Stream classification frameworks are important tools for conserving aquatic resources. Yet despite their utility, most classification frameworks have not incorporated network connectivity. We developed and compared three biologically informed stream classification frameworks considering the effects of variables indexing local habitat and/or connectivity on stream fish communities. The first framework classified streams according to local environmental variables largely following the precedent set by previous stream classifications. The second framework classified streams according solely to network connectivity variables, while the third framework considered both local and connectivity variables. Using fish community data from 291 wadeable streams in South Carolina, USA, we used conditional inference tree analyses to identify either seven or eight discrete types of wadeable streams within each framework. Classifications were evaluated on their ability to describe community composition at a subset of sites not used in model training, and canonical correspondence analysis suggested that each framework performed similarly in describing overall community variation, with about 19% of variation explained. After accounting for the effects of biogeography and land use in our analytical approach, each classification explained a substantially higher amount of community variation with 46% of variation explained by our connectivity-informed classification and 42% explained by our locally informed classification. Classifications differed in their ability to describe elements of community structure; a classification incorporating connectivity predicted species richness better than the one that did not. This study ultimately addresses an important knowledge gap in the classification literature while providing broader implications for the conservation of aquatic organisms and their habitats.

中文翻译：

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将网络连接纳入流分类框架

河流分类框架是保护水生资源的重要工具。然而，尽管它们很实用，但大多数分类框架并没有包含网络连接。考虑到索引当地栖息地和/或连通性的变量对河流鱼类群落的影响，我们开发并比较了三个生物学信息流分类框架。第一个框架根据本地环境变量对流进行分类，很大程度上遵循以前的流分类设定的先例。第二个框架仅根据网络连接变量对流进行分类，而第三个框架同时考虑了本地和连接变量。使用来自美国南卡罗来纳州 291 条可涉水溪流的鱼类群落数据，我们使用条件推理树分析来识别每个框架内的七种或八种离散类型的可涉水流。分类是根据它们在模型训练中未使用的站点子集描述群落组成的能力进行评估的，规范对应分析表明，每个框架在描述整体群落变异方面的表现相似，解释了大约 19% 的变异。在我们的分析方法中考虑了生物地理学和土地利用的影响后，每个分类解释了大量的群落变异，其中 46% 的变异由我们的连通性信息分类解释，42% 由我们的本地信息分类解释。分类在描述群落结构要素的能力上有所不同；结合连通性的分类比没有的分类更好地预测了物种丰富度。这项研究最终解决了分类文献中的一个重要知识差距，同时为水生生物及其栖息地的保护提供了更广泛的影响。