Using large-scale community science data and computer vision to evaluate thermoregulation as an adaptive driver of physiological color change in Anolis carolinensis

We found support for both the thermoregulatory hypothesis and social signaling hypothesis dependent on time of year, which suggests multiple independent drivers are influencing physiological color changes in A. carolinensis. Further, this work shows the power of leveraging large-scale digital field images and machine learning to derive insights about how species can regulate phenotype to maintain their thermal and biotic niche optima.