Likely locations of sea turtle stranding mortality using experimentally-calibrated, time and space-specific drift models

Abstract

Sea turtle stranding events provide an opportunity to study drivers of mortality, but causes of strandings are poorly understood. A general 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.

Regional Variation in Kemp’s Ridley Sea Turtle Diet Composition and Its Potential Relationship With Somatic Growth

“…The majority of turtles included in our study stranded in the spring, summer, and fall when sea surface temperatures, and thereby decomposition rates, would have been relatively high (Higgins et al, 2007). Therefore, in order for stranding to occur before carcasses dissociated due to decomposition, drift times and distances would have needed to be low (∼2-5 days, 15-30 km; Nero et al, 2013;Santos et al, 2018). Second, Kemp's ridleys display relatively high intra-and inter-annual site fidelity to nearshore, shallow (<50 m depth) foraging areas (generally <1,000 km 2 ) that are well constrained spatially within our defined geographic regions (Renaud and Williams, 2005;Schmid and Witzell, 2006;Shaver and Rubio, 2008;Seney and Landry, 2011;Coleman et al, 2017).…”

Predicted distributions and abundances of the sea turtle ‘lost years’ in the western North Atlantic Ocean

“…As a first step toward examining the utility of this model, we compared predicted abundance to a long-term timeseries of sea turtle strandings in the coastal regions of the Gulf of Mexico. Strandings represent a complex interaction among anthropogenic and environmental conditions that influence mortality, the probability of washing ashore and the probability of being reported (Nero et al 2013, Santos et al 2018. Nonetheless, we presume that the more turtles present in an area, the more likely it is that one will wash ashore and be reported; thus, annual differences in the number of strandings are likely to be related to turtle abundance across that region (Hart et al 2006).…”

“…Nonetheless, we presume that the more turtles present in an area, the more likely it is that one will wash ashore and be reported; thus, annual differences in the number of strandings are likely to be related to turtle abundance across that region (Hart et al 2006). Owing to the inherent limitations of interpreting strandings data (Baskale et al 2018, Lalire and Gaspar 2018), we aggregated strandings over large spatial extents (> 500 km of coastline) and temporal periods (annually), to damp higher-frequency signals associated with pulses in sea turtle mortality (Santos et al 2018, Foley et al 2019.…”

Improving emergence location estimates for Argos pop-up transmitters

“…If regions have accurate and localized tide and current predictions, researchers could incorporate oceanographic drift models (e.g., Lagrangian drift models) into emergence location estimates. For example, recent research has used hydrodynamic drift models to predict mortality locations from sea turtle satellite-tag data [25,26]. Experimental tests using drift models that account for wind and tide were also used to estimate at-sea mortality locations of cetaceans and resulted in differences in accuracy of approximately 30 km between true stranding locations and estimated locations [27].…”