Effects of Land Cover on Stream Ecosystems: Roles of Empirical Models and Scaling Issues
Effects of Land Cover on Stream Ecosystems: Roles of Empirical Models and Scaling Issues
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DOI：
10.1007/pl00021506
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发表时间：
2003
期刊：
影响因子：
3.7
通讯作者：
D. Strayer;R. Beighley;Lisa C. Thompson;S. Brooks;C. Nilsson;G. Pinay;R. Naiman
中科院分区：
文献类型：
--
作者：
D. Strayer;R. Beighley;Lisa C. Thompson;S. Brooks;C. Nilsson;G. Pinay;R. Naiman
我们建立了经验模型，估计土地覆盖对河流生态系统的影响，在大西洋中部地区（美国），并评估这种模型是最有效的空间尺度。预测变量包括土地覆盖在流域，在河边走廊，和附近的研究地点，和水坝和点源在流域的数量和位置。响应变量是每年的硝酸盐通量;鱼类，底栖大型无脊椎动物和水生植物的物种丰富度;和水生植物和河岸植被的覆盖。所有数据都来自公开数据库，主要是互联网上的数据。土地覆被与所有生态响应变量显著相关。ModeledR 2范围为0.07至0.5，但大型数据集通常使我们能够以可接受的精度估计回归系数，这些系数表达与土地覆盖单位变化相关的生态条件变化。大坝和点源变量在预测溪流和河流的生态条件方面是无效的，可能是因为数据集的不足。最有效的预测生态响应变量的空间视角（整个流域，河滨走廊，或本地）不同的响应变量，显然是在一致的机制，控制这些变量。我们发现一些证据表明，预测能力下降，在非常小的流域（小于1-10平方公里），这表明景观斑块的空间布局可能成为关键在这些小尺度。经验模型可以取代，约束，或与更多的机械模型相结合，以了解土地覆盖变化对河流生态系统的影响。
We built empirical models to estimate the effects of land cover on stream ecosystems in the mid-Atlantic region (USA) and to evaluate the spatial scales over which such models are most effective. Predictive variables included land cover in the watershed, in the streamside corridor, and near the study site, and the number and location of dams and point sources in the watershed. Response variables were annual nitrate flux; species richness of fish, benthic macroinvertebrates, and aquatic plants; and cover of aquatic plants and riparian vegetation. All data were taken from publicly available databases, mostly over the Internet. Land cover was significantly correlated with all ecological response variables. ModeledR2ranged from 0.07 to 0.5, but large data sets often allowed us to estimate with acceptable precision the regression coefficients that express the change in ecological conditions associated with a unit change in land cover. Dam- and point-source variables were ineffective at predicting ecological conditions in streams and rivers, probably because of inadequacies in the data sets. The spatial perspective (whole watershed, streamside corridor, or local) most effective at predicting ecological response variables varied across response variables, apparently in concord with the mechanisms that control each of these variables. We found some evidence that predictive power fell in very small watersheds (less than 1–10 km2), suggesting that the spatial arrangement of landscape patches may become critical at these small scales. Empirical models can replace, constrain, or be combined with more mechanistic models to understand the effects of land-cover change on stream ecosystems.