Chen Ying, Qu Yajing, Zhao Wenhao, Wu Xiaochen, Yang Anfu, Hu Yulin, Chen Haiyan, Wang Meiying, Cai Yuxuan, Ma Jin, Wu Fengchang
State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China.
Hainan Research Academy of Environmental Sciences, Haikou 570100, China.
J Hazard Mater. 2025 Aug 15;494:138483. doi: 10.1016/j.jhazmat.2025.138483. Epub 2025 May 3.
Global industrialization has resulted in severe contamination of soil with heavy metals (HMs). Nevertheless, it is unclear if it affects the depth-resolved bacterial communities. Herein, we collected soil samples at different depths from a typical HM-contaminated site and used amplicon sequencing to determine the differences in depth-resolved bacterial communities and to assess the thresholds and ecological impacts of HMs. Results revealed that HM levels reduced markedly with soil depth. The bacteria in upper soil exhibited higher community diversity and a more complex and stable ecological network structure. As depth increased, the proportion of negative interactions gradually elevated, indicating more competitive interspecies behavior. Threshold analyses based on machine learning revealed that arsenic (As) and copper (Cu) exhibited nonlinear impacts on ecosystems. Cu demonstrated a low-threshold effect, with its ecological consequences manifested at extremely low concentrations. Our results highlighted the utility of microbial monitoring in assessing the adverse effects of HMs on soil health to support environmental management and ecological restoration.
全球工业化导致土壤受到重金属（HMs）的严重污染。然而，目前尚不清楚这是否会影响深度解析的细菌群落。在此，我们从一个典型的重金属污染场地采集了不同深度的土壤样本，并使用扩增子测序来确定深度解析的细菌群落差异，以及评估重金属的阈值和生态影响。结果表明，重金属含量随土壤深度显著降低。上层土壤中的细菌表现出更高的群落多样性以及更复杂和稳定的生态网络结构。随着深度增加，负相互作用的比例逐渐升高，表明种间竞争行为更为激烈。基于机器学习的阈值分析表明，砷（As）和铜（Cu）对生态系统表现出非线性影响。铜表现出低阈值效应，其生态后果在极低浓度时就会显现。我们的结果突出了微生物监测在评估重金属对土壤健康的不利影响以支持环境管理和生态恢复方面的作用。
