Ecol Appl. 2015 Jun;25(4):943-55. doi: 10.1890/14-0935.1.
Lake water quality is affected by local and regional drivers, including lake physical characteristics, hydrology, landscape position, land cover, land use, geology, and climate. Here, we demonstrate the utility of hypothesis testing within the landscape limnology framework using a random forest algorithm on a national-scale, spatially explicit data set, the United States Environmental Protection Agency's 2007 National Lakes Assessment. For 1026 lakes, we tested the relative importance of water quality drivers across spatial scales, the importance of hydrologic connectivity in mediating water quality drivers, and how the importance of both spatial scale and connectivity differ across response variables for five important in-lake water quality metrics (total phosphorus, total nitrogen, dissolved organic carbon, turbidity, and conductivity). By modeling the effect of water quality predictors at different spatial scales, we found that lake-specific characteristics (e.g., depth, sediment area-to-volume ratio) were important for explaining water quality (54-60% variance explained), and that regionalization schemes were much less effective than lake specific metrics (28-39% variance explained). Basin-scale land use and land cover explained between 45-62% of variance, and forest cover and agricultural land uses were among the most important basin-scale predictors. Water quality drivers did not operate independently; in some cases, hydrologic connectivity (the presence of upstream surface water features) mediated the effect of regional-scale drivers. For example, for water quality in lakes with upstream lakes, regional classification schemes were much less effective predictors than lake-specific variables, in contrast to lakes with no upstream lakes or with no surface inflows. At the scale of the continental United States, conductivity was explained by drivers operating at larger spatial scales than for other water quality responses. The current regulatory practice of using regionalization schemes to guide water quality criteria could be improved by consideration of lake-specific characteristics, which were the most important predictors of water quality at the scale of the continental United States. The spatial extent and high quality of contextual data available for this analysis makes this work an unprecedented application of landscape limnology theory to water quality data. Further, the demonstrated importance of lake morphology over other controls on water quality is relevant to both aquatic scientists and managers.
湖泊水质受局部和区域驱动因素的影响，包括湖泊物理特征、水文学、景观位置、土地覆盖、土地利用、地质和气候。在这里，我们使用随机森林算法在美国环境保护署 2007 年国家湖泊评估的全国范围内具有空间显式数据集上展示了假设检验在景观水文学框架中的应用。对于 1026 个湖泊，我们测试了水质驱动因素在不同空间尺度上的相对重要性、水力学连通性在调节水质驱动因素中的重要性以及两者的重要性如何在五个重要的湖泊内水质指标（总磷、总氮、溶解有机碳、浊度和电导率）的响应变量之间存在差异。通过对不同空间尺度下水质预测因子的建模，我们发现湖泊特有特征（例如深度、沉积物面积与体积比）对于解释水质（解释 54-60%的方差）非常重要，区域化方案比湖泊特定指标的效果差得多（解释 28-39%的方差）。流域尺度的土地利用和土地覆盖解释了 45-62%的方差，森林覆盖和农业土地利用是最重要的流域尺度预测因子之一。水质驱动因素并非独立作用；在某些情况下，水力学连通性（上游地表水特征的存在）调节了区域尺度驱动因素的影响。例如，对于上游有湖泊的湖泊的水质而言，区域分类方案比湖泊特有变量作为预测因子的效果差得多，而对于没有上游湖泊或没有地表水流入的湖泊则相反。在美国大陆的尺度上，电导率由在较大空间尺度上起作用的驱动因素解释，而不是其他水质响应。考虑到湖泊特有特征，当前使用区域化方案来指导水质标准的监管实践可以得到改善，湖泊特有特征是美国大陆尺度上水质的最重要预测因子。这种分析可用的上下文数据的空间范围和高质量使这项工作成为景观水文学理论在水质数据方面的空前应用。此外，湖泊形态对水质控制的其他因素的重要性与水生科学家和管理者都有关。
