Brian Dennis – 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

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.

R is the amazing, free, open-access software package for scientific graphs and calculations used by scientists worldwide. The R Student Companion is a student-oriented manual describing how to use R in high school and college science and mathematics courses. Written for beginners in scientific computation, the book assumes the reader has just some high school algebra and has no computer programming background. The author presents applications drawn from all sciences and social sciences and includes the most often used features of R in an appendix. In addition, each chapter provides a set of computational challenges: exercises in R calculations that are designed to be performed alone or in groups. Several of the chapters explore algebra concepts that are highly useful in scientific applications, such as quadratic equations, systems of linear equations, trigonometric functions, and exponential functions. Each chapter provides an instructional review of the algebra concept, followed by a hands-on guide to performing calculations and graphing in R. R is intuitive, even fun. Fantastic, publication-quality graphs of data, equations, or both can be produced with little effort. By integrating mathematical computation and scientific illustration early in a student‘s development, R use can enhance one's understanding of even the most difficult scientific concepts. While R has gained a strong reputation as a package for statistical analysis, The R Student Companion approaches R more completely as a comprehensive tool for scientific computing and graphing.