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.

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Presentation transcript:

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 1 Lab. Ecologie & Modèle pour l’Halieutique (IFREMER, Nantes, France) 2 Departement d’Economie Maritime (IFREMER, Brest, France) 3 AZTI, Spain… François Bastardie 1, Dominique Pelletier 1, Stéphanie Mahevas 1, Claire Macher 2, Olivier Guyader 2, Olivier Thébaud 2, Northern hake CS staff 3 …

Marine Protected Areas Spatio-temporal effort reduction Mesh size regulations etc. > EFIMAS/COMMIT european projects > Evaluation of current and alternative management scenarios 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) > Need for spatio-temporal multi-stocks and multi-fleets model

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

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 –… 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.)

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

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

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

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 ‘Fisheries library in R’ (FLR): a set of R packages

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

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

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

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

ISIS-FLR: user assistant Editing data

ISIS-FLR: user assistant Create FLR objects from data

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

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)

ISIS-FLR: user assistant running this set of simulations

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

ISIS-FLR: user assistant Drawing data on the region

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

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

fleet model code steps from ‘Isis-Fish’ uniformly distributing the effort by metier over cells of metier zones At each time steps, on each 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

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)

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

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

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

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 F stock from multi-fleet model Uniformly redistributing the abundance on zones At each time step, depending on zones: class change migrations reproduction recrutement t+1t+εt natural & fishing mortality redistribution on zones

Management rules : example for MPA 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 Dynamic effort allocation depending on total or partial zone closure (e.g. a forbidden gear or metier, etc.)

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