Whale Museum

Abstract Highly pathogenic avian influenza (HPAI) H5N1 clade 2.3.4.4b has expanded rapidly across the Southern Ocean since 2023, causing extensive mortality in sub-Antarctic wildlife. Yet its penetration into Antarctica and impacts on ice-dependent species remain poorly resolved primarily due to surveillance constraints. We report the first confirmed H5N1 infection in an Antarctic ice-dependent seal (crabeater seal; Lobodon carcinophaga ) and document mortality of crabeater seals across the northern Weddell Sea during November–December 2024. Combining genomic, serological and observational data across nine species, we detected H5N1 RNA in a crabeater seal and a kelp gull ( Larus dominicanus ), and recovered complete HA, NA and M2 gene sequences from both. Phylogenetic analyses allowed us to identify at least two independent introductions of HPAI H5N1 clade 2.3.4.4b into the northern Antarctic Peninsula region. Serology provided strong evidence of prior exposure in scavenging birds, but no detectable H5 immunity in penguins or pinnipeds. Together, the results demonstrate ongoing novel viral incursions into Antarctica, likely facilitated by at-sea processes e.g. animal interactions on ice floes, that remain invisible to land-based surveillance. These findings highlight the vulnerability of ice-dependent pinnipeds to HPAI H5N1 clade 2.3.4.4b and the urgent need for expanded integrated Antarctic monitoring frameworks that pair serology, opportunistic carcass sampling and genomic sequencing.

ABSTRACT Photo‐identification (photo‐ID) is a widely used, non‐invasive method for monitoring individual animals, including humpback whales ( Megaptera novaeangliae ; HBWs), and has provided valuable insights into their population dynamics, movement patterns, and social structures. Traditional identification relies on the trailing edge and ventral pigment patterns of the tail fluke (fluke‐ID); however, not all whales present their flukes, limiting identification and re‐sighting opportunities. We developed a novel aerial‐identification (aerial‐ID) approach using drone imagery to identify individual HBWs based on the arrangement of two features, tubercles (TB) and cookiecutter shark scars (CCS). Between January and March 2022, we sampled 1498 HBWs, including repeated individuals, capturing fluke‐ID images for 772 and aerial‐ID images for 1437. Fluke‐ID yielded 164 re‐sightings (76 lactating females, 88 others), while aerial‐ID yielded 372 (249 and 123, respectively), representing a 227% increase for lactating females and 40% for others. We extended this approach to a multi‐year, cross‐regional dataset (2018–2025) of 54 individuals verified with fluke‐ID. All were matched using aerial‐ID, with the longest re‐sight spanning 2737 days (6.5 years), representing the maximum interval within our study period. Aerial‐ID thus offers a powerful complement to fluke‐ID, expanding demographic coverage, increasing re‐sighting rates, and enabling long‐term, cross‐regional monitoring.

Ocean nourishment sequestering carbon dioxide in the deep ocean Phytoplankton have been essential to life on Earth for over 35 billion years. Through photosynthesis, they consume carbon dioxide on a scale comparable to that of forests and other land plants. Edwina Tanner from the WhaleX Foundation shares insights on this and discusses the potential for plankton-based solutions in marine carbon dioxide removal (mCDR) and ocean nourishment. Great whales act as climate engineers, stimulating productivity by providing nutrients to microscopic phytoplankton, the planet’s real climate giants. Millions of tiny phytoplankton are critical components of the Earth’s system, producing at least 50% of the oxygen we breathe and playing a crucial role in the global carbon cycle. They are the invisible forest that represents around 80% of the biomass in the ocean that is eaten or dies and then sinks as marine snow to sequester gigatonnes of atmospheric carbon dioxide annually. Measurements by satellites show that phytoplankton represent the ‘fast’ carbon cycle that operates on the scale of days to weeks compared to decades or centuries, as do the forests on land. The UN Plankton Manifesto recognizes the heavy lifting that phytoplankton do, outlining the triple planetary crisis - biodiversity, climate, and pollution that plankton-based solutions can address (United Nations, 2024).

Species distribution modeling helps understand how environmental factors influence species distribution, creating profiles to predict presence in unexplored areas and assess ecological impacts. This study examined the habitat use and population ecology of the Chilean dolphin in Seno Skyring, Chilean Patagonia. We used three models-random forest (RF), generalized linear model (GLM), and artificial neural network (ANN)-to predict dolphin distribution based on environmental and biotic data like water temperature, salinity, and fish farm density. Our research has determined that the RF model is the most precise tool for predicting the habitat preferences of Chilean dolphins. The results indicate that these dolphins are primarily located within six kilometers of the coast, strongly correlating with areas featuring numerous fish farms, sheltered waters close to the shore with river inputs, and shallow productive zones. This suggests a potential association between dolphin presence and fish-farming activities. These findings can guide targeted conservation measures, such as regulating fish-farming practices and protecting vital coastal areas to improve the survival prospects of the Chilean dolphin. Given the extensive fish-farming industry in Chile, this research highlights the need for greater knowledge and comprehensive conservation efforts to ensure the species' long-term survival. By understanding and mitigating the impacts of fish farming and other human activities, we can better protect the habitat and well-being of Chilean dolphins.

BackgroundTo reduce heat loss underwater, marine mammals cover their bodies with insulation. Cetaceans in particular rely solely on blubber for insulation which has low conductivity. Blubber establishes a great thermal gradient between the warmer body core and cooler body surface, reducing heat transfer to the environment. A temperature gradient within the blubber determines the conductive heat transfer from the body trunk, where conduction and convection are the primary heat transfer mechanisms in cetaceans. Therefore, measuring the temperature at the innermost part of the blubber, i.e., the temperature at the muscle–blubber interface (Tmbi), can enhance our understanding of thermoregulatory mechanisms in cetaceans. In thermoregulation, activity-induced heat produced by increased muscle metabolism is another factor that plays an important role, however, the effects of activity on Tmbi have not been investigated in cetaceans. To assess this relationship in free-swimming cetaceans, we measured Tmbi and activity levels in a captive Risso’s dolphin (Grampus griseus) using an implantable biologging device.ResultsTmbi and activity data were analyzed for 11 days. The average Tmbi was 35.1 ± 0.6 ºC and the temperature gradient between Tmbi and the water temperature was 13.0 ± 0.7 ºC. Tmbi was higher during the daytime and lower in the early morning. The variation in Tmbi was best explained by both the activity levels and time of day. Tmbi did not simply increase with activity levels; it appeared to remain relatively constant at most activity levels. However, Tmbi appeared to decrease when the animal was inactive and increase when it was intensely active.ConclusionOur results provide important insights into how a dolphin regulates its body temperature underwater. Thermal insulation by blubber and heat production by activity were suggested to play important roles in thermoregulation. Whole-body heat dissipation might be used to regulate temperature increases when heat production is excessive due to intense activity. During inactive periods, decreasing temperature gradient may help reduce heat loss from the body.

Appendix 1. Recording of sounds produced by killer whales feeding on a pygmy sperm whale 370 km west of Monterey, California. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Abstract Land- and boat-based surveys were conducted to collect data during whale watching excursions in July/August and October 2020. Occurrence and distribution of cetaceans south of Pico Island (Azores) were determined. Ten species were sighted, 63 schools of Delphinids and 96 individuals of large whales. The most frequently sighted species were: Sperm whale (Physeter macrocephalus), Sei whale (Balaenoptera borealis), Atlantic spotted dolphin (Stenella frontalis), Common bottlenose dolphin (Tursiops truncatus), and Risso’s dolphin (Grampus griseus). An unusual high numbers of sightings of Sei whale in August and especially in October was noticed, and is not mentioned in the literature so far.

Humpback whales (Megaptera novaeangliae) have recently been observed feeding in the New York Bight (NYB), the section of ocean from Montauk, New York to Cape May, New Jersey, United States (US). This feeding technique brings humpback whales to the surface of the water which puts them at a greater risk of vessel strike. The NYB is already an area of concern due to shipping traffic leading to the Ports of New York and New Jersey (PNYNJ). In this study, data collected by Gotham Whale from 2011 to 2019 were analyzed on humpback whales lunge feeding in the NYB apex, near the entrance to the PNYNJ. Clusters of lunge feeding were investigated, along with the water depths of lunge feeding locations. Using ArcGISPro, six significant hot spot clusters were identified, and water depth of lunge feeding locations ranged from 4.50 to 35.00 m with a mean of 14.83 m. The results of this study provide the first documentation on potential lunge feeding hot spot clusters in the NYB apex. Future studies should obtain comprehensive data looking at the amount of time humpback whales in the NYB are spending on the surface and time they are spending feeding in shipping lanes. This information will be important for the management of marine mammals in this area and may help to mitigate and reduce the incidence of boat strikes to humpback whales in this region.

Abstract Humpback whales (Megaptera novaeangliae) produce calls across age and sex class and throughout their migratory range. Despite growing interest in calling behavior, the function of most calls is unknown. Among identified call types, the ‘whup’ is ubiquitous, and innate, and may serve as a contact call. We conducted an acoustic playback experiment combined with passive acoustic monitoring and visual observations to test the function of the whup on a Southeast Alaskan foraging ground. Using a before-during-after design, we broadcasted either a control sound or a unique whup call sequence. We investigated the change in whup rates (whups/whale/10 minutes) in response to treatment (whup or control) and period (before, during, or after). In 100% of the conspecific trials, whup rates increased during broadcasts, and whup rates were significantly higher than in before or after periods. There was no significant difference in whup rates between before and after periods during conspecific trials. In control trials, there were no significant differences in whup rates between before, during, or after periods. Neither whups nor control playbacks elicited an approach response. Humpback whale vocal responses to whup playbacks suggest that whups function as a contact call, but not necessarily as an aggregation signal.

BackgroundDeep learning algorithms have been built for the detection of systemic and eye diseases based on fundus photographs. The retina possesses features that can be affected by gender differences, and the extent to which these features are captured via photography differs depending on the retinal image field.ObjectiveWe aimed to compare deep learning algorithms’ performance in predicting gender based on different fields of fundus photographs (optic disc–centered, macula-centered, and peripheral fields).MethodsThis retrospective cross-sectional study included 172,170 fundus photographs of 9956 adults aged ≥40 years from the Singapore Epidemiology of Eye Diseases Study. Optic disc–centered, macula-centered, and peripheral field fundus images were included in this study as input data for a deep learning model for gender prediction. Performance was estimated at the individual level and image level. Receiver operating characteristic curves for binary classification were calculated.ResultsThe deep learning algorithms predicted gender with an area under the receiver operating characteristic curve (AUC) of 0.94 at the individual level and an AUC of 0.87 at the image level. Across the three image field types, the best performance was seen when using optic disc–centered field images (younger subgroups: AUC=0.91; older subgroups: AUC=0.86), and algorithms that used peripheral field images had the lowest performance (younger subgroups: AUC=0.85; older subgroups: AUC=0.76). Across the three ethnic subgroups, algorithm performance was lowest in the Indian subgroup (AUC=0.88) compared to that in the Malay (AUC=0.91) and Chinese (AUC=0.91) subgroups when the algorithms were tested on optic disc–centered images. Algorithms’ performance in gender prediction at the image level was better in younger subgroups (aged <65 years; AUC=0.89) than in older subgroups (aged ≥65 years; AUC=0.82).ConclusionsWe confirmed that gender among the Asian population can be predicted with fundus photographs by using deep learning, and our algorithms’ performance in terms of gender prediction differed according to the field of fundus photographs, age subgroups, and ethnic groups. Our work provides a further understanding of using deep learning models for the prediction of gender-related diseases. Further validation of our findings is still needed.