A statistical methodology for estimating assortative mating for phenotypic traits that are labile or measured with error. Issue 12 (28th July 2017)

• Record Type: Journal Article
• Authors: Class, Barbara; Dingemanse, Niels J.; Araya‐Ajoy, Yimen G.; Brommer, Jon E.
• Editors: Schielzeth, Holger
• Is Part Of: Methods in ecology and evolution. Volume 8:Issue 12(2017)
• Page Start: 1910
• Page End: 1919
• Publication Date: 2017-07-28
• Subjects: assortative mating -- bivariate model -- common environment -- mixed‐effect modelling -- non‐random assortment -- quantitative genetics -- repeatability; Ecology -- Periodicals; Evolution -- Periodicals; 577
• DOI: 10.1111/2041-210X.12837
• Languages: English
• ISSNs: 2041-210X

Abstract

Abstract: Assortative mating in wild populations is commonly reported as the correlation between males' and females' phenotypes across mated pairs. Theories of partner selection and quantitative genetics assume that phenotypic resemblance of partners captures associations in "intrinsically determined" trait values. However, when considering traits with a repeatability below one (labile traits or traits measured with error), the correlation between phenotypes of paired individuals can arise from shared environmental effects on the phenotypes of paired individuals or correlated measurement error. We introduce statistical approaches to estimate assortative mating in labile traits or traits measured with error in the presence of shared environmental effects. These approaches include (1) the correlation between the mean phenotypes of males and females, (2) the correlation between randomized values of individuals and (3) the between‐pair correlation derived from a bivariate mixed model. We use simulations to show that the performance of these different approaches depends on the number of repeated measures within individuals or pairs, which is determined by study design, and rates of survival and divorce. We conclude that short‐term environmental effects on phenotypes of paired individuals likely inflate estimates of assortative mating when not statistically accounted for. Our approach allows investigation of this important issue in assortative mating studies for labile traits (e.g. behaviour, physiology, or metabolism) in both socially monogamous and other mating systems, and groupings of individuals outside a mating context.