The concept of fragmentation is explored in this book as it applies to arid, pastoral systems throughout the world. Global significance of the world's vast rangelands is large. Arid and semiarid rangelands make up almost 25% of the earth's landscapes and support more than 20 million people whose livelihoods depend on these lands. It is the home of the planet's last remaining megafauna and many other important species. The case is developed that fragmentation arises from different natural, social and economic conditions worldwide but creates similar outcomes for human and natural systems. With information from nine sites around the world the authors examine how fragmentation occurs, the patterns that result, and the consequences of fragmentation for ecosystems and the people who depend on them for their livelihoods.

With detailed data from nine sites around the world, the authors examine how the so-called 'fragmentation' of these fragile landscapes occurs and the consequences of this break-up for ecosystems and the people who depend on them. 'Rangelands' make up a quarter of the world's landscape, and here, the case is developed that while fragmentation arises from different natural, social and economic conditions worldwide, it creates similar outcomes for human and natural systems.

Bayesian modeling has become an indispensable tool for ecological research because it is uniquely suited to deal with complexity in a statistically coherent way. This textbook provides a comprehensive and accessible introduction to the latest Bayesian methods--in language ecologists can understand. Unlike other books on the subject, this one emphasizes the principles behind the computations, giving ecologists a big-picture understanding of how to implement this powerful statistical approach. Bayesian Models is an essential primer for non-statisticians. It begins with a definition of probability and develops a step-by-step sequence of connected ideas, including basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and inference from single and multiple models. This unique book places less emphasis on computer coding, favoring instead a concise presentation of the mathematical statistics needed to understand how and why Bayesian analysis works. It also explains how to write out properly formulated hierarchical Bayesian models and use them in computing, research papers, and proposals. This primer enables ecologists to understand the statistical principles behind Bayesian modeling and apply them to research, teaching, policy, and management. * Presents the mathematical and statistical foundations of Bayesian modeling in language accessible to non-statisticians* Covers basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and more* Deemphasizes computer coding in favor of basic principles* Explains how to write out properly factored statistical expressions representing Bayesian models