Flow, Fish and Fishing: Building Spatial Fishing Scenarios Dave Siegel, James Watson, Chris...

Flow, Fish and Fishing: Building Spatial Fishing

Scenarios

Dave Siegel, James Watson, Chris Costello, Crow White, Satoshi Mitarai, Dan Kaffine, Will White, Bruce Kendall, Steve Gaines

UC Santa Barbara

What is F3?

• Flow – how are fish populations connected? – Resource Connectivity

• Fish – heterogeneity of stock growth & recruitment – Dynamic Externality and Spatial Heterogeneity

• Fishing – spatial harvesting, economic objectives, distributional impacts over time – Economic Optimality

Focus on Larval Connectivity

Flow

Fish

Settlement

HabitatRecruitment

Harvest

RegulationFisherm

en

Market INFO

Climate

Flow

Fish

Settlement

Recruitment

Flow

Fish

Settlement

HabitatRecruitment

Harvest

RegulationFisherm

en

INFO

The F3 Approach

• Circulation & Larval Transport – time / space scales of

larval transport & their settlement

• Stock / Harvest Dynamics – implications of uncertainty

on fish stocks, yields & profits

• Fleet Dynamics – How do fishermen choose when,

where & how to fish?

• Value of Information – How does amount & quality of

data available inform the management process?

Constructing Fishery Scenarios

• Build fishing scenarios for SoCal Bight

• Goal: optimal spatial management of a stock given complete information

• Pieces – Domain– Stock demographics– Connectivity – Harvest strategy

• Optimizing it is hard – see the next talk…

Southern California Bight

Southern California Bight

48 patches

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Stock / Harvest Modeling

Next generation stocks =

survivors - harvest + new recruits

SURVIVORS are surviving adults from previous time

HARVEST are those extracted from the fishery

NEW RECRUITS are a function of fecundity of the

survivors, larval dispersal & mortality, settlement &

recruitment to adult stages

Stock = Kelp Bass• Adults are nearly sedentary

– Mature at 3 years

• Settlement, recruitment & survivability– Multi-year analysis of larval settlement & survey

observations by W. White & J. Caselle [in review]– Intra-cohort density dependence on recruitment with a

positive association with kelp density – Annual adult survival = f(adult density)

• Larval connectivity via passive dispersal– Settlement window = 26 to 36 days– Spawning season = May-September– Larvae are found near the sea surface

Kelp Cover Distribution

Multiyear Kelp Cover from Cal F&G

% cover for each patch

Lagrangian Particle Trajectories

Velocity fields from Oey et al. [2003] data assimilation product

Quality good where/when there are data available

Connectivity MatrixS

our

ce P

atc

he

s (j

)

Destination Patches (i)

Self Settlement Line

Hydrodynamic Connectivity only!!!

Catalina Island

Role of Larval Life History

PLD = 18 d PLD = 72 d

• PLD alters connectivity (no one pattern holds)• Shorter PLD’s show more self-settlement

Sou

rce

Pat

ches

(j)

Destination Patches (i) Destination Patches (i)

Interannual Variation in Connectivity

19941998

Sou

rce

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ches

(j)

Destination Patches (i) Destination Patches (i)

• Hydrodynamic connectivity differs year by year in both strength and location

Let’s make some scenarios…• Focus on long term assessment of fishing

yields

– Use long-time mean connectivity & kelp distributions

• Assume a fishing policy

Let TAC = c [Stock] where value of c achieves maximum yield

Spatial allocation by ideal free distribution

Assumes complete knowledge of system

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Biomass for Optimal Yield Case

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x 105

• Highest biomass corresponds to kelp density via recruitment success – though not always

San Miguel Is

Pt Sal

Naples

Biomass Distribution

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Optimal Yield

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San Miguel Is

Spatial Harvest Yield

recruitment

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hydrodynamic connectivity

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Hydrodynamic vs. Realized Connectivity

• Realized connectivity couples hydrodynamics, larval production & habitat factors

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rce

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Destination Patches (i) Destination Patches (i)

Flow Only Includes Production & Habitat

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Closures

Impose Spatial Closures

Close 20% sites semi-randomly

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Optimal Yield with MPA

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x 105Spatial Harvest Yield

• Yield scenarios can be used for impact assessments

Close 20% sites semi-randomly

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with closures

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Harvest Fraction

20% locations closed

no closures

20% locations closed

no closures

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Harvest Fraction

Black – no closures Red – previous caseGreen – 20 random closures

Conclusions• Constructed fishing scenarios for SoCal Bight

Link hydrodynamics & fish biology with management

Couples to the Ocean Observatory Initiative

• Modest spatial closures with management outside often lead to increased fishery yields

Heterogeneity in connectivity & demographics leads to increased productivity & yields

Not optimal economic sol’n

See Chris Costello’s talk next…

Flow, Fish & Fishing Webpage

www.icess.ucsb.edu/~satoshi/f3

Lagrangian Particle Trajectories

Velocity fields from Oey et al. [2003] data assimilation product

Quality good where/when there are data available

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Biomass for Optimal Yield Case with MPA

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x 105Biomass Distribution