Population Mean Fitness Research Articles

How density dependence, genetic erosion and the extinction vortex impact evolutionary rescue.

Following severe environmental change that reduces mean population fitness below replacement, populations must adapt to avoid eventual extinction, a process called evolutionary rescue. Models of evolutionary rescue demonstrate that initial size, genetic variation and degree of maladaptation influence population fates. However, many models feature populations that grow without negative density dependence or with constant genetic diversity despite precipitous population decline, assumptions likely to be violated in conservation settings. We examined the simultaneous influences of density-dependent growth and erosion of genetic diversity on populations adapting to novel environmental change using stochastic, individual-based simulations. Density dependence decreased the probability of rescue and increased the probability of extinction, especially in large and initially well-adapted populations that previously have been predicted to be at low risk. Increased extinction occurred shortly following environmental change, as populations under density dependence experienced more rapid decline and reached smaller sizes. Populations that experienced evolutionary rescue lost genetic diversity through drift and adaptation, particularly under density dependence. Populations that declined to extinction entered an extinction vortex, where small size increased drift, loss of genetic diversity and the fixation of maladaptive alleles, hindered adaptation and kept populations at small densities where they were vulnerable to extinction via demographic stochasticity.

Niche construction and the environmental term of the price equation: How natural selection changes when organisms alter their environments.

Organisms construct their own environments and phenotypes through the adaptive processes of habitat choice, habitat construction, and phenotypic plasticity. We examine how these processes affect the dynamics of mean fitness change through the environmental change term of the Price Equation. This tends to be ignored in evolutionary theory, owing to the emphasis on the first term describing the effect of natural selection on mean fitness (the additive genetic variance for fitness of Fisher's Fundamental Theorem). Using population genetic models and the Price Equation, we show how adaptive niche constructing traits favorably alter the distribution of environments that organisms encounter and thereby increase population mean fitness. Because niche-constructing traits increase the frequency of higher-fitness environments, selection favors their evolution. Furthermore, their alteration of the actual or experienced environmental distribution creates selective feedback between niche constructing traits and other traits, especially those with genotype-by-environment interaction for fitness. By altering the distribution of experienced environments, niche constructing traits can increase the additive genetic variance for such traits. This effect accelerates the process of overall adaption to the niche-constructed environmental distribution and can contribute to the rapid refinement of alternative phenotypic adaptations to different environments. Our findings suggest that evolutionary biologists revisit and reevaluate the environmental term of the Price Equation: owing to adaptive niche construction, it contributes directly to positive change in mean fitness; its magnitude can be comparable to that of natural selection; and, when there is fitness G × E, it increases the additive genetic variance for fitness, the much-celebrated first term.

A computational investigation of cis-gene regulation in evolution

In biological processes involving gene networks, genes regulate other genes that determine the phenotypic traits. Gene regulation plays an important role in evolutionary dynamics. In a genetic algorithm, a trans-gene regulatory mechanism was shown to speed up adaptation and evolution. Here, we examine the effect of cis-gene regulation on an adaptive system. The model is haploid. A chromosome is partitioned into regulatory loci and structural loci. The regulatory genes regulate the expression and functioning of structural genes via the cis-elements in a probabilistic manner. In the simulation, the change in the allele frequency, the mean population fitness and the efficiency of phenotypic selection are monitored. Cis-gene regulation increases adaption and accelerates the evolutionary process in comparison with the case involving absence of gene regulation. Some special features of the simulation results are as follows. A low ratio of regulatory loci and structural loci gives higher adaptation for fixed total number of loci. Plasticity is advantageous beyond a threshold value. Adaptation is better for large number of total loci when the ratio of regulatory loci to structural loci is one. However, it reaches a saturation beyond which the increase in the total loci is not advantageous. Efficiency of the phenotypic selection is higher for larger value of the initial plasticity.

Beyond mean fitness: Demographic stochasticity and resilience matter at tree species climatic edges

AbstractAimLinking local population dynamics and species distributions is crucial to predicting the impacts of climate change. Although many studies focus on the mean fitness of populations, theory shows that species distributions can be shaped by demographic stochasticity or population resilience. Here, we examine how mean fitness (measured by invasion rate), demographic stochasticity and resilience (measured by the ability to recover from disturbance) constrain populations at the edges compared with the climatic centre.LocationEurope: Spain, France, Germany, Finland and Sweden.PeriodForest inventory data used for fitting the models cover the period from 1985 to 2013.Major taxaDominant European tree species; angiosperms and gymnosperms.MethodsWe developed dynamic population models covering the entire life cycle of 25 European tree species with climatically dependent recruitment models fitted to forest inventory data. We then ran simulations using integral projection and individual‐based models to test how invasion rates, risk of stochastic extinction and ability to recover from stochastic disturbances differ between the centre and edges of the climatic niches of species.ResultsResults varied among species, but in general, demographic constraints were stronger at warm edges and for species in harsher climates. Conversely, recovery was more limiting at cold edges. In addition, we found that for several species, constraints at the edges were attributable to demographic stochasticity and capacity for recovery rather than mean fitness.Main conclusionsOur results highlight that mean fitness is not the only mechanism at play at the edges; demographic stochasticity and population capacity to recover also matter for European tree species. To understand how climate change will drive species range shifts, future studies will need to analyse the interplay between population mean growth rate and stochastic demographic processes in addition to disturbances.

Habitat suitability correlates with mean population fitness of a threatened marsupial predator

Context Measuring the quality of habitats necessary for the survival of threatened species is a priority for conservation management, but traditional metrics are often too costly to implement. As a result, many practitioners rely on proxies such as habitat suitability, which are measured by relating environmental variables to species occurrence data using habitat suitability models. However, little research has examined how these proxies relate to actual measures of habitat quality, such as body condition. Aim By testing the relationship between habitat suitability and habitat quality – as characterised by mean population fitness – the aim of this study was to improve our understanding of ways in which we can reliably map habitat of high importance for a particular species, as well as habitats where populations are most susceptible to local extinction. Methods We used data from a large-scale monitoring program on the northern quoll (Dasyurus hallucatus), a threatened marsupial predator, which collected data on three measures of population mean fitness (measured as body mass accounting for size, tail circumference, and body mass). We correlated these measures with habitat suitability derived from a habitat suitability model. Key results We found quoll mean population fitness increased with increasing habitat suitability. In addition, we found mean population fitness increased with increasing topographic ruggedness, annual rainfall, rainfall variability, and decreasing distance to water, consistent with previous studies that suggest quolls are able to persist better in habitat where resource availability (shelter, food) is higher. Conclusions Our findings demonstrate the usefulness of habitat suitability models for predicting habitat quality for a threatened predator at a large scale, and that predictions of habitat suitability can correspond with measures of fitness. In addition, they support previous studies in highlighting the importance of topographically complex habitat for this species. Implications These findings have important implications for identifying both source populations, where species recruitment is likely to exceed mortality, and more vulnerable populations that may require targeted conservation interventions to ensure their long-term persistence and stability.

Surrogate ensemble assisted large-scale expensive optimization with random grouping

Many fitness evaluations are often needed for large-scale evolutionary optimization to find the optimal solution. Therefore, evolutionary algorithms are impeded to solve computationally expensive problems. Surrogate assisted evolutionary algorithms (SAEAs) have been shown to have good capability in a finite computational budget. However, not many SAEAs, have been proposed for large-scale expensive problems. The main reason is that a proper surrogate model is challenging to be trained due to the curse of dimension. In this paper, we propose to employ the random grouping technique to divide a large-scale optimization problem into several low-dimensional sub-problems. Then a surrogate ensemble is trained for each sub-problem to assist the sub-problem optimization. The next parent population for large-scale optimization will be generated by the horizontal composition of the populations for sub-problem optimization. Furthermore, the best solution found so far for the sub-problem with the best population mean fitness value will be used to replace the best solution found so far for the large-scale problem on its corresponding dimensions, and the new solution will be evaluated using the expensive objective function. The experimental results on CEC’2013 benchmark problems show that the proposed method is effective and efficient for solving large-scale expensive optimization problems.

Suppressors of fixation can increase average fitness beyond amplifiers of selection

Evolutionary dynamics on graphs has remarkable features: For example, it has been shown that amplifiers of selection exist that-compared to an unstructured population-increase the fixation probability of advantageous mutations, while they decrease the fixation probability of disadvantageous mutations. So far, the theoretical literature has focused on the case of a single mutant entering a graph-structured population, asking how the graph affects the probability that a mutant takes over a population and the time until this typically happens. For continuously evolving systems, the more relevant case is that mutants constantly arise in an evolving population. Typically, such mutations occur with a small probability during reproduction events. We thus focus on the low mutation rate limit. The probability distribution for the fitness in this process converges to a steady state at long times. Intuitively, amplifiers of selection are expected to increase the population's mean fitness in the steady state. Similarly, suppressors of selection are expected to decrease the population's mean fitness in the steady state. However, we show that another set of graphs, called suppressors of fixation, can attain the highest population mean fitness. The key reason behind this is their ability to efficiently reject deleterious mutants. This illustrates the importance of the deleterious mutant regime for the long-term evolutionary dynamics, something that seems to have been overlooked in the literature so far.

Genetic load: genomic estimates and applications in non-model animals.

Genetic variation, which is generated by mutation, recombination and gene flow, can reduce the mean fitness of a population, both now and in the future. This 'genetic load' has been estimated in a wide range of animal taxa using various approaches. Advances in genome sequencing and computational techniques now enable us to estimate the genetic load in populations and individuals without direct fitness estimates. Here, we review the classic and contemporary literature of genetic load. We describe approaches to quantify the genetic load in whole-genome sequence data based on evolutionary conservation and annotations. We show that splitting the load into its two components - the realized load (or expressed load) and the masked load (or inbreeding load) - can improve our understanding of the population genetics of deleterious mutations.

Species distribution models rarely predict the biology of real populations

Species distribution models (SDMs) are widely used in ecology. In theory, SDMs capture (at least part of) species' ecological niches and can be used to make inferences about the distribution of suitable habitat for species of interest. Because habitat suitability is expected to influence population demography, SDMs have been used to estimate a variety of population parameters, from occurrence to genetic diversity. However, a critical look at the ability of SDMs to predict independent data across different aspects of population biology is lacking. Here, we systematically reviewed the literature, retrieving 201 studies that tested predictions from SDMs against independent assessments of occurrence, abundance, population performance, and genetic diversity. Although there is some support for the ability of SDMs to predict occurrence (~53% of studies depending on how support was assessed), the predictive performance of these models declines progressively from occurrence to abundance, to population mean fitness, to genetic diversity. At the same time, we observed higher success among studies that evaluated performance for single versus multiple species, pointing to a possible publication bias. Thus, the limited accuracy of SDMs reported here may reflect the best‐case scenario. We discuss the limitations of these models and provide specific recommendations for their use for different applications going forward. However, we emphasize that predictions from SDMs, especially when used to inform conservation decisions, should be treated as hypotheses to be tested with independent data rather than as stand‐ins for the population parameters we seek to know.

Social Selection and the Evolution of Maladaptation.

Evolution by natural selection is often viewed as a process that inevitably leads to adaptation or an increase in population fitness over time. However, maladaptation, an evolved decrease in fitness, may also occur in response to natural selection under some conditions. Social selection, which arises from the effects of social partners on fitness, has been identified as a potential cause of maladaptation, but we lack a general rule identifying when social selection should lead to a decrease in population mean fitness. Here we use a quantitative genetic model to develop such a rule. We show that maladaptation is most likely to occur when social selection is strong relative to nonsocial selection and acts in an opposing direction. In this scenario, the evolution of traits that impose fitness costs on others may outweigh evolved gains in fitness for the individual, leading to a net decrease in population mean fitness. Furthermore, we find that maladaptation may also sometimes occur when phenotypes of interacting individuals negatively covary. We outline the biological situations where maladaptation in response to social selection can be expected, provide both quantitative genetic and phenotypic versions of our derived result, and suggest what empirical work would be needed to test it. We also consider the effect of social selection on inclusive fitness and support previous work showing that inclusive fitness cannot suffer an evolutionary decrease. Taken together, our results show that social selection may decrease population mean fitness when it opposes individual-level selection, even as inclusive fitness increases.

Embodied Computational Evolution: Feedback Between Development and Evolution in Simulated Biorobots.

Given that selection removes genetic variance from evolving populations, thereby reducing exploration opportunities, it is important to find mechanisms that create genetic variation without the disruption of adapted genes and genomes caused by random mutation. Just such an alternative is offered by random epigenetic error, a developmental process that acts on materials and parts expressed by the genome. In this system of embodied computational evolution, simulated within a physics engine, epigenetic error was instantiated in an explicit genotype-to-phenotype map as transcription error at the initiation of gene expression. The hypothesis was that transcription error would create genetic variance by shielding genes from the direct impact of selection, creating, in the process, masquerading genomes. To test this hypothesis, populations of simulated embodied biorobots and their developmental systems were evolved under steady directional selection as equivalent rates of random mutation and random transcriptional error were covaried systematically in an 11 × 11 fully factorial experimental design. In each of the 121 different experimental conditions (unique combinations of mutation and transcription error), the same set of 10 randomly created replicate populations of 60 individuals were evolved. Selection for the improved locomotor behavior of individuals led to increased mean fitness of populations over 100 generations at nearly all levels and combinations of mutation and transcription error. When the effects of both types of error were partitioned statistically, increasing transcription error was shown to increase the final genetic variance of populations, incurring a fitness cost but acting on variance independently and differently from genetic mutation. Thus, random epigenetic errors in development feed back through selection of individuals with masquerading genomes to the population’s genetic variance over generational time. Random developmental processes offer an additional mechanism for exploration by increasing genetic variation in the face of steady, directional selection.

Non-vertical cultural transmission, assortment and the evolution of cooperation.

Cultural evolution of cooperation under vertical and non-vertical cultural transmission is studied, and conditions are found for fixation and coexistence of cooperation and defection. The evolution of cooperation is facilitated by its horizontal transmission and by an association between social interactions and horizontal transmission. The effect of oblique transmission depends on the horizontal transmission bias. Stable polymorphism of cooperation and defection can occur, and when it does, reduced association between social interactions and horizontal transmission evolves, which leads to a decreased frequency of cooperation and lower population mean fitness. The deterministic conditions are compared to outcomes of stochastic simulations of structured populations. Parallels are drawn with Hamilton’s rule incorporating relatedness and assortment.

Assortative mating can help adaptation of flowering time to a changing climate: Insights from a polygenic model

Several empirical studies report fast evolutionary changes in flowering time in response to contemporary climate change. Flowering time is a polygenic trait under assortative mating, since flowering time of mates must overlap. Here, we test whether assortative mating, compared with random mating, can help better track a changing climate. For each mating pattern, our individual‐based model simulates a population evolving in a climate characterized by stabilizing selection around an optimal flowering time, which can change directionally and/or fluctuate. We also derive new analytical predictions from a quantitative genetics model for the expected genetic variance at equilibrium, and its components, the lag of the population to the optimum and the population mean fitness. We compare these predictions between assortative and random mating, and to our simulation results. Assortative mating, compared with random mating, has antagonistic effects on genetic variance: it generates positive associations among similar allelic effects, which inflates the genetic variance, but it decreases genetic polymorphism, which depresses the genetic variance. In a stationary environment with substantial stabilizing selection, assortative mating affects little the genetic variance compared with random mating. In a changing climate, assortative mating however increases genetic variance compared to random mating, which diminishes the lag of the population to the optimum, and in most scenarios translates into a fitness advantage relative to random mating. The magnitude of this fitness advantage depends on the extent to which genetic variance limits adaptation, being larger for faster environmental changes and weaker stabilizing selection.

The power of randomization by sex in multilocus genetic evolution

BackgroundMany hypotheses have been proposed for how sexual reproduction may facilitate an increase in the population mean fitness, such as the Fisher-Muller theory, Muller’s ratchet and others. According to the recently proposed mixability theory, however, sexual recombination shifts the focus of natural selection away from favoring particular genetic combinations of high fitness towards favoring alleles that perform well across different genetic combinations. Mixability theory shows that, in finite populations, because sex essentially randomizes genetic combinations, if one allele performs better than another across the existing combinations of alleles, that allele will likely also perform better overall across a vast space of untested potential genotypes. However, this superiority has been established only for a single-locus diploid model.ResultsWe show that, in both haploids and diploids, the power of randomization by sex extends to the multilocus case, and becomes substantially stronger with increasing numbers of loci. In addition, we make an explicit comparison between the sexual and asexual cases, showing that sexual recombination is the cause of the randomization effect.ConclusionsThat the randomization effect applies to the multilocus case and becomes stronger with increasing numbers of loci suggests that it holds under realistic conditions. One may expect, therefore, that in nature the ability of an allele to perform well in interaction with existing genetic combinations is indicative of how well it will perform in a far larger space of potential combinations that have not yet materialized and been tested. Randomization plays a similar role in a statistical test, where it allows one to draw an inference from the outcome of the test in a small sample about its expected outcome in a larger space of possibilities—i.e., to generalize. Our results are relevant to recent theories examining evolution as a learning process.ReviewersThis article was reviewed by David Ardell and Brian Golding.

Exploitative leaders incite intergroup warfare in a social mammal

Collective conflicts among humans are widespread, although often highly destructive. A classic explanation for the prevalence of such warfare in some human societies is leadership by self-serving individuals that reap the benefits of conflict while other members of society pay the costs. Here, we show that leadership of this kind can also explain the evolution of collective violence in certain animal societies. We first extend the classic hawk-dove model of the evolution of animal aggression to consider cases in which a subset of individuals within each group may initiate fights in which all group members become involved. We show that leadership of this kind, when combined with inequalities in the payoffs of fighting, can lead to the evolution of severe intergroup aggression, with negative consequences for population mean fitness. We test our model using long-term data from wild banded mongooses, a species characterized by frequent intergroup conflicts that have very different fitness consequences for male and female group members. The data show that aggressive encounters between groups are initiated by females, who gain fitness benefits from mating with extragroup males in the midst of battle, whereas the costs of fighting are borne chiefly by males. In line with the model predictions, the result is unusually severe levels of intergroup violence. Our findings suggest that the decoupling of leaders from the costs that they incite amplifies the destructive nature of intergroup conflict.

Parameter estimation of photovoltaic models using an improved marine predators algorithm

The abundance of solar energy as one of the clean energy forms offers a great advantage as an alternative to non-renewable energy sources. The photovoltaic system is a promising technology that directly converts sunlight into a direct current. Parameter estimation of photovoltaic systems is a challenging task that has a significant influence on the efficiency of these systems. Most of the existing methods employed for identifying parameters of photovoltaic systems suffer from high computing burdens, fall into local optima, or struggle with the intricate adjustment required of the algorithm parameters to provide the best performance. This paper, therefore, proposes an improved algorithm based on the new metaheuristic marine predators algorithm to extract the optimal values of photovoltaic parameters. The improved marine predators algorithm employs a population improvement strategy to enhance the quality of the solutions by utilizing two different ways to handle the solutions inside the population-based on the population mean fitness. The location of a high-quality solution is improved using an adaptive mutation operation, while the location of a low-quality solution is updated according to the location of the best-obtained solution and the location of a good solution selected from the population. A good solution is chosen from the first half of the population after sorting its solutions in ascending order. The results of several experiments show the superior performance of the proposed algorithm compared to existing algorithms on a range of photovoltaic models. The results show that the proposed algorithm is highly correlated with the measured current–voltage data so that it can offer a useful alternative for parameter estimation of photovoltaic models.

Gene expression noise can promote the fixation of beneficial mutations in fluctuating environments.

Nongenetic phenotypic variation can either speed up or slow down adaptive evolution. We show that it can speed up evolution in environments where available carbon and energy sources change over time. To this end, we use an experimentally validated model of Escherichia coli growth on two alternative carbon sources, glucose and acetate. On the superior carbon source (glucose), all cells achieve high growth rates, while on the inferior carbon source (acetate) only a small fraction of the population manages to initiate growth. Consequently, populations experience a bottleneck when the environment changes from the superior to the inferior carbon source. Growth on the inferior carbon source depends on a circuit under the control of a transcription factor that is repressed in the presence of the superior carbon source. We show that noise in the expression of this transcription factor can increase the probability that cells start growing on the inferior carbon source. In doing so, it can decrease the severity of the bottleneck and increase mean population fitness whenever this fitness is low. A modest amount of noise can also enhance the fitness effects of a beneficial allele that increases the fraction of a population initiating growth on acetate. Additionally, noise can protect this allele from extinction, accelerate its spread, and increase its likelihood of going to fixation. Central to the adaptation-enhancing principle we identify is the ability of noise to mitigate population bottlenecks, particularly in environments that fluctuate periodically. Because such bottlenecks are frequent in fluctuating environments, and because periodically fluctuating environments themselves are common, this principle may apply to a broad range of environments and organisms.

Intraspecific variability in fluctuating environments: mechanisms of impact on species diversity.

Recent studies have found considerable trait variations within species. The effect of such intraspecific trait variability (ITV) on the stability, coexistence, and diversity of ecological communities received considerable attention and in many models it was shown to impede coexistence and decrease species diversity. Here we present a numerical study of the effect of genetically inherited ITV on species persistence and diversity in a temporally fluctuating environment. Two mechanisms are identified. First, ITV buffers populations against varying environmental conditions (portfolio effect) and reduces variation in abundances. Second, the interplay between ITV and environmental variations tends to increase the mean fitness of diverse populations. The first mechanism promotes persistence and tends to increase species richness, while the second reduces the chance of a rare species population (which is usually homogeneous) to invade, thus decreasing species richness. We show that for large communities the portfolio effect is dominant, leading to ITV promoting species persistence and richness.

Predicting heterosis and inbreeding depression from population size and density to inform management efforts

Abstract Effective population size should be positively related to census size and density, and it is expected to influence the strength of genetic drift, inbreeding and response to selection, and thus the distribution of the genetic load across populations. We examined whether census population size and density predict the strength of inbreeding depression, heterosis and population mean fitness at the seed stage in the terrestrial orchid Gymnadenia conopsea by conducting controlled crosses (self, outcross within and between populations) in 20 populations of varying size (7–30,000 individuals) and density (1–12.8 individuals/m2). In the largest population, we also examined how local density affects the occurrence of self‐pollination with a pollen staining experiment. The majority of populations expressed strong inbreeding depression at the seed stage (mean δID: min–max = 0.26: −0.53 to 0.51), consistent with a mainly outcrossing mating system and substantial genetic load. The effect of between‐population crosses varied from strong outbreeding depression to heterosis (mean δOD: min–max = 0.05: −0.22 to 0.92), indicating varying influence of drift and selection among populations. Census population size did not significantly predict the strength of inbreeding depression, heterosis or population mean fitness. However, inbreeding depression was positively and heterosis negatively correlated with population density. The proportion of self‐massulae deposition was three times higher in sparse patches compared to dense ones (41% vs. 14%). Combined effects of density‐dependent pollinator behaviour and limited seed dispersal may cause stronger genetic sub‐structuring in sparse populations and reduce the strength of the correlation between census and effective population size. The results point to the importance of considering population density in addition to size when evaluating the distribution of recessive deleterious alleles across populations. Synthesis and applications. Management plans for threatened species often involve crosses between populations to restore genetic variation, a process termed genetic rescue. This study indicates that such conservation efforts should be more successful if designed on the basis of population density in addition to population size, because we found population density predicted both the strength of heterosis and inbreeding depression across populations of Gymnadenia conopsea.

Stabilizing selection and adaptive evolution in a combination of two traits in an arctic ungulate.

Stabilizing selection is thought to be common in wild populations and act as one of the main evolutionary mechanisms, which constrain phenotypic variation. When multiple traits interact to create a combined phenotype, correlational selection may be an important process driving adaptive evolution. Here, we report on phenotypic selection and evolutionary changes in two natal traits in a semidomestic population of reindeer (Rangifer tarandus) in northern Finland. The population has been closely monitored since 1969, and detailed data have been collected on individuals since they were born. Over the length of the study period (1969-2015), we found directional and stabilizing selection toward a combination of earlier birth date and heavier birth mass with an intermediate optimum along the major axis of the selection surface. In addition, we demonstrate significant changes in mean traits toward earlier birth date and heavier birth mass, with corresponding genetic changes in breeding values during the study period. Our results demonstrate evolutionary changes in a combination of two traits, which agree closely with estimated patterns of phenotypic selection. Knowledge of the selective surface for combinations of genetically correlated traits are vital to predict how population mean phenotypes and fitness are affected when environmentschange.