Abstract
Many flies and moths mimic the frontal appearance of jumping spiders. This type of mimicry, which we term as partial mimicry, can be distinguished from Batesian mimicry since the mimic has spider resembling patterns only in certain parts of the body, and not the entire body. The presence of spider-like patterns is obvious only at certain angles suggesting that the mimic is frequently targeted by its predators from particular angles. We tested this hypothesis using Deep Convolutional Neural Networks (DCNNs). First, we trained the network on images of forward facing jumping spiders, where features such as the large principal eyes, small lateral eyes and outstretched legs were evident. Then we tested the classifier on images of jumping spider mimicking flies and moths. A probability value according to the likelihood of the image being a jumping spider or not was assigned by the classifier. We show that the classifier was more likely to misidentify mimicking flies and moths as jumping spiders, but that this probability varied according to the species tested. We further tested it on images of flies from different angles and by taking into consideration the visual acuity of potential predators. Our results suggest that neural networks can be efficient tools for testing evolutionary hypotheses, and that partial mimicry may be a result of the effect of the signaling angle and orientation of the mimics in combination with the likelihood that predators may depend on cognitive shortcuts to identify insects as prey. Further experiments incorporating the properties of the visual system of predators (such as vision in ultraviolet) would result in a better understanding of the evolution of partial mimicry.
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