ArticleRoyal Society open science2020
Behavioural inference from signal processing using animal-borne multi-sensor loggers: a novel solution to extend the knowledge of sea turtle ecology.
Article in Royal Society open science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Refining Accelerometer-Based Animal Behaviour Classifications With Sequence-Informed Post-Processing.Ecology and evolution · 2026Article
- Observing abilities of satellite-tagged sea turtles: comparison of reconstructed temperature profiles with ocean model data in the Adriatic and Ionian Seas.Scientific reports · 2026Article
- Review
- Incorporating fine-scale behaviors into habitat suitability modeling: A case study for sea turtles.Ecological applications : a publication of the Ecological Society of America · 2025Article
- Fibropapillomatosis Dynamics, Severity and Demographic Effect in Caribbean Green Turtles.EcoHealth · 2025Article
- A benchmark for computational analysis of animal behavior, using animal-borne tags.Movement ecology · 2024Article
- Automatic identification of the endangered hawksbill sea turtle behavior using deep learning and cross-species transfer learning.The Journal of experimental biology · 2024Article
- Short-Term Effects of Attaching Animal-Borne Devices on the Behavior of Juvenile Green Turtles.Ecology and evolution · 2024Article
- The response of sea turtles to vocalizations opens new perspectives to reduce their bycatch.Scientific reports · 2024Article
- The urban lives of green sea turtles: Insights into behavior in an industrialized habitat using an animal-borne camera.Ecology and evolution · 2024Article
- Predictive model of sperm whale prey capture attempts from time-depth data.Movement ecology · 2023Article
- The role of individual variability on the predictive performance of machine learning applied to large bio-logging datasets.Scientific reports · 2022Article
- Multivariate analysis of biologging data reveals the environmental determinants of diving behaviour in a marine reptile.Royal Society open science · 2022Article
- Estimation of the Maternal Investment of Sea Turtles by Automatic Identification of Nesting Behavior and Number of Eggs Laid from a Tri-Axial Accelerometer.Animals : an open access journal from MDPI · 2022Article
- Article
- Equine Activity Time Budgets: The Effect of Housing and Management Conditions on Geriatric Horses and Horses with Chronic Orthopaedic Disease.Animals : an open access journal from MDPI · 2021Article
- Assessment of Machine Learning Models to Identify Port Jackson Shark Behaviours Using Tri-Axial Accelerometers.Sensors (Basel, Switzerland) · 2020Article
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Authors and funding
27 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The identification of sea turtle behaviours is a prerequisite to predicting the activities and time-budget of these animals in their natural habitat over the long term. However, this is hampered by a lack of reliable methods that enable the detection and monitoring of certain key behaviours such as feeding. This study proposes a combined approach that automatically identifies the different behaviours of free-ranging sea turtles through the use of animal-borne multi-sensor recorders (accelerometer, gyroscope and time-depth recorder), validated by animal-borne video-recorder data. We show here that the combination of supervised learning algorithms and multi-signal analysis tools can provide accurate inferences of the behaviours expressed, including feeding and scratching behaviours that are of crucial ecological interest for sea turtles. Our procedure uses multi-sensor miniaturized loggers that can be deployed on free-ranging animals with minimal disturbance. It provides an easily adaptable and replicable approach for the long-term automatic identification of the different activities and determination of time-budgets in sea turtles. This approach should also be applicable to a broad range of other species and could significantly contribute to the conservation of endangered species by providing detailed knowledge of key animal activities such as feeding, travelling and resting.
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