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Macroinvertebrate traits distinguish unregulated rivers subject to water abstraction

机译:大型无脊椎动物性状区分了需抽水的未管制河流

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摘要

The biological effect of water abstraction from unregulated streams in New South Wales, Australia, was assessed with data on macroinvertebrate assemblages in riffles (445 samples) and pool edges (718 samples) obtained from a national assessment of river condition in 1995 to 2000. We used limiting environmental difference (LED) modeling to predict the macroinvertebrate assemblage expected for each sample in the absence of water abstraction and estimated effect by comparing predicted and observed assemblages for sites with upstream abstraction. We found a high likelihood of effect on macroinvertebrates in 30% of riffles with upstream abstraction and 22% of edges. Complex combinations of biological traits appeared to determine the sensitivities and tolerances of individual taxa to water abstraction. For example, rheophilous families generally were rarer than predicted in apparently affected riffles, where thermophilic families seemed tolerant of the effects of water abstraction, and free-swimming families were less common than predicted in apparently affected edges, where families with tegument respiration appeared unaffected by water withdrawals. The trait response was clearer for the riffle fauna than for the edge fauna, perhaps because the physical and chemical effects of abstraction are more consistent for riffles than for edges. Our approach can support management planning by identifying taxa that are most at risk from water abstraction and sites where apparent effects are most evident.
机译:利用从1995年至2000年的全国河流状况评估获得的浅滩(445个样本)和池边缘(718个样本)的大型无脊椎动物组合数据,评估了澳大利亚新南威尔士州不受管制的河流取水的生物效应。我们使用极限环境差异(LED)建模来通过比较上游提取位点的预测和观察到的组合来预测在没有取水和估计效果的情况下每个样品的预期大型无脊椎动物组合。我们发现有30%的浅滩具有上游提取和22%的边缘对大型无脊椎动物产生影响的可能性很高。生物学特征的复杂组合似乎决定了单个分类单元对取水的敏感性和耐受性。例如,在明显受影响的浅滩中,嗜温性家庭通常比预期的少,嗜热家庭似乎能忍受抽水的影响,而在明显受影响的边缘,自由游泳的家庭不如预期的那样普遍,那里有铁皮呼吸的家庭似乎不受其影响取水。浅滩动物区系的特征响应比边缘动物区系更清晰,这可能是因为浅滩的抽象物理和化学效应比边缘区更一致。我们的方法可以通过识别最容易因取水和明显影响最明显的地点而分类的类群来支持管理计划。

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