Chimpanzee identification and social Network construction through an online citizen science platform

Citizen science has grown rapidly in popularity in recent years due to its
potential to educate and engage the public while providing a means to
address a myriad of scientific questions. However, the rise in popularity
of citizen science has also been accompanied by concerns about the quality
of data emerging from citizen science research projects. We assessed data
quality in the online citizen scientist platform Chimp&See, which
hosts camera trap videos of chimpanzees (Pan troglodytes) and other
species across Equatorial Africa. In particular, we compared detection and
identification of individual chimpanzees by citizen scientists to that of
experts with years of experience studying those chimpanzees. We found that
citizen scientists typically detected the same number of individual
chimpanzees as experts, but assigned far fewer identifications (IDs) to
those individuals. Those IDs assigned, however, were nearly always in
agreement with the IDs provided by experts. We applied the data sets of
citizen scientists and experts by constructing social networks from each.
We found that both social networks were relatively robust and shared a
similar structure, as well as having positively correlated individual
network positions. Our findings demonstrate that, although citizen
scientists produced a smaller data set based on fewer confirmed IDs, the
data strongly reflect expert classifications and can be used for
meaningful assessments of group structure and dynamics. This approach
expands opportunities for social research and conservation monitoring in
great apes and many other individually identifiable species.