Inbunden, Engelska, 2015
Applied Hierarchical Modeling in Ecology: Analysis of distribution, abundance and species richness in R and BUGS
Volume 1:Prelude and Static Models
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Beskrivning
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
Produktinformation
- Utgivningsdatum: 2015-11-27
- Mått: 191 x 235 x 44 mm
- Vikt: 1 810 g
- Format: Inbunden
- Språk: Engelska
- Antal sidor: 808
- Förlag: Elsevier Science
- ISBN: 9780128013786
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Mer om författaren
Dr. Marc Kéry is a senior scientist at the Swiss Ornithological Institute, a non-profit NGO with about 200 employees dedicated primarily to bird research, monitoring, and conservation. Marc was trained as a plant population ecologist at the universities of Basel and Zürich, Switzerland. After a 2-year postdoc at the (then) USGS Patuxent Wildlife Center in Laurel, USA, he moved into animal population ecology and during the last 25 years has worked at the interface between population ecology, biodiversity monitoring, wildlife management, and applied statistics. He has published more than 150 peer-reviewed journal articles and six textbooks on applied statistical modeling. He has taught more than 60 one-week workshops all over the world to biologists and wildlife managers about the concepts and practice of modern statistical analysis in their fields, 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.
Recensioner i media
"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
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