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Plant Science Today (PST)

Research Articles

Vol. 12 No. 4 (2025)

Habitat suitability modeling for the conservation of Exacum bicolor Roxb.: An endangered, medicinal and ornamental species of Kerala’s lateritic hillocks under current and future climatic scenarios

DOI
https://doi.org/10.14719/pst.8130
Submitted
7 March 2025
Published
24-10-2025 — Updated on 05-11-2025
Versions

Abstract

Exacum bicolor Roxb. (Gentianaceae) is a perennial herb with striking flowers and medicinal beneficial properties. Endemic to Peninsular India, it is currently listed as endangered. Despite its aesthetic and therapeutic value, the species remains underexplored and underutilized. Preserving the genetic diversity through in-situ or ex-situ conservation is essential to meet the growing demand for its medicinal and ornamental uses. A Maximum Entropy (MaxEnt) modeling approach was used to estimate the potential distribution of E. bicolor in Kerala, India, considering both present and future climatic scenarios, due to its efficiency with presence-only data. The MaxEnt model predicted habitat suitability for E. bicolor with an area of approximately 1126 km², showing high probability zones concentrated in the northern parts of Kerala. To evaluate habitat suitability, 22 environment parameters - including bioclimatic variables, soil types, agroecological zones, topographical features - were incorporated into the MaxEnt model. The most influential variables were precipitation of driest quarter (Bio17), water vapour pressure (January), annual precipitation (Bio12), mean diurnal range (Bio2), solar radiation (July and March), Topographic Position Index (TPI) and slope. Habitat suitability for E. bicolor was projected under future climate scenarios (2020-2100) using Shared Socioeconomic Pathways (SSPs) 126, 245, 370 and 585. In the SSP585 scenario, highly suitable areas for E. bicolor are anticipated to decrease by 14.20 % during the period of 2080-2100 compared to the current scenario. This decline is likely attributed to rising temperatures and decreasing rainfall, underscoring the need for climate-resilient conservation planning.

References

  1. 1. Sreelatha U, Sankar M, Anupama TV. Potential indigenous ornamentals of hillocks of Kerala. Indian Horticulture. 2019;64(4).
  2. 2. Sreelatha U, Baburaj TS, Narayanan Kutty C, Nazeem PA, Jyoti Baskara. Cultivation prospects of Exacum bicolor Roxb. An endangered, ornamental and anti-diabetic herb. Natural Product Radiance. 2007;6(5):402-4.
  3. 3. Jeeshna MV, Paulsamy S. Habitat and Phytosociological characters of the endangered plant species, Exacum bicolor RoxB. Kongunadu Research Journal. 2018;5(2):56-61. https://doi.org/10.26524/krj272
  4. 4. Zurell D, Franklin J, König C, Bouchet PJ, Dormann CF, Elith J, et al. A standard protocol for reporting species distribution models. Ecography. 2020;43(9):1261-77. https://doi.org/10.1111/ecog.04960
  5. 5. Franklin J. 2009. Mapping species distributions: spatial inference and prediction. Cambridge University Press.
  6. 6. Franklin J. Moving beyond static species distribution models in support of conservation biogeography. Diversity Distribution. 2010;16(3):321-30. https://doi.org/10.1111/j.1472-4642.2010.00641.x
  7. 7. Barbosa MA, Sillero N, Martínez-Freiría F, Real R. Ecological niche models in mediterranean herpetology: past, present and future. In: Ecological modeling. Hauppauge (NY): Nova Science Publishers; 2012. p. 173-204. https://doi.org/10.13140/2.1.3746.6560
  8. 8. Chefaoui RM, Assis J, Duarte CM, Serrão EA. Large-scale prediction of seagrass distribution integrating landscape metrics and environmental factors: the case of Cymodocea nodosa (Mediterranean–Atlantic). Estuaries and Coasts. 2016;39:123-37. https://doi.org/10.1007/s12237-015-9966-y
  9. 9. Kumar S, Stohlgren TJ. Maxent modeling for predicting suitable habitat for threatened and endangered tree Canacomyrica monticola in New Caledonia. Journal of Ecology and Natural Environment. 2009;1(4):94-8.
  10. 10. Saran S, Joshi R, Sharma S, Padalia H, Dadhwal VK. Geospatial modeling of Brown oak (Quercus semecarpifolia) habitats in the Kumaun Himalaya under climate change scenario. Journal of the Indian Society of Remote Sensing. 2010;38:535-47.
  11. 11. Jaynes ET. Information theory and statistical mechanics. Physical Review. 1957;106(4):620. https://doi.org/10.1103/PhysRev.106.620
  12. 12. Shilky, Kishore BS, Kumar G, Saikia P, Kumar A. Application of species distribution modeling for conservation and restoration of forest ecosystems. In: Ecosystem and species habitat modeling for conservation and restoration. Singapore: Springer; 2023. p. 249-64. https://doi.org/10.1007/978-981-99-0131-9_13
  13. 13. Feng X, Park DS, Liang Y, Pandey R, Papeş M. Collinearity in ecological niche modeling: confusions and challenges. Ecology and Evolution. 2019;9(18):10365-76. https://doi.org/10.1002/ece3.5555
  14. 14. Kebede AS, Nicholls RJ, Allan A, Arto I, Cazcarro I, Fernandes JA, et al. Applying the global RCP–SSP–SPA scenario framework at sub-national scale: A multi-scale and participatory scenario approach. Science of the Total Environment. 2018;635:659-72. https://doi.org/10.1016/j.scitotenv.2018.03.368
  15. 15. O’Neill BC, Kriegler E, Riahi K, Ebi KL, Hallegatte S, Carter TR, et al. A new scenario framework for climate change research: the concept of shared socioeconomic pathways. Climatic Change. 2014;122:387-400.
  16. 16. Dione PM, Faye C, Mohamed A, Alarifi SS, Mohammed MA. Assessment of the impact of climate change on current and future flows of the ungauged Aga-Foua-Djilas watershed: a comparative study of hydrological models CWatM under ISIMIP and HMF-WA. Applied Water Science. 2024;14(7):163. https://doi.org/10.1007/s13201-024-02219-x
  17. 17. Li SY, Miao LJ, Jiang ZH, Wang GJ, Gnyawali KR, Zhang J, et al. Projected drought conditions in Northwest China with CMIP6 models under combined SSPs and RCPs for 2015–2099. Advances in Climate Change Research. 2020;11(3):210-7. https://doi.org/10.1016/j.accre.2020.09.003
  18. 18. Theertha PC, Sincy J, Drishya NS, Atheena K, Anusree N. Floristic diversity and phytosociological studies of selected area in Kanayannur, Kannur District, Kerala. International Journal of Creative Research Thoughts. 2021;9(3).
  19. 19. Kumar BM. Agroforestry systems and practices of Kerala. Agroforestry systems and practices of India. New Delhi, India: New India Publishing Agency; 2007. p. 459-83.
  20. 20. Thomas J, Joseph S, Thrivikramji KP, Arunkumar KS. Sensitivity of digital elevation models: the scenario from two tropical mountain river basins of the Western Ghats, India. Geoscience Frontiers. 2014;5(6):893-909. https://doi.org/10.1016/j.gsf.2013.12.008
  21. 21. Baghdadi N, Mallet C, Zribi M. QGIS and generic tools. John Wiley & Sons; 2018. https://doi.org/10.1002/9781119457091
  22. 22. Nair HC, Joseph A, Padmakumari Gopinathan V. GIS Based landform classification using digital elevation model: a case study from two river basins of Southern Western Ghats, Kerala, India. Modeling Earth Systems and Environment. 2018;4:1355-63. https://doi.org/10.1007/s40808-018-0490-5
  23. 23. Saupe EE, Barve V, Myers CE, Soberón J, Barve N, Hensz CM, et al. Variation in niche and distribution model performance: the need for a priori assessment of key causal factors. Ecological Modelling. 2012;237:11-22. https://doi.org/10.1016/j.ecolmodel.2012.04.001
  24. 24. Rawat S, Padua S. Potential spatio-temporal distribution of invasive Charru Mussel, Mytella strigata (Hanley, 1843) under different climate scenarios. Thalassas: An International Journal of Marine Sciences. 2024;40(1):411-22. https://doi.org/10.1007/s41208-023-00610-0
  25. 25. Zhan P, Wang F, Xia P, Zhao G, Wei M, Wei F, et al. Assessment of suitable cultivation region for Panax notoginseng under different climatic conditions using MaxEnt model and high-performance liquid chromatography in China. Industrial Crops and Products. 2022;176:114416. https://doi.org/10.1016/j.indcrop.2021.114416
  26. 26. Thapa A, Wu R, Hu Y, Nie Y, Singh PB, Khatiwada JR, et al. Predicting the potential distribution of the endangered red panda across its entire range using MaxEnt modeling. Ecology and Evolution. 2018;8(21):10542-54. https://doi.org/10.1002/ece3.4526
  27. 27. Ghorbani A, Zou J. Data shapley: equitable valuation of data for machine learning. In: International conference on machine learning. PMLR; 2019 May 24. p. 2242-51.
  28. 28. Shcheglovitova M, Anderson RP. Estimating optimal complexity for ecological niche models: a jackknife approach for species with small sample sizes. Ecological Modelling. 2013;269:9-17. https://doi.org/10.1016/j.ecolmodel.2013.08.011
  29. 29. Yang XQ, Kushwaha SP, Saran S, Xu J, Roy PS. Maxent modeling for predicting the potential distribution of medicinal plant, Justicia adhatoda L. in Lesser Himalayan foothills. Ecological Engineering. 2013;51:83-7. https://doi.org/10.1016/j.ecoleng.2012.12.004
  30. 30. Zhao Y, Cao H, Xu W, Chen G, Lian J, Du Y, et al. Contributions of precipitation and temperature to the large scale geographic distribution of fleshy-fruited plant species: growth form matters. Scientific Reports. 2018;8(1):17017. https://doi.org/10.1038/s41598-018-35436-x
  31. 31. Thuiller W, Richardson DM, Pyšek P, Midgley GF, Hughes GO, Rouget M. Niche‐based modelling as a tool for predicting the risk of alien plant invasions at a global scale. Global Change Biology. 2005;11(12):2234-50. https://doi.org/10.1111/j.1365-2486.2005.001018.x
  32. 32. Swets JA. Measuring the accuracy of diagnostic systems. Science. 1988;240(4857):1285-93. https://doi.org/10.1126/science.3287615
  33. 33. Elith J, Phillips SJ, Hastie T, Dudík M, Chee YE, Yates CJ. A statistical explanation of MaxEnt for ecologists. Diversity and Distributions. 2011;17(1):43-57. https://doi.org/10.1111/j.1472-4642.2010.00725.x
  34. 34. Shen T, Yu H, Wang YZ. Assessing the impacts of climate change and habitat suitability on the distribution and quality of medicinal plant using multiple information integration: take Gentiana rigescens as an example. Ecological Indicators. 2021;123:107376. https://doi.org/10.1016/j.ecolind.2021.107376
  35. 35. Bian WH, Lv X, Feng GX, Sheng ZM. Advances in Orchidacea medicinal plant Cremastra appendiculata. Guizhou Agricultural Sciences. 2017;45(7):88-92.
  36. 36. Seif A. Using topography position index for landform classification (case study: Grain Mountain). Bulletin of Environment, Pharmacology and Life Sciences. 2014;3(11):33-9.
  37. 37. Li WN, Zhao Q, Guo MH, Lu C, Huang F, Wang ZZ, et al. Predicting the potential distribution of the endangered plant Cremastra appendiculata (Orchidaceae) in China under multiple climate change scenarios. Forests. 2022;13(9):1504. https://doi.org/10.3390/f13091504
  38. 38. Jung C, Schindler D. A review of recent studies on wind resource projections under climate change. Renewable and Sustainable Energy Reviews. 2022;165:112596. https://doi.org/10.1016/j.rser.2022.112596

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