Predicting downed woody material carbon stocks in forests of the conterminous United States.

Autor: Smith JE; USDA Forest Service, Northern Research Station, 271 Mast Road, Durham, NH 03824, USA. Electronic address: james.smith6@usda.gov., Domke GM; USDA Forest Service, Northern Research Station, 1992 Folwell Avenue, St. Paul, MN 55108, USA. Electronic address: grant.m.domke@usda.gov., Woodall CW; USDA Forest Service, Northern Research Station, 271 Mast Road, Durham, NH 03824, USA. Electronic address: christopher.w.woodall@usda.gov.
Jazyk: angličtina
Zdroj: The Science of the total environment [Sci Total Environ] 2022 Jan 10; Vol. 803, pp. 150061. Date of Electronic Publication: 2021 Sep 03.
DOI: 10.1016/j.scitotenv.2021.150061
Abstrakt: Downed woody material (DWM) is a unique part of the forest carbon cycle serving as a pool between living biomass and subsequent atmospheric emission or transference to other forest pools. Thus, DWM is an individually defined pool in national greenhouse gas inventories. The diversity of DWM carbon drivers (e.g., decay, tree mortality, or wildfire) and associated high spatial variability make this a difficult-to-predict component of forest ecosystems. Using the now fully established nationwide inventory of DWM across the United States (US), we developed models, which substantially improved predictions of stand-level DWM carbon density relative to the current national-reporting model ('previous' model, here). The previous model was developed from published DWM carbon densities prior to the NFI DWM inventory. Those predictions were tested using NFI DWM carbon densities resulting in a poor fit to the data (coefficient of determination, or R 2  = 0.03). We present new random forest (RF) and stochastic gradient boosted (SGB) regression models to prediction DWM carbon density on all NFI plots and spatially on all forest land pixels. We evaluated various biotic and abiotic regression predictors, and the most important were standing dead trees, long-term annual precipitation, and long-term maximum summer temperature. A RF model scored best for expanding predictions to NFI plots (R 2  = 0.31), while an SGB model was identified for DWM carbon predictions based on purely spatial data (i.e., NFI-plot-independent, with R 2  = 0.23). The new RF model predicts conterminous US DWM carbon stocks to be 15% lower than the previous model and 2% higher than NFI data expanded according to inventory design-based inference. The new NFI data-driven models not only improve the predictions of DWM carbon density on all plots, they also provide flexibility in extending these predictions beyond the NFI to make spatially explicit and spatially continuous estimates of DWM carbon on all forest land in the US.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Published by Elsevier B.V.)
Databáze: MEDLINE
Externí odkaz: