bioRxivAnimal Behavior & Cognition
Boundary Strength Analysis: Combining colour pattern geometry and coloured patch visual properties for use in predicting behaviour and fitness
Abstract
Colour patterns are used by many species to make decisions that ultimately affect their Darwinian fitness. Colour patterns consist of a mosaic of patches that differ in geometry and visual properties. Although traditionally pattern geometry and colour patch visual properties are analysed separately, these components are likely to work together as a functional unit. Despite this, the combined effect of patch visual properties, patch geometry, and the effects of the patch boundaries on animal visual systems, behaviour and fitness are relatively unexplored. Here we describe Boundary Strength Analysis (BSA), a novel way to combine the geometry of the edges (boundaries among the patch classes) with the receptor noise estimate ({Delta}S) of the intensity of the edges. The method is based upon known properties of vertebrate and invertebrate retinas. The mean and SD of{Delta} S (m{Delta}S, s{Delta}S) of a colour pattern can be obtained by weighting each edge class{Delta} S by its length, separately for chromatic and achromatic{Delta} S. This assumes those colour patterns, or parts of the patterns used in signalling, with larger m{Delta}S and s{Delta}S are more stimulating and hence more salient to the viewers. BSA can be used to examine both colour patterns and visual backgrounds. BSA was successful in assessing the estimated conspicuousness of colour pattern variants in two species, guppies (Poecilia reticulata) and Gouldian finches (Erythrura gouldiae), both polymorphic for patch colour, luminance and geometry. The pattern difference between chromatic and achromatic edges in both species reveals the possibility that chromatic and achromatic edges could function differently. BSA can be applied to any colour pattern used in intraspecific and interspecific behaviour. Seven predictions and four questions about colour patterns are presented.
- assay: imaging
- research focus: cell type annotation
- research focus: clustering
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: deep learning
- approach: statistical model
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