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1 October 2006 ESTIMATION OF ENDOCRANIAL VOLUME THROUGH THE USE OF EXTERNAL SKULL MEASURES IN THE CARNIVORA (MAMMALIA)
John A. Finarelli
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Abstract

Allometric relationships of brain and body size in the mammalian order Carnivora have been studied with respect to the pattern and timing of brain-size evolution and aspects of life history, adaptation, and ecology. Analyses of character evolution are aided by inclusion of relevant fossil data; however, endocranial volumes for fossil taxa have been limited to a few cases of extraordinary preservation, reducing the power of analyses. A method for estimating the volume of the brain from external braincase measurements of extant carnivorans is developed here. Multiple linear regression, combined with Akaike information criterion–based model averaging, was employed to predict endocranial volume from 3 external skull measures for 825 museum specimens of 126 extant terrestrial taxa spanning the Carnivora. The AIC-averaged model accurately predicted endocranial volumes for carnivoran taxa (R2 = 0.983), and the results were robust to potential sampling problems. Moreover, the model did not reflect phylogenetic autocorrelation, nor did it represent an induced correlation due to a general body-size scaling. Rather, the averaged model was an accurate predictor of brain volume that should be easily extendable to the carnivoran fossil record, and additionally can be generalized to construct similar models for a broad range of mammalian clades. This in turn will allow comparative studies within and among distantly related mammalian lineages and reconstructions of brain volumes for a diverse array of fossil taxa.

John A. Finarelli "ESTIMATION OF ENDOCRANIAL VOLUME THROUGH THE USE OF EXTERNAL SKULL MEASURES IN THE CARNIVORA (MAMMALIA)," Journal of Mammalogy 87(5), 1027-1036, (1 October 2006). https://doi.org/10.1644/05-MAMM-A-430R1.1
Accepted: 1 March 2006; Published: 1 October 2006
KEYWORDS
Akaike Information Criterion
Brain allometry
Carnivora
Mammalia
model-averaging
multiple linear regression
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