a simulation model of the dynamics of aquatic macroinvertebrate communities_1

7/29/2019 A Simulation Model of the Dynamics of Aquatic Macroinvertebrate Communities_1

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*1

V. V. Gertseva, , a

, J. E. Schindler, d, 1

, V. I. Gertsev, b, 2

, N. Y. Ponomarev, c, 3

and W. R.

English, e, 4

a

Mark O. Hatfield Marine Science Center, 2030 S.E. Marine Science Drive, Newport, OR 97365, USAbDepartment of Mathematics, Rybinsk State Academy of Technology, 57 Pushkin Street, Rybinsk 152934,

Russiac 600 SE Jamar Street, Apt. 657, Pullman, WA 99163, USAd Department of Biological Sciences, Clemson University, 132 Long Hall, Clemson, SC 29634, USAe

Department of Forest Resources, Clemson University, 261 Lehotsky Hall, Clemson, SC 29634, USA

Received 3 March 2003; Revised 24 September 2003; accepted 27 October 2003. Available online 26

February 2004.

The main objective of this research is to develop a model of the dynamics of stream aquatic

macroinvertebrate communities. This research involves both theoretical and experimental aspects. The

theoretical portion includes the development of a simulation model of macroinvertebrate community

dynamics. This model is written using STELLA software [Stella Software Technical Documentation]. The

experimental portion focuses on the validation of the model by comparing its simulations with observations

from Holly Springs Creek in South Carolina, USA. The model developed through this research reflects the

most important links between the aquatic insects, the dominant macroinvertebrates of most streams, and their

environment, enabling insight into relationships in lotic ecosystems. This knowledge provides a theoretical

basis for a better understanding of various environmental interactions in streams, making it possible to use the

model for the theoretical analysis of macroinvertebrate community functioning, studies of lotic ecosystems,

and stream management.

Author Keywords: Stream ecosystem; Macroinvertebrate community; Functional feeding group; STELLA

software

1. Introduction

2. Methods

2.1. Model description

2.1.1. Functional feeding group module

2.1.2. Module of abiotic factors

2.1.3. Biotic interactions module2.2. Sampling

3. Results and discussion

3.1. Model simulations

3.1.1. The role of temperature in maintaining the dynamics of the aquatic macroinvertebrate community

3.1.2. The role of other abiotic factors in maintaining the dynamics of the aquatic macroinvertebrate

community

3.2. Model validation

4. Implications of the results

Appendix A

mulation model of the dynamics of aquatic macroinvertebrate communi... http://www.bugs.nau.edu/bio322/labs/labs/example_a

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References

Since the 1970s aquatic insects, the dominant macroinvertebrates of most streams, have become a primary

focus of many ecological studies in lotic habitats because of their critical role in breaking down the organic

matter, thereby recycling nutrients and transferring energy. These studies have included most areas of

autecological and synecological inquiry, animal behavior, physiological and trophic ecology, populationdynamics, and preypredator interactions (Merritt and Cummins, 1996a). Despite all of this attention,

investigations using mathematical modeling have been scarce. At the same time, the application of such

models in ecology is necessary if we want to understand the function of complex biological systems. It is

simply not possible to understand the many components and interrelations in a macroinvertebrate community

without the use of a model as a synthesis tool.

Until recently, the mathematical expressions of community dynamics were limited to analytical models based

on differential equations. These models, however, are useful under restricted conditions when relatively few

equations can be solved at the same time. This limitation produces phenomenological models that are not

applicable to dynamic natural systems. Recent advances in computer technology make development of

computational approaches in ecological theory more feasible, realistic and practical. These new approaches

make it possible to take into account a great number of environmental relationships, enabling theoreticalinsight into important mechanisms controlling the dynamics of such complex systems as a community. Since

the mid-1980s applicable computer simulations appeared, including the STEPPE model (Coffin and

Lauenroth, 1989), JABOWA model ( Botkin, 1993), and FORET model ( Shugart and West, 1977). However,

all of these models are restricted to the terrestrial environments and primary level of the ecosystem. In the

late 1980s Forrester introduced STELLA, a software with a user-friendly interface and availability of array

functions ( High Performance Systems, Inc., 1996). Given the fact that STELLA can be applied to a variety

of dynamic systems, it achieved broad recognition and use among ecologists and was successfully applied in a

number of studies ( Milke et al., 1998; Krivtsov et al., 1999; Krivtsov et al., 2000; Angelini and Petrere, 2000;

Gertseva et al., 2003 and Li and Yakupitiyage, 2003). This research simulates the dynamics of stream

macroinvertebrate community using STELLA software.

Until the 1920s the community research assumed a population to be the basic ecological unit. As a result,most of these early studies were taxonomic inventories of the organisms inhabiting a specific ecosystem. This

perspective, in many respects, limited the solving of certain function- and process-oriented questions. In the

beginning of the 20th century, ecologists began to develop a new view of the community structure. Elton

(1927) pioneered the trophic level analysis ( Kingsland, 1991). He maintained that in a complex community,

organisms can be aggregated into major trophic categories or levels depending on the functional roles they

play and source of energy they assimilate. Elton (1927) concluded that green plants occupy the first trophic

level, the producer level; herbivores the second level, or the primary consumer level; carnivores that eat

herbivores the third level, or the secondary consumer level, etc. Eltons (1927) ideas about trophic levels,

which helped to clarify many problems in ecology, are still extensively used in the research of aquatic and

terrestrial communities today ( Pianka, 1981).

This approach, however, was difficult to apply to aquatic macroinvertebrates because studies of their feedingshowed that it is impossible to relate macroinvertebrates to a definite trophic level, since essentially all

aquatic insects are omnivorous. For instance, insects that chew leaf litter in streams ingest not only leaf tissue

and associated microbiota, such as fungi, bacteria, protozoa, and microarthropods, but also diatoms and other

algae that may be attached to the leaf surface, as well as small macroinvertebrates. Therefore, an alternative

approach for looking at the macroinvertebrate community had to be formulated. Cummins (1973) proposed a

functional analysis of invertebrate feeding based on morphobehavioral mechanisms of food acquisition.

Called the functional feeding group approach, this method was based on the associations between a limited

set of feeding adaptations found in freshwater invertebrates and their basic nutritional resource categories.

Although food intake could be expected to change from season to season, from habitat to habitat, and with


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growth stage, limitation is in food acquisition mechanisms have been shaped over evolutionary time and, as

such, are relatively fixed. Currently, the functional feeding group approach dominates the study of aquatic

macroinvertebrate communities.

The model, presented here, describes the community of aquatic macroinvertebrates within the framework of

functional feeding groups. This approach allowed us to aggregate species into morphobehavioral groups and

meet the challenge of modeling the dynamics of entire community. There are other techniques to model

biological communities, such as modeling a single community metric that summarizes the general structure of

biological community (Wasgington, 1984) and multivariate statistical analysis ( Zamrella and Bunnell, 1998

and Childress et al., 1998). These techniques, however, have a number of shortcomings. For example, thesingle community metric approach poorly represents the characteristics of the community. Multivariate

statistical analysis does not reproduce a quantitative framework from which the composition of the

community can be predicted and the importance of the environmental variables can be assessed ( Olden,

2003). At the same time, the functional feeding group approach allows us not only tractable studying of the

complex multispecies assemblages, but also a feasible simulation of the interactions between the aquatic

macroinvertebrate community and its environment.

2.1. Model descriptionFunctional feeding groups approach involves classification of macroinvertebrates into groups of organisms

similar to each other with respect to their trophic functions. Generally, the aquatic macroinvertebratecommunity is comprised of four functional feeding groups: shredders, scrapers, collectors and predators.

Therefore, the model includes four blocks, each of which corresponds to a functional feeding group within the

community. Each block is comprised of three modules: the module of the functional feeding group, the

module of the abiotic factors affecting the functional feeding group and the module of the biotic interactions

among organisms within the community. A simplified diagram of the dynamics of number of organisms in a

functional feeding group within stream macroinvertebrate community is shown in Fig. 1. The values for model

parameters are taken from published data and then calibrated in order to improve parameter estimation.

Summary of basic equations used to simulate the dynamics of the functional feeding group with a STELLA

model is given in the appendix (Supplementary data on-line). Initial values of variables and parameter values

are presented in tables in the appendix.

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Fig. 1. Simplified diagram of the dynamics of number of organisms in a functional feeding group within a stream macroinvertebrate

community.

2.1.1. Functional feeding group moduleThe functional feeding group module reflects the initial links, defining the number of organisms in a functional

feeding group. The increase in the number of organisms in a functional feeding group depends on the

recruitment of new organisms through oviposition and subsequent hatching. Since most adult aquatic

macroinvertebrates live in the terrestrial environment, recruitment depends on the reproductive success of the

population as a whole. Even if the adults, who have emerged from a definite subpopulation, fail in theirattempt to reproduce, adults from another subpopulation still may come and lay eggs. Therefore, recruitment

in the model is represented by a constant value. This value, however, can vary depending on the parameters

of the environment inhabited by the macroinvertebrates. If environmental factors are at an optimum, then

recruitment will be at the maximum, on the other hand, if one or several environmental factors are not at the

optimum, then recruitment will not reach its maximum value. In the model the term habitat suitability refers

to the environmental factors influencing the number of organisms recruited into a functional feeding group

and affecting the increase in the number of organisms.

Elimination of the organisms from the functional feeding group depends on the number of organisms in this

group and, therefore, is represented in the model by a rate, not a specific number. The rate of the elimination

of the organisms from the community includes two components. The first one is a coefficient reflecting the


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elimination of the macroinvertebrate when all of environmental factors fall into tolerance range. This

coefficient is designated as natural mortality. The second component is stochastic elimination, which varies

depending on the conditions of the environment and determines actual elimination of the organisms from the

community.

2.1.2. Module of abiotic factorsThe module of abiotic factors reflects the influence of the physical environment on the dynamics of a

functional feeding group. Water temperature, water flow, dissolved oxygen, pH and nutrients are considered

as the most ecologically significant abiotic factors for the aquatic macroinvertebrate communities. All are

characterized by temporal variability. Aquatic macroinvertebrates have evolved different strategies fordealing with natural variation in their environment. These strategies allow them to withstand fluctuations in

the environment within their tolerance ranges. These fluctuations, however, can alter the suitability of a

particular habitat. Habitat suitability is represented in the model by an index with a range from 0 to 1,

determined by multiplying the coefficients of the influential abiotic factors.

Environmental fluctuations, however, may sometimes exceed the organisms tolerance ranges, causing the

elimination of a massive number of organisms from a functional feeding group. These severe fluctuations are,

as a rule, unpredictable and, as such, are stochastic. Stochastic elimination in the model is also represented by

an index, calculated by adding the coefficients of the influential abiotic factors. This index always is larger

than 0 and unlimited from above.

2.1.3. Biotic interactions module

Although streams are often considered to be physically controlled environments, biotic interactions have thepotential to influence the dynamics of aquatic macroinvertebrate communities (Peckarsky, 1983). Biotic

interactions in the model are represented by predation and competition.

Competition indirectly influences the number of organisms in a functional feeding group. By definition,

competition occurs when two or more populations interfere with or inhibit one another (Pianka, 1981). The

organisms concerned typically use some common resource that is in short supply and, therefore, reduce the

resource availability to each other. This, in turn, influences the suitability of the habitat and, as a result,

decreases the number of organisms recruited into the functional feeding group. The strength of the

competition depends primarily on the number of competing organisms. The competing organisms occupy a

particular area of habitat that can support only a limited number of individuals. When the number of

organisms in a functional feeding group exceeds the limit density which can be sustained by the environment,

competition becomes stronger.The primary predators of macroinvertebrates are vertebrates, such as fish and salamanders, and predacious

macroinvertebrates. In the model we do not consider vertebrate predators. Having a variety of feeding roles

and being very mobile, vertebrate predators can produce a diffuse effect on the benthic communities that

makes it difficult to determine the effect of a particular predator on the macroinvertebrates (Allan, 1983). In

the model, emphasis was placed on the effect of predacious macroinvertebrates comprising the same

community as their prey. The pressure of predators depends primarily on the number of predators, their

trophic activity and the amount of prey available. Through feeding, predators directly decrease the number of

organisms in all other functional feeding groups.

This far, only the dynamics in the number of individuals of a single functional feeding group has been

considered. Since the macroinvertebrate community is comprised of four such groups, the community model

must include four blocks each of which corresponds to a functional feeding group.

2.2. SamplingTo test how well the model represents the actual community dynamics, the methods of simulation and

comparison were applied (Hall and Day, 1977). The model simulations were compared with observations

from Holly Springs Creek in Clemson University Experimental Forest, South Carolina, USA. Holly Spring

Creek is a small, heavily shaded, headwater stream with leaf material as the primary food source for

macroinvertebrates.

Chemistry monitoring and macroinvertebrate community study methods based on the protocols established by

EPA in document EPA-841-B-97-003 were used as sampling techniques (Barbour et al., 1997). Temperature

and water flow in Holly Springs Creek were measured every hour with the Model 4230 Bubbler Flow Meter.


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Total nitrite, nitrate and phosphate water samples from Holly Spring Creek were taken each week. These

samples were brought to the laboratory, where they were analyzed using Lamott Co. equipment. During each

sampling event, standard handheld meters were used to record dissolved oxygen and pH. Dissolved oxygen

was measured using a YSI Model 51 Dissolved Oxygen Meter, which was calibrated before each event using

YSI protocol, while pH was measured using a VWRbrand pHastchek Pocket pH Meter, which was also

calibrated before each sampling using the instructions on the meter.

Macroinvertebrate samples were taken every week. In the field, samples were stored in plastic bags in a

cooler on ice. When sampling was complete, the samples were taken to the laboratory where organisms were

picked from the detritus and identified to species (Brigham et al., 1982). This process was completedimmediately or the samples were stored in a refrigerator at 510 C for identification within 24 h or preserved

in 80% ethanol for use at a later date. After identification, organisms were separated into the four functional

feeding groups of shredders, scrapers, collectors and predators, according to Merritt and Cummins (1996b),

and then counted. To estimate how well the simulations fit the real data, a Chi square test, with the null

hypothesis that the model is appropriate and simulations fit observations well, was used. To do Chi square test

we exported the results of simulations from STELLA to Microsoft Excell where compared them with the data

of natural observation.

3.1. Model simulations

3.1.1. The role of temperature in maintaining the dynamics of the aquatic macroinvertebratecommunityAccording to Sweeney (1984), temperature is often considered the most significant factor controlling growth,

metabolism, emergence, and reproduction of aquatic macroinvertebrates in undisturbed streams. In the model

these parameters are aggregated into habitat suitability and stochastic elimination. Habitat suitability reflects

the environmental conditions at which macroinvertebrates meet requirements for recruitment and

development. Stochastic elimination includes not only the mortality of the organisms from stochastic events

but also the emergence of adult insects because the emergence constitutes the greatest loss from the aquatic

macroinvertebrate community. Although emergence is considered a life history parameter of aquatic insects

with a certain temperature as a trigger for the emergence of massive numbers of organisms, the time when this

temperature will be reached has a degree of uncertainty.

The influence of temperature on the dynamics of aquatic macroinvertebrate communities is realized throughthe habitat suitability and stochastic elimination indices. As the habitat suitability index reaches one, more

organisms are recruited into the community. As the stochastic elimination index reaches one, more organisms

emerge from the community. The annual dynamics of the aquatic macroinvertebrates depending on habitat

suitability and stochastic elimination defined by temperature can be seen in the STELLA graph in Fig. 2.

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Fig. 2. STELLA graph of the annual dynamics of the number of organisms in an aquatic macroinvertebrate community depending on

habitat suitability and stochastic elimination defined by temperature.

As this graph in Fig. 2 indicates, the maximum abundance of organisms in a stream occurs each year during

the spring and late autumn. In the model it is assumed that there is a sufficient number of eggs in diapause

available for hatching. In early spring, when temperature reaches an optimum level for recruitment and

development, but has not yet reached the point when the insects start to emerge, the number of organisms

increases rapidly, forming the spring peak. In late spring, when temperature is suitable for recruitment and for

emergence, these two processes overlap, leading to a decrease in the number of organisms in the community.

By the end of spring, temperature becomes too high for maximum recruitment and this process declines.

However, temperature is still optimum for emergence, and therefore, numbers of macroinvertebrates continue

to decline until the temperature reaches the point where it is too high for emergence. As a rule, this occurs in

the beginning of the summer. During summer, macroinvertebrates experience only slight changes in numbers


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as a result of fluctuations in other environmental factors such as water flow, dissolved oxygen, pH, and

nutrients, but generally the community remains more or less stable. The cooler temperatures in early autumn

reinitiate emergence and recruitment. In late autumn, the number of macroinvertebrates again starts to

increase because the temperature is cold enough for emergence, while still being within the optimum range for

recruitment and development. In winter, a small decrease in the abundance of organisms is observed. This

decrease, however, is explained by the natural mortality of the larvae caused by other abiotic factors, not by

the decrease in temperature alone.

Model sensitivity is examined by varying the values of different temperature parameters and recording

alternative reactions of aquatic macroinvertebrates. The results of the sensitivity analysis presented heresuggest that aquatic macroinvertebrates are fairly sensitive to changes in temperature parameters, which then

influence the habitat suitability and stochastic elimination. Most of all, the lotic macroinvertebrate community

is sensitive to parameter T4S

reflecting the temperature at which recruitment completely stops (Fig. 3) and

parameter T5Ereflecting the temperature at which the emergence rate becomes one half of the maximum

(Fig. 4). Both these parameters strongly influence the abundance of organisms during summer. This effect

combines the consequences of both recruitment and emergence processes. It is evident that it would be

advantageous if these parameters could be directly measured by conducting laboratory experiments.

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Fig. 3. Sensitivity analysis of the number of aquatic macroinvertebrates for different values of parameter T4Sreflecting the temperature

at which recruitment completely stops.

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Fig. 4. Sensitivity analysis of the number of aquatic macroinvertebrates for different values of parameter T5Ereflecting the

temperature at which the emergence rate becomes one half of the maximum.

Generally, for those years when water temperatures are mild, that is high enough to initiate recruitment but

not emergence, the spring and autumn peaks in macroinvertebrate abundance are high. However, for those

years when an early temperature increase is observed, the conditions promote rapid emergence processes just

after new organisms are recruited into the community. This finding supports the findings ofVannote and

Sweeney (1980), who discovered that high temperatures initiate the development of adult tissue in both small

and large larva at about the same time, reduce the overall growth potential for individual larva and accelerate

the rate of adult tissue maturation, leading to early emergence but at a reduced size. This results in low peaks

in abundance in early spring and late autumn, and an overall decrease in number throughout the year.

3.1.2. The role of other abiotic factors in maintaining the dynamics of the aquatic macroinvertebrate

community

Various scenarios involving the manipulating water flow, dissolved oxygen, pH, and nutrients showed that theresponses of the macroinvertebrate community to these factors were as expected. When the values of these

factors exceed either upper or lower thresholds, macroinvertebrates experience an apparent decrease in

number. Since in undisturbed streams the values of these factors lie within the organisms tolerance range and

define the fluctuations in the number of organisms during summer and winter when the temperature is either

too high or too low to initiate developmental processes, it was not attempted to determine these thresholds.

However, further improvements in the model may focus on determining these tolerance ranges through

laboratory experiments aimed at studying a particular functional feeding group or particular population.

3.2. Model validationFig. 5, Fig. 6, Fig. 7 and Fig. 8 show the comparisons between the number of organisms in each functional


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feeding group obtained by both natural observations and model simulations.

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Fig. 5. Comparison of numbers of shredders simulated and observed.

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Fig. 6. Comparison of numbers of scrapers simulated and observed.

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Fig. 7. Comparison of numbers of collectors simulated and observed.

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Fig. 8. Comparison of numbers of predators simulated and observed.

Overall, the simulation curves of the model fit the data of macroinvertebrate dynamics from Holly Spring

Creek well. For three functional feeding groups, the shredders, scrapers, and predators, the Chi square testshowed that the simulations fit the observations (P=0.118, P=0.1655, and P=0.165 for shredders, scrapers

and predators respectively). On the other hand, for the collectors, the Chi square test indicated that the

simulations do not fit observations well (P=0.042). However, the exact fit between the simulations and

observations would be difficult to obtain, requiring substantially more research. Since the objective of this

research was to achieve initial broad representation of the dynamics of the macroinvertebrate community,

reproduction of every detail of such a complex system was not even attempted. However, this model can be

considered as a framework for further, more detailed experimental research focusing on defining the exact

values of the parameters for a particular functional feeding group or particular population inhabiting

undisturbed low-order streams. It should be noted that this model is not limited only to the study of these

particular streams. Although only a first-order stream was used to illustrate the model, this approach is

flexible: it can be adjusted to incorporate factors depending on the ecosystem studied.

The model developed through this research reflects the most important links between the aquatic

macroinvertebrate community and its environment, enabling insight into relationships in lotic ecosystems.

This knowledge provides a theoretical basis both for a better understanding of various environmental

interactions in streams and for certain ecological manipulations, making it possible to use the model for the

theoretical analysis of macroinvertebrate community functioning, studies of lotic ecosystems, and stream

management.

To develop an effective management strategy it would be necessary to predict the consequences of actions


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that would be too expensive, difficult, or destructive to study using real systems. Such a task could best be

solved by applying a general simulation model, reflecting the most important processes, variables and factors

affecting the ecosystem in question. There are many examples when mathematical models contribute finding

the successful solution for the environmental management (Olden, 2003; Tong and Chen, 2002; Krivtsov et

al., 2001 and Gertsev and Gertseva, 1999). The model developed here also appears to fit this purpose well.

One specific application of the results from this research could be their use in stream restoration. The model

gives a representation of different factors and the implications of their change, indicating the features that

should be taken into account when a stream is designed. For example, important application involves the

consideration of temperature. Model simulations showed that temperature in the early spring is a veryimportant factor affecting the macroinvertebrate community. Since temperature in small streams is very

sensitive to shading, changing the canopy created by riparian vegetation may be one of the most important

variables in small stream restoration. Selecting the vegetation near the stream, i.e. choosing between

deciduous and evergreen or early and late spring species, could influence significantly the macroinvertebrates.

Although this model gives realistic simulations of the dynamics of the macroinvertebrate community, it could

be improved by further research. It has been shown, for example, that vertebrate predation does not usually

play a significant role in structuring a lotic macroinvertebrate community (Allan, 1983). Therefore, in the

model little emphasis is placed on the effect of fish. However, under certain conditions fish predation may be

important in the dynamics of benthic organisms and its omission may lead to errors in the simulations. Thus,

the incorporation of fish would expand the model, giving it wider applications.

This approach, using an accurate model of a stream community, can be applied to study not only lotic, butalso lentic communities. The final impact of such studies has the potential to contribute the primary objectives

of current ecological inquiry, one that emphasizes quantifying the processes and dynamics of natural systems.

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Pinelands streams.Ecol. Appl.8, pp. 654658.

Parameters of abiotic factors for shredders


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Limit_Density=35; Natural_Mortality=0.13; Recruitment=1.09.

Parameters of abiotic factors for scrapers


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Parameters of abiotic factors for collectors


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Parameters of abiotic factors for predators


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a simulation model of the dynamics of aquatic macroinvertebrate communities_1

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