000			02415cam a2200289zu 4500
001			88904891
003			FRCYB88904891
005			20250107233644.0
006			m o d
007			cr un
008			250108s2020 fr | o|||||0|0|||eng d
020			_a9780691180496
035			_aFRCYB88904891
040			_aFR-PaCSA _ben _c _erda
100	1		_aRailsback, Steven F.
245	0	1	_aModeling Populations of Adaptive Individuals _c['Railsback, Steven F.', 'Harvey, Bret C.']
264		1	_bPrinceton University Press _c2020
300			_a p.
336			_btxt _2rdacontent
337			_bc _2rdamdedia
338			_bc _2rdacarrier
650		0	_a
700	0		_aRailsback, Steven F.
700	0		_aHarvey, Bret C.
856	4	0	_2Cyberlibris _uhttps://international.scholarvox.com/netsen/book/88904891 _qtext/html _a
520			_aEcologists 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.
999			_c73422 _d73422