Transcript FishBase goes FishBayes R, JAGS and Bayesian Statistics

FishBase goes FishBayes
New Approaches toward
Best Available Knowledge
Rainer Froese
iMarine Workshop, 15 May 2013
DG Connect, Brussels, Belgium
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)
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
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?
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
Example how to get new information:
MSY from Catch Data
Catch-MSY in a Nutshell
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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
Excellent Match with Estimates from full Stock Assessments
Available from FishBase, but runs for several minutes
Challenge: General Scientific Method
to Summarize Widely Different Information
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: 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
(after about 5 minutes...)
Example: FishBase Online
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
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)
Questions?