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<p><i>Vibrionaceae</i> (vibrios) are ubiquitous and opportunistic marine bacteria that are emerging as sensitive bioindicators of environmental change. However, their temporal dynamics in coastal environments and their taxon-specific temperature responses remain to be fully explored. Here, time-series temperature analyses were conducted and these revealed distinct temporal patterns (“Summer”, “Winter” and “Mixed-pattern”) within <i>Vibrionaceae</i>, primarily shaped by temperature but also modulated by physical–chemical parameters. Temperature emerged as the dominant driver, strongly correlating with the abundance (both free-living and culturable) and community structure of vibrios, shaping seasonal patterns and driving sharp shifts in key seasonal taxa (e.g. <i>Vibrio bathopelagicus</i> and <i>V. caribbeanicus</i>) at specific thresholds. Crucially, machine learning models built on representative <i>Vibrionaceae</i> communities explained ~ 91% of the temperature variance in the testing dataset, validating their extreme thermal sensitivity and precise integrated multi-taxa responses. Furthermore, exploratory analyses of the taxa-specific correlations with inorganic nutrients suggest their potential for reflecting coastal nutrient dynamics. Together, these findings elucidate the temperature-driven succession of <i>Vibrionaceae</i> and highlight their potential as bioindicators providing vital insights into the ecological status and biological response processes of marine environments under climate change.</p>

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Vibrionaceae: sensitive bioindicators for identifying the ecological impacts of ocean warming

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月28日

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  • Yulin Zhang,
  • Derui Song,
  • Xiaolei Wang,
  • Shaodong Zhu,
  • Peng Yao,
  • Fabiano L. Thompson,
  • Chun-Xu Xue,
  • Jiwen Liu,
  • Xiao-Hua Zhang

摘要

Vibrionaceae (vibrios) are ubiquitous and opportunistic marine bacteria that are emerging as sensitive bioindicators of environmental change. However, their temporal dynamics in coastal environments and their taxon-specific temperature responses remain to be fully explored. Here, time-series temperature analyses were conducted and these revealed distinct temporal patterns (“Summer”, “Winter” and “Mixed-pattern”) within Vibrionaceae, primarily shaped by temperature but also modulated by physical–chemical parameters. Temperature emerged as the dominant driver, strongly correlating with the abundance (both free-living and culturable) and community structure of vibrios, shaping seasonal patterns and driving sharp shifts in key seasonal taxa (e.g. Vibrio bathopelagicus and V. caribbeanicus) at specific thresholds. Crucially, machine learning models built on representative Vibrionaceae communities explained ~ 91% of the temperature variance in the testing dataset, validating their extreme thermal sensitivity and precise integrated multi-taxa responses. Furthermore, exploratory analyses of the taxa-specific correlations with inorganic nutrients suggest their potential for reflecting coastal nutrient dynamics. Together, these findings elucidate the temperature-driven succession of Vibrionaceae and highlight their potential as bioindicators providing vital insights into the ecological status and biological response processes of marine environments under climate change.