Abstract
Himalaya, the highest mountain system in the world and house of important biodiversity hotspot, is sensitive to projected warming by climate change. Rhizocarpon geographicum (map lichen), a crustose lichen, grows in high mountain ranges, is a potential indicator species of climate change. In the present study, MaxEnt species distribution modeling algorithm was used to predict the suitable habitat for R. geographicum in current and future climate scenarios. Nineteen bioclimatic variables from WorldClim database, along with elevation, were used to predict the current distribution and three representative concentration pathway (RCP) scenarios by integrating three general circulation models (GCMs) for future distribution of species covering years 2050 and 2070. Furthermore, we performed change analysis to identify the precise difference between the current and future distribution of suitable areas of the species for delineating habitat range expansion (gain), habitat contraction (loss), and stable habitats. The final ensemble model obtained had average test value 0.968, and its predicted ~ 27.5% of the geographical area in the Indian Himalayan Region is presently climatically suitable for the species. The predicted highly suitable area for R. geographicum is observed to be declining in Northwestern Himalaya, and it is shifting towards the higher elevation areas of the Eastern Himalaya. The projected distribution in future under the RCP scenarios (RCP 4.5, 6.0, and 8.5) showed the range expansion towards higher elevations, and it is more pronounced for the extreme future scenarios (RCP 8.5) than for the moderate and intermediate climate scenarios (RCP 4.5 and RCP 6.0). However, assuming that species can migrate to previously unoccupied areas, the model forecasts a habitat loss of 10.86–16.51% for R. geographicum, which is expected due to increase in mean annual temperature by 1.5–3.7 °C. The predictive MaxEnt modeling approach for mapping lichen will contribute significantly to the understanding of the impact of climate change in Himalayan ecosystems with wide implications for drawing suitable conservation plans and to take adaptation and mitigation measures.
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Data availability
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Abbreviations
- AUC:
-
area under the curve
- CCAFS:
-
CGIAR Research Program on Climate Change, Agriculture and Food Security
- CMIP5:
-
Coupled Model Intercomparison Project Phase 5
- ENM:
-
ecological niche modeling
- GCM:
-
global climate model
- GHG:
-
greenhouse gases
- IHR:
-
Indian Himalayan Region
- IPCC:
-
Intergovernmental Panel on Climate Change
- MaxEnt:
-
maximum entropy
- MRI-CGCM3:
-
Meteorological Research Institute Coupled Global Climate ModelVersion-3
- RCP:
-
representative concentration pathway
- ROC:
-
receiver operating characteristic curve
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Acknowledgements
This study is part of projects entitled “Timberline and Altitudinal Gradient Ecology of Himalayas, and Human Use Sustenance in a Warming Climate” under National Mission on Himalayan Studies (NMHS) Program (Grant# NMHS/2015-16/LG03/03), and an Institute In-House Project#4 entitled “Mainstreaming Himalayan Biodiversity for Sustainable Development” funded by Ministry of Environment Forest & Climate Change (MoEF&CC), New Delhi, India. The authors are grateful to the Directors, G.B. Pant National Institute of Himalayan Environment (NIHE), Almora, Uttarakhand, and CSIR-National Botanical Research Institute (NBRI), Lucknow, U.P., India, for their encouragement and supports.
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DK and AP collected field data. DK, RB, and DKU compiled data from Herbarium. DKU, SPS, DK, and RB designed the MS. DK, SPS, AP, SR, and MJ wrote the first draft of the manuscript, and all authors contributed to subsequent revisions. The authors read and approved the final manuscript.
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Kumar, ., Pandey, A., Rawat, S. et al. Predicting the distributional range shifts of Rhizocarpon geographicum (L.) DC. in Indian Himalayan Region under future climate scenarios. Environ Sci Pollut Res 29, 61579–61593 (2022). https://doi.org/10.1007/s11356-021-15624-5
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DOI: https://doi.org/10.1007/s11356-021-15624-5