Beyond "greening" and "browning": Trends in grassland ground cover fractions across Eurasia that account for spatial and temporal autocorrelation.

Grassland ecosystems cover up to 40% of the global land area and provide many ecosystem services directly supporting the livelihoods of over 1 billion people. Monitoring long-term changes in grasslands is crucial for food security, biodiversity conservation, achieving Land Degradation Neutrality goals, and modeling the global carbon budget. Although long-term grassland monitoring using remote sensing is extensive, it is typically based on a single vegetation index and does not account for temporal and spatial autocorrelation, which means that some trends are falsely identified while others are missed. Our goal was to analyze trends in grasslands in Eurasia, the largest continuous grassland ecosystems on Earth. To do so, we calculated Cumulative Endmember Fractions (annual sums of monthly ground cover fractions) derived from MODIS 2002-2020 time series, and applied a new statistical approach PARTS that explicitly accounts for temporal and spatial autocorrelation in trends. We examined trends in green vegetation, non-photosynthetic vegetation, and soil ground cover fractions considering their independent change trajectories and relations among fractions over time. We derived temporally uncorrelated pixel-based trend maps and statistically tested whether observed trends could be explained by elevation, land cover, SPEI3, climate, country, and their combinations, all while accounting for spatial autocorrelation. We found no statistical evidence for a decrease in vegetation cover in grasslands in Eurasia. Instead, there was a significant map-level increase in non-photosynthetic vegetation across the region and local increases in green vegetation with a concomitant decrease in soil fraction. Independent environmental variables affected trends significantly, but effects varied by region. Overall, our analyses show in a statistically robust manner that Eurasian grasslands have changed considerably over the past two decades. Our approach enhances remote sensing-based monitoring of trends in grasslands so that underlying processes can be discerned.

Environment and Genotype Influence Quantitative and Qualitative Variation in Condensed Tannins in Aspen.

Condensed tannins (CTs) are abundant, ecologically-relevant secondary metabolites in many plants, which respond to variables associated with anthropogenic environmental change. While many studies have reported how genetic and environmental factors affect CT concentrations, few have explored how they influence CT molecular structure. Here, using trembling aspen (Populus tremuloides) as a model organism, we report how foliar CT concentrations, polymer sizes, representation of procyanidins and prodelphinidins, and stereochemistry vary in response to changes in air temperature (warming and freeze damage), air composition (elevated CO2 and O3), soil quality (nutrients and microbiome), and herbivory (mammal and lepidopteran). Use of multiple aspen genotypes enabled assessment of genetic influences on aspen CTs. CT concentration and composition were analyzed by thiolysis-ultra high performance liquid chromatography/mass spectrometry in archived leaf samples from prior experiments. All environmental variables explored except for soil microbiome influenced both CT quantity and quality, with climate factors appearing to have larger effect magnitudes than herbivory. Climate, soil, and herbivory effects varied among genotypes, while air composition effects were consistent across genotypes. Considering that CT properties (concentrations and molecular structures) mediate functions at the organismal through ecosystem scales, intraspecific variation in responses of CT properties to environmental factors could provide a pathway through which environmental change exerts selective pressure on Populus populations. Future studies are needed to identify the molecular-level mechanisms by which environmental factors influence CT concentrations and structures, and to establish their ecological and evolutionary significance.

Phenotypic Variation in Phytochemical Defense of Trembling Aspen in Western North America: Genetics, Development, and Geography.

Trembling aspen (Populus tremuloides) is arguably the most important deciduous tree species in the Intermountain West of North America. There, as elsewhere in its range, aspen exhibits remarkable genetic variation in observable traits such as morphology and phenology. In contrast to Great Lakes populations, however, relatively little is known about phytochemical variation in western aspen. This survey of phytochemistry in western aspen was undertaken to assess how chemical expression varies among genotypes, cytotypes (diploid vs. triploid), and populations, and in response to development and mammalian browsing. We measured levels of foliar nitrogen, salicinoid phenolic glycosides (SPGs) and condensed tannins (CTs), as those constituents influence organismal interactions and ecosystem processes. Results revealed striking genotypic variation and considerable population variation, but minimal cytotype variation, in phytochemistry of western aspen. Levels of SPGs and nitrogen declined, whereas levels of CTs increased, with tree age. Browsed ramets had much higher levels of SPGs, and lower levels of CTs, than unbrowsed ramets of the same genotype. We then evaluated how composite chemical profiles of western aspen differ from those of Great Lakes aspen (assessed in earlier research). Interestingly, mature western aspen trees maintain much higher levels of SPGs, and lower levels of CTs, than Great Lakes aspen. Phenotypic variation in chemical composition of aspen - a foundation species - in the Intermountain West likely has important consequences for organismal interactions and forest ecosystem dynamics. Moreover, those consequences likely play out over spatial and temporal scales somewhat differently than have been documented for Great Lakes aspen.

Intraspecific variation in plant economic traits predicts trembling aspen resistance to a generalist insect herbivore.

Patterns of trait expression within some plant species have recently been shown to align with the leaf economics spectrum paradigm. Resistance to herbivores is also expected to covary with leaf economics traits. We selected 36 mature Populus tremuloides genotypes in a common garden to assess whether aspen leaf economics patterns follow those observed among species globally. We also evaluated leaf economics strategies in the context of insect resistance by conducting bioassays to determine the effects of plant traits on preference and performance of Lymantria dispar. We found that: (1) intraspecific trait patterns of P. tremuloides parallel those exhibited by the interspecific leaf economics spectrum, (2) herbivores preferred leaves from genotypes with resource-acquisitive strategies, and (3) herbivores also performed best on genotypes with resource-acquisitive strategies. We conclude that a leaf economics spectrum that incorporates defense traits is a useful tool for explaining intraspecific patterns of variation in plant strategies, including resistance to herbivores.

Growing up aspen: ontogeny and trade-offs shape growth, defence and reproduction in a foundation species.

BACKGROUND AND AIMS: Intraspecific variation in foundation species of forest ecosystems can shape community and ecosystem properties, particularly when that variation has a genetic basis. Traits mediating interactions with other species are predicted by simple allocation models to follow ontogenetic patterns that are rarely studied in trees. The aim of this research was to identify the roles of genotype, ontogeny and genotypic trade-offs shaping growth, defence and reproduction in aspen. METHODS: We established a common garden replicating >500 aspen genets in Wisconsin, USA. Trees were measured through the juvenile period into the onset of reproduction, for growth, defence chemistry (phenolic glycosides and condensed tannins), nitrogen, extrafloral nectaries, leaf morphology (specific leaf area), flower production and foliar herbivory and disease. We also assayed the TOZ19 sex marker and heterozygosity at ten microsatellite loci. KEY RESULTS: We found high levels of genotypic variation for all traits, and high heritabilities for both the traits and their ontogenetic trajectories. Ontogeny strongly shaped intraspecific variation, and trade-offs among growth, defence and reproduction supported some predictions while contradicting others. Both direct resistance (chemical defence) and indirect defence (extrafloral nectaries) declined during the juvenile stage, prior to the onset of reproduction. Reproduction was higher in trees that were larger, male and had higher individual heterozygosity. Growth was diminished by genotypic allocation to both direct and indirect defence as well as to reproduction, but we found no evidence of trade-offs between defence and reproduction. CONCLUSIONS: Key traits affecting the ecological communities of aspen have high levels of genotypic variation and heritability, strong patterns of ontogeny and clear trade-offs among growth, defence and reproduction. The architecture of aspen's community genetics - its ontogeny, trade-offs and especially its great variability - is shaped by both its broad range and the diverse community of associates, and in turn further fosters that diversity.

Genomic and transcriptomic analyses reveal polygenic architecture for ecologically important traits in aspen (Populus tremuloides Michx.).

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 30 ecologically important traits using a common garden of aspen through genomic and transcriptomic analyses. A multilocus association model revealed that most traits displayed a highly 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 1243 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 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.

Statistical tests for non-independent partitions of large autocorrelated datasets.

Large sets of autocorrelated data are common in fields such as remote sensing and genomics. For example, remote sensing can produce maps of information for millions of pixels, and the information from nearby pixels will likely be spatially autocorrelated. Although there are well-established statistical methods for testing hypotheses using autocorrelated data, these methods become computationally impractical for large datasets. • The method developed here makes it feasible to perform F-tests, likelihood ratio tests, and t-tests for large autocorrelated datasets. The method involves subsetting the dataset into partitions, analyzing each partition separately, and then combining the separate tests to give an overall test. • The separate statistical tests on partitions are non-independent, because the points in different partitions are not independent. Therefore, combining separate analyses of partitions requires accounting for the non-independence of the test statistics among partitions. • The methods can be applied to a wide range of data, including not only purely spatial data but also spatiotemporal data. For spatiotemporal data, it is possible to estimate coefficients from time-series models at different spatial locations and then analyze the spatial distribution of the estimates. The spatial analysis can be simplified by estimating spatial autocorrelation directly from the spatial autocorrelation among time series.