Abstract:
:Morphological relationships change with overall body size and body size often varies among populations. Therefore, quantitative analyses of individual traits from organisms in different populations or environments (e.g., in studies of phenotypic plasticity) often adjust for differences in body size to isolate changes in allometry. Most studies of among population variation in morphology either (1) use analysis of covariance (ANCOVA) with a univariate measure of body size as the covariate, or (2) compare residuals from ordinary least squares regression of each trait against body size or the first principal component of the pooled data (shearing). However, both approaches are problematic. ANCOVA depends on assumptions (small variance in the covariate) that are frequently violated in this context. Residuals analysis assumes that scaling relationships within groups are equal, but this assumption is rarely tested. Furthermore, scaling relationships obtained from pooled data typically mischaracterize within-group scaling relationships. We discuss potential biases imposed by the application of ANCOVA and residuals analysis for quantifying morphological differences, and elaborate and demonstrate a more effective alternative: common principal components analysis combined with Burnaby's back-projection method.
journal_name
Oecologiajournal_title
Oecologiaauthors
McCoy MW,Bolker BM,Osenberg CW,Miner BG,Vonesh JRdoi
10.1007/s00442-006-0403-6subject
Has Abstractpub_date
2006-07-01 00:00:00pages
547-54issue
4eissn
0029-8549issn
1432-1939journal_volume
148pub_type
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