Colonization at expanding range edges often involves few founders, reducing effective population size. This process can promote the evolution of self‐fertilization, but implicating historical processes as drivers of trait evolution is often difficult and requires an explicit model of biogeographic history. In plants, contemporary limits to outcrossing are often invoked as evolutionary drivers of self‐fertilization, but historical expansions may shape mating system diversity, with leading‐edge populations evolving elevated selfing ability. In a widespread plant, Campanula americana, we identified a glacial refugium in the southern Appalachian Mountains from spatial patterns of genetic drift among 24 populations. Populations farther from this refugium have smaller effective sizes and fewer rare alleles. They also displayed elevated heterosis in among‐population crosses, reflecting the accumulation of deleterious mutations during range expansion. Although populations with elevated heterosis had reduced segregating mutation load, the magnitude of inbreeding depression lacked geographic pattern. The ability to self‐fertilize was strongly positively correlated with the distance from the refugium and mutation accumulation—a pattern that contrasts sharply with contemporary mate and pollinator limitation. In this and other species, diversity in sexual systems may reflect the legacy of evolution in small, colonizing populations, with little or no relation to the ecology of modern populations.

In flowering plants, shifts from outcrossing to partial or complete self-fertilization have occurred independently thousands of times, yet the underlying adaptive processes are difficult to discern. Selfing's ability to provide reproductive assurance when pollination is uncertain is an oft-cited ecological explanation for its evolution, but this benefit may be outweighed by costs diminishing its selective advantage over outcrossing. We directly studied the fitness effects of a self-compatibility mutation that was backcrossed into a self-incompatible (SI) population of Leavenworthia alabamica, illuminating the direction and magnitude of selection on the mating-system modifier. In array experiments conducted in two years, self-compatible (SC) plants produced 17-26% more seed, but this advantage was counteracted by extensive seed discounting-the replacement of high-quality outcrossed seeds by selfed seeds. Using a simple model and simulations, we demonstrate that SC mutations with these attributes rarely spread to high frequency in natural populations, unless inbreeding depression falls below a threshold value (0.57 ≤ δ ≤ 0.70) in SI populations. A combination of heavy seed discounting and inbreeding depression likely explains why outcrossing adaptations such as self-incompatibility are maintained generally, despite persistent input of selfing mutations, and frequent limits on outcross seed production in nature.

Forecasting the risk of pathogen spillover from reservoir populations of wild or domestic animals is essential for the effective deployment of interventions such as wildlife vaccination or culling. Due to the sporadic nature of spillover events and limited availability of data, developing and validating robust, spatially explicit, predictions is challenging. Recent efforts have begun to make progress in this direction by capitalizing on machine learning methodologies. An important weakness of existing approaches, however, is that they generally rely on combining human and reservoir infection data during the training process and thus conflate risk attributable to the prevalence of the pathogen in the reservoir population with the risk attributed to the realized rate of spillover into the human population. Because effective planning of interventions requires that these components of risk be disentangled, we developed a multi-layer machine learning framework that separates these processes. Our approach begins by training models to predict the geographic range of the primary reservoir and the subset of this range in which the pathogen occurs. The spillover risk predicted by the product of these reservoir specific models is then fit to data on realized patterns of historical spillover into the human population. The result is a geographically specific spillover risk forecast that can be easily decomposed and used to guide effective intervention. Applying our method to Lassa virus, a zoonotic pathogen that regularly spills over into the human population across West Africa, results in a model that explains a modest but statistically significant portion of geographic variation in historical patterns of spillover. When combined with a mechanistic mathematical model of infection dynamics, our spillover risk model predicts that 897,700 humans are infected by Lassa virus each year across West Africa, with Nigeria accounting for more than half of these human infections.

It is often expected that temperate plants have expanded their geographical ranges northward from primarily southern refugia. Evidence for this hypothesis is mixed in eastern North American species, and there is increasing support for colonization from middle latitudes. We studied genome‐wide patterns of variation in RADseq loci to test hypotheses concerning range expansion in a North American forest herb (Campanula americana). First, spatial patterns of genetic differentiation were determined. Then phylogenetic relationships and divergence times were estimated. Spatial signatures of genetic drift were also studied to identify the directionality of recent range expansion and its geographical origins. Finally, spatially explicit scenarios for the spread of plants across the landscape were compared, using variation in the population mutation parameter and Tajima's D. We found strong longitudinal subdivision, with populations clustering into groups west and east of the Mississippi River. While the southeastern region was probably part of a diverse Pleistocene refugium, there is little evidence that range expansion involved founders from these southern locales. Instead, declines in genetic diversity and the loss of rare alleles support a westward colonization wave from a middle latitude refugium near the southern Appalachian Mountains, with subsequent expansion from a Pleistocene staging ground in the Mississippi River Valley (0.51–1.27 million years ago). These analyses implicate stepping stone colonization from middle latitudes as an important mechanism of species range expansion in eastern North America. This study further demonstrates the utility of population genetics as a tool to infer the routes travelled by organisms during geographical range expansion.

Inbreeding depression is dependent on the ploidy of populations and can inhibit the evolution of selfing. While polyploids should generally harbor less inbreeding depression than diploids at equilibrium, it has been unclear whether this pattern holds in non-equilibrium conditions following bottlenecks. We use stochastic individual-based simulations to determine the effects of population bottlenecks on inbreeding depression in diploids and autotetraploids, in addition to cases where neo-autotetraploids form from the union of unreduced gametes. With a ploidy-independent dominance function based on enzyme kinetics, inbreeding depression is generally lower in autotetraploids for fully and partially recessive mutations. Due to the sampling of more chromosomes during reproduction, bottlenecks generally reduce inbreeding depression to a lesser extent in autotetraploids. All else being equal, population bottlenecks may have ploidy-dependent effects for another reason-in some cases matings between close relatives temporarily increase inbreeding depression in autotetraploids by increasing the frequency of the heterozygous genotype harboring the most harmful mutations. When neo-autotetraploids are formed by few individuals, inbreeding depression is dramatically reduced, given extensive masking of harmful mutations following whole genome duplication. This effect persists as nascent tetraploids reach mutation-selection-drift balance, providing a transient period of permissive conditions favoring the evolution of selfing.

Lassa virus is a significant burden on human health throughout its endemic region in West Africa, with most human infections the result of spillover from the primary rodent reservoir of the virus, the natal multimammate mouse, M. natalensis. Here we develop a Bayesian methodology for estimating epidemiological parameters of Lassa virus within its rodent reservoir and for generating probabilistic predictions for the efficacy of rodent vaccination programs. Our approach uses Approximate Bayesian Computation (ABC) to integrate mechanistic mathematical models, remotely-sensed precipitation data, and Lassa virus surveillance data from rodent populations. Using simulated data, we show that our method accurately estimates key model parameters, even when surveillance data are available from only a relatively small number of points in space and time. Applying our method to previously published data from two villages in Guinea estimates the time-averaged R 0 of Lassa virus to be 1.74 and 1.54 for rodent populations in the villages of Bantou and Tanganya, respectively. Using the posterior distribution for model parameters derived from these Guinean populations, we evaluate the likely efficacy of vaccination programs relying on distribution of vaccine-laced baits. Our results demonstrate that effective and durable reductions in the risk of Lassa virus spillover into the human population will require repeated distribution of large quantities of vaccine.

32Lassa virus is a significant burden on human health throughout its endemic region in 33 West Africa, with most human infections the result of spillover from the primary rodent 34 reservoir of the virus, the natal multimammate mouse, M. natalensis. Here we develop a 35 Bayesian methodology for estimating epidemiological parameters of Lassa virus within its 36 rodent reservoir and for generating probabilistic predictions for the efficacy of rodent 37 vaccination programs. Our approach uses Approximate Bayesian Computation (ABC) to 38 integrate mechanistic mathematical models, remotely-sensed precipitation data, and Lassa 39 virus surveillance data from rodent populations. Using simulated data, we show that our 40 method accurately estimates key model parameters, even when surveillance data are available 41 from only a relatively small number of points in space and time. Applying our method to 42 previously published data from two villages in Guinea estimates the time-averaged of Lassa 0 43 virus to be 1.658 and 1.453 for rodent populations in the villages of Bantou and Tanganya, 44 respectively. Using the posterior distribution for model parameters derived from these 45 Guinean populations, we evaluate the likely efficacy of vaccination programs relying on 46 distribution of vaccine-laced baits. Our results demonstrate that effective and durable 47 reductions in the risk of Lassa virus spillover into the human population will require repeated 48 distribution of large quantities of vaccine. 49 3 50 Author Summary 51 Lassa virus is a chronic source of illness throughout West Africa, and is considered to be a threat 52 for widespread emergence. Because most human infections result from contact with infected 53 rodents, interventions that reduce the number of rodents infected with Lassa virus represent 54 promising opportunities for reducing the public health burden of this disease. Evaluating how 55 well alternative interventions are likely to perform is complicated by our relatively poor 56 understanding of viral epidemiology within the reservoir population. Here we develop a novel 57 statistical approach that couples mathematical models and viral surveillance data from rodent 58 populations to robustly estimate key epidemiological parameters. Applying our method to 59 existing data from Guinea yields well-resolved parameter estimates and allows us to simulate a 60 variety of rodent vaccination programs. Together, our results demonstrate that rodent 61 vaccination alone is unlikely to be an effective tool for reducing that public health burden of 62 Lassa fever within West Africa. 63 65 Lassa virus is a zoonotic pathogen endemic to West Africa where it poses a significant 66 burden on human health (Buckley et al. 1970). Although only a relatively small proportion of 67 human cases result in severe symptoms and mortality, human infection is common, with 8-52% 68 of the human population within Sierra Leone seropositive (McCormick et al. 1987; Bausch et al. 69 2001; Dan-Nwafor et al. 2019), indicative of past...

The danger posed by emerging infectious diseases necessitates the development of new tools that can mitigate the risk of animal pathogens spilling over into the human population. One promising approach is the development of recombinant viral vaccines that are transmissible, and thus capable of self-dissemination through hard to reach populations of wild animals. Indeed, mathematical models demonstrate that transmissible vaccines can greatly reduce the effort required to control the spread of zoonotic pathogens in their animal reservoirs, thereby limiting the chances of human infection. A key challenge facing these new vaccines, however, is the inevitability of evolutionary change resulting from their ability to self-replicate and generate extended chains of transmission. Further, carrying immunogenic transgenes is often costly, either in terms of metabolic burden, increased competition with the pathogen, or due to unintended interactions with the viral host regulatory network. As a result, natural selection is expected to favor vaccine strains that down-regulate or delete these transgenes resulting in increased rates of transmission and reduced efficacy against the target pathogen. In addition, efficacy and evolutionary stability will often be at odds; as when longer, more efficacious antigens experience faster rates of evolutionary decay. Here we ask how such trade-offs influence the overall performance of transmissible vaccines. We find that evolutionary instability can substantially reduce performance, even for vaccine candidates with the ideal combination of efficacy and transmission. However, we find that, at least in some cases, vaccine stability and overall performance can be improved by the inclusion of a second, redundant antigen. Overall, our results suggest that the successful application of recombinant transmissible vaccines will require consideration of evolutionary dynamics and epistatic effects, as well as basic measurements of epidemiological features.