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Springer, Tree Genetics and Genomes, 3(19), 2023

DOI: 10.1007/s11295-023-01598-7

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Abiotic factors predict taxonomic composition and genetic admixture in populations of hybridizing white oak species (Quercus sect. Quercus) on regional scale

This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

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Abstract

Abstract Knowing which drivers affect the spatial distribution of hybridizing species and their admixed individuals on local or regional scale can leverage our understanding about processes that shape taxonomic diversity. Hybridizing white oak species (Quercus sect. Quercus) represent an ideal study system to elucidate which environmental factors determine their relative abundance and admixture levels within admixed forest stands. To elaborate these relationships, we used 58 species-diagnostic single-nucleotide polymorphism (SNP) markers and high-resolution topographic and soil data to identify the environmental factors associated with taxonomic composition of individuals and populations in 15 mixed stands of Q. petraea and Q. pubescens in the Valais, an inner-Alpine valley in Switzerland. At the individual tree level, generalized linear models (GLMs) explained a relatively small part of variation (R2 = 0.32). At the population level, GLMs often explained a large part of variation (R2 = 0.54–0.69) of the taxonomic indices. Mean taxonomic composition of the sites depended mainly on altitude and geographic position. Moreover, the more within-site variation we found in predictors related to topographic position, the higher was the average genetic admixture of single trees. Our results show that a multitude of topographic and edaphic factors affect the taxonomic composition and admixture levels of these two hybridizing oak species on local scale and that regional heterogeneity of these factors promote taxonomic diversity and admixture. Overall, our study highlights the prospects of using tailored genetic resources and high-resolution environmental data to understand and predict taxonomic composition in response to changing environments.