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We present an ocean-basin-scale dataset that includes tail fluke photographic identification (photo-ID) and encounter data for most living individual humpback whales (Megaptera novaeangliae) in the North Pacific Ocean. The dataset was built through a broad collaboration combining 39 separate curated photo-ID catalogs, supplemented with community science data. Data from throughout the North Pacific were aggregated into 13 regions, including six breeding regions, six feeding regions, and one migratory corridor. All images were compared with minimal pre-processing using a recently developed image recognition algorithm based on machine learning through artificial intelligence; this system is capable of rapidly detecting matches between individuals with an estimated 97–99% accuracy. For the 2001–2021 study period, a total of 27,956 unique individuals were documented in 157,350 encounters. Each individual was encountered, on average, in 5.6 sampling periods (i.e., breeding and feeding seasons), with an annual average of 87% of whales encountered in more than one season. The combined dataset and image recognition tool represents a living and accessible resource for collaborative, basin-wide studies of a keystone marine mammal in a time of rapid ecological change.

The cosmopolitan distribution of humpback whales (Megaptera novaeangliae) is largely driven by migrations between winter low-latitude breeding grounds and summer high-latitude feeding grounds. Southern Hemisphere humpback whales faced intensive exploitation during the whaling eras and recently show evidence of population recovery. Gene flow and shared song indicate overlap between the western (A) and eastern (B1, B2) Breeding Stocks in the South Atlantic and Indian Oceans (C1). Here, we investigated photo-identification evidence of population interchange using images of individuals photographed during boat-based tourism and research in Brazil and South Africa from 1989 to 2022. Fluke images were uploaded to Happywhale, a global digital database for marine mammal identification. Six whales were recaptured between countries from 2002 to 2021 with resighting intervals ranging from 0.76 to 12.92 years. Four whales originally photographed off Abrolhos Bank, Brazil were photographed off the Western Cape, South Africa (feeding grounds for B2). Two whales originally photographed off the Western Cape were photographed off Brazil, one traveling to the Eastern Cape in the Southwestern Indian Ocean (a migration corridor for C1) before migrating westward to Brazil. These findings photographically confirm interchange of humpback whales across the South Atlantic and Indian Oceans and the importance of international collaboration to understand population boundaries.

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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.

We present an ocean-basin-scale dataset that includes tail fluke photographic identification (photo-ID) and encounter data for the majority of living individual humpback whales (Megaptera novaeangliae) in the North Pacific Ocean. The dataset was built through a broad collaboration combining 39 separate curated photo-ID catalogs supplemented with community science data. All available images were compared using a recently developed machine learning artificial intelligence image recognition algorithm able to rapidly and accurately detect matches between individuals. For the study period of 2001 to 2021, a total of 27,956 unique individuals were documented in 157,379 encounters, with each individual encountered, on average, in 5.6 sampling periods (i.e., breeding and feeding seasons), and with an annual average of 87.1% of whales encountered in more than one season. The combined dataset and image recognition tool represents a living and accessible resource for collaborative, basin-wide studies of a keystone marine mammal in a time of rapid ecological change.

We present an ocean-basin-scale dataset that includes tail fluke photographic identification (photo-ID) and encounter data for most living individual humpback whales (Megaptera novaeangliae) in the North Pacific Ocean. The dataset was built through a broad collaboration combining 39 separate curated photo-ID catalogs, supplemented with community science data. Data from throughout the North Pacific were aggregated into 13 regions, including six breeding regions, six feeding regions, and one migratory corridor. All images were compared with minimal pre-processing using a recently developed machine learning artificial intelligence image recognition algorithm capable of rapidly detecting matches between individuals to an estimated 97–99% accuracy. For the study period of 2001 to 2021, a total of 27,956 unique individuals were documented in 157,350 encounters. Each individual was encountered, on average, in 5.6 sampling periods (i.e., breeding and feeding seasons), with an annual average of 87% of whales encountered in more than one season. The combined dataset and image recognition tool represents a living and accessible resource for collaborative, basin-wide studies of a keystone marine mammal in a time of rapid ecological change.