基于人类干扰响应特征的黄河上游干流鱼类指示物种筛选
CSTR:
作者:
作者单位:

1.中国科学院水生生物研究所;2.青海省渔业技术推广中心青海省渔业环境监测中心

作者简介:

通讯作者:

中图分类号:

基金项目:

国家重点研发计划(2021YFC3200103) 和国家自然科学基金面上项目(32171659; U22A20454)


Screening of Fish Indicator Species in the upper Yellow River Based on Responses to Anthropogenic Disturbance.
Author:
Affiliation:

Institute of Hydrobiology, Chinese Academy of Sciences

Fund Project:

National Key Research and Development Program (2021YFC3200103); National Natural Science Foundation of China(32171659; U22A20454)

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 附件
  • |
  • 文章评论
    摘要:

    鱼类指示物种法是水生态健康评价的重要工具,其关键在于筛选出对环境变化敏感且具有代表性的物种。为构建适用于黄河上游干流的鱼类指示物种筛选体系,本研究基于2022-2023年野外调查数据,结合鱼类群落分布特征及其与环境因子的响应关系,通过多维度分析方法筛选指示物种,并利用指示值法对筛选结果进行了验证。首先,冗余分析分析了鱼类分布与水环境因子的关联性,并分析物种分布对每一环境因子的响应;其次,利用RLQ和四角分析量化鱼类功能性状与环境梯度的耦合关系;最后,基于以上两种方法筛选出对环境变化具有显著响应的物种作为指示物种。最终确定黄河裸裂尻鱼(Schizopygopsis pylzovi)、骨唇黄河鱼(Chuanchia labiosa)、花斑裸鲤(Gymnocypris eckloni)、极边扁咽齿鱼(Platypharodon extremus)、厚唇裸重唇鱼(Gymnodiptychus pachycheilus)和拟鲇高原鳅(Triplophysa siluroides)等6种鱼类为黄河上游干流关键指示物种。然后基于指示值法评估物种对特定生境的指示强度,对筛选结果进行验证,结果发现:6种指示物种中的黄河裸裂尻鱼、骨唇黄河鱼、极边扁咽齿鱼、厚唇裸重唇鱼和拟鲇高原鳅等5种鱼类均具有显著指示作用。随后利用随机森林模型分析了指示物种的出现与否和环境扰动之间的关系及其响应趋势,发现水库年龄、水产养殖年限以及大坝数量是预测指示物种出现与否的重要变量,随着水库年龄、水产养殖年限和大坝数量的增加,指示物种出现频率显著下降。本研究成果可为指示物种筛选提供方法学参考,同时也为流域水生态保护与管理提供了科学依据。

    Abstract:

    The indicator species approach is an important tool for assessing aquatic ecosystem health. Its effectiveness lies on the selection of species that are both sensitive to environmental changes and representative of the local ecological community. This study aimed to establish a fish-based indicator species framework suited to the upper reaches of the Yellow River. Drawing on field survey data collected from 2022 to 2023, Drawing on field survey data collected from 2022 to 2023, we integrated fish community distribution patterns with species-environment relationships using a multi-faceted analytical approach and validated the results using the Indicator Value (IndVal) method. First, redundancy analysis (RDA) was employed to explore the associations between fish distributions and environmental variables, assessing species-specific responses to individual environmental gradients. Subsequently, RLQ and Fourth-corner analyses were used to quantify the coupling between fish functional traits and environmental gradients. Species that exhibited significant responses in both analyses were selected as candidate indicator species. Six key indicator species were ultimately identified for the upper reaches of the Yellow River: Schizopygopsis pylzovi, Chuanchia labiosa, Gymnocypris eckloni, Platypharodon extremus, Gymnodiptychus pachycheilus, and Triplophysa siluroides. The IndVal method was then used to evaluate the strength of these species as indicators of specific habitats. Among them, five species—S. pylzovi, C. labiosa, P. extremus, G. pachycheilus, and T. siluroides—demonstrated statistically significant indicator values. Furthermore, the Random Forest Model was applied to examine the relationships between indicator species occurrence and environmental factors. Results revealed that the age of reservoir, age of aquaculture, and the cumulative number of dams were the most important predictors of species presence or absence. As these variables increased, the occurrence frequency of indicator species declined significantly. This study provides a methodological reference for the selection of indicator fish species and offers a scientific basis for the ecological protection and management of river basins. These findings offer a methodological basis for the identification of indicator species and provide valuable insights to support ecological monitoring, conservation planning, and watershed management in the Yellow River Basin.

    参考文献
    相似文献
    引证文献
引用本文
相关视频

分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2025-07-08
  • 最后修改日期:2025-12-29
  • 录用日期:2025-12-29
  • 在线发布日期: 2026-04-29
  • 出版日期:
文章二维码
您是第    位访问者
地址:南京市江宁区麒麟街道创展路299号    邮政编码:211135
电话:025-86882041;86882040     传真:025-57714759     Email:jlakes@niglas.ac.cn
Copyright:中国科学院南京地理与湖泊研究所《湖泊科学》 版权所有:All Rights Reserved
技术支持:北京勤云科技发展有限公司

苏公网安备 32010202010073号

     苏ICP备09024011号-2