Applied Statistical Modelling for Ecologists

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.

• 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