Download A comparison of whole-community and ecosystem approaches

Journal of Plankton Research Vol.17 no.6 pp.1273-1305, 1995
A comparison of whole-community and ecosystem approaches
(biomass size distributions, food web analysis, network analysis,
simulation models) to study the structure, function and regulation
of pelagic food webs
Ursula Gaedke
Limnologisches Institut, Universitdt Konstanz, PO Box 5560, D-78434 Konstanz,
FRG
Abstract A step-by-step procedure for investigating the structure, function and regulation of pelagic
communities as an entirety is suggested which proceeds along gradients of increasing requirements
for data and knowledge, and of growing understanding of ecosystem functioning. It comprises
methodologies based on biomass size distributions, followed by food web analysis, network analysis
and dynamic simulation models. The different approaches are compared with respect to data
requirements, theoretical foundations, operational problems, time and computational effort, and the
different types of information they provide on food web structure and dynamics. These ideas are
illustrated with data from Lake Constance. Biomass size distributions provide a structural and
energetic food web analysis based only on measurements of abundances and body sizes, and a few
general assumptions mainly on size relationships of metabolic activities and trophodynamics. Food
web analysis considers binary webs depicting qualitatively trophic links between species or trophic
guilds and provides profound information about the food web structure. Mass-balanced flow
diagrams (trophic webs) take into account the magnitude of flows between living and non-living
compartments, and provide comprehensive descriptions of fluxes and cycling of matter and the
trophic food web structure when evaluated by network analysis. These three static approaches are
contrasted with tactical dynamic simulation models depicting interaction webs and representing
unique possibilities of studying the dynamic nature, spatio-temporal organization, direct and indirect
cause-effect relationships, and impact of physical forcing. However, these capabilities are only
achievable on the expenditure of a very large research effort.
Introduction
Now, more than ever, a comprehensive understanding of the structure, function
and regulation of major ecosystems is necessary to face the world's ever growing
environmental problems. Urgently needed is a strategic approach to predict the
development of community structures and the functional responses of
ecosystems to different anthropogenic environmental impacts. The dynamics
and regulation of food webs cannot be understood, however, unless one
simultaneously considers processes at the level of individual populations in
concert with those acting over the domain of the entire community. All
populations are embedded in an ecological context, i.e. processes that accrue at
the population level permanently change properties of the whole system. These,
in turn, impose new constraints back upon the individual populations (e.g. Mann,
1988; Pahl-Wostl, 1993b, 1995).
Approaching this idea in practice is made problematic, however, by the
extreme complexity of natural ecosystems which does not allow us to treat
ecosystem dynamics in their entirety. Rather, one is forced to abstract from the
situation, i.e. to select only certain data and questions to be asked and to neglect
O Oxford University Press
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U.Gaedke
the rest. Which of the many possible conceptions one chooses to elaborate and
examine depends on many factors. For example, one's choice is strongly
influenced by the availability of data, their training, working conditions and
sociocultural milieu. The same factors affect the temporal and spatial scales with
which an investigator observes ecosystem development. Therefore, one must
approach the attributes of ecosystems from as many angles as possible—a task
made feasible to an extent by the use of conceptual and mathematical models.
Because they are simplified descriptions of the real world, there is no reason to
expect any individual approach to be unequivocally right or wrong. Rather, each
will differ in its capabilities to portray distinct features of the same natural
system. In the end, however, they should complement each other, by virtue of
the fact that each separate abstraction poses somewhat different questions and
uses distinct types of information.
An operational procedure that takes these problems into account could take
the form of a step-by-step procedure for the analysis of ecological communities
and ecosystems. Investigation proceeds in a stepwise fashion that requires ever
more data and knowledge at each juncture, but which in turn yields a progressive
understanding of ecosystem functioning (Figure 1). It takes explicit account of
the effort and operational problems involved in the measurement of features of
pelagic systems and in the subsequent data analysis. Along the way, the
perspective changes from a focus on the community to an ecosystem point of
low
high
low
n*gl»ot«d
low
high
low
high
oonsldsrsd
high
effort & Information content
of the data base
rel. contribution of local
field work
blomass sirs
distribution
food wab analysis
(binary food waba)
network analysis
(trophic food waba)
dynamic simulation
modal
(Intaractlon waba)
- rigid
adapthra
potential contribution from
other ecosystems
& lab. studies
interactions
with the abiotic
environment
mechanlstical
details
Fig. 1. Sketch of a hierarchical order of different ecosystem approaches underlying Table I which
provides more detailed information. The effort to establish the data base and the information content
is roughly estimated by the multiplicity of knowledge required to apply a specific methodology. The
increasing effort required to obtain additional information beyond standing stocks goes along with an
increase of generality of this knowledge for similar ecosystems. For example, the abundance of a
species in a particular ecosystem is relatively easy to measure as compared to its feeding habits. The
latter, in tum, may be more alike among different habitats (for details see the text).
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Investigating pelagic food webs
view by explicitly incorporating more numerous abiotic processes that may
significantly affect food web dynamics. Prior or supplementary to setting up a
dynamic, fully interactive simulation model for a particular ecosystem, it may be
appropriate to explore other available methodologies, such as biomass size
distributions, food web analysis and network analysis. Because they demand
fewer data, these methodologies are usually less time consuming and expensive.
Furthermore, they are likely to involve a smaller number of assumptions that can
be communicated more succinctly. Table I provides a summary of the data
requirements, potentials and limitations for various methods of studying entire
food webs. Only those approaches that are capable of characterizing food webs
in their entirety, and for which more or less well-defined theoretical foundations
have already been developed, are considered. Hence, phenomena or modelling
techniques that pertain only to individual populations (e.g. individual-based
modelling) are excluded from the discussion. The contribution to theoretical
ecology made by strategic models formulated in terms of analytically solvable
differential equations is explicitly acknowledged (e.g. those that address the coexistence of species in a fluctuating environment; Ebenhoh, 1988; Yodzis, 1989),
but a full consideration of these techniques lies beyond the scope of this paper.
Some methodologies pertain mostly to pelagic and locally sustained food webs,
but most technologies are more generally applicable. I illustrate most methods
with examples from the pelagic zone of Lake Constance.
Table I. Overview of the data requirements, concepts and potential insights to be gained by various
ecosystem approaches
Observational basis
Concepts and techniques
Potential insights
abundances and body mass
—» biomass
biological species,
allometric relationships,
time-series analysis,
biomass size distribution,
trophic continuum concept
species list, quantitative
importance of species in respect
to biomass (and process rates)
size-conversion efficiency,
variation in time and space,
(structure, function, regulation)
+ abiotic parameters
regression analysis
potential abiotic impacts
+ trophic interactions
—> binary web
aggregation of species
to trophic guilds,
food web analysis
food web structure,
connectance, linkage density,
potential direct interactions
+ process rates (e.g.
production, respiration) and
diet compositions
-»trophic web
population dynamics,
flow analysis, network
analysis, cycle analysis,
trophic level concept
mass-balanced flow diagrams,
recycling of C and nutrients,
trophic transfer efficiencies,
trophic structure and function
(direct and indirect effects)
consistency checks
+ regulation of flows
-> rigid interaction web
dynamic simulation models
with a set community structure
direct and indirect effects and
interactions, dynamic
consistency checks
+ adaptability
-» adaptive interaction web
folly reactive and predictive
simulation models with a
flexible community structure
prediction of community structure
and function for different
environmental scenarios
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U.Gaedke
Lake Constance (in German: Bodensee) is a large (476 km2), deep (zmax = 256 m,
mean depth 100 m), mesotrophic lake situated at the northern fringe of the Alps. It
represents a large open-water body with presumably little labile allochthonous
imports and a pronounced seasonally. A comprehensive data base is available
on almost all abiotic and biotic parameters. For example, most physicochemical
parameters and the abundance of all plankton organisms have been assessed by
weekly sampling and elaborate microscopic counting techniques for several
years. Additionally, production and other metabolic rates have been studied in
detail. The measurements have been performed by a large team of scientists
within the special collaborative programme 'Cycling of matter in Lake
Constance'. This outstanding data set has been analysed in detail at the
population level (e.g. Simon, 1988; Tilzer and Beese, 1988; Glide, 1990; Muller et
al., 1991; Weisse, 1991) and at the community level (Geller et al., 1991) using
biomass size distributions (Gaedke, 1992a,b, 1993), food web analysis
(M.M.Lang, unpublished), and network analysis (Gaedke and Straile, 1994a,b;
Straile, 1994; Gaedke et al., 1995). Further references on sampling techniques
and production estimates are given by Gaedke (1992a) and Gaedke and Straile
(1994b), and the literature cited therein.
To conclude, the basic objective here is to suggest a strategy with which to
investigate the functioning of a distinct pelagic community at the ecosystem level.
A full discussion is given describing the measurements required to understand
specific details of food web structure and function. Similarly, given a specific set
of data, I discuss what kind of approach is the most suitable to apply. The paper
aims to provide a comparison of methodologies by examining for each particular
approach data requirements, theoretical foundation, time and computational
effort, and capabilities and limitations of understanding of ecosystem structure,
function and regulation. References for more comprehensive reviews and recent
developments of the individual approaches and unresolved problems are given
to the best of my knowledge. After some general remarks on an appropriate
sampling strategy, I examine in depth the four key modelling approaches:
biomass size spectra, food web analysis, network analysis and dynamic
simulation models.
Measurement strategy
Field observations may be performed with different accuracy, and with different
spatial and temporal resolution. Phytoplankton biomass and species composition, for example, may be roughly but more rapidly assessed by microscopy
which requires up to 10 h of work time per sample, depending on the requested
accuracy and taxonomic resolution. It may also be assessed by measuring
pigment concentrations either in situ or by remote sensing. The space of
potential sampling schemes may be imagined as a three-dimensional cube where
the j-axis represents the temporal, the >»-axis the spatial resolution and the z-axis
the effort in making the observations (Figure 2). Of course, it is most desirable to
achieve high-quality measurements with a high spatio-temporal resolution.
Given the usual limitations of a typical sampling programme, however, this will
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Investigating pelagic food webs
quality of
measurements
spatial resolution
of measurements
temporal frequency
of measurements
Fig. 2. Three-dimensional cube to illustrate the potential sampling space along the gradients of
temporal and spatial resolution and of the accuracy of measurements. Owing to the limited sampling
capacity, trade-offs between the different optimization criteria are in general required. Total
sampling effort should be distributed over various sampling strategies represented by different points
within the cube in order to obtain information on different spatio-temporal scales.
not be possible in general. Consequently, less accurate techniques (e.g.
fluorescent probes or remote sensing) will have to be used in order to increase
the spatio-temporal resolution. Long-term data series, where data acquisition is
repeatedly performed in the same way, show a great potential for many studies
and are essential for some. However, ecosystem functioning is determined by a
multitude of processes acting on a large range of spatio-temporal scales which
have to be studied appropriately (e.g. Field et ai, 1985). Concepts such as
stability, resilience or equilibrium are contingent on the spatial and temporal
scales of observation (e.g. Pahl-Wostl, 1993a,c, 1995, and references cited
therein), and cross-system comparisons of food web structure and dynamics are
only meaningful when one accounts for the spatio-temporal variability. Thus, the
available sampling capacity should not be concentrated on one point within the
cube of potential sampling schemes, but different locations in it have to be
covered providing information on different aspects of food web dynamics.
Community analysis based on measurements of abundances and body sizes:
biomass size distributions (trophic continuum)
When starting research on a particular open-water ecosystem, organismal
abundances and body masses are among the first biological observations as they
are relatively easy to establish in pelagic environments and essential for all
subsequent analyses. They form the basis for the hierarchy of methodologies
suggested in Table I by providing basic information on the species list and the
quantitative importance of the different groups as assessed by biomass. Using
allometric relationships, contributions to metabolic community processes like
production and respiration may be roughly inferred as well (see below).
Organismal abundances have to be observed specifically for individual
ecosystems and periods of seasonal succession, and can hardly be inferred
reliably from knowledge about other ecosystems. In contrast, estimates of body
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masses and conversion factors from body length or fresh weight to units of dry
weight and carbon may be gained for many taxa from studies undertaken in
similar ecosystems.
The spatio-temporal variability and pattern formation in standing stocks may
be investigated by time-series analysis and other techniques that correlate the
various parameter combinations (e.g. herbivorous and algal biomass). This
analysis allows the formulation of first hypotheses concerning biological
interactions. Accompanying measurements of physicochemical factors (e.g.
temperature, light, nutrient concentrations), which are commonly obtained
simultaneously with the basic biological parameters, allow a first investigation of
the reaction of the biological system to environmental fluctuations occurring
during the period of investigation (Table I). These techniques generally do not
take a whole food web viewpoint, but provide basic information for the models
that are to follow. In contrast, a complete analysis of species size distributions of
abundance, biomass and metabolic activity can provide information about
structural and energetic aspects of pelagic food webs in their entirety based on
these data, as illustrated below.
General description and data requirements
Biomass size distributions are constructed by allocating all organisms into
logarithmically spaced size classes according to their individual body weight and
summing up the biomass in each size class. Such spectra have been established
for small and large limnetic and marine systems, and show the remarkable
regularity with which biomass is frequently distributed in a continuous manner
across the entire range of logarithmic size classes. In large open-water
ecosystems like Lake Constance and the open ocean, no gaps (i.e. size classes
without detectable biomass) are observed along the size gradient (e.g. Sheldon et
al., 1972; Rodriguez and Mullin, 1986; Gaedke, 1992a). Furthermore, biomass
tends to be in general evenly distributed over all size classes ranging from
bacteria (10 14 g C cell ') to carnivorous crustaceans (10^* g C ind."1) on a
seasonal average in Lake Constance (Figure 3). This regularity implies that the
abundance of organisms per size class is inversely proportional to their body
weight, which enables an estimate of the minimum counting volumes required to
assess abundances of all size categories with equal accuracy. Some of the oceanic
systems show a gradual and regular decrease of biomass with body size.
Microscopic counting of an entire plankton community ensures detailed
insight into the species composition, which enables a more profound functional
analysis and provides the basis for the subsequent approaches. However,
because it requires a great amount of time, it becomes impossible to attain a
good spatio-temporal resolution. The latter may be improved considerably at the
expense of taxonomic resolution by using automated particle analysers like
inductive particle counters (e.g. the Coulter counter) or, more powerful, flow
cytometers and (in the future) other optical techniques, as well as echo-sounders
for crustaceans and larger organisms. Coulter counters were used to establish the
first particle size distributions for many different regions in the open ocean
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Investigating pelagic food webs
10.000
1.000
E
100
o
11
14
17
20
23
26
log 2 (body weight) [pg C]
Fig. 3. A plankton biomass size distribution of Lake Constance, time-averaged over the seasonal
course in 1987 (redrawn from Gaedke, 1992a).
(Sheldon et al., 1972), but do not allow discrimination between living and dead
particles. The most advanced flow cytometers equipped with different scatters,
excitations and emission channels, as well as sorting capacities, will permit a
rough identification of shapes and emission spectra over a broad size range,
making living organisms and dead material distinguishable. Furthermore,
measurements of autofluorescence enable a distinction to be made between
pigmented (i.e. autotrophs) and non-pigmented cells (i.e. heterotrophs). Future
development of staining techniques (e.g. for proteins) may improve the
capabilities of this method to estimate biomass. Given that some knowledge of
the species composition, biomass conversion factors and food web structure is
available, biomass size spectra based on such data are an effective technique to
study the structure of and the energy flow within the food web (see below). This
approach appears particularly useful for long-term monitoring and for large
heterogeneous areas with possibly fast dynamics.
Theoretical framework and underlying assumptions
Body mass is frequently the single most useful quantity for study. Elton (1927)
has already attempted to reduce the enormous complexity of pelagic food webs
by emphasizing the generalizing capacity of body size. Allometric relationships
between body size and weight-specific process rates enable a rough prediction of
metabolic properties of (parts of) the entire community from body mass (e.g.
Banse and Mosher, 1980; Platt et al., 1984). The metabolic activity, M,, of the
organisms within size class i may be estimated from the size class-specific
biomass, Bh and the average body mass, w,, of the respective size class: M, = c Bt
w[*. The constant c depends on the physiological process under consideration
(e.g. production, respiration). Its value may differ among major taxonomic
groups (e.g. homeotherms and heterotherms, osmotrophs and phagotrophs;
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Moloney and Field, 1989) and can probably not yet be specified as reliably as the
scaling exponent b (e.g. Platt, 1985). A value of 0.25 is commonly assumed for b
for all kind of processes (e.g. Peters, 1983; Moloney and Field, 1989). However,
this value might be too large for (small) plankton organisms (Platt and Silvert,
1981; Banse, 1982; Sommer, 1989). A reasonable agreement was found between
seasonal changes in the metabolic activity of the eukaryotic plankton community
in Lake Constance derived by this method and of more direct and largely
independent production estimates (e.g. 14C technique; Geller et al., 1991;
Gaedke, 1993). This allometnc approach was used to parameterize a dynamic
simulation model in a coherent way for a region where direct measurements of
process rates were mostly lacking (Moloney and Field, 1991; see below).
Additionally, body size has great predictive power for ecological processes
(e.g. Platt et al., 1984; Calder, 1985; Cousins, 1985; Platt, 1985). In Lake
Constance, for example, spring succession (i.e. the response time to an external
perturbation) of dominant herbivores (ciliates, rotifers and crustaceans like
daphnids) was strongly related to body size, as was the magnitude of seasonal
fluctuations of plankton standing stocks per size class. In pelagic systems, body
size gains additional predictive power at the community level since body size and
trophic position are related to each other, especially if the grazing and detritus
chain are considered separately (see below). This regularity derives from the fact
that autotrophs tend to be relatively small and predators are generally to a
certain extent larger than their prey, i.e. the predator size allows predictions
about the size of food particles eaten.
After the first empirical establishment of continuous and regularly shaped
biomass size distributions, some models were developed to explain this regularity
(e.g. Sheldon et al., 1977; Platt and Denman, 1978; Silvert and Platt, 1978, 1980;
Borgmann, 1987) which formed the theoretical foundation for all subsequent
analyses. They have basically three characteristic features, (i) They utilize the
correlation between body size and trophic position in pelagic systems and
describe a mostly continuous flow of matter from small to larger organisms, (ii)
They are based on allometric relationships between body weight and metabolic
processes, (iii) (Constant) predator-prey weight ratios, indicating the width of
each trophic step along the size gradient, are used in some models to relate the
flow of matter along the size gradient to trophic transfer efficiencies. The
suitability of models assuming size-dependent metabolic rates and trophodynamics was experimentally analysed for lake plankton using a radiotracer (32P)
(Vezina, 1986). A more detailed model considering differently sized autotrophs,
heterotrophs and detrital particles has been suggested by Cousins (1985).
Bacteria are the exception to models of this type since the basic assumptions of a
flow of matter along the size gradient and predictability of growth rates from
allometric relationships are not valid for these organisms (Giide, 1990).
A continuum of biomass in respect to body size indicates the existence of a
continuum of functional guilds in the planktonic food web owing to the close
relationship between body size and physiological and ecological features. For
example, herbivorous plankton ranges over a large size range and exhibits a
correspondingly wide and continuous range of response times to a phytoplank1280
Investigating pelagic food webs
ton bloom. Fast reactions are commonly achieved at the expense of other factors
like vulnerability to grazing. This perception is in contrast to a more traditional
view which emphasized differences in life history features among distinct
taxonomic groups. Perceiving the pelagic community as a continuum of ecotypes
has recently stimulated some preliminary hypotheses to explain the emergence
of regular size distributions as an interplay between processes at the population
and system level (Silvert, 1984; Gaedke, 1992b; Pahl-Wostl, 1995).
Predator biomasses equal or exceeding those of their prey are energetically
possible because small organisms have higher weight-specific metabolic rates
than larger ones. Flat size spectra indicate that size-related differences in
weight-specific metabolic rates compensate for the losses implied by the
transfer of biomass from one trophic level to the next. The steepness of a size
spectrum's slope reflects the efficiency of transferring biomass from small to
larger sized organisms. A more negative slope of a straight line fitted to the
(normalized) biomass size spectrum implies that a relatively large amount of
small organisms supports only a relatively small biomass of larger ones and vice
versa. By these means, biomass size distributions provide insight into the
energy flow and transfer efficiency along the size gradient in complex pelagic
food webs without the necessity to define distinct trophic levels or to
distinguish taxonomic gToups as long as the size range under consideration is
sufficiently broad to justify the assumption of an energy flow along the size
gradient. Between others, this technique may provide rough predictions of
potential fish yield from plankton measurements (Mann, 1988). The efficiency
of transferring biomass along the size gradient by trophic interactions (i.e. not
growth) depends on usual trophic transfer efficiencies (including exploitation
and growth efficiencies) involved in each predator-prey interaction, and on the
predator-prey weight ratios which determine the 'step size' by which biomass is
transferred to larger sized organisms. If predators are only moderately larger
than their prey items, a larger number of trophic interactions is required to
transfer e.g. bacterial production to large crustaceans as compared to food
chains dominated by filter feeders with large predator-prey weight ratios.
Thus, trophic transfer efficiencies may be derived from the slope of biomass
size spectra if average predator-prey weight ratios can be estimated (Gaedke,
1993). According to expectations, slopes become less negative with increasing
eutrophication (Ahrens and Peters, 1991).
Standardized measures, time and computational effort
A biomass spectrum is mostly characterized by two features: the overall slope of
a fitted line and the deviation from this line, which indicates the smoothness of
the spectrum. The spectrum's shape reacts sensitively to insufficient assessment
of particular size ranges, whereas the slope on which the functional analysis is
based is less affected (Gaedke, 1992a, and references cited therein). Computations of biomass size spectra and related measures are straightforward and
quickly done when the data are arranged in a proper way. This, however, may
demand some effort, as measurements of abundance of all organisms have to be
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merged with corresponding body sizes or size frequency distributions. Analyses
of size spectra may be performed on PCs without specific training in mathematics
or computer science.
Comparison with other approaches
The data base required to establish size distributions is close to the form in which
the raw data are obtained. Models for the energetic analysis demand the
parameterization of only very few constants as compared to network analysis
and tactical simulation models. These highly aggregated models appear capable
of explaining the major energy flows (Vezina, 1986; Gaedke, 1994a), although
they do not account for mechanistic details, e.g. a detailed evaluation of the
recycling of matter via the microbial loop. It has been postulated that size-related
models may achieve a larger predictive power than (less aggregated) mechanistic
box models (Mann, 1988; see below). The double logarithmic plot of biomass
versus body size provides only a coarse representation of the community
structure and reflects only substantial changes.
A major shortcoming of actual models on biomass size distributions and flow
networks as compared to dynamic simulation models is that they are nondynamic. One way to depict seasonal changes is to discretize the plankton
development artificially by splitting the seasonal course into different time
intervals. Size distributions and trophic food webs may then be established for
each time interval assuming steady-state conditions. This procedure may give
reasonable information on the average structure and fluxes during the respective
periods, but it is not suitable for grasping fast dynamics and the processes driving
them properly. Truly dynamic models for size distributions have rarely been
developed (Silvert and Platt, 1978, 1980; Parkin and Cousins, 1981) and have
hardly been tested with a particular data set (but see Vezina, 1986), partially
because they provide a very idealized view of the energy flow (see the section on
interaction webs for tactical simulation models using body size as aggregation
scheme).
Conclusion and future development
Size-related approaches allow evaluations of the structure and energy flow in
complex pelagic food webs. Abundance, metabolic activity, seasonal variability,
reaction time to external perturbations, trophic position, prey size ranges and
other attributes may be predicted from body mass. This empirical evidence (as
well as recently developed theory) suggests that size-related descriptions have
some potential for the analysis and modelling of pelagic food webs which needs
to be explored more strongly in the future. Size-related approaches may turn out
to be relatively cost efficient and reproducible for different pelagic ecosystems
since only relatively accessible measurements (especially when modern automated particle analysers become generally available) and a few general
assumptions are required (see also Platt, 1985). To utilize this potential, more
effort should be directed to a better quantification of aUometric relationships for
small plankton organisms and to analyse 'size conversion efficiencies' as
compared to trophic transfer efficiencies.
1282
Investigating pelagic food webs
Community analysis based on additional information of trophic interactions:
food web analysis (binary food webs)
General description and data requirements
Based on a species list, binary community food webs may be established by
compiling knowledge on all trophic interactions occurring within the food web
(Table I). The name binary web indicates that only the presence or absence of a
feeding link is considered, but not its magnitude or interaction strength. All
organisms within an ecosystem influence each other directly or indirectly, which
makes detailed information on the food web structure essential for functional
ecosystem analysis. This is one reason why binary food webs were established for
almost all types of ecosystems ranging from large open-water bodies and deserts
to water-filled tree holes. Furthermore, binary webs are used to search for
phenomenological regularities in food web structures (e.g. the number of feeding
links per species, the number of predators per prey species, food chain lengths,
the connectance; see 'standardized measures' below) across different habitats,
and their dependence e.g. on food web size (i.e. the number of species or guilds),
habitat characteristics (e.g. productivity, environmental fluctuations, size, pelagic
versus terrestric, dimensionality) and the history of assembly (e.g. Cohen et al.,
1990; Havens, 1992; Hall and Raffaelli, 1993). Such regularities are of major
concern for disputes on food web stability and resilience which were investigated
using dynamic population models, the behaviour of which depended on their
trophic structure (e.g. Yodzis, 1988). For example, species-rich and highly
connected model systems tended to be less 'stable' and it was postulated that
natural webs should display patterns which enhance stability (Pimm et al., 1991;
but see also Yodzis, 1993). To conclude, most studies of binary food webs were
either performed as essential prerequisites of more functional analyses, or in the
context of cross-system comparisons and stability analyses. The potential direct
contribution of food web analysis to the solution of applied problems in
particular ecosystems requires further evaluation. Figure 4 displays an example
of a binary food web model of the pelagic community of Lake Constance
(M.M.Lang and U.Gaedke, unpublished).
Knowledge on potential trophic interactions between pelagic organisms may
be derived from direct observation, stomach content and faecal pellet analysis,
by immunological and isotopic techniques and, to some extent, from laboratory
studies and the morphology and feeding behaviour of a potential predator
combined with the relative prey size and its potential predator avoidance
strategies [for a more comprehensive discussion, see Paine (1988)]. Diet
compositions of large zooplankton and fish are in general well established as
compared to smaller plankton. Observations on feeding links obtained for a
particular ecosystem may be considerably supplemented from the literature since
adaptation of feeding behaviour to prey availability (e.g. by adjusting mesh sizes)
and temporal and geographical diet heterogeneity may be regarded as low,
especially for small plankton. Parasitic interactions were generally ignored in
pelagic systems. Including or excluding a less known or quantitatively minor
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•D
O
S
Fig. 4. A binary food web of the pelagic community of Lake Constance. Approximately 280
morphologically different forms were aggregated into 26 trophic guilds. (1) Bacteria; (2) autotrophic
picoplankton (APP); (3-8) larger phytoplankton; (5) heterotrophic flagellates; (9-13) ciliates; (14-17)
rotifers; (18-22) crustaceans; (23-26) fish (larvae) (M.M.Lang and U.Gaedke, unpublished).
feeding link in a particular web remains to some extent subjective [for further
discussion, see Yodzis (1993)]. Comprehensive and standardized documentation
may partially overcome this problem (Cohen et al., 1993).
Food web analysis is basically a non-dynamic approach. Spatio-temporal
variability of the food web structure may be inferred from changes in the species
lists. However, the absence of a species in the (probably varying) counting
volume does not necessarily imply its absence in the natural habitat. Most species
occur year round, but in very different densities. Thus, defining a species and its
feeding links as present or absent generally requires the definition of an arbitrary
threshold which tends to complicate cross-system comparisons (Winemiller,
1990; Closs and Lake, 1994).
Theoretical framework and underlying assumptions
Binary food web 'models' are purely descriptive and empirical graphs.
Consequently, operational issues concerning e.g. the aggregation, definition of
system boundaries, and the quality and comparability of the data base are the
dominating factors determining their reliability.
Aggregation. In food web studies, it appears most logical to take a trophic point
of view and to aggregate or split biological species into trophic guilds (synonym:
trophospecies) which do not represent units of reproduction, but units of
organisms which share the same predators and prey (e.g. Cohen et al., 1993;
Yodzis, 1993). Ontogenetic changes of predators and prey ranges of individual
1284
Investigating pelagic food webs
species (e.g. by cyclopoid copepods) may exceed the differences between
biological species (e.g. Daphnia hyalina and D.galeata) and very small plankton
organisms can hardly be distinguished at the biological species level.
Additionally, intra-guild predation is avoided by aggregation at the level of
trophic guilds which facilitates subsequent computations. Aggregating or
splitting biological species into trophic guilds may also help to reduce the effort
to be spent on establishing the matrix of trophic links, and to avoid the
impression of unjustified accuracy. Although the majority of all potential trophic
interactions in species-rich communities may immediately be ruled out by
common sense, many thousands remain to be checked when working at the
biological species level, suggesting that a trade-off between the call for
exhaustiveness and practicalities will be required even if funding is generous.
The use of trophic guilds as the basic unit in food web analysis has been criticized
because their definition is to some extent tautological and subjective, and this
motivated recent work on the biological species level (e.g. Closs and Lake, 1994).
Body size has been suggested as a more objective criterion for aggregation, i.e.
defining for each size range the same number of trophic guilds (Pahl-Wostl,
1993a). The expressions trophic guilds and species are used interchangeably in
the following text.
The definition of system boundaries (e.g. the inclusion or exclusion of littoral,
benthic, migrating or transient species). Its necessity arises in all kinds of
ecosystem studies. It is, however, particularly difficult to resolve for binary food
webs because the magnitude of fluxes or interaction strength is not a suitable
decision criterion.
The actual quality and comparability of the data base. This varies greatly because
most studies of binary food webs available so far were not primarily designed for
systematic cross-system comparisons (however, see e.g. Winemiller, 1990; Polis,
1991; Martinez, 1993; Closs and Lake, 1994), but were often by-products of
research with other aims. Consequently, emphasis given to various parts of the
food webs and the spatio-temporal scale varies between studies. For example,
commercial fish species may receive more attention than other organisms and
diet compositions of small organisms are less studied than those of large
plankton, which favours a stronger aggregation at the lower end of the size
gradient. A more detailed discussion on operational problems and potential
improvements is given, for example, by Paine (1988), Cohen et al. (1993), and
Hall and Raffaelli (1993).
Standardized measures
Purely graphical representations of complex food webs are incomprehensible
and a number of different measures have been suggested to summarize relevant
structural properties of complex food webs (e.g. Cohen et al., 1990; Yodzis, 1993,
and literature cited therein). Basic statistics include the number of species (5)
and links (L) per web, the proportions of top (T), intermediate (/) and basal (B)
1285
U.Gtedke
trophic species, and the ratio of the number of prey species to the number of
predator species (or consumers), ((T + /)/(/ + B)). The mean chain length
represents the average number of links connecting top to basal species. Related
descriptors include the maximum chain length and maximum number of trophic
levels, as well as the frequency distribution of chain lengths connecting basal and
top species. The number of links per species (linkage density: D = US) and the
ratio of the number of observed links to the number of all possible links (directed
connectance: C = US2 = D/S) describe the degree of connectedness within
community food webs.
Empirically established values of the connectance and related metrics were
used to parameterize dynamic models studying the relationship between stability
and complexity. The dependence and sensitivity of these quantities to the size of
the natural web and the aggregation of the food web model are under debate
owing to the weakness of the data base (see below) (e.g. Cohen et al., 1990;
Martinez, 1992, 1993; Hall and Raffaelli, 1993). Inconsistencies in the resolution
of web entities both within and between webs so far prevent clear statements
about the relationship between the above-mentioned measures and habitat
characteristics, and about their latent information on functional or dynamic
properties of the respective communities. Scale invariance has been postulated
for the connectance which would imply that the linkage density increases
proportionally with S. Consequently, species would maintain on average more
trophic interactions in large than in small webs (e.g. have a broader diet
composition) which might have consequences for system stability. This
hypothesis is contrasted by the iink-species scaling law' which conjectures an
approximately constant linkage density independent of S (i.e. the connectance
decreases hyperbolically with S) [see the review by Hall and Raffaelli (1993)].
Time and computational effort
Computation of most measures is straightforward if no cycles between trophic
guilds occur (cycles between living components are rare and usually of very small
magnitude in natural food webs; Pimm, 1982), but may demand a powerful PC if
the web is large. Standardized programmes are not yet available. Calculations
demand little time as compared to the effort usually required to establish the
feeding links, and to compare the results with other studies. If carefully done, the
latter may become very time consuming owing to the above-mentioned
dependencies of many indices on model assumptions and lack of standardization
(e.g. Closs and Lake, 1994).
Comparison with other approaches
A major characteristic of this food web approach is that it requires no
quantitative information on biomasses, diet compositions and fluxes which
allows a high resolution of the food web and avoids the introduction of a large
degree of uncertainty if the data base is weak. Food web analysis has a great
power to summarize structural patterns of complex food webs at the expense of
probably little capacity to study functional and dynamic aspects directly.
1286
Investigating pelagic food webs
Disregarding the actual operational problems, a better understanding of food
web organization can contribute to applied questions like strategies on wildlife
conservation and ecosystem stress detection. Food web analysis may complement the common practice of inspecting species lists or the occurrence or
absence of characteristic species by taking a whole community viewpoint and
including indirect interactions. For example, a recent study showed that several
parameters of lake food webs reacted sensitively to strong acidification (Havens,
1993).
Conclusion and future development
Food web analysis provides an effective tool for the structural analysis of
complex food webs which is an important prerequisite for more functionally
orientated approaches. Universal properties of food webs from different habitats
are searched for in order to understand principal differences between types of
ecosystems. However, the many imperfections in the current data base strongly
complicate the unambiguous establishment of regularities in food web structures.
It is a common feeling that future work should be directed to straightening out
and using standardized aggregation procedures, which advance the evaluation of
the ecological implications of the numerous food web descriptors, and of the
mechanisms of how the observed patterns may have evolved (Hall and Raffaelli,
1993).
Community analysis based on additional information of the magnitudes of flows:
mass-balanced flow diagrams and network analysis (trophic food webs)
General description and data requirements
The binary food web is extended to a trophic one by considering the quantitative
importance of the individual flows, and by including the fluxes to and from the
pool of dead organic matter (Table I). The trophic food web is depicted by a
number of compartments to which all organisms are allocated and which are
interconnected by fluxes of matter. To estimate the magnitude of these fluxes,
measurements or estimates on major process rates like ingestion, respiration,
production and the release of dead organic matter are required for each living
compartment, as well as quantitative information on the diet composition of
omnivores. Mass-balance conditions have to be fulfilled for individual compartments (i.e. ingestion representing the input must balance the sum of the outputs
consisting of e.g. respiration, production, egestion, release of organic substances), as well as for the entire system. For example, total primary production
must balance the sum of community respiration, sedimentation and changes in
standing stocks in autochthonous systems. A typical example for such a massbalanced flow diagram is shown in Figure 5.
Flux estimates are obtained by a broad range of techniques depending on the
processes and organisms under consideration. In situ process rates, especially on
respiration and the release of organic substances, tend to be more difficult to
obtain than data on standing stocks. Fortunately, the increasing effort proceeds
1287
U.Gaedke
with an increase of alternative possibilities to estimate the range a flux may
achieve (Figure 1). Information may be derived from laboratory studies, and
from measurements on potential weight-specific rates, assimilation and net
growth efficiencies. Furthermore, the mass-balance constraints reduce the
potential range of flux values. Given a sufficient data base, they enable a
rigorous consistency check of the different bits of information used to quantify
the flow diagram, and allow 'guestimates' of some fluxes which are particularly
inaccessible to measurements.
Most flow diagrams to date have been quantified in units of carbon, indicating
the flow of energy. However, some studies use the most limiting nutrient. In
natural systems, mass-balance conditions are fulfilled for all biogenic elements.
The consideration of multiple commodities (e.g. carbon, nutrients, oxygen,
energy) in balanced flow diagrams and its computational handling is still in its
infancy (Vezina and Platt, 1988; Constanza and Hannon, 1989; Jackson and
Eldridge, 1992; L.Stone, S.Barry and S.Hochstadter, unpublished). It improves
the realism of the analysis and imposes additional constraints which reduce the
degrees of freedom when setting up the flow model. However, the representativeness of point observations and laboratory studies for the entire ecosystem
should be questioned owing to the adaptability, dynamic nature and spatial
heterogeneity of natural systems. Natural systems vary on many scales and these
variabilities are beyond the potential of observation, which enlarges the
inevitability of gaps in the observational variables. For these and other reasons,
177 |
herb. cm.
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O fish
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h 79
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281
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o !._:_.!
131
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67
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38
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Fig. 5. A mass-balanced trophic food web of the pelagic community of Lake Constance in high
summer 1987, quantified in units of carbon (mg C m"2 day"1) (Gaedie and Straile, 1994b; Straile,
1994). HF stands for heterotrophic flagellates; herb. cru. and cam. cru. stand for predominantly
herbivorous and carnivorous crustaceans, respectively. Circles within compartments represent
changes of standing stocks (increase if <0 and decrease if >0), circles outside compartments are intracompartmental predation. X symbolires respiration.
1288
Investigating pelagic food webs
a considerable degree of uncertainty about the magnitude of individual fluxes
and the diet compositions will remain even for systems which are very well
studied according to actual standards (e.g. Stdne et al., 1993; Gaedke and Straile,
1994b).
Consequently, different techniques were suggested to establish mass-balanced
charts from incomplete data sets (e.g. Vezina and Platt, 1988; Wulff et al., 1989,
and references cited therein; Jackson and Eldridge, 1992). Some account
explicitly for errors in the measurements and stochasticity of the environment by
including a consistency range for each flux and biomass estimate in the input
data set (e.g. best estimate 100 units, reasonable range 50-200 units). This
enables, for example, the computation of the minimum and maximum value a
(group of) fluxes may take without causing inconsistencies with the other data
(McManus, 1991; Stone et al., 1993). Questions like 'What is the maximum value
algal dark respiration may take without causing unreasonable values for other
fluxes and efficiencies in the food web model?' are evaluated. The relevance of
uncertainty about individual processes for the overall results may be assessed
systematically and this has the potential to facilitate an efficient allocation of
future research directions.
Network analysis, as defined by Wulff et al. (1989), extracts from massbalanced flow charts comprehensive information on the flow and cycling of
matter, the trophic structure and transfer efficiencies, and the organization of the
food web (see below). Detailed studies were performed for Chesapeake Bay
(Baird and Ulanowicz, 1989), Narragansett Bay (Kremer and Nixon, 1989), an
open ocean plankton system (Ducklow et al., 1989), Lake Kinneret (Stone et al.,
1993), Lake Constance (Gaedke and Straile, 1994b; Straile, 1994), and some
other systems (Christensen and Pauly, 1993), partially in combination with
dynamic simulation models (e.g. Field et al., 1989; Moloney et al., 1991).
Theoretical framework and underlying assumptions
Ecological network analysis has several roots. Many concepts and computational
techniques were developed primarily in other disciplines that include economics,
control theory and electrical networks, and later adapted to ecological networks
(Wulff et al., 1989). Influenced by thermodynamics, network analysis relies
entirely on the exchange of material between organismal groups and their
environment which is a prerequisite for life to exist (Ulanowicz and Platt, 1985).
Quantitative flows are assumed to integrate and reflect to a large extent the
various ways by which organisms interact in nature. However, the magnitude of
matter exchange is not necessarily directly proportional to the flow of
information which restricts the potential of network analysis (see below). For
example, from the view point of plants pollination by bees involves an extremely
small flow of energy, which is, however, of outstanding importance for
population dynamics.
At the present state of the art, restricted knowledge on flows and diet
compositions generally demands a stronger aggregation of trophic webs than that
used for binary ones. This complicates a satisfactory accounting of the reticulate
1289
U.Gaedlte
trophic interconnections of real food webs (Polis and Strong, 1994). Allocating
organisms to a small number of trophic compartments and following the flow of
matter through this model food web may relate to the discrete trophic level
concept sensu Lindeman which has frequently been criticized, e.g. for the
operational problems to handle omnivores unambiguously (e.g. Cousins, 1987).
Trophic analysis in the context of network analysis accounts for omnivorous
nutrition by describing the trophic food web structure in two ways. First, the
trophic position of a compartment is calculated as the weighted average of the
trophic positions of its prey compartments. Thus, the trophic positions of
omnivores are non-integer values which reflect the average number of trophic
transfers their prey items have passed before consumption. Second, the
contributions of the individual compartments to the distinct trophic levels sensu
Lindeman are calculated based on their relative share of ingestion (or biomass),
i.e. omnivorous compartments are distributed over several trophic levels
depending on their diet compositions.
Knowledge of indirect effects which are transmitted by two or more (trophic)
interactions between different members of a food web is essential for the overall
understanding of ecosystem functioning (Yodzis, 1988). Interactions which
appear detrimental when regarding only the direct effects at the population level
may turn out to be advantageous in the community context and vice versa
(Stone, 1990; Stone and Roberts, 1991). For example, an increase in algal
abundance may enhance growth of herbivorous ciliates (positive direct effect),
but it may also be detrimental to ciliates as densities of omnivorous ciliate
predators like daphnids may also increase with increasing algal food supply
(negative indirect effect). Some techniques have been suggested to evaluate
indirect effects from flow diagrams (e.g. Ulanowicz and Puccia, 1990). Such
computations are, however, unlikely to reflect true mutual dependencies for
several reasons (cf. also Wiegert and Kozlowski, 1984). First, flow diagrams are
commonly quantified and analysed in one commodity, either carbon or a limiting
nutrient. However, the interplay between energy and nutrients may present a
key factor in ecosystem functioning, and the strength of the effects may depend
strongly on the commodity used (Stone and Weisburd, 1992). Secondly, the
importance of an interaction is deduced from the relative amounts of flows
between the organisms. This relationship may not hold for several reasons
(Power, 1992). For example, omnivorous predators may be able to switch to
another prey if the previous one is depleted. Thus, their dependency may be
overestimated by a rigid and non-adaptive model (e.g. Polis and Strong, 1994).
Third, evaluations of indirect effects may demand a comparison of positive and
negative effects (Stone, 1990) for which controversial definitions have been
suggested. Like binary webs, flow diagrams were used as basis for computations
of interaction strengths between compartments near equilibrium (de Ruiter et
al., 1995). This subject requires further evaluations.
Standardized measures
Similar to food web analysis, comparative indices have been established to
quantify trophic structure, intercompartmental dependencies, nutrient cycling,
1290
Investigating pelagic food webs
compartmental and system residence times, and the organization of trophic
webs. A comprehensive overview of their theoretical basis and application in
marine systems can be found in Wulff et al. (1989) and the literature cited
therein. The ecological interpretation of most measures is largely unambiguous
and straightforward, and does not demand assumptions other than those made
when establishing the flow chart. Such measures include a cycling index which
represents the portion of flows that is recycled compared with the total flow
within the system. Long cycles involving several living compartments and a
substantial amount of matter are regarded as indicators of undisturbed systems.
The average path length measures the average number of trophic transfers a unit
of matter travels from its entry into the system (e.g. by primary production) until
it leaves the system (e.g. by respiration or sedimentation) and, thus, provides a
weighted average of the food 'chain' length. The total system throughput
represents the sum of all fluxes within a trophic web and may be regarded as an
indicator of its size. So-called 'dependency coefficients' inform about the direct
and indirect contribution of one compartment to the diet of any other one. For
example, the importance of microbial production for the nutrition of larger
zooplankton may be evaluated by these means. In Lake Constance, fish derive
2-8% of their nutrition from bacterial production, the remaining fraction
originates directly from autotrophs (via the grazing chain) (Straile, 1994). The
effective trophic position as defined above is obtained from a so-called
theoretical 'Lindeman Matrix' which also provides an abstract food chain
consisting of the various trophic levels ('Lindeman Spine'). Graphical
representations of the latter display the quantitative importance of flows
between the levels, and the respiration and recycling of matter from each level
(Figure 6). Additionally, the contribution of individual compartments to distinct
trophic levels may be examined. The ascendency is defined as the product of the
total system throughput and the food web organization inferred from flow
diversity (Ulanowicz, 1986). Its maximum value is called developmental capacity
since it was primarily speculated that the ascendency increases as the system
matures (Ulanowicz, 1986), which is in conflict with recent findings (Christensen,
1994; Straile, 1994). Another measure tackling a very important and demanding
issue, but yet delivering disputable results (e.g. Loehle, 1990), are the coefficients
of indirect effects (see above).
Mass-balanced flow diagrams can additionally be used to formulate simple
linear models which trace the pathway of one unit of matter ('tracer', e.g.
primary production or organic matter taken up by bacteria) through the food
web as a function of time. The velocity by which organic matter is channelled
through, and lost from, the food web, and its accumulation within different
compartments of the food web, are computed based on compartmental residence
times, i.e. the ratio of biomass to ingestion. Residence times, accumulation and
elimination of toxic or otherwise harmful substances may be evaluated by these
means (e.g. Anderson, 1983; Jackson and Eldridge, 1992; Eldridge and Jackson,
1993; Higashi et al, 1993; Gaedke et al, 1995). In Lake Constance, the residence
time of primary production was low in spring when 50% of the algal standing
stock was lost from the system after 2 days. In early and midsummer, system
1291
U.Gaedke
124.6
F'
.3
2014
140
8 75
4.86
0 22
0 07
*
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u
282
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48.8
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IV
341
V
0.38
VI
*
*
VII
VIII
•
<
-
Fig. 6. A 'Lindemans Spine' of the pelagic community of Lake Constance (early spring, 1987). The
Roman numbers within the boxes indicate the trophic level (D stands for the pool of dead organic
matter). The numbers above the boxes provide exports from the system and the numbers below the
recycling of dead material. 4 symbolizes respiration. Values are only given for fluxes >0.01 mg C m"2
day"1 (Straile, 1994).
residence times were 2-3 times as long which may reflect an increase of
community organization. The temporal development of the relative distribution
of the tracer within the food web exhibited pronounced seasonal changes as well.
In early spring, primary production was passed quickly to the ciliate
compartment and then to the pool of dead organic material which implied that
cycling of considerable amounts of carbon was performed within a few days. In
summer, herbivores and predators with longer residence times prevailed, which
resulted in a slower flow to bacteria and fish than in early spring.
In addition to the static descriptions of the trophic food web structure
mentioned so far, dynamic considerations of food web regulation were
developed which assume among other things that the major groups of
organisms can be allocated to discrete trophic levels which represent functional
units. Such concepts delivered reasonable predictions and management tools
for some systems. For example, a trophic cascade may be found where large
populations of secondary carnivores (e.g. piscivorous fish) suppress primary
carnivores (e.g. planktivorous fish), which leads to high standing stocks of
herbivores and correspondingly low abundances of primary producers
(Benndorf et al., 1988; Carpenter and Kitchell, 1993). However, such
appealingly simple mechanisms of food web regulation appear to be restricted
to a limited number of systems (Reynolds, 1994; Polis and Strong, 1994). The
reasons for the diverging findings can partly be analysed at the level of static
flow diagrams (e.g. composition of trophic levels, importance of omnivorous
nutrition), but may also require knowledge on dynamic interactions as
described in the following section.
The present level of data acquisition (i.e. standing stocks, body sizes and
quantitative trophic interactions) additionally allows the computation of average
predator-prey weight ratios for parts or the entire food web which are weighted
according to their quantitative importance (Table I). These are relevant for all
size-related energy transfer models.
Time and computational effort
A network analysis for a particular ecosystem may in general be performed
within a few years, depending on the availability of biomass and flux estimates,
1292
Investigating pelagic food webs
and on the degree of aggregation (e.g. number of compartments and fluxes,
spatio-temporal resolution). Achieving mass-balance conditions and calculating
the various network indices requires a considerable computational effort, even
for small systems with up to 10 compartments. Two (non-commercial) software
packages are readily available for personal computers which provide limited
facilities to obtain mass-balanced charts and compute the measures mentioned
above (NETWRK and AUTOMOD, Ulanowicz and Kay, 1991; ECOPATH,
Christensen and Pauly, 1992) (for other programmes, see above). Careful
interpretation of the results sometimes demands detailed knowledge of the
underlying algorithms which are mostly based on matrix operations.
Comparison with other approaches
The compartmental model approach and numeric simulation models as
described below promote a perception of the pelagic community as being
composed of distinct non-overlapping groups of organisms in contrast to a
trophic spectrum as it is emphasized by size distributions. The philosophy of
aggregation may influence the results. In contrast to biomass size distributions,
network analysis relies mostly on flux estimates rather than observations of
standing stocks which, however, may influence flux estimates if weight-specific
rates are used rather than independent rate measurements (e.g. by the I4C
technique). Given sufficient knowledge, network analysis allows more detailed
evaluations of the trophic structure and energy flow than biomass size
distributions, e.g. concerning the relevance of the detritus chain. The larger
reductionism is commonly achieved at the expense of demanding a considerable
number of parameter estimates, some of which are impossible to determine with
sufficient accuracy (e.g. on diet compositions) and ad hoc considerations. Despite
these differences, the two approaches may deliver similar results, e.g. with
respect to the overall energy transfer through the eukaryotic food web. The
absolute values and the seasonal course of trophic transfer efficiencies derived by
network analysis and from the slope of biomass size distributions using
allometric relationships and average predator-prey weight ratios were in
reasonable agreement for most of the seasonal course of 1987 in Lake Constance
(Gaedke and Straile, 1994a). Although magnitudes of fluxes do not necessarily
indicate their overall importance, a trophic web is likely to provide considerably
more information on ecosystem functioning than binary webs. Nevertheless, the
potential power of network analysis (i.e. ignoring all uncertainties in the model
assumptions) to explain the probable causality of food web flow dynamics in a
mechanistic sense is restricted when compared to dynamic adaptive simulation
models.
Summary and future development
Mass-balanced flow diagrams evaluated by network analysis and related
techniques provide an outstanding tool for the description of fluxes and cycling
of matter in food webs. They overcome a number of limitations of actual sizerelated and food web approaches. Future work in this area should be directed (i)
1293
U.Gaedke
to formalize, optimize and standardize mass-balancing procedures which account
explicitly for errors in the measurements and for natural variability, (ii) to
improve the theoretical and computational basis to deal with different units like
carbon and nutrients simultaneously, (iii) to reconsider the methods to evaluate
indirect effects from flow diagrams, and (iv) to further improve the (non-trivial)
consideration of the dynamic nature and spatio-temporal organization of natural
systems at the ecosystem level, e.g. by establishing time-resolved measures (e.g.
ascendency) (Pahl-Wostl, 1990, 1993a, 1995).
Ecosystem analysis based on additional information of flux regulation and
adaptability: dynamic simulation models (interaction webs)
General description and data requirements
The transition from a trophic to an interaction web describing most direct and
indirect interdependencies between the groups of organisms and their abiotic
environment is one of the most difficult and challenging tasks in systems ecology,
even for a set community structure and environment. It requires in addition to
quantitative flux estimates, a profound understanding of the forces which drive
them (Table I). Information on direct and indirect interdependencies is again
less accessible by direct in situ measurements and has often to be inferred from
long-term data series, cross-system comparisons, pulse and press perturbation
experiments (Bender et al., 1984), exclusion- and laboratory experiments,
indirect evidence, and first principles. Thus, qualitative and informal knowledge
considerably complements hard data which are mostly used for model calibration
and validation (W.Ebenhoh, in preparation). In general, extensive quantitative
data bases are available for some interdependencies (e.g. the relationship
between temperature and physiological rates under laboratory conditions) and
lacking for others (e.g. in situ growth rates of a predator at various
concentrations of differently exploitable prey items). The interaction strength
representing a pairwise per capita effect of one group on another is not
necessarily proportional to the magnitude of flow [see above and, for example,
Polis and Strong (1994)], nor to its importance for the community and its stability
as a whole owing to, for example, indirect effects (Hall and Raffaelli, 1993; de
Ruiter et al., 1995) and specific life history features which makes reliable
predictions of system behaviour largely elusive based on such data (Power,
1992). The results of simulated press perturbation experiments in a modelling
study were highly indeterminate in respect to the direction of changes of standing
stocks, and to the relative importance of individual interactions when the
interaction strengths were assumed to be known within an order of magnitude
(Yodzis, 1988). Empirical interaction webs have so far only been established for
parts of complex natural ecosystems (e.g. Paine, 1992). Inference on whole
community interaction webs from inevitably incomplete data sets requires again
a mathematical formalism which may be provided by dynamic simulation
models.
Dynamic simulation models describing explicitly the major dynamics and
interactions between the important functional groups and with abiotic processes
1294
Investigating pelagic food webs
in a particular ecosystem, are called tactical simulation models, in contrast to
strategic ones which aim to identify possible ecological principles by considering
few state variables. The latter provide mostly abstract descriptions of individual
processes isolated from the ecosystem context and are often solved analytically.
Large tactical models are based on non-linear systems of coupled differential or
difference equations which have to be solved numerically on a large computer
(e.g. Jorgensen, 1986). Examples include models for coastal areas (Kremer and
Nixon, 1978; Baretta and Ruardij, 1988; Moloney and Field, 1991; Taylor et al.,
1993; Baretta et al., 1994,1995) and lakes (Scavia and Robertson, 1979).
Complexity and scale. The difficulty of transcending scale is inherent to all
ecosystem studies since the many-layered complexity of natural ecosystems can
never be depicted in a single (mathematical) model, and the principal aim of any
kind of model is not a perfect reproduction of the natural system, but to reduce
the incredible natural complexity to a degree we can hope to cope with. If a
mathematical model were to approach a complexity similar to that of nature, the
investigation of its behaviour would be almost as difficult as that of the natural
system itself. Consequently, mathematical models are always designed to answer
more or less narrowly defined questions and should only include the processes
which are regarded as essential for this purpose. Carefully chosen trade-offs are
required between realism, generality of the results, possibilities to analyse the
model behaviour, and the effort spent in model construction and testing. An
unsystematic incorporation of details into the model is counterproductive for a
better understanding and fit of the model, for various reasons (e.g. Wiegert,
1977; W.Ebenhoh, in preparation). The criteria and the level of aggregation
influence essential properties of model behaviour like stability, elasticity,
adaptability to changing conditions, and the capacities for temporal and spatial
serf-organization. The latter demand internal degrees of freedom and redundancies e.g. in the model food web and community structure, and the
introduction of additional dynamic variables which describe properties of other
state variables, e.g. by reflecting the species composition of functional groups
(Ebenhoh, 1994). Most frequently component organisms are aggregated into
functional groups which may have the side effect that the expression of chaotic
behaviour is reduced (EbenhOh et al., 1995). After realizing the predictive power
of body size for physiological and ecological properties (see the section on
biomass size distributions), body size is increasingly used as an aggregation
scheme in dynamic simulation models (e.g. Moloney and Field, 1991; Silvert,
1993). Discrete trophic levels represent an alternative aggregation scheme.
However, its applicability seems to be restricted to a limited number of systems
(see above). To conclude, finding the optimal degree of aggregation for a model
appropriate to answer specific questions is in itself a challenging task (Silvert,
1981), as are related subjects like parameterization, a comprehensive discussion
of which is beyond the scope of the present paper. Nevertheless, being forced to
select and model in mathematical terms the processes thought to be most
relevant is of great value for the participating scientists as this promotes
systematic thinking on system behaviour (Baretta and Ruardij, 1988), even if the
1295
U.Gaedke
model outcome has serious deficiencies. This work may be facilitated by all kinds
of studies which improve the understanding of ecosystem functioning, e.g.
analysing the system with the previously mentioned methodologies, and
developing tactical and strategic submodels for different key processes on a
lower level of organization (e.g. phytoplankton growth in dependence on the
physicochemical environment, competition of algae and bacteria for limiting
nutrients).
The restriction of the model to a particular question also implies the
consideration of a particular scale in time and space. This is complicated by the
fact that the intrinsic temporal and spatial scales of the component organisms of
the entire food web differ by orders of magnitude, which requires the coupling of
different scales in the model. The time step of pelagic simulation models on the
ecosystem level is often 1 day, and simulation runs cover a few years which
accounts for the usual predominance of the seasonal cycle as primary signal of
interest (e.g. Baretta et al., 1995), and excludes consideration of evolutionary
processes. Effects of processes acting on a finer time scale than 1 day may be
introduced into the main model using time-averaged results from temporally
finer resolved submodels. Large-scale spatial heterogeneity is often considered
by subdividing the area of investigation into discrete spatial compartments
(horizontally and/or vertically) for which simulations are performed separately
using different boundary conditions. In this case, the biological model is in
general linked to a physical one which describes the inter-compartmental
horizontal and vertical transport and mixing of dissolved and particulate
substances, including plankton organisms. Like short-term variation, small-scale
patchiness is generally not modelled explicitly in the main model, but its (often
significant) impact has to be considered when selecting suitable parameter values
or, if available, by using averaged results from spatially finer resolved submodels.
At least part of the year, physical forcing (e.g. by temperature, irradiance,
stratification and mixing) can be the dominant factor determining the structure and
function of pelagic communities, especially when regarding the lower trophic levels
(Baretta et al., 1994). Its adequate representation in the model may require an
interactive coupling of physical and ecological models, which again increases the
complexity to be coped with. The seasonal courses of abiotic parameters which are
only marginally influenced by the biological system at the spatio-temporal scale
under consideration (e.g. surface irradiance, wind) are introduced as independent
time series into the model. Dealing with multiple commodities is well established in
numerical simulation models. The most advanced models consider the mass balance
of the flows of carbon as a substitute for energy as well as those of the limiting
nutrients and all other conservative substances which influence the biological
system significantly (e.g. oxygen or sulphate concentrations) (e.g. Baretta and
Ruardij, 1988; EbenhOh et al., 1995). In this context, the definition of system
boundaries needs careful consideration (e.g. accounting for interacting processes
with benthic and/or littoral systems may be required for a sufficiently realistic
description of remineralization and nutrient dynamics; Baretta etal., 1994).
Predictability. A clear distinction should be made between simulation models
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Investigating pelagic food webs
aiming to reproduce natural patterns observed in a particular ecosystem and
those designed to derive additionally predictions outside the experience on
which they are based, e.g. the system's response to major environmental changes.
Predictive models require sufficiently realistic capabilities to adapt to alternative
environmental scenarios, e.g. by modifying their community and food web
structure and related parameters accordingly. Reliance mostly on field
observations rather than well-designed experiments and extensive cross-system
comparisons limits our knowledge to the naturally occurring combination of
abiotic factors during the observational period. The predictive power of most
models is further reduced by the usual necessity to fit some of the parameters,
inbuilt predetermination, lack of adaptability, and in any case by the existence of
positive feedback mechanisms, the chaotic nature of many ecological processes
(e.g. Hastings et al., 1993; Pahl-Wostl, 1993c) and the (hardly known) selforganizing capacity of the ecosystem. Fully responsive and predictive ecosystem
models have not yet been achieved, they present major objects of investigation
for international programmes to be established during the next years. For the
reasons mentioned above, we will always have to accept an irreducible extent of
uncertainties (Breckling and MUller, 1994) which stimulated a fruitful and
ongoing effort to Gnd useful ecosystem models (Ulanowicz and Platt, 1985;
Mann, 1988). Present evidence suggests that the uncertainties are smaller on the
higher levels of aggregation (e.g. the production and dynamics of individual
species are less predictable than those of large functional guilds) (e.g. PahlWostl, 1995). A reasonable degree of predictability may be expected for spatially
and temporally averaged qualitative patterns at the system or community level,
but not for small-scale processes at the population level (e.g. the local outburst of
specific pest species). Nevertheless, the improved understanding of ecosystem
functioning and regulation gained during model development increases the
reliability of qualitative predictions made by the modellers themselves.
Constructing and analysing ecologically reasonable simulation models,
considering many functional guilds explicitly without major flaws, is a science
in itself and cannot be dealt with here in further detail, although it has hardly
been discussed in the published literature (Baretta et al., 1995; W.Ebenhoh, in
preparation). The objective of the following section is to briefly contrast tactical
simulation models to the previously mentioned approaches in order to allow the
reader a first judgement about the suitability and practicability of this technique
for her or his project.
Theoretical framework and underlying assumptions
Simulation models represent a mechanistic approach which explicitly postulates a
dominant role of cause-effect relationships. However, recent developments
towards a more 'fuzzy' way of modelling aim to consider naturalflexibilityas well
(Breckling and MUller, 1994; W.Ebenhoh, 1994 and in preparation). The
theoretical framework underlying tactical simulation models is less well defined
as compared to the previous mentioned approaches. It involves an entire body of
theoretical concepts derived from all fields of theoretical ecology, system,
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U.Gaedke
information and chaos theory, and related disciplines. A general theory of complex
systems is still lacking although interdisciplinary studies identified some common
features of complex systems like non-predictability, but universal order in chaos,
and spatio-temporal self-organization. In ecosystems, the latter may derive from
the co-existence of many similar species which may both rely on and increase local
spatio-temporal variability and decrease global fluctuations (Pahl-Wostl, 1995).
The actually very limited understanding of the organization principles of natural
ecosystems is a major difficulty in developing realistic models (W.Ebenhoh, in
preparation).
Time and computational effort
The time scope required for model evaluation and testing depends on the
particular goal since a refinement of the questions we ask demands an increase in
the complexity of the models we need to answer these questions. Investigations
on fully responsive and predictive models require a large and interdisciplinary
team of scientists collaborating intensively for years. Apart from a well-defined
scientific scope, such models require a long-term funding scheme which favours
the free exchange of data and ideas. Long-term group selection may need to be
strengthened as compared to the actually common way of exclusively judging
individual scientists frequently. Nowadays there is a trend of coding site-specific
models in such a way that they can easily be modified to apply to similar systems,
which greatly reduces the effort of future model development, and implies an
efficient transfer of knowledge between systems and working groups (Baretta el
al., 1995; Ruardij et al., 1995; W.Ebenhoh, personal communication). The
analysis of simulation results of large models with many state variables demands
computational aid and automated procedures. Results may be summarized using
previously described techniques (e.g. flow diagrams, Lindeman spines; Moloney
et al., 1991; Ebenhoh et al., 1995).
Regarding computations, most tactical models require larger computers than
PCs, e.g. well-equipped workstations or main frames. Commercial and noncommercial simulation environments (i.e. software packages) for models of
different size, goals and complexity are available which facilitate, for example,
compiling and linking of submodels, definition of model parameters, error
analysis, integration, and the numerical and graphical output as well as the
comparison with field data (e.g. Baretta and Ruardij, 1988; Ruardij et al., 1995).
Comparison with other approaches
The advantages of dynamic simulation models, as compared to the previously
mentioned approaches, include the unique possibility of studying the dynamic
nature and spatio-temporal organization, which are of outstanding importance in
natural systems (e.g. Ebenhoh, 1988; Gaedke and Ebenhoh, 1991; Stone and
Berman, 1993). Simulation models represent a coherent way to investigate direct
and indirect cause-effect relationships of a large number of dynamic interacting
processes, although their predictive power should not be overestimated [Yodzis
(1988) and see above]. They may overcome many of the restrictions of network
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Investigating pelagic food webs
analysis mentioned above, particularly if they involve sufficient adaptation
capabilities. The often considerable impact of physical forcing can be analysed
explicitly, which is less feasible with the above-mentioned static approaches.
Dynamic simulation models offer powerful tools for consistency checks of input
data and assumptions, testing of hypotheses, systematic integration of qualitative
and quantitative knowledge of an interdisciplinary team, and the transfer of this
knowledge to other ecosystems and research groups (e.g. Baretta and Ruardij,
1988).
Tactical models have recently been criticized for the way they portray a far too
mechanistic and rigid picture of natural ecosystems. Also, the large number of
parameters that are required introduce numerous assumptions and uncertainties
which might render model results meaningless for poorly specified systems.
Deterministic models are often misinterpreted as having the ability to provide
unjustifiably arbitrary precision. In most cases, they are unable to describe the
natural variability that is ubiquitous in real ecosystems, which prevents
meaningful confidence limits being assigned to their outputs. The research
effort is great and the number of potential pitfalls is numerous. The almost
unlimited freedom in model construction is achieved at the expense that all
concepts, equations, parameters and assumptions have to be laid open in order
to enable a full understanding of the model outcome. Complex models
developed by cooperating groups of scientists are tedious to check by single
scientists, which partially reduces their acceptance in the scientific community.
Summary and future development
Tactical simulation models with built-in degrees of freedom for self-organization,
and changes of community structure and species composition have an
outstanding potential to promote a functional ecosystem understanding. Such
models require the mathematical formulation of non-linear functions which
describe the interdependencies between the numerous physicochemical and
biological state variables, and detailed information on the adaptation and
organization principles of the system under study. Unfortunately, much of this
information is hardly available. This suggests that building and analysing such
complex models without major errors is a most difficult and time-consuming
task, and appropriate techniques are only now being developed. Although the
overall potentials and limitations are heavily debated, environmental threats like
global warming, as well as the increasingly successful utilization of simulations in
other sciences, promote further efforts to develop a new generation of ecological
simulation models aiming to account for the criticism mentioned above
(Breckling and Mtiller, 1994).
Discussion and conclusions
The present considerations are thought to provide a pragmatic guide to pelagic
ecosystem research that describes well-established ecosystem approaches.
Alternative ways of ecosystem description are under development and it is to
be hoped that they will take into account the above-mentioned problems, reflect
1299
U.Gaedke
recent changes in ecological perceptions and complement the current views.
Most recent developments include concepts based on information theory
(Hauhs, 1992) and individual orientated approaches which, for example, aim
to explain properties emergent at the ecosystem level with processes acting at a
local scale. Some goal functions like emergy, exergy and ascendency (e.g.
Jorgensen, 1986, 1992; Ulanowicz, 1986; Odum, 1988; Christensen, 1994) have
been suggested as highly aggregated measures of the overall development of
ecosystems.
The actual comparison of different methodologies proceeded largely towards
the consideration of more and more data and details apparently providing a deeper
and deeper insight into ecosystem functioning. As briefly mentioned in the last
section, an excessive continuation of this trend is neither feasible nor desirable, but
various trade-offs need to be evaluated. The complete reducibility of ecosystems to
physics and chemistry by some form of algorithmic reasoning has to be questioned,
and 'hyper-complex' models of any kind are unlikely to deliver complete
understanding and reliable predictions for many reasons, some of which were
touched on above (Mann, 1988; Yodzis, 1988; Wissel, 1992). Empirical evidence
shows that (highly aggregated) models may have predictive power independent of
a successful analysis of the underlying mechanisms (Peters, 1991; Hauhs, 1992).
However, these predictions are only reliably applicable to previously experienced
conditions. To face environmental problems like global warming, we need
methodologies based on a more comprehensive understanding of ecosystem
functioning, regulation and adaptation.
It has previously been acknowledged that empirical ecosystem studies demand
simultaneous investigations of all relevant biotic and abiotic parameters by a
team of field workers. In analogy, modellers favouring different theoretical
frameworks and modelling techniques should combine their efforts as well by
analysing the same ecosystem with different methodologies. First results from
Lake Ciso (Pedros-Alio and Guerrer, 1993) and Lake Constance (U.Gaedke, in
preparation) indicate that this enables an efficient supplement of insights and
more profound comparisons of the capacity of the various approaches to portray
essential features of the natural system.
Acknowledgements
The present study, as well as data acquisition, were performed within the Special
Collaborative Programme (SFB) 248 'Cycling of Matter in Lake Constance'
supported by the Deutsche Forschungsgemeinschaft. Martin M.Lang performed
most of the work on binary food webs in Lake Constance, as did Dietmar Straile
and Silke Hochstadter on the trophic webs which were used for illustration. A
considerable fraction of the information on dynamic simulation models
originates from personal communications and unpublished manuscripts by
Wolfgang EbenhOh, which were kindly provided to me. I thank Job Baretta,
Hans Glide, Martin M.Lang, Claudia Pahl-Wostl, Bill Silvert, Dietmar Straile
and an anonymous referee who contributed stimulating comments on earlier
drafts. I am indebted to Lewi Stone for extended discussions on the content and
considerable improvement of the English, and to Bob Ulanowicz who rephrased
1300
Investigating pelagic food webs
the original English in parts of the Introduction. First ideas on this subject were
discussed with Walter Geller in 1989.
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Received on July 18, 1994; accepted on February 8, 1995
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