Abstract
Avoiding detection can provide significant survival advantages for prey, predators, or the military; conversely, maximizing visibility would be useful for signalling. One simple determinant of detectability is an animal's colour relative to its environment. But identifying the optimal colour to minimize (or maximize) detectability in a given natural environment is complex, partly because of the nature of the perceptual space. Here for the first time, using image processing techniques to embed targets into realistic environments together with psychophysics to estimate detectability and deep neural networks to interpolate between sampled colours, we propose a method to identify the optimal colour that either minimizes or maximizes visibility. We apply our approach in two natural environments (temperate forest and semi-arid desert) and show how a comparatively small number of samples can be used to predict robustly the most and least effective colours for camouflage. To illustrate how our approach can be generalized to other non-human visual systems, we also identify the optimum colours for concealment and visibility when viewed by simulated red–green colour-blind dichromats, typical for non-human mammals. Contrasting the results from these visual systems sheds light on why some predators seem, at least to humans, to have colouring that would appear detrimental to ambush hunting. We found that for simulated dichromatic observers, colour strongly affected detection time for both environments. In contrast, trichromatic observers were more effective at breaking camouflage.
Highlights
Interest in camouflage among evolutionary biologists has grown considerably [1], and many of the basic principles of how to conceal oneself have become far clearer
To address the challenges presented by a large multidimensional parameter space, without needing to impose artificial constraints, we propose a method for identifying optimal colours, patterns or colour/pattern combinations in any given context that uses deep neural networks
In order to be able to interpolate and predict reaction times for target colours that had not been sampled during the experiment, and to take account of intersubject variability in responses, residual deep neural network models were built using the high-level neural network API Keras 2.1.2 [18] running on top of neural network library TensorFlow 1.5.0 [19], separately for each combination of geographical location and chromatic condition
Summary
Interest in camouflage among evolutionary biologists has grown considerably [1], and many of the basic principles of how to conceal oneself have become far clearer. Studies measuring the effectiveness of camouflage tend follow the same basic format: a small number of colours, patterns or colour/pattern combinations are generated that capture the proposed camouflage principles; and the utility of the camouflage is evaluated, perhaps in the field by measuring predation rates, or in the laboratory measuring detection speed and accuracy, identification ability, or capture rate, using either human or non-human subjects. If the question is ‘what is the optimal camouflage strategy to employ in a given context?’, the approach is ineffective: the range of possible patterns is too large
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