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A global meta-analysis reveals effects of heavy metals on soil microorganisms
Dale Li1, Xiujuan Zhang2, Hong Zhang2
1Institute of Loess Plateau, Shanxi University, Taiyuan, Shanxi 030006, China; Department of Resources and Environmental Engineering, Shanxi Institute of Energy, Jinzhong, Shanxi 030600, China.
Abstract:
Heavy metal (HM) contamination disrupts soil ecosystem functions. Microorganisms are pivotal for sustaining soil health, but accurately assessing the ecological risks of HM contamination to microorganisms remains challenging. Here, we conducted a meta-analysis synthesizing 914 datasets from 72 studies to quantify and evaluate the impacts of HMs on microorganisms. The overall effect value results indicate that HM negatively impacts most microbiological indicators, with bacterial abundance (-38 %), fungal abundance (-18 %), microbial biomass carbon (-42 %), microbial biomass nitrogen (-44 %), arylsulfatase (-45 %) and dehydrogenase activity (-66 %) were significantly reduced (p < 0.01), suggesting they can act as sensitivity indicators for assessing ecological risk of microorganisms. Compared to bacteria, fungal indicators (e.g., fungal community structure and Shannon index) are less responsive to HM contamination. At low potential ecological risk index (RI < 150), HM contamination positively impacted certain microbial indicators, such as fungal abundance, fungal Shannon index, and β-glucosidase activity. With increasing RI levels, the negative effects of HMs on microorganisms became more pronounced. Microbiological indicators in acidic soils (pH < 6.5), coarse textured soils, and mining soils were more negatively affected by HMs. Random forest and structural equation modeling analysis also identified RI levels and pH as crucial factors in determining the microbial response to HMs. Adjusted RI (adRI) were calculated using adjusted toxicity factors (adTF). The adRI demonstrated stronger correlations with microbial indicators and lower root-mean-square error (RMSE) in the random forest model than the RI, indicating that adTF is a more effective method for evaluating the effects of HMs on microorganisms. This study enhances the accuracy of quantifying and assessing HM impacts on microorganisms, offering crucial scientific basis for environmental protection and soil remediation.
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