Leseprobe
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Dynamic Modeling in Behavioral Ecology
Colin Whitcomb Clark, Marc Mangel
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Naturwissenschaften, Medizin, Informatik, Technik / Naturwissenschaften allgemein
Beschreibung
This book describes a powerful and flexible technique for the modeling of behavior, based on evolutionary principles. The technique employs stochastic dynamic programming and permits the analysis of behavioral adaptations wherein organisms respond to changes in their environment and in their own current physiological state. Models can be constructed to reflect sequential decisions concerned simultaneously with foraging, reproduction, predator avoidance, and other activities.
The authors show how to construct and use dynamic behavioral models. Part I covers the mathematical background and computer programming, and then uses a paradigm of foraging under risk of predation to exemplify the general modeling technique. Part II consists of five "applied" chapters illustrating the scope of the dynamic modeling approach. They treat hunting behavior in lions, reproduction in insects, migrations of aquatic organisms, clutch size and parental care in birds, and movement of spiders and raptors. Advanced topics, including the study of dynamic evolutionarily stable strategies, are discussed in Part III.
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Poisson point process, State variable, Expected value, Phenotypic plasticity, Prior probability, Biomass (ecology), Flocking (behavior), Population dynamics, Group selection, Schematic, Organism, Environmental factor, Zooplankton, Poisson distribution, Prediction, Fitness function, Probability distribution, Calculation, Result, Selection algorithm, Decision theory, Control function (econometrics), Alternative hypothesis, Optimal control, Stochastic differential equation, Natural selection, Trophic level, Control theory, Oviparity, Biology, Risk aversion, Mathematical and theoretical biology, Central place foraging, Behavioral ecology, Trade-off, Optimal foraging theory, Parasitism, Theory, Decision-making, Bayesian statistics, Behavioral modeling, Reinforcement learning, Loss function, Mathematical optimization, Reproductive value (population genetics), Foraging, Dynamic programming, Posterior probability, Stochastic optimization, Insect, Density dependence, Monte Carlo method, Ecology, Evolutionary biology, Utility, Fecundity, Optimization problem, Sensitivity analysis, Evolutionary ecology, Production function, Stochastic dynamic programming, Random variable, Field experiment, Iteration, Probability, Survival function, Predation, Subroutine, Bayesian inference, Parameter