Jonathan B. Dain

Highly pathogenic avian influenza viruses (HPAIV) have had disastrous, worldwide effects on wild birds and domestic poultry since the emergence of the A/goose/Guangdong/1/1996 (Gs/GD/96) lineage. The currently circulating H5N1 clade 2.3.4.4b has an expanded set of susceptible hosts, including many migratory wild birds, and is associated with higher transmission rates, increased susceptibility among wild bird hosts, and a greater number of wildlife reservoirs. Certain wild bird life‐history strategies and behaviors have been suggested to explain avian hosts’ susceptibility and exposure to HPAIV. These biological traits include gregariousness, such as colonial nesting and mixed flock foraging, predation or scavenging on wild birds, and association with aquatic habitats. Variation in host infection responses (e.g., infectability, shedding rates and duration, mortality rate, antibody development) informs the overall infection risk across avian species, yet the specific role of biological traits is often inconsistent and unclear across taxa. Moreover, the interactions and potential compounding effects among these biological traits remain largely unknown. To develop a more holistic understanding of cumulative risk across bird species, we integrate existing information on infection risk factors (i.e., susceptibility, immunological response, and behavioral traits) into a qualitative multivariate analysis. This approach enabled us to examine how infection risk factors relate to biological traits (e.g., phylogeny, physiology, behavior, species range) and to begin disentangling their complex interactions. We quantified and summarized these risk factors across host species and qualitatively ranked species by their viral responses along a proposed HPAIV response continuum, guided by expectations of traits and metrics associated with competence or vulnerability to HPAIV. In doing so, we aimed to better understand how viral responses and biological traits synergistically interact to influence cumulative risk across wild bird species. This work broadly expands on the previous avian influenza literature, which has focused on Anseriformes and Charadriiformes as primary viral reservoirs. We tie our findings to effective disease management responses with links to risk components, including descriptions of potential surveillance strategies applied to research and One Health goals, as well as a fuller understanding of how resources may be better deployed for rapid response when spillovers do inevitably occur. Additionally, we identified numerous areas where vital epidemiological information is lacking to best characterize the spread of these viruses. Ultimately, this improved understanding will help identify and inform disease management needs and decision making.

The practical utility of many modern phylogenetic comparative methods can depend on how accurately mathematical models capture the evolutionary process of traits. Boucher and Démery (2016) described a new quantitative trait model, Brownian motion with reflective limits, that they anticipated might be of use in testing hypotheses about a particular sort of constraint on phenotypic character evolution. Since their analytic solution for the probability function under this bounded evolutionary scenario was not practical to evaluate for reasonably-sized trees, Boucher and Démery (2016) also identified a creative technique for computing the likelihood of their model. The basis of this methodology derives from the convergence of an equal-rates, symmetric, ordered Markov chain and continuous stochastic diffusion in the limit as the number of steps in our chain goes to ∞ (or, alternatively, as their widths decrease towards zero). We refer to this convergence in the limit as the discretized diffusion approximation or (more compactly) the discrete approximation. We realized that this discrete approximation of Boucher and Démery (2016) unlocked a number of additional models for the phylogenetic comparative analysis of discrete and continuous trait data, and we explore several of these in the present article. Specifically, we examine application of this discretized diffusion approximation to the threshold model from evolutionary quantitative genetics, to a new “semi-threshold” trait evolution model, to a joint model of discrete and continuous traits in which the discrete trait influences the rate of evolution of our continuous character, as well as a model where precisely the converse is true, and to a discrete character dependent multi-trend trended continuous trait evolution model. We conclude with some context for the origins of our article and discussion of other possible applications of this powerful approach.