ON THE EVOLUTION OF MIGRATION IN HETEROGENEOUS ENVIRONMENTS

Populations often experience variable conditions, both in time and space. Here we develop a novel theoretical framework to study the evolution of migration under the influence of spatially and temporally variable selection and genetic drift. First, we examine when polymorphism is maintained at a locus under heterogeneous selection, as a function of the pattern of spatial heterogeneity and the migration rate. In a second step, we study how levels of migration evolve under the joint action of kin competition and local adaptation at a polymorphic locus. This analysis reveals the existence of evolutionary bistability, whereby a low or a high migration rate may evolve depending on the initial conditions. Last, we relax several assumptions regarding selection heterogeneity commonly made in previous studies and explore the consequences of more complex spatial and temporal patterns of variability in selection on the evolution of migration. We found that small modifications in the pattern of environmental heterogeneity may have dramatic effects on the evolution of migration. This work highlights the importance of considering more general scenarios of environmental heterogeneity when studying the evolution of life‐history traits in ecologically complex settings.     

Variability of cross-tissue X-chromosome inactivation characterizes timing of human embryonic lineage specification events

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Contrasting patterns of density‐dependent selection at different life stages can create more than one fast–slow axis of life‐history variation

There has been much recent research interest in the existence of a major axis of life‐history variation along a fast–slow continuum within almost all major taxonomic groups. Eco‐evolutionary models of density‐dependent selection provide a general explanation for such observations of interspecific variation in the "pace of life." One issue, however, is that some large‐bodied long‐lived “slow” species (e.g., trees and large fish) often show an explosive “fast” type of reproduction with many small offspring, and species with “fast” adult life stages can have comparatively “slow” offspring life stages (e.g., mayflies). We attempt to explain such life‐history evolution using the same eco‐evolutionary modeling approach but with two life stages, separating adult reproductive strategies from offspring survival strategies. When the population dynamics in the two life stages are closely linked and affect each other, density‐dependent selection occurs in parallel on both reproduction and survival, producing the usual one‐dimensional fast–slow continuum (e.g., houseflies to blue whales). However, strong density dependence at either the adult reproduction or offspring survival life stage creates quasi‐independent population dynamics, allowing fast‐type reproduction alongside slow‐type survival (e.g., trees and large fish), or the perhaps rarer slow‐type reproduction alongside fast‐type survival (e.g., mayflies—short‐lived adults producing few long‐lived offspring). Therefore, most types of species life histories in nature can potentially be explained via the eco‐evolutionary consequences of density‐dependent selection given the possible separation of demographic effects at different life stages.     

EVOLUTION AND EXTINCTION IN A CHANGING ENVIRONMENT: A QUANTITATIVE-GENETIC ANALYSIS

Because of the ubiquity of genetic variation for quantitative traits, virtually all populations have some capacity to respond evolutionarily to selective challenges. However, natural selection imposes demographic costs on a population, and if these costs are sufficiently large, the likelihood of extinction will be high. We consider how the mean time to extinction depends on selective pressures (rate and stochasticity of environmental change, and strength of selection), population parameters (carrying capacity, and reproductive capacity), and genetics (rate of polygenic mutation). We assume that in a randomly mating, finite population subject to density-dependent population growth, individual fitness is determined by a single quantitative-genetic character under Gaussian stabilizing selection with the optimum phenotype exhibiting directional change, or random fluctuations, or both. The quantitative trait is determined by a finite number of freely recombining, mutationally equivalent, additive loci. The dynamics of evolution and extinction are investigated, assuming that the population is initially under mutation-selection-drift balance. Under this model, in a directionally changing environment, the mean phenotype lags behind the optimum, but on the average evolves parallel to it. The magnitude of the lag determines the vulnerability to extinction. In finite populations, stochastic variation in the genetic variance can be quite pronounced, and bottlenecks in the genetic variance temporarily can impair the population's adaptive capacity enough to cause extinction when it would otherwise be unlikely in an effectively infinite population. We find that maximum sustainable rates of evolution or, equivalently, critical rates of environmental change, may be considerably less than 10% of a phenotypic standard deviation per generation.     

Mean growth rate when rare is not a reliable metric for persistence of species

The coexistence of many species within ecological communities poses a long‐standing theoretical puzzle. Modern coexistence theory (MCT) and related techniques explore this phenomenon by examining the chance of a species population growing from rarity in the presence of all other species. The mean growth rate when rare, , is used in MCT as a metric that measures persistence properties (like invasibility or time to extinction) of a population. Here we critique this reliance on and show that it fails to capture the effect of temporal random abundance variations on persistence properties. The problem becomes particularly severe when an increase in the amplitude of stochastic temporal environmental variations leads to an increase in , since at the same time it enhances random abundance fluctuations and the two effects are inherently intertwined. In this case, the chance of invasion and the mean extinction time of a population may even go down as increases.     

Life‐history evolution under fluctuating density‐dependent selection and the adaptive alignment of pace‐of‐life syndromes

We present a novel perspective on life‐history evolution that combines recent theoretical advances in fluctuating density‐dependent selection with the notion of pace‐of‐life syndromes (POLSs) in behavioural ecology. These ideas posit phenotypic co‐variation in life‐history, physiological, morphological and behavioural traits as a continuum from the highly fecund, short‐lived, bold, aggressive and highly dispersive ‘fast’ types at one end of the POLS to the less fecund, long‐lived, cautious, shy, plastic and socially responsive ‘slow’ types at the other. We propose that such variation in life histories and the associated individual differences in behaviour can be explained through their eco‐evolutionary dynamics with population density – a single and ubiquitous selective factor that is present in all biological systems. Contrasting regimes of environmental stochasticity are expected to affect population density in time and space and create differing patterns of fluctuating density‐dependent selection, which generates variation in fast versus slow life histories within and among populations. We therefore predict that a major axis of phenotypic co‐variation in life‐history, physiological, morphological and behavioural traits (i.e. the POLS) should align with these stochastic fluctuations in the multivariate fitness landscape created by variation in density‐dependent selection. Phenotypic plasticity and/or genetic (co‐)variation oriented along this major POLS axis are thus expected to facilitate rapid and adaptively integrated changes in various aspects of life histories within and among populations and/or species. The fluctuating density‐dependent selection POLS framework presented here therefore provides a series of clear testable predictions, the investigation of which should further our fundamental understanding of life‐history evolution and thus our ability to predict natural population dynamics.     

Univariate Community Assembly Analysis (UniCAA): Combining hierarchical models with null models to test the influence of spatially restricted dispersal,environmental filtering,and stochasticity on community assembly

Identifying the influence of stochastic processes and of deterministic processes, such as dispersal of individuals of different species and trait‐based environmental filtering, has long been a challenge in studies of community assembly. Here, we present the Univariate Community Assembly Analysis (UniCAA) and test its ability to address three hypotheses: species occurrences within communities are (a) limited by spatially restricted dispersal; (b) environmentally filtered; or (c) the outcome of stochasticity—so that as community size decreases—species that are common outside a local community have a disproportionately higher probability of occurrence than rare species. The comparison with a null model allows assessing if the influence of each of the three processes differs from what one would expect under a purely stochastic distribution of species. We tested the framework by simulating “empirical” metacommunities under 15 scenarios that differed with respect to the strengths of spatially restricted dispersal (restricted vs. not restricted); habitat isolation (low, intermediate, and high immigration rates); and environmental filtering (strong, intermediate, and no filtering). Through these tests, we found that UniCAA rarely produced false positives for the influence of the three processes, yielding a type‐I error rate ≤5%. The type‐II error rate, that is, production of false negatives, was also acceptable and within the typical cutoff (20%). We demonstrate that the UniCAA provides a flexible framework for retrieving the processes behind community assembly and propose avenues for future developments of the framework.     

Why is sterility virulence most common in sexually transmitted infections? Examining the role of epidemiology

Sterility virulence, or the reduction in host fecundity due to infection, occurs in many host–pathogen systems. Notably, sterility virulence is more common for sexually transmitted infections (STIs) than for directly transmitted pathogens, while other forms of virulence tend to be limited in STIs. This has led to the suggestion that sterility virulence may have an adaptive explanation. By focusing upon finite population models, we show that the observed patterns of sterility virulence can be explained by consideration of the epidemiological differences between STIs and directly transmitted pathogens. In particular, when pathogen transmission is predominantly density invariant (as for STIs), and mortality is density dependent, sterility virulence can be favored by demographic stochasticity, whereas if pathogen transmission is predominantly density dependent, as is common for most directly transmitted pathogens, sterility virulence is disfavored. We show these conclusions can hold even if there is a weak selective advantage to sterilizing.