Ecology of cyanobacteria and algae

Ecology of cyanobacteria and algae
papers from the lecture on microphytobenthos
Putting the lake back together: Reintegrating benthic pathways into lake foo...
Yvonne Vadeboncoeur; M Jake Vander Zanden; David M Lodge
Bioscience; Jan 2002; 52, 1; Academic Research Library
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Phycologia (2008) Volume 47 (5), 437–450
Published 10 September 2008
PHYCOLOGICAL REVIEWS
The ecology of freshwater epipelic algae: an update
ALOISIE POULÍČKOVÁ1*, PETR HAŠLER1, MONIKA LYSÁKOVÁ1
AND
BRYAN SPEARS2
1
Department of Botany, Faculty of Science, Palacký University Olomouc, Svobody 26, CZ – 771 46 Olomouc, Czech Republic
2
Centre for Ecology & Hydrology Edinburgh, Penicuik, Midlothian, Scotland, EH26 0QB
A. POULÍČKOVÁ, P. HAŠLER, M. LYSÁKOVÁ AND B. SPEARS. 2008. The ecology of freshwater epipelic algae: an update.
Phycologia 47: 437– 450. DOI: 10.2216/07-59.1.
Epipelic algae perform a range of ecosystem functions including biostabilisation of sediments, regulation of
benthic–pelagic nutrient cycling, and primary production. There is a growing need to understand their ecological
role in light of current and future alterations in sediment loading resulting from land-use change and land
management practices. Although the majority of recent work on epipelic algal ecology has been conducted within
estuarine ecosystems, significant advances have also been made in freshwaters. We review these recent studies in
combination with more classical freshwater approaches to highlight the importance of freshwater epipelic algal
ecology and to aid discussions regarding future research. A summary of benthic algal groups is given with particular
emphasis on substratum preference and habitat boundaries. No standard methodology exists for sampling
freshwater epipelon, and we discuss the advantages and disadvantages of a suite of currently employed
methodologies. Spatial variability in epipelic community biodiversity is discussed from the micro-scale (i.e. vertical
migration in the sediment surface) to the ecosystem scale (i.e. lake vs river habitats), and finally at the geographic
scale (i.e. the ‘ubiquity’ of epipelic species). Factors regulating epipelon community composition and biomass (e.g.
reproductive strategies, habitat disturbance, grazing pressures, resource limitation, resilience, symbiosis, and
parasitism) are also reviewed. Finally, examples of specific epipelic ecosystem functions (e.g. primary production,
biostabilisation, and regulation of biogeochemical cycling) are given and areas of research requiring particular
focus in the future are outlined.
KEY WORDS: Algae, Cyanobacteria, Diversity, Ecology, Ecosystem function, Epipelon, Phytobenthos
INTRODUCTION
The study of epipelic (cyanobacteria and eukaryotic algae
that live on or in association with fine-grained substrata)
algal ecology was pioneered within freshwater habitats by
Round (1953). However, interest did not develop to the
same extent as in other important areas of freshwater
research, most prominently the study of eutrophication and
phytoplankton ecology. A recent resurgence has been
driven mainly by the requirement to understand the role
of epipelon in regulating the transport and accumulation of
sediments and associated chemical pollutants (Woodruff et
al. 1999; Vadeboncoeur et al. 2003; Poulı́čková et al. 2008).
Although many recent advances have concentrated on
estuarine and intertidal ecosystems (Admiraal 1984; MacIntyre et al. 1996; Decho 2000; Yallop et al. 2000; Moulten
et al. 2004; Droppo et al. 2007), similar (and fewer)
observations have been reported in freshwaters, mainly
from fluvial habitats (i.e. rivers and streams; Passy et al.
1999; Jarvie et al. 2002; Kelly et al. 2003) and to a much
lesser extent in other freshwater habitats (i.e. lakes, ponds,
and springs; Liboriussen & Jeppesen 2003; McMaster &
Schindler 2005; Kilroy et al. 2006). Comparisons between
these two ecosystem types have rarely been reported and
parallel terminologies have been developed making such
comparative work complicated. Although the processes
(e.g. grazing, resource limitation, hydrology, habitat
* Corresponding author ([email protected]).
disturbance, substrate availability) that regulate epipelic
biomass and production are similar across ecosystems,
ecosystem-specific traits exist (e.g. estuary vs river vs lake)
with respect to such processes that make it difficult to draw
inferences across ecosystem boundaries.
A number of epipelic ecosystem functions has recently
been identified in freshwater habitats including significant
contributions to gross primary production (Velasco et al.
2003), trophic interactions (Moulten et al. 2004), ecosystem
engineering (e.g. biostabilisation of sediments; Dodds 2003;
Droppo et al. 2007; Spears et al. 2007b), and the regulation
of nutrient cycling across the sediment–water interface
(Dodds 2003). The extent to which ecosystem-specific
processes govern epipelon distribution and ecosystem
function is, in itself, a contentious issue (Coleman 2002;
Finlay 2002) that is currently the focus of a number of
studies (Poulı́čková & Mann 2006; Lysáková et al. 2007;
Mann et al. 2008; Poulı́čková et al. 2008) and is in need of
an update. In an attempt to provide a general platform, a
benthic algal categorisation scheme coupled with a description of habitat boundaries are provided and the traditional
and modern epipelon sampling methodologies are critically
discussed. Factors affecting epipelic reproduction, symbiosis, susceptibility to grazing losses, parasitism, and resilience
are highlighted, and the variability in epipelon biodiversity
is outlined across a range of spatial scales (i.e. from microscale vertical migrations to ecosystem comparisons and
geographic ‘ubiquity’). Finally, the functional roles of
epipelon are highlighted across a range of freshwater
ecosystems.
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Phycologia, Vol. 47 (5), 2008
Fig. 1. Schematic showing main benthic habitat zones and
gradients in lakes.
HABITAT TERMINOLOGY AND
GENERAL CHARACTERISTICS
There have been many schemes developed for dividing the
benthic habitat of lentic ecosystems (Round 1981). These
are generally centred on light availability to the benthos in
which the habitat is split into two key regions: (1) the
littoral (or photic) zone, and (2) the profundal zone, where
light levels are insufficient to support benthic primary
production (Fig. 1). The terms trophogenic and tropholytic
are also used, implying an upper (autotrophic) and a lower
zone dominated by secondary production (heterotrophic)
and associated remineralisation of organic matter, respectively.
A similar scenario exists within rivers and streams
(Stevenson et al. 1996). The dynamic nature (i.e. flow in
particular) of these systems, and the low nutrient capacities
of the sediments in comparison to lake sediments, ensures
the constant fluctuation of habitat parameters. Compounding this complexity is the fact that a stretch of river or
stream can contain many localised habitat zones, including
slow flowing channels and pools and fast flowing rapids
and riffles (Stevenson 1996).
Viable epipelic cells have also been found in groundwaters (hyporheic zone) below active streams. This community
generally originates from the stream bed where algae
(particularly diatoms and flagellates), which have penetrated into groundwaters by infiltration, can survive (and in
some cases even reproduce) for relatively long periods (e.g.
36 days at 60 cm below the stream bed; Poulı́čková 1987).
BENTHIC ALGAL CATEGORISATION
Benthic algae in freshwater habitats are mainly dominated
by cyanobacteria, green algae, diatoms, and red algae. In
addition, resting stages and settled cells (still capable of
photosynthesis) of planktonic algae can be found in the
benthos making the separation of ecological groups
extremely difficult (Sicko-Goad et al. 1989; Belmonte et
al. 1997). Although these algal groups have great evolutionary, genetic, and chemical differences, they share many
of the same growth forms, which include single cells,
colonies, and filaments (Stevenson et al. 1996). The term
‘periphyton’ (Wetzel & Westlake 1969) or ‘Aufwuchs’ has
been commonly used in the literature to include all
organisms (heterotrophs and autotrophs) growing in
association with submerged substrata. The large number
of algae that make up the autotrophic and mixotrophic
components of periphyton can be separated by their life
traits and preferred substrata. We provide a summary of
the classical definitions of benthic algal communities
(Fig. 2), following terminology published by Round
(1956, 1981) and Margalef (1960).
Although the terminology separating epipelic algae to
epi- and endopelon (see Fig. 2) is clear, such boundaries do
not exist in natural habitats. Additionally, current sampling
methods are insufficient to allow accurate separation of
epipelic and endopelic organisms in muddy organic
sediment environments, and this is further complicated by
the fact that sediment is often composed of a mixture of
sandy, rocky, and muddy materials. Finally, variable
mixing of surface sediments (e.g. wind- or biologicallyinduced mixing) results in an extremely heterogeneous
(both vertical and horizontal) distribution of algae within
surface sediments.
Some organisms exist within the benthos and the water
column, with migration between the two depending on a
number of factors including life histories and susceptibility
to resuspension. These organisms can be categorised as
meroplankton, tychoplankton, or metaphyton (Fig. 2).
Meroplanktonic migration is driven by preferential organismal positioning, e.g. diurnal migration (Hansson 1995)
and grazer avoidance (Hansson 1993); tychoplanktonic
distribution is coincidental, i.e. driven by disturbance events
(Schallenberg & Burns 2004); and metaphyton distribution
has been strongly linked to acidification (Turner et al.
1995).
Many algae form resting stages (epipelic resting stages
discussed later), which further complicates the separation of
functional groups by habitat preference and adds to the
complexity of the taxonomic requirements. Resting stages
can remain viable in sediments for many years (Jewson
1992; McQuoid & Hobson 1996; Hašler et al. 2004;
Poulı́čková et al. 2008) and, therefore, represent a potential
future microautotrophic ‘stock’.
EPIPELON SAMPLING TECHNIQUES
Raw surface sediment can be collected by drawing a length
of glass tube across the sediment and allowing it to fill with
a mixture of surface sediment and water via capillary action
or using some form of suction technique (e.g. peristaltic
pump; Round 1953). However, this collection technique
does not provide adequate quantitative accuracy. Techniques suitable for quantitative work generally require the
collection of known areas of surface sediment and usually
involve the exposure of the sediment surface. These
techniques include: (1) placing a metal disk onto exposed
surface sediment and freezing the area immediately below it
using liquid nitrogen (routinely used in estuarine studies
and recently also in freshwater studies for pigment
quantification) and microscopic assessment using scanning
Poulı́čková et al.: Freshwater epipelon
439
Fig. 2. Schematic showing benthic algal communities categorised by their preferred substrates. Examples of organisms within each
community are given.
electron microscopy (Fig. 3; Greenwood et al. 1999;
HIMOM 2005); (2) using a razor blade to carefully lift a
known area of sediment (extremely difficult in flocculent
sediment habitats); and (3) using sediment core extrusion–
sectioning techniques to remove a known depth and area of
surface sediment. Quantitative techniques are essential for
area-corrected microscopic studies of cell numbers and
biomass (e.g. to express biomass as units of pigment per
area of lake bed). A detailed review of the factors that
should be considered when preparing a sampling strategy
designed to allow multi-site comparisons of benthic
microalgae (including epipelon) in lakes and rivers is
discussed by King et al. (2006) and Kelly et al. (1998),
respectively.
The analysis of bulk surface sediment collected from a
defined area is not sufficient for the quantification of true
epipelic biomass (e.g. photosynthetic pigment analysis) or
production estimates (e.g. 14C uptake method) as bulk
sediment contains not only epipelon but many other
microalgae (e.g. phytoplanktonic contamination of the
epipelon in surface sediment; Fig. 3). For this reason,
Fig. 3. Scanning electron micrograph of lacustrine epipelic diatoms
(1), sedimented phytoplankton (2), and extracellular polymeric
substances (3). Sediment was collected from Loch Leven (Scotland,
21 March 2006) at a depth of 2.3 m. Scale bar 5 10 mm.
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Phycologia, Vol. 47 (5), 2008
attempts are often made to separate the epipelon from the
other algae. However, no standard method of separation
exists and variations in the methods are expected to result
in biased organismal exclusion. Generally, separation
techniques involve the homogenisation of surface sediments
before allowing the mixture to settle in a thin layer within
some form of receptacle (e.g. Petri dish) and rely upon the
vertical phototactic movement of the epipelon back to the
surface. The homogenisation step is essential in that it
results in the ‘resetting’ of the vertical species distribution
(D.G. Mann, personal communication). A ‘trap’ is then
placed on top of the sediment layer and left for a fixed time
over a dark–light cycle (there is also no consensus about the
length of exposure). According to our experiences, traps
should be left for 24 hours when larger diatoms (e.g.
Pinnularia, Amphora; Lysáková et al. 2007) are to be
trapped though time requirements for other species may
vary. The timing of the dark–light cycle and the incubation
light levels are best tested for each specific sample location
as variations are expected in species migration cycles in a
manner similar to that observed in estuarine mudflats
(Round & Eaton 1966; Joint et al. 1982; Saburova &
Polikarpov 2003; Consalvey et al. 2004). A range of trap
materials have also been used and have included glass
(reported . 90% removal; Round 1981), microspheres
(D.M. Paterson, personal communication), and lens tissue
(reported . 90% removal; Round 1981). Screening
measures may also be necessary (e.g. two layers of lens
tissue) to avoid contamination of the trap with other
periphyton biomass; however, in order to supply a ‘bridge’
between the sediment surface and the trap surface, it is
essential to ensure close contact between both. Once
removed, the traps may be used for direct enumeration of
biomass (i.e. glass cover slip of known surface area) or for
production and photosynthetic pigment analysis following
a thorough washing procedure (e.g. washing lens tissue
through a GF/F filter preceding pigment extraction). The
relative success of different trap materials is debated
amongst benthic algal experts; although, no published
work has directly assessed this problem, and although
separation of algae and detritus is possible (Hamilton et al.
2005), no single standard separation method is available
that accurately removes the problem of contamination of
the epipelic community by other autotrophic groups.
BIOLOGICAL CONSEQUENCES AND ADAPTATIONS
Reproductive, survival, and life history strategies of epipelon
In contrast to marine organisms, freshwater epipelon live in
relatively small, enclosed habitats (lakes or ponds surrounded by terrestrial ecosystems). This has resulted in the
development of a number of adaptations to facilitate
dispersal (for example by wind or animal relocation) and
to allow reproduction and survival under extreme conditions (Edlund & Stoermer 1997; Edlund & Spaulding 2006)
and during periods of unfavourable environmental conditions (Table 1). Motility seems to be a common feature of
all autochthonous epipelic cyanobacteria and algae (Hašler
et al. 2008) allowing them to migrate vertically within
sediments (Round & Eaton 1966; Happey-Wood 1988).
Mucilage, important for motility and protection, is secreted
by their vegetative (cyanobacteria; Hašler et al. 2008) or
reproductive stages (euglenophytes, diatoms; Poulı́čková et
al. 2007a). In contrast to the obligatory asexual reproduction of cyanobacteria and euglenophytes (Lee 1999), sexual
reproduction has been observed in Zygnematophyceae
(Coesel & Krienitz 2008) and in most genera of epipelic
pennate diatoms (Lewis 1984; Mann 1999; Chepurnov et al.
2004; Mann & Chepurnov 2005). Although the first
references to diatom sexual reproduction were published
over 150 years ago (Thwaites 1847; Smith 1856), followed
further by Geitler’s observations (Geitler 1973), our
knowledge in this field is still insufficient with a number
of key questions outstanding. For example, diatoms must
abandon cyclic migration to perform sexual reproduction:
where in the sediment does sexual reproduction occur;
when does it occur; what triggers the onset of reproduction;
and do different diatom species, individuals, and functional
groups perform reproduction at the same time? Few
observations (experimentally or otherwise) have been made
in this field or in relation to the life history strategies in
natural populations (Mann 1988; Edlund & Stoermer 1997;
Poulı́čková & Mann 2008).
Symbiosis, parasitism, and grazing
Few examples of epipelic symbionts have been identified in
freshwater habitats. Exceptions include the green symbionts
of Chlorophyta, particularly Chlorella in Paramecium and
Hydra (Reisser 1992; Canter-Lund & Lund 1995). Glaucophyta (Reisser 1992; Lee 1999) and Paulinella chromatophora Lauterborn contain cyanelles (originating from
cyanobacteria) that show evidence of evolution through
endosymbiosis; however, this has resulted in the loss of a
substantial part of the genome via host dependence (Reisser
1992; Lee 1999), and these organisms have yet to be found
in large quantities (Reisser 1992).
The ecological effects of parasitism and grazing are
similar in that they alter the abundance and composition of
algal populations. Although viruses and bacteria can both
infect algae (Canter-Lund & Lund 1995; Andersen 2005),
fungi (Chytridiomycota) and fungi-like organisms (Oomycetes, Chromista) seem to be the most successful parasites
of unicellular epipelic algae (Canter-Lund & Lund 1995;
Bertrand et al. 2004; Ibelings et al. 2004). Observations of
fungal–algal relationships in epipelic diatoms indicate
marked host specificity: Nitzschia sigmoidea–Aphanomycopsis (Scherffel 1925; Friedmann 1952; Mann 1988);
Navicula capitata–Chytridium (Sparrow 1933; Mann
1988). Host specificity, parasite attraction, and chemotaxis
have all been previously studied within the plankton (Powel
1994; Gromov et al. 1999; Ibelings et al. 2004) with recent
molecular approaches providing evidence at the genetic
level. For example, varying susceptibility to infestation by
Zygorhizidium planktonicum Canter strains has been
observed in the genetic isolates of planktonic diatom
Asterionella formosa Hassal (De Bruin et al. 2004). Further
study of host–parasite interaction is required to develop our
understanding of co-evolutionary pathways (Thompson
2002).
Poulı́čková et al.: Freshwater epipelon
441
Table 1. Summary of reproductive and survival strategies of the main epipelic phototrophs.
Group/form
Representatives
Cyanobacteria: coccoid Chroococcus
colonial filamentous Merismopedia
Oscillatoria
Phormidium,
Komvophoron,
Pseudanabaena,
Arthrospira
Euglenophytes:
Euglena
monadoid
Phacus
Trachelomonas
Dinophyta,
Raphidophyceae:
monadoid
Diatoms: unicell,
colonial
Gymnodinium
Gonyostomum
Zygnematophyceae:
coccoid,
filamentous
Cosmarium, Closterium,
Spirogyra, Mougeotia,
Zygnema
Navicula, Sellaphora,
Neidium, Amphora,
Pinnularia, Caloneis,
Nitzschia
Reproduction
Survival/perennation/
protection
asexual:
mucilage (Chroococcus,
fragmentation, cell division
Microcystis, Gleotrichia)
hormogonia, exocytes
akinetes (Nostoc,
(nannocytes, beocytes)
Anabaena)
sexual: none
hormogonia (Arthrospira,
Oscillatoria,
Planktothrix)
asexual:
mucilage
cell division
palmelloid colony
palmelloid colony
cysts
sexual: none
asexual: cell division
hypnospores (Dinophyta)
sexual: isogamy
palmelloid colony
(Dinophyta only)
(Raphidophyceae)
asexual:
mucilage
cell division
resting cells (freshwater,
sexual: oogamy (centric)
under low light, low
isogamy and anisogamy
temperature, low Si)
(pennate)
spores (marine, under
nutrient limitation, but
need enough Si)
asexual:
cell division
sexual:
conjugation (hologamy)
Fungal periodicity is related to host cell density and a
number of other environmental factors (Kudoh & Takahashi 1990; Brunning 1991; Canter-Lund & Lund 1995;
Ibelings et al. 2004). However, a recent study investigating a
fungal epidemic in phytoplankton failed to highlight one
single abiotic driver responsible for triggering infection
(Ibelings et al. 2004). No comparable studies exist in the
case of epipelon.
Although grazing of epipelon is clearly an important
pathway in supporting freshwater benthic food-webs, very
few studies have quantified the energy transfer pathways.
Smith et al. (1996) estimated the estuarine epipelic diatom
consumption rate by a population of amphipods (Corophium volutator Pallas) at about 18 3 106 to 31 3
106 cells m22 d21. As in the case in the plankton, the
effects of grazing on epipelon may depend upon cell
dimensions and other variables. This is the result of two
general phenomena: (1) smaller algae grow faster than
larger (Smith et al. 1996), and (2) zooplankton preferentially graze smaller rather than larger algae (Sieburth et al.
1978). Grazing of epipelon by organisms within higher
trophic levels has also been observed, for example the
exploitation of epipelic algae by tadpoles (WaringerLoschenkohl & Schagerl 2001). Hillebrand (2005) conducted a survey of the available literature in an attempt to
clarify the role of grazing in shaping the periphytic
mucilage, algaenan
(sporopollenin-like
material)
resting zygotes
(Desmidiales)
spores – zygospores,
parthenospores,
aplanospores
(Zygnematales)
References
Lee 1999
Poulı́čková et al. 2004
Lee 1999
Leedale 1967
Lee 1999
Reproduction:
Geitler 1973;
Chepurnov et al.
2004; Mann &
Chepurnov 2005
Resting stages:
Sicko-Goad et al.
1989; Kuwata et al.
1993; McQuoid &
Hobson 1996
Lee 1999
Poulı́čková et al.
2007b
Versteegh & Blokker
2004
community and observed an interaction between light and
grazing pressures where a more ‘edible’ algal community,
and therefore, higher grazing rates, were favoured under
high light conditions. Community composition was influenced by a trade-off between adaptations to harvest light
and to avoid ingestion by grazers. Moulten et al. (2004)
conducted a multi-tiered grazer (various mayfly and shrimp
species) exclusion experiment in a coastal stream (Rio de
Janeiro, Brazil) and observed trophic cascade effects where
periphyton grazing by baetid ephemeropterans (mayfly)
was reduced by the top-down control of Macrobrachium (a
palaemonidae shrimp). Both of these studies considered
periphyton as a benthic algal composite and so may not be
accurately indicative of absolute variations in epipelon
biomass; however, it is clear that where epipelon biomass
and production is high, e.g. in well-lit streams (Velasco et
al. 2003) and shallow lakes (Liboriussen & Jeppesen 2003),
their effect on food-web structure and maintenance will be
increased. Other examples outline the effects of grazing on
benthic algal (including endopelic and epipelic species)
zonation and community composition (Pringle 1996).
Benthic food-web manipulation models of the type
described above need to be applied across a wide range of
ecosystem types (e.g. trophic gradient of lakes and stream
order) before the role of epipelon in ecosystem stability and
function can be adequately addressed.
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Phycologia, Vol. 47 (5), 2008
SPATIAL VARIATION AND BIODIVERSITY
Cardinale et al. (2006) reported that biodiversity (of stream
periphyton) in a given system was jointly determined by the
frequency of disturbances that created new niche opportunities for species in space or time and by the rate at which
biomass accrual leads to successional displacement of
inferior competitors by superior competitors. These findings
complement previous knowledge by showing that multivariate models, which consider interactive effects of community
production and ecosystem disturbance, are, in fact, possible
explanations of much broader patterns of richness in natural
systems (Cardinale et al. 2006). We highlight the complexity
of epipelic biodiversity by considering spatial variation in
community structure from small (i.e. vertical migration
within the upper micrometers of the sediment surface) to
large scale (i.e. the ‘ubiquity’ of epipelon).
Vertical distribution and migration
Few studies have assessed the photosynthetic efficiency of
an epipelic community as it varies with depth in freshwater
sediments. One exception is the photosynthesis–irradiance
measurements (at a microscale) along with light– climate
determinations reported by Dodds (1992). There are,
however, numerous reports of viable microalgal cells at
significant depths below the sediment surface (observed
down to several tens of centimetres in the aphotic
sediments) in a variety of different habitats (Wasmund
1969; Steele et al. 1970; Branch & Pringle 1987; Poulı́čková
1987; MacIntyre et al. 1996, Müllner & Schagerl 2003).
Similarly, elevated chlorophyll a concentrations are often
observed at depths far greater than the upper few
millimetres (Müllner & Schagerl 2003). This is due mainly
to distribution processes other than phototactic cell
movement, acting to reposition epipelon in the sediment,
and is most likely the result of sediment resuspension and
settling mechanisms (e.g. wind-induced mixing and bioturbation preceding deposition from the water column; Hilton
et al. 1986) and not vertical migration.
Epipelic migration in the surface sediments is a dynamic
process that is generally thought to be driven by nutritional
requirements and light, and often involves the positioning
of algae along steep gradients of dissolved plant nutrients
and oxygen (Round 1981; Revsbech et al. 1983). This
migration has been observed to follow circadian or diurnal
rhythms (Round & Palmer 1966) where the algae migrate
vertically through surficial sediments at night and return to
the sediment surface to photosynthesize in daylight
(Richardson & Castenholz 1987; Saburova & Polikarpov
2003). Tidal rhythms have been well described in estuaries
(Palmer & Round 1965). The results of some laboratory
experiments with epipelic diatoms suggested endogenous
rhythms, as the vertical-migration rhythm occurs also
under continuous light and continuous dark (Round &
Eaton 1966). A mosaic of vertical migration patterns
(exogenous and endogenous) have previously been described in epipelic flagellates (Happey-Wood 1988). However, vertical migration (e.g. in Euglena, Cylindrotheca,
Nitzschia) has been linked to both positive (i.e. to secure
higher light conditions) and negative (to avoid high light
and resultant photoinhibition) phototactic strategies
(Round & Happey 1965; Round & Palmer 1966; Kingston
1999) with estimates of cellular propulsion in the range of
1.7 mm h21 (Saburova & Polikarpov 2003). Variation in
the strategies of movement presumably correspond to a
specific selective advantage, in terms of spatial positioning
within the sediment–water interface, and likely contributes
to the highly diverse algal communities observed in
shallow-water sediments (Happey-Wood 1988).
Epipelon of springs
Even though there are many different spring types,
phycological studies have been concentrated mainly within
thermal (Dell’Uomo 1986), saline (Kadlubowska 1985),
tufa-forming (Pentecost 2001), or acid (Cambra & Hindák
1998) springs. Freshwater springs represent steady-state
environments within which temperature, aeration, and
nutrient supply remain relatively constant throughout the
year. As such, seasonal variability in epipelic community
composition is not commonly observed (Zelazna-Wieczorek
& Maninska 2006). In comparison to hot springs (dominated
by few tolerant cyanobacteria), the species diversity in cold
freshwater springs is large and dominated mainly by diatoms
(Werum 2001; Poulı́čková et al. 2005; Cantonati et al. 2006;
Zelazna-Wieczorek & Maninska 2006). Although epipelic
motile diatoms such as Neidium, Stauroneis, Sellaphora,
Navicula, and Cymatopleura can occur (Round 1981),
Surirella spiralis Kutzing and Campylodiscus hibernicus
Ehrenberg are specific to springs (Poulı́čková et al. 2005).
Epipelon of streams
Although the epipelic community in streams is limited to
areas of sediment accumulation, species diversity can be
high at such sites (Passy et al. 1999). The epipelon of rivers
and streams is generally dominated by diatoms (Round
1981; Al-Saadi et al. 1996); although, dense cyanobacterial
mats have also been observed (Bott et al. 1997). Species
richness varies (from 100 to 300 diatom taxa) in different
stream types and with ecological conditions (Moore 1974;
Stevenson 1984; Poulı́čková et al. 1998; Vilbaste 2001;
McGregor et al. 2006). Common diatom genera under
alkaline conditions include Achnanthidium and Fragilaria
sensu lato; whereas, Tabellaria, Eunotia, and Pinnularia
characterize acidic conditions. The structure and function
of stream periphyton communities (including epipelon) are
also influenced by light (Hill et al. 1995; Kiffney et al. 2004;
Schiller et al. 2007). Chlorophyta are more abundant in
unshaded habitats and are commonly represented by the
genera Oedogonium, Mougeotia, Spirogyra, and Zygnema
(Moore 1974).
Epipelon of lakes and ponds
Although many epipelic species can be found in lakes
regardless of trophic status, some species and groups are
clearly more abundant under specific environmental conditions (Round 1957, 1958, 1972; Mann et al. 2008). A recent
study of epipelon in 14 Czech ponds identified variation in
community structure across a trophic gradient (Lysáková et
al. 2007). Co-occurrence and ecological restriction of certain
Poulı́čková et al.: Freshwater epipelon
demes of Sellaphora species complex have been recorded by
Mann et al. (2008). Extensive literature is available outlining
the effects of environmental fluctuations on diatom
community composition and is supplemented by a range
of paleolimnological applications (Stoermer & Smol 1999).
However, similar evidence also exists to suggest that shifts in
the epipelic community as a whole (i.e. not exclusively
diatoms) in lakes can be used as a ‘real-time’ method of
monitoring climate change; although, additional work is
required to accurately define the effects of key driving
variables (Vinebrooke & Leavitt 1999; Havens et al. 2001;
Vadeboncoeur et al. 2003; McMaster & Schindler 2005).
The epipelon of acidic lakes is commonly dominated by
Zygnematophyceae (Cosmarium, Closterium, Spirogyra,
and Zygnema) and pennate diatoms of the genus Eunotia,
Frustulia, Pinnularia, and Brachysira (Round 1981; Graham
& Vinebrooke 1998; Lysáková et al. 2007) in comparison to
Gyrosigma, Navicula, Nitzschia, Anomoeoneis, and Cymatopleura in alkaline lakes (Round 1959; Lysáková et al.
2007). Acidification can alter the algal community structure
(Vinebrooke 1996, Vinebrooke et al. 2002) by suppressing
filamentous cyanobacteria and supporting green algae.
Cyanobacteria in neutral-alkaline lakes are represented by
Komvophoron, Pseudanabaena, Phormidium, and Oscillatoria (Hašler et al. 2008).
Geographic biodiversity
The majority of freshwater epipelic species are observed
across large geographic areas indicating easy dispersal
mechanisms or insufficiently drawn species boundaries.
However, there is very little information concerning specific
dispersal mechanisms. If a species produces resistant wall
material, either in the vegetative or in the spore stage, there
is a possibility of preservation in sediments, and its
continuity in the habitat can be checked (Guillermo &
Zamaloa 2004; Versteegh & Blokker 2004; Zamaloa &
Guillermo 2005; Räsänen et al. 2006; Schmidt et al. 2007).
The positive relationship between local population persistence and abundance, reported recently by Soininen &
Heino (2005), suggests that a high local abundance may
prevent local species extinction.
Ubiquitous distributional patterns were demonstrated in
different protist groups (Fenchel & Finlay 2004; Finlay et
al. 2004). However, the generalisation of this model to all
microorganisms still remains controversial (Coleman 2002;
Lachance 2004; Coesel & Krienitz 2008; Vanormelingen et
al. 2008). Some floristic data indicate a possible geographically restricted distributional pattern in microalgae (Martiny et al. 2006; Vyverman et al. 2007; Coesel & Krienitz
2008). Endemic epipelic species have been observed in
Antarctic region (Komárek & Jankovská 2001; Van de
Vijver et al. 2005) or in some ancient lakes: for example,
Lake Baikal (Mann 1999) and lakes in Mongolia and
Macedonia (Edlund et al. 2006). In a few cases, there is
strong evidence that morphologically distinct taxa have a
restricted distribution that is related more to historical
biogeography than to environmental factors (Tyler 1996).
For example, despite intensive study of apparently appropriate habitats worldwide, the diatom genus Eunophora has
never been found outside New Zealand and Tasmania
443
(Vyverman et al. 1998; Kilroy et al. 2006). Finlay et al.
(2002) recently presented four diatom species as examples
of ubiquitous species, one of which was the epipelic pennate
diatom Navicula cryptocephala Kützing. However, Geitler
(1951, 1952, 1958) observed at least six cryptocephala-like
demes, distinguishable on the basis of cytological and/or
reproductive characteristics, within a small number of
Austrian localities. All of these demes were extremely
similar morphologically (silica frustule morphology). Recent observations (Poulı́čková & Mann 2006) of British and
Czech clones of N. cryptocephala corresponded well to
Geitler’s reports. This discrepancy can be explained in one
of two ways: either N. cryptocephala is highly polymorphic
with respect to reproductive and nuclear characteristics
suggesting agreement with Finlay’s hypothesis, or (at least
in this case) many separate species have been combined
under the same name indicating inadequate taxonomic
separation in past studies, all being extremely similar in the
overall morphology of the cell and the striae pattern of the
valve (Poulı́čková & Mann 2006).
The possibility that ‘cosmopolitan’ morphospecies (morphologically defined species) consist of more than one
biological (i.e. genetically distinct or sexually isolated)
species is also discussed by Potapova & Charles (2002).
Cryptic speciation is well documented in other algae
(Coleman 1959; Blackburn & Tyler 1987; Fawley et al.
2004; Lewis & Flechtner 2004; Zakrys et al. 2004). It could
be argued that endemism is easily overlooked because it
occurs among cryptic species, and some evidence exists that
supports this theory. For example, Rynearson & Armbrust
(2004) detected genetically and physiologically distinct
populations of the cosmopolitan planktonic marine diatom
Ditylum brightwellii (West) Grunow in connected estuaries
in the United States. Evans & Hayes (2004) demonstrated
that geographically separated and genetically distinct
individuals of another planktonic marine diatom (Pseudonitzschia) were unable to interbreed. Cryptic or semicryptic
speciation has also been investigated within the epipelic
species complexes of Sellaphora pupula agg. and S. bacillum
agg. (Evans et al. 2007; Mann et al. 2008).
Regardless of the reason, this debate has helped to shape
further research, which is necessary before ecologists can
accurately address the scale of geographic biodiversity. The
combination of molecular techniques with other approaches, such as investigations of species limits, breeding
behaviour, and morphology (Mann et al. 2008), is necessary
to obtain a better understanding of the nature and scale of
biodiversity at the microbial level and to determine the
ecological and evolutionary implications of biodiversity
within microbial communities.
CONTRIBUTION TO ECOSYSTEM PRODUCTION
The role of benthic algae in ecosystem production has
received little attention when compared to studies concerning phytoplankton primary production in pelagic foodwebs (Wetzel 2001; Vadeboncoeur et al. 2002). Moreover,
the majority of studies that do consider benthic production
do not focus specifically upon the role of epipelon but
444
Phycologia, Vol. 47 (5), 2008
Table 2. Environmental factors affecting primary production of epipelon.
Environmental stress
Water column nutrients
Bathymetry
Wind fetch and internal seiche height
Bioturbation
Temperature
UV radiation
Grazing
Humic dissolved organic matter
Mechanism of suppression
high nutrients result in phytoplanktonic shading
of the benthos
gradient of autotrophy to heterotrophy with
decreasing light and increasing depth
physical habitat disturbance and burial
biological disturbance and burial
rate of metabolism increases with temperature
species-specific photoinhibition
direct loss of biomass
decreased photoinhibition
include the autotrophic benthos as a whole and thus may
not be considered indicative of true epipelic production.
A number of studies have attempted to quantify the
relative contribution of benthic algae to total energy
production in a lake. Vadeboncoeur et al. (2002) reported
a contribution of 45% (average over 29 lakes) total primary
production in the benthos relative to pelagic production
rates. Wetzel (2001) estimated the contribution of benthic
microalgal primary production alone could reach 60% of
total lake ecosystem primary production. Miller & Reed
(1975) reported that the epipelon of ponds in Alaska
contributed 6 to 10 times more carbon fixation than the
phytoplankton, and Clesceri (1979) observed 103 times
higher production in autotrophic vs heterotrophic microorganisms in the sediment of Lake George (New York,
USA).
Of growing interest in aquatic ecology is the process of
ecosystem change. One example is a shift in the productivity
from the benthos to the water column in shallow lakes (i.e.
steady state hypothesis; Scheffer 1998) following an
alteration in environmental pressures (e.g. external nutrient
loading to a lake). The interactive effects of benthic and
pelagic primary production were assessed by Liboriussen &
Jeppesen (2003). Primary production was dominated by
phytoplankton (96%; total annual primary production
190 g C m21 yr21) under turbid conditions and by benthic
algae (77%; total annual primary production 141 g
C m21 yr21) under clear water conditions. Additionally,
relative benthic algal contribution was highest in winter in
the turbid lake, and in both winter and midsummer in the
clear water lake, most likely as a result of light limitation in
the turbid lake and nutrient limitation in the clear lake.
Thus, under oligotrophic conditions benthic algae may
regulate pelagic primary production by intercepting sediment-derived nutrients. Under eutrophic (turbid) conditions benthic algal primary production may be limited by
phytoplanktonic shading. A number of other factors have
also been reported to regulate the relative proportion of
epipelic primary production (Table 2).
Due to hydrological differences (i.e. constant and
variable water flow) the comparative primary production
of phytoplankton in streams is generally less important
than benthic algae; however, epipelic production may be
strongly constrained by habitat disturbance (Biggs 1996). In
fast flowing streams, frequent flushing of bed sediment
limits the colonisation period and results in stronger
reliance of system productivity on allochthonous sources
References
Liboriussen & Jeppesen 2003
Wetzel 2001
Lake 2000
Johnson et al. 1989
King & Schramm 2004
Vinebrook & Leavitt 1996; Kelly et al. 2003
Moulten et al. 2004; Hillebrand 2005
Rae et al. 2001
of carbon and attached benthic algae. This hypothesis was
supported by Rosenfeld & Roff (1991) when they reported
net primary production rates of epilithon of up to
2 mg C m22 d21 with epipelon production estimated at
20% of this value. Total autotrophic primary production
(i.e. autochthonous carbon production) within forested
riffle sites constituted about 30% of the total stream carbon
input. However, this may not hold true in systems with less
variable hydrology. Velasco et al. (2003) conducted a
similar survey within a semi-arid stream and observed high
macrophyte net primary production (49 g C m22 d21)
followed by the epilithon (37 g C m22 d21), and finally
epipelon (23 g C m22 d21). In terms of coverage corrected
production estimates, the epipelon was the major contributor (84% of the total gross primary production) to the
autochthonous carbon input to the stream and resulted in a
net autotrophic system. This finding stresses the need for
coverage corrected gross and net primary production
estimates when assessing ecosystem productivity, even
when epipelic primary production rates are relatively low,
and efforts should be made to standardise such procedures.
This should include a thorough analysis of sampling
methods (see above) and development of selective productivity analyses (Hickman 1969).
REGULATION OF NUTRIENT CYCLING ACROSS
THE SEDIMENT–WATER INTERFACE
Phytoplankton has generally been observed to out-compete
epipelon for nutrients (e.g. Hwang et al. 1998) in the water
column of lakes; although, epipelic uptake (especially of
phosphorus) from the water column has been observed
(Havens et al. 1999). Additionally, uptake of water column
phosphorus into riverine algal biofilms (periphyton; likely a
combination of epilithon and epipelon in these studies) has
been observed to be highest under low-flow conditions
(Jarvie et al. 2002), and recent in situ nutrient addition
experiments suggest that algal biofilms may become
phosphorus-limited at concentrations below 90 mg l21
(Bowes et al. 2007). Preferential uptake has also been
observed both in terms of nutritional source (i.e. water
column or diffusive media), chemical speciation and
species-specific nutritional demand (Pringle 1990). The role
of sediment dwelling algae in phosphorus retention has
been reviewed recently by Dodds (2003); however, these
recent advances stress the need for further investigation.
Poulı́čková et al.: Freshwater epipelon
The epipelon can play a major role in regulating nutrient
transfer across the sediment–water interface. In clear
oligotrophic lakes and in eutrophic lakes recovering from
eutrophication following a reduction in external nutrient
load, nutrient release from the sediment may be a major
source to the water column (Sas 1989; Spears et al. 2006,
2007a). Sediment nutrient release in streams and lakes is
subject to a number of processes regulated by the epipelon
including the buffering of physicochemical nutrient release
via direct sequestration of nutrients (either from the water
column or from sediments and groundwaters; Pringle 1990;
Hagerthy & Kerfoot 1998), a reduction in hypoxic nutrient
release [i.e. redox sensitive phosphorus complexes (Carlton
& Wetzel 1988) and denitrification–nitrification processes
(Jansson 1980)] via the maintenance of oxygenated surface
sediment and a decrease in nutrient release across the
sediment–water interface via flow attenuation (Dodds
2003). However, it is extremely difficult to quantitatively
separate these processes as they occur on very small scales.
One exception is the work of Woodruff et al. (1999), in
which sediment silica release was observed to be limited in
the presence of epipelic diatoms (most likely via direct
uptake) and phosphorus was focused (from the sediment
and water column) in the surface sediment via a combination of direct nutrient sequestration and oxic coprecipitation of phosphorus with iron. These observations are in
agreement with our own laboratory incubation experiments
where nutrient release (SiO2 and PO4–P) was dampened
under light conditions (with respect to dark), most likely as
a combination of altered redox conditions and direct
sequestration (Spears et al. 2008). However, NH4–N release
was enhanced under illuminated conditions, probably via
the enhancement of nutrient cycling as discussed by Jansson
(1980). The extent to which species-specific variations in
nutrient uptake kinetics (discussed in detail by Borchardt
1996) will regulate the transfer of various nutrients across
the sediment–water interface is not known. In lakes where
significant reductions in external nutrient loads have
resulted in a switch in nutritional dependency from the
catchment to the sediment, regulation of sediment nutrient
release to the water column by epipelon may greatly affect
pelagic stoichiometry (Spears et al. 2008).
Sediment resuspension events may result in high magnitude nutrient release through the disruption of chemical
gradients. Additionally, bioturbation has been linked with
elevated sediment nutrient release (Schrage & Downing
2004); although, in the littoral zones of large lakes, windinduced sediment disturbance may play a greater role in
regulating sediment nutrient release than bioturbation.
Recent work within estuarine mudflat ecosystems has
highlighted the role of motile epipelic diatoms in increasing
the sediment stability through the maintenance of a matrix
composed of extracellular polymeric substances (EPS;
illustrated in Fig. 3) in the upper microns of the sediment
surface (Paterson 1994; Decho 2000; Yallop et al. 2000).
Few studies have assessed the role of EPS production in
regulating sediment stability in freshwater ecosystems
although, this process may be important in littoral habitats;
where susceptibility to disturbance and epipelic production
are high. One notable exception is the work reported by Cyr
& Morton (2006) in which carbohydrate concentration (a
445
proxy of EPS) in Canadian Shield lakes was positively
correlated with sediment chlorophyll concentration. This is
in agreement with much of the estuarine mudflat work and
also in line with the few studies available in the literature in
which increased stability has been directly linked with
epipelic biomass and EPS production in shallow lakes
(Droppo et al. 2007; Spears et al. 2007b). The role of
epipelic biodiversity may be critical to the biostabilisation
of freshwater sediments. For example, epipelic diatoms may
reduce sensitivity to erosion via biochemical processes (i.e.
production of EPS; Spears et al. 2007b); whereas, matforming cyanobacteria (e.g. Oscillatoria sp.) may reduce
sensitivity to erosion via the maintenance of physical
barriers (i.e. the mat itself; Dodds 2003). Further investigation is required to assess the relative roles of different
epipelic algae in sediment stability.
IMPLICATIONS FOR FUTURE RESEARCH
We have outlined a number of knowledge gaps with respect
to the ecology and importance of freshwater epipelon in
performing ecosystem functions. These are summarised
below along with our recommended research directions.
1.
2.
3.
4.
5.
6.
Studies on epipelic species diversity, taxonomic
boundaries, distribution, dispersal and autecology
should follow up or couple with improvements in
taxonomy using a combination of molecular and
traditional (morphology, morphometry, cytology,
breeding) methods (Mann 1999). Efforts should also
be made to identify species complexes and cryptic,
semicryptic, or pseudocryptic species (Evans et al.
2007; Mann et al. 2008).
We must better understand the environmental cues
responsible for triggering epipelic sexual reproduction
and spatial and temporal aspects of reproduction with
respect to life history strategies (Mann 1988; Edlund &
Stoermer 1997; Poulı́čková & Mann 2008).
Sampling methods need to be more rigorously tested
and calibrated. This should include an assessment of
the drivers (e.g. light, nutrients, grazing relief) of
migratory rhythms including organismal specificity
(i.e. do cyanobacteria migrate along a different rhythm
than diatoms?) and the formulation of standard
methodologies for measuring migration (Saburova &
Polikarpov 2003).
Greater attention should be paid to the ecological role of
resting stages in freshwaters. In particular, more effort
should be made to quantify the occurrence of resting
stages and to investigate their viability and germination
characteristics in freshwater sediments (McQuoid &
Hobson 1995, 1996; Poulı́čková et al. 2008).
Few studies exist within which grazing pressures on
epipelon have been quantified. Studies of this type are
essential for assessing the importance of epipelon in
energy transfer through the food web (Jewson 1992;
Edlund & Francis 1999).
The structural properties of epipelic communities
should be further investigated with particular emphasis
446
7.
Phycologia, Vol. 47 (5), 2008
on ‘layering’ of different algal groups and their ability to
perform biostabilisation functions (Spears et al. 2007b).
The role of epipelon biodiversity in regulating nutrient
cycling (both diffuse and turbulence related) should be
assessed over a range of habitat types.
ACKNOWLEDGEMENTS
We are grateful to Dr Laurence Carvalho and Alex Kirika
(Centre for Ecology and Hydrology, Edinburgh), Prof.
David Paterson, Irvine Davidson (LTSEM imagery), Jenna
Funnell (Sediment Ecology Research Group, University of
St Andrews), and Dr Roo Perkins (Cardiff University) for
the provision of valuable information. This study was also
supported by the EU Synthesys project GB-TAF-643,
GACR 206/07/0115, and GACR 206/08/0389. We are
grateful to the Royal Botanic Garden Edinburgh for the
use of their facilities during the Synthesys project and to Dr
Emma Defew (Sediment Ecology Research Group, University of St Andrews), the editor, and both reviewers for
commenting on the manuscript.
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Received 26 July 2007; accepted 30 April 2008
Associate editor: Mark Edlund
Hydrobiologia (2013) 709:159–171
DOI 10.1007/s10750-013-1446-4
PRIMARY RESEARCH PAPER
Differential cell size structure of desmids and diatoms
in the phytobenthos of peatlands
Jiřı́ Neustupa • Jana Veselá • Jan Št’astný
Received: 26 July 2012 / Revised: 9 November 2012 / Accepted: 12 January 2013 / Published online: 24 January 2013
Ó Springer Science+Business Media Dordrecht 2013
Abstract The mean cell sizes of microalgae vary in
relation to the abiotic factors, such as nutrients,
temperature, or water transparency. This study focused
on the community cell size of desmids and diatoms,
two dominant groups of the peatland phytobenthos.
Forty samples from various temperate European
peatlands were investigated. The species composition
and the species richness were controlled mainly by the
pH levels. Purely spatial factors also significantly
affected the species composition. Interestingly, diatoms were more strongly geographically restricted than
desmids. The spatial control of the species composition
was limited mostly to the large taxa, which indicated
that dispersal limitation may be an important structuring
factor for phytobenthos at a regional scale. The mean
cell sizes of desmids were related to the ombrominerotrophic gradient, pH, and Ca concentration.
Electronic supplementary material The online version of
this article (doi:10.1007/s10750-013-1446-4) contains
supplementary material, which is available to authorized users.
Handling editor: Judit Padisak
J. Neustupa (&) J. Veselá J. Št’astný
Department of Botany, Faculty of Science, Charles
University of Prague, Benátská 2, Praha, CZ 128 01,
Czech Republic
e-mail: [email protected]
Acidic, ombrotrophic bogs typically contained small
cells, whereas minerotrophic fens had larger desmids.
By contrast, the diatom size structure did not depend on
the ombro-minerotrophic gradient. Thus, the cell sizes
of desmids in peatlands may be used as a proxy for
important environmental processes, such as transition
from minerotrophy to ombrotrophy, or acidification,
whereas diatoms did not primarily respond to these
processes and their size structure is driven by different
factors, such as conductivity.
Keywords Biovolume Desmids Diatoms Microphytobenthos Peat bogs Size structure
Introduction
The size range of microalgal species inhabiting marine
and freshwater habitats varies by over nine orders of
magnitude in terms of cell volume (Finkel et al.,
2010). Consequently, the community size structure
may differ profoundly among natural phytoplankton
and phytobenthos assemblages. The dynamics of
microalgal size structure has been investigated mostly
in phytoplankton communities, and studies show that
it may be driven by various factors, such as the nutrient
status (Friebele et al., 1978; Irwin et al., 2006;
Litchman et al., 2009), temperature (Winder et al.,
2009; Morán et al., 2010), sinking resistance (Passy,
2007), depth of the mixed layer (Litchman et al.,
2009), irradiance (Key et al., 2010), or water
123
160
transparency (Finkel et al., 2009). Small algae typically predominate in oligotrophic waters where,
because of their higher surface-to-volume (S:V) ratios,
they benefit from more efficient nutrient uptake rates
per unit biovolume (Friebele et al., 1978; Passy, 2007).
Larger species become more abundant in eutrophic
conditions (Ruggiu et al., 1998; Irwin et al., 2006).
However, the opposite relationship has also been
documented, driven by the light limitation selecting
the smaller freshwater phytoplankton species with
more efficient light absorption and lower sinking rates
in eutrophic conditions (Finkel et al., 2009).
The microphytobenthos size structure has attracted
considerably less attention, although the predominance of relatively large pennate diatoms in the
phytobenthos over smaller centric diatoms in the
phytoplankton has many times been documented
(Round et al., 1990). Passy (2007) argued that cell
sizes of phytoplankton species of diatoms were
probably limited by their sinking resistance, whereas
size-related dispersal limitations were important for
benthic species. De Nicola et al. (2006) showed that
the area-specific biovolumes of periphyton increased
with the nutrient enrichment of lakes, while Cattaneo
et al. (1997) reported the same relationship for stream
periphyton. By contrast, studies that have focused
solely on benthic diatoms have reported non-significant relationships. Lavoie et al. (2006, 2010) showed
that the community size structure of stream benthic
diatoms did not correlate with the local nutrient status;
therefore, these diatom size data were not useful for
biomonitoring. Likewise, Wunsam et al. (2002)
showed that the relationship between the cell sizes of
benthic diatoms in streams and the trophic levels at
sites was controlled by the water color, rather than the
local phosphorus concentration. Furthermore, Finkel
et al. (2009) found no relationship between the cell
sizes of benthic diatoms in lakes and the concentrations of nutrients. Consequently, Passy (2007) argued
that diatoms may considerably differ from many other
groups of microalgae in terms of the dynamics of their
per biovolume nutrient uptake. The biovolume of
diatom cells has two distinct parts: the metabolically
inactive central vacuole, and the thin layer of
cytoplasm, which contains the cell organelles. The
cytoplasm is located under the frustule and it is
proportionate to the cell surface (Round et al., 1990).
Therefore, Passy (2007) suggested that the per unit
biovolume nutrient uptake is generally size-independent
123
Hydrobiologia (2013) 709:159–171
in diatoms. Other biological aspects of the community
size structure of benthic diatoms have also been
examined. Soininen & Kokocinski (2006) found a
weak positive relationship between the community
cell size and the latitude for boreal stream benthic
diatoms. However, this weak relationship was not
supported by the species temperature optima, and it
could not be readily ascribed to the temperature-size
rule. Heino & Soininen (2006) found that smaller
species of benthic diatoms from boreal streams were
significantly more frequently distributed within their
study region. This suggested that smaller species of
stream phytobenthos may have larger populations and
more efficient passive dispersal; therefore, spatial
factors may be less limiting on their regional
distribution.
In this study, we investigated the parallel community size structure of diatoms (Bacillariophyceae,
Stramenopila) and desmids (Desmidiales, Viridiplantae), which are two major groups forming the microphytobenthos of freshwater peatlands. These wetland
habitats, which typically have a low pH, have not been
investigated with respect to the size structure of their
microalgal communities. The diversity of desmids and
diatoms in peatlands is usually positively related to the
pH values of individual localities (Coesel, 1982;
Mataloni, 1999; Nováková, 2002; Neustupa et al.,
2009). The pH gradient in peatlands has often been
used to distinguish between strongly acidic, ombrotrophic bogs, and more pH-neutral, minerotrophic
fens (Wheeler & Proctor, 2000). This relationship,
although still relevant, is less important in temperate
peatlands, where minerotrophic poor fens may have
very low pH values (Hájek et al., 2006; Neustupa et al.,
2011a). However, the anthropogenic acidification of
Central European ombrotrophic bogs, which was
caused primarily by acid rains in the second half of
the twentieth century, has produced extremely acidic
conditions in these habitats, which now often have pH
values less than 4.0. Interestingly, the pH gradient in
peatlands was also found to be more or less unrelated
to their trophic gradient (Bridgham et al., 1996;
Wheeler & Proctor, 2000). The pH levels and the
corresponding ombro-minerotrophy transition were
reported to be correlated more strongly with the
species composition of vascular plants (Bedford
et al., 1999; Vitt, 2006) or bryophytes (Bragazza &
Gerdol, 2002) than nutrient concentrations. This may
be due to fluctuating and spatially variable nutrient
Hydrobiologia (2013) 709:159–171
concentrations in these localities because any available ions are utilized rapidly by organisms (Kellogg &
Bridgham, 2003). The stressing effects of extremely
low pH values were also linked to changes in the
carbon uptake mechanisms of peatland microalgae
because the concentrations of HCO3- ions rapidly
diminish in the pH levels less than 5.5 (Moss, 1973).
Consequently, in the present study we predicted
that poorly buffered ombrotrophic localities will have
relatively smaller algae with higher S:V ratios, which
may facilitate more efficient nutrient uptake and
higher growth rates, allowing them to cope with
unfavorable conditions in these habitats. By contrast,
comparatively larger sized species were predicted in
minerotrophic fens. It was expected that these predicted trends in the community size structure would
primarily be correlated with the pH values and with the
estimates of ombro- versus minerotrophy of individual
localities based on field observations of their water
supply and hydrography. The current study tested
whether pH-related environmental processes in peatlands, such as acidification, or the transition from a
minerotrophic fen to an ombrotrophic bog stage would
be reflected in the mean cell sizes of phytobenthic
microalgae. Given the possible difference between
diatoms and desmids in terms of their per unit
biovolume nutrient uptake scaling, this study aimed
to determine whether there was a difference in the
community mean size dynamics between these two
major phytobenthic groups.
The effects of geographical factors were also tested,
i.e., whether the spatial distances among localities
reflected large scale processes, such as history,
climate, or dispersal, which may account for a
significant component of the variation in species
structure. This study also tested whether purely spatial
effects would be more pronounced in the relatively
larger species of both groups. The cell size of
individual taxa should be positively correlated with
their dispersal limitations (Heino & Soininen, 2006;
Passy, 2007). Consequently, this factor may play a role
in structuring the local phytobenthic communities in
peatlands, where larger species might be more
spatially structured than species with smaller cells.
Finally, the species composition of desmids and
diatoms was used to test whether there were congruent
patterns in the ordination of sites, and whether the
species richness of both groups followed similar
patterns among the investigated localities.
161
Materials and methods
Localities and sampling
In total, 40 peatland localities were sampled during
June and July 2011 (Supplementary Table 1). The site
selection was aimed to include a range of different
peatland habitats in temperate Europe. The study sites
were positioned in four regions: Krušné Hory Mts.,
Northwest Bohemia, Czech Republic; Dokesko district, Northeast Bohemia, Czech Republic; West
Pomerania, Poland; and Bornholm Island, Denmark.
The pH ranged from 3.3 to 7.3, and the localities
ranged from typical ombrotrophic raised bogs to
minerotrophic fens fed by ground and surface waters.
The position of the localities on the ombrotrophic to
minerotrophic gradient was estimated using a threelevel scale, based on a visual inspection of their
hydrography and physiognomy. The samples taken in
typical ombrotrophic raised bogs (characterized by a
central cupola elevated above the bog margins) that
are fed mostly by the rainwater were assigned with the
lowest score. Conversely, the apparent minerotrophic
localities positioned at the peaty margins of lakes or in
the alluvium of streams that are mostly fed by the
ground or surface waters were assigned with the
highest score. The intermediate localities, such as
mountainous bogs with an active peat cupolla and
substantial precipitation located on slopes, which
increase the relative amount of surface water influx,
were assigned with 2. Thus, each locality was assigned
a score ranging from 1, for purely ombrotrophic bogs,
to 3, for typical minerotrophic fens. In each locality,
approximately 10 9 10 cm of the epipelon was sampled from the uppermost 5 mm layer using a 100 mL
plastic syringe. The pH and conductivity values were
measured in the field using a combined pH/conductometer (WTW 340i, WTW GmbH, Weilheim, Germany). Total nitrogen concentrations were measured
using the chemiluminiscent nitrogen dioxide (NO2)
assessment method, which involves the high-temperature catalytic conversion of ammonium, nitrite, and
nitrate to nitrogen dioxide. Total phosphorus concentrations were evaluated by acid persulfate digestion.
Organic and condensed inorganic forms of phosphates
were converted to orthophosphates by heating with
acid and persulfate. Ca and Fe concentrations were
determined using inductively coupled plasma atomic
emission spectroscopy (ICP-AES). The phytobenthos
123
162
samples were fixed in the field using Lugol’s solution
(3–4% final concentration). Two hundred desmid cells
were identified in each sample during systematic
inspections of the Lugol’s solution-fixed samples at
4009 magnification using an Olympus BX51 light
microscope equipped with a Z5060 digital microphotography equipment. Two hundred diatom cells were
also identified in each sample using Naphrax (Brunel
Microscopes Ltd, Wiltshire, UK) mounted permanent
diatom slides at 1,0009 magnification.
Hydrobiologia (2013) 709:159–171
estimated using S = (2. A) ? (P.c) and V = A.c,
whereas the corresponding values for desmid cells were
estimated based on a general ellipsoid (Vellipsoid), using
a, b, and c values, and the area (Aellipse) and perimeter
(Pellipse) of an ellipse with a and b axes. The volume of a
desmid cell (V) with a generally ellipsoidal layout was
approximated using the formula V/Vellipsoid = A/Aellipse,
i.e., Vx = (Ax. Vellipsoid)/Aellipse. Hence, after the algebraic simplification of trivial geometric formulas for
scalene ellipsoids it gave Vx = (2. Ax. c)/3. Then, the
mean relative biovolume values for individual samples
were estimated as
Species data analysis
spec
P
Vspec kspec
Two-dimensional non-metric multidimensional scaling (NMDS) was used with a Bray-Curtis distance
measure to determine the species composition patterns
of desmids and diatoms at individual localities. The
species data were square-root transformed and Wisconsin double standardized using the metaMDS function in the vegan package of R, ver. 2.13.0. (Oksanen
et al., 2011; R Development Core Team, 2011). The
congruence of the two ordinations was evaluated using
the function procrustes in the vegan package. This
function conducts asymmetric Procrustes superimposition, which minimizes the squared differences
between two ordinations (Bookstein, 1991; PeresNeto & Jackson, 2001). The resulting Procrustes sum
of squares indicated the goodness of fit between the
desmids and diatoms, based on the ordinations of the
localities that were illustrated graphically with a
Procrustean superimposition plot showing the positions of sites in two superimposed ordinations. The
non-randomness of the congruence between the two
ordinations was evaluated using the permutation
Procrustes test, implemented in the function protest
of the vegan package. The randomization of site
assignments was based on 9,999 random replicates.
The sizes of cells were expressed as their biovolumes,
surface areas, and S:V ratios. The algorithm proposed by
Neustupa et al. (2011a) was used to estimate the
biovolumes and surface areas. The frontal views of
desmids and the valvar views of diatoms were used to
compute their area (A), perimeter (P), length (a), and
width (b) using TpsDig ver. 2.16. (Rohlf, 2010). The
maximum thickness of cells (c) was estimated based on
the published width-to-thickness ratios of individual
species, or direct measurements of the cells. The surface
areas (S) and biovolumes (V) of diatom cells were
123
Vsample ¼
i¼1
200
where Vspec is a mean estimated biovolume of a
particular species, kspec is the actual number of cells
counted in this sample out of the total of 200 cells.
Similarly, the surfaces of desmid cells with general
ellipsoidal layouts were approximated using the
formula S = (P. Sellipsoid)/Pellipse. Full details of these
computations and the alternative computations for
desmid cells with multiple radiations are described in
Neustupa et al. (2011a).
Abiotic values, the mean relative desmid and
diatom biovolumes, and the surface areas and S:V
ratios of individual sites were log transformed so that
they could be compared unequivocally using linear
and partial linear correlation analyses with the pH
values at the localities (defined at the log scale) in
PAST, ver. 2.15. (Hammer et al., 2001). In addition,
the effects of abiotic factors on the mean biovolumes
of desmids and diatoms were also evaluated using the
multiple regression analyses with the optimal model
chosen on the basis of the Akaike’s information
criterion (AIC) using the stepAIC function of the
MASS package of R, ver. 2.13.0. (Venables & Ripley,
2002). Prior to the regression analysis, both the mean
biovolume data and the abiotic factors (except for the
pH values) were log transformed. The abiotic factors
were also standardized to zero mean and unit
variance. The forward stepwise search of the optimal
model, avoiding collinearity among closely related
factors, was used (Burnham & Anderson, 2004).
The effects of individual abiotic factors on the
species composition of samples were evaluated using
a permutational multivariate analysis of variance
(permutational MANOVA), which was conducted
Hydrobiologia (2013) 709:159–171
with Hellinger-transformed desmid and diatom species data using the Bray-Curtis distance index
(Anderson, 2001; Oksanen et al., 2011). The permutational MANOVA is a distribution-free function that
partitions the distance matrices (typically based on
species composition data) among external sources of
variation. This method is considered a robust alternative to parametric MANOVA and ordination methods,
such as redundancy analysis (Legendre and Anderson,
Legendre & Anderson, 1999; Oksanen et al., 2011).
Stepwise forward selection based on the F-ratios was
used to generate the optimal model for the decomposition of the variation in species data among individual log-transformed abiotic factors. The significance
of individual effects was assessed using permutation
tests with 9,999 repetitions. Partition of variance in
community structure attributed to purely spatial and
environmental factors was performed using the
redundancy analysis (RDA) based variance partition
(Borcard et al., 1992). This analysis was conducted
using varpart function of the package vegan (Oksanen
et al., 2011) in R, ver. 2.13.0. (R Development Core
Team, 2011). The adjusted R2 values were used for the
partitioning of variance (Peres-Neto et al., 2006). The
original matrix of geographic distances among localities was converted using PAST ver. 2.15 to principal
coordinates that covered the spatial variation. The
varpart function then used the Hellinger-transformed
species data and the standardized environmental factors
(Oksanen et al., 2011). Significance of the testable
fractions (such as pure effects of space and environmental factors) were calculated using permutation tests
with 9,999 repetitions. For parallel analyses of relatively smaller and larger taxa, the species datasets of
both groups were divided into two subgroups, based on
the median values of the cell biovolume, and these
subgroups were evaluated separately.
Results
Species composition
In total, 206 species of desmids and 105 diatom
species were recovered from the peatland samples
(Supplementary Tables 2 and 3). The Procrustes
analysis of the NMDS ordinations of desmid and
diatom datasets demonstrated their non-random congruence (Procrustes correlation r = 0.77, P \ 0.001;
163
Fig. 1 NMDS ordination plot of sites based on the species
composition of desmids (arrow ends) superimposed with the
Procrustes analysis of the NMDS ordination plot based on the
species composition of diatoms (circles)
Fig. 1). However, the relatively large residual distances among identical sites in both ordinations
suggested that the community structure of desmids
and diatoms did not follow exactly the same patterns
relative to the abiotic and spatial factors. This was
confirmed by further analyses. The species composition of desmids was most tightly controlled by the pH
values of localities (Table 1). The permutational
MANOVA model explained approximately 43% of
the variation in the species data. Conductivity was the
second most important factor, while the effect of
spatial distance among localities on the species
composition of desmids was also significant. The
ombro-minerotrophy gradient and Ca concentrations
were correlated with the pH values (Table 2), but they
did not account for much of the additional variation in
the desmid species data that was not related to the pH
gradient (Table 1). However, the effect of the ombrominerotrophy gradient was still marginally significant,
even after accounting for the effect of pH. The other
abiotic factors had no significant effects on the species
composition of desmids in the samples. The species
composition of diatoms was also primarily controlled
by pH (Table 1). The permutational MANOVA model
accounted for about 55% of the total variation. Similar
to the desmids, conductivity and spatial distance had
the second and third most significant effects on the
species composition, which were not related to the pH
gradient (Table 1). The ombro-minerotrophic gradient
and Ca concentrations, i.e., two abiotic factors that
were largely covered by the variation in the pH values,
123
Residuals
Residuals
123
*** P \ 0.001, ** P \ 0.01, * P \ 0.05,
n.s.
pH
1
2.58
8.27
0.16
***
Conductivity
1
1.26
4.06
0.08
***
Spatial
distance
2
1.32
2.12
0.08
**
Ombrominerotrophy
1
0.52
1.67
0.03
*
Total N
1
0.47
1.50
0.03
n.s.
Fe
1
0.40
1.30
0.02
n.s.
Total P
1
0.29
0.93
0.02
n.s.
Ca
1
0.26
0.83
0.02
n.s.
Diatoms, all species—abiotic factors
30
9.35
0.57
pH
1
2.81
14.25
0.22
***
Conductivity
1
1.60
8.08
0.12
***
Spatial
distance
2
1.20
3.05
0.09
***
Ca
1
0.45
2.29
0.03
*
Ombrominerotrophy
1
0.38
1.90
0.03
*
Total N
1
0.31
1.57
0.02
n.s.
Fe
1
0.27
1.35
0.02
n.s.
Total P
1
0.14
0.71
0.01
n.s.
30
5.92
0.45
The effects of individual factors were evaluated sequentially
following the stepwise forward model selection
P [ 0.05
were also marginally significant. The effects of other
factors, including the total nitrogen and phosphorus,
were not significant.
The concentrations of total nitrogen and phosphorus were mutually positively correlated, although they
were not significantly related to pH or conductivity
(Table 2). The species richness of desmids and
diatoms at the sites was mutually positively correlated
(Fig. 2a). The species richness of both groups was also
strongly positively correlated with the pH (Fig. 2b, c)
and measures related to ombro-minerotrophy and the
Ca concentration (Table 2). By contrast, the species
richness of diatoms and desmids was not related to
conductivity, the concentrations of nutrients, or other
abiotic factors.
0.85***
Ca
n.s.
0.10n.s.
Total P
-0.04
0.06n.s./0.09n.s.
0.00n.s/-0.02n.s.
0.75***/0.46**
0.64***/-0.01n.s.
0.61***/-0.05n.s.
Mean cell biovolume—desmids
Mean cell surface—desmids
-0.08
*** P \ 0.001, ** P \ 0.01; * P \ 0.05,
n.s.
P [ 0.05
The numbers in bold represent correlation coefficients with P \ 0.01
0.86***/0.33
Species richness—desmids
/-0.42*
n.s.
/-0.01
n.s.
n.s.
-0.06
n.s.
n.s.
/0.29
-0.28
Mean cell S/V—desmids
n.s.
n.s.
/-0.20
n.s.
0.55***/0.36*
-0.67***/-0.59***
Species richness—diatoms
/0.09
n.s.
/-0.07
0.22
-0.58***/-0.56***
Mean cell S/V—diatoms
n.s.
n.s.
0.08
n.s.
Mean cell surface—diatoms
/-0.05
-0.11
0.20
n.s.
-0.27
n.s.
/0.01
n.s.
n.s.
0.76***/0.10
n.s.
-0.50**/-0.37*
0.73***/0.42*
0.73***/0.44**
0.58***/-0.04
0.08
/0.02
n.s.
0.25
n.s.
n.s.
n.s.
0.31*/-0.01
-0.17
n.s.
0.66***
0.19n.s.
-0.03n.s.
0.68***
n.s.
-0.26
/-0.08
n.s.
n.s.
/-0.04
n.s.
-0.08
n.s.
0.01n.s./-0.01n.s.
-0.02n.s./-0.08n.s.
/0.24
/0.09
n.s.
/-0.04
n.s.
-0.25
0.21
n.s.
-0.26
n.s.
n.s.
/-0.06
n.s.
-0.26
n.s.
0.46**
0.01n.s.
/-0.43*
n.s.
n.s.
0.07
n.s.
n.s.
/0.08
/0.07
n.s.
-0.08
n.s.
0.17n.s./0.02n.s.
0.16n.s./0.05n.s.
-0.18
n.s.
0.36*/0.17
n.s.
-0.34*/-0.27
-0.32*/-0.26
0.09n.s.
0.18n.s.
0.73***/0.36*
-0.40**/-0.23
0.62***/0.16n.s.
0.65***/0.11n.s.
n.s.
n.s.
n.s.
n.s.
/-0.04
/0.23
0.63***/0.10
0.30
n.s.
-0.03
n.s.
0.12n.s./0.29n.s.
-0.05n.s.
-0.30n.s./-0.28n.s.
0.04n.s./0.09n.s.
-0.08n.s./0.03n.s.
-0.08n.s./0.09n.s.
-0.13n.s./-0.04n.s.
-0.26n.s./-0.21n.s.
0.23n.s./0.21n.s.
0.26n.s./0.26n.s.
Fe
Table 1 The results of individual permutational MANOVA
tests partitioning variation in species composition of sites
Mean cell biovolume—diatoms
Fe
-0.23n.s.
Total N
n.s.
0.17n.s.
0.74***
Ombro-/minerotrophy
0.41**
-0.16n.s.
0.19n.s.
0.14n.s.
Conductivity
Ca
P value
Total P
R2
Total N
F ratio
Ombro-/minerotrophy
Sums of
squares
Conductivity
Df
pH
Factor
Table 2 The results of linear correlation analyses/partial linear correlation analyses among individual environmental factors and community parameters
164
Hydrobiologia (2013) 709:159–171
Desmids, all species—abiotic factors
Hydrobiologia (2013) 709:159–171
165
Fig. 2 Linear correlation analyses of the species richness values for diatoms and desmids in samples (a), pH values and species
richness of desmids (b) and diatoms (c), and the mean cell biovolumes of both groups (d)
Size structure
The mean relative biovolumes of desmids and diatoms
(Supplementary Table 4) were positively correlated,
although this relationship was only marginally significant (Fig. 2d). The biovolumes of desmids were
highly positively related to the pH (Fig. 3a), the Ca
concentrations (Fig. 3b), and the ombro-minerotrophy
gradient. The latter correlation was still highly significant after the effects of all the other abiotic factors
were accounted for in the partial linear correlation
analysis (Table 2). In contrast to the desmid assemblages, the biovolumes of diatoms were not linearly
related to the pH gradient (Fig. 3c), although they were
marginally related to the ombro-minerotrophy gradient
(Table 2). However, they were significantly negatively
related to the conductivity values at sites (Fig. 3d;
Table 2). Interestingly, the relationship between the
pH and the mean diatom biovolumes suggested a
unimodal response of diatom mean cell sizes in the
community (Fig. 3c). This relationship was confirmed
by the second order polynomial univariate regression
analysis, which detected a highly significant relationship (F = 9.40, R2 = 0.34, P \ 0.001). The
results of the correlation analyses of the mean relative
surface areas and S:V ratios with abiotic factors were
very similar to the mean biovolumes of both groups
(Table 2). The multiple regression analyses of mean
desmid and diatom biovolumes on the abiotic factors
confirmed the dominant effect of the ombro-minerotrophy gradient on the desmid cell size data. This
qualitative factor was chosen as the first explanatory
variable on the basis of the AIC value (Table 3). The
Ca concentrations of the localities were included as the
second factor of the model that accounted for 58.0% of
the total variation in the mean desmid biovolumes. The
pH values were not included by the model selection
procedure because of their collinearity with the above-
123
166
Hydrobiologia (2013) 709:159–171
Fig. 3 Linear correlation analyses of the mean cell biovolumes of desmids versus pH (a) and Ca concentrations (b), and the mean cell
biovolumes of diatoms versus pH (c) and conductivity (d)
Table 3 The results of
multiple regression analyses
evaluating effects of abiotic
factors on the variation of
mean biovolumes of
desmids and diatoms at the
localities
Factor
Regression
coefficient
Standard
error
t-statistic
P value
***
***
R2/adjusted R2
Desmids, mean biovolumes
0.58/0.56
Intercept
Ombro-minerotrophy
4.29
0.36
0.07
0.10
60.31
3.78
Ca
0.20
0.10
2.09
*
Diatoms, mean biovolumes
The optimal models were
chosen using the forward
stepwise selection based on
the Akaike’s information
criterion
*** P \ 0.001,
** P \ 0.01, * P \ 0.05,
n.s.
P [ 0.05
0.62/0.58
Intercept
Conductivity
3.07
0.05
65.03
***
-0.32
0.05
-6.04
***
0.22
0.05
4.09
-0.14
0.05
-2.88
0.10
0.05
2.15
Ca
Total P
Fe
mentioned factors (Table 2). The mean biovolumes of
diatoms were optimally explained by a set of variables,
including conductivity values, Ca, total P, and Fe
123
***
**
*
concentrations (Table 3). This multiple regression
model explained about 62% of the variation in the
mean diatom biovolumes at the localities.
Hydrobiologia (2013) 709:159–171
167
Fig. 4 Results of variance partitioning into individual fractions
of environmental factors, spatial distances, combined effects of
space and environment (when applicable), and unexplained
variance (a). Individual analyses were based on complete species
data of desmids (b), small (c) and large (d) desmid species, as
well as on complete species data of diatoms (e) and small (f) and
large (g) diatoms. The proportions of unexplained variance were
cut off for better visibility of other fractions. *** P \ 0.001,
** P \ 0.01, * P \ 0.05, n.s. P [ 0.05
Variance partition of the environmental and spatial
factors by a series of partial RDA’s illustrated that the
environmental factors had strongly significant effects
on the species composition of both desmids and
diatoms (Fig. 4). The adjusted R2 values of the
variance explained by environmental factors were
consistently lower for desmid datasets (Fig. 4b–d),
than for diatoms (Fig. 4e–g). Interestingly, the purely
spatial effects were much more pronounced among the
large species of both groups, whereas their fractions
were considerably lower and even insignificant for
datasets consisting of small species (Fig. 4).
strongly acidic environments (Gross, 2000). Single
measurements of the total concentrations of nutrients
did not correlate with the pH levels or the ombrominerotrophic gradient, which supported previous
studies that reported a weak relationship between the
pH and trophic gradients in various types of peatlands
(Bridgham et al., 1996, 1998; Wheeler & Proctor,
2000). By contrast, the pH levels were strongly
correlated with the estimated ombrotrophic or minerotrophic status of sites. This supported a general
distinction between poorly buffered ombrotrophic
bogs and minerotrophic fens based on their pH levels
(Vitt, 2006). However, there were still several strongly
acidic, minerotrophic poor fens that conformed to the
notion that the Central European peatlands, which are
located near the southern limits of the global range of
these habitats, may not be unequivocally differentiated
solely on the basis of their pH levels (Hájek et al.,
2006). Nevertheless, we should note that our study was
only based on a limited number of samples and the
addition of further localities could possibly change the
observed pattern of the relation of peatland hydrography to the observed pH level.
Interestingly, the mean cell size dynamics of
desmids and diatoms was considerably different
among the sites. The mean biovolumes of desmids
were optimally explained by the ombro-minerotrophic
gradient and the Ca concentrations (tightly correlated
with the pH levels) in the multiple regression model,
Discussion
This study illustrated the dominant effect of pH on
species composition and species richness of the
phytobenthos of peatlands. Similar patterns of species
composition of peatland desmids and diatoms along a
pH gradient have been reported in previous studies
(Coesel, 1982; Mataloni, 1999; Lederer & Soukupová,
2002; Falasco & Bona, 2011). The pH was also found
as the main correlate of species richness in the peatland
phytobenthos (Mataloni, 1999; Neustupa et al., 2009).
This pattern was explained by the low availability of
nutrients in low pH conditions (Coesel, 1982) and the
direct stress effects of H? ions on the biological
membranes of unicellular organisms that inhabit
123
168
as well as in the univariate linear correlation analyses.
Conversely, the diatom mean biovolumes were not
unequivocally related to these factors. The purely
ombrotrophic acidified peat bogs, which typically had
low pH values, had considerably smaller desmids than
the more pH-neutral sites. This pattern may probably
be explained by the generally unstable conditions in
these poorly buffered ombrotrophic bogs. These
conditions generally favor smaller species with higher
growth rates and higher surface-to-volume ratios
(Friebele et al., 1978; De Nicola et al., 2006). We
can conclude that the data on the cell size distribution
of desmids in peatlands could possibly be used as a
quantitative measure of the community response to
key environmental gradients in these habitats, such as
the transition from a minerotrophic fen to an ombrotrophic bog stage, or acidification.
The cell size dynamics of diatoms clearly differed
and it was only weakly related to the ombro-minerotrophy gradient in the linear correlation analyses.
Interestingly, the obvious difference between desmids
and diatoms in terms of the relationship between mean
size and the pH gradient was not apparent in the more
acidic part of the scale. The pH levels were significantly unimodally related to the mean biovolumes of
diatoms, and there was an obvious positive relationship between these variables at pH levels of 3.3–5.3.
Consequently, diatoms had the largest mean biovolumes at pH of ca 5.2–5.3, whereas the mean
biovolumes of desmids increased at sites with higher
pH values. The decreasing size of diatoms relative to
the pH at minerotrophic sites where the pH was [5.3
was most likely caused by factors not considered in
this study. The nutrient concentrations and pH values
were not mutually related in sites with higher pH
values, and there was no significant relationship
between the nutrient levels and the mean diatom
biovolumes. The decrease in diatom sizes may be
related to biotic factors, such as increased competition
in less extreme habitats with a higher pH. This pattern
could have been evaluated based on changes in the
absolute quantities of diatoms, desmids, and other
phytobenthic groups in the samples, but the current
study was focused on the relative amounts of individual taxa in the desmid and diatom phytobenthic
assemblages; therefore, the present data were not
appropriate for such an analysis.
Alternatively, the less direct relationship between
ombro-minerotrophy and the mean cell sizes of
123
Hydrobiologia (2013) 709:159–171
diatoms compared to desmids may have been caused
by their different nutrient uptake scaling. Passy (2007)
argued that smaller diatoms may not have significantly
better per unit biovolume maximum nutrient uptake
rates than larger taxa because their cytoplasm is
effectively constrained to a thin layer located beneath
the plasmatic membrane. Therefore, the results of the
current study may also provide indirect support for this
hypothesis. The difference between desmids and
diatoms in their mean community biovolume dynamics, or, in other words, the lack of clear relation
between pH levels of the localities and the mean
biovolumes of diatoms could also be related to the
differences in infraspecific size variation between both
groups. The dimensions of individual desmid taxa are
fairly constant within comparatively narrow limits
(Coesel & Meesters, 2007) and are typically considerably lower than the differences among species.
Conversely, cell sizes of diatom species vary considerably as a result of the gradual size decrease during
their vegetative cell division (Round et al., 1990).
Therefore, natural diatom populations must cope
with regular fluctuations in their S:V ratios. Consequently, this can make the size structure of the diatom
communities in peatlands generally less susceptible to
the actual pH levels or hydrography of the individual
localities.
Interestingly, the mean biovolumes of diatoms were
significantly linearly related to the conductivity. This
relationship was also significant for the mean surface
areas and confirmed also by the multiple regression
analysis of the mean diatom biovolumes at the
localities. The conductivity values at the study sites
ranged from 29 to 245 lS cm-1, and this gradient was
comparable to those commonly reported in various
peatland habitats (Coesel, 1982; Mataloni, 1999;
Neustupa et al., 2012). Snoeijs et al. (2002) reported
significant effects of salinity on the mean cell sizes of
benthic diatoms in the Baltic Sea. However, the
profound salinity gradient of the Baltic Sea, where
localities had 10–100 times higher salinity values than
our samples, probably prevents a direct comparison
with the current study of peatlands. The relationship
between the mean community cell size and conductivity in the current study may reflect a more general
and previously unexplored pattern in diatom size
dynamics in peatlands, which should be investigated
further. It should be noted, however, that both the
multiple regression models explaining the variation in
Hydrobiologia (2013) 709:159–171
the mean biovolumes of desmids and diatoms using
the abiotic factors left relatively high proportions of
the variability unexplained. These unexplained fractions may possibly relate to some other important
abiotic factors that were not accounted in this study,
such as the dissolved organic carbon (DOC). Alternatively, this variation can also be related to the purely
neutral factors that principally cannot be explained by
local physico-chemical variables.
The spatial structure significantly affected the
community structure of desmids and diatoms at the
study sites. Interestingly, the geographic distances
were slightly more pronounced in diatoms, suggesting
that, compared with desmids, their community structure was relatively more strongly structured by large
scale processes, such as dispersal, climate, or history.
Similar significant effects on the community structure
at the regional level were reported in several studies of
benthic diatoms from streams (Soininen et al., 2004;
Heino et al., 2010; Smucker & Vis, 2011; Virtanen &
Soininen, 2012). Individual geographically constrained distribution areas were also recently detected
for several large desmid taxa in the genus Micrasterias
(Neustupa et al., 2011b). In this study, the large
species of desmids and diatoms were clearly more
geographically restricted than taxa with smaller cell
biovolumes. The significant effect of spatial distances
among localities, which was not correlated with
environmental data, although more important for large
diatoms, was still highly significant in large desmids.
By contrast, the datasets of small species lacked any
significant spatial structure that was not accounted for
by environmental factors. This pattern may suggest
that the spatial pattern in the species data was actually
related to dispersal limitations, which should be
considered as a structuring factor for phytobenthic
communities in peatlands. At a regional scale, dispersal limitations are probably more important for
large species, which may have less effective passive
dispersal (Heino & Soininen, 2006; Passy, 2007;
Vanormelingen et al., 2008a). Overall, our results
showed that local environmental parameters are
important for structuring the phytobenthic assemblages of peatlands, but they may mask the important
effects of dispersal-related processes at regional
scales, which are related to the cell sizes of individual
taxa. However, we should also note that species
concepts of microalgae, including desmids and diatoms, are notoriously unstable, and numerous recent
169
studies have detected cryptic or pseudocryptic diversity within traditional morphospecies (Vanormelingen
et al., 2008b; Evans et al., 2009; Poulı́čková et al.,
2010). Small desmid and diatom species typically
have fewer conspicuous morphological discriminatory
characters. Thus, there may be more cryptic species in
the relatively small taxa compared with larger species.
This may lead to an underestimation of species
diversity among small taxa in ecological studies,
including this one, based on morphological species
concepts. Thus, the lack of significant geographic
structure among the small taxa in the current study
may be explained by the low reliability of taxonomic
concepts in these species. To the best of our knowledge, there have been no rigorous analyses of the level
of cryptic species differentiation relative to the cell
size of individual traditional taxa. However, such data
would be very useful for estimating the size-related
dispersal limits of microalgae.
Acknowledgments This study was supported by Grant No.
13-29315S from the Czech Science Foundation. The authors are
indebted to Magda Škaloudová for her sampling assistance. The
authors thank Bioedit proofreading service for the language and
style corrections. We thank the anonymous reviewers for their
recommendations that led us to the improvements of the
manuscript.
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Vanormelingen, P., V. A. Chepurnov, D. G. Mann, K. Sabbe &
W. Vyverman, 2008b. Genetic divergence and reproductive
barriers among morphologically heterogeneous sympatric
clones of Eunotia bilunaris sensu lato (Bacillariophyta).
Protist 159: 73–90.
Venables, W. N. & B. D. Ripley, 2002. Modern Applied Statistics with S. Springer, New York.
Virtanen, L. & J. Soininen, 2012. The roles of environment and
space in shaping stream diatom communities. European
Journal of Phycology 47: 160–168.
Vitt, D. H., 2006. Functional characteristics and indicators of
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Boreal Peatland Ecosystems. Springer, Berlin: 9–24.
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study from streams in the Laurentians (Quebec, Canada).
Freshwater Biology 47: 325–340.
123
Electronic supplementary material, Table 1. The abiotic characteristics of investigated localities
No.
Region / Locality
B1
Denmark, Bornholm, Bastemose
B2
Denmark, Bornholm, Kohullet
B3
Denmark, Bornholm, Langemose
B4
B5
Denmark, Bornholm, wetlands near Kohullet
pool
Denmark, Bornholm, Gamlemosen
B6
Denmark, Bornholm, Gregers Myr
B7
Denmark, Bornholm, Barmose
B8
Denmark, Bornholm, Oksemyr
B9
B10
Denmark, Bornholm, an unnamed lake near
Gamledam
Denmark, Bornholm, a pool close to Majdal
B11
Denmark, Bornholm, Majdal
B12
Denmark, Bornholm, Grydedal
D1
D7
Czech Republic, Doksy region, a littoral of the
Mácha's Lake (Hirschberger Großteich)
Czech Republic, Doksy region, a pool in the
Northern “Swamp” mire
Czech Republic, Doksy region, a pool in the
Northern “Swamp” mire
Czech Republic, Doksy region, a pool in the
Eastern “Swamp” mire
Czech Republic, Doksy region, pool in the
Eastern “Swamp” mire
Czech Republic, Doksy region, a pool in the
Southern “Swamp” mire
Czech Republic, Doksy region, Břehyně mire
D8
Czech Republic, Doksy region, Břehyně mire
D2
D3
D4
D5
D6
Geographic
coordinates
55°07´36.67´´ N
14°56´43.57´´ E
55°07´22.41´´ N
14°53´29.50´´ E
55°07´19.26´´ N
14°53´43.74´´ E
55°07´31.45´´ N
14°53´29.75´´ E
55°08´15.97´´ N
14°54´31.35´´ E
55°07´54.83´´ N
14°53´43.17´´ E
55°07´55.73´´ N
14°56´33.05´´ E
55°05´33.25´´ N
15°06´10.10´´ E
55°05´53.05´´ N
15°06´11.15´´ E
55°05´44.42´´ N
15°05´15.97´´ E
55°05´39.75´´ N
15°05´06.62´´ E
55°05´21.84´´ N
15°05´37.27´´ E
50°34´59.74´´ N
14°38´15.51´´ E
50°35´39.39´´ N
14°38´35.34´´ E
50°35´41.58´´ N
14°38´44.39´´ E
50°34´33.92´´ N
14°40´15.29´´ E
50°34´46.10´´ N
14°40´04.29´´ E
50°34´41.68´´ N
14°39´41.17´´ E
50°35´02.65´´ N
14°43´01.99´´ E
50°35´04.12´´ N
14°42´24.83´´ E
pH
7.3
Conductivity
(μS.cm-1)
200
TN
(mg.l-1)
0.71
TP
(mg.l-1)
0.026
Ca
(mg.l-1)
21.2
Fe
(μg.l-1)
121
Ombro- /
Minerotrophy
3
5.7
74
0.62
0.01
3.20
243
3
6.2
118
1.65
0.059
12.7
149
3
5.2
55
0.73
0.032
2.70
183
3
7.1
233
0.64
0.03
28.0
157
3
6.7
111
0.87
0.026
12.1
232
3
5.3
36
1.09
0.023
2.43
524
3
6.0
44
1.10
0.046
2.93
349
3
5.1
57
1.65
0.072
2.18
521
3
5.0
30
1.25
0.043
0.59
232
2
6.5
76
0.69
0.039
7.20
394
3
5.6
76
2.84
0.141
4.18
2060
3
6.3
245
0.69
0.023
39.4
377
3
5.2
40
0.91
0.01
4.87
847
3
5.6
51
1.57
0.031
2.52
6670
3
5.3
65
0.58
0.01
4.59
393
3
4.3
83
1.23
0.01
4.83
516
3
7.0
224
0.84
0.01
27.6
160
3
5.1
86
1.83
0.024
6.02
807
3
6.4
191
1.78
0.062
19.3
304
3
D9
Czech Republic, Doksy region, Břehyně mire
D10
Czech Republic, Doksy region, Břehyně mire
K1
P2
Czech Republic, Krušné Hory Mts., Spáleniště
peat bog
Czech Republic, Krušné Hory Mts., Spáleniště
peat bog
Czech Republic, Krušné Hory Mts., a small
mire near Horní Blatná
Czech Republic, Krušné Hory Mts., Velký
Močál peat bog
Czech Republic, Krušné Hory Mts., Velký
Močál peat bog
Czech Republic, Krušné Hory Mts., Volárna
peat bog
Czech Republic, Krušné Hory Mts., an
unnamed peat bog near Lícha
Czech Republic, Krušné Hory Mts., Lícha
mire
Czech Republic, Krušné Hory Mts., a mire
near Přebuz
Czech Republic, Krušné Hory Mts., a mire
near Přebuz
Czech Republic, Krušné Hory Mts., an
unnamed peat bog near Roudné
Poland, Pomerania, Wierzchomińskie Bagno
peat bog
Poland, Pomerania, Warnie Bagno peat bog
P3
Poland, Pomerania, Warnie Bagno peat bog
P4
Poland, Pomerania, Warnie Bagno peat bog
P5
Poland, Pomerania, a pool near Podborsko
P6
Poland, Pomerania, a pool near Dobrowieckie
Małe Lake
Poland, Pomerania, a pool near Dobrowieckie
Małe Lake
K2
K3
K4
K5
K6
K7
K8
K9
K10
K11
P1
P7
50°35´00.64´´ N
14°42´15.34´´ E
50°34´24.45´´ N
14°41´46.32´´ E
50°23´54.22´´ N
12°49´15.83´´ E
50°23´50.67´´ N
12°49´21.26´´ E
50°22´52.37´´ N
12°44´22.03´´ E
50°23´43.27´´ N
12°38´04.56´´ E
50°23´47.61´´ N
12°38´15.08´´ E
50°24´03.07´´ N
12°38´10.53´´ E
50°23´28.98´´ N
12°37´13.21´´ E
50°23´29.54´´ N
12°37´26.68´´ E
50°22´39.80´´ N
12°36´24.89´´ E
50°22´41.42´´ N
12°36´39.83´´ E
50°21´29.56´´ N
12°38´45.26´´ E
54°09´23.89´´ N
15°56´54.07´´ E
54°08´51.27´´ N
15°55´55.32´´ E
54°08´30.08´´ N
15°56´11.72´´ E
54°08´27.89´´ N
15°55´46.62´´ E
53°56´06.68´´ N
16°06´38.64´´ E
53°57´12.43´´ N
16°05´58.25´´ E
53°57´07.11´´ N
16°06´20.06´´ E
5.3
68
2.61
0.056
14.1
2380
3
5.1
56
2.300
0.028
10.3
1950
3
3.7
76
0.80
0.021
0.46
225
1
3.5
83
0.97
0.01
0.47
528
1
4.8
69
0.50
0.01
2.01
142
3
3.4
93
0.95
0.027
0.61
669
1
3.3
150
1.21
0.01
0.46
1120
1
3.4
107
0.93
0.01
0.34
504
1
3.4
125
1.45
0.02
0.57
914
1
3.6
68
0.81
0.01
0.87
1530
2
5.1
49
0.50
0.01
1.93
1700
2
5.2
48
0.55
0.01
2.40
477
2
3.8
75
0.95
0.01
0.39
635
1
3.5
175
1.76
0.034
2.24
888
2
3.3
177
2.00
0.051
0.89
592
1
3.5
182
2.81
0.366
1.86
586
2
3.6
154
2.44
0.039
0.56
469
2
4.1
48
1.23
0.032
0.19
145
3
4.1
33
0.68
0.01
0.25
107
3
4.0
29
0.78
0.041
0.48
270
2
Electronic supplementary material, Table 2. Species list of the samples – desmids. The names of individual taxa were arranged alphabetically and their
nomenclature follows Algaebase (http://www.algaebase.org) and Index Nominum Algarum (http://ucjeps.berkeley.edu/INA.html).
Actinotaenium cucurbita
Actinotaenium inconspicuum
Actinotaenium perminutum
Actinotaenium silvae-nigrae
Actinotaenium turgidum
Bambusina borreri
Closterium acutum
Closterium angustatum
Closterium archerianum var. pseudocynthia
Closterium attenuatum
Closterium baillyanum var. alpinum
Closterium baillyanum var. baillyanum
Closterium calosporum var. calosporum
Closterium closterioides var. intermedium
Closterium cornu
Closterium costatum
Closterium delpontei
Closterium dianae var. arcuatum
Closterium dianae var. dianae
Closterium dianae var. minus
Closterium dianae var. pseudodianae
Closterium dianae var. rectius
Closterium didymotocum
Closterium directum
Closterium ehrenbergii
Closterium gracile
Closterium idiosporum
Closterium incurvum
Closterium intermedium
Closterium juncidum
Closterium kützingii
Closterium leibleinii
Closterium lineatum var. elongatum
Closterium lineatum var. lineatum
Closterium lunula
Closterium moniliferum
Closterium navicula
Closterium parvulum
Closterium praelongum var. brevius
Closterium pronum
B1
0
0
0
0
0
0
2
0
0
0
0
0
1
1
0
2
1
0
1
0
0
1
0
0
3
0
1
1
3
0
5
2
0
0
1
0
0
3
1
1
B2
0
0
0
0
0
0
0
0
0
0
0
0
0
7
1
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
B3
0
1
0
0
0
0
0
0
0
0
0
6
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
B4
0
0
0
0
0
0
11
0
0
0
0
0
0
0
0
4
0
0
0
24
0
0
0
0
0
0
0
17
0
1
0
0
0
0
2
0
0
19
0
0
B5
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
13
0
0
0
0
14
0
0
0
4
0
3
0
0
0
0
11
0
63
0
0
B6
0
0
0
0
0
0
1
0
0
0
0
1
7
0
0
0
1
0
4
13
0
0
0
0
0
25
0
0
0
0
0
0
0
0
0
0
0
5
0
1
B7
0
0
0
0
0
19
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
7
0
0
0
0
0
0
0
0
0
0
3
B8
0
0
0
0
0
0
9
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
1
0
0
0
B9
0
0
0
0
0
0
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
B10
37
0
0
0
0
0
88
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
B11
0
0
0
0
0
0
0
0
0
0
0
0
4
0
0
7
0
0
0
3
0
0
0
0
0
4
3
1
0
0
5
0
1
0
0
1
0
4
0
0
B12
0
0
0
0
0
0
0
0
12
0
0
0
3
0
0
12
0
75
0
0
0
0
0
0
0
0
8
38
0
0
18
0
0
0
0
0
0
5
0
0
D1
0
0
3
0
5
0
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
26
0
0
0
0
5
0
0
0
0
9
0
25
0
0
D2
0
0
0
0
0
0
2
0
0
0
1
0
0
0
90
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
D3
1
0
0
0
0
0
147
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
D4
69
1
0
0
0
6
0
0
0
0
5
0
0
0
0
0
0
0
0
5
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
D5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
D6
0
0
0
0
0
0
0
0
0
0
1
0
14
0
0
0
0
0
0
26
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
D7
0
0
0
0
0
29
1
1
0
1
17
2
0
1
2
0
0
1
0
14
0
0
0
0
0
42
0
0
3
10
0
0
0
0
1
0
0
0
0
0
D8
0
0
0
0
5
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
1
0
0
1
0
1
0
2
0
0
0
0
13
0
0
8
0
0
Closterium ralfsi var. hybridum
Closterium rostratum
Closterium setaceum
Closterium strigosum
Closterium striolatum
Closterium sublaterale
Closterium tumidulum
Closterium tumidum
Closterium turgidum
Closterium venus
Cosmarium abbreviatum var. germanicum
Cosmarium amoenum
Cosmarium angulosum var. angulosum
Cosmarium angulosum var. concinnum
Cosmarium bioculatum
Cosmarium blytti var. novae-sylvae
Cosmarium boeckii
Cosmarium botrytis var. tumidum
Cosmarium caelatum
Cosmarium connatum
Cosmarium conspersum var. latum
Cosmarium contractum var. contractum
Cosmarium contractum var. minutum
Cosmarium debaryi
Cosmarium depressum
Cosmarium difficile
Cosmarium formosulum
Cosmarium humile
Cosmarium impressulum
Cosmarium margaritatum
Cosmarium margaritiferum
Cosmarium obliquum
Cosmarium obsoletum
Cosmarium ochthodes
Cosmarium ornatum
Cosmarium orthopunctulatum
Cosmarium ovale
Cosmarium pachydermum
Cosmarium paragranatoides
Cosmarium phaseolus var. elevatum
Cosmarium polygonatum
Cosmarium praemorsum
Cosmarium pseudoornatum
Cosmarium pseudopyramidatum
Cosmarium pseudoretusum
1
0
0
0
0
0
0
0
1
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
1
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
0
0
1
19
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
10
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
18
0
0
0
0
0
0
0
0
0
0
0
0
0
0
7
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
0
0
0
0
0
0
0
22
0
0
0
0
0
0
4
19
0
0
0
0
0
0
0
0
0
0
0
0
18
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
3
1
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
1
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
0
0
0
0
0
0
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
17
0
0
0
0
0
0
1
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
1
0
2
1
0
3
0
0
0
0
0
0
0
0
0
0
0
3
0
0
0
0
19
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
16
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
7
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
10
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
25
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
17
0
0
0
0
0
175
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
10
0
0
0
0
0
1
0
6
0
0
0
2
0
2
0
1
1
0
0
0
0
0
1
0
1
1
0
0
0
0
16
0
0
3
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
4
1
0
2
0
0
7
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
1
2
0
0
0
0
0
0
0
0
0
0
0
0
13
0
0
0
6
0
0
0
0
0
0
0
Cosmarium punctulatum var. subpunctulatum
Cosmarium pygmaeum var. heimerlii
Cosmarium pygmaeum var. pygmaeum
Cosmarium pyramidatum
Cosmarium quadratulum var. boldtii
Cosmarium quadratum
Cosmarium regnellii
Cosmarium reniforme
Cosmarium simplicius
Cosmarium sphagnicolum
Cosmarium striolatum
Cosmarium subadoxum
Cosmarium subcostatum var. minus
Cosmarium subgranatum
Cosmarium subtumidum
Cosmarium tenue
Cosmarium tetraophtalmum
Cosmarium thwaitesii var. penioides
Cosmarium tinctum var. intermedium
Cosmarium tinctum var. tinctum
Cosmarium ungerianum var. subtriplicatum
Cosmarium variolatum
Cosmarium varsoviense
Desmidium aptogonum
Desmidium grevillei
Desmidium swartzii
Euastrum ansatum var. ansatum
Euastrum ansatum var. rhomboidale
Euastrum bidentatum var. speciosum
Euastrum binale var. gutwinskii
Euastrum dubium
Euastrum gayanum
Euastrum humerosum
Euastrum oblongum
Euastrum pectinatum
Euastrum pulchellum
Euastrum subalpinum
Euastrum verrucosum
Gonatozygon brebissonii
Haplotaenium indentatum
Haplotaenium minutum
Haplotaenium rectum
Hyalotheca dissiliens var. dissiliens
Hyalotheca dissiliens var. minor
Hyalotheca mucosa
1
0
0
0
1
1
3
1
0
0
0
0
0
3
0
0
0
0
0
4
0
0
0
0
0
80
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
16
0
0
0
0
0
0
12
0
0
0
0
0
0
0
0
0
0
0
0
9
0
0
0
0
0
0
17
0
0
2
0
4
0
0
0
0
0
0
0
0
0
0
10
0
0
0
0
0
0
0
0
4
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
154
1
0
0
0
0
9
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
10
3
0
0
0
0
0
0
0
0
0
0
0
6
0
0
0
0
0
0
9
0
0
2
0
19
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
9
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5
1
0
0
0
0
0
0
0
0
0
0
0
19
15
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
8
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
24
0
0
0
0
0
0
1
0
0
4
0
60
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
2
0
3
0
0
0
0
0
0
0
0
0
0
14
0
0
0
0
0
0
7
0
0
0
0
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
0
0
0
0
0
0
0
0
0
0
0
0
17
0
0
0
0
0
107
2
0
0
0
0
8
0
0
0
0
0
0
0
0
0
0
0
14
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
41
0
0
0
52
0
0
0
2
2
0
0
0
0
2
2
0
0
3
0
0
0
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
8
0
0
0
0
0
24
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
8
0
0
0
0
14
0
0
0
0
0
0
0
17
0
0
6
6
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
23
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
3
0
0
0
0
0
0
6
0
0
0
0
0
0
0
0
1
6
0
21
0
4
0
0
0
0
0
0
1
0
0
0
0
0
0
0
30
0
0
0
0
0
11
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
2
0
0
0
0
4
0
0
2
0
0
0
0
1
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
3
0
0
0
0
2
0
0
2
0
0
0
0
0
0
0
0
0
3
0
0
8
0
0
0
0
0
0
8
6
6
0
3
1
0
0
3
0
0
0
Micrasterias americana
Micrasterias apiculata
Micrasterias brachyptera
Micrasterias crux-melitensis
Micrasterias fimbriata
Micrasterias jenneri
Micrasterias pinnatifida
Micrasterias rotata
Micrasterias semiradiata
Micrasterias thomasiana var. notata
Micrasterias truncata var. quadrata
Micrasterias truncata var. truncata
Penium cylindrus
Penium spirostriolatum
Pleurotaenium archeri
Pleurotaenium coronatum
Pleurotaenium crenulatum
Pleurotaenium ehrenbergii
Pleurotaenium nodulosum
Pleurotaenium trabecula
Pleurotaenium truncatum
Sphaerozosma filiforme
Spondylosium pulchellum
Staurastrum alternans
Staurastrum arachne
Staurastrum avicula
Staurastrum boreale
Staurastrum borgeanum var. minor
Staurastrum brachiatum
Staurastrum controversum
Staurastrum crassangulatum
Staurastrum dilatatum
Staurastrum dispar
Staurastrum furcatum var. aciculiferum
Staurastrum furcatum var. furcatum
Staurastrum hirsutum
Staurastrum inflexum
Staurastrum lapponicum
Staurastrum manfeldtii
Staurastrum margaritaceum
Staurastrum micron
Staurastrum minimum
Staurastrum oligacanthum
Staurastrum orbiculare
Staurastrum oxyacanthum
3
1
0
1
11
0
0
0
1
0
0
0
0
0
2
0
0
1
0
0
0
0
0
0
0
23
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
1
0
0
0
0
0
0
0
0
93
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
3
0
6
0
0
0
12
0
0
0
5
0
0
0
0
0
0
0
1
0
12
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
6
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
0
1
0
0
0
0
0
0
0
7
0
20
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
8
0
16
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
7
0
0
0
22
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
12
0
0
4
0
0
0
0
0
0
0
0
0
0
0
5
0
0
0
0
2
0
0
5
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
6
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
37
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
6
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
19
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5
0
0
0
1
3
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
17
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
2
0
0
0
12
0
4
0
0
0
0
0
0
2
0
0
0
0
0
0
3
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
27
0
0
0
0
0
28
1
2
0
0
0
2
0
2
0
0
0
0
0
0
0
0
9
9
4
0
0
10
0
0
0
0
0
0
4
9
0
0
0
0
0
0
0
0
0
0
1
5
0
Staurastrum paradoxum
Staurastrum polymorphum var. pygmaeum
Staurastrum polytrichum
Staurastrum pseudotetracerum
Staurastrum punctulatum
Staurastrum pungens
Staurastrum simonyi
Staurastrum striatum
Staurastrum striolatum
Staurastrum teliferum
Staurastrum tetracerum
Staurodesmus brevispina
Staurodesmus convergens
Staurodesmus dejectus var. apiculatus
Staurodesmus dejectus var. dejectus
Staurodesmus extensus var. extensus
Staurodesmus extensus var. isthmosus
Staurodesmus glaber
Staurodesmus incus var. incus
Staurodesmus incus var. indentatus
Staurodesmus omaerae
Staurodesmus patens
Teilingia granulata
Tetmemorus brebissonii var. minor
Tetmemorus granulatus
Tetmemorus laevis var. laevis
Tetmemorus laevis var. minutus
Xanthidium antilopaeum
Xanthidium armatum
Xanthidium cristatum
Xanthidium octocorne
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
0
0
0
0
0
0
0
0
0
0
0
0
7
0
0
0
0
0
0
0
2
0
0
0
0
2
0
1
0
0
0
0
0
3
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
7
0
0
0
0
0
0
0
0
0
3
0
0
0
0
0
0
0
2
0
0
0
0
1
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
2
1
0
0
0
0
0
1
0
0
0
0
12
0
0
0
0
0
0
0
36
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
53
0
0
0
0
0
0
9
0
0
0
0
0
11
0
0
0
0
0
9
0
0
0
21
0
0
34
0
4
3
0
0
0
0
12
0
4
0
0
0
0
4
0
35
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
1
0
1
0
0
8
0
0
0
0
0
0
0
0
12
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
3
0
0
6
0
0
0
0
0
0
0
0
2
0
15
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4
0
0
3
4
0
0
0
0
0
0
6
27
0
0
0
1
0
20
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
18
8
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
12
31
0
0
9
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
2
0
0
0
0
0
0
0
14
0
0
0
0
0
1
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
9
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
0
0
0
0
0
0
Electronic supplementary material, Table 2 (cont)
Actinotaenium cucurbita
Actinotaenium inconspicuum
Actinotaenium perminutum
Actinotaenium silvae-nigrae
Actinotaenium turgidum
Bambusina borreri
Closterium acutum
Closterium angustatum
Closterium archerianum var. pseudocynthia
Closterium attenuatum
Closterium baillyanum var. alpinum
Closterium baillyanum var. baillyanum
Closterium calosporum var. calosporum
Closterium closterioides var. intermedium
Closterium cornu
Closterium costatum
Closterium delpontei
Closterium dianae var. arcuatum
Closterium dianae var. dianae
Closterium dianae var. minus
Closterium dianae var. pseudodianae
Closterium dianae var. rectius
Closterium didymotocum
Closterium directum
Closterium ehrenbergii
Closterium gracile
Closterium idiosporum
Closterium incurvum
Closterium intermedium
Closterium juncidum
Closterium kützingii
Closterium leibleinii
Closterium lineatum var. elongatum
Closterium lineatum var. lineatum
Closterium lunula
Closterium moniliferum
Closterium navicula
Closterium parvulum
Closterium praelongum var. brevius
Closterium pronum
Closterium ralfsi var. hybridum
Closterium rostratum
D9
0
0
0
0
0
0
6
0
0
0
0
0
0
0
0
4
0
0
1
0
7
0
0
0
0
0
0
0
3
11
1
0
0
38
0
0
9
0
0
9
3
0
D10
0
0
0
0
0
40
0
0
0
0
0
0
0
0
0
17
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
K1
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
K2
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
K3
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
K4
10
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
K5
37
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
K6
23
0
0
7
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
K7
27
0
0
88
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
K8
5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
7
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
K9
2
0
0
0
0
0
0
0
0
0
0
0
0
0
23
9
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
36
0
0
0
81
K10
1
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
5
0
0
0
1
K11
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
18
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
P1
25
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
P2
39
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
P3
119
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
P4
160
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
P5
18
0
0
0
0
85
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4
0
0
0
0
0
0
0
0
0
0
0
0
P6
0
0
0
0
0
16
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
P7
41
0
0
0
0
4
22
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
15
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Closterium setaceum
Closterium strigosum
Closterium striolatum
Closterium sublaterale
Closterium tumidulum
Closterium tumidum
Closterium turgidum
Closterium venus
Cosmarium abbreviatum var. germanicum
Cosmarium amoenum
Cosmarium angulosum var. angulosum
Cosmarium angulosum var. concinnum
Cosmarium bioculatum
Cosmarium blytti var. novae-sylvae
Cosmarium boeckii
Cosmarium botrytis var. tumidum
Cosmarium caelatum
Cosmarium connatum
Cosmarium conspersum var. latum
Cosmarium contractum var. contractum
Cosmarium contractum var. minutum
Cosmarium debaryi
Cosmarium depressum
Cosmarium difficile
Cosmarium formosulum
Cosmarium humile
Cosmarium impressulum
Cosmarium margaritatum
Cosmarium margaritiferum
Cosmarium obliquum
Cosmarium obsoletum
Cosmarium ochthodes
Cosmarium ornatum
Cosmarium orthopunctulatum
Cosmarium ovale
Cosmarium pachydermum
Cosmarium paragranatoides
Cosmarium phaseolus var. elevatum
Cosmarium polygonatum
Cosmarium praemorsum
Cosmarium pseudoornatum
Cosmarium pseudopyramidatum
Cosmarium pseudoretusum
Cosmarium punctulatum var. subpunctulatum
Cosmarium pygmaeum var. heimerlii
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
20
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
16
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
176
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
6
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4
0
0
0
0
0
0
0
0
0
0
0
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
145
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
11
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Cosmarium pygmaeum var. pygmaeum
Cosmarium pyramidatum
Cosmarium quadratulum var. boldtii
Cosmarium quadratum
Cosmarium regnellii
Cosmarium reniforme
Cosmarium simplicius
Cosmarium sphagnicolum
Cosmarium striolatum
Cosmarium subadoxum
Cosmarium subcostatum var. minus
Cosmarium subgranatum
Cosmarium subtumidum
Cosmarium tenue
Cosmarium tetraophtalmum
Cosmarium thwaitesii var. penioides
Cosmarium tinctum var. intermedium
Cosmarium tinctum var. tinctum
Cosmarium ungerianum var. subtriplicatum
Cosmarium variolatum
Cosmarium varsoviense
Desmidium aptogonum
Desmidium grevillei
Desmidium swartzii
Euastrum ansatum var. ansatum
Euastrum ansatum var. rhomboidale
Euastrum bidentatum var. speciosum
Euastrum binale var. gutwinskii
Euastrum dubium
Euastrum gayanum
Euastrum humerosum
Euastrum oblongum
Euastrum pectinatum
Euastrum pulchellum
Euastrum subalpinum
Euastrum verrucosum
Gonatozygon brebissonii
Haplotaenium indentatum
Haplotaenium minutum
Haplotaenium rectum
Hyalotheca dissiliens var. dissiliens
Hyalotheca dissiliens var. minor
Hyalotheca mucosa
Micrasterias americana
Micrasterias apiculata
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
1
0
0
0
0
0
81
0
0
0
5
0
0
0
0
0
0
0
0
0
0
0
8
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
95
0
5
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
4
0
0
0
156
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
181
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
182
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
162
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
170
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
79
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
36
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
7
0
0
0
0
0
0
151
0
16
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
13
0
0
0
0
0
0
0
112
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
131
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
75
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
42
0
0
0
0
0
0
0
14
0
0
14
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
0
0
0
0
0
0
0
1
0
0
0
0
0
0
4
0
0
0
15
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
60
0
0
0
0
0
0
Micrasterias brachyptera
Micrasterias crux-melitensis
Micrasterias fimbriata
Micrasterias jenneri
Micrasterias pinnatifida
Micrasterias rotata
Micrasterias semiradiata
Micrasterias thomasiana var. notata
Micrasterias truncata var. quadrata
Micrasterias truncata var. truncata
Penium cylindrus
Penium spirostriolatum
Pleurotaenium archeri
Pleurotaenium coronatum
Pleurotaenium crenulatum
Pleurotaenium ehrenbergii
Pleurotaenium nodulosum
Pleurotaenium trabecula
Pleurotaenium truncatum
Sphaerozosma filiforme
Spondylosium pulchellum
Staurastrum alternans
Staurastrum arachne
Staurastrum avicula
Staurastrum boreale
Staurastrum borgeanum var. minor
Staurastrum brachiatum
Staurastrum controversum
Staurastrum crassangulatum
Staurastrum dilatatum
Staurastrum dispar
Staurastrum furcatum var. aciculiferum
Staurastrum furcatum var. furcatum
Staurastrum hirsutum
Staurastrum inflexum
Staurastrum lapponicum
Staurastrum manfeldtii
Staurastrum margaritaceum
Staurastrum micron
Staurastrum minimum
Staurastrum oligacanthum
Staurastrum orbiculare
Staurastrum oxyacanthum
Staurastrum paradoxum
Staurastrum polymorphum var. pygmaeum
0
0
1
0
0
0
0
4
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
2
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
21
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
17
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
27
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
0
0
0
0
0
0
0
0
0
0
0
0
0
46
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
78
0
0
0
0
0
0
0
0
0
0
54
0
0
0
0
0
0
0
0
0
0
0
6
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
0
0
0
0
0
0
0
0
0
0
6
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Staurastrum polytrichum
Staurastrum pseudotetracerum
Staurastrum punctulatum
Staurastrum pungens
Staurastrum simonyi
Staurastrum striatum
Staurastrum striolatum
Staurastrum teliferum
Staurastrum tetracerum
Staurodesmus brevispina
Staurodesmus convergens
Staurodesmus dejectus var. apiculatus
Staurodesmus dejectus var. dejectus
Staurodesmus extensus var. extensus
Staurodesmus extensus var. isthmosus
Staurodesmus glaber
Staurodesmus incus var. incus
Staurodesmus incus var. indentatus
Staurodesmus omaerae
Staurodesmus patens
Teilingia granulata
Tetmemorus brebissonii var. minor
Tetmemorus granulatus
Tetmemorus laevis var. laevis
Tetmemorus laevis var. minutus
Xanthidium antilopaeum
Xanthidium armatum
Xanthidium cristatum
Xanthidium octocorne
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
31
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
147
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
6
0
0
0
0
0
0
0
0
0
0
5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
19
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
56
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
30
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
38
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
16
0
7
0
1
1
0
0
Electronic supplementary material, Table 3. Species list of the samples – diatoms. The names of individual taxa were arranged alphabetically and their
nomenclature follows Algaebase (http://www.algaebase.org) and Index Nominum Algarum (http://ucjeps.berkeley.edu/INA.html).
Achnanthes conspicua
Achnanthes linearioides
Achnanthidium minutissimum
Adlafia minuscula
Amphipleura pellucida
Brachysira brebissonii
Brachysira neglectissima
Brachysira neoexilis
Brachysira serians
Caloneis tenuis
Caloneis undulata
Chammaepinnularia mediocris
Chammaepinnularia hassiaca
Cocconeis pseudolineata
Cymbella helvetica
Cymbella lanceolata
Cymbopleura naviculiformis.
Diadesmis contenta
Encyonema minutum
Encyonema neogracile
Encyonema silesiacum
Encyonopsis cesatii
Encyonopsis falaisensis
Eolimna minima
Eunotia arculus
Eunotia arcus
Eunotia bilunaris
Eunotia boreotenuis
Eunotia botuliformis
Eunotia circumborealis
Eunotia glacialifalsa
Eunotia glacialis
Eunotia groenlandica
Eunotia implicata
Eunotia incisa
Eunotia inflata
Eunotia jemtlandica
Eunotia meisterii
Eunotia microcephala
Eunotia minor
B1
3
7
33
0
17
0
20
0
0
0
0
0
0
6
1
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
1
0
6
2
0
0
0
0
0
B2
0
0
0
0
0
0
8
0
0
0
0
8
0
0
0
0
0
0
0
0
0
0
0
0
12
0
12
0
0
0
0
0
0
3
0
0
0
26
0
0
B3
0
0
24
0
0
0
9
0
0
0
0
0
0
0
0
0
0
0
3
12
0
0
0
0
13
0
34
0
0
0
1
0
0
12
0
0
0
1
0
0
B4
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
5
0
45
0
0
0
0
0
0
1
0
0
0
5
0
0
B5
0
12
99
0
0
0
0
0
0
0
0
0
0
1
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
12
0
0
0
0
0
5
B6
0
9
66
0
0
0
3
24
0
0
0
1
0
0
0
0
0
0
1
0
0
0
0
0
0
0
1
0
1
0
0
0
0
26
0
0
2
0
0
0
B7
0
0
2
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
2
0
0
0
0
0
21
0
0
0
0
1
0
0
B8
0
0
4
0
0
0
40
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
1
5
0
7
0
0
0
0
0
0
0
0
0
0
0
0
0
B9
0
0
0
0
0
0
3
0
0
0
0
0
0
0
0
0
0
0
0
3
0
0
0
0
1
0
19
0
0
0
0
0
0
0
0
0
1
8
0
0
B10
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
159
0
0
0
0
0
0
1
2
0
0
0
0
0
B11
0
0
9
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
3
0
0
0
6
0
0
16
0
0
0
0
0
0
0
0
0
0
0
0
0
B12
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
60
0
0
0
0
0
0
68
7
0
0
0
0
0
D1
0
0
52
0
0
0
5
0
0
4
0
0
0
0
0
0
0
0
1
0
0
0
1
0
0
0
32
1
0
0
0
0
0
23
0
0
0
0
0
0
D2
0
0
0
0
0
6
1
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
71
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
D3
0
0
0
0
0
0
0
0
128
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
D4
0
0
0
0
0
11
4
0
58
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5
0
2
0
0
0
5
0
0
0
0
0
0
0
0
0
D5
0
0
1
0
0
0
0
0
10
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
26
0
0
0
0
0
0
0
0
0
D6
0
0
25
0
0
0
80
0
0
0
0
6
8
0
0
0
0
0
0
0
0
2
19
0
3
1
0
0
0
0
0
0
1
9
0
0
0
0
0
0
D7
0
0
0
0
0
0
26
0
0
0
0
3
0
0
0
0
0
0
0
2
0
0
0
0
38
0
18
0
0
0
0
0
0
0
0
0
0
0
0
0
D8
0
0
15
0
0
0
0
0
0
4
0
5
11
0
0
0
1
0
3
3
0
0
3
2
0
1
2
0
0
0
1
0
0
4
0
0
0
0
0
0
Eunotia naegelii
Eunotia nymanniana
Eunotia paludosa
Eunotia rhomboidea
Eunotia subarcuatoides
Eunotia tenella / exigua
Eunotia tetraodon
Eunotia trinacria
Eunotia ursamoioris
Fallacia vitrea
Fragilaria capucina
Fragilaria exigua
Fragilaria gracilis
Fragilaria virescens
Frustulia erifuga
Frustulia saxonica / crassinervia
Gomphonema acidoclinatum
Gomphonema acuminatum
Gomphonema angustum
Gomphonema clavatum
Gomphonema gracile
Gomphonema minutum
Gomphonema parvulum
Gomphonema subtile
Kobayasiella parasubtilissima
Meridion circulare
Navicula cryptocephala
Navicula cryptotenella
Navicula gregaria
Navicula pseudostauron
Navicula radiosa
Navicula tridentula
Neidium hercynicum
Nitzschia acidoclinata
Nitzschia paleacea
Nitzschia paleaformis
Peronia fibula
Pinnularia biceps
Pinnularia complexa
Pinnularia cruxarea
Pinnularia divergens
Pinnularia erratica
Pinnularia gibbiformis
Pinnularia julma
Pinnularia microstauron var. microstauron
3
0
1
1
0
0
0
0
0
0
0
0
7
0
0
0
0
0
0
0
1
0
0
0
0
0
2
2
0
1
16
0
0
38
0
10
0
0
0
0
0
0
0
0
0
5
0
0
25
0
10
0
0
0
0
0
0
0
0
0
9
3
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
7
2
0
0
0
0
0
0
0
0
0
0
0
1
1
4
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
10
0
7
0
0
0
0
0
0
0
0
0
0
0
0
10
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
7
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
10
0
35
0
0
0
0
3
0
0
0
0
6
0
0
1
0
0
0
1
0
0
0
5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
10
7
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5
0
1
0
0
0
0
0
0
0
0
0
3
0
0
10
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
116
0
0
0
0
0
0
0
0
0
25
0
0
2
0
0
0
0
0
1
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
8
0
6
0
0
3
0
29
0
0
0
0
0
0
0
0
0
13
0
9
16
0
0
0
0
0
0
0
0
0
0
0
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
83
0
0
0
0
0
0
0
0
0
1
0
2
15
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
12
0
0
2
0
1
0
0
0
0
0
0
46
0
0
0
0
0
0
0
0
0
5
0
0
0
0
0
0
1
0
0
0
74
0
0
0
0
0
2
0
0
0
0
0
29
0
0
6
21
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
0
0
0
4
3
0
0
0
12
3
0
0
0
0
0
0
0
1
0
0
21
0
0
0
0
4
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
103
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
0
0
0
0
0
0
11
0
0
0
0
0
0
0
17
0
0
0
0
0
0
0
0
0
41
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
3
0
0
6
1
0
5
0
0
0
0
0
4
0
0
0
79
2
0
0
0
0
0
0
0
6
0
0
0
0
0
0
0
0
0
0
0
0
6
0
0
0
0
4
0
0
0
0
0
0
0
124
0
0
0
0
0
0
0
0
0
35
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
10
0
0
0
0
0
0
1
0
4
0
0
0
0
0
0
0
1
0
0
0
0
10
0
0
0
0
0
0
0
0
0
0
9
0
0
0
0
0
0
0
0
3
0
2
0
7
0
0
0
0
0
0
0
36
0
0
0
31
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
9
0
0
2
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
72
2
0
0
0
0
3
1
0
3
0
3
5
2
0
14
0
0
0
7
1
0
0
29
1
0
0
0
0
0
0
0
0
0
Pinnularia neomajor var. inflata
Pinnularia pseudogibba
Pinnularia rhombarea var. rhombarea
Pinnularia rupestris
Pinnularia stomatophora
Pinnularia subcapitata
Pinnularia subgibba var. subgibba
Pinnularia subrupestris
Pinnularia viridiformis
Pinnularia viridis
Psammothidium subatomoides
Pseudostaurosira brevistriata
Pseudostaurosira robusta
Sellaphora laevissima
Sellaphora seminulum
Stauroneis phoenicenteron
Stenopterobia delicatissima
Stenopterobia densistriata
Synedra ulna
Tabellaria flocculosa
0
0
0
0
0
0
0
0
2
0
0
0
0
3
0
0
0
0
2
13
0
2
0
1
0
11
6
0
0
0
0
0
0
0
0
0
0
1
0
48
0
0
0
0
1
3
2
0
2
0
0
0
0
0
0
0
0
0
0
57
0
0
0
0
0
23
3
0
0
0
0
0
0
0
0
0
0
1
0
95
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5
3
0
0
0
0
0
0
0
5
0
0
0
0
0
0
0
0
0
0
0
37
3
0
0
0
2
1
0
1
0
0
0
0
0
0
0
2
0
0
0
31
1
0
0
0
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
62
0
0
0
0
0
4
0
0
0
3
0
0
0
0
0
1
0
0
0
32
0
0
0
0
0
3
0
0
3
0
0
0
0
0
0
0
0
0
0
11
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
21
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
25
0
0
1
0
0
0
0
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
7
0
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
7
0
0
0
0
0
2
6
3
0
0
2
0
0
0
0
0
0
9
0
3
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Electronic supplementary material, Table 3 (cont).
Achnanthes conspicua
Achnanthes linearioides
Achnanthidium minutissimum
Adlafia minuscula
Amphipleura pellucida
Brachysira brebissonii
Brachysira neglectissima
Brachysira neoexilis
Brachysira serians
Caloneis tenuis
Caloneis undulata
Chammaepinnularia hassiaca
Chammaepinnularia mediocris
Cocconeis pseudolineata
Cymbella helvetica
Cymbella lanceolata
Cymbopleura naviculiformis.
Diadesmis contenta
Encyonema neogracile
Encyonema minutum
Encyonema silesiacum
Encyonopsis cesatii
Encyonopsis falaisensis
Eolimna minima
Eunotia arculus
Eunotia arcus
Eunotia bilunaris
Eunotia boreotenuis
Eunotia botuliformis
Eunotia circumborealis
Eunotia glacialifalsa
Eunotia glacialis
Eunotia groenlandica
Eunotia implicata
Eunotia incisa
Eunotia inflata
Eunotia jemtlandica
Eunotia meisterii
Eunotia microcephala
Eunotia minor
Eunotia naegelii
Eunotia nymanniana
D9
0
0
0
0
0
0
1
0
0
1
0
6
15
0
0
0
0
0
6
0
0
0
0
0
2
0
34
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
D10
0
0
0
3
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
46
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
K1
0
0
0
0
0
1
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
K2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
K3
0
0
0
0
0
78
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
3
0
0
0
0
0
0
0
0
0
0
0
K4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
K5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
K6
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
K7
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
K8
0
0
0
0
0
0
0
0
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
29
0
0
0
1
0
0
0
0
0
0
12
0
0
0
0
K9
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
1
0
0
0
0
0
0
0
11
0
0
2
K10
0
0
2
0
0
2
0
0
0
0
3
0
0
0
0
0
0
0
3
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
1
0
0
0
1
0
2
K11
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
P1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
P2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
P3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
P4
0
0
0
0
0
1
0
0
6
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
P5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
136
16
P6
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
1
0
0
0
0
0
0
0
0
0
0
0
12
6
P7
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Eunotia paludosa
Eunotia rhomboidea
Eunotia subarcuatoides
Eunotia tenella / exigua
Eunotia tetraodon
Eunotia trinacria
Eunotia ursamoioris
Fallacia vitrea
Fragilaria capucina
Fragilaria exigua
Fragilaria gracilis
Fragilaria virescens
Frustulia erifuga
Frustulia saxonica / crassinervia
Gomphonema acidoclinatum
Gomphonema acuminatum
Gomphonema angustum
Gomphonema clavatum
Gomphonema gracile
Gomphonema minutum
Gomphonema parvulum
Gomphonema subtile
Kobayasiella parasubtilissima
Meridion circulare
Navicula cryptocephala
Navicula cryptotenella
Navicula gregaria
Navicula pseudostauron
Navicula radiosa
Navicula tridentula
Neidium hercynicum
Nitzschia acidoclinata
Nitzschia paleacea
Nitzschia paleaformis
Peronia fibula
Pinnularia biceps
Pinnularia complexa
Pinnularia cruxarea
Pinnularia divergens
Pinnularia erratica
Pinnularia gibbiformis
Pinnularia julma
Pinnularia microstauron var. microstauron
Pinnularia neomajor var. inflata
Pinnularia pseudogibba
0
0
0
1
0
0
0
0
0
0
1
0
0
13
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
25
0
52
0
0
9
2
0
0
0
0
0
0
0
6
0
0
6
0
0
0
0
0
0
0
0
0
104
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
6
0
0
0
0
0
0
0
0
0
5
0
4
0
0
0
0
0
0
0
0
0
0
0
0
17
0
0
0
0
0
0
0
0
175
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
25
0
0
0
0
0
0
0
0
0
0
0
0
168
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
38
0
0
0
0
0
0
0
0
0
0
0
12
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
30
0
0
0
0
12
0
0
0
0
0
0
0
149
0
0
0
0
0
0
0
0
8
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
95
0
0
0
0
38
0
0
0
0
0
0
0
8
0
0
0
0
0
0
0
0
59
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
63
0
0
0
0
5
0
0
0
0
0
0
0
13
0
0
0
0
0
0
0
0
118
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
111
0
0
0
0
8
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
72
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
13
0
1
0
0
0
0
0
1
0
39
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
6
0
0
0
0
0
0
0
0
0
4
9
0
75
0
0
1
0
0
0
0
11
11
5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
7
0
0
0
0
1
0
0
0
0
2
0
1
42
0
22
11
0
0
0
0
0
0
4
8
11
0
0
0
0
0
0
4
0
1
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
2
5
0
0
0
4
0
0
0
0
1
0
0
0
0
0
0
0
0
0
192
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
195
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
188
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
200
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
159
0
0
1
0
10
0
0
0
0
0
0
0
7
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
24
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
24
0
0
0
0
0
0
0
0
0
0
0
0
27
0
0
0
0
0
0
0
0
0
0
0
151
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
196
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Pinnularia rhombarea var. rhombarea
Pinnularia rupestris
Pinnularia stomatophora
Pinnularia subcapitata
Pinnularia subgibba var. subgibba
Pinnularia subrupestris
Pinnularia viridiformis
Pinnularia viridis
Psammothidium subatomoides
Pseudostaurosira. brevistriata
Pseudostaurosira robusta
Sellaphora laevissima
Sellaphora seminulum
Stauroneis phoenicenteron
Stenopterobia delicatissima
Stenopterobia densistriata
Synedra ulna
Tabellaria flocculosa
0
0
19
11
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
16
0
0
2
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
7
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
67
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
6
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
18
0
38
0
0
36
0
0
0
0
0
0
0
0
0
0
0
0
0
0
16
0
0
0
0
0
0
0
0
0
2
5
9
0
24
0
0
0
2
0
0
0
3
0
0
0
0
0
1
0
1
0
61
0
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
10
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
7
0
0
0
0
0
0
1
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Electronic supplementary material, Table 4. The mean biovolumes, surface areas and S:V ratios of individual samples
No.
B1
B2
B3
B4
B5
B6
B7
B8
B9
B10
B11
B12
D1
D2
D3
D4
D5
D6
D7
D8
D9
D10
K1
K2
K3
K4
K5
K6
K7
K8
K9
K10
K11
P1
P2
P3
P4
P5
P6
P7
Mean cell biovolume desmids
(μm3)
188805.7
6203.8
30047.3
76412.4
412880.8
31158.4
19848.3
14033.8
8239.6
15994.6
12119.1
107598.4
96435.9
14269.5
27544.8
68865.0
133383.7
13577.5
43592.4
396641.8
74194.2
66300.4
873.9
662.5
24843.9
1547.5
2458.3
2369.9
11267.7
10188.4
127564.8
11941.9
3253.6
6829.9
5226.4
7661.1
12962.0
9321.0
102564.1
22323.0
Mean cell surface desmids
(μm2)
30831.2
1833.0
7436.6
21921.3
25384.5
4387.4
6047.3
4270.7
2822.9
3537.2
2921.0
21282.1
13922.4
3364.6
5134.3
12115.8
23391.6
3633.1
8774.2
68397.5
19904.8
13324.6
600.9
452.8
5778.3
619.7
791.5
747.0
2326.0
3299.4
21023.8
2352.2
1382.7
1931.8
1463.6
1868.2
3066.7
2819.8
18534.8
4971.8
Mean S:V ratio desmids
(μm-1)
0.37
0.90
0.45
0.67
0.41
0.72
0.86
0.60
0.68
0.69
0.81
0.30
0.40
0.78
0.91
0.23
0.17
0.58
0.65
0.28
0.35
0.23
1.06
0.99
0.28
1.10
1.00
1.04
0.59
0.57
0.33
0.38
0.89
0.79
0.86
0.59
0.25
0.51
0.55
0.42
Mean cell biovolume diatoms
(μm3)
1211.1
1125.7
1275.8
1003.5
1707.3
613.2
3660.9
1605.8
2075.0
1753.0
417.1
837.1
1003.3
1227.9
4817.4
3301.3
4339.2
307.9
1645.3
600.4
2834.9
6606.9
488.3
1607.8
1103.5
1454.2
248.2
347.6
411.3
8717.9
3511.1
7499.9
2201.9
159.6
180.3
135.3
416.4
616.6
1527.1
1823.0
Mean cell surface diatoms
(μm2)
801.5
793.1
803.4
867.4
643.4
477.6
1028.5
775.6
869.6
1196.2
485.9
799.5
603.1
1046.7
2149.7
1749.3
1884.1
335.3
1031.3
502.7
1273.5
1951.1
483.1
1313.2
797.3
1188.3
308.5
388.9
366.3
2658.9
1225.4
1913.3
1606.8
237.8
253.8
219.5
377.2
788.1
1274.0
1472.7
Mean S:V ratio diatoms
(μm-1)
1.52
1.19
1.23
1.04
1.92
1.52
1.79
1.35
1.61
0.92
1.82
1.04
1.41
0.97
0.60
0.77
1.16
1.50
1.16
1.48
1.51
0.92
1.16
0.91
0.94
1.01
1.51
1.32
1.43
0.76
1.21
0.96
0.80
1.60
1.59
1.62
1.51
1.51
0.90
0.82
Journal of
Ecology 2006
94, 609– 618
Geographic patterns of diversity in streams are predicted
by a multivariate model of disturbance and productivity
Blackwell Publishing Ltd
BRADLEY J. CARDINALE, HELMUT HILLEBRAND* and
DONALD F. CHARLES†
Department of Ecology, Evolution and Marine Biology, University of California – Santa Barbara, Santa
Barbara, California, USA, *Institute for Botany, University of Cologne, Köln, Germany, and †Patrick Center
for Environmental Research, Academy of Natural Sciences, Philadelphia, Pennsylvania, USA
Summary
1 Univariate explanations of biodiversity have often failed to account for broad-scale
patterns in species richness. As a result, increased attention has been paid to the development and testing of more synthetic multivariate hypotheses. One class of multivariate
hypotheses, founded in successional diversity theory, predict that species richness is
jointly influenced by periodic disturbances that create new niche opportunities in space
or time, and the production of community biomass that speeds displacement of inferior
by superior competitors.
2 While the joint response of diversity to disturbance and productivity has gained support
from theoretical and small-scale experimental studies, evidence that corresponding
patterns of biodiversity occur broadly across natural systems is scarce.
3 Using a data set that employed standardized methods to sample 85 streams throughout
the mid-Atlantic United States of America, we show that biogeographical patterns of
primary producer diversity in stream ecosystems are consistent with the predictions of
a multivariate model that incorporates disturbance frequency and community biomass
production as independent variables. Periphyton species richness is a concave-down
function of disturbance frequency (mean no. floods year−1) and of biomass production
(µg of biomass accrual cm−2 day−1), and an increasing function of their interaction.
4 Changes in richness across the disturbance × productivity response surface can be
related to several predicted life-history traits of the dominant species.
5 Our findings complement prior studies by showing that multivariate models which
consider interactive effects of community production and ecosystem disturbance are, in fact,
candidate explanations of much broader patterns of richness in natural systems. Because
multivariate models predict synergistic effects of ecological variables on species diversity,
human activities – which are simultaneously altering both the disturbance regime and
productivity of streams – could be influencing biodiversity more than previously anticipated.
Key-words: species diversity, disturbance, primary production, freshwater algae,
periphyton, stream ecosystem functioning, flood, river
Journal of Ecology (2006) 94, 609–618
doi: 10.1111/j.1365-2745.2006.01107.x
Introduction
One of the enduring challenges in ecology is to identify
those factors that explain patterns of biodiversity across
large spatial scales (Hutchinson 1961; MacArthur &
© 2006 The Authors
Journal compilation
© 2006 British
Ecological Society
Correspondence: Bradley J. Cardinale, Department of Ecology,
Evolution, and Marine Biology, University of California –
Santa Barbara, Santa Barbara, CA 93106 (tel. +1608 893
4157; e-mail [email protected]).
Wilson 1967; Connell 1978; May 1986; Godfray &
Lawton 2001; Hubbell 2001; Ricklefs 2004). Most
hypotheses that account for geographical variation in
diversity invoke single explanatory factors. For example,
the Intermediate Disturbance Hypothesis (Connell 1978;
Sousa 1984; Mackey & Currie 2001; Shea et al. 2004)
predicts that the number of species in an ecosystem will
be a unimodal function of the frequency of disturbance,
because organisms face inherent trade-offs in their ability
to compete for resources at low levels of disturbance and
610
B. J. Cardinale,
H. Hillebrand &
D. F. Charles
© 2006 The Authors
Journal compilation
© 2006 British
Ecological Society,
Journal of Ecology,
94, 609–618
their ability to recolonize habitats at high levels. The
Productivity Hypothesis (Rosenzweig & Abramsky
1993; Waide et al. 1999; Mittelbach et al. 2001; Currie
et al. 2004), on the other hand, predicts that species
richness is a unimodal function of ecosystem productivity
– more accurately, the availability of resources that limit
production – which regulates the strength of species
interactions and the local coexistence of competitors.
Each of these univariate hypotheses have had some
success in explaining patterns of diversity, particularly in
controlled experiments and observational studies where
study units have been carefully selected to vary in just one
explanatory factor (e.g. Buckling et al. 2000; Kassen
et al. 2000; Rainey et al. 2000; Molino & Sabatier 2001;
Chase & Leibold 2002; Scholes et al. 2005). However,
ecologists have increasingly realized that models based on
single causal factors are often inadequate descriptions
of broad patterns of species richness in nature (Wootton
1998; Waide et al. 1999; Wilkinson 1999; Mackey &
Currie 2001; Mittelbach et al. 2001; Roxburgh et al.
2004). When coupled with the recognition that potential
causal factors often covary in nature, this has prompted
much interest in merging hypotheses into more synthetic, multivariate explanations of biodiversity.
Some of the earliest attempts to merge hypotheses
proposed that diversity is the result of a dynamic
equilibrium between rates of community biomass production that hasten competitive exclusion, and the
frequency or magnitude of disturbances that reduce
the impact of competition on community dynamics
(Huston 1979). However, as subsequent theory showed
that disturbance does not, by itself, reduce the impact
of competition on species population sizes (Chesson
& Huntly 1997), the door was open for improvements.
One improvement was proposed by Kondoh (2001), who
used the patch occupancy models of Hastings (1980)
and Tilman (1994) to explore the coupled impacts of
disturbance and productivity on species richness. Like
many of its predecessors, Kondoh’s model predicted
that the relationships of species richness to productivity and disturbance are both unimodal, but the level of
one factor that maximizes species richness depends on
the level of the second factor – an interaction that leads
to patterns not easily anticipated from univariate models
of diversity (Proulx & Mazumder 1998). An advance of
this model was that it relied on more plausible mechanisms of species coexistence that require disturbance
to create new niche opportunities in space and/or time.
For example, new opportunities can result when patchy
disturbance allows species to differentially express
life history trade-offs, such as between their ability to
compete for limiting resources, colonize open space or
specialize on the exploitation of resource rich patches
(MacArthur & Wilson 1967; Armstrong 1976; Tilman
1994; Pacala & Rees 1998; Amarasekare 2003). Importantly, the expression of these life-history traits is also a
function of productivity, which influences the rate
of biomass accumulation in open patches, dispersal of
propagules across patches and, ultimately, the speed of
successional displacement of inferior by superior competitors. As a result, disturbance and productivity are
predicted to jointly determine the variety of life-history
traits that can be expressed in a system.
Several empirical tests of multivariate diversity theory
have now shown clear interactive effects of disturbance
and productivity (or resource supply) on species richness
(e.g. Wilson & Tilman 2002; Kneitel & Chase 2004;
Scholes et al. 2005). The results of these studies have
been bolstered by meta-analyses of experimental manipulations of herbivory and nutrient supply in aquatic
habitats that suggest the interactive effects of biotic
disturbance and productivity on the richness of primary
producers are likely to be general (Worm et al. 2002). In
stream ecosystems, which are the focus of our research,
an increasing number of studies have explored how
disturbances (biotic or abiotic) interact with limiting
resources to dictate the rate of biomass accumulation
and the diversity of primary producers (Mulholland et al.
1991; Rosemond et al. 1993; Death & Winterbourn
1995; Pollock et al. 1998; Proulx & Mazumder 1998;
Biggs et al. 1999). This work has prompted the
development of conceptual models that predict how lifehistory traits of algal species change across disturbance
× productivity continua (McCormick 1996; Stevenson
1997; Biggs et al. 1998). Yet, even while a growing body
of theory and small-scale empirical work lends support
to multivariate models of diversity, there is relatively
little convincing evidence that patterns of biodiversity
in natural ecosystems are a joint function of a system’s
disturbance regime and level of biomass production.
In part, this is because the interpretation of patterns in
studies performed in natural systems is often clouded
by inconsistent use of methodologies across sites. Even
those studies that have successfully overcome this
limitation (e.g. Death & Winterbourn 1995; Pollock
et al. 1998; Biggs & Smith 2002) tend to be limited to
a few sites in a relatively small geographical area.
Here we investigate whether species diversity and the
life-history traits of primary producers across a broad
array of natural stream ecosystems are consistent with
a multivariate model that jointly considers the importance of disturbance and productivity. We analyse a data
set where standardized methods were used to measure
disturbance frequency (number of floods per year),
primary production (the rates of biomass accrual of
benthic periphyton) and species diversity (richness and
evenness) of primary producers in 85 streams across
a 3.6 × 105 km2 area of the mid-Atlantic United States
of Ameria (Cardinale et al. 2005). Cardinale et al. (2005)
used this data set to explore how algal diversity and
biomass production covary across geographical regions
having distinct hydrologic characteristics. Here we examine
in more detail a potential explanation of their patterns
by evaluating whether species richness varies across
a disturbance/productivity response surface like that
predicted by Kondoh (2001). This analysis is coupled
with new data showing how species evenness, taxonomic
composition and the life-history traits of dominant
611
Stream diversity,
disturbance and
productivity
species covary with stream flood frequency and rates of
community biomass accrual.
Materials and methods
 
© 2006 The Authors
Journal compilation
© 2006 British
Ecological Society,
Journal of Ecology,
94, 609–618
Of the numerous forms of disturbance that impact stream
organisms, flooding is one of the most pervasive. High
discharge events are known to reduce population sizes
of nearly every major group of stream organism, not
only because flow directly scours individuals from the
stream channel, but also because sediment mobility
is a significant source of mortality to flora and fauna
(Resh et al. 1988; Allan 1995; Bond & Downes 2000).
Here we define disturbance as a flood of bank-full
magnitude or more which is likely to mobilize stream
sediments and impose considerable mortality on benthic
algae. We characterized the frequency of flooding in all
stream ecosystems in the mid-Atlantic United States
of America (Maryland, North Carolina, Pennsylvania,
Virginia and West Virginia) that are gauged and
monitored by the US Geological Survey (NWISWeb).
Records of mean daily discharge were obtained for
a 12-year period preceding the study (1 October 1989 to
the date of sampling in 2001, updated from Cardinale
et al. (2005) who used records only available to 30
September 1999). Streams having more than 365 days
missing from the record, and which were too ephemeral
or too deep to sample were eliminated from the data set
(mean daily flow < 0.85 or > 113 m3 second−1).
For the 473 streams remaining, the mean number of
floods per year was calculated using a flood frequency
analysis of the annual maximum event series (Black
1991). A flood was defined as any discharge exceeding
the 1.67- year recurrence interval – a definition that was
chosen on both practical as well as biological grounds.
Biologically, the size of this event is important because
it is thought to represent a ‘bank-full’ discharge where
physical forces are sufficient to reform the channel and
rearrange the streambed (Leopold et al. 1964; Poff &
Ward 1989; Leopold 1997). As the physical movement
of substrates ranks among the most common cause of
mortality to stream organisms (Resh et al. 1988; Allan
1995; Bond & Downes 2000), a discharge of this size
almost certainly represents a significant disturbance to
stream periphyton. Smaller discharge events can also move
sediments and impose mortality on benthic organisms,
but their characterization would be difficult, if not
impossible across the large number of streams we
studied (Lorang & Hauer 2003). Thus, we use a practical
definition of a flood that can be reasonably considered
to represent a major biological disturbance across a wide
variety of streams.
  
A standardized assay was used to quantify the diversity
and biomass production of benthic periphyton in 85
Fig. 1 Map showing location of 85 stream study sites
throughout the mid-Atlantic United States of America. Streams
were selected to represent a large gradient in the frequency of
flooding based on analysis of long-term discharge records
available for 474 streams in this region that are gauged and
monitored by the US Geological Survey (see Methods).
streams for which estimates of flood frequency were
available. Cardinale et al. (2005) used 83 of these streams
to describe two discrete study groups that differed in
six aspects of hydrologic variability. Here we focus on
the continuum of flood frequency across the streams
(Fig. 1), adding two new records that did not fall within
the original hydrologic groupings (because samples from
one of the hydrologic pairs could not be collected), but
which extend the range of variables considered here.
In the summer of 2001 (28 June to 16 October),
unglazed ceramic tiles (six sets, each with fifteen
5.29-cm2 tiles connected in a 3 × 5 rectangular array)
were staked flush with the surface of the streambed
across two riffle habitats in each stream. Tiles were
incubated for 15 days (SD = 1, range = 14–17), after
which a randomly selected tile was collected from each
of the six sets, and periphyton removed and preserved,
as a composite sample, in glutaraldehyde. The density
of all Bacillariophyceae, Cyanobacteria, Chlorophyceae,
Euglenophyceae and Pyrrophyta were determined from
material allowed to settle in a Utermöhl chamber. A
minimum of 400 units (individuals, colonies or filaments)
were identified from randomly selected transects at 480×
magnification. Diatom frustules were then cleaned by
boiling in 30% hydrogen peroxide, mounted in Hyrax,
and identified at 1500×. Individual cells were approximated to a geometric shape and, using dimensions from
at least 10 individuals per species, the total biovolume
of each population was determined (Hillebrand et al.
1999). Biovolume was converted to biomass assuming
a specific gravity of 1.0, summed across taxa and divided
by tile area and incubation period to derive the rate of
periphyton biomass accrual (µg cm−2 day−1).
Biomass accrual is a common metric used to estimate
productivity in aquatic ecosystems (Hall & Moll 1975),
although several limitations should be noted. First,
biomass accrual represents the sum of immigration,
growth, death and emigration processes. It is likely that
losses of biomass from cell death and sloughing were
negligible in this study because incubation times were
612
B. J. Cardinale,
H. Hillebrand &
D. F. Charles
kept short to approximate the exponential phase of
biofilm growth (Stock & Ward 1989; Peterson &
Stevenson 1992; Cardinale et al. 2001). We cannot,
however, distinguish the influence of immigration from
local cell division. Still, we specifically chose biomass
accrual over other metrics because in aquatic systems
of microalgae, biomass accrual is the only metric that
allows one to examine species-specific contributions to
biomass production, which is vital for interpreting
patterns.
Although tile substrates are a standardized means of
sampling periphyton, they are only useful to the extent
that they provide an accurate depiction of species composition on natural substrata (Lowe et al. 1996). To
assess whether the sampling tiles provided a reasonable
approximation of species composition in the streams,
we simultaneously sampled periphyton on n = 6 median
sized natural particles (d50 = 4 – 6 cm, 2nd axis diameter) in a subset of 24 study streams chosen because they
complemented a separate study of periphyton assemblages in Pennsylvania (Charles, unpublished data).
For this comparison, we focused on the dominant group
of primary producers (Bacillariophyceae), which represented > 90% of all taxa.
Results and discussion
    

    
Kondoh’s (2001) model of successional diversity gives
the qualitative prediction that species richness will be a
concave-down function of disturbance frequency and
of community biomass production, and an increasing
function of their interaction. We tested this prediction
by fitting our data to a second-order polynomial function of the form:
S = b0 + b1P + b2D + b3P2 + b4D2 + b5P × D + ε
eqn 1
© 2006 The Authors
Journal compilation
© 2006 British
Ecological Society,
Journal of Ecology,
94, 609–618
performed two complementary analyses. First, we
modelled the evenness in final species biomass on the
sampling tiles (as Simpson’s index) as the dependent
variable in equation 1. To interpret the curve fit
we examined the dominant species in streams having
strong influence over the polynomial coefficients,
particularly b3 and b4, which regulate curvilinearity.
Second, we used a Principal Components Analyses
(PCA) on the covariance matrix of species relative
biomasses to identify dominant trends in species composition across the response surface. For this analysis
we focused only on abundant and widespread species,
including those found in ≥ 10 streams and representing
≥ 10% of periphyton biomass in at least one of those
streams. We found that the first three eigenvectors from
the PCA explained 71% of all variation in relative biomass,
and that these eigenvectors were strongly associated
with the distributions of just four common algal species
(| factor loadings | > 0.60). Thus, we summarize these
results by simply showing trends in the relative biomass
of these four taxa.
where D is the frequency of disturbance (mean no.
floods year−1), and P is periphyton biomass production
on the sampling tiles (µg cm−2 day−1). There was no significant correlation between flood frequency and rates
of biomass accumulation (r = 0.15, P > 0.10); thus, the
two variables were not confounded. As is true for many
taxonomic groups (Willig et al. 2003; Hillebrand 2004),
preliminary analyses revealed a latitudinal trend in
periphyton species richness (S = 87.57 − 1.61 × Latitude,
F = 23.48, P < 0.01, r2 = 0.22). Given this, we used the
residual richness after accounting for the latitudinal
trend as the dependent variable S. It is important to
note that there was no correlation between stream latitude and disturbance frequency (r = 0.03, P = 0.79,
also see Fig. 1), or between stream latitude and the rate
of biomass production (r = −0.06, P = 0.57) that might
generate a ‘spurious’ relationship (sensu Prairie & Bird
1989) between these variables and S. Thus, our conclusions are robust after accounting for latitudinal trends.
To examine trends in species composition across
the productivity × disturbance response surface, we
With all 85 streams included in the statistical model,
equation 1 explained a significant fraction of variation
in species richness among the sampling tiles (F = 3.87,
P < 0.01, r2 = 0.20), but only coefficients for biomass
production differed significantly from zero (b1 = 0.37
± 0.10 mean and SE, t = 3.90, P < 0.01, b3 = −0.01 ± 0.004,
t = −2.63, P = 0.01). A single stream (USGS gauge no.
01465798) was both a statistical outlier and had strong
influence on model fit (studentized residual = 4, leverage
= 0.8, open circle in Fig. 2a). More than 80% of the biomass at this site was dominated by the filamentous
green alga, Zygnema sp. (open circle, Fig. 3a), which
was found at this site and no other. With this data point
excluded, the polynomial function remained a significant fit to the data, accounting for 30% of all variation
in periphyton species richness (Table 1). There were
significant quadratic trends for productivity (b3 = −0.02
± SE 0.004, t = −4.81, P < 0.01) and disturbance
frequency (b4 = −4.57 ± 2.61, t = −1.75, P = 0.08), and
a positive term for their interaction (b5 = 0.60 ± 0.17,
t = 3.47, P < 0.01). We used information criteria to
assess whether statistical models with quadratic terms
were a significantly better fit to the data than those
without (the reader not familiar with information
theoretic approaches to model selection can see Johnson
& Omland 2004 for a summary). Akaike weights indicate
that exclusion of b3 and b4 led to models that were
highly improbable explanations of the data (P < 0.01),
arguing the quadratic terms were necessary for satisfactory model fit. In support of this conclusion, we found
that maximum richness occurred within the measured
range of productivity given mean values of disturbance
613
Stream diversity,
disturbance and
productivity
Fig. 2 Patterns of periphyton species richness across streams in the mid-Atlantic United States of America. (a) Species richness
on the sampling tiles as a function of the rate of biomass production (µg cm−2 day−1) and stream disturbance frequency (no. floods
year−1). The same data are presented as a two-dimensional contour plot in (b). Trends in (a) and (b) are fit with a quadratic
smoothing function parameterized with polynomial coefficients in Table 1. Positive and negative values occur because richness
is the residual after accounting for a latitudinal gradient in diversity (see Methods), and both productivity and disturbance were
centred for analyses. Open circle represents the stream excluded from curve fitting (see Results). (c) The number of diatom species
on sampling tiles as a function of diatom richness on natural substrata in a subset of 24 study streams. (d) Patterns of diatom
richness on natural substrata as a function of biomass production of diatoms (measured from accumulation on tiles) and stream
disturbance frequency.
© 2006 The Authors
Journal compilation
© 2006 British
Ecological Society,
Journal of Ecology,
94, 609–618
(max S at P = −b1/2b3 = 10.5 for S′ = 0 with D centred at
mean = 0), and within the measured range of disturbance given mean values of productivity (max S at
D = −b2/2b4 = 0.32 for S′ = 0 with P centred at mean = 0).
These results collectively indicate that patterns of
periphyton species richness across these streams fit
our predictions, being best explained as concave-down
functions of productivity and disturbance frequency, and
an increasing function of their interaction (Fig. 2a,b).
Analysis of diatom species richness on natural substrata collected from a subset of 24 study streams lends
further support to the conclusions above. The number
of diatom species on the standardized sampling tiles
was positively, and linearly related to the number of
diatom species on natural stream substrates, with one
out of every 4 –5 species being detected, on average
(Fig. 2c). As occurred on the tiles, variation in diatom
richness on natural substrata was best explained by an
interaction between the productivity of diatoms and the
frequency of disturbance (b5 = 1.53 ± 0.72, t = 2.13,
P = 0.05, n = 24, Fig. 2d). Unlike patterns on the tiles,
quadratic terms in the statistical model were not significant, indicating no evidence of curvilinear relationships
between richness and either productivity or disturbance.
It is, however, unclear whether this inconsistency can
be meaningfully interpreted since the range of these
variables was constrained in the subset of 24 streams
relative to the full data set (−10% for productivity, −66%
for disturbance frequency). Either way, the results indicate that patterns of species richness are best explained by
an interaction of disturbance and productivity (compare
Fig. 2b–d).
    
 
Much effort has gone into predicting the adaptive
life-history traits of species expected to dominate
614
B. J. Cardinale,
H. Hillebrand &
D. F. Charles
Table 1 Coefficients for the model y = b0 + b1P + b2D + b3P2 + b4D2 + b5P × D + ε, where y = species richness (, F = 6.71,
P < 0.01, r2 = 0.30), or evenness (, F = 2.90, P = 0.02, r2 = 0.16), P = periphyton biomass production (µg cm−2 day−1), and
D = disturbance frequency (no. floods year−1). P and D were centred for analyses, data for species richness represent the residuals
after accounting for a latitudinal gradient (see Methods), and species evenness was transformed using the Box-Cox
transformation, E2.3, to meet statistical assumptions
Source
Species
richness bi (SE)
t
P
Species
evenness bi (SE)
t
P
Intercept
P
D
P2
D2
P×D
2.31 (0.75)
0.42 (0.09)
2.96 (1.84)
− 0.02 (0.004)
− 4.59 (2.61)
0.60 (0.17)
3.09
4.86
1.61
− 4.82
−1.76
3.46
< 0.01
< 0.01
0.11
< 0.01
0.08
< 0.01
0.60 (0.04)
0.01 (0.004)
0.02 (0.09)
− 0.007 (0.002)
− 0.22 (0.13)
0.01 (0.01)
16.14
3.41
0.23
−3.26
−1.68
0.92
< 0.01
< 0.01
0.82
< 0.01
0.10
0.36
Fig. 3 Patterns in species composition. (a) The evenness of biomass among periphyton species on sampling tiles as a function of
the rate of biomass production (µg cm−2 day−1) and stream disturbance frequency (no. floods year−1). The same data are plotted
as a two-dimensional contour plot in (b). In (a), the dominant species in streams having a strong influence on downward trends
are noted at each corner. In (b), insets show the relative biomass (z-axis) of four common species as a function of productivity (xaxis) and disturbance frequency (y-axis). The combined distributions of these species explained 71% of all variation in dominance
(see explanation of Principal Components Analysis in Methods). Scales of the x- and y-axes in each inset are the same as in main
figure.
© 2006 The Authors
Journal compilation
© 2006 British
Ecological Society,
Journal of Ecology,
94, 609–618
various combinations of disturbance and production
in terrestrial plant assemblages (e.g. Grime 1979;
Huston 1994), and analogous conceptual models have
recently been developed for primary producers in stream
ecosystems (McCormick 1996; Stevenson 1997; Biggs
et al. 1998). The underlying assumptions of some of
these models have been questioned on theoretical grounds
(e.g. Chesson & Huntly 1997), but many give qualitatively
similar predictions. When disturbance frequency is
low relative to the rate of biomass accrual, conditions
are expected to favour dominance by competitively
superior species because (i) there is sufficient time for
species to recruit to patches, even if they are slow growing
or poor dispersers, (ii) local interactions have time to
influence community structure, and (iii) inferior
competitors can be driven extinct from the system. In
contrast, when disturbance frequency is high relative
to the rate of biomass production, conditions favour
dominance by species that are either adapted for
dispersal, and therefore able to colonize and exploit the
resources in spatial ‘refuges’ unoccupied by superior
competitors, or are specifically adapted to resist disturbance and/or recover quickly.
We found that the evenness of algal species was highest
at intermediate levels of productivity and disturbance
frequency, but low at each of the four extremes (Fig. 3a,
Table 1). At the low P × low D and low P × high D
extreme, trends were influenced primarily by the diatom
615
Stream diversity,
disturbance and
productivity
Cocconeis placentula (Fig. 3a), which dominated the
majority of streams characterized by low biomass
accumulation (Fig. 3b and inset). Cocconeis placentula
is often associated with high disturbance frequency
because its adnate growth form confers resistance to
scour by floods (Peterson 1996; Biggs et al. 1998). Studies
have also associated this taxon with a broad range of
stream productivity (Biggs et al. 1998), but while we found
C. placentula in streams spanning the whole spectrum
of biomass production, it was only a minor component
of periphyton assemblages in streams with higher
rates of accumulation. We suspect this is because, as an
adnate growth form, C. placentula is unlikely to tolerate
shading at the bottom of a periphytic mat and tends
to be replaced by higher profile, often competitively
superior species.
In support of this possibility, we found that trends at
the high P × low D extreme were the result of dominance
by the filamentous green alga Schizomeris sp. (bottom
right of Fig. 3a), and the stalked diatom Gomphonema
parvulum (Fig. 3b and inset). Both of these species are
loosely attached growth forms that tend to be particularly sensitive to high velocity and the scour of floods
(Hoagland et al. 1982; Burkholder et al. 1990; Biggs 1996;
Peterson 1996). However, overstorey species such as
these also tend to be superior competitors for light
and nutrients and are typical of late-successional
algal assemblages (Burkholder et al. 1990). Gomphonema
parvulum is also particularly well known to tolerate
organic pollution (Stevenson & Bahls 1999), which
may further explain its dominance at the high P × low
D extreme.
At the high P × high D extreme, trends were influenced
by streams dominated by the diatoms Didymosphenia
germinata and Melosira varians (Fig. 3a,b and inset),
and by the filamentous green alga Stigeoclonium
(Fig. 3b and inset). These taxa are often classified as
late-successional species that dominate assemblages in
eutrophic waters (Biggs et al. 1998; Kociolek & Spaulding
2003; Stoermer & Julius 2003). Didymosphenia germinata,
which frequently grows as a mucilaginous mat (Kociolek
& Spaulding 2003), and Stigeoclonium, which has basal
cells that are highly resistant to shear (Biggs 1996), are
well-adapted for persistence in flood prone environments. Melosira varians, on the other hand, is known to
exhibit low resistance to scour disturbance (Biggs &
Thomsen 1995; Passy 2002), but can reproduce rapidly
to yield high biomass. Thus, we suspect that the prevalence of M. varians in frequently disturbed streams
stems from its high resilience following space-clearing
disturbance.
   
© 2006 The Authors
Journal compilation
© 2006 British
Ecological Society,
Journal of Ecology,
94, 609–618
As a large-scale survey of biodiversity, it should be clear
that we cannot link the documented patterns to any
underlying theoretical model or biological mechanism
to the exclusion of all other explanations. However, we
think it is noteworthy that the biogeographical patterns
reported here are consistent with those of experimental
tests (e.g. Wilson & Tilman 2002; Kneitel & Chase 2004;
Scholes et al. 2005), meta-analyses (Worm et al. 2002)
and mathematical theory (Kondoh 2001) detailing how
disturbance and community biomass production
interact to influence biodiversity. Our results are
valuable because they suggest that inferences derived
from more controlled, but less realistic avenues of research
are applicable to much broader patterns of species
richness in natural systems.
Our inferences do, however, have several limitations.
First, algal species diversity and production were both
characterized from short-term incubations of periphyton
on artificial substrates. This method was chosen as a
standardized means of sampling across a large number
of streams having very different characteristics. Our
intent was that the substrates would serve as passive
samplers that portray patterns of diversity and rates of
biomass accrual in open patches for the stream as a
whole, but our conclusions are only valid to the extent
that this assumption is true. For measures of species
richness, the assumption was partly verified using independent samples of periphyton from natural substrata,
which suggest we can have some confidence that the
trends in species richness are robust. However, rates of
biomass accrual on tile substrates are, at best, a limited
characterization of primary production. Not only are
we unable to account for certain loss terms that might
influence the rate of biomass accrual (e.g. herbivory),
we do not know if the rates of accrual are representative
of those that occur naturally. Although it would have
been ideal to measure the development of primary production on natural substrates following floods, ‘tracking’ storm events for 85 streams spanning 3.6 × 105 km2
of the mid-Atlantic USA would have been impractical,
if not impossible. It might have also been better to
measure stream metabolism to obtain more direct estimates of production, but such methods suffer from not
being able to distinguish between the contributing
species. Thus, in the absence of practical alternatives,
our use of sampling tiles to measure biomass accrual
provides useful information that would be otherwise
difficult to obtain.
Secondly, we chose to define disturbance as any
discharge exceeding the 1.67 years recurrence interval,
because this magnitude of event is commonly thought
to represent a ‘bank-full’ flood where physical forces
are sufficient to scour streambed sediments (Leopold
1968; Gordon et al. 1992). We think it is reasonable
to assume that this definition does indeed characterize
major, infrequent disturbances across the variety of
sites that were studied, although it is also likely that
each stream is subjected to smaller, more frequent events
that scour algae from the streambed. It would have been
preferable to obtain a direct measure of geomorphic
disturbance at each site, but this requires the use of discharge/critical shear stress relationships that are often
labourious to work out, or that must be assumed from
theoretical relationships which generally do a poor job
616
B. J. Cardinale,
H. Hillebrand &
D. F. Charles
of predicting sediment stability (Downes et al. 1998;
Lorang & Hauer 2003). Thus, we were forced to define
a flood as a discharge large enough to be safely considered a disturbance across a wide variety of stream
types. Given our somewhat crude definition, it strikes
us as particularly interesting that clear relationships still
emerged between flood frequency, algal productivity
and species richness. We suspect that improved methodology and more refined metrics of disturbance would
only serve to make these relationships more apparent.
Conclusions and implications
© 2006 The Authors
Journal compilation
© 2006 British
Ecological Society,
Journal of Ecology,
94, 609–618
Our study shows that patterns of periphyton species
richness in streams throughout the mid-Atlantic
United States of America are consistent with a multivariate model that predicts disturbance frequency and
community biomass production will interact to influence community diversity (Kondoh 2001). We found
that the relationship between species richness and
biomass production was unimodal, but the level of
productivity that maximized richness increased as a
function of disturbance frequency. Similarly, the relationship between species diversity and disturbance frequency was unimodal, but the level of disturbance that
maximized richness increased with increasing productivity. Changes in community composition along these
environmental gradients suggest these patterns may
have resulted from opposing effects of productivity and
disturbance on species interactions. In streams where
the frequency of floods was high relative to the rate of
biomass production, periphyton assemblages were
dominated by species specialized for coping with disturbance, such as adnate diatoms that are likely poor
competitors, but whose small size and growth form
makes them highly resistant to scour from high flow
events. In streams where the frequency of floods is low
relative to the rate of biomass production, assemblages
were dominated by stalked and filamentous algal
species that are especially prone to scour and have characteristics that imply low resilience (slow reproduction
and/or poor dispersal), but which tend to be competitively superior because they grow upright above
the periphyton canopy to sequester nutrients and light.
Species richness was maximized along the positive
diagonal of the disturbance × productivity gradient,
which theory argues is where the opposing effects of
productivity and disturbance allow for the greatest
expression of life-history traits.
When results of this study are interpreted alongside
those of prior research, the balance of evidence
suggests that the maintenance of species diversity in
a given system is jointly determined by the frequency of
disturbances that create new niche opportunities for
species in space or time, and the rate at which biomass
accrual leads to successional displacement of inferior
by superior competitors. To the extent this conclusion
is correct, it argues that univariate explanations of
diversity (e.g. the Intermediate Disturbance and
Productivity Hypotheses) are antiquated, limited to too
narrow a range of variables to represent generalities
or, perhaps, altogether incorrect. Because multivariate
models predict synergistic effects of ecological variables
on species diversity, they further suggest that human
activities – which are changing both the rates of disturbance and the productivity of ecosystems (Vitousek
et al. 1997) – could impact biodiversity more than
expected from prior ecological theory. In stream
ecosystems, dams, urbanization and destruction of
riparian zones are well known to alter the frequency of
flooding (Poff et al. 1997), and excessive nutrient loading has increased the productivity of aquatic systems
around the globe (Allan & Flecker 1993; Bennett et al.
2001). Attempts to mitigate the effects of altered disturbances rates rarely consider the concurrent impacts
of eutrophication on productivity; similarly, attempts
to mitigate eutrophication rarely consider how altered
flow regimes change the frequency of disturbance.
Multivariate models of diversity suggest that, for any
attempt to mitigate human impacts on biodiversity, a
significant but important challenge is that we conserve
and/or restore both the natural levels of environmental
variation that impose disturbance, and rates of resource
supply that limit biomass production.
Acknowledgements
This work was supported by NSF grant IBN 0104768
to B.J.C. We thank M. Agbeti and L. Marr for analysis
of periphyton samples. J. Chase, M. Helmus, A. Ives,
M. Kondoh, C. Nilsson, S. Passy, J. Stevenson, K.
Tilmon, B. Worm and an anonymous referee provided
comments that greatly improved this manuscript.
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Received 3 June 2005
revision accepted 16 November 2005
Handling editor: Christer Nilsson
Internat. Rev. Hydrobiol.
88
2003
3–4
243–254
ALEXANDRA N. MÜLLNER and MICHAEL SCHAGERL1
Institute of Botany, Dept. of Higher Plant Systematics and Evolution, University of Vienna,
Rennweg 14, A-1030 Vienna, Austria
1
Institute of Ecology and Conservation Biology, Dept. of Hydrobotany, University of Vienna,
A-1090 Vienna, Austria; e-mail: [email protected]
Abundance and Vertical Distribution of the Phytobenthic Community
within a Pool and Riffle Sequence of an Alpine Gravel Stream
key words: microphytobenthos, stream, vertical distribution, carotenoids, HPLC, algae
Abstract
In the 2nd order mountain brook “Oberer Seebach” (Austria), diatoms and cyanoprokaryotes dominate the microphytobenthos, with the diatoms forming the richest species group.
Comparative investigations of different habitats clearly show seasonal variations in algal species
composition and biomass throughout the year. Pool habitats hold a higher number of taxa than riffle
sites. In addition, phytobiomass is two- to nearly fourfold higher in pools. Based on investigations of
the chlorophyll-a vertical profiles within the coarse gravel sediments, the light discontinuity layer was
determined to be situated at 7–10 cm sediment depth. Vertical distribution showed a maximum of taxa
a few centimeters below the sediment surface. The same diatom taxa were found throughout all sampling dates, sediment depths and sampling sites.
The results of exposed perforated metal tubes clearly indicate that the occurrence of phytobenthos in
aphotic zones is primarily controlled by saltatory movements of the upper sediment layers driven by
discharge.
1. Introduction
Benthic algae are a major component of aquatic food webs. They are now accepted to be
the primary energy source in many medium sized (third to sixth order) streams (VANNOTE
et al., 1980). With increasing stream size, autotrophic production also gains in importance,
but the relative contribution of periphyton declines as plant communities may shift to macrophytes (LAMBERTI, 1996) or phytoplankton (VANNOTE et al., 1980). In alpine rivulets, allochthonuous material plays the most important role as an energy source, but periphyton,
especially epilithic algae, must not be neglected. Frequently, the periphytic layer is visible
as a typical coloured pattern within streams (GEITLER, 1927 a). The coloration, indicative of
the algal species composition, varies seasonally depending on environmental variables such
as light and nutrient supply, water level and temperature changes (KANN, 1978).
The present investigation was designed to estimate the benthic algal biomass in the “Oberer
Seebach”, a summer-cold 2nd order brook located in the Alps of Lower Austria. This location
is not only well known since the intensive ecosystem investigations in its “Ritrodat-Lunz
study area” (BRETSCHKO, 1981), but also because of the early work of GEITLER (1927a) on the
typical coloration in alpine brooks in this area. One advantage of confining a study to a small
reach of a stream like the “Ritrodat” is that the confounding effects of differing nutrient concentrations and experimental artefacts can be factored out.
Up to now only a few studies are available dealing with the horizontal and vertical distribution of microphytobenthos in alpine brooks. These investigations focused mainly on algae
attached directly to the sediment surface, without consideration of the hyporheal (GEITLER,
© 2003 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim 1434-2944/03/3-407-0243 $ 17.50+.50/0
244
A. N. MÜLLNER and M. SCHAGERL
1927a, b; BUDDE, 1928; BACKHAUS 1968a, b, c; KANN, 1978; ROTT and PFISTER, 1988;
PFISTER, 1992a, b, 1993; PIPP and ROTT, 1993, 1994). Studies dealing with the vertical
distribution of attached algae and their pigments were performed mainly on lake sediments
(e.g. ZÜLLIG, 1981, 1982; STEENBERGEN and KORTHALS, 1988; YACOBI et al., 1991) and on
marine sand and mud flats. Especially on the latter topic there exists a wide range of investigations (e.g. PAMATMAT, 1968; FENCHEL and STRAARUP, 1971; ROUND, 1979; REPETA and
GAGOSIAN, 1982, 1987; SKJOLDAL, 1982; RIDOUT et al., 1984; PATERSON, 1986; RIAUX-GOBIN
et al., 1987; FURLONG and CARPENTER, 1988; HAPPEY-WOOD and JONES, 1988; DELGADO, 1991;
JÖNSSON et al., 1994). POULÍČKOVÁ (1987) studied the phytoplankton community in the
hyporheic zone respectively in the groundwater of a river. However, no previous study has
analysed the phytobenthic algae within a coarse gravel brook along a vertical depth gradient.
Thus, one main objective of our study was to investigate to which depth autotrophic periphyton
can be found and what are the processes influencing the vertical algal distribution. For macrozoobenthos, SCHWOERBEL (1961, 1964) described the hyporheal as a nursery and refuge of the
stream fauna. Meanwhile, the nursery role has clearly been supported by numerous studies,
whereas the refuge role is still a convenient model, which has not really been proved or
disproved (PALMER et al., 1992). POULÍČKOVÁ (1987) investigated the seasonal changes in the
relative abundance of phytoplankton in the groundwater of a gravel stream compared to that
in the free-flowing surface water. When the total number of algae in surface water was set to
100%, the amount of algae found in the groundwater ranged from 60 to 103% in spring and
autumn, but only 2 to 36% during the main vegetation period. Similar relations were also
noticed by ČISTÍN and HIMMEL (1980). From that it could be hypothesized that water below
the free-flowing surface represents an environment in which algae more easily survive
unfavourable periods. This study addresses the question for periphyton communities.
Besides coarse particulate organic material (CPOM), which is the primary food source for
macrozoobenthos (shredders) in small order rivers, also periphyton is of great importance.
Grazers exploit this microlayer as a food source by scraping and rasping (ALLAN, 1995).
Because water velocity and the associated physical forces represent one of the most important
environmental factors affecting the organisms of running waters (ALLAN, 1995), the differences
in algal biomass and in taxonomic composition at the class level from a pool and riffle
sequence were studied in the brook “Oberer Seebach”.
For studies of seasonal and vertical variability quantitative data are necessary. However,
attached algae are difficult to quantify. For the most part cell counting is not possible because
algae are destroyed by scraping. An estimation of different algal species on a semiquantitative scale may be used to get an impression of overall frequencies. One promising approach
in solving the quantification of the relationships of taxonomic groups within the periphyton
is the quantification of pigments from algal groups using class-specific pigment markers,
which was done in this study. This practicable method by means of high performance liquid
chromatography (HPLC) represents a powerful tool for getting information on algal class
composition (BIANCHI et al., 1993; DOWNES et al., 1993; JEFFREY et al., 1997; LATASA et al.,
1992; MILLIE et al., 1993 a, b; SCHAGERL and DONABAUM, 1998).
Main objectives of this study were to investigate the seasonal variations of A) the vertical distribution of algae within the sediments of the stream and B) the algal quantities and
community composition at a pool and a riffle site with different hydraulic characteristics.
2. Materials and Methods
Phytobenthos was analyzed at a pool and a riffle site in the alpine 2nd order brook “Oberer Seebach”
within the Ritrodat area, Lunz, Lower Austria (BRETSCHKO, 1981). General characteristics of the brook
are listed in Table 1; Fig. 1 shows temperature regime and water level fluctuations. The “Oberer Seebach” drains a limestone dominated, densely forested catchment. Climate and weather are consistent with
245
Phytobenthos in an Alpine Gravel Stream
Table 1. General characteristics of the brook “Oberer Seebach”; means with ±95% confidence-limits (combined from BRETSCHKO and MOSER, 1993; BRETSCHKO, 1998; MÜLLNER,
1998).
Discharge (1980/84, l · s–1)
MQ
720
M max. Q
2400
M min. Q
320
Max.
17500
Min.
320
Water temperature (1982/86, °C)
Annual mean
6.8
Mean Maximum
11.1
Mean Minimum
1.9
Chemistry
pH
Conductivity (µS cm–1)
Alkalinity (meq · l–1)
Ca++ (meq · l–1)
Mg++ (meq · l–1)
Ptot (mg · l–1)
Ntot (mg · l–1)
N-NO3 (µg · l–1)
N-NO2 (µg · l–1)
N-NH4 (µg · l–1)
O2 (mg · l–1)
Si-SiO4 (mg · l–1)
P-PO4 (µg · l–1)
Grain size frequency distribution (quartiles, mm)
10.6 ± 1.2
Q25
Median
23.1 ± 2.0
47.6 ± 1.6
Q75
Fractions (%) smaller than 1.0 mm
LL
4.4
mean
7.4
UL
9.3
porosity:
1979/82
8.1 ± 0.1
216 ± 6
2.18 ± 0.007
2.04 ± 0.06
0.52 ± 0.04
0.01
1.011 ± 0.238
–
–
–
–
–
–
>24%
March – Dec. 1997, this study
8.3 ± 0.1
204
2.23 ± 0.15
1.87 ± 0.12
–
–
–
1197 ± 261
0.8 ± 0.5
10 ± 8
12.1 ± 0.8
0.7 ± 0.05
5 ± 2.5
the location, inside the northern fringe of the Eastern Alps. The Ritrodat area is a 100 m long stretch of
the stream, 320 m upstream of its inflow into lake “Untersee”. The mean breadth of the channel is
14.0 ± 1.6 m at bankfull discharge, the mean slope is 0.41 ± 0.003 cm/m, averaged over the years 1980/82
(BRETSCHKO, 1983). Hydrologically, “Oberer Seebach” is a typical “flashy river”, with extremely steep
discharge increases, as is to be expected from the karstic catchment. Temperatures of the surface water
are always low, characterizing “Oberer Seebach” as a typical summer cold stream (Table 1, Fig. 1). The
chemical composition of the water reflects the geology of the catchment: it is well buffered and without any seasonal pattern (Table 1).
10
220
9
200
8
180
7
6
160
5
140
4
120
3
100
2
summer autumn
1996
Figure 1.
winter
1997
spring
summer
autumn
Water level fluctuations and temperature regime of the brook “Oberer Seebach”.
246
A. N. MÜLLNER and M. SCHAGERL
To investigate the seasonal variations of the algal quantities and community composition at a pool
and a riffle site, sampling was conducted nine times during 1997 (1.3., 6.4., 4.5., 1.6., 3.7., 16.7., 10.8.,
8.9., 10.12.) by scraping algae from individual sediment particles (i.e. stones). Algae were removed
separately from the top, bottom and lateral surfaces of the stones. The lateral surface (flank) was sampled
separately because of its special hydraulic features (AMBÜHL, 1959). A modified periphyton sampler
(DOUGLAS, 1958), with its sampling area minimized down to 3.5 cm2, was applied to also include smaller
pebbles (total number of investigated stones: n = 43; scraped top, bottom and lateral areas: n = 236).
Hydraulic characterization of the sampling sites (pool, riffle) was done by means of FST standard
hemispheres (STATZNER and MÜLLER, 1989), which summarize key hydraulic characteristics such as
shear stress, boundary Reynolds number and Froude number (FST numbers: pool always <1; riffle:
mean 8.43, std dev. 1.68, n = 37).
For studying the vertical distribution of algae within the sediment, a 1 m2 area was marked and stones
cleared away in 5 cm steps down to 20 cm below surface. Each 5 cm layer was collected separately and
immediately brought to the laboratory. Sampling was done by means of the same Douglas-sampler
(DOUGLAS, 1958) as used for the investigations of seasonal variations of the surface algal quantities and
community composition at pool and riffle site, while differentiating between top, flank and bottom surfaces of the pebbles (total number of investigated stones: n = 176; scraped top, flank and bottom areas:
n = 1024; flank sampled separately from top and bottom surfaces only in the first depth layer). For data
interpretation, obtained chl-a values (top, flank, bottom) were converted into 2 cm steps.
For investigating the processes influencing the vertical algal distribution, especially down to aphotic
sediment layers, three perforated metal tubes (diameter 6 cm, length 100 cm, perforations 0.8 × 0.8 cm)
filled with sterilized (180 °C, 5 h) natural substrate (grain size diameter c. 4–6 cm) where exposed in
the bed sediments at a riffle site for two, three and six months, respectively.
For each sample, subsamples were created for dry and ash mass determination, pigment analysis and
diatom preparation, respectively. Determination of dry mass and ash content was made by filtering homogenized and resuspended subsamples onto precombusted and preweighted Whatman GF/F
filters. After drying the filtered material at 95 °C for 24 hrs, it was reweighted for dry mass determination. Then the material was combusted at 500 °C for 2 hrs for ash mass (incl. carbonate) determination.
For pigment analysis, subsamples were filtered (Whatman GF/C) and stored at –30 °C. Pigment
extraction was done by grinding the filters in 90% cold acetone following extraction in darkness at
+2 °C for 12 hrs. The suspension was centrifuged and the supernatant analyzed by means of HPLC
according to WRIGHT et al. (1991; Merck-Hitachi HPLC system; ternary low pressure gradient with
distilled water, acetone and acetonitril; prederivatisation of samples with tetrabutylammoniahydroxide;
column: Merck-Superspher RP-18 250/4, precolumn: Merck-Lichrospher RP-8 endcapped). Peakdetection and integration was done at 440 nm (SCHAGERL, 1993; SCHAGERL et al., 1996).
Percentages of individual algal classes were determined by calculation of their respective chlorophyll-a
(chl) part of the total chl-a using class-specific pigment ratios (Bacillariophyceae including Synurophyceae: fucoxanthin; Cyanobacteria: echinenone; Chlorophyta: chl-b; SCHAGERL and DONABAUM, 1998;
WILHELM et al., 1991). For microscopical work, a Reichert Polyvar supplied with differential interference contrast optics was used. For diatom determinations, combusted samples were embedded in
Naphrax following the Naphrax manual.
3. Results
3.1. Seasonal Distribution – Riffle Site
The algal biomass expressed as chl-a (µg cm–1) was low at the beginning of March and even
decreased in April because of a spate, followed by only a slight increase until May due to
continued changes in water discharge (Fig. 2). Subsequent constant hydrological conditions as
well as the rising water temperature towards the summer season resulted in an increase of
the algal biomass in June, lasting until beginning of July. A spate in July caused an almost
total reduction of the phytobenthic vegetation in the whole brook. Whereas algal biomass
showed only a slight increase in the first month after the flood event, a month later the
phytobenthos biomass had more than doubled compared to the values at the beginning of
Phytobenthos in an Alpine Gravel Stream
247
Figure 2. Seasonal changes of periphyton at the riffle site (A) class composition expressed as black –
fuco-group, squared grey – greens, white – blue-greens and (B) total chlorophyll-a per surface area and
water level fluctuations.
July. A small spate at the beginning of October, as well as the following suboptimal environmental conditions, caused a strong decrease of algal biomass in winter.
A very high abundance of the fucoxanthin containing group including the Bacillariophyceae
and Synurophyceae was observed at the riffle site (Fig. 2; subsequently addressed as the fucogroup). With the exception of a single sampling date (first spate-maximum in July), this group
always constituted the dominant fraction of the phytobenthos (50–100%). The most abundant
species were Phaeodermatium rivulare HANSG. and Hydrurus foetidus (VILL.) TRÉV. (Synurophyceae) as well as the diatoms Achnanthes minutissima KÜTZ., Cocconeis placentula EHRENB.,
Cymbella minuta HILSE ex RABENH. and Gomphonema angustum AGH.
3.2. Seasonal Distribution – Pool Site
Compared to the riffle site, similar trends in whole phytobenthos biomass dynamics could
be found at the pool site. A marked difference was an at least twofold higher chl-a value at
the pool site on each sampling date with the exception of the big spate in July (Fig. 3).
A typical dominance of the non-fuco-group was observed throughout the year (most abundant
species: Chlorophyta – Gongrosira spp. KÜTZING, Cyanoprocaryota – Homoeothrix varians
GEITLER, Chamaesiphon spp. A. BRAUN et GRUNOW, Phormidium spp. KÜTZING), except the
sampling dates in August and September, when flood events had also removed nearly the
complete algal vegetation at this site. The subsequent development of the phytobenthos vegetation was similar to that of the riffle site and therefore first of all consisted of fast
growing pioneers (Synurophyceae, Bacillariophyceae).
248
A. N. MÜLLNER and M. SCHAGERL
Figure 3. Seasonal changes of periphyton at the pool site (A) class composition expressed as black –
fuco-group, squared grey – greens, white – blue-greens and (B) total chlorophyll-a per surface area and
water level fluctuations.
3.3. Vertical Distribution – Riffle Site
Detailed analysis showed that after longer lasting stable hydrological conditions without
strong saltatory sediment movements, a minimum chl-a amount was always found at 7–10 cm
sediment depth (1.3., 1.6., 3.7., 8.9.), also referred to as the light discontinuity layer. Thus,
Figure 4. Vertical chlorophyll-a distribution within the sediment at the riffle site, 2 cm steps were
obtained by conversion from the raw data set (top, flank, bottom of pebbles). Light discontinuity layer
was determined at 7–10 cm sediment depth.
249
Phytobenthos in an Alpine Gravel Stream
Table 2. Vertical distribution of total species numbers of algae within the coarse gravel
sediments of the brook “Oberer Seebach” in 1997. Numbers in the top line indicate sampling depth, cm. (T = top, B = bottom, F = flank of stones; nm = not measured; in brackets
number of diatom species).
date
0–5 T
0–5 F
0–5 B
5–10 T
5–10 B 10–15 T 10–15 B 15–20 T
1.3.
6.4.
4.5.
1.6.
3.7.
16.7.
10.8.
8.9.
20 (8)
34 (27)
29 (22)
27 (22)
33 (22)
5 (0)
8 (6)
29 (26)
22
41
39
18
37
4
11
31
20
49
28
29
20
23
33
32
30
31
11
2
35
3
38
5
16
16
8
2
18
(10)
(32)
(29)
(12)
(23)
(0)
(9)
(28)
(10)
(43)
(23)
(22)
(12)
0
0
20 (17)
(12)
(28)
(27)
(22)
(23)
(7)
(0)
(32)
(3)
(32)
(0)
(10)
(14)
(6)
(0)
(18)
21 (13)
33 (29)
nm
0
0
12 (6)
0
0
8 (8)
20 (20)
nm
0
0
3(0)
0
0
2 (0)
23 (22)
nm
0
0
nm
0
0
15–20 B
1 (0)
0
nm
0
0
nm
0
0
the photic zone goes down to a depth that corresponds to SCHWOERBEL’s (1964) postulated
position of about four to five times the mean grain size diameter. Only after strong flood
pulses (6.4., 16.7.) was the sharpness of this boundary obscured, a fact which documents the
influence of spates down to deeper regions of the sediments (Fig. 4). Two weeks after the
strong summer spate which resulted in a total destruction of the phytobenthos, a thin algal
pioneer biofilm had already been established, again showing the typical sharp chl-a decline
towards the light discontinuity layer.
The investigation of the vertical distribution of diatoms showed the maximum number of
taxa not at the sediment surface but always in deeper sediment layers, which was also
true for the non-diatoms (Table 2). In addition, the same diatom taxa were abundant at all
sampling dates, sediment depths and sampling sites. Thus, the hydraulic heterogeneity of the
stream bed was not leading to a patchwork of community types.
3.4. Sediment-Pipes
At all sediment pipe sampling dates (10.8., 8.9., 10.12.), the exposed metal tubes were covered with a gravel layer of 8–20 cm, which impressively displays the importance of saltatory
movements on the vertical distribution of algae. The surface of the sterilized stones inside the
sediment pipes contained a few diatom cells at the first sampling date (10.8.: Achnanthes minutissima, Gomphonema angustum), but no cells could be found at the other two days (8.9., 10.9.).
The pebbles stacked above the pipes by saltatoriy movements were covered by an algal vegetation resembling the one of the surrounding riverbed area.
4. Discussion
There is an increasing emphasis on the role of hydraulic features in determining the spatial
distribution of organisms in streams (STATZNER et al., 1988; DAVIS and BARMUTA, 1989; YOUNG,
1992; BIGGS and HICKEY, 1994; LORENZ et al., 1997; BEISEL et al., 1998; GIBERSON
and CAISSIE, 1998; STATZNER et al., 1998; FREEMAN et al., 1999; LAMOUROUX et al., 1999;
ADAMS et al., 2000). Flowing water can affect phytobenthos communities through several
processes. Algal resistance to scour is strongly dependent on the stability of benthic substrata. If substrata are relocated very frequently because of spates, slow growing filamentous
Cyanobacteria and Chlorophyta will not be able to establish dense layers and rapidly grow-
250
A. N. MÜLLNER and M. SCHAGERL
ing diatoms and Synurophyceae will dominate the microphytobenthos. The strong influence of
dicharge pattern on the type of algal association was observed also in this study. A development from a pioneer-dominated algal vegetation (Synurophyceae, Bacillariophyceae) to a
mature Chlorophyta-Cyanoprokaryota association was observed in the course of longer periods
of intermediate and stable water discharge in March and June. In July there was a nearly
complete removal of the epilithic algal vegetation by extreme spates; only endolithic cyanoprokaryota (e.g. Leptolyngbya perforans (GEITL.) ANAGN. et KOM.) and lime-incrusting green
algae (e.g. Gongrosira incrustans (REINSCH) SCHMIDLE) survived.
Fast flowing water can also increase metabolic rates by reducing the thickness of the
diffusive boundary layer, which can be seen as a barrier for metabolites to and from cells
(WHITFORD and SCHUMACHER, 1961; LOCK and JOHN, 1979; RIBER and WETZEL, 1987). As
water velocity increases so does skin friction and drag on the community; the shape, size
and orientation of organisms to flow becomes more and more important in their bid to hold
station (VOGEL, 1981). At our sampling site, diatoms, capable of high mucilage secretion
and therefore being able to buffer environmental forces, dominated at the riffles together
with the thin layers of the Synurophyceae Phaeodermatium rivulare, staying within the
boundary layer of the natural substrate.
The hydrological regime exerts important control over the biota of rivers. In this study, a
great influence of water level stability on periphyton density was recognized. In unshaded
streams, the flood disturbance regime is one of the fundamental variables determining habitat
suitability and pattern for benthic algae (BIGGS, 1996), with intermediate current velocities
generally leading to highest algal biomass (STEVENSON, 1996). During low water situations,
there is an improvement in the light climate. At permanently submerged locations the algal
vegetation is denser than in areas near the bank subjected to a fluctuating water regime. On
the other hand, a decrease of the algal biomass results from the impaired light climate in
deeper regions of the stream channel. Between those extremes a zone is marked, where
light climate and hydrology allow an optimum development of benthic algal communities.
According to SCHWOERBEL (1964), the boundary layer between benthal and hyporheal in
flowing waters with relatively homogenous sediments is strongly characterized by a rapid
decline of light towards the hyporheal. In this study the light discontinuity layer was
determined by the coloration of the pebbles at 7–10 cm sediment depth thus confirming
SCHWOERBEL’s (1964) postulated size of layer in dependence on the mean grain size diameter.
POULÍČKOVÁ (1987) compared phytoplankton from surface water with that of the interstitial of three streams in the basin of the Morava river (15, 30, 60, 100 cm below the bottom).
Generally, the species composition of the sediment waters corresponded to the composition of
the surface water. These results strongly indicate the dependence of the ground water phytoplankton from inoculations from the stream. The expected increase in colourless flagellates
in greater depths was not observed. These facts are congruent with conclusions to be drawn
from our Sediment-Pipe experiments. No confirmation could be found as to the hypothesis,
that parts of algal benthic mats scraped off the surface stones are transported into the sediments and settle down. In addition to that no heterotrophic algae were found growing on the
exposed stones. After intense spates, the most important pool for algal recolonization is probably surface water itself which transports algal diaspores. The occurence of phytobenthos in
aphotic zones is primarily controlled by saltatory movements and other forms of displacement of stones of the upper sediment layers driven by water discharge. Concerning the
diatoms, POULÍČKOVÁ (1987) found an irregular distribution in different sediment layers.
She assumed that diatoms do not penetrate into the sediment evenly, some species exhibit
a greater probability of intruding into deeper horizons. In this study the vertical distribution
of diatoms showed the maximum number of taxa not at the sediment surface but always
in the deeper sediment layers. The same three to four taxa, raphe bearing and being able
to perform vertical movements, were dominant or abundant at all sampling dates, sediment
Phytobenthos in an Alpine Gravel Stream
251
depths and sampling sites: Achnanthes minutissima, Gomphonema angustum and Cocconeis
placentula. Responses of phytobenthic algae to hydraulic habitat features in streams are
complex and it is difficult to uncover universal hydraulic habitat preferences for this community.
By investigating the vertical distribution of chl-a within beach sand, SKJOLDAL (1982)
found that sediment turbulences brought a part of the algal population down into layers of
low or no light, thereby exerting a strong influence on the algal distribution, production and biomass. Thus, under conditions of high sediment disturbance the vertical distribution of chl-a
was generally more uniform, with a lower content in the surface layer than in more stable
sediments. This study confirmed this observation also for coarse sediments. High water
events caused a more uniform distribution of chl-a due to strong sediment movements. In
contrast to fine sediments, the movements of coarse sediments cause a much higher mortality
rate of the phytobenthos community.
In addition to physical sediment mixing, also other factors influence the vertical distribution of algae and chl-a. These include chemical gradients, vertical distribution of grazers,
the physiological and metabolic capacity of the algae for dark metabolism, and the motility
of the algae (e.g. diatoms, see above). The importance of the latter has been clearly demonstrated by a broad variety of studies and experiments (e.g. FENCHEL and STRAARUP, 1971;
PATERSON, 1986; HAPPEY-WOOD and JONES, 1988; JÖNSSON et al., 1994). Chemical gradients
and vertical distribution of grazers within the sediments of the study site “RITRODAT”
have been investigated by a variety of studies (e.g. BRETSCHKO, 1981; BRETSCHKO, 1998;
BRETSCHKO and KLEMENS, 1986; BRETSCHKO and LEICHTFRIED, 1988; PANEK 1991).
In this investigation HPLC pigment analysis revealed important information about the
structure of benthic algal communities and the patterns of succession. However, it must be
considered that algal class composition is derived from algal pigments, which are strongly
influenced by the physiological state. Additionally, chl-a per unit biomass shows species
specific as well as class specific variations. Further investigations are necessary to fully
validate this method.
5. Acknowledgements
We are very grateful to GERNOT BRETSCHKO, head of the Biological Station Lunz, for his readiness
to help. Many thanks also to the staff, who supported this study in many ways. Part of the work was
financially supported by a grant of the ÖN-IAD and the cultural department of the board of the Lower
Austrian government.
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Journal of
Ecology 2007
95, 745–754
Differential cell size optimization strategies produce
distinct diatom richness–body size relationships in stream
benthos and plankton
Blackwell Publishing Ltd
SOPHIA I. PASSY
Department of Biology, University of Texas at Arlington, Box 19498, Arlington, TX 76019, USA
Summary
1 The relationship between species richness and body size is one of the most thoroughly
studied subjects in animal ecology; however, this relationship and its underlying
mechanisms are largely unknown in photosynthetic organisms, especially protists.
2 In this continental study, I first examined the number of diatom species across the cell
size spectrum in benthic and planktonic stream habitats. The relationship was rightskewed unimodal and was significantly different between the benthos and the plankton;
larger sizes were more speciose in the benthos, and smaller sizes in the plankton. The
species richness peaks were explained with allometric trade-offs between maximum
nutrient uptake rate and dispersal in the benthos but maximum nutrient uptake rate and
sinking resistance in the phytoplankton.
3 I also explored the cell size similarity among species and across environments. Small
diatoms were significantly more similar in size than large diatoms, and benthic diatoms
were significantly more similar than planktonic diatoms.
4 This is the first continental study on the richness–body size relationship in algae,
which suggests that the environmental differences between benthic and planktonic
habitats generated allometric trade-offs that have driven the cell size optimization
towards larger species in the benthos but smaller in the plankton. The patterns of cell
size similarity revealed a higher niche overlap in the benthos than in the phytoplankton
and among small species than among large species. These findings indicate that
interspecific competition in stream diatoms, which is a function of niche differentiation,
is habitat-specific and inversely related to cell size.
Key-words: allometric scaling laws, benthos, biodiversity, diatom, macroecology,
NAWQA, periphyton, phytoplankton, species richness, trade-offs.
Journal of Ecology (2007) 95, 745–754
doi: 10.1111/j.1365-2745.2007.01248.x
Introduction
© 2007 The Author
Journal compilation
© 2007 British
Ecological Society
The relationships of species richness with sampling
area, productivity and body size are probably the three
most extensively studied topics in ecology. While the
first two relationships have been examined by animal
and plant ecologists alike, the third one has been
grossly overlooked in the plant literature despite a long
and inspiring history of research in animal ecology (for
an exhaustive review see Allen et al. 2006). The change
in species richness with body size, i.e. the richness–body
Correspondence: Sophia I. Passy
(e-mail [email protected]).
size relationship, most often conforms to a rightskewed unimodal distribution (May 1986; Siemann
et al. 1996). Still, what mechanisms generate the
richness–body size relationship and whether it is
consistent with scale, latitude and habitat heterogeneity
and across taxonomic categories are subjects of active
research (Blackburn & Gaston 1994; Bakker & Kelt
2000; Gaston et al. 2001; Kozlowski & Gawelczyk
2002; Niklas et al. 2003; Knouft 2004; McClain 2004).
Traditionally, the unimodal shape of the richness–
body size relationship has been explained with various
allometric trade-offs, whereby small and large sizes are
advantageous under different conditions and the
richness peaks at intermediate sizes, which can be
746
S. I. Passy
© 2007 The Author
Journal compilation
© 2007 British
Ecological Society,
Journal of Ecology,
95, 745–754
beneficial under most conditions. However, as the
number of intermediate size species increases so does
the competition among them, which in turn forces
species towards sizes of lower adaptive value but
comparatively less intense negative interspecific
interactions. For example, a trade-off between speciation
and extinction, with speciation favouring small species
and extinction, small or large species, depending upon
environmental stability, generates unimodal distributions (Dial & Marzluff 1988). A unimodal distribution
could be the result of a trade-off between energy uptake
and energy conversion to offspring, with large species
being superior in resource acquisition and small species
superior in conversion of resources into reproductive
work (Brown et al. 1993). A trade-off between food
patch size increasing with consumer body size and
tolerance to low resource concentration in the patch
decreasing with consumer body size provided the
theoretical basis of the synthetic theory of biodiversity,
which predicted a unimodal but left-skewed richness–
body size relationship and higher body size similarity
in larger species (Ritchie & Olff 1999). A model
incorporating allometric scaling laws of the number of
individuals and speciation rate decreasing with body
size and active dispersal increasing with body size
produced maximum species richness at intermediate
body sizes (Etienne & Olff 2004). A trade-off between
production and mortality has also been invoked to
explain the unimodal body size distribution (Kozlowski
& Weiner 1997; Kindlmann et al. 1999).
A completely different suite of driving forces has
been proposed in elucidating the unimodal rightskewed richness–body size relationships in plants. It
has been suggested that large plant species were selected
against because they exhibit lower niche differentiation
and fecundity allocation, and require environmental
conditions that were uncommon throughout evolutionary history (Aarssen et al. 2006). The richness–
body size relationship in algae has received very little
attention and primarily in plankton studies (Havlicek
& Carpenter 2001; Scheffer & van Nes 2006). In the
periphyton this relationship remains unknown at
continental scales, although slightly right-skewed and
symmetrical distributions have been reported at regional
and watershed scales, respectively (Soininen &
Kokocinski 2006).
Diatoms differ from both animals and plants and it
is to be expected that the mechanisms generating their
richness–body size relationships are also different. For
example, diatom dispersal involves the mature organism
(like in animals) and not the reproductive structures
such as spores or seeds (like in plants). However, like
plants, diatoms are passive dispersers despite the
ability of many raphe-bearing species to move in their
immediate vicinity; therefore dispersal should scale
negatively with body size. The resources used by
diatoms, unlike animals, do not form discrete units such
as prey or patches of vegetation; consequently, the size
optimization in diatoms is not driven by the size of their
food. Unlike both animals and plants, diatom mortality is
a response to unfavourable environmental changes or
grazing but not a natural completion of their lives when
cell division, i.e. reproduction, takes place. Therefore,
mortality can affect but cannot govern the body size
distributions in diatoms. Finally, streams are highly
heterogeneous ecosystems where, even within a single
reach, there are areas of low and high disturbance (e.g.
the margins vs. the thalweg) and areas of lower and
higher nutrient levels (e.g. hard vs. soft substrates).
Hence, in each stream reach there are conditions of low
disturbance and higher nutrient levels that would support
large diatoms. This contrasts with terrestrial systems,
where undisturbed and nutrient-rich habitats required
by large plants were historically rare (Aarssen et al. 2006).
Important properties of algal communities, such
as taxonomic organization and biomass–richness
relationships, were shown to be significantly different
between planktonic and benthic stream algae (Passy &
Legendre 2006a,b). Planktonic communities comprised
fewer but more distantly related species and reached
peak biomass at lower species richness than benthic
communities. These differences were attributed to the
existence of fewer and shorter environmental gradients
in the phytoplankton, forcing a stronger niche differentiation among species. Taxonomic and biomassrichness structures of a community correlate with
organismal body size, which is thus expected to exhibit
different distributional patterns in the plankton and
benthos. To test this hypothesis, I examined the richness–
body size distributions of planktonic and benthic
diatoms collected from all major watersheds in the
United States by the National Water-Quality Assessment
(NAWQA) Program. I hypothesized that the richness–
body size relationship would be governed by similar
physiological but different ecological factors in the two
habitats. Nutrient uptake rate, which is determined by
algal cell size irrespective of the habitat, i.e. freshwater
or marine (Grover 1989; Aksnes & Egge 1991; Stolte
et al. 1994; Wen et al. 1997), is expected to drive the cell
size optimization in both planktonic and benthic diatoms. However, the environmental conditions in these
two habitats are vastly different and dispersal can be
hindered in the heterogeneous benthic environments.
Successful dispersal must include: dislodgement from
the substrate (emigration), entraining and remaining in
the water flow (suspension), and reaching suitable
substrates (immigration), where individuals can establish
and start reproducing. Dispersal is of much lesser
importance in the plankton, where algae dwell and
reproduce in their characteristic habitat or are constantly
imported from the benthos. Sinking, on the other hand,
which is strongly dependent on cell size, can interfere
with life-supporting processes in the phytoplankton
such as photosynthesis and growth. Therefore, it is
suggested here that cell size distributions can be
determined by the combined effect of nutrient uptake
rate and dispersal in the benthos, and by nutrient
uptake rate and sinking in the phytoplankton. While
747
Cell size
organization in
stream diatoms
data exist on algal nutrient uptake and sinking velocity
(discussed below), to my knowledge there is no comprehensive research on diatom dispersal but only
partial studies on immigration and emigration, which
do influence dispersal but do not equate with it. Here,
dispersal was specifically examined in an independent
data set, derived from an extensive 2-year spatial
survey of three reaches in Batavia Kill, a highland
stream in New York State (Passy & Blanchet 2007).
The objectives of the present study were: (i) to determine the continental richness–body size relationships in planktonic and benthic diatoms; (ii) to assess
whether these relationships could be explained with the
interplay of physiological and ecological allometries,
derived from independent investigations; and (iii) to
determine how similar in biovolume diatoms are along
their cell size spectrum, which has implications for their
niche differentiation and consequently competition
strength.
Materials and methods
 
The NAWQA data set
© 2007 The Author
Journal compilation
© 2007 British
Ecological Society,
Journal of Ecology,
95, 745–754
The NAWQA data set analysed here contains 4778
diatom samples from more than 50 major river basins
and aquifers across the USA, including sites in Alaska
and Hawaii. Three habitat types, defined by the
NAWQA, were quantitatively sampled for algae: hard
substrates in richest targeted habitats (RTH), soft
substrates in depositional targeted habitats (DTH),
and phytoplankton (for details see http://water.usgs.gov/
nawqa/protocols/OFR02-150/OFR02-150.pdf). RTH
maintain the taxonomically richest community and
encompass the following habitats: (i) shallow riffles in
areas with coarse-grained substrates (epilithon); (ii)
woody snags in reaches with fine-grained substrates
(epidendron); and (iii) macrophytes where riffles or
woody snags are absent (epiphyton). DTH microalgae
are found in organically rich or sandy depositional
areas along the stream margins, including epipelic and
epipsammic habitats. Both RTH and DTH were
sampled from a defined area of substrate. Phytoplankton
is a community of suspended algae collected from 1 L
of water in nutrient-rich streams or 5 L of water in
unproductive, nutrient-poor streams. More information
on the habitats and sampling techniques is given in
Passy & Legendre (2006b). The NAWQA data set
comprises 2699 RTH, 1682 DTH and 397 phytoplankton
samples collected year round between March 1993 and
September 2003.
Sample processing and algal enumeration and identification, followed by assessment of algal biovolumes,
were carried out by specialized phycology laboratories
(for details see http://diatom.acnatsci.org/nawqa/
protocols.asp). Briefly, soft algae and diatoms were
enumerated, followed by diatom identification in
permanent mounts prepared from acid-digested samples.
Biovolume was calculated for all taxa in a sample after
approximation to simple geometric figures. In this
study only the diatoms were considered because they
are truly single celled algae and diatom cell biovolume
is equivalent to organismal body size. Many diatoms
form colonies but there is no exchange of materials
among cells and therefore the colonial habit is unlikely
to directly influence the nutrient uptake rate. Colonies
and cell shapes deviating from a sphere can greatly
reduce the sinking velocity of planktonic diatoms and
this was partially taken into consideration in the
calculation of sinking resistance (see below).
The Batavia Kill data set
Spatial surveys of algae were carried out in 2001 and
2002 in three 100-m cobble-bottom reaches within a
5-km stretch of Batavia Kill, an upland stream within
the New York City Watershed (Passy & Blanchet 2007).
The three reaches differed in geomorphic status and
canopy cover. The upstream reach was geomorphically
stable, with significantly greater depth and larger particle
size than the other two reaches. The midstream and
downstream reaches were geomorphically unstable.
After the first survey, the downstream treatment reach
was subjected to a large-scale restoration, including the
re-channelling and bank stabilization of 1.6 km of
stream. Canopy cover, measured in the three reaches in
both years, averaged 82% in the stable reach and 17%
in the midstream reach. The treatment reach was
completely deforested before and after the first year of
restoration. Algae were collected in a regular spatial
grid approximately every 50 cm across the stream and
every 10 m along the stream from 7.55 cm2 of rock surface.
Samples were preserved in 4% formaldehyde. The
surveys were conducted during 11–13 July 2001 and
9 –11 July 2002, with 248 and 223 samples collected,
respectively. For diatom identification, samples were
digested with acids and mounted in Naphrax®
(PhycoTech Inc., St Joseph, MI, USA). At least 300
frustules were counted with a 100 × 1.35 NA oil immersion
objective. Biovolume data for the diatom species were
obtained from the NAWQA data set, which includes
numerous sites in New York State.
 
The NAWQA data set
Body size frequency distributions were explored in the
following way. The average cell biovolume for each
species across all samples was calculated and lntransformed. In each habitat, the ln-biovolume
spectrum was subdivided into 12 classes (bin size of one
unit on a ln-scale) and the number of species in each
class was counted. The results were generally robust to
increases in bin size; a decrease in bin size allowed the
emergence of small local peaks but did not alter the
position of the major peak, which remained invariant.
748
S. I. Passy
The three habitats were analysed with a contingency
table testing the null hypothesis that species richness
distributions were independent of habitat type. The
significance of skewness and kurtosis in ln-biovolume
was determined by dividing the absolute value of the
respective statistic by its standard error; if the ratio was
greater than 2 then the statistic was significantly
different from zero ( 11 2004).
For all samples in a habitat, the ratio of larger-tosmaller diatom species adjacent in size (henceforth
referred to as biovolume ratio) was examined as a
function of the ln-biovolume of the larger diatom using
a LOWESS smoother. The smoothing technique suggested a threshold behaviour of the dependent variable,
which showed a different linear response on both sides
of a threshold value of the predictor. This behaviour
was modelled with a piece-wise linear regression using
 11. The model was defined in  as follows:
¥ = b0 + b1x + b2(x – z)(x > z)
eqn 1
if x < z then ¥ = b0 + b1x
if x > z then ¥ = b0 – b2z + (b1 + b2)x
where ¥ = estimate of biovolume ratio, b 0 = intercept of the first regression line, b1 = slope of the first
regression line, b0 – b2z = intercept of the second regression line, b1 + b2 = slope of the second regression line,
z = a threshold value of x where the slope changes,
x = ln-biovolume of the larger species in the ratio. In
order to standardize the variables across habitats
and remove the influence of extreme values biovolume ratios larger than 10 were removed from all
analyses.
The Batavia Kill data set
© 2007 The Author
Journal compilation
© 2007 British
Ecological Society,
Journal of Ecology,
95, 745–754
The frequency of each species in the samples from each
of the three reaches was used as a measure of dispersal,
i.e. the likelihood of a particular species reaching all
habitats. Thus, species with high frequencies are
good dispersers and vice versa. While dispersal has
traditionally been measured in actively dispersing
species in terms of the distance they can travel away
from a source, to my knowledge there are no investigations
that explicitly measured the dispersal capabilities of
diatom species in nature. Diatoms are passive dispersers
and their successful dissemination depends on their
size. Smaller cells have lower probability of sinking (see
eqn 3 below) and can thus travel farther than large cells;
moreover, they have a higher probability of benthic
immigration, especially at high current velocities
(Stevenson 1996). In addition, dispersal is controlled
by the population size (Finlay et al. 2002), i.e. the higher
the number of potential colonists the higher the chance
that at least one individual would reach a location.
However, the number of individuals is an allometric
function (Etienne & Olff 2004; S. I. Passy, unpublished
data) and therefore the examination of species frequency
as a function of body size would account for the number
of individuals as well. Species frequencies within and
across individual reaches in the 2 years studied were
examined with a curve-fitting program (TableCurve
2D 5.01). The following parsimonious equation, which
produced consistently good fits of the frequency–
biovolume relationships, was used:
¥ = b0 + b1x–1
eqn 2
where ¥ = species frequency, b0 = intercept, b1 = slope,
and x = ln-biovolume. Homogeneity of slopes across
reaches was tested with , which ensured that
this relationship was not dependent on reach type. The
dispersal in the benthic samples from the NAWQA
data set was subsequently calculated as the inverse of
ln-biovolume and standardized to range between 0 and 1.
  
Maximum nutrient uptake rate, ρmax (µmol cell–1 h–1),
which reflects the sustained nutrient flux required to
support maximal growth, is a power function of cell
biovolume, V (µm3): ρmax ∝ V 0.66 (Irwin et al. 2006).
Although maximum uptake rate per unit biovolume
has been suggested to decrease with cell size in algae
(Irwin et al. 2006 and references therein), in the case of
diatoms, which have specific cell structure, it is expected
to be size-independent for the following reasons.
Diatom biovolume comprises two distinct parts: the
cytoplasm with all cell organelles, including chloroplasts,
and a large central vacuole, which contains mostly water
but also ions, salts and sugars, and is used primarily as
a depot for nutrients and storage products. The
cytoplasm is metabolically active and requires nutrients
to execute all anabolic processes, whereas the vacuole is
comparatively inert. The cytoplasm is confined to a
thin layer under the frustule, particularly pronounced
in pennates, and being proportionate to the cell surface
scales with the square of cell length (V 2/3). Thus,
expressing nutrient uptake per unit anabolically active
biovolume, i.e. the part of the cell biovolume that needs
nutrients, would give ρmax ∝ V 0.660V –0.667 = V –0.007. An
exponent of –0.007 is sufficiently close to zero to
suggest size-independence of per biovolume nutrient
uptake.
The maximum nutrient uptake rate was calculated as
V0.66 for diatoms in all three studied habitats and standardized to range between 0 and 1.
 
Sinking velocity for phytoplankton species is a
function of their size and form resistance of nonspherical cells as indicated by the Ostwald’s modification
of Stoke’s law (Graham & Wilcox 2000):
2 2
−1 −1
vs = gr ( q ′ − q )v φ
eqn 3
9
749
Cell size
organization in
stream diatoms
where vs = sinking velocity, g = gravitational acceleration of the earth, r = radius of a spherical volume,
equivalent to that of the algal cell, q′ = density of the
algal cell, q = density of water, ν = viscosity of water,
and φ = dimensionless form resistance. All terms in this
equation except r and φ are considered constants and
therefore with no effect on variable standardization.
Using the NAWQA biovolume V (µm3) data, sinking
velocity was calculated as V 2/3, which is proportionate
to the square of the cell radius. The sinking velocity was
then converted to sinking resistance by calculating the
inverse of vs, i.e. V –2/3, and the resistance was standardized
to a range between 0 and 1. Standardization was carried
out separately for centrics and pennates, which have
comparatively low and high φ, respectively (Reynolds
1984). This rationale was used to determine the lower
and upper bound of v s for species differing in form
resistance.
Results
In all habitats, the biovolume frequency distributions,
which were equivalent to biovolume–species richness
distributions (measurements of ln(Biovolume) were
equivalent to the numbers of species in each habitat),
were unimodal, significantly right-skewed (|Skewness |/
SE of Skewness > 2), and significantly leptokurtic
(| Kurtosis |/SE of Kurtosis > 2) (Table 1, Fig. 1). While
skewness was comparable among the three habitats,
kurtosis was much more severe in the benthos than in
the plankton, which had a flatter species richness–lnbiovolume distribution. Contingency table analysis
revealed a significant difference between benthos and
plankton (χ2 = 52.499, d.f. = 22, P = 0.0002), rejecting
the null hypothesis of habitat-independent biovolume
distributions. The maximum species richness was
observed at ln-biovolume between 8 and 9 (at 8.9) in the
benthos, while in the phytoplankton, the peak spanned
several biovolume classes, from 6 to 9, with ln-biovolume
between 6 and 7 being slightly more speciose than the
Fig. 1 Diatom richness–biovolume distributions in benthic
and planktonic stream habitats from all major watersheds in
the US. DTH = depositional targeted habitats, RTH = richest
targeted habitats.
rest (Fig. 1). These findings indicate that smaller sizes
were more speciose in the plankton and larger sizes in
the benthos. Species richness distributions were not
significantly different in the two benthic habitats
(χ2 = 2.939, d.f. = 11, P = 0.992). Notably, there were
substantially more species to the left of the optimum
than to the right in the benthos, i.e. smaller biovolume
classes were much more speciose than larger ones.
This trend also occurred in the phytoplankton but was
much less pronounced (Fig. 1).
The frequency, a proxy measure of dispersal, was
calculated for all diatom species in the three studied
reaches of Batavia Kill individually and altogether,
within and across years. Frequency was a negative power function of ln-biovolume (Fig. 2) and
this relationship was independent of reach type as
Table 1 Basic statistics of ln-biovolume in the three habitats.
Note that n equals the number of observations, which is also
equal to the number of species as each observation of lnbiovolume was taken from a different species. SES,
SEK = standard error of skewness and kurtosis, respectively
© 2007 The Author
Journal compilation
© 2007 British
Ecological Society,
Journal of Ecology,
95, 745–754
Basic statistics
DTH
RTH
Phytoplankton
n
Minimum
Maximum
Mean
95% CI
Skewness
SES
Skewness/SES
Kurtosis
SEK
Kurtosis/SEK
1512
2.0
13.88
7.38
7.29–7.48
0.34
0.06
5.42
0.90
0.13
7.17
1644
2.0
13.88
7.36
7.27–7.45
0.32
0.06
5.23
0.94
0.12
7.76
722
2.7
13.88
6.91
6.78–7.05
0.47
0.09
5.12
0.62
0.18
3.40
Fig. 2 Frequency of 49 diatoms in 2001 and 65 diatoms in
2002 within three 100-m reaches of Batavia Kill, NY, as a
function of their ln-biovolume. Data fits were generated from
the following regression model: frequency = b0 + b1(lnbiovolume)–1. In 2001 b0 = –59.37, b1 = 970.13; in 2002 b0 =
–53.77, b1 = 752.67. These regression parameters were not
significantly different between the 2 years and were statistically
equivalent to the respective parameters in the individual reaches.
750
S. I. Passy
Table 2  of species frequency as a function of ln (biovolume)–1, reach, and their interaction in the 2 years of study of
Batavia Kill. To eliminate correlation between effects, the independent variable was centred by subtracting the mean.
R2 = coefficient of determination, n = number of observations, SS = sum of squares, MS = mean square, and d.f. = degrees of
freedom
Source
SS
d.f.
MS
F-ratio
P-value
2001, n = 135, R2 = 0.19
Biovolume
Reach
Biovolume × Reach
Error
23583.11
2677.46
323.03
113770.6
1
2
2
129
23583.11
1338.73
161.51
881.94
26.74
1.52
0.18
0.00001
0.22
0.83
2002, n = 158, R2 = 0.13
Biovolume
Reach
Biovolume × Reach
Error
13343.19
54.74
631.88
91424.72
1
2
2
152
13343.19
27.37
315.94
601.48
22.18
0.05
0.53
0.00001
0.96
0.59
demonstrated by  (Table 2). Biovolume explained
19% of the frequency variance in 2001 and 13% in 2002
and these results were highly significant (Table 2). Therefore,
despite substantial differences in geomorphic and light
conditions among the three reaches, the relationship
between diatom dispersal and biovolume remained
invariant. Pooling all reaches together produced a
frequency–biovolume relationship that was statistically
equivalent to the relationships in individual reaches.
This indicates that dispersal was also insensitive to the
scale of observation, i.e. local within-reach vs. broader
between-reach. Dispersal in the two benthic habitats of
the NAWQA data set was estimated as the inverse of
species ln-biovolume. Nutrient uptake rate was calculated
and plotted as a function of ln-biovolume together
with the dispersal estimates (Fig. 3a,b). The largest
ln-biovolume (14.88) in all habitats was identified as an
outside value by stem-and-leaf plots and was not
included in the calculations of nutrient uptake rate,
dispersal, and sinking resistance (below). Optimal
biovolume was expected at the intersection of dispersal
and nutrient uptake, i.e. at ln-biovolume values where
neither of these processes was limiting. Such intersection
was observed at ln-biovolume of 8.9 in DTH and RTH,
which exactly corresponded to the value of ln-biovolume
with the highest number of species.
Sinking resistance was determined separately for the
two large diatom groups (centrics and pennates), which
differ in form resistance. An optimal biovolume range
was expected between the intersections of the standardized nutrient uptake rate and sinking resistance.
Indeed, these intersections were observed at ln-biovolume
of approximately 5.9 and 7.2 (Fig. 3c), which corresponded
to the most speciose ln-biovolume values (Fig. 1).
The biovolume ratio of species adjacent in size
increased significantly (P < 0.00001) and non-linearly
with ln-biovolume of the larger species in all habitats
(Table 3). Piece-wise linear regressions explained
between 31 and 34% of the variance in biovolume ratio
and showed that its rate of increase was not uniform
along the biovolume gradient. This rate was very low
(0.04 ≤ b1 ≤ 0.07) at lower values of ln-biovolume but
changed abruptly at a threshold value of ln-biovolume,
above which the biovolume ratio rapidly increased
(1.96 ≤ (b1 + b2) ≤ 2.32). The biovolume ratio was close
to 1 below the threshold but much higher than 1 above
it (Fig. 4a–c), indicating that species adjacent in size
had nearly identical biovolumes below the threshold
© 2007 The Author
Journal compilation
© 2007 British
Ecological Society,
Journal
of Ecology,
Fig. 3 Nutrient
uptake rate and dispersal as power functions of ln-biovolume in DTH (a) and RTH (b). Nutrient uptake rate and sinking resistance as
power
functions of ln-biovolume in phytoplankton (c).
95,
745–754
751
Cell size
organization in
stream diatoms
Table 3 Coefficients of determination (R2), regression coefficients, and 95% confidence intervals (CI) of piece-wise
linear regressions of biovolume ratio vs. ln (biovolume) in the
three studied habitats. Regression parameters that are not
significantly different at P < 0.05 across habitats are in italic.
P < 0.00001 in all regressions. n = number of observations;
b0 = intercept; z = threshold value of the independent variable,
where the slope of the fitting curve changes from b1 to b1 + b2
Coefficients
DTH
RTH
Phytoplankton
n
R2
b0
95% CI
b1
95% CI
b1 + b2
95% CI
z
95% CI
79 072
0.34
0.905
0.895–0.915
0.039
0.037–0.041
1.958
1.933–1.982
9.296
9.286–9.305
95 822
0.31
0.861
0.850–0.872
0.054
0.052–0.055
2.010
1.983–2.037
9.228
9.216–9.241
10 566
0.33
0.757
0.707–0.807
0.073
0.065–0.081
2.320
2.213–2.428
9.307
9.262–9.353
but disparate ones above the threshold. The rate of
biovolume ratio increase with ln-biovolume was
significantly different across habitats; it was highest in
phytoplankton and lowest in DTH (Table 3, Fig. 4d).
© 2007 The Author
Journal compilation
© 2007 British
Ecological Society,
Journal of Ecology,
95, 745–754
The threshold value was significantly higher in
phytoplankton and DTH than in RTH.
Discussion
The richness–biovolume relationship was unimodal
right-skewed in both benthic and planktonic diatoms
from the US running waters. However, there were
significant differences between the two habitats. In the
benthos, the biovolume distributions were more strongly
leptokurtic, displaying a distinct peak of species
richness at ln-biovolume between 8 and 9. The position
of this peak was exactly predicted by an allometric
trade-off between nutrient uptake rate and dispersal.
Smaller species (to the left of the peak) were limited by
their ability to acquire resources, whereas larger species
(to the right of the peak) were limited by their dispersal
capabilities. Here I examined only the physiological
aspect of the resource uptake limitation but it should
be noted that it is further exacerbated by the strong
competition in the benthos where larger species, by
virtue of their taller stature, gain better access to light
and nutrients from the water column and can create a
resource-depleted environment for the smaller species
Fig. 4 Biovolume ratio as a function of ln-biovolume of the larger species in the pair in DTH (a), RTH (b) and phytoplankton
(c). The fits for the three habitats, produced by piece-wise linear regressions, are plotted together in (d).
752
S. I. Passy
© 2007 The Author
Journal compilation
© 2007 British
Ecological Society,
Journal of Ecology,
95, 745–754
in the lower stories. The richness peak can be interpreted
as an outcome of cell size optimization, where species
were not severely limited by their ability to either
sequester resources or disperse. Considering that the
majority of species (61%) was observed to the left of the
richness peak and only a minor portion of the species
to the right of it (13%) suggests that constraints on
dispersal in the benthos are much more important than
physiological restrictions for diatom cell size organization.
Tolerance for low nutrient levels, well documented in
small diatoms (Cattaneo et al. 1997; Wunsam et al. 2002),
and production of storage vacuoles can offset a poor
nutrient sequestration and explain why there are so
many species with suboptimal abilities to obtain
nutrients. The ineffectiveness of large diatoms to reach
all locations where they would be competitively superior
due to mechanical constraints and lower numbers of
individuals takes a toll on their diversification, which is
evident in their minor contribution to the overall species
richness. Additionally, high profile diatoms have a
higher susceptibility to physical disturbance from shear
stress or grazing (Steinman 1996; Stevenson 1996) and
chemical disturbance from various forms of pollution
(Cattaneo et al. 1998), which will further limit their
diversification.
Phytoplankton had a flatter biovolume distribution
with much higher relative richness of smaller size
classes than the benthos. An allometric trade-off
between nutrient uptake rate and sinking resistance
accounted for part of the richness peak containing the
most speciose biovolume, i.e. ln-biovolume between 6
and 7. However, unlike dispersal, sinking resistance
varies not only with cell size but also with a number of
other factors such as form resistance, density of the
cell, water temperature and turbulence, and is therefore
more adequately represented by a band rather than a
curve. Here, I treated the centric and pennate diatoms
as two homogeneous groups, implicitly differing in
form resistance. This, however, is an oversimplification
because there is substantial variability of form resistance and cytoplasmic density within each group,
which, if accounted for, would have generated a much
larger spectrum of sinking resistance variability. Smaller
species (to the left of the peak) were limited by nutrient
uptake and represented 15% of all planktonic diatoms,
while large species (to the right of the peak) were controlled by sinking resistance and accounted for 11% of
all species. As both tails of the biovolume distribution
in the phytoplankton contained a comparable number
of species, it is logical to assume that the two mechanisms described here exert similar impact on diatom
cell size organization. The reason may be in their
common cellular roots, e.g. the amount of nutrient per
cell determines nutrient uptake but also sinking resistance, as the excess nutrients stored in the cell as oils
reduce its density and consequently its sinking velocity.
Selective grazing can also contribute to a reduced
number of smaller species that are more vulnerable to
herbivory than larger diatoms. Finally, large diatoms
are more likely to be exported into the phytoplankton
following disturbance events in the benthos.
The continental richness–biovolume relationships
of freshwater diatoms (class Bacillariophyceae) were
significantly right-skewed in all habitats, which is
common for higher taxonomic levels (e.g. class) and at
larger geographical scales (Kozlowski & Gawelczyk
2002). There are different hypotheses for the prevalence
of right-skewed distributions, including size-biased
speciation and extinction, macroevolutionary constraint
on small sizes, the fractal nature of the environment
and body size optimization (Kozlowski & Gawelczyk
2002). Probably a combination of these mechanisms is
responsible for the shape of the present distributions;
however, the macroevolutionary constraint on small
sizes, also known as the ‘reflecting barrier’, is particularly relevant in diatoms. According to this hypothesis,
distributions cannot extend below the natural barrier
of the lower limit of body size, where physiological
constraints reflect diversification toward larger sizes
(Kozlowski & Gawelczyk 2002 and references
therein). This is why richness–body size distributions
are not simply left truncated but humped. While this
mechanism may not be unequivocal across animal
lineages of widely varying lower size, it is apparent in
diatoms with a lower size limit set by the minimum size
of the eukaryotic cell, which, on average, does not fall
below 10 µm in diameter. Spherical shapes with the
same biovolume as the smallest diatoms in the NAWQA
data set would exhibit diameters as low as 2.4 µm, i.e.
below the lower limit of most eukaryotic cells, where
algal maximum nutrient uptake rate, and metabolic
and nutrient storage abilities are expected to be
diminished. Thus, the existence of a left barrier and an
optimization mechanism (maximizing nutrient uptake
rate) that drives diversification away from it can account
for the right-skewed richness–biovolume distributions
in diatoms.
In all three habitats, the biovolume ratio increased
along the cell size gradient with a distinct change of
rate at a threshold ln-biovolume value of around 9.3
(very close to the richness peak). The piece-wise response
of biovolume ratio to diatom size indicated that there
were two distinct groups of species. Below the threshold,
the biovolume ratios approached 1, i.e. diatoms adjacent
in size were nearly identical. Above the threshold,
diatoms displayed large distances in size. This finding
is exactly opposite to the predictions of the synthetic
theory of biodiversity (Ritchie & Olff 1999), which postulated a decreasing body size ratio with organismal size
in local communities. The reason for this controversy is
in the underlying trade-offs. The synthetic theory
assumes a decreasing number of available patches (only
the large ones) but increasing tolerance for low resource
concentration in large species. Although this theory
was supported by empirical data on large mammals
and terrestrial plants, its premises cannot apply
to unicellular algae. Diatoms inhabit a continuously
changing environment with pronounced gradients
753
Cell size
organization in
stream diatoms
© 2007 The Author
Journal compilation
© 2007 British
Ecological Society,
Journal of Ecology,
95, 745–754
of nutrient and light (Borchardt 1996; Hill 1996) and
not discrete patches (pertains mostly to benthic forms).
In addition, algae encompass small species that are
generally better adapted to low resource concentrations
than large species (Cattaneo et al. 1997; Li 2002;
Wunsam et al. 2002; Jiang et al. 2005; Irwin et al. 2006)
(pertains to benthic and planktonic diatoms alike).
The analysis of biovolume ratios in this study reveals
that small species form a continuum where there is a
species for every possible size and the distance between
species adjacent in size approaches zero. The lack of
discontinuities in the biovolume ratios suggests that
smaller species (below the biovolume threshold) form a
single guild. The notion that species identical in size
within trophic levels are also ecologically equivalent
has become an ecological axiom. Therefore, small
species with near nil differences in biovolume are expected
to occupy very similar niches, and in coexistence to
experience high niche overlap, the amount of which can
be estimated from the size similarity among species.
Thus, the size similarity was the greatest in DTH but
the lowest in phytoplankton, suggesting a significantly
higher niche overlap in the benthos than in the phytoplankton. This hypothesis was put forth to explain the
higher taxonomic distances in the phytoplankton
compared with the benthos (Passy & Legendre 2006a)
and is fully corroborated by the present rigorous
analysis of the cell size structure of diatom communities.
The highest biovolume similarity in DTH was probably
forced by environmental conditions of unstable substrates
and sediment-borne contaminants favouring a
comparatively taxonomically uniform community of
low-profile, motile and pollution-tolerant species
(Passy & Legendre 2006a).
The high number of small species in a community
has been explained mechanistically with the corresponding number of suitable patches. For example,
the complex behaviour of large species may involve
the utilization of a combination of patches, each one
being sufficient to maintain a different small species
(Hutchinson & MacArthur 1959). Furthermore, small
species can exploit patches that are unavailable to large
species due to their inability to perceive them or fit within
them (Morse et al. 1985; Ritchie & Olff 1999; Aarssen
et al. 2006). The remarkable biovolume similarity of
small species documented here offers a complementary,
functional explanation of the high richness of small
species, i.e. small species exhibit much higher niche
overlap, which is counterbalanced by their high tolerance to resource limitation (and environmental adversity in general). Indeed, species sufficiently similar to
possess equal competitive abilities have been shown in
a theoretical study to maintain stable coexistence and
high diversity (Scheffer & van Nes 2006). Additionally,
the superior dispersal of small species prevents local
extinctions by a constant re-supply of new colonists.
Larger species (above the biovolume threshold)
displayed substantial differences in cell size, which
suggests the existence of multiple guilds. The members
of different guilds have differential resource requirements
and experience less competition in sympatry than the
members of the same guild. In addition, the superior
capabilities of large species to acquire resources would
further relax their resource competition. The piecewise linear nature of the diatom biovolume similarity
can also be interpreted as a competition gradient whereby
competition remains strong up to a threshold value of
ln-biovolume, beyond which the negative interspecific
interactions sharply decline. The classical paper by
Hutchinson (1959) explored the size ratio of co-occurring
competitors within mammals and birds and suggested
that a ratio of about 1.28 is necessary for niche differentiation. Across the three habitats in this study, the
average biovolume ratio of species below the ln-biovolume
threshold was between 1.14 and 1.21, but between 2.02
and 3.05 above that threshold. This indicates that small
species of diatoms experience much stronger interspecific
competition than multicellular organisms, whereas the
competition among large diatoms is alleviated.
In conclusion, this is the first continental study on
the richness–body size relationship in algae to suggest
that the environmental differences between the benthos
and the plankton are responsible for allometric tradeoffs driving cell size optimization towards larger species in
the benthos and smaller species in the plankton. The
patterns of biovolume similarity revealed a higher niche
overlap in the benthos than in the phytoplankton and
among small species than among large species. Therefore,
the interspecific competition in stream diatoms, being a
function of niche differentiation, is habitat-specific and
inversely related to cell size.
Acknowledgements
I thank Don Charles for kindly providing the data set
and Mark Ritchie for his comments on this project. I
am grateful to Jim Grover for stimulating discussions
and an insightful review, Christer Nilsson, Barney
Davies and two anonymous reviewers for their suggestions,
which substantially improved the manuscript. Financial
support under grant #C004307 from the New York
State Department of Environmental Conservation is
gratefully acknowledged. This manuscript is submitted
for publication with the understanding that the United
States Government is authorized to reproduce and
distribute reprints for governmental purposes. The
views and conclusions contained in this document are
those of the author and should not be interpreted
as necessarily representing the official policies, either
expressed or implied, of the US Government.
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Received 8 November 2006; revision accepted 22 March 2007
Handling Editor:Christer Nilsson
Fundamental and Applied Limnology
Archiv für Hydrobiologie
Vol. 174/3: 205–213, April 2009
© E. Schweizerbart’sche Verlagsbuchhandlung 2009
Diatom community structure along environmental and
spatial gradients in lakes and streams
Janne Soininen1, * and Jan Weckström1
With 4 figures and 1 table
Abstract: The aims of this study were to examine whether lentic and lotic diatom communities exhibit distinct
groupings following habitat preferences and whether lentic and lotic diatom communities differ in terms of spatial
structure at regional scales in Finnish Lapland. Principal Component Analysis (PCA) was used to describe the
main gradients in water chemistry. Non-metric Multidimensional Scaling (NMDS) was used to examine patterns
in diatom community structure. Redundancy Analysis (RDA) with variation partitioning was employed to examine
the spatial structure of lentic and lotic communities. Principal coordinates of neighbour matrices was used to model
the spatial variables in variation partitioning. The PCA showed that the main gradients in water chemistry of lakes
and streams were relatively similar in the study area in northern Finland although the main PCA gradient was more
related to water pH in lakes, whereas in streams the main PCA gradient was mainly related to alkalinity and conductivity. According to NMDS, however, the lake and stream diatom communities differed sharply. RDA with variation
partitioning showed that pure spatial factors explained more of the explained variation for the streams (28 %) than
for the lakes (9 %). Pure environmental factors nonetheless explained most of the variation both for the lakes (74 %)
and streams (58 %). Our results lent support to a notion that diatoms are probably jointly structured by neutral,
dispersal-related processes and species sorting by the environment, as both environmental and spatial factors had
a notable effect on community structure. We emphasize though that local environmental control was overall much
stronger than the effects of large-scale geographical and dispersal-related factors both in lakes and streams.
eschweizerbartxxx ingenta
Key words: diatoms, lentic, lotic, NMDS, PCNM, spatial structure, unicellular organisms, variation partitioning.
Introduction
Until recently community ecology centred mainly on
understanding community organization at local scales
largely neglecting the surrounding environment and
the effect of regional factors on local communities.
Recent decades have, however, shown that biological
communities are structured by the interplay between
local and regional factors and communities should not
be studied in isolation by concentrating only on deterministic local abiotic and biotic filters (Ricklefs 1987,
Leibold et al. 2004). Although it is now widely accepted that communities are under local and regional
control, the effects of independent factors contributing
to community structure are often poorly understood.
In freshwaters, for example, it is often insufficiently
known whether the water chemistry or local physical
habitat constrains the species distribution. Therefore,
much interest lies in whether freshwater algal communities, for example, are alike in similar water chemistry but in different types of freshwater habitats.
For freshwater benthic algae, some early studies
compared communities from different habitats and
found notable qualitative differences (e.g., Golubić
1967, reviewed by Round 1981). However, detailed
quantitative knowledge on whether algal communities can be clearly divided into lotic and lentic community types, for example, is still relatively scarce
Authors’ address:
1
Department of Biological and Environmental Sciences, P. O. Box 65, FIN-00014 University of Helsinki, Finland.
* Corresponding author; e-mail: [email protected]
DOI: 10.1127/1863-9135/2009/0174-0205
1863-9135/09/0174-0205 $ 2.25
© 2009 E. Schweizerbart’sche Verlagsbuchhandlung, D-70176 Stuttgart
206
J. Soininen and J. Weckström
from many ecosystem types and geographical areas.
If the separate community types exist, this entails
that communities are largely controlled by the local physical habitat. Alternatively, algal communities
may be structured by regional factors (e.g., variation
in climate) and water chemistry irrespective of the
type of the waterbody thus resulting in overlapping
communities in lakes and streams. Benthic diatoms
(Bacillariophyceae) provide a highly amenable group
to tackle this question because diatoms occur widely
across space and constantly through time both in
lentic and lotic habitats. However, diatom communities may well differ between standing and running
waters given that lakes and streams differ from each
other by several characteristics. Streams are characterized by continuous unidirectional flow and large
fluctuations in discharge resulting in frequent natural
physical disturbances and severe changes in water
chemistry (Allan 1995). Further, a large proportion
of headwater streams are fuelled by allochthonous
organic material and primary production may be relatively limited. By contrast, lakes are often physicochemically more stable environments than headwater streams and should also have larger autotrophic
production. This is especially true for high latitude
remote pristine lakes that are not interconnected by
streams and that have small catchments with sparse
vegetation cover.
Some recent studies have directly addressed the
question whether there are notable quantified differences in community composition between lentic and
lotic habitats. Crump et al. (2007) studied lentic and
lotic bacteria communities and found that community
composition differed substantially between lake and
streams. For macroinvertebrates, Johnson et al. (2004)
found that stream and lake communities differed remarkably in terms of community composition and key
variables contributing to assemblage variation. Robinson & Kawecka (2005) were interested in this question
in diatoms and compared the community structure of
lentic and lotic communities in an Alpine stream-lake
network. They found that there were notable differences between the benthic diatom communities in lakes
and streams in the same study area. Much more information is, however, needed especially at larger spatial
scales that are more pertinent to monitoring and conservation programs in freshwaters.
At broad spatial scales ranging beyond a single
lake-stream network, the distribution patterns of lentic
and lotic diatoms might also differ from each other because lotic communities should be better interconnected via streams and rivulets, while benthic diatoms in
eschweizerbartxxx ingenta
lake littorals might exhibit lower degrees of connectivity among the sites. This is especially true in systems
where lakes or ponds are isolated in a terrestrial matrix
not having inlets or outlets and diatoms therefore exclusively disperse overland passively via air or actively
by, for example, water birds (Kristiansen 1996). Lake
communities may thus be more spatially structured
due to the lower degree of interchange of the colonists
among the localities. Therefore, it would be interesting
to examine the degree of spatial structure in both lentic
and lotic communities in the same study area ranging
across drainage systems.
The aims of this study were first (i) to examine
whether lentic and lotic diatoms exhibit distinct groupings following habitat preferences, that is, whether
lentic and lotic communities in the same geographical
area and more or less similar water chemistry differ
from each other sharply or do lentic and lotic diatoms
show overlapping community types. Second (ii), we
examined whether lentic and lotic diatom communities differ in terms of spatial structure. We expected
that lotic diatom communities show weaker spatial
structure than lentic communities. This is because lotic sites are better interconnected via water pathways
within drainage systems and communities are therefore more easily homogenised across the sampled
sites.
Material and methods
Lentic diatoms
Lentic diatom samples consist of two data sets, a 64 lake set
from northwestern Finland (NWF, centered on 68º N, 22ºE;
Weckström & Korhola 2001) and a 45 lake set from northeastern Finland (NEF, 69ºN, 28ºE; Korhola et al. 1999) (Fig. 1).
The NWF data set was sampled during July 1995 and 1998 and
the NEF data set during July 1996. Surface sediment diatom
assemblages that represent an integrated sample of all habitats
over one to several years were derived from the deepest part of
the lake. Since the lakes were generally simple shaped the integrated sample is considered to represent well the overall diatom flora (see Meriläinen 1971). In general, the lakes are small
(0–115 ha), shallow (1–27 m), acidic to slightly alkaline (pH
5–7.8) low conductivity lakes (5.9–48.6 µS cm–1). Given that
we sampled only small lakes, most of the diatoms represented
littoral periphytic communities (see Meriläinen 1971, Round
1981). This is evidenced by the fact that in most of the samples
planktonic taxa represented < 10 % of the total cell number. Altogether 12 physical and chemical variables (lake area, maximum depth, water temperature, pH, conductivity, alkalinity, K,
Ca, Na, Mg, total organic carbon), that have been shown to have
a strong impact on the structure and distribution of lentic diatom communities in the area, were measured using the standard
procedures described e.g., in Korhola et al. (1999), Weckström
& Korhola (2001), and Weckström (2001).
Diatom communities in lakes and streams
207
Fig. 1. A map showing the location of
two lentic data sets (ellipsoids) and
three lotic data sets (rectangles). The
filled dots represent the single lake
samples and the filled squares the single river samples. The southernmost
lake is included in analyses although
not encircled in ellipses.
Diatom samples were prepared using H2O2 digestion and
HCl-treatment and the cleaned diatoms were mounted on microscope slides with Naphrax. At least 500 diatom valves
from each sample were counted on random transects at 1000×
magnification. Taxonomic identification was mainly based on
Krammer & Lange-Bertalot (1986–1991). The nomenclature
mainly follows Hartley (1986). For a more detailed description
of the methods and the diatom floras used for identification, see
Weckström et al. (1997a) and Weckström (2001). All together
396 diatom taxa were enumerated from these data sets.
eschweizerbartxxx ingenta
Lotic diatoms
15 stream riffles in three drainage systems, 45 sites in total, in
northern Finland were sampled (see Fig. 1). The drainage systems were: Kemijoki (centered on 67 °N, 28°E), Muonionjoki
(68 °N, 24 °E), and Tenojoki (70 °N, 27 °E). Algal sampling
was conducted once for each site during summer base flow
in August 2001 (Kemijoki) or 2004 (Muonionjoki and Tenojoki). Sampling was confined to near-pristine forest headwater
streams. All sampling was conducted by the same field crew
using a strictly standardized field protocol. Each study site
was divided into five or ten cross-stream transects, depending
on stream width. Ten stones were selected randomly in each
transect, and diatoms were scraped off the stones from a predefined area (3.1 cm2) using a rubber template. Most of the samples represented epilithic communities, although in some sites
samples harboured also epipelic and epiphytic (when mosses
or filamentous algae were present) diatoms. Subsamples, ten in
total, were then pooled into a composite sample for each site.
At each site, we measured five water chemistry variables (water
colour, alkalinity, conductivity, pH and total phosphorus) using
national standards, and four physical parameters (current velocity, shading, particle size and moss cover) known to be the most
important determinants of the diversity and community structure of lotic diatoms in our study area (for species-environment
relationships, see Soininen et al. 2004 and Soininen & Heino
2007).
In the laboratory, acid combustion (HNO3: H2SO4; 2:1) was
used to clean frustules of organic material. Cleaned diatoms
were mounted in Naphrax. A total of 500 valves per sample
were identified and counted using phase contrast light microscopy (magnification 1000×). Diatoms were identified according
to Krammer & Lange-Bertalot (1986–1991) and Lange-Bertalot & Metzeltin (1996). There were 236 diatom species in the
lotic data set.
Data analysis
We first used Principal Component Analysis (PCA) to examine whether lakes and streams showed similar water chemistry in terms of key parameters that were measured in both data
sets and are known to be the most important for diatoms in the
study area (pH, alkalinity and conductivity, e.g., Weckström et
al. 1997a). PCA was performed with correlation matrices using
software CANOCO version 4.5 (ter Braak & Šmilauer 2002).
We used non-parametric MANOVA to test whether site scores
of the lake and stream data sets differed significantly from each
other. Non-parametric MANOVA was conducted using software package PAST version 1.79 with 10000 random permutations (Hammer et al. 2001).
Then, we used Non-metric Multidimensional Scaling
(NMDS) for species abundance data to examine the major gradients in floristic variation. We specifically examined whether
208
J. Soininen and J. Weckström
lentic and lotic diatoms formed non-overlapping community types or whether the communities overlapped in ordination space. NMDS is based on ranked distances and is highly
suitable for ecological data containing numerous zero values
(Minchin 1987). We used Multi-Response Permutation Procedures (MRPP) with 9999 random permutations to test whether
lentic and lotic diatom communities differed from each other.
MRPP is a non-parametric procedure for testing the significance of possible differences between a priori defined groups
(Zimmerman et al. 1985). We also used Indicator Species Analysis (IndVal) (Dufrene & Legendre 1997) to identify species
discriminating between the lake and stream communities. The
indicator value of a taxon varies from 0 to 100, and it attains
its maximum value when all individuals of a taxon occur at all
sites of a single group. The method thus selects indicator species based on both high specificity for and high fidelity to a
specific group. IndVal is considered superior to more traditional
methods of identifying indicators (e.g., TWINSPAN) on both
statistical and practical grounds. For example, it is robust to
differences in within-group sample sizes and abundances across
species. The significance of the indicator value for each species
was tested by a Monte Carlo randomization test with 10000
permutations. NMDS, MRPP and IndVal were conducted using
software package PC-ORD version 4.0 (McCune & Mefford
1999).
Finally, we used Redundancy Analysis (RDA) with variation partitioning (Borcard et al. 1992) to assess the relative
importance of space, environment and their combined effect
on diatom communities. We partitioned the variation into components explained by pure space (S), pure environment (E)
and their combined effect (S + E). For modelling the spatial
variables, we used PCNM (Principal Coordinates of Neighbour Matrices) (Borcard & Legendre 2002, Legendre et al.
2005). This is a method that efficiently detects and quantifies
spatial patterns over a wide range of scales by reconstructing the spatial patterns by starting from the fine-scale relationships instead of the large-scale trends eventually covering the
whole range of scales. Instead of connectivity networks, we
used basic coordinates for constructing the PCNM variables
both for lakes and streams because (i) small-sized organisms
such as diatoms are also dispersed via air across long distances
(e.g. Sharma et al. 2006), (ii) we included lotic diatom data
from three different drainage systems that are not connected
via direct water routes and (iii) we were basically interested
whether diatom communities differ in different parts of the
study area and geographical location is thus our key variable.
eschweizerbartxxx ingenta
Principal component analysis
(PCA) correlation biplot of the environmental variables in the combined data set
of 154 sites. The open dots refer to the lentic sites whereas the open squares to the
lotic sites. According to non-parametric
MANOVA, lentic and lotic sites did not
differ from each other (p = 0.34).
Fig. 2.
Diatom communities in lakes and streams
For assessing the importance of environmental factors on diatoms, we used 12 variables in lentic data set and 9 variables
in lotic data set (see the measured variables above). RDA with
variation partitioning was conducted using package vegan in R
(Oksanen et al. 2006). PCNM was conducted using function
quickPCNM available at http://www.bio.umontreal.ca/legendre/. We used adjusted R2-values, Chord-transformed species
data and two PCNM-variables in variation partitioning both
for lakes and streams.
Results
The PCA showed that water chemistry of lakes and
streams was relatively similar as indicated by overlap-
209
ping site scores in ordination space (Fig. 2). According
to non-parametric MANOVA, site scores of the lakes
and streams did not differ from each other (p = 0.34).
The main PCA gradient in water chemistry in lakes
was most related to water pH, whereas in streams the
main gradient was more related to alkalinity and conductivity. The first two PCA axes represented 96.1 %
of the variation in water chemistry.
According to NMDS, diatom community structure
in lakes and streams differed sharply as communities
showed mainly non-overlapping distributions in ordination space (Fig. 3). According to MRPP, lentic and
lotic community structures were significantly different (p < 0.0001). We also ran NMDS excluding the
Table 1. Summary for the most significant indicator species for lentic and lotic sites. Indicator values are based on Indicator Species
Analysis (IndVal) using species abundance data. Shown are only the species with observed indicator value higher than 50. Mean
abundances refer to abundances in lentic or lotic data set.
Observed
indicator
value
Lentic species
Frustulia rhomboides var. saxonica (Rabenh.) De Toni 1891
Pinnularia biceps var. biceps Greg. 1856
Brachysira brebissonii R. Ross in Hartley 1986
Brachysira vitrea (Grun.) R. Ross in Hartley 1986
Pinnularia microstauron (Ehrenb.) Cleve 1891
Semiorbis hemicyclus (Ehrenb.) Patr. in Patr. & Reimer 1966
Cymbella gaeumannii Meister 1934
Achnanthes levanderi Hust. 1933
Cymbella cesatii (Rabenh.) Grun. in A. Schmidt 1881
Nitzschia fonticola Grun. in Van Heurck 1881
Navicula subtilissima Cleve 1891
Achnanthes altaica (Poretzky) A. Cleve-Euler 1953
Stauroneis anceps var. anceps Ehrenb. 1843
Cymbella hebridica (Grun. ex Cleve) Cleve 1894
Achnanthes marginulata (W. Sm.) Grun. in Cleve & Grun. 1880
Navicula mediocris Krasske 1932
eschweizerbartxxx ingenta
Mean
p-value
abundance
%
91.1
88.1
80.3
74.8
71.6
69.7
68.8
62.2
62.2
61.1
57.8
56.9
56.6
55.8
52.7
51.8
6.29
3.31
5.00
4.69
0.51
0.04
1.51
1.23
0.94
1.02
0.91
0.56
0.45
0.75
1.08
0.74
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
88.7
88.3
85.7
84.3
75.6
73.3
71.1
68.0
.0
66.4
61.4
57.6
56.2
54.5
51.8
2.95
6.08
20.54
8.65
0.66
6.44
1.07
2.03
1.76
0.59
1.68
2.64
1.01
0.51
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
Lotic species
Fragilaria ulna var. ulna (Nitzsch) Ehrenb. 1836
Fragilaria capucina var. gracilis (Oestrup) Hustedt 1950
Achnanthes minutissima Kützing 1933
Tabellaria flocculosa (Roth) Kützing 1844
Eunotia pectinalis var. minor f. impressa (Ehr.) Hust.
Fragilaria capucina var. rumpens (Kutz.) Lange-Bertalot 1991
Gomphonema clavatum Ehr.
Fragilaria vaucheriae var. vaucheriae (Kutz.) J. B. Petersen 1938
Meridion circulare var. circulare (Grev.) Ag. 1831
Cymbella silesiaca Bleisch ex Rabenh. 1864
Gomphonema angustatum var. angustatum (Kutz.) Rabenh. 1864
Achnanthes subatomoides (Hust.) Lange-Bertalot & Archibald 1985
Eunotia curvata (Kutz.) Lagerst. 1884
Nitzschia gracilis Hantzsch 1860
210
J. Soininen and J. Weckström
Fig. 3. Ordination diagram for
eschweizerbartxxx ingenta
Fig. 4. Variation partitioning of the lake and stream diatom
data into fractions explained by environment, space and their
combined effect. For clarity, shown is the variation that was
explained by the environmental and spatial variables of the total variation. Partitioning was based on Redundancy Analysis
(RDA) with spatial variables modelled using PCNM (Principal
Coordinates of Neighbour Matrices).
Non-metric
Multidimensional
Scaling (NMDS) analyses for
the lentic and lotic data set. The
open dots refer to the lentic sites
whereas the filled squares to the
lotic sites. According to Multi-Response Permutation Procedures,
lentic and lotic sites harboured
significantly different diatom
communities (p = 0.0000).
planktonic taxa from the lake data set but lentic and
lotic communities were nevertheless highly different
(results not shown). Moreover, in order to minimise
the possible taxonomical differences caused by two
separate analysts, we ran a NMDS using genus-level
data. Even with this simplification the diatom communities between lakes and streams differed significantly
(p < 0.0001). IndVal showed that there were a large
number of significant indicator species that showed
strong preference for occurring either in lakes or in
streams (Table 1).
According to RDA with variation partitioning environmental and spatial variables jointly explained
16.6 % and 12.4 % of the total variation in diatom
community structure in lakes and streams, respectively. For the streams, pure spatial factors accounted
for more (28 %) of the explained variation than for the
lakes (9 %) indicating that lotic diatoms incorporated
a strong spatial component (Fig. 4). Pure environmental factors nonetheless explained most of the variation
both for the lakes (74 %) and streams (58 %). The proportion of the combined effect was slightly larger for
the lakes (18 %) than for the streams (14 %).
Diatom communities in lakes and streams
Discussion
We showed that despite the relatively similar water
chemistry in lakes and streams in our study area, lentic
and lotic diatom communities differed from each other
sharply. This was further evidenced by the finding that
there were a large number of diatom species that had
a strong preference for either lake or stream habitats
(Table 1). These findings are congruent with Crump
et al. (2007) who found that lakes and streams harboured substantially different bacteria communities in
a watershed in Alaska. The discrepancy between lake
and stream diatom communities can not be explained
by differences in identification of some highly difficult
diatom species because we found clear differences in
communities even on genus-level data. By contrast,
our result probably reflects the differences in, for example, physical factors in terms of different current
and light regimes between the lakes and the streams.
This is especially true because the lakes in our study
area mainly lacked notable canopy vegetation and
streams, by contrast, were characterized by typically
strong shading by the canopy vegetation. Unfortunately, we lack detailed data for the nutrient content of the
studied lakes (because nutrient values were below the
analytical detection limit in most of the lakes) and can
not infer the possible role of different nutrient supply
on community structure between lakes and streams.
Secondly, part of the differences in community structure may have been caused by the fact that lentic communities represented full range of habitats in lakes,
whereas lotic samples represented mostly epilithic
communities, although most of the lotic samples also
harboured epipelic and epiphytic diatoms.
Contrary to our prediction, lake diatom communities showed weaker spatial structure than communities in streams given that pure spatial component was
much smaller in lentic communities. Most plausibly,
this cannot be explained by the differences in study
extents or sampling strategies between the data sets
as both encompassed similar areas and both included
sampling sites from several drainage systems. Our
finding is nonetheless congruent with Crump et al.
(2007) who found that lake bacteria communities were
much more similar across the landscape than stream
communities in the Toolik Lake watershed in the high
Canadian Arctic. Moreover, e.g., Beisner et al. (2006)
found that lake phytoplankton communities were only
weakly spatially structured. At the scales considered in
the present study, both lentic and lotic diatom communities were nevertheless related to local environment,
large-scale spatial factors and their combined effect
eschweizerbartxxx ingenta
211
although the size of these fractions varied between the
lakes and streams. The lake communities, for example, were more related to the pure environmental factors indicating that water chemistry in the lake data set
did not incorporate as large a spatial component as in
the stream data set. Thus, these data lend support to a
notion that diatoms are probably jointly structured by
neutral, dispersal-related processes and species sorting
by the environment, that is, niche-based processes, as
both environmental and spatial factors had a notable
effect on community structure. We emphasize though
that local environmental control was overall much
stronger than the effects of large-scale geographical
and dispersal-related factors both in lakes and streams.
This was especially true for the lakes where pure environmental factors accounted for over 70 % of the variation that could be explained.
The finding that the pure spatial factors nonetheless had a notable effect for diatoms largely parallels
the findings from other biogeographical areas (e.g.,
Potapova & Charles 2002, Reid 2005, reviewed by
Soininen 2007). We showed by variation partitioning that the spatial component in diatoms can not be
caused by mere covariation between geographical and
environmental factors because also the pure spatial
factors contributed to community structure. This finding is supported by the most of the recent findings for
diatoms and other unicellular organisms (e.g., Green
et al. 2004, Horner-Devine et al. 2004, Telford et al.
2006, Vyverman et al. 2007) and shows that species
occupying local sites are drawn from the regional species pools that are not similar, but rather differ substantially from each other. However, our finding disagrees with the recent study by Fierer et al. (2007) who
found that there was no apparent relationship between
community similarity and geographical distance in
stream bacteria in a single watershed. Such a small
study extent compared to ours may explain this lack of
distance effect for stream bacteria.
The reason for the larger spatial component in lotic than in lentic diatoms we found remains speculative at present, but at least three mechanisms can be
put forward. Firstly, some of the spatial effect may
be accounted for by the spatially structured variation
in some environmental factors that went unmeasured
in the field. Although all sampling sites represented
northern boreal to subarctic conditions, spatially structured variation in water temperatures across sites, for
example, may have slightly affected the lotic communities (see e.g., Pienitz et al. 1995, Weckström et
al.1997a,b, Potapova & Charles 2002). However, as
mean July air temperature explained independently
212
J. Soininen and J. Weckström
only 1.6 % of the variation in the lake diatom data
(Weckström 2001), we believe that temperature is not
a strong explanatory variable for diatom composition
at this study area covering geographically a relatively
small spatial extent (i.e., maximum distance between
sites was 350 km). Variation in environmental variables is also generally large in stream environments
probably resulting in larger noise in the lotic data set
than in the lentic data set where communities and environment features were more strongly coupled. In fact,
this was evidenced by the larger proportion of variance
that could be explained by the measured variables for
lakes (16.6 % vs. 12.4 %). Secondly, part of the pure
effect of spatial factors on lotic communities may be
attributed to metacommunity dynamics, namely to
mass effects (see Leibold et al. 2004, Stevens et al.
2007, Vanschoenwinkel et al. 2007), as efficient dispersal among sites may act as a structuring, not homogenizing force (Cottenie et al. 2003, Soininen et
al. 2007). This means that efficient dispersal allows
species to persist in less suitable sites via source-sink
dynamics (Mouquet & Loreau 2002). This is likely in
stream systems that are well connected by direct water
pathways sites therefore forming a metacommunity
in which colonists with different dispersal propensity (e.g., attached vs. free-living species, see Heino
& Soininen 2006) disperse mainly in one direction.
Thirdly, lake samples represent an integrated sample of
several growing seasons, while samples from streams
represent the communities growing in a point of time.
If extinction-colonization dynamics are fast, the timeintegrated sampling may result in a lower influence of
spatial factors in lakes. We thereby conclude that (i)
the pure spatial effect is probably a slight overestimation due to some unmeasured environmental factors
and (ii) that part of the spatial structure (especially)
in the lotic communities may have been caused by efficient dispersal among sites as communities that frequently exchange colonists may be structured not only
by neutral processes and species sorting, but also by
mass effects (Leibold et al. 2004, Cottenie 2005, Vanschoenwinkel et al. 2007).
In conclusion, our findings provide evidence that
despite the largely similar water chemistry, lentic and
lotic diatom communities differ sharply in terms of
community composition. Moreover, communities are
not only structured by local environment but also by
large-scale factors and communities show thus notable
spatial structure across the landscapes. The differences
in community composition are probably attributed to
local physical factors, that is, communities are partly
structured by the local current, disturbance and light
eschweizerbartxxx ingenta
regimes. The mechanisms contributing to the spatial
structure of diatoms remain speculative at present but
these may be partly related to effects of some unmeasured environmental factors, biotic interactions, largescale historical factors as well as dispersal dynamics
given that sites are typically exchanging colonists resulting probably in efficient metacommunity dynamics, that is, mass effects.
Acknowledgements
We would like to thank the Academy of Finland (grant to JS)
and The Kone Foundation (grant to JW) for financial support.
We also thank Kaarina Weckström, Daniel Borcard and Richard Telford for constructive comments on earlier draft of the
manuscript. Finally, we thank Jari Oksanen for help in statistical analyses.
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Hydrobiologia (2009) 624:81–90
DOI 10.1007/s10750-008-9668-6
PRIMARY RESEARCH PAPER
Distribution of epipelic diatoms in artificial fishponds
along environmental and spatial gradients
Aloisie Poulı́čková Æ Jiřı́ Neustupa Æ
Jana Špačková Æ Pavel Škaloud
Received: 17 September 2008 / Revised: 2 December 2008 / Accepted: 4 December 2008 / Published online: 17 December 2008
Ó Springer Science+Business Media B.V. 2008
Abstract Although epipelic diatoms play a key role
in primary production of many ecosystems, many
aspects of their biodiversity, ecology and geographical
distribution are poorly understood. The present study
is based on sampling of 45 man-made fishponds in the
Czech Republic covering an environmental gradient
from oligo/dystrophic highland ponds within protected
areas to the eutrophic/hypertrophic lowland ponds
used for intensive fish production. Diatom distribution
patterns assessed using biomass and species composition variables were assessed along environmental
and geographical gradients. In total, 185 epipelic
diatom taxa were found in the investigated samples.
The differences in species composition between sites
were correlated with environmental parameters, but
not with the geographic distance of the localities. This
pattern might suggest that niche-based control, rather
than the effect of dispersal limitation, is the main
driving force in the species composition of epipelic
Handling editor: J. Padisak
A. Poulı́čková (&) J. Špačková
Department of Botany, Faculty of Science, Palacký
University, Svobody Str. 26, 771-46 Olomouc,
Czech Republic
e-mail: [email protected]
J. Neustupa P. Škaloud
Department of Botany, Faculty of Science, Charles
University of Prague, Benátská 2, 128-01 Prague,
Czech Republic
diatoms in fishponds. The alpha-diversity of sites
correlated with altitude, nitrogen and chlorophyll a
concentrations but did not correlate with the area of the
ponds. The significant relationships between local
abundance of species and their regional occupancy
were very similar to previous studies of diatoms in
boreal streams. In addition, these data concur with
patterns known for multicellular organisms suggesting
that in this respect diatoms may not differ from groups
of organisms with larger body sizes.
Keywords Epipelon Diatoms Geographic distribution
Introduction
Fishponds are man-made shallow water bodies in
which water level, fish stock and, to some extent,
nutrient and fish-food input are under human control
(Fott et al., 1980). Moreover, fishpond ecosystems
exhibit fast changes and are susceptible to unpredictable random external disturbances (Weimann, 1942).
The Central European fishponds have been constructed since the Middle Age, and since then they
have often lost any artificial appearance, looking
nowadays like small lakes within the landscape
(Kořı́nek et al., 1987). However, with their ca.
250–750 years, they are still much younger than
most comparable natural stagnant freshwaters (lakes,
wetland pools). This freshwater habitat type can be
123
82
found in several European countries (e.g. Austria,
Poland, Germany, Hungary, France, Croatia and
Russia) and are typically more abundant than larger
lakes by about 100:1 (Oertli et al., 2005). In the
Czech Republic, they represent the single most
common type of stagnant water habitat (total area
510 km2; Pokorný et al., 2006) replacing a large
proportion of former wetland ecosystems (Fig. 1).
The most recent articles, particularly focused on
invertebrates or macrophytes, highlighting the importance of ponds for the provision of habitat to support
freshwater biodiversity (Céréghino et al., 2008a, b;
Davies et al., 2008; De Bie et al., 2008). The
phytobenthos of fishponds is structurally dominated
by epipelic microalgal assemblages. Epipelic algae
represent a specific functional group adapted to living
on and between fine-grained substrata. The study of
epipelon was pioneered within freshwater habitats by
Round (1953), but epipelic algae in general have
received relatively little attention in comparison to
studies of phytoplankton (Poulı́čková et al., 2008a).
Thus, the distribution patterns of benthic algae in
fishponds remain largely unexplored (Lysáková et al.,
2007; Hašler et al., 2008; Poulı́čková et al., 2008a, b).
On the other hand, the distribution patterns of
freshwater microalgae have recently been the subject
of several studies, specifically investigating the local
abundance versus regional occupancy patterns of
species assemblages and the relation of species
Fig. 1 Map showing total
area and distribution of all
fishponds in the Czech
Republic (grey areas) and
investigated localities
(black areas). For locality
numbers see Table 1
123
Hydrobiologia (2009) 624:81–90
composition similarities between individual localities
to their spatial structure and environmental characteristics (e.g. Soininen, 2007; Soinien et al., 2007). The
relation of local abundance versus regional occupancy
of species and the frequency distribution of species
occupancy across the investigated region are useful
characteristics of distribution patterns in microalgal
species (Heino & Soininen, 2006). In diatoms, Soininen & Heino (2005) illustrated a similar positive
relationship between species local abundance and
regional occupancy. However, comparative data from
other habitat types than boreal streams are lacking.
It has recently been demonstrated that dispersal
limitations do affect the global distribution of freshwater diatoms (Telford et al., 2006; Vyverman et al.,
2007). Moreover, traditional ‘‘cosmopolitan’’ and
‘‘euryvalent’’ species (species complexes) consist of
pseudo-cryptic diatom species with restricted distribution (Evans et al., 2008; Poulı́čková et al., 2008c).
Thus, diatoms now may appear as the group with
possibly much less cosmopolitanism than hypothesized by Finlay et al. (2002). Generally, groups largely
with cosmopolitan distribution of species should have
flat species–area curves with little increase of the
species richness at the level of regions, continents or
at the global level. On the other hand, species–area
curves of groups with strong geographic limits of
distribution increase strongly from the regional to
global levels (Finlay et al., 2002). Thus, strong spatial
Hydrobiologia (2009) 624:81–90
control of diatom distribution at the regional level
should indicate their pronounced geographically
restricted species distribution pattern.
The present study uses an epipelic diatom dataset
from 45 Czech fishponds and aims to address the
following questions:
(a)
Do the epipelic diatom communities in manmade fishponds exhibit strong spatial structuring
at the regional level (suggesting neutrality) or
are they structured by environmental features
(suggesting the niche-based control)?
(b) Which environmental factors correlate with the
alpha-diversity of fishpond epipelic diatoms and
is there any effect of pond size on the single site
alpha-diversity of epipelic diatoms?
(c) Is the relationship between the local abundance
and the regional frequency, and the species–
occupancy frequency distribution of fishpond
epipelic diatoms similar to those observed in
boreal streams (Soininen & Heino, 2005)?
Material and methods
In May 2007, we sampled 45 fishponds in the Czech
Republic (Fig. 1) covering a gradient from oligo/
dystrophic highland ponds within protected areas
(localities Pavlov, U 3 krátkých) to hypertrophic
lowland ponds used for intensive fish production
(Naděje, Starý Kanclı́ř). The geographic positions,
area and selected environmental data of the ponds are
summarized in the Table 1. Other measured environmental variables and species composition of other
algal groups (Cyanobacteria, desmids, euglenophytes)
were published elsewhere (Hašler et al., 2008).
Selected environmental variables (pH, conductivity)
were measured in situ using field instruments (WTW,
Germany). Samples for chemical analyses were stored
in sterile plastic bottles. Total phosphorus and nitrogen were analysed with a DR 2000 spectrophotometer
by HACH (Hach, 1993; Hašler et al., 2008).
Sediment samples were collected using a glass
tube, as described by Round (1953), and transported
to the laboratory in polyethylene bottles. The mud–
water mixtures were then poured into plastic boxes
and allowed to stand in the dark for at least 5 h. The
supernatant was then removed by suction and the
mud covered with lens tissue. Under continuous low-
83
level illumination (ca. 5 lmol photons m-2 s-1),
epipelic algae moved up through the first layer of
lens tissue (separating detritus and inorganic particles) and became attached to the second layer of lens
tissue placed on top. Captured diatoms were cleaned
with a mixture of concentrated sulphuric and nitric
acids and mounted in Naphrax as described previously by Poulı́čková & Mann (2006). Diatom species
were identified according to Krammer & LangeBertalot (1986, 1988, 1991a, b). Relative abundances
of individual diatom species were estimated by
counting 400 valves from each sample.
The Mantel tests of matrix correlations were used
for testing the relationships between matrices of
distance in species composition (evaluated using
Bray–Curtis quantitative distance measure), environmental distance (matrix of Euclidean distances of
ranked values of individual parameters) and the
geographic distance of the localities (in kilometres).
We used both the full (two matrices) Mantel tests and
the partial (three matrices) Mantel tests with either the
effect of environmental similarity or the geographic
distance taken as the co-variate in testing the relation
of the above-mentioned matrices in zt ver. 1.0 (Bonnet
and Van de Peer, 2002). Altogether 10,000 permutations were used to assess the permutation P-value of
significance in matrix correlations. The correlations of
species composition distance, environmental and
geographic distances were illustrated using linear
regression models. Linear correlations of alphadiversity indices and the environmental factors were
evaluated in PAST, ver. 1.80. (Hammer et al., 2001).
Alpha-diversity was evaluated using Shannon and
Menhinick indices after Magurran (2004). The evenness of the diatom community was assessed using the
Simpson index and species dominance using the 1Simpson index (Hammer, 2002; Magurran, 2004).
Results
In total, 185 diatom taxa were recorded across the study
sites. The most common epipelic diatoms were ‘‘cosmopolitan’’ species (sensu Krammer & LangeBertalot, 1986, 1988, 1991a, b) or species complexes
(see Poulı́čková & Mann, 2006; Potapova & Hamilton,
2007; Mann et al., 2008): Navicula capitata Ehrenb.,
N. gregaria Donkin, Amphora copulata (Kütz.)
Schoem. et Archibald, Achnanthidium minutissimum
123
84
Hydrobiologia (2009) 624:81–90
Table 1 Basic characteristics of investigated localities
Area (m2)
pH
TN
(mg l-1)
Chl-a
(lg l-1)
No.
Locality
GPS
Altitude
(m a.s.l.)
1
Drahany
N 49;25;54;8
380
35,200
195
7.38
6.5
16.12
2
Protivanov
615
24,222
201
9.93
6.5
6.38
610
12,300
175
7.50
0.8
3.78
610
6,700
112
6.76
0.3
5.53
673
680
290
10.90
5.9
265.47
674
55,200
125
7.82
0.9
6.17
680
9,400
200
7.28
1.9
9.40
N 49;30;39;4
E 16;49;51;4
630
2,000
181
9.41
5.1
32.84
N 49;07;07;7
430
656,548
220
8.89
0.7
10.68
429
2,247,877
245
8.33
0.5
45.01
435
213,225
245
8.18
3.0
70.40
441
4,270,631
205
8.59
1.9
29.95
445
1,323,151
215
7.60
1.1
34.06
455
339,028
165
10.38
1.8
140.49
Cond
(lS cm-1)
E 16;52;34;9
N 49;28;12;2
E 16;48;41;7
3
Obora
N 49;27;44;3
E 16;47;54;9
4
U 3 krátkých
N 49;28;47;5
E 16;47;35;0
5
Suchý 1 náves
N 49;28;52;5
E 16;45;49;5
6
Suchý 2 tobogán
7
Pavlov
N 49;28;54;5
E 16;45;40;2
N 49;30;57;7
E 16;47;23;6
8
Buková
9
Naděje
E 14;44;31;3
10
Velký Tisý
N 49;04;04;2
E 14;42;25;6
11
Malý Tisý
N 49;03;13;8
E 14;44;57;0
12
Rožmberk
N 49;02;53;3
E 14;45;43;6
13
Opatovický
N 48;59;13;9
E 14;46;43;4
14
Starý kanclı́ř
N 48;58;05;6
15
Hejtman
N 48;57;32;4
469
800,000
132
7.65
0.4
19.26
Staňkov
E 14;56;20;8
N 48;58;31;9
483
1,969,555
133
9.16
1.0
25.79
483
412,628
188
7.76
2.4
66.19
323
4,153
461
9.10
0.1
4.05
316
10,779
429
8.10
0.9
9.34
198
120,525
670
7.93
3.3
69.00
198
127,905
770
7.78
1.7
88.13
207
189,476
422
7.68
1.5
28.02
E 14;53;43;6
16
E 14;57;26;7
17
Špačkov
N 48;58;31;9
E 14;57;26;7
18
Bezednı́k
N 49;17;58;2
E 17;43;35;1
19
Hornı́ Ves
N 49;17;45;0
E 17;42;03;7
20
Záhlinice 1
N 49;17;14;6
E 17;28;41;1
21
Záhlinice 2
N 49;17;14;6
E 17;28;41;1
22
Chropyně
N 49;21;25;4
E 17;22;14;1
123
Hydrobiologia (2009) 624:81–90
85
Table 1 continued
Area (m2)
Cond
(lS cm-1)
pH
TN
(mg l-1)
Chl-a
(lg l-1)
No.
Locality
GPS
Altitude
(m a.s.l.)
23
Tovačov
N 49;26;06;8
206
359,850
267
7.38
2.3
97.52
24
Hrdibořice 1
N 49;28;56;1
213
30,362
726
7.94
1.9
64.38
213
49,824
729
8.00
1.5
37.48
266
2,631,963
303
8.70
1.3
11.20
266
807,966
203
7.09
0.4
10.60
279
10,507
306
7.69
0.9
2.30
E 17;17;35;6
E 17;13;31;2
25
Hrdibořice 2
N 49;28;56;1
E 17;13;31;2
26
Máchovo jezero
N 50;34;34;4
E 14;39;00;0
27
Břehyňský hráz
N 50;34;32;9
E 14;41;35;7
28
Černý
N 50;36;30;6
29
Vavrouškův
N 50;36;35;4
287
30,482
293
8.05
0.3
8.76
Strážovský
E 14;45;01;9
N 50;36;38;3
279
40,000
299
7.77
0.1
6.31
289
9,246
221
7.50
0.1
6.73
287
79,690
245
7.57
0.1
13.46
261
541,141
332
8.90
0.8
7.68
262
9,396
437
8.46
1.8
26.08
220
36,565
748
7.47
2.6
48.32
263
27,036
722
7.17
2.5
42.57
E 14;45;46;1
30
E 14;44;29;8
31
Tůň u letiště
32
Hradčanský
N 50;36;51;0
E 14;43;48;1
N 50;37;05;6
E 14;42;26;5
33
Novozámecký
N 50;37;44;7
E 14;32;12;1
34
Hostivař-vedl.
N 50;02;23;3
E 14;31;53;6
35
Hamerský
N 50;03;08;3
E 14;29;16;7
36
před Vrahem
N 50;01;50;9
37
Vrah
N 50;01;44;1
274
24,503
543
7.32
2.2
33.25
Homolka
E 14;32;50;9
N 50;01;38;4
212
12,364
660
9.82
3.4
6.73
302
23,955
778
7.65
2.0
19.13
419
37,656
556
7.59
2.9
5.06
412
64,661
511
8.17
2.0
45.59
395
176,150
431
8.50
2.2
59.79
418
38,482
534
7.39
2.5
19.30
320
15,000
457
7.56
1.7
45.82
E 14;32;53;4
38
E 14;32;42;1
39
Milı́čov
N 50;01;34;0
40
Požár
N 49;59;15;5
E 14;32;27;0
E 14;45;24;2
41
Louňovický
N 49;59;07;0
E 14;45;59;7
42
Jevanský
N 49;58;43;7
E 14;47;13;8
43
Pařez
N 49;59;05;5
E 14;46;33;5
44
Lı́šnice
N 49;45;42;0
E 16;51;39;0
123
86
Hydrobiologia (2009) 624:81–90
Table 1 continued
No.
Locality
GPS
Altitude
(m a.s.l.)
45
Obectov
N 49;43;39;0
329
Area (m2)
500
Cond
(lS cm-1)
296
pH
7.26
TN
(mg l-1)
Chl-a
(lg l-1)
3.7
185.06
E 16;55;43;0
Cond conductivity, TN total nitrogen, Chl-a chlorophyll a concentration
(Kütz.) Czarn. agg., Navicula cryptocephala Kütz.
agg. and Sellaphora pupula (Kütz.) Mereschk. agg.
The samples from oligotrophic/dystrophic protected
ponds (often with low pH and conductivity; e.g.
Břehyňský, Pavlov) were characterized by the occurrence of Brachysira vitrea (Grunow) Ross, Pinnularia
viridis (Nitzsch) Ehrenb., P. microstauron (Ehrenb.)
Cleve and Tabellaria flocculosa (Roth) Kützing.
Assemblages from eutrophic ponds (Naděje, Rožmberk, Malý Tisý) were characterized by a dominance of
N. cryptocephala, N. capitata, N. gregaria, Nitzschia
palea (Kütz.) W. Smith. The representation of S.
pupula agg. was very low (usually \1%) with the
single exception of the Obectov locality that had 74%
of S. pupula agg. cells in the epipelic community. Rare
taxa included Entomoneis ornata (J.W.Bailey) Reimer
(localities Obora, Suchý 2, Břehyňský) and Amphipleura pelucida (Kütz.) Kütz. (Hradčanský, Tůň u letiště).
The results of Mantel tests of matrix correlation
(Table 2) illustrated strong relations between the
species composition and environmental factors. The
relation of environmental distance and species composition was highly significant both in two-matrices
Fig. 2 Relation of species composition distances of fishpond
epipelic diatoms from the Czech Republic between individual
sites and their environmental (a) and geographic distances (b)
123
Table 2 Results of Mantel tests of matrix correlations
r
P-value
Species composition versus
geographic distance
-0.009
0.41
Species composition versus
environmental distance
0.233
0.0002
Geographic distance versus
environmental distance
0.138
0.0017
Species composition versus
geographic distance with
environmental distance effect
controlled
-0.028
Species composition versus
environmental distance with
geographic distance effect controlled
0.235
0.25
0.0002
Significant correlations are indicated in bold
Mantel test (Fig. 2a) and in the partial Mantel test with
the geographic distance taken as the co-variate.
However, the species compositions of the samples
were not correlated with their spatial position
(Fig. 2b). In other words, the closer localities did not
have more similar diatom species composition when
in kilometre. The linear correlation r and the respective Pvalues are indicated in upper left of the graphs
-0.21
-0.23
0.43***
–
–
20.55***
0.62***
0.45**
–
0.05
-0.09
20.44**
0.35*
0.34*
0.10
0.38*
0.24
–
-0.13
Discussion
Significant values are indicated in bold
* P-value = 0.01–0.05, ** P-value = 0.001–0.01, *** P-value \ 0.001,
Individual values indicate correlation coefficients between evaluated variables
Chlorophyll a
Total N
Total P
pH
Conductivity
Pond area
P-value [ 0.05
–
-0.06
-0.08
-0.19
-0.11
20.30*
–
0.16
0.26
–
Altitude
Evenness
Dominance
Menhinick index
compared to the more distant ones (Table 2). At the
same time, geographic distances of ponds were positively correlated with their environmental distance.
Species diversity indices did not correlate with pond
area (Table 3). The alpha-diversity indices (evaluated
by Shannon and Menhinick indices) were negatively
correlated with site altitude, and there was a significant
negative correlation between Menhinick diversity
index and the total nitrogen and chlorophyll a concentrations (the P-values of the correlation of Shannon
index with these measures were slightly above the 0.05
level). Altitude was negatively correlated with site
conductivity. The total nitrogen, total phosphorus and
chlorophyll a concentrations were strongly positively
correlated with each other. In addition, total phosphorus and conductivity were strongly negatively
correlated with water transparency, and conductivity
was positively correlated with the total phosphorus
concentrations (Table 3).
The local maximum species abundance and the
local mean species abundance were positively correlated with regional frequency (i.e. % of the localities
with the presence of the particular species). This
positive correlation was slightly stronger for the local
maximum abundance, but it was highly significant for
both the local abundance measures (Fig. 3). The
species–occupancy frequency distribution (Fig. 4)
was in agreement with the ‘‘satellite-mode’’, distribution model, i.e. a high proportion of species
occurred at a small number of sites.
0.09
0.25
0.26 -0.01
20.63***
0.06
0.07
-0.07
0.04
-0.04
-0.20
0.20
21.00***
–
–
-0.09
-0.08
0.08
-0.26
0.26
0.04 -0.19 -0.28
-0.04
0.19
0.28
20.40** 20.34*
-0.27
-0.22
0.18
20.50*** -0.12
–
20.46**
-0.11
-0.26
0.46**
-0.01 -0.21 -0.28
0.14
-0.01
20.34*
20.95***
0.95***
0.60***
87
–
Shannon index
Shannon index Menhinick index Evenness Dominance Altitude
Table 3 Linear correlations of diversity measures and environmental variables
Pond area Conductivity pH
Total P Total N
Chlorophyll a Transparency
Hydrobiologia (2009) 624:81–90
In their study of meta-community structure in boreal
wetland ponds, Soinien et al. (2007) recently suggested two distinct models for distribution patterns of
freshwater microalgae in natural ponds:
(a)
The neutrality model concurring with Hubbell’s
(2001) theory. This model is typical of strongly
spatially structured meta-communities with pronounced regional distance decay (negative
correlation of similarity in species composition
with geographic distance of localities). This
pattern suggests that species are spatially limited in their dispersal, but it assumes that there
are no differences in fitness of individuals
within the assemblages (Hubbell, 2001).
123
88
Hydrobiologia (2009) 624:81–90
Fig. 3 The relationship between maximum (a) and mean (b) local abundance and regional occupancy (frequency) of epipelic
fishpond diatom species of the Czech Republic. The linear correlation r and the respective P-values are indicated in lower right
Fig. 4 The species–occupancy frequency distribution of fishpond epipelic diatoms in the Czech Republic
(b)
The niche-based model assuming that individuals
have different fitness in different environmental
conditions. In this model, species composition
similarities between pairs of localities correlate
primarily with similarities in their local environmental characteristics rather than with the
geographic distances. This model assumes that
dispersal limitations does not matter (at least
within an investigated region), and so, the species
pools of individual localities are more or less
identical to the overall regional species pool.
Finlay (2002) and Finlay et al. (2002) extended
this model to the global scale assuming cosmopolitan distribution in most protist species with
dimensions of less than 1 mm.
123
In our study, the differences in species composition between sites (evaluated by the Bray–Curtis
distance measure) were correlated with environmental distance, but they were not related to the
geographic distance of the localities. This pattern
might suggest, at least at the regional level of the
Czech Republic, that niche-based control rather than
the effect of dispersal limitations on the species
composition of epipelic diatoms in fishponds is
important. Within the Czech Republic (ca. hundreds
of kilometres without obvious dispersal barriers as,
for example mountain ridges), the effects of dispersal
limits on epipelic diatom metapopulations can be
ignored. However, we should still bear in mind the
relatively young age of individual fishpond habitats
(ca. 250–750 years). In this relatively very short
evolutionary time, local speciation events, detectable
via morphology-based diatom taxonomy, may not
have taken place (see, e.g. Soininen, 2007 for diatoms
speciation estimates of about 103–104 years). In
addition, most of the fishponds in the Czech Republic
have experienced drastic ecological changes in the
past decades as a result of large-scale eutrophication
and intensive fish-production management (Pechar,
1995), having strong influence on biota (Allan, 2004;
Declerck et al., 2006). This could further support the
‘‘young age-effect’’ which may be superseded by
spatially controlled variation in diatom species composition. The published datasets detailing the diatoms
of natural lakes generally illustrate obvious spatial
effects (Fallu et al., 2002; Bouchard et al., 2005)
Hydrobiologia (2009) 624:81–90
indicating dispersal limitations between localities.
Thus, the ‘‘young age-effect’’ of man-made fishponds
that highlights the environmental control over the
spatial control of the diatoms species composition
could, for the present, be the most plausible explanation of this pattern.
In organisms with ubiquitous dispersal and distribution, the pattern of species frequency distribution
across localities should be characterized by a high
proportion of ‘‘core species’’, i.e. species present at
most of the localities. However, Soininen & Heino
(2005) illustrated that the ‘‘satellite mode’’ (sensu
Hanski, 1982) of diatom species frequency distribution held with most species occurring in a low
proportion of sites in boreal streams. Thus, they
concluded that regional distribution patterns of
stream diatoms ‘‘may not be fundamentally different
from those described previously for multicellular
organisms’’ and suggested that this may even be a
more general pattern for other habitat types and
species groups. Given the difference in morphological and ecological structure of the diatom
community coupled with the obvious differences
between man-made fishponds and boreal streams, the
pattern of diatom species regional frequency distribution in artificial fishponds may provide a test
system from which a more general distribution model
for freshwater diatoms may be constructed.
The significant correlations between local abundance of species and their regional occupancy in this
study were similar to those illustrated by Soininen &
Heino (2005) in their boreal streams study. In
addition, these findings are in agreement with
patterns observed in multicellular organisms, suggesting that diatoms may not differ from other groups
of biota with larger body sizes. The overwhelming
number of satellite species (present in just a few sites
across the region) in a situation where there is no
spatial effect on species composition similarity
implies rather narrow ecological tolerances (niche
breadths) of epipelic diatom species, similarly to
conclusions based on the investigation of the boreal
streams data of Soininen & Heino (2005). This
observed distribution pattern clearly indicated the
importance of habitat variety among Central European fishponds for the sustainability of epipelic
diatom biodiversity. Whether the environmentally
driven species composition control of fishpond epipelic diatoms reflects the absence of dispersal
89
limitations at the regional level or it may really be
a result of their relatively young age in comparison
with most natural stagnant freshwater habitats,
remains to be confirmed or rejected in future studies.
Acknowledgements This study was supported by grant nos.
206/07/0115 and 206/08/0389 of Czech Science Foundation,
partly by the research project of the Czech Ministry of
Education no. 0021620828. We would like to thank Dr. J.
Burian (Department of Geoinformatics, Olomouc) for
preparing a map of all fishpond distribution and Dr. Bryan
Spears (Centre for Ecology & Hydrology, Edinburgh) for
helping to improve the clarity of the manuscript.
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1414(2):
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146-154 (2007)
(2007)
Neutrality, niches, and determinants of plankton
metacommunity structure across boreal
wetland ponds1
Janne SOININEN2, Department of Biological and Environmental Sciences, P.O. Box 65, FIN-00014 University of Helsinki,
Finland, e-mail: [email protected]
Mikolaj KOKOCINSKI, Collegium Polonicum, University of Adam Mickiewicz, ul. Kosciuszki 1, 69-100 Slubice, Poland.
Satu ESTLANDER, Department of Biological and Environmental Sciences, P.O. Box 65, FIN-00014 University of
Helsinki, Finland.
Juho KOTANEN, South Savo Regional Environment Centre, Jääkärinkatu 14, FIN-50100 Mikkeli, Finland.
Jani HEINO, Finnish Environment Institute, Research Department, P.O. Box 413,
FIN-90014 University of Oulu, Finland.
Abstract: We examined the determinants of zooplankton and phytoplankton metacommunity structure across 25 boreal
wetland ponds. Our objective was to determine whether plankton metacommunities exhibit spatial structuring, thus suggesting
neutrality, or are merely structured by local environmental features, suggesting that they are under niche-based control.
According to Redundancy Analysis (RDA), zooplankton community structure was primarily controlled by concentration
of major ions and geographical location of the pond, while phytoplankton community structure was primarily controlled
by major ion concentration, nitrogen concentration, and geographical location. According to variance partitioning in RDA,
zooplankton community structure was attributed more to pure spatial position of the pond (16.7% of variance) than to pure
environmental factors (4.7% of variance). For phytoplankton, community composition was controlled by both spatial and
environmental factors, although the effect of pure spatial position (11.1% of variance) on phytoplankton community structure
was somewhat weaker than for zooplankton. For zooplankton, community similarity was negatively (P < 0.01) related to
both geographical and environmental distance. For phytoplankton, community similarity was negatively (P < 0.05) related
only to geographical distance. Finally, cluster analyses showed that zooplankton and phytoplankton communities formed
highly dissimilar groupings, thus implying low community concordance. Our results suggest that both zooplankton and
phytoplankton comply with both neutral and niche-based models. Our results further imply that even across small spatial
scales and even for small organisms such as plankton, communities might be strongly spatially structured. The finding that
spatial configuration was even more important than local environmental factors in controlling zooplankton community
composition suggests that zooplankton may be dispersal-limited within relatively small spatial scales or that zooplankton
metacommunities might be related to mass effects.
Keywords: environmental control, metacommunity, neutral theory, phytoplankton, ponds, spatial structure, zooplankton.
Résumé : Nous avons examiné les déterminants de la structure des métacommunautés de zooplancton et de phytoplancton
dans 25 étangs boréaux. Notre objectif était de déterminer si les métacommunautés de plancton démontraient une structure
spatiale suggérant la neutralité ou si elles étaient simplement structurées par les facteurs locaux de l’environnement suggérant
alors un contrôle basé sur les niches. Selon une analyse de redondance, la structure des communautés de zooplancton
était principalement contrôlée par la concentration des ions les plus importants et par l’emplacement géographique. Dans le
cas de la structure des communautés de phytoplancton, le contrôle provenait de la concentration des principaux ions et de
l’azote ainsi que de l’emplacement géographique de l’étang. Une répartition de la variance dans l’analyse de redondance a
indiqué que la structure des communautés de zooplancton était plus attribuable à la position seule de l’étang (16,7 % de la
variance) qu’aux seuls facteurs de l’environnement (4,7 % de la variance). Dans le cas du phytoplancton, la composition de la
communauté était contrôlée à la fois par les facteurs spatiaux et environnementaux mais la position seule de l’étang (11,1 %
de la variance) avait un effet un peu plus faible que dans le cas du zooplancton. La similarité des communautés de
zooplancton était reliée négativement (P < 0,01) à la fois à la distance géographique et à celle environnementale, alors que
pour le phytoplancton, la similarité des communautés était reliée négativement (P < 0,05) seulement à la distance
géographique. Finalement, des analyses typologiques ont démontré que les communautés de zooplancton et de phytoplancton
formaient des regroupements très dissimilaires ce qui implique une faible concordance des communautés. Nos résultats
démontrent que le zooplancton et le phytoplancton se conforment tous les deux à la fois au modèle neutre et à celui basé sur
les niches. Nos résultats démontrent de plus que même à de petites échelles spatiales et pour un organisme aussi petit que le
plancton, les communautés peuvent être fortement structurée au niveau spatial. Le fait que la configuration spatiale était
plus importante que les facteurs locaux de l’environnement dans le contrôle de la composition des communautés de
zooplancton démontre que ce dernier est peut être limité dans sa dispersion à des échelles spatiales relativement petites ou
que les métacommunautés de zooplancton peuvent être reliées à des effets de masse.
Mots-clés : contrôle environnemental, étangs, métacommunauté, phytoplancton, structure spatiale, théorie neutre, zooplancton.
Nomenclature: Ettl, 1983; Komárek & Anagnostidis, 1999.
1Rec.
2006-07-04; acc. 2006-10-05.
Associate Editor: Marco Rodriguez.
2Author for correspondence.
ÉCOSCIENCE, vol. 14 (2), 2007
Introduction
A metacommunity is defined as a set of local communities connected by the dispersal of constituent species
(Wilson, 1992). The development of this concept quickly
resulted in the generation of a number of conceptual metacommunity models. Species-sorting, mass-effect, patchdynamics, and neutral models differ in their assumptions
regarding the importance of species traits, environmental
heterogeneity, and dispersal constraints (Leibold et al.,
2004). The neutral model predicts, for example, that community similarity decreases with geographical distance in
the metacommunity because of spatially limited dispersal and assumes that all individuals have identical fitness
(Hubbell, 2001). By contrast, niche-based theories, which
are based on the assumption that individuals differ in their
ability to perform under different environmental conditions,
predict that community similarity decreases with environmental dissimilarities among sites (Tuomisto, Ruokolainen
& Yli-Halla, 2003; Gilbert & Lechowicz, 2004). To date,
metacommunity ecology has been mostly theoretical
(Amarasekare & Nisbet, 2001; Mouquet & Loreau 2002;
2003), and only recently has there been increased interest
in empirical and experimental studies on metacommunities
(Cottenie & De Meester, 2003; Cottenie et al., 2003; Urban,
2004; Cadotte & Fukami, 2005).
Amongst the most suitable environments for studying metacommunity ecology are small freshwater ponds
scattered in a landscape. Ponds constitute discrete habitat
patches that are mainly connected by the overland dispersal
of their biota (Wilbur, 1997). Thus, because of their isolation in the terrestrial matrix, dispersal limitation should
have a major role in structuring pond communities. Local
environmental factors should nevertheless also be important, because freshwater ponds range along a major gradient
from temporary to fish-free permanent to permanent fishcontaining habitats, with consequent changes in the relative
importance of disturbance and predation in shaping their
community structure (Wellborn, Skelly & Werner, 1996).
Even among ponds of the same broad type (e.g., fish-containing ponds) abiotic environmental factors may strongly
determine the community membership of pond organisms
(Arnott & Vanni, 1993), and this environmental control on
species distributions may even overcome the influences of
dispersal as the major determinant of community structure.
Plankton communities represent important elements
of the biota in ponds. Even very small and often acidic
ponds may harbour surprisingly diverse phytoplankton
and zooplankton communities. Traditionally, studies on
the planktonic organisms of ponds and shallow lakes have
emphasized the role of local environmental factors such as
water body area, nutrient supply, and ionic composition of
water in regulating the community structure of both phytoplankton (Leibold, 1999; Dodson, Arnott & Cottingham,
2000; Jeppesen et al., 2000) and zooplankton (Arnott &
Vanni, 1993; Dodson, Arnott & Cottingham, 2000; Jeppesen
et al., 2000). Recently, however, more emphasis has been
placed on regional processes in studies directly addressing
the spatial configuration of ponds (Cottenie & De Meester,
2003; Cottenie et al., 2003; Louette & De Meester, 2005)
and in studies examining the roles of connectivity and the
dispersal capacity of organisms per se (Brendonck, De
Meester & Riddock, 2000; Shurin, 2000; Forbes & Chase,
2002; Cottenie & De Meester, 2004). However, these
studies have centred on addressing metapopulation and
metacommunity dynamics of zooplankton, while spatial
patterns in phytoplankton communities across a set of
ponds have largely been ignored. Although they are tightly
coupled via food-web interactions and share the same lifeform in pond systems, phytoplankton and zooplankton are
likely to show differing responses to major environmental
gradients, and to exhibit dissimilar spatial structuring
because of differences in body size, dispersal capacity, trophic position, and rate of population dynamics (Rothhaupt,
2000). One could envisage, for example, that phytoplankton cells are more easily dispersed among closed ponds
via air and animal vectors than zooplankton due to their
smaller size (Kristiansen, 1996).
Here, we examine the determinants of phytoplankton
and zooplankton community structure in a set of boreal
wetland ponds. Specifically, our objective was to determine
whether plankton metacommunities exhibit strong spatial structuring (thus suggesting neutrality) or are merely
structured by local environmental features (thus suggesting
niche-based control). Therefore, for simplicity, we examined
whether plankton communities corroborate either of these 2
broad metacommunity models (i.e., neutral model versus
niche-based model) or are jointly controlled by environmental factors and spatial position. We expected zooplankton
communities to be more strongly spatially structured than
phytoplankton due to their larger body size and thus lower
dispersal ability (Finlay, Esteban & Fenchel, 1996). We further expected phytoplankton to be more strongly related to
water chemistry compared to zooplankton as phytoplankton
occupy the basic trophic level, often being under relatively
strict environmental or bottom-up control (Paszowski &
Tonn, 2000). Communities at higher trophic levels, such as
zooplankton that graze phytoplankton, are typically more
strongly regulated by food web interactions or top-down
forces (McQueen et al., 1989). We also examined the degree
of concordance in the patterns of phytoplankton and zooplankton communities across the ponds. Our study ponds
are located relatively close to each other, the maximum distance between the ponds being approximately 20 km. This
relatively small spatial extent should ensure that most of the
species have potentially relatively free access to all localities within our study system (see Figuerola & Green, 2002;
Cohen & Shurin, 2003; Havel & Shurin, 2004).
Methods
Study area and study ponds
The study area is located in the River Oulankajoki
watershed in northeastern Finland (Figure 1), with the area
encompassing the sampling sites being 221 km2. The bedrock of the study area is highly variable, with extensive
occurrence of calcareous rocks. Accompanied by considerable altitudinal differences, this is reflected in highly variable vegetation, ranging from old-growth coniferous forests
to nutrient-poor bogs and luxurious fens. These factors
also provide the basis for the high variability of freshwater
habitats across the region. The watershed is characterized
147
Soininen et al.: Plankton metacommunity structure across wetland ponds
Figure 1. A map showing the locations of the 25 ponds in the study landscape.
by numerous ponds and small lakes that range from isolated
wetland ponds to more connected headwater lakes. In this
study, we included only wetland ponds that are surrounded
by bog or fen, and we sampled altogether 25 ponds. Most of
the ponds did not have inlets or outlets and were not interconnected. Aquatic vegetation in the sampled ponds was
very sparse, mainly consisting of bryophytes. At least 9 of
the studied ponds contained fish (perch Perca fluviatilis or
ninespined stickleback Pungitius pungitius), and given that
fish were visually detected even in some of the smallest
ponds (J. Soininen, M. Kokocinski, S. Estlander & J. Heino,
pers. observ.), we assume that all ponds contained fish. All
ponds were close to a natural state at the time of sampling
in July of 2005, with only minor effects of forestry in their
catchments. The duration of ice-free period in these ponds is
5 to 6 months.
Sampling and laboratory analyses
Phytoplankton and zooplankton samples were collected with a tube sampler (V = 2.3 L) from 5 randomly
selected near-shore locations at same depth (0.5 m) and
pooled into one collective sample at each pond. Subsamples
for phytoplankton were mixed, and a sample of 1 L was
148
fixed immediately with acid Lugol’s iodine solution at
each pond. Zooplankton samples were filtered through a
50-µm net and preserved with formaldehyde in the field.
For phytoplankton analyses, samples were sedimented in a
glass cylinder for 48 h, after which the overlying water was
gently decanted off and the lower layer (volume of 5 mL)
was stored in a glass vial in a dark and cool place. The
known volume of concentrated samples was then used for
identification and counting of phytoplankton using a light
microscope (magnification 400x). For each sample, all individuals, whether single cells, colonies, or filaments, were
counted in 400 fields of a Burker chamber. Phytoplankton
was identified mainly according to Ettl (1983) and Komárek
and Anagnostidis (1999). For zooplankton, 2 sub-samples
of each sample were poured into a 50-mL settling chamber (Utermöhl-Chamber) and allowed to settle undisturbed
for 45 min. All zooplankton individuals were counted
from the whole chamber area at a magnification of 125x
using an inverted microscope. Results of the counting were
then used to estimate the number of organisms per cubic
metre of pond water using the formula N = (nd)/V, where
N = number of organisms per cubic metre, n = number of
organisms counted, d = dilution factor of sample (splits),
ÉCOSCIENCE, vol. 14 (2), 2007
and V = volume of pond water filtered. Samples for water
chemistry analyses were taken simultaneously with plankton
sampling at each pond. Water samples were analyzed in the
laboratory for conductivity, pH, hardness, calcium, colour,
total nitrogen, and total phosphorus using Finnish national
standard methods. Pond areas and altitude were determined
using GIS. For means and ranges of environmental variables, see Table I. Pond coordinates were determined in the
field using GPS.
Data analysis
We used redundancy analysis (RDA) to examine the
relative contributions of environmental factors and geographical position to plankton communities (presence–
absence data). We first examined the effects of individual
environmental and geographical variables on plankton
community structure. We next partitioned variability in the
community structure of zooplankton and phytoplankton
between 2 explanatory variable groups: (i) environmental
variables and (ii) geographical location. Geographical variables included altitude and north (N) and east (E) coordinates centred on their respective means and standardized;
subsequently, a third-order spatial polynomial of the form:
Z = b1N + b2E + b3N2 + b4E2 + b5NE
+ b6N2E + b7NE2 + b8N3 + b9E3
describing the spatial location of each pond was constructed. Using altitude and these multiple spatial variables
allows one to model more complex spatial patterns in the
distribution of species than mere north and east coordinates
(Borcard, Legendre & Drapeau, 1992; Legendre, 1993).
Because the majority of ponds did not have inlet or outlet
streams, it was impossible to measure the direct hydrological pathways between ponds. Therefore, we used the above
geographical variables to portray the across-landscape altitudinal and spatial distances between ponds. For partitioning variation in community structure between environmental
variables and spatial location, each group of variables was
first screened using forward selection with a Monte Carlo
randomization test (199 permutations) in RDA (CANOCO
version 4.5, ter Braak & Smilauer, 2002). Only variables
significantly (α = 0.05) related to community structure
were retained in the final models. A series of 3 RDAs was
then run for community structure: (1) matrix constrained
by both significant environmental and geographical location variables (a + b + c; fractions following Legendre &
Legendre, 1998), (2) matrix constrained by environmental
Table I. Means (± SE) and ranges of environmental variables
measured from the study ponds.
Variable
Conductivity (mS/m)
pH
Hardness (°dH)
Calcium (°dH)
Colour (mg Pt·L–1)
Total N (mg·L–1)
Total P (mg·L–1)
Pond area (ha)
Altitude (m asl)
Mean
SE
Min
Max
4.87
7.07
1.28
0.81
115.60
285.40
6.34
0.50
250.72
1.146
0.118
0.328
0.217
8.78
22.28
0.37
0.061
5.52
1.34
6.00
0.27
0.04
30.00
100.00
2.00
0.061
168.00
24.40
8.50
6.89
4.35
190.00
680.00
12.00
1.136
280.00
variables only (a + b), and (3) matrix constrained by geographical position variables only (b + c). Variation in community structure was subsequently partitioned into shared
environmental and geographical position (b = [a + b] + [b +
c] – [a + b + c]), pure environmental (a = [a + b] – [b]), pure
spatial position (c = [b + c] – [b]), and unexplained fractions (d = 1 – [a + b + c]). RDA was used for constrained
ordination because preliminary detrended correspondence
analyses (DCA) showed that gradients were rather short
(< 2 SD units) for zooplankton, but rather long for phytoplankton (> 3 SD units). However, for ease of comparison,
RDA was used for both data sets (see also Cottenie, 2005).
Furthermore, assuming unimodal responses of species to the
underlying gradients, the results of additional canonical correspondence analyses did not appreciably differ from those
of the RDAs.
We next calculated pairwise similarities between all
site pairs using Sørensen’s coefficient on presence–absence
data (McCune & Grace, 2002). This measure ranges from
0 to 1, with high values indicating similar species composition between 2 sites. We also calculated environmental
distances between sites based on the environmental variables measured. Because these variables were measured
using different units, we first standardized all environmental
variables to standard deviate (Legendre & Legendre, 1998)
and then used Euclidean distance to calculate environmental
distances among sites. We calculated geographical distances
between sites using Euclidean distances based on altitude,
north coordinates, and east coordinates. We tested for the
relationship between community similarity and geographical distance and between community similarity and environmental distance using Mantel’s test (Legendre & Legendre,
1998). Because the pairwise similarity and distance values
are not independent, we used Monte Carlo tests (999 permutations) to assess the significance of the relationships.
Mantel tests were run using PC-Ord version 4.25 (McCune
& Mefford, 1999).
Finally, we used cluster analysis to examine if phytoplankton and zooplankton communities showed similar patterns across the ponds. Flexible beta linkage with
Sørensen’s coefficient as the distance measure and beta
value set at –0.50 (McCune and Grace, 2002) was used to
classify the sites into groups based on their zooplankton
and phytoplankton community structure. Finally, we tested
for the concordance (i.e., similarity in community patterns)
between zooplankton and phytoplankton community structure using Mantel’s test with Sørensen’s coefficient. These
analyses were run using PC-Ord version 4.25 (McCune &
Mefford, 1999).
Results
There were 65 and 152 taxa in the zooplankton and
phytoplankton matrices, respectively. Most of the zooplankton taxa were rotifers and cladocerans, and the most typical species were Bosmina coregoni, Conochilus unicornis,
Kellicottia longispina, and Polyarthra dolichoptera. The
phytoplankton communities consisted mainly of diatoms,
cryptomonads, and green algae, and the most typical spe149
Soininen et al.: Plankton metacommunity structure across wetland ponds
cies were Cymbella microcephala, Cryptomonas marssonii,
Fragilaria capucina, Nephrocytium agardhianum, and
Rhodomonas minuta. A list of plankton taxa found can be
obtained from the corresponding author on request.
In general, conductivity, pH, hardness, and calcium
showed strong intercorrelations (r > 0.80), but these variables were not significantly correlated with nutrient concentrations in the ponds. Furthermore, water chemistry
variables, except nutrients, were strongly (r > 0.80) negatively correlated with the altitudinal locations of the ponds,
with ponds high in the landscape having lower conductivity,
pH, hardness, and calcium concentrations than those lower
in the landscape. According to RDAs with each explanatory variable alone, zooplankton community structure was
primarily controlled by the concentration of major ions
and geographical location, given that there were significant
relationships between community structure and conductivity, pH, hardness, calcium, altitude, and both easting and
northing (Table II). Largely paralleling the patterns found
for zooplankton, phytoplankton community structure also
showed a relatively tight relationship to major ionic composition, as well as significant relationships to total nitrogen,
altitude, and easting and northing (Table II).
Partial RDAs showed that both zooplankton and phytoplankton community structure was significantly related to
environmental and geographical variables. For zooplankton,
pH appeared as the only significant environmental variable in the forward selection, while N, N2, and N3 were
significant geographical variables. Environmental variables
(a + b) explained about 9% of variability in community
structure, while geographical variables (b + c) explained
21% of variability in community structure. Both variable
groups together (a + b + c) explained 25.7% of variability
in zooplankton community structure. Variation partitioning
showed (Figure 2) that pure environmental effects were
weak (4.7%), as were shared environmental and spatial
effects (4.3%). More variability in zooplankton community structure was related to geographical effects (16.7%).
However, most of the variability in zooplankton community
structure remained unexplained (74.3%). For phytoplankton,
pH and total nitrogen were the most important environmental variables, and N and N2 were the significant geographi-
cal variables in the RDAs. These environmental variables
(a + b) explained 14% of variability in community structure, and the geographical variables (b + c) also explained
14% of variability in community structure. Both variable
sets together (a + b + c) explained 25.1% of variability in
phytoplankton community structure. Variation partitioning (Figure 2) showed that 11.1% percent was related to
pure environmental effects, 2.9% to shared environmental
and spatial effects, and 11.1% to pure geographical effects;
74.9% remained unexplained. Altitude was no longer significantly related to zooplankton or phytoplankton community structure, likely because it was strongly correlated
with north coordinates (r = 0.76) that overcame the effect of
altitude in the forward selection of RDAs.
For zooplankton, community similarity was negatively
(P < 0.01) related to both geographical and environmental distance (Figure 3a,c). For phytoplankton, community
similarity was negatively (P < 0.05) related to geographical
distance, but community similarity did not decrease significantly with environmental distance (Figure 3b,d).
Cluster analyses showed that zooplankton and phytoplankton communities formed highly dissimilar groupings
(Figure 4). This discrepancy in community patterns was
also demonstrated by a non-significant Mantel correlation
between patterns in zooplankton and phytoplankton communities (r = 0.070, P = 0.285).
Discussion
According to RDAs, pond zooplankton community
structure showed significant relationships with both geographical position and environmental factors, suggesting
that zooplankton probably corroborate both the neutral and
niche-based models. This finding is consistent with that of
Tuomisto, Ruokolainen, and Yli-Halla (2003), who found
that the composition of tropical plant communities was
jointly controlled by dispersal processes and environmental
factors, thus supporting both neutral and niche-based theories. In our study system, however, local environmental fea-
Table II. Results of RDAs for zooplankton and phytoplankton with
each variable alone as explanatory variable. R2 denotes the sum of
canonical eigenvalues or the amount of explained variability. P values were determined via Monte Carlo test (199 permutations).
Variable
Conductivity
pH
Hardness
Calcium
Colour
Total N
Total P
Pond area
Altitude
Easting
Northing
150
Zooplankton
R2
P
0.081
0.090
0.083
0.084
0.051
0.036
0.038
0.048
0.079
0.073
0.090
0.005
0.005
0.005
0.005
0.180
0.645
0.605
0.280
0.005
0.005
0.005
Phytoplankton
R2
P
0.061
0.071
0.062
0.067
0.032
0.070
0.030
0.044
0.055
0.071
0.073
0.035
0.015
0.035
0.200
0.940
0.025
0.895
0.320
0.045
0.025
0.015
F igure 2. Results of variation partitioning in RDA for phytoplankton and zooplankton communities showing 4 fractions: pure environmental, shared environmental and spatial, spatial, and unexplained
fractions of variation.
ÉCOSCIENCE, vol. 14 (2), 2007
Figure 3. Relationships between (a) zooplankton community similarity and geographical distance, (b) phytoplankton and geographical distance,
(c) zooplankton and environmental distance, and (d) phytoplankton and environmental distance. Geographical distances were calculated using easting,
northing, and altitude.
tures showed some degree of geographical structuring, and
the effect of geographical position on community structure
was, therefore, probably also influenced by niche characteristics of species. For zooplankton, neutral processes were,
however, stronger candidates for controlling the community
compositional variation than niche-based processes, because
the pure effects of geographical factors accounted for more
variability in zooplankton community structure than the
pure effects of environmental factors. This pattern was corroborated by distance-decay plots for zooplankton, where
community similarity decreased slightly more strongly with
geographical distance than environmental distance.
The variation in phytoplankton community structure
was similarly attributable to pure environmental factors and
pure geographical position, suggesting that phytoplankton
might also be under both neutral and niche-based control.
Nevertheless, somewhat weaker geographical structuring
and stronger environmental control of phytoplankton communities compared to zooplankton might reflect the fact that
phytoplankton species are unicellular, and are thus likely
to show higher dispersal capacity and weaker geographical structuring than larger metazoans (Finlay, Esteban &
Fenchel, 1996; Hillebrand et al., 2001). Moreover, smaller
organisms occupying low trophic levels respond more
strongly to fine-scale variation in the environment due to
their faster life cycles (Paszowski & Tonn, 2000; Gillooly
et al., 2002). However, distance-decay plots provided somewhat different insights into patterns of geographical structuring versus environmental structuring, as community
similarity decreased significantly only with geographical
distance. This finding suggests that phytoplankton also
might not be completely freely dispersed among the ponds,
as we also found that geographical factors did account for
a similar portion of variability as environmental factors in
the RDAs. These results thus concur with recent studies
on other unicellular organism groups that have suggested
a power law rate of distance decay (Green et al., 2004;
Noguez et al., 2005).
Although a major part of the community variation
remained unexplained, both zooplankton and phytoplankton
showed clear geographical structuring. This suggests at least
some degree of dispersal-limited metacommunity structure
and might reflect variability in the relative importance of
colonization dynamics among the ponds, given that ponds
linked by short-distance aerial dispersal are more likely to
exchange colonists than ponds that are more distant from
each other. Although it is known that zooplankton may
disperse relatively efficiently via waterbirds (Figuerola &
Green, 2002), and phytoplankton also directly via air currents (Kristiansen, 1996), dispersal may not be totally free
across ponds that lack direct interconnecting pathways.
The finding that geographical position was more important
than local environmental factors in controlling zooplankton
community composition suggests that zooplankton might
be dispersal-limited even within such a small spatial extent
(221 km2) as in the present study. This finding is seemingly
in accordance with Cottenie et al. (2003), who found that
even in a system of ponds that are strongly interconnected
by stream corridors, spatial position accounts for a significant part of variability in zooplankton community structure.
They stressed, however, that dispersal acted as a homogenizing, not a limiting force, and that dispersal limitation
was highly unlikely in their study landscape. Pinel-Alloul,
Niyonsenga, and Legendre (1995) found that most of the
151
Soininen et al.: Plankton metacommunity structure across wetland ponds
Figure 4. Clustering (flexible beta linkage) of zooplankton (a) and
phytoplankton (b) communities in the study ponds. Numbers refer to different ponds.
variability in zooplankton communities among lakes across
a more extensive region was accounted for by environmental factors and relatively little by pure geographical position, thus suggesting a minor role for spatially contagious
dispersal in controlling these lake communities. Moreover,
Havel and Shurin (2004) suggested that zooplankton dispersal is highly efficient among lakes separated by short spatial
distances, and local biotic interactions and abiotic factors
should thus limit local communities. This latter suggestion
is not fully supported by the present finding of significant
spatial structure in zooplankton communities. However,
we emphasize that part of the spatial structuring might be
attributed to mass effects, and the strong influence of geographical factors does not reflect mere dispersal limitation,
152
but perhaps also neighbourhood dispersal processes (Cottenie
et al., 2003; Cottenie, 2005).
Even if pond plankton communities were geographically structured, they also showed relationships with a number
of environmental factors. The results of RDAs were consistent with a number of recent studies on lentic organisms,
emphasizing the primacy of major ion concentrations as the
main environmental drivers of community structure (Arnott
& Vanni, 1993; Dixit, Dixit & Smol, 2002; Fallu, Allaire &
Pienitz, 2002; Willén, 2003). The predominance of major
ion concentration in regulating the distributions of species
likely reflects the environmental niches of organisms, with
consequent effects on community composition of ponds.
Contrary to our prediction, however, zooplankton showed
slightly stronger relationships with water ion concentration
than phytoplankton in simple RDAs (Table II). This may
reflect the higher temporal constancy of zooplankton than
phytoplankton, that is, the more rapidly fluctuating phytoplankton communities could not be related as strongly to
ion concentration as variation in zooplankton communities.
Moreover, RDAs showed that the trophic status of ponds
might also be an important driver of phytoplankton communities, although the ponds exhibited only subtle variability in nutrient concentrations (Table I). The tighter coupling
of phytoplankton communities with nutrients was also
reflected in the higher proportion of variation attributable to
pure environmental factors in partial RDAs. The response
of phytoplankton, but not of zooplankton, to nitrogen supply might also contribute to the low community concordance between zooplankton and phytoplankton observed in
cluster analyses and Mantel’s test. Further reasons for low
community concordance might be the different responses
to top-down forces by fish and invertebrate predators. Low
community concordance in freshwater communities is not
uncommon, however (Allen et al., 1999). For example,
Paavola et al. (2003) found only weak concordance among
stream classifications based on fish, macroinvertebrate, and
bryophyte assemblages.
However, we stress that because a major portion of
the community variation could not be explained by the
measured explanatory variables, some potentially important variables were perhaps not measured in this study. For
example, the fact that we did not have detailed information
on fish and invertebrate predators presents a possible caveat
for our approach in general. It is known that fish predation
may have significant effects on zooplankton community
structure (Hessen et al., 2006), and such effects might also
cascade to lower trophic levels (Carpenter, Kitchell &
Hodgson, 1985). Furthermore, although the vegetation in
all ponds was sparse at best, some measure of macrophyte
cover might have increased the amount of explained variability in plankton communities in the RDAs. Finally, we
emphasize that the static measures of community structure
we used do not capture the temporally dynamic nature of
plankton communities, and the variance explained by the
measured explanatory variables may thus have remained
relatively low.
Using only field data may not be enough to assess the
strict roles of different metacommunity processes. More
ÉCOSCIENCE, vol. 14 (2), 2007
rigorous examination of these processes would require
experimental approaches. However, changes of community
similarity across geographical and environmental gradients do echo the relative roles of the underlying processes.
The fact that patterns reveal much about metacommunity
processes has been proven recently in many different ecosystems, and variation partitioning approaches have been
shown to be highly rewarding in this context (Cottenie &
De Meester, 2005). To conclude, this study adds to a growing body of evidence that even across small spatial scales,
and even for small organisms such as plankton, metacommunities might be strongly spatially structured (see also
Jenkins & Buikema, 1998). However, our study showed that
zooplankton and phytoplankton may show slightly differing
degrees of spatial and environmental structuring. We found
compelling evidence that plankton community composition may be jointly regulated by neutral and niche-based
processes. Further studies are needed to infer the relative
importance of neutrality versus niche structuring in determining plankton metacommunity structure in ponds.
Acknowledgements
We thank Oulanka Research Station for logistical support.
This study was financially supported by grants from the Academy
of Finland (to J. Soininen and J. Heino).
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Aquat Ecol (2012) 46:229–239
DOI 10.1007/s10452-012-9394-z
Spatio-temporal community structure of peat bog benthic
desmids on a microscale
Jiřı́ Neustupa • Kateřina Černá • Jan Št’astný
Received: 22 November 2011 / Accepted: 22 March 2012 / Published online: 7 April 2012
Ó Springer Science+Business Media B.V. 2012
Abstract Significant spatial variation in species
composition of microphytobenthos often occurs at
scales of decimetres. This microscale variation is
typically more connected with dispersal-related events
than to environmental factors. In this study, 4 microscale transects were delimited at 4 temperate lowland
peat bog localities to investigate spatial and temporal
microscale variations in benthic desmids (Desmidiales, Viridiplantae). Significant spatial autocorrelation
was detected in most of the transects taken 3 times, in
September and December 2010 and March 2011. The
relative abundance of species data produced more
pronounced spatial patterns than the presence–absence
data. Spatial autocorrelation mostly decreased during
the winter period, possibly due to meteorological
disturbances, resulting in less spatially structured
phytobenthic community in the March transects. In
most cases, spatial distance accounted for a significant part of the variation in a community structure,
even in analyses that controlled for the effects of
Handling Editor: Bas W. Ibelings.
Electronic supplementary material The online version of
this article (doi:10.1007/s10452-012-9394-z) contains
supplementary material, which is available to authorized users.
J. Neustupa (&) K. Černá J. Št’astný
Department of Botany, Faculty of Science, Charles
University of Prague, Benatska 2, Prague 12801, Czech
Republic
e-mail: [email protected]
environmental and temporal factors. This indicated
that pure spatial factors should be considered important
for structuring phytobenthic communities, even across
a temporal time span of 6 months. The reduced data
sets that included only 25 % of the most frequented
species produced very similar patterns in spatial and
temporal autocorrelation as the full data sets. Consequently, we concluded that microscale variation of
benthic desmids may be sufficiently represented by
dynamics of the common species.
Keywords Desmidiales Microscale Microphytobenthos Peat bogs Spatial structure
Introduction
Microscale spatial variation has recently been recognised as one of the important properties of phytobenthic community structure both in marine and in
freshwater habitats (Benedetti-Cecchi 2001; Coleman
2002; Machová-Černá and Neustupa 2009). Recruitment and local extinctions have been proposed as
important drivers of spatial heterogeneity of benthic
algae on scales of centimetres to metres (Saburova
et al. 1995; Rindi and Cinelli 2000; Benedetti-Cecchi
2001; Coleman 2003; Rindi and Batelli 2005; Machová-Černá and Neustupa 2009). Environmental variability at this scale is usually less important and less
correlated with community structure than at higher
scales, such as mesoscale (tens of metres or greater) or
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230
macroscale (kilometres or greater). Therefore, effects
of purely spatial factors or variation random to
environmental factors are typically more important
on a microscale, whereas the meso- and macroscales
may rather reflect habitat structure and other environmental factors (Méléder et al. 2007; Veselá 2009;
Soininen et al. 2011; Astorga et al. 2012). Nevertheless, abiotic factors may still play a significant role
even on a microscale level, for example, in habitats
with high substrate heterogeneity (Machová-Černá
and Neustupa 2009) or in habitats that are primarily
structured by a single physical factor, such as the
current velocity in streams (Passy 2001; Soininen
2003).
Significant spatial autocorrelation of phytobenthos
on a microscale has repeatedly been detected in
samples taken at least 10 cm apart along transects
(Benedetti-Cecchi 2001; Coleman 2002; Azovsky
et al. 2004). The purely spatial autocorrelation usually
diminishes in samples taken several metres apart,
where environmental factors typically account for
most of the variation in the community structure
(Méléder et al. 2007; Černá 2010). Long-term temporal consistency of the small-scale spatial structure of
algal community species composition was illustrated
by Coleman (2002). She suggested that seasonal
changes (such as variation in recruitment levels) had
a rather negligible effect on patterns of spatial
variability of inter- and subtidal phytobenthic communities in subtropical Australia. Conversely, nonseasonal effects operating on small temporal scales
may have been more important in these habitats.
Azovsky et al. (2004) demonstrated that temporal
variation, while still detectable, was much less
important than spatial heterogeneity in describing
community structure of microphytobenthos in boreal
intertidal sandflats. Interestingly, there was about the
same minor part of variation in community structure of
diatoms described by spatial scale in decimetres and
temporal range of 30 days, whereas larger spatial
scales spanned most of the variation. However,
temporal stability of microscale spatial structure was
questioned by Machová-Černá and Neustupa (2009),
who detected consistent changes in spatial autocorrelation of phytobenthic samples taken along microscale
transects in a lowland peat bog 3 times during the year
(12th May, 30th August, and 28th October). The
microscale spatial autocorrelation generally decreased
in most benthic microhabitats, which was explained by
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Aquat Ecol (2012) 46:229–239
colonisation processes that continuously homogenised
the phytobenthic community during the season. The
authors hypothesised that the effects of winter disturbance, including temperature drops, freezing, and
periods of snow cover, may have led to further
decrease in the microscale spatial autocorrelation. In
addition, diversity of the phytobenthic community is
generally increased during the season, so that the
relatively low spring levels may be result of local
extinctions of less frequented species during the
winter disturbance, followed by their subsequent
recolonisation.
Heino and Soininen (2010) illustrated that the
spatial and environmental factors on macroscale levels
may be sufficiently represented by the dynamics of
common species, that is, of 25 % of the most
frequented species in samples. They concluded that
macroscale studies could mostly concentrate on the
common species, as the rare species have little effect
on turnover description along spatial or environmental
gradients in aquatic communities. Conversely, Benedetti-Cecchi et al. (2008) highlighted the role of rare
species in fluctuating environments through densitydependent regulation. They illustrated that rare benthic species of algae and invertebrates may be
important in driving the temporal changes, as they
were highly susceptible to environmental fluctuations.
However, on a microscale level, the role of rare
species in relation to spatial, temporal, or environmental gradients was not investigated.
In this study, we specifically evaluated the effects
of the winter period on microscale spatial structure of
desmid epipelon at 4 different temperate lowland
acidic localities. In addition, the relative effects of
spatial, temporal, and 2 important environmental
factors (pH, conductivity) were evaluated. The pH
values have repeatedly been reported the single most
important physico-chemical structuring factor of
peatland microphytobenthos (Coesel 1982; 2001,
Neustupa et al. 2009). In addition, conductivity, which
approximates the concentration of solute ions, has also
been considered highly correlated with community
structure dynamics (Coesel 1982, Černá 2010). Specifically, we asked whether the spatial structure on a
microscale remained more or less stable during the
winter disturbance, that is, whether the purely spatial
effects spanned a significant part of the total variation
in species composition of samples taken along combined spatial and temporal gradients. To span the
Aquat Ecol (2012) 46:229–239
extent of temporal climatic fluctuations in temperate
Europe, transects were delimited in 2 European
lowland regions with pronounced differences in
annual temperature amplitudes. Two transects in
northern Bohemia, Czech Republic, typically experience prolonged freezing during the winter period,
whereas 2 transects sampled in Aquitaine, France,
usually do not freeze as the mean temperatures in the
winter months remain mostly in positive values.
Finally, we also asked whether the common species
adequately represented variation in the community
structure along the investigated gradients, and whether
the species frequencies data described patterns in
community structure dynamics that were not perceived by the presence–absence-based matrices.
The microphytobenthos in this study was represented by a green algal group of desmids (Desmidiales) that typically form a dominant component of
such assemblages in freshwater acidic wetlands
(Brook 1981; Coesel and Meesters 2007). Desmids
have frequently been used as a model group in
freshwater ecology, especially in studies analysing
the effects of abiotic factors (Spijkerman and Coesel
1998; Černá and Neustupa 2010; Stamenković and
Hanelt 2011), as well as in biomonitoring and
biodiversity studies of peatland habitats (Coesel
1982, 2001; Neustupa et al. 2009). The diversity
optima of desmid communities in phytobenthos are in
moderately acidic (pH, 5.5–7.0) and mesotrophic
peatland habitats such as minerotrophic fens and bogs
(Coesel 1982; Coesel and Meesters 2007). Numerous
temperate desmid species have growth rate optima in
relatively high temperatures (Brook 1981; Spijkerman
and Coesel 1998), but desmid communities form an
omnipresent part of acidic freshwater phytobenthos
year-round (Brook 1981; Neustupa et al. 2011).
Materials and methods
Localities and sampling
The transects were delimited in physiognomically
homogenous shallow pools of the following peatland
localities: Aquitaine 1 (A1), a pool within the Étang
Hardy bog (43°430 09.9800 N, 01°220 07.5200 W, altitude
15 m a.s.l.), area 90 m2, depth of the sampling site
20–25 cm; Aquitaine 2 (A2), a pool close to the
Marais du Cla (44°310 11.1600 N, 00°360 57.4300 W,
231
altitude 67 m a.s.l.), area 400 m2, depth of the
sampling site 25–40 cm; Bohemia 1 (B1), a pool at
the U Klucku minerotrophic fen (50°340 41.3200 N,
14°390 41.3500 E, altitude 265 m a.s.l.), area 200 m2,
depth of the sampling site 20–30 cm; and Bohemia 2
(B2), northern part of the Swamp peat bog
(50°350 42.0800 N, 14°380 44.2700 E, altitude 254 m
a.s.l.), area 275 m2, depth of the sampling site
15–20 cm. The actual geographic distance between
A1 and A2 transects was 107.4 km, and it was 2.2 km
between B1 and B2 pools. The samples were taken 3
times: 6–14 September 2010, 15–22 December 2010,
and 12–19 March 2011. The sampling was performed
on the same days both in Aquitaine and in Bohemian
localities. The annual temperature amplitudes (i.e. the
difference of mean minimum and maximum monthly
values) of Aquitaine localities reached 12.3 °C (Biarritz station, 32 km from A1) and 14.8 °C (Mérignac
station, 39 km from A2), respectively. The lowest
mean winter temperatures are 8.2 °C (A1) and 5.6 °C
(A2), respectively. The annual number of frosty days
(with maximum temperature below 0 °C) typically
varied from 0 to 2 at the Biarritz station and from 0 to 4
at the Mérignac station. The annual temperature
amplitude of the Bohemian localities reached
20.7 °C, with the lowest mean winter temperatures
at -2.8 °C (Prague Ruzyne station, 63 km from B1
and B2). There are 25–80 annual frost days at the
Ruzyne station. The climatic data were acquired from
public sources (http://www.worldclimate.com and
http://www.tutiempo.net/en/).
All the investigated localities represent natural
lowland minerotrophic peatlands with different acidity and conductivity. These values were measured in
the fields using a combined pH/conductometer (WTW
340i; WTW GmbH, Weilheim, Germany). The probes
were submerged, so that the values were always
measured at about 2 cm above the bottom of the pools.
At each locality, a linear 400-cm-long transect was
delimited. In total, 10 samples were taken along each
transect, separated by a distance of 40 cm from each
other. We chose to sample the investigated localities
along the linear transects as it is a good way to clearly
visualise the changes in community structure taking
place along the line. The samples along transects were
taken using a precisely identical pattern at four
investigated localities, and this also allowed a
straightforward comparison of microscale structure
of individual communities. An individual sample
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232
consisted of 2 cm2 of epipelon taken from the
uppermost 5-mm layer by using a 100-ml plastic
syringe. The samples were fixed with Lugol’s solution
in the field (3–4 % final concentration), and later
examined under an Olympus BX 51 light microscope.
In total, 200 desmid cells from each sample were
identified in the course of the systematic inspection of
the microscopic slides at 400 9 magnification.
Species data analysis
The two-dimensional non-metric multidimensional
scaling (NMDS) with a Bray–Curtis distance measure
was used to illustrate patterns of species composition
at individual localities. The coefficients of determination (R2) were computed for each axis to determine the
proportion of variance of the scaled data, which was
accounted for by the NMDS procedure. Reliability of
each NMDS ordination, that is, correspondence of
original multivariate distances among samples to
resulting distances in the NMDS diagram was reported
by Kruskal’s stress values (Borg and Groenen 2005).
Significance of differences in species composition
among individual sampling dates was tested by a nonparametric 2-group analysis of similarities (ANOSIM)
based on Bray–Curtis distance measure (Clarke 1993).
Bray–Curtis distance of 2 samples j and k is defined as
P i xij xik
BC ¼ P
i xij þ xik
where xij and xik are abundances of the i-th species in
samples j and k, respectively (Hammer 2011). The
spatial autocorrelation of species composition along
individual transects was tested by 2- and 3-matrix
(partial) Mantel tests (Fortin and Gurevitch 1993). The
2-matrix tests evaluated correlations between species
composition and spatial distances among individual
sampling points, with no regard to other factors. The
3-matrix tests evaluated these correlations, with the
effects of environmental factors removed. The species
data were represented by Bray–Curtis distance matrices based on their frequencies in samples. Alternatively, the Jaccard index matrices based on the
presence–absence species data were also used. The
spatial matrix was based on actual distances among
individual sampling points. The environmental factors
were depicted as matrices based on the Euclidean
distances among standardised pH and conductivity
123
Aquat Ecol (2012) 46:229–239
values from individual sampling points. The ANOSIM
and Mantel tests were carried out in PAST, ver. 2.08.
(Hammer et al. 2001), with 9999 permutations used.
Partition of variance in community structure attributed to individual factors was performed using 2
parallel approaches. The redundancy analysis (RDA)based variance partition (Borcard et al. 1992) was
conducted using varpart function of the package
vegan (Oksanen et al. 2011) in R, ver. 2.13.0. (R
Development Core Team 2011). The adjusted R2
values were used for the partitioning of variance
(Peres-Neto et al. 2006). The function used the
standardised spatial and temporal factors. Environmental factors were combined from the standardised
pH and conductivity values. Standardisation involved
subtracting mean values of the particular parameter
from the actual values and their subsequent dividing
by standard deviation of the data set. The permutational multivariate analysis of variance using distance
matrices (permutational MANOVA) was conducted
with both Bray–Curtis and binary Jaccard distance
indices (Anderson 2001; Oksanen et al. 2011). The
function adonis of the package vegan in R, ver.
2.13.0., was used. This distribution-free function
partitions distance matrices (typically based on species-in-sites data) among different sources of variation
and has been considered a robust alternative to
parametric MANOVA, as well as to ordination
methods (Legendre and Anderson 1999). The tests
evaluating how variation was attributed to different
factors are sequential, that is, individual terms are
tested in the order as they are quoted in the adonis
function formula. Therefore, the ‘pure’ effects of
individual factors (such as spatial, temporal, or
environmental factors) can be ascertained, their significance evaluated, when they appear as the last at the
predictor part of the formula (Oksanen et al. 2011).
Therefore, several adonis models were conducted,
each with a different order of factors. The significant
p values were assessed using permutation tests on
pseudo-F ratios, with 9999 permutations used. The R2
values corresponded to different factors or to their
joint effects, as well as to the adjusted R2 values of the
RDA-based variance partition and were illustrated
using Venn diagrams.
The 25 % most frequently occurring taxa were
considered as common species (Heino and Soininen
2010), and only these were left in the reduced data sets.
The NMDS ordination patterns of the original and
Aquat Ecol (2012) 46:229–239
reduced data sets were compared using the function
protest of the vegan package in R, ver. 2.13.0. In this
method, the Procrustes superimposition is used to
rotate the matrices of site ordination scores to
maximum similarity by minimising the sum of their
squared differences (Peres-Neto and Jackson 2001).
Significance of the Procrustes statistics was assessed
by permutation tests (9999 permutations) on the
correlation statistics derived from the sum of squares
of superimposed configurations (Oksanen et al. 2011).
The Mantel tests and the permutational MANOVAand RDA-based variance partitions were conducted
with the reduced data sets in the same way as described
above. Differences in Mantel r values between the
original and reduced data sets, between analyses with
species data matrices based on Bray–Curtis and
Jaccard distances, and between full and partial Mantel
r values were evaluated by linear correlation analyses.
Results
In total, there were 129 desmid species recovered in
samples from the investigated transects (Supplementary Tables 1–4). There were also apparent differences
233
in species richness among individual transects. The B1
transect, for which the highest pH values were
recorded (Supplementary Table 5), had a total of 80
species, while there were 38 species recovered in the
transect A1, 36 at A2, and 25 at B2. The pH and
conductivity values were either largely stable across
both the temporal and spatial gradients (such as the pH
values in the B1 or conductivity in the A2 transects) or
they apparently differed among sampling dates, but
remained relatively stable in samples from individual
transects (Supplementary Table 5). The NMDS ordination plots of samples taken along individual transects suggested some degree of temporal effects on
species composition. The temporal separation was
apparent in B1 and, to a lesser degree, also in A1 and
A2 (Fig. 1). These patterns were largely confirmed by
the ANOSIM tests that illustrated significant differences in community structure among all the temporal
groups of samples along the A2 and B1 transects
(Table 1). On the other hand, temporal variability of
samples was seemingly lower in the A1 and B2
transects. However, the September and March samples
from the A1 transect as well as the September and
December samples from the B2 transect were still
significantly different. The NMDS ordination patterns
Fig. 1 The NMDS
ordination plot of samples
from 4 transects based on
their species composition.
The squares correspond to
the September, the stars to
the December, and the
triangles to the March
samples. The R2 values
determine the proportion of
variance accounted for by
the ordination procedure.
The stress values evaluate
the total fits of original
multivariate distances
among samples to the scaled
distances depicted in the
ordination plots
123
234
Aquat Ecol (2012) 46:229–239
Table 1 The results of two-group ANOSIM tests on differentiation among transect samples taken in different seasons.
The R values are indicated in the upper triangle, the permutation p values in the lower triangle of the table. The R and
p values of the full and reduced data sets are separated by
slashes
Sep
Sep
Dec
Mar
A1: 0.11/0.11
A1: 0.37/0.36
A2: 0.50/0.19
A2: 0.67/0.46
B1: 0.64/0.49
B1: 0.87/0.81
B2: 0.20/0.16
Dec
A1: 0.09/0.10
A2: ***/*
A2: 0.25/0.22
B1: ***/***
B1: 0.63/0.55
B2: **/*
Mar
B2: 0.08/0.05
A1: n.s./n.s.
B2: 0.09/0.07
A1: **/**
A1: n.s./n.s.
A2: ***/***
A2: **/**
B1: ***/***
B1: ***/***
B2: n.s./n.s.
B2: n.s./n.s.
*** p \ 0.001; ** 0.001 \ p \ 0.01; * 0.01 \ p \ 0.05; n.s.,
p [ 0.05
based on the reduced data sets (i.e. including the
common species only) were similar with high to
moderate correlations of Procrustes rotations between
and reduced configurations (A1, r = 0.99; A2, r =
0.88; B1, r = 0.92; B2, r = 0.76). All these correlations were significant at the 0.01 % level. In addition,
the ANOSIM tests on reduced data sets illustrated very
similar patterns of temporal differentiation between
samples (Table 1). Whereas the R-statistic values
were generally lower with the reduced data sets than
with the original data tables (i.e. including the rare
species), the significance values illustrated identical
pattern, with strong differentiation of the A2 and B1
transects and weak temporal differences among samples from the A1 and B2 transects.
Spatial autocorrelation along individual transects,
with species data evaluated by the quantitative Bray–
Curtis distance measure, generally decreased with time
(Table 2; Fig. 2a and c). The partial Mantel tests of
species data distances versus spatial distances, with the
environmental factors controlled, illustrated very similar pattern of time-related general decrease in Mantel
r values (linear correlation analysis, Bray–Curtis distance matrices: Pearson’s r = 0.98, p \ 0.001; Jaccard
distance matrices: Pearson’s r = 0.98, p \ 0.001).
However, the presence–absence species data evaluated
123
by Jaccard distance index illustrated rather different
trends in Mantel r values among temporal transects
(Table 2; Fig. 2b and d). Indeed, the linear correlation
analysis illustrated a non-significant relation of the
Mantel r values acquired using species data coded by
Bray–Curtis and by Jaccard distance matrices (linear
correlation analysis of the 2-matrix Mantel r values:
Pearson’s r = 0.29, p [ 0.05; 3-matrix Mantel r values:
Pearson’s r = 0.41, p [ 0.05). The Mantel tests based
on the presence–absence species data illustrated generally less significant spatial autocorrelation than the
Mantel tests based on relative abundances. The reduced
species data sets coded by Bray–Curtis distance matrices produced very similar spatial autocorrelation patterns as the full data sets (2-matrix Mantel r values:
Pearson’s r = 0.93, p \ 0.001; 3-matrix Mantel r values: Pearson’s r = 0.92, p \ 0.001). Conversely, the
correlation between the Mantel r values of the full and
reduced species data sets evaluated by the presence–
absence Jaccard distance matrices was non-significant
(2-matrix Mantel r values: Pearson’s r = 0.34,
p [ 0.05; 3-matrix Mantel r values: Pearson’s
r = 0.32, p [ 0.05). The r values of the Mantel tests
based on frequency data of common species generally
decreased with time, but this pattern was not evident
from analyses based on the presence–absence species
data matrices (Table 2).
The RDA- and permutational MANOVA-based
variance partitions resulted in very similar patterns at
individual transects (Fig. 3; Supplementary Table 6).
The permutational MANOVA models generally
reported low proportions of unexplained variance.
The purely spatial effects were dominant along the A1
and B2 transects, whereas they were moderately (A2)
or weakly important (B1) in other transects. These
purely spatial effects related neither to environmental
nor to temporal factors and were significant in 3 out of
the 4 transects. On the other hand, purely temporal
factors, that is, the effects of temporal change in
species composition, were only significant in samples
from the most species-rich B1 transect. Environmental
factors (pH and conductivity) explained rather modest
variance proportions, but their pure effects on community structure were significant in 2 transects (A2 and
B1). Relatively high temporal variation of environmental factors was reflected by substantial proportion
of community structure explained by correlated temporal and environmental variation along the transects
A1, A2, and B1. However, high proportions of
Aquat Ecol (2012) 46:229–239
235
Table 2 The results of Mantel tests evaluating spatial autocorrelation of individual transects
Two-matrix (full) tests
Mantel r
Three-matrix (partial) tests
Mantel r
0.74***/0.55**/0.56**
Bray–Curtis distance versus spatial distance,
controlled for environmental factors
0.71***/0.53**/0.57**
A1
Bray–Curtis distance
versus spatial distance
Jaccard distance versus
spatial distance
0.72***/0.57**/0.56***
0.26n.s./0.12n.s./0.30*
0.18n.s./0.17n.s./0.12n.s.
Jaccard distance versus spatial distance,
controlled for environmental factors
0.70***/0.54**/0.57**
0.23n.s./0.09n.s./0.28n.s.
0.17n.s./0.19n.s./0.12n.s.
A2
Bray–Curtis distance
versus spatial distance
Jaccard distance versus
spatial distance
0.40*/0.37*/0.16n.s.
n.s.
0.49**/0.26
/0.22
n.s.
0.01
-0.04
/0.31*/0.12
n.s.
/0.29
n.s.
n.s.
n.s.
n.s.
/-0.04
Bray–Curtis distance versus spatial distance,
controlled for environmental factors
0.40*/0.40*/0.22n.s.
Jaccard distance versus spatial distance,
controlled for environmental factors
-0.02n.s./0.33*/0.05n.s.
Bray–Curtis distance versus spatial distance,
controlled for environmental factors
0.55***/0.34*/0.26n.s.
Jaccard distance versus spatial distance,
controlled for environmental factors
0.60***/0.31*/0.18n.s.
Bray–Curtis distance versus spatial distance,
controlled for environmental factors
0.30*/0.44*/0.05n.s.
Jaccard distance versus spatial distance,
controlled for environmental factors
0.43*/0.09n.s./0.02n.s.
-0.05n.s./0.09n.s./-0.07n.s.
0.51**/0.29*/0.29*
-0.04n.s./0.37*/-0.01n.s.
B1
Bray–Curtis distance
versus spatial distance
Jaccard distance versus
spatial distance
0.44**/0.34*/0.29*
n.s.
0.33*/0.18
n.s.
/0.25
0.53***/0.31*/0.13n.s.
n.s.
0.14
/-0.06
n.s.
n.s.
/0.14
0.45**/0.18n.s./0.22n.s.
0.16n.s./-0.07n.s./0.18n.s.
B2
Bray–Curtis distance
versus spatial distance
Jaccard distance versus
spatial distance
0.28*/0.42*/0.04n.s.
n.s.
0.16
/0.38*/0.00
n.s.
n.s.
n.s.
0.44*/0.09 /0.00
-0.07n.s./0.10n.s./-0.26n.s.
0.19n.s./0.40*/0.04n.s.
The Mantel R values and their significance evaluated by permutation tests are indicated. The R values of the September, December,
and March transects are separated by slashes. The full data sets are depicted in upper and the reduced data sets in the lower parts of
cells
*** p \ 0.001; ** 0.001 \ p \ 0.01; * 0.01 \ p \ 0.05; n.s., p [ 0.05
Fig. 2 Spatial autocorrelation indicated by the Mantel r values
of individual transects in September, December, and March
samples with species data matrices based on a the full data sets
and Bray–Curtis distances, b the full data sets and Jaccard
distances, c the reduced data sets and Bray–Curtis distances, and
d the reduced data sets and Jaccard distances. The filled circles
correspond to the A1, the hollow circles to the A2, the filled
triangles to the B1, and the hollow triangles to the B2 transects
unexplained variance indicated that there still were
substantial parts of variation in species data not
perceived by any of the analysed factors. The Jaccard
index–based variance partitions did not substantially
differ from the quantitative Bray–Curtis distancebased matrices. The relative proportions among individual factors remained largely unchanged, but the
proportions of unexplained variance slightly increased
in all transects (data not shown). Variance partitions of
the reduced data sets produced very similar results to
the full data sets analyses. The differences among all
the individual proportions in all transects (including
proportions of the unexplained variation) did not differ
by more than 3 percentage points, so that their overall
proportions remained almost identical as in the original
full data sets, including the rare species. At the same
123
236
Aquat Ecol (2012) 46:229–239
Fig. 3 The Venn diagrams illustrating partition of variance
spanned by individual factors using a the redundancy analysis
and b permutational MANOVA. The values outside the
diagrams represent unexplained variation. The significance
values in the permutational MANOVA plots are represented as
*** p \ 0.001, ** p \ 0.01, * p \ 0.05, n.s. p [ 0.05
time, the results of permutation tests that evaluated
effects of individual pure factors in the adonis procedure using reduced species data sets were generally
identical to the full data sets (data not shown).
During the 2010/2011 winter season, there were 4
frosty days at the Mérignac station and 2 frosty days at
the Biarritz station, respectively. Conversely, there
were 73 frosty days at the Ruzyně station in the Czech
Republic, resulting in a prolonged freezing period at
the investigated Bohemian localities (B1 and B2) in
December 2010 and January 2011. While the B1
transect was the only one with significant pure
temporal effects on community structure, the pattern
of spatio-temporal dynamics of the B2 transect was
largely similar to those of Aquitainian transects.
Machová-Černá and Neustupa (2009) identified similar trends of decreasing microscale spatial autocorrelation of peat bog phytobenthos in a study that
illustrated temporal changes in autocorrelation during
the vegetation season. Our present data indicated that
this trend of decreasing spatial autocorrelation continued over the winter period. Consequently, we
suggest that the microscale spatial structure of peat
bog phytobenthos recovers in spring and in early
summer months, when higher water temperatures lead
to increased growth rates of species from differently
sized overwintering populations, resulting in strong
spatially autocorrelated communities. Sommer (2000)
illustrated that herbivory may increase the small-scale
spatial autocorrelation of microphytobenthos, and
Discussion
Significant spatial autocorrelation was detected at 10
out of the total 12 investigated transects. These results
generally confirmed previous studies from various
habitats that reported significant spatial effects in
community structure in scales of decimetres (Benedetti-Cecchi 2001; Coleman 2002; Rindi and Batelli
2005; Machová-Černá and Neustupa 2009). Interestingly, spatial autocorrelation decreased with time,
both in the Aquitaine and in the Bohemian transects.
This indicates that the effects of winter disturbance
(possibly related to drop in temperatures and enhanced
mixing probability due to high wind intensity and
precipitation) acted in both regions with climatic
conditions that spanned gradient from oceanic to
continental climate. Interestingly, meteorological differences between both regions did not result in any
clear-cut differences in spatial structure effects clearly
distinguishing localities in Aquitaine and Bohemia.
123
Aquat Ecol (2012) 46:229–239
such factors cannot be excluded as drivers of spring
structuring of peat bog benthic communities. However, colonising events during late summer, especially
the autumnal and winter disturbances, seem to
continually decrease spatial autocorrelation, resulting
in communities with low autocorrelation at the scales
of decimetres, as illustrated in this study. This pattern
of spring recovery of microscale spatial structure
should be tested in the future, in studies specifically
designed for the identification of spring changes in
spatial structure of microphytobenthic communities.
Spatial factors proved to be important determinants
of species composition along our microscale transects.
At 2 transects, pure spatial distance among samples was
even the single most important factor describing their
species structure across the time span of 6 months. At
such localities, a distance of no more than 4 m in a single
pool constantly produced more different species composition than samples taken after 6 months at the same
place. This small-scale spatial heterogeneity was often
overlooked in species inventories of microphytobenthos, but always has been intuitively perceived by
practised experts on Desmidiales, who often pointed out
that certain species may consistently occur at spatially
limited spots (Heimans 1969) or that species composition of closely related, seemingly identical localities
may be profoundly different (Messikommer 1942).
Moreover, similar patterns of stable spatial heterogeneity at small scales were also reported for Sphagnuminhabiting testate amoebae (Mitchell et al. 2000) for
intertidal microbenthos (Azovsky et al. 2004) or for
turfing algae (Coleman 2002, 2003). Fraschetti et al.
(2005) stated that small-scale variability has to be
perceived as an inherent property of benthic assemblages in marine coastal habitats. This study illustrated
that this should also apply to freshwater peatland
phytobenthos. Temporal variability was mostly much
less conspicuous, even if purely temporal variation was
still significant at the B1 transect. However, the
temporal gradient was generally more correlated with
the environmental data (pH and conductivity) than the
spatial distances, and most of the temporal variation
could not be distinguished from the environmental
variation. Conversely, the effects of spatial distances on
community structure were uncorrelated with the effects
of environmental factors. This indicated that they were
related to dispersal-related events, such as recruitment,
colonisation, or extinction, rather than to small-scale
environmental heterogeneity.
237
Heino and Soininen (2010) illustrated that macroscale spatial and environmental turnover of community structure may be represented by a dynamics of the
25 % most frequented species. Our results generally
confirmed this pattern on a microscale level. The data
sets reduced for rare species exhibited very similar
patterns of spatial autocorrelation, the differences in
species composition among groups, as well as of the
relative proportions of spatial, environmental, and
temporal factors. Similarly to Heino and Soininen
(2010), we may conclude that microscale variation of
desmid phytobenthos can be adequately described
using common species, which may certainly be of
much use to future studies on spatial structure of these
communities. At the present, longer temporal or
spatial extent of these studies is often limited by
laborious and time-consuming microscopic species
identification, often requiring substantial taxonomic
expertise—see e.g. Coesel and Meesters (2007) or
Št’astný (2010). However, our data suggested that less
detailed enumerating and counting of rare species may
not reduce ecological interpretability of results, possibly enabling a substantially more ambitious experimental design of studies based on a less number of
cells counted in each sample. Heino and Soininen
(2010) based their rarity measure on presence–absence
data sets only. However, our results suggested that
relative abundances of individual species carried an
important piece of information on their microscale
spatial turnover. Whereas the presence–absence data
sets did not much differ in variance partition analyses,
there were important differences in spatial autocorrelation patterns. Most of the spatial signal was lost in
the presence–absence species data, and this pattern
was even more apparent in the reduced data sets. We
can, therefore, agree with Archambault and Bourget
(1996), who reported that microscale community
structure is especially shaped by abundances of
individual taxa and cannot be adequately represented
solely by presence–absence data.
In conclusion, we ascertained that purely spatial
factors typically play a significant role in the microphytobenthic community variation on microscale
gradients. However, the spatial autocorrelation has
an important temporal dynamics, involving effects of
the winter period, and possibly, the strong spatial
structuring in the first half of the vegetation period.
These effects were detected all across the climatic
gradient of temperate European lowland peatlands and
123
238
should, therefore, not be specifically linked to relatively high weather amplitudes typical for continental
localities. Our results were consistent, both with full
and with reduced data sets, which indicated that
relative abundances of common species may adequately represent the microphytobenthic community
structure turnover on a microscale level.
Acknowledgments This study was supported by the grant no.
KJB601110921 of the Science Foundation of the Czech Academy
of Sciences. The authors thank Frans Kouwets for providing
invaluable advice on the Aquitaine localities. They are also
indebted to Yvonne Nemcova, Jana Veselá, and Jana Kreidlová
for their sampling assistance. The authors thank Editage
proofreading service for the language and style corrections.
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Supplementary table 1 Species data of samples taken from the transect A1.
A1
Actinotaenium cucurbita
Bambusina brebissonii
Closterium acutum
Closterium baylianum
var. alpinum
Closterium calosporum
Closterium costatum
Closterium dianae
Closterium intermedium
Closterium juncidum
Closterium kützingii
Closterium lineatum
Closterium lunula
Closterium. ralfsii var.
hybridum
Closterium setaceum
Cosmarium
cymatonothophorum
Cosmarium quadratum
Cosmarium prominulum
var. subundulatum
Cosmarium pyramidatum
Desmidium grevillei
Docidium baculum
Haplotaenium minutum
Haplotaenium rectum
Hyalotheca dissiliens
Euastrum ansatum
Euastrum crassum var.
microcephalum
Euastrum humerosum
Euastrum luetkemuelleri
var. carniolicum
Euastrum oblongum
Euastrum pectinatum
Micrasterias fimbriata
Micrasterias thomasiana
Micrasterias truncata
Pleurotaenium
ehrenbergii
Pleurotaenium archeri
Staurastrum inflexum
Staurastrum teliferum
Tetmemorus brebissonii
Tetmemorus granulatus
S1
0
0
42
19
S2
0
0
32
10
S3
0
0
12
18
S4
0
0
20
44
S5
0
0
13
61
S6
0
0
25
37
S7
0
0
18
49
S8
0
0
13
34
S9
0
0
14
28
S10
0
0
3
28
D1
0
1
5
26
D2
0
14
5
6
D3
0
10
2
9
D4
0
29
5
25
D5
0
0
4
56
D6
0
10
5
55
D7
0
0
0
75
D8
0
0
1
53
D9
0
0
1
38
D10
0
0
5
53
M1
0
0
1
7
M2
0
0
0
11
M3
0
0
1
15
M4
0
11
3
24
M5
0
0
0
26
M6
0
9
0
89
M7
0
0
4
103
M8
0
0
1
97
M9
0
14
4
68
M10
1
0
1
48
2
0
4
7
5
0
0
0
1
0
0
1
1
3
0
0
0
0
0
0
1
3
2
0
0
0
0
0
0
0
0
7
0
0
0
0
0
0
0
0
4
0
0
0
0
1
0
0
3
7
0
0
0
2
1
0
0
1
9
0
0
0
0
0
0
0
3
3
0
0
0
0
0
0
0
4
7
0
0
0
0
0
0
0
2
16
0
0
0
1
1
0
0
19
10
0
2
0
0
0
0
2
2
5
0
0
0
1
0
0
0
4
3
0
1
0
0
0
0
5
11
15
0
3
0
0
0
1
1
8
12
1
3
0
0
0
0
4
8
8
0
1
0
3
0
0
2
6
9
0
0
0
0
1
0
0
0
9
0
1
1
0
0
0
1
34
38
0
0
0
1
0
0
0
6
6
0
0
0
0
0
0
0
7
6
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
1
3
0
0
0
0
0
0
0
3
0
0
0
0
0
0
0
0
8
4
0
0
3
0
0
0
0
3
0
0
1
0
0
0
0
1
17
14
0
2
0
1
0
0
0
15
11
0
0
0
1
0
0
0
12
17
0
0
0
0
0
0
0
26
14
0
3
0
1
11
0
1
0
1
0
5
2
7
0
21
2
30
1
75
0
18
0
5
0
10
0
5
0
4
0
8
0
12
1
26
0
27
0
17
0
7
0
21
0
0
0
0
0
0
0
1
0
2
0
2
0
0
0
0
0
4
0
0
0
0
0
0
1
1
0
0
1
0
0
0
2
1
3
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
10
25
0
6
1
0
1
0
10
55
0
18
3
0
0
0
5
79
0
11
0
0
2
0
5
47
0
21
0
0
0
0
9
29
0
17
1
0
4
0
7
7
0
48
1
0
2
0
10
0
0
38
1
0
2
0
11
10
0
26
0
0
1
0
20
0
0
44
1
8
4
0
54
0
0
46
5
0
0
0
9
52
1
3
1
0
1
0
3
106
0
2
0
0
0
0
4
109
0
1
0
30
0
0
2
19
0
2
0
0
2
0
6
59
0
9
0
0
0
0
7
7
0
11
2
0
2
0
8
16
2
21
4
0
0
0
20
19
0
37
2
0
1
0
8
0
0
14
0
0
5
0
16
0
0
49
1
0
1
0
6
78
0
1
1
0
4
0
0
84
0
0
0
0
1
0
3
113
0
2
0
0
0
0
4
126
0
6
0
10
1
0
16
4
0
7
2
0
8
0
17
15
0
16
2
0
3
0
4
5
0
1
0
0
3
0
8
0
0
13
0
0
1
0
13
0
0
6
0
0
5
0
10
0
0
11
0
7
0
1
0
0
0
0
0
0
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
1
0
0
0
0
1
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
1
0
1
0
2
0
0
0
0
1
0
0
0
1
0
0
0
0
0
1
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
3
0
1
1
0
0
2
0
0
0
1
1
1
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
1
0
1
0
0
0
0
0
1
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
1
0
0
0
0
0
1
1
2
0
1
0
0
0
1
0
0
0
0
0
2
0
0
0
0
0
3
0
0
0
0
0
1
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
1
0
0
0
27
2
33
0
0
7
1
56
0
0
7
1
56
0
2
9
1
31
0
0
9
1
43
0
0
11
0
24
1
0
13
0
22
0
0
15
0
8
1
0
28
0
21
0
0
13
0
24
0
0
6
2
47
0
0
3
0
42
0
0
0
0
23
0
1
7
0
64
0
0
6
0
19
0
0
5
0
44
0
0
8
1
20
0
0
16
1
20
0
0
18
1
33
0
0
15
0
26
0
0
2
1
83
0
0
0
0
101
0
0
1
0
57
0
0
1
0
8
0
1
6
0
110
0
0
5
1
34
0
0
2
0
41
0
0
3
0
49
0
0
11
0
46
0
0
2
1
73
Supplementary table 2 Species data of samples taken from the transect A2.
A2
Actinotaenium cucurbita
Actinotaenijm cucurbitinum
Bambusina brebissonii
Closterium acutum
Closterium baylianum var.
baylianum
Closterium baylianum var.
alpinum
Closterium calosporum
Closterium dianae var.
pseudodianae
Closterium dianae var.
dianae
Closterium gracile
Closterium juncidum
Closterium ralfsii var.
hybridum
Closterium rostratum
Closterium setaceum
Closterium striolatum
Closterium turgidum
Cosmarium canaliculatum
Cosmarium pseudoconnatum
Cosmarium
pseudopyramidatum
Cosmarium margaritiferum
Cosmarium sp. 1
Euastrum ampullaceum
Euastrum ansatum
Euastrum crassum
Haplotaenium indentatum
Haplotaenium minutum
Haplotaenium rectum
Micrasterias thomasiana
Micrasterias truncata var.
quadrata
Penium spirostriolatum
Staurastrum margaritaceum
Staurastrum teliferum
Tetmemorus brebissonii
Tetmemorus granulatus
Tetmemorus laevis
Xanthidium armatum
S1
11
0
0
0
0
S2
9
0
0
1
11
S3
4
0
0
4
8
S4
3
0
0
0
10
S5
9
0
0
0
9
S6
8
0
0
3
8
S7
38
3
5
3
5
S8
16
0
0
2
12
S9
7
0
0
0
0
S10
12
0
0
0
7
D1
6
0
0
0
0
D2
26
0
0
0
0
D3
15
0
0
0
0
D4
16
0
0
0
0
D5
6
0
0
0
0
D6
14
0
0
0
0
D7
14
0
0
2
2
D8
39
0
0
0
8
D9
7
0
0
0
4
D10
59
0
0
0
8
M1
23
0
0
1
2
M2
24
0
0
0
0
M3
18
0
0
2
0
M4
22
0
0
0
1
M5
13
0
0
1
3
M6
16
0
0
2
9
M7
34
0
0
0
8
M8
26
0
0
13
2
M9
29
0
0
0
2
M10
16
0
0
0
0
2
17
4
14
9
15
5
4
5
10
0
0
0
0
0
3
0
1
0
0
3
4
0
11
5
7
2
0
0
0
11
0
11
6
8
0
10
0
11
11
8
8
5
3
13
2
2
2
14
3
0
0
1
0
5
0
0
0
6
0
6
0
6
0
9
0
7
0
0
0
0
0
0
0
2
0
0
0
16
0
0
0
1
0
2
0
0
0
0
0
69
59
71
67
45
37
29
37
31
48
71
35
46
62
39
70
42
43
33
31
53
45
41
60
48
56
32
57
25
48
4
4
0
3
14
1
4
14
0
7
17
0
0
10
0
0
26
0
0
5
0
2
10
0
0
12
0
1
7
0
0
18
0
0
13
0
0
0
0
0
4
0
0
12
0
0
9
0
0
18
0
0
9
0
0
19
0
0
0
0
0
0
0
0
0
0
6
0
0
4
0
0
13
0
0
0
2
0
0
2
0
2
4
0
0
0
0
0
0
0
0
11
11
0
4
0
4
0
12
6
6
9
0
6
0
8
4
4
13
0
13
0
7
7
0
14
0
3
0
37
0
0
9
0
6
0
13
0
0
10
0
0
0
3
0
0
18
1
13
0
8
4
11
6
0
1
0
5
2
2
45
0
2
0
7
2
5
21
0
0
0
0
0
0
6
0
0
0
0
3
0
31
3
9
0
0
15
0
22
0
15
0
0
8
0
12
0
16
0
0
6
3
22
1
6
0
0
3
0
9
0
6
0
2
8
0
10
0
10
0
0
5
0
5
2
5
0
0
0
0
15
0
11
0
0
0
0
19
0
4
0
0
8
0
13
0
11
0
0
0
0
13
0
18
4
0
6
0
20
0
8
2
0
11
0
7
0
4
0
0
11
8
3
0
5
9
0
7
2
9
0
2
0
0
8
0
20
0
8
0
0
2
0
16
0
9
0
0
7
0
49
0
31
1
0
0
0
30
0
23
1
0
20
0
9
4
0
18
4
1
0
0
3
0
0
0
0
23
0
3
0
0
4
0
0
0
0
25
0
0
0
0
0
0
7
0
0
17
7
0
6
0
9
0
0
0
0
18
1
1
0
0
13
0
8
0
0
28
3
1
5
0
16
0
5
0
0
15
5
0
1
0
8
0
2
2
1
23
6
6
0
0
7
0
5
0
0
23
13
2
2
0
12
0
0
0
0
7
2
9
1
0
13
0
9
12
0
34
0
0
0
0
22
0
6
3
0
23
0
3
5
0
37
0
15
0
0
10
0
5
0
0
20
0
4
4
0
30
4
4
0
0
39
0
3
0
0
15
0
3
0
0
26
0
3
6
0
14
0
3
2
0
27
0
4
2
0
25
0
2
0
0
27
0
4
4
0
9
0
0
7
0
26
0
7
4
0
37
0
0
12
0
27
0
0
0
0
8
0
4
3
0
23
0
5
2
0
27
3
3
9
0
31
0
2
4
0
36
0
0
2
0
30
0
6
2
0
28
2
4
4
0
27
2
4
0
0
7
4
0
0
0
11
0
3
3
0
9
0
0
4
0
18
2
0
0
0
11
4
7
3
0
11
0
3
5
0
7
0
2
4
1
14
0
1
0
0
9
2
5
5
0
17
0
5
0
0
16
0
1
12
0
18
0
15
0
0
11
0
0
0
0
0
0
0
0
12
0
0
0
0
0
0
0
0
0
0
0
4
0
8
0
0
0
0
3
0
7
0
0
0
3
0
0
6
0
0
0
3
0
0
8
0
0
0
5
0
0
13
0
0
0
0
4
4
15
0
0
0
2
0
0
33
0
0
0
2
2
0
27
0
0
0
3
6
0
21
0
0
0
0
0
0
22
0
0
0
0
0
0
10
0
0
0
0
4
0
12
0
0
0
0
6
3
30
3
0
0
3
11
0
11
2
0
0
4
4
0
14
0
0
0
5
4
0
21
0
0
0
4
15
0
4
0
0
0
4
4
0
20
0
0
0
1
8
0
11
0
0
0
1
2
0
11
0
1
2
2
12
0
2
0
0
0
4
15
0
11
5
2
0
3
27
0
11
2
0
0
0
24
0
7
10
0
0
2
37
0
5
12
0
0
0
13
0
6
2
0
0
2
5
0
4
4
0
0
4
2
1
15
Supplementary table 3 Species data of samples taken from the transect B1.
S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 D1 D2 D3
B1
Actinotaenium diplosporum
Actinotaenium inconspicuum
Actinotaenium perminutum
Actinotaenium sp.
Actinotaenium turgidum
Closterium acutum
Closterium calosporum var.
brasiliense
Closterium closterioides var.
intermedium
Closterium dianae
Closterium gracile
Closterium intermedium
Closterium kützingii
Closterium cf. macilentum
Closterium parvulum
Cosmarium angulosum
Cosmarium bioculatum var.
depressum
Cosmarium botrytis var.
botrytis
Cosmarium botrytis var.
tumidum
Cosmarium connatum
Cosmarium contractum
Cosmarium depressum
Cosmarium difficile
Cosmarium goniodes
Cosmarium granatum
Cosmarium humile
Cosmarium margaritatum
Cosmarium margaritiferum
Cosmarium moniliforme var.
panduriforme
Cosmarium monochondrum
var. fallax
Cosmarium obtusatum
Cosmarium ovale
Cosmarium paragranatoides
Cosmarium perforatum
Cosmarium phaseolus var.
elevatum
Cosmarium polygonum var.
depressum
Cosmarium pseudoornatum
Cosmarium pseudoretusum
0
1
3
0
5
1
34
0
0
1
0
4
0
15
0
0
0
0
5
0
9
0
0
4
0
2
0
7
1
0
6
0
4
0
5
0
0
6
0
1
0
13
0
1
2
0
2
0
9
0
0
1
0
1
0
12
0
2
5
0
1
0
4
1
0
1
0
0
0
8
0
0
1
0
0
0
24
0
0
2
0
2
0
19
0
3
4
0
8
0
13
D4
0
1
2
0
1
0
38
D5
0
1
3
0
2
0
21
D6
0
0
0
0
2
0
27
D7
1
1
1
0
4
0
29
D8
0
1
4
0
0
0
22
D9
0
1
6
0
3
0
15
D10
0
1
3
0
4
0
30
M1
0
0
0
1
3
0
45
M2
0
0
2
0
4
0
16
M3
0
0
1
0
2
0
28
M4
1
1
0
0
3
0
35
M5
0
0
4
0
1
0
52
M6
0
0
1
0
2
0
45
M7
0
1
0
0
3
0
54
M8
0
1
0
0
3
0
44
M9
1
0
0
0
0
1
39
M10
0
0
2
0
1
0
38
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
23
0
2
0
1
1
1
0
23
0
0
0
0
0
1
0
33
0
0
0
0
1
1
0
19
0
0
0
0
0
2
0
24
0
0
0
0
0
2
0
22
0
0
0
0
0
2
0
29
0
0
0
0
0
0
0
45
0
1
0
0
2
0
0
34
0
0
0
0
0
0
0
42
0
0
0
0
0
1
0
14
0
0
0
0
0
0
1
6
0
0
0
0
0
0
1
14
0
0
0
0
0
0
0
18
0
0
0
0
0
0
0
7
0
0
0
0
0
0
0
17
0
0
0
0
0
0
0
25
1
0
0
0
0
0
0
24
0
0
0
0
0
1
0
11
0
0
0
0
0
1
0
20
0
0
0
0
0
1
0
57
0
1
0
0
0
0
0
25
0
2
0
0
0
0
0
18
0
0
0
0
0
0
0
24
0
0
0
0
0
0
0
38
0
0
1
0
0
0
0
38
0
1
0
0
0
0
0
33
0
1
0
0
0
0
0
47
0
0
0
0
0
0
0
44
0
0
0
0
0
0
0
33
0
0
0
0
0
0
0
0
1
1
0
0
0
2
1
0
0
5
3
3
3
4
1
2
0
3
0
3
2
0
0
0
2
1
3
2
2
1
1
5
4
0
0
5
2
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5
0
1
0
1
1
0
0
0
0
16
0
0
0
1
2
0
0
0
0
10
0
4
0
1
1
1
0
0
0
9
0
4
0
0
2
0
0
0
0
17
0
2
0
0
4
0
0
0
0
3
0
1
0
0
0
1
0
0
2
17
0
2
0
0
2
0
0
0
0
11
0
2
0
0
2
0
0
0
1
19
0
0
0
0
2
0
0
0
0
15
0
1
0
0
1
0
0
0
0
7
1
0
2
1
2
0
0
0
0
9
0
0
2
0
2
0
0
0
0
16
0
2
1
0
2
0
0
0
0
5
0
1
2
0
2
1
0
0
0
12
0
6
2
0
3
0
0
0
1
12
0
0
0
0
8
0
4
0
0
13
0
0
0
0
2
0
0
0
0
20
1
1
0
0
0
0
2
0
0
9
0
4
0
0
3
0
2
1
0
15
0
0
0
0
4
0
1
0
0
10
1
1
0
0
2
0
1
0
0
13
0
1
0
0
5
1
0
2
0
14
0
3
0
0
2
0
1
3
0
10
0
3
0
1
2
0
1
0
0
8
0
0
0
1
0
0
2
0
0
9
0
2
0
0
1
0
0
3
0
7
0
2
0
0
0
0
1
0
1
4
0
3
0
0
0
0
2
1
1
7
0
3
0
0
1
0
3
0
0
7
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
1
0
0
0
0
3
0
0
0
0
5
0
1
0
0
1
0
0
0
0
7
0
0
0
0
5
0
1
0
0
7
0
0
0
0
2
0
0
0
2
1
0
0
4
2
3
0
0
2
2
4
0
0
5
0
7
0
0
0
0
3
0
0
5
1
7
1
0
1
0
1
0
0
0
0
5
0
0
0
0
5
0
0
0
0
5
0
0
0
0
4
0
0
2
1
2
1
0
1
0
4
0
0
4
0
1
0
0
4
0
6
0
0
1
0
1
0
0
3
1
4
1
1
2
2
2
0
0
2
0
3
0
1
1
0
1
0
0
5
0
1
0
0
0
0
0
0
0
0
0
0
1
0
4
0
1
0
2
3
2
3
4
1
0
0
0
0
0
0
0
0
0
28
0
20
0
44
0
45
0
35
0
34
0
36
0
28
0
37
0
35
1
28
0
42
1
38
1
22
0
36
1
34
1
26
0
30
0
37
1
22
0
12
0
18
0
34
0
35
0
18
0
16
0
18
0
19
0
23
0
23
Cosmarium quadratum
Cosmarium regnellii
Cosmarium reniforme
Cosmarium sp. 1
Cosmarium sp. 2
Cosmarium subgranatum
Cosmarium varsoviense
Desmidium aptogonum
Desmidium baileyi var.
coelatum
Desmidium swartzii
Euastrum ansatum var.
ansatum
Euastrum ansatum var.
rhomboidale
Euastrum oblongum
Euastrum pectinatum
Euastrum verrucosum
Gonatozygon aculeatum
Gonatozygon brebissonii
Haplotaenium rectum
Hyalotheca dissiliens
Mirasterias crux-melitensis
Micrasterias pinnatifida
Micrasterias truncata
Pleurotaenium archeri
Pleurotaenium crenulatum
Pleurotaenium trabecula
Sphaerozosma filiforme
Staurastrum alternans
Staurastrum bieneanum
Staurastrum eurycerum
Staurastrum furcigerum
Staurastrum crassangulatum
Staurastrum lapponicum
Staurastrum manfeldtii
Staurastrum muticum
Staurastrum polytrichum
Staurastrum
pseudotetracerum
Staurastrum sebaldi var.
gracile
Staurastrum teliferum
Staurastrum tetracerum
Staurastrum vestitum
Staurodesmus dejectus var.
apiculatus
Teilingia granulata
Tetmemorus granulatus
0
4
1
0
1
8
1
1
0
1
2
1
0
4
8
0
13
0
0
6
1
0
2
18
0
2
0
0
3
1
0
4
17
1
3
0
0
1
0
0
5
12
1
4
0
0
1
2
0
4
15
0
23
0
0
2
0
0
4
16
1
3
0
0
0
0
0
1
15
3
15
0
0
1
1
0
4
10
4
2
0
0
1
0
1
4
14
4
2
0
0
2
0
2
3
7
2
0
4
1
4
0
0
0
17
1
4
4
0
1
0
3
0
7
1
3
0
0
2
0
6
0
8
0
4
0
0
4
1
7
0
14
1
3
4
0
0
0
5
0
13
2
1
1
0
2
0
6
0
12
0
0
0
0
2
0
4
0
10
3
0
0
0
5
0
5
0
12
1
6
0
0
0
0
4
0
11
0
6
0
1
0
0
1
0
8
0
0
8
0
2
0
2
0
6
4
1
0
0
0
0
2
0
8
2
7
6
0
0
0
3
0
8
0
5
0
2
0
0
6
0
5
1
0
0
0
0
0
7
0
9
1
0
0
1
0
0
3
0
7
0
3
0
1
2
1
1
0
4
2
0
5
0
1
0
3
0
7
3
0
10
1
0
0
2
0
5
0
2
9
0
0
2
0
2
0
0
0
0
0
1
0
0
0
1
0
10
0
1
1
3
0
0
0
1
0
0
0
3
1
1
0
4
0
1
0
9
0
1
0
1
0
12
2
3
0
1
0
1
0
0
1
12
0
0
0
0
1
0
0
2
0
1
3
11
7
5
2
5
2
3
4
2
4
0
3
2
3
1
4
2
3
3
5
3
6
3
2
4
2
0
0
0
0
0
0
24
1
3
0
0
0
0
8
3
0
0
0
4
0
1
0
1
25
0
0
0
0
0
0
4
0
2
0
0
1
0
25
7
0
0
0
4
0
1
0
5
27
0
0
0
1
0
0
0
0
6
0
0
0
1
2
2
0
0
0
3
0
3
0
1
22
0
0
0
1
0
0
0
0
4
0
0
0
1
10
4
0
0
1
2
0
3
0
0
29
0
2
0
1
1
0
0
0
3
0
0
1
0
13
6
0
0
0
6
0
1
2
1
18
0
0
0
0
0
0
0
0
3
0
0
0
3
7
2
0
0
0
3
0
1
2
1
26
0
1
0
0
0
0
0
0
4
0
0
1
1
0
2
0
0
3
7
0
0
0
0
34
0
1
0
0
0
0
0
1
3
0
0
0
1
0
0
0
0
0
4
0
0
1
0
26
0
2
0
0
0
0
0
1
5
0
0
0
1
0
4
0
0
1
2
0
3
1
0
27
0
1
1
0
0
0
0
0
3
0
0
0
1
3
8
0
0
0
6
0
0
0
0
32
0
1
0
7
0
1
9
0
2
0
1
0
1
15
6
1
0
0
2
2
2
1
0
14
0
1
0
1
0
0
2
0
2
0
0
0
1
19
6
0
0
2
6
4
1
1
0
15
0
1
0
0
0
1
0
0
7
0
0
0
2
2
8
0
1
1
0
1
3
0
0
21
0
0
0
0
1
1
0
0
1
0
0
0
0
28
4
0
0
0
0
5
2
0
0
18
0
0
0
0
0
0
0
1
3
0
0
0
2
4
4
0
0
2
1
4
1
0
0
19
0
0
0
1
0
1
0
1
6
1
0
0
1
6
9
0
0
0
2
3
2
1
0
19
0
2
0
3
1
1
0
0
1
0
0
0
3
3
5
0
0
1
3
5
1
1
0
15
0
0
0
1
0
1
0
0
2
0
0
0
2
9
7
0
0
0
3
3
4
0
0
23
0
2
1
2
0
0
0
1
2
0
0
0
1
2
3
0
0
1
0
4
2
1
0
30
0
0
0
7
0
3
0
0
3
0
0
0
1
1
5
0
0
1
0
6
2
0
0
24
0
1
0
2
2
0
0
0
1
0
0
0
1
0
4
0
0
0
4
1
0
0
0
10
0
2
0
0
2
1
6
0
2
0
0
0
2
23
4
0
0
0
3
0
0
2
0
10
0
0
0
0
1
0
0
0
2
0
0
0
2
21
2
0
0
0
1
2
1
1
0
15
0
1
0
0
0
0
0
0
5
0
0
0
0
18
5
0
0
0
1
0
0
4
0
9
0
3
1
0
0
0
0
0
0
0
0
0
0
20
5
0
0
0
2
0
1
1
0
15
0
1
0
1
1
1
0
0
2
0
0
0
1
11
6
0
0
0
2
0
1
1
0
11
1
2
0
0
0
0
0
0
1
1
0
0
0
0
7
0
0
0
3
3
1
5
1
8
0
0
0
0
1
0
0
0
3
0
0
0
1
4
5
0
0
1
7
1
0
0
2
16
0
0
2
1
0
0
0
0
2
0
0
0
1
0
12
0
0
0
8
0
0
2
0
6
0
2
1
0
1
0
0
0
2
0
0
0
0
24
7
0
0
1
7
0
2
4
0
5
1
1
5
5
5
4
0
2
5
5
5
4
4
4
5
2
1
2
3
5
1
3
2
1
2
3
2
0
3
2
0
1
0
0
1
2
0
3
1
1
0
1
2
2
0
0
1
1
0
2
2
2
0
1
1
0
0
1
4
2
0
1
1
0
0
1
0
1
0
1
3
1
0
2
1
1
0
2
0
4
2
3
1
5
0
5
1
3
0
1
1
4
0
3
1
5
0
1
1
2
0
3
1
0
0
2
2
1
0
1
3
0
1
0
5
4
0
0
3
3
1
0
1
3
0
4
0
5
0
0
2
2
1
1
2
1
1
3
0
6
0
2
2
0
0
4
2
1
0
6
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
1
0
0
0
0
1
0
0
0
0
0
0
0
0
0
1
0
5
0
0
0
0
0
3
0
2
0
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Supplementary table 4 Species data of samples taken from the transect B2.
B2
Actinotaenium
cucurbita
Bambusina
brebissonii
Closterium baylianum
var. alpinum
Closterium
calosporum
Closterium cynthia
Closterium gracile
Closterium juncidum
Closterium lunula
Closterium navicula
Closterium ralfsii var.
hybridum
Closterium striolatum
Cosmarium
pyramidatum
Desmidium swartzii
Euastrum ansatum
Euastrum humerosum
Micrasterias jenneri
Micrasterias rotata
Micrasterias truncata
Penium cylindrus
Penium
spirostriolatum
Staurastrum hirsutum
Staurastrum
punctulatum
Staurastrum simonyi
Tetmemorus laevis
Tetmemorus
granulatus
S1
2
S2
3
S3
0
S4
1
S5
0
S6
0
S7
0
S8
0
S9
1
S10
0
D1
0
D2
0
D3
0
D4
0
D5
0
D6
0
D7
0
D8
0
D9
0
D10
0
M1
0
M2
0
M3
0
M4
0
M5
0
M6
0
M7
0
M8
0
M9
0
M10
0
12
7
0
0
35
14
18
10
3
20
4
5
2
12
8
12
8
5
0
18
0
0
0
0
14
0
0
0
0
0
7
3
6
4
0
3
0
1
0
0
3
12
1
3
9
2
8
7
12
8
5
6
3
0
8
10
0
1
3
2
4
1
4
3
0
2
0
0
4
5
0
0
3
1
2
5
0
0
0
1
1
2
0
0
0
1
0
1
0
1
1
17
6
2
1
30
0
17
2
0
0
1
0
119
7
0
1
12
0
61
20
0
1
4
0
52
31
0
0
2
0
54
32
0
0
7
0
14
35
0
0
1
0
18
30
0
0
0
0
25
20
0
0
0
0
29
48
0
0
2
0
3
33
0
0
2
0
4
31
1
0
12
0
42
14
0
0
1
0
16
20
0
0
2
0
55
21
0
0
3
0
43
16
0
0
0
0
2
23
1
0
1
0
42
25
0
1
1
0
15
32
0
0
0
0
5
26
0
0
0
0
12
11
1
2
7
0
22
10
3
0
16
0
102
13
0
1
13
0
171
3
0
0
3
0
18
22
0
0
5
0
29
5
1
0
3
0
43
57
0
0
7
0
20
53
0
0
5
0
11
60
1
0
0
0
6
43
0
0
1
27
0
77
0
21
0
30
0
24
0
47
0
10
0
14
0
13
0
25
0
39
0
38
1
17
2
21
0
7
5
10
6
14
15
32
1
20
15
25
16
20
0
32
0
10
0
13
0
26
4
17
4
35
0
32
8
13
2
15
5
0
2
0
0
0
1
1
4
0
2
0
0
0
0
0
2
0
0
0
0
0
1
1
1
0
2
0
0
0
0
1
0
0
1
0
0
0
0
1
0
0
2
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
2
0
0
0
0
0
0
5
1
0
1
0
0
0
0
0
2
0
0
0
0
0
0
0
72
0
0
0
0
1
0
0
49
0
0
5
1
0
1
0
37
1
0
1
1
0
0
0
14
0
0
0
1
0
0
0
20
0
0
0
0
1
0
0
33
3
0
1
1
0
0
0
26
5
0
2
2
0
0
0
1
0
0
0
0
1
0
0
12
2
0
0
0
0
0
0
13
3
0
0
1
0
0
0
20
1
0
0
0
0
3
0
29
0
0
0
0
0
10
0
8
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
13
3
0
0
0
0
0
0
24
1
0
0
1
1
0
0
3
0
0
0
0
0
0
0
5
0
0
0
0
0
0
0
2
2
0
0
0
0
0
0
20
3
2
0
0
0
0
0
2
10
0
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
11
70
0
8
67
0
6
20
1
6
65
0
0
54
0
1
38
0
1
119
0
2
123
0
10
117
0
0
69
0
11
32
0
1
39
0
0
78
0
3
107
0
4
65
0
1
67
0
2
89
0
3
81
0
7
85
0
7
77
0
2
115
0
0
70
0
2
48
0
0
10
0
0
87
0
1
102
0
0
55
0
2
73
0
2
104
0
5
97
Supplementary table 5 Abiotic data (pH and conductivity) of individual samples
A1_Sep_01
A1_Sep_02
A1_Sep_03
A1_Sep_04
A1_Sep_05
A1_Sep_06
A1_Sep_07
A1_Sep_08
A1_Sep_09
A1_Sep_10
A1_Dec_01
A1_Dec_02
A1_Dec_03
A1_Dec_04
A1_Dec_05
A1_Dec_06
A1_Dec_07
A1_Dec_08
A1_Dec_09
A1_Dec_10
A1_Mar_01
A1_Mar_02
A1_Mar_03
A1_Mar_04
A1_Mar_05
A1_Mar_06
A1_Mar_07
A1_Mar_08
A1_Mar_09
A1_Mar_10
pH
5.1
4.8
4.9
4.9
4.8
4.9
4.7
4.9
4.8
4.9
5.1
5.3
5.1
5.0
5.1
4.9
5.1
5.1
5.0
4.7
4.6
5.0
4.8
4.9
5.0
4.7
4.7
4.7
5.0
4.8
conductivity
150
150
152
145
145
146
145
150
142
140
68
66
69
72
73
72
75
69
72
72
57
58
60
52
64
60
60
53
62
61
A2_Sep_01
A2_Sep_02
A2_Sep_03
A2_Sep_04
A2_Sep_05
A2_Sep_06
A2_Sep_07
A2_Sep_08
A2_Sep_09
A2_Sep_10
A2_Dec_01
A2_Dec_02
A2_Dec_03
A2_Dec_04
A2_Dec_05
A2_Dec_06
A2_Dec_07
A2_Dec_08
A2_Dec_09
A2_Dec_10
A2_Mar_01
A2_Mar_02
A2_Mar_03
A2_Mar_04
A2_Mar_05
A2_Mar_06
A2_Mar_07
A2_Mar_08
A2_Mar_09
A2_Mar_10
pH
5.2
5.2
5.1
5.3
5.2
5.1
5.1
5.4
5.1
5.1
6.8
6.8
6.7
6.8
6.8
6.7
6.8
6.6
6.7
6.6
5.7
5.9
5.6
5.8
5.8
5.7
5.4
5.8
5.8
5.7
conductivity
135
134
137
135
134
138
132
133
137
132
122
122
123
123
124
125
126
123
122
123
124
124
125
123
126
124
126
123
124
125
B1_Sep_01
B1_Sep_02
B1_Sep_03
B1_Sep_04
B1_Sep_05
B1_Sep_06
B1_Sep_07
B1_Sep_08
B1_Sep_09
B1_Sep_10
B1_Dec_01
B1_Dec_02
B1_Dec_03
B1_Dec_04
B1_Dec_05
B1_Dec_06
B1_Dec_07
B1_Dec_08
B1_Dec_09
B1_Dec_10
B1_Mar_01
B1_Mar_02
B1_Mar_03
B1_Mar_04
B1_Mar_05
B1_Mar_06
B1_Mar_07
B1_Mar_08
B1_Mar_09
B1_Mar_10
pH
7.3
7.3
7.2
7.3
7.4
7.4
7.4
7.4
7.4
7.4
7.2
7.1
7.1
7.1
7.3
7.1
7.2
7.2
7.3
7.2
7.1
7.1
7.1
7.2
7.2
7.2
7.2
7.2
7.2
7.1
conductivity
194
193
192
196
195
195
194
195
197
198
233
235
234
232
230
229
231
234
232
230
247
248
246
247
247
247
246
247
248
248
B2_Sep_01
B2_Sep_02
B2_Sep_03
B2_Sep_04
B2_Sep_05
B2_Sep_06
B2_Sep_07
B2_Sep_08
B2_Sep_09
B2_Sep_10
B2_Dec_01
B2_Dec_02
B2_Dec_03
B2_Dec_04
B2_Dec_05
B2_Dec_06
B2_Dec_07
B2_Dec_08
B2_Dec_09
B2_Dec_10
B2_Mar_01
B2_Mar_02
B2_Mar_03
B2_Mar_04
B2_Mar_05
B2_Mar_06
B2_Mar_07
B2_Mar_08
B2_Mar_09
B2_Mar_10
pH
6.1
6.0
5.9
5.9
5.7
5.8
5.8
5.6
5.8
5.9
6.1
6.0
5.9
5.3
5.9
5.9
6.1
5.4
5.9
6.2
5.6
5.6
5.9
5.8
5.9
6.0
6.1
6.2
6.3
6.3
conductivity
55
55
55
58
52
56
57
52
54
54
44
40
41
48
45
55
41
37
58
56
75
96
91
74
77
61
62
69
67
72
Supplementary table 6 The results of individual adonis tests partitioning variation in species composition evaluated by Bray-Curtis distance matrices. The
effects of individual factors were evaluated sequentially so that pure effects of a particular factor could be ascertained after the two other were subtracted.
env, environmental factors; spat, spatial factors; temp, temporal factors
A1
Factor Df Sums of Squares
F
R2 p-value
env
2
0.70
4.91 0.18
***
spat
1
1.18
16.48 0.31
***
temp
1
0.14
1.94 0.04
n.s.
residuals 25
1.79
0.47
Factor Df Sums of Squares
F
R2 p-value
env
2
0.70
4.91 0.18
***
temp
1
0.25
3.52 0.07
*
spat
1
1.07
14.89 0.28
***
residuals 25
1.79
0.47
Factor Df Sums of Squares
F
R2 p-value
temp
1
0.62
8.64 0.16
***
spat
1
1.25
17.53 0.33
***
env
2
0.15
1.03 0.04
n.s.
residuals 25
1.79
0.47
Factor Df Sums of Squares
F
R2 p-value
spat
1
1.25
17.53 0.33
***
temp
1
0.62
8.64 0.16
***
env
2
0.15
1.03 0.04
n.s.
residuals 25
1.79
0.47
Factor
env
spat
temp
A2
Df Sums of Squares
2
0.66
1
0.18
1
0.05
F
5.87
3.28
0.98
R2 p-value
0.29
***
0.08
**
0.02
n.s.
residuals 25
1.40
-
0.61
Factor Df Sums of Squares
env
2
0.66
temp
1
0.06
spat
1
0.18
residuals 25
1.40
F
5.87
1.12
3.14
-
R2 p-value
0.29
***
0.03
n.s.
0.08
**
0.61
Factor Df Sums of Squares
temp
1
0.47
spat
1
0.19
env
2
0.12
residuals 25
1.40
F
8.33
3.47
2.11
-
R2 p-value
0.20
***
0.08
**
0.10
*
0.61
Factor Df Sums of Squares
spat
1
0.19
temp
1
0.47
env
2
0.12
residuals 25
1.40
F
3.47
8.33
2.11
-
R2 p-value
0.08
**
0.20
***
0.10
*
0.61
B1
Factor Df Sums of Squares
env
2
0.56
spat
1
0.07
temp
1
0.26
residuals 25
1.14
F
6.10
1.50
5.61
-
R2 p-value
0.28
***
0.03
n.s.
0.13
***
0.56
Factor Df Sums of Squares
env
2
0.56
temp
1
0.25
spat
1
0.08
residuals 25
1.14
F
6.10
5.41
1.70
-
R2 p-value
0.28
***
0.12
***
0.04
n.s.
0.56
Factor
temp
spat
Df Sums of Squares
F
R2 p-value
1
0.53
11.65 0.26
***
1
0.10
2.28 0.05
*
env
2
residuals 25
0.25
1.14
2.69
-
0.12
0.56
**
Factor Df Sums of Squares
F
R2 p-value
spat
1
0.10
2.28 0.05
*
temp
1
0.53
11.65 0.26
***
env
2
0.25
2.69 0.12
**
residuals 25
1.14
0.56
B2
Factor Df Sums of Squares
env
2
0.21
spat
1
0.31
temp
1
0.15
residuals 25
1.85
F
1.41
4.18
2.02
-
R2 p-value
0.08
n.s.
0.12
**
0.06
n.s.
0.73
Factor Df Sums of Squares
env
2
0.21
temp
1
0.15
spat
1
0.31
residuals 25
1.85
F
1.41
2.04
4.16
-
R2 p-value
0.08
n.s.
0.06
n.s.
0.12
**
0.73
Factor Df Sums of Squares
temp
1
0.13
spat
1
0.32
env
2
0.22
residuals 25
1.85
F
1.75
4.29
1.49
-
R2 p-value
0.05
n.s.
0.13
**
0.09
n.s.
0.73
Factor Df Sums of Squares
spat
1
0.32
temp
1
0.13
env
2
0.22
residuals 25
1.85
F
4.29
1.75
1.49
-
R2 p-value
0.13
**
0.05
n.s.
0.09
n.s.
0.73
***, p < 0.001; **, p < 0.01; *, p < 0.05; n.s., p > 0.05
Control of Microbenthic Communities by Grazing and Nutrient Supply
Author(s): Helmut Hillebrand, Maria Kahlert, Ann-Louise Haglund, Ulrike-G. Berninger,
Simone Nagel, Stephen Wickham
Source: Ecology, Vol. 83, No. 8 (Aug., 2002), pp. 2205-2219
Published by: Ecological Society of America
Stable URL: http://www.jstor.org/stable/3072052
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Ecology, 83(8), 2002, pp. 2205-2219
? 2002 by the Ecological Society of America
CONTROL OF MICROBENTHIC COMMUNITIES BY GRAZING
AND NUTRIENT SUPPLY
HELMUTHILLEBRAND,"6
MARIAKAHLERT,'ANN-LOUISEHAGLUND,23ULRIKE-G.BERNINGER,4
SIMONENAGEL,5AND STEPHENWICKHAM5
'Erken Laboratory, Department of Limnology, Evolutionary Biology Centre, University of Uppsala, Norr Malma 4200,
S-76173 Norrtdlje, Sweden
2Department of Energy, Malardalen University, Box 883, S-72123 Vdsteras, Sweden
3Department of Limnology, Evolutionary Biology Centre, University of Uppsala, Norbyvdgen 20, S-75236 Uppsala, Sweden
4Biological Oceanography, Alfred Wegener Institute of Polar and Marine Research, Am Handelshafen 12,
D-2 7570 Bremerhaven, Germany
5Zoological Institute, University of Cologne, Weyertal 119, D-50923 Kiiln, Germany
In periphyton communities, autotrophic algae and prokaryotes live in close
Abstract.
spatial proximity to heterotrophic components such as bacteria and micro- and meiofauna.
In factorial field experiments, we manipulated grazer access and nutrient supply to periphyton communities and measured the effects on algal, ciliate, meiofaunal, and bacterial
biomass. We tested whether grazing macrozoobenthos affects all periphytic components
(generalist consumption), whether nutrient effects propagate through the community, and
whether interactions between the different periphyton groups allow for indirect feedback
mechanisms. The experiments were conducted during three different seasons in a mesoeutrophic lake in Sweden (Lake Erken) and at an adjacent coastal marine site (Vadddo)of
similar productivity, but with contrasting grazer fauna. We found strong direct effects of
nutrients and grazing on algae at both sites. Algal biomass increased in fertilized treatments
and was significantly reduced when grazers were present. The algae clearly dominated the
system quantitatively and were positively correlated to the biomass of ciliates and meiofauna. The effects of grazing and nutrients were more complex for heterotrophs than for
algae. Generally, the presence of grazers tended to increase the biomass of bacteria, ciliates,
and meiofauna. Thus, macrograzers were not generalist consumers of the entire community,
but mainly reduced algae. Furthermore, the results suggested strong indirect effects of
grazing, presumably through changes in nutrient supply and algal size structure. Nutrient
enrichment had weak and inconsistent effects on bacterial, ciliate, and meiofaunal biomass.
There was thus no complete propagation of bottom-up effects through the community, and
strong internal feedback mechanisms within the periphyton mediated the effects of macroconsumers and nutrient enrichment.
Key words: algae; bacteria; bottom-up mechanisms; ciliates; grazing; indirect effects; macroinvertebrates; meiofauna; nutrient regeneration; periphyton; protozoa; top-down mechanisms.
INTRODUCTION
The propagation of nutrient and consumption effects
in communities of trophically linked species has received wide attention in ecology, especially within
aquatic ecosystems (Power 1992, Havens et al. 1996,
Hansson et al. 1998). There is still no consensus about
the mechanisms that determine the role of resources
and consumption for the biomass of different trophic
groups (Power 1992, Polis and Strong 1996). The presence of generalist consumers, possibly feeding on different trophic levels (omnivores), has been identified
as a major factor for the propagation of effects in food
webs (Diehl 1995, Polis and Strong 1996, Hansson et
al. 1998). Omnivory may be especially important in
littoral food webs (Havens et al. 1996), since the base
Manuscriptreceived 30 July 2001; revised 20 December 2001;
accepted 20 December 2001.
6
E-mail: [email protected]
of the food web itself represents a heterogeneous assemblage with a variety of internal trophic interactions.
The high production in both freshwater and marine
littorals is largely based on the assemblage of algae
and heterotrophic organisms that comprise the periphyton. If enough light is available, the periphyton is
generally dominated by photosynthetic organisms, consisting of unicellular, colonial, or filamentous species
from a variety of pro- and eukaryotic phyla. However,
the periphyton assemblage represents a complex community with heterotrophic bacteria, fungi, protozoa,
and small metazoa (meiofauna), as well as autotrophic
components in close spatial proximity. There is a large
body of literature on the effects of resource supply and
herbivory on epilithic algal biomass and diversity,
comprising the competition for nutrients (McCormick
1996) and light (Hill 1996), as well as the impact of
grazing (Feminella and Hawkins 1995, Steinman
1996). A large number of possible interactions exist
among the different components of the periphytic bio-
2205
2206
HELMUT HILLEBRAND ET AL.
predators\
-*
> Consumption
Nutrientsupply
Nutrientregeneration
\
*.............................
............................
meiofauna
ciliates . .
het.
flagel
lates
periphyticalgae
bacteria
FIG. 1. Conceptual diagram of interactions in periphyton
communities. The diagram denotes only direct effects, both
trophic and nutrient-related interactions. Factors manipulated
in the field experiments are highlighted in gray, whereas response variables are presented by rectangular boxes. Predators and heterotrophic flagellates were not included in the
analysis of the experiment.
film, mediating the effects of external factors (Fig. 1).
However, this complexity has been neglected in most
studies on nutrient and consumer effects on periphyton.
Macroconsumers (gastropods, crustaceans, insect
larvae) that graze on periphyton are much larger than
their prey and are capable of ingesting the complete
assemblage (generalist consumption), including bacteria and micro- and meiofauna (Fig. 1). Bacteria are
consumed by macrograzers (Lamberti and Resh 1983,
Mulholland et al. 1991), but presumably are assimilated
less effectively than are algae (Morales and Ward
2000). Studies investigating the link from macroconsumers to meiofauna revealed inconsistent effect
strengths (Bott 1996, Schmid-Araya and Schmid 2000),
with strong reduction in meiofauna biomass due to predation reported from salt marshes and coastal marine
sediments (Bell 1980, Jdnsson et al. 1993, Walters et
al. 1996). Macrograzers exert strong heterogeneous effects on different vertical strata (e.g., isopods preferentially remove canopy algae) or horizontal patches
(e.g., gastropods graze in tracks) of the assemblages
(Feminella and Hawkins 1995, Steinman 1996, Sommer 1997). A natural assemblage of macroinvertebrates
Ecology, Vol. 83, No. 8
is likely to consist of different feeding strategies, including that of generalists. Therefore, we hypothesize
that all periphyton components are reduced when macroconsumers are present.
Nutrients also strongly influence benthic microalgae,
increasing biomass and production and changing species composition and diversity (Pringle 1990, Rosemond et al. 1993, McCormick 1996, Hillebrand and
Sommer 1997). In experiments with simultaneous manipulation of both nutrients and grazer access, grazing
counteracted the effects of nutrients on the algae (Rosemond et al. 1993, Hillebrand et al. 2000). It is less clear
how nutrient supply affects heterotrophic components
of periphyton and their interaction with algae. Bacteria
were shown to be abundant and strongly correlated to
algal biomass, indicating a dependence of bacterial
growth on algal extracellular excretion (Hepinstall and
Fuller 1994, Romani and Sabater 1999), but this relation may not be universal (Findlay et al. 1993). Moreover, bacterial growth may be directly limited by inorganic nutrients in pelagic (Vrede et al. 1999) and
benthic habitats (Neckles et al. 1994). Bacteria may
thus respond to fertilization, either directly via nutrient
supply, or indirectly via algal production. Alternatively, bacteria and algae may compete for nutrients within
the periphyton assemblage, as occurs in pelagic systems (Azam et al. 1983). Additionally, enrichment also
increased meiofauna biomass and grazing rates (Peterson et al. 1983, Nilsson et al. 1991). We hypothesize
that nutrient enrichment effects propagate through the
different trophic groups in an epilithic periphyton community, that is, higher bacterial and algal biomass due
to enrichment transfers into higher biomass of meioand microfauna. This will generate a positive correlation between algal and heterotrophic biomass.
The meio- and microfauna living within the periphyton itself ingest parts of the assemblage (Fig. 1), including algae as well as smaller heterotrophic organisms (Bott and Borchardt 1999). Whereas the impact
of meio- (nematodes, copepods) and microfauna (ciliates) on the algal assemblage has rarely been assessed
in freshwater habitats (McCormick 1991, Bott and Borchardt 1999), several studies exist from the marine intertidal (Romeyn and Bouwman 1983, Epstein et al.
1992, Wickham et al. 2000). These groups of small
consumers may generally have less effect on the biomass of the algae (McCormick 1991), but their selective ingestion may have important impact on the community structure (Romeyn and Bouwman 1983, Bott
and Borchardt 1999). These trophic interactions and
the proposed competition between algae and bacteria
may allow for indirect effects in response to macrograzer and nutrient manipulation. We hypothesize that
the effects of nutrients and grazers will be interactive
and dampened between the trophic groups within the
community.
We manipulated macroconsumer access to periphyton and enhanced nutrient supply in factorial field ex-
CONTROL OF MICROBENTHIC COMMUNITIES
August 2002
periments at two different sites in Sweden, the mesoeutrophic Lake Erken and a site at the Swedish Baltic
coast (Vaddo). The sites were chosen to represent similar levels of habitat productivity, but different macrograzer fauna, due to the salinity (-5 g/kg) of the Baltic
coast. We measured the effects of grazers and nutrients
on algae (including cyanobacteria), meiofauna, ciliates,
and heterotrophic bacteria. The use of multiple sites
and seasons together with the analysis of these periphyton communities allow us to test the following hypotheses:
1) The grazing impact of macroconsumers reduces
the biomass of all periphyton components (generalist
consumption).
2) The biomass of heterotrophic components of the
periphyton is positively correlated to the algal biomass,
but this correlation is weakened by macroconsumer
grazing.
3) Nutrient enrichment results in an increasing algal
and bacterial biomass, and this higher biomass of prey
is transformed into higher biomass of protozoa and
meiofauna.
4) The impacts of nutrient supply and grazer presence are interactive for all components. Nutrient effects
will be dampened in the presence of grazers, whereas
negative grazer effects will be less severe in nutrientenriched communities. Positive grazer effects via nutrient regeneration will be more important in low-nutrient treatments.
METHODS
Experimental sites
The freshwater
site, Lake Erken (59?50' N, 18?37'
E), has an area of 23.7 km2 and a mean depth of 9.0
m. It is usually ice-covered from January to early April.
During the time period of the experiments, mean total
phosphorus (TP) in the lake was 0.94 [Lmol/L,and total
nitrogen (TN) was 46.1 imol/L. The mean TN:TP ratio, calculated on a molar basis, was 49. Mean phytoplankton biomass was 5.6 [Lg chlorophyll a/L, and
mean Secchi depth was 5.8 m (all data from the Erken
Laboratory's monitoring program). The experiment
was placed at 70-90 cm water depth at the southeastern
shore of the lake in a cobblestone area.
Vaddo (59?56' N, 18?55' E) is an island at the Swed-
ish east coast, separated from the mainland by a narrow
sound. Experiments were conducted in a shallow embayment at 70-90 cm water depth. For Singofjarden
near Vadddo,the mean TP is 0.77 [imol/L, and the mean
TN is 23.3 [Lmol/L, giving a molar TN:TP 31. Mean
phytoplankton biomass is 7.0 [Lgchlorophyll a/L, and
mean Secchi depth is 3.9 m (all data from Erken Laboratory's monitoring program).
For both sites, dissolved nutrient concentrations
were monitored throughout the study, which is presented in more detail in Hillebrand and Kahlert (2001).
In short, ambient nutrient concentrations ranged 0.3-
2207
4.1 [Lmol/L dissolved inorganic nitrogen (DIN) and
0.01-0.9 imol/L phosphate (DIP). Ambient dissolved
silicate concentrations were always high (>6 pumol/L)
and were thus considered nonlimiting. We also analyzed natural grazer abundance in the vicinity of the
experiments by sampling five cobblestones once during
each experiment (Hillebrand and Kahlert 2001).
Experimental setup
The experiments were conducted with a factorial
combination of nutrient enrichment and grazer exclusion. At each site, we conducted three experiments to
cover different seasons, i.e., six experiments in total:
early spring directly after ice break (17 Apr-24 May
2000, 38 d), late spring (24 May-19 June 2000, 28 d),
and summer (18 July-22 Aug 2000, 36 d). Because of
a failure of the grazer exclusion treatment, however,
the summer experiment in Lake Erken had to be removed from the analysis. All possible treatment combinations were replicated fourfold, which gave 24 plots
for each experiment (three grazing levels X two nutrient levels X four replicates).
The grazer density was manipulated with metal
frame cages (15 X 15 X 15 cm) which were mounted
on top of concrete plates (40 X 40 cm) and covered
by a 1-mm screen. These screens consisted of clear
polyethylene reducing incoming light by only 8% (Hillebrand et al. 2000). The meshes were produced in the
form of hats, which were tightly attached to the base
of the cage frame by Velcro strips. The screens were
replaced by clean ones every 5-10 d during the experiment. Complete coverage of the cages excluded
macrograzers (hereafter termed absent [ABS]), whereas two adjacent sides of the net were cut out to allow
the macrograzers access to the periphyton (present
press]) . To estimate the effects of the cages, plots
without cages (control [CON]) were established on
concrete plates.
Nutrients were supplied with a granulose slow-release NPK-fertilizer (Plantacote Depot 6M, Urania
Agrochem, Hamburg, Germany), which adds nitrogen
(N as ammonium and nitrate) and phosphorus (P as
water-soluble P205) to the water column. Previous investigations showed that this fertilizer continuously enriched the water column for six weeks (Worm et al.
2000). For our experiment, we added 30 g of the fertilizer in porous plastic boxes to half of the cages and
half of the control plots (enriched [ENR]), whereas the
other half did not receive additional nutrients (ambient
[AMB]). The boxes were attached to the cage frame
(ABS, PRES) or to a screw on the concrete plate
(CON), in both cases at a height of 3-5 cm above the
periphyton. The nutrient supply was related to the background nutrients, the median enrichment factor was 2.4
times ambient for N and 5.0 times ambient for P (Hillebrand and Kahlert 2001).
We used unglazed ceramic tiles (5 X 5 cm) as standard substrata in all experiments. The tiles were pre-
2208
HELMUT HILLEBRAND ET AL.
colonized at each site for 3-12 mo to allow the establishment of natural periphyton communities. At the
start of each experiment, the macrograzers were carefully removed from the overgrown tiles. For each replicate, three tiles were used, two of which were pooled
afterwards for the analysis of algae and micro- and
meiofauna, whereas the third tile was used to analyze
the bacteria. The tiles for the bacteria analysis had been
previously sawn to allow them to be broken into nine
parts of similar size for the different analyses. All tiles
were glued to the concrete plates, either within the
cages (ABS, PRES) or outside (CON). The concrete
plates were placed at least 50 cm apart, resulting in
distance of approximately one meter between plots,
which made interference between treatments very unlikely.
Ecology, Vol. 83, No. 8
values for individual carbon content of the different
groups (Bott and Borchardt 1999) to calculate carbon
biomass.
Ciliate samples were counted by filtering (pressure,
<5 cm Hg) fixed subsamples onto 1.2-[Lm pore size
cellulose nitrate filters and staining with the Skibbe
(1994) modification of the quantitative protargol staining (QPS) method (Montagnes and Lynn 1987). This
stains cilia, ciliary basal bodies, and nuclei, and allows
ciliates to be counted and the majority identified to at
least genus level. The filters were counted in their entirety, and ?40 ciliates were counted per filter. Ciliate
biovolume was estimated using the measured lengths
and widths and common geometric equations. Ciliates
were identified using Kahl (1930-1935), Carey (1992),
and Foissner et al. (1999). Biovolume was converted
to carbon content, allowing for shrinking due to fixaSampling and analysis
tion, using a conversion factor of 0.14 pg C/[Lm3 (Putt
At the end of each experiment, the tiles for algae, and Stoecker 1989). In addition to final total ciliate
micro- and meiofauna were sampled and immediately biomass, we analyzed functional groups (bacterivores,
transferred into plastic bags containing filtered (0.2 [Lm algivores, raptorial feeders, and omnivores) within the
pore size) water from the vicinity of the experiment. ciliate community using literature information on feedFollowing transfer to the laboratory, all processing was ing ecology. No ciliate samples were obtained from the
completed within six hours, and samples were stored summer experiment at Vaddo.
The bacteria biomass was analyzed from the separate
dark and cool (40C) during this time. In the laboratory,
the periphyton was removed from the tiles with razor previously sawn tile in each replicate. The tile was
blades, algal conglomerates were carefully separated broken into nine parts of similar size for the different
with scissors and forceps, and the combined suspension analyses. One piece (four replicates for each treatment)
from both tiles was adjusted to a defined volume (rang- was used to determine bacterial biomass. Every tile
ing 100-200 mL). From this suspension, four different piece was put in 7-mL filter-sterilized (0.2 VLm)lake or
subsamples were taken for each replicate. (1) An ali- coastal water and fixed with formaldehyde to a final
quot was preserved with Lugol's iodine for determi- concentration of 4%. In the laboratory, the periphyton
nation of algal and meiofauna abundance. (2) An ali- was removed from the tiles with razor blades and the
quot was digested with H202 for the mounting of per- periphyton suspension was kept cool (40C) until countmanent diatom slides. (3) An aliquot was preserved ing. The exact area of the tile piece was calculated by
with 5% (final concentration) Bouin's fixative to iden- photocopying the tile pieces, cutting the pieces out of
tify and enumerate the ciliates. (4) An aliquot was used the paper and measuring their masses. Mass was transdirectly for live identification of unsilicified algae and formed to area using the mass of a piece of paper with
known area (1 cm2).
protists.
For microscopy slide preparations, 1 mL of the susUsing permanent and live samples, the identification
of algae was done to species level whenever possible, pension was diluted 10 or 100 times, and a 0.5-mL
otherwise to genus level. Counting was conducted in subsample was taken and diluted with 3.5 ml 4% filter3-mL Uterm6hl chambers under an inverted micro- sterilized (0.2 [Lm)formaldehyde. The suspension was
scope at 400X and 100X magnification. At least 1000 sonicated for one minute at 20 W (Microson Ultrasonic
cells were counted for each sample. Algal species not Celldisruptor, Misonix, Farmingdale, New York, USA)
identifiable in preserved samples were put into groups, to break up bacterial clumps. The green nucleic acid
which were used consistently throughout the experi- stain SYTO 13 (Molecular Probes, Leiden, The Nethments. Biovolume for each species or group was cal- erlands) was used to estimate bacterial abundance and
culated with best fitting geometric models (Hillebrand mean cell volume with epifluorescence microscopy. An
et al. 1999). From biovolume, we calculated algal car- addition of 2 [l SYTO 13 was conducted (final conbon using different equations for diatoms and nonsil- centration 2.5 [Lmol/L), and the sample was incubated
icified algae following the recommendations by Gos- in the dark for 15 min (del Giorgio et al. 1996). The
selain et al. (2000) and using the exponential equations samples were then filtered onto black polycarbonate
membrane filters (0.22-nm pore size; MSI, Westboro,
proposed by Menden-Deuer and Lessard (2000).
Meiofauna were identified to group level (nema- Massachusetts, USA, or Osmonics, Glendale, Arizona,
todes, copepods, rotifers, ostracods, etc.) and counted USA), air dried, mounted onto slides, and counted by
in preserved samples at 100X magnification, scanning epifluorescence microscopy under excitation with blue
the complete chamber area (3 mL). We used published light (Nikon B-2A, Tokyo, Japan) at 1250X magnifi-
CONTROL OF MICROBENTHIC COMMUNITIES
August 2002
Vaddo
Lake Erken
1400
W Amphipoda
M Isopoda
EE3Trichoptera _ Ephemeroptera
Diptera
Gastropoda
1200
'"
2209
1000
>
v
800
...........
..IIII1..
Il.l.1.1._.I.I....::...:::.
J. ..-.,,IU~...
..
600
Sprin
Spring
.......
...0.....
Early...
FIG2.ComosiionofEacrlyoo
............
............
_
............e
.......
Lateo
raSumm
er i
Early
teEke
La n Vdo
S mme
Sbummanes
reie
oh
main groups for each of the three experimental seasons.
cation. Per slide, -400 cells were counted, and linear
dimensions were measured with an ocular micrometer
scale for ?100 bacteria to determine mean cell volume.
Bacterial biomass was calculated as carbon content
(CC) from bacterial abundance and mean cell volume
(cv), using the allometric conversion factor CC = 218
(cv)086 (Loferer-Krbssbacher et al. 1998).
Statistical analysis
Full-factorial ANOVA was used to test for significant impacts on the biomass of algae, meiofauna, ciliates and bacteria (hypotheses 1 and 3). Due to the
missing summer experiment in Lake Erken, we conducted the analyses separately for both sites to avoid
unequal ANOVA design. Independent factors comprised season (Vadd6, N = 3; Erken, N = 2), grazer
presence (ABS and PRES, N = 2), and nutrient enrichment (ENR and AMB, N = 2). The impact of cages
was estimated with an analogous design, where the
factor grazer absence was replaced by cage presence
(PRES and CON, N = 2). This design separated the
test of cage artifacts from the test of grazing effects
(Hillebrand et al. 2000). Log-transformed variables
were used throughout to reduce the observed heterogeneity in the variance.
If we found no cage artifact, we used a planned comparison to contrast the effect of grazer absence (ABS)
against grazer presence (CON and PRES) to test for an
overall grazing effect. Furthermore, we wanted to test
whether enhancement of nutrient availability led to
positive effects of grazer presence at low nutrient supply (hypothesis 4). Therefore, we contrasted grazer ab-
sence (ABS) against grazer presence (both CON and
PRES) separately for either AMB or ENR nutrient addition.
Due to the known variability of biomass in benthic
assemblages and our relatively small values of N, we
discuss significant effects (P < 0.05) and trends (0.05
< P < 0.1), using these criteria throughout the text.
We used Pearson's correlation coefficient (r) between
log-transformed biomass to test for significant relations
between producer biomass and heterotrophic (meiofauna, ciliates, bacteria) biomass (hypothesis 2). We
also tested if grazer presence alters the correlation between algae and heterotrophs (hypotheiss 2) by conducting correlations only for ABS and PRES treatments, respectively. To account for the multiple use of
data in the correlation analyses, we use Bonferroni adjustment of significance level, regarding effects as significant for P < 0.0056 and as nonsignificant trends
for P < 0.011.
RESULTS
Community structure
Macrozoobenthos.-The two sites differed greatly in
ambient densities of herbivorous invertebrates (Fig. 2).
Apart from the numerically dominant chironomids, the
macroconsumer fauna in Lake Erken mainly consisted
of caddis fly larvae (Trichoptera) and gastropods (Theodoxus fluviatilis). Mayfly larvae (Ephemeroptera) became important in late spring, and isopods (Asellus)
did so in summer (Fig. 2). At Vaddd, the grazer fauna
was dominated by Hydrobia and Theodoxus (Gastro-
HELMUT HILLEBRAND ET AL.
2210
+ Ciliates
_-11
* Bacteria
o Meiofauna
100.
-2
0)
(A,
o
1
U)~~~~~~
o
-0
L)
E
+
0
OE
~0.01 ~ ~
0.01
0
(D 0.001
10
00o~00
40
00
00
0
0
9
0
~~~~00
100
1000
Algal biomass (jig C/cm2)
FIG. 3. Correlation between heterotrophic and autotrophic
biomass within periphyton of Lake Erken and Vaddo. The
line represents equal autotrophic and heterotrophic biomass.
poda) and by different crustacean genera, such as Idotea (Isopoda), Gammarus, or Monoporeia (Amphipoda). In contrast to Lake Erken, the grazer fauna at Vaddo showed strong seasonal variation in total abundances. Although we avoided direct counts in the cages
to minimize disturbance, we checked for qualitative
and obvious quantitative differences between the macrograzer density in the cages and in the vicinity at
each sampling date. No such differences were observed.
Periphyton composition.-The algal community was
generally dominated by filamentous forms, comprising
the genera Ulothrix, Spirogyra, Mougeotia, and Stigeoclonium, as well as chain-forming diatoms like Melosira. Other important groups were unicellular diatoms, such as Epithemia, Cymbella, Petroneis, Synedra, or Cyclotella, and cyanobacteria, mainly Oscillatoria (Vaddd) and Anabaena (Lake Erken).
Comparing algal, ciliate, meiofauna, and bacterial biomass revealed that algae (median biomass of 114 [ig
C/cm2) clearly dominated the assemblage (Fig. 3). Bacteria had a median biomass of 5.9 [Lg C/cm2, equivalent
to 1-10% of the algal carbon (Fig. 3). Ciliates (median
biomass 1.41 fig C/cm2) represented slightly >1% of
the algal carbon and 20% of the bacterial carbon. Meiofauna were less important in terms of biomass (median
biomass 0.35 gg C/cm2), representing 0.3% of the algal
biomass. The dominance of bacteria among the heterotrophic components was slightly more pronounced
for Vaddo than for Lake Erken. Biomass of macrozoobenthos was not directly measured, but assuming a
C content of -10% of wet mass and using mean masses
given in the literature (Schwoerbel 1994), the measured
abundances become estimates for macrozoobenthic
biomass of 10-100 [ig C/cm2. This represents at least
an order of magnitude higher biomass in large grazers
compared to the meio- and microfauna.
Numerically, nematodes were the predominant meiofaunal taxon, followed by copepods and rotifers. The
Ecology, Vol. 83, No. 8
ciliate community was dominated by omnivores (e.g.,
belonging to the genera Euplotes and Stentor), contributing a mean of 72% of the total biomass. Additionally, bacterivorous ciliates (e.g., Cyclidium sp., Aspidisca sp.) and raptorial feeders, such as Lacrymaria
spp. and Loxophyllum spp., contributed significantly to
the ciliate fauna, whereas algivores (Thuricola sp., Trithigmostoma sp.) were rare.
Algal biomass was positively correlated to most of
the heterotrophic compartments of periphyton at both
sites (Table 1). In Erken, we found increasing meiofauna abundance and increasing ciliate biomass with
increasing algal biomass, whereas the correlation to
bacterial carbon was positive but nonsignificant. A similar pattern was found for Vadd6, with significant correlation of algal to ciliate biomass, whereas the correlations to meiofauna and bacteria were both positive,
but nonsignificant (Table 1).
The overall correlations did not explain much variance (generally <25%; Table 1). The correlations became stronger when calculated separately for ABS and
PRES treatments, indicating that the positive relation
between algal and heterotrophic biomass was confined
mainly to ungrazed treatments. In Lake Erken, algae
were positively correlated to meiofauna, ciliates, and
to bacteria in ABS treatments, albeit nonsignificantly
(Table 1). However, in PRES treatments, the correlations became nonsignificant both for meiofauna and
ciliates, and even reversed for bacteria. At Vadddo,the
positive correlation between ciliates and algae was consistent in both grazed and ungrazed treatments (Table
1). For bacteria and meiofauna, all correlations were
nonsignificant, but distinctly positive in ABS treatments (Table 1).
Effects of experimental manipulations
Algae.-At both sites, seasonal setting, grazer presence, and nutrient supply significantly affected algal
biomass (Tables 2 and 3, Fig. 4). In Lake Erken, the
algae responded positively to enhanced nutrient supply
Correlations (Pearson's r values, with no. observations in parentheses) between algal biomass and different
heterotrophic components of periphyton in Lake Erken and
at Vaddo.
TABLE 1.
Treatment
Ciliates
Meiofauna
Bacteria
Lake Erken
All
Only ABS
Only PRES
0.417 (45)*
0.698 (16)*
0.212 (15)
0.359 (45)
0.600 (16)t
0.294 (15)
0.138 (43)
0.434 (16)
-0.510 (16)
Voddo
All
Only ABS
Only PRES
0.494 (44)*
0.653 (16)*
0.730 (15)*
0.215 (68)
0.478 (24)
0.167 (24)
0.094 (61)
0.390 (22)
0.100 (22)
Note: The correlations are given for all treatments, for grazer absence only (ABS), and for open cages with grazers
(PRES), respectively.
* P < 0.05, Bonferroni adjusted to 0.0056.
t P < 0.1, Bonferroni adjusted to 0.011.
2211
CONTROL OF MICROBENTHIC COMMUNITIES
August 2002
2.
Erken.
TABLE
Effect of season, grazing, and nutrient supply on periphyton components in Lake
Factor
S
G
N
S XG
S XN
G XN
S XG XN
C
S x C
N x C
S x N x C
Bacteria
Meiofauna
Ciliates
Algae
F
df
F
df
F
df
F
df
4.17
(0.053)
1.71
(0.203)
18.07
(<0.001)
4.69
(0.041)
0.84
(0.369)
1.22
(0.281)
0.03
(0.875)
23
2.66
(0.116)
0.58
(0.456)
3.27
(0.084)
0.14
(0.716)
2.84
(0.106)
2.08
(0.163)
0.24
(0.630)
23
0.91
(0.350)
4.33
(0.049)
1.33
(0.261)
0.61
(0.444)
0.74
(0.399)
<0.01
(0.963)
1.18
(0.301)
23
2.51
(0.127)
0.44
(0.516)
0.05
(0.831)
1.26
(0.274)
2.73
(0.113)
9.94
(0.005)
0.16
(0.693)
22
11.51
(0.003)
0.16
(0.696)
0.12
(0.728)
2.49
(0.129)
21
0.83
(0.372)
0.17
(0.683)
0.02
(0.844)
0.37
(0.550)
21
2.01
(0.171)
0.06
(0.816)
1.55
(0.227)
2.08
(0.164)
21
23
23
23
23
23
23
21
21
21
23
23
23
23
23
23
21
21
21
23
23
23
23
23
23
21
21
21
0.94
(0.343)
6.67
(0.018)
5.83
(0.026)
1.69
(0.209)
22
22
22
22
22
22
19
19
19
19
Notes: Table entries are F ratios (with significance levels in parentheses and degrees of
freedom for error term) from three-factorial ANOVA for the factors (S, season; G, grazer
presence; N, nutrient enrichment; C, cage presence) and their interactions. In all cases, there
was one degree of freedom for the independent term. Log-transformed data were used throughout.
TABLE
3.
Effect of season, grazing, and nutrient supply on periphyton components at Vaddb.
S (2)
G (1)
N (1)
S X G (2)
S X N (2)
G X N (1)
S X G x N (2)
C (1)
S X C (2)
N x C (1)
S X N X C (2)
Meiofauna
Ciliates
Algae
Factor (df)t
Bacteria
F
df
F
df
F
df
F
df
27.74
(<0.001)
15.85
(<0.001)
3.82
(0.059)
3.42
(0.044)
2.01
(0.148)
0.01
(0.910)
0.96
(0.393)
36
25.05
(<0.001)
9.46
(0.005)
2.82
(0.107)
3.38
(0.079)
2.82
(0.107)
0.02
(0.899)
2.85
(0.105)
23
0.10
(0.906)
2.19
(0.147)
0.96
(0.333)
0.41
(0.665)
0.33
(0.719)
0.62
(0.436)
2.18
(0.128)
36
2.09
(0.140)
2.65
(0.113)
0.12
(0.826)
1.10
(0.345)
0.24
(0.784)
0.94
(0.339)
0.61
(0.550)
32
5.05
(0.031)
3.53
(0.041)
0.15
(0.702)
2.00
(0.152)
32
0.94
(0.343)
0.12
(0.728)
0.73
(0.404)
7.12
(0.015)
20
0.28
(0.598)
0.27
(0.762)
0.18
(0.672)
0.58
(0.568)
32
1.19
(0.285)
0.91
(0.414)
0.88
(0.357)
2.52
(0.100)
29
36
36
36
36
36
36
32
32
32
23
23
23
23
23
23
20
20
20
36
36
36
36
36
36
32
32
32
32
32
32
32
32
32
29
29
29
Notes: Table entries are F ratios (with significance levels in parentheses and degrees of
freedom for error term) from three-factorial ANOVA for the factors (S, season; G, grazer
presence; N, nutrient enrichment; C, cage presence). Log-transformed data were used throughout.
t Degrees of freedom for independent term are given in parentheses. For ciliates, all df for
independent terms were 1.
2212
400
HELMUT HILLEBRAND ET AL.
Early spring
Late spring
Summer
Early spring
2.5
Ecology, Vol. 83, No. 8
Late Spring
Summer
Lake Erken
LakeErken
2.0
300
AMB
-
ENR
l AMB
ENR
1.5
200
1.0
E 100
Cn
C,,
CM
E
E 0.5
2- D0.0
400
:
2.5
VaddX
Vaddo
2.0 1
300
1.5
200
1.0
100
0.5
ABS PRES CON ABS PRES CON ABS PRES CON
0.0
ABS PRES CON ABS PRES CON ABS PRES CON
FIG. 4. Algal biomass in Lake Erken (upper panel) and
Vaddo (lower panel). Mean biomass (-+1 SE) is given for
treatments without (AMB) and with (ENR) nutrient enrichment for each of three experimental seasons. Grazer treatments are indicated along the horizontal axis (ABS, absent;
PRES, present; CON, cage artifact control).
FIG. 5. Meiofauna biomass in Lake Erken (upper panel)
and Vaddo (lower panel). Mean biomass (?+1 SE) is given for
treatments without (AMB) and with (ENR) nutrient enrichment for each of three experimental seasons. Grazer treatments are indicated along the horizontal axis (ABS, absent;
PRES, present; CON, cage artifact control).
throughout both experiments (Table 2), whereas a significant grazing impact was confined to one experiment
(significant grazer X season interaction; Table 2) in
early spring (Fig. 4). The enhanced supply of nutrients
led to an increase in algal biomass ranging from a few
percent to almost 100% (early spring; Fig. 4). There
was a nonsignificant trend toward higher algal biomass
in late as compared to early spring, which was, however, not consistent for all treatments. At Vaddd, the
seasons also differed in algal biomass, which was highest in summer and lowest in late spring (Table 3, Fig.
4). Grazer presence significantly reduced algal biomass
throughout (Table 3), which was most evident in summer (Fig. 4) and resulted in a significant grazer X season interaction (Table 3). In summer, grazers reduced
algal biomass at Vaddd to less than half of the ungrazed
biomass (Fig. 4). The algae also increased with nutrient
enrichment (nonsignificant trend; Table 3), especially
in late spring and summer. Significant cage artifacts
occurred at both sites (Tables 2 and 3, Fig. 4); in the
presence of cages, algal biomass in Lake Erken (both
experiments) and at Vdddb (summer only) was reduced
compared to control sites.
It became obvious that grazing negatively affected
most filamentous species, reducing especially Ulothrix
zonata (Lake Erken, early spring), Ulothrix flacca
(Vadddo,late spring), and Spirogyra sp. (Vaddd, early
spring). The chain-forming diatom Melosira nummuloides also decreased with grazer presence (Vaddd,
summer). Grazing had less impact on single-celled species, but decreased the biomass of the cyanobacteria
Oscillatoria sp., whereas other cyanobacteria (Anabaena sp.) tended to increase. Nutrient enrichment increased the abundance of diatom species such as the
chain-forming Fragilaria sp., and of Mougeotia, which
dominated in Lake Erken at ENR nutrient levels,
whereas Spirogyra dominated at AMB levels.
Meiofauna.-While grazing had a negative effect on
algae, there was a significant positive main effect of
grazing in Lake Erken (Table 2, Fig. 5) and similar but
nonsignificant main effects and interactions between
grazer presence, enrichment, and season at Vaddd (Ta-
August2002
2213
CONTROLOF MICROBENTHICCOMMUNITIES
ble 3, Fig. 5). Nutrient amendment had no significant
effects on meiofauna biomass in either Lake Erken or
at Vadd6 (Tables 2 and 3), although higher biomass
was found in several ENR treatments (Fig. 5). No cage
artifacts were detected for meiofauna. Using a planned
comparison, we found that positive effects of grazer
presence in Lake Erken were much stronger at AMB
nutrient levels (F,,33 = 8.00, P = 0.008) compared to
ENR treatments (F,,33 = 4.01, P = 0.054).
The different meiofauna groups did not respond
equally to the treatments. Nematodes were positively
affected by grazing in Lake Erken and at Vaddo in late
spring. Small chironomids were also positively affected
by grazing in summer at Vaddd, whereas they were
rare in the other two seasons. Rotifers were enhanced
by grazer presence at Vaddo in early and late spring,
but reduced in summer. Positive effects of nutrients
were seen for rotifers in Lake Erken in late spring and
for copepods at Vaddo in early spring.
Ciliates.-In Lake Erken, the total biomass of the
ciliate community was positively affected by nutrient
enrichment, showing a positive but nonsignificant trend
from AMB to ENR treatments (Fig. 6, Table 2). Grazer
presence had no significant positive or negative effect
on ciliate biomass. For Vadddo,a strong decrease in
ciliate biomass from early to late spring was evident
(Table 3). Moreover, grazer presence had significant
positive effects on ciliate biomass (Table 3), especially
in late spring (Fig. 6). No nutrient effect on ciliate
biomass was found at Vaddb, although some increase
in biomass with enrichment could be observed (Fig.
6).
Cage effects were absent at both sites. We therefore
compared the effect of grazer presence (both CON and
PRES) with or without nutrient addition with a planned
comparison. For Lake Erken, the grazing effect was
positive at AMB nutrient levels (F133 = 4.95, P =
0.033), but nonsignificant at ENR nutrient levels (F, 33
= 0.04, P = 0.844). In Vddd6, the positive effects were
significant at both AMB (F1 32= 16.53, P < 0.001) and
ENR nutrient levels (F132 = 6.86, P = 0.013).
From the ciliate groups, omnivores were positively
affected by grazing at Vaddd. but not in Lake Erken.
Bacterivorous ciliates also increased with grazer presence, at Vaddo as well as in Lake Erken at ambient
nutrient levels. Biomass of raptorial feeders tended to
increase with nutrient enrichment in Lake Erken and
at Vaddo. Algivores were not significantly affected by
either grazing or nutrient supply.
Bacteria.-In Lake Erken, the effects of grazing and
nutrients on bacterial biomass were interactive and represented a complex pattern with significant grazing X
nutrient interactions (Table 2). Enrichment either increased or decreased bacterial biomass, depending on
grazer presence and season (Fig. 7). An increase with
enrichment was seen in ABS treatments, but a decrease
in PRES treatments. At ENR nutrient levels, grazer
presence reduced bacterial biomass, whereas at AMB
Earlyspring
Latespring
LakeErken
6
LAMB
ENR
5
4
3
2
Cn0
0
a
| Vaddo
5
4
3
2T
ABS
PRES
CON
ABS
PRES
CON
FIG. 6. Ciliate biomass in Lake Erken(upperpanel) and
Vaddo (lower panel). Mean biomass (? 1 SE) is given for
treatmentswithout (AMB) and with (ENR) nutrientenrichmentfor each of two experimentalseasons.Grazertreatments
are indicatedalong the horizontalaxis (ABS, absent;PRES,
present;CON, cage artifactcontrol).
nutrients, grazers increased bacterial biomass. At Vdddo, we found no significant effects or trends (Table 3),
although there was slightly higher biomass in grazed
compared to ungrazed treatments (Fig. 7). Cage effects
on bacterial biomass were only present in Lake Erken,
where a significant cage X season interaction indicated
a slight increase of biomass within cages in early spring
and a slight decrease in late spring.
Comparingeffects of nutrientsand grazers on the
differentcomponents
The simultaneous manipulation of grazers and nutrients had significant impacts on both the heterotrophic
and autotrophic components of periphyton (Fig. 8).
Synthesizing the results described here reveals a pattern
of strong direct effects of both factors on the algae,
which were the dominant part of the assemblage. Overall, nutrients clearly increased and grazers clearly decreased the algal biomass, but the effects were not consistent across seasons. For the heterotrophic compartments, the effects were much more complex. In Lake
Erken, nutrients had weak positive effects on ciliates
2214
HELMUT HILLEBRAND ET AL.
40
Lake Erken
30
ElI
AMB
ENR
20(
__10.
CD0
1-
u
40
c 0
Vaddo
30
20
Ecology, Vol. 83, No. 8
hypothesis 2, which states that biomass of heterotrophic components is positively correlated to algal biomass). Increasing nutrient supply resulted in increased
algal biomass, but effects on bacterial biomass were
ambivalent (refuting part of hypothesis 3, stating that
bacteria would also be affected). The increase in the
biomass of algal prey was related to higher biomass of
ciliates, but not of meiofauna (refuting hypothesis 3
regarding propagating bottom-up effects). The impacts
of nutrient supply and grazer presence were counteractive for algal biomass (supporting hypothesis 4). For
the heterotrophic parts of the periphyton, grazer effects
were generally positive, which was more pronounced
at low nutrient supply, suggesting an impact of grazer
presence on nutrient availability (supporting hypothesis 4).
These results have strong implications for food webs
in general and for the analysis of periphytic assemblages in particular. Following an evaluation of the
experimental setup, we will discuss these implications
in more detail for grazers, nutrients, and the interactions between grazers and nutrients.
Experimental setup
10
0
ABS PRES CON ABS PRES CON ABS PRES CON
FIG. 7.
Bacterial biomass in Lake Erken (upper panel)
and Vidddo(lower panel). Meanbiomass(?+1 SE) is given for
treatmentswithout (AMB) and with (ENR) nutrientenrichment for each of three experimentalseasons. Grazertreatments are indicatedalong the horizontalaxis (ABS, absent;
PRES, present;CON, cage artifactcontrol).
Except for the intrusion of macrozoobenthos in one
experiment, which was removed from the analysis, the
exclusion treatments were successful. In the other experiments, we found no visible grazing tracks on the
tiles in the closed cages (ABS). The nutrient fertilizer
was also shown to be effective (Hillebrand et al. 2000,
Hillebrand and Kahlert 2001). Despite the efforts to
Kem
and both positive and negative effects on bacteria. No
nutrient effects were observed on meiofauna or on the
other heterotrophic groups at Vaddo. Grazing had significant positive effects on meiofauna (Erken) and ciliates (Vadd6). Bacteria responded to grazer presence
only in Lake Erken, where the effect was positive in
AMB and negative in ENR treatments.
EX
0
EQ
V
0\
E
-
E+~~~~~~~~
O
Miates
VO-
m fauna
v-
aV 0
E
V
DISCUSSION
Our experiments revealed strong impacts of grazing
and nutrient supply on the periphyton community, affecting the biomass of algae, bacteria, ciliates, and meiofauna. Macroconsumers reduced algal biomass, but
did not reduce the biomass of heterotrophic periphyton
components. These grazers were thus not strictly generalist consumers (refuting hypothesis 1, which states
that grazing reduces all periphyton), but favored heterotrophs either by selective consumption or indirect
growth enhancement. Autotrophic biomass was clearly
correlated to most of the heterotrophic components of
periphyton, but divergent effects of grazer presence on
the different components resulted in different correlations in grazed and ungrazed treatments (supporting
FIG.8. Summaryof effects of macroconsumerpresence
and nutrient supply on different periphyton components: algae, bacteria, ciliates and meiofauna. The figure shows effects on biomass, as detected by ANOVA. The symbols give
the site (E, Lake Erken; V, Vaiddb) and the sign of the effect:
+, positive; -, negative; ?, both positive and negative observed. A zero implies nonsignificant effects (P > 0.1). Signs
in parentheses represent trends (0.05 < P < 0.1).
August2002
CONTROLOF MICROBENTHICCOMMUNITIES
eliminate cage artifacts, cages reduced algal biomass
at both sites, mainly due to a lower proportion of filamentous algae on tiles in cages compared to outside
(Hillebrand and Kahlert 2001). Since those filamentous
algae present were highly susceptible to grazing (see
Results), we are confident that this artifact does not
generally flaw our conclusions. Cage affects on the
heterotrophic parts of the assemblages were absent, except for a season X cage interaction for bacteria in
Lake Erken.
Two possible limitations of our study should be mentioned. First, we conducted a destructive sampling of
final yields. We have therefore no data to analyze the
short-term population dynamics. The time period of our
experiments will clearly allow predator-prey fluctuations between ciliates and bacteria, and presumably
also for algae and some meiofauna groups (Bott and
Borchardt 1999). We only analyzed for final net effects
of the experimental manipulations on the response variables, integrated over the period of our experiment.
Second, we do not have data on heterotrophic flagellates, which often represent a link between bacteria
and other faunal components of the periphyton. A close
correlation between heterotrophic flagellates and bacteria is known for pelagic systems (Berninger et al.
1991, Sanders et al. 1992), in sediments (Hondeveld
et al. 1994), and in periphyton (Neckles et al. 1994,
Eisenmann et al. 1999). However, although heterotrophic flagellates are usually the main bacterivores in the
pelagial, it is not yet clear whether they play an equally
important role in the benthos and epibenthos (Kemp
1990, Hondeveld et al. 1995). It is possible that flagellate grazing on bacteria weakened the correlation of
bacterial biomass to the other periphyton components
and the response to experimental manipulations.
Grazers
Macroconsumers had strong negative impacts on algal biomass, which is a consistent result for freshwater
(Feminella and Hawkins 1995, Steinman 1996) and
coastal marine (Nicotri 1977, Hillebrand et al. 2000)
periphyton. We found different effect strengths in different seasons: grazing was more important in early
spring (Lake Erken) or summer (Vidd6) than in other
seasons. The effects of grazing were not similar at both
sites for any season, and so were not directly related
to seasonal changes in water temperature or grazer density (Hillebrand and Kahlert 2001). Grazing affected
mostly filamentous and erect chain-forming algal species, which is a persistent pattern in grazing experiments on periphyton, presumably based on higher mechanical vulnerability of filaments (Nicotri 1977, Steinman 1996, Hillebrand et al. 2000).
In contrast to the negative effects on algal biomass,
grazers had net positive effects on meiofauna, ciliates,
and (less consistently) bacteria. Two processes may
interact to produce this result. Macrograzers either did
not consume substantial parts of the heterotrophs, or
2215
they indirectly stimulated the heterotrophic biomass
accrual resulting in a net positive effect despite consumption.
There is still no agreement about the degree of food
selection in grazer-periphyton interactions. As indicated for the preferential consumption of algal growth
types, the macroinvertebrates may not actively select
certain food items, but their feeding type may specifically affect some periphytic growth forms, e.g., erect
growing filaments and chains (Steinman 1996, Hillebrand et al. 2000). Nonselected food may even profit
from grazer presence. The impact of macrograzers on
epilithic meio- and microfauna has rarely been assessed
(Bott 1996), but experiments on other substrata (sediment, plants) revealed inconsistent response of meiofauna to macrofauna presence (Bell 1980, Jinsson et
al. 1993, Walters et al. 1996, Schmid-Araya and
Schmid 2000). On hard substrates, meiofauna and ciliates may escape grazing, through their mobility within
the film. For bacteria, the attachment to particles allows
ingestion and inhibits discrimination of uptake by macroconsumers. Nevertheless, Neckles et al. (1994) reported nonsignificant or even positive responses to
grazer presence, whereas others found negative effects
of macrograzers on periphytic bacteria (Lamberti and
Resh 1983, Morales and Ward 2000). Morales and
Ward (2000) reported a preferential consumption of
algae over bacteria by a grazing snail, and concluded
that the close spatial proximity of both components and
the small prey-to-grazer ratios did not allow selective
ingestion. Instead, they proposed that higher consumption of algae might be due to greater assimilation efficiency, whereas bacteria may pass the gut relatively
undamaged. Besides reduced consumption and reduced
assimilation of bacterial biomass, the fast growth of
bacteria may also reduce the impact of experimental
manipulations on bacterial biomass after four weeks.
Differences in grazing pressure and assimilation efficiency may explain the absence of negative effects
on heterotrophs, but not the increase in biomass with
macroconsumer presence. Grazers may enhance heterotrophic growth either by increasing nutrient availability (see Discussion: Interactions between grazers
and nutrients) or by improving food supply to the small
fauna via removal of large algal species. Grazers were
shown to remove mainly upright, filamentous species
from the periphyton (see Results). Since these large
species are presumably inedible for many small herbivores, the presence of macrograzers could change the
size spectrum and species composition toward a dominance of small cells, which are more easily ingestible
by small herbivores. We calculated the proportion of
algal abundance contributed by small single-celled species (cell volume <4000 [Lm3)and found increased
proportions of this group in PRES treatments at Vadddo
in early spring (ABS, 32% of total abundance; PRES,
46%) and summer (ABS, 22%; PRES, 29%). In other
experiments, however, there was no change or even a
2216
HELMUT HILLEBRAND ET AL.
decrease of the proportion with grazing (Lake Erken,.
late spring). Still, the positive effect of grazing on ciliates was confined to Vaddd, which is in good agreement with these results.
While the ABS treatments excluded macroconsumers, meio- and microfauna were present in all treatments, and their consumption of algae could not be
estimated. Some studies report significant effects of
small herbivores on algal assemblages (Hann 1991,
Bott and Borchardt 1999). In our experiments, total
meiofauna abundances (up to 100 individuals/cm2)
were well in line with the high abundances reported
Coull 1999) and
from sediments (106 individuals/;
on plants (80 individuals/cm2; Rutledge and Fleeger
1993). Therefore a significant impact of meiofauna on
algae may be possible. Feeding rate estimations for
ciliates indicated that ciliate consumption of algae may
also be sufficient to affect algal abundance, but may
be less efficient over extended time periods, particularly for large or filamentous species (Epstein et al.
1992, Balczon and Pratt 1996). The importance of
small herbivores living within the biofilm may be in
their selection of certain algal growth types, rather than
their overall grazing pressure (McCormick 1991).
Nutrients
Nutrient effects on algae were positive, but with different strengths across seasons and sites. In a previous
analysis, we found that nutrient effects were correlated
mainly to light availability and ambient dissolved and
total nitrogen concentrations (Hillebrand and Kahlert
2001). The positive effect of nutrient enrichment on
algal biomass was consistent with previous experiments in freshwater (Pringle 1990, Stelzer and Lamberti 2001) and marine environments (Hillebrand and
Sommer 1997, Hillebrand et al. 2000).
Nutrient effects on heterotrophic components were
comparably weak and did not propagate through the
community. For total meiofaunal biomass, the effects
of nutrients were nonsignificant, and only rotifers were
positively affected by nutrient supply. The period of
the experiments may have been too short to support a
numerical response of metazoans to the nutrient supply,
although immigration is possible. Accordingly, Nilsson
et al. (1991) found strong responses of sediment meiofauna to enrichment within two weeks. The increase
in ciliate biomass with nutrient enrichment was significant for Lake Erken, where there are also strong
effects of nutrient enrichment on algal biomass. The
increase may either be connected to algal biomass or
to bacterial biomass, and algae may positively affect
ciliate biomass by delivering food or habitat structure.
The effects of nutrients on bacteria were weak and
only present in Lake Erken at grazer absence. Positive
effects of nutrient enrichment on bacteria have previously been described for pelagic bacteria in Lake Erken
(Vrede et al. 1999) and also for periphyton-associated
bacteria in other habitats (Hepinstall and Fuller 1994,
Ecology, Vol. 83, No. 8
Neckles et al. 1994), whereas altered nutrient supply
did not affect bacteria or microfauna biomass in stream
experiments (Mulholland et al. 1991). The weak nutrient effects in our experiments may be due to the
absence of inorganic nutrient limitation for bacteria and
a higher importance of interaction with algae, compared to direct nutrient effects (Hepinstall and Fuller
1994). In laboratory experiments, Findlay et al. (1993)
found increases of algal biomass with nutrient enrichment, but no subsequent increase in bacterial biomass.
In addition, the presence of fast-growing predators such
as heterotrophic flagellates may dampen the response
to increased nutrient supply (see Discussion: Experimental setup).
Interactions between grazers and nutrients
Generally, two different scenarios delimit the possible responses of periphyton to top-down and bottomup regulating factors. On one hand, the entire assemblage may respond to the manipulations, i.e., resource
supply and grazing affect all groups. On the other hand,
the trophic interactions within the biofilms allow the
stepwise propagation of manipulative effects from either the top down or the bottom up, including a variety
of indirect effects. For example, fertilization of a tundra
river initially caused a positive response of the entire
food web, which in later years was mediated by a strong
top-down feedback of grazers on algae (Peterson et al.
1993). The divergent responses of different components to grazer presence and nutrient enrichment in our
experiments clearly point towards a complex and counteractive scenario.
Grazing and nutrient effects had opposite impacts on
algal biomass, resulting in similar biomass levels in
controls compared to PRES/ENR treatments. Similar
results have been obtained for stream periphyton (McCormick and Stevenson 1991, Rosemond et al. 1993).
However, we found no significant interaction terms between grazing and nutrients in the ANOVA on algal
biomass, indicating that the two main effects were not
tightly connected.
For the heterotrophic components, grazing and nutrients generally revealed positive effects (although
only weak for nutrients). We found two indications for
an interdependence of these effects. First, the positive
effects of grazing on meiofaunal, ciliate and bacterial
biomass were more pronounced at AMB nutrient levels,
indicating a role of grazer presence in nutrient availability. Secondly, grazer presence also changed the correlation between autotrophic and heterotrophic biomass
in the assemblage. The correlations tended to be significantly positive in the absence of grazers, but the
presence of grazers weakened or even reversed the
trends. Although correlations should be used with care
given the complexity of the environment (Bott 1996),
this might indicate a bottom-up effect regulated at grazer absence, whereas grazing destroys the relation between algal and heterotrophic biomass by preferential
August2002
CONTROLOF MICROBENTHICCOMMUNITIES
removal of algae and changes in nutrient cycling. Mulholland et al. (1991) analyzed the effects of nutrient
reduction and grazing on nutrient cycling in stream
periphyton. Increased nutrient cycling compensated for
nutrient reduction in the overlying water column,
whereas grazing reduced the importance of nutrient cycling. A grazed and simplified periphyton community
might have higher access to water column nutrients at
lower biomass levels, and grazing increased the longitudinal transport of nutrients in streams (Mulholland
et al. 1991). In our experiments, excretions by grazers
may have supplied nutrients to the periphytic assemblage. Grazers are known to affect the nutrient availability by several mechanisms (McCormick and Stevenson 1991). They may increase nutrient uptake by
destroying the boundary layer which slows down nutrient diffusion (Wetzel 1996), or by removing detritus.
They may increase nutrient supply by sloppy feeding
and via excretion products, such as fecal pellets containing nitrogen and phosphorus (Mulholland et al.
1991, Kahlert and Baunsgaard 1999). For Lake Erken,
positive effects of grazer presence on periphyton nutrient content were shown (Hillebrand and Kahlert
2001).
De Mazancourt et al. (1998) identified reduction of
nutrient loss as an important process allowing for positive effects of grazer presence on prey biomass. Although we found no direct evidence, our results suggest
an important role of grazer-mediated nutrient processing within the periphyton. However, it is not well understood if there is a benthic microbial food web (Bott
1996) of similar importance to that in pelagic ecosystems (Azam et al. 1983).
CONCLUSIONS
AND PERSPECTIVES
In a multiseason study at two sites (freshwater and
marine), we found positive effects of nutrients and negative effects of grazing on algal biomass in periphyton
communities. The heterotrophic components of periphyton responded only weakly to nutrient enrichment,
whereas macrograzer presence clearly increased ciliate,
bacteria, and meiofauna biomass, especially if nutrient
supply was low. Apart from the direct effects of consumption and resource supply, more indirect effects,
such as nutrient regeneration, facilitation, and internal
trophic interactions, were identified as possible mechanisms regulating the composition of this microbenthic
community. These interactions may buffer the effects
of changes in the external environment and maintain a
resilient and highly productive assemblage.
Taking these interactions into account will require
changed perception of benthic food webs, but will also
offer the possibility to test complex food web interactions with these communities. In most models, periphyton is considered as a continuous, though spatially
heterogeneous, resource to grazers (Nisbet et al. 1997,
Poff and Nelson-Baker 1997). Our results indicated that
trophic interactions within the community will modify
2217
both top-down and bottom-up mechanisms and the response of the assemblage to enrichment (cf. Nisbet et
al. 1997). Both ciliates and metazoan meiofauna possess a variety of energy acquisition mechanisms, comprising bacterivory, algivory, predation, detritivory,
and omnivory in different degrees of specificity (Bott
1996, Schmid-Araya and Schmid 2000). However, even
complex food web analyses did not include the different microbenthic components (Havens et al. 1996). Perceiving the periphyton as an assemblage of trophically
interacting populations, rather than a basic autotrophic
assemblage, will thus add additional complexity to benthic food web structure (cf. Diehl 1995, Havens et al.
1996, Polis and Strong 1996).
At -the same time, the fast numerical responses and
the close spatial proximity render periphyton assemblages highly valuable tools to test general predictions
on food web structure. Among others, this approach
may be especially valuable with respect to the importance of indirect effects in ecological communities
(Menge 1995) and the effects of nutrient supply ratios
on plant-dominated assemblages (Stelzer and Lamberti
2001, Hillebrand and Kahlert 2001).
ACKNOWLEDGMENTS
We thankKarlHillebrandand ChristaHillebrandfor providing the cages andUlla Holm at VaddoSkjutftltfor giving
us the opportunityto conductour experimentin this military
area. Lars Peters and JoachimKahlerthelped with the macrozoobenthosanalysis. Karin Larsson, Jutta Schade, Erik
Tdrnblom,Lars Peters, Sonja Stendera,Ulf Lindquist,Per
Odelstrtm, Jan Johansson,and Monika Feiling helped with
the conduction of the experimentsand the analysis of the
samples. We acknowledge the financial support from the
DeutscherAkademischerAustauschdienstgrantD/99/08944
andfromthe ErkenLaboratory.The manuscriptprofitedfrom
the commentsby ThorstenBlenckner,RobertStelzer,Stuart
Findlay, and two anonymousreviewers.
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Ecology, 91(1), 2010, pp. 36–41
Ó 2010 by the Ecological Society of America
A distinct latitudinal gradient of diatom diversity is linked
to resource supply
SOPHIA I. PASSY1
Reports
Department of Biology, University of Texas at Arlington, Box 19498, Arlington, Texas 76019-0498 USA
Abstract. For over 200 years, scientists have recognized the nearly ubiquitous poleward
decline of species richness, but none of the theories explaining its occurrence has been widely
accepted. In this continental study of U.S. running waters, I report an exception to this general
pattern, i.e., a U-shaped latitudinal distribution of diatom richness (DR), equally high in
subtropical and temperate regions. This gradient is linked unequivocally to corresponding
trends in basin and stream properties with impact on resource supply. Specifically, DR
distribution was related to wetland area, soil composition, and forest cover in the watershed,
which affected iron, manganese, and macronutrient fluxes into streams. These results imply
that the large-scale biodiversity patterns of freshwater protists, which are seasonal, highly
dispersive, and sheltered by their environment from extreme temperature fluctuations, are
resource driven in contrast to more advanced, perennial, and terrestrial organisms with
biogeography strongly influenced by climate. The finding that wetlands, through iron export,
control DR in streams has important environmental implications. It suggests that wetlands
loss, already exceeding 52 million hectares in the conterminous United States alone, poses a
threat not only to local biota, but also to biodiversity of major stream producers with
potentially harmful consequences for the entire ecosystem.
Key words: algae; biodiversity; iron; latitudinal diversity gradient; macroecology; nutrient limitation;
periphyton; species richness; streams; wetlands.
INTRODUCTION
larger continuous geographic area with a variety of
habitats and refuges, where species can maintain large
populations and become subjects of faster speciation
and lower extinction (Terborgh 1973, Rosenzweig 1995).
Observations that multicellular species tend to originate
in the tropics, or diversify there more rapidly, reinforced
evolutionary and historical explanations of LDG
(Cardillo 1999, Wiens and Donoghue 2004, Jablonski
et al. 2006).
Theoretically, any general pattern of diversity is
ultimately caused by properties of the physical environment (Brown and Gibson 1983). Therefore, a thorough
knowledge of the latitudinal changes in this environment
should allow the source of LDG to be discerned. Diatom
richness (DR) in streams is the product of hierarchical
local and regional environmental influences (Passy 2009)
but its large-scale spatial distribution, both latitudinal
and longitudinal, is unknown. It is hypothesized here
that if DR exhibits significant spatial gradients, they
must be generated by corresponding trends in the stream
and watershed environmental attributes, in which case
we will be able to identify the origin of LDG in the most
speciose group of freshwater algae. To test this
assumption, I examined the latitudinal and longitudinal
distribution of DR and its local (stream) and regional
(watershed) predictors using data from the U.S.
Geological Survey NAWQA (National Water-Quality
Assessment) Program. NAWQA contains not only some
of the most comprehensive community data worldwide
The decline of species richness from the tropics to the
poles, known as latitudinal diversity gradient (LDG),
has been observed in organisms ranging from bacteria to
mammals but ecologists have yet to reach a consensus
on the mechanisms behind this phenomenon (Pianka
1966, Rohde 1992, Rosenzweig 1995, Willig et al. 2003,
Hillebrand 2004). So far, over 30 hypotheses have been
proposed, which were either non-neutral, assuming
some form of latitudinally bound environmental or
biotic dependence of species richness (Willig et al. 2003),
or neutral, viewing LDG as a result of purely geometric,
nonbiological constraints arising from the natural
boundaries of the Earth domain (Colwell and Hurtt
1994). The non-neutral mechanisms fall within the
broader categories of temperature, geographic area,
productivity, diversification, and habitat history (Pianka
1966, Currie 1991, Rohde 1992, Rosenzweig 1995, Willig
et al. 2003, Mittelbach et al. 2007). For example,
temperature, with a marked increase toward lower
latitudes, was implicated in parallel gradients of
metabolic kinetics and speciation rate, setting the
biodiversity patterns (Rohde 1992, Allen et al. 2006).
Compared to temperate regions, the tropics also span a
Manuscript received 26 March 2009; revised 26 June 2009;
accepted 31 August 2009. Corresponding Editor (ad hoc): N. B.
Grimm.
1
E-mail: [email protected]
36
January 2010
DIATOM BIOGEOGRAPHY IS RESOURCE DRIVEN
but also detailed information on stream physicochemistry and watershed features (see Methods). The
present analyses confirmed that the spatial gradients of
diatom biodiversity were determined by spatially constrained watershed and stream features but further
revealed their impact on resource availability.
METHODS
RESULTS
Diatom richness displayed a U-shaped response along
the latitudinal gradient with peaks at approximately 268
and 488 but a minimum at approximately 378 (Fig. 1a),
indicating that subtropical and temperate regions
harbored similar diversity. A weak, quadratic DR
response was detected along the longitudinal gradient
with a mode at approximately 888 W (Fig. 1b). Stepwise
multiple regression of DR vs. all environmental variables in the data set, including stream properties, i.e.,
water chemistry, temperature, and discharge, and
watershed features, i.e., climate, land cover, and soil
composition, selected five highly significant (0.000005 ,
P , 0.0003) variables. These predictors captured 30% of
the DR variance and included the following with their
respective standardized coefficients (in parentheses):
manganese concentration (0.29) and discharge (0.26)
from stream properties and forest cover (0.19),
wetlands (0.18), and soil organic matter (0.15) from
watershed features. Although iron was among the
strongest positive correlates of DR, it did not enter the
regression model due to collinearity.
Regressions of the selected predictors against latitude
revealed significant quadratic responses for all but Mn
(Fig. 1c–f ). Discharge, wetlands, and soil organic matter
(SOM), which correlated positively with DR, displayed
U-shaped latitudinal gradients, closely matching the DR
gradient. Conversely, forest cover, which was a negative
DR predictor, exhibited the opposite, i.e., a unimodal
latitudinal pattern. Variance partitioning showed that
the latitudinal effect on DR was almost entirely
subsumed by the latitudinally dependent environmental
attributes, i.e., SOM, wetlands, forest, and discharge
(Fig. 2). The DR explanatory variables accounted for
most of the variance contributed by longitude (Fig. 2),
suggesting that both spatial gradients were generated by
the same mechanisms. Multiple correlations were
employed to elucidate how the three basin features
included in the DR model may influence local stream
properties with impact on algal diversity (Fig. 3). This
analysis was performed using both the whole data set
and the subset with DOC measurements. Strong and
highly significant relationships were detected between
the three watershed features, i.e., wetlands, forest, and
SOM and stream water chemistry (i.e., DOC, Fe, and
Mn concentrations). All three stream variables increased
with wetlands and SOM but decreased with forest.
Forest cover was negatively correlated with nearly all
nutrients, including those that were positive predictors
of DR, e.g., orthophosphate and ammonia (Pearson r ¼
0.39 and 0.28, respectively, P , 0.000001, N ¼ 530).
DOC was strongly positively correlated with both
micronutrients (Fig. 3) and, because of this, including
it in the already described regression model (in the
subset with DOC measurements) added only 1% to the
explained variance. Diatom richness responded strongly
to variability in DOC, watershed features, and micronutrients (Fig. 3).
Reports
The NAWQA data set was derived from 1059 algal
samples with corresponding chemistry data, collected
from 531 distinct stream localities. A subset of these
data, encompassing 850 samples from 402 localities,
contained dissolved organic carbon (DOC) measurements. Benthic algae from the NAWQA richest-targeted
habitats, including hard substrates or macrophytes
exposed to faster currents, were sampled quantitatively
across latitudinal and longitudinal ranges of 23836 0 and
51842 0 , respectively. To eliminate the influence of
extreme environments, such as low pH, only hard-water
streams, defined as those with Ca concentrations 20
mg/L (Patrick 1961), were considered. They represented
74% of all studied streams. Information on watershed
climate, land cover, and soil composition was gathered
by the USGS for each locality. Between May 1993 and
November 2004, water chemistry, discharge, and temperature were measured for each site within the month
of algal collection. Over the study period, 214 localities
were sampled more than once, i.e., between two and nine
times. For each of these localities, the environmental
data were averaged, while the maximum value of diatom
richness was selected to ensure that biodiversity was not
underestimated. To improve normality, proportional
environmental predictors were arcsine-square-root
transformed, while all others were ln-transformed.
Latitude and longitude were centered on their means
and squared because the centered linear and quadratic
monomials were not collinear unlike the respective raw
variables. Maximum diatom richness was regressed
against all stream and basin variables using iterative
stepwise multiple regression until a parsimonious model,
with condition indices ,9, was obtained. Although some
predictors were correlated, condition indices lower than
15 ascertained that such collinearity was not strong
enough to alter the regression coefficients (Wilkinson et
al. 1996). Notably, a regression of raw richness vs. raw
environmental variables selected the same predictors,
indicating that the modifications of these variables, i.e.,
maximum vs. average, did not affect their relationships.
A series of multiple regressions of maximum DR against
the variables from the environmental model and the
linear and quadratic terms of the centered latitude and
longitude was performed. As a result, the variance in
maximum DR was partitioned into pure and covariance
terms, explained by pure environment, pure latitude (or
longitude), and their covariance, respectively. Pair-wise
correlations among maximum DR and environmental
variables were estimated with Pearson r.
37
SOPHIA I. PASSY
Ecology, Vol. 91, No. 1
Reports
38
FIG. 1. (a, b) Spatial gradients of diatom richness and (c–h) its most important predictors. The distribution of all variables was
fit with a quadratic model of the spatial gradient and the coefficient of determination (R 2) is given. All predictors are plotted against
latitude, including the arcsine square-root transformation (asnsqr) of the proportion of the watershed covered by (d) wetlands and
(e) forest, (c) soil organic matter (measured as percentage by weight), (f ) instantaneous discharge (measured in cubic feet/s [1 cubic
foot ¼ 0.028 m3]), (g) dissolved organic carbon (DOC; measured in mg/L); and (h) Fe (measured in lg/L). Regression results are
shown. N ¼ 531 except for Fe and DOC with N ¼ 525 and 402, respectively.
P , 0.005; à P , 0.000005.
DISCUSSION
Results of this investigation shed light on the
unresolved controversy over the causes of the latitudinal
diversity gradient. They revealed that the diatom LDG
was not neutral but driven by stream and watershed
features linked to resource supply and themselves
showing quadratic latitudinal gradients. These environmental attributes were also responsible for the weaker
longitudinal DR variability and although the following
discussion applies to both gradients, emphasis is given to
LDG. Productivity has not been widely accepted as a
mechanism generating LDG (Rohde 1992, Willig et al.
2003) because it has been unclear why greater productivity would give rise to a greater richness as opposed to
a monopoly of a few competitively superior species, as
postulated by competition theory (Currie 1991). For the
aquatic environment, this paradox was solved by two
recent large-scale investigations. First, a global metaanalysis demonstrated that richness increased with
eutrophication in freshwater as opposed to terrestrial
systems (Hillebrand et al. 2007). Second, a continental
survey explained this discrepancy with the contrasting
spatial organization of these communities, ranging
between simple single-story grasslands vs. complex
multistory freshwater biofilms, where niche dimensionality increases with the number of nutrients at high
supply (Passy 2008). In the present work, productivity
was correlated with Fe and Mn availability, which in
January 2010
DIATOM BIOGEOGRAPHY IS RESOURCE DRIVEN
FIG. 2. Partitioning of the variance in diatom richness into
percentages explained by environmental properties (large
circles), spatial attributes (small circles), and their covariance
(intersection). Numbers in the respective circles outside the
intersection represent the variance explained by pure environment and pure space.
2008). The strongly positive relationship between DOC
and DR observed in the present large-scale study
provides compelling support of this notion, i.e., had
the chelation of Fe by DOC led to Fe-limited conditions
in streams, diatoms would have responded negatively to
elevated concentrations of DOC. The DOC–Fe complexes are soluble and remain suspended in the water
column, which can facilitate their export from wetlands,
shown indeed to be important sources of stream DOC
and Fe (Dillon and Molot 1997, Gorham et al. 1998,
Goulet and Pick 2001). Soil organic matter, which
correlated most strongly and positively with wetland
area (Fig. 3), has also been linked to increased stream
DOC (Aitkenhead et al. 1999) as well as greater Fe and
Mn solubility and availability to biota (Shuman 1988,
Lindsay 1991). Thus, watersheds that contain larger
wetlands and organically rich soils, through DOC–Fe
export, will provide bioavailable Fe to the stream
network, sufficient to maintain high DR. Not surprisingly, the characteristic latitudinal distributions of
wetlands, SOM, DOC, and Fe were paralleled by an
analogous latitudinal DR distribution (Fig. 1). Ushaped latitudinal distributions of wetlands and SOM
in the US can be inferred from existing maps and tables
(Richardson and Vepraskas 2000, Lehner and Döll
2004) but they were firmly established here by regression
modeling. Notably, Fe was not only among the
strongest predictors of DR in individual localities but
was also the best predictor of the richness of the regional
species pool, i.e., the number of species found within a
hydrologic system of adjoining watersheds (Passy 2009),
implying that Fe supply limits DR at all scales.
FIG. 3. Effects of latitudinal and environmental attributes on diatom richness. Latitude had a strong impact on the distribution
of the watershed variables included in the diatom richness (DR) regression model, shown by solid black arrows; the numbers next
to these arrows represent the multiple R from the quadratic regressions in Fig. 1 (N ¼ 531); all other numbers show the Pearson r.
The regression model gauged the direct effects of the predictors (in red) on richness (solid red arrows). However, multiple
correlations of all measured variables suggested pathways of environmental control on DR whereby watershed features (diamonds)
defined stream properties (rectangles) which, in turn, determined richness. Stream variables not included in the regression model are
given in blue. All correlations were significant at P , 0.000005 (except the one with an asterisk [*] for which P , 0.05). N ¼ 525 in
all Pearson correlations not involving DOC (solid arrows), and N ¼ 402 in all correlations of DOC (dotted arrows). SOM is soil
organic matter.
Reports
turn was traced back to SOM, wetland area, and forest
cover (Fig. 3).
Dissolved organic carbon was largely determined by
the amount of SOM and the presence of wetlands in the
watershed (Fig. 3), which is consistent with previous
research (Gorham et al. 1998). DOC binds Fe and
although opinions diverge on whether this complex is
bioavailable (Maranger and Pullin 2003), there has been
mounting evidence that organically complexed Fe is
readily utilized by diatoms (Matsunaga et al. 1998,
Kuma et al. 1999, Deein et al. 2002, Chen and Wang
39
Reports
40
SOPHIA I. PASSY
Although previous research in boreal regions has
suggested a connection between wetland area and
stream Fe (Bjorkvald et al. 2008), to my knowledge,
this is the first investigation to definitively demonstrate
this relationship across a broad range of latitudes and its
influence on LDG of primary producers in lotic systems.
Considering that Fe limitation controls phytoplankton
composition and productivity in large areas of the
world’s ocean (de Baar et al. 2005), it is proposed here
that Fe supply plays a pivotal role in defining large-scale
algal distributional patterns across aquatic habitats,
including marine plankton and freshwater benthos.
Manganese was the best explanatory variable of DR
and, even though it did not display latitudinal dependence, it was positively correlated with DOC and
negatively, with forest cover, both of which were
latitudinally bound (Figs. 1 and 3). Forests are
inhospitable environments for algae because they reduce
light penetration and diminish nutrient flux into streams
through strong nutrient retention (Quinn and Stroud
2002, Poor and McDonnell 2007). Heavily forested
watersheds of lower soil organic matter content,
encompassing streams poor in DOC, Mn, and macronutrients (Fig. 3) peaked at latitudes around 388, where
DR was at its minimum (Fig. 1). Thus, micronutrient
supply, i.e., of Mn and Fe, which was the most
important determinant of diatom richness, was defined
by watershed properties with distinct latitudinal distributions.
This study proposes a pathway of watershed control
on stream biodiversity, whereby wetlands, via iron
export, increase diatom richness in streams. The
environmental implications of this finding are far
reaching because it reveals that a continuing loss of
wetlands, already exceeding 52 million hectares in the
conterminous United States alone (Mitsch and Gosselink 2007), may lead to algal biodiversity decline in
streams with a subsequent reduction in primary
productivity and biomass (Cardinale et al. 2005, Passy
and Legendre 2006 ). It is expected that stream
ecosystem health will improve with wetland restoration
that produces sufficient amounts of soil organic matter,
DOC, and iron in the watershed and facilitates
micronutrient fluxes into streams. For comprehensive
future assessments of freshwater biodiversity, it is
advised that wetland restoration efforts incorporate
biomonitoring of the associated stream network in
addition to the standard inlet–outlet water analyses.
The failure of diatoms to conform to the general
poleward richness decline can be explained with their
biology. Diatoms possess an extraordinary dispersal
capacity due to large population densities and small sizes
that permit the utilization of dispersal vectors ranging
from surface- and groundwater to hurricanes and
migrating fauna (Finlay 2002). Diatoms are thus
relieved from geographical restrictions, e.g., to the
origin of speciation, that less vagile species may
experience. Diatom dispersal diminishes too the role of
Ecology, Vol. 91, No. 1
habitat area, which was identified as one of dominant
factors underlying LDG in terrestrial systems (Rosenzweig 1995). Larger areas of continuous habitat provide
refuges and support greater populations that are less
likely to go extinct but local extinctions can be offset by
unlimited dispersal. Discharge, which was mostly a
function of surface water area in the watershed (Pearson
r ¼ 0.37, P , 0.000001), contributed independently only
5% to the explained variance in DR, which implies that
habitat area is a marginal predictor of richness.
Temperature constraints regulating the distribution of
perennial organisms are of little consequence to the
ephemeral diatoms, whose short generation times and
seasonality (Passy et al. 1999) allow them to quickly
establish after benevolent environments become available, including the temperate zones during warmer
months. Indeed, water temperature, which increased
toward the lowest latitudes (Pearson r ¼ 0.39, P ,
0.000001, N ¼ 528), was not correlated with maximum
DR in this study (Pearson r ¼ 0.07, P ¼ 0.09, N ¼ 528).
However, diatoms are very sensitive to nutrient limitation as most species can survive only at moderate to
ample nutrient supply. Therefore, regardless of temperature or history, locations that offer abundant resources
due to favorable watershed land cover and soil
composition, host the most diverse diatom communities.
These locations, as shown here, display a non-linear
latitudinal pattern, which is closely followed by a
nonlinear distribution of diatom diversity. The present
results suggest that freshwater protists, which are
seasonal, highly dispersive, and sheltered by their
environment from extreme temperature fluctuations,
have resource driven biogeography. In contrast, organisms that are more advanced, perennial, and terrestrial
exhibit large-scale distributions, determined by climate,
evolution, and history.
ACKNOWLEDGMENTS
I am grateful to two anonymous reviewers, whose insightful
suggestions substantially improved this manuscript.
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