Detection of pelagic habitats and abundance of skipjack tuna in relation to the environment in the Indian Ocean around Sri Lanka - Elepathage,Thushani Suleka Madhubha; Tang,Danling (PeerJ Preprints, 2019)
Using remote sensing data of sea surface temperature (SST), chlorophyll-a (Chl-a) together with catch data, the pelagic hotspots of Skipjack tuna (SKPJ) were identified. MODIS/Aqua satellite data and the fish catch data were obtained during 2002-2016 period. Empirical cumulative distribution frequency (ECDF) model of satellite-based oceanographic data in relation to skipjack fishing was used for the initial statistical analysis and the results showed that key pelagic habitat corresponded mainly with the 0.4 – 0.7 mg m-3 Chl-a concentration. Chl-a represents the phytoplankton that attracts the food items of SKPJ like zooplankton and nekton The favorable SST range for SKPJ is 26 - 27 0C which provides suitable thermocline and an optimum level of upwelling to circulate nutrients needed for the primary production. The high total catches and CPUEs were found within the months of September to December and the optimum levels of Chl-a, SST also were observed in similar months. Hence, the South-West monsoon season was identified as the best and peak season of SKPJ fisheries. SST and Chl-a are important indicators to detect the habitats of SKPJ and the maps prepared can be used in the future to cost-effectively and efficiently identify and demarcate the biological conservation regions or fisheries zones of SKPJ. According to GAM the 0.3 - 0.6 mg m-3 Chl-a, 28 - 28.5 0C SST in Western and 0.25 - 0.3 mg m-3 Chl-a and 28.5 - 28.80C SST in Eastern were found as highly correlated predictor variables value ranges with SKPJ abundance. The deviances explained in above areas in GAM were 90.8% and 61.4% respectively. The GAM was considered as a robustly dealing method with nonlinear relationships and it can be used to model the fish catch abundance with influencing variables significantly since it could predict the CPUE values greater than 90% similarly to nominal CPUEs in both subregions of the study area.
Keywords: Chlorophyll-a; Sea Surface Temperature; Bay of Bengal; Indian Ocean; Fisheries; Catch per unit effort; Phytoplankton; Generalized Additive Model