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60 results for “Chelonia”
Relocations for: Satellite-tracking reveals sex-specific migration distance in green turtles (Chelonia mydas)
<p>Relocations from 25 green turtles, tracked in West Africa in 2021.</p>
Figure 2 in Species assemblage and distribution of turtle barnacles (Cirripedia: Coronuloidea) on foraging green sea turtles (Chelonia mydas) in the Persian Gulf
Figure 2. Distribution of Chelonibia testudinaria and Platylepas hexastylos on the carapace (a) and plastron (b) of foraging green sea turtles (Chelonia mydas) in southern Qeshm Island (eastern Persian Gulf). letters on the scutes of the top-left picture show: c) central scutes; l) lateral scutes; n) nuchal scute; s) supracaudal scutes; m) marginal scutes (all unmarked scutes between nuchal and supracaudals are marginal scutes). Letters on the scutes of the below-right picture show: i) intergular scute; g) gular scute; h) humeral scute; p) pectoral scute; ab) abdominal scute; f) femoral scute; a) anal scute; in) inframarginal scutes.
Figure 1 in Species assemblage and distribution of turtle barnacles (Cirripedia: Coronuloidea) on foraging green sea turtles (Chelonia mydas) in the Persian Gulf
Figure 1. Sampling site of green sea turtles (Chelonia mydas) on the southern coast of Qeshm Island, the Persian Gulf.
Figure 2 in Growth rates of wild green turtles, Chelonia mydas, at a temperate foraging habitat in the northern Gulf of Mexico: assessing short-term effects of cold-stunning on growth
Figure 2. Graphical summary of generalized additive model fit for somatic growth, in cm straight carapace length (SCL)/year, for St Joseph Bay, Florida conditioned on two growthrate predictors: (A,B) number of previous cold-stunning events and mean carapace length or (C,D) number of previous cold-stunning events and mean condition index. The response variable (growth rate as cm SCL/year) is shown on the y-axis in each panel as a centred scale to ensure valid point-wise 95% credible intervals and comparison between the covariates across the four panels. The width of the mean factor response (number of previous cold-stunning events: A,C) is proportional to sample size with the 95% confidence interval shown by cross bars. Solid curves in B and D are cubic smoothing spline fits for these continuous covariates conditioned on the cofactor (previous cold-stunnings) while the dotted curves in the same panels are point-wise 95% confidence curves around the fits. The data distribution within (B) and (D) is shown by the vertical bars on the topside of the lower x-axis. For instance, (D) shows that most of the data for the mean condition index occur from 1.1 to 1.5 with some extreme outliers. While not statistically significant, it was apparent that expected growth rates were lower for turtles that were exposed to one or two cold-stunning events (A,C). Neither mean size (B) nor mean condition (D) were significant growth-rate predictors for this sample. The sample size (n551) is too small for this study to draw any robust conclusions about the effect of cold-stunning events on juvenile green turtle somatic growth.
Figure 1 in Growth rates of wild green turtles, Chelonia mydas, at a temperate foraging habitat in the northern Gulf of Mexico: assessing short-term effects of cold-stunning on growth
Figure 1. Location of St Joseph Bay in the northern Gulf of Mexico. Major set-netting sites (filled circles) used throughout the project and location of cold stun strandings (solid arrows) during 2001 and 2003. Site of release (indicated by star) into the Gulf of Mexico after rehabilitation, and the possible path (thin arrows) taken while returning to the southern end of St Joseph Bay.
Figure 4 in No rest for the weary: restricted resting behaviour of green turtles (Chelonia mydas) at a deep-neritic foraging area influences expression of life history traits
Figure 4. Depth versus duration of resting bouts for (a) each individual turtle (n = 12), and (b) average dive depth vs. dive duration for all resting dives by each individual turtle ± 1 standard deviation (R2 = 0.36).
Figure 3 in No rest for the weary: restricted resting behaviour of green turtles (Chelonia mydas) at a deep-neritic foraging area influences expression of life history traits
Figure 3. Dive depth vs. dive duration for (a) all non-resting dives by all turtles (R2 = 0.26, slope = 0.72) and (b) all resting dives by all turtles (R2 = 0.31, slope = 0.43).
Figure 2 in No rest for the weary: restricted resting behaviour of green turtles (Chelonia mydas) at a deep-neritic foraging area influences expression of life history traits
Figure 2. Map of Bahίa de los Angeles study area along the eastern coast of the Baja California Peninsula, Mexico (inset); 10-m baythmetric contours represented by dashed lines; Capture sites: 1. El Barco, 2. La Silica, 3. El Bajo, 4. El Cardon, 5. Pedregal de la Blanca and 6. Playa Blanca.
Evolutionary comparisons of Chelonid alphaherpesvirus 5 (ChHV5) Genomes from Fibropapillomatosis-afflicted green (Chelonia mydas), Olive ridley (Lepidochelys olivacea) and Kemp's ridley (Lepidochelys kempii) sea turtles
<p>The spreading global sea turtle fibropapillomatosis (FP) epizootic is threatening some of Earth's ancient reptiles, adding to the plethora of threats faced by these keystone species. Understanding this neoplastic disease and its likely aetiological pathogen, chelonid alphaherpesvirus 5 (ChHV5), is crucial to understand how the disease impacts sea turtle populations and species and the future trajectory of disease incidence. We generated 20 ChHV5 genomes, from three sea turtle species, to better understand the viral variant diversity and gene evolution of this oncogenic virus. We revealed previously underappreciated genetic diversity within this virus (with an average of 2035 single nucleotide polymorphisms (SNPs), 1.54% of the ChHV5 genome) and identified genes under the strongest evolutionary pressure. Furthermore, we investigated the phylogeny of ChHV5 at both genome and gene level, confirming the propensity of the virus to be interspecific, with related variants able to infect multiple sea turtle species. Finally, we revealed unexpected intra-host diversity, with up to 0.15% of the viral genome varying between ChHV5 genomes isolated from different tumours concurrently arising within the same individual. These findings offer important insights into ChHV5 biology and provide genomic resources for this oncogenic virus.</p>
FIG. 2 in An Ethogram Describing the Nesting Behavior of Green Sea Turtles (Chelonia mŋdas)
FIG. 2.—Schematic representation of the relationships between different nesting stages of Green Sea Turtles (Chelonia mŋdas). Solid lines indicate progression leading to a successful nesting attempt. Dashed lines represent variations that might result in abandoned nesting attempts prior to oviposition. Action patterns within each nesting stage are designated as follows: SQC ¼ simultaneous quadrupedal crawl; RFFSW ¼ rear flipper flick sweep; RFSW ¼ rear flipper sweep; FFSW ¼ front flipper sweep; RFFSC ¼ rear flipper flick scoop; RFK ¼ rear flipper knead.
FIG. 1 in An Ethogram Describing the Nesting Behavior of Green Sea Turtles (Chelonia mŋdas)
FIG. 1.—Illustrations of movements performed by nesting Green Sea Turtles (Chelonia mŋdas): (A) simultaneous rear and front flippers, (B) paired front flippers, (C) alternating rear flippers, (D) single left front flipper, (E) single right front flipper, (F) single left rear flipper, and (G) single right rear flipper. Diagrams modified from Eckert et al. (1999).
Data from: Energy expenditure of adult green turtles (Chelonia mydas) at their foraging grounds and during simulated oceanic migration
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Data from: Assault from all sides: hybridization and introgression threaten the already critically endangered Myuchelys georgesi (Chelonia: Chelidae)
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Evolutionary comparisons of Chelonid alphaherpesvirus 5 (ChHV5) Genomes from Fibropapillomatosis-afflicted green (Chelonia mydas), Olive ridley (Lepidochelys olivacea) and Kemp’s ridley (Lepidochelys kempii) sea turtles
Open the record for dataset details and reuse information.
Figure 1 in No rest for the weary: restricted resting behaviour of green turtles (Chelonia mydas) at a deep-neritic foraging area influences expression of life history traits
Figure 1. Generalised profiles for the six dive types as defined by Seminoff et al. (2006).
Data from: Patterns of sexual size dimorphism in Chelonia
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Transcriptomic analysis of pre-ovipositional embryonic arrest in the green sea turtle (Chelonia mydas)
GEO Series GSE197628. Chelonia mydas. 15 samples. Type: Expression profiling by high throughput sequencing.
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>
FIG. 4 in An Ethogram Describing the Nesting Behavior of Green Sea Turtles (Chelonia mŋdas)
FIG. 4.—The rate of rear flipper movement as a function of the completion of two behavioral stages for video-recorded Green Sea Turtles (Chelonia mŋdas) nesting on beaches in Brevard and Indian River counties, Florida, USA. (A) The digging stage (n ¼ 6); (B) the covering stage (n ¼ 8). See Results for detailed descriptions of behaviors involved in each stage; trend lines were used for visualization purposes only.
FIG. 3 in An Ethogram Describing the Nesting Behavior of Green Sea Turtles (Chelonia mŋdas)
FIG. 3.—The rate of flipper movement as a function of the completion of two behavioral stages for video-recorded Green Sea Turtles (Chelonia mŋdas) nesting on beaches in Brevard and Indian River counties, Florida, USA. (A) The body pitting stage (n ¼ 4); (B) the camouflaging stage (n ¼ 8). See Results for detailed descriptions of behaviors involved in each stage; trend lines were used for visualization purposes only.
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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.