WHERE IS F3 IN MODELING LARVAL DISPERSAL? Satoshi Mitarai, David Siegel University of California,...
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Transcript of WHERE IS F3 IN MODELING LARVAL DISPERSAL? Satoshi Mitarai, David Siegel University of California,...
![Page 1: WHERE IS F3 IN MODELING LARVAL DISPERSAL? Satoshi Mitarai, David Siegel University of California, Santa Barbara, CA Kraig Winters Scripps Institution of.](https://reader035.fdocuments.us/reader035/viewer/2022062515/56649c7b5503460f9492ef47/html5/thumbnails/1.jpg)
WHERE IS F3 IN MODELING LARVAL DISPERSAL?
Satoshi Mitarai, David Siegel University of California, Santa Barbara, CA
Kraig WintersScripps Institution of Oceanography, La Jolla, CA
Flow, Fish & FishingA Biocomplexity Project
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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
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MATHEMATICALLY,...
all y
tx
ty,xy
ty
ty
tx
tx
tx
tx
1tx
R L K) FH(A
)HM (AHAA
❶ # of larvae released at a
source location y
❷ Fraction of larvae transported from
source y to destination x
❸ Fraction of larvae that recruit to adult
Dispersal kernel (or connectivity matrix)
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Self settlement
DIFFUSION MODELS
• Do not account for regional differences• Valid for long term dispersal
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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
Long term kernel (or diffusion model)
Short term kernel(or Markov chain model)
![Page 6: WHERE IS F3 IN MODELING LARVAL DISPERSAL? Satoshi Mitarai, David Siegel University of California, Santa Barbara, CA Kraig Winters Scripps Institution of.](https://reader035.fdocuments.us/reader035/viewer/2022062515/56649c7b5503460f9492ef47/html5/thumbnails/6.jpg)
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
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SIMULATION FIELDS
• Strong upwelling case (summer northern California)
![Page 8: WHERE IS F3 IN MODELING LARVAL DISPERSAL? Satoshi Mitarai, David Siegel University of California, Santa Barbara, CA Kraig Winters Scripps Institution of.](https://reader035.fdocuments.us/reader035/viewer/2022062515/56649c7b5503460f9492ef47/html5/thumbnails/8.jpg)
VALIDATION OF SIMULATION:MEAN TEMPERATURE
Simulation field(mean over 180 days)
CalCoFI data (July, Line #70)
• Shows good agreement with CalCOFI seasonal mean
![Page 9: WHERE IS F3 IN MODELING LARVAL DISPERSAL? Satoshi Mitarai, David Siegel University of California, Santa Barbara, CA Kraig Winters Scripps Institution of.](https://reader035.fdocuments.us/reader035/viewer/2022062515/56649c7b5503460f9492ef47/html5/thumbnails/9.jpg)
VALIDATION OF SIMULATION:LAGRANGIAN STATISTICS
Time scale Length scale Diffusivity
(zonal/merid) (zonal/merid) (zonal/merid)
2.7/2.9 days 29/31 km 4.0/4.3 x107 cm2/s
2.9/3.5 days 32/38 km 4.3/4.5 x107 cm2/sSurface drifter data(Swenson & Niiler)
Simulation data
• Show good agreement with surface drifter data
![Page 10: WHERE IS F3 IN MODELING LARVAL DISPERSAL? Satoshi Mitarai, David Siegel University of California, Santa Barbara, CA Kraig Winters Scripps Institution of.](https://reader035.fdocuments.us/reader035/viewer/2022062515/56649c7b5503460f9492ef47/html5/thumbnails/10.jpg)
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
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LARVAL DISPERSAL
Larval dispersal Sea level (stream line)
Larvae are released every day for 90 days,uniformly distributed in habitat (1280 each day)Red dots: settling larvae
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ONLY THE LARVAE THAT SETTLE
Larval transport Sea level (stream line)
Let us observe where settlers to this subpopulation come from
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ARRIVAL DIAGRAM
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DISPERSAL KERNEL
Simulation Diffusion Model
• Dispersal kernel is heterogeneous• Large spatial structures of “hot spots” exist
Self settlementSelf settlement
Mean
130 km
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THE NEXT GENERATION OF SETTLERS
Year t Year t+1
Self settlementSelf settlement
• Results suggest that dispersal kernel is stochastic
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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
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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?
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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?
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MARKOV CHAIN MODELING:APPLICATION FOR COMPLEX COAST LINE
Dave’s Catalina Island
5000 independent larval release from each cite
Dispersal Kernel
Reasonable?