Gradient Forests (GF) is a machine learning algorithm that is gaining in popularity for studying the environmental drivers of genomic variation and for incorporating genomic information into climate change impact assessments. Here we (i) provide the first experimental evaluation of the ability of "genomic offsets" -a metric of climate maladaptation derived from Gradient Forests -to predict organismal responses to environmental change, and (ii) explore the use of GF for identifying candidate SNPs.We used high-throughput sequencing, genome scans, and several methods, including GF, to identify candidate loci associated with climate adaptation in balsam poplar (Populus balsamifera L.). Individuals collected throughout balsam poplar's range also were planted in two common garden experiments. We used GF to relate candidate loci to environmental gradients and predict the expected magnitude of the response (i.e., the genetic offset metric of maladaptation) of populations when transplanted from their "home" environment to the common gardens. We then compared the predicted genetic offsets from different sets of candidate and randomly selected SNPs to measurements of population performance in the common gardens. We found the expected inverse relationship between genetic offset and performance: populations with larger predicted genetic offsets performed worse in the common gardens than populations with smaller offsets. Also, genetic offset better predicted performance than did "naive" climate transfer distances. However, sets of randomly selected SNPs predicted performance slightly better than did candidate SNPs. Our study provides evidence that genetic offsets represent a first order estimate of the degree of expected maladaptation of populations exposed to rapid environmental change and suggests GF may have some promise as a method for identifying candidate SNPs.

BackgroundPopulation structure inference using the software STRUCTURE has become an integral part of population genetic studies covering a broad spectrum of taxa including humans. The ever-expanding size of genetic data sets poses computational challenges for this analysis. Although at least one tool currently implements parallel computing to reduce computational overload of this analysis, it does not fully automate the use of replicate STRUCTURE analysis runs required for downstream inference of optimal K. There is pressing need for a tool that can deploy population structure analysis on high performance computing clusters.ResultsWe present an updated version of the popular Python program StrAuto, to streamline population structure analysis using parallel computing. StrAuto implements a pipeline that combines STRUCTURE analysis with the Evanno Δ K analysis and visualization of results using STRUCTURE HARVESTER. Using benchmarking tests, we demonstrate that StrAuto significantly reduces the computational time needed to perform iterative STRUCTURE analysis by distributing runs over two or more processors.ConclusionStrAuto is the first tool to integrate STRUCTURE analysis with post-processing using a pipeline approach in addition to implementing parallel computation – a set up ideal for deployment on computing clusters. StrAuto is distributed under the GNU GPL (General Public License) and available to download from http://strauto.popgen.org.

In hybrid zones occurring in marginal environments, adaptive introgression from one species into the genomic background of another may constitute a mechanism facilitating adaptation at range limits. Although recent studies have improved our understanding of adaptive introgression in widely distributed tree species, little is known about the dynamics of this process in populations at the margins of species ranges. We investigated the extent of introgression between three species of the genus Populus sect. Tacamahaca (P. balsamifera, P. angustifolia and P. trichocarpa) at the margins of their distributions in the Rocky Mountain region of the United States and Canada. Using genotyping by sequencing (GBS), we analysed ~ 83,000 single nucleotide polymorphisms genotyped in 296 individuals from 29 allopatric and sympatric populations of the three species. We found a trispecies hybrid complex present throughout the zone of range overlap, including early as well as advanced generation backcross hybrids, indicating recurrent gene flow in this hybrid complex. Using genomic cline analysis, we found evidence of non-neutral patterns of introgression at 23% of loci in hybrids, of which 47% and 8% represented excess ancestry from P. angustifolia and P. balsamifera, respectively. Gene ontology analysis suggested these genomic regions were enriched for genes associated with photoperiodic regulation, metal ion transport, maintenance of redox homeostasis and cell wall metabolites involved in regulation of seasonal dormancy. Our study demonstrates the role of adaptive introgression in a multispecies hybrid complex in range-edge populations and has implications for understanding the evolutionary dynamics of adaptation in hybrid zones, especially at the margins of species distributions.