气候变化领域集成服务门户

Understanding biogeographic patterns and the mechanisms underlying them has been a main issue in macroecology and biogeography, and has implications for biodiversity conservation and ecosystem sustainability. Evergreen broad-leaved woody plants (EBWPs) are important components of numerous biomes and are the main contributors to the flora south of 35 degrees N in China. We calculated the grid cell values of species richness (SR) for a total of 6265 EBWP species in China, including its four growth-forms (i.e., tree, shrub, vine, and bamboo), and estimated their phylogenetic structure using the standardized phylogenetic diversity (SPD) and net relatedness index (NRI). Then we linked the three biogeographical patterns that were observed with each single environmental variable representing the current climate, the last glacial maximum (LGM)present climate variability, and habitat heterogeneity, using ordinary least squares regression with a modified t-test to account for spatial autocorrelation. The partial regression method based on a general linear model was used to decompose the contributions of current and historical environmental factors to the bio-geographical patterns observed. The results showed that most regions with high numbers of EBWP species and phylogenetic diversity were distributed in tropical and subtropical mountains with evergreen shrubs extending to Northeast China. Current mean annual precipitation was the best single predictor. Topographic variation and its effect on temperature variation was the best single predictor for SPD and NRI. Partial regression indicated that the current climate dominated the SR patterns of Chinese EBWPs. The effect of paleo-climate variation on SR patterns mostly overlapped with that of the current climate. In contrast, the phylogenetic structure represented by SPD and NRI was constrained by paleo-climate to much larger extents than diversity, which was reflected by the LGMpresent climate variation and topography-derived habitat heterogeneity in China. Our study highlights the importance of embedding multiple dimensions of biodiversity into a temporally hierarchical framework for understanding the biogeographical patterns, and provides important baseline information for predicting shifts in plant diversity under climate change.