Cloudy with a chance of sardines: forecasting sardine distributions using regional climate models

摘要

Despite the significant advances in making monthly or seasonal forecasts of weather, ocean hypoxia, harmful algal blooms and marine pathogens, few such forecasting efforts have extended to the ecology of upper trophic level marine species. Here, we test our ability to use short-term (up to 9months) predictions of ocean conditions to create a novel forecast of the spatial distribution of Pacific sardine, Sardinops sagax. Predictions of ocean conditions are derived using the output from the Climate Forecast System (CFS) model downscaled through the Regional Ocean Modeling System (ROMS). Using generalized additive models (GAMs), we estimated significant relationships between sardine presence in a test year (2009) and salinity and temperature. The model, fitted to 2009 data, had a moderate skill [area under the curve (AUC)=0.67] in predicting 2009 sardine distributions, 5-8months in advance. Preliminary tests indicate that the model also had the skill to predict sardine presence in August 2013 (AUC=0.85) and August 2014 (AUC=0.96), 4-5months in advance. The approach could be used to provide fishery managers with an early warning of distributional shifts of this species, which migrates from the U.S.-Mexico border to as far north as British Columbia, Canada, in summers with warm water and other favorable ocean conditions. We expect seasonal and monthly forecasts of ocean conditions to be broadly useful for predicting spatial distributions of other pelagic and midwater species.