Spatio‐temporal model for mariculture suitability of Japanese scallop (Mizuhopecten yessoensis) in Funka and Mutsu Bays, Japan *Christopher Mulanda Aura1,2, Sei‐Ichi Saitoh1, Yang Liu1 and Toru Hirawake1 1Laboratory of Marine Environment and Resource Sensing, Graduate School of Fisheries Sciences, Hokkaido University, 3‐1‐1, Minato, Hakodate, Hokkaido, 041‐ 8611, Japan. 2Kenya Marine and Fisheries Research Institute, P.O. Box 81651‐ 80100, Mombasa, Kenya. *Email: [email protected] W1: Identifying critical multiple stressors of North Pacific marine Ecosystems and indicators to assess their impacts Oct. 12th 2012, Hiroshima, Japan Introduction: Global & local Scallop Production Mainly in China and Japan (FAO 2009)! Over 40% of scallop production in Japan is from Aquaculture (FAO, 2009). It is cultivated in Japan because of its good food quality & high productivity (Ito, 1991). Introduction Minimize risk of environmental load Sustainability Solve coastal space demand Site selection Modelling Determine potentiality of coastal firms Determine indicators/stressors Introduction • World aquaculture vulnerable to adverse impacts of natural, socioeconomic, environmental & technological conditions (FAO, 2009). • E.g. Marine cage culture in Chile, Oyster farming in France & Shrimp farming in Mozambique had high mortality-loss of production (FAO, 2009). • Funka & Mutsu are semi-enclosed bays, in same eco- region, similar types of currents, hanging scallop mariculture facing competition from other ventures & in rapid growth. Objective Sustainability: Indicators and Stressors Sensor Social‐Infrastructure Scallop mariculture Constraints (River mouth, Habor, township & Industrial areas) To develop & assess the spatio‐temporal models using coastal ecosystem indicators and stressors for scallop mariculture suitability in Funka & Mutsu Bays’ ecoregion. Materials and Methods Study Area Oyashio cold current Tsugaru warm current Dominant scallop sites ≤60 m delineation Suitability Model ParameterModel Sub-model TypeIndicators/Stressors of Data Resolution SourceFinal of Data model Chlorophyll‐a Satellite 1 km MODIS/Aqua MCE‐Weighted Linear Sea surface 0.40 Combination Sea surface temperature Satellitetemperature1 km MODIS/Aqua (AHP) (Saaty, 0.30 Chlorophyll‐a 1997) Secchi disk depth (*SDD) Satellite 1 km Kd490 (MODIS/Aqua) 0.60 Biophysical 0.20 Secchi disk depth i = ∑ rw)V(x ijj 0.10 Model Bathymetry j Bathymetry Digital 150 m JODC 0.46 Distance to town Social‐infrastructure/constraints Satellite 10 m ALOS AVNIR‐2 2011cal 0.40 Socio‐infra 0.31 Distance to pier ysi oph temporal Bi ‐ a nfr 0.23 Distance to land‐facil o‐i Scallop production Analog/digital Funka = MARINENEToci t S ain str HOKKAIDOCon Website Spatio River mouth Mutsu = Aomori Constraints Industrial Area Prefecture website *SDD =1.04 x K (490)‐0.82 Harbor d Township [Chen et al., 2007.] Consistency Ratio = 0.00 (<0.1 accepted) wj = weight, Σwj = 1, r = attribute transformed to spatial score (1‐8) ij Most preferred alternative is maximum V(xi) value Results and Discussion: Monthly Model Example 2009 monthly model Jan. Feb. Mar. Apr. Suitability scores Land Constraint 1 2 3 4 5 6 7 8 Results and Discussion: Monthly Model Example 2009 monthly model May. Jun. Jul. Aug. Suitability scores Land Constraint 1 2 3 4 5 6 7 8 Results and Discussion: Monthly Model Example 2009 monthly model Sep. Oct. Nov. Dec. Suitability scores Land Constraint 1 2 3 4 5 6 7 8 Biophysical and Socio‐infrastructure Sub‐models 2008 2009 2010 2011 H:\FMy early maps\Funk a2008env .tif H:\FMyearly maps\Funka2011env.tif Suitability scores Land Funka = 53.1% Constraint Mutsu = 55.7% 1 2 3 4 5 6 7 8 Final models and Validation 2008 2009 Funka 2010Mutsu 2011 H:\FMyearly maps\Funka2010.tif ) 100 90 80 70 >6 score >6 ( 60 y 50 40 30 20 10 % Consistenc 0 2008 2009 2010 2011 Model Period with greatest impact on scallop growth? May & Nov. peaks Area (Km2) Funka Mutsu Potential area 1024 856 Constraints 265 250 Scallop growth variation (Apr‐Nov)? 2008 2009 2010 2011 Linear (2008) Linear (2009) Linear (2010) Linear (2011) 7.5 ∆suitability score = ∆growth (Silva et al., 2012) 7.0 2009 6.5 2010 6.0 2008 5.5 R2 = 0.711 2 5.0 2011 R = 0.6811 Funka score R2 = 0.72 4.5 2 R = 0.6265 4.0 4 4.5 5 5.5 6 6.5 Mutsu score Indicator/Stressor/Model performance Indicator/Stressor/Model performance Biophysical indicator: Chlorophyll‐a ≤2 Nezline et al. (2005) Site: F= 3.50, p=0.006 Yearly: F=5.84, p=0.01 Seasonally: F=4.03, p=0.00 Funka Mutsu Biophysical indicator: SDD Site: F=1.54, p=0.04 Yearly: F=3.55, p=0.002 Seasonally: F=3.94, p=0.00 Funka Mutsu Biophysical indicator: SST 7‐13 & Restrictive >20 Sites: F=8.23, p=0.09 Yearly: F=7.49, p=0.07 Seasonally: F=23.22, p=0.003 Funka Mutsu Further validation Funka Bay Consistency = 73% Further validation Mutsu Bay Mutsu scallop mortality 2010 120 100 80 rate y 60 40 M o rtalit 20 0 Hiranai Sotogahama Aomori Yokohama Mutsu Consistency = 71% Sites Conclusions • Funka Bay high score of 7 & Mutsu Bay high score of 6. • Spatio‐temporal variations in suitability scores & indicator/stressor concentrations within & between both bays. • Constraints (stressors) limited scallop potential area. • Elevated SST in August & September (>20°C in both bays) ‐scallop mortality in Mutsu Bay in 2010‐stressor • Chl‐a (>5 mg m‐3 in Funka Bay) in 2010 model ‐ stressor. • Low SDD in high scallop production sites‐a stressor . Conclusions • Model displayed a high degree of reliability due to consistency with existing scallop mariculture. • GIS‐based MCE‐ascertain degree of stressors & indicators in coastal ecosystems & delineation of scallop potential sites‐in Ecosystem Approach to Aquaculture (EAA) & Integrated Coastal Zone Management (ICZM). • Further addition of environmental impact (EC) & currents (velocity) indicators would strengthen the models further. Thank you *Christopher Mulanda Aura1,2, Sei‐Ichi Saitoh1, Yang Liu1 and Toru Hirawake1 1Laboratory of Marine Environment and Resource Sensing, Graduate School of Fisheries Sciences, Hokkaido University, 3‐1‐1, Minato, Hakodate, Hokkaido, 041‐8611, Japan. 2Kenya Marine and Fisheries Research Institute, P.O. Box 81651‐80100, Mombasa, Kenya. *Email: [email protected].