Ongoing Programs

Good Practices in Stock Asessment Modeling

One ongoing Program at CAPAM is the Good Practices in Stock Assessment Modeling, which includes a number of Projects that address the theories, estimators, and assumptions used in contemporary stock assessment model development, e.g., selectivity, catchability, growth, natural mortality, spawning stock-recruitment relationship, covariates, spatial structure, data/likelihoods weighting, multispecies and ecosystem considerations, and diagnostics.

White Seabass Stock Assessment

CAPAM Staff are conducting a Stock Assessment for White Seabass (Atractoscion nobilis). The white seabass is a nearshore finfish species found in the coastal waters of California and Baja California, and to a much lesser extent, along the coast of Oregon and Washington. The species supports important California recreational and commercial fisheries, which are managed through State regulations. CAPAM staff will use the fisheries stock assessment modeling platform Stock Synthesis (SS) to assess the California population of White Seabass. All available information on population dynamics, movement patterns, life history, age, growth, maturity, fecundity, range, catch history, catch per unit effort (CPUE) time series, electronic tagging data, hatchery data and survey data will be evaluated to determine which are appropriate for inclusion in the assessment. This project is led by Dr. Juan Valero and Lynn Waterhouse. An independent peer-review of the stock assessment work took place in La Jolla during May 2 and 3, 2016. 

Education and Trainning

CAPAM Staff in collaboration with scientists from NOAA and other institutions are organizing short-courses on Fisheries Stock Assessment. One course was completed during December 9-13, 2013 at Scripps Institution of Oceanography, La Jolla CA. Upcoming courses are going to be taught in the University of Miami (January 27-29, 2014), Argentina (February 17-21, 2014) and Chile (March 3-7, 2014). For more information visit our Education and Trainning page.