Sweepstakes reproductive success is absent in a New Zealand snapper (Chrysophrus auratus) population protected from fishing despite “tiny” Ne/N ratios elsewhere

A landmark study published in 2002 estimated a very small N_e/N ratio (around 10^–5) in a population of pink snapper (Chrysophrys auratus, Forster, 1801) in the Hauraki Gulf in New Zealand. It epitomized the tiny N_e/N ratios (<10^–3) reported in marine species due to the hypothesized operation of sweepstakes reproductive success (SRS). Here we re‐evaluate the occurrence of SRS in marine species and the potential effect of fishing on the N_e/N ratio by studying the same species in the same region, but in a population that has been protected from fishing since 1975. We combine empirical, simulation and model‐based approaches to estimate N_e (and N_b) from genotypes of 1,044 adult fish and estimate N using recapture‐probabilities. The estimated N_e/N ratio was much larger (0.33, SE: 0.14) than expected. The magnitude of estimates of population‐wide variance in individual lifetime reproductive success (10–18) suggested that the sweepstakes effect was negligible in the study population. After evaluating factors that could explain the contrast between studies – experimental design, life history differences, environmental effects and the influence of exploitation on the N_e/N ratio – we conclude that the low N_e of the Hauraki Gulf population is associated with demographic instability in the harvested compared to the protected population despite circumstantial evidence that the 2002 study may have underestimated N_e. This study has broad implications for the prevailing view that reproductive success in the sea is largely driven by chance, and for genetic monitoring of populations using the N_e/N ratio and N_b.     

Simple method for calculating confidence limits around inbreeding rate and effective population size estimates from pedigrees

An important concept in population genetics is effective population size (N_e), which describes the expected rate of loss of genetic variability from a population. One way to estimate N_e is using a pedigree. However, there are no methods for comparing the N_e estimated from a pedigree with that expected from life-history models. In the paper we show how N_e can be estimated from the change in inbreeding rate (f) estimated from a pedigree. The mean individual inbreeding rate in a population at a given time must be calculated from averaged values for males and females, where each age class is weighted by its reproductive value. We show an exact method for placing confidence intervals around f and N_e using a binomial distribution, and present a method for approximating this interval for large N_es using a Poisson distribution. These confidence intervals can be used to compare f and N_e from a pedigree to expected values from demographic models, and to compare N_es of two populations.     

INCREASED PROBABILITY OF EXTINCTION DUE TO DECREASED GENETIC EFFECTIVE POPULATION SIZE: EXPERIMENTAL POPULATIONS OF CLARKIA PULCHELLA

We established replicated experimental populations of the annual plant Clarkia pulchella to evaluate the existence of a causal relationship between loss of genetic variation and population survival probability. Two treatments differing in the relatedness of the founders, and thus in the genetic effective population size (N_e), were maintained as isolated populations in a natural environment. After three generations, the low N_e treatment had significantly lower germination and survival rates than did the high N_e treatment. These lower germination and survival rates led to decreased mean fitness in the low N_e populations: estimated mean fitness in the low N_e populations was only 21% of the estimated mean fitness in the high N_e populations. This inbreeding depression led to a reduction in population survival: at the conclusion of the experiment, 75% of the high N_e populations were still extant, whereas only 31% of the low N_e populations had survived. Decreased genetic effective population size, which leads to both inbreeding and the loss of alleles by genetic drift, increased the probability of population extinction over that expected from demographic and environmental stochasticity alone. This demonstrates that the genetic effective population size can strongly affect the probability of population persistence.     

Effects of population characteristics and structure on estimates of effective population size in a house sparrow metapopulation

Effective population size (N_e) is a key parameter to understand evolutionary processes and the viability of endangered populations as it determines the rate of genetic drift and inbreeding. Low N_e can lead to inbreeding depression and reduced population adaptability. In this study, we estimated contemporary N_e using genetic estimators (LDNE, ONeSAMP, MLNE and CoNe) as well as a demographic estimator in a natural insular house sparrow metapopulation. We investigated whether population characteristics (population size, sex ratio, immigration rate, variance in population size and population growth rate) explained variation within and among populations in the ratio of effective to census population size (N_e/N_c). In general, N_e/N_c ratios increased with immigration rates. Genetic N_e was much larger than demographic N_e, probably due to a greater effect of immigration on genetic than demographic processes in local populations. Moreover, although estimates of genetic N_e seemed to track N_c quite well, the genetic N_e‐estimates were often larger than N_c within populations. Estimates of genetic N_e for the metapopulation were however within the expected range (<N_c). Our results suggest that in fragmented populations, even low levels of gene flow may have important consequences for the interpretation of genetic estimates of N_e. Consequently, further studies are needed to understand how N_e estimated in local populations or the total metapopulation relates to actual rates of genetic drift and inbreeding.     

The influence of variation in female fecundity on effective population size

Understanding the relationship between effective population size (N_e) and the number of adults in a population (N) is important for predicting genetic change in small populations. In general, N_e is expected to be close to N/2, i.e. in the range N/4-3N/4, provided that the powerful effect of population bottlenecks on reducing N_e is factored out (using the harmonic mean of N). However, some very low published estimates of N_e/N(< 0.1) raise the possibility that other factors acting to reduce N_e have been underestimated. Here one such factor, variation in female fecundity, is investigated. Its effect on N_e depends on the standardized variance in fecundity (per breeding season), a measure that is generally independent of mean fecundity. Empirical estimates of this standardized variance from 16 animal studies yielded an average value of 0.44, and a maximum value less than 1.5. To investigate the effect of such values, three kinds of fecundity variation were modelled: random (seasonal): individual; and age-related. Fixed individual differences among females reduce N_e the most. However, to reduce N_e to N/10, the resulting standardized variance must usually be 10 or more. Random differences need to be even larger to achieve the same reduction. One possible mechanism, the random loss of whole families, requires very high family mortality (90% or more). The third model, fecundity that increases linearly with age, is ineffective at causing a marked decrease in N_e. Given the finding that very unusual conditions are required to reduce N_e below N_e/10, low estimates of N_e/N need to be examined critically: the lowest published ratio, for a natural population of oysters, was found to be questionable because of possible immigration into the population by cultivated oysters.     

Genetic substructure and admixture as important factors in linkage disequilibrium‐based estimation of effective number of breeders in recovering wildlife populations

The number of effective breeders (N_b) and effective population size (N_e) are population parameters reflective of evolutionary potential, susceptibility to stochasticity, and viability. We have estimated these parameters using the linkage disequilibrium‐based approach with LDNE through the latest phase of population recovery of the brown bears (Ursus arctos) in Finland (1993–2010; N = 621). This phase of the recovery was recently documented to be associated with major changes in genetic composition. In particular, differentiation between the northern and the southern genetic cluster declined rapidly within 1.5 generations. Based on this, we have studied effects of the changing genetic structure on N_b and N_e, by comparing estimates for whole Finland with the estimates for the two genetic clusters. We expected a potentially strong relationship between estimate sizes and genetic differentiation, which should disappear as the population recovers and clusters merge. Consistent with this, our estimates for whole Finland were lower than the sum of the estimates of the two genetic clusters and both approaches produced similar estimates in the end. Notably, we also found that admixed genotypes strongly increased the estimates. In all analyses, our estimates for N_e were larger than N_b and likely reflective for brown bears of the larger region of Finland and northwestern Russia. Conclusively, we find that neglecting genetic substructure may lead to a massive underestimation of N_b and N_e. Our results also suggest the need for further empirical analysis focusing on individuals with admixed genotypes and their potential high influence on N_b and N_e.     

GENOME SIZE IS NOT CORRELATED WITH EFFECTIVE POPULATION SIZE IN THE ORYZA SPECIES

Genome sizes vary widely across the tree of life and the evolutionary mechanism underlined remains largely unknown. Lynch and Conery (2003) proposed that evolution of genome complexity was driven mainly by nonadaptive stochastic forces and presented the observation that genome size was negatively correlated with effective population size (N_e) as a strong support for their hypothesis. Here, we analyzed the relation between N_e and genome size for 10 diploid Oryza species that showed about fourfold genome size variation. Using sequences of more than 20 nuclear genes, we estimated N_e for each species after correction for the effects of demography and heterogeneity of mutation rates among loci and species. Pairwise comparisons and correlation analyses did not detect a negative relationship between N_e and genome size despite about 6.5‐fold interspecies N_e variation. By calculating phylogenetically independent contrasts (PICs) for N_e, we repeated correlation analysis and did not find any correlation between N_e and genome size. These observations suggest that the genome size variation in the Oryza species cannot be explained simply by the effect of effective population size.     

A ROLE FOR NONADAPTIVE PROCESSES IN PLANT GENOME SIZE EVOLUTION?

Genome sizes vary widely among species, but comprehensive explanations for the emergence of this variation have not been validated. Lynch and Conery (2003) hypothesized that genome expansion is maladaptive, and that lineages with small effective population size (N_e) evolve larger genomes than those with large N_e as a consequence of the lowered efficacy of natural selection in small populations. In addition, mating systems likely affect genome size evolution via effects on both N_e and the spread of transposable elements (TEs). We present a comparative analysis of the effects of N_e and mating system on genome size evolution in seed plants. The dataset includes 205 species with monoploid genome size estimates (corrected for recent polyploidy) ranging from 2Cx = 0.3 to 65.9 pg. The raw data exhibited a strong positive relationship between outcrossing and genome size, a negative relationship between N_e and genome size, but no detectable N_e× outcrossing interaction. In contrast, phylogenetically independent contrast analyses found only a weak relationship between outcrossing and genome size and no relationship between N_e and genome size. Thus, seed plants do not support the Lynch and Conery mechanism of genome size evolution. Further work is needed to disentangle contrasting effects of mating systems on the efficacy of selection and TE transmission.     

Limited gene flow among brown bear populations in far Northern Europe? Genetic analysis of the east–west border population in the Pasvik Valley

Noninvasively collected genetic data can be used to analyse large‐scale connectivity patterns among populations of large predators without disturbing them, which may contribute to unravel the species’ roles in natural ecosystems and their requirements for long‐term survival. The demographic history of brown bears (Ursus arctos) in Northern Europe indicates several extinction and recolonization events, but little is known about present gene flow between populations of the east and west. We used 12 validated microsatellite markers to analyse 1580 hair and faecal samples collected during six consecutive years (2005–2010) in the Pasvik Valley at 70°N on the border of Norway, Finland and Russia. Our results showed an overall high correlation between the annual estimates of population size (N_c), density (D), effective size (N_e) and N_e/N_c ratio. Furthermore, we observed a genetic heterogeneity of ～0.8 and high N_e/N_c ratios of ～0.6, which suggests gene flow from the east. Thus, we expanded the population genetic study to include Karelia (Russia, Finland), Västerbotten (Sweden) and Troms (Norway) (477 individuals in total) and detected four distinct genetic clusters with low migration rates among the regions. More specifically, we found that differentiation was relatively low from the Pasvik Valley towards the south and east, whereas, in contrast, moderately high pairwise F_ST values (0.91–0.12) were detected between the east and the west. Our results indicate ongoing limits to gene flow towards the west, and the existence of barriers to migration between eastern and western brown bear populations in Northern Europe.     

Reconciling Genetic and Demographic Estimators of Effective Population Size in the Anuran Amphibian Bufo Calamita

We used genetic and demographic methods to estimate the variance effective population sizes (N _e) of three populations of natterjack toads Bufo calamita in Britain. This amphibian breeds in temporary pools where survival rates can vary among families. Census population sizes (N) were derived from spawn string counts. Point and coalescent-based maximum likelihood estimates of N _e based on microsatellite allele distributions were similar. N _e/N ratios based on genetic estimates of N _e ranged between 0.02 and 0.20. Mean demographic estimates of N _e were consistently higher (2.7–8.0-fold) than genetic estimates for all three populations when variance in breeding success was evaluated at the point where females no longer influence their progeny. However, discrepancies between genetic and demographic estimators could be removed by using a model that included extra variance in survivorship (above to Poisson expectations) among families. The implications of these results for the estimation of N _e in wild populations are discussed.     

Decomposing demographic contributions to the effective population size with moose as a case study

Levels of random genetic drift are influenced by demographic factors, such as mating system, sex ratio and age structure. The effective population size (N_e) is a useful measure for quantifying genetic drift. Evaluating relative contributions of different demographic factors to N_e is therefore important to identify what makes a population vulnerable to loss of genetic variation. Until recently, models for estimating N_e have required many simplifying assumptions, making them unsuitable for this task. Here, using data from a small, harvested moose population, we demonstrate the use of a stochastic demographic framework allowing for fluctuations in both population size and age distribution to estimate and decompose the total demographic variance and hence the ratio of effective to total population size (N_e/N) into components originating from sex, age, survival and reproduction. We not only show which components contribute most to N_e/N currently, but also which components have the greatest potential for changing N_e/N. In this relatively long‐lived polygynous system we show that N_e/N is most sensitive to the demographic variance of older males, and that both reproductive autocorrelations (i.e., a tendency for the same individuals to be successful several years in a row) and covariance between survival and reproduction contribute to decreasing N_e/N (increasing genetic drift). These conditions are common in nature and can be caused by common hunting strategies. Thus, the framework presented here has great potential to increase our understanding of the demographic processes that contribute to genetic drift and viability of populations, and to inform management decisions.     

Estimating effective population size using RADseq: Effects of SNP selection and sample size

Effective population size (N_e) is a key parameter of population genetics. However, N_e remains challenging to estimate for natural populations as several factors are likely to bias estimates. These factors include sampling design, sequencing method, and data filtering. One issue inherent to the restriction site‐associated DNA sequencing (RADseq) protocol is missing data and SNP selection criteria (e.g., minimum minor allele frequency, number of SNPs). To evaluate the potential impact of SNP selection criteria on N_e estimates (Linkage Disequilibrium method) we used RADseq data for a nonmodel species, the thornback ray. In this data set, the inbreeding coefficient F_IS was positively correlated with the amount of missing data, implying data were missing nonrandomly. The precision of N_eestimates decreased with the number of SNPs. Mean N_e estimates (averaged across 50 random data sets with2000 SNPs) ranged between 237 and 1784. Increasing the percentage of missing data from 25% to 50% increased N_e estimates between 82% and 120%, while increasing the minor allele frequency (MAF) threshold from 0.01 to 0.1 decreased estimates between 71% and 75%. Considering these effects is important when interpreting RADseq data‐derived estimates of effective population size in empirical studies.     

Sexual selection has minimal impact on effective population sizes in species with high rates of random offspring mortality: An empirical demonstration using fitness distributions

The effective population size (N_e) is a fundamental parameter in population genetics that influences the rate of loss of genetic diversity. Sexual selection has the potential to reduce N_e by causing the sex‐specific distributions of individuals that successfully reproduce to diverge. To empirically estimate the effect of sexual selection on N_e, we obtained fitness distributions for males and females from an outbred, laboratory‐adapted population of Drosophila melanogaster. We observed strong sexual selection in this population (the variance in male reproductive success was ～14 times higher than that for females), but found that sexual selection had only a modest effect on N_e, which was 75% of the census size. This occurs because the substantial random offspring mortality in this population diminishes the effects of sexual selection on N_e, a result that necessarily applies to other high fecundity species. The inclusion of this random offspring mortality creates a scaling effect that reduces the variance/mean ratios for male and female reproductive success and causes them to converge. Our results demonstrate that measuring reproductive success without considering offspring mortality can underestimate N_e and overestimate the genetic consequences of sexual selection. Similarly, comparing genetic diversity among different genomic components may fail to detect strong sexual selection.     

Environmental and topographic variables shape genetic structure and effective population sizes in the endangered Yosemite toad

Aim Describing the landscape variables that accurately reflect how environmental and topographic variations affect population connectivity and demography is a major goal of landscape genetics and conservation biology. However, few landscape genetics studies have quantified the relationships between landscape variables and effective population size (N_e), although N_e is a key conservation and population genetics parameter. In this study, I estimated genetic structure and effective population sizes in the Yosemite toad (Bufo canorus) and tested for associations with environmental and geographic variables. Location Yosemite National Park, California, USA. Methods I estimated F_ST, D_ps and N_e using 10 microsatellite loci amplified from 781 individuals from 24 populations. I used three landscape variables (environmental variation, topography and slope) to generate geographic distance models and a series of regression analyses to identify the variables that contributed to genetic structure in this species. I also tested for correlations between N_e and a suite of variables, including geographic and genetic isolation, habitat suitability, elevation, temperature and precipitation. Results I found substantial variation in genetic distances between populations (F_ST = 0.004–0.396, D_ps = 0.045–0.839) and in effective population sizes (N_e = 9–52). Environmental variation and slope played important roles in explaining variation in genetic distances, and precipitation variables were significantly correlated with N_e. Main conclusions These results show that environmental and topographic variables are both important for understanding population connectivity in B. canorus and provide some of the first evidence, in any species, for a link between environmental variables and effective population size.     

When are genetic methods useful for estimating contemporary abundance and detecting population trends?

The utility of microsatellite markers for inferring population size and trend has not been rigorously examined, even though these markers are commonly used to monitor the demography of natural populations. We assessed the ability of a linkage disequilibrium estimator of effective population size (N_e) and a simple capture-recapture estimator of abundance (N) to quantify the size and trend of stable or declining populations (true N = 100–10,000), using simulated Wright–Fisher populations. Neither method accurately or precisely estimated abundance at sample sizes of S = 30 individuals, regardless of true N. However, if larger samples of S = 60 or 120 individuals were collected, these methods provided useful insights into abundance and trends for populations of N = 100–500. At small population sizes (N = 100 or 250), precision of the N_e estimates was improved slightly more by a doubling of loci sampled than by a doubling of individuals sampled. In general, monitoring N_e proved a more robust means of identifying stable and declining populations than monitoring N over most of the parameter space we explored, and performance of the N_e estimator is further enhanced if the N_e/N ratio is low. However, at the largest population size (N = 10,000), N estimation outperformed N_e. Both methods generally required ≥ 5 generations to pass between sampling events to correctly identify population trend.     

Temporal genetic samples indicate small effective population size of the endangered yellow-eyed penguin

There is an increasing awareness that the long-term viability of endemic island populations is negatively affected by genetic factors associated with population bottlenecks and/or persistence at small population size. Here we use contemporary samples and historic museum specimens (collected 1888–1938) to estimate the effective population size (N _e) for the endangered yellow-eyed penguin (Megadyptes antipodes) in South Island, New Zealand, and evaluate the genetic concern for this iconic species. The South Island population of M. antipodes—constituting almost half of the species’ census size—is thought to be descended from a small number of founders that reached New Zealand just a few hundred years ago. Despite intensive conservation measures, this population has shown dramatic fluctuations in size over recent decades. We compare estimates of the harmonic mean N _e for this population, obtained using one moment and three likelihood based-temporal methods, including one method that simultaneously estimates migration rate. Evaluation of the N _e estimates reveals a harmonic mean N _e in the low hundreds. Additionally, the inferred low immigration rates (m = 0.003) agree well with contemporary migration rate estimates between the South Island and subantarctic populations of M. antipodes. The low N _e of South Island M. antipodes is likely affected by strong fluctuations in population size, and high variance in reproductive success. These results show that genetic concerns for this population are valid and that the long-term viability of this species may be compromised by reduced adaptive potential.     

Effective number of breeding adults in Oregon spotted frogs (<Emphasis Type="Italic">Rana</Emphasis><Emphasis Type="Italic">pretiosa</Emphasis>): genetic estimates at two life stages

We used genetic methods to estimate the effective number of breeders (N _b) in a population of Rana pretiosa, an imperiled amphibian in western North America. Microsatellite data was gathered from large samples of adults, eggs, and juveniles collected in 2006. We wished to determine where in the life cycle the greatest reductions in N _b occur, and to compare genetic estimates of N _b to an egg mass count estimate of the number of breeding adults. We predicted that N _b estimated at the metamorph stage would be reduced by increased variance in family size due to egg mass mortality. Contrary to our prediction, estimates of N _b at the egg and metamorph stages were similar. Thus, we found no evidence of inflated variance in family size between the two stages. If our results for this population are typical for R. pretiosa, then increased variance in family size during the egg to metamorph stage may not be a strong factor in reducing the effective population sizes (N _e) relative to the census sizes (N) in this species.     

Temporal-method estimates of Ne from highly polymorphic loci

The temporal method is used widely to estimategenetic effective population size (N _e), a parameter of fundamental interestto studies of evolutionary and conservationbiology. The statistical properties oftemporal-method estimates have not beenexplored for highly polymorphic DNA markersthat often contain many alleles occurring invery low frequencies. We used a Monte Carlosimulation approach to assess accuracy andprecision of the temporal method whenimplemented with haplotypic/allelic data atmitochondrial (mt)DNA and nuclear-encodedmicrosatellite DNA loci. Estimates of N _e were between 2%–106% greater thantheir true values in 48 simulationsparameterized using different demographicscenarios, models of mutation, and samplesizes. Overestimation of N _e resultsfrom a bias in the approximation used by Waples(1989) to derive the relationship between theexpected temporal variance (F) and N _e when allele frequencies are very closeto 0 or 1. Our results show that one commonlyapplied solution to this problem, binning oflow-frequency alleles, results in a trade-offof accuracy and precision in some cases. Weshow that both chi-square and normalapproximations are appropriate for estimating95% confidence intervals of N _e andwe develop a power analysis based on thechi-square distribution to estimate samplesizes and allelic diversity required toevaluate specific hypotheses. For highlypolymorphic loci like mtDNA andmicrosatellites, the increased precisionafforded by the presence of rare allelesoutweighs the upward bias in temporal-methodestimates of N _e.     

Estimating effective population size from linkage disequilibrium: severe bias in small samples

Effective population size (N _e) is a central concept in evolutionary biology and conservation genetics. It predicts rates of loss of neutral genetic variation, fixation of deleterious and favourable alleles, and the increase of inbreeding experienced by a population. A method exists for the estimation of N _e from the observed linkage disequilibrium between unlinked loci in a population sample. While an increasing number of studies have applied this method in natural and managed populations, its reliability has not yet been evaluated. We developed a computer program to calculate this estimator of N _e using the most widely used linkage disequilibrium algorithm and used simulations to show that this estimator is strongly biased when the sample size is small (<‰100) and below the true N _e. This is probably due to the linkage disequilibrium generated by the sampling process itself and the inadequate correction for this phenomenon in the method. Results suggest that N _e estimates derived using this method should be regarded with caution in many cases. To improve the method’s reliability and usefulness we propose a way to determine whether a given sample size exceeds the population N _e and can therefore be used for the computation of an unbiased estimate.     

neogen: A tool to predict genetic effective population size (Ne) for species with generational overlap and to assist empirical Ne study design

Molecular genetic estimates of population effective size (N_e) lose accuracy and precision when insufficient numbers of samples or loci are used. Ideally, researchers would like to forecast the necessary power when planning their project. neogen (genetic N_e for Overlapping Generations) enables estimates of precision and accuracy in advance of empirical investigation and allows exploration of the power available in different user‐specified age‐structured sampling schemes. neogen provides a population simulation and genetic power analysis framework that simulates the demographics, genetic composition, and N_e, from species‐specific life history, mortality, population size, and genetic priors. neogen guides the user to establish a tractable sampling regime and to determine the numbers of samples and microsatellite or SNP loci required for accurate and precise genetic N_e estimates when sampling a natural population. neogen is useful at multiple stages of a study's life cycle: when budgeting, as sampling and locus development progresses, and for corroboration when empirical N_e estimates are available. The underlying model is applicable to a wide variety of iteroparous species with overlapping generations (e.g., mammals, birds, reptiles, long‐lived fishes). In this paper, we describe the neogen model, detail the workflow for the point‐and‐click software, and explain the graphical results. We demonstrate the use of neogen with empirical Australian east coast zebra shark (Stegostoma fasciatum) data. For researchers wishing to make accurate and precise genetic N_e estimates for overlapping generations species, neogen facilitates planning for sample and locus acquisition, and with existing empirical genetic N_e estimates neogen can corroborate population demographic and life history properties.