ABSTRACTCitizen science programs can be effective at collecting information at large temporal and spatial scales. However, sampling bias is a concern in citizen science datasets and can lead to unreliable estimates. We address this issue with a novel approach in a first-of-its-kind citizen science survey of Weddell seals for the entire coast of Antarctica. Our citizen scientists inspected very high-resolution satellite images to tag any presumptive seals hauled out on the fast ice during the pupping period. To assess and reduce the error in counts in term of bias and other factors, we ranked surveyors on how well they agreed with each other in tagging a particular feature (presumptive seal), and then ranked these features based on the ranking of surveyors placing tags on them. We assumed that features with higher rankings, as determined by “the crowd wisdom,” were likely to be seals. By comparing citizen science feature ranks with an expert’s determination, we found that non-seal features were often highly ranked. Conversely, some seals were ranked low or not tagged at all. Ranking surveyors relative to their peers was not a reliable means to filter out erroneous or missed tags; therefore, we developed an effective correction factor for both sources of error by comparing surveyors’ tags to those by the expert. Furthermore, counts may underestimate true abundance due to seals not being present on the ice when the image was taken. Based on available on-the-ground haul-out location counts in Erebus Bay, the Ross Sea, we were able to correct for the proportion of seals not detected through satellite images after accounting for year, time-of-day, location (islet vs. mainland locations), and satellite sensor effects. We show that a prospective model performed well at estimating seal abundances at appropriate spatial scales, providing a suitable methodology for continent-wide Weddell Seal population estimates.
Habitat Use by Weddell Seals and Emperor Penguins Foraging in the Ross Sea, Antarctica