The chemical complexity of the rhizosphere has obscured our understanding of it but is critically important to understand plant-soil linkages, plant-soil feedbacks and how these change across environmental gradients. In an attempt to unveil the unknown relationships at the plant/soil interface, we have taken advantage of modern developments in metabolomics and statistical analysis. Past chemical research of the rhizosphere is often centered on single compounds or families of compounds, biasing our interpretation of plant soil feedbacks. To understand the chemical complexity of the rhizosphere, we utilized clones of Populus angustifolia collected from six riparian populations from AZ to MT and grown in a common environment. This design allowed for the exploration of the genetic component of variation in the root and rhizosphere soil metabolome to pursue three related hypotheses: First, there will be large scale variation in the chemical composition of the rhizosphere from trees collected from unique populations. Second, variation in soils reflect the variation in root chemical variation that conditioned them. Finally, the variation in chemical complexity among these populations is driven by trade-offs in physiological processes, instead of variation in belowground root productivity.
Results/Conclusions
Redundancy analysis on a dataset of over ten thousand known and unidentified chemical compounds suggest that plant population of origin can account for up to 34% of the variation in root chemistry and 30% of the variation in rhizosphere soil chemistry in a common environment. Further, exploration of plants conditioning soil was conducted with a co-inertia analysis and found that variation in plant root chemistry explains ~16% of the variation in soil chemistry. Further analysis reveals that the variation in rhizosphere chemistry of each population are driven by compounds unique to that population, not simply a result of differential belowground root productivity (i.e., compounds suggestive of cellular metabolism, such as NADH and ATP are not driving the observed trends). These data show that metabolomics can be used to address important ecological questions impacting plant soil linkages and feedbacks and have utility in broadening our understanding of what has often been kept in “the black box”. These data also show that approaches such as these are getting us closer to a predictive understanding of the chemical complexity of the rhizosphere within and across environmental gradients, which has both basic and applied implications for conservation and restoration.