Changing effects of energy and water on the richness distribution pattern of the Quercus genus in China

Front Plant Sci. 2024 Jan 17:15:1301395. doi: 10.3389/fpls.2024.1301395. eCollection 2024.

Abstract

Climate varies along geographic gradients, causing spatial variations in the effects of energy and water on species richness and the explanatory power of different climatic factors. Species of the Quercus genus are important tree species in China with high ecological and socioeconomic value. To detect whether the effects of energy and water on species richness change along climatic gradients, this study built geographically weighted regression models based on species richness and climatic data. Variation partition analysis and hierarchical partitioning analysis were used to further explore the main climatic factors shaping the richness distribution pattern of Quercus in China. The results showed that Quercus species were mainly distributed in mountainous areas of southwestern China. Both energy and water were associated with species richness, with global slopes of 0.17 and 0.14, respectively. The effects of energy and water on species richness gradually increased as energy and water in the environment decreased. The interaction between energy and water altered the effect of energy, and in arid regions, the effects of energy and water were relatively stronger. Moreover, energy explained more variation in species richness in both the entire study area (11.5%) and different climate regions (up to 19.4%). The min temperature of coldest month was the main climatic variable forming the richness distribution pattern of Quercus in China. In conclusion, cold and drought are the critical climatic factors limiting the species richness of Quercus, and climate warming will have a greater impact in arid regions. These findings are important for understanding the biogeographic characteristics of Quercus and conserving biodiversity in China.

Keywords: Quercus genus; climatic determinants; energy and water; geographically weighted regression; richness distribution pattern.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was financially supported by the Special Foundation for National Science and Technology Basic Resources Investigation of China [grant number 2019FY202300], the Natural Science Foundation of Shandong [grant number ZR2023QC234, ZR2023QC238, ZR2023QC253], the Postdoctoral Innovation Project of Shandong [grant number SDCX-ZG-202203031], the Natural Science Foundation of Qingdao [grant number 23-2-1-42-zyyd-jch], and the Research Foundation of Qingdao Forest Ecosystem [grant number 11200005071603].