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Ensemble of machine learning and global circulation models coupled with geospatial databases for niche mapping of Bell Rhododendron under climate chan2.83 MB
Published VersionCC BY V4.0, Open Access
Journal article
Ensemble of machine learning and global circulation models coupled with geospatial databases for niche mapping of Bell Rhododendron under climate change
Geocarto International, Vol.39(1), pp.1-17
2024
Abstract
Himalayan species conservation faces major challenges due to unprecedented climate change. Alpine Rhododendrons are crucial components of Himalaya, yet their vulnerability to climate change remains poorly understood. This study examines niche shifting of Rhododendron campanulatum, a keystone species of alpine treeline, under different climate change scenarios using ensemble models. The study presents extensive use of four machine learning models and three global circulation models for niche modelling. Models achieved True Skill Statistic ≥0.8, Area Under Curve ≥0.9, Cohen's Kappa ≥0.7, and overall accuracy of ≥0.9. Results showed distribution of R. campanulatum is governed by annual temperature range, minimum temperature of coldest month and precipitation of warmest quarter. Analyses revealed niche contraction and expansion of a 3-5%. Contractions are particularly evident at lower treeline boundaries. Both upward and downward shifts are anticipated under future climatic scenarios.
Details
- Title
- Ensemble of machine learning and global circulation models coupled with geospatial databases for niche mapping of Bell Rhododendron under climate change
- Authors
- K. V. Satish - Banaras Hindu UniversityPrashant K. Srivastava (Corresponding Author) - Banaras Hindu UniversityMukund Dev Behera - Indian Institute of Technology KharagpurMohammed Latif Khan - Dr. Hari Singh Gour UniversitySrishti Gwal - Banaras Hindu UniversitySanjeev Kumar Srivastava - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
- Publication details
- Geocarto International, Vol.39(1), pp.1-17
- Publisher
- Taylor & Francis
- Date published
- 2024
- DOI
- 10.1080/10106049.2024.2421233
- ISSN
- 1752-0762; 1010-6049
- Copyright note
- © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
- Data Availability
- Data will be shared upon reasonable request.
- Grant note
- We are thankful to National Mission on Himalayan Studies, G.B. Pant National Institute of Himalayan Environment, Almora, India for funding this research.
- Organisation Unit
- School of Science, Technology and Engineering; Sustainability Research Cluster
- Language
- English
- Record Identifier
- 991087293602621
- Output Type
- Journal article
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web Of Science research areas
- Environmental Sciences
- Geosciences, Multidisciplinary
- Imaging Science & Photographic Technology
- Remote Sensing
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