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Predicting suitable habitat for the european lobster, by Ibon Galparsoro, EURO-BASIN Training
Predicting suitable habitat for the european lobster, by Ibon Galparsoro, EURO-BASIN Training
Predicting suitable habitat for the european lobster, by Ibon Galparsoro, EURO-BASIN Training
Predicting suitable habitat for the european lobster, by Ibon Galparsoro, EURO-BASIN Training
Predicting suitable habitat for the european lobster, by Ibon Galparsoro, EURO-BASIN Training
Predicting suitable habitat for the european lobster, by Ibon Galparsoro, EURO-BASIN Training
Predicting suitable habitat for the european lobster, by Ibon Galparsoro, EURO-BASIN Training
Predicting suitable habitat for the european lobster, by Ibon Galparsoro, EURO-BASIN Training
Predicting suitable habitat for the european lobster, by Ibon Galparsoro, EURO-BASIN Training
Predicting suitable habitat for the european lobster, by Ibon Galparsoro, EURO-BASIN Training
Predicting suitable habitat for the european lobster, by Ibon Galparsoro, EURO-BASIN Training
Predicting suitable habitat for the european lobster, by Ibon Galparsoro, EURO-BASIN Training
Predicting suitable habitat for the european lobster, by Ibon Galparsoro, EURO-BASIN Training
Predicting suitable habitat for the european lobster, by Ibon Galparsoro, EURO-BASIN Training
Predicting suitable habitat for the european lobster, by Ibon Galparsoro, EURO-BASIN Training
Predicting suitable habitat for the european lobster, by Ibon Galparsoro, EURO-BASIN Training
Predicting suitable habitat for the european lobster, by Ibon Galparsoro, EURO-BASIN Training
Predicting suitable habitat for the european lobster, by Ibon Galparsoro, EURO-BASIN Training
Predicting suitable habitat for the european lobster, by Ibon Galparsoro, EURO-BASIN Training
Predicting suitable habitat for the european lobster, by Ibon Galparsoro, EURO-BASIN Training
Predicting suitable habitat for the european lobster, by Ibon Galparsoro, EURO-BASIN Training
Predicting suitable habitat for the european lobster, by Ibon Galparsoro, EURO-BASIN Training
Predicting suitable habitat for the european lobster, by Ibon Galparsoro, EURO-BASIN Training
Predicting suitable habitat for the european lobster, by Ibon Galparsoro, EURO-BASIN Training
Predicting suitable habitat for the european lobster, by Ibon Galparsoro, EURO-BASIN Training
Predicting suitable habitat for the european lobster, by Ibon Galparsoro, EURO-BASIN Training
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Predicting suitable habitat for the european lobster, by Ibon Galparsoro, EURO-BASIN Training

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Key lecture for the EURO-BASIN Training Workshop on Introduction to Statistical Modelling for Habitat Model Development, 26-28 Oct, AZTI-Tecnalia, Pasaia, Spain (www.euro-basin.eu)

Key lecture for the EURO-BASIN Training Workshop on Introduction to Statistical Modelling for Habitat Model Development, 26-28 Oct, AZTI-Tecnalia, Pasaia, Spain (www.euro-basin.eu)

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  • 1. EURO-BASIN Training Workshop on Introduction to statistical modelling tools, for habitat models development Predicting suitable habitat for the European lobster (Homarus gammarus), on the Basque continental shelf (Bay of Biscay), using Ecological-Niche Factor Analysis Ibon Galparsoro AZTI-Tecnalia; Marine Research Division igalparsoro@azti.esPasaia 126-28 October 2011 EURO-BASIN, www.euro-basin.eu Introduction to Statistical Modelling Tools for Habitat Models Development, 26-28th Oct 2011
  • 2. Background In the Basque Country, a marine habitat mapping programme started in 2004Determine habitat suitability for some key speciesAlthough this fishery is limited, its socio-economic importance in some ports isvery highHowever, there is a lack of information on the H. gammarus fishery and on theofficial registration of catches, leading to an underestimate of the population sizeThis makes it difficult to understand the stock and its management to maintain asustainable fishery. © AZTI-Tecnalia 2
  • 3. Objetives!  (i) the identification of seafloor morphological characteristics, together with wave energy conditions, that determine the presence of European lobster (Homarus gammarus);!  (ii) to habitat suitability model for the lobster, using ENFA. © AZTI-Tecnalia 3
  • 4. Study area and lobster sampling estrategy 7th June and 10th August, 2007 Total of 17 lobster pot lines were laid Each line was 650 m long, including 60 pots The initial, middle (or bearing change) and final positions Pots were deployed in the afternoon and recovered in the morning © AZTI-Tecnalia 4
  • 5. Multibeam echosounder dataSeaBat 7125 and SeaBat 8125 MBES1 m resolution seafloor DEM Seafloor morphologic feature extraction multiscale analysis (15mX15m; 45mX45m; 135mX135m) Bathymetry Slope Aspect Curvature (planimetric and profile) Benthic Positon Index (Broad and Fine Scale) Rugosity Distance to rock © AZTI-Tecnalia 5
  • 6. Wave flux over the seafloor Most representative wave characteristics were obtained from databases Coastal hydrodynamic numerical modelling software (SMC) Waves were propagated up to the coast Mean wave flux, per metre of fetch over the first metre above the seafloor was calculated © AZTI-Tecnalia 6
  • 7. Ecological-Niche Factor Analysis and habitat suitability map production The ENFA approach (Hirzel et al., (2002)) computes suitability functions by comparing the species distribution in the eco-geographical variables space, with that of the whole set of cells It does not require ‘absence data’ Marginality (M) represents the ecological distanceFrequency Global 〈 m − mS 〉 Species M= G between the species optimum and the mean habitat 1.96δ G within the reference area σG Specialisation (S) is defined as the ratio of the ∂ σS S= G standard deviation of the global distribution ( ∂G ), to µG ∂S µS Altitude that of the focal species (∂S ) Multi-scale analysis © AZTI-Tecnalia 7
  • 8. Results 92 lobsters were caught, in 17 pot line deployments (average= 5.3) The pot were located on the lowest part of a steep slope, at the boundary with the sandy bottom© AZTI-Tecnalia 8
  • 9. ResultsScale (pixel) Marginality Specialisation 3x3 0.983 2.418 Best results were obtained the 9x9 1.196 2.138 maximum resolution analysis 27x27 1.514 2.261 Multiscale 1.861 1.618 The cross-validation of the model quality, predicted to expected ratio for the overall curve, resulted in a Boyce Index of 0.98 ± 0.06 © AZTI-Tecnalia 9
  • 10. Results Environmental Overall area Presence areas variables Standard Standard Maximum Minimum Mean Deviation Maximum Minimum Mean Deviation Euclidean distance to rock (m) 3950 0 597 243 158 0 30 44 Broad sacale Benthic Position Index 28 -17 0.5 2.71 9 -7 -1.1 2.9 Slope (º) 65 0 3 3.94 44 0 6 6 Wave flux (kWhm-1) 12 0 0.2 0.37 0.63 0.09 0.3 0.09 Bathymetry (m, below Chart Datum) -88 -1 -47 19.6 -47 -30 -37 4.14These results indicate:1.  Lobster habitat differs considerably from the mean environmental conditions over the study area2.  It is restrictive in the range of conditions in which it dwells © AZTI-Tecnalia 10
  • 11. Results© AZTI-Tecnalia 11
  • 12. DiscusionResults are comparable to those obtained for other lobster species in terms ofthe seafloor morphological characteristics that best explain the presence of thelobster.Wilson et al., 2007, identified multi-scale ENFA approach as providing betterresults than the one-scale analysis.This observation suggests that bottom topography is importantSpecial care should be taken in the representativeness of the lobster samplingFuture work will focus upon the realisation of specific surveys, with randomsampling, in order to quantify statistically the reliability of the lobsterdistribution model. © AZTI-Tecnalia 12
  • 13. This study was funded by the Basque government:Department of Environment and Regional PlanningDepartment of Agriculture, Fishing and Alimentation © AZTI-Tecnalia 13
  • 14. Predicting suitable habitat for Zostera noltii inthe Oka estuary (Basque Country) and itsmodification under mean sea-level rise scenario Mireia Valle, Ángel Borja, Ibon Galparsoro, Joxe M. Garmendia and Guillem Chust © AZTI-Tecnalia 14
  • 15. INTRODUCTION Zostera noltii Hornem., 1832: Widely distributed within the intertidal zones of the northeast Atlantic Cantabrian Sea © AZTI-Tecnalia 15Vermaat et al., 1993; Phillippart et al.; 1995; Auby and Labourg, 1996; Laborda et al., 1997; Milchakova et al., 1999; Pérez Llorens, 2004
  • 16. INTRODUCTION Habitats Directive (92/43/EEC)Water Framework Directive (2000/60/EC) Fitoplancton Macroalgas Bentos Factores fisico-químicos (agua) Garmendia et al., 2008 Peces © AZTI-Tecnalia 16
  • 17. INTRODUCTION Global Warming Mean Sea-Level Rise 60 St. Jean de Luz +49 cm49 cm at the end of 21st 40 Santander Bilbao Sea level rise (cm) Century SRES A2 + MinMelt SRES A1B + MaxMelt +29 cm 20(Chust et al., 2010 ECSS 87:113-124) 0 -20 1940 1960 1980 2000 2020 2040 2060 2080 2100 © AZTI-Tecnalia Year 17
  • 18. OBJECTIVES1.  Determine the main environmental variables explaining Zostera noltii distribution, within the Oka estuary 2.  Evaluate the modification of the present suitable habitats under the mentioned sea-level rise scenario © AZTI-Tecnalia 18
  • 19. MATERIAL AND METHODS Marginality (0-1)Ecological NicheFactor Analysis Specialization BioMapper (Hirzel et al. 2002) Distribution of focal species Distribution of any EGV © AZTI-Tecnalia 19
  • 20. MATERIAL AND METHODS Ecogeographical variables HabitatSediment characteristics Suitability Map Ocean currents Ecological LiDAR derived Niche topographic height Factor Analysis Presence data © AZTI-Tecnalia 20
  • 21. RESULTS•  Marginality 1.004: Z. noltii’s habitat differs from Main EGV determining the mean environmental species presence: conditions over the study area 1.  Mean grain size•  Specialization 6.209: 2.  Redox potencial restrictive in the range of 3.  Sediment selection conditions which it dwells. Narrow ecological niche 4.  Slope 5.  Velocity of flood tide•  Cross-Validation 0.95 ± 0.15 6.  % of gravel Topographic characteristic high importance © AZTI-Tecnalia 25
  • 22. RESULTSActual HSM SLR Scenario HSM Habitat Suitability: 0-33 à 33-67 à 67-100à © AZTI-Tecnalia 26
  • 23. RESULTSSurface percentage modification for Habitat Suitability (HS) areas: Present SLR scenario17.52% 6.84% HS>50 HS>50 HS<50 HS<50 82.48% 93.16% © AZTI-Tecnalia 27
  • 24. DISCUSION AND PERPECTIVES•  Applicability of the method à van der Heide et al., 2009; Fonseca and Kenoworthy, 1987; Cabaço et al. 2009•  Rising sea level may adversely impact Z. noltii meadows. HS under the SLR scenario show the vulnerability of this species, which highlights the importance of the recovery tasks in the remainders estuaries where the species is not present. © AZTI-Tecnalia 28
  • 25. FUTURE PERSPECTIVES•  Validation of the model à Bidasoa and Lea estuaries à improvement of the accuracy of the model.•  SLR scenario à take into account changes in current patterns à erode seagrass beds and create new areas for seagrass colonization à increase the suitable areas for focal species. © AZTI-Tecnalia 29
  • 26. This research has been supported by: Thank you very much for your attention! Merci beaucoup! © AZTI-Tecnalia 30EURO-BASIN, www.euro-basin.eu Introduction to Statistical Modelling Tools for Habitat Models Development, 26-28th Oct 2011

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