investigation of the role of larval behavior in determining nearshore habitat connectivity satoshi...

Investigation of the role of larval behavior in determining nearshore

habitat connectivity

Satoshi Mitarai, David Siegel, Robert WarnerUniversity of California, Santa Barbara, CA

Kraig WintersScripps Institution of Oceanography, La Jolla, CA

Flow, Fish & FishingA Biocomplexity Project

GOAL OF THIS WORK

• Investigate the role of vertical positioning in determining habitat connectivity

Q: Does vertical positioning do this?

Distance from larval source (km)

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Taken from Steneck, Science (2006)

Dispersal kernel

TARGET AREA

• Central California– Wind is dominant

• Mean wind stress– Along the coast– Stronger in summer,

weaker in winter

• Wind is variable– Larval dispersal in

turbulence Mean windMean offshore current at surface

IDEALIZED SIMULATIONTop view

Alongshore pressure gradient obtained from observation data

Stochastic wind stressestimated from observation data

Side view

Periodic

Periodic

Wal

l

Ope

n Poleward

SEA SURFACE TEMPERATURE

Summer Winter

Offshore transport: strong Offshore transport: weak

MODELED LARVAE

● Release many (105) particles as modeled larvae

● Modeled after typical rocky reef fish– Habitat: within 20 km from coast

– Release: one season (90 days)

– Competency window: one month (20 to 40 days)

– Settlement: in habitat during competency

● Passive transport horizontal

VERTICAL POSITIONING

Release location

1) Surface 2) Surface 3) Surface 4) Centered at 30 m5) Centered at 20 m6) Centered at 40 m

Migration location

-> Surface -> Passive transport-> Centered at 30 m -> Centered at 30 m-> Centered at 37.5 m -> Centered at 55 m

Shifts occur 5 days after release (post-flexion)

LARVAL DISPERSAL

Red dots: settling larvae

SummerSurface -> passive

WinterSurface -> passive

DISPERSAL KERNELSample dispersal kernel

(from a 10-km subpopulation)Ensemble averaged

(& normalized)

Gaussian fit

• Non-Gaussian kernel (unless ensemble averaging) is general

(south) (north) (south) (north)

ENSEMBLE-AVERAGED DISPERSAL KERNELS (SUMMER)

-106 ± 61 km -110 ± 63 km -85 ± 67 km -78 ± 69 km -78 ± 66 km -67 ± 68 km

Retention

• Change in dispersal scale is insignificant

• Surface-released larvae can increase retention probability by vertical migration (90%)

(south) (north)

1) Surface -> surface2) Surface -> passive3) Surface -> 30 m4) 30 m -> 30 m5) 20 m -> 37.5 m6) 40 m -> 55 m

ENSEMBLE-AVERAGED DISPERSAL KERNELS (WINTER)

-67 ± 72 km -66 ± 71 km -56 ± 77 km -45 ± 76 km -52 ± 77 km -39 ± 83 km

1) Surface -> surface2) Surface -> passive3) Surface -> 30 m4) 30 m -> 30 m5) 20 m -> 37.5 m6) 40 m -> 55 m

Retention

(south) (north)

• Change in dispersal scale is insignificant

• High retention probability

• Change in retention probability is insignificant

SETTLEMENT RATESSummer

Winter

Settlement increases with migration (72%)

No significant change for non-surface released larvae

No significant change in winter

CONCLUSIONS

● Simulation results suggest that, in Central California, larval vertical positioning– Does not change dispersal scale (not as in

Steneck’s figure)

– Yet, can significantly increase retention if larvae are released near surface in summer

● Dispersal kernel is not smooth Gaussian– Will create uncertainties in fishery management

FUTURE PLANS

● Investigate the role of other behaviors– e.g., swimming toward shore, diel migration,

turbulence avoidance

● Investigate the role of head land– May create consistent connectivity between

particular subpopulations every year

● Investigate stochasticity in dispersal kernel– How behavior affects?

FUTURE PLANS (2)

● Investigate the temperature time series of settlers -> Moose

– (diel variations are not captured, though)

ONLY SETTLERSSummer

Surface -> passiveWinter

Surface -> passive

Red dots: settling larvae

LARVAL DISPERSAL (SIDE VIEW)

SummerSurface -> passive

WinterSurface -> passive

Red dots: settling larvae

ONLY SETTLERS (SIDE VIEW)Summer

Surface -> passiveWinter

Surface -> passive

Red dots: settling larvae

SIMULATION VALIDATION: MEAN TEMPERATURE (SUMMER)

Simulation

• Shows good agreement with CalCOFI seasonal mean (Line 70)

CalCOFI seasonal mean

SIMULATION VALIDATION:MEAN TEMPERATURE (WINTER)

Simulation CalCOFI seasonal mean

• Shows good agreement with CalCOFI seasonal mean (Line 70)

SIMULATION VALIDATION:LAGRANGIAN STATISTICS

Time scale Length scale Diffusivityzonal/meridional zonal/meridional zonal/meridional

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

Data set

• Shows good agreement with surface drifter data

CONNECTIVITY MATRIX (SUMMER)

Surface -> surface 40 m -> 55 m Gaussian

• Connectivity changes with vertical positioning

EXTREME CASE (SUMMER)

-106 ± 61 km -110 ± 63 km 23 ± 106 km -78 ± 69 km -78 ± 66 km -67 ± 68 km

Retention

• Significant change in dispersal scale• Insignificant increase in retention probability(south) (north)

1) Surface -> surface2) Surface -> passive3) Surface -> 200 m4) 30 m -> 30 m5) 20 m -> 37.5 m6) 40 m -> 55 m

HABITAT CONNECTIVITY WILL BE A FUNCTION OF…

● Spawning timing, locations & structures

● Interactions with small scale turbulence

● Mesoscale transport (currents, eddies, waves)

● Larval behaviors

● Larval development, growth rate & mortality

● Complex geometry

Q: what to be included in “realistic” models?

LARVAL DISPERSAL & EDDY

Eddies sweep newly released larvae together into “packets” which stay coherent through much of their pelagic stage

SETTLEMENT IS EPISODIC

Onshore Ekmann transport is not the only process

Larvae settle in infrequent pulses