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Peer-reviewedCC BYGeocarto International

Spatial habitat suitability modelling of Astraeus Hygrometricus using explainable machine learning and GeoAI approaches

  • Chiranjit Singha1
  • Kishore Chandra Swain1
  • Amlan Mahata2
  • Bikash Ranjan Parida3
  • Biswajeet Pradhan4
  • Abdullah Alamri5
5 affiliations
  1. 1Department of Agricultural Engineering, Institute of Agriculture, Visva-Bharati (A Central University), Sriniketan, West Bengal, India
  2. 2Department of Microbiology, Burdwan University, Burdwan, West Bengal, India
  3. 3Department of Geoinformatics, School of Natural Resource Management, Central University of Jharkhand, Ranchi, India
  4. 4Centre 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
  5. 5Department of Geology and Geophysics, College of Science, King Saud University, Riyadh, Saudi Arabia
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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.

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Cite

BibTeXRIS
BibTeX
@article{singha2026spatial,
  title = {{Spatial habitat suitability modelling of Astraeus Hygrometricus using explainable machine learning and GeoAI approaches}},
  author = {Chiranjit Singha and Kishore Chandra Swain and Amlan Mahata and Bikash Ranjan Parida and Biswajeet Pradhan and Abdullah Alamri},
  journal = {Geocarto International},
  year = {2026},
  month = nov,
  doi = {10.1080/10106049.2026.2757955},
  url = {https://doi.org/10.1080/10106049.2026.2757955},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Spatial habitat suitability modelling of Astraeus Hygrometricus using explainable machine learning and GeoAI approaches
AU  - Chiranjit Singha
AU  - Kishore Chandra Swain
AU  - Amlan Mahata
AU  - Bikash Ranjan Parida
AU  - Biswajeet Pradhan
AU  - Abdullah Alamri
PY  - 2026
DA  - 2026/11/05
JO  - Geocarto International
DO  - 10.1080/10106049.2026.2757955
UR  - https://doi.org/10.1080/10106049.2026.2757955
AB  - 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.
SN  - 1752-0762
SN  - 1010-6049
ER  -