Using serosurveys to optimize surveillance for zoonotic pathogens.

Zoonotic pathogens pose a significant risk to human health, with spillover into human populations contributing to chronic disease, sporadic epidemics, and occasional pandemics. Despite the widely recognized burden of zoonotic spillover, our ability to identify which animal populations serve as primary reservoirs for these pathogens remains incomplete. This challenge is compounded when prevalence reaches detectable levels only at specific times of year. In these cases, statistical models designed to predict the timing of peak prevalence could guide field sampling for active infections. Here we develop a general model that leverages routinely collected serosurveillance data to optimize sampling for elusive pathogens. Using simulated data sets we show that our methodology reliably identifies times when pathogen prevalence is expected to peak. We then apply our method to two putative Ebolavirus reservoirs, straw-colored fruit bats (Eidolon helvum) and hammer-headed bats (Hypsignathus monstrosus) to predict when these species should be sampled to maximize the probability of detecting active infections. In addition to guiding future sampling of these species, our method yields predictions for the times of year that are most likely to produce future spillover events. The generality and simplicity of our methodology make it broadly applicable to a wide range of putative reservoir species where seasonal patterns of birth lead to predictable, but potentially short-lived, pulses of pathogen prevalence.

The probability of parallel genetic evolution from standing genetic variation.

Parallel evolution is often assumed to result from repeated adaptation to novel, yet ecologically similar, environments. Here, we develop and analyse a mathematical model that predicts the probability of parallel genetic evolution from standing genetic variation as a function of the strength of phenotypic selection and constraints imposed by genetic architecture. Our results show that the probability of parallel genetic evolution increases with the strength of natural selection and effective population size and is particularly likely to occur for genes with large phenotypic effects. Building on these results, we develop a Bayesian framework for estimating the strength of parallel phenotypic selection from genetic data. Using extensive individual-based simulations, we show that our estimator is robust across a wide range of genetic and evolutionary scenarios and provides a useful tool for rigorously testing the hypothesis that parallel genetic evolution is the result of adaptive evolution. An important result that emerges from our analyses is that existing studies of parallel genetic evolution frequently rely on data that is insufficient for distinguishing between adaptive evolution and neutral evolution driven by random genetic drift. Overcoming this challenge will require sampling more populations and the inclusion of larger numbers of loci.

Multidimensional (co)evolutionary stability.

The complexity of biotic and abiotic environmental conditions is such that the fitness of individuals is likely to depend on multiple traits. Using a synthetic framework of phenotypic evolution that draws from adaptive dynamics and quantitative genetics approaches, we explore how the number of traits under selection influences convergence stability and evolutionary stability in models for coevolution in multidimensional phenotype spaces. Our results allow us to identify three different effects of trait dimensionality on stability. First are (i) a "combinatorial effect": without epistasis and genetic correlations, a higher number of trait dimensions offers more opportunities for equilibria to be unstable; and (ii) epistatic interactions, that is, fitness interactions between traits, which tend to destabilize evolutionary equilibria; this effect increases with the dimension of phenotype space. These first two effects influence both convergence stability and evolutionary stability, while (iii) genetic correlations (due, e.g., to pleiotropy or linkage disequilibrium) can affect only convergence stability. We illustrate the general prediction that increased dimensionality destabilizes evolutionary equilibria using examples drawn from well-studied classical models of frequency-dependent competition for resources, adaptation to a spatially heterogeneous environment, and antagonistic coevolution. In addition, our analyses show that increased dimensionality can favor diversification, for example, in the form of local adaptation, as well as evolutionary escape.

Crossing the threshold: gene flow, dominance and the critical level of standing genetic variation required for adaptation to novel environments.

Genetic architecture plays an important role in the process of adaptation to novel environments. One example is the role of allelic dominance, where advantageous recessive mutations have a lower probability of fixation than advantageous dominant mutations. This classic observation, termed 'Haldane's sieve', has been well explored theoretically for single isolated populations adapting to new selective regimes. However, the role of dominance is less well understood for peripheral populations adapting to novel environments in the face of recurrent and maladaptive gene flow. Here, we use a combination of analytical approximations and individual-based simulations to explore how dominance influences the likelihood of adaptation to novel peripheral environments. We demonstrate that in the face of recurrent maladaptive gene flow, recessive alleles can fuel adaptation only when their frequency exceeds a critical threshold within the ancestral range.

The effects of migration and drift on local adaptation to a heterogeneous environment.

Local adaptation experiments are widely used to quantify the levels of adaptation within a heterogeneous environment. However, theoretical studies generally focus on the probability of fixation of alleles or the mean fitness of populations, rather than local adaptation as it is commonly measured experimentally or in field studies. Here, we develop mathematical models and use them to generate analytical predictions for the level of local adaptation as a function of selection, migration and genetic drift. First, we contrast mean fitness and local adaptation measures and show that the latter can be expressed in a simple and general way as a function of the spatial covariance between population mean phenotype and local environmental conditions. Second, we develop several approximations of a population genetics model to show that the system exhibits different behaviours depending on the rate of migration. The main insights are the following: with intermediate migration, both genetic drift and migration decrease local adaptation; with low migration, drift decreases local adaptation but migration speeds up adaptation; with high migration, genetic drift has no effect on local adaptation. Third, we extend this analysis to cases where the trait under selection is continuous using classical quantitative genetics theory. Finally, we discuss these results in the light of recent experimental work on local adaptation.

The contribution of parasitism to selection on floral traits in Heuchera grossulariifolia.

Parasites are ubiquitous and have well-documented ecological consequences. In contrast, the extent to which parasites drive phenotypic evolution in hosts remains obscure. We use a recently developed statistical technique--selective source analysis--to analyse the strength of phenotypic selection acting on floral traits in the plant Heuchera grossulariifolia attributable to attack by the seed-parasitic moth, Greya politella. This analysis spanned 3 years and included two sympatric populations of the host plant H. grossulariifolia that differ in ploidy. Our analyses revealed that attack by G. politella contributed to phenotypic selection for flowering time and floral display size, favouring earlier flowering in the polyploid population, later flowering in the diploid population and increased floral display size in the polyploid population. Although selection imposed by parasite attack was generally quite weak, in one of the 3 years parasites generated a modestly strong selection gradient (beta = -0.059) that explained 38.6% of total observed phenotypic selection for earlier flowering in the polyploid population. Together, our results demonstrate parasites can generate significant phenotypic selection, but that such selection may be sporadic across populations and time.

Dos and don'ts of testing the geographic mosaic theory of coevolution.

The geographic mosaic theory of coevolution is stimulating much new research on interspecific interactions. We provide a guide to the fundamental components of the theory, its processes and main predictions. Our primary objectives are to clarify misconceptions regarding the geographic mosaic theory of coevolution and to describe how empiricists can test the theory rigorously. In particular, we explain why confirming the three main predicted empirical patterns (spatial variation in traits mediating interactions among species, trait mismatching among interacting species and few species-level coevolved traits) does not provide unequivocal support for the theory. We suggest that strong empirical tests of the geographic mosaic theory of coevolution should focus on its underlying processes: coevolutionary hot and cold spots, selection mosaics and trait remixing. We describe these processes and discuss potential ways each can be tested.

Coevolution between hosts and parasites with partially overlapping geographic ranges.

Many host species interact with a specific parasite within only a fraction of their geographical range. Where host and parasite overlap geographically, selection may be reciprocal constituting a coevolutionary hot spot. Host evolution, however, may be driven primarily by selection imposed by alternative biotic or abiotic factors that occur outside such hot spots. To evaluate the importance of coevolutionary hot spots for host and parasite evolution, we analyse a spatially explicit genetic model for a host that overlaps with a parasite in only part of its geographical range. Our results show that there is a critical amount of overlap beyond which reciprocal selection leads to a coevolutionary response in the host. This critical amount of overlap depends upon the explicit spatial configuration of hot spots. When the amount of overlap exceeds this first critical level, host-parasite coevolution commonly generates stable allele frequency clines rather than oscillations. It is within this region that one of the primary predictions of the geographic mosaic theory is realized, and local maladaptation is prevalent in both species. Past a further threshold of overlap between the species oscillations do evolve, but allele frequencies in both species are spatially synchronous and local maladaptation is absent in both species. A consequence of such transitions between coevolutionary dynamics is that parasite adaptation is inversely proportional to the fraction of its host's range that it occupies. Hence, as the geographical range of a parasite increases, it becomes increasingly maladapted to the host. This suggests a novel mechanism through which the geographical range of parasites may be limited.

Plant polyploidy and non-uniform effects on insect herbivores.

Genomic duplication through polyploidy has played a central role in generating the biodiversity of flowering plants. Nonetheless, how polyploidy shapes species interactions or the ecological dynamics of communities remains largely unknown. Here we provide evidence from a 4 year study demonstrating that the evolution of polyploidy has reshaped the interactions between a widespread plant and three species of phytophagous moths. Our results show that polyploidy has produced non-uniform effects, with polyploids less attacked by one insect species, but significantly more attacked by two other species. These results suggest that the evolution of plant polyploidy may not generally confer uniform resistance to multiple species of insect herbivores. In the absence of such a uniform release, the extreme evolutionary success of polyploid plants is probably due to factors other than escape from herbivory. Together, these results suggest that a primary consequence of plant polyploidy may be to shape the ecological structure of plant-insect interactions, thereby providing opportunities for diversification in both plant and insect taxa.

Coevolutionary clines across selection mosaics.

Much of the dynamics of coevolution may be driven by the interplay between geographic variation in reciprocal selection (selection mosaics) and the homogenizing action of gene flow. We develop a genetic model of geographically structured coevolution in which gene flow links coevolving communities that may differ in both the direction and magnitude of reciprocal selection. The results show that geographically structured coevolution may lead to allele-frequency clines within both interacting species when fitnesses are spatially uniform or spatially heterogeneous. Furthermore, the results show that the behavior and shape of clines differ dramatically among different types of coevolutionary interaction. Antagonistic interactions produce dynamic clines that change shape rapidly through time, producing shifting patterns of local adaptation and maladaptation. Unlike antagonistic interactions, mutualisms generate stable equilibrium patterns that lead to fixed spatial patterns of adaptation. Interactions that vary between mutualism and antagonism produce both equilibrium and dynamic clines. Furthermore, the results demonstrate that these interactions may allow mutualisms to persist throughout the geographic range of an interaction, despite pockets of locally antagonistic selection. In all cases, the coevolved spatial patterns of allele frequencies are sensitive to the relative contributions of gene flow, selection, and overall habitat size, indicating that the appropriate scale for studies of geographically structured coevolution depends on the relative contributions of each of these factors.