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
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"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