Summary The extent to which ectomycorrhizal (ECM) fungi enable plants to access organic nitrogen (N) bound in soil organic matter (SOM) and transfer this growth‐limiting nutrient to their plant host, has important implications for our understanding of plant–fungal interactions, and the cycling and storage of carbon (C) and N in terrestrial ecosystems. Empirical evidence currently supports a range of perspectives, suggesting that ECM vary in their ability to provide their host with N bound in SOM, and that this capacity can both positively and negatively influence soil C storage. To help resolve the multiplicity of observations, we gathered a group of researchers to explore the role of ECM fungi in soil C dynamics, and propose new directions that hold promise to resolve competing hypotheses and contrasting observations. In this Viewpoint, we summarize these deliberations and identify areas of inquiry that hold promise for increasing our understanding of these fundamental and widespread plant symbionts and their role in ecosystem‐level biogeochemistry.

Microbial communities are ubiquitous and often influence macroscopic properties of the ecosystems they inhabit. However, deciphering the functional relationship between specific microbes and ecosystem properties is an ongoing challenge owing to the complexity of the communities. This challenge can be addressed, in part, by integrating the advances in DNA sequencing technology with computational approaches like machine learning. Although machine learning techniques have been applied to microbiome data, use of these techniques remains rare, and user-friendly platforms to implement such techniques are not widely available. We developed a tool that implements neural network and random forest models to perform regression and feature selection tasks on microbiome data. In this study, we applied the tool to analyze soil microbiome (16S rRNA gene profiles) and dissolved organic carbon (DOC) data from a 44-day plant litter decomposition experiment. The microbiome data includes 1709 total bacterial operational taxonomic units (OTU) from 300+ microcosms. Regression analysis of predicted and actual DOC for a held-out test set of 51 samples yield Pearson’s correlation coefficients of.636 and.676 for neural network and random forest approaches, respectively. Important taxa identified by the machine learning techniques are compared to results from a standard tool (indicator species analysis) widely used by microbial ecologists. Of 1709 bacterial taxa, indicator species analysis identified 285 taxa as significant determinants of DOC concentration. Of the top 285 ranked features determined by machine learning methods, a subset of 86 taxa are common to all feature selection techniques. Using this subset of features, prediction results for random permutations of the data set are at least equally accurate compared to predictions determined using the entire feature set. Our results suggest that integration of multiple methods can aid identification of a robust subset of taxa within complex communities that may drive specific functional outcomes of interest.

We examined the effect of soil microbial communities on plant physiological responses to drought. Bouteloua gracilis seeds were planted in sterilized sand with (inoculated) and without (controls) soil microbial communities. After substantial growth, drought was imposed by completely withholding water. Before soil moisture declined to zero, inoculated plants germinated faster, were significantly taller, and maintained greater soil moisture than controls. The greater soil moisture of the inoculated plants allowed greater photosynthesis but also induced lower tissue drought tolerance (as indicated by turgor loss point) compared to controls. The inoculated plants were more susceptible to severe drought compared to control plants as indicated by significantly lower mean stomatal conductance, as well as marginally significantly greater mean wilting score, for the entire severe drought period after soil moisture declined to zero. Inoculated plants exhibited enhanced growth and photosynthesis and dampened drought stress over short timescales, but also increased susceptibility to drought over long timescales. This work demonstrates (1) an unexpected insight that microbes can have positive initial effects on plant performance, but negative impacts on plant performance during severe drought, and (2) that microbially altered effects on plant function during well-watered and moderate drought conditions can influence plant function under subsequent severe drought.

Discovering widespread microbial processes that drive unexpected variation in carbon cycling may improve modeling and management of soil carbon (Prescott, 2010; Wieder et al., 2015a, 2018). A first step is to identify community features linked to carbon cycle variation. We addressed this challenge using an epidemiological approach with 206 soil communities decomposing Ponderosa pine litter in 618 microcosms. Carbon flow from litter decomposition was measured over a 6-week incubation. Cumulative CO 2 from microbial respiration varied twofold among microcosms and dissolved organic carbon (DOC) from litter decomposition varied five-fold, demonstrating large functional variation despite constant environmental conditions where strong selection is expected. To investigate microbial features driving DOC concentration, two microbial community cohorts were delineated as "high" and "low" DOC. For each cohort, communities from the original soils and from the final microcosm communities after the 6-week incubation with litter were taxonomically profiled. A logistic model including total biomass, fungal richness, and bacterial richness measured in the original soils or in the final microcosm communities predicted the DOC cohort with 72 (P < 0.05) and 80 (P < 0.001) percent accuracy, respectively. The strongest predictors of the DOC cohort were biomass and either fungal richness (in the original soils) or bacterial richness (in the final microcosm communities). Successful forecasting of functional patterns after lengthy community succession in a new environment reveals strong historical contingencies. Forecasting future community function is a key advance beyond correlation of functional variance with end-state community features. The importance of taxon richness-the same feature linked to carbon fate in gut microbiome studies-underscores the need for increased understanding of biotic mechanisms that can shape richness in microbial communities independent of physicochemical conditions.

As the only endemic member in New Zealand of the ancient conifer family, Araucariaceae, Agathis australis is an ideal species to study putatively long-evolved mycorrhizal symbioses. However, little is known about A. australis root and nodular arbuscular mycorrhizal fungi (AMF), and how mycorrhizal colonisation occurs. We used light, scanning and transmission electron microscopy to characterise colonisation, and 454-sequencing to identify the AMF associated with A. australis roots and nodules. We interpreted the results in terms of the edaphic characteristics of the A. australis-influenced ecosystem. Representatives of five families of Glomeromycota were identified via high-throughput pyrosequencing. Imaging studies showed that there is abundant, but not ubiquitous, colonisation of nodules, which suggests that nodules are mostly colonised by horizontal transmission. Roots were also found to harbour AMF. This study is the first to demonstrate the multiple Glomeromycota lineages associated with A. australis including some that may not have been previously detected.