Work

Urbanization impacts the environmental microbial community and its antibiotic phenotypic resistance potential by reshaping the diversity and network complexity

Year: 2026

Type: article

Abstract: Urbanization imposes severe anthropogenic pressures on ecosystems, yet its impacts on the structural cohesion of environmental microbiomes and the co-occurrence of antimicrobial resistance (AMR) remain poorly understood. Here, we combined metabarcoding (16S/ITS2) with in vitro AMR screening of cultivable isolates across aquatic and terrestrial ecosystems in Nouvelle-Aquitaine (France). Random Forest models (RFM), co-occurrence and bipartite networks were used to quantify ecological drivers and evaluate network resilience via percolation simulations. While urbanization categorical diversity indices did not significantly differ between urban and natural habitats (p > 0.05), RFM revealed that microbial richness is driven by distinct, continuous local gradients. Urbanization triggered structural compartmentalization of microbial networks, marked by a collapse in edge density, higher modularity, and eradication of critical connectors. Percolation analysis confirmed this fragility, showing accelerated network collapse under targeted attacks in urban settings. Conversely, the bipartite networks focusing on the environmental phenotypic AMR potential revealed an interesting paradox: whilst urbanization compartmentalized global networks, it significantly densified the bipartite AMR-network. This phenotypic resistance potential, structured around nodes of cefalexin, amoxicillin, and fosfomycin, exhibited increased topological robustness, a doubling of inter-domain connectivity (bacterial-fungal), and an increase in positive correlation. To conclude, this study reveals that urbanization induces a trade-off between the overall cohesion of microbial ecosystems and the topological robustness of specialized AMR-networks. It highlights the interest of integrating high-resolution network analysis into One Health surveillance of AMR, to accurately predict and intercept the AMR spread in anthropized landscapes, even if this AMR-network constructed using culture-based methods have limitations.

Source: Environmental Research

DOI: 10.1016/j.envres.2026.125675