ArticleMovement ecology2021
Using tri-axial accelerometer loggers to identify spawning behaviours of large pelagic fish.
Article in Movement ecology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 41 citations in OpenAlex.
- Practical guidelines for validation of supervised machine learning models in accelerometer-based animal behaviour classification.The Journal of animal ecology · 2025Pooled it
- Stroke rate detection from accelerometer data on marine mammals.The Journal of experimental biology · 2026Article
- Six decades of animal accelerometry: trends, applications, and future directions.Movement ecology · 2026Review
- Structure and composition of yellowtail kingfish (Seriola lalandi) otoliths: implications for using aquaculture-reared fish to reconstruct life histories.Journal of fish biology · 2025Article
- Article
- From biologging to conservation: Tracking individual performance in changing environments.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Use of accelerometry to measure the dynamics of activity patterns of Atlantic bluefin tuna after tagging and release.Movement ecology · 2025Article
- A benchmark for computational analysis of animal behavior, using animal-borne tags.Movement ecology · 2024Article
- How to treat mixed behavior segments in supervised machine learning of behavioural modes from inertial measurement data.Movement ecology · 2024Article
- African dryland antelope trade-off behaviours in response to heat extremes.Ecology and evolution · 2024Article
- Heart Rate and Acceleration Dynamics during Swim-Fitness and Stress Challenge Tests in Yellowtail Kingfish (Biology · 2024Article
- Acoustic accelerometer transmitters and their growing relevance to aquatic science.Movement ecology · 2023Review
- The role of individual variability on the predictive performance of machine learning applied to large bio-logging datasets.Scientific reports · 2022Article
- Classifying Goliath Grouper (Sensors (Basel, Switzerland) · 2021Article
- Plan ahead, or wing it? How storm-petrel parents adjust food delivery to young chicks.Behavioral ecology : official journal of the International Society for Behavioral EcologyArticle
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundTri-axial accelerometers have been used to remotely describe and identify in situ behaviours of a range of animals without requiring direct observations. Datasets collected from these accelerometers (i.e. acceleration, body position) are often large, requiring development of semi-automated analyses to classify behaviours. Marine fishes exhibit many "burst" behaviours with high amplitude accelerations that are difficult to interpret and differentiate. This has constrained the development of accurate automated techniques to identify different "burst" behaviours occurring naturally, where direct observations are not possible.
methodsWe trained a random forest machine learning algorithm based on 624 h of accelerometer data from six captive yellowtail kingfish during spawning periods. We identified five distinct behaviours (swim, feed, chafe, escape, and courtship), which were used to train the model based on 58 predictive variables.
resultsOverall accuracy of the model was 94%. Classification of each behavioural class was variable; F
conclusionOur findings provide a novel approach of applying a supervised machine learning model on free-ranging animals, which has previously been predominantly constrained to direct observations of behaviours and not predicted from an unseen dataset. Additionally, our findings identify typically ambiguous spawning and courtship behaviours of a large pelagic fish as they naturally occur.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.