Statistical analyses
Our first set of analyses using univariate mixed-effects models examined individual differences in trait expression and how trait expression changed with age or time within a day. Our second set of analyses using bivariate mixed-effect models estimated within-sex among- and within-individual correlations between traits.
Univariate models. The univariate models fitted individual identity as a random effect and (within-individual mean-centred) observation day and (mean-centred) time of day as covariates. We used the following error structure for the response variable: body mass was modelled with Gaussian errors, plant use (found in narrow-leafed plants (1) or not (0)) and calling activity (exhibiting calling (1) or not (0)) were modelled with binomial errors. Despite including a quadratic term of observation day (i.e., age) as an additional covariate in the univariate models, we did not observe quadratic ageing patterns in any of the traits (results not included). Consequently, the univariate models did not include a quadratic term of observation day. In addition to the random intercept model, we used a random slope model to estimate among-individual variation in age-related plasticity. We found no variation in the age-related slopes (results not included).
All univariate mixed-effects models were implemented in ASReml (version 4.1, VSN Interaction, Hemel Hempstead, UK) and solved using the restricted maximum likelihood method. The significance of fixed effects was assessed through conditional Wald F tests. Statistical significance for variance was determined using a likelihood-ratio test (LRT). To examine deviations from zero variances, LRTs were conducted, involving the difference in deviance (-2 × log likelihood) between the full model and a model where the variance was removed. The P value was computed under the assumption of an equal combination of P (χ2, df = 0) and P(χ2, df = 1) (hereafter, χ20/1).
Bivariate models. We constructed sets of bivariate models, where we fitted two traits from plant use, calling activity, and body mass as the response variables. These models included individual identity as a random effect and had no fixed effects. In the bivariate models, we constrained the among-individual covariance between plant use and other traits to zero because the among-individual variance in plant use was not significantly positive (see Results). The within-individual variances in plant use and calling activity were fixed to one because they were not estimable with binary data. Thus we were not able to calculate a within-individual covariance between calling activity and plant use. We fitted the bivariate models within a Bayesian framework using the MCMCglmm package (Hadfield 2010) in R (version 3.2.0). To minimise autocorrelation among samples, 1,300,000 Markov Chain Monte Carlo (MCMC) iterations were performed, which were sampled at 1,000-iteration intervals after an initial burn-in period of 300,000 iterations, using Gamma priors. This resulted in a total of 1000 samples from the posterior distribution. Convergence was attained by visual inspection of output plots and by ensuring that autocorrelation between consecutive samples did not exceed 0.1 (Hadfield 2010).