Search

Population genetics is transitioning into a data-driven discipline thanks to the availability of large-scale genomic data and the need to study increasingly complex evolutionary scenarios. With likelihood and Bayesian approaches becoming either intractable or computationally unfeasible, machine learning, and in particular deep learning, algorithms are emerging as popular techniques for population genetic inferences. These approaches rely on algorithms that learn non-linear relationships between the input data and the model parameters being estimated through representation learning from training data sets. Deep learning algorithms currently employed in the field comprise discriminative and generative models with fully connected, convolutional, or recurrent layers. Additionally, a wide range of powerful simulators to generate training data under complex scenarios are now available. The application of deep learning to empirical data sets mostly replicates previous findings of demography reconstruction and signals of natural selection in model organisms. To showcase the feasibility of deep learning to tackle new challenges, we designed a branched architecture to detect signals of recent balancing selection from temporal haplotypic data, which exhibited good predictive performance on simulated data. Investigations on the interpretability of neural networks, their robustness to uncertain training data, and creative representation of population genetic data, will provide further opportunities for technological advancements in the field. [ABSTRACT FROM AUTHOR]

The measurement of biodiversity at all levels of organization is an essential first step to understand the ecological and evolutionary processes that drive spatial patterns of biodiversity. Ecologists have explored the use of a large range of different summary statistics and have come to the view that information‐based summary statistics, and in particular so‐called Hill numbers, are a useful tool to measure biodiversity. Population geneticists, on the other hand, have focused largely on summary statistics based on heterozygosity and measures of allelic richness. However, recent studies proposed the adoption of information‐based summary statistics in population genetics studies. Here, we performed a comprehensive assessment of the power of this family of summary statistics to inform regarding spatial patterns of genetic diversity and we compared it with that of traditional population genetics approaches, namely measures based on allelic richness and heterozygosity. To give an unbiased evaluation, we used three machine learning methods to test the performance of different sets of summary statistics to discriminate between spatial scenarios. We defined three distinct sets, (i) one based on allelic richness measures which included the Jaccard index, (ii) a set based on heterozygosity that included FST and (iii) a set based on Hill numbers derived from Shannon entropy, which included the recently proposed Shannon differentiation, ΔD. The results showed that the last of these performed as well or, under some specific spatial scenarios, even better than the traditional population genetics measures. Interestingly, we found that a rarely or never used genetic differentiation measure based on allelic richness, Jaccard dissimilarity (J), showed the highest discriminatory power to discriminate among spatial scenarios, followed by Shannon differentiation ΔD. We concluded, therefore, that information‐based measures as well as Jaccard dissimilarity represent excellent additions to the population genetics toolkit. [ABSTRACT FROM AUTHOR]

Common garden experiments are precious to study adaptive phenomenon and adaptive potential, in that they allow to study local adaptation without the confounding effect of phenotypic plasticity. The QST − FST comparison framework, comparing genetic differentiation at the phenotypic and molecular level, is the usual way to test and measure whether local adaptation influences phenotypic divergence between populations.Here, we highlight that the assumptions behind the expected equality QST = FST under neutrality correspond to a very simple model of population genetics. While the equality might, on average, be robust to violation of such assumptions, more complex population structure can generate strong evolutionary noise.Synthesis. We highlight recent methodological developments aimed at overcoming this issue and at providing a more general framework to detect local adaptation, using less restrictive assumptions. We invite empiricists to look into these methods and theorists to continue developing even more general methods. [ABSTRACT FROM AUTHOR]

Abstract: Biological diversity is a key concept in the life sciences and plays a fundamental role in many ecological and evolutionary processes. Although biodiversity is inherently a hierarchical concept covering different levels of organization (genes, population, species, ecological communities and ecosystems), a diversity index that behaves consistently across these different levels has so far been lacking, hindering the development of truly integrative biodiversity studies. To fill this important knowledge gap, we present a unifying framework for the measurement of biodiversity across hierarchical levels of organization. Our weighted, information‐based decomposition framework is based on a Hill number of order q = 1, which weights all elements in proportion to their frequency and leads to diversity measures based on Shannon's entropy. We investigated the numerical behaviour of our approach with simulations and showed that it can accurately describe complex spatial hierarchical structures. To demonstrate the intuitive and straightforward interpretation of our diversity measures in terms of effective number of components (alleles, species, etc.), we applied the framework to a real data set on coral reef biodiversity. We expect our framework will have multiple applications covering the fields of conservation biology, community genetics and eco‐evolutionary dynamics. [ABSTRACT FROM AUTHOR]

Marine ecologists have been slow at adopting the metapopulation paradigm in their research. As explained in the landmark book Marine Metapopulations, marine ecology was focused on local processes and used mainly experimental approaches that neglected the potential role of demographic connections among local populations, but this is no longer the case. Metapopulation theory is now integrated into marine ecology research and is being used as a helpful framework for describing spatial population processes, for asking new research questions, and for planning the management and conservation of marine species. Nevertheless, the use of the metapopulation paradigm in marine sciences is challenging because of the complex life histories of marine species and the peculiarities of the marine environment. Here, I highlight the many challenges faced by ecologists who study marine metapopulations and mention some of the approaches that can be used to overcome them. In particular, I focus on the problem of estimating connectivity, an important attribute of metapopulations that represents a fundamental knowledge gap in marine ecology. Following the repeated calls for integrative approaches that combine all sources of information I propose the use of the hierarchical Bayesian framework and provide an example that considers the joint analysis of the three leading approaches in connectivity research, genetics, microchemistry and biophysical modelling. [ABSTRACT FROM AUTHOR]

Microsatellite-based genetic assignment is used broadly to monitor contemporary effective dispersal among populations. The need to investigate the robustness of this method to common genotyping errors was emphasized more than a decade ago, but it remains unaddressed., We evaluate here for the first time the effect of mistaken and null alleles on estimates of contemporary seed and pollen migration rates obtained with genetic assignment methods. We also introduce a novel Bayesian approach to jointly estimate seed and pollen migration rates, genotyping error rates and null allele frequencies, not requiring independent reference or duplicate genotypic data., Unaccounted-for mistaken alleles caused positive bias and increased the root mean square error ( RMSE) of pollen migration rate estimates, whereas seed migration rate estimates were weakly sensitive to mistyping. Jointly estimating mistyping rates minimized the bias and RMSE they introduce on pollen migration estimates, while yielding seed migration rate estimates with similar or slightly larger bias and RMSE than those obtained when ignoring mistyping., Ignoring genotyping errors can be especially problematic when there is no actual migration, because it can lead to the wrong conclusion that there is statistically significant exchange of pollen and/or seeds among populations that are actually isolated., Unaccounted-for null alleles are problematic when among-population pollen dispersal is present, leading to underestimation of pollen migration rates and overestimation of seed migration rates. Jointly estimating null allele frequencies minimized these two biases, reduced the RMSE of seed migration rate estimates and produced relatively small changes in the RMSE of pollen dispersal estimates., Synthesis. Disregarding genotyping errors and null alleles can produce biased and less accurate estimates of the rates at which present-day plant populations are exchanging seed and pollen. An approach is proposed here to minimize the effect of genotyping problems on contemporary migration rate estimates, which should help avoiding erroneous migration inference, monitoring and management, especially when dealing with low migration rates and their associated uncertainty. [ABSTRACT FROM AUTHOR]

Inferring and quantifying recent barriers to connectivity is increasingly important for conservation and management in a world undergoing rapid environmental change. Traditional measures of genetic differentiation can take many generations to reflect a new barrier to connectivity. Although methods that use the linkage disequilibrium signal in mixed genetic samples are able to reflect recent levels of gene flow, they are not suitable for use in situations with low levels of genetic differentiation. Kinship‐based methods, those that assess the spatio‐temporal distribution of related individuals, have been used in this context, but a formal statistical framework for such approaches has been lacking. In this issue of Molecular Ecology Resources, Escoda, et al. adapt the assortativity coefficient, AC, to analyse the networks of kin relationships in the Pyrenean desman (Galemys pyrenaicus) across potential barriers to dispersal. Their modified AC quantifies the proportion of missing kin relationships across putative dispersal barriers with respect to the expected proportion if there was no barrier. This application highlights that AC can be used to test the null hypothesis that a putative barrier has no effect on gene flow, in which case AC is not significantly different from 0. The method represents a useful step forward in conservation genomics by developing and adapting tools to assess contemporary connectivity using genomic data. [ABSTRACT FROM AUTHOR]

Highly fecund marine species with dispersive life-history stages often display large population sizes and wide geographic distribution ranges. Consequently, they are expected to experience reduced genetic drift, efficient selection fueled by frequent adaptive mutations, and high migration loads. This has important consequences for understanding how local adaptation proceeds in the sea. A key issue in this regard, relates to the genetic architecture underlying fitness traits. Theory predicts that adaptation may involve many genes but with a high variance in effect size. Therefore, the effect of selection on allele frequencies may be substantial for the largest effect size loci, but insignificant for small effect genes. In such a context, the performance of population genomic methods to unravel the genetic basis of adaptation depends on the fraction of adaptive genetic variance explained by the cumulative effect of outlier loci. Here, we address some methodological challenges associated with the detection of local adaptation using molecular approaches. We provide an overview of genome scan methods to detect selection, including those assuming complex demographic models that better describe spatial population structure. We then focus on quantitative genetics approaches that search for genotype-phenotype associations at different genomic scales, including genome-wide methods evaluating the cumulative effect of variants. We argue that the limited power of single locus tests can be alleviated by the use of polygenic scores to estimate the joint contribution of candidate variants to phenotypic variation. [ABSTRACT FROM AUTHOR]

Conservation of ecological communities requires deepening our understanding of genetic diversity patterns and drivers at community-wide scales. Here, we use seascape genetic analysis of a diversity metric, allelic richness (AR), for 47 reef species sampled across 13 Hawaiian Islands to empirically demonstrate that large reefs high in coral cover harbour the greatest genetic diversity on average. We found that a species's life history (e.g. depth range and herbivory) mediates response of genetic diversity to seascape drivers in logical ways. Furthermore, a metric of combined multi-species AR showed strong coupling to species richness and habitat area, quality and stability that few species showed individually. We hypothesize that macro-ecological forces and species interactions, by mediating species turnover and occupancy (and thus a site's mean effective population size), influence the aggregate genetic diversity of a site, potentially allowing it to behave as an apparent emergent trait that is shaped by the dominant seascape drivers. The results highlight inherent feedbacks between ecology and genetics, raise concern that genetic resilience of entire reef communities is compromised by factors that reduce coral cover or available habitat, including thermal stress, and provide a foundation for new strategies for monitoring and preserving biodiversity of entire reef ecosystems. [ABSTRACT FROM AUTHOR]

Living at high altitude is one of the most difficult challenges that humans had to cope with during their evolution. Whereas several genomic studies have revealed some of the genetic bases of adaptations in Tibetan, Andean, and Ethiopian populations, relatively little evidence of convergent evolution to altitude in different continents has accumulated. This lack of evidence can be due to truly different evolutionary responses, but it can also be due to the low power of former studies that have mainly focused on populations from a single geographical region or performed separate analyses on multiple pairs of populations to avoid problems linked to shared histories between some populations. We introduce here a hierarchical Bayesian method to detect local adaptation that can deal with complex demographic histories. Our method can identify selection occurring at different scales, as well as convergent adaptation in different regions. We apply our approach to the analysis of a large SNP data set from low- and high-altitude human populations from America and Asia. The simultaneous analysis of these two geographic areas allows us to identify several candidate genome regions for altitudinal selection, and we show that convergent evolution among continents has been quite common. In addition to identifying several genes and biological processes involved in high-altitude adaptation, we identify two specific biological pathways that could have evolved in both continents to counter toxic effects induced by hypoxia. [ABSTRACT FROM AUTHOR]

What shapes variation in genetic structure within a community of codistributed species is a central but difficult question for the field of population genetics. With a focus on the isolated coral reef ecosystem of the Hawaiian Archipelago, we assessed how life history traits influence population genetic structure for 35 reef animals. Despite the archipelago's stepping stone configuration, isolation by distance was the least common type of genetic structure, detected in four species. Regional structuring (i.e. division of sites into genetically and spatially distinct regions) was most common, detected in 20 species and nearly in all endemics and habitat specialists. Seven species displayed chaotic (spatially unordered) structuring, and all were nonendemic generalist species. Chaotic structure also associated with relatively high global FST. Pelagic larval duration ( PLD) was not a strong predictor of variation in population structure ( R2 = 0.22), but accounting for higher FST values of chaotic and invertebrate species, compared to regionally structured and fish species, doubled the power of PLD to explain variation in global FST (adjusted R2 = 0.50). Multivariate correlation of eight species traits to six genetic traits highlighted dispersal ability, taxonomy (i.e. fish vs. invertebrate) and habitat specialization as strongest influences on genetics, but otherwise left much variation in genetic traits unexplained. Considering that the study design controlled for many sampling and geographical factors, the extreme interspecific variation in spatial genetic patterns observed for Hawaìi marine species may be generated by demographic variability due to species-specific abundance and migration patterns and/or seascape and historical factors. [ABSTRACT FROM AUTHOR]

Detecting bottlenecks is a common task in molecular ecology. While several bottleneck detection methods exist, evaluations of their power have focused only on severe bottlenecks (e.g. to Ne ~10). As a component of a recent review, Peery et al. () analysed the power of two approaches, the M-ratio and heterozygote excess tests, to detect moderate bottlenecks (e.g. to Ne ~100), which is realistic for many conservation situations. In this Comment, we address three important points relevant to but not considered in Peery et al. Under moderate bottleneck scenarios, we test the (i) relative advantage of sampling more markers vs. more individuals, (ii) potential power to detect the bottleneck when utilizing dozens of microsatellites (a realistic possibility for contemporary studies) and (iii) reduction in power when postbottleneck recovery has occurred. For the realistic situations examined, we show that (i) doubling the number of loci shows equal or better power than tripling the number of individuals, (ii) increasing the number of markers (up to 100) results in continued additive gains in power, and (iii) recovery after a moderate amount of time or gradual change in size reduces power, by up to one-half. Our results provide a practical supplement to Peery et al. and encourage the continued use of bottleneck detection methods in the genomic age, but also emphasize that the power under different sampling schemes should be estimated, using simulation modelling, as a routine component of molecular ecology studies. [ABSTRACT FROM AUTHOR]

Delimiting species is very conflicting in the case of very young taxa that are in the process of diversification, and even more difficult if the species inhabit a heterogeneous environment. In this case, even population delimitation is controversial. The South American genus of subterranean rodents Ctenomys is highly speciose, with 62 species that appeared in the lapse of 3 Myr. Within the genus, the 'perrensi' group, formed by three named species and a group of forms of unknown taxonomic status, inhabits the Iberá wetland, in northern Argentina. Almost every locality shows a particular chromosomal complement. To understand the relationships and the evolutionary process among species and populations, we examined mitochondrial DNA sequences and microsatellite genotypes. We found an isolation-by-distance pattern with evidence of cluster-like behaviour of the system. The mitochondrial DNA network revealed two different groups, separated by one of the main rivers of the region. Clustering methods delimited 12 different populations and five metapopulation lineages that seem to be evolving independently. We found evidence of ancient migration among localities at the centre of the distribution but no signals of current migration among the 12 delimited clusters. Some of the genetic clusters found included localities with different chromosomal numbers, which points to the existence of gene flow despite chromosomal variation. The evolutionary future of these five lineages is controlled by the dynamics of their habitat: if stable, they may become distinct species; otherwise, they may collapse into a hybrid swarm, forming a single evolving metapopulation. [ABSTRACT FROM AUTHOR]

Understanding speciation is a fundamental aim of evolutionary biology and a very challenging one. Speciation can be viewed as the dynamics of genetic differentiation between populations resulting in substantial reproductive isolation (). It was generally accepted that very small levels of migration prevent genetic differentiation among populations and, therefore, speciation. However, recent theoretical work showed that sympatric speciation is possible (). Nevertheless, providing empirical evidence that gene flow occurred during speciation is challenging because several gene flow scenarios can explain observed patterns of genetic differentiation. Positive migration rate estimates alone do not prove ongoing gene flow during divergence. We also need to know whether migration took place before, during or after speciation. There is no statistical method specifically aimed at estimating gene flow timing, but several studies used the isolation with migration model (; ) to estimate this parameter and make inferences about speciation scenarios. It is tempting to use statistical methods to estimate important evolutionary parameters even if they do not appear explicitly in the inference model. Nevertheless, before doing so, we need to determine whether they can provide reliable results. In this issue of Molecular Ecology, present a simulation study that examines the degree to which gene flow timing estimates obtained from IMa2 () can be used to make inferences about speciation mode. Their results are sobering; gene flow timing estimates obtained in this way are not reliable and cannot be used to unequivocally establish if gene flow was ongoing during speciation. [ABSTRACT FROM AUTHOR]

We review a model-based approach to estimate local population FST’s that is based on the multinomial-Dirichlet distribution, the so-called F-model. As opposed to the standard method of estimating a single FST value, this approach takes into account the fact that in most if not all realistic situations, local populations differ in their effective sizes and migration rates. Therefore, the use of this approach can help better describe the genetic structure of populations. Despite this obvious advantage, this method has remained largely underutilized by molecular ecologists. Thus, the objective of this review is to foster its use for studying the genetic structure of metapopulations. We present the derivation of the Bayesian formulation for the estimation of population-specific FST’s based on the multinomial-Dirichlet distribution. We describe several recent applications of the F-model and present the results of a small simulation study that explains how the F-model can help better describe the genetic structure of populations. [ABSTRACT FROM AUTHOR]

The spatial structuring of intraspecific genetic diversity is the result of random genetic drift, natural selection, migration, mutation, and their interaction with historical processes. The contribution of each has been typically difficult to estimate, but recent advances in statistical genetics have provided valuable new investigative tools to tackle such complexity. Using a combination of such methods, we examined the roles of environment (i.e., natural selection), random genetic processes (i.e., drift), and demography and life histories (e.g., feeding migrations) on population structure of a widely distributed and abundant marine pelagic fish of economic importance, Atlantic herring ( Clupea harengus). Individuals were collected during peak spawning time from 19 spawning locations spanning the region from the western North Sea to the eastern Baltic Sea ( N= 1859, eight microsatellite loci). We carried out separate analyses of neutral and selected genetic variation, which allowed us to establish that the two most important factors affecting population structure were selection due to salinity at spawning sites and feeding migrations. The genetic signal left by the demographic history of herring, on the other hand, seems to have been largely eroded, which is not surprising given the large reproductive potential and presumed enormous local effective population sizes of pelagic fish that constrain the effect of stochastic processes. The approach we used can in principle be applied to any abundant and widely distributed aquatic or terrestrial species. [ABSTRACT FROM AUTHOR]

We present a new multilocus genotype method that makes inferences about recent immigration rates and identifies the environmental factors that are more likely to explain observed gene flow patterns. It also estimates population-specific inbreeding coefficients, allele frequencies, and local population FST's and performs individual assignments. We generate synthetic data sets to determine the region of the parameter space where our method is and is not able to provide accurate estimates. Our simulation study indicates that reliable results can be obtained when the global level of genetic differentiation (FST) is >1%, the number of loci is only 10, and sample sizes are of the order of 50 individuals per population. We illustrate our method by applying it to Pakistani human data, considering altitude and geographic distance as explanatory factors. Our results suggest that altitude explains better the genetic data than geographic distance. Additionally, they show that southern low-altitude populations have higher migration rates than northern high-altitude ones. [ABSTRACT FROM AUTHOR]

Assignment methods, which use genetic information to ascertain population membership of individuals or groups of individuals, have been used in recent years to study a wide range of evolutionary and ecological processes. In applied studies, the first step of articulating the biological question(s) to be addressed should be followed by selection of the method(s) best suited for the analysis. However, this first step often receives less attention than it should, and the recent proliferation of assignment methods has made the selection step challenging. Here, we review assignment methods and discuss how to match the appropriate methods with the underlying biological questions for several common problems in ecology and conservation (assessing population structure; measuring dispersal and hybridization; and forensics and mixture analysis). We also identify several topics for future research that should ensure that this field remains dynamic and productive. [Copyright &y& Elsevier]

Examines the pattern of colonization in metapopulation of grey seals. Emigration of colonists; Determination on the frequency and scale of recolonization; Minimization of genetic differentiation.

In coastal British Columbia, Canada, marine megafauna such as humpback whales (Megaptera novaeangliae) and fin whales (Balaenoptera physalus velifera) have been subject to a history of exploitation and near extirpation. While their populations have been in recovery, significant threats are posed to these vulnerable species by proposed natural resource ventures in this region, in addition to the compounding effects of anthropogenic climate change. Genetic tools play a vital role in informing conservation efforts, but the associated collection of tissue biopsy samples can be challenging for the investigators and disruptive to the ongoing behaviour of the targeted whales. Here, we evaluate a minimally intrusive approach based on collecting exhaled breath condensate, or respiratory 'blow' samples, from baleen whales using an unoccupied aerial system (UAS), within Gitga'at First Nation territory for conservation genetics. Minimal behavioural responses to the sampling technique were observed, with no response detected 87% of the time (of 112 UAS deployments). DNA from whale blow (n = 88 samples) was extracted, and DNA profiles consisting of 10 nuclear microsatellite loci, sex identification and mitochondrial (mt) DNA haplotypes were constructed. An average of 7.5 microsatellite loci per individual were successfully genotyped. The success rates for mtDNA and sex assignment were 80% and 89% respectively. Thus, this minimally intrusive sampling method can be used to describe genetic diversity and generate genetic profiles for individual identification. The results of this research demonstrate the potential of UAS‐collected whale blow for conservation genetics from a remote location. see also the Perspective by Elena Valsecchi. [ABSTRACT FROM AUTHOR]

Explores the effects of demographic parameters and different patterns of migration to the maintenance of genetic variability in a single sink or sinkhole population that receives migrants from a source population and whose size changes with time. Demographic equilibrium of sink with constant number of migrants; Bernoulli and Poison migrations; Demographic stochasticity.

Identifying genomic regions influenced by natural selection provides fundamental insights into the genetic basis of local adaptation. However, it remains challenging to detect loci under complex spatially varying selection. We propose a deep learning-based framework, DeepGenomeScan, which can detect signatures of spatially varying selection. We demonstrate that DeepGenomeScan outperformed principal component analysis- and redundancy analysis-based genome scans in identifying loci underlying quantitative traits subject to complex spatial patterns of selection. Noticeably, DeepGenomeScan increases statistical power by up to 47.25% under nonlinear environmental selection patterns. We applied DeepGenomeScan to a European human genetic dataset and identified some well-known genes under selection and a substantial number of clinically important genes that were not identified by SPA, iHS, Fst and Bayenv when applied to the same dataset. [ABSTRACT FROM AUTHOR]

Geographic patterns of human genetic variation provide important insights into human evolution and disease. A commonly used tool to detect and describe them is principal component analysis (PCA) or the supervised linear discriminant analysis of principal components (DAPC). However, genetic features produced from both approaches could fail to correctly characterize population structure for complex scenarios involving admixture. In this study, we introduce Kernel Local Fisher Discriminant Analysis of Principal Components (KLFDAPC), a supervised non-linear approach for inferring individual geographic genetic structure that could rectify the limitations of these approaches by preserving the multimodal space of samples. We tested the power of KLFDAPC to infer population structure and to predict individual geographic origin using neural networks. Simulation results showed that KLFDAPC has higher discriminatory power than PCA and DAPC. The application of our method to empirical European and East Asian genome-wide genetic datasets indicated that the first two reduced features of KLFDAPC correctly recapitulated the geography of individuals and significantly improved the accuracy of predicting individual geographic origin when compared to PCA and DAPC. Therefore, KLFDAPC can be useful for geographic ancestry inference, design of genome scans and correction for spatial stratification in GWAS that link genes to adaptation or disease susceptibility. [ABSTRACT FROM AUTHOR]

Ever since the introduction of allozymes in the 1960s, evolutionary biologists and ecologists have continued to search for more powerful molecular markers to estimate important parameters such as effective population size and migration rates and to make inferences about the demographic history of populations, the relationships between individuals and the genetic architecture of phenotypic variation (; ). Choosing a marker requires a thorough consideration of the trade-offs associated with the different techniques and the type of data obtained from them. Some markers can be very informative but require substantial amounts of start-up time (e.g. microsatellites), while others require very little time but are much less polymorphic. Amplified fragment length polymorphism (AFLP) is a firmly established molecular marker technique that falls in this latter category. AFLPs are widely distributed throughout the genome and can be used on organisms for which there is no a priori sequence information (). These properties together with their moderate cost and short start-up time have made them the method of choice for many molecular ecology studies of wild species (). However, they have a major disadvantage, they are dominant. This represents a very important limitation because many statistical genetics methods appropriate for molecular ecology studies require the use of codominant markers. In this issue, present an innovative hierarchical Bayesian method that overcomes this limitation. The proposed approach represents a comprehensive statistical treatment of the fluorescence of AFLP bands and leads to accurate inferences about the genetic structure of natural populations. Besides allowing a quasi-codominant treatment of AFLPs, this new method also solves the difficult problems posed by subjectivity in the scoring of AFLP bands. [ABSTRACT FROM AUTHOR]

The article discusses various issues published within the issue, including one on spatial autocorrelation statistics and the correlations among values at different spatial scales for conducting genetic inferences at multiple spatial scales, another on the relationship between classical population genetics models and individual-based landscape genetics models, as well as a report on an approximate Bayesian computation (ABC) approach for making inferences about complex demographic processes.

colonise is a user-friendly Microsoft® Windows software for the study of colonization events in natural populations. It determines the composition of the newly colonized population and makes inferences about the factors that influenced individuals to establish a new population. colonise uses a hierarchical Bayesian model to combine multilocus genotype data with demographic and environmental data, and a reversible jump Markov chain Monte Carlo (MCMC) algorithm to perform posterior density estimations. The software is written in C and C + + for the graphical user interface and is available at . The software integrates a tool to draw posterior density functions (histogram, running mean, traces, etc.) and to estimate parameters from them (mean, mode, variance, HPDI, etc.). [ABSTRACT FROM AUTHOR]

How emerging adaptive variants interact is an important factor in the evolution of wild populations, but the opportunity to empirically study this interaction is rare. We recently documented the emergence of an adaptive phenotype "curly-wing" in Hawaiian populations of field crickets (Teleogryllus oceanicus). Curly-wing inhibits males' ability to sing, protecting them from eavesdropping parasitoid flies (Ormia ochracea). Surprisingly, curly-wing co-occurs with similarly protective silent "flatwing" phenotypes in multiple populations, in which neither phenotype has spread to fixation. These two phenotypes are frequently coexpressed, but since either sufficiently reduces song amplitude to evade the fly, their coexpression confers no additional fitness benefit. Numerous "off-target" phenotypic changes are known to accompany flatwing, and we find that curly-wing, too, negatively impacts male courtship ability and affects mass and survival of females under lab conditions. We show through crosses and genomic and mRNA sequencing that curly-wing expression is associated with variation on a single autosome. In parallel analyses of flatwing, our results reinforce previous findings of X-linked single-locus inheritance. By combining insights into the genetic architecture of these alternative phenotypes with simulations and field observations, we show that the co-occurrence of these two adaptations impedes either from fixing, despite extreme fitness benefits, due to fitness epistasis. This co-occurrence of similar adaptive forms in the same populations might be more common than is generally considered and could be an important force inhibiting adaptive evolution in wild populations of sexually reproducing organisms. [ABSTRACT FROM AUTHOR]

Abstract: Local adaptation and phenotypic plasticity are two important characteristics of alpine plants to overcome the threats caused by global changes. Among alpine species, Arabis alpina is characterised by an unusually wide altitudinal amplitude, ranging from 800 to 3,100 m of elevation in the French Alps. Two non‐exclusive hypotheses can explain the presence of A. alpina across this broad ecological gradient: adaptive phenotypic plasticity or local adaptation, making this species especially useful to better understand these phenomena in alpine plant species. We carried out common garden experiments at two different elevations with maternal progenies from six sites that differed in altitude. We showed that (1) key phenotypic traits (morphotype, total fruit length, growth, height) display significant signs of local adaptation, (2) most traits studied are characterised by a high phenotypic plasticity between the two experimental gardens and (3) the two populations from the highest elevations lacked morphological plasticity compared to the other populations. By combining two genome scan approaches (detection of selection and association methods), we isolated a candidate gene (Sucrose‐Phosphate Synthase 1). This gene was associated with height and local average temperature in our studied populations, consistent with previous studies on this gene in Arabidopsis thaliana. Synthesis. Given the nature of the traits involved in the detected pattern of local adaptation and the relative lack of plasticity of the two most extreme populations, our findings are consistent with a scenario of a locally adaptive stress response syndrome in high elevation populations. Due to a reduced phenotypic plasticity, an overall low intra‐population genetic diversity of the adaptive traits and weak gene flow, populations of high altitude might have difficulties to cope with, e.g. a rise of temperature. [ABSTRACT FROM AUTHOR]

Accurately estimating patterns of population connectivity in marine systems remains an elusive goal. Current genetic approaches have focused on assigning individuals back to their natal populations using one of two methods: parentage analyses and assignment tests. Each of these approaches has their relative merits and weaknesses. Here, we illustrate these tradeoffs using a forward-time agent-based model that incorporates relevant natural history and physical oceanography for 135 Kellet's whelk (Kelletia kelletii) populations from Southern California. Like most marine organisms, Kellet's whelks live in large meta-populations where local populations are connected by dispersive larvae. For estimating population connectivity, we found parentage analyses to be relatively insensitive to the amount of genetic differentiation among local populations, but highly sensitive to the proportion of the meta-population sampled. Assignment tests, on the other hand, were relatively insensitive to the proportion of the meta-population sampled, but highly sensitive to the amount of genetic differentiation found among local populations. Comparisons between the true connectivity matrices (generated by using the true origin of all sampled individuals) and those obtained via parentage analyses and assignment tests reveal that neither approach can explain >26% of the variation in true connectivity. Furthermore, even with perfect assignment of all sampled individuals, sampling error alone can introduce noise into the estimated population connectivity matrix. Future work should aim to improve the number of correct assignments without the expense of additional incorrect assignments, perhaps by using dispersal information obtained from related individuals as priors in a Bayesian framework. These analyses dispel a number of common misconceptions in the field and highlight areas for both future research and methodological improvements. [ABSTRACT FROM AUTHOR]

Features the electronic journal 'Conservation Genetics,' edited by A.R. Hoelzel. Promotion of the application of molecular genetic methodologies; Answer to queries on the conservation of biological diversity; Emphasis on the genetic structure and/or phylogeography of populations.

Background: The study of local adaptation processes is a very important research topic in the field of population genomics. There is a particular interest in the study of human populations because they underwent a process of rapid spatial expansion and faced important environmental changes that translated into changes in selective pressures. New mutations may have been selected for in the new environment and previously existing genetic variants may have become detrimental. Immune related genes may have been released from the selective pressure exerted by pathogens in the ancestral environment and new variants may have been positively selected due to pathogens present in the newly colonized habitat. Also, variants that had a selective advantage in past environments may have become deleterious in the modern world due to external stimuli including climatic, dietary and behavioral changes, which could explain the high prevalence of some polygenic diseases such as diabetes and obesity. Results: We performed an enrichment analysis to identify gene sets enriched for signals of positive selection in humans. We used two genome scan methods, XPCLR and iHS to detect selection using a dense coverage of SNP markers combined with two gene set enrichment approaches. We identified immune related gene sets that could be involved in the protection against pathogens especially in the African population. We also identified the glycolysis & gluconeogenesis gene set, related to metabolism, which supports the thrifty genotype hypothesis invoked to explain the current high prevalence of diseases such as diabetes and obesity. Extending our analysis to the gene level, we found signals for 23 candidate genes linked to metabolic syndrome, 13 of which are new candidates for positive selection. Conclusions: Our study provides a list of genes and gene sets associated with immunity and metabolic syndrome that are enriched for signals of positive selection in three human populations (Europeans, Africans and Asians). Our results highlight differences in the relative importance of pathogens as drivers of local adaptation in different continents and provide new insights into the evolution and high incidence of metabolic syndrome in modern human populations. [ABSTRACT FROM AUTHOR]

Identifying genomic regions targeted by positive selection has been a long-standing interest of evolutionary biologists. This objective was difficult to achieve until the recent emergence of next-generation sequencing, which is fostering the development of large-scale catalogues of genetic variation for increasing number of species. Several statistical methods have been recently developed to analyse these rich data sets, but there is still a poor understanding of the conditions under which these methods produce reliable results. This study aims at filling this gap by assessing the performance of genome-scan methods that consider explicitly the physical linkage among SNPs surrounding a selected variant. Our study compares the performance of seven recent methods for the detection of selective sweeps (iHS, nSL, EHHST, xp-EHH, XP-EHHST, XPCLR and hapFLK). We use an individual-based simulation approach to investigate the power and accuracy of these methods under a wide range of population models under both hard and soft sweeps. Our results indicate that XPCLR and hapFLK perform best and can detect soft sweeps under simple population structure scenarios if migration rate is low. All methods perform poorly with moderate-to-high migration rates, or with weak selection and very poorly under a hierarchical population structure. Finally, no single method is able to detect both starting and nearly completed selective sweeps. However, combining several methods (XPCLR or hapFLK with iHS or nSL) can greatly increase the power to pinpoint the selected region. [ABSTRACT FROM AUTHOR]

Background Demographic bottlenecks can severely reduce the genetic variation of a population or a species. Establishing whether low genetic variation is caused by a bottleneck or a constantly low effective number of individuals is important to understand a species' ecology and evolution, and it has implications for conservation management. Recent studies have evaluated the power of several statistical methods developed to identify bottlenecks. However, the false positive rate, i.e. the rate with which a bottleneck signal is misidentified in demographically stable populations, has received little attention. We analyse this type of error (type I) in forward computer simulations of stable populations having greater than Poisson variance in reproductive success (i.e., variance in family sizes). The assumption of Poisson variance underlies bottleneck tests, yet it is commonly violated in species with high fecundity. Results With large variance in reproductive success (Vk ⩾ 40, corresponding to a ratio between effective and census size smaller than 0.1), tests based on allele frequencies, allelic sizes, and DNA sequence polymorphisms (heterozygosity excess, M-ratio, and Tajima's D test) tend to show erroneous signals of a bottleneck. Similarly, strong evidence of population decline is erroneously detected when ancestral and current population sizes are estimated with the model based method MSVAR. Conclusions Our results suggest caution when interpreting the results of bottleneck tests in species showing high variance in reproductive success. Particularly in species with high fecundity, computer simulations are recommended to confirm the occurrence of a population bottleneck [ABSTRACT FROM AUTHOR]

Computer simulations are excellent tools for understanding the evolutionary and genetic consequences of complex processes whose interactions cannot be analytically predicted. Simulations have traditionally been used in population genetics by a fairly small community with programming expertise, but the recent availability of dozens of sophisticated, customizable software packages for simulation now makes simulation an accessible option for researchers in many fields. The in silico genetic data produced by simulations, along with greater availability of population-genomics data, are transforming genetic epidemiology, anthropology, evolutionary and population genetics and conservation. In this Review of the state-of-the-art of simulation software, we identify applications of simulations, evaluate simulator capabilities, provide a guide for their use and summarize future directions. [ABSTRACT FROM AUTHOR]

A plant species' genetic population structure is the result of a complex combination of its life history, ecological preferences, position in the ecosystem and historical factors. As a result, many different statistical methods exist that measure different aspects of species' genetic structure. However, little is known about how these methods are interrelated and how they are related to a species' ecology and life history. In this study, we used the IntraBioDiv amplified fragment length polymorphisms data set from 27 high-alpine species to calculate eight genetic summary statistics that we jointly correlate to a set of six ecological and life-history traits. We found that there is a large amount of redundancy among the calculated summary statistics and that there is a significant association with the matrix of species traits. In a multivariate analysis, two main aspects of population structure were visible among the 27 species. The first aspect is related to the species' dispersal capacities and the second is most likely related to the species' postglacial recolonization of the Alps. Furthermore, we found that some summary statistics, most importantly Mantel's r and Jost's D, show different behaviour than expected based on theory. We therefore advise caution in drawing too strong conclusions from these statistics. [ABSTRACT FROM AUTHOR]

Understanding the forces that influence natural variation within and among populations has been a major objective of evolutionary biologists for decades. Motivated by the growth in computational power and data complexity, modern approaches to this question make intensive use of simulation methods. Approximate Bayesian Computation (ABC) is one of these methods. Here we review the foundations of ABC, its recent algorithmic developments, and its applications in evolutionary biology and ecology. We argue that the use of ABC should incorporate all aspects of Bayesian data analysis: formulation, fitting, and improvement of a model. ABC can be a powerful tool to make inferences with complex models if these principles are carefully applied. [Copyright &y& Elsevier]

Background: The population genetic structure of subterranean rodent species is strongly affected by demographic (e.g. rates of dispersal and social structure) and stochastic factors (e.g. random genetic drift among subpopulations and habitat fragmentation). In particular, gene flow estimates at different spatial scales are essential to understand genetic differentiation among populations of a species living in a highly fragmented landscape. Ctenomys australis (the sand dune tuco-tuco) is a territorial subterranean rodent that inhabits a relatively secure, permanently sealed burrow system, occurring in sand dune habitats on the coastal landscape in the south-east of Buenos Aires province, Argentina. Currently, this habitat is threatened by urban development and forestry and, therefore, the survival of this endemic species is at risk. Here, we assess population genetic structure and patterns of dispersal among individuals of this species at different spatial scales using 8 polymorphic microsatellite loci. Furthermore, we evaluate the relative importance of sex and habitat configuration in modulating the dispersal patterns at these geographical scales. Results: Our results show that dispersal in C. australis is not restricted at regional spatial scales (~ 4 km). Assignment tests revealed significant population substructure within the study area, providing support for the presence of two subpopulations from three original sampling sites. Finally, male-biased dispersal was found in the Western side of our study area, but in the Eastern side no apparent philopatric pattern was found, suggesting that in a more continuous habitat males might move longer distances than females. Conclusions: Overall, the assignment-based approaches were able to detect population substructure at fine geographical scales. Additionally, the maintenance of a significant genetic structure at regional (~ 4 km) and small (less than 1 km) spatial scales despite apparently moderate to high levels of gene flow between local sampling sites could not be explained simply by the linear distance among them. On the whole, our results support the hypothesis that males disperse more frequently than females; however they do not provide support for strict philopatry within females. [ABSTRACT FROM AUTHOR]

Bayesian methods have become extremely popular in molecular ecology studies because they allow us to estimate demographic parameters of complex demographic scenarios using genetic data. Articles presenting new methods generally include sensitivity studies that evaluate their performance, but they tend to be limited and need to be followed by a more thorough evaluation. Here we evaluate the performance of a recent method,bayesass, which allows the estimation of recent migration rates among populations, as well as the inbreeding coefficient of each local population. We expand the simulation study of the original publication by considering multi-allelic markers and scenarios with varying number of populations. We also investigate the effect of varying migration rates and FST more thoroughly in order to identify the region of parameter space where the method is and is not able to provide accurate estimates of migration rate. Results indicate that if the demographic history of the species being studied fits the assumptions of the inference model, and if genetic differentiation is not too low ( FST ≥ 0.05), then the method can give fairly accurate estimates of migration rates even when they are fairly high (about 0.1). However, when the assumptions of the inference model are violated, accurate estimates are obtained only if migration rates are very low ( m = 0.01) and genetic differentiation is high ( FST ≥ 0.10). Our results also show that using posterior assignment probabilities as an indication of how much confidence we can place on the assignments is problematical since the posterior probability of assignment can be very high even when the individual assignments are very inaccurate. [ABSTRACT FROM AUTHOR]

Summary 1 Many red seaweeds are characterized by a haploid-diploid life cycle in which populations consist of dioecious haploid (gametophyte) and diploid (tetrasporophyte) individuals as well as an additional diploid zygote-derived sporangium (carposporophyte) stage. A demographic analysis of Gracilaria gracilis populations was carried out to explore and evaluate the population dynamics and stage structure of a typical haploid-diploid red seaweed. 2 Four G. gracilis populations were studied at two sites on the French coast of the Strait of Dover. Survival, reproduction and recruitment rates were measured in each population for up to 4 years. Eight two-sex stage-based population projection matrices were built to describe their demography. 3 All four populations were characterized by high survival and low recruitment rates. Population growth rates (λ) were similar between populations and between years and ranged from 1.03 to 1.17. In addition, generation times were found to be as long as 42 years. 4 Sex and ploidy ratios were variable across populations and over time. Female frequencies ranged from 0.31 to 0.59 and tetrasporophyte frequencies from 0.44 to 0.63. However, in most cases, the observed population structures were not significantly different from the calculated stage distributions. 5 Eigenvalue elasticity analysis showed that λ was most sensitive to changes in matrix transitions that corresponded to survival of the gametophyte and tetrasporophyte stages. In contrast, the contribution of the fertility elements to λ was small. Eigenvector elasticity analysis also showed that survival elements had the greatest impact on sex and ploidy ratios. [ABSTRACT FROM AUTHOR]

The article focuses on ancestral genetic variation fuels repeated ecotype formation in bottlenose dolphins and repeated adaptation can provide insights into the mechanisms allowing species to adapt to novel environments. It mentions parallel linked selection acted upon ancient alleles in geographically distant coastal populations, which were present as standing genetic variation in the pelagic populations and recurrent bouts of selection through time.

The deep sea has been described as the last major ecological frontier, as much of its biodiversity is yet to be discovered and described. Beaked whales (ziphiids) are among the most visible inhabitants of the deep sea, due to their large size and worldwide distribution, and their taxonomic diversity and much about their natural history remain poorly understood. We combine genomic and morphometric analyses to reveal a new Southern Hemisphere ziphiid species, Ramari's beaked whale, Mesoplodon eueu, whose name is linked to the Indigenous peoples of the lands from which the species holotype and paratypes were recovered. Mitogenome and ddRAD-derived phylogenies demonstrate reciprocally monophyletic divergence between M. eueu and True's beaked whale (M. mirus) from the North Atlantic, with which it was previously subsumed. Morphometric analyses of skulls also distinguish the two species. A time-calibrated mitogenome phylogeny and analysis of two nuclear genomes indicate divergence began circa 2 million years ago (Ma), with geneflow ceasing 0.35–0.55 Ma. This is an example of how deep sea biodiversity can be unravelled through increasing international collaboration and genome sequencing of archival specimens. Our consultation and involvement with Indigenous peoples offers a model for broadening the cultural scope of the scientific naming process. [ABSTRACT FROM AUTHOR]

Metapopulation theory assumes a balance between local decays/extinctions and local growth/new colonisations. Here we investigate whether recent population declines across part of the UK harbour seal range represent normal metapopulation dynamics or are indicative of perturbations potentially threatening the metapopulation viability, using 20 years of population trends, location tracking data (n = 380), and UK-wide, multi-generational population genetic data (n = 269). First, we use microsatellite data to show that two genetic groups previously identified are distinct metapopulations: northern and southern. Then, we characterize the northern metapopulation dynamics in two different periods, before and after the start of regional declines (pre-/peri-perturbation). We identify source–sink dynamics across the northern metapopulation, with two putative source populations apparently supporting three likely sink populations, and a recent metapopulation-wide disruption of migration coincident with the perturbation. The northern metapopulation appears to be in decay, highlighting that changes in local populations can lead to radical alterations in the overall metapopulation's persistence and dynamics. [ABSTRACT FROM AUTHOR]

As species recover from exploitation, continued assessments of connectivity and population structure are warranted to provide information for conservation and management. This is particularly true in species with high dispersal capacity, such as migratory whales, where patterns of connectivity could change rapidly. Here we build on a previous long-term, large-scale collaboration on southern right whales (Eubalaena australis) to combine new (n new ) and published (n pub ) mitochondrial (mtDNA) and microsatellite genetic data from all major wintering grounds and, uniquely, the South Georgia (Islas Georgias del Sur: SG) feeding grounds. Specifically, we include data from Argentina (n pub mtDNA/microsatellite = 208/46), Brazil (n new mtDNA/microsatellite = 50/50), South Africa (n new mtDNA/microsatellite = 66/77, n pub mtDNA/microsatellite = 350/47), Chile–Peru (n new mtDNA/microsatellite = 1/1), the Indo-Pacific (n pub mtDNA/microsatellite = 769/126), and SG (n pub mtDNA/microsatellite = 8/0, n new mtDNA/microsatellite = 3/11) to investigate the position of previously unstudied habitats in the migratory network: Brazil, SG, and Chile–Peru. These new genetic data show connectivity between Brazil and Argentina, exemplified by weak genetic differentiation and the movement of 1 genetically identified individual between the South American grounds. The single sample from Chile–Peru had an mtDNA haplotype previously only observed in the Indo-Pacific and had a nuclear genotype that appeared admixed between the Indo-Pacific and South Atlantic, based on genetic clustering and assignment algorithms. The SG samples were clearly South Atlantic and were more similar to the South American than the South African wintering grounds. This study highlights how international collaborations are critical to provide context for emerging or recovering regions, like the SG feeding ground, as well as those that remain critically endangered, such as Chile–Peru. [ABSTRACT FROM AUTHOR]

Demographic compensation arises when vital rates change in opposite directions across populations, buffering the variation in population growth rates, and is a mechanism often invoked to explain the stability of species geographic ranges. However, studies on demographic compensation have disregarded the effects of temporal variation in vital rates and their temporal correlations, despite theoretical evidence that stochastic dynamics can affect population persistence in temporally varying environments. We carried out a seven‐year‐long demographic study on the perennial plant Arabis alpina (L.) across six populations encompassing most of its elevational range. We discovered demographic compensation in the form of negative correlations between the means of plant vital rates, but also between their temporal coefficients of variation, correlations and elasticities. Even if their contribution to demographic compensation was small, this highlights a previously overlooked, but potentially important, role of stochastic processes in stabilising population dynamics at range margins. [ABSTRACT FROM AUTHOR]

To what degree are patterns of genetic structure in fragmented systems the result of contemporary landscape versus history? We examined the distribution of genetic diversity as a function of colonization history and contemporary landscape in four fish species inhabiting a hierarchically fragmented, unaltered system, the Kogaluk drainage (Labrador): lake trout (Salvelinus namaycush), longnose sucker (Catostomus catostomus), round whitefish (Prosopium cylindraceum), and lake chub (Couesius plumbeus). The footprint of colonization history was still observable in the three species where this issue was examined regardless of the generations since their arrival. Approximate Bayesian computation (ABC) analyses suggest colonization took place from the southwest. The species exhibit similar diversity patterns despite different --> N ^ e --> values and generation intervals. Contemporary gene flow was largely negligible except for gene flow from a centrally located lake. These results suggest landscape has driven colonization history, which still has influence on genetic structuring. The species are widespread. Understanding how they behave in the pristine Kogaluk provides a baseline against which to evaluate how other anthropogenically perturbed systems are performing. Improved understanding of historical and contemporary processes is required to fully explain diversity patterns in complex metapopulations. [ABSTRACT FROM AUTHOR]

The recent availability of next-generation sequencing (NGS) has made possible the use of dense genetic markers to identify regions of the genome that may be under the influence of selection. Several statistical methods have been developed recently for this purpose. Here, we present the results of an individual-based simulation study investigating the power and error rate of popular or recent genome scan methods: linear regression, Bayescan, BayEnv and LFMM. Contrary to previous studies, we focus on complex, hierarchical population structure and on polygenic selection. Additionally, we use a false discovery rate (FDR)-based framework, which provides an unified testing framework across frequentist and Bayesian methods. Finally, we investigate the influence of population allele frequencies versus individual genotype data specification for LFMM and the linear regression. The relative ranking between the methods is impacted by the consideration of polygenic selection, compared to a monogenic scenario. For strongly hierarchical scenarios with confounding effects between demography and environmental variables, the power of the methods can be very low. Except for one scenario, Bayescan exhibited moderate power and error rate. BayEnv performance was good under nonhierarchical scenarios, while LFMM provided the best compromise between power and error rate across scenarios. We found that it is possible to greatly reduce error rates by considering the results of all three methods when identifying outlier loci. [ABSTRACT FROM AUTHOR]

Effective population size (N e ) determines the strength of genetic drift and can influence the level of genetic diversity a population can maintain. Assessing how changes in demographic rates associated with environmental variables and management actions affect N e thus can be crucial to the conservation of endangered species. Calculation of N e through demographic models makes it possible to use elasticity analyses to study this issue. The elasticity of N e to a given vital rate is the proportional change in N e associated with a proportional increase in that vital rate. In addition, demographic models can be used to study N e and population growth rate (λ) simultaneously. Simultaneous examination is important because some vital rates differ diametrically in their associations with λ and N e . For example, in some cases increasing these vital rates increases λ and decreases N e . We used elasticity analysis to study the effect of stage-specific survival and flowering rates on N e, annual effective population size (Na), and λ in seven populations of the endangered plant Austrian dragonhead (Dracocephalum austriacum). In populations with λ≥ 1, the elasticities of Ne and Na were similar to those of λ. Survival rates of adults were associated with greater elasticities than survival rates of juveniles, flowering rates, or fecundity. In populations with λ < 1, Ne and Na exhibited greater elasticities to juvenile than to adult vital rates. These patterns are similar to those observed in other species with similar life histories. We did not observe contrasting effects of any vital rate on λ and Ne; thus, management actions that increase the λ of populations of Austrian dragonhead will not increase genetic drift. Our results show that elasticity analyses of Ne and Na can complement elasticity analysis of λ. Moreover, such analyses do not require more data than standard matrix models of population dynamics. [ABSTRACT FROM AUTHOR]