Uncovering the determinants of biodiversity hotspots in China: Evidence from the drivers of multiple diversity metrics on insect assemblages and implications for conservation

Understanding large-scale biodiversity patterns and underlying mechanisms during the formation process is essential for guiding conservation efforts. However, previous studies on the identification and formation mechanism of diversity hotspots in China were often limited to a single (alpha) diversity metric, while multiple (beta or zeta) diversity has rarely been used for exploring drivers and conservation actions. Here, a comprehensive species distribution dataset consisting of representative families of three insect orders was compiled to explore biodiversity hotspots based on dif-ferent algorithms. Furthermore, to assess the effects of environmental factors on hotspots, we fitted generalized addi-tive mixed-effects models (GAMMs) for species richness, generalized dissimilarity models (GDMs) and multi-site generalized dissimilarity modeling (MS-GDM) for the total beta and zeta diversity. Our results showed that biodiver-sity hotspots were mainly concentrated in central and southern China, especially in mountainous areas with complex topography, which indicated the insects' affinity to montane environments. Further analyses based on multiple models showed that water-energy factors exerted the strongest explanatory power for the insect assemblage diversity in hotspots of both alpha and beta (or zeta) levels. Additionally, anthropogenic factors also exerted a significant effect on hotspots, and this effect was higher for beta diversity than for alpha diversity. Overall, our study elucidates a com-prehensive analysis of the identification and underlying mechanism of biodiversity hotspots in China. Despite several limitations, we still believe that our findings can provide some new insights for conservation efforts in Chinese hotspots.

Automated summary

Understanding the patterns and mechanisms of large-scale biodiversity is crucial for conservation efforts. Previous studies on diversity hotspots in China have often focused on single diversity metrics, neglecting multiple diversity measures. In this study, we compiled a comprehensive species distribution dataset and used various algorithms to identify biodiversity hotspots. We also assessed the effects of environmental factors on hotspots using statistical models. Our results revealed that biodiversity hotspots in China are concentrated in central and southern regions, specifically in mountainous areas with complex topography. Water-energy factors were found to be the strongest drivers of insect diversity in hotspots, followed by anthropogenic factors. This study provides valuable insights for conservation efforts in Chinese hotspots.