The use of agent-based modelling of genetics in conservation genetics studies

Animal Landscape and Man Simulation System a genetically explicit agent-based model was used to obtain measures for the genetic and demographic status of simulated populations. This investigation aimed to test the applicability of this approach for assessing the effect of environmental perturbations on populations’ temporal and spatial dynamics. This was achieved by assessing how three simple scenarios with increasing degree of environmental disturbance, simulated by populations bottlenecks repeated at different intervals, affected the genetic and demographic characteristics of the simulated population. Model outputs from a simplified landscape scenario concurred with theoretical expectations validating the model in a qualitative way. Differences in medians, means and coefficient of variation of the observed (H_o) and expected heterozygosity (H_e), population census size (N), effective population size (N_e), inbreeding coefficient (F) and N_e/N ratio were observed for simulated populations. Impacts occurred rapidly after simulated bottleneck events and genetic estimates were less variable, and therefore more reliable, than demographic estimates. Precise genetic consequences of the bottlenecks repeated at different intervals, and resulting population perturbations, are a complex balance between effects on population sub-structure, size and founding events. Agent-based models are appropriate tools to simulate these interactions, being sufficiently flexible to mimic real population processes under a range of environmental conditions. Such models incorporating explicit genetics provide a promising new approach to evaluate the impact of environmental changes on genetic composition of populations.     

Inference with selection,varying population size,and evolving population structure: application of ABC to a forward–backward coalescent process with interactions

Genetic data are often used to infer demographic history and changes or detect genes under selection. Inferential methods are commonly based on models making various strong assumptions: demography and population structures are supposed a priori known, the evolution of the genetic composition of a population does not affect demography nor population structure, and there is no selection nor interaction between and within genetic strains. In this paper, we present a stochastic birth-death model with competitive interactions and asexual reproduction. We develop an inferential procedure for ecological, demographic, and genetic parameters. We first show how genetic diversity and genealogies are related to birth and death rates, and to how individuals compete within and between strains. This leads us to propose an original model of phylogenies, with trait structure and interactions, that allows multiple merging. Second, we develop an Approximate Bayesian Computation framework to use our model for analyzing genetic data. We apply our procedure to simulated data from a toy model, and to real data by analyzing the genetic diversity of microsatellites on Y-chromosomes sampled from Central Asia human populations in order to test whether different social organizations show significantly different fertilities.Subject terms: Genetic variation, Phylogenetics, Ecological genetics     

Extinction of populations due to inbreeding depression with demographic disturbances

The process of population extinction due to inbreeding depression with constant demographic disturbances every generation is analysed using a population genetic and demographic model. The demographic disturbances introduced into the model represent loss of population size that is induced by any kind of human activities, e.g. through hunting and destruction of habitats. The genetic heterozygosity among recessive deleterious genes and the population size are assumed to be in equilibrium before the demographic disturbances start. The effects of deleterious mutations are represented by decreases in the growth rate and carrying capacity of a population. Numerical simulations indicate rapid extinction due to synergistic interaction between inbreeding depression and declining population size for realistic ranges of per-locus mutation rate, equilibrium population size, intrinsic rate of population growth, and strength of demographic disturbances. Large populations at equilibrium are more liable to extinction when disturbed due to inbreeding depression than small populations. This is a consequence of the fact that large populations maintain more recessive deleterious mutations than small populations. The rapid extinction predicted in the present study indicates the importance of the demographic history of a population in relation to extinction due to inbreeding depression.     

Inferring population decline and expansion from microsatellite data: a simulation-based evaluation of the Msvar method

Reconstructing the demographic history of populations is a central issue in evolutionary biology. Using likelihood-based methods coupled with Monte Carlo simulations, it is now possible to reconstruct past changes in population size from genetic data. Using simulated data sets under various demographic scenarios, we evaluate the statistical performance of Msvar, a full-likelihood Bayesian method that infers past demographic change from microsatellite data. Our simulation tests show that Msvar is very efficient at detecting population declines and expansions, provided the event is neither too weak nor too recent. We further show that Msvar outperforms two moment-based methods (the M-ratio test and Bottleneck) for detecting population size changes, whatever the time and the severity of the event. The same trend emerges from a compilation of empirical studies. The latest version of Msvar provides estimates of the current and the ancestral population size and the time since the population started changing in size. We show that, in the absence of prior knowledge, Msvar provides little information on the mutation rate, which results in biased estimates and/or wide credibility intervals for each of the demographic parameters. However, scaling the population size parameters with the mutation rate and scaling the time with current population size, as coalescent theory requires, significantly improves the quality of the estimates for contraction but not for expansion scenarios. Finally, our results suggest that Msvar is robust to moderate departures from a strict stepwise mutation model.     

The genetic underpinnings of population cyclicity: establishing expectations for the genetic anatomy of cycling populations

Despite extensive research into the mechanisms underlying population cyclicity, we have little understanding of the impacts of numerical fluctuations on the genetic variation of cycling populations. Thus, the potential implications of natural and anthropogenically‐driven variation in population cycle dynamics on the diversity and evolutionary potential of cyclic populations is unclear. Here, we use Canada lynx Lynx canadensis matrix population models, set up in a linear stepping‐stone, to generate demographic replicates of biologically realistic cycling populations. Overall, increasing cycle amplitude predictably reduced genetic diversity and increased genetic differentiation, with cyclic effects increased by population synchrony. Modest dispersal rates (1–3% of the population) between high and low amplitude cyclic populations did not diminish these effects suggesting that spatial variation in cyclic amplitude should be reflected in patterns of genetic diversity and differentiation at these rates. At high dispersal rates (6%) groups containing only high amplitude cyclic populations had higher diversity and lower differentiation than those mixed with low amplitude cyclic populations. Negative density‐dependent dispersal did not impact genetic diversity, but did homogenize populations by reducing differentiation and patterns of isolation by distance. Surprisingly, temporal changes in diversity and differentiation throughout a cycle were not always consistent with population size. In particular, negative density‐dependent dispersal simultaneously decreased differences in genetic diversity while increasing differences in genetic differentiation between numerical peaks and nadirs. Combined, our findings suggest demographic changes at fine temporal scales can impact genetic variation of interacting populations and provide testable predictions relating population cyclicty to genetic variation. Further, our results suggest that including realistic demographic and dispersal parameters in population genetic models and using information from temporal changes in genetic variation could help to discern complex demographic scenarios and illuminate population dynamics at fine temporal scales.     

Reconstructing shifts in vital rates driven by long‐term environmental change: a new demographic method based on readily available data

Frequently, vital rates are driven by directional, long‐term environmental changes. Many of these are of great importance, such as land degradation, climate change, and succession. Traditional demographic methods assume a constant or stationary environment, and thus are inappropriate to analyze populations subject to these changes. They also require repeat surveys of the individuals as change unfolds. Methods for reconstructing such lengthy processes are needed. We present a model that, based on a time series of population size structures and densities, reconstructs the impact of directional environmental changes on vital rates. The model uses integral projection models and maximum likelihood to identify the rates that best reconstructs the time series. The procedure was validated with artificial and real data. The former involved simulated species with widely different demographic behaviors. The latter used a chronosequence of populations of an endangered cactus subject to increasing anthropogenic disturbance. In our simulations, the vital rates and their change were always reconstructed accurately. Nevertheless, the model frequently produced alternative results. The use of coarse knowledge of the species' biology (whether vital rates increase or decrease with size or their plausible values) allowed the correct rates to be identified with a 90% success rate. With real data, the model correctly reconstructed the effects of disturbance on vital rates. These effects were previously known from two populations for which demographic data were available. Our procedure seems robust, as the data violated several of the model's assumptions. Thus, time series of size structures and densities contain the necessary information to reconstruct changing vital rates. However, additional biological knowledge may be required to provide reliable results. Because time series of size structures and densities are available for many species or can be rapidly generated, our model can contribute to understand populations that face highly pressing environmental problems.     

Detecting past population bottlenecks using temporal genetic data

Population bottlenecks wield a powerful influence on the evolution of species and populations by reducing the repertoire of responses available for stochastic environmental events. Although modern contractions of wild populations due to human-related impacts have been documented globally, discerning historic bottlenecks for all but the most recent and severe events remains a serious challenge. Genetic samples dating to different points in time may provide a solution in some cases. We conducted serial coalescent simulations to assess the extent to which temporal genetic data are informative regarding population bottlenecks. These simulations demonstrated that the power to reject a constant population size hypothesis using both ancient and modern genetic data is almost always higher than that based solely on modern data. The difference in power between the modern and temporal DNA approaches depends significantly on effective population size and bottleneck intensity and less significantly on sample size. The temporal approach provides more power in cases of genetic recovery (via migration) from a bottleneck than in cases of demographic recovery (via population growth). Choice of genetic region is critical, as mutation rate heavily influences the extent to which temporal sampling yields novel information regarding the demographic history of populations.     

The Counteracting Effects of Demography on Functional Genomic Variation: The Roma Paradigm

Demographic history plays a major role in shaping the distribution of genomic variation. Yet the interaction between different demographic forces and their effects in the genomes is not fully resolved in human populations. Here, we focus on the Roma population, the largest transnational ethnic minority in Europe. They have a South Asian origin and their demographic history is characterized by recent dispersals, multiple founder events, and extensive gene flow from non-Roma groups. Through the analyses of new high-coverage whole exome sequences and genome-wide array data for 89 Iberian Roma individuals together with forward simulations, we show that founder effects have reduced their genetic diversity and proportion of rare variants, gene flow has counteracted the increase in mutational load, runs of homozygosity show ancestry-specific patterns of accumulation of deleterious homozygotes, and selection signals primarily derive from preadmixture adaptation in the Roma population sources. The present study shows how two demographic forces, bottlenecks and admixture, act in opposite directions and have long-term balancing effects on the Roma genomes. Understanding how demography and gene flow shape the genome of an admixed population provides an opportunity to elucidate how genomic variation is modeled in human populations.     

Population genetics and the evolution of geographic range limits in an annual plant

Abstract Theoretical models of species' geographic range limits have identified both demographic and evolutionary mechanisms that prevent range expansion. Stable range limits have been paradoxical for evolutionary biologists because they represent locations where populations chronically fail to respond to selection. Distinguishing among the proposed causes of species' range limits requires insight into both current and historical population dynamics. The tools of molecular population genetics provide a window into the stability of range limits, historical demography, and rates of gene flow. Here we evaluate alternative range limit models using a multilocus data set based on DNA sequences and microsatellites along with field demographic data from the annual plant Clarkia xantiana ssp. xantiana. Our data suggest that central and peripheral populations have very large historical and current effective population sizes and that there is little evidence for population size changes or bottlenecks associated with colonization in peripheral populations. Whereas range limit populations appear to have been stable, central populations exhibit a signature of population expansion and have contributed asymmetrically to the genetic diversity of peripheral populations via migration. Overall, our results discount strictly demographic models of range limits and more strongly support evolutionary genetic models of range limits, where adaptation is prevented by a lack of genetic variation or maladaptive gene flow.     

Demographic fluctuations lead to rapid and cyclic shifts in genetic structure among populations of an alpine butterfly,Parnassius smintheus

Many populations, especially in insects, fluctuate in size, and periods of particularly low population size can have strong effects on genetic variation. Effects of demographic bottlenecks on genetic diversity of single populations are widely documented. Effects of bottlenecks on genetic structure among multiple interconnected populations are less studied, as are genetic changes across multiple cycles of demographic collapse and recovery. We take advantage of a long‐term data set comprising demographic, genetic and movement data from a network of populations of the butterfly, Parnassius smintheus, to examine the effects of fluctuating population size on spatial genetic structure. We build on a previous study that documented increased genetic differentiation and loss of spatial genetic patterns (isolation by distance and by intervening forest cover) after a network‐wide bottleneck event. Here, we show that genetic differentiation was reduced again and spatial patterns returned to the system extremely rapidly, within three years (i.e. generations). We also show that a second bottleneck had similar effects to the first, increasing differentiation and erasing spatial patterns. Thus, bottlenecks consistently drive random divergence of allele frequencies among populations in this system, but these effects are rapidly countered by gene flow during demographic recovery. Our results reveal a system in which the relative influence of genetic drift and gene flow continually shift as populations fluctuate in size, leading to cyclic changes in genetic structure. Our results also suggest caution in the interpretation of patterns of spatial genetic structure, and its association with landscape variables, when measured at only a single point in time.     

Modelling the effects of genetics and habitat on the demography of a grassland herb

There is growing evidence that genetic and ecological factors interact in determining population persistence. The demographic effects of inbreeding depression can largely depend on the ecological milieu. We used demographic data of the perennial herb Succisa pratensis from six populations in grazed and ungrazed sites with different soil moisture. We built an individual-based model assessing the demographic consequences of inbreeding depression in populations with different management and habitat. Today this plant has to cope with severe landscape fragmentation, deteriorating habitat conditions in terms of decreasing grazing intensity, and the effects of inbreeding depression. For each population we performed simulations testing two inbreeding depression hypotheses (partial dominance and overdominance) and three epistatic functions among loci. The results indicated stronger inbreeding depression effects for populations in unfavourable sites without grazing or in xeric habitats compared to populations in favourable mesic sites with grazing. Overall, we found stronger effects with overdominance, a result that emphasizes the importance of understanding the genetic mechanisms of inbreeding depression. Hence, management practices can interact with the genetic consequences of inbreeding depression in population dynamics, which may have important implications for plant population ecology and evolutionary dynamics of inbreeding depression.     

Joint inference of population assignment and demographic history

A new approach to assigning individuals to populations using genetic data is described. Most existing methods work by maximizing Hardy-Weinberg and linkage equilibrium within populations, neither of which will apply for many demographic histories. By including a demographic model, within a likelihood framework based on coalescent theory, we can jointly study demographic history and population assignment. Genealogies and population assignments are sampled from a posterior distribution using a general isolation-with-migration model for multiple populations. A measure of partition distance between assignments facilitates not only the summary of a posterior sample of assignments, but also the estimation of the posterior density for the demographic history. It is shown that joint estimates of assignment and demographic history are possible, including estimation of population phylogeny for samples from three populations. The new method is compared to results of a widely used assignment method, using simulated and published empirical data sets.     

Forward-time simulations of human populations with complex diseases

Due to the increasing power of personal computers, as well as the availability of flexible forward-time simulation programs like simuPOP, it is now possible to simulate the evolution of complex human diseases using a forward-time approach. This approach is potentially more powerful than the coalescent approach since it allows simulations of more than one disease susceptibility locus using almost arbitrary genetic and demographic models. However, the application of such simulations has been deterred by the lack of a suitable simulation framework. For example, it is not clear when and how to introduce disease mutants—especially those under purifying selection—to an evolving population, and how to control the disease allele frequencies at the last generation. In this paper, we introduce a forward-time simulation framework that allows us to generate large multi-generation populations with complex diseases caused by unlinked disease susceptibility loci, according to specified demographic and evolutionary properties. Unrelated individuals, small or large pedigrees can be drawn from the resulting population and provide samples for a wide range of study designs and ascertainment methods. We demonstrate our simulation framework using three examples that map genes associated with affection status, a quantitative trait, and the age of onset of a hypothetical cancer, respectively. Nonadditive fitness models, population structure, and gene–gene interactions are simulated. Case-control, sibpair, and large pedigree samples are drawn from the simulated populations and are examined by a variety of gene-mapping methods.     

Inferring contemporary levels of gene flow and demographic history in a local population of the leaf beetle Gonioctena olivacea from mitochondrial DNA sequence variation

We have studied mitochondrial DNA variation in a local population of the leaf beetle species Gonioctena olivacea, to check whether its apparent low dispersal behaviour affects its pattern of genetic variation at a small geographical scale. We have sampled 10 populations of G. olivacea within a rectangle of 5 x 2 km in the Belgian Ardennes, as well as five populations located approximately along a straight line of 30 km and separated by distances of 3-12 km. For each sampled individual (8-19 per population), a fragment of the mtDNA control region was polymerase chain reaction-amplified and sequenced. Sequence data were analysed to test whether significant genetic differentiation could be detected among populations separated by such relatively short distances. The reconstructed genealogy of the mitochondrial haplotypes was also used to investigate the demographic history of these populations. Computer simulations of the evolution of populations were conducted to assess the minimum amount of gene flow that is necessary to explain the observed pattern of variation in the samples. Results show that migration among populations included in the rectangle of 5 x 2 km is substantial, and probably involves the occurrence of dispersal flights. This appears difficult to reconcile with the results of a previous ecological field study that concluded that most of this species dispersal occurs by walking. While sufficient migration to homogenize genetic diversity occurs among populations separated by distances of a few hundred metres to a few kilometres, distances greater than 5 km results in contrast in strong differentiation among populations, suggesting that migration is drastically reduced on such distances. Finally, the results of coalescent simulations suggest that the star-like genealogy inferred from the mtDNA sequence data is fully compatible with a past demographic expansion. However, a metapopulation structure alone (without the need to invoke a population expansion event) cannot be dismissed as the cause of this star shape.     

Effects of the demographic transition on the genetic variances and covariances of human life‐history traits

The recent demographic transitions to lower mortality and fertility rates in most human societies have led to changes and even quick reversals in phenotypic selection pressures. This can only result in evolutionary change if the affected traits are heritable, but changes in environmental conditions may also lead to subsequent changes in the genetic variance and covariance (the G matrix) of traits. It currently remains unclear if there have been concomitant changes in the G matrix of life‐history traits following the demographic transition. Using 300 years of genealogical data from Finland, we found that four key life‐history traits were heritable both before and after the demographic transition. The estimated heritabilities allow a quantifiable genetic response to selection during both time periods, thus facilitating continued evolutionary change. Further, the G matrices remained largely stable but revealed a trend for an increased additive genetic variance and thus evolutionary potential of the population after the transition. Our results demonstrate the validity of predictions of evolutionary change in human populations even after the recent dramatic environmental change, and facilitate predictions of how our biology interacts with changing environments, with implications for global public health and demography.     

The importance of sampling scale in the assessment of intraspecific life-history differences in plains killifish,Fundulus zebrinus,populations (Pisces: Cyprinodontidae)

Synopsis The purpose of this study was to determine the effects of unpredictable environmental fluctuations on the demographic and genetic structure of Fundulus zebrinus populations. Collections of F. zebrinus were taken from three rivers in the Arkansas River basin: the Arkansas, Chikaskia, and Ninnescah. Fish were sampled from three sites on each river on nine collection dates throughout 1984 and 1985. Totals of 2100 fish and 6000 fish were included in electrophoretic and demographic analyses, respectively. The results of the study indicate that within a limited geographic region (i.e. within rivers) spatial differences and temporal changes in both demographic and genetic population characteristics occur frequently and are primarily stochastic. However, on a larger spatial scale (i.e. across rivers), general trends emerge for demographic and especially for genetic population characteristics. These results illustrate the importance of sampling scale for conclusions of life-history evolution in fluctuating environments. In addition, it was found that regulation of Fundulus zebrinus populations includes an important density-independent component. Stochastic demographic differences across space and changes through time and spatially and temporally heterogeneous allele frequencies, are both indicative of density-independent regulation. Variation in population parameters, both demographic and genetic, was observed between populations sampled from each river. These population differences were attributed to differences between the rivers themselves.     

Evaluation of model fit of inferred admixture proportions

Model based methods for genetic clustering of individuals, such as those implemented in structure or ADMIXTURE, allow the user to infer individual ancestries and study population structure. The underlying model makes several assumptions about the demographic history that shaped the analysed genetic data. One assumption is that all individuals are a result of K homogeneous ancestral populations that are all well represented in the data, while another assumption is that no drift happened after the admixture event. The histories of many real world populations do not conform to that model, and in that case taking the inferred admixture proportions at face value might be misleading. We propose a method to evaluate the fit of admixture models based on estimating the correlation of the residual difference between the true genotypes and the genotypes predicted by the model. When the model assumptions are not violated, the residuals from a pair of individuals are not correlated. In the case of a bad fitting admixture model, individuals with similar demographic histories have a positive correlation of their residuals. Using simulated and real data, we show how the method is able to detect a bad fit of inferred admixture proportions due to using an insufficient number of clusters K or to demographic histories that deviate significantly from the admixture model assumptions, such as admixture from ghost populations, drift after admixture events and nondiscrete ancestral populations. We have implemented the method as an open source software that can be applied to both unphased genotypes and low depth sequencing data.     

Is urbanization scrambling the genetic structure of human populations? A case study

Recent population expansion and increased migration linked to urbanization are assumed to be eroding the genetic structure of human populations. We investigated change in population structure over three generations by analysing both demographic and mitochondrial DNA (mtDNA) data from a random sample of 2351 men from 22 Iranian populations. Potential changes in genetic diversity (theta) and genetic distance (F(ST)) over the last three generations were analysed by assigning mtDNA sequences to populations based on the individual's place of birth or that of their mother or grandmother. Despite the fact that several areas included cities of over one million inhabitants, we detected no change in genetic diversity, and only a small decrease in population structure, except in the capital city (Tehran), which was characterized by massive immigration, increased theta and a large decrease in F(ST) over time. Our results suggest that recent erosion of human population structure might not be as important as previously thought, except in some large conurbations, and this clearly has important implications for future sampling strategies.     

A range-wide genetic bottleneck overwhelms contemporary landscape factors and local abundance in shaping genetic patterns of an alpine butterfly (Lepidoptera: Pieridae: Colias behrii)

Spatial and environmental heterogeneity are major factors in structuring species distributions in alpine landscapes. These landscapes have also been affected by glacial advances and retreats, causing alpine taxa to undergo range shifts and demographic changes. These nonequilibrium population dynamics have the potential to obscure the effects of environmental factors on the distribution of genetic variation. Here, we investigate how demographic change and environmental factors influence genetic variation in the alpine butterfly Colias behrii. Data from 14 microsatellite loci provide evidence of bottlenecks in all population samples. We test several alternative models of demography using approximate Bayesian computation (ABC), with the results favouring a model in which a recent bottleneck precedes rapid population growth. Applying independent calibrations to microsatellite loci and a nuclear gene, we estimate that this bottleneck affected both northern and southern populations 531–281 years ago, coinciding with a period of global cooling. Using regression approaches, we attempt to separate the effects of population structure, geographical distance and landscape on patterns of population genetic differentiation. Only 40% of the variation in F_ST is explained by these models, with geographical distance and least‐cost distance among meadow patches selected as the best predictors. Various measures of genetic diversity within populations are also decoupled from estimates of local abundance and habitat patch characteristics. Our results demonstrate that demographic change can have a disproportionate influence on genetic diversity in alpine species, contrasting with other studies that suggest landscape features control contemporary demographic processes in high‐elevation environments.     

Statistical guidelines for detecting past population shifts using ancient DNA

Populations carry a genetic signal of their demographic past, providing an opportunity for investigating the processes that shaped their evolution. Our ability to infer population histories can be enhanced by including ancient DNA data. Using serial-coalescent simulations and a range of both quantitative and temporal sampling schemes, we test the power of ancient mitochondrial sequences and nuclear single-nucleotide polymorphisms (SNPs) to detect past population bottlenecks. Within our simulated framework, mitochondrial sequences have only limited power to detect subtle bottlenecks and/or fast post-bottleneck recoveries. In contrast, nuclear SNPs can detect bottlenecks followed by rapid recovery, although bottlenecks involving reduction of less than half the population are generally detected with low power unless extensive genetic information from ancient individuals is available. Our results provide useful guidelines for scaling sampling schemes and for optimizing our ability to infer past population dynamics. In addition, our results suggest that many ancient DNA studies may face power issues in detecting moderate demographic collapses and/or highly dynamic demographic shifts when based solely on mitochondrial information.