E-Book, Englisch, 808 Seiten, Format (B × H): 191 mm x 235 mm
Volume 1:Prelude and Static Models
E-Book, Englisch, 808 Seiten, Format (B × H): 191 mm x 235 mm
ISBN: 978-0-12-801486-8
Verlag: Academic Press
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)
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
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
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)
12. Spatial models I (CAR, spatial exponential)