flow, fish and fishing: building spatial fishing scenarios dave siegel, james watson, chris...
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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
Pat
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
0
1
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10x 10
5
San Miguel Is
Spatial Harvest Yield
recruitment
5 10 15 20 25 30 35 40 45
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25
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35
40
45
0
0.005
0.01
0.015
0.02
hydrodynamic connectivity
5 10 15 20 25 30 35 40 45
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0
0.01
0.02
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Hydrodynamic vs. Realized Connectivity
• Realized connectivity couples hydrodynamics, larval production & habitat factors
Sou
rce
Pat
ches
(j)
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
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.40
1
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7x 10
4
Yie
ld
Ho
with closures
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.40
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10x 10
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Abu
ndan
ce
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with closures
Reg
iona
lA
bund
ance
Reg
iona
lY
ield
Harvest Fraction
20% locations closed
no closures
20% locations closed
no closures
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4-2
0
2
4
6
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10x 10
5
Abu
ndan
ce
Ho
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4-1
0
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8x 10
4
Yie
ld
Ho
Reg
iona
lA
bund
ance
Reg
iona
lY
ield
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
-121.5 -121 -120.5 -120 -119.5 -119 -118.5
33.5
34
34.5
35
35.5
36
Biomass for Optimal Yield Case with MPA
2
4
6
8
10
12
14
x 105Biomass Distribution