Habitat loss can trigger migration network collapse by isolating migratory bird breeding grounds from nonbreeding grounds. Theoretically, habitat loss can have vastly different impacts depending on the site's importance within the migratory corridor. However, migration‐network connectivity and the impacts of site loss are not completely understood. We used GPS tracking data on 4 bird species in the Asian flyways to construct migration networks and proposed a framework for assessing network connectivity for migratory species. We used a node‐removal process to identify stopover sites with the highest impact on connectivity. In general, migration networks with fewer stopover sites were more vulnerable to habitat loss. Node removal in order from the highest to lowest degree of habitat loss yielded an increase of network resistance similar to random removal. In contrast, resistance increased more rapidly when removing nodes in order from the highest to lowest betweenness value (quantified by the number of shortest paths passing through the specific node). We quantified the risk of migration network collapse and identified crucial sites by first selecting sites with large contributions to network connectivity and then identifying which of those sites were likely to be removed from the network (i.e., sites with habitat loss). Among these crucial sites, 42% were not designated as protected areas. Setting priorities for site protection should account for a site's position in the migration network, rather than only site‐specific characteristics. Our framework for assessing migration‐network connectivity enables site prioritization for conservation of migratory species.

Habitat availability determines the distribution of migratory waterfowl along their flyway, which further influences the transmission and spatial spread of avian influenza viruses (AIVs). The extensive habitat loss in the East Asian-Australasian Flyway (EAAF) may have potentially altered the virus spread and transmission, but those consequences are rarely studied. We constructed 6 fall migration networks that differed in their level of habitat loss, wherein an increase in habitat loss resulted in smaller networks with fewer sites and links. We integrated an agent-based model and a susceptible-infected-recovered model to simulate waterfowl migration and AIV transmission. We found that extensive habitat loss in the EAAF can 1) relocate the outbreaks northwards, responding to the distribution changes of wintering waterfowl geese, 2) increase the outbreak risk in remaining sites due to larger goose congregations, and 3) facilitate AIV transmission in the migratory population. In addition, our modeling output was in line with the predictions from the concept of “migratory escape”, i.e., the migration allows the geese to “escape” from the location where infection risk is high, affecting the pattern of infection prevalence in the waterfowl population. Our modeling shed light on the potential consequences of habitat loss in spreading and transmitting AIV at the flyway scale and suggested the driving mechanisms behind these effects, indicating the importance of conservation in changing spatial and temporal patterns of AIV outbreaks. What are the possible consequences of extensive habitat loss on the spread and transmission of avian influenza viruses (AIVs)? We used a logistic regression model to select the suitable habitats of Greater white-fronted goose Anser albifrons in the East Asian-Australasian Flyway, which has extensive habitat loss and seasonal passing waterfowl population. We treated these habitats as sites to construct 6 fall migration networks by removing sites in order of habitat loss (i.e., site removal). We then simulated geese migration in these networks and explored the impacts of habitat loss on AIV spread and transmission. We found that extensive habitat loss can cause relocation of the geese and outbreaks and promote the AIV spread and transmission. Our modeling outputs also suggested a “migratory escape” pattern for changing the spatial and temporal pattern of infection prevalence in the population. Overall, our study showed that habitat loss can affect AIV spread and transmission, suggesting the importance of habitat conservation in changing the spatial and temporal pattern of disease prevalence.

Wild waterbirds, and especially wild waterfowl, are considered to be a reservoir for avian influenza viruses, with transmission likely occurring at the agricultural-wildlife interface. In the past few decades, avian influenza has repeatedly emerged in China along the East Asian-Australasian Flyway (EAAF), where extensive habitat conversion has occurred. Rapid environmental changes in the EAAF, especially distributional changes in rice paddy agriculture, have the potential to affect both the movements of wild migratory birds and the likelihood of spillover at the agricultural-wildlife interface. To begin to understand the potential implications such changes may have on waterfowl and disease transmission risk, we created dynamic Brownian Bridge Movement Models (dBBMM) based on waterfowl telemetry data. We used these dBBMM models to create hypothetical scenarios that would predict likely changes in waterfowl distribution relative to recent changes in rice distribution quantified through remote sensing. Our models examined a range of responses in which increased availability of rice paddies would drive increased use by waterfowl and decreased availability would result in decreased use, predicted from empirical data. Results from our scenarios suggested that in southeast China, relatively small decreases in rice agriculture could lead to dramatic loss of stopover habitat, and in northeast China, increases in rice paddies should provide new areas that can be used by waterfowl. Finally, we explored the implications of how such scenarios of changing waterfowl distribution may affect the potential for avian influenza transmission. Our results provide advance understanding of changing disease transmission threats by incorporating real-world data that predicts differences in habitat utilization by migratory birds over time.

Low pathogenic avian influenza virus can mutate to a highly pathogenic strain that causes severe clinical signs in birds and humans. Migratory waterfowl, especially ducks, are considered the main hosts of low pathogenic avian influenza virus, but the role of geese in dispersing the virus over long-distances is still unclear. We collected throat and cloaca samples from three goose species, Bean goose (Anser fabalis), Barnacle goose (Branta leucopsis) and Greater white-fronted goose (Anser albifrons), from their breeding grounds, spring stopover sites, and wintering grounds. We tested if the geese were infected with low pathogenic avian influenza virus outside of their wintering grounds, and analysed the spatial and temporal patterns of infection prevalence on their wintering grounds. Our results show that geese were not infected before their arrival on wintering grounds. Barnacle geese and Greater white-fronted geese had low prevalence of infection just after their arrival on wintering grounds in the Netherlands, but the prevalence increased in successive months, and peaked after December. This suggests that migratory geese are exposed to the virus after their arrival on wintering grounds, indicating that migratory geese might not disperse low pathogenic avian influenza virus during autumn migration.