Palaeoclimate explains a unique proportion of the global variation in soil bacterial communities

The legacy impacts of past climates on the current distribution of soil microbial communities are largely unknown. Here, we use data from more than 1,000 sites from five separate global and regional datasets to identify the importance of palaeoclimatic conditions (Last Glacial Maximum and mid-Holocene) in shaping the current structure of soil bacterial communities in natural and agricultural soils. We show that palaeoclimate explains more of the variation in the richness and composition of bacterial communities than current climate. Moreover, palaeoclimate accounts for a unique fraction of this variation that cannot be predicted from geographical location, current climate, soil properties or plant diversity. Climatic legacies (temperature and precipitation anomalies from the present to ~20 kyr ago) probably shape soil bacterial communities both directly and indirectly through shifts in soil properties and plant communities. The ability to predict the distribution of soil bacteria from either palaeoclimate or current climate declines greatly in agricultural soils, highlighting the fact that anthropogenic activities have a strong influence on soil bacterial diversity. We illustrate how climatic legacies can help to explain the current distribution of soil bacteria in natural ecosystems and advocate that climatic legacies should be considered when predicting microbial responses to climate change.

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Fig. 1: Relative contribution of the different predictors used to model bacterial composition and diversity.
Fig. 2: Structural equation model accounting for the direct and indirect (plant diversity and/or soil properties) effects of climatic legacies on the diversity of bacteria across the five datasets used.
Fig. 3: Contribution of the different predictors for a subset of data from Australia.

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Acknowledgements

M.D.-B. acknowledges support from the Marie Sklodowska-Curie Actions of the Horizon 2020 Framework Programme H2020-MSCA-IF-2016 under REA grant agreement no. 702057. We acknowledge the contribution of the BASE project partners (DOI: 10.4227/71/561c9bc670099), an initiative supported by Bioplatforms Australia with funds provided by the Australian Commonwealth Government through the National Collaborative Research Infrastructure Strategy. B.K.S. and M.D-B are supported by the Australian Research Council projects (DP13010484 and DP170104634). D.J.E. was supported by the Hermon Slade Foundation. N.F was supported by grants from the US National Science Foundation (PLR 1241629 and DEB 1542653). The work from J.-Z.H. and the China dataset were supported by the Natural Science Foundation of China (grant no. 41230857) and the Chinese Academy of Sciences (grant no. XDB15020200). The work of F.T.M. and the Global Drylands database were supported by the European Research Council (ERC grant agreements 242658 [BIOCOM] and 647038 [BIODESERT]).

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M.D.-B. conceived the idea of this study in consultation with N.F. The microbial datasets of the Global Drylands were originally compiled by F.T.M, B.K.S. and M.D.-B.; those of the Americas by N.F.; Australia by A.B.; China by J.-Z.H., Y.-R.L. and J.-T.W.; and New South Wales by D.J.E., B.K.S. and K.H. Statistical modelling was conducted by M.D.-B. The manuscript was written by M.D.-B. with contributions from all co-authors.

Corresponding author

Correspondence to Manuel Delgado-Baquerizo.

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Supplementary Information

Supplementary Appendices 1–3, Supplementary Tables 1, 3, 4, 8–12, Supplementary Figures 1–15

Supplementary_Table_2

Correlation (Pearson) among bioclimatic variables across different time periods. Worldclim number of climatic variables are shown in Supplementary Table 1

Supplementary_Table_5

Standardized direct effects from s.e.m. in Fig. 2 and Supplementary Figs. 6 and 7

Supplementary_Table_6

Correlations (standardized effects) from s.e.m. in Fig. 2 and Supplementary Figs. 6 and 7

Supplementary_Table_7

Results from random forest analyses aiming to identify the most important bacterial composition predictors of selected palaeoclimatic legacies (AMT or PDM). Acronyms of climatic variables are shown in Supplementary Table 1. Importance is calculated as the per cent increase in the mean square error in our models

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Delgado-Baquerizo, M., Bissett, A., Eldridge, D.J. et al. Palaeoclimate explains a unique proportion of the global variation in soil bacterial communities. Nat Ecol Evol 1, 1339–1347 (2017). https://doi.org/10.1038/s41559-017-0259-7

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