Chimpanzee identification and social network construction through an online citizen science platform. Issue 4 (16th December 2020)

• Record Type: Journal Article
• Authors: McCarthy, Maureen S.; Stephens, Colleen; Dieguez, Paula; Samuni, Liran; Després‐Einspenner, Marie‐Lyne; Harder, Briana; Landsmann, Anja; Lynn, Laura K.; Maldonado, Nuria; Ročkaiová, Zuzana; Widness, Jane; Wittig, Roman M.; Boesch, Christophe; Kühl, Hjalmar S.; Arandjelovic, Mimi
• Is Part Of: Ecology and evolution. Volume 11:Issue 4(2021)
• Page Start: 1598
• Page End: 1608
• Publication Date: 2020-12-16
• Subjects: camera trap -- chimpanzee -- citizen science -- Pan troglodytes -- social network analysis; Ecology -- Periodicals; Evolution -- Periodicals; 577.05
• DOI: 10.1002/ece3.7128
• Languages: English
• ISSNs: 2045-7758

Abstract

Abstract: 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 with 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. Abstract : Citizen science has expanded rapidly in popularity, leading to a growing need to ensure data accuracy in citizen science research projects. We validated the accuracy of chimpanzee detection and individual identification data from the online citizen scientist platform Chimp&See, which hosts camera trap videos of chimpanzees and other species across Equatorial Africa. When comparing data from citizen scientists with those of experts who coded the same videos, we found a high level of agreement in detecting and identifying chimpanzees, although citizen scientists assigned fewer IDs overall. The resulting citizen scientist data could be used to construct a robust social network with a similar structure and network positions to that constructed from expert data coded from the same camera trap videos.