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.

Provides a synthesis of important classes of models about distribution, abundance, and species richness while accommodating imperfect detectionPresents models and methods for identifying unmarked individuals and speciesWritten in a step-by-step approach accessible to non-statisticians and provides fully worked examples that serve as a template for readers' analysesIncludes companion website containing data sets, code, solutions to exercises, and further information

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)