Artificial intelligence reveals environmental constraints on colour diversity in insects

Shipher Wu, Chun-Min Chang, Guan-Shuo Mai, Dustin R. Rubenstein, Chen-Ming Yang, Yu-Ting Huang, Hsu-Hong Lin, Li-Cheng Shih, Sheng-Wei Chen () and Sheng-Feng Shen ()
Additional contact information
Shipher Wu: Academia Sinica
Chun-Min Chang: Academia Sinica
Guan-Shuo Mai: Academia Sinica
Dustin R. Rubenstein: Columbia University
Chen-Ming Yang: Academia Sinica
Yu-Ting Huang: Academia Sinica
Hsu-Hong Lin: Taiwan Endemic Species Research Institute
Li-Cheng Shih: Taiwan Endemic Species Research Institute
Sheng-Wei Chen: Academia Sinica
Sheng-Feng Shen: Academia Sinica

Nature Communications, 2019, vol. 10, issue 1, 1-9

Abstract: 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.

Date: 2019
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-019-12500-2 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-12500-2

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-019-12500-2

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().