Soil Microbial Co-Occurrence Networks Along a Grassland Degradation Gradient: Nonlinear Thresholds, Divergent Bacterial–Fungal Responses, and Environmental Drivers

Grassland degradation is a global ecological crisis that profoundly alters aboveground vegetation and soil properties, yet its impacts on soil microbial co-occurrence networks remain poorly understood. Here, we investigated soil bacterial and fungal communities along a well-defined five-stage degradation gradient spanning from non-degraded Leymus chinensis grassland to extremely degraded bare saline patches in the Songnen meadow steppe of northeastern China, using Illumina MiSeq sequencing and co-occurrence network analysis. Our results revealed that bacterial α-diversity exhibited a unimodal (hump-shaped) response peaking at the moderately degraded MD stage, whereas fungal diversity declined monotonically along the gradient, indicating greater sensitivity of fungi to degradation stress. Both bacterial and fungal community compositions shifted directionally with degradation, driven primarily by soil alkalization (pH) and electrical conductivity (EC). Network complexity followed a unimodal pattern for both kingdoms, maximizing at MD and collapsing at SD, suggesting a critical ecological threshold between moderate and severe degradation, while the increased proportion of positive correlations under severe degradation implied enhanced microbial cooperation in response to environmental stress. Structural equation models further revealed distinct regulatory pathways: bacterial networks were governed by both direct environmental filtering and indirect diversity-mediated effects, whereas fungal networks responded more strongly to direct pH/EC constraints and compositional shifts. Our findings demonstrate that microbial networks exhibit nonlinear threshold responses to grassland degradation, with fungi serving as more sensitive bioindicators than bacteria, and highlight the importance of integrating network-level properties into degradation monitoring and restoration frameworks.