Abstract
Cleaner fish engage in mutualistic interactions by removing ectoparasites from client species, a behaviour that has traditionally been quantified through labour-intensive manual video analysis. This method is not only time-consuming but also susceptible to human error and bias. In this study, we developed a semi-automated system to track and classify cleaning interactions between the cleaner wrasse (Labroides dimidiatus) and the powder blue tang (Acanthurus leucosternon) in a controlled three-dimensional laboratory setting. We employed DeepLabCut (DLC), a deep learning-based tool for markerless pose estimation, to track both fish species simultaneously. The resulting model reliably tracked both individuals with low error rates. Using the tracking data, we designed a classification algorithm that detected cleaning interactions with 90% accuracy. Although the algorithm misclassified approximately 15% of non-interactions as interactions, it successfully identified 25% of video content as containing interactions, thereby reducing the amount of footage requiring manual annotation by 75%. This approach significantly decreases human labour while maintaining high classification performance. Overall, our system represents a valuable step toward automating behavioural analysis in marine mutualisms and can serve as a foundation for broader applications in ethology and conservation research.
Similar content being viewed by others
AI-driven classification and precision cutting algorithms using machine vision in a customer-operated fish processing system
Applying deep learning and the ecological home range concept to document the spatial distribution of Atlantic salmon parr (Salmo salar L.) in experimental tanks
In situ swimming behavior of the Mariana snailfish Pseudoliparis swirei
Acknowledgements
We thank all members of the Behavioural Complexity Lab and Eve Otjacques for stimulating discussions and comments on this manuscript. We thank Professor Helena Nunes, for their invaluable guidance and insightful feedback.
Funding
The project that gave rise to these results received the support of a fellowship from the “la Caixa” Foundation (ID 100010434). The fellowship code is LCF/BQ/PR24/12050006. This work was supported by FLAD Science Award Atlantic—AtlanticDiversa (Proj. 2026/0095) funded by FLAD— Fundação Luso-Americana para o Desenvolvimento. This work was also supported by FCT—Fundação para a Ciência e Tecnologia, I.P., within the grant PTDC/BIA-BMA/0080/2021—ChangingMoods (https://doi.org/10.54499/PTDC/BIA-BMA/0080/2021) to JRP. This work was supported by the strategic project of MARE – Centro de Ciências do Mar e do Ambiente (Reference: UID/04292/2025; DOI: https://doi.org/10.54499/UID/04292/2025), by the Portuguese Recovery and Resilience Plan (PRR) funding attributed to MARE (Reference: UID/PRR/04292/2025; DOI: https://doi.org/10.54499/UID/PRR/04292/2025), by the EQUIPAR+2 programme attributed to MARE (Reference: UID/PRR2/04292/2025), and by the Associate Laboratory ARNET – Aquatic Research Infrastructure Network (Reference: LA/P/0069/2020; DOI: https://doi.org/10.54499/LA/P/0069/2020).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Supplementary Material 1 (download DOCX )
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Reprints and permissions
About this article
Cite this article
Oliveira, R., Garcia, N.C. & Paula, J.R. Semi-automated detection of cleaning interactions using supervised machine learning.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-56200-6
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-026-56200-6
Keywords
- Animal behaviour
- Automation
- Cleaning mutualisms
- DeepLabCut
- Interaction classification
Labroides dimidiatus
Source: Ecology - nature.com
