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December 2020 Data fusion model for speciated nitrogen to identify environmental drivers and improve estimation of nitrogen in lakes
Erin M. Schliep, Sarah M. Collins, Shirley Rojas-Salazar, Noah R. Lottig, Emily H. Stanley
Ann. Appl. Stat. 14(4): 1651-1675 (December 2020). DOI: 10.1214/20-AOAS1371

Abstract

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).

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Erin M. Schliep. Sarah M. Collins. Shirley Rojas-Salazar. Noah R. Lottig. Emily H. Stanley. "Data fusion model for speciated nitrogen to identify environmental drivers and improve estimation of nitrogen in lakes." Ann. Appl. Stat. 14 (4) 1651 - 1675, December 2020. https://doi.org/10.1214/20-AOAS1371

Information

Received: 1 September 2019; Revised: 1 March 2020; Published: December 2020
First available in Project Euclid: 19 December 2020

MathSciNet: MR4194242
Digital Object Identifier: 10.1214/20-AOAS1371

Rights: Copyright © 2020 Institute of Mathematical Statistics

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Vol.14 • No. 4 • December 2020
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