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Quantifying river habitat structure in ecologically meaningful ways

机译:以生态有意义的方式量化河流栖息地结构

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Physical habitat structure (cf. water quality) is a poorly understood aspect of the relationships between river organisms and their environment. Habitat structure varies across a wide range of spatial and temporal scales, both naturally and in response to numerous human activities: usually control (e.g. channelisation) or exploitation (e.g. abstraction). Such changes have major biodiversity implications, making an understanding of the links between organisms and physical habitat structure vital for river management - as reflected in the Water Framework Directive (WFD). A fundamental challenge in making these links is finding an effective way of describing physical habitat structure and modification, given the variability and structural complexity ofriver habitats. We describe an approach to parameterising river habitats, identifying, then quantifying, gradients in both semi-natural habitat and in anthropogenic modification. The complexity/resolution of the resulting habitat indices can be varied according to the desired application - research or management. Case studies in the River Wye illustrate some of the benefits of such indices. Outputs from this work will support implementation of the WFD by capturing variation in river habitats that can be related to a wide range of organisms. More broadly, the methods we present have potential benefits for simplifying complex data, identifying environmental gradients, describing spatial patterns, quantifying organism habitat preferences and suggesting some rigorous approaches to the analysis of existing databases.
机译:物理栖息地结构(CF.水质)是河流生物体与其环境之间关系的态度不当。栖息地结构在各种各样的空间和时间尺度上变化,自然和响应众多人类活动:通常控制(例如信道)或剥削(例如,抽象)。这种变化具有重大的生物多样性影响,了解有机体和物理栖息地结构对河流管理至关重要的联系 - 在水框架指令(WFD)中反映。鉴于患有人栖息地的变异性和结构性复杂性,在制作这些链接时,鉴于患有人栖息地的变异性和结构性复杂性,这是一种基本挑战。我们描述了一种参与河流栖息地的方法,识别,然后定量半自然栖息地和人为改性。由此产生的栖息地指数的复杂性/解决方案可以根据所需的应用程序 - 研究或管理而变化。 Wye河的案例研究说明了这些指数的一些好处。从这项工作的产出将通过捕获河流栖息地的变化来支持粮食计划署的实施,这些河流栖息地可能与各种生物相关。更广泛地,我们存在的方法具有简化复杂数据的潜在好处,识别环境梯度,描述空间模式,量化生物栖息地偏好,并对现有数据库分析一些严格的方法。

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