Skip to main navigation menu Skip to main content Skip to site footer
Plant Science Today (PST)

Research Articles

Early Access

Bioclimatic modeling of Tulipa fosteriana and Tulipa ingens: Predicting the effects of climate change on the distribution of endangered wild tulips

DOI
https://doi.org/10.14719/pst.9007
Submitted
22 April 2025
Published
09-06-2025
Versions

Abstract

Bioclimatic modeling is an essential tool for predicting species distributions under changing environmental conditions. T. fosteriana and  T. ingens, rare and endemic tulip species in Uzbekistan, are currently facing increasing threats from habitat loss and climate change. Understanding their potential range under current and future climate scenarios is crucial for conservation planning. The present study employed Maximum Entropy (MaxEnt) modeling to assess the habitat suitability of T. fosteriana and T. ingens using occurrence data from field surveys, herbarium records and biodiversity databases. Environmental predictors included climatic, soil and topographical variables. Model accuracy was evaluated using the Area Under the Curve (AUC) and future habitat projections were generated under Ssp126 (moderate emissions) and Ssp585 (high emissions) scenarios for 2041-2060. The results suggest that T. fosteriana may expand its range, particularly in the Hissar and Bobotog mountain ranges, while T. ingens is projected to suffer severe habitat reduction, losing over 90 % of its suitable areas under the high-emission scenario. The most influential environmental variables were precipitation in the coldest quarter and depth to bedrock, highlighting the role of moisture availability and soil structure in habitat suitability. High AUC values (above 0.98) confirm model robustness. These findings emphasize the contrasting responses of the two species to climate change. While T. fosteriana may benefit from rising temperatures, T. ingens is at high risk of habitat loss, requiring urgent conservation efforts. This study provides valuable insights for biodiversity management in Central Asia, highlighting the need for protected areas, in-situ conservation and potential ex-situ preservation strategies.

References

  1. 1. Araújo MB, Peterson AT. Uses and misuses of bioclimatic envelope modeling. Ecology. 2012;93(7):1527-39. https://doi.org/10.1890/11-1930.1
  2. 2. Bourg NA, McShea WJ, Gill DE. Putting a CART before the search: successful habitat prediction for a rare forest herb. Ecology. 2005;86(10):2793–804. https://doi.org/10.1890/04-1666
  3. 3. Broennimann O, Treier UA, Müller-Schärer H, Thuiller W, Peterson AT, Guisan A. Evidence of climatic niche shift during biological invasion. Ecology Letters. 2007;10(8):701–9. https://doi.org/10.1111/j.1461-0248.2007.01060.x
  4. 4. Shukrullozoda R Sh., Dekhkonov DB, Khaydarov KK, Kadirov BE, Tojibaev K. Morphology and distribution patterns of Tulipa fosteriana and Tulipa ingens. Plant Science Today.2023;10(2):426–38. https://doi.org/10.14719/pst.2296
  5. 5. Eyring V, Bony S, Meehl GA, Senior CA, Stevens B, Stouffer RJ, et al. Overview of the coupled model intercomparison project phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development. 2016;9(5):1937–58. https://doi.org/10.5194/gmd-9-1937-2016
  6. 6. Fadrique B, Báez S, Duque Á, Malizia A, Blundo C, Carilla J, et al. Widespread but heterogeneous responses of Andean forests to climate change. Nature. 2018;564(7735):207–12. https://doi.org/10.1038/s41586-018-0715-9
  7. 7. Feria TP, Peterson AT. Using point occurrence data and inferential algorithms to predict local communities of birds. Diversity and Distributions. 2002;8:49–56. https://doi.org/10.1046/j.1472-4642.2002.00127.x
  8. 8. Fick SE, Hijmans RJ. WorldClim 2: New 1‐km spatial resolution climate surfaces for global land areas. International Journal of Climatology. 2017;37(12):4302-15. https://doi.org/10.1002/joc.5086
  9. 9. Hengl T, Wheeler I, Wright MN, Batjes NH, Bauer-Marschallinger B, Blagotić A et al. SoilGrids250m: Global gridded soil information based on machine learning. PLoS One. 2017;12(2). https://doi.org/10.1371/journal.pone.0169748
  10. 10. Islam K, Rahman MF, Islam KN, Nath TK, Jashimuddin M. Modeling spatiotemporal distribution of Dipterocarpus turbinatus Gaertn. F in Bangladesh under climate change scenarios. Journal of Sustainable Forestry. 2020;39(3):221–41. https://doi.org/10.1080/10549811.2019.1632721
  11. 11. Lee JY, Marotzke J, Bala G, Cao L, Corti S, Dunne JP, et al. Future global climate: scenario-based projections and near-term information. IPCC. 2021;1:1–195. https://doi.org/10.1017/9781009157896.006
  12. 12. O’Neill BC, Tebaldi C, Van Vuuren DP, Eyring V, Friedlingstein P, Hurtt G, et al. The scenario model intercomparison project (ScenarioMIP) for CMIP6. Geoscientific Model Development. 2016;9(9):3461–82. https://doi.org/10.5194/gmd-9-3461-2016
  13. 13. Phillips SJ anderson RP, Schapire RE. Maximum entropy modeling of species geographic distributions. Ecological Modelling. 2006;190(3–4):231–59.
  14. 14. Raxworthy CJ, Martínez-Meyer E, Horning N, Nussbaum RA, Schneider GE, Ortega-Huerta MA, et al. Predicting distributions of known and unknown reptile species in Madagascar. Nature. 2003;426(6968):837–41.
  15. 15. Shukrullozoda R, Kadirov B, Khaydarov K, Dekhkonov D, Umurzakova Z, Ruziev F, et al. Enhancing biotechnological approaches for the in vitro micropropagation: Protecting endangered wild tulip species in Samarkand, Uzbekistan. Plant Science Today. 2024;11(2). https://doi.org/10.14719/pst.3653
  16. 16. Thuiller W, Lavorel S, Araújo MB, Sykes MT, Prentice IC. Climate change threats to plant diversity in Europe. Proceedings of the National Academy of Sciences. 2005;102(23):8245–50. https://doi.org/10.1073/pnas.0409902102
  17. 17. Wayne GP. Representative concentration pathways. Skeptical Science. 2014:24.
  18. 18. Williams PH, Hannah L, Аndelman S, Midgley GF, Araújo MB, Hughes G, et al. Planning for climate change: Identifying minimum-dispersal corridors for the cape proteaceae. Conservation Biology. 2005;19(4):1063–74. https://doi.org/10.1111/j.1523-1739.2005.00080.x
  19. 19. Akbarov FI, Jabborov AM, Tojibayev K Sh. Modeling and analysis of the geographical distribution of Ranunculus rubrocalyx Regel ex Kom Bulletin of the Khorezm Mamun Academy. 2021;1:29–37.
  20. 20. Akbarov FI, Tojibayev K Sh. Creation of a bioclimatic model of some endemic species of the flora of Surkhandarya region. Scientific Bulletin of Namangan State University. 2022;4:127–33.
  21. 21. Daminova NE. Farg’ona vodiysi dendroflorasi: Diss. avtoref. b.f.f.d. – Toshkent. 2023.
  22. 22. Dehqonov DB. New views on Tulipa L species: Morphology, distribution, molecular research and protection issues: Tashkent. 2023:254.
  23. 23. Gulomov RK. Phlomoides moench genus distributed in the Fergana valley (taxonomy, geography, ecology and protection measures). Tashkent. 2022.
  24. 24. Olanova MV, Gudkova PD, Shomurodov XF, Adilov BA, Rakhimova NK, Khabibullaev B Sh et al. I. Creating a bioclimatic model of species: A task for practical work and methodological instructions for their implementation. Tashkent: Institute of Botany. 2021.
  25. 25. Shukrullozoda R, Kadirov B, Khaydarov K, Dekhkonov D, Umurzakova Z, Ruziev F, et al. Enhancing biotechnological approaches for the in vitro micropropagation: Protecting endangered wild tulip species in Samarkand, Uzbekistan. Plant Science Today. 2024;11(2). https://doi.org/10.14719/pst.3653
  26. 26. Zokirov KK. Examination of the environmental impacts of renewable energies. Procedia Environmental Science, Engineering and Management. 2024;11(2):186–95.
  27. 27. Zokirov KK. Development and validation of a wearable biosensor for continuous glucose monitoring. Procedia Environmental Science, Engineering and Management. 2024;11(2):175–86.
  28. 28. Maślanka, Małgorzata, Prokopiuk B. Bulb organogenesis of Tulipa tarda in vitro cultures in relation to light environment. Acta Agriculturae Scandinavica. Section B - Soil & Plant Science. 2019;69:1-7. https://doi.org/10.1080/09064710.2019.1583361
  29. 29. Bhat MH, Fayaz M, Kumar A, Fayaz M, Najar RA, Anjum M, et al. Micropropagation of Tulipa species. The Global Floriculture Industry. 2020:39-58.
  30. 30. Ibrahim, Majid. Effect of different concentrations of benzyl adenine on the shoot multiplication of tulip (Tulipa gesneriana L. cv. Arma) buds. Dysona. Applied Science. 2020:96-100. https://doi.org/10.30493/DAS.2020.240387
  31. 31. Podwyszyńska, Małgorzata, Marasek-Ciolakowska, Agnieszka. Micropropagation of tulip via somatic embryogenesis. Agronomy. 2020. https://doi.org/10.10.3390/agronomy10121857
  32. 32. Sochacki, Dariusz Marciniak, Przemysław Ciesielska, Maria Zaród, Janina Sutrisno. The influence of selected plant growth regulators and carbohydrates on in vitro shoot multiplication and bulbing of the Tulip (Tulipa L.). Plants. 2023;12:1134. https://doi.org/10.3390/plants12051134
  33. 33. Salybekova NN, Yusupov BY, Alpamyssova GB, Nagiyeva AG, Serzhanova AE, Babayeva GA. Biotechnological features of microclonal reproduction of Tulipa L. species. International Research Journal. 2024;2:91–7. https://doi.org/10.52578/2305-9397-2024-3-2-91-97
  34. 34. Muminov MA, Nosirov MG, Mukimov T, Normuradov DS, Khodjitbabayev K, Bohodirkhodjaev I, et al. Multi-faceted analysis of land use impact on rangeland health: Insights from normalized difference vegetation index assessment in stream, road and mining areas. Journal of Ecological Engineering. 2023:196–203. https://doi.org/10.12911/22998993/159472
  35. 35. Eliboev I, Ishankulov A, Berdimurodov ET, Chulpanov K, Nazarov M, Jamshid B, et al. Advancing analytical chemistry with carbon quantum dots: Comprehensive review. Anal Methods. 2025;17:2627-49. https://doi.org/10.1039/d4ay02237h
  36. 36. Jakhonkulovna SM, Shichiyakh R, Ishankulov A. Electrochemical biosensors for early detection of Alzheimer’s disease. Clinica Chimica Acta. 2025;572:120278 https://doi.org/10.1016/j.cca.2025.120278

Downloads

Download data is not yet available.