Disentangling composite colour patterns in a poison frog species

Abstract

A phylogenetic approach was performed to infer whether variation in conspicuous colour-patterns of a poison frog (Dendrobatidae: Dendrobates tinctorius) has evolved neutrally or under selection. Colour and pattern were split into components that were separately analysed and subsequently re-grouped via principal component analysis. This revealed four different 'displayed' factors on the dorsal and lateral views versus one 'concealed' factor on the ventral view. Based on the assumption that current patterns of trait variation contain information about the evolutionary history of the phenotype, we correlated these trait components to a neutrally evolving gene fragment (cytochrome b). The concealed factor was significantly correlated with the marker fragment, which identified it as having evolved under genetic drift. Noncorrelation of all displayed factors with the marker may indicate the evolution of colour patterns on dorsum and flanks under selection. In our example, colour pattern should therefore be regarded as a multicomponent signal system.

Monitoring Strategy for Eight Amphibian Species in French Guiana, South America

“…Within each species, individuals were recognized with their natural unique color patterns [23]–[24].…”

Stream Noise, Hybridization, and Uncoupled Evolution of Call Traits in Two Lineages of Poison Frogs: Oophaga histrionica and Oophaga lehmanni

“…color and pattern) communication signals [32], [35], [36] that are heritable ([37], [38], Amézquita, unpublished data) and often exhibit intraspecific geographic variation [32], [39]. Among the mechanisms allegedly promoting geographic variation in their complex signals are divergent female choice preferences, adaptation to local predators, mimetic processes, and genetic drift [32], [39]–[41]. We expect here to add to this knowledge by testing the simultaneous effect of natural selection represented by stream noise and hybridization in the evolution of an auditory signal.…”

Analyzing Visual Signals as Visual Scenes

“…A full integration of multicomponent signals is no simple task because the space is high dimensional (n color dimensions  luminance  n space dimensions  time) [Rosenthal, 2007] and so requires a vector-based approach. One strategy is to collect large numbers of variables, use PCA or another dimension reduction technique to find a suitable number of axes of variation and take a distance metric in this space such as Euclidian distance as a signal difference measure [Tanaka and Mori, 2007;Wollenberg et al, 2008]. However this requires measuring as many traits as can be conceived to minimize the risk that the researcher's a priori expectations about signal structure drive the derived representation.…”