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Relative importance of macrophyte community versus water quality variables for predicting fish assemblages in coastal wetlands of the Laurentian Great Lakes

机译:大型植物群落与水质变量对预测洛朗山脉五大湖沿岸湿地鱼类种群的相对重要性

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Fish have been shown to be sensitive indicators of environmental quality in Great Lakes coastal wetlands. Fish composition also reflects aquatic macrophyte communities, which provide them with critical habitat. Although investigators have shown that the relationship between water quality and fish community structure can be used to indicate wetland health, we speculate that this relationship is a result of the stronger, more direct relationship between water quality and macrophytes, together with the ensuing interconnection between macrophyte and fish assemblages. In this study, we use data collected from 115 Great Lakes coastal marshes to test the hypothesis that plants are better predictors of fish species composition than is water quality. First we use canonical correspondence analysis (CCA) to conduct an ordination of the fish community constrained by water quality parameters. We then use co-correspondence analysis (COCA) to conduct a direct ordination of the fish community with the plant community data. By comparing the statistic 'percent fit,' which refers to the cumulative percentage variance of the species data, we show that plants are consistently better predictors of the fish community than are water quality variables in three separate trials: all wetlands in the Great Lakes basin (whole: 21.2% vs 14.0%; n = 60), all wetlands in Lakes Huron and Superior (Upper: 20.3% vs 18.8%; n = 32), and all wetlands in Georgian Bay and the North Channel (Georgian Bay: 18% vs 17%; n=70). This is the largest study to directly examine plant-fish interactions in wetlands of the Great Lakes basin.
机译:鱼类已被证明是大湖沿岸湿地环境质量的敏感指标。鱼的成分还反映了水生植物群落,为它们提供了重要的栖息地。尽管研究人员已经表明水质和鱼类群落结构之间的关系可以用来表示湿地健康,但我们推测这种关系是水质与大型植物之间更紧密,更直接的关系以及大型植物之间相互联系的结果。和鱼群。在这项研究中,我们使用从115个大湖沿岸沼泽收集的数据来检验以下假设:植物比水质更好地预测鱼类的组成。首先,我们使用规范对应分析(CCA)对受水质参数约束的鱼类群落进行排序。然后,我们使用协同对应分析(COCA)对鱼类群落和植物群落数据进行直接排序。通过比较统计“百分比拟合”(指物种数据的累积百分比变化),我们表明在三个独立的试验中,与水质变量相比,植物始终是更好的鱼类群落预测指标:大湖流域的所有湿地(整体:21.2%比14.0%; n = 60),休伦湖和苏必利尔湖所有湿地(上层:20.3%vs 18.8%; n = 32),乔治亚湾和北海峡所有湿地(乔治亚湾:18) %和17%; n = 70)。这是直接检验大湖流域湿地中植物与鱼类相互作用的最大研究。

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