A statistical power analysis of woody carbon flux from forest inventory data

James Westfall (), Christopher Woodall and Mark Hatfield

Climatic Change, 2013, vol. 118, issue 3, 919-931

Abstract: At a national scale, the carbon (C) balance of numerous forest ecosystem C pools can be monitored using a stock change approach based on national forest inventory data. Given the potential influence of disturbance events and/or climate change processes, the statistical detection of changes in forest C stocks is paramount to maintaining the net sequestration status of these stocks. To inform the monitoring of forest C balances across large areas, a power analysis of a forest inventory of live/dead standing trees and downed dead wood C stocks (and components thereof) was performed in states of the Great Lakes region, U.S. Using data from the Forest Inventory and Analysis (FIA) program of the U.S. Forest Service, it was found that a decrease in downed wood C stocks (−1.87 Mg/ha) was nearly offset by an increase in standing C stocks (1.77 Mg/ha) across the study region over a 5-year period. Carbon stock change estimates for downed dead wood and standing pools were statistically different from zero (α = 0.10), while the net change in total woody C (−0.10 Mg/ha) was not statistically different from zero. To obtain a statistical power to detect change of 0.80 (α = 0.10), standing live C stocks must change by at least 0.7 %. Similarly, standing dead C stocks would need to change by 3.8 %; while downed dead C stocks require a change of 6.9 %. While the U.S.’s current forest inventory design and sample intensity may not be able to statistically detect slight changes (>1 %) in forest woody C stocks at sub-national scales, large disturbance events (>3 % stock change) would almost surely be detected. Understanding these relationships among change detection thresholds, sampling effort, and Type I (α) error rates allows analysts to evaluate the efficacy of forest inventory data for C pool change detection at various spatial scales and levels of risk for drawing erroneous conclusions. Copyright U.S. Government 2013

Date: 2013
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DOI: 10.1007/s10584-012-0686-z

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