This book introduces readers to a set of powerful and extremely flexible modelling 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 behaviour. 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 behaviour, ecology, anthropology, conservation biology, and related fields.

The world's marine fisheries are in trouble, as a direct result of overfishing and the overcapacity of fishing fleets. Despite intensive management efforts, the problems still persist in many areas, resulting in many fisheries being neither sustainable nor profitable. Using bio-economic models of commercial fisheries, this book demonstrates that new management methods, based on individual or community catch quotas, are required to resolve the overfishing problem. Uncertainty about marine systems may be another factor contributing to overfishing. Methods of decision analysis and Bayesian inference are used to discuss risk management and the precautionary principle, arguing that extensive marine reserves may be the best way to protect fisheries, alongside a controlled catch quota system. This book will be of interest to environmental scientists, economists and fisheries managers, providing novel insights into many well-known but poorly understood aspects of fisheries management.

The classical theory of natural selection, as developed by Fisher, Haldane, and 'Wright, and their followers, is in a sense a statistical theory. By and large the classical theory assumes that the underlying environment in which evolution transpires is both constant and stable - the theory is in this sense deterministic. In reality, on the other hand, nature is almost always changing and unstable. We do not yet possess a complete theory of natural selection in stochastic environ ments. Perhaps it has been thought that such a theory is unimportant, or that it would be too difficult. Our own view is that the time is now ripe for the development of a probabilistic theory of natural selection. The present volume is an attempt to provide an elementary introduction to this probabilistic theory. Each author was asked to con tribute a simple, basic introduction to his or her specialty, including lively discussions and speculation. We hope that the book contributes further to the understanding of the roles of "Chance and Necessity" (Monod 1971) as integrated components of adaptation in nature."

This book introduces readers to a set of powerful and extremely flexible modelling 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 behaviour. This book also explains how to construct, test, and use dynamic state variable models in a wide range of contexts in evolutionary ecology.