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34 results for “hawksbill turtle”
Hawksbill turtle ddRAD raw sequencing data
<p>Pleistocene environmental changes are generally assumed to have dramatically affected species' demography via changes in habitat availability, but this is challenging to investigate due to our limited knowledge of how Pleistocene ecosystems changed through time. Here, we tracked changes in shallow marine habitat availability resulting from Pleistocene sea level fluctuations throughout the last glacial cycle (120 – 14 thousand years ago; kya) and assessed correlations with past changes in genetic diversity inferred from genome-wide SNPs, obtained via ddRAD sequencing, in Caribbean hawksbill turtles, which feed in coral reefs commonly found in shallow tropical waters. We found sea level regression resulted in an average 75% reduction in shallow marine habitat availability during the last glacial cycle. Changes in shallow marine habitat availability correlated strongly with past changes in hawksbill turtle genetic diversity, which gradually declined to ~1/4th of present-day levels during the Last Glacial Maximum (LGM; 26 – 19 kya). Shallow marine habitat availability and genetic diversity rapidly increased after the LGM, signifying a population expansion in response to warming environmental conditions. Our results suggest a positive correlation between Pleistocene environmental changes, habitat availability and species' demography, and that demographic changes in hawksbill turtles were potentially driven by feeding habitat availability. However, we also identified challenges associated with disentangling the potential environmental drivers of past demographic changes, which highlight the need for integrative approaches. Our conclusions underline the role of habitat availability on species' demography and biodiversity, and that the consequences of ongoing habitat loss should not be underestimated.</p>
F in Habitat utilization by juvenile hawksbill turtles (Eretmochelys imbricata, Linnaeus, 1766) around a shallow water coral reef
F. 6. Schematic showing the movement of hawksbill turtles between foraging and resting sites: (1) foraging on reef flat; (2) ascending to the surface once foraging has ended; (3) descending down reef face; (4) resting site (typically sandy bottomed); (5) ascending to surface following period of rest and return to foraging site.
F in Habitat utilization by juvenile hawksbill turtles (Eretmochelys imbricata, Linnaeus, 1766) around a shallow water coral reef
F. 7. Comparison of foraging depth (active and stationary combined) with the depth of the resting site (post-foraging) (open circle). Line of equivalence (i.e. foraging depth= resting depth). Data represent occasions when the turtle was observed to swim repeatedly between foraging and resting sites (N=11) and not when observed at either site independently. Superimposed are mean dive depth data (±1 SD) for juvenile hawksbills taken from table 3 in van Dam and Diez (1996) (closed circle).
F in Habitat utilization by juvenile hawksbill turtles (Eretmochelys imbricata, Linnaeus, 1766) around a shallow water coral reef
F. 5. Mean depth (±1 SD) for different behaviours at the six study sites combined. SF, stationary foraging; AF, active foraging; R, resting; AR, assisted resting.
F in Habitat utilization by juvenile hawksbill turtles (Eretmochelys imbricata, Linnaeus, 1766) around a shallow water coral reef
F. 3. Comparison of actual and estimated sizes of the four mock-up carapaces (±1 SD). Data shown represent a combination of all observers (N=6). Line of equivalence (i.e. actual size=estimated size) is shown.
Data from: High rates of growth recorded for hawksbill sea turtles in Anegada, British Virgin Islands
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Data from: Natal foraging philopatry in eastern Pacific hawksbill turtles
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Hawksbill turtle ddRAD raw sequencing data
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Data from: Genetic variation, multiple paternity and measures of reproductive success in the critically endangered hawksbill turtle (Eretmochelys imbricata)
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Data from: Reconstructing paternal genotypes to infer patterns of sperm storage and sexual selection in the hawksbill turtle
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F in Habitat utilization by juvenile hawksbill turtles (Eretmochelys imbricata, Linnaeus, 1766) around a shallow water coral reef
F. 2. Side view of mock-up carapace attached to the seabed during the calibration experiment.
Accelerometer, gyroscope and pressure data associated with behaviors of free-ranging hawksbill sea turtles (Chelonia mydas)
<p> </p> <p>Data accompanying the paper: Jeantet, L., Vigon, V., Geiger, S., & Chevallier, D. (2021). Fully convolutional neural network: A solution to infer animal behaviours from multi-sensor data. <em>Ecological Modelling</em>, <em>450</em>, 109555.. doi : <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.ecolmodel.2021.109555" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.ecolmodel.2021.109555</a></p> <p> </p> <p>In this paper we developped a fully convolutional network, the V-Net, to automatically identify the behaviors of green turtle from acceleration, gyroscope, depth sensor data. With minimal preprocessing, we obtained a F1-score of 81.1% and a Global accuracy of 97.2%. </p> <p> </p> <p><strong>Associated Github with the V-Net script : </strong><a href="https://github.com/jeantetlorene/Vnet_seaturtle_behavior">https://github.com/jeantetlorene/Vnet_seaturtle_behavior</a></p> <p> </p> <p>The dataset comprised the raw acceleration, gyroscope and depth sequence of 13 free-ranging green 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 not provided in this dataset. </p> <p>For each individual, the data collected by the devices was correlated with observed behaviors from video recordings. Unlabeled sequences, primarily night recordings, were excluded, resulting in the creation of one file per day of deployment for each individual. A total of 46 behaviors were observed and are described in detail in Jeantet et al. (2020). The labels for these behaviors are found in the column "beh." The behaviors were grouped into six main categories: Breathing, Feeding, Gliding, Resting, Scratching, and Swimming. Any other observed behavior was categorized as Other. The associated labels for the categories can be found in the column "beh_merge."</p> <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> <p>"In total, the green turtle dataset contained 68.6 hours of labelled sequences from 13 individuals (approximately 5.29 hours per individual, max = 14.67 hours, min = 0.96 hours, standard deviation = 3.39 hours). The predominant behavior observed in the videos was Resting, totaling over 34.3 hours, followed by Swimming and Breathing, with 22.3 hours and 5.7 hours, respectively. The other behaviors were expressed in minority (Gliding: 2.3 hours, Feeding: 1.8 hours, Scratching: 1.2 hours and Other: 1 hour). "</p> <p> </p> <p>The folder contains 16 Python matrices, each with 11 columns (AccX, AccY, AccZ, GyrX, GyrY, GyrZ, Depth, beh, beh_merge, Pressure_corr, Pressur_diff) and a number of rows corresponding to the deployment duration. The title of each file indicates the camera number used (CC-07-XX) and the deployment day (DD-MM-YYYY), with an additional number if the file was split due to unlabeled sequences.</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 "beh" column. Two dictionaries (behInd_to_behName_cat, behName_to_behInd_cat) that specify the behavioral categories associated with each number used as a label in the "beh_merge" 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><strong>Please cite this dataset as :</strong> </p> <p>Jeantet, L., Planas-Bielsa, V., Benhamou, S., Geiger, S., Martin, J., Siegwalt, F., Lelong, P., Gresser, J., Etienne, D., Hielard, G., Arque, A., Regis, S., Lecerf, N., Frouin, C., Benhalilou, A., Murgale, C., Maillet, T., Andreani, L., Campistron, G., … Chevallier, D. (2024). Accelerometer, gyroscope and pressure data associated with behaviors of free-ranging hawksbill sea turtles (Chelonia mydas) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.11643602</p> <p> </p>
F in Habitat utilization by juvenile hawksbill turtles (Eretmochelys imbricata, Linnaeus, 1766) around a shallow water coral reef
F. 4. Frequency distribution showing estimated sizes of hawksbill turtles (after corrections) observed at the six study sites between 8 March 2000 and 1 April 2000. Additionally marked is the mean size (N=9) and range of nesting hawksbills at Cousin Island, Seychelles (Diamond, 1976).
F in Habitat utilization by juvenile hawksbill turtles (Eretmochelys imbricata, Linnaeus, 1766) around a shallow water coral reef
F. 1. Map of the Mahé and the inner islands showing the location of the six study sites: (1) Sainte Anne National Marine Park; (2) Cap Ternay National Marine Park; (3) Ansé Royal; (4) Ansé Soleil; (5) Ilé Silhouette National Marine Park; (6) Ilé Seché. Inset: the location of the granitic Seychelles in the Indian Ocean.
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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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.