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Application to the hake-nephrops fishery in the Bay of Biscay-Celtic Shelf

A Bioeconomic Model to Evaluate Mixed Fisheries Dynamics Under a Range of Policy Options. Application to the hake-nephrops fishery in the Bay of Biscay-Celtic Shelf. François Bastardie 1 , Dominique Pelletier 1 , Stéphanie Mahevas 1 ,

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Application to the hake-nephrops fishery in the Bay of Biscay-Celtic Shelf

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  1. A Bioeconomic Model to Evaluate Mixed Fisheries Dynamics Under a Range of Policy Options Application to the hake-nephrops fishery in the Bay of Biscay-Celtic Shelf François Bastardie1, Dominique Pelletier1, Stéphanie Mahevas1, Claire Macher2, Olivier Guyader2, Olivier Thébaud2, Northern hake CS staff3… 1 Lab. Ecologie & Modèle pour l’Halieutique (IFREMER, Nantes, France) 2 Departement d’Economie Maritime (IFREMER, Brest, France) 3 AZTI, Spain…

  2. > EFIMAS/COMMIT european projects • Develop operational tools for evaluation of management procedure (full feedback) • Based on the FLR framework (i.e. all the steps of the management loop could be modelled using FLR in R) > Evaluationof current and alternative management scenarios • Marine Protected Areas • Spatio-temporal effort reduction • Mesh size regulations • etc. > Need for spatio-temporal multi-stocks and multi-fleets model

  3. Underlying world Operating model Fleet adaptation model Economic simulation model Biological Simulation model Human world / management procedure Implementation model: • Control & enforcement Knowledge production model: • Sampling • Assessing • Advising Management decision model: E.g. Harvest Control Rule

  4. A bioeconomical model for mixed fisheries • Spatially and seasonnally explicit • A grid of cells of ices rectangles • From a yearly to a monthly time step • Dynamic mixed fisheries • Multi-stocks • Multi-fleets displayed several metiers with effort by metier • Revenue computation by fleet by metier • Dynamic management rules • TAC • MPA (total or partial closure) • Gear selectivity • …

  5. A bioeconomical model for mixed fisheries • ‘Metier’ (e.g. FLMetier) a unique arrangement of a target species, a gear and a fishing zone • ‘Fleet’ (e.g. FLSetOfVessels) Sets of vessels sharing physical characteristics and practising possible metiers following strategies. A strategy uses the same sequence of metiers over the year with partition of the fleet activity split up into metiers • ‘Catch’ (e.g. FLCatch) store stock-specific parameters (catchability, etc.) and catches, landings and discards for each metier • ‘FLCostMetier’ & ‘FLCostFleet’ various input economical costs at fleet or at metier level • ‘FLGear’, ‘FLDiscards’, ‘FLMarkets’, ‘FLCost’, etc. store the gear properties (selectivity, standard factor, etc.)

  6. A spatially and seasonnally model for mixed fisheries nephrops zones spatial & seasonnal availability depending on life cycle fishing effort not uniformly distributed hake zones metiers zones interaction technical interactions zones and seasons for management rules

  7. ‘Isis-FLR’ A spatially and seasonnally bioeconomic model for mixed fisheries = ISIS-FISH in FLR

  8. R: a language for statistical computing and graphics > Advantages • Open Source, free and readily downloadable • Existing statistical and graphical capabilities • Available for a range of platforms • Can be extended via add-on packages (>500) > R supports object-oriented programming S4 style of programming enable the construction of composite objects

  9. ‘Fisheries library in R’ (FLR): a set of R packages FLCore: Contains definitions of the base classes and methods such as FLFleet, FLStock, FLBiol, etc. FLEDA: Exploratory Data Analysis FLSQL: Connectivity to SQL databases for FLR objects FLXSA: Extended Survivors Analysis FLAdapt: Adaptive Stock Assessment FLBayes: Bayesian models for S/R and surplus production FLBRP: Biological reference points for FLStocks FLSTF: Short term forecasting for FLStocks FLEcon: Fleet based economic data FLGrowth: Self starting growth models

  10. ISIS-FLR : a data hierarchisation for fishery dynamics FLSetOfVessels FLCostFleet 1 1 “name” 1 FLRevenueFleet “nbvessels” “hoursatsea” “efficiency” “activity” “metiers” 1 FLDiscards 1 1…n FLCostMetier 1 FLMetier 1 FLRevenueMetier 1 “catches” “name” 1…n 1 1 “market” “effortmet” FLMarket FLCatch 1 “gear” “traveltime” 1 “metier” “name” “zoneperseason” FLGear “catch.q” “landings.wt” “f” “catches.wt” “discards.wt” “price” No method attached to classes

  11. FLRevenueFleet FLCostFleet FLCostMetier FLRevenueMetier Additional objects to be linked (economy) Link with fleet link with metier “name” “name” “OCLh” “CREAW.SOCIAL.INSURANCE” “FUELC” “sharecost” “OIR” “OWNER.SOCIAL INSURANE” “BAITC” “LANDING.COST” “INITIAL.CAPITAL.VALUE” “OVEC” “ICEC” “CREWSIZE” “CREW.PREMIUM” “INTC” “CSRATE” “FOODC” “INRATE” “DEPRATE” “lic” “OVAC” FLMarket “name” “name” “name” “vesselshare” “depreciation” “netwage” “alphas” “investment” “grosssurplus” “grossreturn” crewshare “betas” “capital” “fullequityprofit” “netrevenue” netcrewshare “fixed price” “totnetcrewshare” “netprofit” “returntobeshared” “omargin” “totalsurplus” “laborsurplus” “grosswage” “vmargin”

  12. FLGear FLDiscards Additional objects to be linked link with metier “name” “name” “desc” “desc” “stocknames” “technparam” “minsize” “strdfactor” selectivity “minage” equselectivity “ogive”

  13. ISIS-FLR: user assistant Loading existing simus Read data from a database Set up for running a set of simus

  14. ISIS-FLR: user assistant Editing data

  15. ISIS-FLR: user assistant Create FLR objects from data

  16. ISIS-FLR: user assistant Set up rules (e.g. MPA)

  17. ISIS-FLR: user assistant Choose values of changing management rules (e.g. 2 simus: MPA/noMPA but possibly a combination of rules could be used)

  18. ISIS-FLR: user assistant running this set of simulations

  19. ISIS-FLR: user assistant After simulations, plotting various graphs

  20. ISIS-FLR: user assistant Drawing data on the region

  21. ISIS-FLR: user assistant Plotting different simulations on the same graph

  22. fleet model code steps from ‘Isis-Fish’ At each time step, explicit calculation of effort

  23. fleet model code steps from ‘Isis-Fish’ At each time steps, on each metier zones: • uniformly distributing the effort by metier over cells of metier zones • Computing fishing mortality F by metier by species • Computing fishing mortality F by stock to link with biological model • Computing catches, landings & discards by fleet by metier by stock

  24. Economical model code steps Static or dynamic model (i.e. optimization of the activity partition depending on the specific revenue of the metiers) • Computing stock price from markets • Computing revenue from total landings of each metier • adding revenue from other species (i.e. not explicitly simulated)

  25. Economical model code steps Is it the same for spanish fleets??

  26. Other metiers, other species • adding revenue from other species (i.e. not explicitly simulated) • adding revenue from other non-described metiers of the fleet

  27. Dynamic allocation of effort • Static effort partition into metiers according to an activity calendar from data analysis (see TECTAC report) • Dynamic allocation at each time step using: A Random Utility Model If ‘i’ a particular choice (e.g. a metier), with x1, x2 explicative variables (pastVPUE, travelTime, etc.) and beta1, beta2, etc. parameters assessed using likelihood maximization on trip data

  28. migrations redistribution on zones reproduction t t+ε t+1 class change natural & fishing mortality recrutement Biological model code steps • Computing step to step class change from Bertalanffy’s relationship • Applying migration coefficients between zones • If reproduction period, computing egg production • Computing egg mortality • If recruitment period, compute new recruited • Applying fishing mortality Fstock from multi-fleet model • Uniformly redistributing the abundance on zones At each time step, depending on zones:

  29. Management rules : example for MPA Dynamic effort allocation depending on total or partial zone closure (e.g. a forbidden gear or metier, etc.) If MPA is enabled, feedback on fleet effort (fishermen’s response): • The activity of the possible metier is reallocated to the remaining cells of the metier zone • If all metier cells are closed for a given metier, the activity of the concerned metier is reallocated to other possible metiers of the fleet • If all metier cells are closed for all possible metiers, the activity of the concerned fleet is set to 0

  30. Possible outputs and graphics • ‘Raw’ ouputs from one or a set of simulations Time serie abundances per stock per species per age per zone Catches, landings, discards per fleet per metier per age per zone Effort, fishing mortality per fleet per metier per zone Effort per stock zones Fish price per age Revenues variables per fleet per metier per zones: owner margin, vessel margin, grosssurplus, etc. • Indices for helping diagnostic Ratio between catches for a given simu and catches from a simu of reference Essayer de decrire l’interet de chacun

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