Background: One major frontier in ecology is predicting ecosystem-level change in response to climate-driven evolution of plant phenotypes. More explicitly, understanding how plant phenotypes and soil microbiome interactions vary along environmental gradients is pivotal for exploring ecosystem (dis)assembly and adaptation from genes to ecosystems. Question: We utilize two approaches to understand how climate, plant genetics, and soil microbial communities interact to influence both phenology and ecosystem-level traits. Methods: Our first approach combines observational and common garden studies to examine how climate warming drives evolution of plant phenotypes, and how such evolution alters plant-soil linkages across the geographic distribution of a foundation tree species, Populus angustifolia. In our second approach, we use a microbial inoculation experiment in the same system to address our overarching hypothesis that climate, plant genetics, and soil microbes predictably affect plant phenology in a common environment.
Results: We detected patterns of selection on phenology and growth among warm populations, and patterns attributable to drift among cool populations. We found warm populations have 24% less carbon (C), 40% less nitrogen (N), and 28% higher fungal abundance in their associated soils, relative to cool populations. We then show trees from warm populations differentially influence soil nutrient pools and microbial abundances by exerting a 12% stronger conditioning effect on both soil C and N, which is driven by a significant interaction between genetically based differences in phenology and soil microbiomes. Conclusion: Overall, our results show bud break phenology is mediated by both a strong plant genetic effect and a soil microbiome effect driven by variation in temperature. Integrating above and belowground perspectives along the climatic gradients that shape biological interactions may prove critical in how we: 1) understand phenotypic variation on the landscape, 2) forecast the potential for local adaptation to future climatic scenarios, and 3) develop data to populate species distribution models and range shift projections.