Login / Signup

Evolution of wing shape in geometrid moths: phylogenetic effects dominate over ecology.

Kadri UdeErki ÕunapAnts KaasikRobert B DavisJuhan JavoišVineesh NedumpallyStênio Ítalo Araújo FoersterToomas Tammaru
Published in: Journal of evolutionary biology (2024)
Locomotory performance is an important determinant of fitness in most animals, including flying insects. Strong selective pressures on wing morphology are therefore expected. Previous studies on wing shape in Lepidoptera have found some support for hypotheses relating wing shape to environment-specific selective pressures on aerodynamic performance. Here, we present a phylogenetic comparative study on wing shape in the lepidopteran family Geometridae, covering 374 species of the northern European fauna. We focused on 11 wing traits including aspect ratio, wing roundness, and the pointedness of the apex, as well as the ratio of forewing and hindwing areas. All measures were taken from images available on the internet, using a combination of tools available in Fiji software and R. We found that wing shape demonstrates a phylogenetically conservative pattern of evolution in Geometridae, showing similar or stronger phylogenetic signal than many of its potential predictors. Several wing traits showed statistically significant associations with predictors such as body size, phenology, and preference for forest habitats. Overall, however, all of these associations remained notably weak, with no wing shape being excluded for any value of the predictors, including body size. We conclude that, in geometrids, wing traits do not readily respond to selective pressures optimizing aerodynamic performance of the moths in different environments. Selection on wing shape may nevertheless operate through other functions of the wings, with the effectiveness of crypsis at rest being a promising candidate for further studies.
Keyphrases
  • systematic review
  • healthcare
  • physical activity
  • deep learning
  • social media
  • machine learning
  • health information
  • case control
  • convolutional neural network
  • genetic diversity