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  1. Abstract Aim

    Efforts to predict the responses of soil fungal communities to climate change are hindered by limited information on how fungal niches are distributed across environmental hyperspace. We predict the climate sensitivity of North American soil fungal assemblage composition by modelling the ecological niches of several thousand fungal species.


    One hundred and thirteen sites in the United States and Canada spanning all biomes except tropical rain forest.

    Major Taxa Studied


    Time Period



    We combine internal transcribed spacer (ITS) sequences from two continental‐scale sampling networks in North America and cluster them into operational taxonomic units (OTUs) at 97% similarity. Using climate and soil data, we fit ecological niche models (ENMs) based on logistic ridge regression for all OTUs present in at least 10 sites (n = 8597). To describe the compositional turnover of soil fungal assemblages over climatic gradients, we introduce a novel niche‐based metric of climate sensitivity, the Sørensen climate sensitivity index. Finally, we map climate sensitivity across North America.


    ENMs have a mean out‐of‐sample predictive accuracy of 73.8%, with temperature variables being strong predictors of fungal distributions. Soil fungal climate niches clump together across environmental space, which suggests common physiological limits and predicts abrupt changes in composition with respect to changes in climate. Soil fungi in North American climates are more likely to be limited by cold and dry conditions than by warm and wet conditions, and ectomycorrhizal fungi generally tolerate colder temperatures than saprotrophic fungi. Sørensen climate sensitivity exhibits a multimodal distribution across environmental space, with a peak in climates corresponding to boreal forests.

    Main Conclusions

    The boreal forest occupies an especially precarious region of environmental space for the composition of soil fungal assemblages in North America, as even small degrees of warming could trigger large compositional changes characterized mainly by an influx of warm‐adapted species.

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  2. Aim Ectomycorrhizal fungi (ECMF) are partners in a globally distributed tree symbiosis implicated in most major ecosystem functions. However, resilience of ECMF to future climates is uncertain. We forecast these changes over the extent of North American Pinaceae forests. Location About 68 sites from North American Pinaceae forests ranging from Florida to Ontario in the east and southern California to Alaska in the west. Taxon Ectomycorrhizal fungi (Asco‐ and Basidiomycetes). Methods We characterized ECMF communities at each site using molecular methods and modelled climatic drivers of diversity and community composition with general additive, generalized dissimilarity models and Threshold Indicator Taxa ANalysis (TITAN). Next, we projected our models across the extent of North American Pinaceae forests and forecast ECMF responses to climate changes in these forests over the next 50 years. Results We predict median declines in ECMF species richness as high as 26% in Pinaceae forests throughout a climate zone comprising more than 3.5 million square kilometres of North America (an area twice that of Alaska state). Mitigation of greenhouse gas emissions can reduce these declines, but not prevent them. The existence of multiple diversity optima along climate gradients suggest regionally divergent trajectories for North American ECMF, which is corroborated by corresponding ECMF community thresholds identified in TITAN models. Warming of forests along the boreal–temperate ecotone results in projected ECMF species loss and declines in the relative abundance of long‐distance foraging ECMF species, whereas warming of eastern temperate forests has the opposite effect. Main Conclusions Our results reveal potentially unavoidable ECMF species‐losses over the next 50 years, which is likely to have profound (if yet unclear) effects on ECMF‐associated biogeochemical cycles. 
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  3. Abstract

    Soil microbial communities play critical roles in various ecosystem processes, but studies at a large spatial and temporal scale have been challenging due to the difficulty in finding the relevant samples in available data sets as well as the lack of standardization in sample collection and processing. The National Ecological Observatory Network (NEON) has been collecting soil microbial community data multiple times per year for 47 terrestrial sites in 20 eco‐climatic domains, producing one of the most extensive standardized sampling efforts for soil microbial biodiversity to date. Here, we introduce the neonMicrobe R package—a suite of downloading, preprocessing, data set assembly, and sensitivity analysis tools for NEON’s newly published 16S and ITS amplicon sequencing data products which characterize soil bacterial and fungal communities, respectively. neonMicrobe is designed to make these data more accessible to ecologists without assuming prior experience with bioinformatic pipelines. We describe quality control steps used to remove quality‐flagged samples, report on sensitivity analyses used to determine appropriate quality filtering parameters for the DADA2 workflow, and demonstrate the immediate usability of the output data by conducting standard analyses of soil microbial diversity. The sequence abundance tables produced byneonMicrobecan be linked to NEON’s other data products (e.g., soil physical and chemical properties, plant community composition) and soil subsamples archived in the NEON Biorepository. We provide recommendations for incorporatingneonMicrobeinto reproducible scientific workflows, discuss technical considerations for large‐scale amplicon sequence analysis, and outline future directions for NEON‐enabled microbial ecology. In particular, we believe that NEON marker gene sequence data will allow researchers to answer outstanding questions about the spatial and temporal dynamics of soil microbial communities while explicitly accounting for scale dependence. We expect that the data produced by NEON and theneonMicrobeR package will act as a valuable ecological baseline to inform and contextualize future experimental and modeling endeavors.

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