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An effect size for comparing the strength of morphological integration across studies

Understanding how and why phenotypic traits covary is a major interest in evolutionary biology. Biologists have long sought to characterize the extent of morphological integration in organisms, but comparing levels of integration for a set of traits across taxa has been hampered by the lack of a rel...

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Detalles Bibliográficos
Autores principales: Conaway, Mark A., Adams, Dean C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9804739/
https://www.ncbi.nlm.nih.gov/pubmed/35971251
http://dx.doi.org/10.1111/evo.14595
Descripción
Sumario:Understanding how and why phenotypic traits covary is a major interest in evolutionary biology. Biologists have long sought to characterize the extent of morphological integration in organisms, but comparing levels of integration for a set of traits across taxa has been hampered by the lack of a reliable summary measure and testing procedure. Here, we propose a standardized effect size for this purpose, calculated from the relative eigenvalue variance, [Formula: see text]. First, we evaluate several eigenvalue dispersion indices under various conditions, and show that only [Formula: see text] remains stable across samples size and the number of variables. We then demonstrate that [Formula: see text] accurately characterizes input patterns of covariation, so long as redundant dimensions are excluded from the calculations. However, we also show that the variance of the sampling distribution of [Formula: see text] depends on input levels of trait covariation, making [Formula: see text] unsuitable for direct comparisons. As a solution, we propose transforming [Formula: see text] to a standardized effect size (Z‐score) for representing the magnitude of integration for a set of traits. We also propose a two‐sample test for comparing the strength of integration between taxa, and show that this test displays appropriate statistical properties. We provide software for implementing the procedure, and an empirical example illustrates its use.