Abstract
Automatic classification models of pelagic species based on trawl–acoustic survey data link echotrace characteristics observed in echosounder echograms to species caught in pelagic trawl hauls. In multispecies ecosystems, developing such models is particularly challenging because pelagic trawls provide probabilistic, proportion‑based species information rather than unambiguous labels for individual schools, which limits the applicability of strictly supervised training and motivates weakly-supervised (probabilistic) learning approaches. This study aims to improve the performance of a previously developed weakly-supervised multi-output classification model through a conservative self-training technique. Using the model’s probabilistic multispecies outputs, self-training gradually transforms the most confident predictions among the poorly-labelled cases into pseudo-labelled ones, increasing the ratio of labelled data and strengthening both model learning and evaluation for the next iterations. The approach was tested on a multi-output classification model trained on small pelagic species in the Bay of Biscay. Self-training improved overall out-of-sample accuracy from 63.5% to 72% (F1-score from 53.7% to 60.7%), maintaining or improving performance for each individual species. Notably, it substantially enhanced performance for species that are abundant but rarely captured in monospecific trawls, such as sardine, whose accuracy increased from 50% to 86% (F1-score from 27.3% to 52.7%). In contrast, performance for species with enough labelled cases remained stable throughout the process.
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Subjects
- Ecology
- Ocean sciences
- Zoology
Acknowledgements
We would like to thank the crew of the R/V ‘Ángeles Alvariño’ and R/V ‘Emma Bardán’ and the AZTI and IEO-CSIC staff who took part in the JUVENA survey, allowing the collection of the samples used in this study. A.L. has benefited from a Basque Government scholarship (PRE_2022_2_0096). The JUVENA survey was funded by the ‘Viceconsejería de Agricultura, Pesca y Políticas Alimentarias – Departamento de Desarrollo Económico y Sostenibilidad y Medio Ambiente’ of the Basque Government, the ‘Secretaría General de Pesca, Ministerio de Agricultura, Alimentación y Medio Ambiente’ of the Spanish Government and by the Spanish Institute of Oceanography (IEO). This is contribution number 1318 from AZTI, Marine Research, Basque Research and Technology Alliance (BRTA).
Funding
A.L. received a doctoral fellowship from the Basque Government to carry out this study (grant code: PRE_2022_2_0096). G.B. received funding to conduct the JUVENA survey from the ‘Viceconsejería de Agricultura, Pesca y Políticas Alimentarias – Departamento de Desarrollo Económico y Sostenibilidad y Medio Ambiente’ of the Basque Government; the ‘Secretaría General de Pesca, Ministerio de Agricultura, Alimentación y Medio Ambiente’ of the Spanish Government; and the Spanish Institute of Oceanography (IEO). This work was also partially funded by the European Union, within the framework of the Horizon Europe project MarineGuardian (Project No. 101212608). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or CINEA. Neither the European Union nor the granting authority can be held responsible for them.
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No experiments on live animals were conducted as part of this study. The manuscript is based exclusively on the analysis of acoustic and fisheries data previously collected during scientific surveys aimed at estimating the abundance of anchovy and other small pelagic species. These surveys were conducted under national fisheries data collection programmes funded by the Basque Government and the Spanish Government, in accordance with applicable national and European regulations for fisheries research and monitoring. No additional handling, manipulation, or experimental procedures on animals were performed for the purposes of this study.
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Lekanda, A., Boyra, G., Handegard, N.O. et al. A self-training approach to improve multispecies school classification models in fisheries acoustics data.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-62227-6
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DOI: https://doi.org/10.1038/s41598-026-62227-6
Keywords
- Acoustics
- Echotrace classification
- Self-training model
- Weakly-supervised
- Probabilistic
- School
- Small pelagics.
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