Molecular Proxies for Climate Maladaptation in a Long-Lived Tree (Pinus pinasterAiton, Pinaceae)

Abstract

Understanding adaptive genetic responses to climate change is a main challenge for preserving biological diversity. Successful predictive models for climate-driven range shifts of species depend on the integration of information on adaptation, including that derived from genomic studies. Long-lived forest trees can experience substantial environmental change across generations, which results in a much more prominent adaptation lag than in annual species. Here, we show that candidate-gene SNPs (single nucleotide polymorphisms) can be used as predictors of maladaptation to climate in maritime pine (Pinus pinaster Aiton), an outcrossing long-lived keystone tree. A set of 18 SNPs potentially associated with climate, 5 of them involving amino acid-changing variants, were retained after performing logistic regression, latent factor mixed models, and Bayesian analyses of SNP-climate correlations. These relationships identified temperature as an important adaptive driver in maritime pine and highlighted that selective forces are operating differentially in geographically discrete gene pools. The frequency of the locally advantageous alleles at these selected loci was strongly correlated with survival in a common garden under extreme (hot and dry) climate conditions, which suggests that candidate-gene SNPs can be used to forecast the likely destiny of natural forest ecosystems under climate change scenarios. Differential levels of forest decline are anticipated for distinct maritime pine gene pools. Geographically defined molecular proxies for climate adaptation will thus critically enhance the predictive power of range-shift models and help establish mitigation measures for long-lived keystone forest trees in the face of impending climate change.KEYWORDS climate adaptation; environmental associations; genetic lineages; single nucleotide polymorphisms; fitness estimates P AST and present climate changes are major drivers of species displacements and range-size variation (Hughes 2000;Franks and Hoffmann 2012). Current predictions indicate that the impact of climate change will intensify over the next 20-100 years (Loarie et al. 2009;Bellard et al. 2012), with concomitant phenotypic and genetic effects on wild populations (Gamache and Payette 2004;Franks and Hoffmann 2012;Alberto et al. 2013a). The capability of species to respond to such alterations will rely on phenotypic plasticity, potential for in situ adaptation, and/or migration to more suitable habitats (Aitken et al. 2008). While phenotypic plasticity and migration might be insufficient to cope with these changes (Mclachlan et al. 2005;Malcom et al. 2011;Zhu et al. 2011), successful in situ adaptation will depend on the amount of standing genetic variation and the rate at which new alleles arise, are maintained, and/or get to fixation within populations (Hancock et al. 2011). Thus, our ability to detect present adaptive polymorphisms and to integrate them in predictive models of future maladaptation might be decisive to ensure the persistence of natural p...

The challenge of separating signatures of local adaptation from those of isolation by distance and colonization history: The case of two white pines

“…Combining results across a number of different methods should be standard practice to detect strong candidate genes (De Mita et al., 2013; Lotterhos & Whitlock, 2014, 2015; de Villemereuil et al., 2014). The integration of phenotypic and genotypic information from populations growing in common‐garden experiments might be the most informative approach to discover loci important for local adaptation (Sork et al., 2013; Yeaman et al., 2016), and it could be used to validate candidate SNPs detected by F ST outlier or GEA methods (e.g., De Kort et al., 2014; Jaramillo‐Correa et al., 2015; Yoder et al., 2014). Another promising avenue would be taking advantage of the annual tree rings to establish relationships between annual growth and climatic variation in common‐garden experiments over a number of years.…”

“…Specifically, GEA methods have low power and high rates of false positives when environmental gradients are correlated with the main axes of neutral population structure (De Mita et al., 2013; Lotterhos & Whitlock, 2015; de Villemereuil et al., 2014). Despite the fact that GEA methods have variable performances in such scenarios, many studies only report results from a single method, and very few report the degree of collinearity between environmental gradients and geography (e.g., Lee & Mitchell‐Olds, 2011) or phylogeographic lineages (e.g., Jaramillo‐Correa et al., 2015). …”

Genetic architecture of wood properties based on association analysis and co‐expression networks in white spruce

“…In forest trees, the candidate gene association study (AS) approach has identified SNPs and genes linked to wood and growth traits in many tree species such as Eucalyptus nitens (Thumma et al ., 2009), Populus spp, (Ingvarsson et al ., 2008; Wegrzyn et al ., 2010; Guerra et al ., 2013), pines (Dillon et al ., 2010; Cumbie et al ., 2011; Jaramillo‐Correa et al ., 2015) and spruces (Beaulieu et al ., 2011; Prunier et al ., 2013). However, the variation in quantitative traits explained by individual SNP markers is generally low and rarely exceeds 5% (Dillon et al ., 2010; Guerra et al ., 2013), consistent with multigenic control (Evans et al ., 2014) and the relatively shallow genomic sampling in most studies to date (< 1% and 10% of estimated gene coding loci per genome) (Nystedt et al ., 2013; Neale et al ., 2014).…”

Ecological and association genetics in two Mediterranean pine species

“…Details on DNA extraction and quantification, SNP discovery and genotyping can be found in Annex 1: Plomion et al (2015). In brief, the design of the Illumina Infinium SNP array included SNPs already validated in the species and new SNPs from RNA-seq and amplicon resequencing data.…”

“…Introduction included multiple candidate genes for adaptive traits in pines and other forest trees, as well as several SNPs coming from gene sequence of unknown function. A more detailed description of the maritime pine OPAs is provided in Chapters 4 (for the 384-SNP OPA) and Chapters 1 and 3 and Annex 1 (Plomion et al 2015; for the 6,100-SNPs OPA), and references therein. In addition, 12 nuclear microsatellites (nuSSRs), considered neutral, were genotyped following and references therein.…”