The sex-specific factor SOA controls dosage compensation in Anopheles mosquitoes.

The Anopheles mosquito is one of thousands of species in which sex differences play a central part in their biology, as only females need a blood meal to produce eggs. Sex differentiation is regulated by sex chromosomes, but their presence creates a dosage imbalance between males (XY) and females (XX). Dosage compensation (DC) can re-equilibrate the expression of sex chromosomal genes. However, because DC mechanisms have only been fully characterized in a few model organisms, key questions about its evolutionary diversity and functional necessity remain unresolved1. Here we report the discovery of a previously uncharacterized gene (sex chromosome activation (SOA)) as a master regulator of DC in the malaria mosquito Anopheles gambiae. Sex-specific alternative splicing prevents functional SOA protein expression in females. The male isoform encodes a DNA-binding protein that binds the promoters of active X chromosomal genes. Expressing male SOA is sufficient to induce DC in female cells. Male mosquitoes lacking SOA or female mosquitoes ectopically expressing the male isoform exhibit X chromosome misregulation, which is compatible with viability but causes developmental delay. Thus, our molecular analyses of a DC master regulator in a non-model organism elucidates the evolutionary steps that lead to the establishment of a chromosome-specific fine-tuning mechanism.

A neural network model for the evolution of learning in changing environments.

Learning from past experience is an important adaptation and theoretical models may help to understand its evolution. Many of the existing models study simple phenotypes and do not consider the mechanisms underlying learning while the more complex neural network models often make biologically unrealistic assumptions and rarely consider evolutionary questions. Here, we present a novel way of modelling learning using small neural networks and a simple, biology-inspired learning algorithm. Learning affects only part of the network, and it is governed by the difference between expectations and reality. We use this model to study the evolution of learning under various environmental conditions and different scenarios for the trade-off between exploration (learning) and exploitation (foraging). Efficient learning readily evolves in our individual-based simulations. However, in line with previous studies, the evolution of learning is less likely in relatively constant environments, where genetic adaptation alone can lead to efficient foraging, or in short-lived organisms that cannot afford to spend much of their lifetime on exploration. Once learning does evolve, the characteristics of the learning strategy (i.e. the duration of the learning period and the learning rate) and the average performance after learning are surprisingly little affected by the frequency and/or magnitude of environmental change. In contrast, an organism's lifespan and the distribution of resources in the environment have a clear effect on the evolved learning strategy: a shorter lifespan or a broader resource distribution lead to fewer learning episodes and larger learning rates. Interestingly, a longer learning period does not always lead to better performance, indicating that the evolved neural networks differ in the effectiveness of learning. Overall, however, we show that a biologically inspired, yet relatively simple, learning mechanism can evolve to lead to an efficient adaptation in a changing environment.

The Joint Evolution of Animal Movement and Competition Strategies.

AbstractCompetition typically takes place in a spatial context, but eco-evolutionary models rarely address the joint evolution of movement and competition strategies. Here we investigate a spatially explicit forager-kleptoparasite model where consumers can either forage on a heterogeneous resource landscape or steal resource items from conspecifics (kleptoparasitism). We consider three scenarios: (1) foragers without kleptoparasites, (2) consumers specializing as foragers or as kleptoparasites, and (3) consumers that can switch between foraging and kleptoparasitism depending on local conditions. We model movement strategies as individual-specific combinations of preferences for environmental cues, similar to step-selection coefficients. Using mechanistic, individual-based simulations, we study the joint evolution of movement and competition strategies, and we investigate the implications for the distribution of consumers over this landscape. Movement and competition strategies evolve rapidly and consistently across scenarios, with marked differences among scenarios, leading to differences in resource exploitation patterns. In scenario 1, foragers evolve considerable individual variation in movement strategies, while in scenario 2, movement strategies show a swift divergence between foragers and kleptoparasites. In scenario 3, where individuals' competition strategies are conditional on local cues, movement strategies facilitate kleptoparasitism, and individual consistency in competition strategy also emerges. Even in the absence of kleptoparasitism (scenario 1), the distribution of consumers deviates considerably from predictions of ideal free distribution models because of the intrinsic difficulty of moving effectively on a depleted resource landscape with few reliable cues. Our study emphasizes the advantages of a mechanistic approach when studying competition in a spatial context and suggests how evolutionary modeling can be integrated with current work in animal movement ecology.

Assortative Mating in an Ecological Context: Effects of Mate Choice Errors and Relative Species Abundance on the Frequency and Asymmetry of Hybridization.

AbstractThe frequency and asymmetry of mixed-species mating set the initial stage for the ecological and evolutionary implications of hybridization. How such patterns of mixed-species mating, in turn, are influenced by the combination of mate choice errors and relative species abundance remains largely unknown. We develop a mathematical model that generates predictions for how relative species abundances and mate choice errors affect hybridization patterns. When mate choice errors are small (<5%), the highest frequency of hybridization occurs when one of the hybridizing species is at low abundance, but when mate choice errors are high (>5%), the highest hybridization frequency occurs when species occur in equal proportions. Furthermore, females of the less abundant species are overrepresented in mixed-species matings. We compare our theoretical predictions with empirical data on naturally hybridizing Ficedula flycatchers and find that hybridization is highest when the two species occur in equal abundance, implying rather high mate choice errors. We discuss ecological and evolutionary implications of our findings and encourage future work on hybrid zone dynamics that take demographic aspects, such as relative species abundance, into account.

Modelling the spatial dynamics of oncolytic virotherapy in the presence of virus-resistant tumour cells.

Oncolytic virotherapy is a promising form of cancer treatment that uses native or genetically engineered viruses to target, infect and kill cancer cells. Unfortunately, this form of therapy is not effective in a substantial proportion of cancer patients, partly due to the occurrence of infection-resistant tumour cells. To shed new light on the mechanisms underlying therapeutic failure and to discover strategies that improve therapeutic efficacy we designed a cell-based model of viral infection. The model allows us to investigate the dynamics of infection-sensitive and infection-resistant cells in tumour tissue in presence of the virus. To reflect the importance of the spatial configuration of the tumour on the efficacy of virotherapy, we compare three variants of the model: two 2D models of a monolayer of tumour cells and a 3D model. In all model variants, we systematically investigate how the therapeutic outcome is affected by the properties of the virus (e.g. the rate of viral spread), the tumour (e.g. production rate of resistant cells, cost of resistance), the healthy stromal cells (e.g. degree of resistance to the virus) and the timing of treatment. We find that various therapeutic outcomes are possible when resistant cancer cells arise at low frequency in the tumour. These outcomes depend in an intricate but predictable way on the death rate of infected cells, where faster death leads to rapid virus clearance and cancer persistence. Our simulations reveal three different causes of therapy failure: rapid clearance of the virus, rapid selection of resistant cancer cells, and a low rate of viral spread due to the presence of infection-resistant healthy cells. Our models suggest that improved therapeutic efficacy can be achieved by sensitizing healthy stromal cells to infection, although this remedy has to be weighed against the toxicity induced in the healthy tissue.

Effects of season length and uniparental care efficacy on the evolution of parental care.

Parental care patterns differ enormously among and even within species. This is exemplified by Chinese penduline tits Remiz consobrinus, where biparental care, female-only care, male-only care and biparental desertion all occur in the same population; moreover, the distribution of these care patterns differs systematically between populations. The eco-evolutionary determinants of this diversity are largely unknown. We developed an individual-based model that allows us to investigate the effects of season length and offspring needs (expressed by the efficacy with which a clutch can be raised by a single parent) on the evolution of parental care patterns. The model is largely conceptual, aiming at general conclusions. However, to keep the model realistic, its set-up and the choice of parameters are motivated by field studies on Chinese penduline tits. Exploring a wide range of parameters, we investigate how parental care patterns are affected by season length and offspring needs and whether and under what conditions diverse parental care patterns can stably coexist. We report five main findings. First, under a broad range of conditions, different care patterns (e.g. male care and biparental care) coexist at equilibrium. Second, for the same parameters, alternative evolutionary equilibria are possible; this can explain differences in care patterns across populations. Third, rapid evolutionary transitions can occur between alternative equilibria; this can explain the often-reported evolutionary lability of parental care patterns. Fourth, season length has a strong but nonmonotonic effect on the evolved care patterns. Fifth, when uniparental care efficacy is low, biparental care tends to evolve; however, in many scenarios uniparental care is still common at equilibrium. In addition, our study sheds new light on Trivers' hypothesis that the sex with the highest prezygotic investment is predestined to invest more postzygotically as well. Our study highlights that diversity in parental care can readily evolve and it shows that even in the absence of environmental change parental care patterns can be evolutionary labile. In the presence of directional environmental change, systematic shifts in care patterns are to be expected.

Human genomic data have different statistical properties than the data of randomised controlled trials.

Madole & Harden argue that the Mendelian reshuffling of genes and genomes is analogous to randomised controlled trials. We are not convinced by their arguments. First, their recipe for meeting the demands on randomised experiments is inherently inconsistent. Second, disequilibrium across chromosomes conflicts with their assumption of statistical independence. Third, the genome-wide association study (GWAS) method has many pitfalls, including low repeatability.

The evolution of social philopatry in female primates.

The transition from solitary life to sociality is considered one of the major transitions in evolution. In primates, this transition is currently not well understood. Traditional verbal models appear insufficient to unravel the complex interplay of environmental and demographic factors involved in the evolution of primate sociality, and recent phylogenetic reconstructions have produced conflicting results. We therefore analyze a theoretical model for the evolution of female social philopatry that sheds new light on the question why most primates live in groups. In individual-based simulations, we study the evolution of dispersal strategies of both resident females and their offspring. The model reveals that social philopatry can evolve through kin selection, even if retention of offspring is costly in terms of within-group resource competition and provides no direct benefits. Our model supports the role of predator avoidance as a selective pressure for group-living in primates, but it also suggests that a second benefit of group-living, communal resource defense, might be required to trigger the evolution of sizable groups. Lastly, our model reveals that seemingly small differences in demographic parameters can have profound effects on primate social evolution.

Evolutionary tipping points in the capacity to adapt to environmental change.

In an era of rapid climate change, there is a pressing need to understand how organisms will cope with faster and less predictable variation in environmental conditions. Here we develop a unifying model that predicts evolutionary responses to environmentally driven fluctuating selection and use this theoretical framework to explore the potential consequences of altered environmental cycles. We first show that the parameter space determined by different combinations of predictability and timescale of environmental variation is partitioned into distinct regions where a single mode of response (reversible phenotypic plasticity, irreversible phenotypic plasticity, bet-hedging, or adaptive tracking) has a clear selective advantage over all others. We then demonstrate that, although significant environmental changes within these regions can be accommodated by evolution, most changes that involve transitions between regions result in rapid population collapse and often extinction. Thus, the boundaries between response mode regions in our model correspond to evolutionary tipping points, where even minor changes in environmental parameters can have dramatic and disproportionate consequences on population viability. Finally, we discuss how different life histories and genetic architectures may influence the location of tipping points in parameter space and the likelihood of extinction during such transitions. These insights can help identify and address some of the cryptic threats to natural populations that are likely to result from any natural or human-induced change in environmental conditions. They also demonstrate the potential value of evolutionary thinking in the study of global climate change.

Focus on the success of others leads to selfish behavior.

It has often been argued that the spectacular cognitive capacities of humans are the result of selection for the ability to gather, process, and use information about other people. Recent studies show that humans strongly and consistently differ in what type of social information they are interested in. Although some individuals mainly attend to what the majority is doing (frequency-based learning), others focus on the success that their peers achieve with their behavior (success-based learning). Here, we show that such differences in social learning have important consequences for the outcome of social interactions. We report on a decision-making experiment in which individuals were first classified as frequency- and success-based learners and subsequently grouped according to their learning strategy. When confronted with a social dilemma situation, groups of frequency-based learners cooperated considerably more than groups of success-based learners. A detailed analysis of the decision-making process reveals that these differences in cooperation are a direct result of the differences in information use. Our results show that individual differences in social learning strategies are crucial for understanding social behavior.

Bet-hedging during bacterial diauxic shift.

When bacteria grow in a medium with two sugars, they first use the preferred sugar and only then start metabolizing the second one. After the first exponential growth phase, a short lag phase of nongrowth is observed, a period called the diauxie lag phase. It is commonly seen as a phase in which the bacteria prepare themselves to use the second sugar. Here we reveal that, in contrast to the established concept of metabolic adaptation in the lag phase, two stable cell types with alternative metabolic strategies emerge and coexist in a culture of the bacterium Lactococcus lactis. Only one of them continues to grow. The fraction of each metabolic phenotype depends on the level of catabolite repression and the metabolic state-dependent induction of stringent response, as well as on epigenetic cues. Furthermore, we show that the production of alternative metabolic phenotypes potentially entails a bet-hedging strategy. This study sheds new light on phenotypic heterogeneity during various lag phases occurring in microbiology and biotechnology and adjusts the generally accepted explanation of enzymatic adaptation proposed by Monod and shared by scientists for more than half a century.

Steady states and stability in metabolic networks without regulation.

Metabolic networks are often extremely complex. Despite intensive efforts many details of these networks, e.g., exact kinetic rates and parameters of metabolic reactions, are not known, making it difficult to derive their properties. Considerable effort has been made to develop theory about properties of steady states in metabolic networks that are valid for any values of parameters. General results on uniqueness of steady states and their stability have been derived with specific assumptions on reaction kinetics, stoichiometry and network topology. For example, deep results have been obtained under the assumptions of mass-action reaction kinetics, continuous flow stirred tank reactors (CFSTR), concordant reaction networks and others. Nevertheless, a general theory about properties of steady states in metabolic networks is still missing. Here we make a step further in the quest for such a theory. Specifically, we study properties of steady states in metabolic networks with monotonic kinetics in relation to their stoichiometry (simple and general) and the number of metabolites participating in every reaction (single or many). Our approach is based on the investigation of properties of the Jacobian matrix. We show that stoichiometry, network topology, and the number of metabolites that participate in every reaction have a large influence on the number of steady states and their stability in metabolic networks. Specifically, metabolic networks with single-substrate-single-product reactions have disconnected steady states, whereas in metabolic networks with multiple-substrates-multiple-product reactions manifolds of steady states arise. Metabolic networks with simple stoichiometry have either a unique globally asymptotically stable steady state or asymptotically stable manifolds of steady states. In metabolic networks with general stoichiometry the steady states are not always stable and we provide conditions for their stability. In order to demonstrate the biological relevance we illustrate the results on the examples of the TCA cycle, the mevalonate pathway and the Calvin cycle.

The importance of mechanisms for the evolution of cooperation.

Studies aimed at explaining the evolution of phenotypic traits have often solely focused on fitness considerations, ignoring underlying mechanisms. In recent years, there has been an increasing call for integrating mechanistic perspectives in evolutionary considerations, but it is not clear whether and how mechanisms affect the course and outcome of evolution. To study this, we compare four mechanistic implementations of two well-studied models for the evolution of cooperation, the Iterated Prisoner's Dilemma (IPD) game and the Iterated Snowdrift (ISD) game. Behavioural strategies are either implemented by a 1 : 1 genotype-phenotype mapping or by a simple neural network. Moreover, we consider two different scenarios for the effect of mutations. The same set of strategies is feasible in all four implementations, but the probability that a given strategy arises owing to mutation is largely dependent on the behavioural and genetic architecture. Our individual-based simulations show that this has major implications for the evolutionary outcome. In the ISD, different evolutionarily stable strategies are predominant in the four implementations, while in the IPD each implementation creates a characteristic dynamical pattern. As a consequence, the evolved average level of cooperation is also strongly dependent on the underlying mechanism. We argue that our findings are of general relevance for the evolution of social behaviour, pleading for the integration of a mechanistic perspective in models of social evolution.

How superdiffusion gets arrested: ecological encounters explain shift from Lévy to Brownian movement.

Ecological theory uses Brownian motion as a default template for describing ecological movement, despite limited mechanistic underpinning. The generality of Brownian motion has recently been challenged by empirical studies that highlight alternative movement patterns of animals, especially when foraging in resource-poor environments. Yet, empirical studies reveal animals moving in a Brownian fashion when resources are abundant. We demonstrate that Einstein's original theory of collision-induced Brownian motion in physics provides a parsimonious, mechanistic explanation for these observations. Here, Brownian motion results from frequent encounters between organisms in dense environments. In density-controlled experiments, movement patterns of mussels shifted from Lévy towards Brownian motion with increasing density. When the analysis was restricted to moves not truncated by encounters, this shift did not occur. Using a theoretical argument, we explain that any movement pattern approximates Brownian motion at high-resource densities, provided that movement is interrupted upon encounters. Hence, the observed shift to Brownian motion does not indicate a density-dependent change in movement strategy but rather results from frequent collisions. Our results emphasize the need for a more mechanistic use of Brownian motion in ecology, highlighting that especially in rich environments, Brownian motion emerges from ecological interactions, rather than being a default movement pattern.

Sex-ratio control erodes sexual selection, revealing evolutionary feedback from adaptive plasticity.

Female choice is a powerful selective force, driving the elaboration of conspicuous male ornaments. This process of sexual selection has profound implications for many life-history decisions, including sex allocation. For example, females with attractive partners should produce more sons, because these sons will inherit their father's attractiveness and enjoy high mating success, thereby yielding greater fitness returns than daughters. However, previous research has overlooked the fact that there is a reciprocal feedback from life-history strategies to sexual selection. Here, using a simple mathematical model, we show that if mothers adaptively control offspring sex in relation to their partner's attractiveness, sexual selection is weakened and male ornamentation declines. This weakening occurs because the ability to determine offspring sex reduces the fitness difference between females with attractive and unattractive partners. We use individual-based, evolutionary simulations to show that this result holds under more biologically realistic conditions. Sexual selection and sex allocation thus interact in a dynamic fashion: The evolution of conspicuous male ornaments favors sex-ratio adjustment, but this conditional strategy then undermines the very same process that generated it, eroding sexual selection. We predict that, all else being equal, the most elaborate sexual displays should be seen in species with little or no control over offspring sex. The feedback process we have described points to a more general evolutionary principle, in which a conditional strategy weakens directional selection on another trait by reducing fitness differences.

Temperature dependence of the mutation rate towards antibiotic resistance.

Objectives: Environmental conditions can influence mutation rates in bacteria. Fever is a common response to infection that alters the growth conditions of infecting bacteria. Here we examine how a temperature change, such as is associated with fever, affects the mutation rate towards antibiotic resistance. Methods: We used a fluctuation test to assess the mutation rate towards antibiotic resistance in Escherichia coli at two different temperatures: 37°C (normal temperature) and 40°C (fever temperature). We performed this measurement for three different antibiotics with different modes of action: ciprofloxacin, rifampicin and ampicillin. Results: In all cases, the mutation rate towards antibiotic resistance turned out to be temperature dependent, but in different ways. Fever temperatures led to a reduced mutation rate towards ampicillin resistance and an elevated mutation rate towards ciprofloxacin and rifampicin resistance. Conclusions: This study shows that the mutation rate towards antibiotic resistance is impacted by a small change in temperature, such as associated with fever. This opens a new avenue to mitigate the emergence of antibiotic resistance by coordinating the choice of an antibiotic with the decision of whether or not to suppress fever when treating a patient. Hence, optimized combinations of antibiotics and fever suppression strategies may be a new weapon in the battle against antibiotic resistance.

Emergence of phenotypic plasticity through epigenetic mechanisms.

Plasticity is found in all domains of life and is particularly relevant when populations experience variable environmental conditions. Traditionally, evolutionary models of plasticity are non-mechanistic: they typically view reactions norms as the target of selection, without considering the underlying genetics explicitly. Consequently, there have been difficulties in understanding the emergence of plasticity, and in explaining its limits and costs. In this paper, we offer a novel mechanistic approximation for the emergence and evolution of plasticity. We simulate random "epigenetic mutations" in the genotype-phenotype mapping, of the kind enabled by DNA-methylations/demethylations. The frequency of epigenetic mutations at loci affecting the phenotype is sensitive to organism stress (trait-environment mismatch), but is also genetically determined and evolvable. Thus, the "random motion" of epigenetic markers enables developmental learning-like behaviors that can improve adaptation within the limits imposed by the genotypes. However, with random motion being "goal-less," this mechanism is also vulnerable to developmental noise leading to maladaptation. Our individual-based simulations show that epigenetic mutations can hide alleles that are temporarily unfavorable, thus enabling cryptic genetic variation. These alleles can be advantageous at later times, under regimes of environmental change, in spite of the accumulation of genetic loads. Simulations also demonstrate that plasticity is favored by natural selection in constant environments, but more under periodic environmental change. Plasticity also evolves under directional environmental change as long as the pace of change is not too fast and costs are low.

Does insurance against punishment undermine cooperation in the evolution of public goods games?

In a public goods game, cooperation can be a stable outcome if defectors are facing efficient punishment. In some public goods systems, punishment is undermined by an insurance system where speculators buy a policy that sequentially covers all punishment costs. Here, we study a simple model to investigate the question whether stable cooperation can break down in the presence of such speculation. We do indeed find scenarios where speculation either leads to the reduction of the basin of attraction of the cooperative equilibrium or even the loss of stability of this equilibrium. This however only happens if the costs of the insurance are lower than the expected fines faced by a defector. We argue that an insurance of this type is not viable and conclude that under realistic assumptions speculation does not destabilize cooperation.

Life-history trade-offs favour the evolution of animal personalities.

In recent years evidence has been accumulating that personalities are not only found in humans but also in a wide range of other animal species. Individuals differ consistently in their behavioural tendencies and the behaviour in one context is correlated with the behaviour in multiple other contexts. From an adaptive perspective, the evolution of animal personalities is still a mystery, because a more flexible structure of behaviour should provide a selective advantage. Accordingly, many researchers view personalities as resulting from constraints imposed by the architecture of behaviour (but see ref. 12). In contrast, we show here that animal personalities can be given an adaptive explanation. Our argument is based on the insight that the trade-off between current and future reproduction often results in polymorphic populations in which some individuals put more emphasis on future fitness returns than others. Life-history theory predicts that such differences in fitness expectations should result in systematic differences in risk-taking behaviour. Individuals with high future expectations (who have much to lose) should be more risk-averse than individuals with low expectations. This applies to all kinds of risky situations, so individuals should consistently differ in their behaviour. By means of an evolutionary model we demonstrate that this basic principle results in the evolution of animal personalities. It simultaneously explains the coexistence of behavioural types, the consistency of behaviour through time and the structure of behavioural correlations across contexts. Moreover, it explains the common finding that explorative behaviour and risk-related traits like boldness and aggressiveness are common characteristics of animal personalities.