Literature DB >> 24668762

Multiplicative by nature: Logarithmic transformation in allometry.

Gary C Packard1.   

Abstract

The traditional allometric method, which is at the heart of research paradigms used by comparative biologists around the world, entails fitting a straight line to logarithmic transformations of the original bivariate data and then back-transforming the resulting equation to form a two-parameter power function in the arithmetic scale. The method has the dual advantages of enabling investigators to fit statistical models that describe multiplicative growth while simultaneously addressing the multiplicative nature of residual variation in response variables (heteroscedasticity). However, important assumptions of the traditional method seldom are assessed in contemporary practice. When the assumptions are not met, mean functions may fail to capture the dominant pattern in the original data and incorrect form for error may be imposed upon the fitted model. A worked example from metabolic allometry in doves and pigeons illustrates both the power of newer statistical procedures and limitations of the traditional allometric method.
© 2014 Wiley Periodicals, Inc.

Mesh:

Year:  2014        PMID: 24668762     DOI: 10.1002/jez.b.22570

Source DB:  PubMed          Journal:  J Exp Zool B Mol Dev Evol        ISSN: 1552-5007            Impact factor:   2.656


  2 in total

1.  Response to Packard: make sure we do not throw out the biological baby with the statistical bath water when performing allometric analyses.

Authors:  J F Lemaître; C Vanpé; F Plard; C Pélabon; J M Gaillard
Journal:  Biol Lett       Date:  2015-06       Impact factor: 3.703

2.  Examination of the Effects of Curvature in Geometrical Space on Accuracy of Scaling Derived Projections of Plant Biomass Units: Applications to the Assessment of Average Leaf Biomass in Eelgrass Shoots.

Authors:  Héctor Echavarría-Heras; Cecilia Leal-Ramírez; Enrique Villa-Diharce; Abelardo Montesinos-López
Journal:  Biomed Res Int       Date:  2019-04-23       Impact factor: 3.411

  2 in total

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