We investigated the predator–prey relationship between baleen whales and killer whales by observing predatory scarring (rake marks) on the tail flukes of three mysticete species from the eastern North Pacific Ocean: humpback, blue, and gray whales. We integrated both qualitative scores and new quantitative measures to compare predatory scarring left by killer whales on the three species. We found statistically significant interspecies differences in incidence, location, and accumulation rates of scarring. Gray whales showed a higher incidence of predatory scarring compared to the other two species. Humpback and gray whales bore most of their rake marks on the trailing edge of the tail fluke, while blue whales showed more evidence of predatory scarring on the leading edge of the fluke, potentially consistent with previously hypothesized theories of flee versus fight responses to killer whales for different mysticete species. Of whales with scarring, blue whales were twice as likely to suffer from fluke mutilation compared to humpback and gray whales. Humpback and gray whales were also significantly more likely to accumulate new rake marks over the years compared to blue whales. We examine how these differences provide insight into the prey‐specific hunting behavior of killer whales.

Researchers can investigate many aspects of animal ecology through noninvasive photo–identification. Photo–identification is becoming more efficient as matching individuals between photos is increasingly automated. However, the convolutional neural network models that have facilitated this change need many training images to generalize well. As a result, they have often been developed for individual species that meet this threshold. These single‐species methods might underperform, as they ignore potential similarities in identifying characteristics and the photo–identification process among species. In this paper, we introduce a multi‐species photo–identification model based on a state‐of‐the‐art method in human facial recognition, the ArcFace classification head. Our model uses two such heads to jointly classify species and identities, allowing species to share information and parameters within the network. As a demonstration, we trained this model with 50,796 images from 39 catalogues of 24 cetacean species, evaluating its predictive performance on 21,192 test images from the same catalogues. We further evaluated its predictive performance with two external catalogues entirely composed of identities that the model did not see during training. The model achieved a mean average precision (MAP) of 0.869 on the test set. Of these, 10 catalogues representing seven species achieved a MAP score over 0.95. For some species, there was notable variation in performance among catalogues, largely explained by variation in photo quality. Finally, the model appeared to generalize well, with the two external catalogues scoring similarly to their species' counterparts in the larger test set. From our cetacean application, we provide a list of recommendations for potential users of this model, focusing on those with cetacean photo–identification catalogues. For example, users with high quality images of animals identified by dorsal nicks and notches should expect near optimal performance. Users can expect decreasing performance for catalogues with higher proportions of indistinct individuals or poor quality photos. Finally, we note that this model is currently freely available as code in a GitHub repository and as a graphical user interface, with additional functionality for collaborative data management, via Happywhale.com.

The global expansion of mariculture offers numerous potential benefits but may also pose a threat to wildlife populations. There is currently only one commercial finfish mariculture facility in Hawaiʻi, a nearshore kanpachi (Seriola rivoliana) farm off the west coast of Hawaiʻi Island. This farm lies within the range of several resident odontocete species, and almost daily common bottlenose dolphin (Tursiops truncatus) associations with the farm have been reported since 2007. We analyzed photographs of 35 bottlenose dolphin groups at the farm sighted between 2008 and 2021 in the context of 20 years of survey effort and extensive community science contributions from Hawaiʻi Island. Thirty‐six bottlenose dolphins were identified associated with the farm, representing almost one‐quarter of the estimated total population size. The discovery rate of new individuals at the farm indicates this is a conservative estimate of the total number of individuals associating with the farm, and social network analysis suggests that associations may continue to spread within the population. We also found a high frequency of farm associated bottlenose dolphins showing aggression towards several other species of dolphins, demonstrating impacts to multiple protected species.

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