Data from: Parental behavior exhibits among-individual variance, plasticity and heterogeneous residual variance
Westneat, David F.; Schofield, Matthew; Wright, Jonathan (2013), Data from: Parental behavior exhibits among-individual variance, plasticity and heterogeneous residual variance, Dryad, Dataset, https://doi.org/10.5061/dryad.ch771
Phenotypic variance should have a hierarchical structure because differences arise between species, between individuals within species, and, for labile phenotypes, also within individuals across circumstances. Within-individual variance could exist because of responses to variable environments (plasticity) or because exhibiting variance per se has fitness consequences. To evolve, the latter requires between-individual variance in within-individual variance. Here, we investigate the parental behavior of female red-winged blackbirds (Agelaius phoeniceus) and assess if the distribution of within-individual variance also differs between individuals or changes with respect to environmental conditions. We used a statistical approach that models both the mean and variance iteratively. We found that the amount of food delivered per second on each visit was influenced by female identity, nestling age, and the location (on vs. off territory) where the female foraged. Moreover, we also found that unexplained within-individual variance (residual variance), after controlling for mean effects, independently declined with nestling age and was smaller when females foraged off their mate’s territory. In addition, females differed in residual variance more than expected by chance. These results confirm that phenotypic variance has a hierarchical structure and they support preconditions for the evolution of mean phenotypic values as well as the variance in phenotype. In the case of provisioning as a form of parental care, our data suggest that female red-winged blackbirds could be managing stochastic variance either directly through choice of foraging location or indirectly in how they budget their time, and we discuss these patterns in relation to adaptive variance sensitivity.