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Macrophytes in boreal streams: Characterizing and predicting native occurrence and abundance to assess human impact

机译:北方河流中的大型植物:表征和预测自然发生和丰度以评估人类影响

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Macrophytes are a structurally and functionally essential element of stream ecosystems and therefore indispensable in assessment, protection and restoration of streams. Modelling based on continuous environmental gradients offers a potential approach to predict natural variability of communities and thereby improve detection of anthropogenic community change. Using data from minimally disturbed streams, we described natural macrophyte assemblages in pool and riffle habitats separately and in combination, and explored their variation across large scale environmental gradients. Specifically, we developed RIVPACS-type models to predict the presence and abundance of macrophyte taxa at stream sites in the absence of human influence and, used data from impacted streams to explore the responses of three biotic indices to anthropogenic stress. The indices used, taxonomic completeness (O/E-taxa), a measure of compositional dissimilarity (BC-index) and an index taking into account the abundance of species (AB-index), are based on predicted and observed macrophyte communities. We found that size of the catchment area, altitude, latitude and percentage of lakes in the catchment were the large scale environmental variables that best predicted the natural variation of assemblages. The RIVPACS approach substantially improved both the precision and accuracy to predict the natural communities and the sensitivity to human disturbance. O/E-taxa performed best in relation to the null model decreasing the variation by 20% in pools, 29% in riffles and 32% in combined data. In general, models based on the riffle assemblages performed better than models based on pool assemblages, but including both habitats and predicting abundances instead of only presence/absence yielded the greatest accuracy and sensitivity. Our results support the use of multivariate modelling techniques in predicting reference condition to assess status of stream macrophyte communities. (C) 2016 Elsevier Ltd. All rights reserved.
机译:大型植物是河流生态系统的结构和功能必不可少的组成部分,因此在河流的评估,保护和恢复中必不可少。基于连续环境梯度的建模提供了一种潜在的方法来预测社区的自然变异性,从而改善对人为社区变化的检测。使用来自最小干扰流的数据,我们分别或组合描述了池和浅滩生境中的天然大型植物组合,并探讨了它们在大规模环境梯度中的变化。具体来说,我们开发了RIVPACS类型的模型来预测在没有人类影响的情况下溪流场所大型植物类群的存在和丰度,并使用来自受影响溪流的数据来探索三种生物指标对人为胁迫的响应。所使用的指标,分类学的完整性(O / E-紫杉类),成分差异的度量(BC-index)和考虑物种丰富度的指标(AB-index)均基于预测和观察到的大型植物群落。我们发现,集水区的大小,高度,纬度和集水区中的湖泊百分比是最能预测组合物自然变化的大规模环境变量。 RIVPACS方法大大提高了预测自然群落的准确性和准确性,并提高了对人为干扰的敏感性。与零模型相比,O / E分类单元的效果最佳,池中的变化减少了20%,浅滩的变化为29%,组合数据的变化为32%。通常,基于浅滩组合的模型比基于池组合的模型表现更好,但包括栖息地和预测丰度,而不只是存在/不存在,都产生了最高的准确性和灵敏度。我们的结果支持使用多元建模技术预测参考条件以评估河流大型植物群落的状况。 (C)2016 Elsevier Ltd.保留所有权利。

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