A few north Appalachian populations are the source of European black locust.

The role of evolution in biological invasion studies is often overlooked. In order to evaluate the evolutionary mechanisms behind invasiveness, it is crucial to identify the source populations of the introduction. Studies in population genetics were carried out on Robinia pseudoacacia L., a North American tree which is now one of the worst invasive tree species in Europe. We realized large-scale sampling in both the invasive and native ranges: 63 populations were sampled and 818 individuals were genotyped using 113 SNPs. We identified clonal genotypes in each population and analyzed between and within range population structure, and then, we compared genetic diversity between ranges, enlarging the number of SNPs to mitigate the ascertainment bias. First, we demonstrated that European black locust was introduced from just a limited number of populations located in the Appalachian Mountains, which is in agreement with the historical documents briefly reviewed in this study. Within America, population structure reflected the effects of long-term processes, whereas in Europe it was largely impacted by human activities. Second, we showed that there is a genetic bottleneck between the ranges with a decrease in allelic richness and total number of alleles in Europe. Lastly, we found more clonality within European populations. Black locust became invasive in Europe despite being introduced from a reduced part of its native distribution. Our results suggest that human activity, such as breeding programs in Europe and the seed trade throughout the introduced range, had a major role in promoting invasion; therefore, the introduction of the missing American genetic cluster to Europe should be avoided.

Dealing with paralogy in RADseq data: in silico detection and single nucleotide polymorphism validation in Robinia pseudoacacia L.

The RADseq technology allows researchers to efficiently develop thousands of polymorphic loci across multiple individuals with little or no prior information on the genome. However, many questions remain about the biases inherent to this technology. Notably, sequence misalignments arising from paralogy may affect the development of single nucleotide polymorphism (SNP) markers and the estimation of genetic diversity. We evaluated the impact of putative paralog loci on genetic diversity estimation during the development of SNPs from a RADseq dataset for the nonmodel tree species Robinia pseudoacacia L. We sequenced nine genotypes and analyzed the frequency of putative paralogous RAD loci as a function of both the depth of coverage and the mismatch threshold allowed between loci. Putative paralogy was detected in a very variable number of loci, from 1% to more than 20%, with the depth of coverage having a major influence on the result. Putative paralogy artificially increased the observed degree of polymorphism and resulting estimates of diversity. The choice of the depth of coverage also affected diversity estimation and SNP validation: A low threshold decreased the chances of detecting minor alleles while a high threshold increased allelic dropout. SNP validation was better for the low threshold (4×) than for the high threshold (18×) we tested. Using the strategy developed here, we were able to validate more than 80% of the SNPs tested by means of individual genotyping, resulting in a readily usable set of 330 SNPs, suitable for use in population genetics applications.