Studies on endemism are always of high interest in biogeography and contribute to better understanding of the evolution of species and making conservation plans. The present study aimed to investigate the endemism patterns of planthoppers in China by delimiting centers of endemism and areas of endemism. We collected 6,907 spatial distribution records for 860 endemic planthopper species from various resources. Centers of endemism were identified using weighted endemism values at 1° grid size. Parsimony analysis of endemicity and endemicity analysis were employed to detect areas of endemism at 1°, 1.5°, and 2° grid sizes. Six centers of endemism located in mountainous areas were identified: Taiwan Island, Hainan Island, eastern Yungui Plateau, Wuyi Mountains, western Qinling Mountains, and western Yunnan. We also delimited six areas of endemism, which were generally consistent with centers of endemism. Our findings demonstrated that mountainous areas have an essential role in facilitating the high level of endemism and formation of areas of endemism in planthoppers through the combined effects of complex topography, a long-term stable environment, and geological events. Dispersal ability and distribution of host plants also have important effects on the patterns of planthoppers’ endemism.

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Although many hypotheses have been proposed to understand the mechanisms underlying large-scale richness patterns, the environmental determinants are still poorly understood, particularly in insects. Here, we tested the relative contributions of seven hypotheses previously proposed to explain planthopper richness patterns in China. The richness patterns were visualized at a 1° × 1° grid size, using 14,722 distribution records for 1335 planthoppers. We used ordinary least squares and spatial error simultaneous autoregressive models to examine the relationships between richness and single environmental variables and employed model averaging to assess the environmental variable relative roles. Species richness was unevenly distributed, with high species numbers occurring in the central and southern mountainous areas. The mean annual temperature change since the Last Glacial Maximum was the most important factor for richness patterns, followed by mean annual temperature and net primary productivity. Therefore, historical climate stability, ambient energy, and productivity hypotheses were supported strongly, but orogenic processes and geological isolation may also play a vital role.

1. Identifying the macro-scale patterns and the underlying mechanisms of species richness are key aspects of biodiversity-related research. In China, previous studies on the mechanisms underlying insect richness have primarily focused on the current ecological conditions. Therefore, the impact of historical climate change on these mechanisms is less well understood. 2. Here, we use members of the Delphacidae family to evaluate the relative impact of the current environmental conditions and that of the Last Glacial Maximum on total species richness and endemism. Total species richness and endemic species richness were summed in 1 ∘ × 1 ∘ grid cells that the insects occupied. Generalised linear models, simultaneous autoregressive models, and random forest models were used to assess the effects of different environmental factors on total species richness and endemism. 3. The two patterns of species richness are jointly regulated by the current environment and the Last Glacial Maximum, but their key determinants differ. Winter coldness and the temperature annual range strongly affected the total species richness, but temperature variation during the Last Glacial Maximum also played an important role in the development of species richness. The distribution of endemic species was most strongly affected by the Last Glacial Maximum temperature change. 4. The studies confirm that historical climate change contributes to patterns of insect species richness, particularly patterns of endemism. Considering that China was mildly affected by the last glacial period, we propose that the incorporation of historical climate data into such studies will provide a better understanding of the underlying mechanisms.

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