Applied Statistical Modelling for Ecologists

Dr. Marc works as a senior scientist at the Swiss Ornithological Institute, Seerose 1, 6204 Sempach, Switzerland. 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 Zuerich. 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 modeling. 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, 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. 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