A generic, individual-based approach to modelling higher trophic levels in simulation of terrestrial ecosystems

In this article, a description is given of the manner in which higher trophic levels (animals) are represented in a generally configurable ecosystem model. The animals are modelled using an individual-based approach that is sufficiently generic to allow for the representation of organisms of different types of species via the specification of appropriate sets of parameter values. Animal behaviours and physiological functions are described with simple mechanistic rules that are derived from various assumptions about, for example, growth rates, metabolic requirements, digestion and assimilation of food, or gestation. The animals interact in a detailed, spatially explicit environment that consists of a terrain, an atmosphere, and various species of primary producers. The model has been implemented in simulation to explore population dynamics in multi-species ecosystems configured with two and three trophic levels. Sample simulation results are presented, together with a discussion of the effectiveness of the approach for the representation of animals in ecosystem modelling.

[1]  Keith D. Farnsworth,et al.  Animal foraging from an individual perspective: an object orientated model , 1998 .

[2]  P. Hraber,et al.  Community assembly in a model ecosystem , 1997 .

[3]  S. Levin The problem of pattern and scale in ecology , 1992 .

[4]  Christophe Lett,et al.  Comparison of a cellular automata network and an individual-based model for the simulation of forest dynamics , 1999 .

[5]  O. Schmitz,et al.  Biodiversity and the productivity and stability of ecosystems. , 1996, Trends in ecology & evolution.

[6]  Steven F. Railsback,et al.  Concepts from complex adaptive systems as a framework for individual-based modelling , 2001 .

[7]  Robert J. Naiman,et al.  Animal Influences on Ecosystem DynamicsLarge animals are more than passive components of ecological systems , 1988 .

[8]  Edward L. Mills,et al.  INDIVIDUAL‐BASED MODEL OF YELLOW PERCH AND WALLEYE POPULATIONS IN ONEIDA LAKE , 1999 .

[9]  H. Mooney,et al.  Biodiversity and Ecosystem Function , 1994, Praktische Zahnmedizin Odonto-Stomatologie Pratique Practical Dental Medicine.

[10]  Richard W. Hill,et al.  Comparative physiology of animals: An environmental approach , 1976 .

[11]  Ralph H. Johnson THERMOREGULATION AND BIOENERGETICS , 1976 .

[12]  Robert Kok,et al.  Learning to engineer life: development of a generally configurable model for the simulation of artificial ecosystems , 2000 .

[13]  Larry B. Crowder,et al.  An individual-based, spatially-explicit simulation model of the population dynamics of the endangered red-cockaded woodpecker, Picoides borealis , 1998 .

[14]  John Wegner,et al.  Population effects of landscape model manipulation on two behaviourally different woodland small mammals , 1998 .

[15]  K. Rose,et al.  An individual-based model of lake fish communities: application to piscivore stocking in Lake Mendota , 2000 .

[16]  Christine M. Mayer,et al.  Individual-based model simulations of a zebra mussel (Dreissena polymorpha) induced energy shunt on walleye(Stizostedion vitreum) and yellow perch (Perca flavescens) populations in Oneida Lake, New York , 1999 .

[17]  Henriette I. Jager,et al.  Effects of climatic temperature change on growth, survival, and reproduction of rainbow trout: predictions from a simulation model , 1997 .

[18]  J. Lawton,et al.  Organisms as ecosystem engineers , 1994 .

[19]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[20]  Charles T. Robbins,et al.  Wildlife Feeding and Nutrition , 1984 .

[21]  D. DeAngelis,et al.  New Computer Models Unify Ecological TheoryComputer simulations show that many ecological patterns can be explained by interactions among individual organisms , 1988 .

[22]  Thomas M. Smith,et al.  The potential for application of individual-based simulation models for assessing the effects of global change , 1992 .

[23]  Christian Wissel,et al.  Aims and limits of ecological modelling exemplified by island theory , 1992 .

[24]  Andrew D. Friend,et al.  A process-based, terrestrial biosphere model of ecosystem dynamics (Hybrid v3.0) , 1997 .

[25]  J Uchmański,et al.  Individual-based modelling in ecology: what makes the difference? , 1996, Trends in ecology & evolution.

[26]  P. Hogeweg,et al.  Individual-oriented modelling in ecology , 1990 .

[27]  D. DeAngelis,et al.  Individual-Based Models and Approaches in Ecology , 1992 .

[28]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[29]  V. Grimm Ten years of individual-based modelling in ecology: what have we learned and what could we learn in the future? , 1999 .

[30]  G. Booth Gecko: A continuous 2d world for ecological modeling , 1997 .

[31]  David R. Jefferson,et al.  RAM: Artificial Life for the Exploration of Complex Biological Systems , 1987, IEEE Symposium on Artificial Life.

[32]  C. Wissel,et al.  Reconciling Classical and Individual‐Based Approaches in Theoretical Population Ecology: A Protocol for Extracting Population Parameters from Individual‐Based Models , 1998, The American Naturalist.

[33]  Lael Parrott,et al.  A generic primary producer model for use in ecosystem simulation , 2001 .

[34]  O P Judson,et al.  The rise of the individual-based model in ecology. , 1994, Trends in ecology & evolution.