title: Resampling-based approaches to study variation in morphological modularity.
abstract: Modularity has been suggested to be connected to evolvability because a higher degree of independence among parts allows them to evolve as separate units. Recently, the Escoufier RV coefficient has been proposed as a measure of the degree of integration between modules in multivariate morphometric datasets. However, it has been shown, using randomly simulated datasets, that the value of the RV coefficient depends on sample size. Also, so far there is no statistical test for the difference in the RV coefficient between a priori defined groups of observations. Here, we (1), using a rarefaction analysis, show that the value of the RV coefficient depends on sample size also in real geometric morphometric datasets; (2) propose a permutation procedure to test for the difference in the RV coefficient between a priori defined groups of observations; (3) show, through simulations, that such a permutation procedure has an appropriate Type I error; (4) suggest that a rarefaction procedure could be used to obtain sample-size-corrected values of the RV coefficient; and (5) propose a nearest-neighbor procedure that could be used when studying the variation of modularity in geographic space. The approaches outlined here, readily extendable to non-morphometric datasets, allow study of the variation in the degree of integration between a priori defined modules. A Java application--that will allow performance of the proposed test using a software with graphical user interface--has also been developed and is available at the Morphometrics at Stony Brook Web page (http://life.bio.sunysb.edu/morph/).
authors.affiliation: Department of Biology, University of Konstanz, Konstanz, Germany. Carmelo.Fruciano@uni-konstanz.de
authors.forename: Carmelo
authors.initials: C
authors.lastname: Fruciano
authors.forename: Paolo
authors.initials: P
authors.lastname: Franchini
authors.forename: Axel
authors.initials: A
authors.lastname: Meyer
mesh_terms: D019295:Computational Biology / Q000379:methods*; D012984:Software