Keywords: n Codonopsis affinisn , Climate Change, Endemic, Himalaya, MaxEnt
Recent climate research has revealed that climate change will impact biodiversity, particularly in mountainous regions. Furthermore, species that are endemic and rare will be more significantly impacted. Codonopsis affinis (Campanulaceae) is a rare and endemic twiner in the Darjeeling eastern Himalaya. The present study is focused on species distribution modeling of C. affinis using the MaxEnt algorithm. Models were generated by first collecting occurrence points within the study area, followed by model generation. The modeling throws light on the current and future distribution and range shift of the species with respect to climate change. Modeling was performed with six occurrence points and nine uncorrelated bioclimatic and topographic variables. All the generated models performed well, with AUC value of 0.992, and TSS value of 0.904. The main variable that impacts the species distribution happens to be altitude. The current habitat area is 274.5 sq km after applying the 0.61 minimum training presence threshold. Overall, a sharp decline in the probable suitable habitat is observed in the future models compared to the current one, reducing from 8.83% of the total habitat to about 0 to 1.35% in the future. This indicates that future climate change could negatively impact this endemic species. Furthermore, the taxon is also impacted by other anthropogenic factors, such as changes in land use. This implies urgency for prioritizing this neglected species, and hence, it would be ideal to take measures to conserve this rare species either through ex-situ or in-situ approaches.
Agnihotri P, Husain T, Shirke PA, Sidhu OP, Singh H, Dixit V, Khuroo AA, Amla DV, Nautiyal CS (2017) Climate change-driven shifts in elevation and ecophysiological traits of Himalayan plants during the past century. Curr Sci 112:595–601
Allouche O, Tsoar A, Kadmon R (2006) Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J Appl Ecol 43(6):1223–1232. https://doi.org/10.1111/j.1365-2664.2006.01214.x
Bawa KS, Seidler R (2015) Deforestation and sustainable mixed-use landscapes: a view from the eastern Himalaya. Ann Mo Bot Gard 100(3):141–149. https://doi.org/10.3417/2012019
Boral D, Moktan S (2021) Predictive distribution modeling of Swertia bimaculata in Darjeeling-Sikkim Eastern Himalaya using MaxEnt: current and future scenarios. Ecol Process 10:26. https://doi.org/10.1186/s13717-021-00294-5
Boral D, Moktan S (2024) Modelling current and future potential distribution of medicinal orchids in Darjeeling eastern Himalaya. Plant Ecol. https://doi.org/10.1007/s11258-023-01392-4
CEPF (2005) Ecosystem profile: eastern Himalayas region. In: critical ecosystem partnership fund. https://www.cepf.net/sites/default/files/final.ehimalayas.ep_.pdf
Dhyani S (2023) Are Himalayan ecosystems facing hidden collapse? Assessing the drivers and impacts of change to aid conservation, restoration and conflict resolution challenges. Biodivers Conserv 32:3731–3764
Dhyani A, Kadaverugu R, Nautiyal BP, Nautiyal MC (2021) Predicting the potential distribution of a critically endangered medicinal plant Lilium polyphyllum in Indian Western Himalayan Region. Reg Environ Change 21:1–11. https://doi.org/10.1007/s10113-021-01763-5
Dolgener N, Freudenberger L, Schluck M, Schneeweiss N, Ibisch PL, Tiedemann R (2013) Environmental niche factor analysis (ENFA) relates environmental parameters to abundance and genetic diversity in an endangered amphibian, the Fire- bellied-toad (Bombina bombina). Cons Gen 15:11–21. https://doi.org/10.1007/s10592-013-0517-4
Erdtman G (1960) The acetolysis method: a revised description. Svensk Bot Tidskr 54:561–564
Fick SE, Hijmans RJ (2017) WorldClim 2: new 1 km spatial resolution climate surfaces for global land areas. Int J Climatol 37(12):4302–4315. https://doi.org/10.1002/joc.5086
Gao SM, Liu JS, Wang M et al (2018) Traditional uses, phytochemistry, pharmacology and toxicology of Codonopsis: a review. J Ethnoph 219:50–70. https://doi.org/10.1016/j.jep.2018.02.039
GBIF (2023) GBIF Occurrence Download https://doi.org/10.15468/dl.cqpk32. Accessed 08 Aug 2023
Hamid M, Khuroo AA, Malik AH, Ahmad R, Singh CP (2020) Assessment of alpine summit flora in Kashmir Himalaya and its implications for long-term monitoring of climate change impacts. J Mt Sci 17:1974–1988. https://doi.org/10.1007/s11629-019-5924-7
Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978. https://doi.org/10.1002/joc.1276
IPCC (2014) Climate Change 2014: Synthesis Report. In: Pachauri RK, Meyer LA (eds) Contribution of working groups I, II and III to the Fifth assessment report of the intergovernmental panel on climate change core writing team. IPCC Switzerland
IPCC (2021) Climate Change 2021. In: Masson-DelmotteV et al (eds) The physical science basis. Contribution of working group I to the sixth assessment report of the intergovernmental panel on climate change. United Kingdom, Cambridge University Press
Kaur S, Kaushal S, Adhikari D et al (2023) Different GCMs yet similar outcome: predicting the habitat distribution of Shorea robusta CF Gaertn. in the Indian Himalayas using CMIP5 and CMIP6 climate models. Environ Monitor Assess 195(6):715
Laface VLA, Musarella CM, Tavilla G et al (2023) Current and potential future distribution of endemic Salvia ceratophylloides Ard (Lamiaceae). Land 12(1):247. https://doi.org/10.3390/land12010247
Liang B, Zhang XX, Li R, Gu N (2022) Guanxin V protects against ventricular remodeling after acute myocardial infarction through the interaction of TGF-β1 and Vimentin. Phytomedicine 95:153866
Lobo JM, Jiménez-Valverde A, Real R (2008) AUC: a misleading measure of the performance of predictive distribution models. Glob Ecol Biogeogr 17(2):145–151
Moktan S, Das AP (2014) Plant species richness and phytosociological attributes of the vegetation in the cold temperate zone of Darjiling Himalaya. India Int Res J Environ Sci 3(10):14–19
Nayar MP, Sastry ARK (1987) Red data book of Indian plants, vol 2. Botanical Survey of India, Calcutta
Palkar RS, Janarthanam MK, Sellappan K (2020) Prediction of potential distribution and climatic factors influencing Garcinia indica in the Western Ghats of India using ecological niche modeling. Natl Acad Sci Lett 43:585–591. https://doi.org/10.1007/s40009-020-00918-y
Pandit MK, Sodhi NS, Koh LP, Bhaskar A, Brook BW (2007) Unreported yet massive deforestation driving loss of endemic biodiversity in Indian Himalaya. Biodiver Conserv 16:153–163. https://doi.org/10.1007/s10531-006-9038-5
Pearson RG (2007) Species distribution modelling for conservation educators and practitioners. Synth Am Mus Nat Hist 50:54–89
Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 190:231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026
Qin A, Liu B, Guo Q et al (2017) Maxent modeling for predicting impacts of climate change on the potential distribution of Thuja sutchuenensis Franch., an extremely endangered conifer from southwestern China. Global Ecol Conserv 10:139–146. https://doi.org/10.1016/j.gecco.2017.02.004
Sharma KP, Vorosmarty CJ, Moore B (2000) Sensitivity of the Himalayan hydrology to land-use and climatic changes. Clim Change 47:117–139. https://doi.org/10.1023/A:1005668724203
Shrestha B, Tsiftsis S, Chapagain DJ et al (2021) Suitability of habitats in Nepal for Dactylorhiza hatagirea now and under predicted future changes in climate. Plants 10(3):467. https://doi.org/10.3390/plants10030467
Department of Botany, University of Calcutta, Kolkata, India