{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow 1 , Glasgow, UK",
    "Instituto de Investigação em Ciências do Mar – Okeanos, University of the Azores 2 , Ponta Delgada, Azores, Portugal",
    "Tjärnö Marine Laboratory, University of Gothenburg 3 , Strömstad, Västra Götaland County, Sweden",
    "Gothenburg Global Biodiversity Centre, University of Gothenburg 4 , Gothenburg, Västra Götaland County, Sweden",
    "Department of Geography, School of the Human Environment, University College Cork 5 , Cork, County Cork, Ireland",
    "Centro de Estudios Avanzados de Blanes, Spanish National Research Council 6 , Blanes, Spain",
    "Instituto Español de Oceanografía, IEO-CSIC, Spanish National Research Council 7 , Santander, Spain",
    "The Lyell Centre, Heriot-Watt University 8 , Edinburgh, UK",
    "Centre for Environmental and Marine Studies (CESAM), Department of Biology, University of Aveiro 9 , Aveiro, Aveiro District, Portugal",
    "Department of Earth, Environmental and Life Sciences (DISTAV), University of Genoa 10 , Genoa, Liguria, Italy",
    "Italian National Antarctic Museum (MNA, Section of Genoa), University of Genoa 11 , Genoa, Liguria, Italy",
    "National Biodiversity Future Center (NBFC) 12 , Palmero, Italy",
    "Ocean and Earth Science, University of Southampton 13 , Southampton, UK",
    "Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research 14 , Bremerhaven, HB, Germany",
    "Department of Biology/Chemistry, University of Bremen 15 , Bremen, HB, Germany",
    "Consejo Superior de Investigaciones Científicas (ICM-CSIC), Institut de Ciències del Mar 16 , Barcelona, Spain",
    "IEO-CSIC, Spanish National Research Council, Instituto Español de Oceanografía 17 , Madrid, Community of Madrid, Spain",
    "Scalloway Campus, Shetland College UHI 18 , Lerwick, UK",
    "University of Aberdeen 19 , Aberdeen, UK",
    "School of GeoSciences, The University of Edinburgh 20 , Edinburgh, UK",
    "School of Mathematics & Statistics, University of Glasgow 21 , Glasgow, UK",
    "Department of Computer Science, University of Copenhagen 22 , Copenhagen, Capital Region of Denmark, Denmark"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 20,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Laurence H. De Clippele",
      "orcid": "0000-0002-4097-274X"
    },
    {
      "affs": [
        1
      ],
      "name": "Joao Balsa"
    },
    {
      "affs": [
        2,
        3
      ],
      "name": "Lara M. Beckmann"
    },
    {
      "affs": [
        4
      ],
      "name": "Corie M.B. Boolukos"
    },
    {
      "affs": [
        5
      ],
      "name": "Itziar Burgues"
    },
    {
      "affs": [
        6
      ],
      "name": "Anna De la Torriente Diez"
    },
    {
      "affs": [
        7
      ],
      "name": "Blair A.A. Easton"
    },
    {
      "affs": [
        8
      ],
      "name": "Teresa C. Ferreira"
    },
    {
      "affs": [
        9,
        10,
        11
      ],
      "name": "Alice Guzzi"
    },
    {
      "affs": [
        12
      ],
      "name": "Lucy Harris",
      "orcid": "0009-0006-9756-5957"
    },
    {
      "affs": [
        13,
        14
      ],
      "name": "Marie E. Kaufmann",
      "orcid": "0009-0003-1220-8730"
    },
    {
      "affs": [
        15
      ],
      "name": "Ariadna Martínez-Dios",
      "orcid": "0000-0001-7136-4010"
    },
    {
      "affs": [
        16
      ],
      "name": "Jose C. Mendoza"
    },
    {
      "affs": [
        17
      ],
      "name": "Tanya G. Riley"
    },
    {
      "affs": [
        13,
        14
      ],
      "name": "Vicente I. Villalobos"
    },
    {
      "affs": [
        18
      ],
      "name": "H. Poppy Clark",
      "orcid": "0000-0002-1667-9922"
    },
    {
      "affs": [
        1,
        15
      ],
      "name": "Carlos Dominguez Carrió"
    },
    {
      "affs": [
        19
      ],
      "name": "Johanne Vad"
    },
    {
      "affs": [
        20
      ],
      "name": "Tiffany Vlaar"
    },
    {
      "affs": [
        21
      ],
      "name": "Abraham G. Smith"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3rnk5ea9p1qqx.bib",
  "cited_by": 0,
  "doi": "10.1098/rsos.251833",
  "fulltext_url": null,
  "id": "3rnk5ea9p1qqx",
  "issns": [
    "2054-5703"
  ],
  "landing_url": "https://royalsocietypublishing.org/rsos/article/13/9/251833/483440/Using-machine-learning-effectively-in-marine",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1910|Marine Biology and Ecology Research"
    ]
  ],
  "openalex": "W7214948656",
  "pdf_url": "https://royalsocietypublishing.org/rsos/article-pdf/doi/10.1098/rsos.251833/6168051/rsos.251833.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-30",
  "publisher": "The Royal Society",
  "ris": "https://research.draconitic.com/paper/3rnk5ea9p1qqx.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:45Z",
      "native_id": "10.1098/rsos.251833",
      "source": "crossref",
      "url": "https://royalsocietypublishing.org/rsos/article/13/9/251833/483440/Using-machine-learning-effectively-in-marine"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "openalex:1910|Marine Biology and Ecology Research",
      "subfield": "oceanography"
    }
  ],
  "title": "Using machine learning effectively in marine benthic ecology: a question-driven toolkit",
  "title_text": "Using machine learning effectively in marine benthic ecology: a question-driven toolkit",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:45Z",
  "url": "https://research.draconitic.com/paper/3rnk5ea9p1qqx",
  "venue": "Royal Society Open Science",
  "versions": []
}