Search

Abstract Background Since 2005, highly pathogenic avian influenza A H5N1 viruses have spread from Asia worldwide, infecting poultry, humans and wild birds. Subsequently, global interest in avian influenza (AI) surveillance increased. Objectives Mongolia presents an opportunity to study viruses in wild birds because the country has very low densities of domestic poultry and supports large concentrations of migratory water birds. Methods We conducted AI surveillance in Mongolia over two time periods, 2009–2013 and 2016–2018, utilizing environmental fecal sampling. Fresh fecal samples were collected from water bird congregation sites. Hemagglutinin (HA) and neuraminidase (NA) subtypes of positive samples were identified through viral isolation or molecular assays, with pathogenicity determined by HA subtype or sequencing the HA cleavage site. Results A total of 10,222 samples were collected. Of these, 7,025 fecal samples were collected from 2009 to 2013, and 3,197 fecal samples were collected from 2016 to 2018. Testing revealed 175 (1.7%) positive samples for low‐pathogenicity influenza A, including 118 samples from 2009 to 2013 (1.7%) and 57 samples from 2016 to 2018 (1.8%). HA and NA subtyping of all positives identified 11 subtypes of HA and nine subtypes of NA in 29 different combinations. Within periods, viruses were detected more frequently during the fall season than in the early summer. Conclusion Mongolia's critical wild bird habitat is positioned as a crossroad of multiple migratory flyways. Our work demonstrates the feasibility of using an affordable environmental fecal sampling approach for AI surveillance and contributes to understanding the prevalence and ecology of low‐pathogenicity avian influenza viruses in this important location, where birds from multiple flyways mix.

Abstract Globally, habitat loss has been deemed a major threat to wetland bird populations. However, the underlying mechanism of population declines and variations in the birds’ vulnerability throughout their annual cycle is challenging to determine, yet critical for development of targeted conservation strategies. Over seven years, landscape water availability explained occupancy of breeding territories best when breeding performance, migratory performance, and annual survival of the White-naped Crane (Grus vipio) population in eastern Mongolia were studied. Also, the hatching success of eggs was positively correlated with water availability in addition to plant productivity. High ambient temperatures and large numbers of herder families (and hence more livestock) negatively affected hatching success. High water availability at Luan, a major stopover site increased migration speed during the cranes’ northbound migration to their breeding grounds. In contrast, when water conditions were favorable, the birds stayed longer at the stopover site during southbound migration. Increased human density reduced the use of the stopover site during northbound migration. Finally, cranes arrived early at the breeding grounds when ambient temperature was high in northeast Mongolia. Combining these findings with historical trends in key environmental factors on their breeding grounds explains the general decline observed in this population of cranes in recent decades. Extrapolating our findings with future climate predictions, the outlook seems poor unless urgent action is taken. Knowledge of the mechanisms underlying White-naped Crane population decline in eastern Mongolia identified in this paper should improve the effectiveness of these actions.

Abstract Background Although some migratory birds may take different routes during their outbound and inbound migration, the factors causing these differential migrations to and from the breeding grounds, have rarely been investigated. In Northeast Asia, Demoiselle crane (Anthropoides virgo) performs one of the most extreme “loop” migrations known to date. During outbound migration, they cross the Himalayas to non-breeding sites in northwest India. Contrastingly, during inbound migration to the breeding grounds, they fly around the western end of the Himalayas. We hypothesise that differences in prevailing environmental conditions aloft and/or on-ground during both seasonal migrations are at the core of this phenomenon. Methods Based on the tracking data of 16 individuals of tagged Demoiselle crane, we compared conditions during actual migration with those of simulated “reverse” migration (i.e. by adding 180 degrees to the flight direction and adding and subtracting half a year to the timestamps of outbound and inbound migration, respectively). Results The comparison of actual and simulated “reverse” migration indicated that cranes would have encountered poorer aloft (wind support and thermal uplift) and on-ground conditions (temperature) if they had migrated in a reverse outbound migration and poorer on-ground conditions (Normalised Difference Vegetation Indexes [NDVI]) if they had migrated in a reverse inbound direction. Conclusions Our analyses suggest that both on-ground and aloft conditions play a key role in explaining Demoiselle cranes’ loop migration, during the periods that they chose to use these alternative routes. Knowledge on the determinants of (differential) migration routes allow predicting migration decisions and may be critical in mitigating global change effects on animal migrations.

Long-distance migratory birds travel more rapidly in spring than in autumn, as they face temporal breeding constraints. However, several species travel slower in spring owing to environmental influences, such as food availability and wind conditions. GPS trackers were attached to 17 Whooper Swans (Cygnus cygnus) inhabiting northeastern Mongolia, to determine their migration routes and stopover sites in spring and autumn. Differences between spring and autumn migrations, migration-influencing parameters, and the effect of spring stopover site temperatures were analyzed. Six swans completed perfect tours between their wintering and breeding sites, and these data were used for analysis. Spring migration lasted 57 days, with 49.2 days spent at 3.7 stopover sites. Autumn migration lasted 21.5 days, with 17.5 days spent at 1.0 stopover sites. Thus, the swans traveled more rapidly in autumn than in spring. Migration distance, number of stopovers, migration speed, and straightness were important migration determinants in both spring and autumn. Migration distance, stopover duration, number of stopovers, daily travel speed, travel duration, and migration speed differed significantly between spring and autumn. During spring migration, the temperature at the current stopover sites and that at the future stopover sites displayed significant variations (t ​= ​1585.8, df ​= ​631.6, p ​