1. FishBase goes FishBayes New Approaches toward Best Available Knowledge Rainer Froese iMarine Workshop, 15 May 2013 DG Connect, Brussels, Belgium

2. A Human Dream: Encyclopedias „… to collect knowledge distributed around the globe…to set forth … to the men with whom we live, and transmit it to those who will come after us.. so that the work of preceding centuries will not become useless..and so that our offspring, becoming better instructed, will at the same time become more virtuous and happy...” Diderot (1751)

3. A New Challenge: Knowledge Explosion In most fields, the number of annually published studies far exceeds the ability of the specialists to absorb or even read them „Best available knowledge“ summarizes in a scientifically correct manner all available knowledge, including related knowledge, with margins of uncertainty

4. A Case in Point: 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?

5. MCMC to the Rescue Assemble all relevant facts, with probability distributions Establish their correlations, with probability distributions Select suitable models to explain data and predict key parameters Let the computer test all possible combinations and select those with highest overall probability

6. Example how to get new information: MSY from Catch Data

7. Catch-MSY in a Nutshell Get reliable catch data Select a population growth model Try all possible parameter combinations Select those that, given the catches, do neither crash the stock nor overshoot carrying capacity of the ecosystem

8. Excellent Match with Estimates from full Stock Assessments

9. Available from FishBase, but runs for several minutes

10. Challenge: General Scientific Method to Summarize Widely Different Information

12. 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

13. Example: Length Weight Relationships

15. Example: LWR Across All Studies

16. Example: LWR for Many Studies

17. Example: LWR for One Study Only

18. Example: LWR Priors

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

20. Example: FishBase Online

21. Estimating LWR for ALL Fishes With help from iMarine, computing time for 32,000 species was cut down from over 10 days to less than two days This routine needs to be run every two months There is still room for improvment

22. Next Steps Repeat exercise with growth estimates (ongoing) Repeat exercise with mortality and maturity Estimate intrinisc rate of population increase (the holy grail in biology)

23. Questions?