Microbial community composition controls carbon flux across litter types in early phase of litter decomposition.

Leaf litter decomposition is a major carbon input to soil, making it a target for increasing soil carbon storage through microbiome engineering. We expand upon previous findings to show with multiple leaf litter types that microbial composition can drive variation in carbon flow from litter decomposition and specific microbial community features are associated with synonymous patterns of carbon flow among litter types. Although plant litter type selects for different decomposer communities, within a litter type, microbial composition drives variation in the quantity of dissolved organic carbon (DOC) measured at the end of the decomposition period. Bacterial richness was negatively correlated with DOC quantity, supporting our hypothesis that across multiple litter types there are common microbial traits linked to carbon flow patterns. Variation in DOC abundance (i.e. high versus low DOC) driven by microbial composition is tentatively due to differences in bacterial metabolism of labile compounds, rather than catabolism of non-labile substrates such as lignin. The temporal asynchrony of metabolic processes across litter types may be a substantial impediment to discovering more microbial features common to synonymous patterns of carbon flow among litters. Overall, our findings support the concept that carbon flow may be programmed by manipulating microbial community composition.

Soil Bacterial and Fungal Richness Forecast Patterns of Early Pine Litter Decomposition.

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 CO2 from microbial respiration varied two-fold 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.

Differences in substrate use linked to divergent carbon flow during litter decomposition.

Discovering widespread microbial processes that create variation in soil carbon (C) cycling within ecosystems may improve soil C modeling. Toward this end, we screened 206 soil communities decomposing plant litter in a common garden microcosm environment and examined features linked to divergent patterns of C flow. C flow was measured as carbon dioxide (CO2) and dissolved organic carbon (DOC) from 44-days of litter decomposition. Two large groups of microbial communities representing 'high' and 'low' DOC phenotypes from original soil and 44-day microcosm samples were down-selected for fungal and bacterial profiling. Metatranscriptomes were also sequenced from a smaller subset of communities in each group. The two groups exhibited differences in average rate of CO2 production, demonstrating that the divergent patterns of C flow arose from innate functional constraints on C metabolism, not a time-dependent artefact. To infer functional constraints, we identified features - traits at the organism, pathway or gene level - linked to the high and low DOC phenotypes using RNA-Seq approaches and machine learning approaches. Substrate use differed across the high and low DOC phenotypes. Additional features suggested that divergent patterns of C flow may be driven in part by differences in organism interactions that affect DOC abundance directly or indirectly by controlling community structure.

Machine learning to predict microbial community functions: An analysis of dissolved organic carbon from litter decomposition.

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.

Tracking Replicate Divergence in Microbial Community Composition and Function in Experimental Microcosms.

The study of microbial community functions necessitates replicating microbial communities. Variation in community development over time renders this an imperfect process. Thus, anticipating the likely degree of variation among replicate communities may aid in experimental design. We examined divergence in replicate community composition and function among 128 naturally assembled starting communities obtained from soils, each replicated three times, following a 30-day microcosm incubation period. Bacterial and fungal communities diverged in both composition and function among replicates, but remained much more similar to each other than to communities from different starting inocula. Variation in bacterial community composition among replicates was, however, correlated with variation in dissolved organic carbon production. A smaller-scale experiment testing nine starting communities showed that divergence was similar whether replicates were incubated on sterile or non-sterile pine litter, suggesting the impact of a pre-existing community on replicate divergence is minor. However, replicates in this experiment which were incubated for 114 days diverged more than those incubated for 30 days, suggesting experiments that run over long time periods will likely see greater variation among replicate community composition. These results suggest that while replicates diverge at a community level, such divergence is unlikely to severely impede the study of community function.

Exploring the role of ectomycorrhizal fungi in soil carbon dynamics.

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.

Plant-microbe interactions before drought influence plant physiological responses to subsequent severe drought.

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.

Short-Term Transcriptional Response of Microbial Communities to Nitrogen Fertilization in a Pine Forest Soil.

Numerous studies have examined the long-term effect of experimental nitrogen (N) deposition in terrestrial ecosystems; however, N-specific mechanistic markers are difficult to disentangle from responses to other environmental changes. The strongest picture of N-responsive mechanistic markers is likely to arise from measurements over a short (hours to days) time scale immediately after inorganic N deposition. Therefore, we assessed the short-term (3-day) transcriptional response of microbial communities in two soil strata from a pine forest to a high dose of N fertilization (ca. 1 mg/g of soil material) in laboratory microcosms. We hypothesized that N fertilization would repress the expression of fungal and bacterial genes linked to N mining from plant litter. However, despite N suppression of microbial respiration, the most pronounced differences in functional gene expression were between strata rather than in response to the N addition. Overall, ∼4% of metabolic genes changed in expression with N addition, while three times as many (∼12%) were significantly different across the different soil strata in the microcosms. In particular, we found little evidence of N changing expression levels of metabolic genes associated with complex carbohydrate degradation (CAZymes) or inorganic N utilization. This suggests that direct N repression of microbial functional gene expression is not the principle mechanism for reduced soil respiration immediately after N deposition. Instead, changes in expression with N addition occurred primarily in general cell maintenance areas, for example, in ribosome-related transcripts. Transcriptional changes in functional gene abundance in response to N addition observed in longer-term field studies likely result from changes in microbial composition.IMPORTANCE Ecosystems are receiving increased nitrogen (N) from anthropogenic sources, including fertilizers and emissions from factories and automobiles. High levels of N change ecosystem functioning. For example, high inorganic N decreases the microbial decomposition of plant litter, potentially reducing nutrient recycling for plant growth. Understanding how N regulates microbial decomposition can improve the prediction of ecosystem functioning over extended time scales. We found little support for the conventional view that high N supply represses the expression of genes involved in decomposition or alters the expression of bacterial genes for inorganic N cycling. Instead, our study of pine forest soil 3 days after N addition showed changes in microbial gene expression related to cell maintenance and stress response. This highlights the challenge of establishing predictive links between microbial gene expression levels and measures of ecosystem function.

The arbuscular mycorrhizal fungi colonising roots and root nodules of New Zealand kauri Agathis australis.

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.

Patterns of fungal diversity in New Zealand Nothofagus forests.

The development of protocols for the conservation of fungi requires knowledge of the factors controlling their distribution, diversity, and community composition. Here we compare patterns of variation in fungal communities across New Zealand's Nothofagus forests, reportedly the most myco-diverse in New Zealand and hence potentially key to effective conservation of fungi in New Zealand. Diversity of leaf endophytic fungi, as assessed by culturing on agar plates, is assessed for three Nothofagus sp. growing in mixed stands from four sites. Host species was found to have a greater influence on fungal community assemblage than site. The leaf endophyte communities associated with Nothofagus solandri and Nothofagus fusca (both Nothofagus subgenus Fuscopora), were more similar to each other than either were to the community associated with Nothofagus menziesii (Nothofagus subgenus Lophozonia). The broad taxonomic groups isolated, identified on the basis of internal transcribed spacer (ITS) sequences, were similar to those found in similar studies from other parts of the world, and from an earlier study on the endophyte diversity in four podocarp species from New Zealand, but there were few matches at species level. Average levels of endophyte species diversity associated with single Nothofagus species and single podocarp species were similar, despite historical literature and collection data recording more than twice as many fungal species on average from the Nothofagus species. The significance of these findings to fungal conservation is discussed.