Applied Hierarchical Modeling in Ecology: Analysis of distribution, abundance and species richness in R and BUGS: Volume 1:Prelude and Static Models

Applied Hierarchical Modeling in Ecology: Distribution, Abundance, Species Richness offers a new synthesis of the state-of-the-art of hierarchical models for plant and animal distribution, abundance, and community characteristics such as species richness using data collected in metapopulation designs. These types of data are extremely widespread in ecology and its applications in such areas as biodiversity monitoring and fisheries and wildlife management. This first volume explains static models/procedures in the context of hierarchical models that collectively represent a unified approach to ecological research, taking the reader from design, through data collection, and into analyses using a very powerful class of models. Applied Hierarchical Modeling in Ecology, Volume 1 serves as an indispensable manual for practicing field biologists, and as a graduate-level text for students in ecology, conservation biology, fisheries/wildlife management, and related fields.

Table of Contents:
Preface Part 1: Prelude 1. Distribution, abundance and species richness in ecology 2. What are hierarchical models and how do we analyse them ? 3. Linear models, generalized linear models (GLMs), and random-effects: the components of hierarchical models 4. Introduction to data simulation 5. The Bayesian modeling software BUGS and JAGS Part 2: Models for static systems 6. Modeling abundance using binomial N-mixture models 7. Modeling abundance using multinomial N-mixture models 8. Modeling abundance using hierarchical distance sampling 9. Advanced hierarchical distance sampling 10. Modeling distribution and occurrence using site-occupancy models 11. Community models (incidence- and abundance-based)

About the Author :
Dr. Marc works as a senior scientist at the Swiss Ornithological Institute, Seerose 1, 6204 Sempach, Switzerland. This is a non-profit NGO with about 160 employees dedicated primarily to bird research, monitoring, and conservation. Marc was trained as a plant population ecologist at the Swiss Universities of Basel and Zuerich. After a 2-year postdoc at the (then) USGS Patuxent Wildlife Center in Laurel, MD. During the last 20 years he has worked at the interface between population ecology, biodiversity monitoring, wildlife management, and statistics. He has published more than 100 peer-reviewed journal articles and five textbooks on applied statistical modeling. He has also been very active in teaching fellow biologists and wildlife managers the concepts and tools of modern statistical analysis in their fields in workshops all over the world, something which goes together with his books, which target the same audiences. Dr Royle is a Senior Scientist and Research Statistician at the U.S. Geological Survey's Patuxent Wildlife Research Center. His research is focused on the application of probability and statistics to ecological problems, especially those related to animal sampling and demographic modeling. Much of his research over the last 10 years has been devoted to the development of methods illustrated in our new book. He has authored or coauthored more than 100 journal articles, and co-authored the books Spatial Capture Recapture, Hierarchical Modeling and Inference in Ecology and Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species Occurrence, all published by Academic Press.

Review :
"For the beginning practitioner of hierarchical models,...this is definitely worth the space and may actually be the first place to start developing a strong fundamental understanding of hierarchical modeling. For more seasoned quantitative ecologists, there are lots of useful modeling details for both the unmarked and BUGS/JAGS syntax,...there is value to having it all in one place. Finally, for professors looking to teach a class or short-course on hierarchical models in ecology,...an excellent text for the course." course." --Journal of Agricultural, Biological, and Environmental Statistics