Abstract
Camera traps can be used to address large-scale questions in community ecology by providing systematic data on an array of wide-ranging species. We deployed 225 camera traps across 1,125 km2 in Serengeti National Park, Tanzania, to evaluate spatial and temporal inter-species dynamics. The cameras have operated continuously since 2010 and had accumulated 99,241 camera-trap days and produced 1.2 million sets of pictures by 2013. Members of the general public classified the images via the citizen-science website www.snapshotserengeti.org. Multiple users viewed each image and recorded the species, number of individuals, associated behaviours, and presence of young. Over 28,000 registered users contributed 10.8 million classifications. We applied a simple algorithm to aggregate these individual classifications into a final ‘consensus’ dataset, yielding a final classification for each image and a measure of agreement among individual answers. The consensus classifications and raw imagery provide an unparalleled opportunity to investigate multi-species dynamics in an intact ecosystem and a valuable resource for machine-learning and computer-vision research.
Highlights
Background & SummaryOver the last 20 years, camera traps—remote, automatic cameras—have revolutionized wildlife ecology and conservation and are emerging as a key tool in the broader disciplines of behavioural, population, and community ecology[1]
We describe the datasets generated by Snapshot Serengeti, a large-scale survey that (1) deployed 225 camera traps across a 1,125 km[2] area in the Serengeti National Park, Tanzania from 2010–2013, (2) used a citizen science website to process millions of images, and (3) used a simple algorithm to ensure high reliability of the resultant species classifications
In collaboration with The Zooniverse, the world’s most popular citizen science platform, we developed the website www.snapshotserengeti.org that allowed members of the general public to view and classify each image, identifying species, counting the number of individuals, and characterizing behaviours (Fig. 3)
Summary
Over the last 20 years, camera traps—remote, automatic cameras—have revolutionized wildlife ecology and conservation and are emerging as a key tool in the broader disciplines of behavioural, population, and community ecology[1]. We describe the datasets generated by Snapshot Serengeti, a large-scale survey that (1) deployed 225 camera traps across a 1,125 km[2] area in the Serengeti National Park, Tanzania from 2010–2013, (2) used a citizen science website (www.snapshotserengeti.org) to process millions of images, and (3) used a simple algorithm to ensure high reliability of the resultant species classifications. The consensus dataset provides species-specific capture histories that can be analysed in a number of ways to evaluate population and community dynamics, either within Serengeti or as part of a larger cross-reserve analysis (see Usage Notes for details). The applications for this dataset extend beyond ecological research. Dataset have been used in classrooms to engage students in authentic research that spans ecology, animal behaviour, and computer science (see Usage Notes for examples)
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