ESTIMATION OF PARAMETERS OF INBREEDING AND GENETIC DRIFT IN POPULATIONS WITH OVERLAPPING GENERATIONS

Many long‐lived plant and animal species have nondiscrete overlapping generations. Although numerous models have been developed to predict the effective sizes (N_e) of populations with overlapping generations, they are extremely difficult to apply to natural populations because of the large array of unknown and elusive life‐table parameters involved. Unfortunately, little work has been done to estimate the N_e of populations with overlapping generations from marker data, in sharp contrast to the situation of populations with discrete generations for which quite a few estimators are available. In this study, we propose an estimator (EPA, estimator by parentage assignments) of the current N_e of populations with overlapping generations, using the sex, age, and multilocus genotype information of a single sample of individuals taken at random from the population. Simulations show that EPA provides unbiased and accurate estimates of N_e under realistic sampling and genotyping effort. Additionally, it yields estimates of other interesting parameters such as generation interval, the variances and covariances of lifetime family size, effective number of breeders of each age class, and life‐table variables. Data from wild populations of baboons and hihi (stitchbird) were analyzed by EPA to demonstrate the use of the estimator in practical sampling and genotyping situations.     

Effective Population Size,Extended Linkage Disequilibrium and Signatures of Selection in the Rare Dog Breed Lundehund

The Lundehund is an old dog breed with remarkable anatomical features including polydactyly in all four limbs and extraordinary flexibility of the spine. We genotyped 28 Lundehund using the canine Illumina high density beadchip to estimate the effective population size (N_e) and inbreeding coefficients as well as to identify potential regions of positive selection. The decay of linkage disequilibrium was slow with r² = 0.95 in 50 kb distance. The last 7-200 generations ago, Ne was at 10-13. An increase of N_e was noted in the very recent generations with a peak value of 19 for N_e at generation 4. The FROH estimated for 50-, 65- and 358-SNP windows were 0.87, 087 and 0.81, respectively. The most likely estimates for F_ROH after removing identical-by-state segments due to linkage disequilibria were at 0.80-0.81. The extreme loss of heterozygosity has been accumulated through continued inbreeding over 200 generations within a probably closed population with a small effective population size. The mean inbreeding coefficient based on pedigree data for the last 11 generations (F_Ped = 0.10) was strongly biased downwards due to the unknown coancestry of the founders in this pedigree data. The long-range haplotype test identified regions with genes involved in processes of immunity, olfaction, woundhealing and neuronal development as potential targets of selection. The genes QSOX2, BMPR1B and PRRX2 as well as MYOM1 are candidates for selection on the Lundehund characteristics small body size, increased number of digits per paw and extraordinary mobility, respectively.     

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.     

The estimates of effective population size based on linkage disequilibrium are virtually unaffected by natural selection

The effective population size (N_e) is a key parameter to quantify the magnitude of genetic drift and inbreeding, with important implications in human evolution. The increasing availability of high-density genetic markers allows the estimation of historical changes in N_e across time using measures of genome diversity or linkage disequilibrium between markers. Directional selection is expected to reduce diversity and N_e, and this reduction is modulated by the heterogeneity of the genome in terms of recombination rate. Here we investigate by computer simulations the consequences of selection (both positive and negative) and recombination rate heterogeneity in the estimation of historical N_e. We also investigate the relationship between diversity parameters and N_e across the different regions of the genome using human marker data. We show that the estimates of historical N_e obtained from linkage disequilibrium between markers (N_eLD) are virtually unaffected by selection. In contrast, those estimates obtained by coalescence mutation-recombination-based methods can be strongly affected by it, which could have important consequences for the estimation of human demography. The simulation results are supported by the analysis of human data. The estimates of N_eLD obtained for particular genomic regions do not correlate, or they do it very weakly, with recombination rate, nucleotide diversity, proportion of polymorphic sites, background selection statistic, minor allele frequency of SNPs, loss of function and missense variants and gene density. This suggests that N_eLD measures mainly reflect demographic changes in population size across generations.     

Making sense of genetic estimates of effective population size

The last decade has seen an explosion of interest in use of genetic markers to estimate effective population size, N_e. Effective population size is important both theoretically (N_e is a key parameter in almost every aspect of evolutionary biology) and for practical application (N_e determines rates of genetic drift and loss of genetic variability and modulates the effectiveness of selection, so it is crucial to consider in conservation). As documented by Palstra & Fraser ( 2012 ), most of the recent growth in N_e estimation can be attributed to development or refinement of methods that can use a single sample of individuals (the older temporal method requires at least two samples separated in time). As with other population genetic methods, performance of new N_e estimators is typically evaluated with simulated data for a few scenarios selected by the author(s). Inevitably, these initial evaluations fail to fully consider the consequences of violating simplifying assumptions, such as discrete generations, closed populations of constant size and selective neutrality. Subsequently, many researchers studying natural or captive populations have reported estimates of N_e for multiple methods; often these estimates are congruent, but that is not always the case. Because true N_e is rarely known in these empirical studies, it is difficult to make sense of the results when estimates differ substantially among methods. What is needed is a rigorous, comparative analysis under realistic scenarios for which true N_e is known. Recently, Gilbert & Whitlock ( 2015 ) did just that for both single‐sample and temporal methods under a wide range of migration schemes. In the current issue of Molecular Ecology, Wang ( 2016 ) uses simulations to evaluate performance of four single‐sample N_e estimators. In addition to assessing effects of true N_e, sample size, and number of loci, Wang also evaluated performance under changing abundance, physical linkage and genotyping errors, as well as for some alternative life histories (high rates of selfing; haplodiploids). Wang showed that the sibship frequency (SF) and linkage disequilibrium (LD) methods perform dramatically better than the heterozygote excess and molecular coancestry methods under most scenarios (see Fig. 1, modified from figure 2 in Wang 2016 ), and he also concluded that SF is generally more versatile than LD. This article represents a truly Herculean effort, and results should be of considerable value to researchers interested in applying these methods to real‐world situations.     

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.     

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.     

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.     

Effective population sizes in cattle,sheep, horses,pigs and goats estimated from census and herdbook data

Accurate measures of effective population sizes (N_e) in livestock require good quality data and specialized skills for their computation and analysis. N_e can be estimated by Wright’s equation N_e=4MF/(M+ F) (M, F being sires and dams, respectively), but this requires assumptions which are often not met. Total census sizes N_c of livestock breeds are collated globally. This paper investigates whether estimates of N_e can be made from N_c; this would facilitate conservation monitoring. Some N_e methodologies avoid the assumptions of Wright’s equation and permit measurement, rather than estimation, of N_e. Those considered here employ, respectively, linkage disequilibrium (LD) of single-nucleotide polymorphisms (yielding N_e(LD)), and genealogical analysis (rate of increase of inbreeding, DF), yielding N_e(DF). Considering breeds of cattle, sheep, horses, pigs and goats for which N_c and either N_e(LD) or N_e(DF) are known (totals of 203 breeds and 321 breeds, respectively), proportionality has been investigated between N_c and these measures of N_e. N_e(LD) was found to increase with N_c, significantly in sheep and horses, less so in cattle, but not at all in pigs. N_e(DF) was correlated with log₁₀(N_c) in cattle, sheep and horses (53, 56, 43 breeds, respectively). N_e(LD) was correlated in cattle (73 breeds) and pigs (31 breeds) with the log₁₀ transformation of N_e as calculated by Wright’s equation. Further verification and refinement are needed, particularly of census data, but credible predictions of N_e are obtainable by applying the following multipliers to log₁₀(N_c): cattle 17.61, sheep 97.72, horse 70.78. For cattle and pigs, multiplying log₁₀(N_e(Wright)) by, respectively, 40.69 and 60.09, also gives credible predictions. Such census-based estimates of N_e could in principle be generated by non-specialists and are likely to be suited to audits of conservation activity when financial resources or availability of data are limiting. The ratio N_e/N_c varied among species with an overall median value of 0.03, less than a tenth of that typically observed in wild mammals. Characteristics were also investigated of a distinct herdbook-based methodology, namely the development of Wright’s equation to take into account variances of progeny numbers to yield what has been termed here N_e (Hill). Comparison of these values with N_e (Wright) could help to identify breeds with breeding structures conducive or inimical to genetic conservation. However, N_e(Hill) requires breed-specific values for these variances, and this restricts its applicability.     

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.     

A comparison of single‐sample effective size estimators using empirical toad (Bufo calamita) population data: genetic compensation and population size‐genetic diversity correlations

The accuracy and precision of four single‐sample estimators of effective population size, N_e (heterozygote excess, linkage disequilibrium, Bayesian partial likelihood and sibship analysis) were compared using empirical data (microsatellite genotypes) from multiple natterjack toad (Bufo calamita) populations in Britain (n = 16) and elsewhere in Europe (n = 10). Census size data were available for the British populations. Because toads have overlapping generations, all of these methods estimated the number of effective breeders N_b rather than N_e. The heterozygote excess method only provided results, without confidence limits, for nine of the British populations. Linkage disequilibrium gave estimates for 10 British populations, but only six had finite confidence limits. The Bayesian and sibship methods both produced estimates with finite confidence limits for all the populations. Although the Bayesian method was the most precise, on most criteria (insensitivity to locus number, correlation with other effective and census size estimates and correlation with genetic diversity) the sibship method performed best. The results also provided evidence of genetic compensation in natterjack toads, and highlighted how the relationship between effective size and genetic diversity can vary as a function of geographical scale.     

Mature male parr contribution to the effective size of an anadromous Atlantic salmon (Salmo salar) population over 30 years

We describe temporal changes in the genetic composition of a small anadromous Atlantic salmon (Salmo salar) population from South Newfoundland, an area where salmon populations are considered threatened (COSEWIC 2010). We examined the genetic variability (13 microsatellite loci) in 869 out‐migrating smolt and post‐spawning kelt samples, collected from 1985 to 2011 for a total of 22 annual collections and a 30 year span of assigned cohorts. We estimated the annual effective number of breeders (N_b) and the generational effective population size (N_e) through genetic methods and demographically using the adult sex ratio. Comparisons between genetic and demographic estimates show that the adult spawners inadequately explain the observed N_e estimates, suggesting that mature male parr are significantly increasing N_b and N_e over the study period. Spawning as parr appears to be a viable and important strategy in the near absence of adult males.     

Small localized breeding populations in a widely distributed coastal shark species

Identifying the geographical scale at which natural populations structure themselves is essential for conservation. One way to gauge this structure is by estimating local effective population size (N_e) and the associated measure of effective number of breeders (N_b), as the smaller and more isolated natural populations are, the smaller N_e and N_b they will present. However, as N_e and N_b are greatly influenced by demographic events and by both species’ behavior and biology, assessing the effectiveness of sample design is necessary to ensure the reliability of said estimates. Here, we first test the sample size effect on yearly N_b and generational N_e estimates from a lemon shark Negaprion brevirostris nursery in Bimini (The Bahamas) and subsequently compare these parameters to estimates of the minimal number of breeders based on pedigree reconstruction. We found that yearly estimates of N_b are positively correlated to annual variations in number of breeders estimated via pedigree reconstructions. Moreover, we measured that 30 individuals from a single cohort were sufficient to obtain reliable yearly estimates of N_b in Bimini’s lemon sharks. We then estimated generational N_e in 10 lemon shark nurseries across the Western Atlantic. Almost every nursery sampled represents an independent population on a generational time scale, with N_e rarely higher than 100 individuals. Our study reveals strong local population structure in lemon sharks, and thus their exposure to localized depletion or extirpation, suggesting that studies of coastal shark nursery areas could routinely estimate N_e and N_b to obtain management-relevant information on adult populations.

.

     

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.     

Multiple estimates of effective population size for monitoring a long‐lived vertebrate: an application to Yellowstone grizzly bears

Effective population size (N_e) is a key parameter for monitoring the genetic health of threatened populations because it reflects a population's evolutionary potential and risk of extinction due to genetic stochasticity. However, its application to wildlife monitoring has been limited because it is difficult to measure in natural populations. The isolated and well‐studied population of grizzly bears (Ursus arctos) in the Greater Yellowstone Ecosystem provides a rare opportunity to examine the usefulness of different N_e estimators for monitoring. We genotyped 729 Yellowstone grizzly bears using 20 microsatellites and applied three single‐sample estimators to examine contemporary trends in generation interval (GI), effective number of breeders (N_b) and N_e during 1982–2007. We also used multisample methods to estimate variance (N_eV) and inbreeding N_e (N_eI). Single‐sample estimates revealed positive trajectories, with over a fourfold increase in N_e (≈100 to 450) and near doubling of the GI (≈8 to 14) from the 1980s to 2000s. N_eV (240–319) and N_eI (256) were comparable with the harmonic mean single‐sample N_e (213) over the time period. Reanalysing historical data, we found N_eV increased from ≈80 in the 1910s–1960s to ≈280 in the contemporary population. The estimated ratio of effective to total census size (N_e/N_c) was stable and high (0.42–0.66) compared to previous brown bear studies. These results support independent demographic evidence for Yellowstone grizzly bear population growth since the 1980s. They further demonstrate how genetic monitoring of N_e can complement demographic‐based monitoring of N_c and vital rates, providing a valuable tool for wildlife managers.     

INTERMITTENT BREEDING AND CONSTRAINTS ON LITTER SIZE: CONSEQUENCES FOR EFFECTIVE POPULATION SIZE PER GENERATION (Ne) AND PER REPRODUCTIVE CYCLE (Nb)

In iteroparous species, it is easier to estimate N_b (effective number of breeders in one reproductive cycle) than N_e (effective population size per generation). N_b can be used as a proxy for N_e and also can provide crucial insights into eco‐evolutionary processes that occur during reproduction. We used analytical and numerical methods to evaluate effects of intermittent breeding and litter/clutch size on inbreeding N_b and N_e. Fixed or random litter sizes ≥ 3 have little effect on either effective‐size parameter; however, in species (e.g., many large mammals) in which females can produce only one offspring per cycle, female N_b = ∞ and overall N_b = 4N_b(male). Intermittent breeding reduces the pool of female breeders, which reduces both female and overall N_b; reductions are larger in high‐fecundity species with high juvenile mortality and increase when multiple reproductive cycles are skipped. Simulated data for six model species showed that both intermittent breeding and litter‐size constraints increase N_e, but only slightly. We show how to quantitatively account for these effects, which are important to consider when (1) using N_b to estimate N_e, or (2) drawing inferences about male reproductive success based on estimates of female N_b.     

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.     

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.     

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.     

Effective population size does not predict codon usage bias in mammals

Synonymous codons are not used at equal frequency throughout the genome, a phenomenon termed codon usage bias (CUB). It is often assumed that interspecific variation in the intensity of CUB is related to species differences in effective population sizes (N_e), with selection on CUB operating less efficiently in species with small N_e. Here, we specifically ask whether variation in N_e predicts differences in CUB in mammals and report two main findings. First, across 41 mammalian genomes, CUB was not correlated with two indirect proxies of N_e (body mass and generation time), even though there was statistically significant evidence of selection shaping CUB across all species. Interestingly, autosomal genes showed higher codon usage bias compared to X‐linked genes, and high‐recombination genes showed higher codon usage bias compared to low recombination genes, suggesting intraspecific variation in N_e predicts variation in CUB. Second, across six mammalian species with genetic estimates of N_e (human, chimpanzee, rabbit, and three mouse species: Mus musculus, M. domesticus, and M. castaneus), N_e and CUB were weakly and inconsistently correlated. At least in mammals, interspecific divergence in N_e does not strongly predict variation in CUB. One hypothesis is that each species responds to a unique distribution of selection coefficients, confounding any straightforward link between N_e and CUB.