One step closer to a transmissible vaccine for rabies virus.

A recent study in PLOS Biology shows that a betaherpesvirus circulating with the vampire bat, Desmodus rotundus, could serve as an effective vector for a transmissible vaccine capable of reducing the risk of rabies virus spillover in Peru.

Quantifying the effectiveness of betaherpesvirus-vectored transmissible vaccines.

Transmissible vaccines have the potential to revolutionize how zoonotic pathogens are controlled within wildlife reservoirs. A key challenge that must be overcome is identifying viral vectors that can rapidly spread immunity through a reservoir population. Because they are broadly distributed taxonomically, species specific, and stable to genetic manipulation, betaherpesviruses are leading candidates for use as transmissible vaccine vectors. Here we evaluate the likely effectiveness of betaherpesvirus-vectored transmissible vaccines by developing and parameterizing a mathematical model using data from captive and free-living mouse populations infected with murine cytomegalovirus (MCMV). Simulations of our parameterized model demonstrate rapid and effective control for a range of pathogens, with pathogen elimination frequently occurring within a year of vaccine introduction. Our results also suggest, however, that the effectiveness of transmissible vaccines may vary across reservoir populations and with respect to the specific vector strain used to construct the vaccine.

Predicting the fine-scale spatial distribution of zoonotic reservoirs using computer vision.

Zoonotic diseases threaten human health worldwide and are often associated with anthropogenic disturbance. Predicting how disturbance influences spillover risk is critical for effective disease intervention but difficult to achieve at fine spatial scales. Here, we develop a method that learns the spatial distribution of a reservoir species from aerial imagery. Our approach uses neural networks to extract features of known or hypothesized importance from images. The spatial distribution of these features is then summarized and linked to spatially explicit reservoir presence/absence data using boosted regression trees. We demonstrate the utility of our method by applying it to the reservoir of Lassa virus, Mastomys natalensis, within the West African nations of Sierra Leone and Guinea. We show that, when trained using reservoir trapping data and publicly available aerial imagery, our framework learns relationships between environmental features and reservoir occurrence and accurately ranks areas according to the likelihood of reservoir presence.

Uncovering Cryptic Coevolution.

AbstractStudies of coevolution in the wild have largely focused on reciprocally specialized species pairs with striking and exaggerated phenotypes. Textbook examples include interactions between toxic newts and their garter snake predators, long-tongued flies and the flowers they pollinate, and weevils with elongated rostra used to bore through the defensive pericarp of their host plants. Although these studies have laid a foundation for understanding coevolution in the wild, they have also contributed to the widespread impression that coevolution is a rare and quirky sideshow to the day-to-day grind of ecology and evolution. In this perspective, we argue that the focus of coevolution has been biased toward the obvious and ignored the cryptic. We have focused on the obvious-studies of reciprocally specialized species pairs with exaggerated phenotypes-mainly because we have lacked the statistical tools required to study coevolution in more generalized and phenotypically mundane systems. Building from well-established coevolutionary theory, we illustrate how model-based approaches can be used to remove this barrier and begin estimating the strength of coevolutionary selection indirectly using routinely collected data, thus uncovering cryptic coevolution in more typical communities. By allowing the distribution of coevolutionary selection to be estimated across genomes, phylogenies, and communities and over deep timescales, these novel approaches have the potential to revolutionize the way we study coevolution. As we develop a road map to these next-generation approaches, we highlight recent studies making notable progress in this direction.

Reservoir population ecology, viral evolution and the risk of emerging infectious disease.

The ecology and life history of wild animals influences their potential to harbour infectious disease. This observation has motivated studies identifying empirical relationships between traits of wild animals and historical patterns of spillover and emergence into humans. Although these studies have identified compelling broad-scale patterns, they are generally agnostic with respect to underlying mechanisms. Here, we develop mathematical models that couple reservoir population ecology with viral epidemiology and evolution to clarify existing verbal arguments and pinpoint the conditions that favour spillover and emergence. Our results support the idea that average lifespan influences the likelihood of an animal serving as a reservoir for human infectious disease. At the same time, however, our results show that the magnitude of this effect is sensitive to the rate of viral mutation. Our results also demonstrate that viral pathogens causing persistent infections or a transient immune response within the reservoir are more likely to fuel emergence. Genetically explicit stochastic simulations enrich these mathematical results by identifying relationships between the genetic basis of transmission and the risk of spillover and emergence. Together, our results clarify the scope of applicability for existing hypotheses and refine our understanding of emergence risk.

Bridging the gap: Using reservoir ecology and human serosurveys to estimate Lassa virus spillover in West Africa.

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.

Coevolutionary Arms Races and the Conditions for the Maintenance of Mutualism.

AbstractEmpirical evidence suggests that coevolutionary arms races between flowering plants and their pollinators can occur in wild populations. In extreme cases, trait escalation may result in evolutionary switching from mutualism to parasitism. However, theoretical approaches to studying coevolution typically assume fixed types of ecological interactions and ignore the evolution of absolute fitness. Here, we introduce a novel approach to track the evolution of absolute fitness as a framework to determine when escalatory coevolution results in a switch from mutualism to parasitism. We apply our approach to two previously studied mechanisms mediating selection as a function of phenotype. Our results demonstrate that interactions mediated by a "bigger-is-better" mechanism evolve toward parasitism. In contrast, generalizing the classical trait-matching mechanism so that the fitness of each species is optimized when trait values mismatch by a particular amount, we find theoretical support for indefinite trait exaggeration that preserves mutualistic interactions. Building on our results, we discuss the consequences of coevolutionary arms races for the maintenance of cheating. Moving beyond pairwise interactions, we consider the ramifications of coevolution in a South African pollination network for the evolution of parasitism. Future work extending our approach beyond pairwise interactions can lead to a framework for understanding the evolution of parasitism in mutualistic networks and further insights into the structure and dynamic nature of ecological communities in general.

A white noise approach to evolutionary ecology.

Although the evolutionary response to random genetic drift is classically modelled as a sampling process for populations with fixed abundance, the abundances of populations in the wild fluctuate over time. Furthermore, since wild populations exhibit demographic stochasticity and since random genetic drift is in part due to demographic stochasticity, theoretical approaches are needed to understand the role of demographic stochasticity in eco-evolutionary dynamics. Here we close this gap for quantitative characters evolving in continuously reproducing populations by providing a framework to track the stochastic dynamics of abundance density across phenotypic space using stochastic partial differential equations. In the process we develop a set of heuristics to operationalize the powerful, but abstract theory of white noise and diffusion-limits of individual-based models. Applying these heuristics, we obtain stochastic ordinary differential equations that generalize classical expressions of ecological quantitative genetics. In particular, by supplying growth rate and reproductive variance as functions of abundance densities and trait values, these equations track population size, mean trait and additive genetic variance responding to mutation, demographic stochasticity, random genetic drift, deterministic selection and noise-induced selection. We demonstrate the utility of our approach by formulating a model of diffuse coevolution mediated by exploitative competition for a continuum of resources. In addition to trait and abundance distributions, this model predicts interaction networks defined by niche-overlap, competition coefficients, or selection gradients. Using a high-richness approximation, we find linear selection gradients and competition coefficients are uncorrelated, but magnitudes of linear selection gradients and quadratic selection gradients are both positively correlated with competition coefficients. Hence, competing species that strongly affect each other's abundance tend to also impose selection on one another, but the directionality is not predicted. This approach contributes to the development of a synthetic theory of evolutionary ecology by formalizing first principle derivations of stochastic models tracking feedbacks of biological processes and the patterns of diversity they produce.

Approximate Bayesian estimation of coevolutionary arms races.

Exaggerated traits involved in species interactions have long captivated the imagination of evolutionary biologists and inspired the durable metaphor of the coevolutionary arms race. Despite decades of research, however, we have only a handful of examples where reciprocal coevolutionary change has been rigorously established as the cause of trait exaggeration. Support for a coevolutionary mechanism remains elusive because we lack generally applicable tools for quantifying the intensity of coevolutionary selection. Here we develop an approximate Bayesian computation (ABC) approach for estimating the intensity of coevolutionary selection using population mean phenotypes of traits mediating interspecific interactions. Our approach relaxes important assumptions of a previous maximum likelihood approach by allowing gene flow among populations, variable abiotic environments, and strong coevolutionary selection. Using simulated data, we show that our ABC method accurately infers the strength of coevolutionary selection if reliable estimates are available for key background parameters and ten or more populations are sampled. Applying our approach to the putative arms race between the plant Camellia japonica and its seed predatory weevil, Curculio camelliae, provides support for a coevolutionary hypothesis but fails to preclude the possibility of unilateral evolution. Comparing independently estimated selection gradients acting on Camellia pericarp thickness with values simulated by our model reveals a correlation between predicted and observed selection gradients of 0.941. The strong agreement between predicted and observed selection gradients validates our method.

Recombinant vector vaccine evolution.

Replicating recombinant vector vaccines consist of a fully competent viral vector backbone engineered to express an antigen from a foreign transgene. From the perspective of viral replication, the transgene is not only dispensable but may even be detrimental. Thus vaccine revertants that delete or inactivate the transgene may evolve to dominate the vaccine virus population both during the process of manufacture of the vaccine as well as during the course of host infection. A particular concern is that this vaccine evolution could reduce its antigenicity-the immunity elicited to the transgene. We use mathematical and computational models to study vaccine evolution and immunity. These models include evolution arising during the process of manufacture, the dynamics of vaccine and revertant growth, plus innate and adaptive immunity elicited during the course of infection. Although the selective basis of vaccine evolution is easy to comprehend, the immunological consequences are not. One complication is that the opportunity for vaccine evolution is limited by the short period of within-host growth before the viral population is cleared. Even less obvious, revertant growth may only weakly interfere with vaccine growth in the host and thus have a limited effect on immunity to vaccine. Overall, we find that within-host vaccine evolution can sometimes compromise vaccine immunity, but only when the extent of evolution during vaccine manufacture is severe, and this evolution can be easily avoided or mitigated.

Patterns of floral morphology in relation to climate and floral visitors.

BACKGROUND AND AIMS: The diversity of floral morphology among plant species has long captured the interest of biologists and led to the development of a number of explanatory theories. Floral morphology varies substantially within species, and the mechanisms maintaining this diversity are diverse. One possibility is that spatial variation in the pollinator fauna drives the evolution of spatially divergent floral ecotypes adapted to the local suite of pollinators. Another possibility is that geographic variation in the abiotic environment and local climatic conditions favours different floral morphologies in different regions. Although both possibilities have been shown to explain floral variation in some cases, they have rarely been competed against one another using data collected from large spatial scales. In this study, we assess floral variation in relation to climate and floral visitors in four oil-reward-specialized pollination interactions. METHODS: We used a combination of large-scale plant and pollinator samplings, morphological measures and climatic data. We analysed the data using spatial approaches, as well as traditional multivariate and structural equation modelling approaches. KEY RESULTS: Our results indicate that the four species have different levels of specialization, and that this can be explained by their climatic niche breadth. In addition, our results show that, at least for some species, floral morphology can be explained by the identity of floral visitors, with climate having only an indirect effect. CONCLUSIONS: Our results demonstrate that, even in very specialized interactions, both biotic and abiotic variables can explain a substantial amount of intraspecific variation in floral morphology.

The measurement of coevolution in the wild.

Coevolution has long been thought to drive the exaggeration of traits, promote major evolutionary transitions such as the evolution of sexual reproduction and influence epidemiological dynamics. Despite coevolution's long suspected importance, we have yet to develop a quantitative understanding of its strength and prevalence because we lack generally applicable statistical methods that yield numerical estimates for coevolution's strength and significance in the wild. Here, we develop a novel method that derives maximum likelihood estimates for the strength of direct pairwise coevolution by coupling a well-established coevolutionary model to spatially structured phenotypic data. Applying our method to two well-studied interactions reveals evidence for coevolution in both systems. Broad application of this approach has the potential to further resolve long-standing evolutionary debates such as the role species interactions play in the evolution of sexual reproduction and the organisation of ecological communities.

The adaptive value of epigenetic mutation: Limited in large but high in small peripheral populations.

The fate of populations during range expansions, invasions and environmental changes is largely influenced by their ability to adapt to peripheral habitats. Recent models demonstrate that stable epigenetic modifications of gene expression that occur more frequently than genetic mutations can both help and hinder adaptation in panmictic populations. However, these models do not consider interactions between epimutations and evolutionary forces in peripheral populations. Here, we use mainland-island mathematical models and simulations to explore how the faster rate of epigenetic mutation compared to genetic mutations interacts with migration, selection and genetic drift to affect adaptation in peripheral populations. Our model focuses on cases where epigenetic marks are stably inherited. In a large peripheral population, where the effect of genetic drift is negligible, our analyses suggest that epimutations with random fitness impacts that occur at rates as high as 10-3 increase local adaptation when migration is strong enough to overwhelm divergent selection. When migration is weak relative to selection and epimutations with random fitness impacts decrease adaptation, we find epigenetic modifications must be highly adaptively biased to enhance adaptation. Finally, in small peripheral populations, where genetic drift is strong, epimutations contribute to adaptation under a wider range of evolutionary conditions. Overall, our results suggest that epimutations can change outcomes of adaptation in peripheral populations, which has implications for understanding conservation and range expansions and contractions, especially of small populations.

Detecting the macroevolutionary signal of species interactions.

Species interactions lie at the heart of many theories of macroevolution, from adaptive radiation to the Red Queen. Although some theories describe the imprint that interactions will have over long timescales, we are still missing a comprehensive understanding of the effects of interactions on macroevolution. Current research shows strong evidence for the impact of interactions on macroevolutionary patterns of trait evolution and diversification, yet many macroevolutionary studies have only a tenuous relationship to ecological studies of interactions over shorter timescales. We review current research in this area, highlighting approaches that explicitly model species interactions and connect them to broad-scale macroevolutionary patterns. We also suggest that progress has been made by taking an integrative interdisciplinary look at individual clades. We focus on African cichlids as a case study of how this approach can be fruitful. Overall, although the evidence for species interactions shaping macroevolution is strong, further work using integrative and model-based approaches is needed to spur progress towards understanding the complex dynamics that structure communities over time and space.

Coevolution Slows the Disassembly of Mutualistic Networks.

Important groups of mutualistic species are threatened worldwide, and identifying factors that make them more or less fragile in the face of disturbance is becoming increasingly critical. Although much research has focused on identifying the ecological factors that favor the stability of communities rich in mutualists, much less has been devoted to understanding the role played by historical and contemporary evolution. Here we develop mathematical models and computer simulations of coevolving mutualistic communities that allow us to explore the importance of coevolution in stabilizing communities against anthropogenic disturbance. Our results demonstrate that communities with a long history of coevolution are substantially more robust to disturbance, losing individual species and interactions at lower rates. In addition, our results identify a novel phenomenon-coevolutionary rescue-that mitigates the impacts of ongoing anthropogenic disturbance by rewiring the network structure of the community in a way that compensates for the extinction of individual species and interactions.

Coevolution in multidimensional trait space favours escape from parasites and pathogens.

Almost all species are subject to continuous attack by parasites and pathogens. Because parasites and pathogens tend to have shorter generation times and often experience stronger selection due to interaction than their victims do, it is frequently argued that they should evolve more rapidly and thus maintain an advantage in the evolutionary race between defence and counter-defence. This prediction generates an apparent paradox: how do victim species survive and even thrive in the face of a continuous onslaught of more rapidly evolving enemies? One potential explanation is that defence is physiologically, mechanically or behaviourally easier than attack, so that evolution is less constrained for victims than for parasites or pathogens. Another possible explanation is that parasites and pathogens have enemies themselves and that victim species persist because parasites and pathogens are regulated from the top down and thus generally have only modest demographic impacts on victim populations. Here we explore a third possibility: that victim species are not as evolutionarily impotent as conventional wisdom holds, but instead have unique evolutionary advantages that help to level the playing field. We use quantitative genetic analysis and individual-based simulations to show that victims can achieve such an advantage when coevolution involves multiple traits in both the host and the parasite.

Rethinking Conventional Wisdom: Are Locally Adapted Parasites Ahead in the Coevolutionary Race?

The metaphors of the Red Queen and the arms race have inspired a large amount of research aimed at understanding the process of antagonistic coevolution between hosts and parasites. One approach that has been heavily used is to estimate the strength of parasite local adaptation using a reciprocal cross infection or transplant study. These studies frequently conclude that the locally adapted species is ahead in the coevolutionary race. Here, I use mathematical models to decompose parasite infectivity into components attributable to local versus global adaptation and to develop a robust index of which species is ahead in the coevolutionary race, which I term coevolutionary advantage. Computer simulations of coevolving host-parasite interactions demonstrate that because the magnitudes of local and global adaptation are largely independent, the link between the sign of local adaptation and coevolutionary advantage is tenuous. A consequence of the weak coupling between local adaptation and coevolutionary advantage is that the bulk of existing empirical studies do not sample enough populations for any reliable conclusions to be drawn. Together, these results suggest that the long-standing conventional wisdom holding that locally adapted parasites are ahead in the coevolutionary race should be reconsidered.

Eradicating infectious disease using weakly transmissible vaccines.

Viral vaccines have had remarkable positive impacts on human health as well as the health of domestic animal populations. Despite impressive vaccine successes, however, many infectious diseases cannot yet be efficiently controlled or eradicated through vaccination, often because it is impossible to vaccinate a sufficient proportion of the population. Recent advances in molecular biology suggest that the centuries-old method of individual-based vaccine delivery may be on the cusp of a major revolution. Specifically, genetic engineering brings to life the possibility of a live, transmissible vaccine. Unfortunately, releasing a highly transmissible vaccine poses substantial evolutionary risks, including reversion to high virulence as has been documented for the oral polio vaccine. An alternative, and far safer approach, is to rely on genetically engineered and weakly transmissible vaccines that have reduced scope for evolutionary reversion. Here, we use mathematical models to evaluate the potential efficacy of such weakly transmissible vaccines. Our results demonstrate that vaccines with even a modest ability to transmit can significantly lower the incidence of infectious disease and facilitate eradication efforts. Consequently, weakly transmissible vaccines could provide an important tool for controlling infectious disease in wild and domestic animal populations and for reducing the risks of emerging infectious disease in humans.

Revisiting Darwin's conundrum reveals a twist on the relationship between phylogenetic distance and invasibility.

A key goal of invasion biology is to identify the factors that favor species invasions. One potential indicator of invasiveness is the phylogenetic distance between a nonnative species and species in the recipient community. However, predicting invasiveness using phylogenetic information relies on an untested assumption: that both biotic resistance and facilitation weaken with increasing phylogenetic distance. We test the validity of this key assumption using a mathematical model in which a novel species is introduced into communities with varying ecological and phylogenetic relationships. Contrary to what is generally assumed, we find that biotic resistance and facilitation can either weaken or intensify with phylogenetic distance, depending on the mode of interspecific interactions (phenotype matching or phenotype differences) and the resulting evolutionary trajectory of the recipient community. Thus, we demonstrate that considering the mechanisms that drive phenotypic divergence between native and nonnative species can provide critical insight into the relationship between phylogenetic distance and invasibility.

Predicting rates of interspecific interaction from phylogenetic trees.

Integrating phylogenetic information can potentially improve our ability to explain species' traits, patterns of community assembly, the network structure of communities, and ecosystem function. In this study, we use mathematical models to explore the ecological and evolutionary factors that modulate the explanatory power of phylogenetic information for communities of species that interact within a single trophic level. We find that phylogenetic relationships among species can influence trait evolution and rates of interaction among species, but only under particular models of species interaction. For example, when interactions within communities are mediated by a mechanism of phenotype matching, phylogenetic trees make specific predictions about trait evolution and rates of interaction. In contrast, if interactions within a community depend on a mechanism of phenotype differences, phylogenetic information has little, if any, predictive power for trait evolution and interaction rate. Together, these results make clear and testable predictions for when and how evolutionary history is expected to influence contemporary rates of species interaction.