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Climate change‐induced distributional change of medicinal and aromatic plants in the Nepal Himalaya
Authors:Uttam B Shrestha  Pramod Lamsal  Suresh K Ghimire  Bharat B Shrestha  Sajita Dhakal  Sujata Shrestha  Kishor Atreya
Institution:1. Global Institute for Interdisciplinary Studies, Kathmandu Nepal ; 2. Himalayan Geo‐En Pvt. Ltd., Kathmandu Nepal ; 3. Central Department of Botany, Tribhuvan University, Kathmandu Nepal ; 4. National Herbarium and Plant Laboratories, Lalitpur Nepal ; 5. School of Forestry and Natural Resource Management, Institute of Forestry, Tribhuvan University, Kathmandu Nepal
Abstract:Medicinal and aromatic plants (MAPs) contribute to human well‐being via health and economic benefits. Nepal has recorded 2331 species of MAPs, of which around 300 species are currently under trade. Wild harvested MAPs in Nepal are under increasing pressure from overexploitation for trade and the effects of climate change and development. Despite some localized studies to examine the impact of climate change on MAPs, a consolidated understanding is lacking on how the distribution of major traded species of MAPs will change with future climate change. This study identifies the potential distribution of 29 species of MAPs in Nepal under current and future climate using an ensemble modeling and hotspot approach. Future climate change will reduce climatically suitable areas of two‐third of the studied species and decrease climatically suitable hotspots across elevation, physiography, ecoregions, federal states, and protected areas in Nepal. Reduction in climatically suitable areas for MAPs might have serious consequences for the livelihood of people that depend on the collection and trade of MAPs as well as Nepal''s national economy. Therefore, it is imperative to consider the threats that future climate change may have on distribution of MAPs while designing protected areas and devising environmental conservation and climate adaptation policies.
Keywords:medicinal and aromatic plants  Nepal  niche modeling  species distribution modeling
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