Physical Factors Control Phytoplankton Production and Nitrogen Fixation in Eight Texas Reservoirs
Physical Factors Control Phytoplankton Production and Nitrogen Fixation in Eight Texas Reservoirs
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物理因素控制德克萨斯州八个水库浮游植物的产生和固氮
DOI：
10.1007/s10021-008-9188-2
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发表时间：
2008
期刊：
影响因子：
3.7
通讯作者：
B. Brooks
中科院分区：
文献类型：
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作者：
Margaret G. Forbes;R. Doyle;J. Scott;J. Stanley;Hui;B. Brooks
通过比较回归树分析和多元线性回归模型，探讨了物理因素、土地利用和水质在预测8个南部水库河流和湖泊梯度85个地点浮游植物产量和固氮潜力方面的相对重要性。回归树模型（r2 = 0.73）表明，浮游植物产量差异主要是水深的函数。最高的产磷率（mg C m−3 h−1）发生在浅点（<0.9 m），其产磷率也与总磷（TP）水平有关。在较深的地点，相对排水面积（RDA，排水面积与水面面积之比）低于45的地点的产量更高，可能是由于水力停留时间较长。多元线性回归选择TP、RDA、溶解磷和已开发土地百分比作为显著模型变量（r2 = 0.63）。回归树模型（r2 = 0.67）显示，在相对较小的排水面积上，N2固定电位（mg N m−3 h−1）明显较高（RDA < 45）。在这个亚组中，固定率还与TP值相关（阈值= 41 μg l−1）。多元线性回归模型（r2 = 0.67）也选择RDA作为N2固定的主要预测因子。回归树模型显示养分控制因子（磷）服从于物理因子（如深度和RDA）。我们得出结论，回归树分析非常适合揭示数据中的非线性趋势（例如，深度），但是当应用于线性数据（例如，磷）时产生了很大的不确定性估计。
We compared regression tree analyses and multiple linear regression models to explore the relative importance of physical factors, land use, and water quality in predicting phytoplankton production and N2 fixation potentials at 85 locations along riverine to lacustrine gradients within eight southern reservoirs. The regression tree model (r2 = 0.73) revealed that differences in phytoplankton production were primarily a function of water depth. The highest rates of production (mg C m−3 h−1) occurred at shallow sites (<0.9 m), where rates were also related to total phosphorus (TP) levels. At deeper sites, production rates were higher at sites with relative drainage area (RDA, ratio of drainage area to water surface area) below 45, potentially due to longer hydraulic residence times. In contrast, multiple linear regression selected TP, RDA, dissolved phosphorus, and percent developed land as significant model variables (r2 = 0.63). The regression tree model (r2 = 0.67) revealed that N2 fixation potentials (mg N m−3 h−1) were substantially higher at sites with relatively smaller drainage areas (RDA < 45). Within this subgroup, fixation rates were additionally related to TP values (threshold = 41 μg l−1). The multiple linear regression model (r2 = 0.67) also selected RDA as the primary predictor of N2 fixation. Regression tree models suggest that nutrient controls (phosphorus) were subordinate to physical factors such as depth and RDA. We concluded that regression tree analysis was well suited to revealing nonlinear trends in data (for example, depth), but yielded large uncertainty estimates when applied to linear data (for example, phosphorus).