1. FishBase goes FishBayes R, JAGS and Bayesian Statistics Rainer Froese FIN Seminar, 21 February 2013 Kush Hall, IRRI, Los Baños, Philippines

2. Problem Statement FishBase has compiled thousands of studies on growth, maturity, reproduction, diet How can the information be summarized? How can new studies be informed? How can best estimates for species without studies be derived? Answer: Bayesian Statistics

4. Bayesian Inference in a Nutshell Prior: express existing knowledge (textbook, common sense, logic, best guess, previous studies) with a central value (such as a mean) and a distribution around it (such as a normal distribution and a standard deviation). Likelihood function: analyze new data, get the mean and distribution Posterior: Combine prior and likelihood into a new, intermediate mean and distribution

5. Example: Length Weight Relationships

7. Example: LWR Across All Studies

8. Example: LWR for Many Studies

9. Example: LWR for One Study Only

10. Example: LWR Priors

11. Example: FishBase Online

16. Example: FishBase Online (after about 5 minutes...)

17. Example: FishBase Online

21. Next Steps Assign LWR to all species (32,000) Repeat exercise with growth estimates (ongoing) Repeat exercise with mortality and maturity Estimate intrinisc rate of population increase (the holy grail in biology)

22. Questions?