Genetic assignment at different geographical levels

Genetic markers can assist in the identification of the stock origin in different organisms. Comparative studies of forest tree provenances have demonstrated that forest tree populations differ in performance across environments and at multiple geographic levels: populations nested within regions nested within gene pools. These levels are critical for conservation and sustainable use of genetic resources: regions of provenance are key units for seed marketing, while populations guide reproductive material collection under most seed regulations. Despite their potential, genetic methods have rarely been applied to identify forest tree origins due to methodological (sufficient number of highly discriminatory markers) and practical (construction of a baseline composed of a representative selection of samples) challenges. In our study, we analyzed a genomic dataset comprising 10,185 SNPs from 1579 samples of Pinus pinaster, a species with strong population structure, across 86 populations, 45 regions of provenance, and 10 gene pools, to discriminate among these hierarchical levels and assign individuals to them. We used two software packages to evaluate the reliability of our baseline dataset (i.e., reference data) for genetic discrimination and assignment: RUBIAS, which performs genetic stock identification and associated tasks, and assignPOP, implementing a supervised machine-learning genetic-assignment framework. Using numerical validation analyses, we assessed their suitability and limitations for origin inference at each geographical level. Our results indicate that origin assignment is reliable in P. pinaster at the gene pool and region of provenance levels, but less so at the population level, provided that the 10 K SNP markers and a comprehensive genetic baseline are used. Incomplete baselines may result in wrong assignments at any hierarchical level, irrespective of sampling intensity for sampled candidate origins. We provide an extensive and publicly available baseline for P. pinaster, offering a useful tool for the management of forest genetic resources of this economically and ecologically important tree species.