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基于对历史和当前鱼类及大型无脊椎动物数据建模的美国西部沙质底河流生物评估。

Biological assessment of western USA sandy bottom rivers based on modeling historical and current fish and macroinvertebrate data.

作者信息

Hughes Robert M, Zeigler Meredith, Stringer Shann, Linam Gordon W, Flotemersch Joseph, Jessup Benjamin, Joseph Seva, Jacobi Gerald, Guevara Lynette, Cook Robert, Bradley Patricia, Barrios Kristopher

机构信息

Department of Fisheries, Wildlife, & Conservation Sciences, Oregon State University, Corvallis, Oregon, USA.

New Mexico Environment Department, Santa Fe, New Mexico, USA.

出版信息

River Res Appl. 2022 Jan 11;38(4):639-656. doi: 10.1002/rra.3929.

Abstract

Biological monitoring is important for assessing the ecological condition of surface waters. However, there are challenges in determining what constitutes reference conditions, what assemblages should be used as indicators, and how assemblage data should be converted into quantitative indicator scores. In this study, we developed and applied biological condition gradient (BCG) modeling to fish and macroinvertebrate data previously collected from large, sandy bottom southwestern USA rivers. Such rivers are particularly vulnerable to altered flow regimes resulting from dams, water withdrawals and climate change. We found that sensitive ubiquitous taxa for both fish and macroinvertebrates had been replaced by more tolerant taxa, but that the condition assessment ratings based on fish and macroinvertebrate assemblages differed. We conclude that the BCG models based on both macroinvertebrate and fish assemblage condition were useful for classifying the condition of southwestern USA sandy bottom rivers. However, our fish BCG model was slightly more sensitive than the macroinvertebrate model to anthropogenic disturbance, presumably because we had historical fish data, and because fish may be more sensitive to dams and altered flow regimes than are macroinvertebrates.

摘要

生物监测对于评估地表水的生态状况至关重要。然而,在确定何为参考条件、应将哪些生物群落用作指标以及生物群落数据应如何转化为定量指标分数方面存在挑战。在本研究中,我们开发并应用了生物状况梯度(BCG)模型,对先前从美国西南部大型沙质底河流中收集的鱼类和大型无脊椎动物数据进行分析。此类河流特别容易受到水坝、取水和气候变化导致的水流变化的影响。我们发现,鱼类和大型无脊椎动物的敏感常见分类群已被更具耐受性的分类群所取代,但基于鱼类和大型无脊椎动物群落的状况评估等级有所不同。我们得出结论,基于大型无脊椎动物和鱼类群落状况的BCG模型可用于对美国西南部沙质底河流的状况进行分类。然而,我们的鱼类BCG模型对人为干扰的敏感度略高于大型无脊椎动物模型,推测原因是我们拥有历史鱼类数据,且鱼类可能比大型无脊椎动物对水坝和水流变化更敏感。

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