Detecting past population bottlenecks using temporal genetic data

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

Mid‐Holocene decline in African buffalos inferred from Bayesian coalescent‐based analyses of microsatellites and mitochondrial DNA

Genetic studies concerned with the demographic history of wildlife species can help elucidate the role of climate change and other forces such as human activity in shaping patterns of divergence and distribution. The African buffalo (Syncerus caffer) declined dramatically during the rinderpest pandemic in the late 1800s, but little is known about the earlier demographic history of the species. We analysed genetic variation at 17 microsatellite loci and a 302‐bp fragment of the mitochondrial DNA control region to infer past demographic changes in buffalo populations from East Africa. Two Bayesian coalescent‐based methods as well as traditional bottleneck tests were applied to infer detailed dynamics in buffalo demographic history. No clear genetic signature of population declines related to the rinderpest pandemic could be detected. However, Bayesian coalescent modelling detected a strong signal of African buffalo population declines in the order of 75–98%, starting in the mid‐Holocene (approximately 3–7000 years ago). The signature of decline was remarkably consistent using two different coalescent‐based methods and two types of molecular markers. Exploratory analyses involving various prior assumptions did not seriously affect the magnitude or timing of the inferred population decline. Climate data show that tropical Africa experienced a pronounced transition to a drier climate approximately 4500 years ago, concurrent with the buffalo decline. We therefore propose that the mid‐Holocene aridification of East Africa caused a major decline in the effective population size of the buffalo, a species reliant on moist savannah habitat for its existence.     

Estimating divergence times from DNA sequences

The patterns of genetic variation within and among individuals and populations can be used to make inferences about the evolutionary forces that generated those patterns. Numerous population genetic approaches have been developed in order to infer evolutionary history. Here, we present the “Two-Two (TT)” and the “Two-Two-outgroup (TTo)” methods; two closely related approaches for estimating divergence time based in coalescent theory. They rely on sequence data from two haploid genomes (or a single diploid individual) from each of two populations. Under a simple population-divergence model, we derive the probabilities of the possible sample configurations. These probabilities form a set of equations that can be solved to obtain estimates of the model parameters, including population split times, directly from the sequence data. This transparent and computationally efficient approach to infer population divergence time makes it possible to estimate time scaled in generations (assuming a mutation rate), and not as a compound parameter of genetic drift. Using simulations under a range of demographic scenarios, we show that the method is relatively robust to migration and that the TTo method can alleviate biases that can appear from drastic ancestral population size changes. We illustrate the utility of the approaches with some examples, including estimating split times for pairs of human populations as well as providing further evidence for the complex relationship among Neandertals and Denisovans and their ancestors.     

Combining genetic and ecological data to assess the conservation status of the endangered Ethiopian walia ibex

Knowledge about the phylogenetic history, genetic variation and ecological requirements of a species is important for its conservation and management. Unfortunately, for many species this information is lacking. Here we use multiple approaches (phylogenetics, population genetics and ecological modelling) to evaluate the evolutionary history and conservation status of Capra walie , an endangered flagship species of wild goat endemic to Ethiopia. The analysis of mitochondrial cytochrome b and Y-chromosome DNA sequences suggests that C. walie forms a monophyletic clade with Capra nubiana , but potentially has been isolated for up to 0.8 million years from this closely related species. Microsatellite DNA analyses show that C. walie has very low genetic variation (mean heterozygosity=0.35) compared with other endangered mammals. This reduced variation likely derives from a prolonged demographic decline and small effective population size. Ecological niche modelling using the bioclimatic features of habitats occupied by C. walie , suggests ecological differences between C. walie and C. nubiana , and identifies the areas most suitable for future reintroductions of C. walie . The genetic and bioclimatic data suggest that C. walie is distinct and requires immediate conservation actions including genetic monitoring and reintroductions to establish independent populations. This study illustrates how combining noninvasive sampling along with genetic and ecological (bioclimatic) approaches can help assess conservation status of poorly known species.     

What can genetics tell us about population connectivity?

Genetic data are often used to assess ‘population connectivity’ because it is difficult to measure dispersal directly at large spatial scales. Genetic connectivity, however, depends primarily on the absolute number of dispersers among populations, whereas demographic connectivity depends on the relative contributions to population growth rates of dispersal vs. local recruitment (i.e. survival and reproduction of residents). Although many questions are best answered with data on genetic connectivity, genetic data alone provide little information on demographic connectivity. The importance of demographic connectivity is clear when the elimination of immigration results in a shift from stable or positive population growth to negative population growth. Otherwise, the amount of dispersal required for demographic connectivity depends on the context (e.g. conservation or harvest management), and even high dispersal rates may not indicate demographic interdependence. Therefore, it is risky to infer the importance of demographic connectivity without information on local demographic rates and how those rates vary over time. Genetic methods can provide insight on demographic connectivity when combined with these local demographic rates, data on movement behaviour, or estimates of reproductive success of immigrants and residents. We also consider the strengths and limitations of genetic measures of connectivity and discuss three concepts of genetic connectivity that depend upon the evolutionary criteria of interest: inbreeding connectivity, drift connectivity, and adaptive connectivity. To conclude, we describe alternative approaches for assessing population connectivity, highlighting the value of combining genetic data with capture‐mark‐recapture methods or other direct measures of movement to elucidate the complex role of dispersal in natural populations.     

唐家河大熊猫种群生存力分析

唐家河大熊猫是一个包括3个亚种群的异质种群,借助于游涡模型（vortex 8.21),对唐家河大熊猫未来100a内的种群动态动物了模拟,并分析了不同因子对该种群命运的影响,结果表明,在不考虑近亲繁殖,灾害等因素的情况下,该种群100a内在总体上保持稳定,并略有增长,但种群基因杂合率下降,累积绝灭率增加,尤以薄衣沟亚种群为最,提高环境容纳量,补充外来个体等措施能在不同程度上有利于该种群的长期存活,而近亲繁殖,灾害等因素则大大加速了种群的灭绝步伐,另外,成功的迁移扩散有利于异质种群的稳定与发展,否则对数量稀少的大熊猫种群有害无益,最后提出了针对性的保护与管理建议。     

Historical demography and genetic structure of sister species: deermice (Peromyscus) in the North American temperate rain forest

To investigate the evolutionary and biogeographical history of Peromyscus keeni and P. maniculatus within the coastal forest ecosystem of the Pacific Northwest of North America, we sampled 128 individuals from 43 localities from southeastern Alaska through Oregon. We analysed mitochondrial DNA variation using DNA sequence data from the mitochondrial cytochrome-b (cyt-b) gene and control region, and we found two distinct clades consistent with the morphological designation of the two species. The sequence divergence between the two clades was 0.0484 substitutions per site for cyt-b and 0.0396 for the control region, suggesting that divergence of the two clades occurred during the middle to late Pleistocene. We also examined the historical demography of the two clades using stepwise and exponential expansion models, both of which indicated recent rapid population growth. Furthermore, using the program migrate we found evidence of migration from populations north of the Fraser River (British Columbia) to the south in both clades. This study demonstrates the utility of these model-based demographic methods in illuminating the evolutionary and biogegographic history of natural systems.     

Can effective population size estimates be used to monitor population trends of woodland bats? A case study of Myotis bechsteinii

Molecular approaches to calculate effective population size estimates (Ne) are increasingly used as an alternative to long‐term demographic monitoring of wildlife populations. However, the complex ecology of most long‐lived species and the consequent uncertainties in model assumptions means that effective population size estimates are often imprecise. Although methods exist to incorporate age structure into Ne estimations for long‐lived species with overlapping generations, they are rarely used owing to the lack of relevant information for most wild populations. Here, we performed a case study on an elusive woodland bat, Myotis bechsteinii, to compare the use of the parentage assignment Ne estimator (EPA) with the more commonly used linkage disequilibrium (LD) Ne estimator in detecting long‐term population trends, and assessed the impacts of deploying different overall sample sizes. We used genotypic data from a previously published study, and simulated 48 contrasting demographic scenarios over 150 years using the life history characteristics of this species The LD method strongly outperformed the EPA method. As expected, smaller sample sizes resulted in a reduced ability to detect population trends. Nevertheless, even the smallest sample size tested (n = 30) could detect important changes (60%–80% decline) with the LD method. These results demonstrate that genetic approaches can be an effective way to monitor long‐lived species, such as bats, provided that they are undertaken over multiple decades.     

Genomic diversity,population structure,and migration following rapid range expansion in the Balsam Poplar,Populus balsamifera

Rapid range expansions can cause pervasive changes in the genetic diversity and structure of populations. The postglacial history of the Balsam Poplar, Populus balsamifera, involved the colonization of most of northern North America, an area largely covered by continental ice sheets during the last glacial maximum. To characterize how this expansion shaped genomic diversity within and among populations, we developed 412 SNP markers that we assayed for a range‐wide sample of 474 individuals sampled from 34 populations. We complemented the SNP data set with DNA sequence data from 11 nuclear loci from 94 individuals, and used coalescent analyses to estimate historical population size, demographic growth, and patterns of migration. Bayesian clustering identified three geographically separated demes found in the Northern, Central, and Eastern portions of the species’ range. These demes varied significantly in nucleotide diversity, the abundance of private polymorphisms, and population substructure. Most measures supported the Central deme as descended from the primary refuge of diversity. Both SNPs and sequence data suggested recent population growth, and coalescent analyses of historical migration suggested a massive expansion from the Centre to the North and East. Collectively, these data demonstrate the strong influence that range expansions exert on genomic diversity, both within local populations and across the range. Our results suggest that an in‐depth knowledge of nucleotide diversity following expansion requires sampling within multiple populations, and highlight the utility of combining insights from different data types in population genomic studies.     

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.     

Chloroplast microsatellites reveal colonization and metapopulation dynamics in the Canary Island pine

Chloroplast microsatellites are becoming increasingly popular markers for population genetic studies in plants, but there has been little focus on their potential for demographic inference. In this work the utility of chloroplast microsatellites for the study of population expansions was explored. First, we investigated the power of mismatch distribution analysis and the F(S) test with coalescent simulations of different demographic scenarios. We then applied these methods to empirical data obtained for the Canary Island pine (Pinus canariensis). The results of the simulations showed that chloroplast microsatellites are sensitive to sudden population growth. The power of the F(S) test and accuracy of demographic parameter estimates, such as the time of expansion, were reduced proportionally to the level of homoplasy within the data. The analysis of Canary Island pine chloroplast microsatellite data indicated population expansions for almost all sample localities. Demographic expansions at the island level can be explained by the colonization of the archipelago by the pine, while population expansions of different ages in different localities within an island could be the result of local extinctions and recolonization dynamics. Comparable mitochondrial DNA sequence data from a parasite of P. canariensis, the weevil Brachyderes rugatus, supports this scenario, suggesting a key role for volcanism in the evolution of pine forest communities in the Canary Islands.     

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.     

The mid-depth method and HIV-1: a practical approach for testing hypotheses of viral epidemic history.

We introduce the mid-depth method, a practical approach for testing hypotheses of demographic history using genealogies reconstructed from sequence data. The relative positions of internal nodes within a genealogy contain information about past population dynamics. We explain how this information can be used to (1) test the null hypothesis of constant population size and (2) estimate the growth rate and current population size of an exponentially growing population. Simulation tests indicate that, as expected, estimates of exponential growth rates are sometimes biased. The mid-depth method is computationally rapid and does not require knowledge of the sample's mutation rate. However, it does assume that the reconstructed genealogy is correct and is therefore best suited to the analysis of variation-rich viral data sets. When applied to HIV-1 sequence data, the mid-depth method provides phylogenetic evidence of different exponential growth rates for subtypes A and B. We posit that this difference in growth rate reflects the different transmission routes and epidemiological histories of the two subtypes.     

The incomplete natural history of mitochondria

Mitochondrial DNA (mtDNA) has been used to study molecular ecology and phylogeography for 25 years. Much important information has been gained in this way, but it is time to reflect on the biology of the mitochondrion itself and consider opportunities for evolutionary studies of the organelle itself and its ecology, biochemistry and physiology. This review has four sections. First, we review aspects of the natural history of mitochondria and their DNA to show that it is a unique molecule with specific characteristics that differ from nuclear DNA. We do not attempt to cover the plethora of differences between mitochondrial and nuclear DNA; rather we spotlight differences that can cause significant bias when inferring demographic properties of populations and/or the evolutionary history of species. We focus on recombination, effective population size and mutation rate. Second, we explore some of the difficulties in interpreting phylogeographical data from mtDNA data alone and suggest a broader use of multiple nuclear markers. We argue that mtDNA is not a sufficient marker for phylogeographical studies if the focus of the investigation is the species and not the organelle. We focus on the potential bias caused by introgression. Third, we show that it is not safe to assume a priori that mtDNA evolves as a strictly neutral marker because both direct and indirect selection influence mitochondria. We outline some of the statistical tests of neutrality that can, and should, be applied to mtDNA sequence data prior to making any global statements concerning the history of the organism. We conclude with a critical examination of the neglected biology of mitochondria and point out several surprising gaps in the state of our knowledge about this important organelle. Here we limelight mitochondrial ecology, sexually antagonistic selection, life-history evolution including ageing and disease, and the evolution of mitochondrial inheritance.     

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’.     

Naturally rare versus newly rare: demographic inferences on two timescales inform conservation of Galápagos giant tortoises

Long‐term population history can influence the genetic effects of recent bottlenecks. Therefore, for threatened or endangered species, an understanding of the past is relevant when formulating conservation strategies. Levels of variation at neutral markers have been useful for estimating local effective population sizes (N_e) and inferring whether population sizes increased or decreased over time. Furthermore, analyses of genotypic, allelic frequency, and phylogenetic information can potentially be used to separate historical from recent demographic changes. For 15 populations of Galápagos giant tortoises (Chelonoidis sp.), we used 12 microsatellite loci and DNA sequences from the mitochondrial control region and a nuclear intron, to reconstruct demographic history on shallow (past ~100 generations, ~2500 years) and deep (pre‐Holocene, >10 thousand years ago) timescales. At the deep timescale, three populations showed strong signals of growth, but with different magnitudes and timing, indicating different underlying causes. Furthermore, estimated historical N_e of populations across the archipelago showed no correlation with island age or size, underscoring the complexity of predicting demographic history a priori. At the shallow timescale, all populations carried some signature of a genetic bottleneck, and for 12 populations, point estimates of contemporary N_e were very small (i.e., < 50). On the basis of the comparison of these genetic estimates with published census size data, N_e generally represented ~0.16 of the census size. However, the variance in this ratio across populations was considerable. Overall, our data suggest that idiosyncratic and geographically localized forces shaped the demographic history of tortoise populations. Furthermore, from a conservation perspective, the separation of demographic events occurring on shallow versus deep timescales permits the identification of naturally rare versus newly rare populations; this distinction should facilitate prioritization of management action.     

Evaluating methods for estimating local effective population size with and without migration

Effective population size is a fundamental parameter in population genetics, evolutionary biology, and conservation biology, yet its estimation can be fraught with difficulties. Several methods to estimate N_e from genetic data have been developed that take advantage of various approaches for inferring N_e. The ability of these methods to accurately estimate N_e, however, has not been comprehensively examined. In this study, we employ seven of the most cited methods for estimating N_e from genetic data (Colony2, CoNe, Estim, MLNe, ONeSAMP, TMVP, and NeEstimator including LDNe) across simulated datasets with populations experiencing migration or no migration. The simulated population demographies are an isolated population with no immigration, an island model metapopulation with a sink population receiving immigrants, and an isolation by distance stepping stone model of populations. We find considerable variance in performance of these methods, both within and across demographic scenarios, with some methods performing very poorly. The most accurate estimates of N_e can be obtained by using LDNe, MLNe, or TMVP; however each of these approaches is outperformed by another in a differing demographic scenario. Knowledge of the approximate demography of population as well as the availability of temporal data largely improves N_e estimates.     

Individual heterogeneity in life histories and eco‐evolutionary dynamics

Individual heterogeneity in life history shapes eco‐evolutionary processes, and unobserved heterogeneity can affect demographic outputs characterising life history and population dynamical properties. Demographic frameworks like matrix models or integral projection models represent powerful approaches to disentangle mechanisms linking individual life histories and population‐level processes. Recent developments have provided important steps towards their application to study eco‐evolutionary dynamics, but so far individual heterogeneity has largely been ignored. Here, we present a general demographic framework that incorporates individual heterogeneity in a flexible way, by separating static and dynamic traits (discrete or continuous). First, we apply the framework to derive the consequences of ignoring heterogeneity for a range of widely used demographic outputs. A general conclusion is that besides the long‐term growth rate lambda, all parameters can be affected. Second, we discuss how the framework can help advance current demographic models of eco‐evolutionary dynamics, by incorporating individual heterogeneity. For both applications numerical examples are provided, including an empirical example for pike. For instance, we demonstrate that predicted demographic responses to climate warming can be reversed by increased heritability. We discuss how applications of this demographic framework incorporating individual heterogeneity can help answer key biological questions that require a detailed understanding of eco‐evolutionary dynamics.     

Transient population dynamics impede restoration and may promote ecosystem transformation after disturbance

The apparent failure of ecosystems to recover from increasingly widespread disturbance is a global concern. Despite growing focus on factors inhibiting resilience and restoration, we still know very little about how demographic and population processes influence recovery. Using inverse and forward demographic modelling of 531 post‐fire sagebrush populations across the western US, we show that demographic processes during recovery from seeds do not initially lead to population growth but rather to years of population decline, low density, and risk of extirpation after disturbance and restoration, even at sites with potential to support long‐term, stable populations. Changes in population structure, and resulting transient population dynamics, lead to a > 50% decline in population growth rate after disturbance and significant reductions in population density. Our results indicate that demographic processes influence the recovery of ecosystems from disturbance and that demographic analyses can be used by resource managers to anticipate ecological transformation risk.     

Detecting population expansion and decline using microsatellites

This article considers a demographic model where a population varies in size either linearly or exponentially. The genealogical history of microsatellite data sampled from this population can be described using coalescent theory. A method is presented whereby the posterior probability distribution of the genealogical and demographic parameters can be estimated using Markov chain Monte Carlo simulations. The likelihood surface for the demographic parameters is complicated and its general features are described. The method is then applied to published microsatellite data from two populations. Data from the northern hairy-nosed wombat show strong evidence of decline. Data from European humans show weak evidence of expansion.