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Modeling Larval Connectivity for the SoCal Bight Satoshi Mitarai, James Watson & David Siegel

Modeling Larval Connectivity for the SoCal Bight Satoshi Mitarai, James Watson & David Siegel Institute for Computational Earth System Science University of California, Santa Barbara Changming Dong & James McWilliams Institute of Geophysical and Planetary Physics

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Modeling Larval Connectivity for the SoCal Bight Satoshi Mitarai, James Watson & David Siegel

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  1. Modeling Larval Connectivity for the SoCal Bight Satoshi Mitarai, James Watson & David Siegel Institute for Computational Earth System Science University of California, Santa Barbara Changming Dong & James McWilliams Institute of Geophysical and Planetary Physics University of California, Los Angeles

  2. Why Larval Connectivity? • Some places are better fishing than others • Habitat type & quality, oceanographic connection to other sites, humankind history, etc. • Larval connectivity measures the probability that sites are connected for a given species • Measures larval source / sink characteristics • Required for spatial demographic stock models • Provides scales of connectivity for MPA network design

  3. Why Model Larval Connectivity? • Knowledge of how larvae are transported from one site to any other on all time scales • Need continuous descriptions of velocity on time scales from hours to decades • Data sets are just getting to this point • Models naturally integrate over all scales

  4. HF Radar Current Observations December 14, 2008 December 14, 2007 HF radar surface currents 0.5 to 6.5 km resolution coverage is getting better www.sccoos.org/data/hfrnet/ December 14, 2006

  5. Talk Outline • Introduce the circulation model (ROMS) configuration & its fidelity to observations • Determine dispersal patterns for the SoCal Bight using Lagrangian techniques • Assess patterns of potential & realized larval connectivity for SoCal Bight

  6. Model Configuration Regional Ocean Modeling System (ROMS) (www.myroms.org) One-way nested grids (~20km  ~6.7km  1km) Surface Forcing from NARR obs (MM5 18km, 6km, 2 km) Open Boundary Forcings from obs (SODA, monthly updates) Initialized with climatology Integration from 1996 to 2003 Integration to present in progress 20km 1 km 6.7km Dong & McWilliams CSR (2007)

  7. Model Domain Pacific Ocean Los Angeles Dong & McWilliams CSR (2007)

  8. Dong et al. [in review]

  9. Dong et al. [in review]

  10. Dong et al. (in review)

  11. Comparison to ADCP Current Profiles Profiles from SBC Red observations Blue model Dashes show stdev Mean currents are well modeled Envelope shows variability is also well modeled Dong et al. (in review)

  12. Interannual Variations of Sea Level Topex/Jason - satellite sea level observations Dong et al. (in review)

  13. Sea level (m) Dong et al. (in review)

  14. Eddy Variability cyclonic anticyclonic Pattern is highly dynamic & contains most of the KE Dong & McWilliams (2007)

  15. Sea level (m) Dong et al. (in review)

  16. Modeling of SoCal Bight Circulation • Good fidelity of mean, seasonal & inter-annual circulation patterns • Mean & fluctuating velocity profiles are also well modeled • Currents are fairly uniform with depth • Eddy driven circulation is large • Can be better… but it is best available data for the circulation of the SoCal Bight

  17. Connectivity Domain Work to follow from Mitarai et al (in review - JGR) & Watson et al (in prep)

  18. Example 30 Day Trajectories Red dots: locations after 30 days Surface following…

  19. -2 [ km ] Dispersal from San Nicolas millions of water parcel releases

  20. -2 [ km ] Dispersal from Other Sites Advection time = 30 days

  21. Seasonal Variability Advection time = 30 days

  22. Interannual Variability Advection time = 30 days

  23. Dispersal in the SoCal Bight • Surface water parcels spread out throughout the Bight within 60 days • Strong position-dependence • Poleward transport along mainland • Retention in SB Channel & around San Clemente Island • Strong seasonal & interannual variability Mitarai, Siegel, Watson, Dong & McWilliams (in review)

  24. Quantifying Connectivity Larval life history is now included (PLD, spawning period, etc.) Quantifies probability of larval transport from site to site Repeat for all source sites 135 x 135 source-to-destination matrix (connectivity matrix) Assumes all sites have equal larval production potential connectivity Watson, Mitarai, Caselle, Siegel, Dong & McWilliams (in prep)

  25. Kelp Bass Potential Connectivity PLD = 25 – 33 d, Spawning = Apr – Nov Connectivity is heterogeneous & asymmetric Strong transport from mainland to islands

  26. Kelp Bass Source / Destination Strength Good Sources: Long Beach, Santa Monica, Ventura Good Destinations: Chinese Harbor, Avalon Assumes uniform larval production PLD = 25-33 d; Spawning from Apr to Nov

  27. The Larval Time Course Matters PLD = 3 – 12 days, all year around PLD = 60 – 120 days, Jan – May Watson et al (in prep)

  28. Kelp Bass Realized Connectivity • Account for non-uniform larval production • Kelp bass egg prod from CRANE obs Source Site Mainland North South (S to N) Islands Islands Mainland North South (S to N) Islands Islands Destination Site Watson et al (in prep)

  29. Larval Connectivity in the SoCal Bight • Connectivity is heterogeneous & asymmetric • Mainland sites are good sources while islands are poor potential sources • Nearby islands are often good destinations • Potential connectivity patterns differ for different larval time histories • Realized connectivity patterns differ due to differences in larval production

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