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Combining national and state data improves predictions of microcystin concentration

机译：结合国家和州数据改善了微囊藻浓度的预测

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摘要

Data collected from lakes at national (regional) scales and state (local) scales can provide different insights regarding relationships between environmental factors and biological responses, and combining these two types of data can potentially yield more precise and accurate understanding of ecological phenomena. National data can include many measures, cover large spatial areas, and span broad environmental gradients. Because of these characteristics, analyses of these data can yield accurate estimates of relationships among different lake characteristics. However, the number of samples in a national data set that is available for estimating a relationship specific to waterbodies within a smaller region, like a single state, is limited. Conversely, state monitoring data provide intensive sampling of lakes within a smaller area, but these data span a narrower range of conditions and may only include a subset of relevant measurements. Here, a Bayesian network model is described that represents the causal linkages between observations of chlorophyll a concentration, cyanobacterial biovolume, and microcystin concentration. This network model was fit to national data and provided a context for modeling observations of chlorophyll a and microcystin collected from lakes in Iowa. Using the knowledge inherent in the national network model improved the accuracy of predictions of microcystin concentrations in Iowa compared to a model based only on Iowa data.
机译：从国家（区域）秤和国家（本地）级别收集的数据可以提供有关环境因素与生物反应之间的关系的不同见解，并结合这两种类型的数据可能会产生更精确和准确的生态现象的理解。国家数据可以包括许多措施，覆盖大型空间区域，跨度广泛的环境梯度。由于这些特征，这些数据的分析可以促进不同湖泊特征之间的关系的准确估计。然而，可用于估计特定于较小区域内的Waterbodies的关系的国家数据集中的样本数量有限。相反，状态监测数据在较小区域内提供湖泊的密集采样，但这些数据跨越一个较窄的条件范围，并且可以包括相关测量的子集。这里，描述了贝叶斯网络模型，其代表叶绿素浓度，蓝藻生物组和微囊藻浓度的观察结果之间的因果关系。该网络模型适合国家数据，并为从爱荷华州湖泊收集的叶绿素A和微囊藻苷的观察进行了建模的语境。使用国家网络模型中固有的知识改善了IOWA中微囊藻浓度预测的准确性，而仅与IOOWA数据的模型相比。

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