{
  "abstract": "Astraeus hygrometricus, a wild mushroom, is an important nutrition-rich edible non-timber forest resource, threatened by environmental and climatic change. This study developed an explainable GeoAI framework to model its habitat suitability in the lateritic forests of eastern India. Using 514 field observations and 32 geo-environmental predictors, five machine-learning models were optimised through Bayesian optimisation and evaluated using spatial cross-validation. Explainability and feature selection were assessed using SHAP, Boruta, and nature-inspired algorithms, while CMIP6 scenarios were used to evaluate future suitability. Model performance was consistently high (AUC = 0.979–0.993), with XGB and GBM achieving the highest AUC (0.993). Very-high suitability declined from 26.47% under baseline conditions to 23.03% under SSP245 and 21.10% under SSP585. LULC, radar backscatter, soil temperature, and elevation were important predictors. The framework provides a robust approach for identifying climate-sensitive habitats and supporting conservation and sustainable management of this valuable forest resource.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Agricultural Engineering, Institute of Agriculture, Visva-Bharati (A Central University), Sriniketan, West Bengal, India",
    "Department of Microbiology, Burdwan University, Burdwan, West Bengal, India",
    "Department of Geoinformatics, School of Natural Resource Management, Central University of Jharkhand, Ranchi, India",
    "Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Civil, Environmental and Sociotechnical Engineering, Faculty of Engineering and IT, University of Technology Sydney, Sydney, NSW, Australia",
    "Department of Geology and Geophysics, College of Science, King Saud University, Riyadh, Saudi Arabia"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Chiranjit Singha"
    },
    {
      "affs": [
        0
      ],
      "name": "Kishore Chandra Swain"
    },
    {
      "affs": [
        1
      ],
      "name": "Amlan Mahata"
    },
    {
      "affs": [
        2
      ],
      "name": "Bikash Ranjan Parida"
    },
    {
      "affs": [
        3
      ],
      "name": "Biswajeet Pradhan"
    },
    {
      "affs": [
        4
      ],
      "name": "Abdullah Alamri"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/30erwj6e4aysw.bib",
  "cited_by": 0,
  "doi": "10.1080/10106049.2026.2757955",
  "fulltext_url": "https://www.tandfonline.com/doi/10.1080/10106049.2026.2757955",
  "id": "30erwj6e4aysw",
  "issns": [
    "1752-0762",
    "1010-6049"
  ],
  "landing_url": "https://doi.org/10.1080/10106049.2026.2757955",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "GB3-5030"
    ],
    [
      "openalex",
      "2302|Species Distribution and Climate Change"
    ]
  ],
  "openalex": "W7225238370",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-05",
  "publisher": "Taylor & Francis Group",
  "ris": "https://research.draconitic.com/paper/30erwj6e4aysw.ris",
  "sources": [
    {
      "first_seen": "2026-11-05T14:21:03Z",
      "native_id": "4f06e77d24564eb6a050b45222642701",
      "source": "doaj",
      "url": "https://doi.org/10.1080/10106049.2026.2757955"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:2302|Species Distribution and Climate Change",
      "subfield": "ecology"
    }
  ],
  "title": "Spatial habitat suitability modelling of Astraeus Hygrometricus using explainable machine learning and GeoAI approaches",
  "title_text": "Spatial habitat suitability modelling of Astraeus Hygrometricus using explainable machine learning and GeoAI approaches",
  "type": "peer-reviewed",
  "updated_at": "2026-11-06T14:20:32Z",
  "url": "https://research.draconitic.com/paper/30erwj6e4aysw",
  "venue": "Geocarto International",
  "versions": []
}