Fleet dynamicsof the SW Indian Ocean tuna

Fishery : a bioeconomic approach

C. Chaboud

UMR 212 EME IRD/IFREMER/UM2)

Characteristics of fisheries

Characteristics of resources

• Three main tuna species : skipjack (SKJ), yellowfin tuna (YFT), big eye tuna (BET)

• Migrating and straddling socks (main EEZs : Seychelles, Mauritius, Chagos (UK), Madagascar, four east African countries, Maldives, France …) and international waters

• Seasonal spatial repartition varying among species and age (difference between adults and juveniles ?)

• Most species are long living species

• Sensitivity to climate variability

Characteristics of exploitation

• Natural capital exploited by many fishing methods: purse seines (FAD + free schools), bait boats, longlines and gill nets.

• Differences in costs , impacts on resource components by species or by age (catchability) , in targeted markets and hence in prices.

• Mobility parterns are different : • Purse seins and longlines are very mobile• Bait boats and artisanal units are less mobile

• Different countries or group of countries owning or exploiting tuna resources

• Exogenous forcing drivers : climate, tuna world Market, energy cost, relationships between countries, piracy….

Modeling objectives• Exploitation dynamics representation : resources, activity(fishing effort, fleet dynamics), revenues, costs profits and rents

and their distributions between actors.

• Simulating responses to external forcing drivers (costs, prices, climate)

• Simulating responses to unilateral or collective management measures (licenses, quotas, fees, conditions of fishing agreements, temporal closures, MPAs ….)

Modeling choices• Time step : month• Simulation length : up to 30 years• Age structured model• Three species (SKJ, BET, YFT)• Muti gears

• Purse seiners (PS) with 2 strategies : PS_FS,PS_ FAD)• Long liners • Bait boats (BB)• Artisanal gillnets (GN)

• 13 countries owning and/or exploiting the resource : fra,espa,syc,asie,mdv,gbr,som,mdg,com,moz,tanz, ken,mus, int, other…

Modeling choices• Spatially explicit model with different “layers” showing• legal constraints for access to resources (access to EEZ’s)• Technical constraints for access to resources • Management limits to resources (MPA’s)• Resources• Fleets• exploitation results /• Catch• revenues, costs• profits, rents...current and discounted

The grid : 12 lines x 10 Columns 120 square cells 5° X 5 °

EEZs « legal boundaries layer »

Difficulty :

Many cells are shared by different EEZs

30 E 125 E30 N

30 S

Access of french seiners fleet to resource32,5 37,5 42,5 47,5 52,5 57,5 62,5 67,5 72,5 77,5 82,5 87,5 92,5 97,5 102,5 107,5 112,5 117,5 122,5

c1c2 c3 c4 c5 c6 c7 c8 c9 c10 c11 c12 c13 c14 c15 c16 c17 c18 c19

27,5 l1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

22,5 l2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

17,5 l3 0 0 0 0 0 0,1 0,7 0,7 0 0 0 0,3 0 0 0 0 0 0 0

12,5 l4 0 0 0 0 0 0,3 1 0,9 0 0 0,1 0,7 0,1 0 0 0 0 0 0

7,5 l5 0 0 0 1 1 0,9 1 0,9 0 0 0 0,9 0,5 0 0 0 0 0 0

2,5 l6 0 0 1 1 1 1 1 0,9 0 0,2 0,7 1 0,9 0 0 0 0 0 0

-2,5 l7 0 0 1 1 1 1 1 0,8 0 0,9 1 1 0,9 0,2 0 0 0 0 0

-7,5 l8 0 0 1 1 1 1 1 0,7 0 0,8 1 1 0,9 0,5 0,2 0 0 0 0

-12,5 l9 0 0 1 1 1 1 1 1 0,9 1 1 1 0,7 0 0,6 0,4 0,7 0 0

-17,5 l10 0 1 1 1 1 1 1 1 1 1 1 1 1 0,1 1 1 0,1 0 0

-22,5 l11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

-27,5 l12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Modeling choices : market

Two possibilities:

• Fishery is supposed to be price taker, exogenous prices are defined by species, year classes and fishing gear (realistic if considering an isolated exploitation/management system)

• Inverse demand function P = P(Q) is flexible (response of price to a variation in quantity, realistic if there is some coordination between all Tuna RFMOs for catch control or capacity control).

Modeling choices : costs

• Cost functions specified by fleet ( gear and fishing country)

• Total cost = Fixed Cost + Variable cost (effort, yield, yield value)

• Fixed cost : insurance, depreciation, maintenance fishing license fees…

• Variable costs : • energy, food (function of time at sea)• labor (function of yield value)• royalties (function of catch)

Resource dynamics• Age structured• Catchability defined par species, gear and live stage• Von Bertallanfy growth curves,• Natural mortality can be specified by age• Recruitment : two possibilities :

• No stock recruitment relationship • SSB/R “hockey stick” relationship … what parameters ?

• no species interactions• Spatial monthly repartition per species and life stage (juvenile/adult

for SKJ and BET) is given by a spatial preference matrix• Spillover from high to low abundance cells

Spatial and temporal resource behavior

At every time step resource Na (stock per species in number per age a ) less catch Ca and natural mortality Ma , is redistributed according to a spatial preference matrix SPaij, defined per month, species and life stage , .

R: recrut. C : catch M: natural mortality S : net spillover

taijN

taijC

taijF taijM

ij tija

tija

tija

tija SPMCN ]).[(

1

11

tijaN

Cell ij time t

Total stock

tijttij SPRN *0

1ij

aijSP

1taS

taS

Cell ij time t +1

Spillover : computation of exchanges between cells (per esp. And age..) at T

Export per cellIs proportional to Numbers and equallyDistributed amongAdjacent cells

+

+

-

-

T+1

Net spillover percell

T

Spatial and temporal resource behavior

After the computation on the resource dynamics in number The biomass is obtained (for a species, a cell i,j and at age a) :

The value of biomass is now computed, given an price vector by age (for a species and a gear) :

Biomass values is used as input in the economic module of the model. Biomass value is different for the different gear, because they don’t target the same markets…

atij

atij

a wNB .

ta

tija

tija pBBV .

Temporal Fleets behavior

• The number of boats per fishing fleet (defined par a type of gear for a given country) can follow two types of time behavior.

• Exogenous defined (fixed or varying during the simulation)

• Endogenous defined : entry/exit behavior at the beginning of each year y (every 12 time steps), depending from past year fleet cumulated profit (Smith model, 1968) :

Endogenous fleet dynamics may be constrained by some fleet number upper bounds (licences per fishing country or per ZEE)

)*( 11 yyy NboatNboat

Spatial Fleet behavior : a free ideal distribution approach

• The total number of boats per fishing fleet is redistributed over the space grid at every time step

• The spatial distribution for a fleet in time t is obtained by an ‘attractor matrix’ At taking in account some past characteristics of each cell, the possibility of legal and technical access to that cell. Different choices are possible :• Biomass value per gear of the cell in t-1 and t-12• Revenue per boat (per cell) per cell in t-1 and t-12• Total catch per boat (per gear) per cell in t-1 and t-12.• Profit per boat (per per cell in t-1 and t-12• Information may be perfect or myopic

Spatial Fleet behavior : a two step application of a gravity model

• 1. Choose a harbor (Mahé, Diego, Mauritius, Malé ..)

The attraction of each harbor, for a fleet, is proportional to At /D where D is a distance matrix (for each port). The probability to choose a port is equal to is attraction divided by the sum of the attraction of all harbors. The number of “possible” ports is defined for each fleet (Diego-Suarez and Mahé for the French purse seiners)

• 2. Choose a cellThe probability for a boat of a given fleet form a a given port is

proportional to At /D

• Two step process 1) choo• • Information may be perfect or myopic • Past characteristics of the cells used to compute

attractiveness :• Revenue per boat (per gear/country) per cell in t-1 and t-

12• Total catch per boat (per gear) per cell in t-1 and t-12.• Profit per boat (per per cell in t-1 and t-12

Spatial temporal Fleets behavior : Particularity of the purse seine fishery

At each time step, the purse seine fleets are divided into two strategic components ‘Purse seines FAD’ and ‘Purse seines Free Schools’ according their relative economic results in t-1 and t-12. The variation of the total number of purse seines of one fleet at the beginning of year y is obtained by adding their respective economic cumulated results (profit) over the past year.

Control variables(defined before simulation)• Initial fleet numbers (with possibility of effort multiplier

varying during simulation)• Maximum number of licensed boats during simulation• Fees and royalties• MPA location (one or several grid cells)• Control of access by resource owners• Quotas , total or per ZEE (to be developed)

Impact of MPAcol 32,5 37,5 42,5 47,5 52,5 57,5 62,5 67,5 72,5 77,5 82,5 87,5 92,5 97,5102,5107,5112,5117,5122,5

LI 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

1 27,5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

2 22,5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

3 17,5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

4 12,5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

5 7,5 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0

6 2,5 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0

7 -2,5 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0

8 -7,5 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0

9 -12,5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

10 -17,5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

11 -22,5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

12 -27,5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Model outputs• Biomass per species, cell, age, EEZ (in volume and value).• Fleets number and spatial distribution• Catches per species, age, fleet, cell,fishing country, EEZ , in

volume and value• Private profit per fleet or country current and discounted

(NPV).• Earnings for resource owners countries • Economic rent for states (private profit + net state incomes)

current and discounted (NPV).

Some results :MPA and effort control scenarios

SKJ Purse seine Fishery

Scénario N° Amp X f

1 No 1

2 No 0.7

3 Permanent 1

4 Permanent 0.7

5 Temporary 10 years periods with and without MPA

1

6 Temporary 10 years periods with and without MPA

0.7

Impact on biomass

0 50 100 150 200 250 300 350 4000

500

1000

1500

2000

2500

3000

3500

NO MPA xF1NO MPA xF.7PERM MPA xf1TEMP MPA xF1TEMP MPA Xf .7PERM MP1 Xf .7

Impact on fishermen discounted rent

0 50 100 150 200 250 300 350 400

-200

-100

0

100

200

300

400

500

NO MPA xF1NO MPA xF.7PERM MPA xf1PERM MP1 Xf .7TEMP MPA xF1TEMP MPA Xf .7

Impact on EEZ’s owners discounted rent

0 50 100 150 200 250 300 350 4000

5

10

15

20

25

30

35

40

45

50

NO MPA xF1NO MPA xF.7PERM MPA xf1PERM MP1 Xf .7TEMP MPA xF1TEMP MPA Xf .7