Using machine learning effectively in marine benthic ecology: a question-driven toolkit

Abstract Manual annotation of marine image datasets is time-consuming, driving interest in machine learning (ML) to automate image analysis. However, benthic ecologists often lack the expertise needed to determine when and how ML can be applied effectively to their datasets. To explore the practical use of ML in benthic ecology, user perceptions and model training experiences were collected during a workshop. RootPainter, an interactive, corrective-annotation ML software, was used to introduce ML concepts and workflows. Participants developed models to segment benthic organisms from time-lapse and remotely operated vehicle imagery. Real-time feedback during training improved efficiency and understanding of model behaviour. Pre- and post-project qualitative surveys identified ecological and technical factors influencing model performance. From these findings, we propose five questions to guide efficient, robust annotation of marine benthic images for ML model development: (i) ‘do you have expert taxonomic and machine learning knowledge?’; (ii) ‘what is your ecological research question?’; (iii) ‘is the species sufficiently abundant?’; (iv) ‘are you consistent in your selection of training data?’; and (v) ‘have you anticipated the strengths and limitations of your imaging tool?’. Responses to these questions can provide practical guidance to help marine ecologists make informed choices, accelerate model training and improve performance.

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Publication Details

Journal
Royal Society Open Science
Published
2026-09-30
DOI
https://doi.org/10.1098/rsos.251833
Primary Topic
Species Distribution and Climate Change
Type
article
Field-Weighted Citation Impact
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article

Using machine learning effectively in marine benthic ecology: a question-driven toolkit

Abraham George Smith, Tanya G Riley, Alice Guzzi, Laurence Helene De Clippele et al.
Royal Society Open Science
Species Distribution and Climate Change
article

Using machine learning effectively in marine benthic ecology: a question-driven toolkit

Abraham George Smith, Tanya G Riley, Alice Guzzi, Laurence Helene De Clippele, Ariadna Martínez‐Dios, Lara Beckmann, Corie M. B. Boolukos, Johanne Vad, Itziar Burgués, H. Poppy Clark, Carlos Domínguez Carrió, José Carlos Mendoza, Lucy M. Harris, Anna De la Torriente Diez, Teresa C. Ferreira, Vicente I. Villabos, Joao Balsa, Blair A.A. Easton, Tiffany Vlaar, Marie E. Kaufmann
article en

Abstract

Abstract Manual annotation of marine image datasets is time-consuming, driving interest in machine learning (ML) to automate image analysis. However, benthic ecologists often lack the expertise needed to determine when and how ML can be applied effectively to their datasets. To explore the practical use of ML in benthic ecology, user perceptions and model training experiences were collected during a workshop. RootPainter, an interactive, corrective-annotation ML software, was used to introduce ML concepts and workflows. Participants developed models to segment benthic organisms from time-lapse and remotely operated vehicle imagery. Real-time feedback during training improved efficiency and understanding of model behaviour. Pre- and post-project qualitative surveys identified ecological and technical factors influencing model performance. From these findings, we propose five questions to guide efficient, robust annotation of marine benthic images for ML model development: (i) ‘do you have expert taxonomic and machine learning knowledge?’; (ii) ‘what is your ecological research question?’; (iii) ‘is the species sufficiently abundant?’; (iv) ‘are you consistent in your selection of training data?’; and (v) ‘have you anticipated the strengths and limitations of your imaging tool?’. Responses to these questions can provide practical guidance to help marine ecologists make informed choices, accelerate model training and improve performance.

Royal Society Open ScienceVol. 13(9)
Universidade dos Açores (PT), University of Copenhagen (DK), Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung (DE), Hochschule Bremen (DE), University of Bremen (DE), University of Aberdeen (GB), National Oceanography Centre (GB), University College Cork (IE), Heriot-Watt University (GB), University College Copenhagen (DK), Instituto Español de Oceanografía (ES), Centre d'Estudis Avançats de Blanes (ES), Institut Català de Ciències del Clima (ES), National Research Council (LK), University of Southampton (GB), University of Aveiro (PT), University of Glasgow (GB), University of Genoa (IT), University of Gothenburg (SE), University of Edinburgh (GB)
Life below water
Openalex Percentile: Top 14%
Species Distribution and Climate Change
DOIOpen Access PDFOpenAlex
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