“Unifying Individual Differences in Personality, Predictability and Plasticity: A Practical Guide”, 2021-11-01 (; similar):
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 minimizing 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 (eg. behavioral 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 modeling 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.
[Keywords:
brms, coefficient of variation, DHGLM, Double Hierarchical, location-scale regression, multivariate, repeatability,rstan]…Conclusions And Future Directions: Incorporating predictability into studies of personality and plasticity creates an opportunity to test more nuanced questions about how phenotypic variation is maintained, or constrained. For some traits, it might be adaptive to be unpredictable, such as in predator-prey interactions (2013). For other traits, selection might act to minimise maladaptive imprecision around an optimal mean (Hansen et al 200618ya). The supplementary worked example and open code ( et al 2021) shows between-individual correlations in predictability across multiple behavioral traits, and some correlations of predictability with personality and plasticity. If driven by biological integration and not measurement errors or statistical artefacts, these correlations could hint at genetic integration too; other studies have found additive genetic variance in predictability ( et al 2017; et al 2020). Given that different traits might have different optimal levels of unpredictability, integration of predictability could constrain variation in one trait (resulting in lower than optimal variability) and maintain variation in another (resulting in greater than optimal variability). Because of associations with personality and plasticity, variation in predictability—the lowest level of the phenotypic hierarchy—could have cascading effects upwards ( et al 2015). Empirical estimates of the strength of these associations can inform theoretical models on the simultaneous evolution of means and variances.