Abstract

A full understanding of the origin and maintenance of β‐diversity patterns in a region requires exploring the relationships of both taxonomic and phylogenetic β‐diversity (TBD and PBD, respectively), and their respective turnover and nestedness components, with geographic and environmental distances. Here, we simultaneously investigated all these aspects of β‐diversity for angiosperms in China. Specifically, we evaluated the relative importance of environmental filtering vs dispersal limitation processes in shaping β‐diversity patterns. We found that TBD and PBD as quantified using a moving window approach decreased towards higher latitudes across the whole of China, and their turnover components were correlated with latitude more strongly than their nestedness components. When quantifying β‐diversity as pairwise distances, geographic and climatic distances across China together explained 60 and 53% of the variation in TBD and PBD, respectively. After the variation in β‐diversity explained by climatic distance was accounted for, geographic distance independently explained about 23 and 12% of the variation in TBD and PBD, respectively, across China. Overall, our results suggest that environmental filtering based on climatic tolerance conserved across lineages is the main force shaping β‐diversity patterns for angiosperms in China.

Highlights

  • Values of SAR Coeff. and R2 were derived from spatial autoregressive (SAR) models

  • We report the partial standardized regression coefficients for each explanatory distance variable and the coefficient of determination (R2) of the complete model

  • Explained variation was calculated based on coefficient of determination derived from multiple regression on distance matrices (MRM) analysis

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Summary

Introduction

ECOG-05190 Qian, H., Jin, Y., Leprieur, F., Wang, X. and Deng, T. Diagram showing the neighborhood approach used to calculate β-diversity between the focal cell (green cell, #13) and each of the remaining 24 cells. Results of Simultaneous Autoregressive models relating β-diversity metrics (βsor.tax and βsor.phy), their turnover (βsim.tax and βsim.phy) and nestedness (βnes.tax and βnes.phy) components, and the ratio of βnes to βsor (βratio.tax and βratio.phy) against latitude (LAT), mean annual temperature (MAT) and the first principal component (PC1) of the six climatic variables (see Methods).

Results
Conclusion
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