Evolution of a multifunctional trait: shared effects of foraging ecology and thermoregulation on beak morphology, with consequences for song evolution

Abstract

While morphological traits are often associated with multiple functions, it remains unclear how evolution balances the selective effects of different functions. Birds' beaks function not only in foraging but also in thermoregulating and singing, among other behaviours. Studies of beak evolution abound, however, most focus on a single function. Hence, we quantified relative contributions of different functions over an evolutionary timescale. We measured beak shape using geometric morphometrics and compared this trait with foraging behaviour, climatic variables and song characteristics in a phylogenetic comparative study of an Australasian radiation of songbirds (Meliphagidae). We found that both climate and foraging behaviour were significantly correlated with the beak shape and size. However, foraging ecology had a greater effect on shape, and climate had a nearly equal effect on size. We also found that evolutionary changes in beak morphology had significant consequences for vocal performance: species with elongate-shaped beaks sang at higher frequencies, while species with large beaks sang at a slower pace. The evolution of the avian beak exemplifies how morphological traits can be an evolutionary compromise among functions, and suggests that specialization along any functional axis may increase ecological divergence or reproductive isolation along others.

Keywords: Meliphagidae; beak shape; bird song; foraging; thermoregulation; trade-off.

Figure 1.

(a) Yellow-gaped honeyeater specimen illustrating positions of landmarks (red) and semi-landmarks (magenta). Semi-landmarks were spaced at equal intervals between landmarks using TPSdig. (b) Relative warp grids showing the extreme values of the first two principal component axes (PC1 and PC2). PC1 is referred to as ‘depth’, and PC2 is referred to as ‘curvature’ throughout. (c) Phylomorphospace of honeyeater beaks, using the first two PC axes, which together account for 95% of shape variation. Genera with divergent phenotypes are noted.

Figure 2.

Phylogenetically corrected path analysis models. On the top versus bottom are models describing hypotheses in which axes of beak variation are split into different functions (a,b) or shared among functions (c,d). Predictors described in (e) are listed as abbreviations in models shown above. Here, we describe beak shape PC1 as ‘depth’ and PC2 as ‘curvature’ to reflect the positive direction of each axis. On the left and right are models describing hypotheses where functions exclude interfering indirect effects (a,c) or include them (b,d). Model fit is assessed by the C-statistic information criterion (CICc) following [62]. The best fitting model is shown in (e), with red arrows indicating negative associations and blue arrows indicating positive ones; values shown below each arrow refer to correlation coefficients; values estimated for body size residuals of beak size are shown above arrows.

Figure 3.

Comparison of beak size and shape variances explained by different predictor variables. (a) Values are derived from PGLS analysis of beak size. (b) Values are derived from a Procrustes PGLS analysis of beak shape that is not restricted to a single PC axis [63]. *p < 0.05; ***p < 0.001.

Figure 4.

(a) Forest plot of effect sizes (standardized β) and their 95% confidence intervals for the effect of beak morphology on three different song metrics, as derived from multivariate PGLS analysis. Asterisks below bars represent significance values assessed through bivariate regressions including correction for intraspecific variation in song characteristics. (b) Diagram of general conclusions. **p < 0.01; ***p < 0.001.