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Soil microbiome predictability increases with spatial and taxonomic scale

Abstract

Soil microorganisms shape ecosystem function, yet it remains an open question whether we can predict the composition of the soil microbiome in places before observing it. Furthermore, it is unclear whether the predictability of microbial life exhibits taxonomic- and spatial-scale dependence, as it does for macrobiological communities. Here, we leverage multiple large-scale soil microbiome surveys to develop predictive models of bacterial and fungal community composition in soil, then test these models against independent soil microbial community surveys from across the continental United States. We find remarkable scale dependence in community predictability. The predictability of bacterial and fungal communities increases with the spatial scale of observation, and fungal predictability increases with taxonomic scale. These patterns suggest that there is an increasing importance of deterministic versus stochastic processes with scale, consistent with findings in plant and animal communities, suggesting a general scaling relationship across biology. Biogeochemical functional groups and high-level taxonomic groups of microorganisms were equally predictable, indicating that traits and taxonomy are both powerful lenses for understanding soil communities. By focusing on out-of-sample prediction, these findings suggest an emerging generality in our understanding of the soil microbiome, and that this understanding is fundamentally scale dependent.

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Fig. 1: How can predictability vary as a function of taxonomic scale?
Fig. 2: How can predictability vary as a function of spatial scale?
Fig. 3: Predictability and spatial scale.
Fig. 4: Predictability and taxonomic scale.
Fig. 5: Average predictability of soil fungi and bacteria in the continental United States.

Data availability

All data used to train statistical models are either publicly available in associated studies or were provided on request to original study authors. All data used to validate models are publicly available through the National Ecological Observatory Network data portal (https://data.neonscience.org/). We will provide raw and processed data on request for purposes of replicating the findings of this study.

Code availability

All code needed to process raw data and to replicate these analyses is available at GitHub (https://www.github.com/colinaverill/NEFI_microbe).

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Acknowledgements

The National Ecological Observatory Network is a program sponsored by the National Science Foundation and operated under cooperative agreement by Battelle Memorial Institute. C.A., Z.R.W., M.C.D. and J.M.B. were supported by NSF Macrosystems Biology (no. 1638577). C.A. was supported by an Ambizione Grant (no. PZ00P3_179900) from the Swiss National Science Foundation. K.F.A. was supported by the Boston University BRITE Bioinformatics REU program. D. Maynard gave feedback on an earlier version of this manuscript. L. Stanish helped to access and interpret microbial data from the NEON Network. J. Luecke designed and illustrated Figs. 1 and 2.

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Contributions

C.A., J.M.B. and M.C.D. conceived the study. C.A., Z.R.W. and K.F.A. performed all analysis and computation. All of the authors wrote the manuscript collaboratively.

Corresponding author

Correspondence to Colin Averill.

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The authors declare no competing interests.

Additional information

Peer review information Nature Ecology & Evolution thanks Xiaofeng Xu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Cross-validation within the NEON dataset.

Mean cross-validated R2 relative to the 1:1 prediction across functional and taxonomic groups for (a) bacteria and (b) fungi. All models were trained on 70% of NEON core or plot level data, and the validated using the remaining 30% of the data.

Extended Data Fig. 2 Coefficient of variation across taxonomic and functional groupings.

Coefficient of variation of model predictions vs. observations across functional and taxonomic groups, both in and out of sample for (a) bacteria and (b) fungi.

Extended Data Fig. 3 Principal component analysis of microbial environmental sensitivities.

Principal component analysis of phylogenetic and functional group parameter values in the global calibration dataset for (a) fungi and (d) bacteria. Factor importance in principal component space is indicated by the direction and length of factor vectors. We visualize the strongest correlation between an individual factor effect size and predictability and the calibration dataset (b,e), as well as the correlations for all factors (c,f). Factors include net primary productivity (NPP), whether or not conifers are present (conifer), whether or not a site is a forest (forest), mean annual temperature (MAT), mean annual precipitation (MAP), soil pH (pH), soil percent carbon (%C), soil carbon to nitrogen ratio (C:N), and the relative abundance of ectomycorrhizal trees (relEM).

Extended Data Fig. 4 Qualitatively similar but quantitatively different relationships between Acidobacteria and soil pH.

Relative abundance of bacterial phylum Acidobacteriaplotted as function of soil pH, highlighting differences in trends between independent sources. a, Values from combined calibration dataset and validation dataset, with points and loess curves colored by dataset. The relationship between Acidobacteria and pH within the validation data, sourced from the National Ecological Observatory Network, appears to have strong a systematic bias; however, due to the compositional nature of amplicon sequencing data, it is difficult to determine the source of biases for any given taxon. b, Values from a subset of 5 independent datasets used in calibration, with points and loess curves colored by dataset.

Extended Data Fig. 5 Variance decomposition.

Density plot of variance decomposition for all (a) bacterial and (b) fungal groups modeled at the site level.

Extended Data Fig. 6 Distribution of samples used in this analysis.

Distribution of sampling sites used in this analysis. Sites used for fungal model calibration are in pink, sites used for bacterial model calibration are in blue, and NEON sites used for validation are in yellow.

Supplementary information

Supplementary Information

Supplementary Figs. 1–6 and the caption for Supplementary Data 1.

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Supplementary Data 1

Out of sample R2 and R21:1 values for all bacterial and fungal groups modeled. Values are reported at core, plot and site scales.

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Averill, C., Werbin, Z.R., Atherton, K.F. et al. Soil microbiome predictability increases with spatial and taxonomic scale. Nat Ecol Evol 5, 747–756 (2021). https://doi.org/10.1038/s41559-021-01445-9

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