Fish Base goes Fish Bayes Summarizing all available

Fish. Base goes Fish. Bayes Summarizing all available information for all fishes Rainer Froese 14 th Fish. Base Symposium 2 nd September 2013, Thessaloniki, Greece

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 them or even know about them • „Best available knowledge“ summarizes in a scientifically correct manner all available knowledge, including related knowledge, with indication of uncertainty

A Case in Point: Fish. Base has compiled thousands of studies on growth, maturity, reproduction, diet, etc • How can the information be summarized? • How can new studies be informed? • How can best estimates for species without studies be derived?

Heavy Computing 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 I MSY from Catch Data

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

Excellent Match with Estimates from full Stock Assessments

Available from Fish. Base, 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 II Length Weight Relationships (in press)

Length Weight Relationships

LWR Across All Studies

LWR for Many Studies

LWR for One Study Only

LWR Priors

Fish. Base Online

Estimating LWR for ALL Fishes • Best LWRs are obtained from studies for the species, including relatives with similar body shapes if needed (< 5 LWR) Rank Based on LWR estimates… Species 1 for this Species 410 2 for this Species & Genus-body shape 700 3 for this Species and Subfamily-body shape 1, 419 4 for relatives in Genus-body shape 1, 222 5 for relatives in Subfamily-body shape 18, 410 6 for other species with this body shape 9, 711

Self-Learning Database • When Daniel and Rainer first discussed about Fish. Base, they envisioned an artificial intelligence system • Some years (decades) later, we are getting there: the addition of LWRs for 16 species improved LWR quality for over 400 species

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)

Thank You Questions?