On the importance of being structured: instantaneous coalescence rates and human evolution—lessons for ancestral population size inference?

Most species are structured and influenced by processes that either increased or reduced gene flow between populations. However, most population genetic inference methods assume panmixia and reconstruct a history characterized by population size changes. This is potentially problematic as population structure can generate spurious signals of population size change through time. Moreover, when the model assumed for demographic inference is misspecified, genomic data will likely increase the precision of misleading if not meaningless parameters. For instance, if data were generated under an n-island model (characterized by the number of islands and migrants exchanged) inference based on a model of population size change would produce precise estimates of a bottleneck that would be meaningless. In addition, archaeological or climatic events around the bottleneck''s timing might provide a reasonable but potentially misleading scenario. In a context of model uncertainty (panmixia versus structure) genomic data may thus not necessarily lead to improved statistical inference. We consider two haploid genomes and develop a theory that explains why any demographic model with structure will necessarily be interpreted as a series of changes in population size by inference methods ignoring structure. We formalize a parameter, the inverse instantaneous coalescence rate, and show that it is equivalent to a population size only in panmictic models, and is mostly misleading for structured models. We argue that this issue affects all population genetics methods ignoring population structure which may thus infer population size changes that never took place. We apply our approach to human genomic data.     

Genotype‐free estimation of allele frequencies reduces bias and improves demographic inference from RADSeq data

Restriction‐site associated DNA sequencing (RADSeq) facilitates rapid generation of thousands of genetic markers at relatively low cost; however, several sources of error specific to RADSeq methods often lead to biased estimates of allele frequencies and thereby to erroneous population genetic inference. Estimating the distribution of sample allele frequencies without calling genotypes was shown to improve population inference from whole genome sequencing data, but the ability of this approach to account for RADSeq‐specific biases remains unexplored. Here we assess in how far genotype‐free methods of allele frequency estimation affect demographic inference from empirical RADSeq data. Using the well‐studied pied flycatcher (Ficedula hypoleuca) as a study system, we compare allele frequency estimation and demographic inference from whole genome sequencing data with that from RADSeq data matched for samples using both genotype‐based and genotype free methods. The demographic history of pied flycatchers as inferred from RADSeq data was highly congruent with that inferred from whole genome resequencing (WGS) data when allele frequencies were estimated directly from the read data. In contrast, when allele frequencies were derived from called genotypes, RADSeq‐based estimates of most model parameters fell outside the 95% confidence interval of estimates derived from WGS data. Notably, more stringent filtering of the genotype calls tended to increase the discrepancy between parameter estimates from WGS and RADSeq data, respectively. The results from this study demonstrate the ability of genotype‐free methods to improve allele frequency spectrum‐ (AFS‐) based demographic inference from empirical RADSeq data and highlight the need to account for uncertainty in NGS data regardless of sequencing method.     

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.     

Effects of recent population bottlenecks on reconstructing the demographic history of prairie-chickens

Current methods of DNA sequence analysis attempt to reconstruct historical patterns of population structure and growth from contemporary samples. However, these techniques may be influenced by recent population bottlenecks, which have the potential to eliminate lineages that reveal past changes in demography. One way to examine the performance of these demographic methods is to compare samples from populations before and after recent bottlenecks. We compared estimates of demographic history from populations of greater prairie-chickens (Tympanuchus cupido) before and after recent bottlenecks using four common methods (nested clade analysis [NCA], Tajima's D, mismatch distribution, and MDIV). We found that NCA did not perform well in the presence of bottleneck events, although it did recover some genetic signals associated with increased isolation and the extinction of intermediate populations. The majority of estimates for Tajima's D, including those from bottlenecked populations, were not significantly different from zero, suggesting our data conformed to neutral expectations. In contrast, mismatch distributions including the raggedness index were more likely to identify recently bottlenecked populations with this data set. Estimates of population mutation rate (theta), population divergence time (t), and time to the most recent common ancestor (TMRCA) from MDIV were similar before and after bottlenecks; however, estimates of gene flow (M) were significantly lower in a few cases following a bottleneck. These results suggest that caution should be used when assessing demographic history from contemporary data sets, as recently fragmented and bottlenecked populations may have lost lineages that affect inferences of their demographic history.     

Demographic inferences using short‐read genomic data in an approximate Bayesian computation framework: in silico evaluation of power,biases and proof of concept in Atlantic walrus

Approximate Bayesian computation (ABC) is a powerful tool for model‐based inference of demographic histories from large genetic data sets. For most organisms, its implementation has been hampered by the lack of sufficient genetic data. Genotyping‐by‐sequencing (GBS) provides cheap genome‐scale data to fill this gap, but its potential has not fully been exploited. Here, we explored power, precision and biases of a coalescent‐based ABC approach where GBS data were modelled with either a population mutation parameter (θ) or a fixed site (FS) approach, allowing single or several segregating sites per locus. With simulated data ranging from 500 to 50 000 loci, a variety of demographic models could be reliably inferred across a range of timescales and migration scenarios. Posterior estimates were informative with 1000 loci for migration and split time in simple population divergence models. In more complex models, posterior distributions were wide and almost reverted to the uninformative prior even with 50 000 loci. ABC parameter estimates, however, were generally more accurate than an alternative composite‐likelihood method. Bottleneck scenarios proved particularly difficult, and only recent bottlenecks without recovery could be reliably detected and dated. Notably, minor‐allele‐frequency filters – usual practice for GBS data – negatively affected nearly all estimates. With this in mind, we used a combination of FS and θ approaches on empirical GBS data generated from the Atlantic walrus (Odobenus rosmarus rosmarus), collectively providing support for a population split before the last glacial maximum followed by asymmetrical migration and a high Arctic bottleneck. Overall, this study evaluates the potential and limitations of GBS data in an ABC‐coalescence framework and proposes a best‐practice approach.     

Natural rewilding of the Great Basin: Genetic consequences of recolonization by black bears (Ursus americanus)

Aim

In the mid‐20th century, many populations of large‐bodied mammals experienced declines throughout North America. Fortunately, within the last several decades, some have begun to rebound and even recolonize extirpated portions of their native range, including black bears (Ursus americanus) in the montane areas of the western Great Basin. In this study, we examine genetic variation in source and recolonized areas to better understand the genetic consequences of recolonization.

Location

Western Great Basin, USA.

Methods

Using multiple loci, we characterized genetic variation among source and recently recolonized areas occupied by black bears, tested for population structure and applied approximate Bayesian computation to test competing hypotheses of demographic history. We assessed signals of gene flow using expectations of genetic consequences derived from alternative modes of recolonization (bottleneck, metapopulation, island model) and tested for significant signals of genetic bottlenecks in areas recently recolonized by black bears.

Results

As anticipated from field survey data and hypothesized expectations, genetic variation of western Great Basin black bears retain an overall signature of demographic decline followed by recent rebound. Furthermore, results reveal that bears in the recolonized range are minimally differentiated from the source area, but newly established subpopulations have lower effective population sizes and reduced allelic diversity. Nevertheless, recolonized areas fail to show a significant signal of a genetic bottleneck. Moreover, bears occupying recolonized areas experience asymmetric gene flow, yielding strong support for a model of genetic connectivity that is best described as a metapopulation.

Main Conclusion

This study presents one of the few empirical examples of genetic consequences of natural recolonization in large‐bodied mammals. Furthermore, these results have implications for understanding the complexities associated with the genetic consequences of recent and ongoing recolonization and highlight the need to develop management strategies uniquely tailored to support connectivity between source and recolonized areas.

     

Inferring past demographic changes from contemporary genetic data: A simulation‐based evaluation of the ABC methods implemented in diyabc

Inferring the demographic history of species and their populations is crucial to understand their contemporary distribution, abundance and adaptations. The high computational overhead of likelihood‐based inference approaches severely restricts their applicability to large data sets or complex models. In response to these restrictions, approximate Bayesian computation (ABC) methods have been developed to infer the demographic past of populations and species. Here, we present the results of an evaluation of the ABC‐based approach implemented in the popular software package diyabc using simulated data sets (mitochondrial DNA sequences, microsatellite genotypes and single nucleotide polymorphisms). We simulated population genetic data under five different simple, single‐population models to assess the model recovery rates as well as the bias and error of the parameter estimates. The ability of diyabc to recover the correct model was relatively low (0.49): 0.6 for the simplest models and 0.3 for the more complex models. The recovery rate improved significantly when reducing the number of candidate models from five to three (from 0.57 to 0.71). Among the parameters of interest, the effective population size was estimated at a higher accuracy compared to the timing of events. Increased amounts of genetic data did not significantly improve the accuracy of the parameter estimates. Some gains in accuracy and decreases in error were observed for scaled parameters (e.g., N_eμ) compared to unscaled parameters (e.g., N_e and μ). We concluded that diyabc ‐based assessments are not suited to capture a detailed demographic history, but might be efficient at capturing simple, major demographic changes.     

Assessing models of speciation under different biogeographic scenarios; an empirical study using multi‐locus and RNA‐seq analyses

Evolutionary biology often seeks to decipher the drivers of speciation, and much debate persists over the relative importance of isolation and gene flow in the formation of new species. Genetic studies of closely related species can assess if gene flow was present during speciation, because signatures of past introgression often persist in the genome. We test hypotheses on which mechanisms of speciation drove diversity among three distinct lineages of desert tortoise in the genus Gopherus. These lineages offer a powerful system to study speciation, because different biogeographic patterns (physical vs. ecological segregation) are observed at opposing ends of their distributions. We use 82 samples collected from 38 sites, representing the entire species' distribution and generate sequence data for mtDNA and four nuclear loci. A multilocus phylogenetic analysis in *BEAST estimates the species tree. RNA‐seq data yield 20,126 synonymous variants from 7665 contigs from two individuals of each of the three lineages. Analyses of these data using the demographic inference package ?a?i serve to test the null hypothesis of no gene flow during divergence. The best‐fit demographic model for the three taxa is concordant with the *BEAST species tree, and the ?a?i analysis does not indicate gene flow among any of the three lineages during their divergence. These analyses suggest that divergence among the lineages occurred in the absence of gene flow and in this scenario the genetic signature of ecological isolation (parapatric model) cannot be differentiated from geographic isolation (allopatric model).     

Controlling population evolution in the laboratory to evaluate methods of historical inference

Natural populations of known detailed past demographic history are extremely valuable to evaluate methods of historical inference, yet are extremely rare. As an alternative approach, we have generated multiple replicate microsatellite data sets from laboratory-cultured populations of a gonochoric free-living nematode, Caenorhabditis remanei, that were constrained to pre-defined demographic histories featuring different levels of migration among populations or bottleneck events of different magnitudes. These data sets were then used to evaluate the performances of two recently developed population genetics methods, BayesAss+, that estimates recent migration rates among populations, and Bottleneck, that detects the occurrence of recent bottlenecks. Migration rates inferred by BayesAss+ were generally over-estimates, although these were often included within the confidence interval. Analyses of data sets simulated in-silico, using a model mimicking the laboratory experiments, produced less biased estimates of the migration rates, and showed increased efficiency of the program when the number of loci and sampled genotypes per population was higher. In the replicates for which the pre-bottleneck laboratory-cultured populations did not significantly depart from a mutation/drift equilibrium, an important assumption of the program Bottleneck, only a portion of the bottleneck events were detected. This result was confirmed by in-silico simulations mirroring the laboratory bottleneck experiments. More generally, our study demonstrates the feasibility, and highlights some of the limits, of the approach that consists in generating molecular genetic data sets by controlling the evolution of laboratory-reared nematode populations, for the purpose of validating methods inferring population history.     

INTEGRATIVE TESTING OF HOW ENVIRONMENTS FROM THE PAST TO THE PRESENT SHAPE GENETIC STRUCTURE ACROSS LANDSCAPES

Tests of the genetic structure of empirical populations typically focus on the correlative relationships between population connectivity and geographic and/or environmental factors in landscape genetics. However, such tests may overlook or misidentify the impact of candidate factors on genetic structure, especially when connectivity patterns differ between past and present populations because of shifting environmental conditions over time. Here we account for the underlying demographic component of population connectivity associated with a temporarily dynamic landscape in tests of the factors structuring population genetic variation in an Australian lizard, Lerista lineopunctulata, from 24 nuclear loci. Correlative tests did not support significant effect from factors associated with a static contemporary landscape. However, spatially explicit demographic modeling of genetic differentiation shows that changes in environmental conditions (as estimated from paleoclimatic data) and corresponding distributional shifts from the past to present landscape significantly structures genetic variation. Results from model‐based inference (i.e., from an integrative modeling approach that generates spatially explicit expectations that are tested with approximate Bayesian computation) contrasts with those from correlative analyses, highlighting the importance of expanding the landscape genetic perspective to tests the links between pattern and process, revealing how factors shape patterns of genetic variation within species.     

Implications of the Circumpolar Genetic Structure of Polar Bears for Their Conservation in a Rapidly Warming Arctic

We provide an expansive analysis of polar bear (Ursus maritimus) circumpolar genetic variation during the last two decades of decline in their sea-ice habitat. We sought to evaluate whether their genetic diversity and structure have changed over this period of habitat decline, how their current genetic patterns compare with past patterns, and how genetic demography changed with ancient fluctuations in climate. Characterizing their circumpolar genetic structure using microsatellite data, we defined four clusters that largely correspond to current ecological and oceanographic factors: Eastern Polar Basin, Western Polar Basin, Canadian Archipelago and Southern Canada. We document evidence for recent (ca. last 1–3 generations) directional gene flow from Southern Canada and the Eastern Polar Basin towards the Canadian Archipelago, an area hypothesized to be a future refugium for polar bears as climate-induced habitat decline continues. Our data provide empirical evidence in support of this hypothesis. The direction of current gene flow differs from earlier patterns of gene flow in the Holocene. From analyses of mitochondrial DNA, the Canadian Archipelago cluster and the Barents Sea subpopulation within the Eastern Polar Basin cluster did not show signals of population expansion, suggesting these areas may have served also as past interglacial refugia. Mismatch analyses of mitochondrial DNA data from polar and the paraphyletic brown bear (U. arctos) uncovered offset signals in timing of population expansion between the two species, that are attributed to differential demographic responses to past climate cycling. Mitogenomic structure of polar bears was shallow and developed recently, in contrast to the multiple clades of brown bears. We found no genetic signatures of recent hybridization between the species in our large, circumpolar sample, suggesting that recently observed hybrids represent localized events. Documenting changes in subpopulation connectivity will allow polar nations to proactively adjust conservation actions to continuing decline in sea-ice habitat.     

The ‘New Wave’ in plant demographic inference: more loci and more individuals

Plant population genetic surveys are starting to take full advantage of technological advances in genotyping methods and of methodological advances in demographic inference. In this issue of Molecular Ecology, Keller et al. (2010) illustrate this trend with a particularly convincing study of rangewide genetic variation in a North American poplar, using both SNP and sequence data. They first investigate population genetic structure by estimating the most likely number of genetic clusters using a more formal approach than most other studies to date. They proceed by estimating gene flow among the inferred populations and by testing predictions on the distribution of low frequency alleles derived from recent work on range expansions.     

Sometimes hidden but always there: the assumptions underlying genetic inference of demographic histories

Demographic processes directly affect patterns of genetic variation within contemporary populations as well as future generations, allowing for demographic inference from patterns of both present-day and past genetic variation. Advances in laboratory procedures, sequencing and genotyping technologies in the past decades have resulted in massive increases in high-quality genome-wide genetic data from present-day populations and allowed retrieval of genetic data from archaeological material, also known as ancient DNA. This has resulted in an explosion of work exploring past changes in population size, structure, continuity and movement. However, as genetic processes are highly stochastic, patterns of genetic variation only indirectly reflect demographic histories. As a result, past demographic processes need to be reconstructed using an inferential approach. This usually involves comparing observed patterns of variation with model expectations from theoretical population genetics. A large number of approaches have been developed based on different population genetic models that each come with assumptions about the data and underlying demography. In this article I review some of the key models and assumptions underlying the most commonly used approaches for past demographic inference and their consequences for our ability to link the inferred demographic processes to the archaeological and climate records.This article is part of the theme issue ‘Cross-disciplinary approaches to prehistoric demography’.     

Recent demographic bottlenecks are not accompanied by a genetic signature in banner-tailed kangaroo rats (Dipodomys spectabilis)

Single-sample methods of bottleneck detection are now routine analyses in studies of wild populations and conservation genetics. Three common approaches to bottleneck detection are the heterozygosity excess, mode-shift, and M-ratio tests. Empirical groundtruthing of these methods is difficult, but their performances are critical for the accurate reconstruction of population demography. We use two banner-tailed kangaroo rat (Dipodomys spectabilis) populations from southeastern Arizona (USA) that are known to have experienced recent demographic reductions to search for genetic bottleneck signals with eight microsatellite loci. Over eight total sample-years, neither population showed a genetic bottleneck signature. M-ratios in both populations were large, stable, and never fell below a critical significance value (Mc). The mode shift test did not detect any distortion of allele frequencies, and tests of heterozygosity excess were not significant in postbottleneck samples when we used standard microsatellite mutation models. The genetic effects of bottlenecks like those experienced by our study populations should be strongly influenced by rates of mutation and migration. We used genetic parentage data to estimate a relatively high mutation rate in D. spectabilis (0.0081 mutants/generation/locus), but mutation alone is unlikely to explain the temporal distribution of rare alleles that we observed. Migration (gene flow) is a more likely explanation, despite prior mark-recapture analysis that estimated very low rates of interpopulation dispersal. We interpret our kangaroo rat data in light of the broader literature and conclude that in natural populations connected by dispersal, demographic bottlenecks may prove difficult to detect using molecular genetic data.     

Beyond F<Subscript>ST</Subscript>: Analysis of population genetic data for conservation

Both the ability to generate DNA data and the variety of analytical methods for conservation genetics are expanding at an ever-increasing pace. Analytical approaches are now possible that were unthinkable even five years ago due to limitations in computational power or the availability of DNA data, and this has vastly expanded the accuracy and types of information that may be gained from population genetic data. Here we provide a guide to recently developed methods for population genetic analysis, including identification of population structure, quantification of gene flow, and inference of demographic history. We cover both allele-frequency and sequence-based approaches, with a special focus on methods relevant to conservation genetic applications. Although classical population genetic approaches such as F _st (and its derivatives) have carried the field thus far, newer, more powerful, methods can infer much more from the data, rely on fewer assumptions, and are appropriate for conservation genetic management when precise estimates are needed.     

Does gene flow destroy phylogenetic signal? The performance of three methods for estimating species phylogenies in the presence of gene flow

Incomplete lineage sorting has been documented across a diverse set of taxa ranging from song birds to conifers. Such patterns are expected theoretically for species characterized by certain life history characteristics (e.g. long generation times) and those influenced by certain historical demographic events (e.g. recent divergences). A number of methods to estimate the underlying species phylogeny from a set of gene trees have been proposed and shown to be effective when incomplete lineage sorting has occurred. The further effects of gene flow on those methods, however, remain to be investigated. Here, we focus on the performance of three methods of species tree inference, ESP-COAL, minimizing deep coalescence (MDC), and concatenation, when incomplete lineage sorting and gene flow jointly confound the relationship between gene and species trees. Performance was investigated using Monte Carlo coalescent simulations under four models (n-island, stepping stone, parapatric, and allopatric) and three magnitudes of gene flow (N_em = 0.01, 0.10, 1.00). Although results varied by the model and magnitude of gene flow, methods incorporating aspects of the coalescent process (ESP-COAL and MDC) performed well, with probabilities of identifying the correct species tree topology typically increasing to greater than 0.75 when five more loci are sampled. The only exceptions to that pattern included gene flow at moderate to high magnitudes under the n-island and stepping stone models. Concatenation performs poorly relative to the other methods. We extend these results to a discussion of the importance of species and population phylogenies to the fields of molecular systematics and phylogeography using an empirical example from Rhododendron.     

Genomic data detect corresponding signatures of population size change on an ecological time scale in two salamander species

Understanding the demography of species over recent history (e.g. <100 years) is critical in studies of ecology and evolution, but records of population history are rarely available. Surveying genetic variation is a potential alternative to census‐based estimates of population size, and can yield insight into the demography of a population. However, to assess the performance of genetic methods, it is important to compare their estimates of population history to known demography. Here, we leveraged the exceptional resources from a wetland with 37 years of amphibian mark–recapture data to study the utility of genetically based demographic inference on salamander species with documented population declines (Ambystoma talpoideum) and expansions (A. opacum), patterns that have been shown to be correlated with changes in wetland hydroperiod. We generated ddRAD data from two temporally sampled populations of A. opacum (1993, 2013) and A. talpoideum (1984, 2011) and used coalescent‐based demographic inference to compare alternate evolutionary models. For both species, demographic model inference supported population size changes that corroborated mark–recapture data. Parameter estimation in A. talpoideum was robust to our variations in analytical approach, while estimates for A. opacum were highly inconsistent, tempering our confidence in detecting a demographic trend in this species. Overall, our robust results in A. talpoideum suggest that genome‐based demographic inference has utility on an ecological scale, but researchers should also be cognizant that these methods may not work in all systems and evolutionary scenarios. Demographic inference may be an important tool for population monitoring and conservation management planning.     

Detecting selection along environmental gradients: analysis of eight methods and their effectiveness for outbreeding and selfing populations

Thanks to genome‐scale diversity data, present‐day studies can provide a detailed view of how natural and cultivated species adapt to their environment and particularly to environmental gradients. However, due to their sensitivity, up‐to‐date studies might be more sensitive to undocumented demographic effects such as the pattern of migration and the reproduction regime. In this study, we provide guidelines for the use of popular or recently developed statistical methods to detect footprints of selection. We simulated 100 populations along a selective gradient and explored different migration models, sampling schemes and rates of self‐fertilization. We investigated the power and robustness of eight methods to detect loci potentially under selection: three designed to detect genotype–environment correlations and five designed to detect adaptive differentiation (based on F_ST or similar measures). We show that genotype–environment correlation methods have substantially more power to detect selection than differentiation‐based methods but that they generally suffer from high rates of false positives. This effect is exacerbated whenever allele frequencies are correlated, either between populations or within populations. Our results suggest that, when the underlying genetic structure of the data is unknown, a number of robust methods are preferable. Moreover, in the simulated scenario we used, sampling many populations led to better results than sampling many individuals per population. Finally, care should be taken when using methods to identify genotype–environment correlations without correcting for allele frequency autocorrelation because of the risk of spurious signals due to allele frequency correlations between populations.     

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.