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Individual-based models

This section is based on the papers An Overview of the Swann simulation system, by 94 Swarm Team, Santa Pe Institute and The Swarm simulation. system and individual-based modeling, by D. Hiebler. [Pg.568]

It appears from the above that microcosm and/or mesocosm tests are limited by the constraints of experimentation, in that usually only a limited number of recovery scenarios can be investigated. Consequently, modeling approaches may provide an alternative tool for investigating likely recovery rates under a range of conditions. Generic models, like the logistic growth mode (for example, see Barnthouse 2004) and life history and individual-based (meta)population models, which also may be spatially explicit, provide mathematical frameworks that offer the opportunity to explore the recovery potential of individual populations. For an overview of these life history and individual-based models, see Bartell et al. (2003) and Pastorok et al. (2003). [Pg.213]

K. Cuddington, P.R. Leavitt (1999). An individual-based model of pigment flux in lakes Implications for organic biogeochemistry and paleoecology. Can. J. Fish. Aquat. Sci., 56, 1964-1977. [Pg.543]

Chapter 6 MASTEP An Individual-Based Model to Predict Recovery of... [Pg.8]

Forum for the Co-ordination of Pesticide Fate Models and their Use Individual-based models Level of concern... [Pg.10]

The elements of the matrix can be made dependent on population density or the environment, but then there is no longer a simple solution via eigenvalues. Instead, Equation 3.1 has to be iterated on computers and resembles simple individual-based models (IBMs see next section). [Pg.49]

Individual-based models describe the life cycle of individual (discrete) organisms. The organisms can differ and display autonomous behavior (DeAngelis and Mooij 2005 Grimm and Railsback 2005). The entities of an IBM — individuals, habitat units, and the abiotic environment — are characterized by sets of state variables, for example, sex, age, body mass, location (individuals) vegetation cover, soil moisture, food level (habitat units) or temperature, rainfall, and disturbance rate (environment). [Pg.49]

Mamedov A, Udalov S. 2002. A computer tool to develop individual-based models for simulation of population interactions. Ecol Model 147 53-68. [Pg.113]

Thorbek P, Topping CJ. 2005. The influence of landscape diversity and heterogeneity on spatial dynamics of agrobiont linyphiid spiders an individual-based model. Biocontrol 50 1-33. [Pg.114]

How to choose model type For example, when is a simple matrix model enough, and when should an individual-based model be used ... [Pg.135]

Bart J. 1995. Acceptance criteria for using individual-based models to make management decisions. Ecol Appl 5 411-420. [Pg.138]

Beaudouin R, Monod G, Ginot V. 2008. Selecting parameters for calibration via sensitivity analysis an individual-based model of mosquitofish population dynamics. Ecol Model 218 29 48. [Pg.138]

DeAngelis DL, Mooij WM. 2005. Individual-based modeling of ecological and evolutionary processes. Annu Rev Ecol Evol Syst 36 147-168. [Pg.139]

Goss-Custard JD, Burton NHK, Clark NA, Ferns PN, McGrorty S, Reading CJ, Rehfisch MM, Stillman RA, Townend I, West AD, Worrall DH. 2006. Test of a behavior-based individual-based model response of shorebird mortality to habitat loss. Ecol Appl 16 2215-2222. [Pg.141]

Grimm V. 1999. Ten years of individual-based modelling in ecology what have we learned and what could we learn in the future Ecol Model 115 129-148. [Pg.141]

Grimm V. 2008. Individual-based models. In Jprgensen SE, FathBD, editors, Ecological models. Vol. 3 of Encyclopedia of ecology, 5 vols. Oxford (UK) Elsevier, p 1959-1968. [Pg.141]

Grimm V, Railsback SF. 2005. Individual-based modeling and ecology. Princeton (NJ) Princeton University Press. [Pg.141]

Railsback SF. 2001. Concepts from complex adaptive systems as a framework for individual-based modelling. Ecol Model 139 47-62. [Pg.143]

Railsback SF, Harvey BC. 2002. Analysis of habitat-selection rules using an individual-based model. Ecology 83 1817-1830. [Pg.143]

Wang M, Grimm V. 2007. Home range dynamics and population regulation an individual-based model of the common shrew Sorex araneus. Ecol Model 205 397 -09. [Pg.145]


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