Microbial taxonomic and functional attributes consistently predict soil CO2 emissions across contrasting croplands

8 páginas.- 5 figuras.- 65 referencias.- Supplementary data to this article can be found online at https://doi.org/10.1016/j.scitotenv.2019.134885

Despite distinct roles of soil microbes in regulating carbon (C) respiration in diverse environments, it remains unclear whether microbial taxonomic and functional attributes can consistently predict soil C emissions across contrasting ecosystems. Here, we conducted a large-scale sampling event across two contrasting croplands (rice and wheat-corn crop rotation) to identify specific soil microbial phylotypes and functional genes associated with soil respiration rates. The results of structural equation modeling indicated that bacterial community composition had a strong link with C respiration rates in the two contrasting cropland types; however, this link was weaker for fungal communities. More importantly, we found that the relative abundances of bacterial Solirubrobacterales_480-2, Myxococcales_mle1-27 and fungal Westerdykella had consistently negative correlation with respiration rates across paddy and upland soils. We also identified taxa that are significantly correlated to C respiration in the paddy (e.g. Methylocaldum) and upland soils (e.g. Kribbella), respectively. Further, we found multiple associations between functional genes involved in microbial C metabolism and soil respiration rates. Our findings provide novel insights into understanding microbial predictors of soil CO2 emissions in diverse croplands, which have important implications for improving C emission predictions in terrestrial ecosystems.

This research was supported by the National Natural Science Foundation of China (41877120, 41830756) and the Fundamental Research Funds for the Central Universities (Program No. 2662019PY010). M.D-B. is supported by the Marie Sklodowska-Curie Actions of the Horizon 2020 Framework Program H2020-MSCA-IF-2016 under REA grant agreement n° 702057. Z.Y. acknowledges support from the U.S. National Science Foundation under Grant CBET-1841301.

Peer reviewed