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• W2886868544 abstract "Sea turtle stranding events provide an opportunity to study drivers of mortality, but causes of strandings are poorly understood. A general sea turtle carcass oceanographic drift model was developed to estimate likely mortality locations from coastal sea turtle stranding records. Key model advancements include realistic direct wind forcing on carcasses, temperature driven carcass decomposition and the development of mortality location predictions for individual strandings. We applied this model to 2009–2014 stranding events within the Chesapeake Bay, Virginia. Predicted origin of vessel strike strandings were compared to commercial vessel data, and potential hazardous turtle-vessel interactions were identified in the southeastern Bay and James River. Commercial fishing activity of gear types with known sea turtle interactions were compared to predicted mortality locations for stranded turtles with suggested fisheries-induced mortality. Probable mortality locations for these strandings varied seasonally, with two distinct areas in the southwest and southeast portions of the lower Bay. Spatial overlap was noted between potential mortality locations and gillnet, seine, pot, and pound net fisheries, providing important information for focusing future research on mitigating conflict between sea turtles and human activities. Our ability to quantitatively assess spatial and temporal overlap between sea turtle mortality and human uses of the habitat were hindered by the low resolution of human use datasets, especially those for recreational vessel and commercial fishing gear distributions. This study highlights the importance of addressing these data gaps and provides a meaningful conservation tool that can be applied to stranding data of sea turtles and other marine megafauna worldwide." @default.
• W2886868544 created "2018-08-22" @default.
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• W2886868544 date "2018-10-01" @default.
• W2886868544 modified "2023-10-15" @default.
• W2886868544 title "Likely locations of sea turtle stranding mortality using experimentally-calibrated, time and space-specific drift models" @default.
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• W2886868544 doi "https://doi.org/10.1016/j.biocon.2018.06.029" @default.
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