Metabolic rhythms in flowing waters: An approach for classifying river productivity regimes. (3rd March 2019)

• Record Type: Journal Article
• Authors: Savoy, Philip; Appling, Alison P.; Heffernan, James B.; Stets, Edward G.; Read, Jordan S.; Harvey, Judson W.; Bernhardt, Emily S.
• Is Part Of: Limnology and oceanography. Volume 64:Number 5(2019)
• Page Start: 1835
• Page End: 1851
• Publication Date: 2019-03-03
• Subjects: Limnology -- Periodicals; Oceanography -- Periodicals; Océanographie; Limnologie; Limnology; Oceanography; Computer network resources; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); Periodicals; 551.4805
• DOI: 10.1002/lno.11154
• Languages: English
• ISSNs: 0024-3590

Abstract

Abstract: Although seasonal patterns of ecosystem productivity have been extensively described and analyzed with respect to their primary forcings in terrestrial and marine systems, comparatively little is known about these same processes in rivers. However, it is now possible to perform a large‐scale synthesis on the patterns and drivers of river productivity regimes because of the recent sensor advances allowing for near‐continuous estimates of river productivity. Here, we explore a dataset of 47 U.S. rivers to examine whether there are characteristic river productivity regimes. We use classification approaches to develop a typology of productivity regimes and then use these regimes to examine differences with respect to potential controls of productivity. We identified two distinct metabolic regimes, which we named Summer Peak and Spring Peak Rivers, within our dataset. These regimes meaningfully differed in both the timing and magnitude of productivity and were robust to different approaches to classification. We also found that several variables, including watershed area and characteristics of water temperature or discharge, were able to predict the class membership of these regimes with modest accuracy. Our results support the presence of characteristic metabolic regimes and suggests that these regimes may have common sets of environmental controls. We present classification as one approach to begin exploring the productivity regimes of rivers. The strength of our approach is that it fully leverages these newly available high‐frequency productivity estimates to create classes that can be used to draw inferences about how the controls of river productivity differ between or within systems.