Life history optimisation drives latitudinal gradients and responses to global change in marine fishes.

Within many species, and particularly fish, fecundity does not scale with mass linearly; instead, it scales disproportionately. Disproportionate intraspecific size-reproduction relationships contradict most theories of biological growth and present challenges for the management of biological systems. Yet the drivers of reproductive scaling remain obscure and systematic predictors of how and why reproduction scaling varies are lacking. Here, we parameterise life history optimisation model to predict global patterns in the life histories of marine fishes. Our model predict latitudinal trends in life histories: Polar fish should reproduce at a later age and show steeper reproductive scaling than tropical fish. We tested and confirmed these predictions using a new, global dataset of marine fish life histories, demonstrating that the risks of mortality shape maturation and reproductive scaling. Our model also predicts that global warming will profoundly reshape fish life histories, favouring earlier reproduction, smaller body sizes, and lower mass-specific reproductive outputs, with worrying consequences for population persistence.

A null model for the distribution of fitness effects of mutations.

The distribution of fitness effects (DFE) of new mutations is key to our understanding of many evolutionary processes. Theoreticians have developed several models to help understand the patterns seen in empirical DFEs. Many such models reproduce the broad patterns seen in empirical DFEs but these models often rely on structural assumptions that cannot be tested empirically. Here, we investigate how much of the underlying "microscopic" biological processes involved in the mapping of new mutations to fitness can be inferred from "macroscopic" observations of the DFE. We develop a null model by generating random genotype-to-fitness maps and show that the null DFE is that with the largest possible information entropy. We further show that, subject to one simple constraint, this null DFE is a Gompertz distribution. Finally, we illustrate how the predictions of this null DFE match empirically measured DFEs from several datasets, as well as DFEs simulated from Fisher's geometric model. This suggests that a match between models and empirical data is often not a very strong indication of the mechanisms underlying the mapping of mutation to fitness.

The impact of threshold decision mechanisms of collective behavior on disease spread.

Humans are a hyper-social species, which greatly impacts the spread of infectious diseases. How do social dynamics impact epidemiology and what are the implications for public health policy? Here, we develop a model of disease transmission that incorporates social dynamics and a behavior that reduces the spread of disease, a voluntary nonpharmaceutical intervention (NPI). We use a "tipping-point" dynamic, previously used in the sociological literature, where individuals adopt a behavior given a sufficient prevalence of the behavior in the population. The thresholds at which individuals adopt the NPI behavior are modulated by the perceived risk of infection, i.e., the disease prevalence and transmission rate, costs to adopt the NPI behavior, and the behavior of others. Social conformity creates a type of "stickiness" whereby individuals are resistant to changing their behavior due to the population's inertia. In this model, we observe a nonmonotonicity in the attack rate as a function of various biological and social parameters such as the transmission rate, efficacy of the NPI, costs of the NPI, weight of social consequences of shirking the social norm, and the degree of heterogeneity in the population. We also observe that the attack rate can be highly sensitive to these parameters due to abrupt shifts in the collective behavior of the population. These results highlight the complex interplay between the dynamics of epidemics and norm-driven collective behaviors.

Pathogen evolution during vaccination campaigns.

Following the initiation of the unprecedented global vaccination campaign against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), attention has now turned to the potential impact of this large-scale intervention on the evolution of the virus. In this Essay, we summarize what is currently known about pathogen evolution in the context of immune priming (including vaccination) from research on other pathogen species, with an eye towards the future evolution of SARS-CoV-2.

The economics of managing evolution.

Humans are altering biological systems at unprecedented rates, and these alterations often have longer-term evolutionary impacts. Most obvious is the spread of resistance to pesticides and antibiotics. There are a wide variety of management strategies available to slow this evolution, and there are many reasons for using them. In this paper, we focus on the economic aspects of evolution management and ask: When is it economically beneficial for an individual decision-maker to invest in evolution management? We derive a simple dimensionless inequality showing that it is cost-effective to manage evolution when the percentage increase in the effective life span of the biological resource that management generates is larger than the percentage increase in annual profit that could be obtained by not managing evolution. We show how this inequality can be used to determine optimal investment choices for single decision-makers, to determine Nash equilibrium investment choices for multiple interacting decision-makers, and to examine how these equilibrium choices respond to regulatory interventions aimed at stimulating investment in evolution management. Our results are illustrated with examples involving Bacillus thuringiensis (Bt) crops and antibiotic use in fish farming.

A stochastic analysis of the interplay between antibiotic dose, mode of action, and bacterial competition in the evolution of antibiotic resistance.

The use of an antibiotic may lead to the emergence and spread of bacterial strains resistant to this antibiotic. Experimental and theoretical studies have investigated the drug dose that minimizes the risk of resistance evolution over the course of treatment of an individual, showing that the optimal dose will either be the highest or the lowest drug concentration possible to administer; however, no analytical results exist that help decide between these two extremes. To address this gap, we develop a stochastic mathematical model of bacterial dynamics under antibiotic treatment. We explore various scenarios of density regulation (bacterial density affects cell birth or death rates), and antibiotic modes of action (biostatic or biocidal). We derive analytical results for the survival probability of the resistant subpopulation until the end of treatment, the size of the resistant subpopulation at the end of treatment, the carriage time of the resistant subpopulation until it is replaced by a sensitive one after treatment, and we verify these results with stochastic simulations. We find that the scenario of density regulation and the drug mode of action are important determinants of the survival of a resistant subpopulation. Resistant cells survive best when bacterial competition reduces cell birth and under biocidal antibiotics. Compared to an analogous deterministic model, the population size reached by the resistant type is larger and carriage time is slightly reduced by stochastic loss of resistant cells. Moreover, we obtain an analytical prediction of the antibiotic concentration that maximizes the survival of resistant cells, which may help to decide which drug dosage (not) to administer. Our results are amenable to experimental tests and help link the within and between host scales in epidemiological models.

Demystifying individual heterogeneity.

Among-individual variation in vital rates, such as mortality and birth rates, exists in nearly all populations. Recent studies suggest that this individual heterogeneity produces substantial life-history and fitness differences among individuals, which in turn scale up to influence population dynamics. However, our ability to understand the consequences of individual heterogeneity is limited by inconsistencies across conceptual frameworks in the field. Studies of individual heterogeneity remain filled with contradicting and ambiguous terminology that introduces risks of misunderstandings, conflicting models and unreliable conclusions. Here, we synthesise the existing literature into a single and comparatively straightforward framework with explicit terminology and definitions. This work introduces a distinction between potential vital rates and realised vital rates to develop a coherent framework that maps directly onto mathematical models of individual heterogeneity. We suggest the terms "fixed condition" and "dynamic condition" be used to distinguish potential vital rates that are permanent from those that can change throughout an individual's life. To illustrate, we connect the framework to quantitative genetics models and to common classes of statistical models used to infer individual heterogeneity. We also develop a population projection matrix model that provides an example of how our definitions are translated into precise quantitative terms.

The evolution of age-specific choosiness when mating.

Mate choice is a crucial element of many processes in evolutionary biology. Empirical research has shown that mating preference and choosiness often change with age. Understanding the evolutionary causes of patterns of age-specific choosiness is challenging because different mechanisms can give rise to the same pattern. Instead of focusing on the optimal age-specific choosiness strategy given fitness trade-offs, we approach this question from a more general standpoint and ask how the strength of selection on choosiness changes with the age at which it is expressed. We show that the strength of selection on a modifier of choosiness at a given age depends on the relative contribution of this age class to the pool of offspring but does not depend directly on the strength of selection on fitness components at the age affected by the modifier. We illustrate our results by contrasting two life histories from the literature. We further show how mutation-selection balance at the choosiness locus can shape age-specific choosiness. Our results provide new insights for understanding the evolution of choosiness throughout life, with implications for understanding the evolution of mate choice and reproductive isolation.

Social evolution under demographic stochasticity.

How social traits such as altruism and spite evolve remains an open question in evolutionary biology. One factor thought to be potentially important is demographic stochasticity. Here we provide a general theoretical analysis of the role of demographic stochasticity in social evolution. We show that the evolutionary impact of stochasticity depends on how the social action alters the recipient's life cycle. If the action alters the recipient's death rate, then demographic stochasticity always favours altruism and disfavours spite. On the other hand, if the action alters the recipient's birth rate, then stochasticity can either favour or disfavour both altruism and spite depending on the ratio of the rate of population turnover to the population size. Finally, we also show that this ratio is critical to determining if demographic stochasticity can reverse the direction of selection upon social traits. Our analysis thus provides a general understanding of the role of demographic stochasticity in social evolution.

Resource limitation prevents the emergence of drug resistance by intensifying within-host competition.

Slowing the evolution of antimicrobial resistance is essential if we are to continue to successfully treat infectious diseases. Whether a drug-resistant mutant grows to high densities, and so sickens the patient and spreads to new hosts, is determined by the competitive interactions it has with drug-susceptible pathogens within the host. Competitive interactions thus represent a good target for resistance management strategies. Using an in vivo model of malaria infection, we show that limiting a resource that is disproportionately required by resistant parasites retards the evolution of drug resistance by intensifying competitive interactions between susceptible and resistant parasites. Resource limitation prevented resistance emergence regardless of whether resistant mutants arose de novo or were experimentally added before drug treatment. Our work provides proof of principle that chemotherapy paired with an "ecological" intervention can slow the evolution of resistance to antimicrobial drugs, even when resistant pathogens are present at high frequencies. It also suggests that a broad range of previously untapped compounds could be used for treating infectious diseases.

Fighting the Public Health Burden of AIDS With the Human Pegivirus.

Highly active antiretroviral therapy has revolutionized the battle against human immunodeficiency virus/acquired immune deficiency syndrome (HIV/AIDS). From its current global rollout, HIV/AIDS morbidity and mortality has been greatly reduced, yet there exists substantial interest in the development of new therapies to further mitigate the HIV/AIDS health burden and to inhibit any fallout from the development of antiretroviral drug resistance. One potential intervention is the human pegivirus (HPgV). HPgV is not known to cause disease, and most remarkably it is shown to delay the progression of HIV to AIDS. However, the health benefit of increasing HPgV prevalence in the community of HIV-infected men remains unknown at the public health level. We evaluated the utility of HPgV biovaccination for mitigating the HIV/AIDS health burden using mathematical models. Importantly, our work considers the potential concern that HPgV will, itself, evolve to become disease-causing by permitting mutant disease-causing HPgV strains to potentially arise during treatment. Our findings show that HPgV biovaccination rates of 12.5%-50% annually could prevent 4.2-23.6 AIDS incidences and 3.3-18.8 AIDS deaths, and could save 2.9-18.6 disability-adjusted life years per 1,000 people. Together, these findings indicate that HPgV biovaccination could be an effective therapy for reducing HIV/AIDS morbidity and mortality, and thus warrants further exploration.

The Role of Phenotypic Plasticity in Moderating Evolutionary Conflict.

Evolutionary conflicts arise when the fitness interests of interacting individuals differ. Well-known examples include sexual conflict between males and females and antagonistic coevolution between hosts and parasites. A common feature of such conflicts is that compensating evolutionary change in each of the parties can lead to little overt change in the interaction itself. As a result, evolutionary conflict is expected to persist even if the evolutionary dynamic between the parties reaches an equilibrium. In these cases, it is of interest to know whether certain kinds of interactions are expected to lead to greater or lesser evolutionary conflict at such evolutionary stalemates. Here we present a theoretical analysis showing that when one of the interacting parties can respond to the other through adaptive phenotypic plasticity, evolutionary conflict is reduced. Paradoxically, however, it is the party that does not express adaptive plasticity that experiences less conflict. Conflict for the party displaying adaptive plasticity can increase or decrease, depending on the situation.

Does High-Dose Antimicrobial Chemotherapy Prevent the Evolution of Resistance?

High-dose chemotherapy has long been advocated as a means of controlling drug resistance in infectious diseases but recent empirical studies have begun to challenge this view. We develop a very general framework for modeling and understanding resistance emergence based on principles from evolutionary biology. We use this framework to show how high-dose chemotherapy engenders opposing evolutionary processes involving the mutational input of resistant strains and their release from ecological competition. Whether such therapy provides the best approach for controlling resistance therefore depends on the relative strengths of these processes. These opposing processes typically lead to a unimodal relationship between drug pressure and resistance emergence. As a result, the optimal drug dose lies at either end of the therapeutic window of clinically acceptable concentrations. We illustrate our findings with a simple model that shows how a seemingly minor change in parameter values can alter the outcome from one where high-dose chemotherapy is optimal to one where using the smallest clinically effective dose is best. A review of the available empirical evidence provides broad support for these general conclusions. Our analysis opens up treatment options not currently considered as resistance management strategies, and it also simplifies the experiments required to determine the drug doses which best retard resistance emergence in patients.

Inclusive fitness theory and eusociality.

Arising from M. A. Nowak, C. E. Tarnita & E. O. Wilson 466, 1057-1062 (2010); Nowak et al. reply. Nowak et al. argue that inclusive fitness theory has been of little value in explaining the natural world, and that it has led to negligible progress in explaining the evolution of eusociality. However, we believe that their arguments are based upon a misunderstanding of evolutionary theory and a misrepresentation of the empirical literature. We will focus our comments on three general issues.

Interpreting phenotypic antibiotic tolerance and persister cells as evolution via epigenetic inheritance.

Epigenetic inheritance is the transmission of nongenetic material such as gene expression levels, RNA and other biomolecules from parents to offspring. There is a growing realization that such forms of inheritance can play an important role in evolution. Bacteria represent a prime example of epigenetic inheritance because a large array of cellular components is transmitted to offspring, in addition to genetic material. Interestingly, there is an extensive and growing empirical literature showing that many bacteria can form 'persister' cells that are phenotypically resistant or tolerant to antibiotics, but most of these results are not interpreted within the context of epigenetic inheritance. Instead, persister cells are usually viewed as a genetically encoded bet-hedging strategy that has evolved in response to a fluctuating environment. Here I show, using a relatively simple model, that many of these empirical findings can be more simply understood as arising from a combination of epigenetic inheritance and cellular noise. I therefore suggest that phenotypic drug tolerance in bacteria might represent one of the best-studied examples of evolution under epigenetic inheritance.

The adaptive evolution of virulence: a review of theoretical predictions and empirical tests.

Why is it that some parasites cause high levels of host damage (i.e. virulence) whereas others are relatively benign? There are now numerous reviews of virulence evolution in the literature but it is nevertheless still difficult to find a comprehensive treatment of the theory and data on the subject that is easily accessible to non-specialists. Here we attempt to do so by distilling the vast theoretical literature on the topic into a set of relatively few robust predictions. We then provide a comprehensive assessment of the available empirical literature that tests these predictions. Our results show that there have been some notable successes in integrating theory and data but also that theory and empiricism in this field do not 'speak' to each other very well. We offer a few suggestions for how the connection between the two might be improved.

Disease eradication on large industrial farms.

Many modern farms exhibit all-in-all-out dynamics in which entire cohorts of livestock are removed from a farm before a new cohort is introduced. This industrialization has enabled diseases to spread rapidly within farms. Here we look at one such example, Marek's disease. Marek's disease is an economically important disease of poultry. The disease is transmitted indirectly, enabling the spread of disease between cohorts of chickens who have never come into physical contact. We develop a model which allows us to track the transmission of disease within a barn and between subsequent cohorts of chickens occupying the barn. It is described by a system of impulsive differential equations. We determine the conditions that lead to disease eradication. For a given level of transmission we find that disease eradication is possible if the cohort length is short enough and/or the cohort size is small enough. Marek's disease can also be eradicated from a farm if the cleaning effort between cohorts is large enough. Importantly complete cleaning is not required for eradication and the threshold cleaning effort needed declines as both cohort duration and size decrease.

Pathogen evolution under host avoidance plasticity.

Host resistance consists of defences that limit pathogen burden, and can be classified as either adaptations targeting recovery from infection or those focused upon infection avoidance. Conventional theory treats avoidance as a fixed strategy which does not vary from one interaction to the next. However, there is increasing empirical evidence that many avoidance strategies are triggered by external stimuli, and thus should be treated as phenotypically plastic responses. Here, we consider the implications of avoidance plasticity for host-pathogen coevolution. We uncover a number of predictions challenging current theory. First, in the absence of pathogen trade-offs, plasticity can restrain pathogen evolution; moreover, the pathogen exploits conditions in which the host would otherwise invest less in resistance, causing resistance escalation. Second, when transmission trades off with pathogen-induced mortality, plasticity encourages avirulence, resulting in a superior fitness outcome for both host and pathogen. Third, plasticity ensures the sterilizing effect of pathogens has consequences for pathogen evolution. When pathogens castrate hosts, selection forces them to minimize mortality virulence; moreover, when transmission trades off with sterility alone, resistance plasticity is sufficient to prevent pathogens from evolving to fully castrate.

Could the human papillomavirus vaccines drive virulence evolution?

The human papillomavirus (HPV) vaccines hold great promise for preventing several cancers caused by HPV infections. Yet little attention has been given to whether HPV could respond evolutionarily to the new selection pressures imposed on it by the novel immunity response created by the vaccine. Here, we present and theoretically validate a mechanism by which the vaccine alters the transmission-recovery trade-off that constrains HPV's virulence such that higher oncogene expression is favoured. With a high oncogene expression strategy, the virus is able to increase its viral load and infected cell population before clearance by the vaccine, thus improving its chances of transmission. This new rapid cell-proliferation strategy is able to circulate between hosts with medium to high turnover rates of sexual partners. We also discuss the importance of better quantifying the duration of challenge infections and the degree to which a vaccinated host can shed virus. The generality of the models presented here suggests a wider applicability of this mechanism, and thus highlights the need to investigate viral oncogenicity from an evolutionary perspective.