Bayesian Population Analysis using WinBUGS

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. Michael Schaub is the Head of the Ecology Department at the Swiss Ornithological Institute and a courtesy Professor at the University of Bern. His research interests include population dynamics, capture-recapture models, integrated population models, and migratory birds. He has coauthored approximately 130 peer-reviewed journal publications and the book Bayesian Population Analysis using WinBUGS.

• 1. Introduction2. Very brief introduction to Bayesian statistical modeling3. Introduction to the generalized linear model (GLM): The simplest model for count data4. Introduction to random effects: The conventional Poisson GLMM for count data5. State-space models6. Estimation of population size7. Estimation of survival probabilities using capture-recapture data8. Estimation of survival probabilities using mark-recovery data9. Multistate capture-recapture models10. Estimation of survival and recruitment using the Jolly-Seber model11. Integrated population models12. Metapopulation modeling of abundance using hierarchical Poisson regression13. Metapopulation modeling of species distributions using hierarchical logistic regression14. Concluding remarks