Shinichi Nakagawa , Malgorzata Lagisz, Roxane Francis, Jess Tam, Xun Li, Andrew Elphinstone, Neil Jordan, Justine O'Brien, Benjamin Pitcher, Monique Van Sluys, Arcot Sowmya, Richard Kingsford

1. Machine (especially deep) learning algorithms are changing the way wildlife imagery is processed. They dramatically speed up the time to detect, count, classify animals and their behaviours. Yet, we currently have a very few systematic literature surveys on its use in wildlife imagery.
2. Through a literature survey (a ‘rapid’ review) and bibliometric mapping, we explored its use across: 1) species (vertebrates), 2) image types (e.g., camera traps, or drones), 3) study locations, 4) alternative machine learning algorithms, 5) outcomes (e.g., recognition, classification, or tracking), 6) reporting quality and openness, 7) author affiliation, and 8) publication journal types.
3. We found that increasing number of studies used convolutional neural networks (i.e., deep learning). Typically, studies have focused on large charismatic or iconic mammalian species . Increasing number of studies is published in ecology-specific journals indicating the uptake of deep learning to transform detection, classification and tracking of wildlife. Sharing of code was limited, with only 20% of studies providing links to analysis code.
4. Much of the published research and focus on animals came from India, China, Australia, or the USA. There were relatively few collaborations across countries. Given the power of machine learning, we recommend increasing collaboration and sharing approaches to utilise increasing amounts of wildlife imagery more rapidly and transform and improve understanding of wildlife behaviour and conservation.
5. Our survey augmented with bibliometric analyses provide valuable signposts for future studies to resolve and address shortcomings, gaps, and biases.