群体遗传学
东亚
生物
进化生物学
地理
遗传学
人口
人口学
中国
考古
社会学
作者
Wuqin Xu,Hans Peter Comes,Yu Feng,Yong-Hua Zhang,Ying-Xiong Qiu
摘要
Aim Several hypotheses are available to predict change in genetic diversity at expanding peripheral ranges. However, empirical evidence to test predictions of the centre–periphery hypothesis (CPH) at contracting range limits is scarce. To address this issue, we assessed spatial patterns of genetic variation, effective population size, and contemporary and historical gene flow in a widespread, Tertiary relict tree species from subtropical China. Location Warm-temperate deciduous forests of subtropical China. Taxon Emmenopterys henryi (Rubiaceae) Methods We applied kernel density estimation to determine the centre of the species’ geographical range. Using microsatellite markers, we assessed genetic structure and diversity in 36 populations (503 individuals) sampled in the centre and periphery across the species’ range. We further examined both historical and contemporary gene flow. Finally, we applied coalescent methods to simulate population demography. Results In support of CPH predictions, the highest density of E. henryi coincided with the geographical centre of the species’ distribution range, and genetic diversity significantly declined with distance from this range centre. Historical migration from the core to the edge was significantly higher than in the opposite direction, whereas contemporary migration followed an opposite pattern. Central and peripheral populations had similar levels of genetic differentiation. Main conclusions Core-to-edge patterns of genetic diversity, but not genetic differentiation, were consistent with the CPH in E. henryi. Also in line with the CPH, historical (but not contemporary) migration from the core to the edge was significantly higher than in the opposite direction. Results suggest that the complex topography in subtropical China and population demographic processes may strongly influence whether CPH predictions are met in different population genetic parameters.
科研通智能强力驱动
Strongly Powered by AbleSci AI