Improving Stock Assessment through Meta-Analysis Using the RAM Legacy Stock Assessment Database

Thursday, August 21, 2014: 10:50 AM
301A (Centre des congrès de Québec // Québec City Convention Centre)
Olaf Jensen , Institute for Marine and Coastal Studies, Rutgers University, New Brunswick, NJ
Julia K. Baum , Biological Sciences, University of Victoria, Victoria, BC, Canada
Trevor A. Branch , School of Aquatic and Fishery Sciences, University of Washington, Seattle, WA
Ray Hilborn , School of Aquatic and Fishery Sciences, University of Washington, Seattle, WA
Philipp Neubauer , Dragonfly Science, Wellington, New Zealand
James Thorson , Fisheries Research Assessment and Monitoring, NOAA/NMFS/NWFSC, Seattle, WA
Katyana Vert-pre , Fishery and Aquatic Sciences, University of Florida, Gainesville, FL
Meta-analyses of stock assessments can provide novel insight into the population dynamics and ecology of fished species. Controlled manipulation of fish populations in open marine systems is rarely possible, but fisheries data provide a valuable substitute for such manipulations. The RAM Legacy Stock Assessment Database facilitates meta-analysis of commercially exploited marine fishes and invertebrates by providing time series of total biomass, spawner biomass, recruitment, fishing mortality and catch/landings for more than 350 stocks in a common format.  Recent analyses of the database have shown that: (1) productivity of fish stocks is frequently better explained by a series of “productivity regimes” than by simple production models; (2) there is little or no evidence for depensation across the range of stock sizes observed for fish populations in the database; (3) the failure of many fish stocks to recover can generally be explained by insufficient reductions in fishing mortality.  Taken together, these results offer support for the standard (non-depensatory) population models used to predict stock recovery, but also provide a caution about the large uncertainty introduced by a changing marine environment.