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Inter-population connectivity of southern elephant seals and the likely intra-species transmission pathways of high pathogenicity avian influenza

McMahon, C.; Hindell, M.; Harcourt, R.; Lerpiniere, I.; Jonsen, I.; Guinet, C.; Woods, R.; Bester, M.; Younger, J. L.; Fountain Jones, N. M.; Burgess, T.

2026-07-08 ecology
10.64898/2026.07.07.737127 bioRxiv
Show abstract

High Pathogenicity Avian Influenza (HPAI) H5N1 clade 2.3.4.4b has spread beyond birds to affect seals across the Southern Ocean and sub-Antarctic region, with southern elephant seals (Mirounga leonina) particularly devastated. The virus, likely introduced via spillover from infected migratory birds, has killed tens of thousands of adult seals and pups throughout most of their range, though Macquarie Island remains unaffected so far. We used twenty years of elephant seal movement data from the southern Indian and Pacific oceans to assess whether seal-to-seal transmission could spread HPAI H5N1 between breeding colonies, despite the vast distances separating them (Marion Island, Iles Crozet, Iles Kerguelen, and Macquarie Island). There was substantial overlap in seals' at-sea distributions during their winter post-moult trips, when seals travel for weeks at average speeds of 3.5 km/h. Two transmission pathways were examined: (1) terrestrial "stepping stone" routes, where infected seals could pass the virus between colonies during short intervals to remain infectious were feasible from Marion Island to Kerguelen but not from Kerguelen to Macquarie Island; and (2) at-sea encounters between seals, which occurred frequently enough to enable transmission. The findings suggest that once established at Macquarie Island, the virus could potentially spread further to New Zealand's sub-Antarctic islands and mainland New Zealand. While seal-to-seal transmission appears possible, we conclude this is unlikely. Nonetheless, understanding at-sea contact rates enhances knowledge of H5N1 epidemiology and demonstrates the value of combining long-term population monitoring with movement data to understand wildlife disease dynamics.

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