Maia, R. | Search bioRxiv

bioRxiv2018

pavo 2.0: new tools for the spectral and spatial analysis of colour in R

O_LIBiological colouration presents a canvas for the study of ecological and evolutionary processes. Enduring interest in colour-based phenotypes has driven, and been driven by, improved techniques for quantifying colour patterns in ever-more relevant ways, yet the need for flexible, open frameworks for data processing and analysis persists.\nC_LIO_LIHere we introduce pavo 2, the latest iteration of the R package pavo. This release represents the extensive refinement and expansion of existing methods, as well as a suite of new tools for the cohesive analysis of the spectral and (now) spatial structure of colour patterns and perception. At its core, the package retains a broad focus on (a) the organisation and processing of spectral and spatial data, and tools for the alternating (b) visualisation, and (c) analysis of data. Significantly, pavo 2 introduces image-analysis capabilities, providing a cohesive workflow for the comprehensive analysis of colour patterns.\nC_LIO_LIWe demonstrate the utility of pavo with a brief example centred on mimicry in Heliconius butterflies. Drawing on visual modelling, adjacency, and boundary strength analyses, we show that the combined spectral (colour and luminance) and spatial (pattern element distribution and boundary salience) features of putative models and mimics are closely aligned.\nC_LIO_LIpavo 2 offers a flexible and reproducible environment for the analysis of colour, with renewed potential to assist researchers in answering fundamental questions in sensory ecology and evolution.\nC_LI

animal behavior and cognition↗

bioRxiv2017

Comparing colours using visual models

Lay SummaryAn outstanding challenge for the study of colour traits is how best to use \"colour spaces\" to represent their visual perception, particularly when asking questions of colour-difference (e.g. the (dis)similarity of males and females, mimics and models, or sister species, to a given viewer). We use simulations to show that existing methods fail to statistically and biologically estimate the separation of groups in colour space, and we suggest a flexible, robust, alternative that avoids those pitfalls.\n\nAbstractColour in nature presents a striking dimension of variation, though understanding its function and evolution largely depends on our ability to capture the perspective of relevant viewers. This goal has been radically advanced by the development and widespread adoption of colour spaces, which allow for the viewer-subjective estimation of colour appearance. Most studies of colour in camouflage, aposematism, sexual selection, and other signalling contexts draw on these models, with the shared analytical objective of estimating how similar (or dissimilar) colour samples are to a given viewer. We summarise popular approaches for estimating the separation of samples in colour space, and use a simulation-based approach to test their efficacy with common data structures. We show that these methods largely fail to estimate the separation of colour samples by neglecting (i) the statistical distribution and within-group variation of the data, and/or (ii) the discriminability of groups relative to the observers visual capabilities. Instead, we formalize the two questions that must be answered to establish both the statistical presence and theoretical magnitude of colour differences, and propose a two-step, permutation-based approach that achieves this goal. Unlike previous methods, our suggested approach accounts for the multidimensional nature of visual model data, and is robust against common colour-data features such as heterogeneity and outliers. We demonstrate the pitfalls of current methods and the flexibility of our suggested framework using an example from the literature, with recommendations for future inquiry.

animal behavior and cognition↗