UC Santa Barbara

Abstract: Climate is a fundamental driver of macroecological patterns in functional trait variation. However, many of the traits that have outsized effects on thermal performance are complex, multi‐dimensional, and challenging to quantify at scale. To overcome this challenge, we leveraged techniques in deep learning and computer vision to quantify hair coverage and lightness of bees, using images of a diverse and widely distributed sample of museum specimens. We demonstrate that climate shapes variation in these traits at a global scale, with bee lightness increasing with maximum environmental temperatures (thermal melanism hypothesis) and decreasing with annual precipitation (Gloger's Rule). We found that deserts are hotspots for bees covered in light‐coloured hairs, adaptations that may mitigate heat stress and represent convergent evolution with other desert organisms. These results support major ecogeographical rules in functional trait variation and emphasize the role of climate in shaping bee phenotypic diversity. Read the free Plain Language Summary for this article on the Journal blog.