Description
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 detection- Presents models and methods for identifying unmarked individuals and species- Written in a step-by-step approach accessible to non-statisticians and provides fully worked examples that serve as a template for readers' analyses- Includes companion website containing data sets, code, solutions to exercises, and further information
Table of Contents
PrefacePart 1: Prelude1. Distribution, abundance and species richness in ecology2. What are hierarchical models and how do we analyse them ?3. Linear models, generalized linear models (GLMs), and random-effects: the components of hierarchical models4. Introduction to data simulation5. The Bayesian modeling software BUGS and JAGSPart 2: Models for static systems6. Modeling abundance using binomial N-mixture models7. Modeling abundance using multinomial N-mixture models8. Modeling abundance using hierarchical distance sampling9. Advanced hierarchical distance sampling10. Modeling distribution and occurrence using site-occupancy models11. Community models (incidence- and abundance-based)
