ALBERT

Description: Individuals of the same population differ consistently from each other in the average expression of behavioral and physiological traits. Often, such traits are integrated and thus correlated with each other. However, the underlying proximate mechanisms generating and maintaining this among-individual covariation are still poorly understood. The melanocortin hypothesis suggests that the melanocortin pathways can have pleiotropic effects linking the expression of melanin-based coloration with physiological and behavioral traits. In the present study, we test this hypothesis in adult male guppies (Poecilia reticulata), by estimating among individual correlations between behaviors (activity, feeding, boldness, display, and chase during courtship), stress response (peak metabolic rate), and coloration (black spot, fuzzy black, and orange). The lack of correlation of any behavior or metabolism with black coloration indicates that the melanocortin hypothesis is not supported in this species. However, we observed covariation among coloration traits, as well as among behavioral traits. Our findings suggest that, although there appear to be constraints within sets of related traits, coloration, physiology, and behaviors can potentially evolve as independent modules in response to selection in this species.

Description: Quantifying individual variation in labile physiological or behavioral traits often involves repeated measures through time, so as to test for consistency of individual differences (often using repeatability, “R”) and/or individual differences in trendlines over time. Another form of temporal change in behavior is temporal autocorrelation, which predicts observations taken closely together in time to be correlated, leading to nonrandom residuals about individual temporal trendlines. Temporal autocorrelation may result from slowly changing internal states (e.g., hormone or energy levels), leading to slowly changing behavior. Autocorrelation is a well-known phenomenon, but has been largely neglected by those studying individual variation in behavior. Here, we provide two worked examples which show substantial temporal autocorrelation (r 〉 0.4) is present in spontaneous activity rates of guppies (Poecilia reticulata) and house mice (Mus domesticus) in stable laboratory conditions, even after accounting for temporal plasticity of individuals. Second, we show that ignoring autocorrelation does bias estimates of R and temporal reaction norm variances upwards, both in our worked examples and in separate simulations. This bias occurs due to the misestimation of individual-specific means and slopes. Given the increasing use of technologies that generate behavioral and physiological data at high sampling rates, we can now study among- and within-individual changes in behavior in more detailed ways, including autocorrelation, which we discuss from biological and methodological perspectives and provide recommendations and annotated R code to help researchers implement these models on their data.