Articles | Volume 20, issue 13
https://doi.org/10.5194/bg-20-2707-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-20-2707-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 Jul 2023
Research article |
| 12 Jul 2023
| 12 Jul 2023
Assessing carbon storage capacity and saturation across six central US grasslands using data–model integration
Kevin R. Wilcox
CORRESPONDING AUTHOR
Department of Ecosystem Science and Management, University of Wyoming,
Laramie, WY 82071, USA
Department of Biology, University of North Carolina Greensboro,
Greensboro, NC 27412, USA
Scott L. Collins
Department of Biology, University of New Mexico, Albuquerque, NM
87131, USA
Alan K. Knapp
Department of Biology & Graduate Degree Program in Ecology,
Colorado State University, Fort Collins, CO 80523, USA
William Pockman
Department of Biology, University of New Mexico, Albuquerque, NM
87131, USA
Zheng Shi
Department of Microbiology and Plant Biology, University of Oklahoma,
Norman, OK 73019, USA
Melinda D. Smith
Department of Biology & Graduate Degree Program in Ecology,
Colorado State University, Fort Collins, CO 80523, USA
School of Integrative Plant Science, Cornell University, Ithaca, NY
14853, USA
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Short summary
The capacity for carbon storage (C capacity) is an attribute that determines how ecosystems store carbon in the future. Here, we employ novel data–model integration techniques to identify the carbon capacity of six grassland sites spanning the US Great Plains. Hot and dry sites had low C capacity due to less plant growth and high turnover of soil C, so they may be a C source in the future. Alternately, cooler and wetter ecosystems had high C capacity, so these systems may be a future C sink.
The capacity for carbon storage (C capacity) is an attribute that determines how ecosystems...
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