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Abstract BackgroundAnimal movement data are increasingly used to make ecological inferences, as well as to inform conservation and management actions. While advanced statistical methods to estimate behavioral states from these datasets have become widely available, the large number to choose from may make it difficult for practitioners to decide which method best addresses their needs. To guide decisions, we compared the behavioral state estimates and inferences from three methods (movement persistence models [MPM], hidden Markov models [HMM], and mixed-membership method for movement [M4]) when analyzing animal telemetry data. Tracks of post-breeding adult male green sea turtles (Chelonia mydas) were treated as an empirical example for this method comparison. The effect of temporal scale on behavioral state estimates was also investigated (at 1, 4, and 8 h time steps). ResultsThe HMM and M4 models produced relatively similar behavioral state estimates (compared to the MPM) and estimated anywhere from three to five states depending on the time interval of the tracks and the method used. Likewise, for all three methods, sampling movement at coarser time scales smoothed estimates of behavioral transitions. Additionally, the selection of movement metrics for analysis by the HMM and M4 also appeared to be a critical decision regarding state estimation and interpretation. At the longest time step (8 h), all three models were able to distinguish area-restricted search (ARS) behavior from migratory behavior, with greater nuance estimated by the HMM and M4 methods. By comparison, the MPM was the only model that was able to identify fine-scale behavioral patterns when analyzing the shortest time step (1 h). Moreover, the analysis of tracks with short time steps via MPM identified likely periods of resting during long-distance migration, which had only previously been hypothesized in green turtles. ConclusionsWhile there is no single best method to estimate behavioral states, our findings demonstrate that results can vary widely among different statistical methods and that model assumptions should be thoroughly checked during the model fitting process to reduce any potential biases. Thus, practitioners should carefully consider which methods best address their needs while also accounting for the inherent properties of their telemetry dataset. 

Satellite tracking has revolutionized our understanding of the behavior and ecology of sea turtles. However, most satellite tracking of sea turtles has been performed on breeding females, leaving knowledge gaps with regard to males. To inform future studies, we examined the peer-reviewed literature to summarize the contribution that satellite tracking has made to the spatial ecology of male turtles and describe how the published research varies across time, species, life stages, seasonal cycle phases, regions, and research topics. We systematically reviewed 61 publications reporting tracking of male sea turtles across 6 species. Loggerheads emerged as the most studied species, featuring in 49% of the publications, with green turtles and loggerheads having the highest number of tracked males. The North Atlantic was the most represented region (39% of publications), followed by the Mediterranean (29%). Most tracking of males has been undertaken at foraging areas (77%), often providing information on home range residency. This is followed by migration publications (44%), which have revealed differences among populations and between sexes, with the breeding period being the least studied. We highlight differences in the spatial ecology of males and females (e.g. residency in breeding areas and migration distance) and identified unanswered research questions about male sea turtles. To overcome the lack of knowledge about this important demographic group, efforts should be undertaken to increase the sample size and geographic coverage of tracked males, with special focus on flatback and Kemp’s ridleys, as well as studies in the Indian and Pacific Oceans. 

Abstract Species distribution models (SDMs) have become increasingly popular for making ecological inferences, as well as predictions to inform conservation and management. In predictive modeling, practitioners often use correlative SDMs that only evaluate a single spatial scale and do not account for differences in life stages. These modeling decisions may limit the performance of SDMs beyond the study region or sampling period. Given the increasing desire to develop transferable SDMs, a robust framework is necessary that can account for known challenges of model transferability. Here, we propose a comparative framework to develop transferable SDMs, which was tested using satellite telemetry data from green turtles (Chelonia mydas). This framework is characterized by a set of steps comparing among different models based on (1) model algorithm (e.g., generalized linear model vs. Gaussian process regression) and formulation (e.g., correlative model vs. hybrid model), (2) spatial scale, and (3) accounting for life stage. SDMs were fitted as resource selection functions and trained on data from the Gulf of Mexico with bathymetric depth, net primary productivity, and sea surface temperature as covariates. Independent validation datasets from Brazil and Qatar were used to assess model transferability. A correlative SDM using a hierarchical Gaussian process regression (HGPR) algorithm exhibited greater transferability than a hybrid SDM using HGPR, as well as correlative and hybrid forms of hierarchical generalized linear models. Additionally, models that evaluated habitat selection at the finest spatial scale and that did not account for life stage proved to be the most transferable in this study. The comparative framework presented here may be applied to a variety of species, ecological datasets (e.g., presence‐only, presence‐absence, mark‐recapture), and modeling frameworks (e.g., resource selection functions, step selection functions, occupancy models) to generate transferable predictions of species–habitat associations. We expect that SDM predictions resulting from this comparative framework will be more informative management tools and may be used to more accurately assess climate change impacts on a wide array of taxa. 

IntroductionAn understanding of animal behavior is critical to determine their ecological role and to inform conservation efforts. However, observing hidden behaviors can be challenging, especially for animals that spend most of their time underwater. Animal-borne devices are valuable tools to estimate hidden behavioral states. MethodsWe investigated the fine-scale behavior of internesting hawksbill turtles using the mixed-membership method for movement (M4) which integrated dive variables with spatial components and estimated latent behavioral states. ResultsFive latent behavioral states were identified: 1) pre-nesting, 2) transit, 3) quiescence, and 4) area restricted search within and 5) near the residence of turtles. The last three states associated with a residency period, showed lower activity levels. Notably, when compared to other behaviors the pre-nesting exhibited shallower and remarkably long dives of up to 292 minutes. We noted high fidelity to residence core areas and nesting beaches, within and between nesting seasons, with residence areas decreasing within a season. DiscussionThe latent behaviors identified provide the most detailed breakdown of turtle movement behaviors during the internesting period to date, providing valuable insights into their ecology and behavior. This information can inform marine turtle conservation and management efforts since utilization distributions of individual behavioral states can be used to determine spatially-explicit susceptibility of turtles to various threats based on their behavior. The analyses of utilization distribution revealed a minimal overlap with existing marine protected areas (0.4%), and we show how a new proposal would expand protection to 30%. In short, this study provides valuable guidance for conservation and management of internesting marine turtles at a fine spatiotemporal resolution and can be used to enhance national action plans for endangered species, including the expansion of existing Marine Protected Areas. By flexibly incorporating biologically informative parameters, this approach can be used to study behavior outside of the hawksbill breeding season or even beyond this species.