Modeling Populations of Adaptive Individuals (Paperback)

Ecologists now recognize that the dynamics of populations, communities, and ecosystems are strongly affected by adaptive individual behaviors. Yet until now, we have lacked effective and flexible methods for modeling such dynamics. Traditional ecological models become impractical with the inclusion of behavior, and the optimization approaches of behavioral ecology cannot be used when future conditions are unpredictable due to feedbacks from the behavior of other individuals. This book provides a comprehensive introduction to state- and prediction-based theory, or SPT, a powerful new approach to modeling trade-off behaviors in contexts such as individual-based population models where feedbacks and variability make optimization impossible. Modeling Populations of Adaptive Individuals features a wealth of examples that range from highly simplified behavior models to complex population models in which individuals make adaptive trade-off decisions about habitat and activity selection in highly heterogeneous environments. Steven Railsback and Bret Harvey explain how SPT builds on key concepts from the state-based dynamic modeling theory of behavioral ecology, and how it combines explicit predictions of future conditions with approximations of a fitness measure to represent how individuals make good-not optimal-decisions that they revise as conditions change. The resulting models are realistic, testable, adaptable, and invaluable for answering fundamental questions in ecology and forecasting ecological outcomes of real-world scenarios.

General

Imprint:	Princeton University Press
Country of origin:	United States
Series:	Monographs in Population Biology
Release date:	May 2020
First published:	2020
Authors:	Steven F. Railsback • Bret C. Harvey
Dimensions:	235 x 156 x 15mm (L x W x T)
Format:	Paperback - Trade
Pages:	208
ISBN-13:	978-0-691-19528-5
Categories:	Books > Science & Mathematics > Biology, life sciences > Zoology & animal sciences > Animal ecology
LSN:	0-691-19528-5
Barcode:	9780691195285

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