Determining which species are at greatest risk, where they are most vulnerable, and what are the trajectories of their communities and populations is critical for conservation and management. Globally distributed, wide-ranging whales and dolphins present a particular challenge in data collection because no single research team can record data over biologically meaningful areas. Flukebook.org is an open-source web platform that addresses these gaps by providing researchers with the latest computational tools. It integrates photo-identification algorithms with data management, sharing, and privacy infrastructure for whale and dolphin research, enabling the global collaborative study of these global species. With seven automatic identification algorithms trained for 15 different species, resulting in 37 species-specific identification pipelines, Flukebook is an extensible foundation that continually incorporates emerging AI techniques and applies them to cetacean photo identification through continued collaboration between computer vision researchers, software engineers, and biologists. With over 2.0 million photos of over 52,000 identified individual animals submitted by over 250 researchers, the platform enables a comprehensive understanding of cetacean populations, fostering international and cross-institutional collaboration while respecting data ownership and privacy. We outline the technology stack and architecture of Flukebook, its performance on real-world cetacean imagery, and its development as an example of scalable, extensible, and reusable open-source conservation software. Flukebook is a step change in our ability to conduct large-scale research on cetaceans across biologically meaningful geographic ranges, to rapidly iterate population assessments and abundance trajectories, and engage the public in actions to protect them.

There have been no long-term field studies of the potential effect of spay/neuter programs on free-roaming domestic cat population sizes. To address that gap via citizen science, we are developing a novel approach to photographic mark-recapture population research that engages volunteers as both smartphone-wielding data collectors and as online data processors in building capture histories from submitted photos. Here, we present a validation study testing the accuracy of cat advocate volunteers at matching smartphone photos of cats, and we compare their success to a reference group of life science university students. We also examine feline photographic identification from two additional perspectives: what makes a volunteer better at cat identification, and what makes a cat photo more identifiable? 151 cat advocates and 17 students completed 37,800 pairwise photo comparisons using our online platform. Cat advocates' matching attempts (n = 34,080) were correct 98.1% of the time compared with students' 97.5% (n = 3,720). Volunteers who reported a pet cat increased their accuracy. Volunteers who held less than a bachelor's degree, or those who volunteered with cats previously, had reduced accuracy. If a cat was a color other than black, its ability to be identified increased. We demonstrated that our citizen science volunteer sample was not only adequate at identifying individual cats in smartphone photos, but performed better than our sample of life science students-a labor pool commonly trusted to organize data from camera trap research. While photographs are the data foundation of many studies of free-roaming cats, we are the first to analyze by-eye visual identification in this species.