Abstract

Biodiversity plays a vital role in maintaining ecosystem functioning. Quantifying the impact of biotic and abiotic factors on plant diversity and creating a prediction map of biodiversity on the Qinghai-Tibet Plateau (QTP) can provide data and mechanism support for biodiversity conservation and restoration. Species richness (SR) serves as one of the indicators of biodiversity. In this study, we developed a SR estimation model based on the random forest algorithm, using 275 SR observation data, soil attribute data, meteorological data, topographical data, and human activity data. We assessed the pattern of SR on the QTP from 2000 to 2020, analyzed its spatiotemporal variation, and further evaluated significant environmental factors influencing vegetation alpha diversity. Our results showed that (1) Climate factor is the main influencing factor of SR spatial variation on the QTP, followed by terrain conditions. (2) Machine learning can account for 56% of SR and unveil distribution patterns showing a decrease in species richness from southeast to northwest on the QTP. (3) Over the past 20 years, there has been an increase in SR, particularly in the southeastern region.

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