Accounting for missing data in the estimation of contemporary genetic effective population size (Ne)

Theoretical models are often applied to population genetic data sets without fully considering the effect of missing data. Researchers can deal with missing data by removing individuals that have failed to yield genotypes and/or by removing loci that have failed to yield allelic determinations, but despite their best efforts, most data sets still contain some missing data. As a consequence, realized sample size differs among loci, and this poses a problem for unbiased methods that must explicitly account for random sampling error. One commonly used solution for the calculation of contemporary effective population size (N_e) is to calculate the effective sample size as an unweighted mean or harmonic mean across loci. This is not ideal because it fails to account for the fact that loci with different numbers of alleles have different information content. Here we consider this problem for genetic estimators of contemporary effective population size (N_e). To evaluate bias and precision of several statistical approaches for dealing with missing data, we simulated populations with known N_e and various degrees of missing data. Across all scenarios, one method of correcting for missing data (fixed‐inverse variance‐weighted harmonic mean) consistently performed the best for both single‐sample and two‐sample (temporal) methods of estimating N_e and outperformed some methods currently in widespread use. The approach adopted here may be a starting point to adjust other population genetics methods that include per‐locus sample size components.     

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.     

A comparison of breeding population estimators using nest and brood monitoring data

For many species, breeding population size is an important metric for assessing population status. A variety of simple methods are often used to estimate this metric for ground‐nesting birds that nest in open habitats (e.g., beaches, riverine sandbars). The error and bias associated with estimates derived using these methods vary in relation to differing monitoring intensities and detection rates. However, these errors and biases are often difficult to obtain, poorly understood, and largely unreported. A method was developed to estimate the number of breeding pairs using counts of nests and broods from monitoring data where multiple surveys were made throughout a single breeding season (breeding pair estimator; BPE). The BPE method was compared to two commonly used estimation methods using simulated data from an individual‐based model that allowed for the comparison of biases and accuracy. The BPE method underestimated the number of breeding pairs, but generally performed better than the other two commonly used methods when detection rates were low and monitoring frequency was high. As detection rates and time between surveys increased, the maximum nest and brood count method performs similar to the BPE. The BPE was compared to four commonly used methods to estimate breeding pairs for empirically derived data sets on the Platte River. Based on our simulated data, we expect our BPE to be closest to the true number of breeding pairs as compared to other methods. The methods tested resulted in substantially different estimates of the numbers of breeding pairs; however, coefficients from trend analyses were not statistically different. When data from multiple nest and brood surveys are available, the BPE appears to result in reasonably precise estimates of numbers of breeding pairs. Regardless of the estimation method, investigators are encouraged to acknowledge whether the method employed is likely to over‐ or underestimate breeding pairs. This study provides a means to recognize the potential biases in breeding pair estimates.     

Estimation of migration rates from marker‐based parentage analysis

Coupled with rapid developments of efficient genetic markers, powerful population genetic methods were proposed to estimate migration rates (m) in natural populations in much broader spatial and temporal scales than the traditional mark‐release‐recapture (MRR) methods. Highly polymorphic (e.g. microsatellites) and genomic‐wide (e.g. SNPs) markers provide sufficient information to assign individuals to their populations or parents of origin and thereby to estimate directly m in a way similar to MRR. Such direct estimates of current migration rates are particularly useful in understanding the ecology and microevolution of wild populations and in managing the populations in the future. In this study, I proposed and implemented, in the software MigEst, a likelihood method to use marker‐based parentage assignments in jointly estimating m and candidate parent sampling proportions (x) in a subset of populations, investigated its power and accuracy using data simulated in various scenarios of population properties (e.g. the actual m, number, size and differentiation of populations) and sampling properties (e.g. the numbers of sampled parent candidates, offspring and markers), compared it with the population assignment approach implemented in the software BayesAss and demonstrated its usefulness by analysing a microsatellite data set from three natural populations of Brazilian bats. Simulations showed that MigEst provides unbiased and accurate estimates of m and performs better than BayesAss except when populations are highly differentiated with very small and ecologically insignificant migration rates. A valuable property of MigEst is that in the presence of unsampled populations, it gives good estimates of the rate of migration among sampled populations as well as of the rate of migration into each sampled population from the pooled unsampled populations.     

A potential bias in the temporal method for estimating Ne in admixed populations under natural selection

Indirect genetic methods are frequently used to estimate the effective population size (N(e)) or effective number of breeders (N(b)) in natural populations. Although assumptions behind these methods are often violated, there have been few attempts to evaluate how accurate these estimates really are in practice. Here we investigate the influence of natural selection following a population admixture on the temporal method for estimating N(e). Our analytical and simulation results suggest that N(e) is often underestimated in this method when subpopulations differ substantially in allele frequencies and in reproductive success. The underestimation is exacerbated when true N(e) and the fraction of the low-fitness group are large. As an empirical example, we compared N(b) estimated in natural populations of steelhead trout (Oncorhynchus mykiss) using the temporal method (N(b[temp])) with estimates based on direct demographic methods (N(b[demo])) and the linkage disequilibrium method (N(b[LD])). While N(b[LD]) was generally in close agreement with N(b[demo]), N(b[temp]) was much lower in sample sets that were dominated by nonlocal hatchery fish with low reproductive success, as predicted by the analytical results. This bias in the temporal method, which arises when genes associated with a particular group of parents are selected against in the offspring sample, has not been widely appreciated before. Such situations may be particularly common when artificial propagation or translocations are used for conservation. The linkage disequilibrium method, which requires data from only one sample, is robust to this type of bias, although it can be affected by other factors.     

Smoothing population size estimates for time-stratified mark-recapture experiments using Bayesian P-splines

Petersen-type mark-recapture experiments are often used to estimate the number of fish or other animals in a population moving along a set migration route. A first sample of individuals is captured at one location, marked, and returned to the population. A second sample is then captured farther along the route, and inferences are derived from the numbers of marked and unmarked fish found in this second sample. Data from such experiments are often stratified by time (day or week) to allow for possible changes in the capture probabilities, and previous methods of analysis fail to take advantage of the temporal relationships in the stratified data. We present a Bayesian, semiparametric method that explicitly models the expected number of fish in each stratum as a smooth function of time. Results from the analysis of historical data from the migration of young Atlantic salmon (Salmo salar) along the Conne River, Newfoundland, and from a simulation study indicate that the new method provides more precise estimates of the population size and more accurate estimates of uncertainty than the currently available methods.     

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.     

Improving the accuracy of population size estimates for burrow‐nesting seabirds

Seabird numbers can change rapidly as a result of environmental processes, both natural and anthropogenic. Informed management and conservation of seabirds requires accurate and precise monitoring of population size. However, for burrow‐nesting species this is rarely achieved due to spatial and temporal heterogeneity in burrow occupancy. Here, we describe a novel method for deriving more accurate population size estimates that employs mark‐recapture methods to correct for unknown variation in nest occupancy throughout a breeding season. We apply it to estimate breeding numbers of a colonial, burrowing seabird, the Little Penguin Eudyptula minor, on the Summerland Peninsula, Phillip Island, Australia. Estimates of active burrow numbers during the September 2008 to February 2009 breeding season were adjusted to numbers of breeding birds based on burrow occupation and modelled population demographics at six, fortnightly monitored reference sites. The population was estimated to be 26 100 (95% CI: 21 100–31 100) and 28 400 (23 800–33 000) breeding Penguins in two temporally separated surveys within one breeding season. We demonstrate using simulation that the method is robust to variation in burrow occupancy throughout the breeding season, providing consistent and more accurate estimates of population size. The advantage of using the corrected method is that confidence intervals will include the true population size. Confidence limits widened as burrow occupancy declined, reflecting the increased uncertainty as larger adjustments for low burrow occupancy were required. In contrast, the uncorrected method that uses burrow occupancy alone as a measure of breeding numbers was inconsistent and significantly underestimated population size across much of the breeding season. Although requiring considerably more survey effort, the corrected approach provides a more accurate means for monitoring population changes in colonially breeding animals while collecting demographic data that can help diagnose the drivers of population change.     

Interpreting recruitment limitation in forests

Studies of tree recruitment are many, but they provide few general insights into the role of recruitment limitation for population dynamics. That role depends on the vital rates (transitions) from seed production to sapling stages and on overall population growth. To determine the state of our understanding of recruitment limitation we examined how well we can estimate parameters corresponding to these vital rates. Our two-part analysis consists of (1) a survey of published literature to determine the spatial and temporal scale of sampling that is basis for parameter estimates, and (2) an analysis of extensive data sets to evaluate sampling intensity found in the literature. We find that published studies focus on fine spatial scales, emphasizing large numbers of small samples within a single stand, and tend not to sample multiple stands or variability across landscapes. Where multiple stands are sampled, sampling is often inconsistent. Sampling of seed rain, seed banks, and seedlings typically span <1 yr and rarely last 5 yr. Most studies of seeding establishment and growth consider effects of a single variable and a single life history stage. By examining how parameter estimates are affected by the spatial and temporal extent of sampling we find that few published studies are sufficiently extensive to capture the variability in recruitment stages. Early recruitment stages are especially variable and require samples across multiple years and multiple stands. Ironically, the longest duration data sets are used to estimate mortality rates, which are less variable (in time) than are early life history stages. Because variables that affect recruitment rates interact, studies of these interactions are needed to assess their full impacts. We conclude that greater attention to spatially extensive and longer duration sampling for early life history stages is needed to assess the role of recruitment limitation in forests.     

‘True’ null allele detection in microsatellite loci: a comparison of methods,assessment of difficulties and survey of possible improvements

Null alleles are alleles that for various reasons fail to amplify in a PCR assay. The presence of null alleles in microsatellite data is known to bias the genetic parameter estimates. Thus, efficient detection of null alleles is crucial, but the methods available for indirect null allele detection return inconsistent results. Here, our aim was to compare different methods for null allele detection, to explain their respective performance and to provide improvements. We applied several approaches to identify the ‘true’ null alleles based on the predictions made by five different methods, used either individually or in combination. First, we introduced simulated ‘true’ null alleles into 240 population data sets and applied the methods to measure their success in detecting the simulated null alleles. The single best‐performing method was ML‐NullFreq_frequency. Furthermore, we applied different noise reduction approaches to improve the results. For instance, by combining the results of several methods, we obtained more reliable results than using a single one. Rule‐based classification was applied to identify population properties linked to the false discovery rate. Rules obtained from the classifier described which population genetic estimates and loci characteristics were linked to the success of each method. We have shown that by simulating ‘true’ null alleles into a population data set, we may define a null allele frequency threshold, related to a desired true or false discovery rate. Moreover, using such simulated data sets, the expected null allele homozygote frequency may be estimated independently of the equilibrium state of the population.     

Spatial data replacing temporal data in population viability analyses: An empirical investigation for plants

In conservation management, there is an urgent need for estimates of population viability and for knowledge of the contributions of different life-history stages to population growth rates. Collection of long-term demographic data from a study population is time-consuming and may considerably delay the start of proper management actions. We examined the possibility of replacing a long-term temporal data set (demographic data from several years within a population) with a short-term spatial data set (demographic data from different populations for the same subset of two continuous years) for stochastic estimates of population viability. Using matrix population models for ten perennial plant species, we found that the matrix elements of spatial data sets often deviated from those of temporal data sets and that matrix elements generally varied more spatially than temporally. The appropriateness of replacing temporal data with spatial data depended on the subset of years and populations used to estimate stochastic population growth rates (log λ_s). Still, the precision of log λ_s estimates measured as variation in the yearly change of logarithmic population size rarely differed significantly between the spatial and temporal data sets. Since a spatiotemporal comparison of matrix elements and their variation cannot be used to assess whether spatial and temporal data sets are interchangeable, we recommend further research on the topic.     

Estimations of linkage disequilibrium,effective population size and ROH‐based inbreeding coefficients in Spanish Churra sheep using imputed high‐density SNP genotypes

In this study, the availability of the Ovine HD SNP BeadChip (HD‐chip) and the development of an imputation strategy provided an opportunity to further investigate the extent of linkage disequilibrium (LD) at short distances in the genome of the Spanish Churra dairy sheep breed. A population of 1686 animals, including 16 rams and their half‐sib daughters, previously genotyped for the 50K‐chip, was imputed to the HD‐chip density based on a reference population of 335 individuals. After assessing the imputation accuracy for beagle v4.0 (0.922) and fimpute v2.2 (0.921) using a cross‐validation approach, the imputed HD‐chip genotypes obtained with beagle were used to update the estimates of LD and effective population size for the studied population. The imputed genotypes were also used to assess the degree of homozygosity by calculating runs of homozygosity and to obtain genomic‐based inbreeding coefficients. The updated LD estimations provided evidence that the extent of LD in Churra sheep is even shorter than that reported based on the 50K‐chip and is one of the shortest extents compared with other sheep breeds. Through different comparisons we have also assessed the impact of imputation on LD and effective population size estimates. The inbreeding coefficient, considering the total length of the run of homozygosity, showed an average estimate (0.0404) lower than the critical level. Overall, the improved accuracy of the updated LD estimates suggests that the HD‐chip, combined with an imputation strategy, offers a powerful tool that will increase the opportunities to identify genuine marker‐phenotype associations and to successfully implement genomic selection in Churra sheep.     

Dealing with uncertainty in landscape genetic resistance models: a case of three co‐occurring marsupials

Landscape genetics lacks explicit methods for dealing with the uncertainty in landscape resistance estimation, which is particularly problematic when sample sizes of individuals are small. Unless uncertainty can be quantified, valuable but small data sets may be rendered unusable for conservation purposes. We offer a method to quantify uncertainty in landscape resistance estimates using multimodel inference as an improvement over single model‐based inference. We illustrate the approach empirically using co‐occurring, woodland‐preferring Australian marsupials within a common study area: two arboreal gliders (Petaurus breviceps, and Petaurus norfolcensis) and one ground‐dwelling antechinus (Antechinus flavipes). First, we use maximum‐likelihood and a bootstrap procedure to identify the best‐supported isolation‐by‐resistance model out of 56 models defined by linear and non‐linear resistance functions. We then quantify uncertainty in resistance estimates by examining parameter selection probabilities from the bootstrapped data. The selection probabilities provide estimates of uncertainty in the parameters that drive the relationships between landscape features and resistance. We then validate our method for quantifying uncertainty using simulated genetic and landscape data showing that for most parameter combinations it provides sensible estimates of uncertainty. We conclude that small data sets can be informative in landscape genetic analyses provided uncertainty can be explicitly quantified. Being explicit about uncertainty in landscape genetic models will make results more interpretable and useful for conservation decision‐making, where dealing with uncertainty is critical.     

Genetic diversity and effective size of the Atlantic salmon Salmo salar L. inhabiting the River Eo (Spain) following a stock collapse

Historic angling records suggest the occurrence of a drastic decline in the River Eo (Spain) Atlantic salmon population size during the past two decades, as a result of overexploitation and habitat deterioration. In recent years, the population has been apparently recovering, and the present study is aimed to report information on the level of genetic diversity and the effective size of the current population as these may have immediate consequences for its conservation. Eighty-six salmon from two temporal groups (1998–1999 and 2004–2006), representing three generations, were genotyped using a panel of eight microsatellites. Inspite of the recent decline in census numbers and the detection of the signs of a population bottleneck, the population exhibits a high level of genetic diversity, similar to that from other populations, and almost unchanged during the period of study [average allelic richness ( A ) = 14·0 and 13·9, and average heterozygosity ( H _e) = 0·843 and 0·851 in 1998–1999 and 2004–2006, respectively]. The effective population size ( N _e) estimated by two different temporal methods showed a consistent value around 80 salmon, whereas the estimates from the linkage disequilibrium (LD) method provided a value around 165 individuals for either sample. The recent growing number of salmon, as indicated by fisheries records, the relatively large estimates of the ratio N _e/ N (with range 0·23–0·44 for the temporal estimates and 0·31–0·59 for the LD estimates) and the high levels of diversity found suggest that the population has not been greatly affected by the historical census declines and can be expected to recover in the future.     

Modeling the Spatial and Temporal Dependence in fMRI Data

Summary Functional magnetic resonance imaging (fMRI) data sets are large and characterized by complex dependence structures driven by highly sophisticated neurophysiology and aspects of the experimental designs. Typical analyses investigating task‐related changes in measured brain activity use a two‐stage procedure in which the first stage involves subject‐specific models and the second‐stage specifies group (or population) level parameters. Customarily, the first‐level accounts for temporal correlations between the serial scans acquired during one scanning session. Despite accounting for these correlations, fMRI studies often include multiple sessions and temporal dependencies may persist between the corresponding estimates of mean neural activity. Further, spatial correlations between brain activity measurements in different locations are often unaccounted for in statistical modeling and estimation. We propose a two‐stage, spatio‐temporal, autoregressive model that simultaneously accounts for spatial dependencies between voxels within the same anatomical region and for temporal dependencies between a subject's estimates from multiple sessions. We develop an algorithm that leverages the special structure of our covariance model, enabling relatively fast and efficient estimation. Using our proposed method, we analyze fMRI data from a study of inhibitory control in cocaine addicts.     

Conducting animal censuses amongst abrupt topography; a GIS‐based alternative to Distance

Management often bases decisions on estimates of animal density and population size. Aerial sampling is expensive whilst current ground methods, noticeably Distance sampling, assume a single detection function for each habitat and that visibility in a given habitat type declines in a smooth, increasing manner with distance from the observer. If the visibility within a habitat varies widely or is suddenly cut off, as amongst abrupt topography, these assumptions are invalidated. We present an affordable, accessible and accurate method for conducting repeated annual censuses on large herbivores in such terrain. We used a GIS habitat‐based approach. Monthly, over a two year period, we conducted repeated road transects in Ithala Game Reserve, South Africa, using GPS to record the geographic position of each sighting. These records (n = 8742) were then imported into a GIS and overlaid onto an existing habitat‐type GIS layer. With the sampled area thus defined as an irregular polygon in the GIS encompassing all records, we calculated densities of each herbivore species by habitat type and extrapolated total population size estimates. Estimates of maximum population size for wildebeest and zebra correlated (±15%) with management's estimates based on aerial surveys, walked transects and experience. White rhino are individually counted in the reserve and our estimates (year 1: 52; year 2: 57) matched these known numbers (year 1: 50; year 2: 53). Our method also yielded realistic numbers for impala, but unrealistic numbers for kudu and warthog. Our GIS‐based census technique produced realistic maximum population estimates for abundant grazing and mixed‐feeder mesoherbivores and for scarce, but highly visible, megaherbivores, but not for cryptic species. With the increasing availability of GIS data, the technique is recommended to those working in abrupt terrain frustrated by the inability of current ground census techniques, principally Distance sampling, to produce realistic population estimates.     

metapop2: Re‐implementation of software for the analysis and management of subdivided populations using gene and allelic diversity

Management programmes often have to make decisions based on the analysis of the genetic properties and diversity of populations. Expected heterozygosity (or gene diversity) and population structure parameters are often used to make recommendations for conservation, such as avoidance of inbreeding or migration across subpopulations. Allelic diversity, however, can also provide complementary and useful information for conservation programmes, as it is highly sensitive to population bottlenecks, and is more related to long‐term selection response than heterozygosity. Here we present a completely revised and updated re‐implementation of the software metapop for the analysis of diversity in subdivided populations, as well as a tool for the management and dynamic estimation of optimal contributions in conservation programmes. This new update includes computation of allelic diversity for population analysis and management, as well as a simulation mode to forecast the consequences of taking different management strategies over time. Furthermore, the new implementation in C++ includes code optimization and improved memory usage, allowing for fast analysis of large data sets including single nucleotide polymorphism markers, as well as enhanced cross‐software and cross‐platform compatibility.     

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

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

Body size estimation of small‐bodied humans: Applicability of current methods

Body size (stature and mass) estimates are integral to understanding the lifeways of past populations.Body size estimation of an archaeological skeletal sample can be problematic when the body size or proportions of the population are distinctive. One such population is that of the Holocene Later Stone Age (LSA) of southern Africa, in which small stature (mean femoral length = 407 mm, n = 52) and narrow pelves (mean bi‐iliac breadth = 210 mm, n = 50) produce a distinctive adult body size/shape, making it difficult to identify appropriate body size estimation methods. Material culture, morphology, and culture history link the Later Stone Age people with the descendant population collectively known as the Khoe‐San. Stature estimates based on skeletal “anatomical” linear measures (the Fully method) and on long bone length are compared, along with body mass estimates derived from “morphometric” (bi‐iliac breath/stature) and “biomechanical” (femoral head diameter) methods, in a LSA adult skeletal sample (n = 52) from the from coastal and near‐coastal regions of South Africa. Indices of sexual dimorphism (ISD) for each method are compared with data from living populations. Fully anatomical stature is most congruent with Olivier's femur + tibia method, although both produce low ISD. McHenry's femoral head body mass formula produces estimates most consistent with the bi‐iliac breadth/staturemethod for the females, although the males display higher degrees of disagreement among methods. These results highlight the need for formulae derived from reference samples from a wider range of body sizes to improve the reliability of existing methods. Am J Phys Anthropol, 2010. © 2009 Wiley‐Liss, Inc.