Enhancing assessments of blue carbon stocks in marsh soils using Bayesian mixed-effects modeling with spatial autocorrelation — proof of concept using proxy data
Virginia Institute of Marine Science
Center for Coastal Resources Management (CCRM)
Frontiers in Marine Science
Our paper showcases the potential gain in scientific insights about blue carbon stocks (or total organic carbon) when additional rigor, in the form of a spatial autocorrelation component, is formally incorporated into the statistical model for assessing the variability in carbon stocks. Organic carbon stored in marsh soils, or blue carbon (BC), is important for sequestering carbon from the atmosphere. The potential for marshes to store carbon dioxide, mitigating anthropogenic contributions to the atmosphere, makes them a critical conservation target, but efforts have been hampered by the current lack of robust methods for assessing the variability of BC stocks at different geographic scales. Statistical model-based extrapolation of information from soil cores to surrounding tidal marshes, with rigorous uncertainty estimates, would allow robust characterization of spatial variability in many unsampled coastal habitats.
blue carbon stocks, marsh soils, Chesapeake Bay Virginia, Statistical model-based extrapolation
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
This article was submitted to Ocean Observation, a section of the journal Frontiers in Marine Science.
Chiu GS, Mitchell M, Herman J, Longo C and Davis K (2023) Enhancing assessments of blue carbon stocks in marsh soils using Bayesian mixed effects modeling with spatial autocorrelation — proof of concept using proxy data. Front. Mar. Sci. 9:1056404. doi: 10.3389/fmars.2022.1056404
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