Soil organisms represent the most biologically diverse community on land and govern the turnover of the largest organic matter pool in the terrestrial biosphere. The highly complex nature of these communities at local scales has traditionally obscured efforts to identify unifying patterns in global soil biodiversity and biogeochemistry. As a result, environmental covariates have generally been used as a proxy to represent the variation in soil community activity in global biogeochemical models. Yet over the past decade, broad-scale studies have begun to see past this local heterogeneity to identify unifying patterns in the biomass, diversity, and composition of certain soil groups across the globe. These unifying patterns provide new insights into the fundamental distribution and dynamics of organic matter on land.

The evolutionary and environmental factors that shape fungal biogeography are incompletely understood. Here, we assemble a large dataset consisting of previously generated mycobiome data linked to specific geographical locations across the world. We use this dataset to describe the distribution of fungal taxa and to look for correlations with different environmental factors such as climate, soil and vegetation variables. Our meta-study identifies climate as an important driver of different aspects of fungal biogeography, including the global distribution of common fungi as well as the composition and diversity of fungal communities. In our analysis, fungal diversity is concentrated at high latitudes, in contrast with the opposite pattern previously shown for plants and other organisms. Mycorrhizal fungi appear to have narrower climatic tolerances than pathogenic fungi. We speculate that climate change could affect ecosystem functioning because of the narrow climatic tolerances of key fungal taxa.

ETH Z€ urich, 8092 Z€ urich, Switzerland Citation: Ke, P.-J., and J. Wan. 2020. Effects of soil microbes on plant competition: a perspective from modern coexistence theory. Ecological Monographs 90(1):Abstract. Growing evidence shows that soil microbes affect plant coexistence in a variety of systems. However, since these systems vary in the impacts microbes have on plants and in the ways plants compete with each other, it is challenging to integrate results into a general predictive theory. To this end, we suggest that the concepts of niche and fitness difference from modern coexistence theory should be used to contextualize how soil microbes contribute to plant coexistence. Synthesizing a range of mechanisms under a general plant-soil microbe interaction model, we show that, depending on host specificity, both pathogens and mutualists can affect the niche difference between competing plants. However, we emphasize the need to also consider the effect of soil microbes on plant fitness differences, a role often overlooked when examining their role in plant coexistence. Additionally, since our framework predicts that soil microbes modify the importance of plant-plant competition relative to other factors for determining the outcome of competition, we suggest that experimental work should simultaneously quantify microbial effects and plant competition. Thus, we propose experimental designs that efficiently measure both processes and show how our framework can be applied to identify the underlying drivers of coexistence. Using an empirical case study, we demonstrate that the processes driving coexistence can be counterintuitive, and that our general predictive framework provides a better way to identify the true processes through which soil microbes affect coexistence.

Motivation: Widespread interest in the study of the microbiome has resulted in data proliferation and the development of powerful computational tools. However, many scientific researchers lack the time, training, or infrastructure to work with large datasets or to install and use command line tools.

Summary Ecosystems with ectomycorrhizal plants have high soil carbon : nitrogen ratios, but it is not clear why. The Gadgil effect, where competition between ectomycorrhizal and saprotrophic fungi for nitrogen slows litter decomposition, may increase soil carbon. However, experimental evidence for the Gadgil effect is equivocal. Here, we apply resource‐ratio theory to assess whether interguild fungal competition for different forms of organic nitrogen can affect litter decomposition. We focus on variation in resource input ratios and fungal resource use traits, and evaluate our model's predictions by synthesizing prior experimental literature examining ectomycorrhizal effects on litter decomposition. In our model, resource input ratios determined whether ectomycorrhizal fungi suppressed saprotrophic fungi. Recalcitrant litter inputs favored the former over the latter, allowing the Gadgil effect only when such inputs predominated. Although ectomycorrhizal fungi did not always hamper litter decomposition, ectomycorrhizal nitrogen uptake always increased carbon : nitrogen ratios in litter. Our meta‐analysis of empirical studies supports our theoretical results: ectomycorrhizal fungi appear to slow decomposition of leaf litter only in forests where litter inputs are highly recalcitrant. We thus find that the specific contribution of the Gadgil effect to high soil carbon : nitrogen ratios in ectomycorrhizal ecosystems may be smaller than predicted previously.

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The practice of medicine is predicated on discovering commonalities or distinguishing characteristics among patients to inform corresponding treatment. Given a patient grouping (hereafter referred to as a phenotype), clinicians can implement a treatment pathway accounting for the underlying cause of disease in that phenotype. Traditionally, phenotypes have been discovered by intuition, experience in practice, and advancements in basic science, but these approaches are often heuristic, labor intensive, and can take decades to produce actionable knowledge. Although our understanding of disease has progressed substantially in the past century, there are still important domains in which our phenotypes are murky, such as in behavioral health or in hospital settings. To accelerate phenotype discovery, researchers have used machine learning to find patterns in electronic health records, but have often been thwarted by missing data, sparsity, and data heterogeneity. In this study, we use a flexible framework called Generalized Low Rank Modeling (GLRM) to overcome these barriers and discover phenotypes in HHS Public Access

Because interactions between plants and microbial organisms can influence species diversity and rates of nutrient cycling, how plants shape microbial communities is fundamental to understanding the structure of ecosystems. Despite this, the spatial and temporal scales over which plants influence microbial communities is poorly understood, particularly whether past abiotic or biotic legacies strongly constrain microbial community development. We examined biogeochemical cycling and microbial community structure in a coastal landscape where historical patterns of vegetation transition after a large fire in 1995 are well known, allowing us to account for past abiotic and biotic conditions. We found that alternative states in microbial community structure and ecosystem processes emerged under different plant species, regardless of past conditions. Greenhouse studies further demonstrated that these differences arise from direct plant selection of microbes, with selection stronger in roots compared with soils, especially for bacteria. Correlation of microbial community structure with seedling growth rates was also stronger for fungi compared to bacteria. Despite these effects, minimal overlap between seedling and field microbial communities indicates that the effects of initial plant selection are not stable; rather, plant selection initiated alternative successional trajectories after the fire. Using data from a guild where we have abundant natural history information, ectomycorrhizal fungi, we show that greenhouse communities are dominated by ruderal taxa that are also common in the field after the fire and that these ruderal fungi strongly alter spatial patterns in plant–soil feedback, enabling invasion and transformation of soils previously occupied by heterospecific plants, thus potentially acting as keystone mutualists.