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The metabolic regimes of 356 rivers in the United States

A national-scale quantification of metabolic energy flow in streams and rivers can improve understanding of the temporal dynamics of in-stream activity, links between energy cycling and ecosystem services, and the effects of human activities on aquatic metabolism. The two dominant terms in aquatic m...

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Autores principales: Appling, Alison P., Read, Jordan S., Winslow, Luke A., Arroita, Maite, Bernhardt, Emily S., Griffiths, Natalie A., Hall, Robert O., Harvey, Judson W., Heffernan, James B., Stanley, Emily H., Stets, Edward G., Yackulic, Charles B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6289110/
https://www.ncbi.nlm.nih.gov/pubmed/30532078
http://dx.doi.org/10.1038/sdata.2018.292
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author Appling, Alison P.
Read, Jordan S.
Winslow, Luke A.
Arroita, Maite
Bernhardt, Emily S.
Griffiths, Natalie A.
Hall, Robert O.
Harvey, Judson W.
Heffernan, James B.
Stanley, Emily H.
Stets, Edward G.
Yackulic, Charles B.
author_facet Appling, Alison P.
Read, Jordan S.
Winslow, Luke A.
Arroita, Maite
Bernhardt, Emily S.
Griffiths, Natalie A.
Hall, Robert O.
Harvey, Judson W.
Heffernan, James B.
Stanley, Emily H.
Stets, Edward G.
Yackulic, Charles B.
author_sort Appling, Alison P.
collection PubMed
description A national-scale quantification of metabolic energy flow in streams and rivers can improve understanding of the temporal dynamics of in-stream activity, links between energy cycling and ecosystem services, and the effects of human activities on aquatic metabolism. The two dominant terms in aquatic metabolism, gross primary production (GPP) and aerobic respiration (ER), have recently become practical to estimate for many sites due to improved modeling approaches and the availability of requisite model inputs in public datasets. We assembled inputs from the U.S. Geological Survey and National Aeronautics and Space Administration for October 2007 to January 2017. We then ran models to estimate daily GPP, ER, and the gas exchange rate coefficient for 356 streams and rivers across the continental United States. We also gathered potential explanatory variables and spatial information for cross-referencing this dataset with other datasets of watershed characteristics. This dataset offers a first national assessment of many-day time series of metabolic rates for up to 9 years per site, with a total of 490,907 site-days of estimates.
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spelling pubmed-62891102018-12-12 The metabolic regimes of 356 rivers in the United States Appling, Alison P. Read, Jordan S. Winslow, Luke A. Arroita, Maite Bernhardt, Emily S. Griffiths, Natalie A. Hall, Robert O. Harvey, Judson W. Heffernan, James B. Stanley, Emily H. Stets, Edward G. Yackulic, Charles B. Sci Data Data Descriptor A national-scale quantification of metabolic energy flow in streams and rivers can improve understanding of the temporal dynamics of in-stream activity, links between energy cycling and ecosystem services, and the effects of human activities on aquatic metabolism. The two dominant terms in aquatic metabolism, gross primary production (GPP) and aerobic respiration (ER), have recently become practical to estimate for many sites due to improved modeling approaches and the availability of requisite model inputs in public datasets. We assembled inputs from the U.S. Geological Survey and National Aeronautics and Space Administration for October 2007 to January 2017. We then ran models to estimate daily GPP, ER, and the gas exchange rate coefficient for 356 streams and rivers across the continental United States. We also gathered potential explanatory variables and spatial information for cross-referencing this dataset with other datasets of watershed characteristics. This dataset offers a first national assessment of many-day time series of metabolic rates for up to 9 years per site, with a total of 490,907 site-days of estimates. Nature Publishing Group 2018-12-11 /pmc/articles/PMC6289110/ /pubmed/30532078 http://dx.doi.org/10.1038/sdata.2018.292 Text en Copyright © 2018, This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply http://creativecommons.org/licenses/by/4.0/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files made available in this article.
spellingShingle Data Descriptor
Appling, Alison P.
Read, Jordan S.
Winslow, Luke A.
Arroita, Maite
Bernhardt, Emily S.
Griffiths, Natalie A.
Hall, Robert O.
Harvey, Judson W.
Heffernan, James B.
Stanley, Emily H.
Stets, Edward G.
Yackulic, Charles B.
The metabolic regimes of 356 rivers in the United States
title The metabolic regimes of 356 rivers in the United States
title_full The metabolic regimes of 356 rivers in the United States
title_fullStr The metabolic regimes of 356 rivers in the United States
title_full_unstemmed The metabolic regimes of 356 rivers in the United States
title_short The metabolic regimes of 356 rivers in the United States
title_sort metabolic regimes of 356 rivers in the united states
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6289110/
https://www.ncbi.nlm.nih.gov/pubmed/30532078
http://dx.doi.org/10.1038/sdata.2018.292
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