Cross-Biome Drivers of Soil Bacterial Alpha Diversity on a Worldwide Scale
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We lack a defined suite of attributes that allow us to universally predict the distribution of bacterial diversity across and within globally distributed biomes. Using data from a global survey, including 237 locations and multiple environmental predictors, we found that only ultraviolet light, forest environments, soil carbon and pH can be considered as significant and globally consistent predictors of soil bacterial diversity, valid within and across biomes (arid, temperate and continental). Bacterial diversity always peaked in grasslands, with moderate-to-low carbon and ultraviolet light levels, and high soil pH. Using these environmental data, we generated the first global predictive map of the distribution of soil bacterial diversity. Our work helps to identify a unique set of environmental attributes for universally predicting the distribution of soil bacterial diversity. This knowledge is key to help predict changes in ecosystem functioning and the provision of essential services under changing environments.
Keywordsα-diversity terrestrial ecosystems arid continental temperate cross-biome
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant agreement No 702057.
Compliance with Ethical Standards
Conflict of interest
The authors declare no conflict of interest.
- Anderson JM, Ingram JSI, Eds. 1993. Tropical soil biology and fertility: a handbook of methods, 2nd ed. Wallingford: CAB International.Google Scholar
- Archer E. 2016. rfPermute, estimate permutation p-values for random forest importance metrics. R package version 1.5.2.Google Scholar
- Delgado-Baquerizo M, Reith F, Dennis PG, Hamonts K, Powell JR, Young A, Singh BK, Bissett A. 2018b. Ecological drivers of soil microbial diversity and soil biological networks in the Southern Hemisphere. Ecology (in press). https://doi.org/10.1002/ecy.2137.
- Kuhn M, Weston S, Keefer C, Coulter N. 2016. Cubist: rule- and instance-based regression modeling. R package version 0.0.19.Google Scholar
- Maestre FT, Delgado-Baquerizo M, Jeffries TC, Eldridge DJ, Ochoa V, Gozalo B et al. 2015. Increasing aridity reduces soil microbial diversity and abundance in global drylands. Proc Natl Acad Sci USA 112:15684–9.Google Scholar
- Quinlan JR. 1993. C4.5: Programs for machine learning. San Mateo, CA: Morgan Kaufmann Publishers.Google Scholar
- Schermelleh-Engel K, Moosbrugger H, Muller H. 2003. Evaluating the fit of structural equation models, tests of significance descriptive goodness-of-fit measures. Methods Psychol Res Online 8:23–74.Google Scholar
- Szoboszlay M, Dohrmann AB, Poeplau C, Don A, Tebbe CC. 2017. Impact of land-use change and soil organic carbon quality on microbial diversity in soils across Europe. FEMS Microbiol Ecol 1:93.Google Scholar