Effects of Aggregation on Chlorophyll-Phosphorus Relations in Missouri Reservoirs
Effects of Aggregation on Chlorophyll-Phosphorus Relations in Missouri Reservoirs
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DOI：
10.1080/07438149809354104
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发表时间：
1998-03
影响因子：
1.5
通讯作者：
John R. Jones;M. F. Knowlton;M. Kaiser
中科院分区：
文献类型：
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作者：
John R. Jones;M. F. Knowlton;M. Kaiser
利用来自密苏里州119个水库的叶绿素和磷数据，我们展示了数据聚合（将数据平均为季节性均值或长期湖泊均值）如何影响我们从大规模统计回归分析中做出推断的能力。我们证明了最明显的数据聚合现象，即从聚合数据估计的变量之间的关系通常比从未聚合数据估计的相同关系更强。平均减少了叶绿素对磷（Chl-TP）响应的通常较大的变化，这是湖泊中这些变量测量的特征。我们还证明，从统计回归分析中得出的推论仅适用于与用于产生模型的聚合水平相匹配的情况。利用湖均值，我们发现Chl-TP有很强的正相关。然而，研究中湖泊之间的这种强烈的横截面模式并不总是反映这些变量在单个湖泊中的相互关系。和t…
ABSTRACT Using chlorophyll and phosphorus data from 119 Missouri reservoirs we show how data aggregation-averaging data into seasonal means or long-term lake means – influences our ability to make inferences from large-scale statistical regression analyses. We demonstrate the most obvious phenomenon of data aggregation, that relations between variables estimated from aggregated data are generally stronger than the same relations estimated from unaggregated data. Averaging reduces the often large variation in the response of chlorophyll to phosphorus (Chl-TP) that characterizes measurements of these variables in lakes. We also demonstrate that inferences made from statistical regression analyses apply only to situations that match the level of aggregation used to produce the model. Using lake means we found a strong positive Chl-TP relation. This strong cross-sectional pattern among lakes in the study, however, did not always reflect the relation of these variables to one another in individual lakes. And t...