Classifying Lakes to Improve Precision of Nutrient—Chlorophyll Relationships
Classifying Lakes to Improve Precision of Nutrient—Chlorophyll Relationships
复制标题
对湖泊进行分类以提高养分-叶绿素关系的精度
作者：
Lester L. Yuan;A. Pollard
摘要：
当模型易于解释时，对压力源和环境反应之间关系的准确估计可以为管理决策提供最有用的信息。在这里，我们描述了一种对湖泊和水库进行分类的方法，该方法可以改进对总磷 (TP) 和叶绿素 a (chl a) 浓度之间关系的估计，同时保留环境管理者和利益相关者可以轻松解释的模型。我们用分类回归树统计地选择分类变量，其中TP和chl a之间的关系是树的终端节点。我们根据校准数据的引导复制开发了一组分类树，以探索更广泛的可能树。我们根据验证数据集的预测性能选择了最终的树。总 N:TP 质量比是从众多生物、化学和物理候选分类变量中最常选择的分类变量。由此产生的湖泊类别中 TP 和 chl a 之间的关系提供的预测比使用基于奥梅尔尼克 III 级生态区聚合的营养生态区计算的预测要准确得多，但对树木集合进行平均的随机森林模型的预测甚至更准确。因此，这里提出的分类方法牺牲了少量的预测准确性来保留易于解释的树结构。
Abstract:
Accurate and precise estimates of relationships between stressors and environmental responses can inform management decisions most usefully when models can be easily interpreted. Here, we describe an approach for classifying lakes and reservoirs that can improve estimates of the relationships between total P (TP) and chlorophyll a (chl a) concentration, while preserving a model that can be readily interpreted by environmental managers and stakeholders. We selected classification variables statistically with a classification and regression tree in which relationships between TP and chl a were the terminal nodes of the tree. We developed a set of classification trees from bootstrapped replicates of the calibration data to explore a broader range of possible trees. We chose a final tree based on its predictive performance with a validation data set. The total N:TP mass ratio was the classification variable selected most frequently from a broad array of biological, chemical, and physical candidate classification variables. Relationships between TP and chl a in the resulting lake classes provided predictions that were substantially more accurate than predictions computed using nutrient ecoregions based on aggregations of Omernik Level III ecoregions, but predictions from a random forest model that averaged an ensemble of trees were even more accurate. Thus, the classification approach presented here sacrifices a small amount of predictive accuracy to retain a tree structure that is readily interpretable.