Description
Explore 'Modeling Populations of Adaptive Individuals,' a groundbreaking resource that delves into the intricate dynamics of populations shaped by adaptive behaviors. This brand new trade paperback from Princeton University Press (ISBN: 9780691195285) is essential for ecologists and researchers alike. With traditional models often falling short in practical applications, this book introduces state- and prediction-based theory (SPT). SPT is a revolutionary approach that models adaptive behaviors under uncertainty, enabling accurate predictions of future ecological conditions.
Whether you are a scholar or a practitioner, you'll find an array of examples—from simplified behavior models to complex individual-based population models. The expert authors, Steven Railsback and Bret Harvey, illustrate how adaptive trade-offs in habitat and activity selection are managed in diverse environments. With this book, you’ll understand how ecological forecasting intertwines with individual decision-making processes in adaptive contexts.
The insights garnered from 'Modeling Populations of Adaptive Individuals' can assist in addressing fundamental ecological questions, enabling innovative solutions in biodiversity conservation, species management, and predicting ecological outcomes. Perfect for advanced studies in ecology and environmental science, this publication is a valuable addition to your resource library.
Note: Shipping for this item is free. Please allow up to 6 weeks for delivery. Once your order is placed, it cannot be cancelled.
Note: Shipping for this item is free. Please allow up to 6 weeks for delivery. Once your order is placed, it cannot be cancelled.
Condition: BRAND NEW
ISBN: 9780691195285
Format: Trade paperback (US)
Year: 2020
Publisher: Princeton University Press
Description:
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.
Whether you are a scholar or a practitioner, you'll find an array of examples—from simplified behavior models to complex individual-based population models. The expert authors, Steven Railsback and Bret Harvey, illustrate how adaptive trade-offs in habitat and activity selection are managed in diverse environments. With this book, you’ll understand how ecological forecasting intertwines with individual decision-making processes in adaptive contexts.
The insights garnered from 'Modeling Populations of Adaptive Individuals' can assist in addressing fundamental ecological questions, enabling innovative solutions in biodiversity conservation, species management, and predicting ecological outcomes. Perfect for advanced studies in ecology and environmental science, this publication is a valuable addition to your resource library.
Note: Shipping for this item is free. Please allow up to 6 weeks for delivery. Once your order is placed, it cannot be cancelled.
Note: Shipping for this item is free. Please allow up to 6 weeks for delivery. Once your order is placed, it cannot be cancelled.
Condition: BRAND NEW
ISBN: 9780691195285
Format: Trade paperback (US)
Year: 2020
Publisher: Princeton University Press
Description:
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.