I used cluster analysis to examine associations among 20 fish species to develop a classification scheme for 132 large Texas reservoirs. Five major groups of reservoirs were identified by cluster analysis based on species associations. Of 29 reservoirs surveyed previously, 76% were classified into the same species associations from one survey to the next. When 19 environmental variables were used in canonical correlation analysis of the five reservoir groups, I found a general east-to-west separation of species associations by water quality and a northwest-to-southeast separation by surface elevation and growing season. A discriminant functions model based on a reduced set of nine environmental variables had an unbiased error rate of 18% for predicting the species association in unclassified reservoirs. A stratified sampling scheme based on the classification model decreased the variance of statewide electrofishing catch per effort up to 43% for bluegill Lepomis macrochirus and 23% for largemouth bass Micropterus salmoides over a simple random sample of reservoirs.

中文翻译：

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得克萨斯水库的湖泊学和鱼类群落协会分类。

我使用聚类分析检查了20种鱼类之间的关联，从而为132个大型德克萨斯水库制定了分类方案。通过基于物种关联的聚类分析确定了五个主要的水库群。在先前调查的29个水库中，有76％被归类为同一物种协会。当在五个水库群的典范相关性分析中使用19个环境变量时，我发现通过水质实现了物种关联的总体上东西向的分离，并且根据地表海拔和生长季节实现了西南到东南的分离。基于减少的9个环境变量集的判别函数模型的无偏误差率为18％，可预测未分类水库中的物种关联。