An accurate and efficient semiautomated approach to counting birds: Estimating Northern Gannet colony size in Canada

Abstract Improving the efficiency of population monitoring and conservation programs is beneficial, so long as the accuracy of the information collected is not diminished. The need to expeditiously estimate the population size of seabird colonies is especially acute during mass mortality events when aerial surveys can provide information quickly on the extent of effects and total mortality. In 2022, the highly pathogenic avian influenza virus caused outbreaks at most Northern Gannet Morus bassanus colonies worldwide, killing tens of thousands of gannets in eastern Canada. In this study, we evaluated the accuracy and efficiency of a semiautomated method using the free software CountEm for counting Northern Gannet nests by reanalyzing 13 years of aerial photographs from past population surveys (2009–2020 and 2022). The CountEm program uses a geometric sampling method which overlays a grid of quadrats onto photographs in which the user counts objects of interest. We developed a protocol that generated population estimates that are accurate enough to support population management objectives (i.e., within 2%–5% of manual counts) and outline additional ways to improve CountEm accuracy. Additionally, using CountEm was 1100% more efficient than manually counting based on counting time. Since CountEm relies on human identification of objects to be counted, our methods, results, and conclusions are transferable to any taxa that form large aggregations and can be identified and counted in photographs.