2025-06-16 ミシガン大学
New research from researchers at the University of Michigan, New York University and more has shown that bird wings follow Allen’s Rule, which means their size helps with heat regulation. Image credit: John Megahan
<関連情報>
- https://news.umich.edu/ai-vision-system-reveals-bird-wings-evolved-for-heat-regulation-not-just-flight/
- https://onlinelibrary.wiley.com/doi/full/10.1111/geb.70033
- https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.13864
温暖な気候で翼の骨が長くなることは、鳥類の翼の進化における体温調節の役割を示唆している Longer Wing Bones in Warmer Climates Suggest a Role of Thermoregulation in Bird Wing Evolution
Brian C. Weeks, Christina Harvey, Joseph A. Tobias, Catherine Sheard, Zhizhuo Zhou, David F. Fouhey
Global Ecology and Biogeography Published: 04 April 2025
DOI:https://doi.org/10.1111/geb.70033
ABSTRACT
Aim
The tendency for animals in warmer climates to be longer-limbed (Allen’s Rule) is widely attributed to the demands of thermoregulation. The role of thermoregulation in structuring bird wings, however, has been overshadowed by the selective demands placed on wings by flight. We test whether occurrence in warmer climates is associated with longer wing bones.
Methods
Using computer vision, we measure wing-bone length from photographs of museum skeletal specimens for 1520 species of passerine birds. We then model the relationship between wing-bone length and temperature, accounting for allometry, the demands of flight efficiency and manoeuvrability, and a range of ecological and environmental variables.
Results
Wing bones are longer in warmer climates. Our models, largely as a result of allometric effects, explain nearly all the variation in wing-bone length in our data, with a marginal R2 = 0.80 and a conditional R2 > 0.99.
Main Conclusions
Across 1520 species of birds, higher temperatures are associated with longer wing bones, as predicted by Allen’s Rule. The vascularised musculature along these bones is maximally uncovered when birds actively hold their wings away from their bodies to aid in cooling or during flight. Conversely, the musculature along the wing bones is insulated by feathering when at rest, such that wings play a minor role in heat exchange when individuals are less active and may need to retain heat. While our analyses do not directly establish the mechanistic basis underlying the pattern we recover, given the asymmetry in the role of wings in thermoregulation, we interpret the positive relationship between temperature and wing-bone length to reflect increased demand for heat dissipation in warmer climates. Our findings highlight the role of thermoregulation in shaping even the most critical features of vertebrate anatomy.
博物館の骨格標本における機能的形質のハイスループット測定のためのディープ・ニューラル・ネットワーク A deep neural network for high-throughput measurement of functional traits on museum skeletal specimens
Brian C. Weeks, Zhizhuo Zhou, Bruce K. O’Brien, Rachel Darling, Morgan Dean, Tiffany Dias, Gemmechu Hassena, Mingyu Zhang, David F. Fouhey
Methods in Ecology and Evolution Published: 08 April 2022
DOI:https://doi.org/10.1111/2041-210X.13864
Abstract
- Increasingly, natural history museum collections are being used to generate large-scale morphological datasets to address a range of macroecological and macroevolutionary questions. One challenge to this approach is that large numbers of individuals either from a single species or from taxonomically broad sets of species may be necessary to characterize morphology at the relevant spatial, phylogenetic or temporal scales.
- We present ‘Skelevision’, a method for rapidly handling, photographing and measuring skeletal specimens with a computer vision approach that uses a deep neural network to segment the photographs of specimens into individual bones, and identify and measure functional aspects of those bones.
- We demonstrate the scale of what is feasible with Skelevision by estimating 11 functional traits from 11 different bones for 12,450 bird skeletal specimens spanning 1,882 species of passerines (~32% of all passerine diversity). We quantify the accuracy of Skelevision estimates by comparing them to handmade measurements for 174 specimens from 115 species across 79 genera that span 59 families. Skelevision is precise, with a mean standard deviation of 0.86 mm for repeated independent measurements of individual bones, and is extremely accurate, with a mean RMSE of 0.89 mm across all traits when compared to handmade measurements. There is minimal phylogenetic signal in the measurement error (mean Pagel’s λ across traits = 0.13), and Skelevision estimates are robust to variation in the degree to which specimens remain articulated.
- This approach has several important advantages over traditional methods for building large-scale morphological datasets (e.g. measurements from long-term field-based operations or handmade measurements of museum specimens). First, measuring new specimens only requires the collection of photographs, which can then be measured automatically, and effectively instantaneously, with the neural network. This is a significant departure from the time and skill required to measure skeletal specimens by hand. Second, the measurements are repeatable. Third, even as the dataset of photographed specimens expands, the amount of annotation data needed to measure new traits on all of the photographed specimens using the neural network will remain fixed and can be done without re-capturing images.
