Exploring the genetic diversity and population structure of Ailanthus altissima using chloroplast and nuclear microsatellite DNA markers across its native range - Saina, Josphat K.; Li, Zhi-Zhong; Ngarega, Boniface K.; Gituru, Robert W.; Chen, Jin-Ming; Liao, Yi-Ying (Frontiers in Plant Science, 2023)
Understanding how anthropogenic disturbances affect the genetics of tree species is crucial; however, how tree populations in the wild can tolerate these activities remains unexplored. Given the ongoing and intensifying anthropogenic disturbances, we conducted a study using Ailanthus altissima to gain new insights into the effects of these pressures on genetic variability in undisturbed and disturbed forests. We analyzed the genetic diversity and population structure of A. altissima using nuclear (EST-SSR) and chloroplast (cpSSR) microsatellite markers. The genetic diversity across the 34 studied populations based on EST-SSRs was found to be moderate to high (nHE = 0.547–0.772) with a mean nHE of 0.680. Bayesian clustering, principal coordinate analysis (PCoA), and discriminant analysis of principal component (DAPC) consistently divided the populations into three distinct groups based on EST-SSRs. Allelic combinations of 92 different chloroplast size variants from 10 cpSSR loci resulted in a total of 292 chloroplast haplotypes. The mean haplotype diversity was relatively high (cpHE = 0.941), and the mean haplotype richness was 2.690, averaged across the 34 populations of A. altissima. Values of FST in A. altissima from chloroplast and nuclear markers were 0.509 and 0.126, respectively. Modeling results showed evidence for population range contraction during the Last Glacial Maximum with subsequent population expansion in the Holocene and the future. Although genetic variation did not differ substantially across disturbed and undisturbed sites, there were small trends indicating higher genetic diversity and population bottlenecks in disturbed forests. As a result, disrupted ecosystems might display surprising genetic patterns that are difficult to predict and should not be overlooked.