Variation in fitness components can be linked in some cases to variation of key traits. Metric traits that lie at the intersection of development, defense, and ecological interactions may be expected to experience strong environmental selection, informing our understanding of evolutionary and ecological processes. Here, we use quantitative genetic and population genomic methods to investigate disease dynamics in hybrid and non-hybrid populations. We focus our investigation on morphological and ecophysiological traits which inform our understanding of physiology, growth, and defense against a pathogen. In particular, we investigate stomata, microscopic pores on the surface of a leaf which regulate gas exhange during photosynthesis and are sites of entry for various plant pathogens. Stomatal patterning traits were highly predictive of disease risk. Admixture mapping identified a polygenic basis of disease resistance. Candidate genes for stomatal and disease resistance map to the same genomic regions, and are experienceing positive selection. Genes with functions for guard cell homeostasis, the plant immune system, components of constitutive defenses, and growth related transcription factors were identified. Our results indicate positive selection is filtering genetic variance from one of the parental species maladpated to a novel pathogen, and changing suites of stomatal traits which contribute to disease variation in natural populations.

Populus has a wide ecogeographical range spanning the Northern Hemisphere, and exhibits abundant distinct species and hybrids globally. Populus tomentosa Carr. is widely distributed and cultivated in the eastern region of Asia, where it plays multiple important roles in forestry, agriculture, conservation, and urban horticulture. Reference genomes are available for several Populus species, however, our goals were to produce a very high quality de novo, chromosome-level genome assembly in P. tomentosa genome that could serve as a reference for evolutionary and ecological studies of hybrid speciation. Here, combining long-read sequencing and Hi-C scaffolding, we present a high-quality, haplotype-resolved genome assembly. The genome size was 740.2 Mb, with a contig N50 size of 5.47 Mb and a scaffold N50 size of 46.68 Mb, consisting of 38 chromosomes, as expected with the known diploid chromosome number (2n=2x=38). A total of 59,124 protein-coding genes were identified. Phylogenomic analyses revealed that P. tomentosa is comprised of two distinct subgenomes, which we deomonstrate is likely to have resulted from hybridization between Populus adenopoda as the female parent and Populus alba var. pyramidalis as the male parent, approximately 3.93 Mya. Although highly colinear, significant structural variation was also found between the two subgenomes. Our study provides a valuable resource for ecological genetics and forest biotechnology.

Gradient Forests 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 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. We used high-throughput sequencing, genome scans, and several methods (including Gradient Forests) 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 Gradient Forests to relate candidate loci to environmental gradients and to predict the expected magnitude of response (i.e., the genetic offset) of populations when transplanted from their “home” environment to the new environments in the common gardens. We then compared the predicted genetic offsets to measurements of population performance in the common gardens. We found the expected inverse relationship between genetic offset and performance in the common gardens: populations with larger predicted genetic offsets performed worse in the common gardens than populations with smaller offsets. Also, genetic offset better predicted performance in the common gardens than did ‘naive’ climate distances. Our results provide preliminary evidence that genomic offsets may provide a first order estimate of the degree of expected maladaptation of populations exposed to rapid environmental change.