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Drylands account for more than 30% of China's terrestrial area, while the ecological drivers of taxonomic (TD), functional (FD) and phylogenetic (PD) diversity in dryland regions have not been explored simultaneously. Therefore, we selected 36 plots of desert and 32 plots of grassland (10 × 10 m) from a typical dryland region of northwest China. We calculated the alpha and beta components of TD, FD and PD for 68 dryland plant communities using Rao quadratic entropy index, which included 233 plant species. Redundancy analyses and variation partitioning analyses were used to explore the relative influence of environmental and spatial factors on the above three facets of diversity, at the alpha and beta scales. We found that soil, climate, topography and spatial structures (principal coordinates of neighbor matrices) were significantly correlated with TD, FD and PD at both alpha and beta scales, implying that these diversity patterns are shaped by contemporary environment and spatial processes together. However, we also found that alpha diversity was predominantly regulated by spatial structure, whereas beta diversity was largely determined by environmental variables. Among environmental factors, TD was most strongly correlated with climatic factors at the alpha scale, while with soil factors at the beta scale. FD was only significantly correlated with soil factors at the alpha scale, but with altitude, soil and climatic factors at the beta scale. In contrast, PD was more strongly correlated with altitude at the alpha scale, but with soil factors at the beta scale. Environment and space explained a smaller portion of variance in PD than in TD and FD. These results provide robust evidence that the ecological drivers of biodiversity differ among different scales and facets of diversity. Future research that focuses on the comparisons among TD, FD and PD would likely provide new insights into elucidating the underlying community assembly.
Pubmed ID: 30648001
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A set of global climate layers (climate grids) with a spatial resolution of about 1 square kilometer. The data can be used for mapping and spatial modeling in a GIS or with other computer programs. If you are not familiar with such programs, you can try DIVA-GIS or the R raster package.
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