Temporal succession and assembly of marine bacterial communities in Maxwell Bay, Antarctica during summer
Haiyu Zeng, Zhiwei Gao, Zheng Wang, Kaiyi Li, Bo Xu, Zhen Yan, Yan Gu, Weimeng Du, Haitao Ding, Jianjun Wang
Journal:Frontiers in Microbiology
IF:4.5
DOI:10.3389/fmicb.2026.1748960
PMID:41939699
Published:2026-03-19
research field:极地生态学气候变化影响生物地球化学循环环境微生物学海洋微生物生态学
Abstract
Introduction In recent years, ecological feedbacks driven by climate change have become increasingly prominent. The polar amplification effect has made Antarctic ecosystems pivotal indicators for reflecting global climatic impacts. As core drivers of biogeochemical cycling, marine microbes play a central role. Therefore, deciphering their temporal dynamics and assembly mechanisms is crucial for projecting the trajectories of polar ecosystems. However, the intrinsic ecological processes regulating microbial summer succession, particularly the relative contribution of deterministic processes, remain insufficiently quantified. Methods In the present study, Maxwell Bay, Antarctica—a coastal marine region heavily influenced by glacial melt—was selected as the model system. Surface seawater samples were collected sequentially during the 2022 austral summer, followed by 16S rRNA gene amplicon sequencing and phylogenetic null model analysis. Results Our results revealed a distinct shift in the assembly mechanisms of bacterial communities. In January, community structure was shaped jointly by stochastic and deterministic processes, with stochastic processes contributing a greater proportion to assembly. This state transitioned to the predominance of deterministic homogeneous selection (84.68%) in February. Mantel tests, followed by linear regression analyses, confirmed that this phylogenetic transition was driven by shifting environmental factors. Specifically, water temperature served as the primary influencing factor in January, whereas silicate and nitrate concentrations emerged as the key factors in February. Subsequent partial least squares path modeling (PLS-PM) and redundancy analysis (RDA) further validated these findings, demonstrating that the identified environmental variables collectively explained more than 50% of the observed variation in community structure.
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