Biodiversity characteristics of large forest plots in Qinghai area of Qilian Mountain National Park

[Objective] Long-term monitoring of plant community dynamics in large forest plots helps reveal the spatial patterns and underlying mechanisms that sustain species diversity. These insights form a solid scientific foundation for biodiversity conservation in the region. [Methods] Taking the typical forest ecosystem as the research object in the Qinghai area of Qilian Mountain National Park, we used the adjacent grid method to conduct a survey of each tree in a 24 hm2 large sample plot. [Results] There was a total of 35 835 trees, of which Picea crassifolia and Juniperus przewalskii accounted for 57.84% and 23.82%, respectively. Species richness and plant height were 3 species and 10.7 m, respectively. Shannon- Wiener and Simpson index of spruce forest were 0.74 and 0.43, respectively, Shannon-Wiener index was relatively low. The Shannon-Wiener index was significantly influenced by tree height, species richness, and Simpson index. As the tree height increased, the Shannon-Wiener was decreased, while the species richness and Simpson index were increased significantly. The coefficients of determination for the training and testing sets of the machine learning model were 0.95 and 0.93, respectively, with root mean square errors of only 0.06 and 0.08. This indicated that the model had a high explanatory power and prediction accuracy for the Shannon-Wiener data. [Conclusion] The species diversity of the Qinghai spruce forest is relatively low and is significantly influenced by tree height, species richness, and the Simpson index. These factors play a crucial role in maintaining the biodiversity of the region.