Spatial variation of bacterial communities in seasonal snow across northeastern and northwestern China
Year: 2026
Type: preprint
Abstract: . Seasonal snow is a transient microbial habitat that can contribute to nutrient cycling, yet bacterial community variation across mid-latitude seasonal snowpacks remains poorly understood. We examined bacterial diversity, community composition, environmental associations, assembly processes, niche breadth, and co-occurrence network structure using 16S rRNA gene sequencing and environmental measurements from 49 surface snow samples in northeastern (NE) and northwestern (NW) China. Our results showed that NE snow had higher bacterial richness and wider niche breadth, whereas NW snow showed greater among-site variation in community composition. Several Bacteroidia lineages were more abundant in NW snow. Community similarity declined with geographic distance in both regions, with a stronger relationship in NE. Total organic carbon’s association with bacterial community composition mainly reflected regional environmental contrasts, whereas Ca2+ remained associated with composition after accounting for region, suggesting possible mineral dust deposition. Stochastic processes overall made a greater contribution in NE than in NW. Among the individual processes, homogeneous selection contributed more in NE, whereas heterogeneous selection was more prominent in NW, and dispersal limitation was the largest individual process in both regions. Greater environmental variation among NW sites may have contributed to stronger heterogeneous selection. The NW network was larger and denser and contained more connectors, while the NE network was more modular. Greater network complexity in NW did not increase robustness to random node removal, and both networks were most sensitive to the loss of high-degree nodes. These findings show that regional environmental variation and spatial structure jointly shape bacterial community assembly and network organisation in seasonal snow.
Institutions: Dalian University of Technology, Lanzhou University
Language: English
Subfield: Ecology
Field: Environmental Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225620851
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