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    Author(s): Joseph Mascaro; undefined undefined; Flint HughesAmanda Uowolo; Stefan A. Schnitzer
    Date: 2011
    Source: Biotropica (online)
    Publication Series: Scientific Journal (JRNL)
    Station: Pacific Southwest Research Station
    PDF: Download Publication  (165.56 KB)


    Nonlinear regression is increasingly used to develop allometric equations for forest biomass estimation (i.e., as opposed to the raditional approach of log-transformation followed by linear regression). Most statistical software packages, however, assume additive errors by default, violating a key assumption of allometric theory and possibly producing spurious models. Here, we show that such models may bias stand-level biomass estimates by up to 100 percent in young forests, and we present an alternative nonlinear fitting approach that conforms with allometric theory.

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    Mascaro, Joseph; Litton, Creighton M.; Hughes, R. Flint; Uowolo, Amanda; Schnitzer, Stefan A. 2011. Minimizing bias in biomass allometry: Model selection and log transformation of data. Biotropica.


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    allometry, Hawai‘i, heteroscedasticity, linear regression, nonlinear regression analysis, Psidium cattleianum

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