Evolutionary stability of vigilance coordination among social foragers

Coordination can greatly improve the efficiency of anti-predatory vigilance scans by increasing predator detection for a constant proportion of time spent vigilant. However, it has been rarely found in nature and most studies have detected or assumed independent scanning by group members. In this study, we analysed the functional consequences of the coordinated alternation of vigilance scanning by group foragers. We introduce coordination by assuming that interscan intervals (ISIs) follow a modified gamma distribution. Depending on the parameters of the distribution, successive scans can be evenly spaced (coordinated scanning) or may present a high overlap (uncoordinated scanning). Comparing evolutionarily stable strategies for animals that do not coordinate their scanning with animals that do coordinate their anti-predator behaviour shows that coordination has a marked effect on survival probability. Moreover, the coordinating strategy is quite robust against mutants that scan independently with exponential distributions of ISIs. However, coordination breaks down when animals can continuously adjust their level of coordination by deciding the proportion of time they spend monitoring the behaviour of other group members. In this case, coordination is only evolutionarily stable if it can be very easily achieved.     

Evolutionary cheating in Escherichia coli stationary phase cultures

Starved cultures of Escherichia coli are highly dynamic, undergoing frequent population shifts. The shifts result from the spread of mutants able to grow under conditions that impose growth arrest on the ancestral population. To analyze competitive interactions underlying this dynamic we measured the survival of a typical mutant and the wild type during such population shifts. Here we show that the survival advantage of the mutant at any given time during a takeover is inversely dependent on its frequency in the population, its growth adversely affects the survival of the wild type, and its ability to survive in stationary phase at fixation is lower than that of its ancestor. These mutants do not enter, or exit early, the nondividing stationary-phase state, cooperatively maintained by the wild type. Thus they end up overrepresented as compared to their initial frequency at the onset of the stationary phase, and subsequently they increase disproportionately their contribution in terms of progeny to the succeeding generation in the next growth cycle, which is a case of evolutionary cheating. If analyzed through the game theory framework, these results might be explained by the prisoner's dilemma type of conflict, which predicts that selfish defection is favored over cooperation.     

The cyclic AMP phosphodiesterase RegA critically regulates encystation in social and pathogenic amoebas

Amoebas survive environmental stress by differentiating into encapsulated cysts. As cysts, pathogenic amoebas resist antibiotics, which particularly counteracts treatment of vision-destroying Acanthamoeba keratitis. Limited genetic tractability of amoeba pathogens has left their encystation mechanisms unexplored. The social amoeba Dictyostelium discoideum forms spores in multicellular fruiting bodies to survive starvation, while other dictyostelids, such as Polysphondylium pallidum can additionally encyst as single cells. Sporulation is induced by cAMP acting on PKA, with the cAMP phosphodiesterase RegA critically regulating cAMP levels. We show here that RegA is deeply conserved in social and pathogenic amoebas and that deletion of the RegA gene in P. pallidum causes precocious encystation and prevents cyst germination. We heterologously expressed and characterized Acanthamoeba RegA and performed a compound screen to identify RegA inhibitors. Two effective inhibitors increased cAMP levels and triggered Acanthamoeba encystation. Our results show that RegA critically regulates Amoebozoan encystation and that components of the cAMP signalling pathway could be effective targets for therapeutic intervention with encystation.     

Evolutionary dynamics of continuous strategy games on graphs and social networks under weak selection

Understanding the emergence of cooperation among selfish individuals has been a long-standing puzzle, which has been studied by a variety of game models. Most previous studies presumed that interactions between individuals are discrete, but it seems unrealistic in real systems. Recently, there are increasing interests in studying game models with a continuous strategy space. Existing research work on continuous strategy games mainly focuses on well-mixed populations. Especially, little theoretical work has been conducted on their evolutionary dynamics in a structured population. In the previous work (Zhong et al., BioSystems, 2012), we showed that under strong selection, continuous and discrete strategies have significantly different equilibrium and game dynamics in spatially structured populations. In this paper, we further study evolutionary dynamics of continuous strategy games under weak selection in structured populations. By using the fixation probability based stochastic dynamics, we derive exact conditions of natural selection favoring cooperation for the death–birth updating scheme. We also present a network gain decomposition of the game equilibrium, which might provide a new view of the network reciprocity in a quantitative way. Finally, we make a detailed comparison between games using discrete and continuous strategies. As compared to the former, we find that for the latter (i) the same selection conditions are derived for the general 2 × 2 game; especially, the rule b/c > k in a simplified Prisoner's Dilemma is valid as well; however, (ii) for a coordination game, interestingly, the risk-dominant strategy is disfavored. Numerical simulations have also been conducted to validate our results.     

Evolutionary dynamics of fearfulness and boldness

A negative relationship between reproductive effort and survival is consistent with life-history. Evolutionary dynamics and evolutionarily stable strategy (ESS) for the trade-off between survival and reproduction are investigated using a simple model with two phenotypes, fearfulness and boldness. The dynamical stability of the pure strategy model and analysis of ESS conditions reveal that: (i) the simple coexistence of fearfulness and boldness is impossible; (ii) a small population size is favorable to fearfulness, but a large population size is favorable to boldness, i.e., neither fearfulness, nor boldness is always favored by natural selection; and (iii) the dynamics of population density is crucial for a proper understanding of the strategy dynamics.     

Evolutionary dynamics of invasion and escape

Whenever life wants to invade a new habitat or escape from a lethal selection pressure, some mutations may be necessary to yield sustainable replication. We imagine situations like (i) a parasite infecting a new host, (ii) a species trying to invade a new ecological niche, (iii) cancer cells escaping from chemotherapy, (iv) viruses or microbes evading anti-microbial therapy, and also (v) the repeated attempts of combinatorial chemistry in the very beginning of life to produce self-replicating molecules. All such seemingly unrelated situations have a common structure in terms of Darwinian dynamics: a replicator with a basic reproductive ratio less than one attempts to find some mutations that allow indefinite survival. We develop a general theory, based on multitype branching processes, to describe the evolutionary dynamics of invasion and escape.     

Evolutionary dynamics of taxonomic structure

The distribution of species among genera and higher taxa has largely untapped potential to reveal among-clade variation in rates of origination and extinction. The probability distribution of the number of species within a genus is modelled with a stochastic, time-homogeneous birth-death model having two parameters: the rate of species extinction, μ, and the rate of genus origination, γ, each scaled as a multiple of the rate of within-genus speciation, λ. The distribution is more sensitive to γ than to μ, although μ affects the size of the largest genera. The species : genus ratio depends strongly on both γ and μ, and so is not a good diagnostic of evolutionary dynamics. The proportion of monotypic genera, however, depends mainly on γ, and so may provide an index of the genus origination rate. Application to living marine molluscs of New Zealand shows that bivalves have a higher relative rate of genus origination than gastropods. This is supported by the analysis of palaeontological data. This concordance suggests that analysis of living taxonomic distributions may allow inference of macroevolutionary dynamics even without a fossil record.     

Evolutionary dynamics of viral attenuation

The genetic trajectory leading to viral attenuation was studied in a canine parvovirus (CPV) strain grown on dog kidney cells for 115 transfers. Consensus sequences of viral populations at passages 0, 3, 30, 50, 80, and 115 were obtained from PCR products covering 86% of the genome; clones from each of the 80th and 115th passages were also sequenced, covering 69% of the genome. Sixteen changes were fixed in the 115th-passage virus sample. Levels of polymorphism were strikingly different over time, in part because of a plaque-cloning step at passage 112 that reduced variation: passage 80 had 19 variants common among the clones, but passage 115 had only a single common variant. Several mutations increased in the culture at the same time, with most reaching fixation only after the 80th passage. The pattern of evolution was consistent with recombination and not with separate selective sweeps of individual mutations. Thirteen of the changes observed were identical to or at the same positions as changes observed in other isolates of CPV or feline panleukopenia virus.     

Evolutionary dynamics of zero-sum games

Aim model in terms of differential equations is used to explain mammalian ovulation control, in particular regulation for a prescribed number of mature eggs. NIH Grant RO1 GM 32153-01GE     

Evolutionary dynamics of intratumor heterogeneity

Intraneoplastic diversity in human tumors is a widespread phenomenon of critical importance for tumor progression and the response to therapeutic intervention. Insights into the evolutionary events that control tumor heterogeneity would be a major breakthrough in our comprehension of cancer development and could lead to more effective prevention methods and therapies. In this paper, we design an evolutionary mathematical framework to study the dynamics of heterogeneity over time. We consider specific situations arising during tumorigenesis, such as the emergence of positively selected mutations ("drivers") and the accumulation of neutral variation ("passengers"). We perform exact computer simulations of the emergence of diverse tumor cell clones over time, and derive analytical estimates for the extent of heterogeneity within a population of cancer cells. Our methods contribute to a quantitative understanding of tumor heterogeneity and the impact of heritable alterations on this tumor trait.     

Evolutionary dynamics of biological auctions

Many scenarios in the living world, where individual organisms compete for winning positions (or resources), have properties of auctions. Here we study the evolution of bids in biological auctions. For each auction, n individuals are drawn at random from a population of size N. Each individual makes a bid which entails a cost. The winner obtains a benefit of a certain value. Costs and benefits are translated into reproductive success (fitness). Therefore, successful bidding strategies spread in the population. We compare two types of auctions. In “biological all-pay auctions”, the costs are the bid for every participating individual. In “biological second price all-pay auctions”, the cost for everyone other than the winner is the bid, but the cost for the winner is the second highest bid. Second price all-pay auctions are generalizations of the “war of attrition” introduced by Maynard Smith. We study evolutionary dynamics in both types of auctions. We calculate pairwise invasion plots and evolutionarily stable distributions over the continuous strategy space. We find that the average bid in second price all-pay auctions is higher than in all-pay auctions, but the average cost for the winner is similar in both auctions. In both cases, the average bid is a declining function of the number of participants, n. The more individuals participate in an auction the smaller is the chance of winning, and thus expensive bids must be avoided.     

Evolutionary dynamics of habitat use

I examine the evolution of alternate genotypes that use two habitats that differ in vegetative cover, focusing on the interplay between ecological dynamics of the community and changes in selective advantage. Facultative habitat choice can stabilize a predator population that would cycle if isolated in the more open habitat. This has important implications for the evolution of habitat use strategies. Local stability arising from facultative habitat use allows any number of behavioural genotypes to co-exist: selective use of the open habitat, selective use of the dense habitat, opportunistic use of both habitats in proportion to availability, and facultative switching between habitats to maximize energy gain. Co-existence occurs because the fitness landscape is flat at the ecological equilibrium imposed by the facultative genotype. In contrast, ecological instability favours the evolution of genotypes with behavioural flexibility to avoid being in the wrong place at the wrong time or selective exploitation of one of the habitats. Uncertain information about habitat quality erodes the adaptive advantage of otherwise optimal behaviours, favouring a bet-hedging behavioural strategy synonymous with partial habitat preferences. These results suggest that ecological dynamics could have a strong influence on behavioural heterogeneity within forager populations and that a mixed ESS for habitat use should predominate.     

Evolutionary dynamics and strong Allee effects

We describe the dynamics of an evolutionary model for a population subject to a strong Allee effect. The model assumes that the carrying capacity k(u), inherent growth rate r(u), and Allee threshold a(u) are functions of a mean phenotypic trait u subject to evolution. The model is a plane autonomous system that describes the coupled population and mean trait dynamics. We show bounded orbits equilibrate and that the Allee basin shrinks (and can even disappear) as a result of evolution. We also show that stable non-extinction equilibria occur at the local maxima of k(u) and that stable extinction equilibria occur at local minima of r(u). We give examples that illustrate these results and demonstrate other consequences of an Allee threshold in an evolutionary setting. These include the existence of multiple evolutionarily stable, non-extinction equilibria, and the possibility of evolving to a non-evolutionary stable strategy (ESS) trait from an initial trait near an ESS.     

Evolutionary dynamics and strong Allee effects

We describe the dynamics of an evolutionary model for a population subject to a strong Allee effect. The model assumes that the carrying capacity k(u), inherent growth rate r(u), and Allee threshold a(u) are functions of a mean phenotypic trait u subject to evolution. The model is a plane autonomous system that describes the coupled population and mean trait dynamics. We show bounded orbits equilibrate and that the Allee basin shrinks (and can even disappear) as a result of evolution. We also show that stable non-extinction equilibria occur at the local maxima of k(u) and that stable extinction equilibria occur at local minima of r(u). We give examples that illustrate these results and demonstrate other consequences of an Allee threshold in an evolutionary setting. These include the existence of multiple evolutionarily stable, non-extinction equilibria, and the possibility of evolving to a non-evolutionary stable strategy (ESS) trait from an initial trait near an ESS.     

Evolutionary dynamics and highly optimized tolerance

We develop a numerical model of a lattice community based on Highly Optimized Tolerance (HOT), which relates the evolution of complexity to robustness tradeoffs in an uncertain environment. With the model, we explore scenarios for evolution and extinction which are abstractions of processes which are commonly discussed in biological and ecological case studies. These include the effects of different habitats on the phenotypic traits of the organisms, the effects of different mutation rates on adaptation, fitness, and diversity, and competition between generalists and specialists. The model exhibits a wide variety of microevolutionary and macroevolutionary phenomena which can arise in organisms which are subject to random mutation, and selection based on fitness evaluated in a specific environment. Generalists arise in uniform habitats, where different disturbances occur with equal frequency, while specialists arise when the relative frequency of different disturbances is skewed. Fast mutators are seen to play a primary role in adaptation, while slow mutators preserve well-adapted configurations. When uniform and skewed habitats are coupled through migration of the organisms, we observe a primitive form of punctuated equilibrium. Rare events in the skewed habitat lead to extinction of the specialists, whereupon generalists invade from the uniform habitat, adapt to their new surroundings, ultimately leading their progeny to become vulnerable to extinction in a subsequent rare disturbance.     

Bacterial cheating drives the population dynamics of cooperative antibiotic resistance plasmids

Inactivation of β ‐lactam antibiotics by resistant bacteria is a ‘cooperative’ behavior that may allow sensitive bacteria to survive antibiotic treatment. However, the factors that determine the fraction of resistant cells in the bacterial population remain unclear, indicating a fundamental gap in our understanding of how antibiotic resistance evolves. Here, we experimentally track the spread of a plasmid that encodes a β ‐lactamase enzyme through the bacterial population. We find that independent of the initial fraction of resistant cells, the population settles to an equilibrium fraction proportional to the antibiotic concentration divided by the cell density. A simple model explains this behavior, successfully predicting a data collapse over two orders of magnitude in antibiotic concentration. This model also successfully predicts that adding a commonly used β ‐lactamase inhibitor will lead to the spread of resistance, highlighting the need to incorporate social dynamics into the study of antibiotic resistance.     

An expanded phylogeny of social amoebas (Dictyostelia) shows increasing diversity and new morphological patterns

Background  

Social Amoebae or Dictyostelia are eukaryotic microbes with a unique life cycle consisting of both uni- and multicellular stages. They have long fascinated molecular, developmental and evolutionary biologists, and Dictyostelium discoideum is now one of the most widely studied eukaryotic microbial models. The first molecular phylogeny of Dictyostelia included most of the species known at the time and suggested an extremely deep taxon with a molecular depth roughly equivalent to Metazoa. The group was also shown to consist of four major clades, none of which correspond to traditional genera. Potential morphological justification was identified for three of the four major groups, on the basis of which tentative names were assigned.