{
  "abstract": "1. Detecting causal relationships from ecological time series remains challenging because community dynamics are often nonlinear, high-dimensional, noisy, and strongly synchronized. These characteristics can reduce the reliability of existing causal inference methods and complicate the identification of ecological interactions in complex natural systems. 2. I introduce Forest-weighted Causal Correlation (FORCC), a machine-learning-based framework that infers directional causality from adaptive neighborhood structures learned by random forests. Rather than evaluating causal direction through cross-map reconstruction, FORCC quantifies directional dependence from the preservation of neighborhood structure using rank-based associations among forest-weight matrices. 3. Simulations of coupled nonlinear systems showed that FORCC accurately recovered causal direction while substantially reducing synchronization-induced false-positive detections relative to conventional convergent cross mapping. Chatterjee's {xi} exhibited the strongest resistance to synchronization-induced errors among the evaluated rank-based statistics. 4. Application to a 12-year marine community time series from Maizuru Bay, Japan, identified asymmetric relationships between individual taxa and community dynamics, revealing candidate driver-like and passenger-like roles that were not readily explained by seasonal synchrony alone. 5. By combining adaptive neighborhood learning with rank-based causal inference, FORCC provides a scalable framework for causal discovery in ecological time series. The method offers a practical tool for screening causal relationships, generating ecological hypotheses, and investigating complex interactions in multivariate natural systems.",
  "abstract_shown": true,
  "affiliations": [
    "National Agriculture and Food Research Organization"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "S. Kishi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4yn2e95f57qzs.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.01.756118",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.01.756118v1.full",
  "id": "4yn2e95f57qzs",
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  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.01.756118v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "ecology"
    ],
    [
      "openalex",
      "2306|Ecosystem dynamics and resilience"
    ]
  ],
  "openalex": "W7220921198",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.01.756118v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4yn2e95f57qzs.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:26:41Z",
      "native_id": "10.64898/2026.10.01.756118",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.01.756118v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:ecology",
      "subfield": "ecology"
    }
  ],
  "title": "Forest-weighted causal correlation: A scalable framework for causal inference in ecological time series",
  "title_text": "Forest-weighted causal correlation: A scalable framework for causal inference in ecological time series",
  "type": "preprint",
  "updated_at": "2026-10-30T12:26:41Z",
  "url": "https://research.draconitic.com/paper/4yn2e95f57qzs",
  "venue": "bioRxiv",
  "versions": []
}