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.

Keywords: Pan troglodytes; camera trap; chimpanzee; citizen science; social network analysis.

Figure 1

Workflow for chimpanzee identification on the Chimp&See platform. Citizen scientists can begin identifying video clips with chimpanzees in the species ID workflow or at any of the steps marked with an asterisk (*). Blue arrows are steps done by citizen scientists (including moderators). Green arrows are steps that are done automatically via custom Python scripts. Purple arrows are steps undertaken by the moderators only. The “Master Chimp Video List” is located off‐platform in an open access Google sheet

Figure 2

Chimpanzee social networks constructed from (a) citizen scientist and (b) expert data. Shaded regions indicate network community membership using the leading eigenvector method (Newman, 2006). Circles indicate individual chimpanzees (nodes) with blue and red indicating male and female chimpanzees, respectively. Lines (edges) connecting the nodes indicate dyadic associations among individuals

Figure 3

Mean (black line) and 95% confidence intervals (gray lines) for rank correlations of network structure at increasing sample sizes in the citizen scientist social network

Figure 4

Individual measures for strength (a) and eigenvector centrality (b) among the weaned chimpanzees identified in both expert and citizen scientist networks (N = 20). Dots indicate the measures for each individual chimpanzee, and the lines connect the same individuals’ positions in the citizen scientist and expert networks, respectively