Applied Statistical Modelling for Ecologists
A Practical Guide to Bayesian and Likelihood Inference Using R, JAGS, NIMBLE, Stan and TMB
Published on 18. July 2024
520 pages
978-0-443-13716-7 (ISBN)
Description
**2025 PROSE Award Finalist in Environmental Science**Applied Statistical Modelling for Ecologists provides a gentle introduction to the essential models of applied statistics: linear models, generalized linear models, mixed and hierarchical models. All models are fit with both a likelihood and a Bayesian approach, using several powerful software packages widely used in research publications: JAGS, NIMBLE, Stan, and TMB. In addition, the foundational method of maximum likelihood is explained in a manner that ecologists can really understand.This book is the successor of the widely used Introduction to WinBUGS for Ecologists (Kéry, Academic Press, 2010). Like its parent, it is extremely effective for both classroom use and self-study, allowing students and researchers alike to quickly learn, understand, and carry out a very wide range of statistical modelling tasks.The examples in Applied Statistical Modelling for Ecologists come from ecology and the environmental sciences, but the underlying statistical models are very widely used by scientists across many disciplines. This book will be useful for anybody who needs to learn and quickly become proficient in statistical modelling, with either a likelihood or a Bayesian focus, and in the model-fitting engines covered, including the three latest packages NIMBLE, Stan, and TMB.
- Contains a concise and gentle introduction to probability and applied statistics as needed in ecology and the environmental sciences
- Covers the foundations of modern applied statistical modelling
- Gives a comprehensive, applied introduction to what currently are the most widely used and most exciting, cutting-edge model fitting software packages: JAGS, NIMBLE, Stan, and TMB
- Provides a highly accessible applied introduction to the two dominant methods of fitting parametric statistical models: maximum likelihood and Bayesian posterior inference
- Details the principles of model building, model checking and model selection
- Adopts a "Rosetta Stone" approach, wherein understanding of one software, and of its associated language, will be greatly enhanced by seeing the analogous code in other engines
- Provides all code available for download for students, at elsevier.com/books-and-journals/book-companion/9780443137150
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Other editions
Additional editions
Marc Kery | Kenneth F. Kellner
Applied Statistical Modelling for Ecologists
A Practical Guide to Bayesian and Likelihood Inference Using R, JAGS, NIMBLE, Stan and TMB
Book
07/2024
Elsevier
€90.00
Shipment within 15-20 days
Persons
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. Ken Kellner is an Assistant Research Professor at Michigan State University, MI, United States. Prior to his current position, he completed a Ph.D. in forest ecology at Purdue University, IN, United States, and a postdoc at West Virginia University, WV, United States. Ken's research has covered a wide range of topics including forest management, plant demography, and avian and mammal conservation. He has published this research in more than 40 peer reviewed publications. In addition, Ken is particularly focused on the development of open-source software tools for ecological modeling. He has developed or contributed to several software packages that are widely used by ecologists and featured in several books, including the successful R packages jagsUI, unmarked, and ubms.
Content
1. Introduction2. Introduction to statistical inference3. Linear regression models and their extensions to generalized linear, hierarchical and integrated models4. Introduction to general-purpose model-fitting engines and the model of the mean5. Simple linear regression with Normal errors6. Comparison of two groups7. Comparisons among multiple groups8. Comparisons in two classifications or with two categorical covariates9. General linear model with continuous and categorical explanatory variables10. Linear mixed-effects model11. Introduction to the Generalized linear model (GLM): Comparing two groups in a Poisson regression12. Overdispersion, zero-inflation and offsets in a GLM13. Poisson regression with both continuous and categorical explanatory variables14. Poisson mixed-effects model or Poisson GLMM15. Comparing two groups in a Binomial regression16. Binomial GLM with both continuous and categorical explanatory variables17. Binomial mixed-effects model or Binomial GLMM18. Model building, model checking and model selection19. General hierarchical models: Site-occupancy species distribution model (SDM)20. Integrated models21. Conclusion
