1. Utilizing Ecosystem Information to Improve Decision Support for Central California Salmon Project Acronym: Salmon Applied Forecasting, Assessment and Research Initiative (SAFARI) Chavez, F. 1, B.K. Wells 2, E. Danner 2, W. Sydeman 3, Y Chao 4, F Chai 5, S. Ralston 2, J. Field 2, D. Foley 2, J. Santora 3, S. Bograd 2, S. Lindley 2, and W. Peterson 2. 3. 4. 5. 1. 2. 5.

2. Approach Develop strong theoretical basis for forecasting using in situ and satellite data Develop strong theoretical basis for forecasting using in situ and satellite data Develop forecasts using in situ and satellite data Develop forecasts using in situ and satellite data Develop 20 year model hindcast and test theory Develop 20 year model hindcast and test theory Develop 9 month model forecasts Develop 9 month model forecasts Incorporate into salmon decision support system Incorporate into salmon decision support system

3. Recent declines in the fishery implicate the ocean and instigated our interest in this work. Background

4. = winter Lifecycle Adult salmon returnAdult salmon spawnJuveniles emigrateAdult salmon return Jacks return Freshwater Marine

5. San Francisco Monterey Bay Environmental conditions Wells, B.K., J. Field, J. Thayer, C. Grimes, S. Bograd, W. Sydeman, F. Schwing, and R. Hewitt. 2008 Untangling the relationships among climate, prey, and top predators in an ocean ecosystem. Marine Ecology Progress Series. 364:15-29 Developing conceptual models between physics and biology

6. Cape Mendocino Pt. Conception Wind Krill Santora, J.A., W.J. Sydeman, I.D. Schroeder, B.K. Wells, J.C. Field. 2011. Mesoscale structure and oceanographic determinants of krill hotspots in the California Current: Implications for trophic transfer and conservation. Progress in Oceanography. Quantifying the observed relationships between physics and biology

7. A B C Wells, B.K., J.A. Santora, J.C. Field, R.B. MacFarlane, B.B. Marinovic, and W.J. Sydeman. In review. An ecosystem perspective for quantifying the dynamics of juvenile Chinook salmon (Oncorhynchus tshawyscha) and prey in the central California coastal region. Marine Ecology Progress Series. 20 years of trawl catch: Juvenile salmon rear in a plug of T. spinifera located in a relaxed area. Quantifying the observed relationships between physics and biology Advection/Upwelling T. spinifera Salmon

8. Santora, J.A., W.J. Sydeman, I.D. Schroeder, B.K. Wells, J.C. Field. 2011. Mesoscale structure and oceanographic determinants of krill hotspots in the California Current: Implications for trophic transfer and conservation. Progress in Oceanography. http://www.sciencedirect.com/science/article/pii/S0079661111000371 Quantifying the observed relationships between physics and biology

9. Krill SLH With estimates of krill and SLH to Fall we can extend our predictions to the previous cohort. Adult salmon returnAdult salmon spawnJuveniles emigrateAdult salmon return Jacks return SI = T. Spin GOF + SLHfall Quantifying and then forecasting R 2 = 0.75

10. Krill SLH With estimates of krill and SLH to Fall we can extend our predictions to the previous cohort. Adult salmon returnAdult salmon spawnJuveniles emigrateAdult salmon return Jacks return SI = T. Spin GOF + SLHfall Quantifying and then forecasting

11. Physical model run over entire Pacific at 12.5 km resolution Taking the next step: Modeling the ocean environment (ROMS-COSINE) To play 80M.mpg movie click hereclick here.

12. Model Data Sea levelSST

13. Chavez, F. P. M. Messié, and J.T. Pennington (2011) Marine primary production in relation to climate variability and change. Annual Review of Marine Science, 3:227–60, doi:10.1146/annurev.m arine.010908.163917

14. The modeling approach is capable of reproducing the zooplankton climatology demonstrated in empirical studies Modeled zooplankton Observed krill Taking the next step: Modeling the ocean environment (ROMS-COSINE)

15. rho = 0.96* R 2 = 0.95* 2007 2006 2005 2004 20032002 2008 Modeled meso-zooplankton T. spinifiera The modeling approach is capable of reproducing the temporal patterns observed in empirical studies Taking the next step: Modeling the ocean environment (ROMS-COSINE)

16. Nowcasts of Krill SLH forecasted There is potential for management improvement Adult salmon returnAdult salmon spawnJuveniles emigrate Adult salmon return Jacks return Harvest rule Taking the next step: Modeling the ocean environment (ROMS-COSINE) SI = T. Spin GOF + SLHfall 2009

17. 2003 2005 2007 2009 2011 2013 Salmon against climate index 4 years previous

18. DSS, Management, Challenges Regular presentations to salmon working group for Pacific Fisheries Management Council, next October 2012 – requires continual presence Developments incorporated into NOAA’s Integrated Ecosystem Assessment of the CCLME, a decision-support system that uses diverse data and ecosystem models to forecast future conditions How to make models operational