1 / 19

WHERE IS F3 IN MODELING LARVAL DISPERSAL?

Satoshi Mitarai, David Siegel University of California, Santa Barbara, CA Kraig Winters Scripps Institution of Oceanography, La Jolla, CA. Flow, Fish & Fishing A Biocomplexity Project. WHERE IS F3 IN MODELING LARVAL DISPERSAL?. GOAL OF THIS WORK.

honora
Download Presentation

WHERE IS F3 IN MODELING LARVAL DISPERSAL?

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Satoshi Mitarai, David Siegel University of California, Santa Barbara, CA Kraig Winters Scripps Institution of Oceanography, La Jolla, CA Flow, Fish & Fishing A Biocomplexity Project WHERE IS F3 IN MODELING LARVAL DISPERSAL?

  2. GOAL OF THIS WORK • Assess fundamental mechanism of larval dispersal & dispersal kernel in California Current system • Using “idealized” simulations • Develop modeling to establish dispersal kernels from available data sets

  3. Dispersal kernel (or connectivity matrix) MATHEMATICALLY,... ❸ Fraction of larvae that recruit to adult ❶ # of larvae released at a source location y ❷ Fraction of larvae transported from source y to destination x

  4. Self settlement DIFFUSION MODELS • Do not account for regional differences • Valid for long term dispersal

  5. MARKOV CHAIN MODELING(SIEGEL ET AL, 2003) • F3 requires seasonal dispersal kernels • Larval releases ~ 90 days • Decorrelation of larval dispersal ~ 3 days • 30 independent larval release Short term kernel (or Markov chain model) Long term kernel (or diffusion model)

  6. IDEALIZED SIMULATIONS • Based on ROMS (regional ocean model system) • Solves fundamental fluid dynamics equations, given initial & boundary conditions • Initial & boundary conditions are specified using observation data • For strong and weak upwelling cases • “Idealized” = statistically stationary & homogeneous in alongshore direction

  7. SIMULATION FIELDS • Strong upwelling case (summer northern California)

  8. Simulation field (mean over 180 days) CalCoFI data (July, Line #70) • Shows good agreement with CalCOFI seasonal mean VALIDATION OF SIMULATION:MEAN TEMPERATURE

  9. Time scale Length scale Diffusivity (zonal/merid) (zonal/merid) (zonal/merid) Simulation data 2.7/2.9 days 29/31 km 4.0/4.3 x107 cm2/s Surface drifter data (Swenson & Niiler) 2.9/3.5 days 32/38 km 4.3/4.5 x107 cm2/s VALIDATION OF SIMULATION:LAGRANGIAN STATISTICS • Show good agreement with surface drifter data

  10. LARVAL DISPERSAL IN SIMULATIONS • Modeled after typical rocky reef fish • Nearshore habitat = waters shallower than 150 m • Larvae are released daily for 90 days, uniformly distributed in habitat (1280 each day) • Tracked as Lagrangian particles • Settle when they are in habitat within competency time window of 20 to 40 days

  11. Larval dispersal Sea level (stream line) Red dots: settling larvae Larvae are released every day for 90 days, uniformly distributed in habitat (1280 each day) LARVAL DISPERSAL

  12. Larval transport Sea level (stream line) Let us observe where settlers to this subpopulation come from ONLY THE LARVAE THAT SETTLE

  13. ARRIVAL DIAGRAM

  14. Simulation Diffusion Model Mean 130 km Self settlement Self settlement • Dispersal kernel is heterogeneous • Large spatial structures of “hot spots” exist DISPERSAL KERNEL

  15. Year t+1 Year t Self settlement Self settlement THE NEXT GENERATION OF SETTLERS • Results suggest that dispersal kernel is stochastic

  16. CONCLUSION • Simulated results suggest... • Larval settlement is episodic • Dispersal kernels are stochastic & heterogeneous even in homogeneous environment • Large spatial structures of hot spots exist • This results will have important consequences for predicting coastal fish stock variations

  17. NEXT STEPS FOR IDEALIZED SIMULATIONS • Investigate weak upwelling case • Assess the role of topography • Coastline may create consistent “hot spots” • Assess the role of larval behavior • Vertical migration may be important • Will behavior change kernel spatial structures? • Or, just change mortality?

  18. NEXT STEP FOR MODELING DISPERSAL KERNEL • Modify Markov chain model • To account for spatial structures of hot spots • How to obtain necessary information for Markov chain model from available data sets? • Should we simulate Channel Islands?

  19. MARKOV CHAIN MODELING:APPLICATION FOR COMPLEX COAST LINE Dave’s Catalina Island Dispersal Kernel 5000 independent larval release from each cite Reasonable?

More Related