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“IDEALIZED” WEST COAST SIMULATIONS
Numerical domain
Boundary conditions
Forcings
Wind stress: modeled as a Gaussian random process
- Statistics (i.e., mean, variance & time scale) determined
for a typical July using NDBC buoy data off Pt. Sur
Alongshore pressure gradient: modeled as a body force
- Determined using dynamic hight differences between
Pt. Conception and Pt. Arena from July mean CalCoFI
data
SOURCE / DESTINATION RELATIONSHIP FOR LARVAL TRANSPORT
Time series of larval settlement
Alongshore travel distance of settlers
• Source/destination relationship for larval transport
– Short PLD larvae
– Long PLD larvae
Stochastic Larval Settlement in Nearshore Marine Ecosystems S. Mitarai1, D.A. Siegel1 and K.B. Winters2
1Institute for Computational Earth System Science, University of California, Santa Barbara, Santa Barbara, CA 93106-3060 2Integrative Oceanography Division, Scripps Institution of Oceanography, La Jolla, CA 92037
ABSTRACT
Key to the predictive understanding of nearshore marine ecosystems is the transport of larvae by ocean circulation processes. Only a very few lucky larvae successfully settle upon suitable habitat and are able to recruit to adult life stages. Methodologies for predicting this source/settlement relationship for larval transport are still primitive and simple diffusive scaling analyses are used for many important applications. Here, we investigate source/settlement relationships of the larval transport using idealized Regional Ocean Model System simulations of time evolving coastal circulations and Lagrangian particles which are released and tracked as models of planktonic larvae. Simulation results are used to construct dispersal kernels which describe the source/destination relationships of larval transport. These dispersal kernels are strong functions of several time scales including the planktonic larval duration, the frequency and duration of larval release events and inherent coastal circulation time scales. For typical applications (such as fish stock assessment), larval dispersal is far from a simple diffusive process and consideration of the stochastic nature of larval dispersal is required. This work provides new insights into the persistence and spatial structure of nearshore fish stock abundances.
SIMULATION FIELD AND VALIDATION
Instantaneous field: sea level & surface velocity
Mean temperature field: simulation vs. CalCoFI data
PARTICLE TRACKING AND VALIDATION
Sample particle trajectories
Lagrangian statistics
MODELING OF “LARVAL PARTICLES”
Define “larval particles”
Passive particles that move with local currents
Each contains many (e.g., 10,000) larvae – considered as a bolus of
larvae
Larvae settle (stop advecting) when they arrive nearshore
- Nearshore = 2 km from coast
Two species considered
Short PLD larvae: Need to settle within a window of 5 to 10 days
Long PLD larvae: Need to settle within a window of 20 to 40 days
Larval particle release
One release each day for the entire season (90 days)
Releases are made within 64 km from coast (every 4 km)
Near surface
SUMMARY & FUTURE PLANS
Summary
Idealized west coast simulations are presented, in which larval dispersion & settlement is
investigated
Simulations show a reasonable agreement with observation data
Larval settlement is intermittent & heterogeneous when viewed on intergenerational time
scales
The modeling of larval settlement is significantly different from typical diffusion modeling
approaches
Future plans
Develop other flow scenarios (e.g., a Southern California case, winter)
Consider subgrid-scale dispersion and the dispersion of initially-adjacent larvae in
determining the number of independent releases that are modeled
Enable larval particles to have simplified “behaviours” and address its role in larval
dispersion
Construct a simple larval dispersion model for use in fish stock/harvest metapopulation
model
White circle: particle release point
Red circles: particle location 30 days
after particle release
Numbers: particle released date
Blue lines: particle trajectories
Good qualitative agreement with CalCoFI
seasonal mean
● Reasonable agreement with diffusion model
● Does not mean that
source/destination relationships can
be predicted using diffusion models
PLD is time particles have spent in the plankton before settling
Larval particle settlement to a 4 km subpopulation are shown
Larval settlements are intermittent & ranges of PLD are seen
Heterogeneous mapping in simulation
while homogeneous in diffusion model
Some sources produce many
successful settlers while some
produce none
Travel distance (or pattern) differs
depending on source locations
Simulation data
Surface drifter data(Swenson & Niiler,1996)
Time scale Length scale Diffusivity
zonal/meridional zonal/meridional zonal/meridional
Shows vortex structures
Rossby radius of deformation ~ 10 km
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/s
Short PLD larvae Long PLD larvae
Side viewTop view
2-km horizontal resolution
Coast
GOALS FOR THIS STUDY
Understand the role of larval transport in predicting nearshore fish stocks & its proper management
Investigate source/destination relationships for Lagrangian particles which originate & settle in nearshore environments
Use “idealized” realizations of coastal circulation tied to real data
Statistically stationary & homogeneous in the alongshore direction
Use these results to develop simple models of larval dispersal for use in fish stock / harvest models
Periodic
Free-slip
Open BC:
Inflow: nudging
Outflow: radiationWind stress
Pressure
Periodic
Temperaturenudging layer
Simulation(mean over 180 days)
CalCoFI data(Line #70, July)
Results imply that one season (i.e.,
typical time interval for larval production)
is not long enough to achieve
homogeneous dispersion (as diffusion
model does)
http://www.icess.ucsb.edu/~satoshi/f3 Flow, Fish & Fishing – A Biocomplexity Project
Simulation Diffusion model
Simulation Diffusion model