Summary: Recent technical and methodological advances have led to a dramatic increase in the use of spectrometry to quantify reflectance properties of biological materials, as well as models to determine how these colours are perceived by animals, providing important insights into ecological and evolutionary aspects of animal visual communication. Despite this growing interest, a unified cross‐platform framework for analysing and visualizing spectral data has not been available. We introduce pavo, an R package that facilitates the organization, visualization and analysis of spectral data in a cohesive framework. pavo is highly flexible, allowing users to (a) organize and manipulate data from a variety of sources, (b) visualize data using R's state‐of‐the‐art graphics capabilities and (c) analyse data using spectral curve shape properties and visual system modelling for a broad range of taxa. In this paper, we present a summary of the functions implemented in pavo and how they integrate in a workflow to explore and analyse spectral data. We also present an exact solution for the calculation of colour volume overlap in colourspace, thus expanding previously published methodologies. As an example of pavo's capabilities, we compare the colour patterns of three African glossy starling species, two of which have diverged very recently. We demonstrate how both colour vision models and direct spectral measurement analysis can be used to describe colour attributes and differences between these species. Different approaches to visual models and several plotting capabilities exemplify the package's versatility and streamlined workflow. pavo provides a cohesive environment for handling spectral data and addressing complex sensory ecology questions, while integrating with R's modular core for a broader and comprehensive analytical framework, automated management of spectral data and reproducible workflows for colour analysis.

Developmental constraints and trade-offs can limit diversity, but organisms have repeatedly evolved morphological innovations that overcome these limits by expanding the range and functionality of traits. Iridescent colours in birds are commonly produced by melanin-containing organelles (melanosomes) organized into nanostructured arrays within feather barbules. Variation in array type (e.g. multilayers and photonic crystals, PCs) is known to have remarkable effects on plumage colour, but the optical consequences of variation in melanosome shape remain poorly understood. Here, we used a combination of spectrophotometric, experimental and theoretical methods to test how melanosome hollowness—a morphological innovation largely restricted to birds—affects feather colour. Optical analyses of hexagonal close-packed arrays of hollow melanosomes in two species, wild turkeys (Meleagris gallopavo) and violet-backed starlings (Cinnyricinclus leucogaster), indicated that they function as two-dimensional PCs. Incorporation of a larger dataset and optical modelling showed that, compared with solid melanosomes, hollow melanosomes allow birds to produce distinct colours with the same energetically favourable, close-packed configurations. These data suggest that a morphological novelty has, at least in part, allowed birds to achieve their vast morphological and colour diversity.

Studies of trilled vocalizations provide a premiere illustration of how performance constraints shape the evolution of mating displays. In trill production, vocal tract mechanics impose a trade-off between syllable repetition rate and frequency bandwidth, with the trade-off most pronounced at higher values of both parameters. Available evidence suggests that trills that simultaneously maximize both traits are more threatening to males or more attractive to females, consistent with a history of sexual selection favoring high-performance trills. Here, we identify a sampling limitation that confounds the detection and description of performance trade-offs. We reassess 70 data sets (from 26 published studies) and show that sampling limitations afflict 63 of these to some degree. Traditional upper-bound regression, which does not control for sampling limitations, detects performance trade-offs in 33 data sets; yet when sampling limitations are controlled, performance trade-offs are detected in only 15. Sampling limitations therefore confound more than half of all performance trade-offs reported using the traditional method. An alternative method that circumvents this sampling limitation, which we explore here, is quantile regression. Our goal is not to question the presence of mechanical trade-offs on trill production but rather to reconsider how these trade-offs can be detected and characterized from acoustic data.