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<p>River–reservoir systems generate strong hydrological and environmental heterogeneity, often associated with shifts in bacterial communities, yet how spatiotemporal environmental differences jointly shape community variation, assembly processes, and co-occurrence patterns remains poorly resolved at the system scale. Here, we combined 16S ribosomal RNA (rRNA) gene sequencing with water-quality measurements to characterize bacterial dynamics across sampled riverine and reservoir sections of the Hanjiang River (China) during contrasting seasons. Community composition and diversity differed across sections and seasons, and the distance–decay relationship was steeper in the warm season, suggesting more evident spatial structuring. Assembly mechanisms also shifted: stochastic processes were relatively more prominent in reservoir samples and during the cold season, whereas heterogeneous selection became more evident in warm-season riverine sections. Co-occurrence networks showed seasonal reorganization, transitioning from denser cold-season networks to more modular warm-season structures, with cross-module connectivity increasingly concentrated in topology-defined connector taxa. Among environmental correlates, water temperature—together with covarying conditions reflecting productivity, nutrient availability, and organic-matter status—was consistently associated with community variation, and Threshold Indicator Taxa Analysis (TITAN2) identified a system-specific community-level transition near 19.4&#xa0;°C along this gradient. Partial least squares path modeling further suggested that temperature was statistically linked to bacterial attributes both directly and indirectly via covarying water-quality conditions, jointly accounting for 66% of community variation within the model. Collectively, these results may support temperature-aware bacterial monitoring and water-quality management in river–reservoir systems.</p>

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Spatiotemporal patterns of bacterial communities and their responses to environmental gradients in a river–reservoir system

  • 期刊论文

  • 发表日期：

    2026年7月1日

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  • Yuhai Zhuo,
  • Nan Li,
  • Bobo Liu,
  • Jianwu Li,
  • Anli Song,
  • Hao Mu,
  • Fei Zhang,
  • Xiang Liu,
  • Zhi Yang,
  • Tinglin Huang,
  • Hongxi Zheng

摘要

River–reservoir systems generate strong hydrological and environmental heterogeneity, often associated with shifts in bacterial communities, yet how spatiotemporal environmental differences jointly shape community variation, assembly processes, and co-occurrence patterns remains poorly resolved at the system scale. Here, we combined 16S ribosomal RNA (rRNA) gene sequencing with water-quality measurements to characterize bacterial dynamics across sampled riverine and reservoir sections of the Hanjiang River (China) during contrasting seasons. Community composition and diversity differed across sections and seasons, and the distance–decay relationship was steeper in the warm season, suggesting more evident spatial structuring. Assembly mechanisms also shifted: stochastic processes were relatively more prominent in reservoir samples and during the cold season, whereas heterogeneous selection became more evident in warm-season riverine sections. Co-occurrence networks showed seasonal reorganization, transitioning from denser cold-season networks to more modular warm-season structures, with cross-module connectivity increasingly concentrated in topology-defined connector taxa. Among environmental correlates, water temperature—together with covarying conditions reflecting productivity, nutrient availability, and organic-matter status—was consistently associated with community variation, and Threshold Indicator Taxa Analysis (TITAN2) identified a system-specific community-level transition near 19.4 °C along this gradient. Partial least squares path modeling further suggested that temperature was statistically linked to bacterial attributes both directly and indirectly via covarying water-quality conditions, jointly accounting for 66% of community variation within the model. Collectively, these results may support temperature-aware bacterial monitoring and water-quality management in river–reservoir systems.