Open AccessSelection on plasticity of seasonal life‐history traits using random regression mixed model analysis
Author(s) -
Brommer Jon E.,
Kontiainen Pekka,
Pietiäinen Hannu
Publication year - 2012
Publication title -
ecology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.17
H-Index - 63
ISSN - 2045-7758
DOI - 10.1002/ece3.60
Subject(s) - bivariate analysis , regression , norm (philosophy) , statistics , covariance , selection (genetic algorithm) , mathematics , linear regression , econometrics , multivariate statistics , computer science , artificial intelligence , political science , law
Theory considers the covariation of seasonal life‐history traits as an optimal reaction norm, implying that deviating from this reaction norm reduces fitness. However, the estimation of reaction‐norm properties (i.e., elevation, linear slope, and higher order slope terms) and the selection on these is statistically challenging. We here advocate the use of random regression mixed models to estimate reaction‐norm properties and the use of bivariate random regression to estimate selection on these properties within a single model. We illustrate the approach by random regression mixed models on 1115 observations of clutch sizes and laying dates of 361 female Ural owl Strix uralensis collected over 31 years to show that (1) there is variation across individuals in the slope of their clutch size–laying date relationship, and that (2) there is selection on the slope of the reaction norm between these two traits. Hence, natural selection potentially drives the negative covariance in clutch size and laying date in this species. The random‐regression approach is hampered by inability to estimate nonlinear selection, but avoids a number of disadvantages (stats‐on‐stats, connecting reaction‐norm properties to fitness). The approach is of value in describing and studying selection on behavioral reaction norms (behavioral syndromes) or life‐history reaction norms. The approach can also be extended to consider the genetic underpinning of reaction‐norm properties.