Abstract
Lodgepole pine (Pinus contorta var. latifolia) and jack pine (Pinus banksiana) hybridize in western Canada, an area of recent mountain pine beetle range expansion. Given the heterogeneity of the environment, and indications of local adaptation, there are many unknowns regarding the response of these forests to future outbreaks. To better understand this we aim to identify genetic regions that have adaptive potential.We used data collected on 472 single nucleotide polymorphism (SNP) loci from 576 tree samples collected across 13 lodgepole pine-dominated sites and four jack pine-dominated sites. We looked at the relationship of genetic diversity with the environment, and we identified candidate loci using both frequency-based (arlequin and bayescan) and correlation-based (matsam and bayenv) methods.We found contrasting relationships between environmental variation and genetic diversity for the species. While we identified a number of candidate outliers (34 in lodgepole pine, 25 in jack pine, and 43 interspecific loci), we did not find any loci in common between lodgepole and jack pine. Many of the outlier loci identified were correlated with environmental variation.Using rigorous criteria we have been able to identify potential outlier SNPs. We have also found evidence of contrasting environmental adaptations between lodgepole and jack pine which could have implications for beetle spread risk.
Highlights
A fundamental goal of evolutionary biology is to identify adaptive variation within a genome
We identified 24 linkage groups in lodgepole pine and 20 groups in jack pine; nine of these were shared between the species
We found that the proportion of associations with the environmental variables was statistically different between lodgepole and jack pine based on two-sample binomial t-tests for minimum temperature – coldest period, precipitation – driest period, longitude, maximum temperature – warmest period, and average potential evapotranspiration (PET) following Bonferroni correction (a = 0.004)
Summary
A fundamental goal of evolutionary biology is to identify adaptive variation within a genome. With the advent of single nucleotide polymorphism (SNP)-chip genotyping and next-generation sequencing (Hudson, 2008), and an increase in bioinformatic capability, we are better able to study nonmodel organisms and begin to identify and characterize adaptive variation (Luikart et al, 2003; Ekblom & Galindo, 2011). This comes at a critical time where many species are faced with changing environments as a result of anthropogenic stresses, invasive species and climate change (Thomas et al, 2004; Hoffman & Sgro, 2011). Through understanding sources of adaptive variation we can promote healthy forests through improved seed-stock selection and assisted migration (McLachlan et al, 2007; Aitken et al, 2008)
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