“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