{
  "abstract": "Global compilations of coral bleaching surveys are increasingly used to train machine-learning models of bleaching severity, but such models are often evaluated with random train–test splits that ignore duplicated records, spatial dependence and differences among contributing survey programmes. We re-examined an earlier analysis of the Global Coral Bleaching Database (BCO-DMO dataset 773466) that reported a random-split random forest R² of 0.688. The result was reproducible, but it was obtained on 32,716 records, 34.5% of which duplicated another record exactly because Reef Check surveys are stored once per substrate category. After collapsing records to 21,527 sampling events, we compared random, site-grouped, spatial-block (1–10°), leave-one-realm-out and forward temporal validation for models using coordinates, thermal-stress metrics, other environmental variables, or indicators of the contributing data source. Random-forest R² fell from 0.60 (random) to 0.53 (new sites), 0.18 (1° blocks) and 0.02 (10° blocks), and was at most 0.06 in held-out realms. Coordinates alone predicted worse than the mean under all block designs. Indicators of the contributing source alone explained 0.35–0.37 of the variance under every cross-validation design, more than any environmental model under spatial validation. Within the single-protocol Reef Check subset, no predictor set achieved positive R² under spatial blocks. The all-predictor model retained moderate ability to rank events with at least 50% bleaching (AUC 0.73 under 5° blocks). These results indicate that the apparent skill of pooled bleaching-severity models largely reflects record duplication, interpolation between nearby surveys, and heterogeneity in how bleaching was measured, rather than transferable environment–bleaching relationships.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "name": "Arav Siddharth Mestry"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fy2eazfgwek3y.bib",
  "cited_by": 0,
  "doi": "10.31223/x56c02",
  "fulltext_url": null,
  "id": "fy2eazfgwek3y",
  "issns": [],
  "landing_url": "https://eartharxiv.org/repository/view/15638/",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/legalcode",
  "metadata_licence": null,
  "native_subjects": [
    [
      "bepress",
      "Climate"
    ],
    [
      "bepress",
      "Environmental Indicators and Impact Assessment"
    ],
    [
      "bepress",
      "Marine Biology"
    ],
    [
      "bepress",
      "Oceanography"
    ],
    [
      "bepress",
      "Statistical Methodology"
    ],
    [
      "bepress",
      "coral bleaching"
    ],
    [
      "bepress",
      "data heterogeneity"
    ],
    [
      "bepress",
      "data leakage"
    ],
    [
      "bepress",
      "machine learning"
    ],
    [
      "bepress",
      "spatial cross-validation"
    ],
    [
      "bepress",
      "thermal stress"
    ],
    [
      "openalex",
      "2303|Coral and Marine Ecosystems Studies"
    ]
  ],
  "openalex": "W7223124745",
  "pdf_url": "https://eartharxiv.org/repository/object/15638/download/27089/",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-21",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/fy2eazfgwek3y.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:49Z",
      "native_id": "15638",
      "source": "eartharxiv",
      "url": "https://eartharxiv.org/repository/view/15638/"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "bepress:Marine Biology",
      "subfield": "ecology"
    },
    {
      "field": "earth",
      "origin": "bepress:Climate",
      "subfield": "climate"
    },
    {
      "field": "earth",
      "origin": "bepress:Oceanography",
      "subfield": "oceanography"
    }
  ],
  "title": "Random-split validation overstates the skill of machine-learning models of global coral bleaching severity",
  "title_text": "Random-split validation overstates the skill of machine-learning models of global coral bleaching severity",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:49Z",
  "url": "https://research.draconitic.com/paper/fy2eazfgwek3y",
  "venue": "EarthArXiv",
  "versions": []
}