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

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.

Camera trapping with photos and videos: implications for ecology and citizen science

“…However, since the study was conducted when few juvenile animals were present and most male deer still had their antlers making them easier to recognize, further analyses across seasons are needed to determine more precisely the ability of citizen scientist to identify age and sex of animals accurately from photo and video footage. It is likely that more citizen science projects will start requiring human observers to move beyond species classifications; provision of additional detail is already evident in projects asking participants to identify age and sex (Thel et al., 2021) and individual ID (McCarthy et al., 2021; Tagg et al., 2018). Further research is needed to establish optimum camera settings for accurate identification of these traits, although video appears to offer clear benefits over traditional photos.…”

“…Camera trap videos could allow for easier species identification because movement can make animals easier to locate within the footage, and because more information is available to an observer, such as different views of an animal, their gait or movement profile, and sound. However, probably owing to the concerns outlined above, most camera trap citizen science projects use photographs and there has been little assessment of citizen science classification accuracy of videos (but see McCarthy et al., 2021). Gaining adequate numbers of classifications is important for timely processing and for combining multiple classifications to achieve higher confidence in classification accuracy (Anton et al., 2018; Egna et al., 2020; Hsing et al., 2018; Swanson et al., 2016).…”

Discovering features in gravitational-wave data through detector characterization, citizen science and machine learning

“…These recommendations overlap with best practices advised for citizen-science projects to produce reliable results [78]. Previous studies have shown that citizen-science volunteers can produce high accuracy results, performing classifications comparable to professional scientists [79][80][81], and even detect novel features in data [82][83][84]; here, we have shown volunteers can go beyond this, and identify new classes analogously to experts. When provided with suitable resources, citizen-science volunteers can make significant scientific discoveries based upon complex data sets.…”

Discovering features in gravitational-wave data through detector characterization, citizen science and machine learning

“…These recommendations overlap with best practices advised for citizen-science projects to produce reliable results [65]. Previous studies have shown that citizen-science volunteers can produce high accuracy results, performing classifications comparable to professional scientists [66][67][68], and even detect novel features in data [69][70][71]; here, we have shown volunteers can go beyond this, and identify new classes analogously to experts. When provided with suitable resources, citizen-science volunteers can make significant scientific discoveries based upon complex data sets.…”