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Importance of Natural and Anthropogenic Environmental Factors to Fish Communities of the Fox River in Illinois

机译:自然和人为环境因素对伊利诺伊州福克斯河鱼类群落的重要性

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摘要

The dominant environmental determinants of aquatic communities have been a persistent topic for many years. Interactions between natural and anthropogenic characteristics within the aquatic environment influence fish communities in complex ways that make the effect of a single characteristic difficult to ascertain. Researchers are faced with the question of how to deal with a large number of variables and complex interrelationships. This study utilized multiple approaches to identify key environmental variables to fish communities of the Fox River Basin in Illinois: Pearson and Spearman correlations, an algorithm based on information theory called mutual information, and a measure of variable importance built into the machine learning algorithm Random Forest. The results are based on a dataset developed for this study, which uses a fish index of biological integrity (IBI) and its ten component metrics as response variables and a range of environmental variables describing geomorphology, stream flow statistics, climate, and both reach-scale and watershed-scale land use as independent variables. Agricultural land use and the magnitude and duration of low flow events were ranked by the algorithms as key factors for the study area. Reach-scale characteristics were dominant for native sunfish, and stream flow metrics were rated highly for native suckers. Regression tree analyses of environmental variables on fish IBI identified breakpoints in percent agricultural land in the watershed (~64 %), duration of low flow pulses (~12 days), and 90-day minimum flow (~0.13 cms). The findings should be useful for building predictive models and design of more effective monitoring systems and restoration plans.
机译:水生社区的主要环境决定因素多年来一直是一个持续存在的话题。水生环境中自然特征和人为特征之间的相互作用以复杂的方式影响鱼类群落,从而难以确定单个特征的影响。研究人员面临着如何处理大量变量和复杂的相互关系的问题。这项研究使用多种方法来识别伊利诺伊州福克斯河盆地鱼类群落的关键环境变量:Pearson和Spearman相关性,基于信息论的算法(称为互信息),以及在机器学习算法随机森林中建立的变量重要性度量。结果基于针对本研究开发的数据集,该数据集使用鱼类生物完整性指数(IBI)及其十个成分指标作为响应变量,并使用一系列环境变量来描述地貌,河流流量统计数据,气候以及两者的范围-规模和流域规模的土地利用作为自变量。通过算法将农业土地利用以及低流量事件的强度和持续时间排定为研究区域的关键因素。到达比例特征是本地翻车鱼的主要特征,溪流指标被本地吮吸者高度评价。对鱼类IBI的环境变量进行回归树分析后,确定了流域农业用地的断点(约64%),低流量脉冲的持续时间(约12天)和90天的最小流量(约0.13 cms)。研究结果对于建立预测模型以及设计更有效的监测系统和恢复计划应该是有用的。

著录项

  • 来源
    《Environmental Management》 |2016年第2期|389-411|共23页
  • 作者单位

    Ven Te Chow Hydrosystems Laboratory, Department of Civil and Environmental Engineering, University of Illinois, Urbana-Champaign, IL, USA;

    Ven Te Chow Hydrosystems Laboratory, Department of Civil and Environmental Engineering, University of Illinois, Urbana-Champaign, IL, USA;

    Illinois State History Survey, Prairie Research Institute, University of Illinois, Urbana-Champaign, IL, USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Stream fish; Stream flow; Land use; Mutual information; Regression tree; Fish IBI;

    机译:溪流鱼;水流;土地利用;相互信息;回归树;鱼IBI;
  • 入库时间 2022-08-17 13:25:51

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