hundreds of thousands of individuals (Read et al. 2006). Most types of fishing gear (e.g. gillnets, long lines, and pot/trap lines) are known to cause entanglements (

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Long-finned pilot whales and killer whales are widely distributed across the North Atlantic, but few studies have reported their occurrence in Icelandic coastal waters. Here, we use sightings data from research platforms and whale watching tours in six regions of Iceland from 2007 to 2020 to show that the occurrence of long-finned pilot and killer whales varied with region and season. Killer whales were regularly encountered in the south of Iceland during summer and west of Iceland during winter/spring. Long-finned pilot whales were only seen during the summer and were most often encountered in the south, west, and northwest of Iceland. Long-finned pilot whale occurrence in the south of Iceland appeared to increase during the study period but killer whale occurrence showed no noticeable changes. Long-finned pilot whales were sighted often in the areas that were also frequented by killer whales and interspecific interactions were commonly observed when both species co-occurred. Interactions appeared to be antagonistic, with killer whales often avoiding long-finned pilot whales and sometimes fleeing at high speed, similar to what has been described elsewhere in the North Atlantic. In the majority of interactions observed (68%), killer whales avoided long-finned pilot whales by moving away, but in 28% avoidance was at high speed with both species porpoising. This variability in the type of behavioural responses indicates that interactions may be more complex than previously described. We discuss regional trends in long-finned pilot whale and killer whale sightings and potential drivers of the observed interactions.

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.

In Iceland, as in many places globally, the detrimental impacts of whale interactions with fishing gear on both fisheries and whales are not well understood and managed. This study conducted anonymous questionnaires of Icelandic fishers and interviews of capelin purse seine boat captains to gather first-hand knowledge of the issues fishers face due to whale interaction with their fishing gear. Results suggest that the humpback whale is the large whale species that is most often entangled or encircled in fishing gear and causing damage, however on occasion other large whale species are interacting with gear as well. Interactions between humpback whales and fishing gear appears to be primarily concentrated in the north/northeast and southwest of the country where there is high fishing effort and known humpback whale feeding habitat. Humpback whale interactions with gear occurred most often with capelin purse seines, which are targeting humpback whale prey, and data suggests that bycatch of whales in this fishery may be underreported. Damage and losses due to whale collisions with gear were reported to cost fishers up to 55.000.000ISK, suggesting this can be a costly issue for which mitigation measures should be explored. The use of acoustic “pingers” is one mitigation measure that has been previously tested by capelin purse-seiners and is something that captains indicated they would be interested in continuing to try. The creation of a whale entanglement/whale-gear interaction reporting system in Iceland would aid in gathering more data and quantifying how often these events are witnessed and what the consequences of these events are to both the fishers and the whales. This study provides new information about the consequences of large whale interactions with Icelandic fisheries and suggests that future collaboration with fishers can provide insight contributing to best management practices for sustainable fishing and whale conservation.