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Understanding the interplay between ecological processes and the evolutionary dynamics of quantitative traits in natural systems remains a major challenge. Two main theoretical frameworks are used to address this question, adaptive dynamics and quantitative genetics, both of which have strengths and limitations and are often used by distinct research communities to address different questions. In order to make progress, new theoretical developments are needed that integrate these approaches and strengthen the link to empirical data. Here, we discuss a novel theoretical framework that bridges the gap between quantitative genetics and adaptive dynamics approaches. ‘Oligomorphic dynamics’ can be used to analyse eco‐evolutionary dynamics across different time scales and extends quantitative genetics theory to account for multimodal trait distributions, the dynamical nature of genetic variance, the potential for disruptive selection due to ecological feedbacks, and the non‐normal or skewed trait distributions encountered in nature. Oligomorphic dynamics explicitly takes into account the effect of environmental feedback, such as frequency‐ and density‐dependent selection, on the dynamics of multi‐modal trait distributions and we argue it has the potential to facilitate a much tighter integration between eco‐evolutionary theory and empirical data.

 

Theoretical models suggest that infectious diseases could play a substantial role in determining the spatial extent of host species, but few studies have collected the empirical data required to test this hypothesis. Pathogens that sterilize their hosts or spread through frequency‐dependent transmission could have especially strong effects on the limits of species' distributions because diseased hosts that are sterilized but not killed may continue to produce infectious stages and frequency‐dependent transmission mechanisms are effective even at very low population densities. We collected spatial pathogen prevalence data and population abundance data for alpine carnations infected by the sterilizing pathogenMicrobotryum dianthorum, a parasite that is spread through both frequency‐dependent (vector‐borne) and density‐dependent (aerial spore transmission) mechanisms. Our 13‐year study reveals rapid declines in population abundance without a compensatory decrease in pathogen prevalence. We apply a stochastic, spatial model of parasite spread that accommodates spatial habitat heterogeneity to investigate how the population dynamics depend on multimodal (frequency‐dependent and density‐dependent) transmission. We found that the observed rate of population decline could plausibly be explained by multimodal transmission, but is unlikely to be explained by either frequency‐dependent or density‐dependent mechanisms alone. Multimodal pathogen transmission rates high enough to explain the observed decline predicted that eventual local extinction of the host species is highly likely. Our results add to a growing body of literature showing how multimodal transmission can constrain species distributions in nature.

 

The clear need to mitigate zoonotic risk has fueled increased viral discovery in specific reservoir host taxa. We show that a combination of viral and reservoir traits can predict zoonotic virus virulence and transmissibility in humans, supporting the hypothesis that bats harbor exceptionally virulent zoonoses. However, pandemic prevention requires thinking beyond zoonotic capacity, virulence, and transmissibility to consider collective “burden” on human health. For this, viral discovery targeting specific reservoirs may be inefficient as death burden correlates with viral, not reservoir, traits, and depends on context-specific epidemiological dynamics across and beyond the human–animal interface. These findings suggest that longitudinal studies of viral dynamics in reservoir and spillover host populations may offer the most effective strategy for mitigating zoonotic risk.

 

How social behaviours evolve remains one of the most debated questions in evolutionary biology. An important theoretical prediction is that when organisms interact locally due to limited dispersal or strong social ties, the population structure that emerges may favour cooperation over antagonism. We carry out an experimental test of this theory by directly manipulating population spatial structure in an insect laboratory model system and measuring the impact on the evolution of the extreme selfish behaviour of cannibalism. We show that, as predicted by the theory, Indian meal moth larvae that evolved in environments with more limited dispersal are selected for lower rates of cannibalism. This is important because it demonstrates that local interactions select against selfish behaviour. Therefore, the ubiquitous variation in population structure that we see in nature is a simple mechanism that can help to explain the variation in selfish and cooperative behaviours that we see in nature.

 

The management of future pandemic risk requires a better understanding of the mechanisms that determine the virulence of emerging zoonotic viruses. Meta-analyses suggest that the virulence of emerging zoonoses is correlated with but not completely predictable from reservoir host phylogeny, indicating that specific characteristics of reservoir host immunology and life history may drive the evolution of viral traits responsible for cross-species virulence. In particular, bats host viruses that cause higher case fatality rates upon spillover to humans than those derived from any other mammal, a phenomenon that cannot be explained by phylogenetic distance alone. In order to disentangle the fundamental drivers of these patterns, we develop a nested modeling framework that highlights mechanisms that underpin the evolution of viral traits in reservoir hosts that cause virulence following cross-species emergence. We apply this framework to generate virulence predictions for viral zoonoses derived from diverse mammalian reservoirs, recapturing trends in virus-induced human mortality rates reported in the literature. Notably, our work offers a mechanistic hypothesis to explain the extreme virulence of bat-borne zoonoses and, more generally, demonstrates how key differences in reservoir host longevity, viral tolerance, and constitutive immunity impact the evolution of viral traits that cause virulence following spillover to humans. Our theoretical framework offers a series of testable questions and predictions designed to stimulate future work comparing cross-species virulence evolution in zoonotic viruses derived from diverse mammalian hosts.

 

Infectious diseases may cause some long-term damage to their host, leading to elevated mortality even after recovery. Mortality due to complications from so-called ‘long COVID’ is a stark illustration of this potential, but the impacts of such post-infection mortality (PIM) on epidemic dynamics are not known. Using an epidemiological model that incorporates PIM, we examine the importance of this effect. We find that in contrast to mortality during infection, PIM can induce epidemic cycling. The effect is due to interference between elevated mortality and reinfection through the previously infected susceptible pool. In particular, robust immunity (via decreased susceptibility to reinfection) reduces the likelihood of cycling; on the other hand, disease-induced mortality can interact with weak PIM to generate periodicity. In the absence of PIM, we prove that the unique endemic equilibrium is stable and therefore our key result is that PIM is an overlooked phenomenon that is likely to be destabilizing. Overall, given potentially widespread effects, our findings highlight the importance of characterizing heterogeneity in susceptibility (via both PIM and robustness of host immunity) for accurate epidemiological predictions. In particular, for diseases without robust immunity, such as SARS-CoV-2, PIM may underlie complex epidemiological dynamics especially in the context of seasonal forcing.

 

Abstract While the negative effects that pathogens have on their hosts are well-documented in humans and agricultural systems, direct evidence of pathogen-driven impacts in wild host populations is scarce and mixed. Here, to determine how the strength of pathogen-imposed selection depends on spatial structure, we analyze growth rates across approximately 4000 host populations of a perennial plant through time coupled with data on pathogen presence-absence. We find that infection decreases growth more in the isolated than well-connected host populations. Our inoculation study reveals isolated populations to be highly susceptible to disease while connected host populations support the highest levels of resistance diversity, regardless of their disease history. A spatial eco-evolutionary model predicts that non-linearity in the costs to resistance may be critical in determining this pattern. Overall, evolutionary feedbacks define the ecological impacts of disease in spatially structured systems with host gene flow being more important than disease history in determining the outcome.