Applications of satellite derived meso-scale features and in-situ bycatch to understand sea turtle habitats along the Indian Coast

Sea turtles in Indian waters are impacted by incidental catch in fisheries, coastal development, habitat loss and depredation of eggs. Incidental catch in fishing gear is considered to be one of the major factors responsible for declining population of sea turtles and other marine megafauna. Turtles are particularly vulnerable to entanglement and drowning in gillnets and associated gears, as the rough skin on their head and flippers catches easily on the meshes of these nets. Incidental bycatch of sea turtles at a hooking rate of 0.108 for every 1000 hooks during the exploratory tuna longlining was carried out by the Fishery Survey of India in the Indian Exclusive Economic Zone (EEZ) during 2005– 2008. Maximum hooking rate of 0.302% of 1000 hooks was reported from the Bay of Bengal. It is observed that the biological and physical oceanographic processes are closely coupled at some locations of oceans, indicating an enhanced production where fishery resources accumulate for foraging. Remotely sensed data on chlorophyll-a concentration (CC) and sea-surface temperature (SST) has been used in India for identifying meso-scales features like eddies, fronts, rings for potential fishing zones (PFZs) forecast. In this study, we used CC, SST and sea-surface height anomaly (SSHa) derived from spaceborne data to correlate the reported instances of sea turtle interactions in the tuna longline fishery for identification of areas with high risk of sea turtle bycatch. Figure 1 shows the area of study. Datasets and their sources included in the study are listed in Table 1. Data on the sea turtle bycatch in the experimental longline fishing targeting yellowfin tuna conducted onboard four tuna longline research vessels of Fishery Survey of India namely Yellow fin, Matsya Vrushti, Matsya Drushti and Blue Marlin during 2005–2008 were used to estimate the bycatch rate in long line. Sea turtles were incidentally caught in the long line gear either by entangling in the ganglion and float line or are hooked in the mouth. Abundance index is expressed in terms of hooking rate (HR), the number of turtles caught per 1000 hooks. Weekly IRS-Ocean Colour Monitor images were corrected for atmospheric effects, and CC computation was carried out using OC2 algorithm, then resampled to 4 km for comparison with SST and SSHa images. OSCAR (Ocean Surface Current Analysis–Real time) which is a 5-day modelled product derived using quasi-linear and steady flow momentum equations has also been used for the study. Maximum rates of turtle bycatch in the exploratory long line were observed along the Orissa coast in the Bay of Bengal. The pattern of seasonal variability in satellite derived SSHa, SST and CC and oceanic features was studied in the context with sea turtle incidental catches. Meso-scale oceanographic features within the geographic range covered by turtles were most intense in off Orissa/Andhra coast. Turtle bycatch per 1000 hooks is higher in east coast as compared to the west coast which can be explained by the existence of mass nesting sites along the east coast (Table 2). In terms of oceanographic variables, there is a discernable difference in CC for both coasts. Similar is in the case of SSHa but there is less observed variation in SST values for both coasts. Satellite derived SSHa, SST, CC and wind/current data are important in defining sea turtle habitat. The images derived from multi satellite sensor show the bycatch points along the productive edges of eddy (Figure 2) and frontal features (Figure 3). The bycatch points were found associated with meso-scale oceanographic features observed in satellite derived chlorophyll, SST and SSHa (Figures 2 and 3). OSCAR-generated surface current vectors indicate their locational influence on turtle bycatch. Also, the bycatch locations are along the current direction. In case of Figure 2 bycatch points are distributed around the boundary zone of nutrient-rich cyclonic eddy’s cold core. Species caught in these areas are olive ridley (Lepidochelys olivacea) and green (Chelonia mydas) turtles. Planktons are patchily distributed in the oceans, although certain factors may lead to elevated levels of productivity at meso-scales (tens of kilometers), including the presence of oceanographic features such as rings, eddies and fronts. These features essentially persist for longer durations. Oceanographic conditions may vary in different ocean basins and sometimes meso-scale features may move/swift and dissipate fast. However, the relatively static nature of mesoscale features in oceans suggests that sea turtles and many fishes like tuna, sharks targeting these features for foraging at oceanic features. Meso-scale