Data from: Just Google it: assessing the use of Google Images to describe geographical variation in visible traits of organisms

Describing spatial patterns of phenotypic traits can be important for
evolutionary and ecological studies. However, traditional approaches, such
as fieldwork, can be time-consuming and expensive. Information
technologies, such as Internet search engines, could facilitate the
collection of these data. Google Images is one such technology that might
offer an opportunity to rapidly collect information on spatial patterns of
phenotypic traits. We investigated the use of Google Images in extracting
data on geographical variation in phenotypic traits visible from
photographs. We compared the distribution of visual traits obtained from
Google Images with four previous studies: colour morphs of black bear
(Ursus americanus); colouration and spottiness in barn owl (Tyto alba);
colour morphs of black sparrowhawk (Accipiter melanoleucus) and the
distribution of hooded (Corvus corone) and carrion crows (Corvus cornix)
across their European hybrid zone. Additionally, we develop and present a
web application (Morphic), which facilitates the human data capture
process of this method. We found good agreement between fieldwork data and
Google Images data across all studies. Indeed, there was strong agreement
between the data obtained from the original study and from the Google
Images method for the colour morphs of black bear (R2 = 80%) and for two
barn owl plumage traits (R2 = 64% and 53%). Our approach also successfully
matched the clinal variation of black sparrowhawks morphs across South
Africa. Our method also gave a good agreement between the distribution of
hooded and carrion crows (with 86% placed on the correct side of the
hybrid zone line). Our results suggest that this method can work well for
visible traits of common and widespread species that are objective,
binary, and easy to see irrespective of angle. The Google Images method is
cost-effective and rapid and can be used with some confidence when
investigating patterns of geographical variation, as well as a range of
other applications. In many cases, it could therefore supplement or
replace fieldwork.