Bonnet, T. | Search bioRxiv

bioRxiv2026

Dispersal, adaptation and persistence of H5N1 in the sub-Antarctic and Antarctica

High pathogenicity avian influenza virus (HPAIV) H5N1 reached the sub-Antarctic and Antarctica in 2023, subsequently spreading to remote locations within this region where it had devastating impacts on seal, penguin and albatross populations. The threat to marine wildlife over this broad area exemplifies the need to understand H5N1 long-distance dispersal and evolution. We obtained 104 novel viral genomic sequences from samples that we collected at South Georgia, Kerguelen, Crozet, Prince Edward, Falklands/Malvinas Islands and the Antarctic Peninsula in a region spanning 8,000 kilometers. Using recent phylogeographic modeling advances we show that H5N1 spread encompassed numerous transmission events between distant locations, accumulating mammalian-adaptive mutations in the process. Seals are the most affected species, but we reveal that the long-distance eastward virus dispersal better aligns with the long-distance movements of large petrels and albatrosses. The risk of H5N1 endemisation, dispersal to other locations and ongoing evolution are highly concerning.

microbiology↗

bioRxiv2018

Heritability, selection, and the response to selection in the presence of phenotypic measurement error: effects, cures, and the role of repeated measurements

Quantitative genetic analyses require extensive measurements of phenotypic traits, a task that is often not trivial, especially in wild populations. On top of instrumental measurement error, some traits may undergo transient (i.e. non-persistent) fluctuations that are biologically irrelevant for selection processes. These two sources of variability, which we denote here as measurement error in a broad sense, are possible causes for bias in the estimation of quantitative genetic parameters. We illustrate how in a continuous trait transient effects with a classical measurement error structure may bias estimates of heritability, selection gradients, and the predicted response to selection. We propose strategies to obtain unbiased estimates with the help of repeated measurements taken at an appropriate temporal scale. However, the fact that in quantitative genetic analyses repeated measurements are also used to isolate permanent environmental instead of transient effects, requires a re-assessment of the information content of repeated measurements. To do so, we propose to distinguish \"short-term\" from \"long-term\" repeats, where the former capture transient variability and the latter the permanent effects. We show how the inclusion of the corresponding variance components in quantitative genetic models yields unbiased estimates of all quantities of interest, and we illustrate the application of the method to data from a Swiss snow vole population.

evolutionary biology↗