Novel Microsatellite Tags Hold Promise for Illuminating the Lost Years in Four Sea Turtle Species.

After hatching, sea turtles leave the nest and disperse into the ocean. Many years later, they return to their natal coastlines. The period between their leaving and their returning to natal areas, known as the "Lost Years", is poorly understood. Satellite tracking studies aimed at studying the "Lost Years" are challenging due to the small size and prolonged dispersal phases of young individuals. Here, we summarize preliminary findings about the performance of prototype microsatellite tags deployed over a three-year period on 160 neonate to small juvenile sea turtles from four species released in the North Atlantic Ocean. We provide an overview of results analyzing tag performance with metrics to investigate transmission characteristics and causes of tag failure. Our results reveal that, despite certain unfavorable transmission features, overall tag performance was satisfactory. However, most track durations were shorter than those observed on individuals of similar size in other studies and did not allow for detailed analyses of trajectories and turtle behavior. Our study further suggests that tracking durations are correlated with the targeted species, highlighting a lack of robustness against some neritic behaviors. Unprecedented diving data obtained for neonate sea turtles in this study suggest that the vertical behaviors of early juveniles are already too strenuous for these miniaturized tags. Our findings will help to inform the biologging research community, showcasing recent technological advances for the species and life stages within our study.

Incorporating multidimensional behavior into a risk management tool for a critically endangered and migratory species.

Conservation of migratory species exhibiting wide-ranging and multidimensional behaviors is challenged by management efforts that only utilize horizontal movements or produce static spatial-temporal products. For the deep-diving, critically endangered eastern Pacific leatherback turtle, tools that predict where turtles have high risks of fisheries interactions are urgently needed to prevent further population decline. We incorporated horizontal-vertical movement model results with spatial-temporal kernel density estimates and threat data (gear-specific fishing) to develop monthly maps of spatial risk. Specifically, we applied multistate hidden Markov models to a biotelemetry data set (n = 28 leatherback tracks, 2004-2007). Tracks with dive information were used to characterize turtle behavior as belonging to 1 of 3 states (transiting, residential with mixed diving, and residential with deep diving). Recent fishing effort data from Global Fishing Watch were integrated with predicted behaviors and monthly space-use estimates to create maps of relative risk of turtle-fisheries interactions. Drifting (pelagic) longline fishing gear had the highest average monthly fishing effort in the study region, and risk indices showed this gear to also have the greatest potential for high-risk interactions with turtles in a residential, deep-diving behavioral state. Monthly relative risk surfaces for all gears and behaviors were added to South Pacific TurtleWatch (SPTW) (https://www.upwell.org/sptw), a dynamic management tool for this leatherback population. These modifications will refine SPTW's capability to provide important predictions of potential high-risk bycatch areas for turtles undertaking specific behaviors. Our results demonstrate how multidimensional movement data, spatial-temporal density estimates, and threat data can be used to create a unique conservation tool. These methods serve as a framework for incorporating behavior into similar tools for other aquatic, aerial, and terrestrial taxa with multidimensional movement behaviors.

Incorporación del comportamiento multidimensional a una herramienta de gestión de riesgos para una especie migratoria en peligro crítico Resumen La conservación de especies migratorias con comportamientos amplios y multidimensionales se enfrenta a los esfuerzos de gestión que sólo utilizan movimientos horizontales o que producen resultados espaciotemporales estáticos. La tortuga laúd, una especie de las profundidades en peligro crítico, necesita con urgencia herramientas que pronostiquen los lugares en donde las tortugas tienen mayor riesgo de interactuar con las pesquerías para prevenir una mayor declinación poblacional. Incorporamos los resultados de un modelo de movimiento horizontal-vertical a las estimaciones de la densidad del núcleo espaciotemporal y de los datos de amenaza (equipo de pesca específico) para desarrollar mapas mensuales del riesgo espacial. De manera más concreta, aplicamos modelos ocultos multiestado de Markov a un conjunto de datos de biotelemetría (n=28 rastros de tortugas laúd, 2004-2007). Usamos los rastros con información de inmersión para caracterizar el comportamiento de las tortugas como uno de tres estados: en tránsito, inmersión mixta o por residencia e inmersión profunda o por residencia. Integramos los datos recientes del esfuerzo de pesca tomados de Global Fishing Watch a los comportamientos pronosticados y las estimaciones del uso mensual del espacio para crear mapas del riesgo relativo de las interacciones tortuga-pesquería. La pesca con palangre de deriva (pelágica) tuvo el promedio mensual más alto de esfuerzo de pesca en la región de estudio. Los índices de riesgo indicaron que este equipo también tiene el potencial más elevado de interacciones de alto riesgo con las tortugas en estado residencial o de inmersión profunda. Añadimos los comportamientos y las superficies de riesgo relativo mensuales a South Pacific Turtle Watch (SPTW) (https://www.upwell.org/sptw), una herramienta dinámica para la gestión de esta población de laúdes. Estos cambios pulirán la capacidad de SPTW para proporcionar predicciones importantes de las áreas con potencial alto de riesgo de pesca accesoria para las tortugas con comportamientos específicos. Nuestros resultados demuestran cómo los datos de movimiento multidimensional, las estimaciones de densidad espaciotemporal y los datos de amenaza pueden ser usados para crear una herramienta única de conservación. Estos métodos sirven como marco para incorporar el comportamiento a herramientas similares para otros taxones acuáticos, aéreos y terrestres con comportamientos multidimensionales.