Expansive study areas, such as those used by highly-mobile species, provide numerous logistical challenges for researchers. Community science initiatives have been proposed as a means of overcoming some of these challenges but often suffer from low uptake or limited long-term participation rates. Nevertheless, there are many places where the public has a much higher visitation rate than do field researchers. Here we demonstrate a passive means of collecting community science data by sourcing ecological image data from the digital public, who act as “eco-social sensors,” via a public photo-sharing platform—Flickr. To achieve this, we use freely-available Python packages and simple applications of convolutional neural networks. Using the Weddell seal (Leptonychotes weddellii) on the Antarctic Peninsula as an example, we use these data with field survey data to demonstrate the viability of photo-identification for this species, supplement traditional field studies to better understand patterns of habitat use, describe spatial and sex-specific signals in molt phenology, and examine behavioral differences between the Antarctic Peninsula’s Weddell seal population and better-studied populations in the species’ more southerly fast-ice habitat. While our analyses are unavoidably limited by the relatively small volume of imagery currently available, this pilot study demonstrates the utility an eco-social sensors approach, the value of ad hoc wildlife photography, the role of geographic metadata for the incorporation of such imagery into ecological analyses, the remaining challenges of computer vision for ecological applications, and the viability of pelage patterns for use in individual recognition for this species.
Comparing the reliability of relative bird abundance indices from standardized surveys and community science data at finer resolutions