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Methods for Studying Morphological Integration and Modularity

Published online by Cambridge University Press:  21 July 2017

Anjali Goswami and
P. David Polly
Anjali Goswami
Affiliation:
Department of Genetics, Evolution and Environment and Department of Earth Sciences, University College London, Wolfson House 408, 4 Stephenson Way, London NW1 2HE UK
P. David Polly
Affiliation:
Department of Geological Sciences, Indiana University, 1001 E. 10th Street, Bloomington, IN 47401 USA
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Abstract

Morphological integration and modularity are closely related concepts about how different traits of an organism are correlated. Integration is the overall pattern of intercorrelation; modularity is the partitioning of integration into evolutionarily or developmentally independent blocks of traits. Modularity and integration are usually studied using quantitative phenotypic data, which can be obtained either from extant or fossil organisms. Many methods are now available to study integration and modularity, all of which involve the analysis of patterns found in trait correlation or covariance matrices. We review matrix correlation, random skewers, fluctuating asymmetry, cluster analysis, Euclidean distance matrix analysis (EDMA), graphical modelling, two-block partial least squares, RV coefficients, and theoretical matrix modelling and discuss their similarities and differences. We also review different coefficients that are used to measure correlations. We apply all the methods to cranial landmark data from and ontogenetic series of Japanese macaques, Macaca fuscata to illustrate the methods and their individual strengths and weaknesses. We conclude that the exploratory approaches (cluster analyses of various sorts) were less informative and less consistent with one another than were the results of model testing or comparative approaches. Nevertheless, we found that competing models of modularity and integration are often similar enough that they are not statistically distinguishable; we expect, therefore, that several models will often be significantly correlated with observed data.

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Type
Morphological Data
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The Paleontological Society Papers , Volume 16: Quantitative Methods in Paleobiology , October 2010, pp. 213 - 243
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Copyright © 2010 by the Paleontological Society 

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