Abstract

Heavy metal contaminates have become a significant threat to soil ecosystems due to their chronicity and universality in soil. Soil microbial metabolism plays a vital role in biogeochemical cycles and soil functions. However, the response of microbial metabolism to heavy metal contamination in soil remains elusive despite potentially offering important insight into the health and ecological consequences of soil ecosystems under such contamination. This study used extracellular enzyme stoichiometry models to identify the response of microbial metabolism to various heavy metal contaminants, while also revealing potential implications of heavy metal contaminates in soil ecosystems. Results showed that microbial metabolism was restricted by soil carbon (C) and phosphorus (P) within a heavy metal polluted area in Northwest China. Heavy metal stress significantly increased microbial C limitation while decreasing microbial P limitation. However, microbial C and P limitations both responded consistently to different heavy metals (i.e., Cd, Pb, Zn, and Cu). Heavy metals had the greatest effect on microbial C limitation (i.e., 0.720 of the total effects) compared to other soil properties, and soil with the lowest heavy metal concentration exhibited the lowest microbial C limitation, and vice versa. These results indicated that microbial metabolic limitation can robustly and sensitively reflect the degree of heavy metals pollution in soil. Additionally, increased microbial C limitation caused by heavy metal contaminants could potentially escalate C release by promoting soil C decomposition as well as increasing investments in enzyme production and the maintenance of metabolic processes. Consequently, potential C loss induced by heavy metal pollution on soil ecosystems may be extensive and significant. Generally, our results suggest the usefulness of extracellular enzyme stoichiometry as a new method from which to evaluate heavy metal soil pollution, while microbial metabolic limitation could potentially be a promising indicator.

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