J. M. Cushing – författare

It is impossible to predict the exact behavior of all biological systems and how these same systems are exemplified by patterns of complexity and regularity. Decades of research in ecology have documented how these sorts of patterns are the consequences of deceptively simple rules that determine the nature of the patterns created. Chaos in Ecology will explain how simple beginnings result in complicated results.Chaos in Ecology is the inaugural volume of Theoretical Ecology Series. The authors of this volume have employed data from a proven model system in population dynamics. As a result, this book will be of interest to anyone interested in the ecology of populations.- It is impossible to predict the exact behavior of almost all biological systems and yet these same systems are exemplified by patterns of complexity and regularity. Decades of research in ecology have documented that these sorts of patterns are the consequence of deceptively simple rules that determine the nature of the patterns created. In essence, simple beginnings result in complicated results- This realization is captured in the mathematical notion of "chaos" and is rendered intuitive by the oft-repeated metaphor: "A butterfly beats its wings in China and causing a thunderstorm in the Midwest." Thus, seemingly trivial initial conditions (e.g. a butterfly in China) cascade through a series of intermediate events to create a significant large-scale event (e.g. a thunderstorm)- Chaos in Ecology is the inaugural volume of Theoretical Ecology Series. The authors of this volume have employed data from a proven model system in population dynamics. As a result, this book will be of interest to anyone interested in the ecology of populations

It is impossible to predict the exact behavior of all biological systems and how these same systems are exemplified by patterns of complexity and regularity. Decades of research in ecology have documented how these sorts of patterns are the consequences of deceptively simple rules that determine the nature of the patterns created. Chaos in Ecology will explain how simple beginnings result in complicated results.

Chaos in Ecology is the inaugural volume of Theoretical Ecology Series. The authors of this volume have employed data from a proven model system in population dynamics. As a result, this book will be of interest to anyone interested in the ecology of populations.

It is impossible to predict the exact behavior of almost all biological systems and yet these same systems are exemplified by patterns of complexity and regularity. Decades of research in ecology have documented that these sorts of patterns are the consequence of deceptively simple rules that determine the nature of the patterns created. In essence, simple beginnings result in complicated results This realization is captured in the mathematical notion of "chaos" and is rendered intuitive by the oft-repeated metaphor: "A butterfly beats its wings in China and causing a thunderstorm in the Midwest." Thus, seemingly trivial initial conditions (e.g. a butterfly in China) cascade through a series of intermediate events to create a significant large-scale event (e.g. a thunderstorm) Chaos in Ecology is the inaugural volume of Theoretical Ecology Series. The authors of this volume have employed data from a proven model system in population dynamics. As a result, this book will be of interest to anyone interested in the ecology of populations

Complex Population Dynamics: Theory and Data brings together over two decades of research and reflection on experimental nonlinear population dynamics. The broad theme of the book is the interface of population data and mathematical models. The authors establish a cornerstone example of a low-dimensional mathematical population model for which quantitative predictions, often unexpected, were borne out in controlled and replicated experiments. Central messages include the importance of low-dimensional, mechanistic models that serve as testable hypotheses; the importance of model validation on independent data; the prediction of novel outcomes in response to parameter manipulation; the interaction of nonlinearity and stochasticity—and how stochasticity illuminates, rather than obscures, deterministic forces; abrupt transitions in dynamic regimes in response to interventions; and how chaotic dynamics are expressed in discrete-state, noisy population systems. The book explores nonlinear phenomena in experimental data, including equilibria, cycles, bifurcations, invariant loops, multiple attractors, resonance and attenuance, saddles, stable and unstable manifolds, basins of attraction, basin boundaries, and chaos, and shows how these dynamics manifest in real data in both time series and state space plots.The book offers an invaluable resource to professional ecologists and applied mathematicians. Although primarily a reference text, it is written to be accessible and engaging to a student audience and could be used as supplementary reading in an advanced ecological modeling class.FeaturesWritten in an engaging style with minimal mathematical prerequisites, making it accessible to an audience of general ecologists as well as a more specialized mathematical audienceA complete accompanying data set from all experiments is freely available on DryadReaders can conveniently explore the model dynamics under discussion with an online LPA simulator.

Complex Population Dynamics: Theory and Data brings together over two decades of research and reflection on experimental nonlinear population dynamics. The broad theme of the book is the interface of population data and mathematical models. The authors establish a cornerstone example of a low-dimensional mathematical population model for which quantitative predictions, often unexpected, were borne out in controlled and replicated experiments. Central messages include the importance of low-dimensional, mechanistic models that serve as testable hypotheses; the importance of model validation on independent data; the prediction of novel outcomes in response to parameter manipulation; the interaction of nonlinearity and stochasticity—and how stochasticity illuminates, rather than obscures, deterministic forces; abrupt transitions in dynamic regimes in response to interventions; and how chaotic dynamics are expressed in discrete-state, noisy population systems. The book explores nonlinear phenomena in experimental data, including equilibria, cycles, bifurcations, invariant loops, multiple attractors, resonance and attenuance, saddles, stable and unstable manifolds, basins of attraction, basin boundaries, and chaos, and shows how these dynamics manifest in real data in both time series and state space plots.

The book offers an invaluable resource to professional ecologists and applied mathematicians. Although primarily a reference text, it is written to be accessible and engaging to a student audience and could be used as supplementary reading in an advanced ecological modeling class.

Features

Written in an engaging style with minimal mathematical prerequisites, making it accessible to an audience of general ecologists as well as a more specialized mathematical audience A complete accompanying data set from all experiments is freely available on Dryad Readers can conveniently explore the model dynamics under discussion with an online LPA simulator.

Complex Population Dynamics: Theory and Data brings together over two decades of research and reflection on experimental nonlinear population dynamics. The broad theme of the book is the interface of population data and mathematical models. The authors establish a cornerstone example of a low-dimensional mathematical population model for which quantitative predictions, often unexpected, were borne out in controlled and replicated experiments. Central messages include the importance of low-dimensional, mechanistic models that serve as testable hypotheses; the importance of model validation on independent data; the prediction of novel outcomes in response to parameter manipulation; the interaction of nonlinearity and stochasticity—and how stochasticity illuminates, rather than obscures, deterministic forces; abrupt transitions in dynamic regimes in response to interventions; and how chaotic dynamics are expressed in discrete-state, noisy population systems. The book explores nonlinear phenomena in experimental data, including equilibria, cycles, bifurcations, invariant loops, multiple attractors, resonance and attenuance, saddles, stable and unstable manifolds, basins of attraction, basin boundaries, and chaos, and shows how these dynamics manifest in real data in both time series and state space plots.

The book offers an invaluable resource to professional ecologists and applied mathematicians. Although primarily a reference text, it is written to be accessible and engaging to a student audience and could be used as supplementary reading in an advanced ecological modeling class.

Features

Written in an engaging style with minimal mathematical prerequisites, making it accessible to an audience of general ecologists as well as a more specialized mathematical audience A complete accompanying data set from all experiments is freely available on Dryad Readers can conveniently explore the model dynamics under discussion with an online LPA simulator.

This book offers an introduction to the use of matrix theory and linear algebra in modeling the dynamics of biological populations. Matrix algebra has been used in population biology since the 1940s and continues to play a major role in theoretical and applied dynamics for populations structured by age, body size or weight, disease states, physiological and behavioral characteristics, life cycle stages, or any of many other possible classification schemes. With a focus on matrix models, the book requires only first courses in multi-variable calculus and matrix theory or linear algebra as prerequisites.The reader will learn the basics of modeling methodology (i.e., how to set up a matrix model from biological underpinnings) and the fundamentals of the analysis of discrete time dynamical systems (equilibria, stability, bifurcations, etc.). A recurrent theme in all chapters concerns the problem of extinction versus survival of a population. In addition to numerous examples that illustrate these fundamentals, several applications appear at the end of each chapter that illustrate the full cycle of model setup, mathematical analysis, and interpretation. The author has used the material over many decades in a variety of teaching and mentoring settings, including special topics courses and seminars in mathematical modeling, mathematical biology, and dynamical systems.

These notes are, for the most part, the result of a course I taught at the University of Arizona during the Spring of 1977. Their main purpose is to inves­ tigate the effect that delays (of Volterra integral type) have when placed in the differential models of mathematical ecology, as far as stability of equilibria and the nature of oscillations of species densities are concerned. A secondary pur­ pose of the course out of which they evolved was to give students an (at least elementary) introduction to some mathematical modeling in ecology as well as to some purely mathematical subjects, such as stability theory for integrodifferentia1 systems, bifurcation theory, and some simple topics in perturbation theory. The choice of topics of course reflects my personal interests; and while these notes were not meant to exhaust the topics covered, I think they and the list of refer­ ences come close to covering the literature to date, as far as integrodifferentia1 models in ecology are concerned. I would like to thank the students who took the course and consequently gave me the opportunity and stimulus to organize these notes. Special thanks go to Professor Paul Fife and Dr. George Swan who also sat in the course and were quite helpful with their comments and observations. Also deserving thanks are Professor Robert O'Malley and Ms. Louise C. Fields of the Applied Mathematics Program here at the University of Arizona. Ms. Fields did an outstandingly efficient and accu­ rate typing of the manuscript.