Spatial assessment of fishing effort around European marine reserves:

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 SYMPOSIUM(1)

2. INTROUCTION

3. Main goal/ 1.to improve fishing conditions • 2. to enhance fishing yields • 3. to conserve marine habitats The reasons to establish MPA • 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

4. MATERIAL and METHODS

5. Materials

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

7. Mpa’s Patterns Partial take zone Partial take zone Partial take zone Partial take zone FMZBL No-Take Zone FMZTL #1 pattern #2 pattern

8. Data Collection Spatial datum Attribute datum IMAGE about total/ no take zone Temporal datum the area of total/ no take zone IMAG E about habitats Years of sampling between 2000 and 2005 depth range IMAGE about depth IMAGE about bottom Type of bottom IMAGE about boat sampled The numbers of Boats and effort sampled IMAGE about fishing gear deployment Position of fishing gear deployment

9. 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) 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) 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.

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

11. Methods

12. GIS GRID MODEL INVOKE SUPERIMPOSING , REGULATE PREDICTION GRID & CALCULATED EXPLANATORY VARIABLE FOR EACH CELL NEB.MODUL ASSIGN SPATIAL DATASET GIS-BASED PROCESS TEMPRAL DATASET ATTRIBUTE DATASET DATA BASE TOPOGRAPHIC SURFACES SELECT & OPERATION Fig. m-1

13. GIS-BASED MODEL’S PROCESS • POINT VIEWSHEDS • the nearestport • 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 CLASSIFING LINE AND POLYGON CONTOUR POINTS VIEWSHE-DS 3D PROFILES BANY-ULS CABO- DE CARR-Y MALT-A MEDE-S ISLAN-D Fig. M-1(continue)

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

15. GETTING A MULTIVARITE STATISTICAL MODEL CONCEPTION Y1~K= α + β y1•X1+ β y2•X2+…+βyk•Xk+ ξ Where Y1~K= effort densities(ED) α =thresholds X 1-k=explanatory variables β yk= environment parameters y1~k= parameter marks ξ=error

16. [1]disMPA [1]disMPA [2]disPort [5]disAr Banyuls Malta Carry [12]dis Dsh [2]disPort [3]Depth YCabode =α + β y1•[1]+β y2•[2]+β y4•[4]+β y7•[7]+β y8•[8] +β y9•[9]+β y10•[10] +βy11•[11]+ξ [4]disPos [3]Depth YMedes= α + β y1•[1]+ β y2•[2]+ β y3•[3]+β y4•[4]+β y5•[5]+β y6•[6]+ ξ EXPLANATORY VARIABLES WITH 5 MPAS YCarry = α + β y1•[1]+β y2•[2]+ β y3•[3]+β y4•[4]+β y5•[5]+ ξ [1]disMPA [3]Depth [2]disPort [1]disMPA YBanyuls = α + β y1•[1]+β y2•[2]+β y3•[3]+ ξ [11]disDet [2]disPort YMalta= α + β y1•[2]+β y3•[3]+β y12•[12]+ξ [6]disRiver [2]disPort [10]disMudco Medes Cabo de [4]dis-Pos [5]disAr [3]Depth [9]disCor [7]disFS [4]disPos [8]disCym

17. Fig. m-2 YCabode =α + β y1•[1]+β y2•[2]+β y4•[4]+βy7•[7] +β y8•[8] +β y9•[9]+β y10•[10] +βy11•[11]+ξ National Taiwan Ocean University /National Taiwan Ocean University /National Taiwan Ocean University CORRECTING THE ESTIMATES OF EFFORT DENSITY to THE SPATIAL STRUCTURE WITHIN THE RESIDUALS YMedes= α + β y1•[1]+ β y2•[2]+ β y3•[3]+βy4•[4] +β y5•[5]+β y6•[6]+ ξ Merging layer with respective Marine Protection Area or we say YCarry = α+β y1•[1]+β y2•[2]+β y3•[3]+βy4•[4] +β y5•[5]+ ξ Generating a trend map and present the spatial structure in the YBanyuls = α+β y1•[1]+β y2•[2]+β y3•[3]+ ξ YMalta= α+β y1•[2]+β y3•[3]+β y12•[12]+ξ MODELLINGPREDICTIVE FORMULA, SELECTING BY AIC ,and COMPUTIMG THOSE TO SHOW EXPLANATORY VARIABLE AND EFFORT DENSITY DATA RESPECTIVELY CONTINOUS MAPS OF THE RESIDOALS National Taiwan Ocean University /National Taiwan Ocean University /National Taiwan Ocean University GRID-BASED LAYER TREND MAP a b d e c

18. Fig. m-2(continue) USING THE ROBUST MODULUS ESTIMATOR COMPUTED SEMIVARIOGRAM BY CALCULATING THE SEMIVARIANCE BETWEEN DATA POINT OUTLINE THE SPATIAL CORRELATION DATA CORRECTING THE ESTIMATES OF EFFORT DENSITY to THE SPATIAL STRUCTURE WITHIN THE RESIDUALS BY USING A WEIGHTED LEAST SQUARES FITTED PARAMETERS OF SPHERICAL MODELS BY USING A ORDINARY POINT KRIGING CONTINOUS MAPS OF THE RESIDUALS TREND MAP

19. Fig. m-3 • CONTINOUS MAPS OF • THE RESIDUALS • TREND MAP Combining • regression kriging performing Produce a continuous maps of effort density • A map of effort density • a map of effort density • Yielding higher accuracy to the nearest linear distance or port At the same time

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

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

22. RESULTS

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. Carry (2) Banyuls(1) (3) (3) Malta(2) Cabo de (1) (3) Medes (3) 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).

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

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

28. DISCUSSION

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

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

31. ACKNOWLEDGEMENTS

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.