Cloudy with a chance of sardines: forecasting sardine distributions using regional climate models ISAAC C. KAPLAN, 1 * GREGORY D. WILLIAMS, 1 NICHOLAS A. BOND, 2,3 ALBERT J. HERMANN 2,3 AND SAMANTHA A. SIEDLECKI 3 1 Conservation Biology Division,NOAA Northwest Fisheries Science Center, 2725 Montlake Blvd E., Seattle, WA 98112, U.S.A. 2 NOAA Pacific Marine Environmental Laboratory, 7600 Sand Point Way NE, Seattle, WA 98115, U.S.A. 3 Joint Institute for the Study of the Atmosphere and Ocean, University of Washington, 3737 Brooklyn Ave NE, Seattle, WA 98105, U.S.A. ABSTRACT Despite the significant advances in making monthly or seasonal forecasts of weather, ocean hypoxia, harmful algal blooms and marine pathogens, few such forecast- ing efforts have extended to the ecology of upper trophic level marine species. Here, we test our ability to use short-term (up to 9 months) predictions of ocean conditions to create a novel forecast of the spa- tial distribution of Pacific sardine, Sardinops sagax. Pre- dictions 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 distribu- tions, 5–8 months in advance. Preliminary tests indi- cate that the model also had the skill to predict sardine presence in August 2013 (AUC = 0.85) and August 2014 (AUC = 0.96), 4–5 months in advance. The approach could be used to provide fishery man- agers 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. Key words: climate forecast system, ecological fore- casting, Pacific sardine, regional ocean modeling sys- tem, Sardinops sagax INTRODUCTION The evolving science of ecological forecasting has emerged as an imperative to anticipate environmental change for human society (Clark et al., 2001). Ecolog- ical forecasts help decision-makers and managers plan for the future, make informed decisions regarding alternative management choices and take appropriate actions to better manage natural resources. Conse- quently, ecological forecasting is considered one of the key science capabilities required to support U.S. coastal ecosystems into the future (Brandt et al., 2006; Murawski and Matlock, 2006). Short-term forecasts of physics, on the scale of days to seasons, are familiar – we are accustomed to forecasts of tomorrow’s weather or the outlook for the next hurricane season. However, to date, in marine systems ecological forecasts at this time scale have been primarily focused on the predic- tion of algal blooms, hypoxia and pathogens (Greene et al., 2009; Stumpf et al., 2009; Ali, 2011). Despite the significant groundwork in forecasting provided by these applications and by weather and climate science, few such forecasting efforts have extended to the ecol- ogy of upper trophic level marine species. One forecasting tool that operates at this seasonal time scale and at the interface of physics and ecology is J-SCOPE: JISAO’s Seasonal Coastal Ocean Prediction of the Ecosystem (http://www.nanoos.org/ products/j-scope/home.php/). J-SCOPE has been developed for the northern California Current System along the west coast of North America to provide pro- jections of physical, chemical and biological ocean properties on 6- to 9-month time horizons. The projec- tions are testable and designed to be relevant to man- agement decisions for fisheries, protected species and the ecosystem. These forecasts are derived using the *Correspondence. e-mail: Isaac.Kaplan@noaa.gov Received 12 September 2014 Revised version accepted 9 October 2015 © 2015 John Wiley & Sons Ltd doi:10.1111/fog.12131 15 FISHERIES OCEANOGRAPHY Fish. Oceanogr. 25:1, 15–27, 2016