Concentrations of nitrogen provide a critical metric for understanding ecosystem function and water quality in lakes. However, varying approaches for quantifying nitrogen concentrations may bias the comparison of water quality across lakes and regions. Different measurements of total nitrogen exist based on its composition (e.g., organic versus inorganic, dissolved versus particulate), which we refer to as nitrogen species. Fortunately, measurements of multiple nitrogen species are often collected and can, therefore, be leveraged together to inform our understanding of the controls on total nitrogen in lakes. We develop a multivariate hierarchical statistical model that fuses speciated nitrogen measurements, obtained across multiple methods of reporting, in order to improve our estimates of total nitrogen. The model accounts for lower detection limits and measurement error that vary across lake, species and observation. By modeling speciated nitrogen, as opposed to previous efforts that mostly consider only total nitrogen, we obtain more resolved inference with regard to differences in sources of nitrogen and their relationship with complex environmental drivers. We illustrate the inferential benefits of our model using speciated nitrogen data from the LAke GeOSpatial and temporal database (LAGOS).

中文翻译：

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特定氮的数据融合模型，以识别环境驱动因素并改善湖泊中氮的估算

氮的浓度为理解湖泊的生态系统功能和水质提供了关键指标。但是，不同的量化氮浓度的方法可能会使湖泊和地区之间水质的比较产生偏差。根据总氮的组成（例如有机对无机，溶解对颗粒），存在不同的总氮测量方法，我们称其为氮。幸运的是，经常收集多种氮素的测量值，因此可以一起利用这些信息，以帮助我们了解湖泊中总氮的控制方法。我们开发了一个多元层次统计模型，该模型融合了通过多种报告方法获得的特定氮测量值，以改善我们对总氮的估计。该模型说明了较低的检出限和测量误差，这些检出限和测量误差随湖泊，物种和观测值而变化。通过对特定氮进行建模，与之前大多数只考虑总氮的工作相反，我们获得了关于氮源差异及其与复杂环境驱动因素之间关系的更可靠的推断。我们使用来自LAke GeOS时空数据库（LAGOS）的指定氮数据说明了我们模型的推论优势。