Applied Statistical Modelling for Ecologists: A Practical Guide to Bayesian and Likelihood Inference Using R, JAGS, NIMBLE, Stan and TMB

Applied Statistical Modelling for Ecologists: A Practical Guide to Bayesian and Likelihood Inference Using R, JAGS/Nimbl

Table of Contents:
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

About the Author :
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.