Aggregation and emergence in ecological modelling: integration of ecological levels

Modelling ecological systems implies to take into account different ecological levels: the individual, population, community and ecosystem levels. Two large families of models can be distinguished among different approaches: (i) completely detailed models involving many variables and parameters; (ii) more simple models involving only few state variables. The first class of models are usually more realistic including many details as for example the internal structure of the population. Nevertheless, the mathematical analysis is not always possible and only computer simulations can be performed. The second class of models can mathematically be analysed, but they sometimes neglect some details and remain unrealistic. We present here a review of aggregation methods, which can be seen as a compromise between these two previous modelling approaches. They are applicable for models involving two levels of organisation and the corresponding time scales. The most detailed level of description is usually associated to a fast time scale, while the coarser one rather corresponds to a slow time scale. A detailed model is thus considered at the individual level, containing many micro-variables and consisting of two parts: a fast and a slow one. Aggregation methods allow then to reduce the dimension of the initial dynamical system to an aggregated one governing few global variables evolving at the slow time scale. We focus our attention on the emerging properties of individual behaviours at the population and community levels.

[1]  P. Haccou Mathematical Models of Biology , 2022 .

[2]  Pierre Auger,et al.  Macroscopic Dynamic Effects of Migrations in Patchy Predator-prey Systems , 1997 .

[3]  S Rinaldi,et al.  Singular homoclinic bifurcations in tritrophic food chains. , 1998, Mathematical biosciences.

[4]  Y. Iwasa,et al.  Aggregation in model ecosystems. I. Perfect aggregation , 1987 .

[5]  Pierre Auger,et al.  Aggregation and emergence in systems of ordinary differential equations , 1998 .

[6]  S. Orszag,et al.  "Critical slowing down" in time-to-extinction: an example of critical phenomena in ecology. , 1998, Journal of theoretical biology.

[7]  D. L. DeAngelis,et al.  Dynamics of Nutrient Cycling and Food Webs , 1992, Population and Community Biology Series.

[8]  C. M. Place Dynamical Systems: Differential Equations, Maps, and Chaotic Behaviour , 1992 .

[9]  Sergio Rinaldi,et al.  Slow-fast limit cycles in predator-prey models , 1992 .

[10]  Sven Erik Jørgensen,et al.  Structural dynamic model , 1986 .

[11]  P. Auger,et al.  Population Dynamics Modelling in an Hierarchical Arborescent River Network: An Attempt with Salmo trutta , 1998 .

[12]  Pierre Auger,et al.  AGGREGATION METHODS IN DISCRETE MODELS , 1995 .

[13]  Dmitriĭ Olegovich Logofet,et al.  Matrices and Graphs Stability Problems in Mathematical Ecology , 1993 .

[14]  Pierre Auger,et al.  Time Scales in Density Dependent Discrete Models , 1997 .

[15]  P Auger,et al.  Fast game theory coupled to slow population dynamics: the case of domestic cat populations. , 1998, Mathematical biosciences.

[16]  C. Combes Ethological Aspects of Parasite Transmission , 1991, The American Naturalist.

[17]  Eva Sánchez,et al.  Aggregation methods in population dynamics discrete models , 1998 .

[18]  Pierre Auger,et al.  Predator Migration Decisions, the Ideal Free Distribution, and Predator‐Prey Dynamics , 1999, The American Naturalist.

[19]  R. O'Neill A Hierarchical Concept of Ecosystems. , 1986 .

[20]  Jean-Christophe Poggiale Applications des variétés invariantes à la modélisation de l'hétérogénéité en dynamique des populations , 1994 .

[21]  Lloyd Demetrius,et al.  Statistical mechanics and population biology , 1983 .

[22]  Pierre Auger,et al.  A PREY-PREDATOR MODEL IN A MULTI-PATCH ENVIRONMENT WITH DIFFERENT TIME SCALES , 1993 .

[23]  Yoh Iwasa,et al.  Aggregation in Model Ecosystems II. Approximate Aggregation , 1989 .

[24]  Sven Erik Jørgensen,et al.  State-of-the-art of ecological modelling with emphasis on development of structural dynamic models , 1999 .

[25]  Thomas C. Gard Aggregation in stochastic ecosystem models , 1988 .

[26]  Sergio Rinaldi,et al.  Limit cycles in slow-fast forest-pest models☆ , 1992 .

[27]  Simon A. Levin,et al.  Biologically generated spatial pattern and the coexistence of competing species , 1997 .

[28]  Peter Kareiva,et al.  Spatial ecology : the role of space in population dynamics and interspecific interactions , 1998 .

[29]  Bob W. Kooi,et al.  Aggregation methods in food chains , 1998 .

[30]  Pierre Auger,et al.  A density dependent model describing Salmo trutta population dynamics in an arborescent river network. Effects of dams and channelling , 1998 .

[31]  Pierre Auger,et al.  Parasitism and host patch selection: A model using aggregation methods , 1998 .

[32]  Pierre Auger,et al.  Complex ecological models with simple dynamics: From individuals to populations , 1994 .

[33]  Luis Sanz Lorenzo Métodos de agregación en sistemas discretos , 1998 .

[34]  S. Levin,et al.  Theories of Simplification and Scaling of Spatially Distributed Processes , 2011 .

[35]  P Auger,et al.  Emergence of population growth models: fast migration and slow growth. , 1996, Journal of theoretical biology.

[36]  Alessandra Gragnani,et al.  Pollution Control Policies and Natural Resource Dynamics: A Theoretical Analysis , 1996 .

[37]  P Auger,et al.  Behavioral choices based on patch selection: a model using aggregation methods. , 1999, Mathematical biosciences.

[38]  Joel E. Cohen,et al.  Community Food Webs: Data and Theory , 1990 .

[39]  N. Rashevsky,et al.  Mathematical biology , 1961, Connecticut medicine.

[40]  Robert Poulin,et al.  Risk of parasitism and microhabitat selection in juvenile sticklebacks , 1989 .

[41]  Sergio Rinaldi,et al.  Low- and high-frequency oscillations in three-dimensional food chain systems , 1992 .