Modeling populations of adaptive individuals

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Détails bibliographiques
Auteur principal: Railsback, Steven F. (1957-....). (Auteur)
Autres auteurs: Harvey, Bret C. (1958-...). (Auteur)
Support: E-Book
Langue: Anglais
Publié: Princeton : Princeton University Press, 2020.
Collection: Monographs in population biology. 63
Sujets:
Autres localisations: Voir dans le Sudoc
Résumé: La page de présentation indique : "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."
Accès en ligne: Accès à l'E-book
Lien: Collection principale: Monographs in population biology
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245 1 0 |a Modeling populations of adaptive individuals   |c Steven F. Railsback, Bret C. Harvey. 
256 |a Données textuelles. 
264 1 |a Princeton :  |b Princeton University Press,  |c 2020. 
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490 1 |a Monographs in Population Biology ;  |v 63 
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500 |a Titre provenant de la page de titre du document numérique. 
500 |a La pagination de l'édition imprimée correspondante est de 195 p. 
501 |a Notice mise à jour d'après la consultation, 2021-02-25. 
504 |a Références bibliographiques. Index. 
506 |a L'accès complet à la ressource est réservé aux usagers des établissements qui en ont fait l'acquisition 
520 |a La page de présentation indique : "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." 
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