Intraspecific genetic variation in foundation species such as aspen (Populus tremuloides Michx.) shapes their impact on forest structure and function. Identifying genes underlying ecologically important traits is key to understanding that impact. Previous studies using single-locus genome-wide association (GWA) analyses to identify candidate genes have identified fewer genes than anticipated for highly heritable quantitative traits. Mounting evidence suggests that polygenic control of quantitative traits is largely responsible for this “missing heritability” phenomenon. Our research characterized the genetic architecture of 35 ecologically important traits using a common garden of aspen through genomic and transcriptomic analyses. A multilocus association model revealed that most traits displayed a polygenic architecture, with most variation explained by loci with small effects (likely below the detection levels of single-locus GWA methods). Consistent with a polygenic architecture, our single-locus GWA analyses found only 38 significant SNPs in 22 genes across 15 traits. Next, we used differential expression analysis on a subset of aspen genets with divergent concentrations of salicinoid phenolic glycosides (key defense traits). This complementary method to traditional GWA discovered 1,243 differentially expressed genes for a polygenic trait. Soft clustering analysis revealed three gene clusters (241 candidate genes) involved in secondary metabolite biosynthesis and regulation. Our results support the omnigenic model that complex traits are largely controlled by many small effect loci, most of which may not have obvious connections to the traits of interest. Our work reveals that ecologically important traits governing higher-order community- and ecosystem-level attributes of a foundation forest tree species have complex underlying genetic structures and will require methods beyond traditional GWA analyses to unravel.

Grassland ecosystems cover one-fourth of the global land area and harbor over 30% of the global carbon stored in soils. However, grasslands are subjected to extensive and intensive land degradation, which threatens biodiversity, the well-being and food-security of millions of people, and poses challenges for climate change mitigation. The question is where grasslands have degraded and where long-term greening is taking place. Time series of satellite data can be used for trend analyses, but when testing for statistical significance, it is important to account for temporal and spatial autocorrelation. Here we present our new statistical method to analyze long-term trends in grasslands based on physically-based Cumulative Endmember Fractions (annual sums of monthly ground cover fractions). Our trend analysis incorporates two steps: first we apply an autoregressive time series to each pixel to obtain a slope estimate while accounting for temporal autocorrelation. Second, we apply a general least-square regression to the slope estimates, in which we incorporate spatial covariance structure, as well as explanatory variables. We tested our approach mapping long-term trends in grasslands in Central Asia using MODSI 2001 2019 time series, which we regressed against meteorological measurements. Our results showed long term changes of both, positive (i.e., revegetation; e.g., east part of Central Asia) and negative trajectories (i.e., desiccation; e.g., north-west part of the Central Asia). Importantly, our method is scalable and transferable to other time series of satellite data and regions, and can be implemented in any computational environment, assuring accessibility and reproducibility.