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Unravelling tree diversity patterns and responses to environmental gradients in a tropical forest landscape of the Western Ghats

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摘要 Understanding spatial patterns of plant species diversity and the factors(e.g.,climate and human)that drive these patterns is essential for biodiversity conservation.We used data from 1700.1-ha forest plots in the Shettihalli tropical forest landscape of the Western Ghats biodiversity hotspot,India,to analyse tree community composition and the drivers ofα-diversity(Shannon)andβ-diversity(LCBD).Compositional patterns were visualized using Non-Metric Multidimensional Scaling(NMDS),and hybrid feature selection with structural equation modeling(SEM)was employed to evaluate the direct and indirect effects of environmental variables on diversity.NMDS identified four distinct forest types in the Shettihalli landscape:semi-evergreen,dry deciduous,moist deciduous,and plantation forests,each with distinct plant composition.Shannon diversity and ecological uniqueness was significantly higher in semi-evergreen forest than in deciduous forest plots.The SEMs explained about 79%and 39–45%of the variation inα-diversity andβ-diversity.Our analysis indicated that current diversity patterns result from multiple processes,with structure,disturbance,and edaphic parameters exerting the strongest direct and indirect effects onα-diversity.β-diversity,in contrast,was largely influenced by climate,topography,stand structure,and edaphic factors.Overall,our findings indicate that various factors(e.g.,climate,topography,and human disturbance)interact to shape tree diversity patterns in tropical forests.These findings will help develop unique conservation and management strategies for distinct forest types in tropical forest ecosystems.
出处 《Plant Diversity》 2026年第1期92-106,共15页 植物多样性(英文版)
基金 supported by the Department of Biotechnology,Ministry of Science and Technology,Govt.India,under grant No.BT/Coord.II/10/02/2016/22.03.2018 the Indian Council of Social Science Research,New Delhi,India,for providing a short-term doctoral fellowship(RFD/Short-Term/2022-23/ENV/ST/66).
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