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

6. 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

12. Example: FishBase Online

13. Example: FishBase Online

14. Example: FishBase Online

15. Example: FishBase Online

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

17. Example: FishBase Online

18. Example: FishBase Online

19. Example: FishBase Online

20. 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?