Generation time measures the trade-off between survival and reproduction in a life cycle

AbstractSurvival and fertility are the two most basic components of fitness, and they drive the evolution of a life cycle. A trade-off between them is usually present: when survival increases, fertility decreases?and vice versa. Here we show that at an evolutionary optimum, the generation time is a measure of the strength of the trade-off between overall survival and overall fertility in a life cycle. Our result both helps to explain the known fact that the generation time describes the speed of living in the slow-fast continuum of life cycles and may have implications for the extrapolation from model organisms of longevity to humans

Medawar and Hamilton on the selective forces in the evolution of ageing

Both Medawar and Hamilton contributed key ideas to the modern evolutionary theory of ageing. In particular, they both suggested that, in populations with overlapping generations, the force with which selection acts on traits declines with the age at which traits are expressed. This decline would eventually cause ageing to evolve. However, the biological literature diverges on the relationship between Medawar’s analysis of the force of selection and Hamilton’s. Some authors appear to believe that Hamilton perfected Medawar’s insightful, yet ultimately erroneous analysis of this force, while others see Hamilton’s analysis as a coherent development of, or the obvious complement to Medawar’s. Here, the relationship between the two analyses is revisited. Two things are argued for. First, most of Medawar’s alleged errors that Hamilton would had rectified seem not to be there. The origin of these perceived errors appears to be in a misinterpretation of Medawar’s writings. Second, the mathematics of Medawar and that of Hamilton show a significant overlap. However, different meanings are attached to the same mathematical expression. Medawar put forth an expression for the selective force on age-specific fitness. Hamilton proposed a full spectrum of selective forces each operating on age-specific fitness components, i.e. mortality and fertility. One of Hamilton’s expressions, possibly his most important, is of the same form as Medawar’s expression. But Hamilton’s selective forces on age-specific fitness components do not add up to yield Medawar’s selective force on age-specific fitness. It is concluded that Hamilton’s analysis should be considered neither as a correction to Medawar’s analysis nor as its obvious complement

Selection on age-specific survival: constant versus fluctuating environment

According to a classic result in evolutionary biodemography, selection on age-specific survival invariably declines with reproductive age. The result assumes proportional changes in survival and a constant environment. Here, we look at selection on age-specific survival when changes are still proportional but the environment fluctuates. We find that selection may or may not decline with reproductive age depending on how exactly survival is proportionally altered by mutations. However, interpreted in neutral terms, the mathematics behind the classic result capture a general property that the genetics of populations with age structure possess both in a constant and in a fluctuating environment

The selection force weakens with age because ageing evolves and not vice versa

According to the classic theory of life history evolution, ageing evolves because selection on traits necessarily weakens throughout reproductive life. But this inexorable decline of the selection force with adult age was shown to crucially depend on specific assumptions that are not necessarily fulfilled. Whether ageing still evolves upon their relaxation remains an open problem. Here, we propose a fully dynamical model of life history evolution that does not presuppose any specific pattern the force of selection should follow. The model shows: (i) ageing can stably evolve, but negative ageing cannot; (ii) when ageing is a stable equilibrium, the associated selection force decreases with reproductive age; (iii) non-decreasing selection is either a transient or an unstable phenomenon. Thus, we generalize the classic theory of the evolution of ageing while overturning its logic: the decline of selection with age evolves dynamically, and is not an implicit consequence of certain assumptions

Age‐specific sensitivity analysis of stable, stochastic and transient growth for stage‐classified populations

Sensitivity analysis in ecology and evolution is a valuable guide to rank demographic parameters depending on their relevance to population growth. Here, we propose a method to make the sensitivity analysis of population growth for matrix models solely classified by stage more fine-grained by considering the effect of age-specific parameters. The method applies to stable population growth, the stochastic growth rate, and transient growth. The method yields expressions for the sensitivity of stable population growth to age-specific survival and fecundity from which general properties are derived about the pattern of age-specific selective forces molding senescence in stage-classified populations

Applying symmetries of elasticities in matrix population models

Elasticity analysis is a key tool in the analysis of matrix population models, which describe the dynamics of stage-structured populations in ecology and evolution. Elasticities of the dominant eigenvalue of a matrix model to matrix entries obey certain symmetries. Yet not all consequences of these symmetries are fully appreciated, as they are sometimes hidden in mathematical detail. Here, we propose a method to reason about these symmetries directly by visual inspection of the life cycle graph that corresponds to the matrix model. We present two applications of this method, one in ecology and one in evolution. First, we prove several conjectures about elasticities that were obtained from purely numerical results and that can support population managers in decision-making under scarce demographic information. Second, we show how to identify candidates for invariant trade-offs in evolutionary optimal life cycles. The method extends to the elasticity analysis of non-dominant eigenvalues, of the stochastic growth rate and, in next-generation matrices, of the basic reproduction number

Introspection dynamics: a simple model of counterfactual learning in asymmetric games

Social behavior in human and animal populations can be studied as an evolutionary process.Individuals often make decisions between different strategies, and those strategies that yield afitness advantage tend to spread. Traditionally, much work in evolutionary game theory considerssymmetric games: individuals are assumed to have access to the same set of strategies, and theyexperience the same payoff consequences. As a result, they can learn more profitable strategies byimitation. However, interactions are oftentimes asymmetric. In that case, imitation may beinfeasible (because individuals differ in the strategies they are able to use), or it may be undesirable(because individuals differ in their incentives to use a strategy). Here, we consider an alternativelearning process which applies to arbitrary asymmetric games,introspection dynamics. Accordingto this dynamics, individuals regularly compare their present strategy to a randomly chosenalternative strategy. If the alternative strategy yields a payoff advantage, it is more likely adopted. Inthis work, we formalize introspection dynamics for pairwise games. We derive simple and explicitformulas for the abundance of each strategy over time and apply these results to severalwell-known social dilemmas. In particular, for the volunteer’s timing dilemma, we show that theplayer with the lowest cooperation cost learns to cooperate without delay

Memory shapes microbial populations

Correct decision making is fundamental for all living organisms to thrive under environmental changes. The patterns of environmental variation and the quality of available information define the most favourable strategy among multiple options, from randomly adopting a phenotypic state to sensing and reacting to environmental cues. Cellular memory—the ability to track and condition the time to switch to a different phenotypic state—can help withstand environmental fluctuations. How does memory manifest itself in unicellular organisms? We describe the population-wide consequences of phenotypic memory in microbes through a combination of deterministic modelling and stochastic simulations. Moving beyond binary switching models, our work highlights the need to consider a broader range of switching behaviours when describing microbial adaptive strategies. We show that memory in individual cells generates patterns at the population level coherent with overshoots and non-exponential lag times distributions experimentally observed in phenotypically heterogeneous populations. We emphasise the implications of our work in understanding antibiotic tolerance and, in general, bacterial survival under fluctuating environments

GNG5 Controls the Number of Apical and Basal Progenitors and Alters Neuronal Migration During Cortical Development

Cortical development is a very complex process in which any temporal or spatial alterations can give rise to a wide range of cortical malformations. Among those malformations, periventricular heterotopia (PH) is characterized by clusters of neurons that do not migrate to the correct place. Cerebral organoids derived from patients with mutations in DCHS1 and FAT4, which have been associated with PH, exhibit higher levels of GNG5 expression in a patient-specific cluster of neurons. Here we investigate the role of GNG5 during the development of the cerebral cortex in mice and human cerebral organoids. GNG5, highly expressed in progenitors and downregulated in neurons, is critical for controlling the number of apical and basal progenitors and neuronal migration. Moreover, forced expression of GNG5 recapitulates some of the alterations observed upon downregulation of Dchs1 and Fat4 in mice and human cerebral organoids derived from DCHS1 and FAT4 patients, suggesting a critical role of GNG5 in cortical development