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N. Aubone, M. Saraceno, M. L. Torres Alberto, J. Campagna, L. Le Ster, B. Picard, M. Hindell, C. Campagna, C. R. Guinet. (2021). Physical changes recorded by a deep diving seal on the Patagonian slope drive large ecological changes (Vol. 223).
Keywords: Elephant seals Malvinas current Patagonian shelf slope Southwestern Atlantic Ocean
Programme: 1201
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. (2023). Improved accuracy and spatial resolution for bio-logging-derived chlorophyll a fluorescence measurements in the Southern Ocean (Vol. 10).
Keywords: bio-logging tag chla fluorescence Sensor calibration Southern elephant seal Southern Ocean Submesoscale
Programme: 1201
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. (2022). Ice fog observed at cirrus temperatures at Dome C, Antarctic Plateau (Vol. 22).
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. (2016). (Vol. 39).
Keywords: High Arctic Paraglacial Sedimentary flux Submarine and aerial coastal evolution Svalbard
Programme: 1223
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. (2024). Spatial distribution of selenium-mercury in Arctic seabirds (Vol. 343).
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. (2023). (Vol. 96).
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Olivia Hicks, Akiko Kato, Frederic Angelier, Danuta M. Wisniewska, Catherine Hambly, John R. Speakman, Coline Marciau, Yan Ropert-Coudert. (2020). Acceleration predicts energy expenditure in a fat, flightless, diving bird (Vol. 10).
Keywords: Ecology Ecophysiology
Programme: 1091
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. (2022). The role of individual variability on the predictive performance of machine learning applied to large bio-logging datasets (Vol. 12).
Abstract: Animal-borne tagging (bio-logging) generates large and complex datasets. In particular, accelerometer tags, which provide information on behaviour and energy expenditure of wild animals, produce high-resolution multi-dimensional data, and can be challenging to analyse. We tested the performance of commonly used artificial intelligence tools on datasets of increasing volume and dimensionality. By collecting bio-logging data across several sampling seasons, datasets are inherently characterized by inter-individual variability. Such information should be considered when predicting behaviour. We integrated both unsupervised and supervised machine learning approaches to predict behaviours in two penguin species. The classified behaviours obtained from the unsupervised approach Expectation Maximisation were used to train the supervised approach Random Forest. We assessed agreement between the approaches, the performance of Random Forest on unknown data and the implications for the calculation of energy expenditure. Consideration of behavioural variability resulted in high agreement (>?80%) in behavioural classifications and minimal differences in energy expenditure estimates. However, some outliers with?70% of agreement, highlighted how behaviours characterized by signal similarity are confused. We advise the broad bio-logging community, approaching these large datasets, to be cautious when upscaling predictions, as this might lead to less accurate estimates of behaviour and energy expenditure.
Keywords: Behavioural ecology Ecological modelling Ecophysiology Machine learning
Programme: 1091
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. (2024). (Vol. 204).
Keywords: Antarctica Breeding habitat quality Human disturbance Population dynamics Population monitoring Pygoscelis adeliae
Programme: 1091
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. (2023). (Vol. 11).
Keywords: Antarctica basal corticosterone disturbance Human activity Pygoscelis adeliae seabird stress response stress-induced corticosterone
Programme: 1091
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