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1. Using data from nesting beach monitoring and satellite telemetry to improve estimates of marine turtle clutch frequency and population abundance

   https://doi.org/https://doi.org/10.1007/s00227-021-03983-z  

   Santos, A.J.B.; Vieira, D.H.G.; Bellini, C.; Corso, G.; Ceriani, S.; Fuentes, M. M. (January 2021, Marine biology)

   Population abundance data are often used to define species’ conservation status. Abundance of marine turtles is typically estimated using nesting beach monitoring data such as nest counts and clutch frequency (CF, i.e., the number of nests female turtles lay within a nesting season). However, studies have shown that CF determined solely from nesting beach monitoring data can be underestimated, leading to inaccurate abundance estimates. To obtain reliable estimates of CF for hawksbill turtles in northeastern Brazil (6.273356° S, 35.036271° W), the region with the highest nesting density in the South Atlantic, data from beach monitoring and satellite telemetry were combined from 2014 to 2019. Beach monitoring data indicated the date of first nesting event, while state-space modeling of satellite telemetry data indicated the departure date of turtles, allowing calculations of residence length at breeding site and CF estimates based on internesting intervals. Females were estimated to nest up to six times within the nesting season with CF estimates between 4.5 and 4.8 clutches per female. CF estimates were used to determine the number of nesting females at the study site based in two approaches: considering and not considering transient turtles. Our approach and findings highlight that transients heavily influence CF estimates and need for reconsideration of how this key parameter is commonly determined for marine turtle populations and the use of beach monitoring data and satellite telemetry for estimations of CF 

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

   https://doi.org/10.1111/cobi.14114  

   Barbour, Nicole; Shillinger, George_L; Gurarie, Eliezer; Hoover, Aimee_L; Gaspar, Philippe; Temple‐Boyer, Julien; Candela, Tony; Fagan, William_F; Bailey, Helen (July 2023, Conservation Biology)

   Abstract 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. 

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3. Effectiveness and design of marine protected areas for migratory species of conservation concern: A case study of post-nesting hawksbill turtles in Brazil.

   https://doi.org/https://doi.org/10.1016/j.biocon.2021.109229  

   Santos, A. J.; Santos, E. A.; Sales, G.; Ramos, R.; Vieira, D. H.; Fuentes, M. M. (January 2021, Biological conservation)

   Marine protected areas (MPAs) are among the most widely used strategy to protect marine ecosystems and are typically designed to protect specific habitats rather than a single and/or multiple species. To inform the con- servation of species of conservation concern there is the need to assess whether existing and proposed MPA designs provide protection to these species. For this, information on species spatial distribution and exposure to threats is necessary. However, this information if often lacking, particularly for mobile migratory species, such as marine turtles. To highlight the importance of this information when designing MPAs and for assessments of their effectiveness, we identified high use areas of post-nesting hawksbill turtles (Eretmochelys imbricata) in Brazil as a case study and assessed the effectiveness of Brazilian MPAs to protect important habitat for this group based on exposure to threats. Most (88%) of high use areas were found to be exposed to threats (78% to artisanal fishery and 76.7% to marine traffic), where 88.1% were not protected by MPAs, for which 86% are exposed to threats. This mismatch is driven by a lack of explicit conservation goals and targets for turtles in MPA management plans, limited spatial information on species' distribution and threats, and a mismatch in the scale of conservation initiatives. To inform future assessments and design of MPAs for species of conservation concern we suggest that managers: clearly state and make their goals and targets tangible, consider ecological scales instead of political boundaries, and use adaptative management as new information become available. 

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4. Revealing Sea Turtle Behavior in Relation to Fishing Gear Using Color-Coded Spatiotemporal Motion Patterns With Deep Neural Networks

   https://doi.org/10.3389/fmars.2021.785357  

   Reavis, Janie L.; Demir, H. Seckin; Witherington, Blair E.; Bresette, Michael J.; Blain Christen, Jennifer; Senko, Jesse F.; Ozev, Sule (November 2021, Frontiers in Marine Science)

   Incidental capture, or bycatch, of marine species is a global conservation concern. Interactions with fishing gear can cause mortality in air-breathing marine megafauna, including sea turtles. Despite this, interactions between sea turtles and fishing gear—from a behavior standpoint—are not sufficiently documented or described in the literature. Understanding sea turtle behavior in relation to fishing gear is key to discovering how they become entangled or entrapped in gear. This information can also be used to reduce fisheries interactions. However, recording and analyzing these behaviors is difficult and time intensive. In this study, we present a machine learning-based sea turtle behavior recognition scheme. The proposed method utilizes visual object tracking and orientation estimation tasks to extract important features that are used for recognizing behaviors of interest with green turtles ( Chelonia mydas ) as the study subject. Then, these features are combined in a color-coded feature image that represents the turtle behaviors occurring in a limited time frame. These spatiotemporal feature images are used along a deep convolutional neural network model to recognize the desired behaviors, specifically evasive behaviors which we have labeled “reversal” and “U-turn.” Experimental results show that the proposed method achieves an average F1 score of 85% in recognizing the target behavior patterns. This method is intended to be a tool for discovering why sea turtles become entangled in gillnet fishing gear. 

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5. Consistent Nest Site Selection by Turtles across Habitats with Varying Levels of Human Disturbance

   https://doi.org/10.3390/d15020275  

   Folkerts Caldwell, Molly; López-Pérez, Jorge E.; Warner, Daniel A.; Wolak, Matthew E. (February 2023, Diversity)

   Human disturbance impacts the breeding behavior of many species, and it is particularly important to understand how these human-caused changes affect vulnerable taxa, such as turtles. Habitat alteration can change the amount and quality of suitable nesting habitat, while human presence during nesting may influence nesting behavior. Consequently, both habitat alteration and human presence can influence the microhabitat that females choose for nesting. In the summer of 2019, we located emydid turtle nests in east-central Alabama, USA, in areas with varying levels of human disturbance (high, intermediate, low). We aimed to determine whether turtles selected nest sites based on a range of microhabitat variables comparing maternally selected natural nests to randomly chosen artificial nests. We also compared nest site choice across areas with different levels of human disturbance. Natural nests had less variance in canopy openness and average daily mean and minimum temperature than artificial nests, but microhabitat variables were similar across differing levels of disturbance. Additionally, we experimentally quantified nest predation across a natural to human-disturbed gradient. Nest predation rates were higher in areas with low and intermediate levels of disturbance than in areas with high human disturbance. Overall, these results show that turtles are not adjusting their choices of nest microhabitat when faced with anthropogenic change, suggesting that preserving certain natural microhabitat features will be critical for populations in human-disturbed areas. 

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