Deciphering life history transcriptomes in different environments.

We compared whole transcriptome variation in six pre-adult stages and seven adult female ages in two populations of cactophilic Drosophila mojavensis reared on two host plants to understand how differences in gene expression influence standing life history variation. We used singular value decomposition (SVD) to identify dominant trajectories of life cycle gene expression variation, performed pairwise comparisons of stage and age differences in gene expression across the life cycle, identified when genes exhibited maximum levels of life cycle gene expression, and assessed population and host cactus effects on gene expression. Life cycle SVD analysis returned four significant components of transcriptional variation, revealing functional enrichment of genes responsible for growth, metabolic function, sensory perception, neural function, translation and ageing. Host cactus effects on female gene expression revealed population- and stage-specific differences, including significant host plant effects on larval metabolism and development, as well as adult neurotransmitter binding and courtship behaviour gene expression levels. In 3- to 6-day-old virgin females, significant upregulation of genes associated with meiosis and oogenesis was accompanied by downregulation of genes associated with somatic maintenance, evidence for a life history trade-off. The transcriptome of D. mojavensis reared in natural environments throughout its life cycle revealed core developmental transitions and genome-wide influences on life history variation in natural populations.

Incorporating evolutionary processes into population viability models.

We examined how ecological and evolutionary (eco-evo) processes in population dynamics could be better integrated into population viability analysis (PVA). Complementary advances in computation and population genomics can be combined into an eco-evo PVA to offer powerful new approaches to understand the influence of evolutionary processes on population persistence. We developed the mechanistic basis of an eco-evo PVA using individual-based models with individual-level genotype tracking and dynamic genotype-phenotype mapping to model emergent population-level effects, such as local adaptation and genetic rescue. We then outline how genomics can allow or improve parameter estimation for PVA models by providing genotypic information at large numbers of loci for neutral and functional genome regions. As climate change and other threatening processes increase in rate and scale, eco-evo PVAs will become essential research tools to evaluate the effects of adaptive potential, evolutionary rescue, and locally adapted traits on persistence.

Robustness and evolvability.

Why isn't random variation always deleterious? Are there factors that sometimes make adaptation easier? Biological systems are extraordinarily robust to perturbation by mutations, recombination and the environment. It has been proposed that this robustness might make them more evolvable. Robustness to mutation allows genetic variation to accumulate in a cryptic state. Switching mechanisms known as evolutionary capacitors mean that the amount of heritable phenotypic variation available can be correlated to the degree of stress and hence to the novelty of the environment and remaining potential for adaptation. There have been two somewhat separate literatures relating robustness to evolvability. One has focused on molecular phenotypes and new mutations, the other on morphology and cryptic genetic variation. Here, we review both literatures, and show that the true distinction is whether recombination rates are high or low. In both cases, the evidence supports the claim that robustness promotes evolvability.

The generation and maintenance of genetic variation by frequency-dependent selection: constructing polymorphisms under the pairwise interaction model.

Frequency-dependent selection remains the most commonly invoked heuristic explanation for the maintenance of genetic variation. For polymorphism to exist, new alleles must be both generated and maintained in the population. Here we use a construction approach to model frequency-dependent selection with mutation under the pairwise interaction model. The pairwise interaction model is a general model of frequency-dependent selection at the genotypic level. We find that frequency-dependent selection is able to generate a large number of alleles at a single locus. The construction process generates multiallelic polymorphisms with a wide range of allele-frequency distributions and genotypic fitness relationships. Levels of polymorphism and mean fitness are uncoupled, so constructed polymorphisms remain permanently invasible to new mutants; thus the model never settles down to an equilibrium state. Analysis of constructed fitness sets reveals signatures of heterozygote advantage and positive frequency dependence.

Evolution of fitnesses in structured populations with correlated environments.

The outcome of selection in structured populations with spatially varying selection pressures depends on the interaction of two factors: the level of gene flow and the amount of heterogeneity among the demes. Here we investigate the effect of three different levels of spatial heterogeneity on the levels of genetic polymorphisms for different levels of gene flow, using a construction approach in which a population is constantly bombarded with new mutations. We further compare the relative importance of two kinds of balancing selection (heterozygote advantage and selection arising from spatial heterogeneity), the level of adaptation and the stability of the resulting polymorphic equilibria. The different levels of environmental heterogeneity and gene flow have a large influence on the final level of polymorphism. Both factors also influence the relative importance of the two kinds of balancing selection in the maintenance of variation. In particular, selection arising from spatial heterogeneity does not appear to be an important form of balancing selection for the most homogeneous scenario. The level of adaptation is highest for low levels of gene flow and, at those levels, remarkably similar for the different levels of spatial heterogeneity, whereas for higher levels of gene flow the level of adaptation is substantially reduced.

Complex dynamics occur in a single-locus, multiallelic model of general frequency-dependent selection.

We examine the characteristics of non-equilibrium dynamics produced by a simple well-known model of frequency-dependent selection at a single diploid locus. An examination of the parameter space of this "pairwise-interaction model" (PIM) revealed non-equilibrium dynamics for polymorphisms of 3, 4 and 5 alleles; both allele-frequency cycling and aperiodic trajectories were detected. We measured the number, cycle length and domains of attraction of the various attractors produced by the model. The domains of attraction tended to be smaller, and the cycles longer, for systems with larger number of alleles. Fitnesses that parametrized negative frequency-dependent selection were more likely to allow cycling, and these cycles also had larger domains of attraction. Aperiodic trajectories were detected only in cases with 4 or 5 alleles. The genetic cycles produced by the model do not have periods as short as those predicted in ecological models with cycling (such as predator-prey population cycles, etc.). Consequently, in a real-world system, PIM allele-frequency cycling is likely to be indistinguishable from stable equilibria when observed over short time scales.

Frequency-dependent selection and the maintenance of genetic variation: exploring the parameter space of the multiallelic pairwise interaction model.

When individuals' fitnesses depend on the genetic composition of the population in which they are found, selection is then frequency dependent. Frequency-dependent selection (FDS) is often invoked as a heuristic explanation for the maintenance of large numbers of alleles at a locus. The pairwise interaction model is a general model of FDS via intraspecific competition at the genotypic level. Here we use a parameter-space approach to investigate the full potential for the maintenance of multiallelic equilibria under the pairwise interaction model. We find that FDS maintains full polymorphism more often than classic constant-selection models and produces more skewed equilibrium allele frequencies. Fitness sets with some degree of rare advantage maintained full polymorphism most often, but a wide variety of nonobvious fitness patterns were also found to have positive potential for polymorphism. An example is put forth suggesting possible explanations for multiallelic polymorphisms maintained despite positive FDS on individual alleles.

Divergence in age patterns of mortality change drives international divergence in lifespan inequality.

In the past six decades, lifespan inequality has varied greatly within and among countries even while life expectancy has continued to increase. How and why does mortality change generate this diversity? We derive a precise link between changes in age-specific mortality and lifespan inequality, measured as the variance of age at death. Key to this relationship is a young-old threshold age, below and above which mortality decline respectively decreases and increases lifespan inequality. First, we show for Sweden that shifts in the threshold's location have modified the correlation between changes in life expectancy and lifespan inequality over the last two centuries. Second, we analyze the post-World War II (WWII) trajectories of lifespan inequality in a set of developed countries-Japan, Canada, and the United States-where thresholds centered on retirement age. Our method reveals how divergence in the age pattern of mortality change drives international divergence in lifespan inequality. Most strikingly, early in the 1980s, mortality increases in young U.S. males led to a continuation of high lifespan inequality in the United States; in Canada, however, the decline of inequality continued. In general, our wider international comparisons show that mortality change varied most at young working ages after WWII, particularly for males. We conclude that if mortality continues to stagnate at young ages yet declines steadily at old ages, increases in lifespan inequality will become a common feature of future demographic change.

Birth-order differences can drive natural selection on aging.

Senescence-the deterioration of survival and reproductive capacity with increasing age-is generally held to be an evolutionary consequence of the declining strength of natural selection with increasing age. The diversity in rates of aging observed in nature suggests that the rate at which age-specific selection weakens is determined by species-specific ecological factors. We propose that, in iteroparous species, relationships between parental age, offspring birth order, and environment may affect selection on senescence. Later-born siblings have, on average, older parents than do first borns. Offspring born to older parents may experience different environments in terms of family support or inherited resources, factors often mediated by competition from siblings. Thus, age-specific selection on parents may change if the environment produces birth-order related gradients in reproductive success. We use an age-and-stage structured population model to investigate the impact of sibling environmental inequality on the expected evolution of senescence. We show that accelerated senescence evolves when later-born siblings are likely to experience an environment detrimental to lifetime reproduction. In general, sibling inequality is likely to be of particular importance for the evolution of senescence in species such as humans, where family interactions and resource inheritance have important roles in determining lifetime reproduction.

Cryptic genetic variation can make "irreducible complexity" a common mode of adaptation in sexual populations.

The existence of complex (multiple-step) genetic adaptations that are "irreducible" (i.e., all partial combinations are less fit than the original genotype) is one of the longest standing problems in evolutionary biology. In standard genetics parlance, these adaptations require the crossing of a wide adaptive valley of deleterious intermediate stages. Here, we demonstrate, using a simple model, that evolution can cross wide valleys to produce "irreducibly complex" adaptations by making use of previously cryptic mutations. When revealed by an evolutionary capacitor, previously cryptic mutants have higher initial frequencies than do new mutations, bringing them closer to a valley-crossing saddle in allele frequency space. Moreover, simple combinatorics implies an enormous number of candidate combinations exist within available cryptic genetic variation. We model the dynamics of crossing of a wide adaptive valley after a capacitance event using both numerical simulations and analytical approximations. Although individual valley crossing events become less likely as valleys widen, by taking the combinatorics of genotype space into account, we see that revealing cryptic variation can cause the frequent evolution of complex adaptations.

Models of frequency-dependent selection with mutation from parental alleles.

Frequency-dependent selection (FDS) remains a common heuristic explanation for the maintenance of genetic variation in natural populations. The pairwise-interaction model (PIM) is a well-studied general model of frequency-dependent selection, which assumes that a genotype's fitness is a function of within-population intergenotypic interactions. Previous theoretical work indicated that this type of model is able to sustain large numbers of alleles at a single locus when it incorporates recurrent mutation. These studies, however, have ignored the impact of the distribution of fitness effects of new mutations on the dynamics and end results of polymorphism construction. We suggest that a natural way to model mutation would be to assume mutant fitness is related to the fitness of the parental allele, i.e., the existing allele from which the mutant arose. Here we examine the numbers and distributions of fitnesses and alleles produced by construction under the PIM with mutation from parental alleles and the impacts on such measures due to different methods of generating mutant fitnesses. We find that, in comparison with previous results, generating mutants from existing alleles lowers the average number of alleles likely to be observed in a system subject to FDS, but produces polymorphisms that are highly stable and have realistic allele-frequency distributions.

Beyond the Mean: Sensitivities of the Variance of Population Growth.

Populations in variable environments are described by both a mean growth rate and a variance of stochastic population growth. Increasing variance will increase the width of confidence bounds around estimates of population size, growth, probability of and time to quasi-extinction. However, traditional sensitivity analyses of stochastic matrix models only consider the sensitivity of the mean growth rate. We derive an exact method for calculating the sensitivity of the variance in population growth to changes in demographic parameters. Sensitivities of the variance also allow a new sensitivity calculation for the cumulative probability of quasi-extinction. We apply this new analysis tool to an empirical dataset on at-risk polar bears to demonstrate its utility in conservation biology We find that in many cases a change in life history parameters will increase both the mean and variance of population growth of polar bears. This counterintuitive behaviour of the variance complicates predictions about overall population impacts of management interventions. Sensitivity calculations for cumulative extinction risk factor in changes to both mean and variance, providing a highly useful quantitative tool for conservation management. The mean stochastic growth rate and its sensitivities do not fully describe the dynamics of population growth. The use of variance sensitivities gives a more complete understanding of population dynamics and facilitates the calculation of new sensitivities for extinction processes.