Minimizing Bias in Biomass Allometry: Model Selection and Log-Transformation of Data

Authors

  • Joseph Mascaro,

    1. Department of Global Ecology, Carnegie Institution for Science, Stanford, California 96025, U.S.A.
    2. Smithsonian Tropical Research Institute, Apartado 2072, Balboa, Republic of Panama
    3. Department of Biological Sciences, University of Wisconsin – Milwaukee, Milwaukee, Wisconsin 53211, U.S.A.
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  • Creighton M. Litton,

    1. Department of Natural Resources and Environmental Management, University of Hawai‘i at Mânoa, Honolulu, Hawaii 96822, U.S.A.
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  • R. Flint Hughes,

    1. Institute of Pacific Islands Forestry, USDA Forest Service, Hilo, Hawaii 96720, U.S.A.
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  • Amanda Uowolo,

    1. Institute of Pacific Islands Forestry, USDA Forest Service, Hilo, Hawaii 96720, U.S.A.
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  • Stefan A. Schnitzer

    1. Smithsonian Tropical Research Institute, Apartado 2072, Balboa, Republic of Panama
    2. Department of Biological Sciences, University of Wisconsin – Milwaukee, Milwaukee, Wisconsin 53211, U.S.A.
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ABSTRACT

Nonlinear regression is increasingly used to develop allometric equations for forest biomass estimation (i.e., as opposed to the traditional 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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