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Data from: pavo: an R package for the analysis, visualization and organization of spectral data

Cite this dataset

Maia, Rafael et al. (2013). Data from: pavo: an R package for the analysis, visualization and organization of spectral data [Dataset]. Dryad.


1. 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. 2. Despite this growing interest, a unified cross-platform framework for analyzing 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) analyze data using spectral curve shape properties and visual system modeling for a broad range of taxa. 3. In this paper, we present a summary of the functions implemented in pavo and how they integrate in a workflow to explore and analyze spectral data. We also present an exact solution for the calculation of colour volume overlap in colourspace, thus expanding previously published methodologies. 4. 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. 5. 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.

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