Structure and function of the
decomposer food webs of forests along a
European North-South-transect with special
focus on Testate Amoebae (Protozoa)
Cover design: Rolf & Dagmar Schröter
Current address: Dagmar Schröter, Department of Global Change & Natural Systems, Potsdam
Institute for Climate Impact Research; P.O. Box 60 12 03, 14412 Potsdam, Germany; Phone: +49-331288 2639, Fax: +49-331-288 2600, e-mail: [email protected]
Citation of this thesis is recommended as follows:
Schroeter, D. (2001). Structure and function of the decomposer food webs of forests along a European
North-South-transect with special focus on Testate Amoebae (Protozoa). PhD-thesis, Department of
Animal Ecology, University Giessen.
Structure and function of the
decomposer food webs of forests along a
European North-South-transect with special
focus on Testate Amoebae (Protozoa)
Dissertation zur Erlangung des Doktorgrades
der Naturwissenschaftlichen Fakultät
der Justus-Liebig-Universität Giessen
durchgeführt am Institut für Allgemeine und Spezielle Zoologie
Bereich Tierökologie
vorgelegt von
Dagmar Schröter
Giessen, März 2001
Dekan: Prof. Dr. Rainer Renkawitz
1. Berichterstatter: Prof. Dr. Volkmar Wolters, Universität Giessen
2. Berichterstatter: Prof. Dr. Peter C. De Ruiter, University of Utrecht, Niederlande
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Table of contents
Abbreviations...............................................................................................................................................
1
Introduction .............................................................................................................................1
1.1
Coniferous forests...................................................................................................................2
1.2
European North-South-transect ..............................................................................................3
1.3
The decomposer system.........................................................................................................4
1.3.1
Decomposition ........................................................................................................................4
1.3.2
The decomposer food web......................................................................................................5
1.3.3
Testate Amoebae (Rhizopoda, Protozoa) ...............................................................................6
1.3.4
Interactions within the decomposer food web .........................................................................8
1.3.5
Quantification of fluxes within the decomposer food web......................................................10
1.4
Structure and aims of this study............................................................................................11
1.4.1
Testate Amoebae community structure - Part 1...................................................................12
1.4.2
Decomposer food web function - Part 2...............................................................................13
1.4.3
Main hypotheses...................................................................................................................14
2
The study sites......................................................................................................................15
2.1
Site description .....................................................................................................................15
2.2
Sampling scheme and sample treatment..............................................................................21
3
Material and methods............................................................................................................23
3.1
Functional groups of organisms ............................................................................................23
3.1.1
Microflora: fungi and bacteria................................................................................................23
3.1.1.1
Chloroform fumigation extraction method (CFE): microbial carbon (Cmic) .............................23
3.1.1.2
Ergosterol..............................................................................................................................25
3.1.1.3
Direct counting of bacteria ....................................................................................................25
3.1.1.4
Metabolic potential and metabolic quotient qCO2..................................................................25
3.1.2
Testate Amoebae..................................................................................................................26
3.1.2.1
Fixation and staining of substrate samples for quantitative analyses....................................27
3.1.2.2
Direct counting of Testate Amoebae.....................................................................................27
3.1.2.3
Flotation method: extraction of empty shells .........................................................................29
3.1.2.4
Batch cultures .......................................................................................................................29
3.1.2.5
Live observations ..................................................................................................................29
3.1.2.6
Taxonomic determination......................................................................................................29
3.1.2.6.1 Distinguishing Centropyxis aerophila sphagnicola and C. sylvatica ......................................32
3.1.2.6.2 Distinguishing Cyclopyxis eurystoma and Phryganella acropodia.........................................32
3.1.2.6.3 The taxon Euglypha cf. strigosa............................................................................................32
3.1.2.6.4 The taxa Nebela parvula/tincta and N. tincta major/bohemica/collaris ..................................32
3.1.2.6.5 Distinguishing Trinema enchelys and T. lineare....................................................................33
3.1.2.7
Slide preparation with Euparal ..............................................................................................33
3.1.2.8
Slide preparation with Naphrax .............................................................................................34
3.1.2.9
Slide preparation with glycerine ............................................................................................34
3.1.2.10 Photography..........................................................................................................................34
Table of Contents
3.1.3
3.1.4
3.1.4.1
3.1.4.2
3.1.5
3.2
3.2.1
3.2.2
3.2.3
3.2.4
3.3
3.3.1
3.3.2
3.3.3
4
4.1
4.2
4.2.1
4.2.2
4.2.3
4.3
4.4
5
5.1
5.1.1
5.1.2
5.1.2.1
5.1.2.2
5.1.2.3
5.1.3
5.1.3.1
5.1.3.2
5.1.3.3
5.1.4
5.2
5.2.1
5.2.2
5.2.2.1
5.2.2.2
5.2.3
5.2.3.1
5.2.3.2
5.2.3.3
5.2.3.4
Nematoda .............................................................................................................................34
Microarthropoda (Collembola and Acari)...............................................................................35
Collembola ............................................................................................................................35
Acari .................................................................................................................................36
Enchytraeidae .......................................................................................................................36
Abiotic parameters ................................................................................................................37
Water content (WC) ..............................................................................................................37
pH of organic layer................................................................................................................37
Thickness of organic layer, mass-to-area-ratio and bulk density...........................................37
C:N-ratio of organic layer ......................................................................................................37
Statistical analyses ...............................................................................................................38
Analysis of variance ..............................................................................................................38
PEARSON product-moment correlation...................................................................................39
Canonical correspondence analysis (CCA)...........................................................................39
The food web model..............................................................................................................41
Detritus pool and resource quality.........................................................................................45
Model parameters .................................................................................................................46
Death rates ...........................................................................................................................46
Assimilation and production efficiencies................................................................................46
Feeding preferences .............................................................................................................47
Adapting the food web model to specific climatic conditions.................................................47
Biomass estimates................................................................................................................51
Results..................................................................................................................................53
Testate Amoebae community structure.................................................................................53
Size structure and biomass...................................................................................................53
Species pattern .....................................................................................................................55
Species richness, diversity and evenness.............................................................................55
Species rank plots.................................................................................................................57
Relative biomass structure....................................................................................................58
Testate Amoebae communities in their environment ............................................................62
Other decomposer biota........................................................................................................62
Abiotic environment...............................................................................................................66
Multivariate analysis relating Testate Amoebae communities and environment ...................68
Total abundance, biocoenosis and necrocoenosis ...............................................................73
Testate Amoebae and the functioning of the food web .........................................................78
Schematic view of the decomposer food web .......................................................................78
Literature survey on the trophic relationships of Testate Amoebae.......................................79
Food sources of Testate Amoebae .......................................................................................79
Testate Amoebae as food source .........................................................................................81
Quantifying trophic interactions.............................................................................................81
Physiological parameters......................................................................................................81
Feeding preferences .............................................................................................................82
Biomasses of functional groups ............................................................................................83
Simulated estimates of total C and N mineralisation.............................................................86
Table of Contents
5.2.3.5
Contribution of functional groups to C mineralisation............................................................88
5.2.3.6
Contribution of functional groups to N mineralisation............................................................90
6
Discussion.............................................................................................................................93
6.1
Testate Amoebae community structure.................................................................................93
6.1.1
Total number of Testate Amoebae species...........................................................................93
6.1.2
Similarity between Testate Amoebae communities...............................................................94
6.1.3
Comparison of species biomass pattern ...............................................................................95
6.1.4
Environmental factors explaining community structure .........................................................96
6.1.5
Size structure and biomass...................................................................................................98
6.1.6
Abundance of Testate Amoebae...........................................................................................99
6.1.7
Comparison of bio- and necrocoenosis...............................................................................100
6.2
Testate Amoebae and the decomposer food web...............................................................101
6.2.1
Total food web biomass and mineralisation along the transect...........................................101
6.2.2
Site specific characteristics of the food webs along the transect.........................................102
6.2.3
Common characteristics of the food webs along the transect .............................................105
6.2.4
Evaluation of model estimates ............................................................................................106
6.3
Research needs..................................................................................................................107
7
Conclusions ........................................................................................................................109
8
Summary.............................................................................................................................111
9
Zusammenfassung..............................................................................................................115
10
Appendix.............................................................................................................................119
10.1
Parameters for calculation of decomposer fauna biomass..................................................119
10.2
Importance of food web biomass structure in the model .....................................................120
11
References..........................................................................................................................121
List of figures.........................................................................................................................................131
List of tables..........................................................................................................................................133
Acknowledgements...............................................................................................................................135
curriculum vitae.....................................................................................................................................137
Abbreviations
Only the variables and constants that occur in a wider context are included in this glossary. In other
cases see explanation beneath formula.
*
**
A
a
AB-DLO
ANOVA
asl
B
BML
C
CANIF
CCA
cf.
CFE
Cmic
cum(lA)
d
DE
DIC
DW
E
EU
F
f.
FR
GCTE
GLOBIS
HSD
IBP
IGBP
IPCC
i
j
K
max
MC
min
significant (p-level of significance £ 0.05)
significant (p-level of significance < 0.01)
assimilation
assimilation efficiency (resp. when used as unit of a variable a = year)
Research Institute for Agrobiology and Soil Fertility – Agricultural Research
Department, Netherlands
analysis of variance
above sea level
biomass
Bundesministerium für Ernährung,Landwirtschaft und Forsten
consumption
Carbon and Nitrogen Cycling in Forest Ecosystems (EU funded research project)
canonical correspondence analysis
confer
chloroform fumigation extraction method
microbial carbon
cumulative explanatory power (variance explained)
death rate
study site in Germany (Waldstein)
differential interference contrast
dry weight
excretion
European Union
feeding rate
forma
study site in France (Aubure)
Global Change and Terrestrial Ecosystems
Global Change and Biodiversity in Soils (EU funded research project)
honest significant difference
International Biological Programme
International Geosphere-Biosphere Programme
Intergovernmental Panel on Climate Change
index for prey
index for predator
deaths through predation ("kill")
maximum
carbon mineralisation (organic C ® CO2)
minimum
Abbreviations
MN
n
n.s.
NIPHYS
NPP
N-SE
mic
P
p
q
qCO2
rC
rpm
SOM
S-SE
TERI
w
w/w
WHC
nitrogen mineralisation (organic N ® NOx-, NH4+, N2)
number of potential prey groups (model equations), number of replicates (ANOVA tables)
not significant (p-level of significance > 0.05)
EU funded project: Nitrogen Physiology of Forest Plants and Soils
net primary production
study site in Northern Sweden (Åheden)
microbial
production
productions efficiency resp. level of significance
C:N-ratio
metabolic quotient
canonical correlation coefficient
rounds per minute
soil organic matter
study site in Southern Sweden (Skogaby)
Terrestrial Ecosystem Research Initiative
feeding preference
weight-to-weight-ratio
water holding capacity
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Chapter 1
Introduction
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1 Introduction
"Woods are more than a group of trees."
Emily Carr (1871-1945),
Canadian painter
Trees are a component of forest ecosystems that is rarely overlooked. These primary producers use
solar energy, carbon dioxide, water and nutrients to build organic matter. The belowground counterpart
of the primary producers, the decomposer system, remains largely unseen. All the same decomposition
is a process equivalent to photosynthesis in its importance for the biogeochemical cycling of energy and
nutrients (Heal et al. 1997).
The share of net primary production (NPP) that is not consumed alive by herbivores enters the
decomposer system directly as dead organic matter. In forests more then 90 % of NPP ends up on the
ground, forming the organic layer (Swift et al. 1979, Ellenberg et al. 1986, Vedrova 1995). The amount
of energy and matter that persists in the time lag between primary production and decomposition is the
resource that all heterotrophic organisms expend their lives on. If the time lag between primary
production and decomposition is especially long, dead organic matter is stored in what we refer to as
‘fossil fuels’.
Decomposition serves two key ecological functions (sensu Likens 1992): the mineralisation of essential
nutrient elements and the formation of soil organic matter (SOM). These functions link the decomposer
system with the primary producers in a way that both systems determine each other (Wardle 1999).
Without decomposition, primary producers could not assimilate carbon dioxide due to shortage of
essential nutrients in available form. Without formation of SOM, the vegetation would lack the physical
substrate to take root in and water would run off before it could be taken up. Conversely, without primary
production, the decomposer system would lack its energy and nutrient resource.
Decomposition is a consequence of the trophic activities of the decomposer biota (Swift et al. 1979,
Verhoef and Brussaard 1990). The decomposer biota form a complex web of interacting organisms: the
decomposer food web. The ecological understanding of the decomposer system and its contributions to
biogeochemical cycling is essential to environmental management purposes and questions of global
change (Currie 1999). For example, the latest report of the IPCC (Third Report of the Intergovernmental
Panel on Climate Change, IPCC, 2001) mentions for the first time the effect of rising temperatures on
1
1
Introduction
the activity of decomposer microorganisms and the possibility of increased decomposition leading to
increased release of the greenhouse gas CO2, a self-stimulating process.
The soil has been described as our most precious non-renewable resource (Marshall et al. 1982).
Today the need to protect this resource just in the same way as e.g. the atmosphere or the hydrosphere
is understood by scientists and most policy makers (Hågvar 1998). This milestone in environmental
protection concepts is the consequence of a great effort of ecologists throughout the world that have
agreed on a common research goal within an initiative that was started in the 1960s, called the
International Biological Programme (IBP). Aim of this programme was to investigate the biological basis
of productivity and human welfare. Since then, human activity has been recognised to effect
ecosystems on a global scale through anthropogenic induced land-use and climate change. A new
initiative, the International Geosphere-Biosphere Programme (IGBP) has established a core project on
Global Change and Terrestrial Ecosystems (GCTE) with the prime objective of predicting the effects of
such global change on terrestrial agricultural and forest ecosystems. The European contribution to
GCTE is the Terrestrial Ecosystem Research Initiative (TERI). Within the framework of TERI a number
of projects have been started to provide the basic understanding of ecological processes essential to
any attempt to predict effects of global change. The study reported here was part of such a project on
Carbon and Nitrogen Cycling in Forest Ecosystems funded by the European Union (CANIF, Contract N°
ENV4-CT95-0053, see Schulze 2000).
1.1 Coniferous forests
It is estimated that globally 1580 Gt (= 1580 1015 g) of carbon is stored in soils and detritus, and
610 Gt C is fixed in the vegetation (Schimel 1995). The largest part of terrestrial primary production,
namely 40 Gt C a-1 of carbon is annually fixed in forests. The flux to grasslands and agricultural fields is
considerably smaller (15 Gt C a-1; Hobbie and Melillo 1984). Since the amounts of anthropogenic CO2
and nutrients that enter the ecosystems through industrial emissions have become a major concern, the
net carbon storage of the terrestrial biosphere is of great interest. The Kyoto Protocol demands
strategies to balance industrial emissions by biological fixation (IGBP Terrestrial Carbon Working Group
1998, WGBU 1998). Net carbon storage in ecosystems is determined by the balance between carbon
gain (photosynthesis) and carbon loss (auto- and heterotrophic respiration) (Schimel 1995). Of the
processes involved, decomposition is certainly the least understood (Hobbie and Melillo 1984).
Biogeochemistry research indicates that the organic layer of forests is a key component in the
responses of forests to aspects of global change (Currie 1999). Recent studies suggest that European
2
1
Introduction
forests act as a carbon sink on a global scale (Valentini et al. 2000), however, budgets of the exchange
between biosphere and atmosphere are estimates involving many dynamic variables that need to be
evaluated and refined in the long term (Schimel 1995).
One of the Earth's largest terrestrial C pools is the boreal coniferous forest system (Bolger et al. 2000).
During the last century coniferous plantations have replaced deciduous forests in large areas of CentralEurope because of their industrial profitability. In Germany, one of the most forested areas of the
European Union, 66 % of the forest is coniferous (BML 1999). Today coniferous forests in the boreal
and temperate zone play an important role in the world's biogeochemical cycles (Bolger et al. 2000). In
this context the sites of this study were chosen to be coniferous forests along a European North-Southtransect.
Apart from their relevance to biogeochemical cycling, forest ecosystems are important reservoirs of
biodiversity. The decomposer systems of forests tend to be especially species rich and may represent
biodiversity hot spots in the landscape (Hågvar 1998).
1.2 European North-South-transect
To discover general patterns within the functioning of ecosystems, studies on large geographical scale
are needed (Menaut and Struwe 1994, Lawton 1999). When comparing forests along a large
geographical gradient major shifts within the decomposer food webs and the mineralisation rates can be
expected because of systematic changes in the physico-chemical environment. Environmental factors
such as temperature and moisture have been shown to alter the impact of community structure of the
decomposer food web on mineralisation processes (Sulkava et al. 1996). The sites investigated within
this study lie on a transect that extends from close to the polar circle in Northern Sweden over ca. 2000
km to the North-East of France. A wide range of latitudes and climatic conditions is covered.
The input of biologically reactive N compounds to forests has increased rapidly over much of Europe
and Eastern North America, relative to pre-industrial levels. Nitrogen Emissions to the atmosphere
remain elevated in industrialised countries and are accelerated in many developing regions (Galloway
1995). Atmospheric deposition of nitrogen is expected to alter C and N fluxes in forest systems because
such systems are considered to be mostly N limited (Currie 1999).
In this context the sites for this study were chosen to be subject to different levels of atmospheric N
deposition, ranging from virtually non-polluted to heavy loads of N input.
3
1
Introduction
Intensive site-level experiments can be questioned as to their generality or applicability over a larger
region, whereas regional surveys can be questioned due to their simultaneous change in several
environmental parameters (e.g., climate, geology, soils) along any spatial transect (Aber, et al. 1998). In
this study the second strategy is applied, being aware of its limitations. Furthermore, on such scale the
number of sites that can be investigated is limited due to practical reasons. Generalisations from this
transect study have to be evaluated in the light of other large scale studies within Europe.
1.3 The decomposer system
1.3.1 Decomposition
The decomposer system is located in the interface between atmosphere and geosphere, i.e. in the
organic layer of the soil. The process of decomposition is understood as the main link between the two
largest terrestrial C pools: plant biomass (primary production) and soil organic matter (SOM) (Sollins et
al. 1996).
The process of decomposition has already been studied approx. 160 years ago by scientists dealing
with the pressing task to increase agricultural soil fertility and to end the famine threatening most people
of that time (Heal, et al. 1997; and Liebig 1840, Lawes 1861 and Müller 1887 cited therein). Since then
the interest and concern for the decomposer system has increased and the first formalised paradigm of
decomposition was published in 1979 (Swift et al. 1979). Today the decomposer system is recognised
as an important component of global C and N cycles. Our understanding of decomposition was recently
reviewed by Heal et al. (1997).
Decomposition is a process of continuous breakdown and re-synthesis, resulting in a heterogeneous
mixture of products (Andrén et al. 1990). It includes secondary production of microbial and animal
biomass and organic metabolites, which in turn become resources. Decomposition of any resource is
the result of three processes: (i) catabolism, i.e. chemical changes such as mineralisation of organic
matter to inorganic forms (largely CO2, H2O, NH4+, NO3-, SO4-), and the synthesis of decomposer
biomass and humus, (ii) comminution, i.e. physical reduction in particle size and selective redistribution
of litter, and (iii) leaching, i.e. the abiotic transport of labile resources down the soil profile (Heal et al.
1997).
The rate of decomposition, is controlled by the physico-chemical environment and the quality of the
resource (Swift et al. 1979, Heal et al. 1997, Lavelle 1997). Since the process of decomposition is
4
1
Introduction
almost entirely mediated biologically, both groups of factors act by regulating the organisms of the
decomposer food web (Heal et al. 1997).
The main pathways of carbon into the decomposer system are the shedding of litter by the trees and the
input of soluble organic matter via abiotic leaching or biotic exudation by roots (Swift et al. 1979, Elliott
et al. 1984). The mineralisation of essential nutrient elements from these resources influences the
energy flux within the ecosystem in two ways. First it controls the influx of energy by regulating primary
production through controlling the availability of limiting nutrients. Second, it regulates the efflux of
energy as nutrient availability interacts with substrate quality, and thereby controls decomposition rates
(Elliott et al. 1984).
1.3.2 The decomposer food web
Areas of especially large species richness have been called 'biotic frontiers', a term signifying the need
to explore these systems. Two major biotic frontiers are the tropical rainforest canopy (Erwin 1983 as
cited in Hågvar 1998) and the deep-sea benthos (Grassle & Maciolek 1992 as cited in Hågvar 1998). By
now the soil is acknowledged as the third biotic frontier (André et al. 1994, Lawton et al. 1996, Hågvar
1998). However, the biodiversity within these decomposer systems is as poorly known as that of remote
environments like the ocean floor (e.g. Elliott et al. 1984, Hobbie and Melillo 1984, Copley 2000). The
functional diversity of soil biota is considered to be essential for the process of decomposition (Wolters
1996, Huhta et al. 1998, Wardle 1999).
Decomposition is a process in which a web of heterotrophic organisms from almost the complete range
of life forms is involved. With the exception of Echinodermata, every major phylum and many minor
phyla of invertebrates are represented by species in the decomposer community (Swift et al. 1979,
Anderson et al. 1981). Such are the microflora (bacteria, fungi, actinomycetes and yeasts), mircofauna
(Protozoa, Nematoda, Rotatoria and Tardigrada), mesofauna (Enchytraeidae, Acari, Collembola) and
macrofauna (e.g. earthworms, Diptera larvae, millipedes, woodlice, insects, slugs and snails). Thus, in
contrast to the ‘one-plant-business’ photosynthesis, the process of decomposition is a real team effort.
The decomposer biota range in size across five orders of magnitude (10-7 to 10-2 m) (Brussaard and
Juma 1996). Because of this body size range the living space important to the various organisms is very
different, ranging from a structural scale of millimeters for microarthropods down to a scale of macromolecular level, on which e.g. microflora can define their resources (Swift et al. 1979). Over this wide
spatial scale the organisms interact with each other. For example, even though their size is very
5
1
Introduction
different, competition between microflora and detritivorous fauna for high-quality resources appears to
be intense (Seastedt 2000).
Within this study the most important groups of microflora, micro- and mesofauna in coniferous forest
floors were investigated: bacteria and fungi, Testate Amoebae (Protozoa), Nematoda, Microarthropoda
(Collembola and Acari), and Enchytraeidae (Persson et al. 1980, Petersen and Luxton 1982). To reduce
the complexity of the system the functional group concept was applied (sensu Moore et al. 1988).
Special emphasis was put on the Testate Amoebae, the most important group of Protozoa in coniferous
forest systems (Schönborn 1992c).
1.3.3 Testate Amoebae (Rhizopoda, Protozoa)
Together with Nematoda, the Protozoa form an important component of the decomposer system: the
microfauna. However, due to their small size and resulting methodological difficulties, Protozoa are
often neglected in studies of the decomposer food web. Even if included, they are mostly considered
with only coarse taxonomic resolution. Information on species distribution and diversity of such small
organisms is rare (Foissner 1987). Almost all the relationships dealt with in macroecology (e.g. largescale gradients in diversity) are formulated for macroscopic organisms. It has been shown that patterns
found in such macroscopic groups of organisms may be different or absent for microscopic organisms
like Protozoa (Hillebrand et al. 2001).
The Testate Amoebae are a polyphyletic group of Protozoa that belong to the Rhizopoda (Meisterfeld
2001a, b). They distinguish from naked Amoebae in their ability to form an outer shell. This shell has an
opening through which the pseudopodia extend, referred to as pseudostome. The shell length of the
Testate Amoebae ranges between 15 and 170 mm. The more common species tend to be smaller than
45 mm (Stout et al. 1982). They live in water filled soil pores and within the thin water-film around
detritus or soil particles.
The Testate Amoebae are a comparably well-known group of soil Protozoa and so far approximately
200 species have been reported in terrestrial ecosystems (Foissner 1996). They are accompanied by
around 400 species of ciliates, 260 species of flagellates and 60 species of Naked Amoebae (Foissner
1996). Among the Protozoa the study of Testate Amoebae is facilitated because taxonomy is based on
their well-defined shell structure which allows simultaneous identification and counting (Coûteaux and
Darbyshire 1998).
Important environmental factors determining Protozoan communities are moisture, food availability, pH,
temperature, atmosphere, organic matter content, geometry of soil pores, root exudates, litter and humus
6
1
Introduction
type, soil type, vegetation cover, and site history (Stout 1980, Stout et al. 1982, Stout 1984, Foissner
1987, Bonnet 1988b, Cowling 1994, Ekelund and Ronn 1994, Meisterfeld 1995, Bamforth 1997).
The heterogeneous soil environment imposes some general restrictions on the inhabiting organisms.
Testate Amoebae meet these challenges with various physiological and morphological adaptations,
such as protecting the cytoplasm with a shell (Schönborn 1962, Schönborn 1966). Further adaptations,
like reduced size to reach small soil pores and inhabit thin water films around organic particles, and
invagination of the pseudostome to protect against desiccation, have been described in detail
(Schönborn 1968, Bonnet 1975). Testate Amoebae have been shown to be less sensible to decreasing
moisture tension than other Protozoa (Stout and Heal 1967). Like most other Protozoa, they outwear
periods of unfavourable conditions by encystment to dormant forms. Some species can secrete an
internal protective membrane (epiphragm, Figure 1.1) and are able to survive several months without
food in this temporal precystic form (Bonnet 1964, Coûteaux and Ogden 1988). Those species that
possess resistant cysts, combined with the ability for rapid encystment and excystment, tend to
dominate soil protozoan populations (Cowling 1994).
precyst
epiphragm
Pseu
shell
20 µm
Figure 1.1 Nebela lageniformis. 400x, DIC, in Euparal. Pseu = pseudostome.
7
1
Introduction
1.3.4 Interactions within the decomposer food web
The organisms within the decomposer food web interact on a multiplicity of spatial, temporal and
organisational scales within a heterogeneous habitat (Lee 1994). In the following a short overview of the
major trophic strategies within the decomposer food web is given focussing on groups of major
importance in coniferous forest systems.
Saprotrophy and detritivory
The feeding on dead organic matter is termed saprotrophy (microflora) or detritivory (fauna).
Saprotrophic microflora, i.e. bacteria and fungi, account for the largest share of the carbon dioxide efflux
from the forest floor (Persson et al. 1980). Microflora generally immobilise nutrients (having e.g. C:Nratios below that of their resources) and thus compete with plants for potentially limiting elements
(Anderson et al. 1981). Ectomycorrhizal fungi, as symbiotic partners of trees, aid nutrient and water
uptake in exchange of plant derived carbon (Read 1991, Leake and Read 1997, Lindahl et al. 2001).
Ectomycorrhizal fungi obtain considerable amounts of energy from the symbiotic tree through allocation
of assimilates to the roots. They expend this energy to form extracellular enzymes that enable
saprotrophic feeding via the extraradical mycelium.
Detritivorous fauna ingest dead organic material usually after it has been conditioned by microflora. To
what extent the microflora colonizing the organic matter is used as food source during detritivorous
feeding is difficult to quantify. It is assumed that most groups feeding on detritus are also microbivorous.
For this feeding strategy of mites Luxton (1972) coined the term 'panphytophagous'. In this study the term
is used generally for functional groups that are both, detritivorous and microbivorous. Many species of
Testate Amoebae, Microarthropoda and Enchytraeidae are considered to be panphytophagous.
Detritivorous and panphytophagous decomposer mesofauna significantly enhance decomposition through
so-called 'indirect effects' of their feeding activity (Anderson et al. 1981, Petersen and Luxton 1982,
Anderson 1995). Such effects are litter fragmentation or comminution, translocation and mixing of litter
material, improvement of soil structure, re-concentration of limiting nutrients, and stimulation, transport
and inoculation of microbes (Anderson and Ineson 1984, Lussenhop 1992).
The 'direct' (i.e. metabolic) contribution of these fauna groups to carbon flux is usually very small
compared with microbial activity (only a few percent of the total C mineralised) (Reichle 1977). Only
Protozoan respiration may directly contribute a significant amount to the C flux from the organic layer
(Foissner 1987). Significant enhancing effects of panphytophagous Microarthropoda and Enchytraeidae
on both, C and N mineralisation, have been observed experimentally (Petersen and Luxton 1982,
8
1
Introduction
Woods et al. 1982, Coleman et al. 1983, Seastedt 1984, Verhoef and Brussaard 1990, Beare et al.
1992, Huhta et al. 1998). This enhancement of fluxes is assumed to be a result of the described indirect
effects that stimulate microbial activity by reducing growth limiting factors. The detritivorous and
panphytophagous fauna may be seen as catalysts for nutrient circulation.
Microbivory
The direct contribution of microbivorous fauna to nitrogen mineralisation via excretion can be
considerable, because they usually have higher C:N-ratios than their food (Anderson et al. 1985, De
Ruiter et al. 1993b, Bolger et al. 2000). Especially the Protozoa increase microbial turnover rates and
release nutrients immobilised in microbial tissue by grazing on microflora (Stout 1980, Clarholm 1981,
Elliott et al. 1984, Kajak 1995, Berg 1997, Coûteaux and Darbyshire 1998). Interactions between
Protozoa and microflora are not only concerned with ingestion of microbial biomass. Protozoa are
believed to secrete metabolites that stimulate bacterial metabolism (Darbyshire 1994).
Another important group of microbial grazers are microbivorous Nematodes, belonging also to the
microfauna. Nematodes occur with low biomass compared to other biota. Nevertheless their indirect
impact, e.g microbial inoculation and stimulation via grazing, is considered to be large (Yeates 1979,
Anderson et al. 1981, Bardgett et al. 1999).
Predaceous feeding
Predaceous groups kill and consume animals. Besides fascinating examples like fungi feeding on
Protozoa (Darbyshire 1994) or Testate Amoebae feeding on Nematoda (Yeates and Foissner 1995) the
most important predaceous interactions occur among e.g. Testate Amoebae feeding on other Testate
Amoebae, Nematoda feeding on Nematoda, and predaceous Microarthropoda feeding on
Microarthropoda and Nematoda. Although panphytophagous fauna may regularly ingest Protozoa,
hence other animals, this is usually not referred to as predaceous feeding, but traditionally seems to be
included in the term 'microbivorous'.
The direct impact of predaceous groups on decomposition is small. Nevertheless, functionally important
top-down control by predators on lower trophic levels may occur even though decomposer food webs
are generally considered to be donor-controlled (Kajak 1995, Salminen et al. 1997, Mikola and Setälä
1998, Laakso and Setälä 1999a).
9
1
Introduction
1.3.5 Quantification of fluxes within the decomposer food web
Studies manipulating key functional components of the decomposer food web have identified important
effects on ecosystem function (Anderson et al. 1981, Ingham et al. 1985, Setälä and Huhta 1991,
Bengtsson et al. 1995, Alphei et al. 1996, Wardle 1999). Such studies, like e.g. microcosm experiments,
provide details of how soil organisms and processes interact under different environmental conditions
(Moore et al. 1996).
However, to specify the contribution of decomposer groups to C and N mineralisation and to assign
particular flux rates to these groups is a difficult task. Direct measurements are impossible due to the
multitude of organisms, their small size, and their invisibility and inaccessibility within the substrate they
inhabit. Measurements of carbon efflux, nitrogen leaching or tree seedling growth in experimental
systems or in situ treat the biota mediating these fluxes and effects as ‘black box’.
Stable isotopes are useful tools to disentangle the complex structure of food webs (Eggers and Jones
2000). But even with those means the quantification of fluxes within the decomposer system is difficult.
Stable isotope techniques rely on models calculating flux rates from differences in isotope signatures
between food web components. There is uncertainty about the applicability of these models to the
decomposer food web. The omnivory of the majority of functional groups within such webs might cause
a need for refined descriptions of the fractionation process (Wolters, pers. com.). Nevertheless such
techniques will undoubtedly enhance the understanding of the structure and function of decomposer
food webs and the role of particular functional groups (Eggers and Jones 2000).
Estimates of the contribution of functional groups to C and N mineralisation have been obtained from
calculations based on the relationship between biomass and O2 consumption (resp. CO2 production)
(Huhta and Koskenniemi 1975, Persson and Lohm 1977, Persson et al. 1980, Andrén et al. 1990). For
Testate Amoebae a special method was developed making use of the fact that within this group necroand biocoenosis can be assessed due to empty shells remaining after cell death (Lousier 1974a,
Schönborn 1975). However, both approaches fail to consider synergistic effects due to feeding
relationships within the food web.
Such effects are considered in the food web model approach used by De Ruiter et al. (1993b, based on
O'Neill 1969, and Hunt et al. 1987). The model includes the interactions of groups feeding on each
other, e.g. the indirect effect that consumers have by stimulating the turnover of the organisms they feed
upon. Food web modelling is recognised as a promising approach to quantify the flux of C and N across
and within food webs (Coûteaux et al. 1988, Heal et al. 1997, Smith et al. 1998). The food web model
applied in this study was originally developed for the short grass prairie (Hunt et al. 1987) and has since
10
1
Introduction
then been applied to agricultural systems and grasslands (De Ruiter et al. 1993a, De Ruiter et al. 1994,
1998) and to a forest ecosystem (Berg 1997).
1.4 Structure and aims of this study
In this study the decomposer food webs of four coniferous forest sites along a North-South-transect
across Europe were investigated. The sites cover a broad latitudinal and climatic range (Persson et al.
2000a). Moreover, they were chosen to be subject to different levels of atmospheric N deposition
(Persson et al. 2000a).
The study is divided into two parts. Part one is a descriptive approach on population ecological level.
Within this part special attention is paid to an often neglected group of Protozoa, the Testate Amoebae.
The structure of their communities at the different sites is described on species level. The diversity of
the Testate Amoebae communities is investigated and their similarity is analysed. The Testate
Amoebae communities are set in the context of the other major taxa of the decomposer food web of
coniferous forests. A multivariate statistical approach is used to ordinate the dominance pattern of the
communities and to test the correlation of Testate Amoebae species with environmental variables. The
population size is estimated and active organisms, dormant cells and the necrocoenosis (empty shells)
are monitored.
In the second part population data are related to ecosystem processes (Figure 1.2). This second step
intents to go beyond description towards a functional understanding, scaling-up from population to
ecosystem science, a necessity that has been emphasised (e.g. Lawton 1994, Bengtsson et al. 1995).
For this, descriptions of the decomposer food webs at the sites are used in a modelling approach to
estimate the mineralisation of carbon and nitrogen by each particular functional group and by the whole
food web. With this approach the structure of the food webs is linked to their function (Figure 1.2).
General patterns in the functioning of ecosystems are sought by studying sites on a large geographical
scale (Lawton 1999).
In the following the aims of the two parts of this study are formulated and a short summary of the
foundation underlying the hypotheses is given (section 1.4.1 and 1.4.2). A summary of the main
hypotheses investigated in this study can be found in section 1.4.3.
11
1
Introduction
The community structure
of Testate Amoebae.
The Testate Amoebae community
as part of the decomposer food web.
The decomposer food web as part of the forest ecosystem,
mediating the flux of energy and matter.
Figure 1.2 The ecological scales investigated within this study: linking the soil biota to ecosystem function
(C and N flux).
1.4.1 Testate Amoebae community structure - Part 1
The Testate Amoebae communities inhabiting the transect sites are described on species level. The
abundance and biomass structure is investigated, dormant forms and the necrocoenosis are considered
separately. Trends in species, abundance and biomass patterns as well as similarity between the
communities along the transect are sought. The Testate Amoebae communities are regarded in the
context of other decomposer biota and the abiotic environment. The data set is analysed to identify
those parameters that best explain the structure of Testate Amoebae communities found at the different
sites.
Like larger metazoan taxa, protozoan abundance and species richness are believed to increase with
decreasing latitude and altitude (Foissner 1987, Smith 1996, Coûteaux and Darbyshire 1998, Chown
and Gaston 2000). Moreover, increased N supply has been shown to enhance abundance and species
richness of Protozoa (Chardez et al. 1972, Berger et al. 1986). Based on the equilibrium theory that a
species community is determined by a balance between immigration and extinction (MacArthur and
Wilson 1967) and accepting that a greater distance is a larger migration barrier, it is generally assumed
that two communities are less similar the further they are apart. Among the local factors determining
12
1
Introduction
protozoan communities moisture and food availability have been proposed to be most important (Stout
1984, Cowling 1994). Due to the expected latitudinal trend in turnover rates (see Part 2 below) a larger
necrocoenosis of Testate Amoebae is expected at the boreal site as a consequence of low
decomposition rates (Meisterfeld 1980).
1.4.2 Decomposer food web function - Part 2
The structure of the decomposer food webs along the transect is described by calculating the
biomasses of the functional groups involved and creating a schematic illustration of the trophic
interactions within the webs. A food web model approach considering site specific climate and resource
quality is applied to estimate the mineralisation of carbon and nitrogen by each particular functional
group at each site. The total food web biomass and the simulated total C and N fluxes mediated by the
decomposer biota at the different sites are compared. The estimated mineralisation rates are evaluated
with experimentally obtained data from the project databank. The contribution of functional groups to
biomass and C and N mineralisation rates is compared. Patterns in the importance of particular groups
along the transect are sought. The estimated fluxes are related to the environmental conditions of each
site.
Total decomposer food web biomass tends to decrease towards North due to adverse climate and
limited nutrient availability (Swift et al. 1979). Hence it is hypothesised that total C and N mineralisation
will be smaller towards the North. Considering the general views of Parmelee (1995) and Tietema
(1998) a gradual shift between two extreme types of decomposer systems is expected: a fungal-based
food web with slow turnover rates in the N-limited North and a bacterial-based food web with fast
turnover rates in the strongly N-polluted South. Considering that Testate Amoebae are mainly
bacterivorous (Bonnet 1964) and based on the review on Protozoan diversity by Coûteaux et al. (1998)
the relative importance of Testate Amoebae for total fluxes is hypothesised to increase towards South.
Due to their climatic sensitivity the Microarthropoda are expected to be of less importance for the food
web functions in the North (e.g. limited feeding capabilities during ice formation; Seastedt 2000) and to
increase in importance towards South. Following Huhta et al. (1998) Enchytraeidae are expected to be
of larger importance to mineralisation at the Northern sites than in the South.
13
1
Introduction
1.4.3 Main hypotheses
Part 1
·
Due to decreasing latitude and increasing N deposition the abundance and diversity of Testate
Amoebae increases from North to South.
·
Similarity between Testate Amoebae communities increases with decreasing distance between the
sites.
·
Among the environmental parameters explaining the Testate Amoebae community structure,
moisture and microbial parameters are the most important factors.
·
The ratio of Testate Amoebae biocoenosis to necrocoenosis increases towards South, due to
enhanced decomposition (disappearance of empty shells).
Part 2
·
Total decomposer food web biomass, C and N mineralisation increase towards South.
·
The expected structural changes within the food web along the transect determine ecosystem
function (e.g. C and N mineralisation).
·
The importance of fungi to C and N fluxes within the food web decreases towards South, while that
of bacteria increases.
·
The importance of decomposer fauna to C and N fluxes within the food web increases towards
South.
14
Chapter 2
The Study Sites
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2 The study sites
2.1 Site description
The study sites were selected to form a North-South-transect of European coniferous forests: N-SE
(Northern Sweden, Åheden), S-SE (Southern Sweden, Skogaby), DE (Germany, Waldstein) and FR
(France, Aubure) (Figure 2.1). They cover a range of latitudes and depositional loads and belong to a
number of forest sites that were investigated within the European projects NIPHYS (Nitrogen Physiology
of Forest Plants and Soils) and CANIF (Carbon and Nitrogen Cycling in Forest Ecosystems).
Figure 2.1 Schematic map of the study sites lying on a North-South transect within
Europe. Northern latitude is given beneath site abbreviation. Total N deposition is
indicated: 0 = very low; N = intermediate; NN = high. See Table 2.1 for details and site
abbreviations.
15
2
The Study Sites
The climate ranges from boreal at N-SE, over humid oceanic at S-SE and FR, to humid continental at
DE (Table 2.1). The altitudinal difference between the sites partly counteracts the latitudinal gradient of
the transect due to increasing altitude at the sites of lower latitude. Both, mean annual temperature and
mean annual precipitation are lowest at the boreal site N-SE (Table 2.1) and strong temperature
extremes occur (Figure 2.2). Mean monthly temperatures at N-SE reach a maximum of 14.9°C in July,
which is close to the maximum temperatures at the other sites. However, the minimum temperature
(-11.9°C) lies far below and the time of the year when temperatures are below zero is considerably
longer. In contrast to the strongly fluctuating temperatures the precipitation pattern at N-SE is more
balanced than at the other site but lies on a considerably lower level (Figure 2.2). Taking the
temperature curve into account, the availability of liquid water is bound to be relatively limited at N-SE.
The other sites are quite similar concerning their temperature curves, DE being constantly a little colder
and S-SE being the warmest site, due to its comparably low altitude (Figure 2.2, Table 2.1). Both, FR
and S-SE are wet sites, with high fluctuations in precipitation pattern. DE is subject to much higher
amounts of precipitation than N-SE, but receives less precipitation than FR and S-SE.
The Northern Swedish site N-SE receives next to no nitrogen via the atmosphere, while the Southern
Swedish and the French site are subject to intermediate levels of atmospheric N deposition. DE
receives the highest amount of N. Atmospheric S deposition follows the same pattern. The litter at all
sites consists almost entirely of spruce needles (Picea abies (L.) KARST.), except for the Northern
Swedish forest site that is a mixed stand. The nutrient content of the litter reflects the characteristic
history and environmental conditions of each site. Nutrient concentrations were usually higher in the
needle litter of Central European sites, e.g. the concentration of N, P, S and K was higher in needles
from FR and DE than in needles from the Swedish sites (Bauer et al. 2000).
16
2
The Study Sites
25.0
N-SE
air temperature (°C)
20.0
S-SE
15.0
DE
FR
10.0
5.0
0.0
-5.0
-10.0
-15.0
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
precipitation (mm)
A
200
N-SE
180
S-SE
160
DE
FR
140
120
100
80
60
40
20
0
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
B
Figure 2.2 Mean monthly temperature (A) and precipitation (B) at the sites.
17
2
The Study Sites
A detailed description of the sites and their history can be found in Persson et al. (2000c). In the
following a short summary of the major characteristics of each particular site will be given (see also
Table 2.1, and section 5.1.3.2., Table 5.9 for further abiotic parameters):
N-SE (Northern Sweden, Åheden)
N-SE, the site in Northern Sweden (Åheden), is a 180-year-old unmanaged pine forest (Pinus sylvestris
L.), mixed with spruce (Picea abies (L.) KARST.) and birch (Betula pubescens EHRHART). P. sylvestris is
the dominant species. This site lies at 175 m asl and is characterised by a bottom layer of forest
mosses. The field layer is dominated by the dwarf-shrubs Vaccinium myrtillus L. and Vaccinium vitisidaea L. The climate is boreal, with the bud break in early June and a mean annual temperature of
1.0°C (Figure 2.3). The soil type is a regolsol on sand. N deposition is very low and the site may be
considered as virtually undisturbed.
S-SE (Southern Sweden, Skogaby)
S-SE, the site in Southern Sweden (Skogaby) is a young homogeneous P. abies plantation situated
about 20 km from the sea in the South-Western part of Sweden at around 105 m asl. The P. abies stand
is the second-generation forest and was planted to replace a planted pine forest in 1966. Until 1913 the
area was grazed Calluna heathland. S-SE is the site with the youngest and most productive tree stand
(Scarascia-Mugnozza et al. 2000). About 50 % of the bottom layer is made up by mosses. The grass
Deschampsia flexuosa (L.) TRIN. occurs only in glades or wider gaps between adjacent tree stands. The
climate is humid oceanic, with the bud break in mid may and a mean annual temperature of 7.6°C. The
soil type is a haplic podzol on sandy loam. The site is subject to an intermediate level of N-deposition.
DE (Germany, Waldstein)
The German site DE (Waldstein) is located at the North-Western border of the Fichtel Mountains (NorthEast Bavaria) at 700 m asl. Forest plantation in this area began as early as in the 16th century. The
area consists mainly of planted P. abies forests. The P. abies stand at DE is a 146-year-old plantation
with a dense field layer vegetation dominated by Vaccinium myrtillus, Calamagrostis villosa (CHAIX) J. F.
GMEL., and Deschampsia flexuosa. The climate is humid continental with the bud break in late April and
a mean annual temperature of 5.5°C. The soil type is a cambic podzol on loamy sand.
18
2
The Study Sites
FR (France, Aubure)
The French site FR (Aubure) is located in the Strengbach catchment at the North-Eastern side of the
Vosges Mountains at 1050 m asl. The P. abies stand is situated at a South-facing slope and is a 92year-old forest planted after an old grazed declining fir forest. Since 1983 the canopy is partly defoliated
(about 30 %) and some needles are yellow and deficient of magnesium. Patches of fern (Dryopteris filixmas (L.) SCHOTT) and grass (Deschampsia flexuosa) make up the field layer. The climate of the site is
humid oceanic, with the bud break in late April and a mean annual temperature of 5.4°C. The soil type
is a dystric cambisol on sandy loam.
Figure 2.3 Astrid Taylor & Anne Pflug during our autumn sampling at Åheden, N-SE.
19
Table 2.1 Characteristics of the four selected coniferous sites (from data given in Persson et al. 2000c).
N-SE
dominant tree species
understorey
vegetation
latitude, longitude
DE
FR
Pinus sylvestris
Picea abies
Betula pendula
Picea abies
Picea abies
Picea abies
dense layer of forest
mosses, dwarf-shrubs
occasional mosses
dense field layer of
grasses and dwarf-shrubs
patches of grass
and fern
64°13’ N, 19°30’ E
56°33’ N, 13°13’ E
50°12’ N, 11°53’ E
48°12’ N, 07°11’ E
175
95-115
700
1050
boreal
humid oceanic
humid continental
humid oceanic
1.0
7.6
5.5
5.4
488
1237
890
1192
early June
mid May
late April
late April
180
33
142
92
natural
planted
planted
planted
6
13
17
12
2
16
20
15
0.21
39
0.08
29
0.11
22
0.16
26
22.6
27.9
38.9
29.5
regosol on sand
haplic podsol on
sandy loam
2nd generation, planted, former
grazed Calluna heathland
cambic podsol on
loamy sand
planted
dystric cambisol on
sandy loam
planted, former grazed
declining fir forest
altitude asl (m)
climate
mean annual air
temperature (°C)
mean annual
precipitation (mm)
bud break
stand age in
1995 (a)
type of stand
total S deposition
(kg S ha-1 y-1)
total N deposition
(kg N ha-1 y-1)
P:N ratio of needlesa
C:N-ratio organic
layerb
organic layer C
(10-3 kg C ha-1)
soil type
site history
S-SE
umanaged, virtually undisturbed
site
a
calculated from Bauer et al. (2000).
b
calculated from Persson et al. (2000c)
2
The Study Sites
2.2 Sampling scheme and sample treatment
Samples were collected at four sampling times (Table 2.2): (i) October / November 1996, (ii) May / June
1997, (iii) September 1997, and (iv) March / April 1998. At each time, between 80 and 110 samples (soil
corer: Ø 5 cm, length 12 cm) of the organic layer (litter, fermentation and humus layer, LFH) were taken
at each site.
Table 2.2 Sampling times at the four sites. See Table 2.1 for site abbreviations.
N-SE
S-SE
DE
FR
1st sampling
02.11.1996
06.11.1996
08.10.1996
16.10.1996
2nd sampling
27.06.1997
21.06.1997
28.05.1997
27.05.1997
3rd sampling
07.09.1997
15.09.1997
29.09.1997
29.09.1997
4th sampling
18.04.1998
16.04.1998
29.03.1998
29.03.1998
At the first sampling occasion 10 bulk samples were obtained at each site by merging 10 single soil
cores per sample. An additional 10 soil cores were drawn to determine site specific organic layer
thickness, bulk density and dry-mass-to-area ratio of the organic layer. After material had been taken
out for the extraction of Microarthropoda, the 10 bulk samples were bulked again in pairs to deliver 5
bulk samples for the remaining measurements. This scheme results in 10 resp. 5 replicate samples of
organic layer per site.
For the subsequent sampling times (samplings 2-4) the sampling scheme was slightly altered. Eight
bulk samples were obtained by merging 7-10 single soil cores per sample. An additional 24 (3 ´ 8)
single soil cores were drawn and treated separately. Of these single soil cores 8 were used to extract
Nematoda, another 8 to extract Enchytraeidae and the remaining 8 to extract Microarthropoda. The site
specific organic layer thickness, bulk density and dry-mass-to-area ratios were also obtained from these
24 single soil cores. All other measurements were made with material from the bulk samples. This
scheme results in 8 replicate samples of organic layer per site and sampling time.
In the laboratory the single soil cores for faunal extractions were separated from living plants (mosses
etc.).
The bulk samples were mixed cautiously but thoroughly by hand, and bigger pieces of wood, twigs and
living plant material were carefully removed.
All fauna extractions and microbial measurements started within 3 days time after sampling. The
samples were stored in the dark at 4° C in sealed polyethylene bags. Prior to microbial analyses and pH
measurements the material was sieved using a 4 mm mesh.
21
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Chapter 3
Material and Methods
Dies ist keine leere Seite
3 Material and methods
3.1 Functional groups of organisms
3.1.1 Microflora: fungi and bacteria
Soil microbial carbon (Cmic) was determined using the fumigation extraction method (section 3.1.1.1). To
distinguish bacterial from fungal biomass additional measurements were undertaken with material
collected at the 4th sampling time (ergosterol, section 3.1.1.2; direct counting of bacteria, section
3.1.1.3). A site specific bacterial-to-fungal-biomass-ratio was calculated and used to calculate the
biomass pools of bacteria and fungi from the Cmic measurements. It was assumed that the
measurements at the 4th sampling time appropriately estimate the average bacterial-to-fungal-biomassratio at each site.
To estimate microbial activity the metabolic potential of the microflora was measured and a metabolic
quotient was calculated (section 3.1.1.4). Prior to microbial analyses the material was sieved using a
4 mm mesh.
3.1.1.1
Chloroform fumigation extraction method (CFE): microbial carbon (Cmic)
Chloroform fumigation causes cell lysis of microorganisms. The increase in extractable C following
chloroform fumigation of soil was used to estimate the amounts of C held in the microbial biomass (Cmic)
(Vance et al. 1987).
From each sample (sieved, fresh organic material) two aliquots of an amount corresponding to 2 g DW
each were weighed into two bottles. Half the aliquots were fumigated prior to extraction. For this, open
bottles were put into an exsiccator. Two empty bottles served as controls. The bottom of the exsiccator
was covered with moist paper towels and the exsiccator contained a beaker whose ground was covered
with lime pellets. Among the sample bottles a beaker with approx. 50 mL ethanol-free chloroform
(CHCl3) containing boiling stones was put. The exsiccator was evacuated until the chloroform boiled
vigorously for approx. 2 min. The exsiccator was closed and the samples remained in the chloroform
atmosphere in the dark at 20°C for 24 h. The exsiccator was then aerated and the paper towels as well
as the beakers containing lime pellets and remaining chloroform were removed. The exsiccator was
then repeatedly (at least ten times) evacuated and aerated to completely remove chloroform fumes
(until no more smell of chloroform was detected from the samples).
Fumigated as well as unfumigated samples were extracted using potassium sulfate solution. To each
23
3
Material and Methods
sample as well as to the controls 90 mL of K2SO4 solution was added (ratio sample to extractant: 1:45
w/w). The samples were shaken for 30 min (250 rpm) and percolated over filters. The extract was stored
in polyethylene vials at –18°C.
The extracts were analysed photometrically after defrosting using a continuous flow system (PERSTORP
ANALYTICAL GmbH, Perstorp, Sweden). For this the samples were acidified with sulphuric acid
(1 N H2SO4) to convert mineral carbon to carbon dioxide (CO2), which was trapped in sodium hydroxide
solution (1 N NaOH). The CO2-free samples were then merged with saturated persulfate solution
(K2S2O8) and irradiated with UV radiation to ensure complete oxidation of organic carbon to CO2. The
carbon dioxide was diffused through a silicone membrane and received by a weakly buffered
phenolphthalein indicator solution. The decrease in the colour of the indicator is proportional to the
carbon concentration in the extracts and was measured photometrically. The method was calibrated
using potassium biphthalate solution (HO2CC6H5CO2K).
Calculation
The C content of the organic material was calculated from the C concentrations of the extracts as
follows (equations 3.1 and 3.2):
m=
m
n
Vextr
VH2O
DW
n × (Vextr + VH2O )
1000 × DW
equation 3.1
C content of organic material (mg g–1 DW)
C concentration of extract (mg L-1)
volume of extractant (mL)
volume of water in sample (mL)
dry weight of sample (g)
mbiomass = (mfumigated – munfumigated) k
mbiomass
mfumigated
munfumigated
k
equation 3.2
microbial C (mg g-1 DW)
C content in fumigated sample (mg g–1 DW)
C content in unfumigated sample (mg g–1 DW)
proportionality factor (k = 2.22, taken from Wu et al. 1990)
Equipment & reagents
80 mL bottles with screw caps; shaker; filter (SCHLEICHER & SCHÜLL 595 ½); glass funnels; 100 mL
beakers; exsiccators; paper towels; boiling stones; lime pellets (NaCO3); polyethylene vials (ROTH,
24
3
Material and Methods
article no. 0794.1); 0.5 M K2SO4 solution; chloroform (MERCK, article no. 2445, stabilised with 20 ppm 2methyl-2-buten).
3.1.1.2
Ergosterol
The ergosterol content of soil is an indicator of living fungal biomass (Ekblad et al. 1998). It may be
used as a marker of the biomass of saprophytic and ectomycorrhizal fungi (Nylund and Wallander
1992). It was determined using HPLC analysis (Djajakirana et al. 1996) in the laboratory of Dr. Rainer
Joergensen (Institut für Bodenwissenschaft, Georg-August-Universität, Göttingen).
3.1.1.3
Direct counting of bacteria
The number of living bacteria cells and their cell volume was measured using automatic confocal laserscanning microscopy picture analysis after fluorescent staining and bacterial biomass was calculated
from these parameters (Bloem et al. 1997). The measurements were carried out in the laboratory of Dr.
Jaap Bloem (Research Institute for Agrobiology and Soil Fertility (AB-DLO), Haren, The Netherlands).
3.1.1.4
Metabolic potential and metabolic quotient qCO2
The metabolic potential of the microflora within the organic material was estimated by measuring the
metabolic release of carbon dioxide (respiration) at 10 °C and at an optimal water content (300 % DW).
The amount of CO2 released per unit microbial biomass (metabolic quotient qCO2 (µg CO2-C g-1 DW h1))
is a measure of the microbial activity and was calculated by dividing the CO2 release by the microbial
biomass in the same sample.For comparison with other studies it must be kept in mind that this
particular metabolic quotient was calculated using the potential metabolic release of CO2 and thus
represents a potential metabolic quotient.
The material was sieved (4 mm mesh size) and an amount of fresh litter that equals 10 g DW (dry
weight) is weighed into a microcosm (jar, volume 0.75 L) which was then sealed air-tight. For each
sample two replicate microcosms were set up. Additionally four empty microcosms were treated
identically to serve as control sets. The microcosms were pre-incubated in the dark at 10°C for five days
to let the microbial community adapt to 'normal' activity after the sieving which usually causes a
respiration peak due to release of substrate after tearing of fungal hyphae etc.
After pre-incubation the water content was adjusted to 300 % DW by adding tab water if necessary.
Then a small vessel filled with 4 mL 1.0 N NaOH was put into each microcosm and the microcosms
were sealed air-tight. The microcosms were incubated for 6 days at 10°C in the dark.
25
3
Material and Methods
During the incubation period carbon dioxide (CO2) that evolved from the organic material was trapped in
the sodium hydroxide solution. After the incubation BaCl2 was added to the vessels in excess and
reacted with dissolved CO2 to form BaCO3, an insoluble salt that precipitates and was thus removed
from the reaction equilibrium, therefore shifting the reaction towards a complete consumption of the
CO2. Simultaneously the lye is neutralised while NaCl is formed:
2 NaOH + BaCl2 + CO2 ¾® 2 NaCl + BaCO3 +H2O
After the incubation two aliquots of 0.5 mL were taken from each vessel and titrated with hydrochloric
acid (0.1 N), using phenolphthalein as indicator. By means of substraction, the amount of metabolically
formed CO2 that reacted with the sodium hydroxide was determined.
Calculation of results
The rate of respiration was calculated as follows:
R=
R
VC
VS
E
N
D
T
tinc
DW
(VC - VS )E × N × D ×T
DW × t inc
equation 3.3
respiration rate (mg CO2-C g-1 DW h-1)
volume of acid needed to titrate the NaOH in control sets (mL)
volume of acid needed to titrate the NaOH of sample (mL)
equivalent weight of CO2-C (= 6 mg mL-1)
normality of the acid (mol L-1)
factor of dilution of NaOH (= 8; because aliquots of 0.5 ml are taken from the vessels containing
a total of 4 mL)
titer of the acid
incubation time (h)
dry weight of sample (g)
Equipment & reagents
Microcosms (jars, volume 0.75 L) with air-tight lids; pipette; beakers; dark chamber with constant
temperature of 10° C; burette or titration automat; small vessels for the alkali; glass-vials with air-tight
lids for storage of the aliquots prior to titration; sodium hydroxide (NaOH) solution, 1.0 N; barium
chloride (BaCl2) solution, 0.5 M; phenolphthalein indicator; hydrochloric acid (HCl) solution, 0.1 N.
3.1.2 Testate Amoebae
Testate Amoebae were counted on species level. Most probable number culturing techniques as used
for Naked Amoebae and Flagellata are insufficient to estimate the density of Testate Amoebae (Bunt
and Tchan 1955, Foissner 1987, Ekelund and Ronn 1994). Thus a direct counting method using an
26
3
Material and Methods
inversed microscope was used (Meisterfeld 1980, modified as described below). Testate Amoebae
species were subsumed into five size classes (see section 5.1.1, Table 5.1 and 5.2) and biomass was
calculated from the abundance of shells that were filled with cytoplasm using conversion factors from
the literature (Volz 1951, Schönborn 1975, Schönborn 1977, 1981, 1982, Lousier and Parkinson 1984,
Lousier 1985, Schönborn 1986, Wanner 1991). If useful measures from the literature were lacking the
biomass of the species was calculated using measurements of cell length, width and height of at least
10 specimen and an ellipsoid formula (Heal 1965, Schönborn 1977). Specific gravity of the cytoplasm
and C content of Protozoa were taken from the literature (specific gravity: 1.05 g mL-1 Schönborn 1981;
C content 50 % DW, see Table 10.1, Berg 1997). Testate Amoebae species were assembled into two
feeding groups: panphytophagous and predaceous species. The latter group comprises of the genera
Nebela and Heleopera (Bonnet 1964 in Coûteaux 1976, Laminger 1980). See section and 5.2.2 for
details.
3.1.2.1
Fixation and staining of substrate samples for quantitative analyses
An aliquot of fresh material from bulk samples (1.00-16.00 g) was weighed into polyethylene vials and
topped with alcoholic aniline blue within two days after soil sampling. The staining with aniline blue
allows differentiation of three types of shells: empty shells, shells that were active at the moment of
fixation, and cysts (Schönborn 1978, Aescht and Foissner 1992).
Equipment & reagents
1.5 g L-1 aniline blue ('waterblue') in 70 % alcohol (shake solution for 20 min); polyethylene vials (ROTH,
article no. 0794.1).
3.1.2.2
Direct counting of Testate Amoebae
The fixed sample was washed from the polyethylene vial into a 100 mL measuring cylinder with tab
water. The volume was recorded and the suspension was transferred completely into a 250 mL or
500 mL round-bottomed flask, depending on density of material and expected amount of Protozoa in the
sample. The polyethylene vial and the measuring cylinder were rinsed several times to transfer the
entire material. When rinsing, the additional amount of water used was recorded each time. The
suspension in the round-bottomed flask was filled up with tab water to a volume of 200 resp. 450 mL.
The suspension was shaken for 20 min on a shaker with circular motions (600-700 rpm). Instantly after
the shaker stopped an aliquot of 100 mL or 500 mL (depending on the density of the suspension) was
taken from the middle of the round-bottomed flask using an EPPENDORF pipette. The very tip of the
27
3
Material and Methods
pipette (approx. 1 mm) was clipped off to widen the opening and avoid the selective intake of smaller
soil particles. The aliquot droplet was laid into a counting chamber that had been pre-filled with tab
water and was already sat in the inversed microscope observation chamber holder, thus allowing the
suspension to settle evenly. The round-bottomed flask with the remaining suspension was sealed with a
rubber cap and stored in the dark at 4°C. The suspension in the counting chamber was allowed to settle
for at least 10 min. The entire chamber was then observed with an inversed microscope at 100´
magnification. Each specimen of Testate Amoebae found was recorded on species level (see section
3.1.2.6). Empty shells, active cells and cysts were differentiated.
At least two aliquots of the aqueous suspension were counted. If the total of specimen found in one
counting chamber deviated more than 10 % from the previous count, further aliquots were counted until
their deviation from the mean of the previous counts was £ 10 %.
Larger species of Testate Amoebae occurred with much smaller frequency than small species.
Therefore two different soil suspensions were counted for each soil sample. A light one, resulting in
around 0.0005 g DW of substrate in the counting chamber, and a dense suspension, resulting in around
0.002 g DW of substrate in the counting chamber. In the light suspension larger shells occurred only
rarely. But even small and hyaline species (e.g. Corythion dubium, Trinema lineare, Cryptodifflugia
oviformis) could easily be detected and the problem of losing smaller Testate Amoebae that may be
masked by soil particles (Foissner 1987) was circumvented. In the dense suspension the smaller shells
were hidden by soil particles, but the larger shells (length of 70 µm and more, e.g. Nebela spec.,
Trigonopyxis arcula, Centropyxis matthesi) could easily be seen and were recorded with a frequency
that allowed extrapolation to the abundance in the sample. From each soil sample at least 2 aliquots of
a light suspension and at least 2 more aliquots of a dense suspension were counted.
Equipment & reagents
Inverse microscope (ZEISS Axiovert 135, magnifications: 100´, 250´, 400´, 1000´); counting chamber
made of perspex with cover slip bottom (Æ 1.5 cm, depth 0.3 cm); 100 µL EPPENDORF pipette; 500 µL
EPPENDORF pipette; 100 mL measuring cylinder; 250 and 500 mL round-bottomed flasks; funnel;
spatula; 1 L water bottle; shaker; cat's whisker or human eyelash in collar holder (see sections 3.1.2.6
below); embedding fluids (Euparal, Naphrax, glycerine; see sections 3.1.2.7-9 below); object slides,
cover-slips; micropipette (micro-capillary and flexible hose with mouth piece); conc. alcohol (approx.
96 %) (see section 3.1.2.6 below).
28
3
3.1.2.3
Material and Methods
Flotation method: extraction of empty shells
A simple method to extract empty shells from soil samples is the flotation-method (Chardez 1959). With
this enrichment method empty Testate Amoebae shells can easily be accumulated for qualitative
studies, e.g. to obtain enough specimen for exhaustive taxonomic evaluation and the compilation of a
species list for a certain site.
250-500 mL soil material was spread out and left at room temperature until air-dry. The material was
then passed subsequently through a coarse (mesh size 1 mm) and a fine (mesh size 0.250 mm) sieve.
The sieving residue was discarded and the remaining, fine material was mixed with tab water in a large
beaker using a glass rod for stirring. The mixture was then agitated using an aquarium's pump to bubble
air through the sample for 1 min. The mixture was left to settle for another minute. While most soil
particles sedimented the air-filled empty Testate Amoebae shells floated at the water surface and were
collected at the wall of the beaker using a pipette. They were collected in a vial and put into an
exsiccator. The exsiccator was evacuated so that the shells sedimented. The suspension was fixated
with conc. formaldehyd (final concentration approx. 2 %) and stored in the dark at room temperature.
3.1.2.4
Batch cultures
To be sure not to miss any species, batch cultures from each sampling occasion were kept for several
weeks. To obtain the cultures 50-100 mL of organic soil material were put into jars and kept moist in the
dark at 10°C for at least 6 weeks. Once a week liquid from the cultures was extracted by gently pressing
the material to obtain run-off. The run-off was observed under the inverse microscope for live specimen
(magnification 100´).
3.1.2.5
Live observations
For direct live observations 1-5 mL of organic soil material was suspended in 2-10 mL of tab water, and
stirred or shaken for at least 1 min. An aliquot of this suspension was observed under the inverse
microscope for living specimen (magnification 100´).
3.1.2.6
Taxonomic determination
In most cases the shell morphology allowed species determination. For this the shell often needed to be
examined from all sides and the interior structures needed to be made visible. The shell was tossed
around using a cat's whisker or a human eyelash fixed to a collar holder, manipulating the shell carefully
without disturbing the rest of the sample. Magnification was increased from 100´ to 250´, 400´, 1000´
29
3
Material and Methods
(oil) if necessary. Differential interference contrast (DIC) as well as phase contrast was used to increase
visibility of low-contrast hyaline structures.
In rare cases the shell was sucked out of the counting chamber using a micropipette and embedded in
fixation fluid on an object slide to facilitate observation (e.g. glycerine, Euparal, Naphrax; see sections
3.1.2.7-9 below). Capped with a cover slip the slide served as a reference and may be stored for several
years (edges were sealed with nail polish).
No complete standard taxonomic works or determination keys can be found for Testate Amoebae.
Therefore original species' descriptions, monographies of genera and compilation of biometric data as
well as reviews of certain genera occurring in soil and freshwater were used. Tables 3.1 and 3.2 give an
overview of the taxonomic literature.
Table 3.1 General taxonomic literature for the determination of Testate Amoebae.
General determination literature
Meisterfeld (2001a, b)
Bonnet and Thomas (1960)
Grospietsch (1965)
Lüftenegger et al. (1988)
Lüftenegger and Foissner (1991)
Ogden and Hedley (1980)
Rauenbusch (1987)
Richter (1995)
Schroeter (1995)
30
Short description
key to the genera, review of most important species
review of species occurring in soil
key to the genera
review of biometric data
review of biometric data
species descriptions (REM-illustrations)
soil species descriptions (REM-illustrations)
soil species descriptions
soil species descriptions
Table 3.2 Specialised taxonomic literature and monographies for the determination of Testate Amoebae.
genus
Arcella EHRENBERG 1830
Argynnia VUCETICH 1974
Assulina EHRENBERG 1871
Centropyxis STEIN 1957
Corythion TARANEK 1881
Cryptodifflugia PENARD 1890
Cyclopyxis DEFLANDRE 1929
Difflugia LECLERC 1815
Edaphonobiotus SCHÖNBORN, FOISSNER & MEISTERFELD 1983
Euglypha DUJARDIN 1841
Geopyxella BONNET & THOMAS 1955
Heleopera LEIDY 1879
Hyalosphenia STEIN 1857
Microchlamys COCKERELL 1911
Microcorycia COCKERELL 1911
Nebela LEIDY 1874
Phryganella PENARD 1902
Plagiopyxis PENARD 1910
Playfairina THOMAS 1961
Pseudawerintzewia BONNET 1959
Schoenbornia DECLOITRE 1964
Schwabia JUNG 1942
Tracheleuglypha DEFLANDRE 1928
Trachelocorythion BONNET 1979
Trigonopyxis PENARD 1912
Trinema DUJARDIN 1841
determination literature
Deflandre (1928b), Decloitre (1976a, 1979b, 1982)
Deflandre (1929), Decloitre (1978, 1979b, a, 1982), Rauenbusch (1987)
Decloitre (1960)
Grospietsch (1964), Page (1966)
Decloitre (1977a, 1979b, 1982)
Ogden (1983), Ogden and Zivkovic (1983)
Coûteaux, et al. (1979), Chardez (1987b, a), Decloitre (1962, 1976b, 1979b, 1982)
Bonnet (1974)
Grospietsch (1965a)
Deflandre (1936), Decloitre (1977b, 1979b, 1982), Meisterfeld and Schüller (1982), Heal (1963)
Chardez (1969)
Thomas (1958)
Schönborn, et al. (1987)
Deflandre (1928a), Coûteaux and Ogden (1988)
Decloitre (1981, 1982)
3
Material and Methods
3.1.2.6.1
Distinguishing Centropyxis aerophila sphagnicola and C. sylvatica
Although characteristic in their shell size the definite distinctive feature separating Centropyxis sylvatica
(DEFLANDRE 1929) BONNET & THOMAS 1955 and C. aerophila sphagnicola DEFLANDRE 1929 is an inner
diaphragm. This diaphragm is formed in C. sylvatica by the ventral lip of the pseudostome extending
well into the shell and by a rim reaching from the dorsal shell wall toward the ventral lip (ventral = the
side of the shell that opens in a pseudostome; dorsal = the side of the shell opposite to the
pseudostome). C. aerophila sphagnicola lacks the dorsal rim. In this study the two species were
differentiated by their length, because of the difficulty to judge the inner architecture of the shells under
the optical conditions when counting in watery suspension. Shells shorter than 68 mm were assigned to
C. aerophila sphagnicola, longer shells (³ 68 mm) were assigned to C. sylvatica.
3.1.2.6.2
Distinguishing Cyclopyxis eurystoma and Phryganella acropodia
The genera Phryganella is distinguished from the lobose genera Cyclopyxis and Difflugia only by its
reticulolobose form of pseudopodia (Chardez 1969). Most observations within this study, however, were
made with fixed material, rendering the observation of pseudopodia impossible. Therefore the species
Cyclopyxis eurystoma DEFLANDRE 1929 and Phryganella acropodia (HERTWIG & LESSER 1874)
HOPKINSON 1909, having a very similar shell architecture, were differentiated from each other according
to a size limit (see circumstantial discussion in Schroeter 1995). Shells with a diameter below 55 mm
were assigned to P. acropodia.
3.1.2.6.3
The taxon Euglypha cf. strigosa
Some specimen of Euglypha could not without doubt be identified as Euglypha strigosa (EHRENBERG
1872) WAILES & PENARD 1911 because the mouth scales were obscured by debris. Furthermore
specimen with and without spines and every degree of spine covering in between were found. It seems
likely that E. strigosa sometimes loses its spines secondarily during the course of its lifetime. All these
specimen were assigned to the taxon Euglypha cf. strigosa. Possibly this morphologic group also
included the very similar species and forms Euglypha ciliata (EHRENBERG 1848) LEIDY 1878, E. ciliata f.
glabra WAILES 1915, E. pseudociliata CHARDEZ 1962 and E. pseudociliata f. glabra CHARDEZ 1962.
3.1.2.6.4
The taxa Nebela parvula/tincta and N. tincta major/bohemica/collaris
Nebela parva CASH & HOPKINSON 1909 and N. tincta (LEIDY 1879) AWERINTZEW 1906 differ from each
other by the presence of two lateral pores above the pseudostome of the latter. Since this feature may
32
3
Material and Methods
not be diagnosed unambiguously under the given optical conditions when counting, the two species
were not distinguished but pooled as the taxon N. parvula/tincta.
Another group of shells has the appearance of N. parvula/tincta but exceeds a length of 95 mm. This
group consists of Nebela tincta major DEFLANDRE 1936, N. bohemica TARANEK 1881 and N. collaris
(EHRENBERG 1848) LEIDY 1879. N. tincta major differs from the other two in having two lateral pores
above the pseudostome but, as above, these cannot explicitly be judged in watery suspension. N.
collaris has a pseudostome with a curved rim in lateral view while the rim of N. bohemica appears as a
straight line. However, both species cannot unequivocally be distinguished from each other (Heal 1963).
Thus the three species were pooled together as the taxon N. tincta major/bohemica/collaris (cf.
Schroeter 1995).
3.1.2.6.5
Distinguishing Trinema enchelys and T. lineare
Following Lüftenegger et al. (1988) Trinema enchelys (EHRENBERG 1838) LEIDY 1878 may be
distinguished from T. lineare PENARD 1890 by its length being above 40 mm. Risk of misidentification is
considered to be very small, since the sizes overlap only in rare cases. Therefore, in this study length
was used as distinctive feature.
3.1.2.7
Slide preparation with Euparal
Embedding the Testate Amoebae in Euparal facilitates the examination of the interiors of the shell. This
is due to the higher refraction index of Euparal (compared to water) making the shell and cell parts
appear to be lighter.
Under microscopic observation one or several specimen were transferred from watery suspension into
the centre of a cover-slip lying on top of an object slide using a micropipette with a flexible hose and a
mouth piece. A droplet of 96 % alcohol was used to supersede the water. When most of the alcohol had
evaporated a small droplet of Euparal was put on top of the specimen and the cover-slip was turned and
laid on the object slide. If the specimen were big, two short clips of human hair were laid alongside the
specimen into the Euparal before turning the cover-slip to provide some footing and prevent the shell
from being squashed when the Euparal dried. The slide preparation was kept upside down at room
temperature, so that the specimen stayed close to the cover-slip and microscopic observation was not
deterred by a thick layer of Euparal between the cover-slip and the specimen.
33
3
Material and Methods
3.1.2.8
Slide preparation with Naphrax
This slide preparation method was used for empty idiosome shells, especially of the genus Euglypha.
The embedding fluid Naphrax (which is also used to embed diatoms) increases the contrast of the
hyaline structures of silicious oxide shells that are characteristic of Testate Amoebae with ideosomes.
The specimen were transferred from watery suspension into the centre of a cover-slip lying on top of an
object slide using a micropipette with a flexible hose and a mouth piece. The shells were left to dry out
completely and Naphrax was dropped on top of them. The cover-slip was turned and put on the slide.
The slide preparation was carefully heated to about 50°C to liquefy the medium and expel bubbles of
air. The slide was stored upside down at room temperature like the Euparal slide preparations.
3.1.2.9
Slide preparation with glycerine
Like Euparal (section 3.1.2.7) glycerine lightens Testate Amoebae shells in the microscopic observation
due to its optical density. Especially shells filled with cytoplasm were embedded in glycerine, since this
medium imposes only a minor osmotic stress on the cytoplasm and nucleus and such structures are
well-preserved. This method is especially well suited for observations using differential interference
contrast (DIC). The handling was the same as for Euparal slide preparations, save that pure glycerine
was taken instead of the Euparal. Glycerine slide preparations were not stored.
3.1.2.10 Photography
The inverse microscope had an extra tubus equipped with a camera (CONTAX 167 MT). For microscopic
photography the following films were used: Agfa Agfapan APX 25, Ilford b/w 125, Agfacolor 100.
3.1.3 Nematoda
A modified O'CONNOR-wet-funnel-extraction followed by milk-filter-cleaning (s'Jakobs and van Bezooijen
1984) was used to extract Nematoda (1.5 d at 20°C + overnight temperature-increase to 60°C in wet
funnels while cooling the sample below to 15°C, followed by 2 d milk-filter-cleaning at 20°C). The
nematodes of the sampling occasions 1-3 were counted alive without further taxonomic determination.
Nematodes of the 4th sampling occasion were killed by heat, stored in 4 % formaldehyde, counted on
genus level (distinguishing juveniles from adults) and assembled into four feeding groups (see Table
3.3, extracted from the detailed Table of feeding habits of nematode genera in Yeates, et al. 1993). The
taxonomic Nematode work was carried out by Dr. Ralf Lenz (Mainz, Germany). Nematode biomass was
calculated from genus specific abundances using conversion factors from the literature (Berg 1997,
34
3
Material and Methods
Ekschmitt et al. 1999) or calculated using the formula from Andrássy (1956) and length and width
estimates from Bongers (1994). Body volume of juveniles was estimated to be on average 22 % of the
adult body volume (Ilja Sonnemann, pers. com.). A list of the conversion factors used is given in the
appendix (chapter 10, Table 10.1 and 10.2).
Table 3.3 Nematode genera found on the sites and their feeding habits according to Yeates
(1993). Following the simplified classification for the food web model further food sources or
feeding modes that may occur are given in parentheses.
Genus
Acrobeloides
Acrolobus
Alaimus
Bunonema
Geomonhystera
Heterocephalobus
Metateratocephalus
Panagrolaimus
Plectus
Prismatolaimus
Pristionchus
Rhabditis
Teratocephalus
Wilsonema
Aphelenchoides
Hexatylus
Laimaphelenchus
Tylencholaimus
Prionchulus
Seinura
Eudorylaimus
Filenchus
Malenchus
Tylenchus
feeding habit
bacterivorous
bacterivorous
bacterivorous
bacterivorous
bacterivorous (detritivorous)
bacterivorous
bacterivorous
bacterivorous
bacterivorous
bacterivorous
bacterivorous (predaceous)
bacterivorous
bacterivorous
bacterivorous
fungivorous (epidermal cell and root hairs, migratory
endoparasite of plants, algae and lichens)
fungivorous
fungivorous (predaceous, algae and lichens)
fungivorous
predaceous ("ingester")
predaceous ("piercer")
omnivorous (predaceous)
omnivorous (epidermal cell and root hairs)
omnivorous (epidermal cell and root hairs)
omnivorous (algae and lichens, fungivorous)
3.1.4 Microarthropoda (Collembola and Acari)
Microarthropods were extracted by means of the high-gradient-canister method (Macfadyen 1953,
Kempson et al. 1963, Wolters 1983) into ethyleneglycol and transferred to ethanol (70 %) for storage.
Organisms were sorted and counted using a dissecting microscope (Leica Wild M3C, magnification 4064 ´). For more detailed information on the extraction procedure and determination see Pflug (2001).
3.1.4.1
Collembola
All Collembola work was carried out by Anne Pflug (Department of Animal Ecology & Zoology,
University Giessen, Germany). Collembola were counted on species level and aggregated into two
35
3
Material and Methods
feeding groups. The genus Frisea is judged as predaceous, the remaining genera were judged as being
panphytophagous. The body length of each species was calculated by taking the mean of all length
measurements given in the determination keys (Gisin 1960, Palissa 1964, Fjellberg 1980, Zimdars and
Dunger 1994, Jordana et al. 1997, Fjellberg 1998, Pomorski 1998). Collembola biomass was then
calculated using the formula given by Persson & Lohm (1977) and the C content of Collembola given by
Berg (1997, see Table 10.1 in the Appendix). Some regression coefficients for species were also taken
from the calculation of Tanaka (1970) and Petersen (1975). For species not mentioned in these
publications parameters of species of the same genus, family or suborder with similar body shape were
used. Juveniles and adults were treated separately in the biomass calculations to account for the
species specific smaller size of juvenile Collembola (for details see Pflug 2001).
3.1.4.2
Acari
The Acari work was carried out by Astrid R. Taylor (Department of Animal Ecology & Zoology, University
Giessen, Germany). Acari were counted on species level and sorted into two feeding groups (Luxton
1972, Walter and Proctor 1999): panphytophagous (juvenile and adult Cryptostigmata, Astigmata,
Prostigmata and unidentified juveniles) and predaceous (juvenile and adult Mesostigmata) Acari. Length
and width of a particular species were either taken from the literature that was used for species
determination (Sellnick 1928, Willmann 1931, Giljarov and Krivolutsky 1975, Berg et al. 1990, Wunderle
et al. 1990, Beck and Woas 1991) or measured for at least 3-10 specimen. Biomass was then
calculated using the formulas and the dry-weight-to-live weight-ratio given by Persson & Lohm (1977)
and the C content of Acari given by Berg (1997, see Table 10.1 in the Appendix). Nymphs and adults
were treated separately to account for the species specific smaller size of juvenile Acari (for details see
Taylor 2001).
3.1.5 Enchytraeidae
Enchytraeidae were extracted using a modified O'CONNOR-wet-funnel method (O'Connor 1955, stepwise
increase of temperature from 20-50°C in 5°C steps lasting 0.5 h each, while cooling the sample below
to 15°C, entire procedure: 3.5 h). Enchytraeidae were counted alive under dissecting microscopes at
10´ magnification without further taxonomic determination. Enchytraeid biomass was calculated from
abundances using a conversion factor from the literature (Heal 1967, Persson and Lohm 1977,
Petersen and Luxton 1982, Dunger and Fiedler 1989, Górny and Grüm 1993, Berg 1997, see Table
10.1 in the Appendix).
36
3
Material and Methods
3.2 Abiotic parameters
3.2.1 Water content (WC)
From each sample about 3.00 g fresh weight (FW) of organic material was weighed into a beaker of
known weight. The material was dried at 105°C for at least 24 h, cooled in an exsiccator over silicagel
and weighed again (Alef 1991). The water content (WC) is expressed as percentage of dry weight (DW):
WC (% DW).
3.2.2 pH of organic layer
The organic material was sieved (mesh size 4 mm) before pH determination. Two sub-samples were
drawn from each sample. For each sub-sample an amount of fresh litter equalling 2.00 g dry weight
(DW) was weighed into a beaker. 20 mL distilled water (ratio litter-to-water 1:10) was added and the
mixture was stirred with a glass rod until all needles were soaked. The samples were shaken for 60 min
using a mechanical shaker. A calibrated pH-meter was used to measure the pH in the supernatant of
each sample while gently moving the electrode.
3.2.3 Thickness of organic layer, mass-to-area-ratio and bulk density
Thickness of organic layer (litter, fermentation and humus layer, LFH layer) was measured from single
soil cores in the field (see sampling scheme section 2.2). Soil cores were then weighed and the massto-area-ratios and bulk density was calculated from the dry weight and the base area of the soil corer.
3.2.4 C:N-ratio of organic layer
Total C and N content of the organic material was measured at the 4th sampling time only. The material
was sieved (4 mm mesh size) and dried at 105°C. The material was powdered and the elements were
measured gas chromatographically after dry combustion with oxygen using a Carlo Erba Elemental
Analyser. The measurements were carried out in the laboratory of Dr. Rainer Joergensen (Institut für
Bodenwissenschaft, Georg-August-Universität, Göttingen). The C:N-ratio was calculated from the C and
N contents (%).
37
3
Material and Methods
3.3 Statistical analyses
Statistical analyses were carried out using STATISTICA 5.0 (Statsoft Inc., Tulsa, USA) and CANOCO for
Windows 4.02 (Research Institute for Agrobiology and Soil Fertility (AB-DLO), Wageningen, The
Netherlands; ter Braak and Smilauer 1998).
3.3.1 Analysis of variance
Analyses of variance (ANOVAs) were performed to test for significant differences between means.
Whenever possible two-way-ANOVAs were carried out including the factors 'site' and 'time'. All sites were
sampled on four occasions covering the seasons of a whole year (see Table 2.2). However, this study
was not designed to investigate population dynamics of the groups of organisms observed, nor to
characterise fluctuations of the abiotic parameters assessed. Rather the aim of this study was to
characterise the study sites with yearly averages reflecting the long term situation at each site.
Nonetheless the factor 'time' is not neglected from the analyses, because it often explains an important
part of the variance within the data sets. Strong fluctuations with time are mentioned whenever this is
necessary to understand restrictions within the comparison of mean values per site or when the
variance with time delivers some insight of general interest.
Whenever necessary, data were adequately transformed prior to statistical analyses (arcsin for
percentage values; logarithm for non-percentage values). The Tukey honest significant difference test
(Tukey HSD test) was used to perform multiple comparisons between pairs of means of parameters.
Homogeneity of variances was tested using the SEN & PURI-test. Lindman (1974) however showed that
the F statistic is quite robust against violations of the homogeneity of variances assumption. Therefore,
when this assumption was violated due to a number of zero values in the data set the ANOVA was
occasionally calculated anyhow. Whenever this was the case it is pointed out clearly in the text.
ANOVAs with model results
The food web model described in detail in chapter 4 was run with the biomass input data from four
separate sampling occasions per site, resulting in four sets of estimates for each site. Analyses of
variance revealed no significant main effect of ‘time’ (sampling time) on the biomass and C and N
mineralisation rates of any functional group nor the total biomass and total mineralisation of C and N.
Therefore the four sets of modelling estimates per site were treated as replicate estimates of the
mineralisation rates and one-way ANOVAs were calculated to compare the sites.
38
3
Material and Methods
3.3.2 PEARSON product-moment correlation
PEARSON product-moment correlations were used to test for significant relationships between two
variables. The correlation coefficient (r) determines the extent to which values of two variables are
linearly related to each other.
3.3.3 Canonical correspondence analysis (CCA)
The canonical correspondence analysis (CCA) is a combination of ordination and multiple regression
assuming unimodal (bell-shaped) response curves of species abundance to environmental variables
(Jongman et al. 1987, ter Braak and Smilauer 1998). This assumption is an advantage over ordination
techniques based on linear response models, such as principal component analysis (PCA), since a
linear relationship between species abundance and environmental variables is rather unlikely within
biological systems.
The CCA is used to sort the sites according to their environmental conditions and to relate Testate
Amoebae species abundance patterns to environmental information (faunal and microbial parameters,
abiotic variables) and to test the data set for trends of ecological preferences of particular species. The
analysis is performed in two steps (cf. ter Braak 1986). First the dominant pattern of variation in
community composition is extracted from the species data by an ordination technique. Second this
pattern is related to environmental variables.
CANOCO uses an iterative ordination algorithm based on weighted averaging that is detailed in the
appendix of ter Braak & Prentice (1988). Statistical significance of the regression with the ordination
axes was determined at the 5 % significance level (p < 0.05) using a MONTE CARLO permutation test with
999 unrestricted permutations (ter Braak 1996).
The sites are plotted on a two-dimensional factorial plane, representing a number of environmental
variables, to understand the conditions at each site in relation to the other sites (biplot of sites). The
Testate Amoebae species are plotted in the same way to test the data set for trends of ecological
preferences of particular species (biplot of species).
39
Chapter 4
The Food Web Model
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4 The food web model
The food web model approach of De Ruiter et al. (1993b, based on O'Neill 1969, and Hunt et al. 1987)
was applied to the decomposer communities of the different sites. The principal output of the food web
model are the carbon and nitrogen mineralisation by individual functional groups as well as the total
mineralisation by the entire decomposer food web.
Figure 4.1 gives a schematic view of the steps taken in this study to model the C and N mineralisation
rates. The first necessity was to identify the organisms involved in the decomposer food web and to
compile a list of the most important taxa. In the next step taxa were aggregated into functional groups on
the grounds of their trophic function and energy processing rates (sensu Moore et al. 1988). Based on the
comprehension of the trophic interconnectivity between the functional groups a diagram of the food web
was constructed in form of a connectedness web (see Figure 5.13, section 5.2.1). The death rates were
adapted to the climate of the specific study site (see section 4.2.3). Then energy flow descriptions of the
food web were computed, in which the feeding rates were calculated from the observed population sizes
(biomasses), the observed detritus pool and resource quality, and physiological parameters characterising
the metabolism of the functional groups. Using the estimated feeding rates and the C:N-ratios of the
interacting groups the C and N mineralisation were then calculated for each functional group and for the
total food web.
41
4
The Food Web Model
compile participating organisms
aggregate into functional groups
comprehend trophic relationships
draw sketch of the food web:
connectedness web
measure
biomasses
(variables)
calculate
energy flow
measure
resource quality
compile physiological
parameters
describe climate
(forcing function)
calculate
feeding rates
calculate
mineralisation rates (C + N)
Figure 4.1 Practical steps in applying the food web model approach to estimate C and N
mineralisation rates (schematic view).
The mathematic model is based on three principal assumptions:
I. The decomposer system is in steady state, i.e. the annual average production of the organisms
balances the annual loss through natural deaths and predation.
II. The top predators suffer from natural deaths only. Calculation of feeding rates starts from the top
predator and proceeds working backwards to the lowest trophic levels.
III. If a predator feeds on more than one prey type, both, the preference of the predator for a given prey
and the relative population sizes of the prey types are taken into account.
42
4
The Food Web Model
The assumption that the annual growth rates of the populations balance the annual natural death rates
(see section 4.2.1) and the death rates due to predation leads to the following equation (Hunt et al.
1987, De Ruiter et al. 1993b):
production = natural deaths + deaths due to predation
equation 4.1
The population biology equation states that production is the part of consumption (food incorporated)
that is neither excreted nor respired but transformed into biomass (Figure 4.2).
consumption
assimilation
efficiency
assimilation
excretion
(faeces)
production
efficiency
production
(biomass)
mineralisation
(CO2, NH4+, etc.)
Figure 4.2 Schematic illustration of the population biology
equation, the pathway of energy from consumption to production
resp. mineralisation.
The assimilation efficiency (a) is the rate with which the material consumed by a functional group is
assimilated into the body (see section 4.2.2, equation 4.6). The production efficiency (p) is the rate with
which the assimilated material is used for production. In other words (1-p) is the rate with which
assimilated material is respired (see section 4.2.2, equation 4.7). Given that production depends on the
feeding rate as well as on the efficiency of assimilation and production, equation 4.1 leads to equation
4.2 (Hunt et al. 1987, De Ruiter et al. 1993b).
43
4
The Food Web Model
a j p j Fj = d jB j + K j
Û Fj =
aj
pj
Fj
dj
Bj
Kj
j
equation 4.2
d jB j + K j
aj pj
assimilation efficiency
production efficiency
feeding rate (kg C ha-1 a-1)
death rate (a-1)
biomass (kg C ha-1)
loss through predation (kg C ha-1 a-1)
index of functional group
The top predator of the food web is considered not to be preyed upon. Thus the feeding rate of the top
predator may be calculated from equation 4.2 by setting the deaths due to predation Kj = 0. Then the
feeding rates of the trophic level below the top predator can be calculated and the calculation proceeds
downwards working through the food web to the lowest trophic level, the primary decomposers.
Most predators feed on more than one prey. If this is the case, both the preference of the predator for a
given prey, and the relative population size (biomass) of the prey types are taken into account. To do so
the relative feeding preference (wij, see section 4.2.3) of a predator j for a prey i is introduced (equation
4.3, Hunt et al. 1987, De Ruiter et al. 1993b). The total feeding rate of predator j (Fj) is split into prey
specific feeding rates (Fij).
Fij =
w ij B i
n
åw
kj
Fj
Bk
k =1
Þ Fij =
wij
n
j
i
44
biomass that predator j consumes from prey i
Fj
total biomass consumed by predator j
feeding preference
number of prey types consumed by predator
index of predator
index of prey
equation 4.3
4
The Food Web Model
The mineralisation rates are derived from the feeding rates (equation 4.4, Hunt et al. 1987, De Ruiter et
al. 1993b). Carbon mineralisation is that part of the consumed biomass C that is assimilated (aj) and not
incorporated into biomass but respired (1-pj):
M C j = a j (1 - p j )Fj
MC j
equation 4.4
carbon mineralisation (kg CO2-C ha-1 a-1)
To calculate the nitrogen mineralisation both, the C:N-ratio of the consumed material (C:N-ratio of the
prey) and that of the produced biomass (C:N-ratio of the predator) must be taken into account (equation
4.5, Hunt et al. 1987, De Ruiter et al. 1993b):
æ 1 pj ö
M N ij = a j ç - ÷ Fij
çq q ÷
j ø
è i
MN ij
q
equation 4.5
nitrogen mineralisation (kg Nmin ha-1a-1)
C:N-ratio
4.1 Detritus pool and resource quality
The size of the detritus pool (organic layer) at each site was calculated from the dry-mass-to-area ratios
(Table 2.1). Since naturally occurring organic matter is a complicated mixture of degradable and
recalcitrant substances (Andrén et al. 1990, Tezuka 1990, Sollins et al. 1996) it cannot be assumed that
the entire C pool is equally available to primary decomposers. Instead for the food web model it was
assumed that only 20 % of the total detritus pool is realisable for the decomposer organisms. Making
use of the 14C-signature the mean residence time of carbon in the LF-layer at the sites was estimated to
be 5-6 years (Harrison et al. 2000) which supports this assumption.
The resource quality was characterised by the observed C:N-ratios of the organic layer (Table 2.1).
Since the total C:N-ratio of the substrate in a heterogeneous environment like the organic layer may
differ from the C:N-ratio of the material available to the primary consumers (Tezuka 1990, Hammel
1997) the site specific C:N-ratios of the substrate were reduced by 20 % for fungi and by 30 % for
bacteria. This reflects the specific ability of fungi and bacteria to use recalcitrant substances (De Ruiter
et al. 1993b, Dighton 1997) while the characteristic C:N-ratios, and thus the specific resource quality at
45
4
The Food Web Model
the particular site, are still taken into account.
4.2 Model parameters
Parameters needed to run the food web model are the average C:N-ratio of the biomass of the
functional groups, the death rate, the assimilation and production efficiencies and the feeding
preferences. Estimates for these parameters were taken from the literature and are based on field
budgets, laboratory measurements and laboratory cultures (see Table 5.14, section 5.2.3).
4.2.1 Death rates
The nominal death rate (d) used in the food web model is defined by Hunt et al. (1987) as the inverse of
the maximal life span observed under ideal laboratory conditions. This death rate determines the rate at
which 'natural deaths' (non-predaceous losses) occur. The death rates used in this study are taken from
the literature (see Table 5.14, section 5.2.3) (Hunt et al. 1987, De Ruiter et al. 1993a). They refer to a
temperature of 10°C (De Ruiter et al. 1993a) and have to be adapted to the specific climatic conditions
of a given site (see section 4.3).
4.2.2 Assimilation and production efficiencies
The efficiency with which the consumed food is used for production depends on the efficiency with
which the consumed food is assimilated (equation 4.6) and on the efficiency with which the assimilated
food is used for production (equation 4.7).
a=
a
C
E
A
p=
46
(C - E ) = A
C
C
equation 4.6
assimilation efficiency
consumption
excretion
assimilation
A-R P
=
A
A
equation 4.7
4
p
R
P
The Food Web Model
production efficiency
respiration
production
4.2.3 Feeding preferences
The feeding preferences are entered in form of a matrix of wij values describing the relative preference
of predators on their prey (see Table 5.15, section 5.2.3). Within this matrix a value of ³ 1 indicates that
one functional group is predator of another functional group. Values > 1 put emphasis on a certain
trophic link, with the weighting being dependent on the total prey biomass consumed by the predator.
E.g. the feeding rate of panphytophagous Testate Amoebae Fpata is split to a feeding rate on three kinds
of prey: fungi (Ffung,pata), bacteria (Fbact,pata) and detritus (Fdetr,pata). According to equation 4.3 the rate with
which the panphytophagous Testate Amoebae feed on fungi, Ffung,pata is calculated as follows:
Ffung,pata =
w fung,pata B fung
(B total )
× Fpata
equation 4.8
Û Ffung,pata =
Btotal
fung
pata
bact
detr
(w
w fung,pata B fung
fung,pata
B fung + w bact,pata B bact + w detr,pata B detr )
× Fpata
total biomass consumed by predator
index of fungi
index of panphytophagous Testate Amoebae
index of bacteria
index of detritus
Hence, the amount of biomass that panphytophagous Testate Amoebae feed from fungi depends on
their preference for fungi and the relative availability of all their food sources, including fungi.
4.3 Adapting the food web model to specific climatic conditions
Besides resource quality, climate is one of the most important determinants for soil biota (Swift et al.
1979). Climatic influences on the decomposer food web enter the model in indirect and direct form.
Indirectly, as the observed food webs (the connectedness of the functional groups and their biomasses)
are a function of the long term site specific conditions and reflect the climatic and all other
environmental influences. Directly, as temperature and moisture regime of a given site have an
47
4
The Food Web Model
influence on metabolic rates of the organisms of the food web. This is accounted for by making the
death rate of the functional groups climate dependent. Climate has a massive influence on the death
rate (natural deaths per year) of the functional groups in the field. This should be understood not so
much in the terms of climatic extremes having a lethal effect but in terms of climatic influences on the
metabolisms of the organism determining their number of generations per year. Climatic conditions that
enhance the metabolism (e.g. increased temperature and moisture) will cause a higher turnover of the
organisms, accounted for within the model by an increase in the death rates.
Usually metabolic rates are adjusted to temperature using the Q10 rate constant. The Q10 value is the
increase in activity for a 10°C rise in temperature. This connection is expressed in the so called 'power
function':
(T - Tb )
E T = Q 10 10
ET
Q10
T
Tb
equation 4.9
temperature factor
rate constant
temperature
base temperature i.e. the temperature corresponding to ET=1
For decomposition modelling values of Q10 are usually within the range of 2-5 (Andrén et al. 1990),
which means that with a temperature increase by 10°C metabolic rates at least double.
The Q10-adaptation is not entirely satisfactory because it does not take soil moisture into account.
Therefore, in this modelling study a method was used that considers both, temperature and soil
moisture regime of the sites.
Within the CANIF-project Persson et al. (2000a, 2000b) estimated respiration (CO2-evolution) and N
mineralisation using a laboratory incubation method with material collected from the same study sites as
studied here. C and N mineralisation rates were measured in the laboratory at a certain constant
reference temperature (Tref) and a certain constant reference soil moisture (reference water potential
yref). To extrapolate from these standard conditions to the soil temperature and moisture in the field
Seyferth (1998) within the working group of Tryggve Persson determined response functions in the
laboratory, using material from the humus layer at a site close to Skogaby (S-SE). The range of
temperatures and moistures studied was wide, taking account of both temperate and boreal systems:
the studied incubation temperatures ranged from -4.0 to +25.0°C at water-holding capacities from
48
4
The Food Web Model
15 to 100 % (Seyferth 1998).
Seyferth (1998) found that the temperature dependence of mineralisation rates in forest soils could well
be described by a quadratic function (equation 4.10, Ratkowsky et al. 1982):
M = b 2 (T - Tmin )
equation 4.10
2
M
Tmin
T
b
mineralisation rate at temperature T
the minimum temperature at which activity starts (°C)
actual temperature (°C)
slope of temperature function (°C-1)
Seyferth (1998) empirically determined Tmin, the minimum temperature at which activity started, to be 6.2°C and calculated a correction factor for the activity at a temperature T as follows (equation 4.11):
b 2 (T - Tmin )
M
= 2
M ref b ref (Tref - Tmin )2
2
M
M ref
bref
Tref
equation 4.11
correction factor
slope at the reference temperature Tref (°C-1)
reference temperature (°C)
In further experiments she found the slope of this function to be moisture dependent according to a loglinear function (equation 4.12):
b = m log (ψ ) + n
b
m
y
n
equation 4.12
slope of temperature function (equation 4.11) dependent on moisture
slope of the moisture function
water potential (Mpa)
constant
For the calculation of M/Mref equations 4.11 and 4.12 can be combined:
49
4
The Food Web Model
(m log (ψ ) + n) × (T - Tmin )
M
=
M ref (m log (ψ ref ) + n)2 (Tref - Tmin )2
2
2
yref
equation 4.13
reference water potential (Mpa)
The combination leads to equation 4.13, which Persson (2000a, 2000b) used to calculate monthly
correction factors M/Mref in order to correct extrapolations from the laboratory to the field according to
temperature and moisture regime of a specific site.
Persson (2000a, 2000b) extrapolated from reference temperature of Tref = 15°C at optimal moisture
conditions (60 % water holding capacity (WHC), corresponding to a site specific water potential yref that
was obtained from the CANIF database; e.g. for Skogaby, S-SE, yref = 0.0035 MPa).
To be able to use Persson's correction factor M/Mref for the food web model the factors had to be
modified to account for a difference in reference temperature. The death rates used for the model were
obtained at Tref = 10°C (see De Ruiter et al. 1993a). All other variables and parameters stay constant,
thus the term (Tref - Tmin)² is the only term that has to be recalculated (equation 4.14):
M'
M (Tref - Tmin )
=
×
M ' ref M ref (T' ref -Tmin )2
2
M'
M ' ref
M
M ref
Tref
T'ref
equation 4.14
correction factor accounting for new reference temperature
correction factor given by Persson
Persson's reference temperature (15°C)
new reference temperature (10°C)
For each month, Persson’s site specific monthly correction factor M/Mref was recalculated to obtain a
corrected monthly M'/M'ref (equation 4.14). The recalculated correction factor M'/M'ref takes the reference
temperature into account at which the death rates of the food web model were obtained. It makes use of
site specific mean monthly temperature and moisture estimates from the CANIF databank. For each
month and functional group a corrected death rate di' was obtained (equation 4.15):
50
4
di ' = d ×
M'
M ' ref
di'
d
climate corrected mean monthly death rate
nominal death rate (Hunt et al. 1987, De Ruiter et al. 1993a)
The Food Web Model
equation 4.15
From the monthly corrected di' an annual corrected death rate d'' is obtained for each functional group
(equation 4.16):
d''=
1 12
å di '
12 i=1
d''
equation 4.16
mean annual death rate adapted to site specific climate
The site specific annual corrected death rates of each functional group are given in Table 5.14, section
5.2.3.
4.4 Biomass estimates
The actual variables of the food web model are the observed biomasses of the functional groups at
each site (section 5.2.3.3, Table 5.17). For each sites mean values of four sampling times covering the
seasons of a year are used, representing an average biomass that takes part of the seasonal variation
of the functional groups into account. The population dynamics of the functional groups were not
studied. Rather the aim was to characterise the food web by yearly averages representing the standing
crop biomass of the groups. To get realistic estimates of the standing crop biomass of the food webs at
a certain site four samplings per year are sufficient (De Ruiter, pers. com.).
51
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Chapter 5
Results
Dies ist keine leere Seite
5 Results
The result chapter is divided into two parts. In part one (section 5.1) the Testate Amoebae communities
are described and analysed. The Testate Amoebae are related to the abundance of other decomposer
biota and to the abiotic environment at the sites along the transect.
In part two (section 5.2) the results of the food web modelling approach described in chapter 4 are
presented. The focus on Testate Amoebae is broadened towards the entire decomposer community
and towards the forest ecosystem.
5.1 Testate Amoebae community structure
5.1.1 Size structure and biomass
A total of 42 species were found on the study sites (Table 5.1). They were grouped into five size classes
I-V according to biomass, ranging from 0.0004 mg C ind-1 to 0.0210 mg C ind-1 (Table 5.2). The size
range was very large; the biomass per individual of the largest class being almost 53 fold that of the
smallest class. Among the Testate Amoebae found, 14 species were assigned to the 1st (smallest), 8 to
the 2nd resp. 3rd and 6 to the 4th resp. 5th size class (Table 5.2).
The abundance of Testate Amoebae within a size class decreased gradually from the smallest to the
largest class; small species occurred with higher abundance (Figure 5.1). A different picture emerged
when the size classes were used to calculate the Testate Amoebae biomass per unit area. Larger
species made up the larger share of the overall Testate Amoebae biomass (Figure 5.1). According to
their size, Testate Amoebae species have been rated to be r-strategic (small species) or K-strategic
(large, xenosome species) (Bamforth 1997). Both strategic groups similarly contribute to the total
biomass of the community.
Because body size varied so much between the different species of the Testate Amoebae community,
abundance (number of individuals per unit area) cannot logically be linked to resource division by
species (cf. Tokeshi 1993). Biomass measures serve this purpose better. On the other hand,
abundance may more suitably reflect the activity of a species than does its biomass. In the comparison
of the Testate Amoebae communities of the different sites emphasis will be put on biomass measures.
53
5
Results
Table 5.1 List of species that were found on the study sites and classification into size
classes. See Table 5.2 for definition of size classes.
size
species
class
Arcella catinus PENARD 1890
IV
Arcella arenaria compressa CHARDEZ 1957
III
Assulina muscorum GREEFF 1889
I
Assulina seminulum (EHRENBERG 1848) LEIDY 1879
II
V
Bullinularia indica (PENARD 1907) DEFLANDRE 1953
Centropyxis aerophila sphagnicola DEFLANDRE 1929
III
Centropyxis gauthieri THOMAS 1959
III
Centropyxis matthesi RAUENBUSCH 1987
V
IV
Centropyxis sylvatica (DEFLANDRE 1929) BONNET & THOMAS 1955
Corythion dubium TARANEK 1882
I
Cryptodifflugia oviformis PENARD 1890
I
Cyclopyxis eurystoma DEFLANDRE 1929
III
V
Cyclopyxis kahli DEFLANDRE 1929
Difflugia lucida PENARD 1890
II
Difflugia minuta RAMPI 1950
I
Edaphonobiotus campascoides SCHÖNBORN, FOISSNER & MEISTERFELD 1983
I
I
Euglypha laevis (PERTY 1849) SCHÖNBORN 1992
Euglypha rotunda minor WAILES 1915
I
Euglypha cf. strigosa (EHRENBERG 1872) WAILES & PENARD 1911a
III
Heleopera sylvatica PENARD 1890
III
III
Hyalosphenia subflava CASH & HOPKINSON 1909
Microchlamys patella (CLAPAREDE & LACHMANN 1958) COCKERELL 1911
II
Microcorycia flava (GREEFF 1866) PENARD 1902
II
Nebela lageniformis PENARD 1890
IV
II
Nebela militaris PENARD 1890
Nebela parvula/tinctab
IV
Nebela tincta major/bohemica/collarisc
IV
Phryganella acropodia (HERTWIG & LESSER 1874) HOPKINSON 1909
II
II
Phryganella paradoxa alta BONNET & THOMAS 1960
Plagiopyxis declivis BONNET & THOMAS 1955
V
Plagiopyxis intermedia BONNET 1959
IV
Plagiopyxis labiata PENARD 1910
V
Schoenbornia humicola (SCHÖNBORN 1964) DECLOITRE 1964
I
Schoenbornia viscicula SCHÖNBORN 1964
I
Tracheleuglypha dentata (PENARD 1890) DEFLANDRE 1928
I
I
Trachelocorythion pulchellum (PENARD 1890) BONNET 1979
Trigonopyxis arcula (LEIDY 1879) PENARD 1912
V
Trigonopyxis minuta SCHÖNBORN & PESCHKE 1988
III
Trinema complanatum PENARD 1890
I
I
Trinema enchelys (EHRENBERG 1838) LEIDY 1878
Trinema lineare PENARD 1890
I
Trinema penardi THOMAS & CHARDEZ 1958
II
total number of species found 42
a
b
c
Euglypha cf. strigosa is treated as a morphologic group. See section 3.1.2.6.3.
Nebela parva CASH & HOPKINSON 1909 and N. tincta (LEIDY 1879) AWERINTZEW 1906 were pooled
as the taxon N. parvula/tincta. See section 3.1.2.6.4.
Nebela tincta major DEFLANDRE 1936, N. bohemica TARANEK 1881 and N. collaris (EHRENBERG
1848) LEIDY 1879 were pooled as the taxon N. tinctamajor/bohemica/collaris. See section
3.1.2.6.4.
54
5
Results
Table 5.2 Size classes of Testate Amoebae and number of
species found belonging to each size class.
conversion factor
(µg C/individual)
0.0004
0.0016
0.0035
0.0077
0.0210
size class
I
II
III
IV
V
number of species
per size class
14
8
8
6
6
12
1000
8
500
4
0
0
I
II
III
IV
-1
1500
biomass (kg C ha )
16
6
-2
abundance (10 m )
2000
V
size class
Figure 5.1 The abundance (columns) resp. biomass (dots) of living Testate Amoebae in
the five size classes. See Table 5.1 and 5.2 for characterisation of size classes. Whiskers
represent standard deviation.
5.1.2 Species pattern
5.1.2.1
Species richness, diversity and evenness
Analysis of variance revealed significant effects of 'site' and 'time' on mean species number, diversity
and evenness (Table 5.3). The F-value (inter-group variance divided by intra-group variance) indicates
which of the tested effects accounts for the larger share of variance within the data set. The higher the
F-value, the higher the amount of variance that is explained by the effect. The F-values in Table 5.3
show that the factor 'site' explains the largest part of variance within the data set of species number and
diversity, while the variance of evenness is explained almost equally by both factors. The significant
interaction between the two factors ‘site’ and ‘time’ reflect site specific fluctuations of the evenness with
time resp. time specific relations of the sites in the equitability of the species abundances (Table 5.3).
55
5
Results
Table 5.3 Results of the two-way ANOVAs on the effect of 'site' and 'time' on
number of species, diversity and evenness. df-Effect = 3 (site, time); dfEffect = 9 (interaction site ´ time); F = inter-group variance divided by intragroup variance; p = p-level of significance: n.s. > 0.05 (not significant);
* £ 0.05; ** < 0.01, n = 50.
site
number of species
diversity
evenness
F
20.1
21.5
6.4
p
**
**
**
time
F
p
6.3 **
6.6 **
8.0 **
site ´ time
F
p
1.3 n.s.
0.1 n.s.
2.5 *
There was little difference in the total number of Testate Amoebae species (species richness) found at
the four sites, apart from a considerable larger value of 40 species at FR (Table 5.4). Besides the total
number, the mean species number of each site was calculated in order to be able to perform statistical
tests. While the total number of species is the cumulative number of species that were found at each
site, the mean number of species is the average number of species encountered per sample.
Concerning the mean species numbers, two groups distinguish from each other: the Swedish sites NSE and S-SE with significantly lower values than the Southern sites DE and FR. To facilitate
comparison with other studies, overall community diversity and evenness were estimated using the
Shannon-Wiener diversity index (as reviewed in Krebs 1999). Parallel to the pattern of species richness,
diversity was smallest at N-SE (3.2), increased towards South over 3.5 at S-SE, to a maximum of 3.7 at
DE (Table 4.5). As a consequence of the evenness at S-SE being significantly higher than at N-SE, the
only significant difference between the Shannon-Wiener diversities was that of N-SE having a lower
diversity than all other sites (Table 4.5). The evenness estimates confirm a rather well-balanced species
abundance pattern at FR, DE and S-SE, while at N-SE only 72 % of the maximum Shannon-Wiener
diversity is reached (Table 5.4).
Table 5.4 Total number of species and mean number of species found at each
site. For the calculation of diversity and evenness the relative abundance of living
Testate Amoebae cells (active cells + cysts) was used; empty shells were not
taken into consideration. In comparison within a specific row values labelled with
identical letters are not significantly different from each other according to the
Tukey HSD test (see Table 5.3 for further details on ANOVA).
number of species (total)
number of species (mean)
diversity
evenness
56
N-SE
34
21A
3.2A
0.72A
S-SE
35
23A
3.5B
0.78B
DE
35
27B
3.7B
0.78B
FR
40
27B
3.6B
0.76AB
5
5.1.2.2
Results
Species rank plots
The most versatile approach of presenting species abundance data to facilitate comparison among
different data sets is plotting logarithmic abundance resp. biomass against arithmetic rank order of
species in a so-called rank plot (Tokeshi 1993). The rank plot was carried out with biomass data (see
remarks on biomass and abundance measures in section 5.1.1). For such a plot the species that occur
on each site are placed in rank order with the most dominant first. The total biomass is then logtransformed (base 10) and plotted against the ranks of the species. The rank curves of the four sites
revealed strong similarities between community structures at N-SE, S-SE, DE and FR. All curves
display a rather shallow non-continuous decrease towards the rarer species with more than one slope.
This suggests that biomass is quite evenly distributed among species (Magurran 1988). The curve for SSE exhibits a fast drop at the beginning of the curve, indicating that at this site a few species are of
great relative importance.
100000
N-SE
S-SE
DE
FR
-1
biomass (g C ha )
log scale
10000
1000
100
10
B
1
0
10
20
30
species in rank order
40
Figure 5.2 Species biomass rank plots. The total Testate Amoebae biomass on a log scale are
plotted against the ranks of the species (Tokeshi 1993).
57
5
Results
5.1.2.3
Relative biomass structure
The community structure of Testate Amoebae is shown in detail in Table 5.5. The relative biomass is
compared to indicate the relative importance within resource division among a species community (see
section 5.1.1). Species are subdivided into 6 groups (A-G) to identify patterns and to aid the comparison
of community structures at the different sites (Table 5.5).
Group A consists of species that were equally common at all sites. Among these, many species also
occurred with relatively high biomasses, e.g. Trigonopyxis arcula, Nebela parvula/tincta, Trinema
lineare, Corythion dubium, Nebela tincta major/bohemica/collaris, Plagiopyxis declivis, Centropyxis
sylvatica, Schoenbornia humicola and Phryganella acropodia, Nebela militaris (Figure 5.3 C, D and E).
Some species rank among the most important even though their individual biomass is small (size
class I), e.g. Trinema lineare, Corythion dubium and Schoenbornia humicola. Consequently these
species occur in very high abundances.
Group B comprises of species that exhibited relatively higher biomass towards North. Three of those
four species, namely Arcella catinus, Cryptodifflugia oviformis and Difflugia lucida showed a clear
biomass maximum at N-SE.
Group C combines nine species that tended to occur with relatively higher biomass towards South,
including five species that were not found at N-SE and/or S-SE, but occurred at the two Southern sites.
Two species, Hyalosphenia subflava and Heleopera sylvatica (Figure 5.3 F), were absent from N-SE but
quite common at all other sites. Trachelocorythion pulchellum and Trinema enchelys were absent from
N-SE and S-SE but found at the two Southern sites.
Group D consists of Tracheleuglypha dentata and Plagiopyxis intermedia that were found at all sites
except S-SE. The latter species was very rare at N-SE.
Group E unites two species (Bullinularia indica and Centropyxis gauthieri) that were lacking only at DE
(Figure 5.3 A). Both species furthermore occurred with higher relative biomasses at N-SE than at S-SE
and FR.
Group F comprises of Plagiopyxis labiata and Cyclopyxis kahli, two species that were found at N-SE
and S-SE but were lacking from the two Southern sites. The latter species was very rare at S-SE.
Group G combines rare species that were found in low biomasses at one or two sites. Among those
Edaphonobiotus campascoides (Figure 5.3 B) inhabited DE, but was absent from the other sites.
Difflugia minuta and Nebela lageniformis (Figure 1.1) were restricted to, and very rare at FR.
58
5
Results
Table 5.5 The community structure of Testate Amoebae. Asterisks represent relative biomass (%). Species
are arranged according to their occurrence or absence at certain sites. Dotted lines indicate groups labelled
with capital letters (A-G). For explanation of the grouping see section 5.1.2.3. ****** > 31.9 %; ***** = 10.0 to
31.9 %; **** = 3.2 to 9.9 %; *** = 1.0 to 3.1 %; ** = 0.32 to 0.99 %; * < 0.32 %; empty space = 0 %; ° = found
only once during counting or occurred only when analysing enriched material from flotations or batch cultures.
A
B
C
D
E
F
G
Trigonopyxis arcula
Nebela parvula/tincta
Trinema lineare
Corythion dubium
Nebela tincta major/bohemica/collaris
Plagiopyxis declivis
Centropyxis sylvatica
Schoenbornia humicola
Phryganella acropodia
Microchlamys patella
Euglypha cf. strigosa
Assulina muscorum
Nebela militaris
Trinema complanatum
Cyclopyxis eurystoma
Centropyxis aerophila sphagnicola
Assulina seminulum
Euglypha rotunda minor
Trinema penardi
Microcorycia flava
Arcella catinus
Cryptodifflugia oviformis
Difflugia lucida
Euglypha laevis
Schoenbornia viscicula
Trigonopyxis minuta
Centropyxis matthesi
Hyalosphenia subflava
Heleopera sylvatica
Phryganella paradoxa alta
Trachelocorythion pulchellum
Trinema enchelys
Tracheleuglypha dentata
Plagiopyxis intermedia
Bullinularia indica
Centropyxis gauthieri
Plagiopyxis labiata
Cyclopyxis kahli
Edaphonobiotus campascoides
Arcella arenaria compressa
Difflugia minuta
Nebela lageniformis
N-SE
****
*****
****
****
***
****
*****
****
***
*****
***
***
***
**
**
**
**
*
*
**
*****
****
***
**
**
*
*
*
°
**
**
*
*
S-SE
******
*****
***
****
****
***
***
***
***
***
***
***
***
**
**
*
*
*
*
***
*
*
*
*
*
*
*
****
**
*
DE
*****
*****
****
****
****
****
****
***
****
***
****
***
***
***
***
**
*
*
*
*
**
*
*
***
**
****
***
****
**
°
*
*
*
*
*
*
**
°
FR
******
*****
****
***
****
****
***
****
****
***
***
**
**
**
**
*
*
*
*
*
**
**
**
**
***
**
**
****
***
*
*
*
*
*
*
*
*
°
°
°
°
59
5
Results
dorsal lip
ventral
50 µm
A
10 µm
Pseu
Pseu
N
E
shel
colla
n
C
N
10 µm
Pseu
cys
T
P
B
10 µm
Pseu
Ne
D
20 µm
F
20 µm
Figure 5.3 Testate Amoebae species from the study sites. A. Bullinularia indica, focus on the dorsal
and on the ventral lip. 200x, bright-field, in Euparal. B. Edaphonobiotus campascoides, lateral view
of the trumpet-like shell with cytoplasm and vesicular nucleus. 400x, DIC, in Euparal. C.
Schoenbornia humicola, nucleus and cytoplasm stained with aniline blue. 400x, bright-field, in
watery suspension. D. Nematode and Trinema lineralis, 400x, bright-field, in watery suspension. E.
Nebela militaris, cytoplasm stained with aniline blue. 400x, bright-field, in watery suspension. F.
Heleopera sylvatica cyst, stained with aniline blue. 200x, bright-field, in watery suspension. Pseu =
pseudostome; N = nucleus; n = nucleolus; P = cytoplasm; T = Trinema lineare; Ne = Nematode.
60
5
Results
The Testate Amoebae communities at the transect sites were analysed using two similarity indices; the
number of unique species and the Bray & Curtis index (Bray and Curtis 1957, Southwood 1994). The
number of unique species in comparison of a pair of sites is an important component of the
complementarity of these sites (Colwell and Coddington 1994). The highest number of unique species
was rendered by the comparison of site DE and N-SE (Table 5.6). In the comparison of FR and DE only
5 unique species were found.
Table 5.6 Total number of species, and matrices of the number of
unique species (Colwell and Coddington 1994) and Bray & Curtissimilarities (Bray and Curtis 1957, Southwood 1994) in comparison
of the four sites.
total number of species
number of
N-SE
unique species
S-SE
DE
FR
Bray & CurtisN-SE
similarity (%)
S-SE
DE
FR
N-SE S-SE DE
34
35 35
7
11
10
8
9
5
67
34
46
43
56
70
FR
40
The Bray & Curtis-similarity index is based on species specific abundance values (Bray and Curtis
1957, Southwood 1994). Again the highest similarity was found when comparing the abundance
patterns of DE and FR, followed by the similarity between N-SE and S-SE (Table 5.6). The number of
unique species and the Bray & Curtis similarity were plotted against the geographical distance between
the respective sites (Figure 5.4). With few exceptions the Bray & Curtis similarity increased with
decreasing geographical distance between the sites, however this correlation is not significant
(PEARSON correlation r = -0.68, p = 0.13; Figure 5.4). The number of unique species showed the reverse
trend in a significant correlation (PEARSON correlation r = 0.89, p < 0.05; Figure 5.4). Both measures
indicate an increasing dissimilarity of the Testate Amoebae communities with increasing geographical
distance between the sites.
61
Results
12
80
Bray & Curtis similarity (%)
70
10
60
8
50
6
40
30
4
20
2
10
D/F
0
0
400
D/S S/N F/S
800
D/N
1200
1600
F/N
number of unique species
5
0
2000
distance (km)
Bray & Curtis similarity (%)
number of unique species
Figure 5.4 The Bray & Curtis similarity index and the number of unique species plotted against
geographical distance between the sites. Bray & Curtis = dotted line (r = -0.68, p = 0.13);
unique species = unbroken line (r = 0.89, p < 0.05). On the abscissa the distance between pairwise compared sites is indicated using the following abbreviations: D = DE; F = FR; S = S-SE;
N = N-SE.
5.1.3 Testate Amoebae communities in their environment
The sites that were studied have been described in chapter 2 by general geographic and site historic
variables (Table 2.1). It was part of the soil ecological survey carried out at each site to gather further
information on the environmental conditions, in order to characterise the ecosystems in terms of
microbial community, abiotic conditions, and faunal groups besides Testate Amoebae.
5.1.3.1
Other decomposer biota
Besides Testate Amoebae other important biota of the decomposer food web were monitored:
microorganisms (bacteria and fungi), Nematoda, Enchytraeidae and Microarthropoda (Collembola and
Acari) and the main effect of 'site' and 'time' was analysed via two-way ANOVA. Both had a significant
effect on all variables, except for the effect of ‘site’ on the abundance of Nematoda that was not
significant (Table 5.7). According to the F-value, 'time' often explained the variance within the data set to
a larger extent than 'site'. Significant interactions of 'time ´ site' indicated that the dynamics of the biota
62
5
Results
were site specific and/or that the site-to-site relation of abundances varied with time.
Table 5.7 Results of the two-way ANOVAs on the effect of 'site' and 'time' on microbial and faunal parameters. dfEffect = 3 (site, time); df-Effect = 9 (interaction site ´ time); F = inter-group variance divided by intra-group
variance; p-level of significance: n.s. > 0.05 (not significant); * £ 0.05; ** < 0.01.
m-2
metabolic potential (mg C
microbial biomass C (g m-2)
metabolic quotient (mg C-CO2 g-1 Cmic)
bacteria (% of total Cmic)a
bacteria, frequency of dividing cells (%)a
Nematoda (103 m-2)
Enchytraeidae (103 m-2)
Collembola (103 m-2)
Acari (103 m-2)
a
h-1)
site
F P
20.4 **
26.0 **
22.2 **
19.9 **
3.8 *
1.3 n.s.
100.5 **
14.7 **
3.1 *
time
F p
46.0 **
53.7 **
24.2 **
22.8
6.4
10.5
7.3
**
**
**
**
site ´ time
F p
4.8 **
21.9 **
11.5 **
4.6
2.1
4.1
3.7
**
*
**
**
n
111
113
110
32
32
116
116
115
55
since these variables were monitored only at the last sampling occasion one-way ANOVAs were carried out.
Microbial activity, expressed by the metabolic quotient that relates metabolic potential (CO2 evolution at
10°C and water content 300 % DW) to unit microbial biomass, was similar at N-SE, S-SE and DE, and
significantly lowest at FR (Figure 5.5). Interestingly microbial biomass (Cmic) was highest at N-SE and
FR, while the relative inactivity of the microflora at FR resulted in a comparably low metabolic quotient at
this site (Figure 5.5). The high metabolic quotient at N-SE was a combination of relatively high microbial
biomass and strong metabolic potential.
63
1.6
70
1.4
60
1.2
A
50
40
A
1.0
A
A
A
A
B
BC
30
C
C
B
0.8
0.6
B
20
0.4
10
0.2
0
0.0
N-SE
metabolic potential
S-SE
DE
microbial biomass C
-1
-2
microbial biomass C (g C mic m )
-1
-2
80
metabolic quotient (mg CO2-C g Cmic)
Results
metabolic potential (mg CO2-C m h )
5
FR
metabolic quotient
Figure 5.5 Microbial parameters (metabolic potential, microbial biomass C, metabolic quotient) along the
transect. Symbols of the same shading labelled with identical letters are not significantly different from each other
according to the Tukey HSD test (see Table 5.7 for further details on ANOVA). Whiskers represent standard
deviation.
At all sites bacteria made up the smaller part of the total microbial biomass (4.8 – 12.6 %, Figure 5.6)
and fungi accounted for around 90 %. The share of bacteria was significantly highest at the Southernmost site FR. The trend of the metabolic quotient (Figure 5.5) to some extent fitted the frequency of
dividing bacterial cells which was significantly highest at N-SE (Figure 5.6).
64
5
Results
18.0
16.0
14.0
B
12.0
%
10.0
A
8.0
6.0
A
A
B
A
B
B
4.0
2.0
0.0
N-SE
S-SE
bacteria
DE
FR
frequency of dividing cells
Figure 5.6 Percentage of bacterial from total microbial biomass C and frequency of dividing bacterial
cells. Columns of the same shading labelled with identical letters are not significantly different from
each other according to the Tukey HSD test (see Table 5.7 for further details on ANOVA). Whiskers
represent standard deviation.
The sites also differed from each other with respect to other faunal groups (Figure 5.7) with the
exception of Nematodes, which were found in similar abundances at all sites. Like the Testate Amoebae
the Enchytraeidae showed a significant increase in abundance towards South, ranging from a minimum
of 20 102 m-2 at N-SE over 50 102 m-2 at S-SE, to a maximum of 380 102 m-2 resp. 430 102 m-2 at DE
resp. FR (Figure 5.7). The abundance of Collembola ranged from 59 103 m-2 to 154 103 m-2, with the
maximum abundance found at FR and the minimum at S-SE. The abundance of Acari ranged from 135
103 m-2 to 255 103 m-2, and revealed the opposite pattern of Collembola: the maximum Acari abundance
was found at S-SE and the minimum at FR.
65
5
Results
700
3
-2
Nematoda, Collembola, Acari (10 m )
800
600
C
2
-2
Enchytraeidae (10 m )
500
400
A C
300
A
AB
200
100
A
A
A
AB
AB
B
A
B
A
N-SE
S-SE
DE
C
B
0
Nematoda
Enchytraeidae
Collembola
FR
Acari
Figure 5.7 Abundance of the major faunal groups besides Testate Amoebae. Columns of the same
shading labelled with identical letters are not significantly different from each other according to the Tukey
HSD test (see Table 5.7 for further details on ANOVA). Whiskers represent standard deviation.
5.1.3.2
Abiotic environment
Both 'site' and 'time' had a significant effect on the environmental variables and significant interactions
between the two factors indicate site specific dynamics and/or time specific site patterns (Table 5.8).
The F-values tell that for all variables except water content the effect 'site' explains more of the total
variance within the data set than does 'time'. In Table 5.9 mean, minimum/maximum, and standard
deviation (in parantheses) of the parameters are given in order to show the range of the abiotic
environmental conditions at each site.
66
5
Results
Table 5.8 Results of the two-way ANOVAs on the effect of 'site' and 'time' on abiotic parameters. dfEffect = 3 (site, time); df-Effect = 9 (interaction site ´ time); F = inter-group variance divided by intragroup variance; p-level of significance: n.s. > 0.05 (not significant); * £ 0.05; ** < 0.01.
thickness of organic layer (cm)
bulk density (g DW mL-1)
water content (% DW)
pHH2O
total C (% DW)a
total N (% DW)a
C:N-ratioa
a
site
F p
18.1 **
38.7 **
17.7 **
136.6 **
24.0 **
66.8 **
218.1 **
time
F p
15.8 **
2.0 n.s.
38.2 **
18.8 **
site ´ time
F p
20.5 **
11.1 **
23.9 **
18.2 **
n
116
116
115
116
32
32
32
since these variables were monitored only at the 4th sampling occasion one-way ANOVAs were
carried out.
In the light of the precipitation patterns (Figure 2.2) the variance within the data set of the water content
of the organic layer is easily understood (Table 5.9). The surprisingly high maximum water content at NSE resulted from snow that was collected with the organic material on two sampling occasions. The
moisture conditions at the sites is better reflected by the monthly precipitation patterns (Figure 2.2) than
by the mean water contents.
The structure of the organic layer, its thickness and density, leading to a certain amount of organic
material per unit area, was characteristic at the sites. At S-SE and FR the organic layer was significantly
thinner (4.7 resp. 5.1 cm) than at N-SE and DE (both 6.2 cm, Table 5.9). The density of material at NSE was fairly small (0.09 g DW mL-1, Table 5.9). The bulk density at the other sites was higher (0.14 to
0.16 g DW mL-1). This was due to the nature of the material: at S-SE, DE and FR it comprised mainly of
spruce needles, at N-SE it was a mixture of moss as well as lichen litter, spruce and pine needles and
occasional birch leaves. The standard deviation of the layer thickness at DE and N-SE indicates that at
these two sites, in contrast to the other two sites, thickness of organic layer varied considerably with
time. At DE it was very much increased in September, probably due to increased needle fall. At N-SE it
was increased during September and April, presumably due to increased litter fall in September and
frost in April.
Another group of environmental variables is strongly correlated with each other: pH and total N. These
variables followed the patterning of total N and S deposition at the sites: minimum N content resp.
acidity at N-SE, maxima at DE, followed by FR, and intermediate values at S-SE (Table 2.1 for
67
5
Results
deposition, and Table 5.9 for environmental parameters). This was accompanied by high C:N-ratios at
N-SE, intermediate values at S-SE and lower values at DE and FR. The somewhat unexpected
(regarding N content) low C:N-ratio at FR resulted from significantly lower total C content at this site
(Table 5.9). The C:N-ratios given in Table 5.9 do not fully correspond to those in Table 2.1 (CANIFdatabank). This is ascribed to natural variation and heterogeneity. They are however presented
because the values in Table 5.9 are extracted from a full data set which allowed statistical testing. For
the ratios given in Table 2.1 the raw data set was not available.
Table 5.9 Mean, minimum/maximum (2nd row) and standard deviation (2nd row, in parentheses) of the environmental
parameters at the sites. In comparison within a specific row values labelled with identical letters are not significantly
different from each other according to the Tukey HSD test (see Table 5.8 for further details on ANOVA).
N-SE
water content (% DW)
278 A
S-SE
183 B
DE
FR
216 C
250 A
n
116
156/503(157)
143/227(35)
130/272(61)
176/338(66)
A
B
A
B
4.7
6.2
5.1
thickness of organic layer (cm)
6.2
116
4.3/9.4(2.4)
4.5/5.0(0.2)
5.1/9.1(1.9)
4.6/5.9(0.6)
bulk density (g DW mL-1)
0.09 A
0.14 B
0.14 B
0.16 C
0.05/0.13(0.04)
0.12/0.14(0.01)
0.12/0.15(0.01)
0.12/0.19(0.03) 116
A
B
C
3.9
3.6
3.4
3.5 D
pHH2O
116
3.8/4.1(0.1)
3.5/3.8(0.1)
3.1/3.6(0.2)
3.3/3.6(0.1)
total C (% DW)a
42 A
44 AB
46 B
36 C
32
39/46(2)
41/48(2)
42/49(2)
31/41(3)
1.1 A
1.5 B
1.9 C
1.5 B
total N (% DW)a
32
0.9/1.2(0.1)
1.4/1.6(0.1)
1.7/2.2(0.2)
1.3/1.8(0.2)
a
A
B
C
C
C:N-ratio
39.6
29.2
23.5
23.2
32
35.8/42.9(2.5)
27.1/31.1(1.2)
22.4/24.7(0.8)
22.6/23.8(0.5)
a since these variables were monitored only at the last sampling occasion one-way ANOVAs were carried out. These
values do not fully correspond to C:N-ratios given in Table 2.1 (from the CANIF-databank) due to natural variation
and heterogeneity. They are presented because the full data set obtained from measurements within this study
allowed statistical testing.
5.1.3.3
Multivariate analysis relating Testate Amoebae communities and environment
The information on the study sites was explored focussing on its explanatory value for the Testate
Amoebae species patterns found. A multivariate statistical method (canonical correspondence analysis,
CCA) was used to relate the environmental data set to the Testate Amoebae species biomass pattern.
In a first approach the entire environmental data set was included in the CCA and the most important
variables were identified using 'forward selection'. The environmental variables judged as being most
important were included into the final CCA. Those variable were 'total atmospheric pollution' (a
combination of the parameters total atmospheric nitrogen and total atmospheric sulfur deposition), pH of
organic layer, water content, mean annual precipitation mean annual temperature, and metabolic
potential as well as microbial biomass C. Data on other components of the fauna did not deliver
sufficient canonical correlation coefficients. Geographical information was not included into the analysis
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Results
to avoid grouping of sites according to nominal data like ‘country’. In the resulting biplots sites are
grouped together on the grounds of actual measurements of abiotic parameters in the field instead of
pre-assumed classifications.
The first canonical axis explains 13 % of the total variance within the data set, the second axis adds 8 %
(eigenvalues, Table 5.10). The first two axes explain 27 % of the variance within the species data set
and 73 % of the variance within the species-environment relation (cumulative percentage, Table 5.10).
Table 5.10 Summary of the CCA of species biomass pattern and environmental variables.
eigenvalue
cumulative % variance of species data
cumulative % variance of species-environment relation
axis 1
0.13
17
46
axis 2
0.08
27
73
Including the explanatory variables one after the other using forward selection, total atmospheric
pollution is estimated to account for the biggest share of the total variance of the data set (cum(lA) in
Table 5.11). This variable shows a significant inter-set correlation coefficient (rC) with axis 1 (rC = –0.77,
Table 5.11). To a lesser extent axis 1 represents substrate pH (rC = 0.60, Table 5.11), a factor that
naturally has a strong negative correlation to atmospheric deposition of S and N. The second
environmental variable with significant explanatory value is microbial biomass C. This variable is to a
large extent represented by the second canonical axis (rC = -0.68, Table 5.11). Two further variables
added significantly to the canonical regression model: mean annual temperature and precipitation. This
is at least partly due to a correlation of the former with total atmospheric pollution (due to the fact that
the more Southern and warmer sites are more polluted. According to their interrelation these
explanatory parameters may be combined into three groups of factors: atmospheric pollution (S and N
deposition, pH), microbial biomass, and climate (mean annual temperature and precipitation).
In a two-dimensional diagram axes 1 and 2 are combinations of the environmental variables included in
the CCA. On the factorial plane spanned by axes 1 and 2 the samples from each site and sampling time
are plotted to understand the conditions at each site in relation to the other sites (biplot of sites, Figure
5.8). The environmental variables are depicted by arrows. The length of an arrow is a relative measure
of its importance in influencing the data set.
In a scatterplot of the samples the replicates from N-SE are grouped together at the far end of axis 1
(Figure 5.8). On the opposite side the samples from DE are found, sub-clustered among each other
according to sampling time (number behind site abbreviation, Figure 5.8). The samples from S-SE and
FR are scattered in between the clusters formed by samples from DE and N-SE, building sub-clusters
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according to sampling date. A cluster of samples from FR at sampling times 3 and 4 is separated along
axis 2 towards the microbial measures. Samples from S-SE and those from FR that were collected at
the 1st and 2nd sampling time are scattered alongside the environmental arrow representing water
content. Judging from its relative length, this arrow is important in explaining the variance of the data
set. It adds a further dimension, being neither correlated to atmospheric pollution nor to microbial
measures and independent of, or almost contrary to, precipitation. This may reflect a methodological
problem, since water content measurements were obscured by snow at two sampling times in N-SE
(see section 5.1.3.2).
In relation to each other and with respect to the factors of significant explanatory value the
characteristics of the sites may be summarised as follows: N-SE is the site with the lowest pollution and
acidification. This site is characterised by high microbial biomass and high metabolic potential and is
subject to low amounts of precipitation and low temperatures. With respect to climate the conditions at
S-SE, DE, FR are less severe and similar due to altitude counteracting latitudinal changes. The site DE
is furthermore distinguished from the others by especially high loads of atmospheric pollution, resulting
in low pH. The French site FR distinguishes from S-SE and DE in microbial features. This site had a
high microbial biomass (likewise N-SE) but the significantly lowest metabolic quotient (in contrast to NSE).
Table 5.11 Conditional effects of including the environmental variables into the CCA one after the
other using forward selection. cum(lA) = cumulative explanatory power (variance explained) by
including the environmental variable; rC = canonical correlation coefficient of the inter-set
correlations of environmental variables with the axes. p-level of significance: n.s. > 0.05 (not
significant); * £ 0.05; ** < 0.01.
atmospheric pollution
microbial biomass C
mean annual temperature
mean annual precipitation
pH
water content
metabolic potential
70
cum(lA)
0.12
0.17
0.21
0.24
0.26
0.27
0.28
p
0.001
0.001
0.006
0.033
0.068
0.155
0.538
rC with rC with
F axis 1 axis 2
**
8.6 -0.77 0.21
**
4.5 0.01 -0.68
**
3.3 -0.17 0.03
*
2.0 -0.15 -0.38
n.s. 1.8 0.60 -0.03
n.s. 1.4 0.32 0.29
n.s. 0.8 0.15 -0.35
Results
axis 2
+1.0
5
3DE2
1DE2
2DE2
4DE1
1DE1
2FR2
2DE1
5DE1
4FR2
atmospheric pollution
2DE4
3SS1
3DE4
3DE1
1DE4
axis 1
temperature
2SS1
1SS1 1FR2
2SS2
1SS2
3FR1
water content
1SS4
3SS4
2SS4
3SS2
1FR1
2FR1
3SS3
2SS3
1SS3
1DE3
3DE3
1NS1
pH
2NS1
3NS3
1NS4
2NS3
1NS3
2NS4
3NS4
1NS2
3NS2
3NS1
2NS2
1FR4 3FR4
precipitation
3FR3
2FR4
metabolic potential
2DE3
1FR3
2FR3
-1.0
microbial biomass C
-1.0
+1.0
Figure 5.8 Biplot of sites. The dots represent replicate samples from different sampling times at each site.
Samples are identified by the replicate number followed by site abbreviation (NS = N-SE, SS = S-SE) and
sampling time (1 = 1st sampling in Oct/Nov, 2 = 2nd sampling in May/Jun; 3 = 3rd sampling in Sept, 4 = 4th
sampling in Mar/Apr).
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In the biplot of species (Figure 5.9) most species are grouped around the origin of the plot. The origin
represents the overall mean of all explanatory variables. Thus the majority of Testate Amoebae species
are characterised as being generalists with respect to the environmental variables and considering the
breadth of the gradients studied. However, some interesting ‘outliers’ are found. Edaphonobiotus
campascoides is depicted along the atmospheric deposition arrow and seems thus to be associated
with high deposition resp. low pH and higher temperature. Cyclopyxis kahli, Arcella catinus,
Cryptodifflugia oviformis and Centropyxis gauthieri are spread out in the opposite direction, along the pH
arrow, seemingly preferring higher pH, less atmospheric pollution and lower temperatures.
The species Bullinularia indica and Phryganella paradoxa alta seem to be strongly related to microbial
parameters and are stretched out along the arrows metabolic potential and microbial biomass C (Figure
5.9). Furthermore, Schoenbornia humicola and S. viscicula are depicted in close proximity of the arrow
representing metabolic potential. Trigonopyxis minuta is separated from the others in a direction
indicating the association with high pollution, precipitation and microbial biomass.
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Results
axis 2
+1.0
5
water content
atmospheric pollution
Eca
Erm
Nmb Cdu
Tde Csp
Pla Mpa
Cma Est
Nmi
Mfl
temperature Ceu Tco Pin Amu
Csy
Tpe
Tli Tpu
Dlu
Ela Npt
Pde
Pac
Hsu Ase
Shu
Tar
Tmi
precipitation
Hsy
Ten
Svi
Cov
Aca
Cga
pH
axis 1
Cka
Bin
Ppr
metabolic potential
-1.0
microbial biomass C
-1.0
+1.0
Figure 5.9 Biplot of species. Aca = Arcella catinus; Amu = Assulina muscorum; Ase = Assulina seminulum;
Bin = Bullinularia indica; Cga = Centropyxis gauthieri; Cma = Centropyxis matthesi; Csp = Centropyxis
sphagnicola; Csy = Centropyxis sylvatica; Cdu = Corythion dubium; Cov = Cryptodifflugia oviformis; Ceu =
Cyclopyxis eurystoma; Cka = Cyclopyxis kahli; Dlu = Difflugia lucida; Dmi = Difflugia minuta; Eca =
Edaphonobiotus campascoides; Ela = Euglypha laevis/rotunda; Erm = Euglypha rotunda minor; Est =
Euglypha cf. strigosa; Hsy = Heleopera sylvatica; Hsu = Hyalosphenia subflava; Mpa = Microchlamys patella;
Mfl = Microcorycia flava; Nla = Nebela lageniformis; Nmi = Nebela militaris; Npt = Nebela parvula/tincta; Nmb
= Nebela tincta major/bohemica/collaris; Pac = Phryganella acropodia; Ppr = Phryganella paradoxa alta; Pde
= Plagiopyxis declivis; Pin = Plagiopyxis intermedia; Pla = Plagiopyxis labiata; Shu = Schoenbornia humicola;
Svi = Schoenbornia viscicula; Tde = Tracheleuglypha dentata; Tpu = Trachelocorythion pulchellum; Tar =
Trigonopyxis arcula; Tmi = Trigonopyxis minuta; Tco = Trinema complanatum; Ten = Trinema enchelys; Tli =
Trinema lineare; Tpe = Trinema penardi.
5.1.4 Total abundance, biocoenosis and necrocoenosis
The direct counting method applied in this study allowed differentiation of living cells and empty shells of
Testate Amoebae. In this way biocoenosis and necrocoenosis may be compared. Furthermore, within
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the biocoenosis, dormant specimen (cysts) were recorded separately from active cells.
The effect of 'site' and 'time' on various parameters of the Testate Amoebae community was tested
(Table 5.12). Both effects and the interaction were significant for all variables. Because of the apparent
high variablity of the parameters, illustration of the results is restricted to some main features of the dat
set.
Table 5.12 Results of the two-way ANOVAs on the effect of 'site' and 'time' on various markers of the Testate
Amoebae community. df-Effect = 3 (site, time); df-Effect = 9 (interaction site ´ time); F = inter-group variance
divided by intra-group variance; p-level of significance: n.s. > 0.05 (not significant); * £ 0.05; ** < 0.01.
site ´ time
F p
3.1 **
12.0 **
7.0 **
8.5 **
3.9 **
10.8 **
time
F p
59.4 **
46.6 **
211.5 **
266.6 **
34.8 **
51.2 **
4500
100
4000
90
3500
80
70
3000
B
60
2500
50
2000
C
B
1500
1000
500
A
A
B
40
B
30
B
A
C
A
20
empty shells (% of total count)
abundance (106 m-2)
living cells (106 m-2)
empty shells (106 m-2)
living cells (% of total count)
empty shells (% of total count)
cysts (% of living cells)
Testate Amoebae biomass (kg C ha-1)
site
F p
28.3 **
25.7 **
8.0 **
10.3 **
6.9 **
62.2 **
10
A
0
0
N-SE
living cells
S-SE
empty shells
DE
FR
empty shells (%)
Figure 5.10 The abundance of living cells (10-6 m-2) and empty shells (10-6 m-2 resp. %) along
the transect. In comparison of dots and columns of the same shading those labelled with
identical letters are not significantly different from each other according to the Tukey HSD test
(see Table 5.12 for further details on ANOVA). Whiskers represent standard deviation.
74
n
50
50
50
50
50
50
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Results
The number of living cells was similar at N-SE and S-SE and ranged around 700 106 m-2 (Figure 5.10).
At both Southern sites the densities of Testate Amoebae were significantly higher. The highest density
of living cells as well as empty shells was found at DE (2700 106 m-2 living cells resp. 1200 106 m-2
empty shells), further South, at FR, numbers were somewhat lower (1800 106 m-2 living cells resp. 800
106 m-2 empty shells). At all sites the average abundance of living cells was greater than that of empty
shells.
The relative size of the necrocoenosis (percentage of empty shells) was 24-37% of the total count, with
the necrocoenosis at N-SE being significantly smaller (Figure 5.10). Large standard deviations indicate
that the relationship of necro- to biocoenosis varied considerably with time. The F-values in Table 5.12
do indeed reveal that 'time' accounted for a larger share of the variance within the data set of those
variables related to the proportion of bio- to necrocoenosis and the occurrence of dormant forms
(percentage of living cells, empty shells and cysts). It seems thus reasonable to look at the dynamic of
the overall averages of these variables (Figure 5.11).
100
90
A
A
A
80
living cells
cysts
70
%
60
50
B
40
30
20
10
0
B
A
Mar/Apr
A
A
May/Jun
Sept
Oct/Nov
Figure 5.11 Occurrence of living cells (% of all specimen found) and cysts (% of living cells) at the
different sampling times (averages over all sites). In comparison of columns of the same shading those
labelled with identical letters are not significantly different from each other according to the Tukey HSD
test (see Table 5.12 for further details on ANOVA). Whiskers represent standard deviation.
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While the proportions of bio- to necrocoenosis remained fairly constant at three of the sampling times,
the biocoenosis was significantly decreased at the summer sampling at all sites (May/June, Figure
5.11). At this sampling time the percentage of living cells of total count ranged from 43 % at N-SE over
25 % at S-SE to 12 resp. 11 % at DE resp FR. The percentage of dormant cells ranged from 0.1 and
2.3 % of the total living cells at three of the four sampling times. Corresponding to the decrease in living
cells at the May/June sampling, however, the number of dormant cells (cysts) was significantly
increased at this sampling time, ranging from 3.2 % at N-SE over 5.1 % at DE to 9.4 % at FR to a
maximum of 13.7 at S-SE.
For the modelling approach applied in this study the Testate Amoebae biomass is of particular interest
(see section 5.2). The biomass of the total Testate Amoebae community at each site and its variation
with sampling time is shown in Figure 5.12. Both, 'site' and 'time', had a significant effect on the total
Testate Amoebae biomass, with the effect of 'site' explaining a larger amount of the total variance within
the data set (Table 5.12). The significant interaction ('site ´ time') indicates that the biomass dynamics
were unique at each site and/or that at each sampling time the biomass at the sites related differently to
each other. Figure 5.12 delivers an argument for the latter explanation since it reveals a common trend
in the biomass dynamics at all sites, displaying seemingly unfavourable conditions at the May/June
sampling. In accordance with the decrease in living cells and increase in cysts (Figure 5.11) the overall
Testate Amoebae biomass dropped at the May/June sampling at all sites, which is significant for DE
and FR (Figure 5.12). The two Southern sites showed a greater dynamic of Testate Amoebae biomass
than the two Swedish sites (Figure 5.12). At the two Swedish sites the biomass of Testate Amoebae
remained rather constantly at low values.
The relationship between bio- and necrocoenosis of the Testate Amoebae was similar at the four sites,
apart from the surprisingly low relative size of the necrocoenosis at N-SE. All sites revealed a common
dynamic in the parameters concerning the realtionship between bio- and necrocoenosis that hinted to
the importance of moisture availability. This common dynamics of parameters, reflecting the activity of
the communities, was paralled by Testate Amoebae total biomass. The biomass of Testate Amoebae at
the two Southern sites was significantly higher and showed higher variation with time than at the two
Swedish sites.
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120
100
80
60
-1
Testate Amoebae (kg C ha )
40
20
0
A
A
Mar/Apr
A
May/Jun
A
A
Sept
Oct/Nov
Mar/Apr
May/Jun
N-SE
120
A
A
Sept
Oct/Nov
A
S-SE
C
100
80
A
A
A
AC
60
C
40
B
20
0
Mar/Apr
B
May/Jun
Sept
DE
Oct/Nov
Mar/Apr
May/Jun
Sept
Oct/Nov
FR
Figure 5.12 Testate Amoebae biomass at the different sites and sampling times. Within a site
specific sub-plot columns labelled with identical letters are not significantly different from each
other according to the Tukey HSD test (see Table 5.12 for further details on ANOVA). Whiskers
represent standard deviation.
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5.2 Testate Amoebae and the functioning of the food web
5.2.1 Schematic view of the decomposer food web
For each site a list of the most important functional groups was compiled. The diets of these groups
were identified following the works of Hunt (1987) and De Ruiter et al. (1993a) and other literature on
specific taxa (see section 5.2.2 on the trophic relationships of Testate Amoebae and section 3.1.3 on
feeding groups of Nematoda). The trophic relationships between the functional groups of the
decomposer web are schematically illustrated by a connectedness web (Figure 5.13). Within this sketch
of the food web feeding relationships are indicated by arrows pointing from prey to predator.
Food web complexity as the number of trophic groups times connectance, where connectance is the
fraction of trophic interactions of all possible interactions (sensu Paine 1988), was very similar at the
sites. Merely the functional group of predaceous Nematoda was absent at S-SE and FR, reducing food
web complexity, as defined above, from 3.5 (N-SE, DE) to 3.2 (S-SE, FR). Keeping this restriction in
mind, Figure 5.13 applies for all sites.
The primary decomposers of the food webs at the four study sites were bacteria and fungi (Figure 5.13).
These microflora groups are saprotrophic and directly metabolise soil organic matter in form of detritus
and humus. The microflora was grazed upon by fungivorous and bacterivorous Nematoda, as well as
panphytophagous Collembola, Acari and Testate Amoebae, and Enchytraeidae. The term
'panphytophagous' refers to functional groups that are microbivorous as well as detritivorous (sensu
Luxton 1972). Many of the functional groups were omnivorous, feeding on several trophic levels (e.g.
omnivorous Nematoda feeding on detritus, microflora and grazers; Figure 5.13). Loss due to secondary
consumers (predators preying on grazers) occurred through predaceous Testate Amoebae, omnivorous
and predaceous Nematoda, predaceous Collembola and predaceous Acari. The top predators of the food
webs were the predaceous mites.
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pred. Acari
Fungi
fung. Nematoda
pred. Collembola
bact. Nematoda
pred. Nematoda
panphyt.
Collembola
omni. Nematoda
panphyt. Acari
Bacteria
pred. Testate
Amoebae
panphyt. Testate
Amoebae
litter
detritus
humus
Enchytraeidae
Figure 5.13 Sketch of the decomposer food web (connectedness web). Feeding relationships are indicated by
arrows pointing from prey to predator. fung. = fungivorous; bact. = bacterivorous; panphyt. = panphytophagous;
omni. = omnivorous; pred. = predaceous.
5.2.2 Literature survey on the trophic relationships of Testate Amoebae
5.2.2.1
Food sources of Testate Amoebae
Terrestrial Testate Amoebae are the main consumers of microbial biomass, exerting a selective grazing
pressure on edible microorganisms (Meisterfeld 1987). They primarily feed on bacteria (Bonnet 1964,
Coûteaux 1976, Stout & Heal 1967 in Lousier and Parkinson 1984) but also ingest fungal hyphae,
spores and yeasts (Barron 1978 in Coûteaux and Devaux 1983, Foissner 1987, Meisterfeld 1987).
Moreover Testate Amoebae feed on algae and on other Protozoa (Bonnet 1964, Laminger 1978, 1980,
Lousier and Parkinson 1984, Laminger & Bucher 1984). Two larger Testate Amoebae species in a New
Zealand forest soil have been proven to feed on micrometazoans: Nebela vas and Difflugia spec.
preyed on 7 different Nematode species (Yeates and Foissner 1995). Schönborn (1965, 1982) verifies
that they can live solely on particulate organic matter like detritus and humus particles. Bamforth (1997)
even concluded that most Testate Amoebae feed on humus particles. Some authors claim that Testate
Amoebae use dissolved organic matter via pinocytosis (Coûteaux 1976, Lousier and Parkinson 1984).
Because of the apparent variety of food sources, Testate Amoebae have been called polyphagous
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(Schönborn 1965). Information on particular species or genera is rare and is summarised in Table 5.13,
regarding genera common in soil.
Table 5.13 Feeding behaviour of some soil Testate Amoebae species resp. genera.
feeding behaviour, food
Genus resp. species
Author
algae
Arcella arenaria var.
Laminger & Bucher (1984)
sphagnicola
algae
Argynnia dentistoma
Laminger & Bucher (1984)
wood detritus, humus particles
Bullinularia
Bonnet (1964) in Laminger (1980)
wood detritus, humus particles
Centropyxis
Bonnet (1964) in Laminger (1980)
algae
Centropyxis aerophila
Bonnet (1964) in Laminger (1980)
wood detritus, humus particles
Cyclopyxis
Bonnet (1964) in Laminger (1980)
detritus particles, fungal spores, bacteria
Euglypha rotunda
Schönborn (1978)
Heleopera petricola humicola Testate Amoebae
Bonnet (1964) in Laminger (1980)
Protozoa
Heleopora petricola
Laminger & Bucher (1984)
Testate Amoebae, humus particles, Sphagnum
Nebela
Schönborn et al. (1987)
Nebela collaris
Nebela collaris
Phryganella acropodia
detritus
Protozoa
detritus particles, fungal spores, bacteria
fungi
Pseudawerintzewia
Schoenbornia humicola
Schwabia
Trigonopyxis
Trigonopyxis arcula
wood detritus, humus particles
humus particles, bacteria
wood detritus, humus particles
wood detritus, humus particles
detritus, fungal spores
Trinema complanatum
Trinema enchelys
detritus particles, fungal spores, bacteria
detritus, bacteria, diatoms, algae, fungal spores,
small Testate Amoebae (Euglypha laevis minor,
Trinema lineare), Naked Amoebae (Limax)
bacteria
Trinema lineare
Laminger & Bucher (1984)
Schönborn (1978)
Coûteaux and Devaux (1983),
Ogden and Pitta (1990)
Bonnet (1964) in Laminger (1980)
Schönborn et al. (1987)
Bonnet (1964) in Laminger (1980)
Bonnet (1964) in Laminger (1980)
Coûteaux (1976), Bonnet (1964) in
Laminger (1980)
Schönborn (1978)
Laminger (1978, 1980)
Laminger (1980)
For the model the Testate Amoebae were divided into two functional groups: the panphytophagous
Testate Amoebae and the predaceous Testate Amoebae (Figure 5.13). The former group is considered
to be primarily bacteria feeding, to graze on fungi to a lesser extent and to use detritus as a minor food
source. The latter group comprises of the genera Nebela and Heleopera, two genera that are reported
as being predaceous (Bonnet 1964 in Coûteaux 1976, Laminger 1980) although some species may
additionally feed on fungal spores, bacteria and detritus (e.g. Nebela collaris, see Table 5.13). Five
predaceous species were found at the sites: Heleopera sylvatica, Nebela lageniformis, N. militaris, N.
parvula/tincta and N. tincta major/bohemica/collaris.
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5.2.2.2
Results
Testate Amoebae as food source
A rarely regarded aspect of the trophic relationships of Protozoa is the fact that apart from grazing on
others, they themselves serve as a food source. In previous production studies predation upon Testate
Amoebae has not been considered (Lousier 1974a, 1985, Meisterfeld 1987, Schönborn 1992b).
Testate Amoebae seem to be directly preyed upon by Nematoda (Varga 1959, 1960 and Foissner 1995
in Yeates and Foissner 1995). Lousier (1974a) considers detritivorous fauna, such as Enchytraeidae, to
be the most important consumers of Testate Amoebae in soil. Testate Amoebae may be consumed
rather indirectly along with large quantities of plant remains that panphytophagous fauna ingest while
grazing on soil microflora. Rusek (1998) mentions Protozoa as food source of Collembola. In this study
Testate Amoebae are considered to be consumed by Enchytraeidae, omnivorous and predaceous
Nematoda, predaceous Collembola, predaceous mites and by predaceous Testate Amoebae (Figure
5.13).
5.2.3 Quantifying trophic interactions
5.2.3.1
Physiological parameters
As described in chapter 4, a number of physiological parameters are needed to quantify the trophic
interactions within the decomposer food webs. These parameters (assimilation and production
efficiencies, C:N-ratios, death rates) and the literature they were obtained from are listed in Table 5.14.
The same assimilation and production efficiencies as well as C:N-ratios of functional groups were taken
for all sites.
Climatic data (mean monthly temperatures and mean monthly precipitation, Figure 2.2) served as
forcing functions and the model was adapted according to the climate of each specific study site. This
was done by adapting basic death rates obtained from Hunt et al. (1987) and De Ruiter et al. (1993a) to
temperature and moisture regime of the specific sites as described in section 4.3. The quality and
quantity of the primary resource is taken into account via the site specific C:N-ratio of the organic
material and the site specific quantity of the C pool (section 4.1 and Table 2.1).
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Table 5.14 Physiological parameters for each functional group. Assimilation and production efficiencies
(a and p) taken from Andrén et al. (1990) and C:N-ratios (q) from Hunt et al. (1987) if not stated
otherwise. Basic death rates (at 10°C) were obtained from Hunt et al. (1987) and De Ruiter et al.
(1993a) and adapted according to temperature and moisture regime of the specific sites.
death rate (a-1)
microflora
Bacteria
Fungi
microfauna
Testate Amoebae
panphytophagous
predaceous
Nematoda
bacterivorous
fungivorous
omnivorous
predaceous
mesofauna
Acari
panphytophagous
predaceous
Collembola
panphytophagous
predaceous
Enchytraeidae
a
p
q
N-SE
S-SE
DE
FR
1.00a
1.00a
0.30a
0.30a
4
10
0.4
0.4
0.7
0.7
0.6
0.6
0.6
0.6
0.70
0.70
0.43
0.43
7
7
2.0
2.0
3.6
3.6
3.0
3.0
3.0
3.0
0.30
0.30
0.60
0.60
0.40
0.40
0.33
0.33
5b
5b
7c
5b
0.9
0.6
1.5
0.5
1.6
1.1
2.6
1.0
1.4
1.0
2.2
0.8
1.3
1.0
2.2
0.8
0.25
0.80
0.40
0.30
5.5b
8
0.6
0.6
1.1
1.1
0.9
0.9
0.9
0.9
0.25
0.80
0.25
0.40
0.30
0.40
8
8
6c
0.6
0.6
1.7
1.1
1.1
3.0
0.9
0.9
2.5
0.9
0.9
2.5
a
Hunt et al. 1987.
Persson 1983.
c Berg 1997.
b
5.2.3.2
Feeding preferences
Since most functional groups fed on more than one food source their feeding preferences were
considered via feeding preferences which enter the model as described in section 4.2.3 (Table 5.15).
Most preferences were taken from Hunt (1987). The preferences concerning Testate Amoebae result
from literature studies as summarised above (section 5.2.2). Within Table 5.15 a value of ³ 1 means
that the functional group indicated in the header of a specific column feeds on the functional group in
the specific row. Values > 1 put emphasis on a certain trophic link, with the weighting being dependent
on the total food biomass consumed by the functional group. The same feeding preferences were used
at all sites.
82
5
Results
Table 5.15 The feeding preferences wij. Explanation see section 4.2.3. prmi = predaceous Acari; prco =
predaceous Collembola; prne = predaceous Nematoda; omne = omnivorous Nematoda; pami =
panphytophagous Acari; paco = panphytophagous Collembola; prta = predaceous Testate Amoebae;
pata = panphytophagous Testate Amoebae; fune = fungivorous Nematoda; bane = bacterivorous
Nematoda; ench = Enchytraeidae; fung = fungi; bact = bacteria; detr = total detritus.
food resp. prey
saprotrophs, grazers, predators
5.2.3.3
prmi
prmi 0
prco 1
prne 1
omne 1
pami 2
paco 2
prta
1
pata 1
fune 1
bane 1
ench 0
fung 0
bact 0
detr
0
prco
0
0
1
1
1
1
1
1
1
1
0
0
0
0
prne
0
0
0
1000
0
0
10
10
1000
1000
0
0
1
0
omne
0
0
0
0
0
0
1
1
1
1
0
1
1
1
pami
0
0
0
0
0
0
0
0
0
0
0
10
100
1
paco
0
0
0
0
0
0
0
0
0
0
0
100
0
1
prta
0
0
0
0
0
0
0
1
0
0
0
0
0
0
pata
0
0
0
0
0
0
0
0
0
0
0
10
1000
1
fune
0
0
0
0
0
0
0
0
0
0
0
1
0
0
bane
0
0
0
0
0
0
0
0
0
0
0
0
1
0
ench
0
0
0
0
0
0
0
1
0
0
0
10
10
1
fung
0
0
0
0
0
0
0
0
0
0
0
0
0
1
bact
0
0
0
0
0
0
0
0
0
0
0
0
0
1
Biomasses of functional groups
Analyses of variance on the effect of 'site' on absolute and relative contributions and on total food web
biomass were performed (Table 5.16). A significant effect of 'site' not only on absolute but also on
relative biomass indicates a structural difference independent of total food web biomass. To be able to
compare the different sites concerning their biomass structure the relative contributions of functional
groups to the total biomass at each site are given in Table 5.17. Such structural differences were found
for bacteria, fungi, bacterivorous, fungivorous and predaceous nematodes, predaceous microarthropods
and enchytraeids (Table 5.16 and 5.19).
83
5
Results
Table 5.16 Results of ANOVAs on the main effect of ‘site’ on biomass (% resp. kg C ha-1) and
on simulated C and N mineralisation (% resp. kg ha-1 a-1) of individual functional groups within
the decomposer food web (one-way ANOVA). Because of negative N mineralisation values
(immobilisation by bacteria) no relative contributions were calculated for this variable. df-effect
= 3; F = inter-group variance divided by intra-group variance; p-level of significance: n.s. > 0.05
(not significant); * ≤ 0.05; ** < 0.01.
biomass
simulated mineralisation rates
(kg C ha-1)
(%)
C (kg ha-1 a-1)
C (%)
N (kg ha-1 a-1)
F p
F p
F p
F p
F p
microflora
Bacteria
9.6 **
575 **
3.1 n.s.
1.7 n.s.
2.5 n.s.
Fungi
0.8 n.s.
12.6 **
4.2 *
2.0 n.s.
14.7 **
microfauna
Testate Amoebae
panphytophagous
2.5 n.s.
2.3 n.s.
3.5 *
2.4 n.s.
3.0 n.s.
predaceous
3.7 *
3.4 n.s.
4.1 *
4.4 *
4.1 *
Nematoda
bacterivorous
8.7 **
4.6 *
4.3 *
22.8 **
4.3 *
fungivorous
7.6 **
3.6 *
4.0 *
21.6 **
4.0 *
omnivorous
1.5 n.s.
1.2 n.s.
1.7 n.s.
1.2 n.s.
4.0 *
92.1 **
38.1 **
97.7 **
89.5 **
92.6 **
predaceousa
mesofauna
Acari
panphytophagous
5.0 *
2.1 n.s.
7.0 **
0.4 n.s.
5.8 *
predaceous
13.8 **
3.5 *
16.2 **
1.0 n.s.
16.1 **
Collembola
panphytophagous
3.8 *
2.0 n.s.
4.1 *
0.7 n.s.
4.2 *
predaceous
7.8 **
12.7 **
8.0 **
21.2 **
8.0 **
Enchytraeidae
14.8 **
28.0 **
31.1 **
13.9 **
11.4 **
total food web
a
0.9 n.s.
4.3 *
39.0 **
due to a number of zero values in this particular data set the homogeneity of variances
assumption is violated according to the SEN & PURI-test. However; Lindman (1974) shows
that the F statistic is quite robust against violations of this assumption.
The total biomass of the decomposer food web ranged from 360 to 540 kg C ha-1 without any statistical
significant differences between sites (Table 5.16 and 5.19). The difference between the food webs thus
principally lay in the pattern of functional group contributions to total food web biomass, in the following
referred to as biomass structure.
More specifically, fungi made up 79 to 91 % of the total food web biomass. Their relative contribution
was significantly highest at N-SE, and gradually decreasing towards South (Table 5.17). Relative
bacterial biomass was significantly different between all sites with a maximum at FR, but no gradual
trend along the transect (Table 5.17). The biomass of fungi strongly dominated over that of bacteria.
There was an overall trend of increasing faunal biomass towards South (Table 5.17), reaching a
maximum of 14 % of the total food web at DE. Within the soil fauna the Testate Amoebae were the most
important contributors, with a particular prominence at the Southern site DE (Table 5.17). Nematoda
84
5
Results
contributed little to total food web biomass and were the only faunal group that showed maximum
biomass at N-SE, with significantly higher relative biomass contributions of predaceous, fungivorous and
bacterivorous Nematodes. Relative microarthropod biomass increased towards South with a significant
maximum for predaceous mites at DE. While the relative biomass of mites was surprisingly low at FR,
predaceous Collembola revealed a significant biomass maximum at this site (Table 5.17).
Enchytraeidae biomass increased significantly towards South.
Table 5.17. Biomasses of functional groups (%) and total (kg C ha-1) at each site. Mean values of
four sampling occasions are shown, standard deviations are given in parentheses. In comparison
within a specific row values labelled with identical letters are not significantly different from each
other according to the Tukey HSD test (see Table 5.16 for details on the ANOVA).
N-SE
microflora
Bacteria
Fungi
microfauna
Testate Amoebae
panphytophagous
predaceous
Nematoda
bacterivorous
fungivorous
omnivorous
predaceous
mesofauna
Acari
panphytophagous
predaceous
Collembola
panphytophagous
predaceous
Enchytraeidae
total food web
S-SE
DE
FR
5.1A
91A
(0.1)
(1)
6.9B
86AB
(0.4)
(5)
4.1C
82B
(0.2)
(4)
11.4D (0.3)
79B
(2)
1.9A
0.4A
(1.2)
(0.3)
4.7A
1.4A
(3.7)
(0.6)
7.7A
2.2A
(4.0)
(1.3)
4.5A
1.1A
(2.6)
(0.6)
0.16A
0.04A
0.02A
0.013A
(0.05)
(0.01)
(0.01)
(0.004)
0.04B
0.02A
0.02A
0.000B
(0.03)
(0.01)
(0.01)
(0.000)
0.06AB
0.02A
0.05A
0.001B
(0.06)
(0.02)
(0.05)
(0.001)
0.07AB
0.02A
0.02A
0.000B
(0.06)
(0.02)
(0.01)
(0.000)
0.3A (0.2)
0.03A (0.01)
0.5A (0.4)
0.08AB (0.04)
0.8A (0.4)
0.15B (0.10)
0.4A (0.1)
0.08AB (0.01)
0.5A (0.3)
0.003A (0.004)
0.1A (0.2)
0.4A (0.2)
0.002A (0.002)
0.4A (0.2)
1.6A (1.4)
0.017A (0.002)
1.9B (0.5)
1.3A (0.9)
0.091B (0.047)
1.8B (0.4)
476A
357A
469A
538A
(105)
(122)
(197)
(195)
Plotting correlation coefficients between the relative biomass structure at different sites (pair-wise
PEARSON correlation) against their geographical distance led to the observation of a similarity trend.
Similarity of biomass structure generally reflected geographical distance; the further apart two sites the
less similar they were. An extreme outlier to this trend was the correlation r between DE and FR, which
revealed that these sites were least similar even though they lie closest to each other (Figure 5.14).
When this pair was omitted, the correlation between food web similarity and geographical distance
became significant (PEARSON correlation r = -0.91, p < 0.05, point FR/DE excluded from regression;
Figure 5.14)
85
5
Results
1.000
S-SE/N-SE
correlation coefficient
0.999
DE/S-SE
0.998
FR/S-SE
0.997
DE/N-SE
0.996
FR/N-SE
0.995
(
0.994
FR/DE)
0.993
0
500
1000
1500
2000
2500
distance (km)
Figure 5.14 Correlation coefficients (r) between the biomass structure at different
sites (relative contributions to total biomass, pair-wise correlation) against the
geographical distance (km) between the sites (straight line: r = -0.91, p < 0.05, point
FR/DE excluded from regression).
5.2.3.4
Simulated estimates of total C and N mineralisation
Simulated total carbon and nitrogen mineralisation were significantly different between the sites (Table
5.16, Figure 5.15). The mineralisation rates did not simply follow the total food web biomasses:
significant differences were found between total mineralisation rates although the total food web
biomasses were similar at all sites (Table 5.16). The simulated rate of carbon mineralisation at N-SE
(800 kg C ha-1 a-1, Figure 5.15 A) was significantly lower than the rates at the two Southern sites DE
and FR (2600 resp. 2400 kg C ha-1 a-1). For S-SE, the site situated in between the boreal and the two
Southern sites, an intermediate C mineralisation rate was estimated (1500 kg C ha-1 a-1).
The pattern of simulated nitrogen mineralisation followed that of carbon (Figure 5.15 B). N
mineralisation was estimated to be almost zero at the boreal site N-SE. At S-SE and FR nitrogen
mineralisation was calculated to be intermediate with 30 resp. 60 kg N ha-1 a-1. The simulated N
mineralisation rate of almost 100 kg N ha-1 a-1 at DE was significantly higher than the rates at the
Swedish sites N-SE and S-SE.
86
-1 -1
C mineralisation (kg ha a )
5
3000
2500
a
500
0
N-SE
-1 -1
N mineralisation (kg ha a )
b
ab
1500
1000
b
observed
2000
A
B
simulated
Results
S-SE
DE
FR
120
simulated
100
c
observed
80
bc
60
40
b
20
0
a
N-SE
S-SE
DE
FR
Figure 5.15 Estimates of C and N mineralisation rates at the
different sites obtained using the food web model ("simulated")
and laboratory incubations of soil cores ("observed"). Laboratory
incubation data are taken from Persson et al. 2000ab. In
comparison of columns of the same shading those labelled with
identical letters are not significantly different from each other
according to the Tukey HSD test (see Table 5.16 for further details
on the ANOVA).
The food web model estimates of total carbon and nitrogen mineralisation were compared to observed
mineralisation rates from extrapolated laboratory incubation experiments for the same forest sites and
soil layers conducted by Persson et al. (Figure 5.15, Persson et al. 2000a, Persson et al. 2000b). At the
two Swedish sites N-SE and S-SE the food web model approach delivered total C mineralisation rates
very similar to the observed rates from the laboratory incubation approach. At DE and FR simulated C
mineralisation rates exceeded the experimentally observed rates (Figure 5.15 A). The simulated N
mineralisation rates were in good agreement with the observed rates at all sites (Figure 5.15 B).
87
5
Results
5.2.3.5
Contribution of functional groups to C mineralisation
In order to see if energy pathways within the decomposer food webs shifted in their importance for total
fluxes, absolute and relative contributions of functional groups to C mineralisation were compared
(Table 5.16, Figure 5.16). When comparing relative contributions the difference in overall mineralisation
totals does not mingle principle differences within the functioning of the food webs. Analyses of variance
revealed significant effects of the factor ‘site’ on absolute and relative C mineralisation of a number of
functional groups (Table 5.16).
The microflora was responsible for around 90 % of the total C mineralisation and only around 10 % was
performed by the soil fauna (Figure 5.16). At the boreal site N-SE carbon mineralisation was estimated
to be largely performed by fungi (bacterial to fungal respiration ca. 30/70), while bacteria and fungi
contributed similarly at the other sites (ca. 40/50). The relative contribution of bacteria and fungi was not
significantly different at the sites (Figure 5.16), in contrast to the relative biomasses (Table 5.17).
The Testate Amoebae as a whole contributed substantially to C mineralisation with a significant
maximum at DE (from 6 % or 44 kg C ha-1 a-1 at N-SE up to 12 % or 343 kg C ha-1 a-1 at DE, Figure
5.16). Nematoda added little to overall carbon mineralisation (0.1-0.2 % or 1-2 kg C ha-1 a-1) reaching a
maximum at N-SE. Only at this site did predaceous Nematoda contribute significantly.
The contribution of total Acari reached a maximum of 0.3 % (or 6 kg C ha-1 a-1) at the German site DE,
and ranged around 0.2 % (or 1-4 kg C ha-1 a-1) at the other sites. Predaceous mites made the largest
contribution at DE. Collembola as a whole mineralised between 0.2-0.3 % (or 2-3 kg C ha-1 a-1) at the
Swedish sites, and up to around 0.6 % (or 10-11 kg C ha-1 a-1) at the two Southern sites. Predaceous
Collembola contributed significantly most at FR.
The contribution of Enchytraeidae became increasingly important from North to South. It ranged from
0.2 % (or 1 kg C ha-1 a-1) at N-SE and 0.4 % (or 5 kg C ha-1 a-1) at S-SE to up to 1.4-1.6 % (or more
than 30 kg C ha-1 a-1) at the two Southern sites (Figure 5.16).
88
Bact
Bacteria
a
Fungi
a
a
a
a
a
a
a
0 10 20 30 40 50 60 70 80 0 10 20 30 40 50 60 70 80 0 10 20 30 40 50 60 70 80 0 10 20 30 40 50 60 70 80
Testate
Amoebae
panphytophagous
a
predaceous
Nematoda
fungivorous
Collembola
predaceous
0.5
a
a
N-SE
1.5
2.0
0.0
0.5
b
b
a
1.0
a
a
a
a
0.0
a
a
a
a
Enchytraeidae
b
a
a
a
predaceous
a
b
a
a
panphytophagous
b
a
a
a
panphytophagous
b
b
b
a
ab
b
b
a
omnivorous
predaceous
Acari
b
a
7a
b
ab
a
bacterivorous
10 a
10 a
5a
1.0
S-SE
1.5
2.0
0.0
0.5
1.0
DE
1.5
2.0
b
0.0
0.5
1.0
1.5
2.0
FR
Figure 5.16 Relative contributions of the functional groups to C mineralisation (%) at the sites. In comparison of the mineralisation by a particular functional group at the sites (i.e.
the horizontal comparison of bars on the same level of the ordinate axis) values labelled with identical letters are not significantly different from each other according to the Tukey
HSD test of a one-way ANOVA on the main effect ‘site’ (see Table 5.16 for further details on the ANOVA). Whiskers on bars represent standard deviation.
5
Results
5.2.3.6
Contribution of functional groups to N mineralisation
The contributions of most functional groups to N mineralisation were significantly different between the
sites (Table 5.16). Negative contributions to N mineralisation represent N immobilisation. At all sites
bacteria were estimated to immobilise N (Table 5.18). Because of the negative contribution of the
bacteria to N mineralisation, relative contribution estimates in percentages are not given.
At N-SE the absolute amount of N immobilisation was smaller than at the other sites (-11 kg N ha-1 a-1 at
N-SE and -14 to –32 kg N ha-1 a-1 at the other sites). At all sites the largest contribution to N
mineralisation is made by panphytophagous Testate Amoebae (9-65 kg N ha-1 a-1), followed by fungi (239 kg N ha-1 a-1), and predaceous Testate Amoebae (0.7-5.9 kg N ha-1 a-1). These three groups reveal
the same pattern in between sites: they contribute most at DE, then FR, less at S-SE and a minimum at
N-SE, but only some of these differences were significant (Table 5.18). N-SE was the only site at which
the fungivorous and predaceous Nematoda contributed to N mineralisation, and the bacterivorous
Nematodes showed a maximum contribution at N-SE (0.34 kg N ha-1 a-1). The mesofauna played a
minor role at N-SE compared to the other sites. Its contribution increased from North to South, with
maxima at the Southern-most site FR for panphytophagous mites, panphytophagous and predaceous
Collembola, and Enchytraeidae (Table 5.18).
The faunal groups are of greater importance for N than for C mineralisation at all sites. At N-SE fungi
alone counterbalanced only about one fifth of bacterial N immobilisation (bacterial to fungal N
mineralisation -11/2), a remaining 11 kg N ha-1 a-1 was mediated by fauna (Table 5.18). At S-SE more
than half of the N immobilisation was counterbalanced by fungi (-24/13), at FR the relationship was
nearly balanced (-32/26). At DE the fungal N mineralisation was estimated to exceed bacterial N
immobilisation (DE –14/39).
90
5
Results
Table 5.18 Contributions of functional groups within the decomposer food web to N mineralisation
(kg N ha-1 a-1). Mean values of four sampling occasions are shown, standard deviations are given
in parentheses. In comparison within a specific row values labelled with identical letters are not
significantly different from each other according to the Tukey HSD test of a one-way ANOVA on
the main effect ‘site’ (see Table 5.16 for details on the ANOVA).
N-SE
S-SE
DE
FR
microflora
Bacteria
-24A (9)
-14A (8)
-32A (20)
-11A (4)
A
AC
B
Fungi
13 (4)
39 (14)
26BC (9)
2 (0)
microfauna
Testate Amoebae
panphytophagous
9A (4)
34A (14)
65A (40)
57A (38)
A
AB
B
predaceous
0.7 (0.5)
3.2 (1.0)
5.9 (3.3)
3.7AB (2.4)
Nematoda
bacterivorous
0.34A (0.07)
0.09B (0.02)
0.13AB (0.13)
0.19AB (0.16)
A
B
AB
fungivorous
0.01 (0.00)
0.00 (0.00)
0.00 (0.00)
0.00AB (0.00)
A
AB
B
omnivorous
0.00 (0.00)
0.00 (0.00)
0.02 (0.02)
0.01AB (0.01)
A
B
B
predaceous
0.01 (0.00)
0.00 (0.00)
0.00 (0.00)
0.00B (0.00)
mesofauna
Acari
panphytophagous
0.15AB (0.10)
0.20AB (0.07)
0.34B (0.15)
0.06A (0.03)
predaceous
0.03A (0.01)
0.10B (0.02)
0.19C (0.05)
0.13BC (0.03)
Collembola
panphytophagous
0.17A (0.11)
0.18A (0.11)
0.68A (0.43)
0.74A (0.04)
A
A
A
predaceous
0.00 (0.00)
0.00 (0.00)
0.03 (0.01)
0.17B (0.11)
A
A
B
Enchytraeidae
0.0 (0.0)
0.0 (0.1)
0.5 (0.3)
0.9B (0.4)
91
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Chapter 6
Discussion
Dies ist keine leere Seite
6 Discussion
6.1 Testate Amoebae community structure
Totalling the observations on Testate Amoebae community structure, it is to say that in spite of the
considerable geographical distance covered by the North-South-transect (approx. 2000 km) the
communities at the sites were quite similar. Differences in latitude, climate, atmospheric pollution and
altitude seemed to have only a modulating effect. Within these small differences in the community
structures some interesting trends emerged.
6.1.1 Total number of Testate Amoebae species
The total number of Testate Amoebae species found was 42. This is in the range previously reported for
spruce forests. In spruce forests in Germany Wanner (1991) found 32 species, Rauenbusch (1987) 16
species and Schroeter (1995) 42-61 species. Coûteaux (1976) reported 71 species from a spruce forest
in France. All species found in this study have previously been found at temperate spruce sites
(Coûteaux 1976, Rauenbusch 1987, Aescht and Foissner 1989, Wanner 1991).
Like other faunal and floral taxa, numbers of Protozoan species are assumed to increase with
decreasing latitude and altitude (Foissner 1987, Cowling 1994, Smith 1996, Coûteaux and Darbyshire
1998, Chown and Gaston 2000). Moreover, increased N supply has been shown to enhance species
richness (Chardez et al. 1972, Berger et al. 1986). Along the transect positive effects of latitude and
increased N availability counteract with negative effects of altitude. Nevertheless, the total number of
Testate Amoebae species increased towards South. The mean number of species, diversity and
evenness was significantly lowest at the boreal site.
It has been argued that biogeographical rules of species richness distribution generally true for larger
animal species do not apply on the microbial scale (Finlay and Fenchel 1996, Finlay and Fenchel 1999,
Hillebrand et al. 2001). Because this study was part of a larger project comparisons with species
numbers of other taxa from the same sites can be made. During the same investigation period
Collembola and ectomycorrhizal fungi were studied (Taylor et al. 2000, Pflug 2001). At the four sites a
range of 30-38 Collembola species were found, with the highest species number at N-SE and the lowest
at S-SE (Pflug 2001). Further sampling may have retrieved further Collembola species at FR and N-SE
(Pflug 2001). Such effort is not expected to render further species of Testate Amoebae due the general
rule that relieable accounts of total Protozoa species richness can be retrieved from 'small samples'
93
6
Discussion
(Finlay and Fenchel 1996). The amount of material investigated in this study was well above the sample
size to which Finlay and Fenchel (1996) refer. Taylor et al. (2000) studied the number of
ectomycorrhizal morphotypes on root tips and found a range between 14-19 species, with the maximum
at N-SE and the minimum at DE. Thus the pattern of species richness was different for the three taxa
Testate Amoebae, Collembola and ectomycorrhizal fungi. The comparison of Testate Amoebae species
numbers with other taxa must be interpreted considering that the species definition within this group,
likewise the group of ectomycorrhizal fungi, is still obscure (Finlay and Fenchel 1996). Most Testate
Amoebae species reproduce asexually by cell division (Grospietsch 1965b). The species described are
classified by their shell morphology and represent morphospecies. Shell morphology is, however, known
to be subject to modifications according to environmental conditions (Schönborn 1992a, Bobrov et al.
1995).
To conclude, the hypothesis that diversity of Testate Amoebae increases with decreasing latitude is
corroborated by the finding that species richness increased towards South. The same trend was
revealed by comparison of the Shannon-Wiener diversities of the communities. The effect of
geographical location on both parameters was significant as tested by ANOVA. However, overall
differences were small and comparison of means by Tukey HSD test indicated high similarity among SSE, DE and FR.
6.1.2 Similarity between Testate Amoebae communities
The similarity between the Testate Amoebae communities was compared by the number of unique
species (Colwell and Coddington 1994) and the Bray & Curtis-similarity index (Bray and Curtis 1957,
Southwood 1994). The latter index takes the abundance pattern of the species into account. Both
measures indicate that, on an overall high level, similarity decreased with increasing geographical
distance, e.g. number of unique species significantly increased between sites that were further apart.
It has been argued that Protozoan species communities are very similar over broad geographical
ranges (Finlay and Fenchel 1996). The large abundances of Protozoa are believed to result in high
migration rates, improbability of extinctions and rarity of allopatric speciation (Finlay and Fenchel 1999).
The observed biogeographical trend in community similarities took place on a high level of similarity
(Bray & Curtis Index 34-70 %). The similarity between Collembola species communities of the same
sites was considerably lower and decreased with increasing geographical distance between the sites
(Bray & Curtis-Index 11-39 %; Pflug, 2001). Thus, in contrast to patterns in species richness, Testate
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Amoebae and Collembola revealed the same pattern of community similarity as measured by the Bray
& Curtis index.
In summary, the similarity between Testate Amoebae communities was large compared to a metazoan
taxon along the same transect. Nevertheless, the general rule that larger distances represent a barrier
for migration may apply also for Testate Amoebae. It has to be considered that, besides migration, local
conditions leading to extinctions of species determine the species community at a given site.
6.1.3 Comparison of species biomass pattern
A comparison of the relative biomass patterns of species at the sites indicated that many species were
'cosmopolitan', i.e. they occurred at all sites studied. Ordination and multivariate regression with
environmental parameters (CCA) further suggested that most species are generalists. It has to be kept
in mind that this assignment concerns only the range of environmental variables taken into account and
the breadth of gradients studied. The range of pH was, for example, quite small (pH 3.1-4.1).
Many Testate Amoebae species occurred with high relative biomasses. A number of those have been
described before as being generalists in acid forest litters, e.g. Nebela parvula/tincta, Trinema lineare,
Corythion dubium, Nebela tincta major/bohemica/collaris, Plagiopyxis declivis, Centropyxis sylvatica,
Trinema complanatum, and Cyclopyxis eurystoma (Bonnet 1988a, Aescht and Foissner 1989, Bonnet
1989b, a, 1990, 1991a, b).
Few species were found that occurred only at a single site. The restriction of Edaphonobiotus
campascoides to DE indicates a possible preference of this species for high N supply and acidity, which
is supported by the species biplot within the CCA. Conversely Bullinularia indica and Centropyxis
gauthieri were found at all sites but DE. The CCA indicates that Bullinularia indica may positively be
linked to higher pH and microbial biomass.
Characteristic species or absence of some species were expected especially for the boreal site N-SE
because of its distinctive features. Three species had higher relative biomasses at N-SE than at all
other sites (Arcella catinus, Cryptodifflugia oviformis and Difflugia lucida). Hyalosphenia subflava and
Heleopera sylvatica were absent from N-SE but occurred with high relative biomass at all temperate
sites. H. subflava has been described to rely on constant humidity and H. sylvatica is judged to be
sensitive to extreme environmental conditions (Bonnet 1990), their absence from the boreal site which is
low in precipitation and subject to climatic extremes might be explained by this sensitivity. However,
there were also two species that were absent only from DE, and two species that were absent only from
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S-SE. Cyclopyxis kahli, a species that was found regularly only at N-SE (and very rarely at S-SE) was
associated to the higher pH at this site according to CCA.
At present there are divergent perspectives on the species distribution of Protozoa. Some believe that
'everything is (almost) everywhere', i.e. that most species are ubiquist cosmopolitans (Finlay and
Fenchel 1996, Fenchel et al. 1997, Finlay and Fenchel 1999). Others argue that increased scientific
effort will discover more and more specialist and endemic species and patterns in species distribution
(Foissner 1987, Foissner 1997, Coûteaux and Darbyshire 1998, Foissner 1998). According to the
definitions of Smith (1978) cosmopolitans occur in all regions of the world, but not necessarily in all
types of habitats, while ubiquists occur in all types of habitats, but not necessarily in all regions of the
world. This study cannot answer the question if Testate Amoebae are generally ubiquists, because only
one type of habitat was studied. However, the findings reported provide some argument that many
Testate Amoebae may be distributed over a wide geographical distance, considering that only a range
of temperate to boreal sites over a transect of 2000 km were studied. Techniques like scanning electron
microscopy were not applied. Such techniques might have resulted in finer species resolution within the
taxonomic groups Euglypha cf. strigosa, Nebela tincta major/bohemica/collaris and N tincta/parvula. It
has to be taken into consideration that very rare specialists species may falsely have been recorded
within these groups. Apart from this taxonomic restriction there is evidence that the species richness
estimates were almost exhaustive. The species richness at the sites was studied not only from
quantitative samples. Additionally, enrichment techniques (flotations, batch cultures) were used in order
to find species that were very rare. However, these techniques did render only one species that had
otherwise been overlooked. It is concluded that the sample size of the quantitative samples were
sufficient to discover a next to complete range of the species present at the sites.
Recapitulating, the hypothesis that the boreal site N-SE will distiguish from the other sites by a number
of species was not confirmed. Only two species were common at all other sites were absent from N-SE.
However, such cases occurred also at other sites. No species was found only at the boreal site but one
species was absent or very rare at all sites except N-SE. One species was restricted to DE and seemed
to be associated to higher N-supply according to CCA.
6.1.4 Environmental factors explaining community structure
The difference between the Testate Amoebae communities was small and lay largely in the pattern of
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relative biomasses of the species, referred to as community structure. When exploring the data set for
factors modulating the Testate Amoebae community structure, the canonical correspondence analysis
(CCA) emphasised the importance of parameters that may be united into three groups of factors,
according to their interrelation. These factors were atmospheric pollution (S and N deposition, pH of
organic layer), microbial biomass, and climate (mean annual temperature and precipitation).
Moisture and food availability (i.e. availability of microbes) have been proposed to be the most important
factors determining Protozoan communities (Stout 1984, Cowling 1994). Positive effects of irrigation
have been observed in forest soils (Lousier 1974a, b). The correlation of Testate Amoebae and
moisture content in mires is, in fact, used in palaeohydrology to reconstruct mire surface wetness based
on qualitative estimates of the hydrological preferences of individual Testate Amoebae species (Tolonen
1986). 'Rhizopod analyses' in palaeoecological studies have established transfer functions for Testate
Amoebae species assemblages to water table and percentage moisture in mires (Woodland et al.
1998). Some studies in spruce forest did not find significant correlation between soil moisture and the
abundance of Testate Amoebae (Petz and Foissner 1989, Wanner and Funke 1989, Wanner 1991),
pointing to overriding effects of other factors or a lack of correlation above a certain threshold value.
Although the aim of this study was not to investigate the abundance and biomass dynamics within the
Testate Amoebae communities, a common trend was observed that seems worth mentioning in the
context of the importance of moisture availability for Testate Amoebae. At the summer sampling relative
number of cysts and empty shells was increased and total biomass decreased. Such decreases in
population size and shifts towards increased number of dormant cells and empty shells have previously
been reported for the dry summer months (Coûteaux 1976, Lousier 1984b, a, Lousier 1984c, 1985).
These observations support the view that moisture availability is a limiting factor for the Testate
Amoebae communities.
The factor microbial biomass probably is important because it indicates food availability for the Testate
Amoebae. Peaks of Protozoan biomass have been observed to quickly follow rain induced peaks of
bacterial biomass, demonstrating the dependence of Protozoa on food availability (Clarholm 1981).
Increased fungal biomass in microcosms has been shown to enhance numbers of the Testate Amoebae
species Phryganella acropodia (Coûteaux and Devaux 1983, Coûteaux 1985). It has been pointed out
that moisture may effect Protozoa indirectly through affecting the availability of microbial food (Foissner
1987).
Literature on the effect of atmospheric deposition on soil Protozoa is quite sparce (Coûteaux et al.
1998). However, positive effects of N fertilisation on the species richness of Testate Amoebae have
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been reported (Chardez et al. 1972, Berger et al. 1986). This study confirms the positive effect of
increased N supply on a larger geographical scale.
In a biplot of sites the CCA was used to aid ordination of the sites according to environmental
parameters. With respect to the three main groups of factors N-SE is clearly differentiated from the other
sites by low atmospheric pollution, comparatively higher pH, severe climate (low temperatures and
precipitation) and high microbial biomass. The other sites are more similar to each other, however,
remarkable features are extremely high loads of atmospheric pollution at DE, leading to low pH.
To conclude, the hypothesis that moisture and microbial parameters are the most important factors
modulating the Testate Amoebae community structure was corroborated. Furthermore, evidence was
found that a group of parameters concerning atmospheric pollution has high explanatory value.
Increased N supply, decreased pH and/or related factors may exert a positive influence on Testate
Amoebae communities. Increased N-supply, like moisture, may act indirectly through enhancing food
supply by stimulating bacterial growth.
6.1.5 Size structure and biomass
Testate Amoebae species within a large biomass range were found. The biggest species had a biomass
of more than 50 times that of the smallest. Because of this range, abundance (number of individuals per
unit area) could not logically be linked to resource division (cf. Tokeshi 1993). Biomass measures serve
this purpose better, while abundance measures may better reflect community activity.
A comparison of both measures revealed that small species occurring with high frequencies and large
species occurring in low frequencies both contribute substantially to the biomass of the total community.
In a study of German spruce forests similar relationships between body size and frequency of Testate
Amoebae were found (Wanner 1991). In fact, a general rule has been stated that numerical density of
individual animal taxa (e.g. numbers per unit area) increases with the inverse of body mass (Peters
1983). Small species like Trinema lineare, Cryptodifflugia oviformis and Euglypha laevis are rated to be
r-selected, while larger species with xenosome shells, e.g. Centropyxis sylvatica, Heleopera sylvatica
and Bullinularia indica, are rated to be K-selected (Wodarz et al. 1992, Bamforth 1997). Among both
groups some species occurred with high relative biomasses, suggesting that even within the group of
Testate Amoebae, strategies along a wide range of the r/K-continuum are successfully realised.
The general rule that rates of growth and metabolism are inversely proportional to body size applies
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also on microscopic scale (Fenchel 1988). Therefore, smaller Testate Amoebae may play a larger role
for energy and nutrient fluxes than bigger species, even though their total biomass is equal or less. See
section 6.2 for further discussion of Testate Amoebae biomass along the transect and within the context
of the decomposer food web.
6.1.6 Abundance of Testate Amoebae
The total abundance of living Testate Amoebae increased towards South, with maximum values found
at DE followed by FR. Counteracting influences seem to take effect. On one hand, both, decreasing
latitude and increasing N supply have a positive effect on Testate Amoebae abundance (Chardez et al.
1972, Berger et al. 1986, Foissner 1987, Foissner 1994). On the other hand, increasing altitude has a
negative effect on abundance (Foissner 1987). Therefore it is suggested that the non-continuous
abundance trend along decreasing latitude may be explained by the counteracting altitudinal gradient
(high altitudes at DE resp. FR: 700 m asl resp. 1050 m asl). In addition to the maximum altitude of FR, N
deposition at this site was lower than at DE.
The total abundance of living cells found (700-2700 106 m-2) were in the upper range or above values
reported from other coniferous forests. In the review by Petersen and Luxton (1982) a range of 1901050 106 m-2 is given for woodland moder. Wanner (1991) reported 20-370 106 m-2 in German spruce
forests, using an extraction method based on the flotation principle (Chardez 1959). Rauenbusch (1987)
found up to 900 106 m-2 Testate Amoebae in a beech forest, using a direct method involving several
dilution steps.
Estimates of the abundance of soil Protozoa are generally obtained with much variation due to spatial
and temporal hererogeneity of their substrate and methodological difficulties related to the small size of
these organisms (Foissner 1987, Ekelund and Ronn 1994). Today direct counting methods from soil
suspensions are preferred for some groups of Protozoa (Foissner 1987, Ekelund and Ronn 1994). In an
extensive review of the different methods Foissner (1987) resumes that the most reliable abundance
values are obtained with direct counting techniques. When directly counting Testate Ameobae from soil
solutions a compromise has to be obtained concerning the concentration of the solution. On the one
hand, in a dense suspension the risk of overlooking specimen that are hidden by soil particles is high.
On the other hand, in a more diluted suspension bigger species that occur with lower frequency are not
found in sufficient numbers for extrapolation. Furthermore a certain inaccuracy is unavoidable in diluting
the samples and such errors accumulate with each dilution step. In this study each sample was
investigated in a direct counting approach involving two steps: (i) counting a sufficient number of sub-
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samples of a dense suspension to obtain estimates of the larger specimen, and (ii) counting a sufficient
number of sub-samples of a thin suspension to obtain estimates of the smaller specimen and to reduce
risk of overlooking.
The hypothesis that the abundance of Testate Amoebae increases from North to South is corroborated.
Considering the results discussed in 6.1.4 this increase could be explained by increasing moisture and
food availability at the sites with milder climate and high N deposition.
6.1.7 Comparison of bio- and necrocoenosis
The number of living cells and cysts (biocoenosis) was on average two to three times the number of
empty shells (necrocoenosis). The relative size of the biocoenosis (63-76 %) was unusually high at all
sites compared to values from the literature (Meisterfeld 1980, Wanner 1991). Meisterfeld (1980) found
5-18 % living Testate Amoebae in a spruce forest and up to 46% in the upper 2 cm of a meadow soil.
At the boreal site N-SE the relative size of the necrocoenosis was significantly smaller than at the other
sites: relative to the total community less empty shells occurred. A small necrocoenosis is a
consequence of high decomposition rates and/or low mortality rates (Meisterfeld 1980). The hypothesis
that the relative size of the necrocoenosis of Testate Amoebae would be larger at the boreal site due to
decreased decomposition is not corroborated. Quite contrary the reverse was found. The low relative
number of empty shells occurring at N-SE cannot be explained by high decomposition rates, since N-SE
revealed smaller rates than all other sites (see section 6.2.1). A possible explanation is that the relative
amount of empty shells was small due to low metabolic rates of the Testate Amoebae in consequence
of the severe climatic conditions. This is in accordance with the climatic adjustment used in the
modelling approach (chapter 4).
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6.2 Testate Amoebae and the decomposer food web
"It is true that mathematical models simplify very much.
But this is not characteristic of mathematical models
– it is characteristic of any attempt to comprehend the world."
Peter Yodzis 1989
6.2.1 Total food web biomass and mineralisation along the transect
A dynamic and interactive set of environmental variables like climate and litter quality determine the
structure and function of the decomposer food web through their long-term influence on the ecosystem.
The latitudinal transect from Northern to Southern Europe formed by the coniferous sites covers a range
of climate and N depositional loads. The conditions at N-SE are rather extreme (boreal, proximately no
N deposition) compared to the three Southern sites (humid-oceanic resp. humid continental, N
deposition ranging from 15-20 kg N ha-1 a-1). In consequence, N-SE clearly differentiates from the other
three sites (see section 6.1.4).
The complexity of the food webs as referring to the number of functional groups times connectance was
similar at all sites (sensu Paine 1988). Results show that opposed to the initial hypothesis total food web
biomasses were also alike. However, the biomass structures of the functional groups were different. A
tendency of increasing dissimilarity between the biomass structure of the food webs with increasing
geographical distance between the sites was observed. However, the correlation beween dissimilarity
and distance was only significant when an outlier was omitted from the analysis: The similarity beween
FR and DE did not fit the trend but was unexpectedly low.
Total C and N mineralisation rates of the decomposer food webs in the organic layer were site specific.
The lowest rates of N and C mineralisation were found at the Northern-most site N-SE, intermediate
rates at the Southern-Swedish site S-SE and maximum rates at the two Southern-most sites DE and
FR.
Since the food web complexity and the total food web biomass at the sites was similar, the pattern of
total mineralisation rates could be a consequence of three remaining aspects: (i) direct climatic effects
on the process rates of the organisms, (ii) differences in resource quality, and (iii) different biomass
structures of the food webs. To test this, the food webs were modelled again without taking climatic
differences and differences in resource quality (C:N-ratio of the organic material) of the sites into
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account. While the absolute estimates changed drastically, the pattern of mineralisation at the different
sites remained (Table 10.3, section 10.2). Hence, the differences in mineralisation rates were to some
extent attributed to the characteristic pattern of functional group contributions to total biomass. This
corroborates the view that the structure and not only the total biomass of the decomposer food web
determines C and N fluxes (Moore et al. 1993, Setälä et al. 1996, Setälä et al. 1998).
6.2.2 Site specific characteristics of the food webs along the transect
At the boreal site N-SE the fungal pathway was of enhanced importance. The bacteria to fungi ratio of C
mineralisation was estimated to be around 30/70. Total N mineralisation at N-SE was estimated to be
negligible. The role of fungi has been stated to be pivotal for boreal forest ecosystems in general
(Näsholm et al. 1998, Lindahl et al. 2001). This can be attributed to their ability to translocate carbon
and nutrients and the capacity of ectomycorrhizal fungi to utilise organic nutrients. These abilities are a
competitive advantage under conditions of low N supply (Leake and Read 1997). On a fertility gradient
of a number of boreal sites, increasing fertility decreased the relative abundance of fungi while total
microbial biomass remained unchanged (Pennanen et al. 1999). The view that fungi find especially
favourable conditions at N-SE is supported by Taylor et al. (2000) who compared the ectomycorrhizal
community of three of the studied sites (namely N-SE, DE and FR) and found the highest species
richness, highest diversity and highest abundance of mycorrhizal root tips at the Northern-most site NSE.
Regarding the decomposer fauna at N-SE the food web structure is shifted in favour of Nematoda and
to the disadvantage of Testate Amoebae in comparison to the other three sites. Nevertheless the
contribution of Testate Amoebae to mineralisation remained higher than that of Nematoda. In
comparison to the other sites, the fauna at N-SE was of relatively less importance for C mineralisation
(7 % compared to 10-14 % at the other sites). Without fauna, however, the microbial N balance would
have been negative at N-SE (bacterial to fungal N mineralisation -11/2). The positive influence of
decomposer fauna, especially Protozoa, on N mineralisation is well established (Ekelund and Ronn
1994, De Ruiter et al. 1993b, Beare et al. 1995).
The contribution of Enchytraeidae to biomass and mineralisation was estimated to be of minor
importance at N-SE and S-SE and increased towards South. At DE and FR the Enchytraeidae were the
second important faunal group for C mineralisation after the Testate Amoebae. The estimates for the
contribution of Enchytraeidae to C mineralisation at N-SE and S-SE were 0.2 resp 0.4 % which
corresponds to 1.4 resp. 5.2 kg C ha-1 a-1. This is in agreement with findings from field investigations
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Discussion
(Huhta and Koskenniemi 1975, Huhta 1976, Berg 1997). For example Huhta and Koskenniemi (1975)
found the C mineralisation by Enchytraeidae to be ca. 3.3 kg C ha-1 a-1 at a boreal site comparable to NSE and ca. 8.7 kg C ha-1 a-1 at a eutrophic spruce site comparable to S-SE. Many microcosm studies
support the view that Enchytraeidae are a keystone group in boreal forest soils (Huhta et al. 1998,
Laakso and Setälä 1999b). The direct relative contributions of Enchytraeidae as estimated in this study
suggest that the importance of this group is largely due to indirect influences which are included in
results from microcosm studies but remain largely unconsidered in the presented approach. However,
Sulkava et al. (1996) point out that microcosm studies may overestimate the effect of Enchytraeidae
due to artificial conditions favouring the growth of semi-aquatic animals which may then reproduce to
densities far exceeding those in the field. In a microcosm experiment where Enchytraeidae were kept at
field density levels their presence had no measurable effect on soil respiration (Hedlund and
Augustsson 1995).
Compared to the fungal-based food web at the boreal site N-SE with slow turnover rates the three
Southern sites may be characterised as being bacterial-based with rather fast turnover rates. With
respect to C mineralisation bacteria and fungi contributed similarily at these sites, the bacteria to fungi
ratio was estimated to be ca. 40/50. A ratio very similar to that has been found in a long term study of a
scots pine stand in central Sweden (i.e. 55/45, Jädraås, Persson et al. 1980).
Apart from this common feature there are some differences among the bacterial-based food webs and
in the nutrient cycling rates at S-SE, DE and FR. In spite of rather mild humid-oceanic climate and
considerable N deposition at S-SE mineralisation rates at this site were lower than at DE and FR.
Collembola and Enchytraeidae were of relatively less importance compared to the food webs at DE and
FR. Similar to N-SE, but to a lesser extent, decomposer fauna was estimated to be essential in order to
counterbalance bacterial N immobilisation. The forest at S-SE revealed a shortage in nutrients
according to markers for N and P availability (C:N-ratio, P:N-ratio, Table 2.1). Firstly, the C:N-ratio of the
organic layer was considerably higher at S-SE than at DE and FR. Secondly, the P:N-ratio of the tree
foliage of 0.08 indicated a P deficiency at S-SE. The ratio between phosphorus and nitrogen in the tree
foliage is an indicator to interpret nutrient status of the soil. P is considered to be deficient at ratios
between 0.10-0.12 (Ingestad 1979 and Nihlgård 1990 cited in Setälä et al. 1997). As a comparably
young tree plantation with the highest net primary production (Scarascia-Mugnozza et al. 2000) N and P
defficiency at S-SE might therefore have effected the plant-soil interaction in a negative way, leading to
decreased activity in the organic layer. The likewise low P:N-ratio (0.11) at DE resulted from increased
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N concentration of the needles and this site does not suffer from shortage of P (Bauer et al. 2000). The
soil nutrient status despite high N deposition at S-SE acknowledges the need to consider site history
(past land use management practices, see e.g. Watson and Mills 1998, Aber et al. 1998).
At FR and DE, likewise two sites with favourable climate and high N deposition, mineralisation rates
were high. The contribution of Testate Amoebae, Microarthropoda and Enchytraeidae to biomass and C
mineralisation was increased at these Southern-most sites. This is principally attributed to less adverse
conditions towards South (Lavelle et al. 1995, Seastedt 2000). The relative contribution of
Enchytraeidae to C mineralisation was considerable and exceeded that of the total microarthropods.
The importance of Enchytraeidae for N and C mineralisation has been reported to be greater than that
of Microarthropoda in microcosm studies of coniferous raw humus (Setälä et al. 1991, Sulkava et al.
1996). Enchytraeids were found to out-compete Microarthropoda under moist conditions (Sulkava et al.
1996).
DE is characterised by an especially high N mineralisation rate. At DE estimates of fungal N
mineralisation exceeded bacterial immobilisation more than 3-fold. For decomposer microflora the
availability of degradable C as energy source is prerequisite to successfully exploit available nutrients
(Paul and Clark 1989, Mikola et al. 1998). Enhanced C input increases microbial activity and
consequently the N flow through the ecosystem (Andrén et al. 1990). Bioavailability of C is considered a
key determinant of soil N cycling (Currie 1999). The increased N mineralisation rate at DE might thus be
explained by increased energy availability originating from different sources. One source may be the
nitrophilous dense understorey vegetation at DE that is likely to supply easily degradable root exudates
to the decomposer system. Another source of energy for the microflora may be the trees, whose N
nutrition was especially good at DE (Bauer et al. 2000, Scarascia-Mugnozza et al. 2000). It has been
suggested that increased nitrogen availability leads to increased carbon allocation to tree roots (Burton
et al. 2001). Well nourished trees might supply especially large amounts of energy to their mycorrhizal
fungi. The ectomycorrhizal fungi could expend this supplementary energy to produce more degradative
enzymes, thus increasing the availability of nutrients within the decomposer system and enhancing
overall turnover rates (Read 1991, Leake and Read 1997, Lindahl et al. 2001).
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Discussion
6.2.3 Common characteristics of the food webs along the transect
Some general characteristics of the food webs can be summarised. A common feature at all sites was
the prominence of the microflora. The contribution of fauna to total C mineralisation was 7-14%. This
estimate is considerably higher than others reported from studies in coniferous forest soils (i.e. 1-5% as
reviewed by Persson 1989). This divergence is suggested to be due to the fact that in this study the
Testate Amoebae, as the major Protozoan group in coniferous forest soils (Schönborn 1992c), were
quantified using a direct counting method instead of a culturing technique. Culturing techniques like the
most probable number (MPN) method that have been used in previous studies are unsuitable for this
group of Protozoa (Foissner 1987, Aescht and Foissner 1992, Ekelund and Ronn 1994).
In general the most important module within the decomposer food web besides fungi seemed to be the
Protozoa feeding mainly on bacteria. The contribution of Testate Amoebae to the C flux ranged from 612 % (or 44 to 343 kg C ha-1 a-1) and the absolute values were similar to values previously reported for
other forest sites (Lousier and Parkinson 1984, Meisterfeld 1986, Schönborn 1992b). The pronounced
positive effect of Protozoan grazing on N mineralisation has been demonstrated in microcosm studies
(Clarholm 1985, Vreeken-Buijs et al. 1997). The need for including Protozoa in studies of the energy
and nutrient cycle in soils that has been stressed by various authors is once again underlined (as
reviewed by Petersen and Luxton 1982, Foissner 1987).
In agricultural and grassland soils bacteria are reported to contribute substantially to N mineralisation
(Hunt et al. 1987, De Ruiter et al. 1993a). In the organic layers of a pine forest, however, they were
estimated to immobilise N (Berg 1997). The results from coniferous forests also suggest that N is
immobilised by the bacteria and released by organisms grazing on them. Possibly this indicates a
principle difference beween agricultural and forest decomposer systems.
Because the resources of secondary consumers are relatively N rich, compared to detritus, the soil
fauna was of much more importance for N than for C mineralisation, a fact that has been recognised in
various studies (Anderson et al. 1981, Hunt et al. 1987, Persson 1989, Setälä et al. 1990, Andrén et al.
1990).
Regarding the estimates of the contributions of predaceous Microarthropoda and Nematoda to
mineralisation, the view that the functional importance of soil fauna is inversely related to the trophic
position of the group is supported (Laakso and Setälä 1999).
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Discussion
6.2.4 Evaluation of model estimates
In the following the estimates of total C and N mineralisation rates along the transect shall be evaluated
in general. The food web model estimates were compared to extrapolated mineralisation rates from
laboratory incubation experiments for the same forest sites and soil layers conducted by Persson et al. (
Persson et al. 2000a, Persson et al. 2000b). The food web model approach delivered values very
similar to the experimentally observed rates except for two cases: total C mineralisation at DE and FR .
It is hard to draw conclusion with respect to what causes these differences. They might be due to the
model formulations, the assumptions underlying the equations and the uncertainties with respect to
particular values of the input parameters. Especially the population sizes of the microflora and their
specific death rates, as well as the C:N ratios of the microbial substrate are difficult to establish and may
have a great impact on the outcome of the model (see e.g. sensitivity analyses carried out by De Ruiter
et al., 1993a). It is however also possible that the discrepancy between model results and observations
are due to uncertainties with respect to the observed rates. This is underlined by the fact that further
mineralisation estimates based on other techniques delivered estimates of C mineralisation that lay
inbetween the estimates reported here (14C technique Harrison et al. 2000, and NUCOM model
simulation by van Oene et al. 2000). The impression is reinforced that there is no flawless method for
estimating mineralisation rates of the decomposer system. A considerable part of the variation within all
approaches is due to deviating estimates of the total C pool of the forest soils, site variability and
differences in sampling frequency (e.g. Persson et al. collected soil cores at a single sampling time
while the food web model runs with estimates from 4 sampling times).
Nevertheless, the food web model calculated mineralisation rates that satisfactorily matched the
observed rates for N mineralisation at all sites and for C mineralisation at two sites. Therefore, the
model calculations were used to estimate the contribution of the various functional groups to the C and
N mineralisation rates, being aware that the estimates of contributions to C mineralisation at DE and FR
should be treated with care. In this study the food web model was applied to explore the data sets of soil
biota and environmental variables, and not as a predictive tool. The model delivered an impression of
how environmental variables may effect the contribution of the various functional groups to nutrient
cycling.
106
6
Discussion
6.3 Research needs
Biogeographical monitoring of Protozoan species has just started. Further studies on a wider range of
habitats and biomes are needed. Moreover, autecological studies are called for to refine the trophic and
physiological classification of Testate Amoebae and the other taxa involved in the decomposer food
web of coniferous forest sites.
Much remains to be studied about the very base of the decomposer food web. The organic matter as
basic resource of the primary decomposers needs to be characterised in more detail. Life strategies like
cycles of activity and inactivity of soil bacteria as well as fungi are poorly understood. Their capacity to
alter their C:N-ratio according to the availability of N needs to be further explored (Tezuka 1990),
especially since the food web model is quite sensitive to the microbial C:N-ratio (De Ruiter et al. 1993a).
Trees as symbiotic organisms and their interchange of energy and nutrients with ectomycorrhizal fungi
are of great importance to the decomposer system (Lindahl et al. 2001). Including direct C and N fluxes
between primary producers and primary decomposer into the food web model would improve the
approach.
Discrepancies between results from microcosm studies and food web model estimates for a functional
group (e.g. Enchytraeidae) point to the indirect effect of mesofauna within decomposition, which is
known to be of great importance (Anderson 1995). A demanding task is to better quantify such indirect
effects and to further include them into the food web model.
Finally, validation of the simulated estimates on functional group level is called for but remains hard to
achieve. Recent studies using stable isotopes have been the most advanced attempts to disentangle
the complex structure of food webs (as reviewed in Eggers and Jones 2000). The combination of stable
isotope techniques and food web modelling may allow further insight into the functioning of belowground
food webs.
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Chapter 7
Conclusions
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7 Conclusions
Part 1
· The Testate Amoebae community structure at four spruce forests sites along a large geographical
transect was remarkably similar. High abundances of this Protozoan taxon may have resulted in high
migration rates and improbability of extinctions. Furthermore a strong influence of the common primary
resource (spruce litter) may have had an equalising effect on community structure.
· Within the tight range of differences found, the hypothesis that the diversity of Testate Amoebae
increases with decreasing latitude was corroborated.
· As hypothesised, the similarity between Testate Amoebae communities increased with decreasing
geographical distance between the sites, even though the overall level of similarity was quite high. The
high similarities found suggest that biogeographical rules of species richness distribution generally true
for larger animal species cannot without restrictions be applied to Protozoan taxa.
· Among the small taxonomic group of Testate Amoebae a wide range of strategies within the r/K-
continuum was realised. Both, r-strategic species and K-strategic species, contributed considerably to
Testate Amoebae biomass.
· The hypothesis that moisture and microbial parameters are the most important factors modulating
Testate Amoebae community structure was corroborated. Moreover, evidence was found that
atmospheric pollution is a relevant factor.
· The hypothesis that the abundance of Testate Amoebae increases from North to South was
confirmed. This increase is suggested to be due to increasing moisture and food availability at the sites
with milder climate and high N deposition.
· The hypothesis that the relative size of the necrocoenosis of Testate Amoebae is larger at the boreal
than at the temperate sites due to decreased decomposition is not corroborated. Quite contrary the
reverse was found. A possible explanation is that the relative amount of empty shells was small due to
low metabolic rates of the Testate Amoebae in consequence of the severe climatic conditions.
109
7
Conclusions
Part 2
· Contrary to expectations the total food web biomass was similar at the sites along the transect,
suggesting a strong influence of the common primary resource (spruce litter). However, a trend of
increasing dissimilarity of the food web biomass structures with increasing geographical distance was
observed.
· The hypothesis that total C and N mineralisation rates of the decomposer food web increase towards
South was corroborated.
· The differences in mineralisation rates were, besides climate and resource quality, to some extent
attributed to the characteristic pattern of functional group contributions to total biomass. This
corroborates the view that the structure of the decomposer food web determines C and N fluxes.
· The hypothesis that the decomposer system at the boreal site N-SE is dominated by fungal pathways
and exhibits 'slow cycles' was corroborated. Severe climate and low N availability seem to favour such
systems.
· Increasing N input and mild climate resulted in gradual realisations of high input systems favouring
bacterial pathways and 'fast cycles'. Especially the two Southern-most sites were nutrient rich with high
turnover rates.
· The importance of fauna to absolute and relative C mineralisation increased from North to South. The
absolute contribution of fauna to N mineralisation showed the same trend. However, relative to the
estimated amount of N immobilisation by the microflora the decomposer fauna, as grazers recycling
microbial N, was of increasing importance towards the North.
· The Testate Amoebae played a larger direct role for C and N fluxes than the total mesofauna. The
impact of this microfauna group on C and N fluxes was estimated to be significant.
· The rating of Enchytraeidae as a keystone group in boreal systems was not corroborated by the
estimates of their direct contribution to the fluxes of C and N. Such differences between microcosm
studies and modelling results may hint to indirect effects having key influences which were only partly
taken into account by the food web model.
· Within the decomposer fauna the highest trophic level (predaceous Microarthropoda and Nematodes)
played a minor direct role for mineralisation, which supports the view that the functional importance of
soil fauna is inversely related to the trophic position of the group.
110
Chapter 8
Summary
Corythion dubium
10 µm
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8 Summary
The decomposer systems of four coniferous forest sites on a European latitudinal transect (North
Sweden to North-East France) were studied. The sites were subject to different climate as well as to
different levels of atmospheric N deposition. The decomposer communities and their abiotic
environment were monitored on four sampling dates. The study links different ecological scales and
attempts to go beyond description towards a functional understanding. It is divided into two parts. In part
one the community structure of Testate Amoebae, the dominant Protozoan group of coniferous forest
soils, was studied on species level. Diversity, abundance of active cells, cysts and empty shells as well
as species biomass pattern at the sites were compared and related to environmental parameters. In part
two the Testate Amoebae community was set into the context of the decomposer food web. The food
web structure and biomass of the functional groups were assessed. A numerical model was applied to
each site to link the structure of the food webs to their function. The C and N flux across and within the
food web was simulated. The contribution of the Testate Amoebae and the other functional groups to C
and N mineralisation at the different sites was compared.
A total of 42 Testate Amoebae species was found. The species number of Testate Amoebae ranged
between 34 to 40 species and increased with decreasing latitude. Diversity and evenness of the Testate
Amoebae communities were lowest at the boreal site. The species biomass pattern at the sites was
quite similar. Many species occurred at all sites and with high relative biomass. The boreal site was not
clearly distinguished from the temperate sites by specialist species. Nevertheless, the similarity between
Testate Amoebae community structure increased with decreasing geographical distance between the
sites. The size range among the Testate Amoebae community was very large, the biggest species
found had an individual biomass of more than 50 times that of the smallest. A wide range of strategies
within the r/K-continuum was realised. Both, r-strategic species and K-strategic species contributed
considerably to Testate Amoebae biomass. A multivariate approach (CCA) suggested that three factors
were most important in explaining the Testate Amoebae community structures: atmospheric pollution,
microbial biomass and climate. Increased N-supply through deposition may impose an indirect positive
effect by stimulating bacterial growth and thus enhancing food supply. The abundance of Testate
Amoebae ranged from 700 106 m-2 at the boreal site to 2700 106 m-2 at the temperate site with the
highest load of atmospheric pollution. The view that abundances increase towards South is supported.
This increase is suggested to be due to increasing moisture and food availability at the sites with milder
climate and high N deposition. The relative size of the necrocoenosis of Testate Amoebae was smaller
111
8
Summary
at the boreal site than at the other sites. This could not be explained by high decompostion rates. A
possible explanation is that the relative amount of empty shells was small due to low metabolic rates of
the Testate Amoebae in consequence of severe climatic conditions.
The food web modelling approach revealed that mineralisation rates were not a simple function of food
web biomass. Besides climate and resource quality, food web structure determined C and N fluxes.
Total food web biomass was similar at all sites, ranging from 360 to 540 kg C ha-1. Total mineralisation
rates ranged from 800 to 2600 kg C ha-1 a-1 and from 0-100 kg N ha-1 a-1. Mineralisation rates were
lowest at the low N-input boreal site with a food web dominated by fungal pathways (bacterial to fungal
respiration ca. 30/70). Further South, as N availability increased, bacterial pathways became more
important (bacterial to fungal respiration ca. 40/50) and the cycling of C and N was accelerated.
Including estimates from direct counts of Testate Amoebae resulted in considerably higher estimates of
faunal mineralisation than have previously been reported (7-14% of total C mineralisation). The
estimated contribution of Testate Amoebae to C mineralisation ranged from 6 % or 44 kg C ha-1 a-1 at
the boreal site to 12 % or 343 kg C ha-1 a-1 at the temperate site with the highest load of atmospheric
pollution. With a similar patterning between sites the estimated contribution of Testate Amoebae to N
mineralisation ranged from 10 to 71 kg N ha-1 a-1. Testate Amoebae were the most important
contributers to N cycling, and were essential to balance bacterial N immobilisation especially in the
North. Within the decomposer fauna the highest trophic level (predaceous Microarthropoda and
Nematodes) played a minor direct role for mineralisation which supports the view that the functional
importance of soil fauna is inversely related to the trophic position of the group. The contribution of
Nematoda to C and N fluxes was estimated to be quite low in general. In contrast to the other faunal
groups, their importance did not increase towards South but was relatively higher at the boreal site. The
microfauna played a larger direct role for C and N fluxes than the mesofauna. The rating of
Enchytraeidae as a keystone group in boreal systems was not corroborated by the estimates of their
direct contribution to C and N fluxes. Such differences between microcosm studies and modelling
results hint to indirect effects having key influences which were only partly taken into account by the
food web model. In general, the importance of decomposer fauna to absolute (kg ha-1 a-1) and relative
(%) C mineralisation increased from North to South. The absolute (kg ha-1 a-1) contribution of fauna to N
mineralisation showed the same trend. However, relative to the estimated amount of N immobilisation
by the microflora, the decomposer fauna was of increasing importance towards the North. Along a
European North-South-transect, changes within the biomass structure of four decomposer food webs
resulted in specific total C and N mineralisation rates. Besides resource quality and climate, the
112
8
Summary
structure of the decomposer food web was essential to C and N fluxes.
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Chapter 9
Zusammenfassung
Corythion dubium
10 µm
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9 Zusammenfassung
Die Zersetzergemeinschaften von vier europäischen Nadelwäldern wurden untersucht. Die Flächen
lagen auf einem Europäischen Transekt von Nord-Schweden bis Nord-Ost–Frankreich, der sich über
eine Länge von ca. 2000 km erstreckte. Sie unterschieden sich hinsichtlich der klimatischen
Bedingungen und des atmosphärischen Stickstoffeintrags. Die Zersetzergemeinschaften und ihre
abiotische Umwelt wurden an 4 Probeterminen erfasst. Die vorliegende Studie untersucht die
Zersetzergemeinschaft auf verschiedenen ökologischen Skalenebenen. Es wurde angestrebt, den
deskriptiven Ansatz zu erweitern und ein funktionelles Verständnis der Lebensgemeinschaft im
Zusammenhang mit ihrer Umgebung zu erlangen. Die Studie ist in zwei Teile untergliedert. Im ersten
Teil wird die Gemeinschaftsstruktur der beschalten Amöben (Rhizopoda, Protozoa) auf Artebene
untersucht. Beschalte Amöben stellen die bedeutenste Protozoengruppe in Fichtenwaldböden dar.
Diversität, Abundanz belebter Zellen, encystierter Tiere und leerer Schalen sowie die Biomasse der
beschalten Amöben an den einzelnen Standorten wurden verglichen und in Bezug zu
Umweltparametern gesetzt. Im zweiten Teil wird die Gemeinschaft der beschalten Amöben in den
Zusammenhang des gesamten Zersetzernahrungsnetzes gesetzt. Die Nahrungsnetzstruktur und die
Biomasse der funktionellen Gruppen wurden erfasst. Ein mathematisches Modell wurde angewendet,
um von der Struktur des Nahrungsnetzes auf seine Funktion zu schließen. Der Kohlenstoff- und
Stickstofffluss durch das gesamte Netz und innerhalb des Netzes wurde simuliert. Der Beitrag der
beschalten Amöben sowie anderer funktioneller Gruppen zur Kohlenstoff- und Stickstoffmineralisation
an den einzelnen Standorten wurde verglichen.
Insgesamt wurden 42 Arten beschalter Amöben gefunden. Der Artenreichtum an den einzelnen
Standorten betrug 34 bis 40 Arten und nahm mit abnehmendem Breitengrad zu. Diversität und
Evenness der Amöbengemeinschaft waren am borealen Standort am niedrigsten. Die Biomassestruktur
der Gemeinschaften an den Standorten war ähnlich. Viele Arten kamen an allen Standorten vor und
wiesen hohe relative Biomassen auf. Die Gemeinschaft der beschalten Amöben am borealen Standort
wurde nicht durch spezialisierte Arten klar von den gemäßigten Standorten abgegrenzt. Dennoch nahm
die Ähnlichkeit zwischen den Amöbengemeinschaften mit zunehmender geographischer Distanz
zwischen den Standorten ab. Die Größenspanne innerhalb der Amöbengemeinschaften war sehr weit.
Die individuelle Biomasse der größten Arten war mehr als fünfzig mal größer als die der kleinsten Arten.
Sowohl r- als auch K-Strategen trugen wesentlich zur Gesamtbiomasse der Amöbengemeinschaft bei.
Innerhalb der Gruppe der beschalten Amöben scheinen eine Reihe von Strategien entlang des r/K-
115
9
Zusammenfassung
Kontinuums erfolgreich umgesetzt zu werden. Anhand multivariater Analyse (kanonische
Korrespondenzanalyse) wurden drei Umweltfaktorengruppen ermittelt, die für die Biomassestruktur der
Amöbengemeinschaft von besonderer Bedeutung waren: Atmosphärischer Schadstoffeintrag,
mikrobielle Biomasse und Klima. Der positive Effekt erhöhter Stickstoffeintrags war möglicherweise
indirekt. Erhöhtes Stickstoffangebot könnte, ähnlich wie erhöhte Bodenfeuchte, bakterielles Wachstum
anregen und somit das Nahrungsangebot für die beschalten Amöben verbessern. Die Abundanz der
beschalten Amöben lag zwischen 700 106 m-2 am borealen Standort und 2700 106 m-2 an dem
gemäßigten Standort mit der höchsten Last an atmosphärischem N-Eintrag. Die Abundanz war im
Süden höher als im Norden. Dies wird auf erhöhte Standortfeuchte und verbessertes Nahrungsangebot
an den Standorten mit milderem Klima und hohem N-Eintrag zurückgeführt. Die relative Größe der
Nekrozönose (leere Schalen beschalter Amöben) war am borealen Standort kleiner als an den anderen
Standorten. Da dies nicht auf erhöhte Abbauraten zurückgeführt werden konnte, wird vermutet, dass die
metabolischen Raten der Amöbengemeinschaft durch widrige klimatische Bedingungen herabgesetzt
waren. Dies könnte die Nekrozönose im Verhältnis zur Biozönose verkleinern.
Ergebnisse des Zersetzernahrungsnetzmodells zeigten, dass die Mineralisationsraten des
Nahrungsnetzes keine simple Funktion der Gesamtbiomasse sind. Zusammen mit Klima und
Ressourcenqualität bestimmte die Nahrungsnetzstruktur die Kohlenstoff- und Stickstoffflüsse. Die
Gesamtbiomasse des Nahrungsnetzes war an allen Standorten ähnlich und betrug zwischen 360 und
540 kg C ha-1. Die simulierten Gesamtmineralisationsraten waren 800 bis 2600 kg C ha-1 a-1 und 0 bis
100 kg N ha-1 a-1. Die Mineralisationsraten am borealen Standort waren am niedrigsten. Die höchsten
Raten wurden an dem gemäßigten Standort mit dem höchsten Eintrag an atmosphärischem N-Eintrag
ermittelt. Am borealen Standort wurde das Nahrungsnetz durch die Pilze dominiert (Verhältnis
bakterieller zu pilzlicher Respiration ca. 30/70). Mit zunehmender Stickstoffverfügbarkeit in Richtung
Süden nahm der bakterielle Abbauweg an Bedeutung zu (Verhältnis bakterieller zu pilzlicher
Respiration ca. 40/50) und die Kreisläufe von C und N waren schneller. Der Beitrag der Fauna an der
gesamten C Mineralisation wurde mit 7-14% vergleichsweise höher eingeschätzt als in anderen
Studien. Dies wird hauptsächlich auf die erhöhte Einschätzung des Protozoenbeitrags zurückgeführt, da
die beschalten Amöben in dieser Studie nicht mittels Kulturmethoden sondern durch direkte
Zählverfahren erfasst wurden. Mit Hilfe des Nahrungsnetzmodells ergab sich ein Beitrag der beschalten
Amöben zur Kohlenstoffmineralisation zwischen 6 % oder 44 kg C ha-1 a-1 am borealen Standort und bis
zu 12 % oder 343 kg C ha-1 a-1 an dem gemäßigten Standort mit der höchsten Last an
atmosphärischem N-Eintrag. Der Beitrag der beschalten Amöben zur Stickstoffmineralisation betrug 10
116
9
Zusammenfassung
bis 71 kg N ha-1 a-1 und zeigte den gleichen Verlauf im Standortvergleich. Die beschalten Amöben
mineralisierten die vergleichsweise größte Menge Stickstoff innerhalb des Nahrungsnetzes. Besonders
im Norden war ihr Beitrag essentiell für den Ausgleich bakterieller N Immobilisation. Innerhalb des
Nahrungsnetzes war der Beitrag der höheren trophischen Ebenen (räuberische Mikroarthropoden und
Nematoden) gering. Somit wurde die Auffassung bestätigt, dass die Bedeutung einer Gruppe für die C
und N Flüsse umgekehrt proportional zu ihrer trophischen Stellung ist. Der Beitrag der Nematoden zum
C und N Fluss wurde insgesamt niedrig eingeschätzt. Im Gegensatz zu den anderen Tiergruppen nahm
ihr Beitrag nicht gen Süden zu, sondern war am borealen Standort vergleichsweise höher. Der Beitrag
der Mikrofauna zum C und N Fluss war insgesamt höher als der der Mesofauna. Die Einschätzung,
dass Enchytraeiden eine Schlüsselgruppe in borealen Wäldern sind, wurde durch die ermittelten
direkten Beiträge zur C und N Mineralisation nicht bestätigt. Solche Differenzen zwischen Ergebnissen
aus Mikrokosmos-Studien und modellierten Werten weisen auf wichtige indirekte Effekte im
Zersetzernahrungsnetz hin. Solche indirekten Effekte werden nur zum Teil durch das
Nahrungsnetzmodell erfasst. Im Allgemeinen nahmen die absoluten (kg ha-1 a-1) und relativen (%)
Beiträge der Zersetzerfauna zur Kohlenstoffmineralisation von Norden nach Süden zu. Die absoluten
(kg ha-1 a-1) Beiträge der Fauna zur Stickstoffmineralisation zeigten den selben Trend. Relativ zur NImmobilisation der Bakterien hingegen wurde der Beitrag der Fauna für die N-Mineralisation des
gesamten Netzes nach Norden hin wichtiger. Entlang eines Europäischen Nord-Süd-Transekts ergaben
standortspezifische Zersetzernahrungsnetze spezifische C und N Mineralisationsraten. Neben
Ressourcenqualität und Klima war die Struktur der Nahrungsnetze für die C und N Umsätze
entscheidend.
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Chapter 10
Appendix
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10 Appendix
10.1
Parameters for calculation of decomposer fauna biomass
Table 10.1 C contents (from Berg 1997) of body dry weight and biomass conversion
factors of decomposer fauna groups (for details see sections 3.1.2-5).
C content of
conversion factor (µg C individual-1)
dry weight (%)
Testate Amoebae
Nematoda
Acari
Collembola
Enchytraeidae
50.0
50.0
47.7
47.5
50.0
size class specific, see Tables 5.1 and 5.2
genus specific, see Table 10.2
genus specific, see Taylor (2001)
species specific, see Pflug (2001)
22.2
Table 10.2 Nematode biomass was calculated from genus specific
abundances using conversion factors from the literature (Ekschmitt et al.
1999) or from calculations using the formula from Andrássy (1956) and
size estimates from Bongers (1994). Body volume of juveniles was
estimated to be on average 22 % of the adult body volume (Ilja
Sonnemann, pers. com.).
conversion factor for
adult specimen
family
genus
(µg C individual-1)
Cephalobidae
Acrobeloides
0.021
Acrolobus
0.013
Alaimidae
Alaimus
0.051
Bunonematidae
Bunonema
0.006
Monhysteridae
Geomonhystera
0.030
Heterocephalobus
0.041
Teratocephalidae
Metateratocephalus
0.009
Panagrolaimidae
Panagrolaimus
0.089
Plectidae
Plectus
0.076
Prismatolaimidae
Prismatolaimus
0.034
Neodiplogasteridae
Pristionchus
0.598
Rhabditidae
Rhabditis
0.598
Teratocephalus
0.009
Wilsonema
0.006
Aphelenchoididae
Aphelenchoides
0.018
Neotylenchidae
Hexatylus
0.248
Laimaphelenchus
0.012
Leptonchidae
Tylencholaimus
0.050
Mononchidae
Prionchulus
0.987
Seinura
0.019
Qudsianematidae
Eudorylaimus
0.416
Tylenchidae
Filenchus
0.007
Malenchus
0.006
Tylenchus
0.055
119
10
Appendix
10.2
Importance of food web biomass structure in the model
To test the importance of food web biomass structure for the estimated mineralisation rates, the food
web model was run without site specific adjustment of climate and resource quality (Scenario "equal").
Within this scenario equal climate (10°C, optimal moisture availability) and resource quality (C:N-ratio
22) was assumed for all sites. Differences in mineralisation estimates between sites from scenario
"equal" are due only to the site specific food web structure. The estimates from the scenario are
compared to those obtained after adequately adjusting the model to site specific conditions (Scenario
"site specific", Table 10.3). The estimates obtained with scenario "equal" are much higher than those of
scenario "site specific". However, a similar pattern of mineralisation rates in comparison of the sites is
observed. Food web biomass structure thus accounts for a considerable part of the differences in
mineralisation rates.
Table 10.3 Simulated N and C mineralisation rates (kg
ha-1 a-1) at the different sites from a scenario assuming
equal climate and resource quality at all sites (Scenario
"equal") compared to the estimates obtained after
adjusting the model to site specific conditions (Scenario
"site specific").
Scenario
Scenario
"equal"
"site specific"
mineralisation (kg ha-1 a-1)
N
C
N
C
N-SE
80
2245
1
764
S-SE
94
2550
28
1514
DE
192
5130
97
2600
FR
176
4913
57
2428
120
Chapter 11
References
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130
List of figures
Figure 1.1 Nebela lageniformis. 400x, DIC, in Euparal. Pseu = pseudostome...........................................................................7
Figure 1.2 The ecological scales investigated within this study: linking the soil biota to ecosystem function (C and N flux). ..12
Figure 2.1 Schematic map of the study sites lying on a North-South transect within Europe. Northern latitude is given
beneath site abbreviation. Total N deposition is indicated: 0 = very low; N = intermediate; NN = high. See Table 2.1 for
details and site abbreviations. ........................................................................................................................................15
Figure 2.2 Mean monthly temperature (A) and precipitation (B) at the sites. ...........................................................................17
Figure 2.3 Astrid Taylor and Anne Pflug during our autumn sampling at Åheden, N-SE. ........................................................19
Figure 4.1 Practical steps in applying the food web model approach to estimate C and N mineralisation rates (schematic
view)................................................................................................................................................................................42
Figure 4.2 Schematic illustration of the population biology equation, the pathway of energy from consumption to production
resp. mineralisation.........................................................................................................................................................43
Figure 5.1 The abundance (columns) resp. biomass (dots) of living Testate Amoebae in the five size classes. See Table 5.1
and 5.2 for characterisation of size classes. Whiskers represent standard deviation.....................................................55
Figure 5.2 Species biomass rank plots. The total Testate Amoebae biomass on a log scale are plotted against the ranks of
the species (Tokeshi 1993).............................................................................................................................................57
Figure 5.3 Testate Amoebae species from the study sites. A. Bullinularia indica, focus on the dorsal and on the ventral lip.
200x, bright-field, in Euparal. B. Edaphonobiotus campascoides, lateral view of the trumpet-like shell with cytoplasm
and vesicular nucleus. 400x, DIC, in Euparal. C. Schoenbornia humicola, nucleus and cytoplasm stained with aniline
blue. 400x, bright-field, in watery suspension. D. Nematode and Trinema lineralis, 400x, bright-field, in watery
suspension. E. Nebela militaris, cytoplasm stained with aniline blue. 400x, bright-field, in watery suspension. F.
Heleopera sylvatica cyst, stained with aniline blue. 200x, bright-field, in watery suspension. Pseu = pseudostome; N =
nucleus;
n
=
nucleolus;
P
=
cytoplasm;
T
=
Trinema
lineare;
Ne
=
Nematode.......................................................................................................................................................................58
Figure 5.4 The Bray & Curtis similarity index and the number of unique species plotted against geographical distance
between the sites. Bray & Curtis = dotted line (r = -0.68, p = 0.13); unique species = unbroken line (r = 0.89, p < 0.05).
On the abscissa the distance between pair-wise compared sites is indicated using the following abbreviations: D = DE;
F = FR; S = S-SE; N = N-SE...........................................................................................................................................62
Figure 5.5 Microbial parameters (metabolic potential, microbial biomass C, metabolic quotient) along the transect. Symbols
of the same shading labelled with identical letters are not significantly different from each other according to the Tukey
HSD test (see Table 5.7 for further details on ANOVA). Whiskers represent standard deviation. ...................................64
Figure 5.6 Percentage of bacterial from total microbial biomass C and frequency of dividing bacterial cells. Columns of the
same shading labelled with identical letters are not significantly different from each other according to the Tukey HSD
test (see Table 5.7 for further details on ANOVA). Whiskers represent standard deviation. ............................................65
Figure 5.7 Abundance of the major faunal groups besides Testate Amoebae. Columns of the same shading labelled with
identical letters are not significantly different from each other according to the Tukey HSD test (see Table 5.7 for
further details on ANOVA). Whiskers represent standard deviation. ................................................................................66
Figure 5.8 Biplot of sites. The dots represent replicate samples from different sampling times at each site. Samples are
identified by the replicate number followed by site abbreviation (NS = N-SE, SS = S-SE) and sampling time (1 = 1st
sampling in Oct/Nov, 2 = 2nd sampling in May/Jun; 3 = 3rd sampling in Sept, 4 = 4th sampling in Mar/Apr). ..................71
Figure 5.9 Biplot of species. Aca = Arcella catinus; Amu = Assulina muscorum; Ase = Assulina seminulum; Bin = Bullinularia
indica; Cga = Centropyxis gauthieri; Cma = Centropyxis matthesi; Csp = Centropyxis sphagnicola; Csy = Centropyxis
131
List of figures
sylvatica; Cdu = Corythion dubium; Cov = Cryptodifflugia oviformis; Ceu = Cyclopyxis eurystoma; Cka = Cyclopyxis
kahli; Dlu = Difflugia lucida; Dmi = Difflugia minuta; Eca = Edaphonobiotus campascoides; Ela = Euglypha
laevis/rotunda; Erm = Euglypha rotunda minor; Est = Euglypha cf. strigosa; Hsy = Heleopera sylvatica; Hsu =
Hyalosphenia subflava; Mpa = Microchlamys patella; Mfl = Microcorycia flava; Nla = Nebela lageniformis; Nmi =
Nebela militaris; Npt = Nebela parvula/tincta; Nmb = Nebela tincta major/bohemica/collaris; Pac = Phryganella
acropodia; Ppr = Phryganella paradoxa alta; Pde = Plagiopyxis declivis; Pin = Plagiopyxis intermedia; Pla =
Plagiopyxis labiata; Shu = Schoenbornia humicola; Svi = Schoenbornia viscicula; Tde = Tracheleuglypha dentata; Tpu
= Trachelocorythion pulchellum; Tar = Trigonopyxis arcula; Tmi = Trigonopyxis minuta; Tco = Trinema complanatum;
Ten = Trinema enchelys; Tli = Trinema lineare; Tpe = Trinema penardi........................................................................73
Figure 5.10 The abundance of living cells (10-6 m-2) and empty shells (10-6 m-2 resp. %) along the transect. In comparison of
dots and columns of the same shading those labelled with identical letters are not significantly different from each
other according to the Tukey HSD test (see Table 5.12 for further details on ANOVA). Whiskers represent standard
deviation. ........................................................................................................................................................................74
Figure 5.11 Occurrence of living cells (% of all specimen found) and cysts (% of living cells) at the different sampling times
(averages over all sites). In comparison of columns of the same shading those labelled with identical letters are not
significantly different from each other according to the Tukey HSD test (see Table 5.12 for further details on ANOVA).
Whiskers represent standard deviation...........................................................................................................................75
Figure 5.12 Testate Amoebae biomass at the different sites and sampling times. Within a site specific sub-plot columns
labelled with identical letters are not significantly different from each other according to the Tukey HSD test (see Table
5.12 for further details on ANOVA). Whiskers represent standard deviation....................................................................77
Figure 5.13 Sketch of the decomposer food web (connectedness web). Feeding relationships are indicated by arrows
pointing from prey to predator. fung. = fungivorous; bact. = bacterivorous; panphyt. = panphytophagous; omni. =
omnivorous; pred. = predaceous. ...................................................................................................................................79
Figure 5.14 Correlation coefficients (r) between the biomass structure at different sites (relative contributions to total
biomass, pair-wise correlation) against the geographical distance (km) between the sites (straight line: r = -0.91, p <
0.05, point FR/DE excluded from regression).................................................................................................................86
Figure 5.15 Estimates of C and N mineralisation rates at the different sites obtained using the food web model ("simulated")
and laboratory incubations of soil cores ("observed"). Laboratory incubation data are taken from Persson et al.
2000ab. In comparison of columns of the same shading those labelled with identical letters are not significantly
different from each other according to the Tukey HSD test (see Table 5.16 for further details on the ANOVA). .............87
Figure 5.16 Relative contributions of the functional groups to C mineralisation (%) at the sites. In comparison of the
mineralisation by a particular functional group at the sites (i.e. the horizontal comparison of bars on the same level of
the ordinate axis) values labelled with identical letters are not significantly different from each other according to the
Tukey HSD test of a one-way ANOVA on the main effect ‘site’ (see Table 5.16 for further details on the ANOVA).
Whiskers on bars represent standard deviation..............................................................................................................89
132
List of tables
Table 2.1 Characteristics of the four selected coniferous sites (from data given in Persson et al. 2000c)...............................20
Table 2.2 Sampling times at the four sites. See Table 2.1 for site abbreviations. ....................................................................21
Table 3.1 General taxonomic literature for the determination of Testate Amoebae. ................................................................30
Table 3.2 Specialised taxonomic literature and monographies for the determination of Testate Amoebae. ............................31
Table 3.3 Nematode genera found on the sites and their feeding habits according to Yeates (1993). Following the simplified
classification for the food web model further food sources or feeding modes that may occur are given in parentheses.35
Table 5.1 List of species that were found on the study sites and classification into size classes. See Table 5.2 for definition of
size classes.....................................................................................................................................................................54
Table 5.2 Size classes of Testate Amoebae and number of species found belonging to each size class. ..............................55
Table 5.3 Results of the two-way ANOVAs on the effect of 'site' and 'time' on number of species, diversity and evenness. dfEffect = 3 (site, time); df-Effect = 9 (interaction site ´ time); F = inter-group variance divided by intra-group variance; p
= p-level of significance: n.s. > 0.05 (not significant); * £ 0.05; ** < 0.01, n = 50. ..........................................................56
Table 5.4 Total number of species and mean number of species found at each site. For the calculation of diversity and
evenness the relative abundance of living Testate Amoebae cells (active cells + cysts) was used; empty shells were
not taken into consideration. In comparison within a specific row values labelled with identical letters are not
significantly different from each other according to the Tukey HSD test (see Table 5.3 for further details on ANOVA)...56
Table 5.5 The community structure of Testate Amoebae. Asterisks represent relative biomass (%). Species are arranged
according to their occurrence or absence at certain sites. Dotted lines indicate groups labelled with capital letters (AG). For explanation of the grouping see section 5.1.2.3. ****** > 31.9 %; ***** = 10.0 to 31.9 %; **** = 3.2 to 9.9 %; ***
= 1.0 to 3.1 %; ** = 0.32 to 0.99 %; * < 0.32 %; empty space = 0 %; ° = found only once during counting or occurred
only when analysing enriched material from flotations or batch cultures........................................................................59
Table 5.6 Total number of species, and matrices of the number of unique species (Colwell and Coddington 1994) and
Bray & Curtis-similarities (Bray and Curtis 1957, Southwood 1994) in comparison of the four sites.............................. 61
Table 5.7 Results of the two-way ANOVAs on the effect of 'site' and 'time' on microbial and faunal parameters. df-Effect = 3
(site, time); df-Effect = 9 (interaction site ´ time); F = inter-group variance divided by intra-group variance; p-level of
significance: n.s. > 0.05 (not significant); * £ 0.05; ** < 0.01. .........................................................................................63
Table 5.8 Results of the two-way ANOVAs on the effect of 'site' and 'time' on abiotic parameters. df-Effect = 3 (site, time); dfEffect = 9 (interaction site ´ time); F = inter-group variance divided by intra-group variance; p-level of significance: n.s.
> 0.05 (not significant); * £ 0.05; ** < 0.01......................................................................................................................67
Table 5.9 Mean, minimum/maximum (2nd row) and standard deviation (2nd row, in parentheses) of the environmental
parameters at the sites. In comparison within a specific row values labelled with identical letters are not significantly
different from each other according to the Tukey HSD test (see Table 5.8 for further details on ANOVA). .....................68
Table 5.10 Summary of the CCA of species biomass pattern and environmental variables. ...................................................69
Table 5.11 Conditional effects of including the environmental variables into the CCA one after the other using forward
selection. cum(lA) = cumulative explanatory power (variance explained) by including the environmental variable; rC =
canonical correlation coefficient of the inter-set correlations of environmental variables with the axes. p-level of
significance: n.s. > 0.05 (not significant); * £ 0.05; ** < 0.01. .........................................................................................70
Table 5.12 Results of the two-way ANOVAs on the effect of 'site' and 'time' on various markers of the Testate Amoebae
community. df-Effect = 3 (site, time); df-Effect = 9 (interaction site ´ time); F = inter-group variance divided by intragroup variance; p-level of significance: n.s. > 0.05 (not significant); * £ 0.05; ** < 0.01.................................................74
Table 5.13 Feeding behaviour of some soil Testate Amoebae species resp. genera. .............................................................80
133
List of tables
Table 5.14 Physiological parameters for each functional group. Assimilation and production efficiencies (a and p) taken from
Andrén et al. (1990) and C:N-ratios (q) from Hunt et al. (1987) if not stated otherwise. Basic death rates (at 10°C)
were obtained from Hunt et al. (1987) and De Ruiter et al. (1993a) and adapted according to temperature and
moisture regime of the specific sites...............................................................................................................................82
Table 5.15 The feeding preferences wij. Explanation see section 4.2.3. prmi = predaceous Acari; prco = predaceous
Collembola; prne = predaceous Nematoda; omne = omnivorous Nematoda; pami = panphytophagous Acari; paco =
panphytophagous Collembola; prta = predaceous Testate Amoebae; pata = panphytophagous Testate Amoebae; fune
= fungivorous Nematoda; bane = bacterivorous Nematoda; ench = Enchytraeidae; fung = fungi; bact = bacteria; detr =
total detritus. ...................................................................................................................................................................83
Table 5.16 Results of ANOVAs on the main effect of ‘site’ on biomass (% resp. kg C ha-1) and on simulated C and N
mineralisation (% resp. kg ha-1 a-1) of individual functional groups within the decomposer food web (one-way ANOVA).
df-effect = 3; F = inter-group variance divided by intra-group variance; p-level of significance: n.s. > 0.05 (not
significant); * ≤ 0.05; ** < 0.01. .......................................................................................................................................84
Table 5.17. Biomasses of functional groups (%) and total (kg C ha-1) at each site. Mean values of four sampling occasions
are shown, standard deviations are given in parentheses. In comparison within a specific row values labelled with
identical letters are not significantly different from each other according to the Tukey HSD test (see Table 5.16 for
details on the ANOVA)......................................................................................................................................................85
Table 5.18 Contributions of functional groups within the decomposer food web to N mineralisation (kg N ha-1 a-1). Mean
values of four sampling occasions are shown, standard deviations are given in parentheses. In comparison within a
specific row values labelled with identical letters are not significantly different from each other according to the Tukey
HSD test of a one-way ANOVA on the main effect ‘site’ (see Table 5.16 for details on the ANOVA). ...............................91
Table 10.1 C contents (from Berg 1997) of body dry weight and biomass conversion factors of decomposer fauna groups (for
details see sections 3.1.2-5). ........................................................................................................................................119
Table 10.2 Nematode biomass was calculated from genus specific abundances using conversion factors from the literature
(Ekschmitt et al. 1999) or from calculations using the formula from Andrássy (1956) and size estimates from Bongers
(1994). Body volume of juveniles was estimated to be on average 22 % of the adult body volume (Ilja Sonnemann,
pers. com.)....................................................................................................................................................................119
Table 10.3 Simulated N and C mineralisation rates (kg ha-1 a-1) at the different sites from a scenario assuming equal climate
and resource quality at all sites (Scenario "equal") compared to the estimates obtained after adjusting the model to
site specific conditions (Scenario "site specific"). .........................................................................................................120
134
Acknowledgements
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Acknowledgements
"Because we do most things relying only on our own
sagacity we become self-interested, turn our backs on reason, and things do
not turn out well. As seen by other people this is sordid, weak, narrow and
inefficient. When one is not capable of true intelligence, it is good to consult
with someone of good sense. [...]
It is, for example, like a large tree with many roots.
One man's intelligence is like a tree
that has been simply stuck in the ground."
(by Yamamoto Tsunetomo 1659-1713,
from 'Hagakure' alias 'The Way of the Samurai',
the inspiration to the film 'Ghost Dog' by Jim Jarmusch)
Prof. Dr. Volkmar Wolters is gratefully acknowledged for supporting this thesis and for giving me the
opportunity to participate within two international research projects (CANIF and GLOBIS). I thank him for
stimulating discussions and for his confidence. Thanks to his support I was able to meet, interact with
and learn from many fellow researchers.
I am very grateful to Prof. Dr. Peter C. De Ruiter, University of Utrecht, whom I first met on one of the
TERI workshops that were hosted in Giessen. He taught me to use the food web model. I greatly
appreciate his help and his friendly and fruitful ways of discussion. I am grateful that he accepted to be
referee of this thesis.
Dr. Ralf Meisterfeld was the supervisor of the Testate Amoebae work within this thesis and I dearly
thank him for his support.
Thank you, Anne Pflug, Astrid Taylor and Jens Dauber, for continuous support and encouragement.
Discussions, many journeys and evenings with these three made my PhD-years worth while. Special
thanks to Anne Pflug for providing the Collembola data, to Astrid Taylor for providing the Acari data, to
Jens for sharing his views and discussing any aspect of ecology, back and forth and back again.
Thanks to all three of them for corrections and helpful comments on many chapters of this thesis.
For statistical advice I thank Prof. Dr. Wolfgang Köhler, Dr. Klemens Ekschmitt and Dr. Gabriel
Schachtel.
I thank Monika Leonardt, Christine Tandler, Birgit Wasmus, Susanne Vesper, Barbara Beier and Martin
Kröckel for their help in the laboratory.
I very much enjoyed the co-operation with the CANIF and GLOBIS partners. - I am particularly grateful
to Niklas Lindberg, Janne Bengtsson and Tryggve Persson for stimulating discussions, introduction to
Bob Hund, support and company, especially on our sampling trips and visits to Uppsala, and during our
twitching tour around Halkidiki.
I dearly thank Tony Harrison and Harmke van Oene for openly sharing information on their modelling
135
Acknowledgements
approaches and the exchange of early drafts of publications.
I thank Andy Taylor for answering many questions on ectomycorrhiza and life (in soil) in general.
Discussing N mineralisation (HA! here it is, the N-word...!) with him was always a source of motivation.
I am very grateful to Karl Reiter and Georg Grabherr for their help with multivariate statistics and an
enjoyable time in Vienna.
For creating a joyous working atmosphere and for their helpful ways I am grateful to Johannes Frisch,
Ilja Sonnemann, Maria Robeck, Anna Kohler, Stephanie Holzhauer and Tobias Purtauf. Special thanks
to the last three for adding a great deal of enthusiasm and new ideas to our working group; to Anna for
correcting the introduction; to Ilja for her help with the Nematoda; to Johannes for help on taxonomic
nomenclature and countless candy...!
Thank you, Heide and Uwe Schröter, for your support, patience and friendship especially during the last
two years.
Thank you, Hanna Tigges, for your heartiness and love of life, that I find worth pursuing. - "The way to
hell is paved with good will" was one of her favorite sayings...
Thank you, Ralph Hückelhoven, Christiane Heineck, Reinhild Biermann, Rolf Schröter, Susann Beetz,
Jürgen Geerlings and Jutta Herzogenrath, friends indeed, for their support. Thanks especially to Ralph
for proof-reading many parts of this thesis and for his trustiness in emergency; to Rolf for his advice
about lay-out and help with the cover, and to Christiane for making our home sweet. Thank you, Eva
Gessner, Jörg Espelta and Mike Fechner for adding essential joy. "Echte Fründe ston zesamme, su wie
eine Jott un Pott...."
Sampling time in Åheden, N-SE.
136
curriculum vitae
name
date of birth
nationality
address
Dagmar Schröter
27.05.1970
German
Gutenbergstr. 90, 14467 Potsdam, Germany
Professional development
since May 2001
Apr 1996 – Dec 00
Dec 1995 – Jan 96
Scientific co-ordinator at the Potsdam Institute for Climate Impact Research
Research assistant at the Institute of Animal Ecology & Zoology,
Justus-Liebig-University Giessen
Research assistant at the Institute of General Biology,
Technical University Aachen
Education
Nov 1995
Nov 1994 – Nov 95
Oct 1989 – Nov 94
May 1989
Aug 1986 – Jul 87
Aug 1987 – May 89
Aug 1980 – Jul 86
Biology Diploma
Diploma thesis at the Institute of Ecology and Zoology,
Technical University Aachen:
"Characterisation of communities of Testate Amoebae (Protozoa)
in spruce forest soils".
Supervisors: Prof. Dr. P. Schmidt and Dr. R. Meisterfeld
Biology studies at the Technical University Aachen
Graduation (Abitur): German high school, Grevenbroich
Graduation: American high school, Little Valley, New York, USA
German high school, Grevenbroich (grades 5 – 10, grades 12 – 13)
137
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