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EMMLi: A maximum likelihood approach to the analysis of modularity

Goswami, A; Finarelli, JA; (2016) EMMLi: A maximum likelihood approach to the analysis of modularity. Evolution , 70 (7) pp. 1622-1637. 10.1111/evo.12956. Green open access

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Abstract

Identification of phenotypic modules, semi-autonomous sets of highly-correlated traits, can be accomplished through exploratory (e.g., cluster analysis) or confirmatory approaches (e.g., RV coefficient analysis). While statistically more robust, confirmatory approaches are generally unable to compare across different model structures. For example, RV coefficient analysis finds support for both two- and six-module models for the therian mammalian skull. Here, we present a maximum likelihood approach that takes into account model parameterization. We compare model log-likelihoods of trait correlation matrices using the finite-sample corrected Akaike Information Criterion, allowing for comparison of hypotheses across different model structures. Simulations varying model complexity and within- and between-module contrast demonstrate that this method correctly identifies model structure and parameters across a wide range of conditions. We further analyzed a dataset of 3-D data, consisting of 61 landmarks from 181 macaque (Macaca fuscata) skulls, distributed among five age categories, testing 31 models, including no modularity among the landmarks, and various partitions of 2, 3, 6, and 8 modules. Our results clearly support a complex six-module model, with separate within- and inter-module correlations. Furthermore, this model was selected for all five age categories, demonstrating that this complex pattern of integration in the macaque skull appears early and is highly conserved throughout postnatal ontogeny. Subsampling analyses demonstrate that this method is robust to relatively low sample sizes, as is commonly encountered in rare or extinct taxa. This new approach allows for the direct comparison of models with different parameterizations, providing an important tool for the analysis of modularity across diverse systems. This article is protected by copyright. All rights reserved.

Type: Article
Title: EMMLi: A maximum likelihood approach to the analysis of modularity
Open access status: An open access version is available from UCL Discovery
DOI: 10.1111/evo.12956
Publisher version: http://dx.doi.org/10.1111/evo.12956
Language: English
Additional information: Copyright © 2016 The Author(s). All rights reserved. This is the peer reviewed version of the following Goswami, A; Finarelli, JA; (2016) EMMLi: A maximum likelihood approach to the analysis of modularity. Evolution , 70 (7) pp. 1622-1637 which has been published in final form at 10.1111/evo.12956 This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving, http://olabout.wiley.com/WileyCDA/Section/id-820227.html#terms
Keywords: Mammals, model selection, phenotypic integration, trait correlations
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences > Genetics, Evolution and Environment
URI: https://discovery.ucl.ac.uk/id/eprint/1494674
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