Detecting fish community structure in open waters using environmental DNA: a case study from the central South China Sea

文献类型： 外文期刊

第一作者： Chen, Ting

作者： Chen, Ting;Zhang, Shuai;Jiang, Peiwen;Chen, Zuozhi;Zhang, Jun;Xu, Shannan;Li, Min;Chen, Ting;Zhang, Shuai;Chen, Zuozhi;Zhang, Jun;Xu, Shannan;Li, Min;Zhang, Shuai;Xu, Shannan;Li, Min

作者机构：

关键词： eDNA; central South China Sea; fish diversity; fish community structure; environmental factors

期刊名称：FRONTIERS IN MARINE SCIENCE （ 影响因子：3.0； 五年影响因子：3.8 ）

ISSN：

年卷期： 2025 年 12 卷

页码：

收录情况： SCI

摘要： Monitoring fish diversity in open ocean environments presents substantial challenges, particularly due to the limitations of traditional sampling methods such as trawling, which are costly, labor-intensive, and ineffective for deeper water layers. Environmental DNA (eDNA) technology offers an economical and efficient alternative, complementing conventional survey techniques. In this study, eDNA analysis was employed to characterize fish species composition and diversity in the central South China Sea (SCS). Additionally, generalized additive models (GAMs) were applied for the 5 m and 200 m depth layers to assess the influence of environmental variables on fish communities. A total of 190 fish species, spanning 32 orders, 68 families, and 135 genera, were detected across eight sampling sites. The 5 m and 200 m depth layers harbored 184 and 178 species, respectively, with 172 species common to both layers. alpha-and beta-diversity analyses revealed no significant differences in fish species composition or diversity between the two depths (p > 0.05). GAM results highlighted temperature as a key environmental driver of fish distribution, with significant effects on species abundance at both depths (p < 0.05). These findings underscore the utility of eDNA for monitoring fish diversity and elucidating the ecological mechanisms shaping vertical species distribution in deep-sea ecosystems. Given the logistical constraints of traditional survey methods in deep-sea environments, eDNA-based approaches offer valuable insights for the sustainable management and conservation of fishery resources in the central SCS.

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