Highly pathogenic H5N1 (HP H5N1) influenza A virus (IAV) has been detected annually in North American ducks since its introduction during 2021, but it is unknown if this virus will follow the same seasonal and geographic patterns that have been observed with low-pathogenicity (LP) IAV in this reservoir. We monitored blue-winged teal in the Mississippi flyway prior to the detection of HP H5N1 and during two post-introduction migration cycles from spring 2022 to spring 2024, testing birds for infection and antibodies to IAV nucleoprotein (NP), hemagglutinin subtype H5, and neuraminidase subtype N1. Antigens representing clade 2.3.4.4b HP H5 and LP North American H5 were used for hemagglutination inhibition (HI) and virus neutralization (VN) tests for H5 antibodies. Virologic results were consistent with historic seasonal and geographic patterns reported for LP IAV with peak infections occurring in pre-migration staging areas in Minnesota during fall 2022. However, the high prevalence of the H5 subtype was exceptional compared to historic prevalence estimates at this same site and for the Mississippi flyway. HP H5N1 was detected on wintering areas in Louisiana and Texas during the fall of that same year and this was followed by an increase in estimated antibody prevalence to NP, H5, and N1 with no HP H5N1 detections during the wintering or spring migration periods of 2022/2023. HP H5N1 was not detected in Minnesota during fall 2023 but was detected from a single bird in Louisiana. However, a similar increase in antibody prevalence was observed during the winter and spring period of 2023 and 2024. Over the two migration cycles, there was a temporal shift in observed prevalence and relative titers against the H5 antigens with a higher proportion of ducks testing positive to the 2.3.4.4b H5 antigen and higher relative titer to that antigen compared to the representative LP North American H5 antigen. The seasonal and geographic patterns observed appear to be driven by population immunity during the migration cycle. Results support an initial high infection rate of HP H5N1 in blue-winged teal in the Mississippi flyway followed by a high prevalence of antibodies to NP, H5, and N1. Although prevalence was much reduced in the second migration cycle following introduction, it is not known if this pattern will persist in the longer term or affect historic patterns of subtype diversity in this reservoir.

Clade 2.3.4.4b A/goose/Guangdong/1/1996 (Gs/GD) H5N1 highly pathogenic (HP) influenza A virus (IAV) (HP H5N1) infection in Blue-winged Teal in the Mississippi Flyway in the United States during the 2022/2023 and 2023/2024 migratory seasons followed a seasonal pattern similar to low pathogenic IAV, with infection peaks in late summer and fall. To determine if this seasonal pattern persisted during the 2024/2025 migration season, we repeated testing of Blue-winged Teal from September 2024 to April 2025 in the Mississippi Flyway. Results from other dabbling duck species sampled concurrently with Blue-winged Teal during the 2023/2024 and 2024/2025 migratory seasons in the Mississippi and Central Flyways also were compared. For Blue-winged Teal in the Mississippi Flyway, a seasonal peak in infection was detected during late fall (November/December 2024) on wintering grounds in Louisiana and Texas corresponding to low H5 and N1 antibody prevalence in juvenile birds. Estimated antibody prevalence increased rapidly following this detection, and from January to April 2025, HP H5N1 infections were not detected. Virologic and serologic results in Mallard, Northern Pintail, American Wigeon, Northern Shoveler, and Green-winged Teal mirrored virologic and serologic results observed in Blue-winged Teal and were consistent between flyways.

As compared to other Anseriformes, data related to influenza A virus (IAV) detection and isolation, and IAV antibody detection in whistling ducks (Dendrocygna spp. and Thalassornis leuconotus; subfamily Dendrocygninae) are limited. To better evaluate the potential role of whistling ducks in the epidemiology of IAV, we (1) conducted surveillance for IAV from black-bellied whistling ducks (BBWD, Dendrocygnaautumnalis) sampled in coastal Louisiana, USA, during February 2018 and 2019, and (2) reviewed the published literature and Influenza Resource Database (IRD) that reported results of IAV surveillance of whistling ducks. In the prospective study, from 166 BBWD sampled, one H10N7 IAV was isolated (0.6% prevalence), and overall blocking enzyme-linked immunosorbent assay (bELISA) antibody seroprevalence was 10%. The literature review included publications and data in the IRD from 1984 to 2020 that reported results from nearly 5000 collected samples. For any given collection, the IAV isolation rate never exceeded 5.5%, and seroprevalence estimates ranged from 0 to 42%. Results from our prospective study in Louisiana are consistent with this historic literature; however, although all data consistently demonstrated a low prevalence of infection, the potential role of this species in the epidemiology of IAV should not be totally discounted. In sum, whistling ducks can be infected with IAV, they represent important species on many areas where waterfowl winter, and their distribution across the globe appears to be changing.

Background: Avian influenza (AI) is a global concern and poses hazards to human health, food security, and wildlife conservation. Avian influenza viruses circulate among wild aquatic birds globally and taxa such as migratory waterfowl are considered to be natural biologic reservoirs. Waterfowl have repeatedly been identified as harboring H5 and H7 hemagglutinin subtype influenza infections in many regions throughout the world including the Neotropics, Mexico, Canada, and the United States. Although several studies have examined the spatial and temporal overlap of waterfowl movement to known disease occurrence locations, poultry facilities, and key waterfowl habitats in Asia, there is a lack of comparable movement studies investigating AI in the Western Hemisphere. There is a recognized need for new methods and tools to help quantify dynamic interaction between wild bird hosts and commercial poultry in North America. Objective: We developed a combined animal movement and disease distribution modeling framework to evaluate the spatial-temporal relationship of migratory waterfowl to past AI occurrences in commercial poultry. Our objective was to leverage multi-year animal movement telemetry and historical poultry disease data to characterize intra-annual AI patterns in North America. Methods: We used satellite-marked blue-winged teal (Anas discors) as a representative sample of the larger North American dabbler duck population and applied Bayesian joint hierarchical modeling to concurrently model species distributions, residency times, migration timing, and disease occurrence probability. Our modeling framework accounted for multiscale spatial patterns, including those associated with wild birds, those among disease event locations, and the spatial overlap between birds and disease locations. Results: Results indicate that migratory waterfowl are positively related to AI occurrence over North America such that as waterfowl occurrence probability or residence time increase at a given location, so too does the chance of an AI outbreak in commercial poultry. Our analyses also suggests that AI occurrence probability is greatest during the months of April and May, a period that is largely coincident with our observed blue-winged teal northward migration, and less during the months of September and October, which is the peak of southward migration. Conclusions: Being able to anticipate the geographic locations and times most vulnerable to AI outbreaks is an asset to the domestic poultry industry and those oversight agencies responsible for disease surveillance and response. Our study offers important insights into migratory waterfowl ecology and AI disease dynamics that aid in better preparing for future outbreaks. When modeling disparate facets of disease systems at the wildlife-agriculture interface, it is essential that multiscale spatial patterns be addressed to avoid mistakenly inferring a disease process, or disease-environment relationship from a pattern evaluated at the improper spatial scale.