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32 results for “Sea Pressure”
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>
Geospatial dataset on human uses, environmental components and MSFD pressure for the Adriatic Sea
<p>Geospatial dataset on human uses, environmental components and MSFD pressure for the Adriatic Sea (reference year 2014-2015). The datasets are derived from ADRIPLAN Portal (http://data.adriplan.eu/) and is suitable for cumulative effects assessment (CEA), maritime use conflict (MUC) analysis and CEA-based marine ecosystem service threat analysis (MES-Threat).</p> <p> </p>
Remote Sensing based Sea Surface partial pressure of CO2 (pCO2) in China Seas (2003-2019)
<p>This dataset is sea surface partial pressure of CO2 (pCO2) in China seas (0-42°N, 105-132°E) over 2003-2019 with a spatial resolution of 1km and temporal resolution of a month. This is our second version of pCO2 in China seas. The first version was published on the SatCO2 website (http://www.satco2.com/index.php?m=content&c=index&a=show&catid=317&id=188).</p> <p>We produce this dataset by creating a boost machine learning algorithm (XGBoost) based on the gradient boost decision tree (GBDT). The input parameters used in this are sea surface temperature (SST), chlorophyll-a concentration (Chl-a), remote sensing reflectance of three bands (Rrs412, 443, 488 nm), the temperature difference in longitude direction (SST_DIF), and the theoretical background <em>p</em>CO<sub>2</sub> (<em>p</em>CO<sub>2</sub>_therm) under corresponding SST. SST_DIF is derived from SST by subtracting the mean value at the same latitude. To calculate <em>p</em>CO<sub>2</sub>_therm, we first assume that the seawater background <em>p</em>CO<sub>2</sub> equals the annual average atmospheric <em>p</em>CO<sub>2</sub> (sea-air <em>p</em>CO<sub>2</sub> balanced under ideal conditions), and the equilibrium temperature is assumed to be the yearly average SST; Then, the background <em>p</em>CO<sub>2</sub> is corrected according to SST to obtain <em>p</em>CO<sub>2</sub>_therm. Air pressure at sea level (SLP) and mole fraction of CO<sub>2</sub> in the air (xCO<sub>2</sub>) are employed when calculating atmospheric <em>p</em>CO<sub>2</sub>. </p> <p>The underway <em>p</em>CO<sub>2</sub> is first gridded to monthly 1 km and then divided into the training and validation sets with the volumes 151009 and 32584 samples. The validation set shows coefficients of determination (R<sup>2</sup>) between the XGBoost model-predicted and in-situ <em>p</em>CO<sub>2</sub> were 0.86, and the root means squared errors (RMSE) for the <em>p</em>CO<sub>2</sub> were 21.1 μatm.</p>
Data from: breeding under pressure: corticosterone is associated with reproductive investment under fluctuating predation risk in a long-lived sea duck
<p>Phenotypic plasticity may enable individuals to cope with predictable and unpredictable environments during their life-cycle. In that context, studying glucocorticoids – corticosterone (CORT) in birds – is relevant because of their primary role in allostasis. Higher baseline CORT levels are classically associated with environmental constraints and lower fitness (the CORT-fitness hypothesis). However, in some environments, higher baseline CORT levels can promote reproduction, therefore being associated with higher fitness (the CORT-adaptation hypothesis). These two hypotheses have been tested in multiple systems, but rarely in a context of fluctuating predation threat.</p> <p>We <span>used a </span>long-term individual-based monitoring of baseline CORT levels in female common eiders <em>Somateria mollissima</em> (n<sub>CORT</sub> = 1537; n<sub>individual</sub> = 790; 2009-2022) to disentangle the context-dependent links between environmental conditions, CORT and fitness. Importantly, the study population has been facing a drastic increase in predation pressure over the past decades, linked to the recovery of the white-tailed eagle <em>Haliaeetus albicilla</em>. Additionally, eiders breed on open or forested islands, further affecting adult and nest predation risk. This system allowed us to disentangle the relative contributions of within- and among-individual variation in baseline CORT levels under predation.</p> <p>Supporting the CORT-adaptation hypothesis, baseline CORT levels were positively associated with reproductive investment (clutch size), age and hatching success. By partitioning within- and among-individual effects, we showed that at the individual level, CORT flexibly increased with clutch size and age. Females displaying higher CORT levels were more successful, suggesting a link between CORT and individual quality.</p> <p><span>At both the population and individual levels, baseline CORT levels decreased over the study period. This decrease was correlated with an increase in predation risk. Females had reduced baseline CORT when nesting under high eagle abundance or adult predation risk (within-individual effect). Interestingly, apparent plasticity towards adult predation risk was only observed on open islands, likely reflecting habitat-dependent strategies.</span></p> <p><span>Consistent with the CORT-adaptation hypothesis, we show that changes in predation regime not only correlate with changes in reproductive investment, but also with rapid plastic adjustment of glucocorticoid levels and therefore individual strategies to cope with predation risk. Given the correlative nature of our study, we encourage further experimental studies testing for a causal relationship between predation and corticosterone levels.</span></p>
Data from: breeding under pressure: corticosterone is associated with reproductive investment under fluctuating predation risk in a long-lived sea duck
Open the record for dataset details and reuse information.
Data for "Northern Pacific Sea-level Pressure Controls Rain-on-Snow in North America"
<p>This is the data for the following work. </p> <p>Rasiya Koya, S., Kar, K.K. & Roy, T. Northern Pacific sea-level pressure controls rain-on-snow in North America. Commun Earth Environ 5, 260 (2024). https://doi.org/10.1038/s43247-024-01431-6</p>
Mean Sea Level Pressure - Hurricane Beryl Analysis with and without a cold pool scheme
<p>This video compares the cold pool scheme, the control scheme, and ERA5 data. For more information, please contact the author.</p>
Sea Temperature and Pressure Data Measured by CTD
Open the record for dataset details and reuse information.
High Altitude (HA) Residents With Pulmonary Vascular Diseseases (PVD), Pulmonary Artery Pressure (PAP) Assessed at HA (2840m) vs Sea Level (LA)
ClinicalTrials.gov study NCT06092424. IPD Sharing: YES. Countries: 1. Publications: 0.
Australian atmospheric pressure and sea level data during the 2022 Hunga-Tonga Hunga-Ha'apai volcano tsunami
<p>This archive contains time series of mean sea level pressure and sea level in Australia before and after the 2022-01-15 Hunga-Tonga Hunga Hapai volcanic tsunami. An associated journal publication has been submitted to the journal Scientific Data, with title "Australian atmospheric pressure and sea level data during the 2022 Hunga-Tonga Hunga-Ha'apai volcano tsunami" </p>
Fig. 5 in Seasonal response of benthic foraminifera to anthropogenic pressure in two stations of the Gulf of Trieste (northern Adriatic Sea, Italy): the marine protected area of Miramare versus the Servola water sewage outfall
Fig. 5: Distribution of the foraminiferal density of those species and genera comprising at least 4% of the assemblage in at least one sample from the studied stations. Red histogram: Ser station; blue histogram: Res station. Note that y-axis scale is variable depending on species foraminiferal density.
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>
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