fishing for profit, not fish: an economic assessment of marine reserve effects on fisheries crow...

FISHING FOR PROFIT, NOT FISH: AN ECONOMIC

ASSESSMENT OF MARINE RESERVE EFFECTS ON

FISHERIES

Crow White, Bruce Kendall, Dave Siegel, and Chris Costello University of California – Santa Barbara

Compared to traditional (open access) management…

…reserves maintain yields:

▪ Hastings and Botsford 1999

…reserves enhance yield:

▪ Gerber et al. 2003 (a review)

▪ Neubert 2003

▪ Gaylord et al. 2005

θ = 5

θ = 0

Cost of catching one fish

= Density of fish at that location

θ

θ = 5

θ = 0

Bottom line for fishermen:

Profit = Revenue - cost



θ

θ = 20

θ = 0





θ

PROFIT = Revenue - Cost

Initial fish density

Final fish density

This year’s harvest at location x

integrate


Final fish density



Incorporating Density Dependence

Post-dispersal: )Hc(Ao

tx

tx

txeRR

sy'all

txyx

ty

ty

tx

tx

tx

tx

1tx R)FLKH(A)HM(AHAA

Larva settlement and/or recruitment success increases with decreasing adult population density at that location.

For coastal fish species:

Myers & Cadigan 1993

Botsford & Hobbs 1995

Carr et al. 1995

Caley et al. 1996

Fokvord 1997

Hixon & Webster 2002

Webster 2003

Skajaa et al. In Prep.

Including stock effect does not influence optimal reserve design

Reserve design that maximizes population density in fishery area:

Maximizes harvest

Minimizes cost of fishing, thereby maximizing profit

0 500 1000 15000

10

20

30

40

50Optimal Reserve Spacing

Distance between reserve centers [km]

Mea

n H

arv

est

Den

sity

[#

fis

h/k

m]

Dd = 100 kmDd = 200 kmDd = 300 km

Reserve = 50% of the coastline

Including stock effect does influence optimal fisheries management

With increasing stock effect severity…

Reserves less effective at increasing profit beyond that attainable under traditional management

Higher escapement and less emphasis on reserves more appropriate for maximizing profit

Max yield without reserves

Max profit without reserves

M = 0.05 (dash) M = 0.1 (solid) P = 1, 2, 3

Cost of fishing: Low Cost of fishing: Moderate

Cost of fishing: Higher Cost of fishing: Highest

Reserves = 20% of the coastline (M = 0.1, P = 1)

θ = 0 - 20

θ = 5 - 20

20% of the coast dedicated to reserves

35% escapement across all fisheries

θ = 0 - 20

θ = 5 - 20



Reserves = 60% of the coastline


θ% escapement across all fisheries

θ = 0 - 20

θ = 5 - 20

Options for fishery management

• Traditional

• No reserves, & high escapement levels (34 - 47% K) individually set for each fishery

• Moderately difficult to enforce

• Mix

• 20 – 50% coast in reserves, & ~30% escapement across all fisheries

• Equivalent/enhanced profit; moderately difficult to enforce

• Reserve

• 60% coast in reserves, & no regulation of escapement

• Equivalent/much enhanced profit; simple to enforce

University of California – Santa Barbara

National Science Foundation

Coastal Environmental Quality Initiative

The Canon National Parks Science Scholars Program

THANK YOU!!

FISHING FOR PROFIT, NOT FISH: AN ECONOMIC

ASSESSMENT OF MARINE RESERVE EFFECTS ON

FISHERIES

Crow White, Bruce Kendall, Dave Siegel, and Chris Costello University of California – Santa Barbara

Compared to traditional (open access) management…

…reserves maintain yields:

▪ Hastings and Botsford 1999

…reserves enhance yield:

▪ Gerber et al. 2003 (a review)

▪ Neubert 2003

▪ Gaylord et al. 2005

θ = 5

θ = 0



θ

θ = 5

θ = 0





θ

θ = 20

θ = 0





θ

No Fishin

g

For coastal fish species:

Myers & Cadigan 1993

Botsford & Hobbs 1995

Carr et al. 1995

Caley et al. 1996

Fokvord 1997

Hixon & Webster 2002

Webster 2003

Skajaa et al. In Prep.

sy'all

txyx

ty

ty

tx

tx

tx

tx


An integro-difference model describing coastal fish population dynamics:

Adult abundance at location x during time-step t+1

Number of adults

harvested

Natural mortality of adults that

escaped being harvested

Fecundity

Larval survival

Larval dispersal (Gaussian)(Siegel et al. 2003)

Larval recruitment at x

Number of larvae that successfully recruit to location x

Incorporating Density Dependence

Post-dispersal: )Hc(Ao

tx

tx

txeRR

sy'all

txyx

ty

ty

tx

tx

tx

tx


Larva settlement and/or recruitment success increases with decreasing adult population density at that location.

Mean lifespan = 1 / Mortality rate

Meta-analysis of 124 nearshore Pacific fishery species (Cailliet 2000):

Most nearshore fishery species live 10++ years

M < 0.1 for most species

Fish Mortality and Lifespan

Atlantic cod: 20+ yrs CA sheephead 20+ yrs Cabezon 17 yrs

http://www.larvalbase.org/

P =

Density independent fish replacement rate per generation: P = F*L

Meta-analysis of

700 fish (Myers et al.

1999)

Mean larval dispersal distance

To maximize profits, should reserves be…

…few and large,

What is the optimal reserve design?

…or many and small?

SLOSS debate



Final fish density


integrate


Final fish density



FEW LARGE RESERVES

SEVERAL SMALL RESERVES

Scale bar = 100 km

0 500 1000 15000

10

20

30

40

50Optimal Reserve Spacing

Distance between reserve centers [km]

Mea

n H

arv

est

Den

sity

[#

fis

h/k

m]

Dd = 100 kmDd = 200 kmDd = 300 km

Reserve = 50% of the coastline

Max yield without reserves

M = 0.1 (solid) P = 1

M = 0.05 (dash) M = 0.1 (solid) P = 1

M = 0.05 (dash) M = 0.1 (solid) P = 1, 2, 3

Cost of fishing: Low Cost of fishing: Moderate

Cost of fishing: Higher Cost of fishing: Highest

12 reserves constituting ~20% of the C.I. coastline

MPA process along the central CA coast.

Deliberating dedicating ~20% of the coast to a network of ~30 reserves(Pending approval by CDFG and Gov. Szchchweschcwchchcnggchcerrrr)

Georges Bank and two nearby reserves constitute

~20-30% of regional groundfish habitat (Murawski 2000)

Reserves = 20% of the coastline (M = 0.1, P = 1)

θ = 0 - 20

θ = 5 - 20




Cost = θ/density (Stop fishing when cost = $1)



Escapement = % of virgin K (K = 100)



Escapement = % of virgin K (K = 100)

Zero-profit escapement level = θ/K = 20%


θ/K = 15/100 = 15%


θ/K = 10/100 = 10%

10%


θ/K = 5/100 = 5%

10%

Reserves = 60% of the coastline


θ% escapement across all fisheries

θ = 0 - 20

θ = 5 - 20

Summary 1. Profit is bottom line for fishermen and fisheries.

2. Fishery yield and profit maximized via…

A small proportion of the coastline in reserves

…A variety of reserve spacing options.

A large proportion of the coastline in reserves

…Several small or few medium-sized reserves.

Summary 4. Reserves effects on fishery profit:

▪ Cost of fishing low/moderate: Increases profit

▪ Cost of fishing exorbitant: Maintains profit




5. Near-maximum profits are maintained across a spectrum of reserve and harvest scenarios:ReservesNone Many

EscapementHigh Low





EscapementHigh Low6. 20-50% coast in reserves and ~30% escapement





EscapementHigh Low6. 20-50% coast in reserves and ~30% escapement

7. 60% coast in reserves and θ% escapement

sy'all

txyx

ty

ty

tx

tx

tx

tx


An integro-difference model describing coastal fish population dynamics:

Adult abundance at location x during time-step t+1

Number of adults

harvested

Natural mortality of adults that

escaped being harvested

Fecundity

Larval survival

Larval dispersal (Gaussian)(Siegel et al. 2003)

Larval recruitment at x

Number of larvae that successfully recruit to location x

Smooth, Gaussian larval dispersal kernel

Based on MCMC particle simulations in a 2-D field characterized by flow velocities obtained from buoys and drifters along the central CA coast.

Siegel et al. 2003

Stochastic larval dispersal kernel

Heterogeneous dispersal pattern due to stochastic ocean flow field dynamics

Siegel et al. 2003

Delivery of larval “packet”

SeaWiFS - NASA

California Current

San Francisco

Santa Barbara

Los Angeles

With fish abundance

Nishimoto & Washburn (2002)

• Elwood Beach

SETTLEMENT TIME SERIES

160 180 200 220 240 260 280 300 3200

10

20

30

40

50

60

70

JD 2001

# se

ttle

rs/d

eplo

ymen

tEllwood Invert Setttlment Time Series

Mytilus Clams (excl razor & HIAARC) any marine snail (excl. veligers, limp)Limpet species Snail veliger any seastar Hiatella arctica

Data Courtesy - PISCO [UCSB]

A STIRRED OCEAN

• Larval dispersal is stochastic driven by turbulent “eddies”– Not smooth diffusion processes

• Makes recruitment and stock distribution predictions challenging

Estimate stochastic dispersal patterns: BIO-PHYSICAL SIMULATIONS

• Simulate coastal circulation processes in California current system– Using ROMS (Rutgers)

– Driven by real data• Buoys and transects

• Release & track “larvae”• Obtain connectivity Mean wind

Mean offshore current at surface due to Coriolis Force

- Creates upwelling

Estimate stochastic dispersal patterns: BIO-PHYSICAL SIMULATIONS

TARGET AREA:• Central California

– Relatively straight coast

– Wind is dominant• Equatorward

Mean windMean offshore current at surface due to Coriolis Force

- Creates upwelling

IDEALIZED SIMULATIONTop view

Alongshore pressure gradient obtained from observation data

Poleward geostrophic force applied as an

external force

Stochastic wind stressestimated from observation data

Predominantly southward

Side viewPeriodic

PeriodicC

oast

= W

all

Ope

n Poleward

SIMULATION VALIDATION: MEAN TEMPERATURE

Simulation

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

CalCOFI seasonal mean

°C °C

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

ADD LARVAE

• Release many (105) Lagrangian particles as model larvae

• Pattern after rocky reef fish– Larvae are released daily for 90 days, uniformly

distributed in habitat – Settlement competency window = day 20-40 – Nearshore habitat = waters within 10 km from

the coast

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

ALL larvaeRed dots = settling larvae

DEPARTURE, ARRIVAL& CONNECTIVITY

• Connectivity is inherently heterogeneous• No bathymetric or coastline variability necessary

x

'x

nxxK ',

THREE MORE REALIZATIONS

• Different realizations -> different connectivity

THREE MORE REALIZATIONS

…

• Connectivity is not diffusive

DISPERSAL KERNELSample dispersal kernel

(from a 10-km subpopulation)Ensemble averaged

(& normalized)

Gaussian fit

Stochastic settlement patterns will be most pronounced for species with…

• Short and periodic spawning seasons

• Late-stage settlement competency windows

• Little/no active swimming behaviors

Population Dynamics Implications of Stochastic Dispersal Kernel

• Habitat connectivity on annual scales is uncertain (“flow-induced uncertainty”)– Hotspots shift from year to year

– Habitat connectivity is heterogeneous & intermittent & NOT diffusive

• Even if coastline and bathymetry is smooth

– Spatial variance in hotspots likely reduced by inclusion of variable bathymetry and coastal contours

• Modeling implications– Must account for massive, local recruitment events

• Add larvae-on-larvae density dependence to represent competition for limited settlement niches at a site

• Even with perfect knowledge of current stock distribution & productivity– Recruitment and future stock predictions uncertain

• Difficult to assess appropriate escapement level– High potential for over-exploitation at recruitment-failure location

• Reliance on reserve management more practical– Network of reserves provide many potential sources

• Consequences of over-exploitation reduced due to reserves

– Fisheries can track locate and exploit “hot spots”• Fishermen can act in real time (managers have to forecast)

Fisheries Management Implications of Stochastic Dispersal Kernel

Flow, Fish, and Fishing (F3) Biocomplexity Project

Dave SiegelPhysical Oceanographer

Kraig WintersPhysical Oceanographer

Bob WarnerPopulation biologist

Steve PolaskyEnvironmental economist

Bruce KendallTheoretical Ecologist

Ray HilbornFishery Scientist

Chris CostelloEnvironmental economist

Steve GainesPopulation ecologist

Satoshi MitaraiOceanography

Post-Doc

University of California – Santa Barbara

National Science Foundation

Coastal Environmental Quality Initiative

The Canon National Parks Science Scholars Program

THANK YOU!!

SIDE VIEW ALL LARVAE

Red dots = settling larvae

Effects of age/stage structure on fish population and fishery dynamics

Per capita production increases (exponentially) with age.

But K (#fish/km) will decrease…



θ = 0 - 20

θ = 5 - 20

fishing for profit, not fish: an economic assessment of marine reserve effects on fisheries crow...

Documents

profit slide

location slide

coastline slide

highest slide

location x slide

density of fish

revenue cost cost

max profit