Scientific Reports (Feb 2024)

Stronger genetic differentiation among within-population genetic groups than among populations in Scots pine provides new insights into within-population genetic structuring

  • Darius Danusevičius,
  • Om P. Rajora,
  • Darius Kavaliauskas,
  • Virgilijus Baliuckas,
  • Algirdas Augustaitis

DOI
https://doi.org/10.1038/s41598-024-52769-y
Journal volume & issue
Vol. 14, no. 1
pp. 1 – 13

Abstract

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Abstract We investigated the presence of spatial genetic groups within forest tree populations and determined if the genetic divergence among these groups is greater than that between populations using Scots pine (Pinus sylvestris) as a model species. We genotyped 890 adult trees of Scots pine in six natural populations in Lithuania at 11 nuclear microsatellite loci. We used a Bayesian clustering approach to identify the within-population genetic groups within each of the six populations. We calculated the differentiation indexes among the genetic groups within each population and among the six populations by ignoring the genetic groups. The Bayesian clustering revealed 2 to 6 distinct genetic groups of varying size as the most likely genetic structures within populations. The genetic differentiation indexes among the genetic groups within populations were nearly tenfold greater (F ST = 0.012–0.070) than those between the populations (F ST = 0.003). We conclude on the existence of markedly stronger structuring of genetic variation within populations than between populations of Scots pine in large forest tracts of northern Europe. Such genetic structures serve as a contributing factor to large within population genetic diversity in northern conifers. We assume that within population mating in Scots pine is not completely random but rather is stratified into genetic clusters. Our study provides pioneering novel key insights into structuring of genetic variation within populations. Our findings have implications for examining within-population genetic diversity and genetic structure, conservation, and management of genetic resources.