Journal
BIOTROPICA
Volume 43, Issue 6, Pages 649-653Publisher
WILEY
DOI: 10.1111/j.1744-7429.2011.00798.x
Keywords
allometry; Hawai'i; heteroscedasticity; linear regression; nonlinear regression analysis; Psidium cattleianum
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Funding
- Applied Ecological Services Inc.
- NSF
- UWM
- National Science Foundation [DEB-0816486]
- Division Of Environmental Biology
- Direct For Biological Sciences [1019436] Funding Source: National Science Foundation
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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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