Quantifying plant mimesis in fossil insects using deep learning

ABSTRACT

As an important combination of behaviour and pattern in animals to resemble benign objects, biolog ical mimesis can effectively avoid the detection of their prey and predators. It at least dates back to the Permian in fossil records. The recognition of mimesis within fossil, however, might be subjective and lack quantitative analysis being only based on few fossils with limited information. To compensate for this omission, we propose a new method using a Siamese network to measure the dissimilarity between hypothetical mimics and their models from images. It generates dissimilarity values between paired images of organisms by extracting feature vectors and calculating Euclidean distances. Additionally, the idea of ‘transfer learning’ is adopted to fine-tune the Siamese network, to overcome the limitations of available fossil image pairs. We use the processed Totally-Looks-Like, a large similar image data set, to pretrain the Siamese network and fine-tune it with a collected mimetic-image data set. Based on our results, we propose two recommended image dissimilarity thresholds for judging the mimicry of extant insects (0–0.4556) and fossil insects (0–0.4717). Deep learning algorithms are used to quantify the mimicry of fossil insects in this study, providing novel insights into exploring the early evolution of mimicry.

KEYWORDS:

• Mimesis
• fossil insects
• similarity
• deep learning
• Siamese network

Acknowledgments

We appreciate the Nanjing Institute of Geology and Palaeontology for the data support and the Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, Wuhan University, China for the experimental equipment support.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1. Data and code are available on https://github.com/fanliaveline/Siamese-Network-Fossil-Mimicry.git

2. We refer to ‘basic model’ as a pretrained architecture normally; ‘top model’ as a similarity calculation network and fully connected layer; and `convolutional layer’ as the initial modules.

Additional information

Funding

This work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB26000000), the Second Tibetan Plateau Scientific Expedition and Research (2019QZKK0706), and the National Natural Science Foundation of China (41688103). E.A.J. thanks the Chinese Academy of Sciences for the support under the President’s International Fellowship Initiative (PIFI). This is a Leverhulme Emeritus Fellowship Contribution for E.A.J.