Oecologia (2001) 126:114–124
DOI 10.1007/s004420000492
Helmut Hillebrand · Frank Watermann · Rolf Karez
Ulrike-G. Berninger
Differences in species richness patterns between unicellular
and multicellular organisms
Received: 8 October 1999 / Accepted: 17 July 2000 / Published online: 29 August 2000
© Springer-Verlag 2000
Abstract For unicellular organisms, a lack of effects of
local species richness on ecosystem function has been
proposed due to their locally high species richness and
their ubiquitous distribution. High dispersal ability and
high individual numbers may enable unicellular taxa to
occur everywhere. Using our own and published data
sets on uni- and multicellular organisms, we conducted
thorough statistical analyses to test whether (1) unicellular taxa show higher relative local species richness compared to multicellular taxa, (2) unicellular taxa show
lower slopes of the species:area relationships and species:individuals relationships, and (3) the species composition of unicellular taxa is less influenced by geographic
distance compared to multicellular taxa. We found higher local species richness compared to the global species
pool for unicellular organisms than for metazoan taxa.
The difference was significant if global species richness
was conservatively estimated but not if extrapolated, and
therefore higher richness estimates were used. Both microalgae and protozoans showed lower slopes between
species richness and sample size (area or individuals)
compared to macrozoobenthos, also indicating higher local species richness for unicellular taxa. The similarity of
species composition of both benthic diatoms and ciliates
decreased with increasing geographic distance. This indicated restricted dispersal ability of protists and the absence of ubiquity. However, a steeper slope between similarity and distance was found for polychaetes and corH. Hillebrand (✉)
Erkenlaboratory, Department of Limnology, EBC,
Uppsala University, Norr Malma 4200,
76173 Norrtaelje, Sweden
e-mail: [email protected]
Fax: +46-176-229315
F. Watermann
Institute of Chemistry and Biology
of the Marine Environment, Marine Station,
Schleusenstrasse 1, 26382 Wilhelmshaven, Germany
R. Karez · U.-G. Berninger
Institut für Meereskunde, Abteilung Meeresbotanik,
Düsternbrooker Weg 20, 24105 Kiel, Germany
als, suggesting a stronger effect of distance on the dispersal of metazoans compared to unicellular taxa. In
conclusion, we found partly different species richness
patterns among uni- and multicellular eukaryotes, but no
strict ubiquity of unicellular taxa. Therefore, the effect of
local unicellular species richness on ecosystem function
has to be reanalyzed. Macroecological patterns suggested for multicellular organisms may differ in unicellular
communities.
Keywords Diatoms · Protists · Meta-analysis ·
Diversity · Similarity
Introduction
Diversity has become a major research topic in recent
years due to increasing concerns about the global loss of
species richness (Pimm et al. 1995). Experimental evidence has indicated an impact of species or functional
diversity on the function and stability of ecosystems
(Naeem et al. 1994, 1996; Tilman and Downing 1994;
Tilman et al. 1996, 1997), but subsequently a critical discussion arose concerning the experimental design (Huston 1997; Hodgson et al. 1998), the statistical inevitability of species richness effects (Doak et al. 1998), and the
respective impact of the presence versus number of species or functional groups (Grime 1997).
Most of these experiments and calculations have been
conducted using macrofauna and -flora. Only a few experiments with microorganisms have been conducted, revealing a positive effect of diversity on some ecosystem
processes (McGrady-Steed et al. 1997; Naeem and Li
1997). However, the direct effect of microbial species
richness on ecosystem function (e.g., biogeochemical
processes and productivity) has generally been questioned for aquatic (Fenchel et al. 1997; Finlay et al. 1997)
and terrestrial (Wardle et al. 1997) ecosystems. Fenchel et
al. (1997) argued that global ciliate diversity is low, but
any local assemblage is comparably highly diverse due to
the high dispersal ability and the high individual numbers
115
of unicellular organisms. They found very high local species richness of ciliates and concluded that “everything is
everywhere” (Fenchel et al. 1997). Because of the high
dispersal ability, Finlay et al. (1997) proposed that microbial diversity has no discrete role to play for ecosystem
function, since every ecological role could be maintained
from the pool of globally dispersing microorganisms.
Although intuitively obvious, these qualitative considerations of unicellular diversity have only been analyzed for ciliates (Finlay and Esteban 1998), but not for
other unicellular taxa or by employing appropriate statistical tests. We are not aware of any direct observation of
the dispersal ability of unicellular organisms, although
some scattered information on aerial and aqueous transport of microalgae has been published (Round 1983).
The proposed high proportion of cosmopolites among diatoms and ciliates has been controversially discussed
(Mann 1989; Pierce and Turner 1993; Mann and Droop
1996; Snoeijs and Potapova 1998). In the present study,
we derive three hypotheses from the verbal consideration
of different diversity patterns for microorganisms. Although the initial statements were based on investigations of free-living ciliates (Finlay et al. 1996, 1997;
Fenchel et al. 1997), they were generalized for all unicellular organisms, allowing us to consider also data on unicellular microalgae. Using our own data on microalgae
and protozoans and data obtained by surveying the literature, we tested the following predictions:
1. Unicellular organisms show a higher relative local
species richness than larger organisms compared to
the global species pool. We tested this prediction by
calculating “relative local species richness” (see below) and comparing this for several unicellular and
multicellular taxa.
2. Unicellular organisms show lower species:sample
size slopes than multicellular organisms. Finlay et al.
(1998) proposed a different species:area relationship
for protists compared to multicellular organisms,
where a low slope of the linearized species:area relationship (SAR) indicated the high dispersal ability.
We analyzed this relationship for a variety of taxa.
Additionally, we used the species:individuals relationship (SIR), since individuals are a size-independent
indicator of sample size.
3. The similarity of the species composition of two protist assemblages is independent of the distance between these two sites, if species composition is dominated by cosmopolitan species. Minimally, the correlation between species composition and geographic
distance should be weaker for unicellular than for
multicellular taxa. We tested this prediction by comparing similarity and geographic distance between
sites for two unicellular and two multicellular species.
Data analysis and treatment
To analyze our hypotheses, we used meta-analysis techniques which have been successfully implemented to
compare effects across a range of published studies
(Gurevitch and Hedges 1993, 1999; Arnquist and Wooster
1995). The most powerful techniques involve the calculation of a standardized effect size and weighting of the effect size by a measure of precision, which is given by the
inverse of the sampling variance (Gurevitch and Hedges
1993). Lacking this measure, an unweighted analysis can
be performed. Table 1 lists the kind of data we obtained
from the literature and the calculated effect sizes (for a
more detailed acount, see Rosenberg et al. 2000) for each
of the hypotheses. We performed weighted meta-analyses
on the correlations between species richness and area as
well as between species richness and individual number.
Z-transformed correlation coefficients are established as
effect sizes in meta-analyses; the variance can be calculated from the number of observations (Rosenberg et al.
2000). Regression slopes are also suitable effect sizes,
where the sampling variance is given by the squared standard error of the slope (J. Gurevitch, personal communication). Whereas a weighted analysis on slopes could be performed for the species:individuals curves, the standard error of slopes was often not reported for the species:area
relationship, leading to an unweighted analysis. For relative local species richness, no estimate of variance was
available and an unweighted analysis was performed. All
calculations were done with MetaWin 2.0 (Rosenberg et
al. 2000). Differences between unicellular and multicellular organisms were tested with an analysis of heterogeneity between groups and within groups, which is analogous
to an analysis of variance (Hedges and Olkin 1985;
Rosenberg et al. 2000).
Meta-analyses may differ in their results if data selection is biased in any direction or if the presence or absence of a single study results in widely diverging conclusions (Englund et al. 1999). We used the ASFA database to find studies reporting local species richness of
certain taxa (hypothesis 1) and data for the SIR (hypothesis 2). We did not apply any criteria a priori or a posteriori to select or dismiss certain studies, since these selection criteria often bias the results (Englund et al.
1999). Instead, we combined very heterogeneous data
with regard to study aim and effort. Thus, we allowed
high variability and are confident that our conclusions
are conservative. We give a complete list of the studies
included in the Appendices.
Background and database
Relative local species richness
To test our first hypothesis, we calculated the proportion
of the global species pool present in a local assemblage.
For the percentage of species represented in a local assemblage, we use the term “relative local species richness.” Cornell and Lawton (1992) proposed a definition
of local and regional species richness based on the factors influencing species composition: on local scales,
ecological factors determine species richness, e.g., competition and grazing, whereas on regional scales, evolu-
116
Table 1 Overview of analyses performed to test three independent
hypotheses on species richness patterns in eukaryotic microorganisms. The table gives the hypotheses, the data obtained from the
literature, the calculated effect size measure and its variance, the
number of observations and the analysis. Unweighted meta-analyses were performed when measures of variance could not be calculated or were not given in the original data. For weighted ana-
lyses, the variance was calculated for correlation coefficients according to Rosenberg et al. (2000) and for slopes by squaring their
SE. Meta-analyses allow a test of differences between groups employing a measure of heterogeneity and subsequent randomizations (Q/random), whereas data randomization was used to compare the real value of Mantel’s r with a random distribution
Hypothesis
Original data
Effect size metric
Variance
n
Analysis
P-level
(1) High local
species richness
Species richness
Local S as percentage
of global pool of S
None
135
Unweighted
Q/random
(2) (a) Species:
area relationship
Regression analysis
Correlation
coefficient r
Regression analysis
Correlation
coefficient r
Slope z
Z-transform of r
Not given
VrZ
127
106
Unweighted
Weighted
Q/random
Q/random
Slope x
Z-transform of r
SE(x)2
VrZ
28
28
Weighted
Weighted
Q/random
Q/random
Site×site matrix
of similarity of species
composition
and distance
None
None
(b) Species:
individuals relationship
(3) Similarity vs
geographic distance
tionary factors like speciation, extinction, and dispersal
become more important. This definition is impractical
for microorganisms, for which the rate of speciation and
dispersal is largely unknown. Therefore, we defined
pragmatically any study to be local if samples were taken in one habitat (e.g., one lake, one coastal site).
Species richness data were obtained from studies listed in the ASFA database (1978–1998) and from our own
studies (see below). At least ten studies were used for
each taxonomic group, comprising a wide range of environmental conditions and study effort (Appendix 1).
Searching the literature for estimates of global species
richness in these taxa, we found highly diverging estimates reflecting different taxonomic schools and extrapolation to include as yet undiscovered species. Therefore, we calculated the relative local species richness in
duplicate, first assuming conservative estimates of global
species richness and afterwards extrapolated estimates.
For both sets of data, we performed an unweighted metaanalysis allowing us to test for significant differences between unicellular and multicellular taxa.
Species richness compared to sampled area
and counted individuals
Species:area relationships (SARs) describe the correlation between the species richness of a certain taxonomic
group and area, which can represent the sampling area,
the area of a habitat (e.g., island, lake) or the regional area (see Rosenzweig 1995 for a comprehensive review).
This relationship has generally been formulated as:
(1)
S = c × Az
where S is species richness, A is area and c and z are coefficients. Eq. 1 is often represented in the linearized form:
log S = log c + z × log A
(2)
Mantel’s
Random
standardized r
Finlay et al. (1998) argued that ciliates show low estimates of z, i.e., species richness increases only weakly
with sampling area because of the high dispersal ability
of unicellular organisms. To test this prediction, we obtained parameter estimates for the SAR from the review
by Connor and McCoy (1979); additional estimates were
obtained from the literature (Appendix 2).
We performed tests on differences in the strength of
the relationship (using Z-transformed correlation coefficients) and on the steepness of the curve (using estimates
of the slope z) between unicellular and multicellular organisms (Table 1).
However, area is not an unbiased estimate of sample
size in a comparison of differently sized organisms.
Therefore, we analyzed additionally the relationship between species richness and number of individuals (SIR):
S=k×Ix
(3)
where S is species richness, I is the number of individuals, k and x are coefficients, which can also be linearized
as Eq. 2. This equation was successfully used to describe
the dependence of insect species richness on the number
of individuals, showing highly significant results over
several size classes (Siemann et al. 1996). We compared
those relationships for taxonomic groups for which individuals could easily be differentiated (unicellular organisms, zoobenthos, insects), whereas Eq. 3 is inadaequate
for colonial organisms (corals, grasses, macroalgae). Data for this analysis were taken from our own investigations (see database) and from studies listed in the ASFA
database, giving the number of individuals together with
the corresponding species number (Appendix 3). Numbers of individuals were not recalculated from abundances, but were obtained only from studies giving the
original counted numbers of individuals. We performed
linear regression analyses on the log number of species
dependent on the log number of individuals (Eq. 3 in lin-
117
earized form). Further parameter estimates for Eq. 3
were taken from recent analyses of insects (Siemann et
al. 1996) and fish (Hall and Greenstreet 1996). As for
the SAR, we performed meta-analyses on correlation coefficients and on slopes for the SIR (Table 1).
Similarity of species composition
Fenchel et al. (1997) and Finlay et al. (1997) proposed
that the species composition of ciliates was dependent on
ecological factors rather than on species dispersal. We
hypothesized that in this case, the similarity between two
species assemblages living under similar environmental
conditions should not be influenced by the distance between these assemblages. We used the Jaccard index, J,
which is based on the presence of species, to estimate the
similarity of species composition (see Krebs 1989 for
equation and constraints) and calculated the geographic
distance between sites from equidistant maps. We analyzed 19 studies for diatoms (see Appendix 4) and corrected the species lists for recent taxonomic changes
(Round et al. 1990) and synonymizations. Furthermore,
we used three reviews on soil ciliate assemblages published by Foissner (1996, 1997, 1999). Here, sticking to
a single author reduced the error introduced by different
taxonomic schools and identification skills. For metazoans, the species composition of coastal polychaete assemblages was used and, additionally, a recent contribution on corals (Sheppard 1998). We are confident that
distance is not correlated with environmental conditions
(e.g., temperature, light, salinity), since the studies employed in our analysis were situated in similar habitats.
For each group, we compared the site×site matrices
for similarity and distance with a Mantel test (Fortin and
Gurevitch 1993). We calculated Mantel’s standardized r,
which like a correlation coeffcient ranges from –1 to +1.
Significance levels were obtained by randomization of
one matrix and calculation of Mantel’s r for each random
pairing (Fortin and Gurevitch 1993). Mantel’s r takes into account the interdependence of data within each matrix. This non-independence does not allow calculation
of standard correlations or error terms for linear regressions, but the slope values themselves are unbiased and
can be obtained from standard linear regression.
Own data
Benthic microalgae were analyzed in experiments investigating the influence of colonization time, nutrients, and
grazing on periphyton growing on porous artificial substrates supplied with different nutrient concentrations.
The experimental setup and results concerning species
composition, biomass, and biochemical composition are
described in detail elsewhere (Hillebrand and Sommer
1997; Hillebrand 1999). All experiments were conducted
in situ with artificial substrates. The algal assemblage,
consisting mainly of diatoms, was analyzed microscopi-
cally. At least 1,000 individuals were counted with an inverted microscope (Leitz DMIRB) and species were determined or morphologically differentiated. Combined
data for all experiments were used to calculate relative
local species richness (Hillebrand 1999), whereas the S:I
relationship was analyzed with data from colonization
experiments (Hillebrand and Sommer 1997; Hillebrand
1999) and from grazing experiments (Hillebrand 1999).
Fossil diatoms were counted in samples originating
from two drill holes (Archive KB 5950 and KB5 752)
covering the entire Holocene. The drilled site is located
in the marshland of Wangerland about 18 km northwest
of Wilhelmshaven close to the Jade Bay (53°38′ N,
7°57′ E). The cores were drilled with a drilling system
provided by the Geological Survey of the Federal State
of Lower Saxony, Germany (Merkt and Streif 1970).
Benthic and pelagic diatoms were isolated from 80 selected samples according to Schrader (1973). To obtain
evidence on relative abundance, counts were done with a
microscope at ×1,000 magnification. Quantitative analyses were based upon counts of 200 frustules, but at
some levels with very low valve concentration, a smaller
number had to be counted. Fragments representing more
than half frustules were counted as a whole (Schrader
and Gersonde 1978). Detailed results on stratigraphy and
species composition have been published elsewhere
(Dellwig et al. 1998, 1999).
Taxonomic identification of diatoms was conducted
by mounting oxidized samples in Naphrax resin. The recent and fossil species were identified using a light microscope at ×1,000 magnification. Additionally, an electron microscope was also used for taxonomic identification of fossil diatoms.
Original data on ciliate diversity were taken from two
different studies: planktonic flagellates and ciliates were
identified and enumerated from epilimnetic water samples collected in a small hypertrophic pond in the
English Lake District (Priest Pot). Over a period of
6 months, integrated water samples spanning the top 1 m
of the water column were collected at intervals of
1–3 days (Berninger et al. 1993). This part of the water
column was oxygenated at all times. Secchi disc transparency varied between about 0.5 m and 1.5 m over the entire sampling period. Samples for the enumeration of ciliates and other microzooplankton were fixed in 5% (f.c.)
HgCl2. Aliquots of the fixed samples were transferred into Sedgewick Rafter counting cells and counted with an
Olympus microscope at ×300 final magnification. For the
analysis of the data presented in Berninger et al. (1993),
organisms were grouped into size classes and functional
groups. The detailed species composition is listed in Berninger (1990). Data on benthic ciliates were taken from a
study on the vertical distribution in a wadden sea sediment (Berninger and Epstein 1995). Undisturbed sediments were collected from a sandy tidal flat on the North
Sea near Cuxhaven (Germany) and kept in the laboratory
in a pump-aided seawater recirculation system. At different times and in different areas of the sediments, small
sediment cores were extracted with a 60-ml plastic sy-
118
Table 2 Mean effect sizes and tests of heterogeneity for the species:area relationship. The table gives mean effect sizes (E+) for
multicellular (m) and unicellular (u) organisms with 95% confidence intervals (CI) and number of studies (n). The test of hetero-
geneity divides total heterogeneity (Qt) in heterogeneity between
(Qb) and within (Qw) groups. Significance levels (P) were determined employing randomization procedures (Rosenberg et al.
2000)
E+ (95% CI)
n
df
Q
P
Measure: slope z (Unweighted analysis)
m
u
0.30 (0.12–0.48)
0.15 (-0.68–0.99)
119
8
Between
Within
1
125
0.16
5.67
Measure: Z-transformed r (Weighted analysis)
m
u
0.91 (0.87–0.96)
0.06 (-0.10–0.22)
99
7
Between
Within
1
104
153.88
531.83
0.058
0.0047
ringe, diameter about 2.5 cm, with the Luer end cut off.
Sediment samples were immediately sectioned into 1- to
2-mm-thick slices after gradual extrusion of the sample
from the syringe with the help of a micrometer screw.
Samples were fixed with Bouin’s fixative mixture. Ciliates were extracted using a modification of the procedure
of Bak and Nieuwland (1989) and identified and enumerated employing the quantitative protargol staining technique (Montagnes and Lynn 1987).
Results
Do unicellular organisms show a higher relative local
species richness than multicellular organisms?
The species richness of local assemblages varied between 10 and 550 for the six taxonomic groups (unicellular: diatoms, desmids, and ciliates; multicellular: nematodes, bivalves, and polychaetes). High local species
richness of more than 100 species was repeatedly reported for all groups. The relative local species richness (as
percent of global species pool) was highly variable, and
additionally depended on the global species richness estimate (Fig. 1). Diatoms, in particular, were affected if a
high estimated global species number was used instead
of a more conservative estimate. In comparison to metazoans, unicellular taxa showed significantly higher relative local species richness for conservative global richness estimates (randomization of unweighted analysis,
P=0.0004), but the difference became non-significant
with extrapolated global estimates (P=0.623). Thus, unicellular organisms show a high relative local species
richness, but the difference compared to metazoans is
not categorical, as is shown by the high relative local
species richness of polychaetes.
Do unicellular organisms show lower slopes in relation
to sample size?
Parameter estimates of the SAR could be obtained easily
for a variety of macroscopic organisms, whereas comparatively few data were available for protists and smaller
metazoans (Fig. 2). Small organisms indeed had small
Fig. 1 Relative local species richness expressed as percentage of
the global species richness for three unicellular and three metazoan taxa. We calculated the percentage using two estimates of
global species richness, a conservative (circles and gray boxes)
and an extrapolated higher (squares and white boxes) estimate.
Values for these estimates are given at the top of the diagram (in
thousand species). The box and whiskers show the quartiles and
the range of the published species richness, respectively; the
points represent the median
Fig. 2 Slopes (z) of the log species:log area relationship (SAR)
for several metazoan taxa, land plants and protists. Values for z
were obtained from the literature and from own data (Appendix 2)
and combined for the following groups: land plants (P), mammals
(M), other land vertebrates (V), fish (F), birds (B), zoobenthos
(Zb), other invertebrates (Inv), arthropods (A), zooplankton (Zp),
protozoans (Pro), and microalgae (Al)
119
Fig. 3 Relationship between
the number of individuals and
the number of species for zoobenthos (a), insects (b), protozoa (c), benthic microalgae (d),
and fish (e). Lines represent
significant regressions reported
for insects (Siemann et al.
1996) and fish (Hall and
Greenstreet 1996). See Appendices 3 and 4 for further references
z-values and larger organisms tended to exhibit steeper
increases of S with increasing area. However, low z-values could also be found for some macroscopic organisms
(e.g., for birds and insects; Fig. 2). The difference between slopes of unicellular and multicelluar taxa was
slightly non-significant (Table 2). However, the strength
of the SAR indicated by Z-transformed correlation coefficients was significantly lower for the unicellular eukaryotes compared to the multicellular organisms (Table 2).
In all investigated groups, species richness increased
with the number of identified individuals (Fig. 3), resulting in significant linear regressions of log species
number on log individuals for almost all studies (Appendix 4). The relationship between individual number
and species richness showed distinctly less variation for
metazoa (Fig. 3a) than for benthic microalgae and protozoa (Fig. 3c,d). For these unicellular taxa, the relationship clearly leveled off in log-log species, indicat-
120
Table 3 Mean effect sizes and
tests of heterogeneity (weighted analysis) for the species:individuals relationship. For details, see Table 2
E+ (95% CI)
n
Measure: slope z
m
u
0.44 (0.40–0.48)
0.24 (0.21–0.28)
17
11
Measure: Z-transformed r
m
u
1.20 (1.09–1.32)
0.78 (0.65–0.90)
17
11
df
Q
P
Between
Within
1
26
57.74
218.80
0.187
Between
Within
1
26
30.00
105.79
0.052
Table 4 Results of standardized Mantel tests on site×site matrices
with Jaccard’s index of similarity and geographic distance. Given
for each of four taxonomic groups are Mantel’s r (with a theoretical range between –1 and +1), the significance level as calculated
from randomizations, and the slope of a linear regression of similarity on distance
Fig. 4 Similarity of species assemblages of diatoms, ciliates, corals and polychaetes as a function of distance between sites. Similarity is expressed as the binary Jaccard index; distance is given as
shortest log-transformed distance between two sites (Appendix 5)
Organisms
Mantel’s r
P-level
Slope
Diatoms
Ciliates
Polychaetes
Corals
–0.591
–0.256
–0.925
–0.396
<0.001
0.049
0.032
<0.001
–0.05
–0.04
–0.15
–0.16
fect of distance on metazoan species composition compared to unicellular taxa (Table 4).
Discussion
ing the lower unicellular species richness at higher sampling intensity. Although the mean slopes of the SIR
(mean weighted effect size and confidence intervals;
Table 3) were lower for unicellular groups, the variation
within both taxa groups was high, resulting in non-significant differences between slopes of unicellular and
multicellular SIRs. Furthermore, for the Z-transformed
correlation coeffcients, the difference between groups
was slightly non-significant (Table 3). The detected differences between unicellular and multicellular eukaryotes were thus not as categorical as stated in our hypothesis.
Is the similarity of species composition dependent
on dispersal distance?
All four taxa investigated showed a decreasing similarity
of species composition with increasing geographic distance (Fig. 4, Table 4). This decrease was significant for
all taxa, although only marginally for ciliates (Table 4).
Thus, all taxa including the unicellular ones are not
strictly ubiquitous in their dispersal ability, but ciliate
and diatom species compositions were affected by dispersal distance. However, distance affected both metazoan taxa more than it did the unicellular organisms
(Fig. 4). The slopes of the linear regression of similarity
on log-transformed distances were similar for diatoms
and soil ciliates, whereas both corals and polychaetes
had steeper negative slopes. This indicated a higher ef-
Our study conducted thorough statistical analyses to
test verbally formulated statements on unicellular diversity patterns, reflecting mainly the ubiquity of protist species due to the high dispersal ability of small organisms (Fenchel et al. 1997; Finlay et al. 1997, 1998).
We found that the predictions from those papers were
partly confirmed but some important differences were
revealed. Relative local species richness was significantly higher for protists than for metazoans, but the
difference was not as clear-cut as initially stated and
depended on the global species richness estimate (hypothesis 1). The increase of species richness with increasing sample size (area or individuals) was weaker
for unicellular than for multicellular taxa, indicating
higher local species richness and a lower global species
pool, although the difference was not statistical significant in some cases (hypothesis 2). Furthermore, the
similarity of species assemblages was more affected by
geographic distance in metazoans (polychaetes and corals) compared to protists (diatoms and ciliates). But
unicellular taxa also showed decreasing similarity with
increasing distance between study sites, indicating the
lack of ubiquitous dispersal for both metazoans and
unicellular taxa (hypothesis 3). We conclude that to exclude the effect of microbial diversity on ecosystem
function is premature, but we also want to emphasize
that ecological patterns found for macroscopic organisms cannot readily be transferred to the level of unicellular eukaryotes (see below).
121
Relative local species richness was significantly higher for protists than for metazoans, if the global species
richness was estimated conservatively, i.e., according to
the number of species described so far. We found the
highest relative species richness values in unicellular
groups. High local species richness has previously been
described for aquatic ciliates (Fenchel et al. 1997; Finlay
and Esteban 1998) and for diatoms (Kingston et al. 1983;
Oh and Koh 1995; Lange-Bertalot and Metzeltin 1996).
However, a similar high relative species richness of local
assemblages was found for polychaetes (Fig. 1). Using
extrapolated estimates of the global species pool, the difference between metazoans and protist became insignificant. Thus, unicellular origanisms do not exhibit generally higher local species richness than metazoans. Additionally, our pragmatic definition of “local” habitats
should include more microhabitats per site for small organisms and comparably higher abundances (Marquet et
al. 1990; Finlay et al. 1998). Hence, if high relative local
species richness is indicative of high dispersal ability, the
dispersal abilities of protists and metazoans do not exhibit categorical differences.
Diatoms showed the highest discrepancy between
conservative and extrapolated estimates of global species
richness, spanning an order of magnitude (Mann and
Droop 1996). The controversial discussion of diatom
taxonomy highlights several problems that arose during
the comparison of species richness of protists and multicellular organisms. The estimated number of synonyms
may be very high for diatoms (cf. May and Nee 1995 for
insects), since species descriptions are often arbitrary
and inconsistent (Cox 1993; Patterson 1999). This may
lead to future combination of taxa which were previously
separated and thus to a reduction in their global species
richness. On the other hand, almost all data on protist
species richness rely entirely on morphological species
concepts (Fenchel et al. 1997; Finlay et al. 1998). For
both diatoms and ciliates, the validity of morphospecies
has been questioned on the basis of molecular differences (Medlin et al. 1991; Manhart and Court 1992), reproduction barriers (Mann 1989), and different physiology (Dini and Nyberg 1999). Structural and molecular
classification may be congruent at the level of phylogenetical lineages (Taylor 1999), but may differ at the species level (Costas et al. 1995). Thus, a combination of
morphology and genetics may be most useful (Manhart
and McCourt 1992). Extending the morphological approach by additional techniques leads to a refined taxonomical distinction of species, and thus to an increase in
the global species pool. Therefore, the proposed cosmopolitan distribution of unicellular species has been repeatedly criticized (Mann and Droop 1996; Snoeijs and
Potapova 1998). At the present time, estimating the effect of this “fine-grained” taxonomy on relative local
species richness of protists is difficult. If taxonomic separation foremostly affects sympatric populations, it
results in higher global and higher local species richness and relative richness will remain largely unchanged.
If allopatric populations are split at the species level, rel-
ative local species richness would fall (see Fig. 1) because of a higher proportion of endemics (Snoeijs and
Potapova 1998).
The species:area relationship (SAR) has a long and
rich history in community ecology (summarized e.g., in
Rosenzweig 1995). A slope z near 0.26 was related to a
log-normal abundance distribution, whereas any biological interpretation of the slope and the intercept was criticized (Connor and McCoy 1979). However, Finlay et al.
(1998) discussed the low slopes of the SAR found for
protists (their study, z=0.043) and for zooplankton
(z=0.051–0.094; Dodson 1991, 1992) as a measure of the
high dispersal ability of these organisms. Our analysis
showed that the SAR is weaker and less steep for unicellular organisms (Table 2). Except for some negative values, the lowest slopes were found for unicellular eukaryotes, for zooplankton, and for flying metazoans (Fig. 2).
Therefore a correleation between z and body size or dispersal ability may be possible.
However, the use of the SAR in the discussion on dispersal ability can be criticized on several points besides
that of the general biological interpretation. The intercept c in the log-log equation (Eq. 2) contributes to the
slope in linear space (Eq. 1), mainly reflecting the initial
increase in species richness in small areas (Rosenzweig
1995). Accordingly, the slope of the SAR depends on the
size range used for its calculation, i.e., values obtained
from larger areas may not apply to the whole relationship (Harte et al. 1999). For small organisms, the low zvalues may be obtained from data on large areas, whereas the SAR may be considerably steeper at small spatial
scales, e.g., at the scale of micropatches (Blanchard
1990; Saburova et al. 1995). Allen et al. (1999) analyzed
the species richness of six taxonomic groups for 186
northeastern U.S. lakes and found a significant positive
correlation between species richness and lake area for
benthos, birds, fish, pelagic crustaceans, and rotifers, but
not for diatoms. The lack of a significant SAR in this
case may simply reflect the different relationship of lake
area to metazoan and diatom body size. If SAR also becomes non-linear in its log-transformed version, organism size has to be considered and a size-based area
equivalent should be used which, e.g., could be the habitat area divided by biomass or the surface of the organisms analyzed (see Hillebrand et al. 1999 for the geometry of surface area calculations). Furthermore, SAR is
not independent of ecological processes, e.g., colonization time (Schoener and Schoener 1981; Anderson
1998), which can also be scaled to organism size. Additionally, a dependence of the slope z on the trophic rank
of the organisms was recently proposed (Holt et al.
1999). These constraints impair the discussion of differences in SAR between multicellular and unicellular species.
The species:individuals relationship (SIR) is less well
established compared to the SAR, but it is advantageous
because it is independent of organism size. The high explanatory power of this relationship was shown for insects differentiated in size classes (Siemann et al. 1996).
122
Although the differences in the Z-transformed correlation coefficients and the slope x were non-significant, the
pattern of the combined data showed marked differences
between unicellular and multicellular taxa (Fig. 3). We
found a remarkable dependence of zoobenthic species
richness on individual number, which was consistent
despite the heterogeneity of sampled communities
(Fig. 3a). The observed protist diversity was clearly not a
monotonic increasing function of increasing sample size
(Fig. 3c,d). At high sampling intensity, few new species
were added, indicating a high local species richness and
an overall low global species pool. No similar decrease
in the slope x could be detected in the zoobenthos data
(Fig. 3a) or in data on deep-sea species richness (Grassle
and Maciolek 1992). The data for the analysis of SIR
consisted of samples of different size (individual number) within a single habitat and also of studies comprising different habitats. The increase in species richness
with increasing individual number was not dependent on
the addition of new habitats, i.e., increasing the sampling
effort by increasing the number of individuals analyzed
per sample or by increasing the number of samples analyzed with the same intensity seems irrelevant. This supports the difference in species richness patterns of unicellular species (see also Finlay and Esteban 1998).
Thus, the SIR for protozoans and benthic microalgae
generally confirmed the predictions made for ciliates and
the SAR (Fenchel et al. 1997; Finlay et al. 1998), but
again the differences with metazoans (e.g., fish) were not
categorical.
The similarity of species composition was negatively
affected by geographic distance for both unicellular and
multicellular species, but the decrease in similarity was
steeper for polychaetes and corals than for any of the
unicellular taxa. The negative impact of geographic distance on the similarity of protist communities contradicts
a cosmopolitan dispersal of these organisms. The negative correlation was significant for all four groups, despite the high variability of J within any distance range.
The decrease of similarity with distance suggests the
possible existence of unicellular endemism or at least regionally restricted species distribution (Snoeijs and
Potapova 1998), but the incidence of endemic species is
poorly known for diatoms and ciliates (see the discussion above). Geologically old or geographically remote
lakes tend to have a large proportion of metazoan endemics (e.g., the Caspian Sea; see Dumont 1998), whereas the rate of endemism is comparably low for diatoms
(Karayeva and Makarova 1973). The global biogeographic distribution of tintinnids (Ciliophora) suggests a
major influence of ecological factors (e.g., temperature)
but only few regionally restricted distributions of tintinnid genera (Pierce and Turner 1993). These patterns
can be interpreted indicating high dispersal ability, but
have also been criticized with respect to a higher resolution in taxonomy, as discussed above (Mann and Droop
1996; Palmer et al. 1997).
Our analysis of our own and literature data revealed
significant differences between protists and macroscopic
taxa regarding local and global species richness, but also
the lack of a ubiquitous dispersal ability of unicellular
organisms. We are therefore reluctant to comment on the
possible role of microbial diversity for the function of
ecosystems. Finlay et al. (1997) proposed that the microbial community will never be so impoverished that it
will not be able to “occupy” all relevant biogeochemical
roles. They conclude that microbial species extinction
does not threaten ecosystem function. However, protist
assemblages are usually highly dominated by a few species, whereas most species are rare. It remains unclear
(1) if one of these rare species would be able to fill in the
gap left by a declining major species, or (2) if the dispersal ability of key (dominant) species is high enough
to ensure relevant ecosystem processes. To deny a possible role of unicellular species richness in ecosystem
function therefore seems premature. Properly defined experiments on the effect of microbial diversity on ecosystem stability and processes have to be conducted and analyzed beyond the level of bulk biomass parameters.
Furthermore, our results may have consequences for the
emerging field of macroecology (Gaston and Blackburn
1999), which is proposed to emphasize general rules in
ecology (Lawton 1999). Almost all the relationships
dealt with in macroecology (e.g., large-scale gradients in
distribution and diversity) are formulated for macroscopic organisms. We have shown in our analysis that these
patterns may be different or even absent for microscopic
organisms, although there adequate data are generally
lacking to analyze the macroecological patterns of unicellular organisms.
Acknowledgements We thank all authors who have contributed
to this study by publishing their original data on species richness
and individual numbers. Our data analysis was greatly improved
by the suggestions of Ulrich Sommer and especially by Jessica
Gurevitch, who guided us through meta-analysis techniques. An
earlier draft of the manuscript profited from comments by Heike
K. Lotze and Ulrich Sommer and two anonymous reviewers. HH
acknowledges financial support by the DAAD.
Appendices
The complete database for this analysis and additional information
on the robustness of data can be found on the journal’s homepage
under http://dx.doi.org/10.1007/s004420000492
• Appendix 1: data on relative local species richness
• Appendix 2: regression parameters of species:area relationships
• Appendix 3: data for the analysis of species:individuals relationships
• Appendix 4: regression parameters of species:individuals relationships
• Appendix 5: data on the influence of geographic distance on the
species composition
123
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