The use of programmable robots (‘mechanical models’ is more accurate) to minimise disturbance while observing wildlife, or to run behavioural experiments in the field, has slowly increased in the last decade and studies across many taxa have utilized this approach (Martins et al., 2005; Partan et al., 2009; Cianca et al., 2013; Macedonia et al., 2013; Clark et al., 2015). I’d argue that “robots” are one for the most important tools for behavioural ecologists studying communication or display behaviour, as they are one of the few ways in which we can conduct field-based experiments – mimicking or manipulating animal behaviour, colour or morphology in any way – in the animal’s natural environment.
We recently published a paper in the Journal of Evolutionary Biology, using robots in playback experiments to test the importance of ornament design for signal detection and conspecific recognition.
Many factors potentially affect signal design, including the need for rapid signal detection and the ability to identify the signal as conspecific. As understanding these different sources of selection on signal design is essential in the larger goal of explaining the evolution of both signal complexity and signal diversity, here we assessed the relative importance of detection and recognition for signal design in the Black-bearded gliding lizard, Draco melanopogon (fig. 1). Lizards of the species-rich genus Draco use large extendible dewlaps for communication, that differ in colour pattern and size between species – in a similar fashion to the anoles.
Figure 1 A. Male D. melanopogan, dewlap naturally extended (image a still from behavioural trials) and the angle of dewlap extension as measured from still; B. robot, dewlap treatments (Bi) solid colour and Bii) two-coloured); and C. artificially extended dewlaps of a male and female D. melanopogan.
To test whether the dewlap colour and pattern function more to facilitate 1. signal detection and 2. conspecific recognition, we presented free-living lizards with robots displaying dewlaps of six different designs, varying in the proportion of the black and white components.
In this case, our robots were just ‘visual flags’ that mimicked the dewlap size and shape, as well as the speed and display pattern of live Draco melanopogan lizards (video 1). Having only the dewlap / visual flag and not the rest of the lizard body allowed us to look solely at the salience of the dewlap colour and pattern itself – without adding any identifying or qualifying information in the form of a body.
Video 1: ‘The floating dewlap’
Our experiment had six colour treatments ranging from “natural” (population typical design, fig. 1) to unnatural (wrong colour, no pattern) – and from very conspicuous (high internal contrast and high contrast against the background for each colour) to very inconspicuous (matching the luminance of the background). Thus, we could test both the ‘detection’ and ‘conspecific recognition’ hypotheses with the same set of treatments.
Predictions for Hypothesis 1: We predicted that should the dewlap colour pattern function in signal detection, that more conspicuous dewlap treatments would be detected sooner than less conspicuous dewlaps. Each of the two-coloured treatments were more conspicuous than the single-coloured treatments, as they had the same high contrast black and white elements, but they also had the high internal contrast of the black against the white (75.02 JND). Provided the receiver has sufficient visual acuity at the viewing distance to be able to distinguish the two colours from one another, internal contrast increases signal conspicuousness, and the more equal the two adjacent colour patches are in size (i.e. 50% of the dewlap black – 50% of the dewlap white) the greater the internal contrast. There is no existing data on the visual acuity of Draco lizards, so for this experiment we stuck to the natural dewlap size and viewing distances, with small oscillations around the natural proportions of black and white. Continue reading Dewlap Design Facilitates Recognition But Not Detection: a Field Test Using Robots