Unifying individual differences in personality, predictability and plasticity: A practical guide. Issue 2 (20th November 2021)

• Record Type: Journal Article
• Authors: O'Dea, Rose E.; Noble, Daniel W. A.; Nakagawa, Shinichi
• Is Part Of: Methods in ecology and evolution. Volume 13:Issue 2(2022)
• Page Start: 278
• Page End: 293
• Publication Date: 2021-11-20
• Subjects: brms -- coefficient of variation -- DHGLM -- Double Hierarchical -- location‐scale regression -- multivariate -- repeatability -- rstan; Ecology -- Periodicals; Evolution -- Periodicals; 577
• DOI: 10.1111/2041-210X.13755
• Languages: English
• ISSNs: 2041-210X

Abstract

Abstract: Organisms use labile traits to respond to different conditions over short time‐scales. When a population experiences the same conditions, we might expect all individuals to adjust their trait expression to the same, optimal, value, thereby minimising phenotypic variation. Instead, variation abounds. Individuals substantially differ not only from each other, but also from their former selves, with the expression of labile traits varying both predictably and unpredictably over time. A powerful tool for studying the evolution of phenotypic variation in labile traits is the mixed model. Here, we review how mixed models are used to quantify individual differences in both means and variability, and their between‐individual correlations. Individuals can differ in their average phenotypes (e.g. behavioural personalities), their variability (known as 'predictability' or intra‐individual variability), and their plastic response to different contexts. We provide detailed descriptions and resources for simultaneously modelling individual differences in averages, plasticity and predictability. Empiricists can use these methods to quantify how traits covary across individuals and test theoretical ideas about phenotypic integration. These methods can be extended to incorporate plastic changes in predictability (termed 'stochastic malleability'). Overall, we showcase the unfulfilled potential of existing statistical tools to test more holistic and nuanced questions about the evolution, function, and maintenance of phenotypic variation, for any trait that is repeatedly expressed.