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

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

MATHEMATICALLY,...

all y

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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)

Self settlement

DIFFUSION MODELS

• Do not account for regional differences• Valid for long term dispersal

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)

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

SIMULATION FIELDS

• Strong upwelling case (summer northern California)

VALIDATION OF SIMULATION:MEAN TEMPERATURE

Simulation field(mean over 180 days)

CalCoFI data (July, Line #70)

• Shows good agreement with CalCOFI seasonal mean

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

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

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

ONLY THE LARVAE THAT SETTLE

Larval transport Sea level (stream line)

Let us observe where settlers to this subpopulation come from

ARRIVAL DIAGRAM

DISPERSAL KERNEL

Simulation Diffusion Model

• Dispersal kernel is heterogeneous• Large spatial structures of “hot spots” exist

Self settlementSelf settlement

Mean

130 km

THE NEXT GENERATION OF SETTLERS

Year t Year t+1

Self settlementSelf settlement

• Results suggest that dispersal kernel is stochastic

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

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?

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?

MARKOV CHAIN MODELING:APPLICATION FOR COMPLEX COAST LINE

Dave’s Catalina Island

5000 independent larval release from each cite

Dispersal Kernel

Reasonable?