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On the emergence of diel solute signals in flowing waters
Author(s) -
Hensley Robert T.,
Cohen Matthew J.
Publication year - 2016
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1002/2015wr017895
Subject(s) - diel vertical migration , environmental science , streams , environmental chemistry , ecology , atmospheric sciences , chemistry , geology , biology , computer network , computer science
Biota imprint their stoichiometry on relative rates of elemental cycling in the environment. Despite this coupling, producer‐driven diel solute variation in rivers and streams is more apparent for some solutes (e.g., dissolved oxygen—DO) than others (e.g., nitrate— NO 3 − ). We hypothesized that these differences arise from atmospheric equilibration, with signals emerging and evolving differently for gaseous and nongaseous solutes. Measurements of DO and NO 3 in a spring‐fed river, where constant inputs isolate in‐stream processing, support this hypothesis, as do results from reactive transport modeling of river solute dynamics. Atmospheric equilibration dramatically shortens the benthic footprint over which signals integrate, facilitating emergence of diel DO signals in response to in‐stream metabolism. In contrast, upstream influences persist much further downstream for nongaseous solutes, confounding and potentially obscuring the diel signals from in‐stream assimilatory processing. Isolating diel NO 3 signals from in‐stream processing requires a two‐station approach wherein metabolic impacts on solute variation are measured by difference between upstream and downstream sensors. Notably, two‐station inference improves markedly when hydraulic controls on signal propagation such as dispersion and storage are explicitly considered. We conclude that the absence of diel signals at a single station for nongaseous solutes such asNO 3 −cannot be interpreted as lack of autotroph demand or element coupling. As advances in sensors enable the acquisition of an increasingly rich array of solute signals, controlling for differences in the emergence and downstream evolution of gaseous versus nongaseous solutes will dramatically improve inferences regarding the timing and magnitude of coupled elemental processing.

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