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Rewiring Micro-Food Webs: How Copper Nanopesticides Can Reshape Nitrogen Fluxes via Trophic Decoupling
Xuchen Yan1, Jason C White2, Erkai He3
1School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
None:
The extensive application of copper-based nanopesticides (Cu-based NPs) in agroecosystems can pose significant ecological risks to soil nitrogen (N) cycling through microbial-mediated mechanisms, yet microfood web-driven regulatory feedbacks remain poorly understood. Through a microcosm experiment with CuO NPs and silica-encapsulated CuO@SiO2 NPs (applied at 20 and 200 mg/kg), we demonstrate how microfood web interactions (bacteria, fungi, protists, and nematodes) govern N transformation dynamics. Integrated enzymatic and ecological network analyses revealed that both NPs reduced microbial biomass nitrogen (MBN) and bioavailable N species. High-dose CuO@SiO2 (200 mg/kg) strongly inhibited nitrite reductase (NiR, 44.8%), creating a denitrification bottleneck. Elevated β-1,4-glucosidase:β-1,4-N-acetyl-glucosaminidase (BG:NAG) ratios indicated decoupled carbon-nitrogen cycling, favoring oligotrophic bacteria (e.g., Sphingomonas) over sensitive fungi (Olpidiomycota). NPs disproportionately impacted high-trophic-level microfauna, restructuring microfood web topology. Network modeling uncovered concentration-specific trophic cascades: CuO@SiO2 intensified protistan predation, disrupting nematode-microbe linkages and weakening top-down control of nitrifiers/denitrifiers. Consequently, this trophic disruption under high-dose CuO@SiO2 NP exposure triggered 71.8% higher N2O emissions, while reducing soil N supply capacity by 36.1% compared to the control. Conversely, low-dose CuO NPs maintained N turnover through reinforced bacterial-protist-fungal connectivity. Our findings establish that microfood web restructuring (not merely microbial toxicity) determines NP effects on biogeochemical cycles. This underscores the imperative to incorporate trophic interactions into nanomaterial risk assessments for accurate prediction of agroecosystem functions.
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