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290 results for “sea turtles”
Fig. 1 in The Northernmost Record Of The Loggerhead Sea Turtle, Caretta Caretta (Testudines, Cheloniidae), In The Black Sea, With The Review Of The Species Occurrence In The Region
Fig. 1. Records of the loggerhead sea turtle in the Black Sea.
Latitudinal cline in the foraging dichotomy of loggerhead sea turtles reveals the importance of East China Sea for priority conservation
<p><strong>Aim:</strong> Quantifying the importance of habitat areas for conservation of highly migratory marine species with complex life histories can be challenging. For example, loggerhead turtles (Caretta caretta) nesting in Japan forage both oceanically and neritically after their reproductive period. Here, we aimed to quantify the proportions of turtles using these two contrasting habitats (foraging dichotomy) to suggest priority conservation areas.</p> <p><strong>Methods:</strong> We examined the occurrence of foraging dichotomy at three nesting sites (Ishigaki, Okinoerabu Islands, and Ichinomiya) based on stable isotope analysis of the egg yolks for 82 turtles and satellite tracking of post-nesting migration for 12 turtles. Moreover, we used the data of three other sites from previous studies (Yakushima Island, Minabe, and Omaezaki).</p> <p><strong>Results:</strong> Two neritic foraging grounds (East China Sea and the coastal area of the Japanese archipelago), and an oceanic ground (North Pacific Ocean) were identified. We found a latitudinal cline with respect to the occurrence of foraging dichotomy; >84% of the females nesting at southern sites (Ishigaki and Okinoerabu Islands), 73% at middle sites (Yakushima Island and Minabe), and <46% at northern sites (Omaezaki and Ichinomiya) were neritic foragers; the proportion of oceanic foragers increased at northern sites. Based on the annual number of nests in the entire nesting region of Japan, satellite tracking, and the latitudinal cline of foraging dichotomy, we estimated that 70% and 9% of annual nesting females in Japan utilise the neritic foraging habitat in the East China Sea and the coastal area of the Japanese archipelago, respectively and that and 22% utilise the oceanic habitat of the North Pacific Ocean.</p> <p><strong>Main conclusions:</strong> The East China Sea represents a critical foraging habitat for the North Pacific populations of endangered loggerhead sea turtles. Our findings emphasise the need for international management to ensure their protection.</p>
Data from: How well do embryo development rate models derived from laboratory data predict embryo development in sea turtle nests?
<p>Development rate of ectothermic animals varies with temperature. Here we use data derived from laboratory constant temperature incubation experiments to formulate development rate models that can be used to model embryonic development rate in sea turtle nests. We then use a novel method for detecting the time of hatching to measure the in situ incubation period of sea turtle clutches to test the accuracy of our models in predicting the incubation period from nest temperature traces. We found that all our models overestimated the incubation period. We hypothesize three possible explanations which are not mutually exclusive for the mismatch between our modeling and empirically measured in situ incubation period: (1) a difference in the way the incubation period is calculated in laboratory data and in our field nests, (2) inaccuracies in the assumptions made by our models at high incubation temperatures where there is no empirical laboratory data, and (3) a tendency for development rate in laboratory experiments to be progressively slower as temperature decreases compared with in situ incubation.</p>
Data from: Network analysis of sea turtle movements and connectivity: a tool for conservation prioritization
<p><strong>Aim</strong>: Understanding the spatial ecology of animal movements is a critical element in conserving long-lived, highly mobile marine species. Analysing networks developed from movements of six sea turtle species reveals marine connectivity and can help prioritize conservation efforts.</p> <p><strong>Location</strong>: Global.</p> <p><strong>Methods</strong>: We collated telemetry data from 1,235 individuals and reviewed the literature to determine our dataset's representativeness. We used the telemetry data to develop spatial networks at different scales to examine areas, connections, and their geographic arrangement. We used graph theory metrics to compare networks across regions and species and to identify the role of important areas and connections.</p> <p><strong>Results</strong>: Relevant literature and citations for data used in this study had very little overlap. Network analysis showed that sampling effort influenced network structure and the arrangement of areas and connections for most networks was complex. However, important areas and connections identified by graph theory metrics can be different than areas of high data density. For the global network, marine regions in the Mediterranean had high closeness while links with high betweenness among marine regions in the South Atlantic were critical for maintaining connectivity. Comparisons among species-specific networks showed that functional connectivity was related to movement ecology, resulting in networks composed of different areas and links.</p> <p><strong>Main conclusions</strong>: Network analysis identified the structure and functional connectivity of the sea turtles in our sample at multiple scales. These network characteristics could help guide the coordination of management strategies for wide-ranging animals throughout their geographic extent. Most networks had complex structures that can contribute to greater robustness, but may be more difficult to manage changes when compared to simpler forms. Area-based conservation measures would benefit sea turtle populations when directed towards areas with high closeness dominating network function. Promoting seascape connectivity of links with high betweenness would decrease network vulnerability.</p>
Data from: Tireless travellers: Sea turtles swim continuously during long-distance movements
<p>While rest and sleep are crucial to animals, our understanding of whether and how long-distance migrants rest has been thwarted by the inability to relay high-resolution data from multi-channel loggers via satellite. We overcame these obstacles for an iconic long-distance migrator by equipping five loggerhead sea turtles (Caretta caretta) with satellite tags and data-loggers providing depth and 3-D acceleration measurements. Turtles were translocated to open-sea locations and induced to complete oceanic migrations of tens of km to return to their nesting beach performing active, oriented movements. Across a total of >600 hours of high-resolution data, we observed (i) constant flipper frequency (ca. 0.5 Hz) indicating that turtles never ceased movement, (ii) intense subsurface swimming (mostly around 1 m) for about 50% of time and (iii) deeper, less active dives up to 80 m, which were made day and night and more frequently in offshore waters. Flipper beat amplitude was much smaller in deep dives; hence the estimated energy expenditure was 37% lower on deep dives compared to subsurface swimming. These findings suggest that turtles, which can complete migrations of >2000 km, alternate between phases of intense near-surface swimming and periods of lower activity at depth, without fully resting during migration.</p>
Accelerometer, gyroscope and pressure data associated with behaviors of free-ranging hawksbill sea turtles (Eretmochelys imbricata)
<p> </p> <p> </p> <div> <div> <div> <div> <div> <p>In this paper, we explored the use of transfer learning across species and taxa, employing fully convolutional neural networks to predict the behaviors of critically endangered hawksbill sea turtles from acceleration data. For this purpose, fully convolutional neural networks (V-net and U-net) were pre-trained on a dataset of green turtles (Zenodo link) and human data (Intensive Care Unit (ICU) HAR dataset, <a href="https://doi.org/10.24432/C54S4K" target="_new" rel="noreferrer">https://doi.org/10.24432/C54S4K</a>) before being fine-tuned on the hawksbill dataset. The results reveal a 8% and 4% improvement in F1-score with transfer learning from the green turtle and human datasets, respectively, compared to training the models from random weight initialization (without transfer learning). </p> </div> </div> </div> </div> </div> <p> </p> <p>The dataset comprised the raw acceleration, gyroscope and depth sequence of 6 free-ranging hawksbill sea turtles associated with the behaviors. The indiviuals were equipped with a on-board video recorder combined with an accelerometer, gyroscope, magnetometer and luminosity, temperature and depth sensors using four suction cups and an automatic release system over a two-day periods (see Jeantet et al. 2020 for details and the associated article). The accelerometer, gyroscope, magnetometer recorded at 20 Hz and the pressure, temperature and luminosity sensors at 1 Hz. The cameras were programmed to record until nightfall (6 pm) and resume at daybreak (6 am). The magnetometer, luminosity and temperature data are provided but not used in the associated study. </p> <p> </p> <div> <div> <div> <div> <div> <p>For each individual, the data collected by the devices was correlated with observed behaviors from video recordings. Unlabeled sequences, mostly comprising night recordings, were excluded, resulting in the creation of one file per day of deployment for each individual. </p> </div> </div> </div> </div> </div> <div> <div> <div> <div> <div> <p>To process the depth data and increase the sampling rate to 20 Hz, we used a linear interpolation technique. We called this new variable "Pressure_corr". Additionally, we calculated the pressure difference ("Pressure_diff") between each measuring point (originally at 1 Hz).</p> </div> </div> </div> </div> </div> <p> </p> <p>"In total, 69.7 hours of multi-sensor sequences were labelled from six different hawksbill turtles (approximately 11.6 hours of recording per individual, max = 17.8 hours, min = 6.3 hours, standard deviation = 3.6 hours). The predominant behavior observed in the videos was <em>Feeding</em>, totaling over 38.6 hours, followed by <em>Resting</em> and <em>Swimming</em>, with 19.1 hours and 7.9 hours, respectively. The other behaviors were expressed in minority (<em>Breathing</em>: 2.2 hours, <em>Gliding</em>: 1 hour, <em>Scratching</em>: 0.8 hour and <em>Other</em>: 0.1 hour). " </p> <p> </p> <p>The folder contains 10 Python matrices, each with 15 columns (AccX, AccY, AccZ, GyrX, GyrY, GyrZ, MagX, MagY, MagZ, Depth, Light, Temperature, Pressure_corr, Pressur_diff, Behavior) and a number of rows corresponding to the deployment duration. The title of each file indicates the camera number used (CC-09-XX) and the deployment day (DD-MM-YYYY), with the last digit specifying whether the matrix corresponds to the first or second day of deployment.</p> <p> </p> <p>The folder also contains two dictionaries (behInd_to_behName, behName_to_behInd) that specify the behaviors associated with each number used as a label in the Behavior column. Additionally, there is a dictionary (dico_info) that provides the names of the matrix columns and the frequence of recording.</p> <p> </p> <p> </p> <p> </p> <p> </p>
Fig. 6 in Observations on the mortality of olive ridley sea turtles (Lepidochelys olivacea) and associated factors along Ganjam coast, east coast of India
Fig. 6 — Association of beach elevation with turtle mortality
Fig. 5 in Observations on the mortality of olive ridley sea turtles (Lepidochelys olivacea) and associated factors along Ganjam coast, east coast of India
Fig. 5 — Changes in the Rushikulya river mouth during the last two decades
Fig. 4 — A dead olive ridley with a in Observations on the mortality of olive ridley sea turtles (Lepidochelys olivacea) and associated factors along Ganjam coast, east coast of India
Fig. 4 — A dead olive ridley with a possible hit mark on its carapace
Fig. 1 in Observations on the mortality of olive ridley sea turtles (Lepidochelys olivacea) and associated factors along Ganjam coast, east coast of India
Fig. 1 — Study area map with survey locations
Supporting data and code for: Individual plasticity in response to rising sea temperatures contributes to an advancement in green turtle nesting phenology.
Open the record for dataset details and reuse information.
Fig. 4 in Twenty Years of Sea Turtle Strandings in New Caledonia.
Fig. 4. Recorded strandings in New Caledonia per age class between 1999 and 2021.
Figure 2 in Variability in Reception Duration of Dual Satellite Tags on Sea Turtles Tracked in the Pacific Ocean
Figure 2. Example of dual tag attachment to a loggerhead turtle.
Genotype data not consistent with clonal transmission of sea turtle fibropapillomatosis or goldfish schwannoma
<p>Recent<b> </b>discoveries of transmissible cancers in multiple bivalve species suggest that direct transmission of cancer cells within species may be more common than previously thought, particularly in aquatic environments. Fibropapillomatosis occurs with high prevalence in green sea turtles (Chelonia mydas) and the geographic range of disease has increased since fibropapillomatosis was first reported in this species. Widespread incidence of schwannomas, benign tumours of Schwann cell origin, reported in aquarium-bred goldfish (Carassius auratus), suggest an infectious aetiology. We investigated the hypothesis that cancers in these species arise by clonal transmission of cancer cells. Through analysis of polymorphic microsatellite alleles, we demonstrate concordance of host and tumour genotypes in diseased animals. These results imply that the tumours examined arose from independent oncogenic transformation of host tissue and were not clonally transmitted. Further, failure to experimentally transmit goldfish schwannoma via water exposure or inoculation suggest that this disease is unlikely to have an infectious aetiology.</p>
Ariano-Sánchez et al-Sand temperatures on sea turtle nesting beaches
<p>Dataset of sand temperatures on sea turtle nesting beaches recorded for 2018-2019 from two dark volcanic sand beaches in the Pacific coast of Guatemala, Central America.</p> <p>R Code for analysis of dataset.</p>
Dietary plasticity linked to divergent growth trajectories in a critically endangered sea turtle
<p>Foraging habitat selection and diet quality are key factors that influence individual fitness and metapopulation dynamics through effects on demographic rates. There is growing evidence that sea turtles exhibit regional differences in somatic growth linked to alternative dispersal patterns during the oceanic life stage. Yet, the role of habitat quality and diet in shaping somatic growth rates is poorly understood. Here, we evaluate whether diet variation is linked to regional growth variation in hawksbill sea turtles (Eretmochelys imbricata), which grow significantly slower in Texas versus Florida (USA), through novel integrations of skeletal growth, gastrointestinal content (GI), and bulk tissue and amino acid (AA)-specific stable nitrogen (δ15N) and carbon (δ13C) isotope analyses. We also used AA δ15N ΣV values (heterotrophic bacterial re-synthesis index) and δ13C essential AA (δ13CEAA) fingerprinting to test assumptions about the energy sources fueling hawksbill food webs regionally. GI content analyses, framed within a global synthesis of hawksbill dietary plasticity, revealed that relatively fast-growing hawksbills stranded in Florida conformed with assumptions of extensive spongivory for this species. In contrast, relatively slow-growing hawksbills stranded in Texas consumed considerable amounts of non-sponge invertebrate prey and appear to forage higher in the food web as indicated by isotopic niche metrics and higher AA δ15N-based trophic position estimates internally indexed to baseline N variation. However, regional differences in estimated trophic position may also be driven by unique isotope dynamics of sponge food webs. AA δ15N ΣV values and δ13CEAA fingerprinting indicated minimal bacterial resynthesis of organic matter (ΣV < 2) and that eukaryotic microalgae were the primary energy source supporting hawksbill food webs. These findings run contrary to assumptions that hawksbill diets predominantly comprise high microbial abundance sponges expected to primarily derive energy from bacterial symbionts. Our findings suggest alternative foraging patterns could underlie regional variation in hawksbill growth rates, as divergence from conventional sponge prey might correspond with increased energy expenditure and reduced foraging success or diet quality. As a result, differential dispersal patterns may infer substantial individual and population fitness costs and represent a previously unrecognized challenge to the persistence and recovery of this critically endangered species.</p>
Sea turtle relative abundance in nearshore waters adjacent to the Mississippi River delta, Gulf of Mexico, United States
<p><span>We measured the relative abundance of sea turtles using standardized transect surveys conducted during the summer and fall of 2013 in neritic waters surrounding the Mississippi River delta in Louisiana, USA. Data comprise sea turtle locations, observation circumstances, and environmental covariates recorded at the beginning of each transect and at the time of each turtle observation. Turtles were recorded by species and size class, as well as location in the water column and the distance the turtle was from the transect line. Transects were performed on an 8.2-meter vessel with two observers atop a 4.5-meter elevated platform, with vessel speed standardized at ~15 km/hr. These data are the first to describe relative abundance of sea turtles observed from small vessels in this region. Detection of turtles <45 cm SSCL and data detail are greater than aerial surveys. The data serve to inform resource managers and researchers regarding these protected marine species. </span></p>
Vulnerability of sea turtle nesting sites to erosion and inundation: a decision support framework to maximize conservation
<p>Sandy beaches provide essential nesting habitat for sea turtles but are threatened globally by a rapidly changing climate. Identifying which nesting sites are at greatest risk from erosion and inundation remains an important goal of sea turtle conservation globally. Yet, efforts to identify at-risk sites have been hindered by the ability to model complex processes and incomplete information on nesting distribution and abundance. To assess the erosion and inundation risk to the reproductive success of a discrete genetic stock of flatback turtles (<em>Natator</em> <em>depressus</em>) across its nesting range in the Pilbara region of Western Australia, we used the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) Coastal Vulnerability Model. A relative exposure index was calculated for 402 nesting beaches in terms of six geophysical variables: wind and wave exposure, surge potential, relief, observed sea level rise and coastal geomorphology, and coupled with published information on the distribution and abundance of turtle tracks in the region. </p> <p>The majority of beaches (74%) had an intermediate to high exposure. In particular, 36% of beaches with a high abundance of flatback tracks (the top 25% of the frequency distribution) had a high exposure (the top 25% of the frequency distribution). This suggests that coastal exposure is a key vulnerability to the reproductive success of sea turtles that nest in this region. Promisingly, five beaches with a high abundance of turtle tracks also had a low exposure (bottom 25% of the frequency distribution) and these beaches may be critical for the long-term resilience of the stock against sea level rise and severe storms. Exposure varied across nesting sites and the approach presented here allows for a rapid and broadscale assessment of relative erosion and inundation risks at a scale most relevant to management. </p>
Data from: Behaviour-specific spatiotemporal patterns of habitat use by sea turtles revealed using biologging and supervised machine learning
<ol> <li>Conservation of threatened species and anthropogenic threat mitigation commonly rely on spatially managed areas selected according to habitat preference. Since the impact of threats can be behaviour-specific, such information could be incorporated into spatial management to improve conservation outcomes. However, collecting spatially explicit behavioural data is challenging.</li> <li>Using multi-sensor biologging tags containing high-resolution movement sensors (e.g., accelerometer, magnetometer, GPS) and animal-borne video cameras, combined with supervised machine learning, we developed a method to automatically identify and geolocate typically ambiguous behaviours for the poorly understood flatback turtle <em>Natator depressus</em>. Subsequently, we evaluated behaviour-specific spatiotemporal patterns of habitat use.</li> <li>Boosted regression trees successfully identified the presence of foraging and resting in 7074 dives (AUC > 0.9), using dive features representing characteristics of locomotory activity, body posture, and three-dimensional dive paths validated by ancillary video data. Foraging was characterised by dives with longer duration, variable depth, tortuous bottom phases; resting was characterised by dives with decreased locomotory activity and longer duration bottom phases.</li> <li>Foraging and resting showed minimal spatial segregation based on 50% and 95% utilisation distributions. Expected diel patterns of behaviour-specific habitat use were superseded by the extreme tides at the near-shore study site. Turtles rested in areas close to the subtidal and intertidal boundary within larger overlapping foraging areas, allowing efficient access to intertidal food resources upon inundation at high tides when foraging was ~25% more likely.</li> <li> <em>Synthesis and applications:</em><span> Using supervised machine learning and biologging tools, we show the potential for dynamic spatial management of flatback turtles to mitigate behaviour-specific threats by prioritising protection of important locations at pertinent times. Although results are a species-specific response to a super-tidal environment</span>, our approach can be generalised to a broad range of taxa and study systems, facilitating a conceptual advance in spatial management.</li> </ol>
Data from: Hidden demographic impacts of fishing and environmental drivers of fecundity in a sea turtle population
<p><span>Fisheries bycatch is a critical threat to sea turtle populations worldwide, particularly because turtles are vulnerable to multiple gear types. The Canary Current is an intensely fished region, yet there has been no demographic assessment integrating bycatch and population management information of the globally significant Cabo Verde loggerhead turtle (<em>Caretta</em> <em>caretta</em>) population. Using Boa Vista island (Eastern Cabo Verde) subpopulation data from capture-recapture and nest monitoring (2013–2019), we evaluated population viability and estimated regional bycatch rates (2016–2020) in longline, trawl, purse-seine, and artisanal fisheries. We further evaluated current nesting trends in the context of bycatch estimates, existing hatchery conservation measures, and environmental (net primary productivity) variability in turtle foraging grounds. We projected that current bycatch mortality rates would lead to the near extinction of the Boa Vista subpopulation. Bycatch reduction in longline fisheries and all fisheries combined would increase finite population growth rate by 1.76% and 1.95%, respectively. Hatchery conservation increased hatchling production and reduced extinction risk, but alone it could not achieve population growth. Short-term increases in nest counts (2013–2021), putatively driven by temporary increases in net primary productivity, may be masking ongoing long-term population declines. When fecundity was linked to net primary productivity, our hindcast models simultaneously predicted these opposing long-term and short-term trends. Consequently, our results showed conservation management must diversify from land-based management. The masking effect we found has broad-reaching implications for monitoring sea turtle populations worldwide, demonstrating the importance of directly estimating adult survival and that nest counts might inadequately reflect underlying population trends.</span></p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.