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

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Presentation transcript:

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

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

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

Example: Length Weight Relationships

Example: LWR Across All Studies

Example: LWR for Many Studies

Example: LWR for One Study Only

Example: LWR Priors

Example: FishBase Online

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

Example: FishBase Online

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)

Questions?