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Utilizing Ecosystem Information to Improve Decision Support for Central California Salmon

Utilizing Ecosystem Information to Improve Decision Support for Central California Salmon Project Acronym: S almon A pplied F orecasting, A ssessment and R esearch I nitiative (SAFARI).

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Utilizing Ecosystem Information to Improve Decision Support for Central California Salmon

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  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. Wells2, E. Danner2, W. Sydeman3, Y Chao4, F Chai5, S. Ralston2, J. Field2, D. Foley2, J. Santora3, S. Bograd2, S. Lindley2, and W. Peterson2. 5. 3. 1. 5. 4. 2.

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

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

  4. Lifecycle Adult salmon spawn Adult salmon return Adult salmon return Juveniles emigrate Jacks return = winter Freshwater Marine

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

  6. Quantifying the observed relationships between physics and biology Krill Wind Cape Mendocino Pt. Conception 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.

  7. Quantifying the observed relationships between physics and biology 20 years of trawl catch: Juvenile salmon rear in a plug of T. spinifera located in a relaxed area. T. spinifera Salmon Advection/Upwelling C B A 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.

  8. Quantifying the observed relationships between physics and biology 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

  9. Quantifying and then forecasting R2 = 0.75 With estimates of krill and SLH to Fall we can extend our predictions to the previous cohort. SI = T. Spin GOF + SLHfall SLH Krill Adult salmon spawn Adult salmon return Adult salmon return Juveniles emigrate Jacks return

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

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

  12. Data Model SST Sea level

  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.marine.010908.163917

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

  15. Taking the next step: Modeling the ocean environment (ROMS-COSINE) The modeling approach is capable of reproducing the temporal patterns observed in empirical studies rho = 0.96* R2 = 0.95* 2008 2003 2002 2004 2007 2005 2006 T. spinifiera Modeled meso-zooplankton

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

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

  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

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