The Allometric Quarter-Power Scaling Model and Its Applicability to Grand Fir and Eucalyptus Trees

Hannah Capes (), Robert J. Maillardet (), Thomas G. Baker (), Christopher J. Weston (), Don McGuire (), Ian C. Dumbrell () and Andrew P. Robinson ()
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Hannah Capes: Empirica Research
Robert J. Maillardet: University of Melbourne (UOM)
Thomas G. Baker: UOM
Christopher J. Weston: University of Melbourne (UOM)
Don McGuire: Synergetic Forestry Services
Ian C. Dumbrell: Forest Products Commission
Andrew P. Robinson: UOM

Journal of Agricultural, Biological and Environmental Statistics, 2017, vol. 22, issue 4, No 8, 562-584

Abstract: Abstract The use of the allometric model $$y = \beta x ^{\alpha }$$ y = β x α to describe the relative growth of morphological traits of trees is a source of contention in ecology. This is particularly so in a specific form, the West, Brown and Enquist model, which predicts values of $$\alpha $$ α that are multiples of 1/4 for various allometric relationships—the quarter-power scaling law. We use statistical techniques to test the appropriateness of the quarter-power scaling allometric model in a number of different relative growth relationships of trees. Two separate datasets are used, one of repeated measures of Abies grandis (Grand fir) trees, another of independent measures of Eucalyptus trees. Nonlinear mixed-effects modelling is used to fit allometric models to the datasets. Generalised additive models, equivalence testing and traditional significance testing are used to assess the adequacy of the allometric models fitted and the values of the estimated exponents relative to those predicted by the WBE model. In only one of the five models fitted was there empirical evidence for the WBE-predicted quarter-power exponent. However, the adequacy of the allometric models was generally supported, though a need for further analysis over a larger range of tree ages/sizes is indicated. Supplementary materials accompanying this paper appear online.

Keywords: Allometric scaling; Quarter-power scaling law; Nonlinear mixed-effects models; Equivalence testing (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s13253-017-0292-7

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