Utilizing citizen science data to rapidly assess changing associations between wild birds and avian influenza outbreaks in poultry.

High pathogenicity avian influenza virus (HPAIV) is a rapidly evolving virus causing significant economic and environmental harm. Wild birds are a key viral reservoir and an important source of viral incursions into animal populations, including poultry. However, we lack a thorough understanding of which species drive incursions and whether this changes over time. We explored associations between the abundances of 152 avian species and outbreaks of highly pathogenic avian influenza (HPAI) in poultry premises across Great Britain between October 2021 and January 2023. Spatial generalized additive models were used, with species abundance distributions sourced from eBird. Associations were investigated at the species-specific level and across species aggregations. During autumn/winter, associations were generally strongest with waterbirds such as ducks and geese; however, we also found significant associations in groups such as non-native gamebirds and rapid change in species-specific associations over time. Our results demonstrate the value of citizen science to rapidly explore wild species as potential facilitators of disease incursions into well-monitored populations, especially in regions where viral surveillance in wild species is limited. This can be a critical step towards prioritizing targeted surveillance that could inform species-specific biosecurity measures; particularly for HPAIV, which has undergone sudden shifts in host range and continues to rapidly evolve.

Genetic diversity, recombination and cross-species transmission of a waterbird gammacoronavirus in the wild.

Viruses emerging from wildlife can cause outbreaks in humans and domesticated animals. Predicting the emergence of future pathogens and mitigating their impacts requires an understanding of what shapes virus diversity and dynamics in wildlife reservoirs. In order to better understand coronavirus ecology in wild species, we sampled birds within a coastal freshwater lagoon habitat across 5 years, focussing on a large population of mute swans (Cygnus olor) and the diverse species that they interact with. We discovered and characterised the full genome of a divergent gammacoronavirus belonging to the Goose coronavirus CB17 species. We investigated the genetic diversity and dynamics of this gammacoronavirus using untargeted metagenomic sequencing of 223 faecal samples from swans of known age and sex, and RT-PCR screening of 1632 additional bird samples. The virus circulated persistently within the bird community; virus prevalence in mute swans exhibited seasonal variations, but did not change with swan age-class or epidemiological year. One whole genome was fully characterised, and revealed that the virus originated from a recombination event involving an undescribed gammacoronavirus species. Multiple lineages of this gammacoronavirus co-circulated within our study population. Viruses from this species have recently been detected in aquatic birds from both the Anatidae and Rallidae families, implying that host species habitat sharing may be important in shaping virus host range. As the host range of the Goose coronavirus CB17 species is not limited to geese, we propose that this species name should be updated to 'Waterbird gammacoronavirus 1'. Non-invasive sampling of bird coronaviruses may provide a tractable model system for understanding the evolutionary and cross-species dynamics of coronaviruses.

Sensitivity of migratory connectivity estimates to spatial sampling design.

BACKGROUND: The use of statistical methods to quantify the strength of migratory connectivity is commonplace. However, little attention has been given to their sensitivity to spatial sampling designs and scales of inference. METHODS: We examine sources of bias and imprecision in the most widely used methodology, Mantel correlations, under a range of plausible sampling regimes using simulated migratory populations. RESULTS: As Mantel correlations depend fundamentally on the spatial scale and configuration of sampling, unbiased inferences about population-scale connectivity can only be made under certain sampling regimes. Within a contiguous population, samples drawn from smaller spatial subsets of the range generate lower connectivity metrics than samples drawn from the range as a whole, even when the underlying migratory ecology of the population is constant across the population. Random sampling of individuals from contiguous subsets of species ranges can therefore underestimate population-scale connectivity. Where multiple discrete sampling sites are used, by contrast, overestimation of connectivity can arise due to samples being biased towards larger between-individual pairwise distances in the seasonal range where sampling occurs (typically breeding). Severity of all biases was greater for populations with lower levels of true connectivity. When plausible sampling regimes were applied to realistic simulated populations, accuracy of connectivity measures was maximised by increasing the number of discrete sampling sites and ensuring an even spread of sites across the full range. CONCLUSIONS: These results suggest strong potential for bias and imprecision when making quantitative inferences about migratory connectivity using Mantel statistics. Researchers wishing to apply these methods should limit inference to the spatial extent of their sampling, maximise their number of sampling sites, and avoid drawing strong conclusions based on small sample sizes.