Deep learning on butterfly phenotypes tests evolution's oldest mathematical model.

Traditional anatomical analyses captured only a fraction of real phenomic information. Here, we apply deep learning to quantify total phenotypic similarity across 2468 butterfly photographs, covering 38 subspecies from the polymorphic mimicry complex of Heliconius erato and Heliconius melpomene. Euclidean phenotypic distances, calculated using a deep convolutional triplet network, demonstrate significant convergence between interspecies co-mimics. This quantitatively validates a key prediction of Müllerian mimicry theory, evolutionary biology's oldest mathematical model. Phenotypic neighbor-joining trees are significantly correlated with wing pattern gene phylogenies, demonstrating objective, phylogenetically informative phenome capture. Comparative analyses indicate frequency-dependent mutual convergence with coevolutionary exchange of wing pattern features. Therefore, phenotypic analysis supports reciprocal coevolution, predicted by classical mimicry theory but since disputed, and reveals mutual convergence as an intrinsic generator for the unexpected diversity of Müllerian mimicry. This demonstrates that deep learning can generate phenomic spatial embeddings, which enable quantitative tests of evolutionary hypotheses previously only testable subjectively.

iCollections methodology: workflow, results and lessons learned.

The Natural History Museum, London (NHMUK) has embarked on an ambitious programme to digitise its collections. The first phase of this programme was to undertake a series of pilot projects to develop the workflows and infrastructure needed to support mass digitisation of very large scientific collections. This paper presents the results of one of the pilot projects - iCollections. This project digitised all the lepidopteran specimens usually considered as butterflies, 181,545 specimens representing 89 species from the British Isles and Ireland. The data digitised includes, species name, georeferenced location, collector and collection date - the what, where, who and when of specimen data. In addition, a digital image of each specimen was taken. A previous paper explained the way the data were obtained and the background to the collections that made up the project. The present paper describes the technical, logistical, and economic aspects of managing the project.

iCollections methodology: workflow, results and lessons learned.

The Natural History Museum, London (NHMUK) has embarked on an ambitious programme to digitise its collections. The first phase of this programme was to undertake a series of pilot projects to develop the workflows and infrastructure needed to support mass digitisation of very large scientific collections. This paper presents the results of one of the pilot projects - iCollections. This project digitised all the lepidopteran specimens usually considered as butterflies, 181,545 specimens representing 89 species from the British Isles and Ireland. The data digitised includes, species name, georeferenced location, collector and collection date - the what, where, who and when of specimen data. In addition, a digital image of each specimen was taken. A previous paper explained the way the data were obtained and the background to the collections that made up the project. The present paper describes the technical, logistical, and economic aspects of managing the project.

iCollections - Digitising the British and Irish Butterflies in the Natural History Museum, London.

BACKGROUND: The Natural History Museum, London (NHMUK) has embarked on an ambitious programme to digitise its collections . The first phase of this programme has been to undertake a series of pilot projects that will develop the necessary workflows and infrastructure development needed to support mass digitisation of very large scientific collections. This paper presents the results of one of the pilot projects - iCollections. This project digitised all the lepidopteran specimens usually considered as butterflies, 181,545 specimens representing 89 species from the British Isles and Ireland. The data digitised includes, species name, georeferenced location, collector and collection date - the what, where, who and when of specimen data. In addition, a digital image of each specimen was taken. This paper explains the way the data were obtained and the background to the collections which made up the project. NEW INFORMATION: Specimen-level data associated with British and Irish butterfly specimens have not been available before and the iCollections project has released this valuable resource through the NHM data portal.