Detection of a Novel Reassortant H9N9 Avian Influenza Virus in Free-Range Ducks in Bangladesh

Wild aquatic birds are the primary natural reservoir for influenza A viruses (IAVs). In this study, an A(H9N9) influenza A virus (A/duck/Bangladesh/44493/2020) was identified via routine surveillance in free-range domestic ducks in Bangladesh. Phylogenetic analysis of hemagglutinin showed that the H9N9 virus belonged to the Y439-like lineage. The HA gene had the highest nucleotide identity to A/Bean Goose (Anser fabalis)/South Korea/KNU 2019-16/2019 (H9N2). The other seven gene segments clustered within the Eurasian lineage.

Modeling Eastern Russian High Arctic Geese (Anser fabalis, A. albifrons) during moult and brood rearing in the ‘New Digital Arctic’

Many polar species and habitats are now affected by man-made global climate change and underlying infrastructure. These anthropogenic forces have resulted in clear implications and many significant changes in the arctic, leading to the emergence of new climate, habitats and other issues including digital online infrastructure representing a ‘New Artic’. Arctic grazers, like Eastern Russian migratory populations of Tundra Bean Goose Anser fabalis and Greater White-fronted Goose A. albifrons, are representative examples and they are affected along the entire flyway in East Asia, namely China, Japan and Korea. Here we present the best publicly-available long-term (24 years) digitized geographic information system (GIS) data for the breeding study area (East Yakutia and Chukotka) and its habitats with ISO-compliant metadata. Further, we used seven publicly available compiled Open Access GIS predictor layers to predict the distribution for these two species within the tundra habitats. Using BIG DATA we are able to improve on the ecological niche prediction inference for both species by focusing for the first time specifically on biological relevant population cohorts: post-breeding moulting non-breeders, as well as post-breeding parent birds with broods. To assure inference with certainty, we assessed it with 4 lines of evidence including alternative best-available open access field data from GBIF.org as well as occurrence data compiled from the literature. Despite incomplete data, we found a good model accuracy in support of our evidence for a robust inference of the species distributions. Our predictions indicate a strong publicly best-available relative index of occurrence (RIO). These results are based on the quantified ecological niche showing more realistic gradual occurrence patterns but which are not fully in agreement with the current strictly applied parsimonious flyway and species delineations. While our predictions are to be improved further, e.g. when synergetic data are made freely available, here we offer within data caveats the first open access model platform for fine-tuning and future predictions for this otherwise poorly represented region in times of a rapid changing industrialized ‘New Arctic’ with global repercussions.

Seasonal and diel activity patterns of the endangered taiga bean goose (Anser fabalis fabalis) during the breeding season, monitored with camera traps

Taiga bean goose (Anser fabalis fabalis) is an endangered subspecies that breeds sporadically in remote habitats in the arctic and boreal zones. Due to its elusive behaviour, there is a paucity of knowledge on the behaviour of taiga bean goose during the breeding season, and survey methods for monitoring numbers in the breeding areas are lacking. Camera traps are a useful tool for wildlife monitoring, particularly when there is a need for non-invasive methods due to the shy nature of the species. In this study, we tested the use of camera traps to investigate seasonal and diel activity patterns of taiga bean goose in Finland over two successive breeding seasons, 2018 and 2019. We did this by modelling counts of geese from images with generalized linear and additive mixed models. The camera type (cameras placed by experts specialized in bean goose ecology vs randomly placed cameras) did not influence the count of taiga bean goose (p = 0.386). However, the activity varied significantly by region, Julian day, time of day and temperature, with the study site (individual peatland) and year adding substantial random variation and uncertainty in the counts. Altogether, the best fitting model explained nearly 70% of the variation in taiga bean goose activity. The peak in activity occurred about a month later in the southernmost region compared to the more northern regions, which may indicate behaviours related to migration rather than breeding and moulting. Our results show that long-term monitoring with game camera traps provide a potential unobtrusive approach for studying the behavioural patterns of taiga bean goose and can increase our ecological knowledge of this little-known subspecies. The results can be applied to planning of the annual censuses and finding the optimal time frame for their execution.

Modeling Eastern Russian High Arctic Geese (Anser fabalis, A. albifrons) during moult and brood rearing with Machine Learning, Open Access GIS and 24 years-long Big Data: Good Evidence for the New Digital Arctic of 2021

Many polar species and habitats are now affected by man-made global climate change and underlying infrastructure; it makes for a New Arctic. Arctic grazers, like Eastern Russian migratory populations of Tundra Bean Goose Anser fabalis and Greater White-fronted Goose A. albifrons, are affected along the entire flyway in East Asia, namely China, Japan and Korea. Here we present the best-available long-term 24 years digitized GIS data for the breeding study area (East Yakutia and Chukotka) and its habitats with ISO-compliant metadata. Further, we used seven publically available GIS predictor layers to predict the distribution for these two species within the tundra habitats. We are able to improve on the ecological niche prediction inference for both species by focusing for the first time specifically on biological relevant aspects: post-breeding moulting non-breeders, as well as post-breeding parent birds with broods. We then assessed it with 4 lines of evidence including alternative best-available open access field data from GBIF.org as well as compiled literature and found a good model accuracy in support of our evidence for a robust inference of these new findings. Our predictions indicate a relative index of occurrence (RIO) based on the quantified ecological niche showing more realistic gradual occurrence patterns and that are not fully in agreement with the current strictly applied parsimonious flyway and species delineations. While our predictions are to be improved further, e.g. when synergetic data are made freely available, here we offer the first open access model platform for fine-tuning and future predictions for this otherwise poorly represented region in times of a highly changing industrialized ‘new’ arctic with global repercussions.