Likely locations of sea turtle stranding mortality using experimentally-calibrated, time and space-specific drift models. (October 2018)

• Record Type: Journal Article
• Authors: Santos, Bianca S.; Friedrichs, Marjorie A.M.; Rose, Sarah A.; Barco, Susan G.; Kaplan, David M.
• Is Part Of: Biological conservation. Volume 226(2018)
• Page Start: 127
• Page End: 143
• Publication Date: 2018-10
• Subjects: Sea turtle strandings -- Sea turtle mortality -- Chesapeake Bay -- Drift simulations -- Fisheries and vessel interactions -- Endangered species -- Marine conservation -- Protected species management; Conservation of natural resources -- Periodicals; Nature conservation -- Periodicals; Ecology -- Periodicals; Environment -- Periodicals; Environmental Pollution -- Periodicals; Electronic journals; 333.9516
• DOI: 10.1016/j.biocon.2018.06.029
• Languages: English
• ISSNs: 0006-3207

Abstract

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. Highlights: Development of a general turtle carcass oceanographic drift model Improvement in our ability to predict sea turtle mortality from stranding data Identified potential locations of hazardous turtle-human interactions in Virginia Quantitative assessments hindered by the low resolutions of human use datasets