Applying computer vision to digitised natural history collections for climate change research: Temperature‐size responses in British butterflies

Abstract

Abstract Natural history collections are invaluable resources for understanding biotic response to global change. Museums around the world are currently imaging specimens, capturing specimen data and making them freely available online. In parallel to the digitisation effort, there have been great advancements in computer vision: the computer trained automated recognition/detection, and measurement of features in digital images. Applying computer vision to digitised natural history collections has the potential to greatly accelerate the use of these collections for biotic response to global change research. In this paper, we apply computer vision to a very large, digitised collection to test hypotheses in an established area of biotic response to climate change research: temperature‐size responses. We develop a computer vision pipeline (Mothra) and apply it to the NHM collection of British butterflies (>180,000 imaged specimens). Mothra automatically detects the specimen and other objects in the image, sets the scale, measures wing features (e.g. forewing length), determines the orientation of the specimen (pinned ventrally or dorsally) and identifies the sex. We pair these measurements and specimen collection data with temperature records for 17,726 specimens across a subset of 24 species to test how adult size varies with temperature during the immature stages of species. We also assess patterns of sexual size dimorphism across species and families for 32 species trained for automated sex ID. Mothra accurately measures the forewing lengths of butterfly specimens compared to manual measurements and accurately determines the sex of specimens, with females as the larger sex in most species. An increase in adult body size with warmer monthly temperatures during the late larval stages is the most common temperature‐size response. These results confirm suspected patterns and support hypotheses based on recent studies using a smaller dataset of manually measured specimens. We show that computer vision can be a powerful tool to efficiently and accurately extract phenotypic data from a very large collection of digital natural history collections. In the future, computer vision will become widely applied to digital collections to advance ecological and evolutionary research and to accelerate their use to investigate biotic response to global change.