Bayesian Population Analysis using WinBUGS: A Hierarchical Perspective

Bayesian Population Analysis using WinBUGS: A Hierarchical Perspective

By: Marc Kery (author), Michael Schaub (author)Paperback

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Bayesian statistics has exploded into biology and its sub-disciplines, such as ecology, over the past decade. The free software program WinBUGS, and its open-source sister OpenBugs, is currently the only flexible and general-purpose program available with which the average ecologist can conduct standard and non-standard Bayesian statistics.

About Author

Dr Kery is a Population Ecologist with the Swiss Ornithological Institute and a courtesy professor ("Privatdozent") at the University of Zurich/Switzerland, from where he received his PhD in Ecology in 2000. He is an expert in the estimation and modeling of abundance, distribution and species richness in "metapopulation designs" (i.e., collections of replicate sites). For most of his work, he uses the Bayesian model fitting software BUGS and JAGS, about which he has published two books with Academic Press (2010 and 2012). He has authored/coauthored 70 peer-reviewed articles and four book chapters. Since 2007, and for a total of 103 days, he has taught 23 statistical modeling workshops about the methods in the proposed book at research institutes and universities all over the world.


1. Introduction 2. Very brief introduction to Bayesian statistical modeling 3. Introduction to the generalized linear model (GLM): The simplest model for count data 4. Introduction to random effects: The conventional Poisson GLMM for count data 5. State-space models 6. Estimation of population size 7. Estimation of survival probabilities using capture-recapture data 8. Estimation of survival probabilities using mark-recovery data 9. Multistate capture-recapture models 10. Estimation of survival and recruitment using the Jolly-Seber model 11. Integrated population models 12. Metapopulation modeling of abundance using hierarchical Poisson regression 13. Metapopulation modeling of species distributions using hierarchical logistic regression 14. Concluding remarks

Product Details

  • ISBN13: 9780123870209
  • Format: Paperback
  • Number Of Pages: 554
  • ID: 9780123870209
  • weight: 900
  • ISBN10: 0123870208

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