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    Author(s): Karen E. Kopper; Donald McKenzie; David L. Peterson
    Date: 2009
    Source: Res. Pap. PNW-RP-582. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 24 p.
    Publication Series: Research Paper (RP)
    Station: Pacific Northwest Research Station
    PDF: Download Publication  (3.24 MB)


    Models and effect-size metrics for meta-analysis were compared in four separate meta-analyses quantifying surface fuels after prescribed fires in ponderosa pine (Pinus ponderosa Dougl. ex Laws.) forests of the Western United States. An aggregated data set was compiled from eight published reports that contained data from 65 fire treatment units. Downed woody and organic fuels were partitioned into five classes, and four meta-analyses were performed on each in a 2 by 2 factorial combination of fixed-effects vs. mixed-effects models with a difference-based metric (Hedges’ d) vs. a ratio-based metric (log-response ratio). All analyses yielded significant effect sizes for each class of fuels, although mixed-effects models had larger confidence intervals around mean effect sizes and smaller ranges in those means. The use of multiple methods produced a robust result for this study, but also carries the danger of selective interpretation if results are contradictory. Meta analysis in fire research merits further consideration because it facilitates inferences across data sets reported by multiple authors, even when reporting is inconsistent. Nevertheless, standardized methodology, consistent measurement protocols, and complete reporting of both significant and nonsignificant results will greatly assist future synthesis efforts using metaanalysis.

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    Kopper, Karen E.; McKenzie, Donald; Peterson, David L. 2009. The evaluation of meta-analysis techniques for quantifying prescribed fire effects on fuel loadings. Res. Pap. PNW-RP-582. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 24 p.


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    Effect size, fuel treatment, Hedges’ d, log-response ratio, mixed-effects model.

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