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Fleet dynamics of the SW Indian Ocean tuna Fishery : a bioeconomic approach C. Chaboud

Fleet dynamics of 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)

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Fleet dynamics of the SW Indian Ocean tuna Fishery : a bioeconomic approach C. Chaboud

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  1. Fleet dynamics of the SW Indian Ocean tuna Fishery : a bioeconomic approach C. Chaboud UMR 212 EME IRD/IFREMER/UM2)

  2. Characteristics of fisheries

  3. 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

  4. 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….

  5. 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 ….)

  6. 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…

  7. 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

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

  9. EEZs « legalboundaries layer » 30 E 125 E 30 N Difficulty : Manycells are shared by differentEEZs 30 S

  10. Access of french seinersfleet to resource

  11. 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).

  12. 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)

  13. 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

  14. 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 Total stock Cellij time t Cellij time t +1

  15. Spillover : computation of exchanges between cells (per esp. And age..) at T Export per cell Is proportional to Numbers and equally Distributed among Adjacent cells T - + - + Net spillover per cell T+1

  16. 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…

  17. 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)

  18. 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’ Attaking 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

  19. 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 cell • The 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

  20. 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.

  21. 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)

  22. Impact of MPA

  23. 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).

  24. Someresults :MPA and effort control scenariosSKJ Purse seine Fishery

  25. Impact onbiomass

  26. Impact on fishermendiscountedrent

  27. Impact on EEZ’sownersdiscountedrent

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