Molecular Ecology · 2023 · 13 citations · 64 references
Life SciencesMicrobiologySoil SciencesMicrobial EcologySoil EcologySoil MicrobiologyEnvironmental MicrobiomesMicrobial Community DynamicsMicrobial DiversityMicrobiome CompositionMicrobiome DiversityMicrobiome DynamicsBeta DiversityBeta Diversity AnalysisCommunity DissimilarityMicrobial Community DiversityMicrobial Diversity ChangeSoil Microbiome DiversitySpatial Diversity PatternsSpatial Microbiome Variation
Abstract Declines in soil multifunctionality (e.gsoil capacity to provide food and energy) are closely related to changes in the soil microbiome (e.g., diversity) Determining ecological drivers promoting such microbiome changes is critical knowledge for protecting soil functions. However, soil‐microbe interactions are highly variable within environmental gradients and may not be consistent across studies. Here we propose that analysis of community dissimilarity (β‐diversity) is a valuable tool for overviewing soil microbiome spatiotemporal changes. Indeed, β‐diversity studies at larger scales (modelling and mapping) simplify complex multivariate interactions and refine our understanding of ecological drivers by also giving the possibility of expanding the environmental scenarios. This study represents the first spatial investigation of β‐diversity in the soil microbiome of New South Wales (800,642 km 2 ), Australia. We used metabarcoding soil data (16S rRNA and ITS genes) as exact sequence variants (ASVs) and UMAP (Uniform Manifold Approximation and Projection) as the distance metric. β‐Diversity maps (1000‐m resolution)—concordance correlations of 0.91–0.96 and 0.91–0.95 for bacteria and fungi, respectively—showed soil biome dissimilarities driven primarily by soil chemistry—pH and effective cation exchange capacity (ECEC)—and cycles of soil temperature—land surface temperature (LST‐phase and LST‐amplitude). Regionally, the spatial patterns of microbes parallel the distribution of soil classes (e.g., Vertosols) beyond spatial distances and rainfall, for example. Soil classes can be valuable discriminants for monitoring approaches, for example pedogenons and pedophenons. Ultimately, cultivated soils exhibited lower richness due to declines in rare microbes which might compromise soil functions over time.
64
R: A Language and Environment for Statistical Computing
R Core Team · 2000 · 353K citations · Full text
Natural Sciences, Computer Science, Statistical Computing +10
DADA2: High-resolution sample inference from Illumina amplicon data
Benjamin J. Callahan, Paul J. McMurdie, Michael Rosen et al. · Nature Methods · 2016 · 36.3K citations
Cutadapt removes adapter sequences from high-throughput sequencing reads
Marcel Martin · EMBnet journal · 2011 · 35.8K citations · Full text