Organismal appearances are shaped by selection from both biotic and abiotic drivers. For example, Gloger’s rule describes the pervasive pattern that more pigmented populations are found in more humid areas. However, species may also converge on nearly identical colours and patterns in sympatry, often to avoid predation by mimicking noxious species. Here we leverage a massive global citizen-science database to determine how biotic and abiotic factors act in concert to shape plumage in the world’s 230 species of woodpeckers. We find that habitat and climate profoundly influence woodpecker plumage, and we recover support for the generality of Gloger’s rule. However, many species exhibit remarkable convergence explained neither by these factors nor by shared ancestry. Instead, this convergence is associated with geographic overlap between species, suggesting occasional strong selection for interspecific mimicry.

Although communicative complexity is often predicted to correlate with social complexity in animal societies, few studies have employed large-scale comparative analyses to test whether socially complex species have more complex systems of communication. I tested this social complexity hypothesis in birds (Class: Aves) using the large amount of natural history information that describes both vocal repertoire and social system in these species. To do so, I marshalled data from primary and secondary records of avian vocal repertoires ( = 253), and for each of the species in the dataset I recorded the reported repertoire size and associated species information. Using phylogenetic comparative methods, I found that cooperative breeding was a strong and repeatable predictor of vocal repertoire size, while other social variables, e.g. group size and group stability, had little or no influence on repertoire size. Importantly, repertoire sizes expanded concurrently with the evolution of cooperative breeding, suggesting a direct link between these two traits. Cooperatively breeding species devoted significantly more of their repertoire to contact calls and alarm calls. Overall, these results therefore lend support to the hypothesis that social complexity via behavioural coordination leads to increases in vocal complexity.

Animals produce a wide array of sounds with highly variable acoustic structures. It is possible to understand the causes and consequences of this variation across taxa with phylogenetic comparative analyses. Acoustic and evolutionary analyses are rapidly increasing in sophistication such that choosing appropriate acoustic and evolutionary approaches is increasingly difficult. However, the correct choice of analysis can have profound effects on output and evolutionary inferences. Here, we identify and address some of the challenges for this growing field by providing a roadmap for quantifying and comparing sound in a phylogenetic context for researchers with a broad range of scientific backgrounds. Sound, as a continuous, multidimensional trait can be particularly challenging to measure because it can be hard to identify variables that can be compared across taxa and it is also no small feat to process and analyse the resulting high‐dimensional acoustic data using approaches that are appropriate for subsequent evolutionary analysis. Additionally, terminological inconsistencies and the role of learning in the development of acoustic traits need to be considered. Phylogenetic comparative analyses also have their own sets of caveats to consider. We provide a set of recommendations for delimiting acoustic signals into discrete, comparable acoustic units. We also present a three‐stage workflow for extracting relevant acoustic data, including options for multivariate analyses and dimensionality reduction that is compatible with phylogenetic comparative analysis. We then summarize available phylogenetic comparative approaches and how they have been used in comparative bioacoustics, and address the limitations of comparative analyses with behavioural data. Lastly, we recommend how to apply these methods to acoustic data across a range of study systems. In this way, we provide an integrated framework to aid in quantitative analysis of cross‐taxa variation in animal sounds for comparative phylogenetic analysis. In addition, we advocate the standardization of acoustic terminology across disciplines and taxa, adoption of automated methods for acoustic feature extraction, and establishment of strong data archival practices for acoustic recordings and data analyses. Combining such practices with our proposed workflow will greatly advance the reproducibility, biological interpretation, and longevity of comparative bioacoustic studies.