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Applied Statistical Modelling for Ecologists: A Practical Guide to Bayesian and Likelihood Inference Using R, JAGS, NIMBLE, Stan and TMB provides an important guide and comparison of powerful new software packages that are now widely used in research publications, including JAGS, Stan, NIMBLE, and TMB. It provides a gentle introduction to the most exciting specialist software that is often used to conduct cutting-edge research, along with Bayesian statistics and frequentist statistics with its maximum likelihood estimation method. In addition, this book is simple and accessible, allowing researchers to carry out and understand statistical modelling. Through examples, the book covers the underlying statistical models widely used by scientists across many disciplines. Thus, this book will be useful for anyone who needs to quickly become proficient in statistical modelling, and in the model-fitting engines covered.
1. Introduction
2. Introduction to statistical inference
3. Linear regression models and their extensions to generalized linear, hierarchical and integrated models
4. Introduction to general-purpose model-fitting engines and the model of the mean
5. Simple linear regression with Normal errors
6. Comparison of two groups
7. Comparisons among multiple groups
8. Comparisons in two classifications or with two categorical covariates
9. General linear model with continuous and categorical explanatory variables
10. Linear mixed-effects model
11. Introduction to the Generalized linear model (GLM): Comparing two groups in a Poisson regression
12. Overdispersion, zero-inflation and offsets in a GLM
13. Poisson regression with both continuous and categorical explanatory variables
14. Poisson mixed-effects model or Poisson GLMM
15. Comparing two groups in a Binomial regression
16. Binomial GLM with both continuous and categorical explanatory variables
17. Binomial mixed-effects model or Binomial GLMM
18. Model building, model checking and model selection
19. General hierarchical models: Site-occupancy species distribution model (SDM)
20. Integrated models
21. Conclusion
Dr Marc Kéry works as a senior scientist at the Swiss Ornithological Institute. This is a non-profit NGO with about 160 employees dedicated primarily to bird research, monitoring, and conservation. Marc was trained as a plant population ecologist at the Swiss Universities of Basel and Zürich, after a 2-year postdoc at the (then) USGS Patuxent Wildlife Center in Laurel, MD. During the last 20 years, he has worked at the interface between population ecology, biodiversity monitoring, wildlife management, and statistics. He has published more than 100 peer-reviewed journal articles and five textbooks on applied statistical modelling. He has also been very active in teaching fellow biologists and wildlife managers the concepts and tools of modern statistical analysis in their fields in workshops all over the world, 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, USA. Prior to his current position, he completed a Ph.D. in forest ecology at Purdue University in Indiana, USA, and a postdoc at West Virginia University. 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 modelling. 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.
