Large-scale assessment of intra- and inter-annual breeding success using a remote camera network.

Changes in the physical environment along the Antarctic Peninsula have been among the most rapid anywhere on the planet. In concert with environmental change, the potential for direct human disturbance resulting from tourism, scientific programs, and commercial fisheries continues to rise in the region. While seabirds, such as the gentoo penguin Pygoscelis papua, are commonly used to assess the impact of these disturbances on natural systems, research efforts are often hampered by limited spatial coverage and lack of temporal resolution. Using a large-scale remote time-lapse camera network and a modeling framework adapted from capture-recapture studies, we assess drivers of intra- and inter-annual dynamics in gentoo penguin breeding success across nearly the entire species' range in the Atlantic sector of the Southern Ocean. We quantify the precise timing of egg/chick mortality within each season and examine the role of precipitation events, tourism visitation, and fishing activity for Antarctic krill Euphausia superba (a principal prey resource in the Antarctic) in these processes. We find that nest failure rates are higher in the egg than the chick stage and that neither krill fishing nor tourism visitation had a strong effect on gentoo penguin breeding success. While precipitation events had, on average, little effect on nest mortality, results suggest that extreme weather events can precipitate sharp increases in nest failure. This study highlights the importance of continuous ecosystem monitoring, facilitated here by remote time-lapse cameras, in understanding ecological responses to environmental stressors, particularly with regard to the timing of events such as extreme weather.

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 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.