Quantifying plant mimesis in fossil insects using deep learning

References

• Agrawal P, Girshick R, Malik J 2014. Analyzing the performance of multilayer neural networks for object recognition. European conference on computer vision; Sep 6-12; Zurich,Switzerland, Springer. p. 329–344.
   Google Scholar
• Appalaraju S, Chaoji V. 2017. Image similarity using deep CNN and curriculum learning. arXiv preprint arXiv:170908761.
   Google Scholar
• Bates HW. 1863. Contributions to an insect fauna of the amazon valley Coleoptera: longicornes. Ann Mag Nat Hist. 12(71):367–381. doi:https://doi.org/10.1080/00222936308681538.
   Google Scholar
• Carlberg U. 1981. Defensive behaviour in females of the stick insect Sipyloidea sipylus (Westwood) (Phasmida). Zool Anz. 207:177–180.
   Google Scholar
• Castner JL, Nickle DA. 1995. Notes on the biology and ecology of the leaf-mimicking katydid Typophyllum bolivari vignon (Orthoptera: Tettigoniidae: Pseudophyllinae: Pterochrozini). J Orth Res. 4:105–109. doi:https://doi.org/10.2307/3503465.
   Google Scholar
• Chopra S, Hadsell R, LeCun Y 2005. Learning a similarity metric discriminatively, with application to face verification. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05); Jun 20-25; San Diego, CA, USA; Vol. 1. IEEE. p. 539–546.
   Google Scholar
• Clune J, Bengio Y, Lipson H. 2014. How transferable are features in deep neural networks? Advances in neural information processing systems; Dec 8-13; Montreal, Quebec, Canada; p. 3320–3328
   Google Scholar
• Cuthill JFH, Guttenberg N, Ledger S, Crowther R, Huertas B. 2019. Deep learning on butterfly phenotypes tests evolution’s oldest mathematical model. Sci Adv. 5(8):eaaw4967. doi:https://doi.org/10.1126/sciadv.aaw4967.
   PubMed Web of Science ®Google Scholar
• De Solan T, Renoult JP, Geniez P, David P, Crochet PA. 2020. Looking for mimicry in a snake assemblage using deep learning. Am Nat. 196(1):74–86. doi:https://doi.org/10.1086/708763.
   PubMed Web of Science ®Google Scholar
• Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L 2009. Imagenet: a large-scale hierarchical image database. 2009 IEEE conference on computer vision and pattern recognition; Jun 20-25; Miami, FL, USA; Ieee. p. 248–255.
   Google Scholar
• Ezray BD, Wham DC, Hill CE, Hine HM. 2019. Unsupervised machine learning reveals mimicry complexes in bumblebees occur along a perceptual continuum. Proc R Soc B. 286(1910):20191501. doi:https://doi.org/10.1098/rspb.2019.1501.
   PubMed Web of Science ®Google Scholar
• Fang H, Labandeira CC, Ma Y, Zheng B, Ren D, Wei X, Liu J, Wang Y. 2020. Liche mimesis in mid-mesozoic lacewings. elife. 9:e59007. doi:https://doi.org/10.7554/eLife.59007.
   PubMed Web of Science ®Google Scholar
• Foottit RG, Adler PH. 2009. Insect biodiversity. Wiley-Blackwell. Oxford.
   Google Scholar
• Garrouste R, Hugel S, Jacquelin L, Rostan P, Steyer JS, Desutter-Grandcolas L, Nel A. 2016. Insect mimicry of plants dates back to the Permian. Nat Commun. 7(1):1–6. doi:https://doi.org/10.1038/ncomms13735.
   Web of Science ®Google Scholar
• Hadsell R, Chopra S, LeCun Y 2006. Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06); Jun 17-22; New York, NY, USA; IEEE.Vol. 2. p. 1735–1742.
   Google Scholar
• He K, Zhang X, Ren S, Sun J 2016. Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition; Jun 17-19; San Juan, PR,USA; IEEE; p. 770–778
   Google Scholar
• Heads SW. 2008. The first fossil Proscopiidae (Insecta, Orthoptera, Eumastacoidea) with comments on the historical biogeography and evolution of the family. Palaeontology 51(2):499–507. doi:https://doi.org/10.1111/j.1475-4983.2008.00756.x.
   Web of Science ®Google Scholar
• Hoffer E, Ailon N 2015. Deep metric learning using triplet network. International Workshop on Similarity-Based Pattern Recognition. OCT 12-14; Copenhagen, Denmark; Springer. p. 84–92.
   Google Scholar
• Koch G, Zemel R, Salakhutdinov R 2015. Siamese neural networks for one-shot image recognition. Proceedings of the 32nd International Conference on Machine Learning, ICML deep learning workshop; Jul 6-11; Lille, France; Vol. 2.
   Google Scholar
• Krizhevsky A, Sutskever I, Hinton GE. 2017. Imagenet classification with deep convolutional neural networks. Commun ACM. 6:84–90. doi:https://doi.org/10.1145/3065386.
   Google Scholar
• Li W, Ding W, Sadasivam R, Cui X, Chen P. 2019a. His-GAN: a histogram-based GAN model to improve data generation quality. Neural Netw. 119:31–45. doi:https://doi.org/10.1016/j.neunet.2019.07.001.
   PubMed Web of Science ®Google Scholar
• Li W, Liang Z, Ma P, Wang R, Cui X, Chen P. 2021. Hausdorff GAN: improving GAN Generation Quality with Hausdorff Metric. IEEE Trans Cybern.doi:https://doi.org/10.1109/TCYB.2021.3062396
   Google Scholar
• Li W, Liu X, Liu J, Chen P, Wan S, Cui X. 2019b. On improving the accuracy with auto-encoder on conjunctivitis. Appl Soft Comput. 81:105489. doi:https://doi.org/10.1016/j.asoc.2019.105489.
   Web of Science ®Google Scholar
• Liu X, Shi G, Xia F, Lu X, Wang B, Engel MS. 2018. Liverwort mimesis in a cretaceous lacewing larva. Curr Biol. 28(9):1475–1481. doi:https://doi.org/10.1016/j.cub.2018.03.060.
   PubMed Web of Science ®Google Scholar
• MartInez-Delclos X, Briggs DE, Penalver E. 2004. Taphonomy of insects in carbonates and amber. Palaeogeogr Palaeoclimatol Palaeoecol. 203(1v2):19–64. doi:https://doi.org/10.1016/S0031-0182(03)00643-6.
   Web of Science ®Google Scholar
• Melekhov I, Kannala J, Rahtu E 2016. Siamese network features for image matching. 2016 23rd International Conference on Pattern Recognition (ICPR). Dec 4-8; CancÚn, Mexico; IEEE. p. 378–383.
   Google Scholar
• Mugleston J, Naegle M, Song H, Bybee SM, Ingley S, Suvorov A, Whiting MF. 2016. Rein-venting the leaf: multiple origins of leaf-like wings in katydids (Orthoptera: Tettigoniidae). Invertebr Syst. 30(4):335–352. doi:https://doi.org/10.1071/IS15055.
   Web of Science ®Google Scholar
• Müller F. 1879. Ituna and thyridia; a remarkable case of mimicry in butterflies (transl. byralph meldola from the original German article in kosmos, may 1879, 100). Tra Entomol SocLond. 1879: xx-xxix
   Google Scholar
• Rosenfeld A, Solbach MD, Tsotsos JK 2018. Totally looks like-how humans compare, compared to machines. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. Jun 18-22; Salt Lake City, UT, USA; p. 1961–1964.
   Google Scholar
• Suzuki TK, Tomita S, Sezutsu H. 2014. Gradual and contingent evolutionary emergence of leaf mimicry in butterfly wing patterns. BMC Evol Biol. 14(1):1–13. doi:https://doi.org/10.1186/s12862-014-0229-5.
   PubMed Web of Science ®Google Scholar
• Szegedy C, Liu W, Jia SP, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A 2015. Going deeper with convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition. Jun 7-12; Boston, MA, USA; IEEE Computer Society; p. 1–9.
   Google Scholar
• Trigueros DS, Meng L, Hartnett M. 2018. Face recognition: from traditional to deep learning methods. arXiv preprint arXiv:181100116.
   Google Scholar
• Ueda K. 2020. Occurrence dataset; [2020 inaturalist research-grade observations.]. [accessed 2020 Dec 10]. doi:https://doi.org/10.15468/ab3s5x.
   Google Scholar
• Valkonen JK, Nokelainen O, Jokimã Ki M, Kuusinen E, Paloranta M, Peura M, Mappes J. 2014. From deception to frankness: benefits of ontogenetic shift in the anti-predator strategy of alder moth acronicta alni larvae. Curr Zool. 60(1):114–122. doi:https://doi.org/10.1093/czoolo/60.1.114.
   Web of Science ®Google Scholar
• Van der Maaten L, Hinton G. 2008. Visualizing data using t-SNE. J Mach Learn Res. 9(Nov):2579–2605.
   Google Scholar
• Vinther J. 2015. A guide to the field of palaeo colour: melanin and other pigments can fossilise: reconstructing colour patterns from ancient organisms can give new insights to ecology and behaviour. BioEssays 37(6):643–656. doi:https://doi.org/10.1002/bies.201500018.
   PubMed Web of Science ®Google Scholar
• Wang Y, Labandeira CC, Shih C, Ding Q, Wang C, Zhao Y, Ren D. 2012. Jurassic mimicry between a hangingfly and a ginkgo from China. Proc Natl Acad Sci U S A. 109(50):20514–20519. doi:https://doi.org/10.1073/pnas.1205517109.
   PubMed Web of Science ®Google Scholar
• Wham DC, Ezray BD, Hines HM. 2019. Measuring perceptual distance of organismal color pattern using the features of deep neural networks. bioRxiv. 736306.doi:https://doi.org/10.1101/73606
   Google Scholar
• Wickler W. 1968. Mimicry in plants and animals. New York: McGraw Hill.
   Google Scholar
• Williams P. 2007. The distribution of bumblebee colour patterns worldwide: possible significance for thermoregulation, crypsis, and warning mimicry. Biol J Linn Soc. 92(1):97–118. doi:https://doi.org/10.1111/j.1095-8312.2007.00878.x.
   Web of Science ®Google Scholar
• Yang H, Shi C, Engel MS, Zhao Z, Ren D, Gao T. 2021. Early specializations for mimicry and defense in a Jurassic stick insect. Natl Sci Rev. 8(1):nwaa056. doi:https://doi.org/10.1093/nsr/nwaa056.
   PubMed Web of Science ®Google Scholar
• Yin X, Chen W, Wu X, Yue H 2017. Fine-tuning and visualization of convolutional neural net-works. 12th IEEE Conference on Industrial Electronics and Applications (ICIEA). Jun 18-22; Siem Reap, Cambodia. p. 1310–1315.
   Google Scholar
• Yosinski J, Clune J, Bengio Y, Lipson H. 2014. How transferable are features in deep neural neural networks? In: Advances in neural information processing systems; Dec 8–13; Montreal, Quebec, Canada ; 3320–3328.
   Google Scholar