This book introduces readers to a set of powerful and extremely flexible modeling techniques, starting at "square one" and continuing with carefully chosen applications. Some of these applications of methodology include insect oviposition behavior, overwinter survival of birds and fish, avian migration, resource management, conservation biology, agroecology, and human behavior. This book also explains how to construct, test, and use dynamic state variable models in a wide range of contexts in evolutionary ecology, and its complete and up-to-date coverage allows readers to immediately begin using the described techniques. Dynamic State Variable Models in Ecology is designed for self-instruction or for use in upper division undergraduate or graduate courses. It is ideal for students and scientists interested in behavior, ecology, anthropology, conservation biology, and related fields.

This book introduces readers to a set of powerful and extremely flexible modeling techniques--starting at "square one"--and is ideal for students and scientists in behavior studies, ecology, anthropology, conservation biology, and related fields.

This book describes a powerful and flexible technique for the modeling of behavior, based on evolutionary principles. The technique employs stochastic dynamic programming and permits the analysis of behavioral adaptations wherein organisms respond to changes in their environment and in their own current physiological state. Models can be constructed to reflect sequential decisions concerned simultaneously with foraging, reproduction, predator avoidance, and other activities. The authors show how to construct and use dynamic behavioral models. Part I covers the mathematical background and computer programming, and then uses a paradigm of foraging under risk of predation to exemplify the general modeling technique. Part II consists of five "applied" chapters illustrating the scope of the dynamic modeling approach. They treat hunting behavior in lions, reproduction in insects, migrations of aquatic organisms, clutch size and parental care in birds, and movement of spiders and raptors. Advanced topics, including the study of dynamic evolutionarily stable strategies, are discussed in Part III.

From controlling disease outbreaks to predicting heart attacks, dynamic models are increasingly crucial for understanding biological processes. Many universities are starting undergraduate programs in computational biology to introduce students to this rapidly growing field. In Dynamic Models in Biology, the first text on dynamic models specifically written for undergraduate students in the biological sciences, ecologist Stephen Ellner and mathematician John Guckenheimer teach students how to understand, build, and use dynamic models in biology. Developed from a course taught by Ellner and Guckenheimer at Cornell University, the book is organized around biological applications, with mathematics and computing developed through case studies at the molecular, cellular, and population levels. The authors cover both simple analytic models--the sort usually found in mathematical biology texts--and the complex computational models now used by both biologists and mathematicians. Linked to a Web site with computer-lab materials and exercises, Dynamic Models in Biology is a major new introduction to dynamic models for students in the biological sciences, mathematics, and engineering.

This book introduces readers to a set of powerful and extremely flexible modeling techniques, starting at "square one" and continuing with carefully chosen applications. Some of these applications of methodology include insect oviposition behavior, overwinter survival of birds and fish, avian migration, resource management, conservation biology, agroecology, and human behavior. This book also explains how to construct, test, and use dynamic state variable models in a wide range of contexts in evolutionary ecology, and its complete and up-to-date coverage allows readers to immediately begin using the described techniques. Dynamic State Variable Models in Ecology is designed for self-instruction or for use in upper division undergraduate or graduate courses. It is ideal for students and scientists interested in behavior, ecology, anthropology, conservation biology, and related fields.

Adaptive radiation is the evolution of diversity within a rapidly multiplying lineage. It can cause a single ancestral species to differentiate into an impressively vast array of species inhabiting a variety of environments. Much of life's diversity has arisen during adaptive radiations. Some of the most famous recent examples include the East African cichlid fishes, the Hawaiian silverswords, and of course, Darwin's Gal--aacute--;pagos finches,. This book evaluates the causes of adaptive radiation. It focuses on the 'ecological' theory of adaptive radiation, a body of ideas that began with Darwin and was developed through the early part of the 20th Century. This theory proposes that phenotypic divergence and speciation in adaptive radiation are caused ultimately by divergent natural selection arising from differences in environment and competition between species. In The Ecology of Adaptive Radiation the author re-evaluates the ecological theory, along with its most significant extensions and challenges, in the light of all the recent evidence. This important book is the first full exploration of the causes of adaptive radiation to be published for decades, written by one of the world's best young evolutionary biologists.

A plethora of different theories, models, and concepts make up the field of community ecology. Amid this vast body of work, is it possible to build one general theory of ecological communities? What other scientific areas might serve as a guiding framework? As it turns out, the core focus of community ecology—understanding patterns of diversity and composition of biological variants across space and time—is shared by evolutionary biology and its very coherent conceptual framework, population genetics theory. The Theory of Ecological Communities takes this as a starting point to pull together community ecology's various perspectives into a more unified whole. Mark Vellend builds a theory of ecological communities based on four overarching processes: selection among species, drift, dispersal, and speciation. These are analogues of the four central processes in population genetics theory—selection within species, drift, gene flow, and mutation—and together they subsume almost all of the many dozens of more specific models built to describe the dynamics of communities of interacting species. The result is a theory that allows the effects of many low-level processes, such as competition, facilitation, predation, disturbance, stress, succession, colonization, and local extinction to be understood as the underpinnings of high-level processes with widely applicable consequences for ecological communities. Reframing the numerous existing ideas in community ecology, The Theory of Ecological Communities provides a new way for thinking about biological composition and diversity.

Introduction and background; Exploratory data analysis and graphics; Deterministic functions for ecological modeling; Probability and stochastic distributions for ecological modeling; Stochatsic simulation and power analysis; Likelihood and all that; Optimization and all that; Likelihood examples; Standar statistics revisited; Modeling variance; Dynamic models.

This book is open access under a CC BY-NC 2.5 license. This book provides an unprecedented synthesis of the current status of scientific and management knowledge regarding global rangelands and the major challenges that confront them. It has been organized around three major themes. The first summarizes the conceptual advances that have occurred in the rangeland profession. The second addresses the implications of these conceptual advances to management and policy. The third assesses several major challenges confronting global rangelands in the 21st century. This book will compliment applied range management textbooks by describing the conceptual foundation on which the rangeland profession is based. It has been written to be accessible to a broad audience, including ecosystem managers, educators, students and policy makers. The content is founded on the collective experience, knowledge and commitment of 80 authors who have worked in rangelands throughout the world. Their collective contributions indicate that a more comprehensive framework is necessary to address the complex challenges confronting global rangelands. Rangelands represent adaptive social-ecological systems, in which societal values, organizations and capacities are of equal importance to, and interact with, those of ecological processes. A more comprehensive framework for rangeland systems may enable management agencies, and educational, research and policy making organizations to more effectively assess complex problems and develop appropriate solutions.

Mathematical modelling is an essential tool in present-day ecological research. Yet for many ecologists it is still problematic to apply modelling in their research. In our experience, the major problem is at the conceptual level: proper understanding of what a model is, how ecological relations can be translated consistently into mathematical equations, how models are solved, steady states calculated and interpreted. Many textbooks jump over these conceptual hurdles to dive into detailed formulations or the mathematics of solution. This book attempts to fill that gap. It introduces essential concepts for mathematical modelling, explains the mathematics behind the methods, and helps readers to implement models and obtain hands-on experience. Throughout the book, emphasis is laid on how to translate ecological questions into interpretable models in a practical way. The book aims to be an introductory textbook at the undergraduate-graduate level, but will also be useful to seduce experienced ecologists into the world of modelling. The range of ecological models treated is wide, from Lotka-Volterra type of principle-seeking models to environmental or ecosystem models, and including matrix models, lattice models and sequential decision models. All chapters contain a concise introduction into the theory, worked-out examples and exercises. All examples are implemented in the open-source package R, thus taking away problems of software availability for use of the book. All code used in the book is available on a dedicated website.