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Abstract: 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.
Keywords: gene pool, genetic assignment, maritime pine, origin identification, region of provenance, SNP marker
Published in DiRROS: 03.12.2025; Views: 212; Downloads: 97
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Abstract: Aim: Marginal tree populations, either those located at the edges of the species' rangeor in suboptimal environments, are often a valuable genetic resource for biologicalconservation. However, there is a lack of knowledge about the genetic consequencesof population marginality, estimated across entire species' ranges. Our study ad-dresses this gap by providing information about several genetic indicators and theirvariability in marginal and core populations identified using quantitative marginalityindices.Location: Southwestern Europe and North Africa.Methods: Using 10,185 SNPs across 82 populations of maritime pine (Pinus pinasterAit.), a widespread conifer characterised by a fragmented range, we modelled therelationship of seven genetic indicators potentially related to population evolution-ary resilience, namely genetic diversity (based on both all SNPs and outlier SNPs),inbreeding, genetic differentiation, recessive genetic load and genomic offset, withpopulation geographical, demo-historical and ecological marginality (as estimated bynine quantitative indices). Models were constructed for both regional (introducinggene pool as a random factor) and range-wide spatial scales.Results: We showed a trend towards decreasing overall genetic diversity and increas-ing differentiation with geographic marginality, supporting the centre-periphery hy-pothesis (CPH). However, we found no correlation between population inbreedingand marginality, while geographically marginal populations had a lower recessive ge-netic load (only models without the gene pool effect). Ecologically marginal popula-tions had a higher genomic offset, suggesting higher maladaptation to future climate,albeit some of these populations also had high genetic diversity for climate outliers.Main Conclusions: Overall genetic diversity (but not outlier-based estimates) and dif-ferentiation patterns support the CPH. Ecologically marginal populations and those atthe southern edge could be more vulnerable to climate change due to higher climate maladaptation, as predicted by genomic offsets, and/or lower potentially adaptive ge-netic diversity. This risk is exacerbated by typically small effective population sizesand increasing human impact in marginal populations.
Keywords: population genetics, conservation genetics, marginal populations, Pinus pinaster, genetic indicators
Published in DiRROS: 29.08.2024; Views: 1029; Downloads: 967
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