ArticleNature communications2019
Artificial intelligence reveals environmental constraints on colour diversity in insects.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed.
- Beyond the Classics: The Synergy of AI and Genomics Reveals an Expanded Repertoire of Pigmentation Genes.Journal of experimental zoology. Part B, Molecular and developmental evolution · 2026Review
- Deep learning models link local cellular features with whole-animal growth dynamics in zebrafish.Life science alliance · 2025Article
- Global hotspots of butterfly diversity are threatened in a warming world.Nature ecology & evolution · 2025Article
- Reconstructing illusory camouflage patterns on moth wings using computer vision.Journal of the Royal Society, Interface · 2025Article
- Automated graptolite identification at high taxonomic resolution using residual networks.iScience · 2024Article
- Convergence in sympatric swallowtail butterflies reveals ecological interactions as a key driver of worldwide trait diversification.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- A high-throughput multispectral imaging system for museum specimens.Communications biology · 2022Article
- Intraspecific trait variability and community assembly in hawkmoths (Lepidoptera:Ecology and evolution · 2021Article
- Domain randomization-enhanced deep learning models for bird detection.Scientific reports · 2021Article
- ?Description of a new species of the genusZooKeys · 2021Article
- Deep learning in deep time.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- An adaptive design approach for defects distribution modeling in materials from first-principle calculations.Journal of molecular modeling · 2020Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Explaining colour variation among animals at broad geographic scales remains challenging. Here we demonstrate how deep learning-a form of artificial intelligence-can reveal subtle but robust patterns of colour feature variation along an ecological gradient, as well as help identify the underlying mechanisms generating this biogeographic pattern. Using over 20,000 images with precise GPS locality information belonging to nearly 2,000 moth species from Taiwan, our deep learning model generates a 2048-dimension feature vector that accurately predicts each species' mean elevation based on colour and shape features. Using this multidimensional feature vector, we find that within-assemblage image feature variation is smaller in high elevation assemblages. Structural equation modeling suggests that this reduced image feature diversity is likely the result of colder environments selecting for darker colouration, which limits the colour diversity of assemblages at high elevations. Ultimately, with the help of deep learning, we will be able to explore the endless forms of natural morphological variation at unpreceded depths.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.