Oceanographic Effects on Rockfish Growth and Productivity in the California Current

Climate effects on ecosystem productivity are well established for the California Current, and climate variability is reflected in bottom-up effects on fish growth and productivity.  Stock assessments have increasingly begun to include these important effects in estimating abundance and production.  In turn, critical metrics estimated in stock assessments (growth, spawning biomass, spawning output and recruitment) vary as a function of current environmental conditions.  In this study, we explore the consequences of poor feeding conditions during warm oceanographic regimes (i.e., El Niño or positive PDO) on female bioenergetic allocation patterns, fecundity and growth.  Moderate El Niño conditions persisted off the coast of California during the summer and early winter of 2009, followed by a cooler and more productive La Niña regime during the spring and summer of 2010.  Collections of rockfish over these two years showed a significantly lower condition factor in female chilipepper rockfish during 2009-2010 compared to the 2010-2011 winter reproductive season.  A decrease in fish condition or storage of fatty lipids is hypothesized to adversely affect fish growth and production of eggs, however this relationship is not well understood.  We collected basic demographic data (length, age, weight, maturity), as well as fecundity and muscle tissue samples from multiple rockfish species off the coast of California to evaluate this hypothesis.  In addition to field collections, an ongoing laboratory experiment was initiated to examine mechanistic links between poor feeding conditions in the fall prior to the winter reproductive season and female fecundity, quality of larvae and timing of parturition.  The effects of poor fish condition on production and possible skipped spawning events will be incorporated into existing stock assessment models of West Coast groundfish for which time-varying growth (and potentially fecundity) have been identified as key factors.  These findings will improve future assessments by increasing precision and decreasing uncertainty in the models.