Abstract

Citizen science approaches for monitoring, and even restoring, coral reefs have grown in popularity though tend to be restricted to those who have taken courses that expose them to the relevant methodologies. Now that cheap (~10 USD), waterproof pouches for smart phones are widely available, there is the potential for mass acquisition of coral reef images by non-scientists. Furthermore, with the emergence of better machine-learning-based image classification approaches, high-quality data can be extracted from low-resolution images (provided that key benthic organisms, namely corals, other invertebrates, & algae, can be distinguished). To determine whether informally captured images could yield comparable ecological data to point-intercept + photo-quadrat surveys conducted by highly proficient research divers, we trained an artificial intelligence (AI), CoralNet, with images taken before and during a bleaching event in 2015 in Chagos (Indian Ocean). The overall percent coral covers of the formal, “gold standard” method and the informal, “tourist diver” approach of 38.7 and 35.1%, respectively, were within ~10% of one another; coral bleaching percentages of 30.5 and 31.8%, respectively, were statistically comparable. Although the AI was prone to classifying bleached corals as healthy in ~one-third of cases, the fact that these data could be collected by someone with no knowledge of coral reef ecology might justify this approach in areas where divers or snorkelers have access to waterproof cameras and are keen to document coral reef condition.