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Spatial assessment of fishing effort around European marine reserves: Implications for successful fisheries management Vanessa Stelzenmuller a,g, Francesc.

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Presentation on theme: "Spatial assessment of fishing effort around European marine reserves: Implications for successful fisheries management Vanessa Stelzenmuller a,g, Francesc."— Presentation transcript:

1 Spatial assessment of fishing effort around European marine reserves: Implications for successful fisheries management Vanessa Stelzenmuller a,g, Francesc Maynou, Guillaume Bernarde, Gwenael Cadiou, Matthew Camilleri, Romain Crec’hriou, Geraldine Criquet, Mark Dimech, Oscar Esparza, Ruth Higgins, Philippe Lenfant b Angel Perez-Ruzafa 沿歐洲海洋保護區周圍對其捕撈努力量之空間評估 : 蘊涵成功之漁業管理 Addresser: 陳安成

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3 — Main goal/ 1.to improve fishing conditions 2. to enhance fishing yields 3. to conserve marine habitats — Based on/ 1.traditional fisheries management has failed 2.sustainable spatial planning and structuring — Social and economic demands/ need to be consistented with ecological functions — Approach/ merging GIS with geostatistics and multivariate statistical techniques The reasons to establish MPA The reasons to establish MPA

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6 2 5 1 3 4 STUDY DOMAIN : 1: Cerbère-Banyuls (France) 2: Cabo de Palos (Spain) 3: Carry-le-Rouet (France) 4: Malta 5: Medes Islands (Spain) 2 Fig. 1. Spain France Malta

7 #1 pattern LOCATION TOTAL/ NO-TAKE AREA(HA) Carry210/85 Medes Island511/93 Banyuls617.4/65 CDPalo s 1,898/27 0 LOCATION TOTAL/ NO-TAKE AREA(HA) MALTA1070,000/_ #2 pattern No-Take Zone Partial take zone FMZ BL FMZ TL

8 Attribute datum Spatial datum Temporal datum The numbers of Boats and effort sampled

9 a Total effort: (Σmean number of days of gear deployment* Σnumber of boats)/year. b Effort sampled: Σnumber of days of gear deployment Gear, Year * Σnumber of boats, Gear Year. C Percent of total effort sampled: effort sampled/(total effort number of sampling years) 100. Table 2 Years of sampling, total number of artisanal fishing vessels, number of vessels sampled, percentage of fleet sampled, the total possible fishing effort per year, the fishing effort sampled, percentage of total fishing effort sampled, the resolution of the summary grids, and the resolution of the prediction grids for the MPAs of Cerbere-Banyuls (Banyuls),Cabo de Palos (CDPalos), Carry-le-Rouet (Carry), Malta, and the Medes Islands (Medes) Table 1 Year of establishment, size, depth range and habitats of the studied MPAs: Cerbere-Banyuls (Banyuls), Cabo de Palos (CDPalos), Carry-le-Rouet (Carry), Malta and the MedesIslands (Medes)

10 HARDWARE: — GPS — BOATS — ARTIFICIAL FISHING GEARS SOFTWARE: — GIS SURFACE-BASED REPRESENTATION MODELS SHAPE MODEL GRID MODEL BOUNDARY GAM GEOSTATICAL MODEL

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12 ATTRIBUTE DATASET TEMPRAL DATASET SPATIAL DATASET GRID MODEL GIS TOPOGRAPHIC SURFACES Fig. m-1 GIS-BASED PROCESS NEB.MODUL DATA BASE

13 — POINT VIEWSHEDS the nearest port oceanic beds rocky reefs reefs coralligeneous sandy bottoms deteritic bottoms mud sea grass beds biomass hot spot hot spot — LINE CONTOURS distance(representing explanatory variable) — POLYGON CONTOURS no-take zone partial take zone — 3D PROFILES depth A GRID SUFACE POINTS VIEWSHE- DS LINE AND POLYGON CONTOUR 3D PROFILE S BANY -ULS CARR -Y MEDE -S ISLAN -D MALT -A CABO - DE Fig. M-1(continue) CLASSIFING GIS-BASED MODEL’S PROCESS

14 NO-TAKE ZONE Port P.Oceanica bed Fine sand bottom Cymodocea Coralligenous Detritic bottom Contaminated Mud River Artificial reefs High biomass bony fish ENVIRONMENT -AL PARAMETERS Dis MPA Dis Cym Dis Mudco Dis Port Dis Cor Dis Pos Dis Det Dis FS Dis Ar Dis River Depth Dis Dsh FISHING GEAR EXPLANATORY VARIABLES

15 Y 1~K = α + β y1 X 1 + β y2 X 2 +… +β yk X k + ξ Where Y 1~K = effort densities(ED) α =thresholds X 1-k =explanatory variables β yk = environment parameters y 1~k = parameter marks ξ=error GETTING A MULTIVARITE STATISTICAL MODEL CONCEPTION

16 [1]disMPA [4]dis-Pos [2]disPort [7]disFS [8]disCym [9]disCor [10]disMudco Cabo de [11]disDet [2]disPort [12]dis Dsh [3]Depth [1]disMPA [3]Depth Banyuls [2]disPort Malta Carry [1]disMPA [5]disAr [3]Depth [2]disPort [4]disPos Medes [2]disPort [1]disMPA [5]disAr [4]disPos [3]Depth [6]disRiver Y Cabode =α + β y1 [1]+β y2 [2]+β y4 [4]+β y7 [7]+β y8 [8] +β y9 [9]+β y10 [10] +β y11 [11]+ξ Y Banyuls = α + β y1 [1]+β y2 [2]+β y3 [3]+ ξ Y Carry = α + β y1 [1]+β y2 [2]+ β y3 [3]+β y4 [4]+β y5 [5]+ ξ Y Medes = α + β y1 [1]+ β y2 [2]+ β y3 [3]+β y4 [4]+β y5 [5]+β y6 [6]+ ξ Y Malta = α + β y1 [2]+β y3 [3]+β y12 [12]+ξ EXPLANATORY VARIABLES WITH 5 MPAS

17 MODELLING PREDICTIVE FORMULA, S ELECTING BY AIC,and COMPUTIMG THOSE TO SHOW EXPLANATORY VARIABLE AND EFFORT DENSITY DATA RESPECTIVELY GRID-BASED LAYER National Taiwan Ocean University /National Taiwan Ocean University /National Taiwan Ocean University Merging layer with respective Marine Protection Area or we say Generating a trend map and present the spatial structure in the National Taiwan Ocean University /National Taiwan Ocean University /National Taiwan Ocean University ab c de Fig. m-2 Y Banyuls = α+β y1 [1]+β y2 [2]+β y3 [3]+ ξ Y Malta = α+β y1 [2]+β y3 [3]+β y12 [12]+ξ Y Carry = α+β y1 [1]+β y2 [2]+β y3 [3]+β y4 [4] +β y5 [5]+ ξ Y Medes = α + β y1 [1]+ β y2 [2]+ β y3 [3]+β y4 [4] +β y5 [5]+β y6 [6]+ ξ Y Cabode =α + β y1 [1]+β y2 [2]+β y4 [4]+β y7 [7] +β y8 [8] +β y9 [9]+β y10 [10] +β y11 [11]+ξ

18 BY CALCULATING THE SEMIVARIANCE BETWEEN DATA POINT BY USING A ORDINARY POINT KRIGING BY USING A WEIGHTED LEAST SQUARES USING THE ROBUST MODULUS ESTIMATOR Fig. m-2(continue)

19 CONTINOUS MAPS OF THE RESIDUALS TREND MAP a map of effort density Produce a continuous maps of effort density At the same time performing Combining Fig. m-3

20 8 small steps in total a semi variogramme Fig. m-5-1 BIG STEP 1 of point kriging (example for depth)

21 and BIG STEP 2 of point kriging (example for depth) Fig. m-5-2 Kriged waterdepth Variance of water depth after kriging 6 small steps in total

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23 Table 3 Final selected effort density (ED) GAMs for: Cerbere-Banyuls, Cabo de Palos, Carry-le-Rouet, Malta bottom longlines (MaltaBL), Malta trammel nets (MaltaTL), and Medes Islands (Medes)  We selected the final GAMs by lowest value,finding model that explained between 38.3% and 78.3% of the overall data variability.  We could identify the variables disMPA and depth as a significant influence on the fishing efffort allocation.  We found the variable disPort,which relates to effort costs to the fishermen.

24 Fig. 2. The fitted spline functions for the predictor variables incorporated in the final GAMs for ED involves: Cerbere-Banyuls (Banyuls), Cabo de Palos (CDPalos), Carry-le-Rouet(Carry), Malta (MaltaBL; bottom longline) and Medes Islands (Medes). Banyuls(1)(2) Malta(2) Cabo de (1) Medes Carry (3)

25 Table 4 Important spatial scales of the MPAs for (1).Cerbere-Banyuls (Banyuls), Cabo de Palos(CDPalos), Carry- le-Rouet (Carry), Malta, and the Medes Islands (Medes) and (2).Threshold values Threshold values were extracted from the FITTED SPLINE FUNCTION ( effort density GAMs),that reflect the range of values were the variables have a positive effect on the effort density estimates.

26 ED dis MPA border ED DEPTH ED dis PORT ED DEPTH THRESHOLD RANGE Banyuls /Cabo de Carry / Malta Banyuls /Medes Island Cabo de / Carry Explanatory Variable We can identify or observe from fig.2

27 Ed Banyuls ED Malta ED Cabo de palos ED Carry ED Medes Fig. 3. Estimated maps of fishing effort density around the MPAs

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29 Significant impact for effort allocation Dis. nearest port Depthth Dis. no-take zone First implication Second implication Second implication Significant impact for effort allocation Maps of fishing effort densities Maps of fishing effort densities Maps of other social pressures  Spillover of biomass getting increased yields.  Trade-off between costs and catch.  The main target species concentrating around the MPA.  Spillover of biomass getting increased yields.  Trade-off between costs and catch.  The main target species concentrating around the MPA. Overlay  Allowing assessment of the potential or spatial conflicts.  Allowing assessment of the potential or spatial conflicts. Significant Impact for Effort Allocation Implication for successful fisheries management

30 Significant impact for effort allocation The last implication The last implication The use of methodology Threshold values  Allowing determination of the spatial scales to the fisheries.  Measuring fisheries benefits.  Allowing determination of the spatial scales to the fisheries.  Measuring fisheries benefits. Fitted spline functions Explanatory variables

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32 Research partners: The fisheries working package of the European Commission Logistical support: a.Institut de Ciencies del Mar (ICM-CSIC),Spain b.UMR 5244 CNRS-EPHE-UPVD, France c. Malta Centre for Fisheries Sciences, Malta d. Grupo de Investigacion Ecologia y Ordenacion de Ecosistemas Marinos Costeros, Spain e. Groupement d’Interet Scientifique (GIS) Posidonie, France f. Universidade dos Acores, PT-9901-862 Horta, Portugal g. CEFAS UK Financial support: a.The European project BIOMEX b.German research foundation (support sponsor, vanessa stezenmuller,only)

33 THE END Thank’ou for listening my presentation, only look forward to instructing and collecting from you.


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