DOMINANT ENVIRONMENTAL FACTORS SHAPING PLANT DIVERSITY IN THE WESTERN TIEN SHAN TRANSBOUNDARY REGION
Keywords:
Western Tien Shan, plant diversity, topographic gradient, PCA, Z-score, ecological drivers, hotspotsAbstract
The Western Tien Shan transboundary region is one of the important floristic centers of Central Asia and is distinguished by high environmental heterogeneity and endemism. The aim of this study was to evaluate the relative effects of climatic, edaphic, and topographic factors shaping plant diversity in the region and to identify the dominant ecological drivers. Based on approximately 18,000 herbarium records and 5 × 5 km grid data, Principal Component Analysis (PCA) and Z-score standardization approaches were applied. The results showed that the ecological gradients identified by PCA were mainly structured by terrain complexity and the elevation–temperature axis. Z-score analysis confirmed that topographic factors, particularly elevation and slope gradients, were the dominant ecological drivers across most of the region. While climatic factors defined the broad-scale ecological background, soil factors acted as strong modulators in certain local zones. The obtained results demonstrate that the spatial structure of the Western Tien Shan flora has a multi-factor and mosaic character, providing an important scientific basis for biodiversity monitoring, identification of ecological hotspots, and the designation of priority conservation areas.
References
1. Aguirre‐Gutiérrez J. et al. Functional susceptibility of tropical forests to climate change //Nature Ecology & Evolution. – 2022. – Т. 6. – №. 7. – С. 878-889.
2. Beck J. et al. Spatial bias in the GBIF database and its effect on modeling species' geographic distributions //Ecological Informatics. – 2014. – Т. 19. – С. 10-15.
3. Dormann C. F. et al. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance //Ecography. – 2013. – Т. 36. – №. 1. – С. 27-46.
4. Fithian W., Hastie T. Finite-sample equivalence in statistical models for presence-only data //The annals of applied statistics. – 2012. – Т. 7. – №. 4. – С. 1917.
5. Guisan A., Zimmermann N. E. Predictive habitat distribution models in ecology //Ecological modelling. – 2000. – Т. 135. – №. 2-3. – С. 147-186.
6. Hawkins, B. A., et al. Energy, water, and broad-scale geographic patterns of species richness //Ecology. – 2003. – Т. 84. – №. 12. – С. 3105-3117.
7. Jolliffe I. T., Cadima J. Principal component analysis: a review and recent developments //Philosophical transactions of the royal society A: Mathematical, Physical and Engineering Sciences. – 2016. – Т. 374. – №. 2065. – С. 20150202.
8. Körner C. The use of ‘altitude’in ecological research //Trends in ecology & evolution. – 2007. – Т. 22. – №. 11. – С. 569-574.
9. Legg S. IPCC, 2021: Climate change 2021-the physical science basis //Interaction. – 2021. – Т. 49. – №. 4. – С. 44-45.
10. Meyer C., Weigelt P., Kreft H. Multidimensional biases, gaps and uncertainties in global plant occurrence information //Ecology letters. – 2016. – Т. 19. – №. 8. – С. 992-1006.
11. Stein A., Gerstner K., Kreft H. Environmental heterogeneity as a universal driver of species richness across taxa, biomes and spatial scales //Ecology letters. – 2014. – Т. 17. – №. 7. – С. 866-880.
12. Werden L. K., Becknell J. M., Powers J. S. Edaphic factors, successional status and functional traits drive habitat associations of trees in naturally regenerating tropical dry forests //Functional Ecology. – 2018. – Т. 32. – №. 12. – С. 2766-2776.
13. Yang W., Ma K., Kreft H. Geographical sampling bias in a large distributional database and its effects on species richness–environment models //Journal of Biogeography. – 2013. – Т. 40. – №. 8. – С. 1415-1426.
