Climate conditions are primary predictors of the regional‐scale spatial diversity patterns of ectomycorrhizal fungi

Abstract

AbstractAimSpatial models are valuable for revealing biodiversity patterns but are less commonly applied to soil microbes than to aboveground macroorganisms. Ectomycorrhizal (EM) fungi are symbiotic microbes with high taxonomic and functional diversity that are associated with forest trees. We aimed to predict regional‐scale spatial patterns of EM fungal richness and community composition.LocationForests and subalpine ecosystems in Japan, from Hokkaido to Okinawa.TaxonEM fungi (Asco‐ and Basidiomycetes).MethodsWe used EM fungal DNA sequence data from 1507 soil cores at 39 sites covering a wide range of environmental conditions. The random forest machine learning approach was applied to determine the relative importance of environmental variables (i.e. climate, soil and ecosystem productivity) and to make spatial predictions. The spatial patterns of EM fungal richness and community composition were mapped at 1‐km2 grid resolution.ResultsTemperature generally had a strong influence on EM fungal richness and community composition dissimilarity. Our regional spatial analysis revealed that (1) EM fungal richness was higher in northeastern and montane regions than in southwestern regions and low‐elevation plains, (2) different EM fungal lineages exhibited contrasting spatial diversity patterns and (3) community composition dissimilarity shifted sharply from high to low elevations, and gradually from northeastern to southwestern regions, mainly in relation to climate gradients. Areas with low applicability for spatial modelling were identified based on multidimensional environmental spaces, which will help to prioritize data collection for future research.Main ConclusionsOur study provides a baseline of the potential spatial patterns of EM fungal communities, which were explained primarily by climate variables and secondarily by soil factors and ecosystem productivity. The predicted spatial patterns may be valuable for identifying diversity hotspots and advancing the assessment of climate change impacts on ecologically important root‐associated fungi.