Overshooting dynamics in a model adaptive radiation

The history of life is punctuated by repeated periods of unusually rapid evolutionary diversification called adaptive radiation. The dynamics of diversity during a radiation reflect an overshooting pattern with an initial phase of exponential-like increase followed by a slower decline. Much attention has been paid to the factors that drive the increase phase, but far less is known about the causes of the decline phase. Decreases in diversity are rarely associated with climatic changes or catastrophic events, suggesting that they may be an intrinsic consequence of diversification. We experimentally identify the factors responsible for losses in diversity during the later stages of the model adaptive radiation of the bacterium Pseudomonas fluorescens. Proximately, diversity declines because of the loss of biofilm-forming niche specialist morphotypes. We show that this loss occurs despite the presence of strong divergent selection late in the radiation and is associated with continued adaptation of resident niche specialists to both the biotic and abiotic environments. These results suggest that losses of diversity in the latter stages of an adaptive radiation may be a general consequence of diversification through competition and lends support to the idea that the conditions favouring the emergence of diversity are different from those that ensure its long-term maintenance.     

A dimensionless number for understanding the evolutionary dynamics of antigenically variable RNA viruses

Antigenically variable RNA viruses are significant contributors to the burden of infectious disease worldwide. One reason for their ubiquity is their ability to escape herd immunity through rapid antigenic evolution and thereby to reinfect previously infected hosts. However, the ways in which these viruses evolve antigenically are highly diverse. Some have only limited diversity in the long-run, with every emergence of a new antigenic variant coupled with a replacement of the older variant. Other viruses rapidly accumulate antigenic diversity over time. Others still exhibit dynamics that can be considered evolutionary intermediates between these two extremes. Here, we present a theoretical framework that aims to understand these differences in evolutionary patterns by considering a virus's epidemiological dynamics in a given host population. Our framework, based on a dimensionless number, probabilistically anticipates patterns of viral antigenic diversification and thereby quantifies a virus's evolutionary potential. It is therefore similar in spirit to the basic reproduction number, the well-known dimensionless number which quantifies a pathogen's reproductive potential. We further outline how our theoretical framework can be applied to empirical viral systems, using influenza A/H3N2 as a case study. We end with predictions of our framework and work that remains to be done to further integrate viral evolutionary dynamics with disease ecology.     

Evolution of virulence in heterogeneous host communities under multiple trade-offs

Many pathogens and parasites are transmitted through hosts that differ in species, sex, genotype, or immune status. In addition, virulence (here defined as disease-induced mortality) and transmission can vary during the infectious period within hosts of different state. Most models of virulence evolution assume that transmission and virulence are constant over the infectious period and that the host population is homogenous. Here, we examine a multispecies susceptible-infected-recovered (SIR) model where transmission occurs within and between species, and transmission and virulence varied during the infectious period. This allows us to understand virulence evolution in a broader range of situations that characterize many emerging diseases. Because emerging pathogens are by definition new to their host populations, they should be expected to rapidly adapt after emergence. We illustrate these evolutionary effects using the framework of adaptive dynamics to examine how virulence evolves after emergence in response to the relative strength of selection on pathogen fitness and mutational variance for virulence. We illustrate the role of evolution by simulating adaptive walks to an evolutionarily stable virulence. We found that the magnitude of between-species transmission and the relative timing of transmission and mortality across species were of primary importance for determining the evolutionarily stable virulence.     

Maintenance of clinal variation for shell colour phenotype in the flat periwinkle Littorina obtusata

Clines can signal spatially varying selection and therefore have long been used to investigate the role of environmental heterogeneity in maintaining genetic variation. However, clinal patterns alone are not sufficient to reject neutrality or to establish the mechanism of selection. Indirect, inferential methods can be used to address neutrality and mechanism, but fully understanding the adaptive significance of clinal variation ultimately requires a direct approach. Ecological model systems such as the rocky intertidal provide a useful context for direct experimentation and can serve as a complement to studies in more traditional genetic model systems. In this study, we use indirect and direct approaches to investigate the role of environmental heterogeneity in the maintenance of shell colour polymorphism in the flat periwinkle snail, Littorina obtusata. We document replicated clines in shell colour morph frequencies over thermal gradients at two spatial scales, contrasting with patterns at previously reported microsatellite loci. In addition, experimental results demonstrate that that shell colour has predictable effects on shell temperature and that these differences in temperature, in turn, coincide with patterns of survivorship under episodic thermal stress. Direct manipulation of shell colour revealed that shell colour, and not a correlated character, was the target of selection. Our study provides evidence that spatially varying selection via thermal regime contributes to the maintenance of shell colour phenotype variation in L. obtusata in the sampled areas of the Gulf of Maine.     

Linking community and disease ecology: the impact of biodiversity on pathogen transmission

The increasing number of zoonotic diseases spilling over from a range of wild animal species represents a particular concern for public health, especially in light of the current dramatic trend of biodiversity loss. To understand the ecology of these multi-host pathogens and their response to environmental degradation and species extinctions, it is necessary to develop a theoretical framework that takes into account realistic community assemblages. Here, we present a multi-host species epidemiological model that includes empirically determined patterns of diversity and composition derived from community ecology studies. We use this framework to study the interaction between wildlife diversity and directly transmitted pathogen dynamics. First, we demonstrate that variability in community composition does not affect significantly the intensity of pathogen transmission. We also show that the consequences of community diversity can differentially impact the prevalence of pathogens and the number of infectious individuals. Finally, we show that ecological interactions among host species have a weaker influence on pathogen circulation than inter-species transmission rates. We conclude that integration of a community perspective to study wildlife pathogens is crucial, especially in the context of understanding and predicting infectious disease emergence events.     

Coexistence and emergent neutrality generate synchrony among competitors in fluctuating environments

Many competitive communities exhibit a puzzling amount of species diversity. In this study, we model a community of symmetric competitors in a fluctuating environment. We use biologically realistic temperature-dependent growth curves with a widely hypothesized trade-off between maximum growth and nice breadth to control the shapes of the curves of different species. We perform three analyses of the community dynamics to investigate the role of environmental fluctuations in community composition and species diversity. We initiate communities with equal abundances of all species and randomize the temperature fluctuations so that there is no correlation between species responses, only noise. We initiate single populations and allow other species to randomly invade the community. We also knock out extant species one by one from an established community and allow them to reinvade after the remaining species have adjusted. We find that competitors with sufficiently different temperature niches coexist via temporal niche differentiation. We also find long-term persistence of species that are very similar to a dominant competitor. This creates communities with species clumped along a temperature niche axis, with stable coexistence between groups and near neutrality within groups. The near neutrality results in interspecific synchrony within the groups, providing an explanation for the maintenance of high diversity in competitive communities where synchrony is commonly observed.     

DnaSAM: Software to perform neutrality testing for large datasets with complex null models

Patterns of DNA sequence polymorphisms can be used to understand the processes of demography and adaptation within natural populations. High-throughput generation of DNA sequence data has historically been the bottleneck with respect to data processing and experimental inference. Advances in marker technologies have largely solved this problem. Currently, the limiting step is computational, with most molecular population genetic software allowing a gene-by-gene analysis through a graphical user interface. An easy-to-use analysis program that allows both high-throughput processing of multiple sequence alignments along with the flexibility to simulate data under complex demographic scenarios is currently lacking. We introduce a new program, named DnaSAM, which allows high-throughput estimation of DNA sequence diversity and neutrality statistics from experimental data along with the ability to test those statistics via Monte Carlo coalescent simulations. These simulations are conducted using the ms program, which is able to incorporate several genetic parameters (e.g. recombination) and demographic scenarios (e.g. population bottlenecks). The output is a set of diversity and neutrality statistics with associated probability values under a user-specified null model that are stored in easy to manipulate text file.     

Niches versus neutrality: uncovering the drivers of diversity in a species-rich community

Ecological models suggest that high diversity can be generated by purely niche-based, purely neutral or by a mixture of niche-based and neutral ecological processes. Here, we compare the degree to which four contrasting hypotheses for coexistence, ranging from niche-based to neutral, explain species richness along a body mass niche axis. We derive predictions from these hypotheses and confront them with species body-mass patterns in a highly sampled marine phytoplankton community. We find that these patterns are consistent only with a mechanism that combines niche and neutral processes, such as the emergent neutrality mechanism. In this work, we provide the first empirical evidence that a niche-neutral model can explain niche space occupancy pattern in a natural species-rich community. We suggest this class of model may be a useful hypothesis for the generation and maintenance of species diversity in other size-structured communities.     

Ecological correlates of species differences in the Lake Tanganyika crab radiation

The endemic crabs of Lake Tanganyika include a phenotypically diverse clade that exhibits recent divergence and low phylogenetic species resolution. There are indications that ecological niche segregation has played a prominent role in the divergence of this clade. We used habitat surveys, gut content analyses and stable isotope analyses to test the extent to which morphological species are ecologically different. Our data show some interspecific segregation in depth, substrate type and mean stable isotope signatures. At the same time, a considerable level of ecological niche overlap is evident among species of Platythelphusa that coexist in rocky littoral habitats. We consider these results in the framework of adaptive radiation theory, and we discuss general ramifications for the maintenance of species diversity in Lake Tanganyika.     

CRISPR-Induced Distributed Immunity in Microbial Populations

In bacteria and archaea, viruses are the primary infectious agents, acting as virulent, often deadly pathogens. A form of adaptive immune defense known as CRISPR-Cas enables microbial cells to acquire immunity to viral pathogens by recognizing specific sequences encoded in viral genomes. The unique biology of this system results in evolutionary dynamics of host and viral diversity that cannot be fully explained by the traditional models used to describe microbe-virus coevolutionary dynamics. Here, we show how the CRISPR-mediated adaptive immune response of hosts to invading viruses facilitates the emergence of an evolutionary mode we call distributed immunity - the coexistence of multiple, equally-fit immune alleles among individuals in a microbial population. We use an eco-evolutionary modeling framework to quantify distributed immunity and demonstrate how it emerges and fluctuates in multi-strain communities of hosts and viruses as a consequence of CRISPR-induced coevolution under conditions of low viral mutation and high relative numbers of viral protospacers. We demonstrate that distributed immunity promotes sustained diversity and stability in host communities and decreased viral population density that can lead to viral extinction. We analyze sequence diversity of experimentally coevolving populations of Streptococcus thermophilus and their viruses where CRISPR-Cas is active, and find the rapid emergence of distributed immunity in the host population, demonstrating the importance of this emergent phenomenon in evolving microbial communities.     

Coalescent under the evolution of coadaptation

Adaptation to novel environments arises either from new beneficial mutations or by utilizing pre‐existing genetic variation. When standing variation is used as the source of new adaptation, fitness effects of alleles may be altered through an environmental change. Alternatively, changes in epistatic genetic backgrounds may convert formerly neutral mutations into beneficial alleles in the new genetic background. By extending the coalescent theory to describe the genealogical histories of two interacting loci, I here investigated the hitchhiking effect of epistatic selection on the amount and pattern of sequence diversity at the linked neutral regions. Assuming a specific form of epistasis between two new mutations that are independently neutral, but together form a coadapted haplotype, I demonstrate that the footprints of epistatic selection differ markedly between the interacting loci depending on the order and relative timing of the two mutational events, even though both mutations are equally essential for the formation of an adaptive gene combination. Our results imply that even when neutrality tests could detect just a single instance of adaptive substitution, there may, in fact, be numerous other hidden mutations that are left undetected, but still play indispensable roles in the evolution of a new adaptation. We expect that the integration of the coalescent framework into the general theory of polygenic inheritance would clarify the connection between factors driving phenotypic evolution and their consequences on underlying DNA sequence changes, which should further illuminate the evolutionary foundation of coadapted systems.     

Emergent neutrality in adaptive asexual evolution

In nonrecombining genomes, genetic linkage can be an important evolutionary force. Linkage generates interference interactions, by which simultaneously occurring mutations affect each other's chance of fixation. Here, we develop a comprehensive model of adaptive evolution in linked genomes, which integrates interference interactions between multiple beneficial and deleterious mutations into a unified framework. By an approximate analytical solution, we predict the fixation rates of these mutations, as well as the probabilities of beneficial and deleterious alleles at fixed genomic sites. We find that interference interactions generate a regime of emergent neutrality: all genomic sites with selection coefficients smaller in magnitude than a characteristic threshold have nearly random fixed alleles, and both beneficial and deleterious mutations at these sites have nearly neutral fixation rates. We show that this dynamic limits not only the speed of adaptation, but also a population's degree of adaptation in its current environment. We apply the model to different scenarios: stationary adaptation in a time-dependent environment and approach to equilibrium in a fixed environment. In both cases, the analytical predictions are in good agreement with numerical simulations. Our results suggest that interference can severely compromise biological functions in an adapting population, which sets viability limits on adaptive evolution under linkage.     

The structure of allelic diversity in the presence of purifying selection

In the absence of selection, the structure of equilibrium allelic diversity is described by the elegant sampling formula of Ewens. This formula has helped to shape our expectations of empirical patterns of molecular variation. Along with coalescent theory, it provides statistical techniques for rejecting the null model of neutrality. However, we still do not fully understand the statistics of the allelic diversity expected in the presence of natural selection. Earlier work has described the effects of strongly deleterious mutations linked to many neutral sites, and allelic variation in models where offspring fitness is unrelated to parental fitness, but it has proven difficult to understand allelic diversity in the presence of purifying selection at many linked sites. Here, we study the population genetics of infinitely many perfectly linked sites, some neutral and some deleterious. Our approach is based on studying the lineage structure within each class of individuals of similar fitness in the deleterious mutation-selection balance. Consistent with previous observations, we find that for moderate and weak selection pressures, the patterns of allelic diversity cannot be described by a neutral model for any choice of the effective population site. We compute precisely how purifying selection at many linked sites distorts the patterns of allelic diversity, by developing expressions for the likelihood of any configuration of allelic types in a sample analogous to the Ewens sampling formula.     

A Novel Statistical Model to Estimate Host Genetic Effects Affecting Disease Transmission

There is increasing recognition that genetic diversity can affect the spread of diseases, potentially affecting plant and livestock disease control as well as the emergence of human disease outbreaks. Nevertheless, even though computational tools can guide the control of infectious diseases, few epidemiological models can simultaneously accommodate the inherent individual heterogeneity in multiple infectious disease traits influencing disease transmission, such as the frequently modeled propensity to become infected and infectivity, which describes the host ability to transmit the infection to susceptible individuals. Furthermore, current quantitative genetic models fail to fully capture the heritable variation in host infectivity, mainly because they cannot accommodate the nonlinear infection dynamics underlying epidemiological data. We present in this article a novel statistical model and an inference method to estimate genetic parameters associated with both host susceptibility and infectivity. Our methodology combines quantitative genetic models of social interactions with stochastic processes to model the random, nonlinear, and dynamic nature of infections and uses adaptive Bayesian computational techniques to estimate the model parameters. Results using simulated epidemic data show that our model can accurately estimate heritabilities and genetic risks not only of susceptibility but also of infectivity, therefore exploring a trait whose heritable variation is currently ignored in disease genetics and can greatly influence the spread of infectious diseases. Our proposed methodology offers potential impacts in areas such as livestock disease control through selective breeding and also in predicting and controlling the emergence of disease outbreaks in human populations.     

Adaptive aging in the context of evolutionary theory

Compelling evidence for an adaptive origin of aging has clashed with traditional evolutionary theory based on exclusively individual selection. The consensus view has been to try to understand aging in the context of a narrow, restrictive evolutionary paradigm, called the Modern Synthesis, or neo-Darwinism. But neo-Darwinism has shown itself to be inadequate in other ways, failing to account for stable ecosystems, for the evolution of sex and the maintenance of diversity and the architecture of the genome, which appears to be optimized for evolvability. Thus aging is not the only reason to consider overhauling the standard theoretical framework. Selection for stable ecosystems is rapid and efficient, and so it is the easiest modification of the neo-Darwinian paradigm to understand and to model. Aging may be understood in this context. More profound and more mysterious are the ways in which the process of evolution itself has been transformed in a bootstrapping process of selection for evolvability. Evolving organisms have learned to channel their variation in ways that are likely to enhance their long-term prospects. This is an expanded notion of fitness. Only in this context can the full spectrum of sophisticated adaptations be understood, including aging, sex, diversity, ecological interdependence, and the structure of the genome.     

A trade-off between neutrality and adaptability limits the optimization of viral quasispecies

Theoretical studies of quasispecies usually focus on two properties of those populations at the mutation-selection equilibrium, namely asymptotic growth rate and population diversity. It has been postulated that, as a consequence of the high error rate of quasispecies replication, an increase of neutrality facilitates population optimization by reducing the amount of mutations with a deleterious effect on fitness. In this study we analyse how the optimization of equilibrium properties is affected when a quasispecies evolves in an environment perturbed through frequent bottleneck events. By means of a simple model we demonstrate that high neutrality may be detrimental when the population has to overcome repeated reductions in the population size, and that the property to be optimized in this situation is the time required to regenerate the quasispecies, i.e. its adaptability. In the scenario described, neutrality and adaptability cannot be simultaneously optimized. When fitness is equated with long-term survivability, high neutrality is the appropriate strategy in constant environments, while populations evolving in fluctuating environments are fitter when their neutrality is low, such that they can respond faster to perturbations. Our results might be relevant to better comprehend how a minority virus could displace the circulating quasispecies, a fact observed in natural infections and essential in viral evolution.     

Revisiting the dimensionality of biological diversity

Biodiversity can be represented by different dimensions. While many diversity metrics try to capture the variation of these dimensions they also lead to a ‘fragmentation’ of the concept of biodiversity itself. Developing a unified measure that integrates all the dimensions of biodiversity is a theoretical solution for this problem, however, it remains operationally impossible. Alternatively, understanding which dimensions better represent the biodiversity of a set of communities can be a reliable way to integrate the different diversity metrics. Therefore, to achieve a holistic understand of biological diversity, we explore the concept of dimensionality. We define dimensionality of diversity as the number of complementary components of biodiversity, represented by diversity metrics, needed to describe biodiversity in an unambiguously and effective way. We provide a solution that joins two components of dimensionality – correlation and the variation – operationalized through two metrics, respectively: evenness of eigenvalues (EE) and importance values (IV). Through simulation we show that considering EE and IV together can provide information that is neglected when only EE is considered. We demonstrate how to apply this framework by investigating the dimensionality of South American small mammal communities. Our example evidenced that, for some representations of biological diversity, more attention is needed in the choice of diversity metrics necessary to effectively characterize biodiversity. We conclude by highlighting that this integrated framework provides a better understanding of dimensionality than considering only the correlation component.     

Maintenance of clonal diversity in Dipsa bifurcata (Fallén, 1810) (Diptera: Lonchopteridae). I. Fluctuating seasonal selection moulds long-term coexistence

The deterministic maintenance of clonal diversity in thelytokous taxa can be seen as a model for understanding how environmental heterogeneity both can stabilize genetic diversity and can allow coexistence of competing species. We here analyze the temporal fluctuations in clonal diversity in the thelytokous Lonchopterid fly, Dipsa bifurcata (Fallén, 1810), at four localities in Sweden over an 8-year period. Estimated fitness values for clones are cyclical, synchronous among populations and correlated with seasonal changes in the environment. Differential winter viability and emergence from overwintering along with differential reproductive rate during the summer appear to be the selective mechanisms by which long-term clonal diversity is maintained. In a companion paper (Tomiuk et al, 2004), we present a model for the maintenance of clonal diversity through the mechanism of differential diapause among clones, utilizing fitness values estimated from the data presented here. In general, our results imply that fluctuating seasonal fitnesses can maintain stable genetic polymorphism within populations, as well as coexistence between closely related competitors, when coupled with differences in diapause phenology.     

Microbial diversity--insights from population genetics

Although many environmental microbial populations are large and genetically diverse, both the level of diversity and the extent to which it is ecologically relevant remain enigmatic. Because the effective (or long-term) population size, N_e, is one of the parameters that determines population genetic diversity, tests and simulations that assume selectively neutral mutations may help to identify the processes that have shaped microbial diversity. Using ecologically important genes, tests of selective neutrality suggest that adaptive as well as non-adaptive types of selection act and that departure from neutrality may be widespread or restricted to small groups of genotypes. Population genetic simulations using population sizes between 10³ and 10⁷ suggest extremely high levels of microbial diversity in environments that sustain large populations. However, census and effective population sizes may differ considerably, and because we know nothing of the evolutionary history of environmental microbial populations, we also have no idea what N_e of environmental populations is. On the one hand, this reflects our ignorance of the microbial world. On the other hand, the tests and simulations illustrate interactions between microbial diversity and microbial population genetics that should inform our thinking in microbial ecology. Because of the different views on microbial diversity across these disciplines, such interactions are crucial if we are to understand the role of genes in microbial communities.     

The labile brain. II. Transients, complexity and selection

The successive expression of neuronal transients is related to dynamic correlations and, as shown in this paper, to dynamic instability. Dynamic instability is a form of complexity, typical of neuronal systems, which may be crucial for adaptive brain function from two perspectives. The first is from the point of view of neuronal selection and self-organizing systems: if selective mechanisms underpin the emergence of adaptive neuronal responses then dynamic instability is, itself, necessarily adaptive. This is because dynamic instability is the source of diversity on which selection acts and is therefore subject to selective pressure. In short, the emergence of order, through selection, depends almost paradoxically on the instabilities that characterize the diversity of brain dynamics. The second perspective is provided by information theory.