During the most recent decade many Bayesian statistical models and software for answering questions related to the genetic structure underlying population samples have appeared in the scientific literature. Most of these methods utilize molecular markers for the inferences, while some are also capable of handling DNA sequence data. In a number of earlier works, we have introduced an array of statistical methods for population genetic inference that are implemented in the software BAPS. However, the complexity of biological problems related to genetic structure analysis keeps increasing such that in many cases the current methods may provide either inappropriate or insufficient solutions.a priori specified hypotheses about genetic population structure can be directly compared using Bayes' theorem. In general, we have improved further the computational characteristics of the algorithms behind the methods implemented in BAPS facilitating the analyses of large and complex datasets. In particular, analysis of a single dataset can now be spread over multiple computers using a script interface to the software.We discuss the necessity of enhancing the statistical approaches to face the challenges posed by the ever-increasing amounts of molecular data generated by scientists over a wide range of research areas and introduce an array of new statistical tools implemented in the most recent version of BAPS. With these methods it is possible, e.g., to fit genetic mixture models using user-specified numbers of clusters and to estimate levels of admixture under a genetic linkage model. Also, alleles representing a different ancestry compared to the average observed genomic positions can be tracked for the sampled individuals, and .The Bayesian modelling methods introduced in this article represent an array of enhanced tools for learning the genetic structure of populations. Their implementations in the BAPS software are designed to meet the increasing need for analyzing large-scale population genetics data. The software is freely downloadable for Windows, Linux and Mac OS X systems at  The past decade has provided an upsurge of methods and software enabling Bayesian statistical analyses of the ancestry and the current genetic structure of natural populations using a variety of molecular information sources, such as microsatellite and single-nucleotide polymorphism markers, as well as mitochondrial and house-keeping DNA sequences, to name a few. Reviews of the existing methods (see e.g. -3) illusWe have earlier demonstrated the versatility of enhancing Bayesian computation through incorporation of analytical integration techniques into the stochastic search over the space of putative models, in the current applied context ,5,8, in In most of the subsections of METHODS, we introduce a number of new statistical tools that are implemented in the most recent version of the BAPS software, to further enhance the possibilities of exploring complex patterns associated with the evolution of populations from a genetics perspective. The novel features are then demonstrated through a set of analyses of molecular data sets in the RESULTS section.a priori upper bounds determined by the user. While such an approach is relevant for most applications, there are situations in which it would be more appropriate to use fixed numbers of clusters specified by auxiliary information available. For instance, the sampling scheme might very likely be known to violate assumptions utilized for most genetic clustering methods, namely that the individuals represent fairly unrelated samples from the target population. With closely related samples it may happen that the estimate of the number of clusters is strongly biased, as the assumption of the Hardy-Weinberg equilibrium fails for such data. By fixing the cluster numbers within a certain confidence range, one can avoid spurious inferences about population structure due to genetic relatedness between samples. Also, biological auxiliary information may limit the number of possible ancestral sources of sampled individuals, say, into two alternatives, in which case it would be beneficial to restrict the analyses by not fitting models with three or more clusters. Such an approach is particularly useful when the molecular data are relatively sparse and the level of genetic differentiation between the ancestral sources is weak.Earlier introduced models and the associated stochastic estimation algorithms available in BAPS have been based on an approach where the number of genetically diverged subpopulations (i.e. genetic clusters) underlying the sample material have been learned statistically, given some reasonable k panmictic parts is determined by a partition S = , which assigns n sampled individuals into k non-empty clusters. Using the molecular information available for the individuals, the inferences about the genetic mixture are obtained from the posterior distributionIn the stochastic partition representation utilized in ,4,8, a gp(data|S) is the marginal likelihood of the molecular data for the genetic mixture S, and p(S) is the prior distribution of this structure parameter, which can be defined in various ways, depending on the availability of biologically relevant non-molecular information.where Θ is the space of possible genetic structures (partitions), a priori uncertainty about the genetic mixture in terms of p(S). Firstly, a default uninformative choice sets the prior equal to a uniform distribution over the set of genetic structures that are only restricted by an integer K (or a set of distinct integers K), 1 <K ≤ n, an upper limit for the number of panmictic components thought to be feasible for the investigated population. The structure S which maximizes the posterior distribution (1) over this set is then sought using a stochastic search algorithm. Secondly, a more informative prior can be derived from the information provided by a commonly applied sampling design, where the strategy is to collect individuals from a number of geographically limited areas, yielding local sample populations. A uniform prior distribution can then be specified over the set of genetic structures satisfying the constraint that any subset of individuals with an origin in the same local sample population should not be assigned into separate clusters. The second prior thus corresponds to clustering of sample populations  instead of the individuals. Although such a prior for genetic structures is quite restrictive, it is still useful, in particular when the molecular information in the likelihood is very sparse (say 1–5 marker loci), or when the levels of genetic distances among populations are rather low.In the earlier introduced BAPS models, five different choices are available for specifying the K is determined application-wise, and the corresponding posterior optimal number of clusters within the range  is estimated using the stochastic learning algorithm. The particular advantage of the spatial priors in contrast to the uniform distribution is that they provide more weight for clustering solutions that are expected to be sensible from the biological perspective a priori, which strengthens the inferences for sparse molecular data. However, for strongly informative markers and clear genetic boundaries in the sample, the two approaches will provide highly similar inferences, as the prior distribution will be dominated by the marginal likelihood of the molecular data.The third and fourth prior alternatives are otherwise analogous to those two already presented, except that the prior distribution over different genetic structures is not uniform, but favors spatial smoothness in the clustering solution. Such priors were derived in , who inta priori determined upper bound K.The final alternative to specifying the prior distribution over the genetic structures is provided by considering trained clustering, where it is assumed that separate baseline data consisting of individuals with known origins is available for at least some of the possible genetic origins underlying the sample data. The molecular information from the baseline is then utilized to update information concerning the allele frequencies in the corresponding subpopulations, which is further used to assess models that assign sample data to these and other putative origins in various configurations. The number of clusters present in the sample data is here otherwise learned similarly as for the previously described models, i.e. under an K. This means that the prior distribution is otherwise analogously determined as according to the five described alternatives, except that it gives non-zero probabilities only for the genetic structures that have exactly the specified number of clusters. It is worth emphasizing that the stochastic maximization of the posterior is still done in a similar fashion as for the models with an upper bound K, by using the analytically calculated marginal likelihoods to evaluate the clustering solutions, however, under the constraint that the number of clusters does not change during the iterations of the estimation algorithm. The particular advantage of utilizing the analytical approach is that it avoids the Monte Carlo error for the estimation of a marginal likelihood for a Bayesian model. Such errors can be substantial even for a relatively large number of Monte Carlo iterations, when the number of clusters is at least considerable and/or the number of samples assigned in a cluster is small, due to the increased instability of the allele frequency samples from the corresponding posterior distribution. In situations where it is still of interest (and appropriate) to compare the statistical adequacy of estimated genetic structures having different numbers of clusters, the obtained maximal marginal likelihood values p(data|S) for the different solution can be used as previously.To provide more flexibility for the genetic mixture estimation, we have in BAPS 5.1 extended each of the above modelling approaches to allow for a fixed number of clusters to be used instead of letting the algorithm learn the value under a given upper bound a priori considered plausible for some set of sample data. Under such circumstances, fitting of genetic clustering models as presented previously can be an inconvenient and indirect way of comparing the existing a priori hypotheses about the genetic structure. To facilitate such comparisons, we have implemented a possibility to do the analysis using the posterior probabilities for the hypotheses corresponding to the fixed genetic mixture models. Let Hi, i = 1, ..., m, be m hypotheses, each specifying unambiguously a value for the partition S in a genetic mixture model, and p(Hi) the corresponding prior probabilities for the hypotheses. The posterior probabilities for the hypotheses are then defined asWhen molecular methods are applied in population genetics, it is common that previously gained context-specific knowledge, or even more general theories, may suggest a range of alternative genetic structures that are and provide a direct way of comparing the relative plausibility of the competing structures.In our earlier work , we intrWhile admixture models are useful for shaping our understanding about the past events in a population, they have a somewhat narrow scope in certain contexts, where it is of interest to identify at which loci an individual might have alleles representing a deviating ancestry. Namely, as the admixture framework is focused on the average proportions of genomic content that can be assigned to particular ancestral sources, it does not directly reveal which alleles or nucleotides should be considered to carry statistically conclusive evidence for an ancestry distinct from the origin assigned to an individual in a genetic mixture analysis. Such an inference task is particularly relevant in analyzing bacterial population structures, since horizontal gene transfer between groups of different origins play an important role in shaping the bacterial gene sequences. Also, for suspected hybrid individuals, it may be of interest to explore how many and which genomic regions show signs of recombination if data are available from the parental lineages. Moreover, migration events in a relatively distant past, for which the traces present in the molecular data are already quite diluted, may go undetected in an admixture analysis when the focus is on average proportions of genomic content, especially if a large number of loci is considered.In order to complement the picture painted by an admixture analysis about the past events in a population, we introduce here a simple statistical tool which can be exploited to discover alleles with a deviating ancestry, given the results for an earlier estimated genetic mixture model. Our approach is based on the use of Bayes factors combined with predictive likelihoods to compare the evidence for alternative ancestral sources at each marker locus observed for a particular individual  at a particular locus (site), we utilize in a combined manner Bayes factors  and postk. Let aij be an observed allele for an individual i at the locus j, and further, let c denote the cluster into which this individual was assigned in the genetic mixture analysis. For notational simplicity, we assume here that the data are haploid. Diploid data is dealt with accordingly by considering both the alleles at a single locus separately. The strength of evidence for an ancestry distinct from c is assessed by the maximal Bayes factorWe assume that a genetic mixture analysis is performed prior to an attempt to discover alleles with a deviating ancestry, to identify the possible putative origins of the alleles. Such an analysis may be done either using the approach where the suitable number of clusters is learned by BAPS, or by specifying the number of clusters in advance. The number of distinct ancestral origins used here is denoted by p(aij|Sc) is the predictive likelihood of allele aij in the genetic origin represented by cluster c after removing aij from the molecular data obtained from sc at locus j, and similarly, p(aij|Sc') is the corresponding predictive likelihood for cluster c' . The predictive likelihood is the probability of observing the allele aij within a particular genetic origin, when the remaining uncertainty about the allele frequencies at locus j after the molecular data obtained from individuals assigned in the particular cluster has been taken into account. Thus, these predictive likelihoods reflect in a statistically sensible manner both the possibly varying levels of polymorphism and missing data over the loci. To identify loci where the statistical evidence for a deviating ancestry is at least substantial, we use 2.3 as a default threshold for the log BF, as suggested by the guidelines discussed in  for these cases. It is also seen that false conclusive evidence for a deviating origin for any other ancestral source population is only obtained for a single allele (loge Bayes factor 3.01), which is inferred to have ancestry in the population labelled A. The pattern in Figure The role of the posterior probabilities is here to aid the interpretation of the allele screening results based on the Bayes factors. Namely, they enable a simultaneous visualization of the pattern of the deviating ancestry suggested for a particular individual, and also, they can be used to further assess the statistical evidence. As a conservative strategy, it is suggested that the evidence for a deviating ancestry is considered strong, if the Bayes factor exceeds at least the default threshold interval .60, 5.98Burkholderia cepacia complex, with the total length of 2773 bases over all the genes. Our simulated dataset was generated by imitating the level of observed molecular divergence among these Burkholderia strains. Firstly, an arbitrary strain was selected as a global strain profile, being the ancestor of the whole simulated population. Then, local ancestors for three subpopulations were generated from the chosen global ancestor, by inserting mutations in the DNA sequence of the global strain, such that they occur at a rate of 0.035 per site. Thus, on average, the local ancestors are separated from the global strain by 0.035*2773 = 97 mutations. Finally, to obtain the synthetic data for the non-admixed strains, mutations were inserted into the DNA sequences of the local ancestors, such that they occur at a rate of 0.0054 per site, i.e. the strains are on the average separated from their local ancestor by 0.0054*2773 = 15 mutations. The numbers of generated strains with non-admixed ancestry were 25, 20 and 20, for the three subpopulations, respectively. Additionally, five strains with a considerable degree of recombination between the subpopulations 1 and 2 were included in the data. The molecular profiles of these strains were created independently of each other, such that at approximately 25% of the sites the observed bases were generated according to the mutation model from the local ancestor of population 2, and correspondingly, at approximately 75% of the sites from the local ancestor of population 1.To reasonably mimic characteristics of datasets for which the linkage mixture and admixture models are intended, we use a real bacterial data published in , to genea priori upper bound K for the number of clusters set equal to 10, and using five replicate runs of the stochastic estimation algorithm. The three underlying clusters were correctly discovered in the analysis, and we performed the linkage admixture analysis conditional on the obtained posterior optimal genetic mixture, where the admixed strains were allocated to the cluster corresponding to the first underlying population. In the admixture analysis based on the default number of simulations used in BAPS 5.1, all the admixed strains were assigned with p-values equal to 0.00, and all other strains with p-values equal to 1.00. This finding reflects the information content of the molecular data, as no false positive or negative results were obtained. The average estimated admixture coefficient for the ancestry in population 2 is 0.09 for the admixed strains, which underestimates to some extent the contribution of this origin to the genomes of the strains. However, it should be noticed that in this type of a DNA sequence data, many of the sites with a deviating ancestry in fact carry identical bases in all underlying populations, which means that they are not informative in the admixture likelihood, and thus, it is expected that the contribution from a deviating ancestral origin cannot be very precisely estimated, even under ideal circumstances from the statistical perspective.The simulated data with 70 strains described above were analyzed using the second-order Markov linkage clustering model of , with tha priori upper limit K = 30 and 10 replicate runs of the estimation algorithm, which yielded the posterior optimal clustering solution with 11 clusters corresponding to random mating units (Table As a final illustration of the issues related to the information provided by the sampling design in genetic mixture clustering, we consider the recent human dataset from . The data priori upper bound values K. The challenge posed by a large-scale dataset with a complex structure, i.e. several small clusters associated with relatively low levels of genetic differentiation from the remaining populations, was clearly illustrated by these data. For small to moderate a priori upper bound values, the stochastic estimation algorithm used in BAPS was unable to detect more than six clusters in the individual level analysis, and the analytical comparison of the solutions' log marginal likelihood values revealed that they were inferior to the earlier derived estimate with 11 clusters (Table a priori upper bound value successively, until the posterior mode estimates started to converge to the vicinity of the 11-cluster solution. From K = 500 upwards, new clusters were successively identified, and with K = 800, the algorithm was finally able to discover a genetic mixture with a higher posterior probability than the 11-cluster solution obtained in the clustering at the sample population level. This new optimum contained 11 clusters as well; however, some of the individuals were re-allocated into alternative clusters different from those containing the majority of their sample population data. We finally performed an admixture analysis conditional on the optimal genetic mixture estimate from the individual level analysis. The posterior mode estimates of the admixture coefficients significant at the 5% level using the default options in BAPS are shown in Figure To investigate the genetic structure of the human data at a finer scale, we also performed genetic mixture clustering at the level of individuals in BAPS, using a range of The apparent popularity of the Bayesian approach to solving inference problems in population genetics has led to a surge of interest to develop new models and methods for analyzing a variety of questions related to the genetic structure of a population, its intrinsic ancestral patterns and the assignment of individuals to distinct origins. Our experiences from the development of the approaches implemented in the BAPS software have crystallized the need for models and algorithms that reasonably scale up to large and complex datasets, both with respect to the time-complexity and the accuracy of the inferences.Our example concerned with the extensive human dataset from  illustraa priori bound K = 30, as the default BAPS analysis initialized using the prior boundary K = 800. This finding suggests that our estimation algorithms could be further improved by utilizing various strategies in the initialization phase, although the currently implemented methods have been successfully applied for a wide range of challenging applications.It is important to notice that the information contained in the sampling design provides often a statistically powerful means for guiding the model estimation towards optimal clustering solutions, as illustrated by the comparison of the sample and individual level genetic mixture analysis of the human data. In fact, we also performed a modified genetic mixture clustering at the individual level, by starting the analysis from the optimum solution obtained from the sample population clustering. This analysis converged to the same global optimum with 11 clusters under the It is clear that any of the existing Bayesian approaches to genetic population structure inference needs to evolve to meet the challenge from the large-scale and/or genome-wide genotyping that is available for a continuously increasing number of organisms. Here we have discussed an array of statistical tools that represent a step towards that direction; however, a multitude of innovative strategies for model learning, such as Bayesian variational estimation and more intelligent ways of screening for initial model estimates, could still be utilized to enhance the possibilities to reliably fit genetic structure models. This will be a priority research area for us in the near future to benefit the whole research community interested in questions related to statistical learning of genetic population structure.All authors contributed equally in writing, analyses and method development.


Heteralocha acutirostris) of New Zealand represents the most extreme example of beak dimorphism known in birds. We used a combination of nuclear genotyping methods, molecular sexing, and morphometric analyses of museum specimens collected in the late 19th and early 20th centuries to quantify the sexual dimorphism and population structure of this extraordinary species. We report that the classical description of Huia as having distinctive sex-linked morphologies is not universally correct. Four Huia, sexed as females had short beaks and, on this basis, were indistinguishable from males. Hence, we suggest it is likely that Huia males and females were indistinguishable as juveniles and that the well-known beak dimorphism is the result of differential beak growth rates in males and females. Furthermore, we tested the prediction that the social organisation and limited powers of flight of Huia resulted in high levels of population genetic structure. Using a suite of microsatellite DNA loci, we report high levels of genetic diversity in Huia, and we detected no significant population genetic structure. In addition, using mitochondrial hypervariable region sequences, and likely mutation rates and generation times, we estimated that the census population size of Huia was moderately high. We conclude that the social organization and limited powers of flight did not result in a highly structured population.The extinct Huia ( Heteralocha acutirostris) had the most extreme sex-linked bill dimorphism known in birds Huia (Philesturnus carunculatus) and the North Island Kokako . Like the Huia, these species are also wattlebirds and are characterised by a pair of colourful, fleshy wattles; strong feet; and short, rounded wings The closest relatives of the Huia are the North Isand Saddleback , especially when using nested PCRs. This preferential W loci amplification fortuitously provided us with very clear sexing results – either W loci amplification only, or just Z loci amplification. Of the 38 specimens successfully sexed, 17 were shown to be males and 21 were females. Morphometric data show that Huia males had an average beak length of 52.8 mm, whereas female beaks were typically much longer, with an average of 77.9 mm. This difference was highly significant using a two-tailed t-test 22 df, assuming unequal variance (P<0.0001). In contrast, the mean female beak depth (13.9 mm) was significantly smaller than that of males (16.9 mm) using a two-tailed t-test 33 df and assuming unequal variance (P<0.0001). Variation in the beak length of males was relatively small (SD ±3.8 mm), while in females it was much larger (SD ±18.2 mm). To test the null hypothesis that there was no difference in the variance between male and female beak lengths, we conducted a one-tailed F-test. The test statistic is the ratio of two sample variances, and the difference was highly significant 20, 16 df (P<0.0001). In addition to beak length, females showed a significantly greater variance in beak depth . Moreover, four DNA-sexed females were indistinguishable from males in terms of beak length  amplified for four or more of the six polymorphic microsatellite DNA loci used. All loci were in Hardy-Weinberg equilibrium . No linkμ  of each locus varied from 3.2% to 20% . Allelic dropout rates also differed significantly among samples . Three false alleles were observed in the dataset (rate of occurrence  = 0.7%).Data for consensus genotypes are presented in % to 20%  with a mAllelic dropout is thought to be a significant problem for genetic census data from degraded DNA samples such as those analysed here. This can result in false genotypes and hence can cause overestimation of population size e(f) = θ/2μ, where μ is the mutation rate per base pair per generation and generation time is defined as the average age at which a female reproduces. A generation time of 6.3 years was used in our analyses, and was based on the generation times of the related North Island Kokako −7 mutations per site per year for the hypervariable region, based on a pedigree study in Adélie penguins −7 mutations per site per year. The former resulted in a mutation rate per generation of 3.47×10−6 for Huia and the latter 2.65×10−7. Using the above values, Ne(f) for Huia was estimated at 1709–4477. The range of estimates of the overall effective population size (Ne) of Huia using this generation time and these mutation rate variables was 3419–8954 breeding adults, assuming an equal sex ratio. The ratio of effective to census population size (Nc) in wild populations is often quite low and it has been suggested that a Ne: Nc ratio of 0.1 is appropriate −7http://evolution.gs.washington.edu/lamarc/).We recorded eight mitochondrial haplotypes among the 21 Huia individuals examined . The resHuia exhibited a greater mean number of alleles per locus than did its relative, the North Island Saddleback, when polymorphic loci alone were considered . Huia alHuia averaged a greater number of alleles per locus over all polymorphic loci than Saddleback, despite fewer Huia samples being genotyped. Moreover, Huia had higher levels of heterozygosity than the Hen Island Saddleback population, from which all contemporary populations derive We suggest that the large variation in bill length and depth of female Huia, along with our molecular sexing data, indicate that female Huia may have began life with short stout bills indistinguishable from those of males and that their beaks grew longer over their lifetime. The primary evidence for this is that we recorded four female Huia that were DNA sexed, and these individuals had bills indistinguishable from males in terms of length. In addition a single Huia specimen labeled as a ‘juvenile’ was sexed as a female but had a very short bill. Several processes could explain the greater coefficient of variation in female bill length. For example, bill length might be controlled by W chromosome-specific loci and females consequently show much greater variation, or alternatively female bill growth may have been influenced by other factors such as diet. There is evidence for both these processes in avian species Our data show that Huia were characterised by a high level of genetic variation. Two genetic studies of Kokako reported low levels of genetic structuring among modern populations for both mtDNA Our estimate of the population size of Huia is relevant to the period prior to the human settlement of New Zealand and, depending on likely mutation rates and generation times, indicates a ‘moderate’ historical population size of 34,187 to 89,539 individuals. It is likely, however, that the pre-human population size was higher than our estimate, as our analyses show a period of population expansion. In combination, these factors suggest moderate to high Huia population densities. As a consequence, gene flow within the population is likely to be significant and will thereby promote genetic homogeneity within the species. In addition, populations of moderate size, such as that of Huia, typically exhibit a level of genetic inertia that may also have contributed to the overall genetic homogeneity of the species. This finding is in contrast to our original expectation that Huia populations would have exhibited high levels of population structure.In conclusion, our molecular sexing results provide evidence that female Huia bills develop from a male-like condition. We suggest that young females may have been indistinguishable from males in terms of bill length, and that the typical differences developed over the lifetime of the individuals. Mitochondrial DNA variation in Huia allowed us to estimate the likely historical census population size for this species. Despite the suggestions from early naturalists that Huia were highly territorial and that the species had limited dispersal capability, no evidence for population genetic structure was detected using a range of nuclear loci. Generally, this work illustrates the potential of both sex-linked and autosomal DNA sequences to improve understanding of the population dynamics and molecular ecology of an extinct species. Furthermore, as many species of conservation concern become rarer in the wild, scientific programmes might benefit from using older specimens in museums. This would have less impact on modern populations of these species and would make use of a valuable museum resource. Hence, similar ancient DNA methods to those used here could be applied to scientific attempts to better understand the causes of species' decline.Twenty-four Huia footpad samples were provided by the Canterbury Museum, 13 from the Auckland Museum, seven from the Naturalis Museum (Leiden), 24 from the American Museum of Natural History, two from Macleay Museum, two from the Australian Museum, and one sample from a private collector . Exact p2 of Huia footpad tissue was removed and cut into several pieces using a sterile razor blade. Huia DNA was extracted by incubating footpad fragments overnight at 50°C in 2.5 ml of extraction buffer , 250 µl of 10% SDS, 15 µl of 200 mg/ml dithiothreitol (DTT), and 25 µl of 50 mg/ml Proteinase-K. Samples were then extracted with phenol followed by chloroform: isoamyl alcohol (24∶1) and then concentrated by centrifugation through a Vivaspin-30  membrane. Negative extraction controls were included with every 6–12 sample extractions. All mitochondrial sequences were obtained by PCR as outlined below and sequenced in the forward and reverse direction from multiple independent amplifications. Huia HVRI sequences are deposited in the GenBank database with accession numbers GU176413–GU176433.Extraction of ancient Huia DNA was performed in a dedicated ancient DNA laboratory physically separated from where contemporary DNA and PCR products were handled. Decontamination was routinely carried out by UV-irradiation and sodium hypochlorite washes. Approximately two mm5′-ATAAACCCAAGTGATCCTACCT) and huiaIIR (5′-TTGAGTAGCTCGGTTCTCGTGA). Amplification products were purified by centrifugation through SephacrylTM S200HR , sequenced using ABI BigDye® Terminator v3.1 chemistry, and analysed using an ABI 3730 Genetic Analyzer. Sequences were edited and aligned in SequencherTM 4.6 (Gene Codes Corporation).One hundred and ninety-nine bp of the Huia hypervariable region I (HVRI) region were amplified from 21 individuals  as outli5′-GTAGGAGCAGAAGATATTCTG). These amplify a region of the sex-linked chromodomain-helicase-DNA gene (CHD)  and eight loci isolated from a Kokako microsatellite library μ) and both alleles of putative heterozygotes were detected twice in order to rule out false alleles. In addition, a subset of four Huia samples were independently extracted and amplified for three loci  at an ancient DNA facility at the University of Auckland.Two techniques were used to avoid genotyping errors. Firstly, an initial screen of the quality of the samples was performed from the first round of PCR reactions O and HE, respectively) and number of alleles, were calculated in Arlequin 2000 (http://cmpg.unibe.ch/software/arlequin/software/2.000/doc/faq/faqlist.htm). GENEPOP version 3.4 was used to test for linkage disequilibrium and deviation from Hardy-Weinberg parameters μ) and the occurrence of false alleles (FAμ) was calculated for each locus and across all loci using the equations below, as recommended by Broquet & Petit Measures of genetic variation, including observed and expected heterozygosities  was determined using FAμ  =  Fj/Aj, where Fj  =  the number of amplifications at locus j where a false allele is observed, and Aj  =  the total number of amplifications (both hetero- and homozygotes).Allelic dropout was calculated using the equation ADOP) was calculated using the equation P = (K)×(K/2) n−1K  =  the ADOμ at each locus and n is the number of repeated amplifications; in this work, n = 7.The probability of false homozygotes at each locus after repeated PCR reactions  were calculated from the mean estimate log-likelihood of each K (lnP(X/K)).STRUCTURE was used with no input of prior population information in relation to individual samples, and admixture was assumed. Allele frequencies among clusters were considered to be independent to prevent overestimation of cluster number μ (the prior probability distribution on K) was set at 1, i.e. equal probabilities of each K were assumed, and the parameter θ  was varied from 1 to 20. The maximum number of source populations was changed with each analysis from 4 to 8. Estimates of the posterior probabilities were made after 50,000 observations of the Markov chain, with the first 5,000 observations omitted as ‘burn-in’.PARTITION was applied only to the Huia samples that possessed a full complement of genotype data because missing data are not permitted in this software package. The parameter http://www2.biology.ualberta.ca/jbrzusto/sharedst.php). The mean number of alleles (NA) per locus and mean expected heterozyosity (HE) were compared between Huia and Saddleback. Data from 41 individuals from the Hen Island population of Saddleback Shared allele distances  between Huia were calculated online (http://beast.bio.ed.ac.uk/Tracer). An ESS of 100–200 has been suggested to indicate convergence. Our estimates were well above this value, in the thousands or greater. After preliminary analyses were completed, a final estimate of θ was performed from 10 replicates, each with the following starting parameters: θ = 0.015, and a linear prior of 0.0001–3. Two initial chains sampled 5000 trees with a sampling increment of 40, of which the first 7000 trees sampled were discarded, followed by 4 final chains to produce the estimate, in which, after a burn-in of 5000 trees, every 50th tree of 5×106 trees was sampled. Adaptive chain heating was used . We also performed separate LAMARC analyses to test for signatures of exponential growth or shrinkage using the same searching strategy.The genetic diversity parameter (θ) was calculated using a Bayesian framework


Sky islands, formed by the highest reaches of mountain tracts physically isolated from one another, represent one of the biodiversity-rich regions of the world. Comparative studies of geographically isolated populations on such islands can provide valuable insights into the biogeography and evolution of species on these islands. The Western Ghats mountains of southern India form a sky island system, where the relationship between the island structure and the evolution of its species remains virtually unknown despite a few population genetic studies.Brachypteryx major, across the montane Shola forests on these islands and also inferred its evolutionary history. We used Bayesian and maximum likelihood-based phylogenetic and population-genetic analyses on data from three mitochondrial markers and one nuclear marker  obtained from 33 White-bellied Shortwing individuals across five islands. Genetic differentiation between populations of the species correlated with the locations of deep valleys in the Western Ghats but not with geographical distance between these populations. All populations revealed demographic histories consistent with population founding and expansion during the Last Glacial Maximum. Given the level of genetic differentiation north and south of the Palghat Gap, we suggest that these populations be considered two different taxonomic species.We investigated how ancient geographic gaps and glacial cycles have partitioned genetic variation in modern populations of a threatened endemic bird, the White-bellied Shortwing Our results show that the physiography and paleo-climate of this region historically resulted in multiple glacial refugia that may have subsequently driven the evolutionary history and current population structure of this bird. The first avian genetic study from this biodiversity hotspot, our results provide insights into processes that may have impacted the speciation and evolution of the endemic fauna of this region. Geographical isolation is one of the major drivers of speciation as exemplified by species on islands. Although most research on biogeographic isolation has been associated with oceanic islands, recently continental islands or “sky-islands” have received increasing attention The Western Ghats mountains of southern India are identified as a global biodiversity hotspot Unlike the well-studied Madrean and Eastern-Arc sky island systems Unlike continuously distributed large mammals (e.g. Asian elephants), animals that inhabit montane sky islands could have different population histories. For example, the sky islands could have been colonised multiple times from a common source population. Alternatively, a single sky island could have been colonised, followed by subsequent dispersal to other islands through successive, serial founder events. The latter, also known as the ‘stepping stone’ model is one where genetic data would reveal a signature of isolation by distance The evolutionary history of a species may also be affected by the conditions prevailing at different periods of time. Because they impact species distributions, global climatic fluctuations  have had a profound impact on the genetic structure and evolution of species and communities Brachypteryx major as our study species because it is a small  passerine bird that is restricted only to the high-elevation Shola forests (above 1400-m) on the sky islands of the Western Ghats south of 13.5°N In order to investigate the impacts of biogeographic barriers, habitat structure and the possible impacts of Quaternary climate transitions on sky-island species in the Western Ghats, we chose the White-bellied Shortwing Myiomela major and White-bellied Blue Robin Myiomela albiventrisCallene by the same author It should be noted that the species has also been referred to as two separate species, the Nilgiri Blue Robin In this paper, we examine the population structure of the White-bellied Shortwing to investigate (a) whether geographical gaps affect its population structure and (b) whether the population genetic structure of the species reflects the island structure of the sky islands of the Western Ghats. Further, we investigate the evolutionary history of this bird to determine whether (a) dispersal between sky islands for the Shortwing follows a stepping stone model or if the population is the product of multiple vicariance events and (b) the timing of these dispersal events correlates with global climatic events.We found that the geographical gaps in the Western Ghats significantly affected the population structure of the Shortwing. The deepest phylogenetic division corresponds to the Palghat Gap as revealed by different methods including Bayesian analysis , S2, S3.The genetic distance  and geographical gaps  correlated with geographical distance  but partial correlations revealed that genetic distance was significantly correlated to gaps  but not to geographical distance  when the third factor was controlled for.We also found that geographical gaps play a more important role in structuring the genetic variation in the species than does geographical distance, with poor support for an isolation-by-distance model. Both genetic distance . This value is higher than that recognized as the threshold value of 7.9% between an average pair of avian species b (cyt b), cytochrome c oxidase subunit I (COI) and Control Region (D-loop), the nuclear marker glyceraldehyde-3-phosphate dehydrogenase intron (G3PDH) and the combined mitochondrial DNA (cyt b and COI) sequences  the three-population-model showed the maximum variance between groups and the lowest among populations within each group . These rutations . The Babb sequence analyses, revealed that the time to most recent common ancestor (TMRCA) for all populations of White-bellied Shortwing was 4.909±0.003 Mya. The southern population is the oldest one with a TMRCA of 1.566±0.001 Mya. Subsequently, the Anamalai group was colonised about 0.342±0.001 Mya, Peppara about 0.224±0.001 Mya and the northern populations of Bababudan and Ooty about 0.255±0.0004 Mya  across a 110-km lowland gap We proposed to examine the effect of geographical gaps on the population genetic structure of the Shortwing. We found that the magnitude of genetic difference between the populations of Shortwings corresponded to the size of the geographical gap and this was supported by multiple analyses: genetic distance, FIn contrast, the Shencottah Gap has not been identified as a geographical barrier for the distribution of species . We find that the two populations are reciprocally monophyletic and hence, can be classified as two different species. We propose that based on the common names followed in Ali and Ripley Brachypteryx major and the southern species the White-bellied Shortwing Brachypteryx albiverntris.Based on plumage and morphological differences between populations of Shortwings across the Palghat Ghat, there has been some debate ST values, thus forming a single population.The results from the current data set reveal that the Shortwings have a complex population structure. In places where breaks between islands correspond to geographical gaps there is a correlation between physiography and genetic structure. In other areas, however, the island structure does not correlate with genetic structure. The central-island complex of Grasshills, Kodaikanal and High-Wavies form a single large population  although they were on two different islands. These samples showed very low genetic distance across all genetic markers despite a comparable high sample size. However, the structure within this region, especially Grasshills and Kodaikanal, is not well resolved. As different genetic markers revealed different sub-structures, we cannot derive strong inferences about the structure within this region with the present data. The observed lack of structure could be due to recent divergence or ongoing gene flow between these populations. Data from faster-mutating regions like microsatellites might clarify our understanding of within-island population structure. For example, although Kodaikanal and Grasshills are on the same island, they are currently not connected by forests . GeneticIn order to gain a better understanding of the impacts of physiography on patterns of genetic structure and to explore why certain populations stayed connected, we mapped the elevational profile of the Western Ghats Pogonocichla stellataIf the Shortwing dispersed between different sky islands following the stepping stone model, we would find the oldest population on one end of the linear island system with younger populations arising from it. However, the evolutionary history of the Shortwing does not follow the stepping stone model. The evolutionary history seems to be more complex, again implicating a strong role of geography and climate in this region. The split between the north and south species at the Palghat Gap is the oldest, about 5 Mya. Primary rainforest songbirds in Africa have been known to show a Miocene origin The most interesting aspect of the evolutionary history of the species is that populations north and south of the gaps show deep and old splits, whereas the present populations within the three clades have diverged very recently, in the Pleistocene, correlating with the Last Glacial Maximum. We also detected signatures of rapid population expansion for all these young populations based on mismatch distributions. Hence, we propose that major species divergence occurred very early during the Pliocene glacial cycle events along the major geographical gaps. It is likely that these ancestral populations underwent a bottleneck or population crash forming multiple glacial refugia on these islands. The current populations are clearly re-colonised and are expanding from a recent founder event, with their dispersal being continually restricted by the geographical gaps. Unlike African montane regions, where some peaks have recent volcanic origin, causing much of the speciation, there is no known volcanic activity in the Western Ghats in the recent times and much of this region has rocks that are of Archean (2400 Mya) origin. The origin of the geographical gaps (>500 Mya) also predates the origin of passerines in the Tertiary Based on our results, for the first time, we can make predictions regarding phylogeographic patterns for species in the Western Ghats mountains of the Indian subcontinent. This is significant not only for the Western Ghats, but for peninsular India as a whole, because this region lacks a phylogeographic paradigm. Our results suggest that biogeographic divides might affect relatively small, endemic Western Ghats species that occupy the sky islands. On the other hand, more vagile, larger species that live at lower elevations are less likely to be affected by these divides. More generally, the extent to which these divides will shape population structure will depend on the elevational range the species occupies and its dispersal ability. The lack of stepping stone based dispersal in the evolutionary history of the Shortwing might be a general pattern shown by other species, or might be limited to species of certain life histories and ecologies. Future studies that use a comparative phylogeographic approach, examining multiple species with different body sizes, ecological niches and life histories would be the best approach to understand shared aspects of evolutionary history and ecological response between species.In conclusion, our study reveals that ancient geographical gaps clearly form barriers for dispersal and gene flow and have caused deep divergences in a montane species, while habitat and climate refugia have influenced recent population history. The gaps and physiography, hence, play a background role that is more proximally affected by habitat and climate changes. For the first time, we demonstrate the impacts of both biogeographic barriers and recent quaternary climatic events on a bird species in the Western Ghats mountain chain in the Indian subcontinent. Our results have important implications for the White-bellied Shortwing, and also provide, much-needed phylogenetic predictions and hypotheses that could be tested in future studies not only in this biodiversity-rich, yet under-studied, region of the world but also in other sky island systems across the world.et al.Blood samples were collected from 33 individuals of the Shortwing from altitudes above 1400 m on the five sky islands namely Bababudan Hills, Nilgiri Hills, Anamalai Hills, Cardamom Hills and Agasthyamalai Hills . Samplinb (cyt b) 1067 base pairs (bp), cytochrome c oxidase subunit I (COI) 715 bp and Control Region (D-loop) 460 bp, and one nuclear marker, glyceraldehyde-3-phosphate dehydrogenase intron (G3PDH) 352 bp for this study. All the PCR reactions were carried out with 1X Taq Buffer B, 2 mM MgCl2, 1.5 U Taq DNA Polymerase , 0.25 mM dNTP mix , 0.2 µM of each primer , and about 1–1.5 µl of DNA extract.DNA was extracted using a DNeasy tissue kit  following the manufacturer's protocols. We used three mitochondrial markers, cytochrome We conducted a standard 35-cycle PCR with different primer sets  to ampliThe sequences were edited manually and assembled using the Mega 3.1 sequence editor b and D-loop) separately, for the combined mitochondrial regions of COI and cyt b (denoted by MtDNA) and for the nuclear marker G3PDH. Pair-wise comparison of FST between populations of sampled Shortwings was also conducted with ARLEQUIN 3.11 To understand population structure and to examine isolation by distance, average genetic distances between and within groups of broad geographical ranges were calculated using the Kimura-2-parameter model in Mega 3.1 b, COI, D-loop, the combined mitochondrial DNA data of cyt b and COI (denoted as MtDNA), and for the segment of the G3PDH marker. An analysis of combined datasets of different markers was limited by the lack of data from an outgroup species. Wherever an outgroup species was available across markers, the combined analysis have been carried out. Prior to phylogenetic analyses, ModelTest Version 3.8 b, HKY+I+G  or HKY (D-loop and G3PDH markers) evolutionary model and by allowing the program to generate all the other parameters independently. Since the best-fit model for MtDNA and cyt b, TVM, is not available in MrBAYES, hence the next closest model GTR was used for analysis (as in Castoe b) data using the median joining (MJ) network algorithm Population structure of the Shortwing was examined using phylogenetic analyses conducted using neighbour joining and maximum likelihood methods in PAUP* Population structure of the Shortwing was also examined using Analysis of variance (AMOVA) computed using ARLEQUIN 3.11 ST) matrix and the geographical distance matrix and also the effect of Gaps by using a third dummy variable indicating the locations of the Gaps using the methods described in Bohonak 2002 http://earth.google.com). The correlation was tested for statistical significance at 0.05 level using a randomization test with 1000 iterations To test for isolation by distance, we conducted a Mantel test b data using BEAST, version 1.4.8 et al.−1 for cyt b has been validated by a recent study by Weir and Schluter −1±0.1%  across 12 taxonomic orders. Hence we used this divergence rate for estimating divergence times across Shortwing clades. Range expansion events have been dated by calculating Tau = 2 µt −1 and generation time of 1 year for this calculation. The differences between the distribution of pairwise differences in a set of sequences to a distribution based on a population expansion model or ‘mismatch distribution’ To investigate the evolutionary history of the Shortwing, we estimated dates of divergence between and within clades with cyt Figure S1Phylogenetic tree (Bayesian) of White-bellied Shortwing on sky-islands of southern India based on 998 bp of the cytochrome-b marker. Branch support values indicate Bayesian, Neighbour-Joining bootstrap support and Maximum Likelihood bootstrap support and values. The samples from a region are coloured by ‘island’ colour in the map.(5.65 MB TIF)Click here for additional data file.Figure S2Phylogenetic tree (Bayesian) of White-bellied Shortwing on sky-islands of southern India based on 714 bp of the cytochrome oxidase-1 marker. Branch support values indicate Bayesian, Neighbour-Joining bootstrap support and Maximum Likelihood bootstrap support and values. The samples from a region are coloured by ‘island’ colour in the map.(3.74 MB TIF)Click here for additional data file.Figure S3Phylogenetic tree (Bayesian) of White-bellied Shortwing on sky-islands of southern India based on 461 bp of the Control Region marker. Branch support values indicate Bayesian, Neighbour-Joining bootstrap support and Maximum Likelihood bootstrap support and values. The samples from a region are coloured by ‘island’ colour in the map.(4.78 MB TIF)Click here for additional data file.Figure S4Phylogenetic tree (Bayesian) of White-bellied Shortwing on sky-islands of southern India based on 352 bp of the nuclear glyceraldehyde-3-phosphate dehydrogenase intron (G3PDH) marker. Branch support values indicate Bayesian, Neighbour-Joining bootstrap support and Maximum Likelihood bootstrap support and values. The samples from a region are coloured by ‘island’ colour in the map.(4.70 MB TIF)Click here for additional data file.Table S1Partitioning of genetic variation of White-bellied Shortwing populations in the Western Ghats sky islands based on mitochondrial DNA  Footnote:*Significant (p<0.05), + Population codes: 1-Bababudan, 2- Ooty, 3- Grasshills, 4-Kodiakanal, 5- High-Wavies, 6- Peppara; Brackets indicate grouping of population. Groupings that include populations on either side of the gaps , 3, 4, 6; , 4, 5, 6; 1, , 4, 5, 6) result in negative among group variance, and hence are not included in this table.(0.05 MB DOC)Click here for additional data file.


Mycobacterium tuberculosis. Sixty-four direct, decontaminated, and serially diluted smears were prepared from sputa obtained from 19 patients suspected to have M. tuberculosis infection. Slides were stained with auramine orange and evaluated as being positive or negative for M. tuberculosis with both the new portable fluorescence microscope and a laboratory grade fluorescence microscope. Concordant results were obtained in 98.4% of cases. This highly portable, low cost, fluorescence microscope may be a useful diagnostic tool to expand the availability of M. tuberculosis testing at the point-of-care in low resource settings.This study describes the design and evaluation of a portable bright-field and fluorescence microscope that can be manufactured for $240 USD. The microscope uses a battery-operated LED-based flashlight as the light source and achieves a resolution of 0.8 µm at 1000× magnification in fluorescence mode. We tested the diagnostic capability of this new instrument to identify infections caused by the human pathogen,  Mycobacterium tuberculosis infection M. tuberculosis control programs In developing countries, bright field microscopic evaluation of stained sputum smears remains the primary method for confirmation of M. tuberculosis countries as identified by the World Health Organization and weighs 9.48 kg. A compact, light-weight, battery-operated, low-cost fluorescence microscope has the potential to enable diagnosis and screening of M. tuberculosis in remote settings with limited infrastructure, providing the proven effectiveness of fluorescence microscopy to the point-of-care.Recent advances in fluorescence microscopy, including the use of low-cost light-emitting diodes (LEDs) as a light source, have helped to make the technology more widely available M. tuberculosis bacilli from sputum smears obtained from clinical specimens.To address this need, we developed the Global Focus microscope: a portable, battery-powered, inverted bright field and fluorescence microscope with up to 1000× magnification that can be manufactured for approximately $240 USD. In this report, we compare the diagnostic performance of the Global Focus microscope to a conventional fluorescence microscope  for the detection of M. tuberculosis bacilli from sputa. Sputum smears were derived from clinical specimens submitted for M. tuberculosis case finding under routine conditions at the Pasteur Institute . Nineteen sputum samples were collected from 19 presumptive patients. Each sputum sample was used to generate a decontaminated preparation. Direct slides were prepared from 18 of the same specimens. For nine of the samples, an additional three preparations were generated: 1∶2, 1∶4, and 1∶8 dilutions from the decontaminated pellet. For the direct group, sputa were deposited onto the slides without additional treatment. For the decontaminated and diluted groups, sputa were chemically treated with sodium hypochlorate to lyse the cells and then centrifuged to concentrate the sample before application to the slide. Following the application of sputum, all slides were heat-fixed. Slides were stained using an auramine orange fluorescent dye kit  no more than one hour before evaluation. This study was reviewed by the Rice University IRB and found to be exempt from IRB review.The diagnostic performance of the Global Focus microscope in fluorescence mode was compared to a Nikon E400 for the detection of The 64 slides were randomized, assigned unique identification numbers, and reviewed by a blinded mycobacteriologist. During the evaluation, a randomly selected subset of 16 slides (25%) was concurrently reviewed by a blinded pathologist. All slides were evaluated first using the Global Focus microscope , and subA Mann-Whitney U non-parametric analysis  was performed to test the hypothesis that the two microscopes yielded equivalent results using data from the direct and decontaminated groups.M. tuberculosis bacilli could be clearly resolved at 400× magnification, as seen in The maximum spatial resolution of the Global Focus microscope in fluorescence mode was measured to be 0.8 µm at 1000× magnification Seventeen of the 19 decontaminated specimens examined with the two microscopes resulted in concordant evaluations (89.4% concordance). In two cases, samples judged as doubtful with the Nikon E400 microscope were judged as negative with the Global Focus microscope.Fourteen of the 18 direct specimens examined with the two microscopes resulted in concordant evaluations (77.7% concordance). In one case, a sample judged as 1+ with the Global Focus microscope was judged as 3+ with the Nikon E400. In two cases, samples judged as doubtful with the Nikon E400 microscope were judged as negative with the Global Focus microscope. In one case, a sample evaluated as negative with the Nikon E400 microscope was classified as 1+ with the Global Focus microscope; the decontaminated preparation from this patient was evaluated as 1+ with both microscopes.Twenty-six of the 27 diluted specimens examined with both microscopes resulted in concordant evaluations (96.3% concordance). In one case, a sample classified as doubtful with the Global Focus microscope was classified as negative with the Nikon E400 microscope.Using IUATLD guidelines, concordant results were obtained in 56 of 64 specimens (87.5% concordance). When samples were evaluated as positive or negative, results with the two microscopes agreed in all but one case (98.4% concordance).M. tuberculosis bacilli in the following groups: direct sputum smear ; and the decontaminated smear .No statistical differences were measured between the Global Focus and Nikon E400 microscopes for detecting M. tuberculosis in sputum smears. The compact size (7.5×13×18 cm), minimal weight (1 kg), and minimal power requirements (two AA batteries) of the Global Focus microscope provide a favorable option for diagnostic settings with minimal laboratory infrastructure. This study indicates that evaluation of smears using the Global Focus microscope yields similar results as evaluation with a standard clinical laboratory grade fluorescence microscope. Importantly, the patient specimens tested herein were collected in a remote region of Iran, and therefore represent the prototypical samples that would be evaluated in the field with the Global Focus microscope. While future field studies are planned to evaluate the reliability and ease of use of this microscope, the results presented here serve as a proof of principle that diagnosis of M. tuberculosis is possible using low-cost, portable fluorescence microscopy.We have developed a bright field and fluorescence microscope that provides an alternative to conventional fluorescence microscopy for the diagnosis of


Eunicea flexuosa, a widely distributed Caribbean octocoral. Eco-phenotypic and genetic effects were evaluated by reciprocal transplants of colonies inhabiting opposite ends of the depth gradient and analysis of population genetics of mitochondrial and nuclear genes, respectively.Clonal marine organisms exhibit high levels of morphological variation. Morphological differences may be a response to environmental factors but also they can be attributed to accumulated genetic differences due to disruption of gene flow among populations. In this study, we examined the extensive morphological variation (of 14 characters) in natural populations observed in the gorgonian Significant differences (P < 0.001) in 14 morphological traits were found among colonies inhabiting 12 locations distributed in seven reefs in southwest Puerto Rico. Results from principal component analysis indicated the presence of two groups based on depth distribution, suggesting the presence of two discrete morphotypes . A discriminant function analysis based on a priori univariate and multivariate analyses (which separated the colonies in morphotypes) correctly classified 93% of the colonies for each environment. Light, water motion and sediment transport might influence the distribution of the two morphotypes. Reaction norms of morphological characters of colonies reciprocally transplanted showed gradual significant changes through the 15 months of transplantation. Sclerites of shallow water colonies became larger when transplanted to deeper environments and vice versa, but neither of the two transplanted groups overlapped with the residents' morphology. Genetic analysis of mitochondrial and nuclear genes suggested that such discrete morphology and non-overlapping phenotypic plasticity is correlated with the presence of two independent evolutionary lineages. The distribution of the lineages is non-random and may be related to adaptational responses of each lineage to the environmental demands of each habitat.Eunicea flexuosa corresponds to two distinct genetic lineages with narrower distributions and more rigid phenotypic plasticity than the original description. The accepted description sensu Bayer (1961) of E. flexuosa is a complex of at least two distinct genetic lineages, adapted to different habitats and do not exchange genetic material despite living in sympatry. The present study highlights the importance of correctly defining species, because the unknowingly use of species complexes can overestimate geographical distribution, population abundance, and physiological tolerance.The extensive distribution and ample morphological variation of  The phenotype is considered the product of inherited genetic information and its interaction with the environment. Thus, differences in the phenotype can be explained by variations in environmental conditions, but also such phenotypic differences may reflect accumulated genetic variation due to disruption of gene flow between populations, and their subsequent speciation into biological species.First, phenotypic plasticity enhances the survival and reproductive success of individuals by contributing to their ability to cope with environmental changes and to potentially adapt to new niches. Plasticity is an emergent property of the genotype and therefore also susceptible to natural selection . The chaPhenotypic plasticity has been studied in algae , spongesIn the absence of local adaptation, the high dispersal potential of marine propagules usually results in genetic homogeneity over large distances -22. HoweApart from allopatric divergence, sympatric divergence is also plausible. Speciation has occurred in spawning organisms with larvae capable of long dispersal -28 and gEunicea flexuosa (Lamouroux 1821), in shallow forereef areas are susceptible to high water motion and are generally taller; grow in a single plane with thicker branches and bigger calices. In contrast, colonies in deeper environments are exposed to low water motion and less light. There, the colonies exhibit multiplane growth, are smaller with fewer terminal branches than their shallow counterparts with smaller and more sparse calices  of amonmsh1 and 18S using the maximum likelihood (ML) method in PAUP* [msh1 and 18S, respectively. For the heuristic searches in ML, data were bootstrapped 100 times, and sequences were added randomly ten times. Phylogenetic relationships were also constructed using neighbor-joining and maximum parsimony with 1000 bootstrap replicates as implemented in PAUP*. Given the uncertain phylogenetic position of E. flexuosa and its conspecifics [P. homomalla, Muricea muricata and Eunicea spp. Regardless the outgroup, identical topologies were obtained. All sequences have been deposited in GenBank (Accession numbers EF659469-EF659598 and EF659599- EF659741)Gene genealogies were constructed for in PAUP* . Hierarcin PAUP*  suggestein PAUP*  and JC mpecifics , severalCP conceived the project, collected the data, carried out the analyses and wrote the manuscript. PY and NVS advised on analyses, NVS provided samples and lab materials. PY and NVS participated in discussions. All authors read and approved the final manuscript.


Identification of global livestock diversity hotspots and their importance in diversity maintenance is essential for making global conservation efforts. We screened 52 sheep breeds from the Eurasian subcontinent with 20 microsatellite markers. By estimating and weighting differently within- and between-breed genetic variation our aims were to identify genetic diversity hotspots and prioritize the importance of each breed for conservation, respectively. In addition we estimated how important within-species diversity hotspots are in livestock conservation.Bayesian clustering analysis revealed three genetic clusters, termed Nordic, Composite and Fat-tailed. Southern breeds from close to the region of sheep domestication were more variable, but less genetically differentiated compared with more northern populations. Decreasing weight for within-breed diversity component led to very high representation of genetic clusters or regions containing more diverged breeds, but did not increase phenotypic diversity among the high ranked breeds. Sampling populations throughout 14 regional groups was suggested for maximized total genetic diversity.During initial steps of establishing a livestock conservation program populations from the diversity hot-spot area are the most important ones, but for the full design our results suggested that approximately equal population presentation across environments should be considered. Even in this case, higher per population emphasis in areas of high diversity is appropriate. The analysis was based on neutral data, but we have no reason to think the general trend is limited to this type of data. However, a comprehensive valuation of populations should balance production systems, phenotypic traits and available genetic information, and include consideration of probability of success. Ovis aries) has been an economically and culturally important farm animal species since its domestication in the Near East approximately 9,000 years B.P. )Kinghorn  suggesteKinghorn  proposedlgorithm .MT supervised the molecular analysis, consistency of allele calling, coordinated or performed statistical analysis and wrote the final drafts of the paper. MO did the genotyping and most of the writing and statistical analyses for the first draft. IT had significant contribution both to statistical analyses and manuscript writing. MAT contributed to analysis design and molecular co-ancestry based analyses. NM, MC, GG, TK and MM have collaborated in study design, sampling and interpretation of the results. In addition, MC and TK did part of the molecular analyses. JK was in charge of the overall study including it's design, sample collection, statistical analysis, manuscript writing and coordinating the author contributions. All authors read and approved the final manuscript.Table S1 - Marker diversity parameters. PDF file with list of microsatellites and their chromosomal location, total number of alleles, expected unbiased heterozygosity, and estimates of within-population (f) and among-population (θ) fixation indices.Click here for fileTable S2 - Table of the name of sheep breeds, their origin, demographic status and diversity parameters. PDF file with data on per population sample size, expected heterozygosity, within-breed fixation index (f), allelic richness, and number of private alleles.Click here for fileFigure S1 - Additional synthetic maps. PDF file synthetic maps for within-breed diversity and breed differentiation.Click here for fileTable S3 - Breed-wise optimal contributions to a core-set for different weightings of the within-breed variation. PDF file with detailed data summarized in Table Click here for fileTable S4 - Distribution of core-set contributions using genetic clustering. PDF file with table similar to Table Click here for fileTable S5 - Breeds, having equal contributions to the core set when the number of breeds conserved is fixed. PDF file with table of included breeds when the number of included breeds is fixed at 5, 10, 15 or 20.Click here for file


FST and DEST), and effective population size (Ne). The primary goal of any recovery effort is to produce a long-term self-sustaining population, and these genetic measures provide a metric by which we can gauge our progress and help make important management decisions.Our ability to monitor populations or species that were once threatened or endangered and in the process of recovery is enhanced by using genetic methods to assess overall population stability and size over time. This can be accomplished most directly by obtaining genetic measures from temporally-spaced samples that reflect the overall stability of the population as given by changes in genetic diversity levels , degree of population differentiation  was delisted in 1994 and 1999, respectively, and its abundance will be monitored by the species Recovery Team every three years until 2015. Although the United States Fish and Wildlife Service makes a distinction between tundrius and anatum subspecies, our genetic results based on eleven microsatellite loci suggest limited differentiation that can be attributed to an isolation by distance relationship and warrant no delineation of these two subspecies in its northern latitudinal distribution from Alaska through Canada into Greenland. Using temporal samples collected at Padre Island, Texas during migration , no significant differences in genetic diversity or significant population differentiation in allele frequencies between time periods were observed and were indistinguishable from those obtained from tundrius/anatum breeding locations throughout their northern distribution. Estimates of harmonic mean Ne were variable and imprecise, but always greater than 500 when employing multiple temporal genetic methods.The peregrine falcon in North America (Ne, suggest a stable or growing population, which is consistent with ongoing field-based monitoring surveys. Therefore, historic and continuing efforts to prevent the extinction of the peregrine falcon in North America appear successful with no indication of recent decline, at least from the northern latitude range-wide perspective. The results also further highlight the importance of archiving samples and their use for continual assessment of population recovery and long-term viability.These results, including those from simulations to assess the power of each method to estimate  Ne), gene flow or dispersal, and population admixture can provide information useful for making future management decisions to prevent further population decline and extinction. For species previously considered threatened or endangered, section 4(g)(1) of the Endangered Species Act requires the U.S. Fish and Wildlife Service in cooperation with the States to monitor a species for a minimum of five years after being removed from the List of Endangered and Threatened Wildlife and Plants to ensure they maintain non-threatened status. The incorporation of genetic monitoring into such programs can provide information on the progress made in creating and maintaining a self-sustaining population, regardless if genetic measures were addressed in the original rulemaking or listing process . This approach can be particularly important with populations or species that have wide geographic distributions over challenging terrain  where accurate demographic measures from the field are costly or difficult to obtain.In cases where populations or species have a recent history of decline followed by increase, the use of genetic data can be a powerful tool for monitoring progress in conservation efforts Falco peregrinus) provides an example species recovery plan that could benefit from ongoing genetic monitoring. Globally, the peregrine falcon consists of nineteen subspecies and is found on every continent with the exception of Antarctica F. p. pealei is a year-round resident of the Pacific Northwest from the coasts of northern Washington and British Columbia extending to the Aleutian Islands in Alaska. F. p. tundrius breeds throughout the Arctic tundra of Alaska, Canada and western Greenland, and F. p. anatum breeds south of the tundra to northern Mexico, except in coastal areas in the Pacific Northwest. Both F. p. tundrius and anatum are migratory with tundrius wintering as far south as central Argentina and Chile in South America .The peregrine falcon (F. p. anatum) were essentially extirpated F. p. anatum) was also significantly reduced with only 15 (29%) of 51 known historic nest sites occupied in 1964 2F. p. tundrius) were less severe; however, it is estimated that their abundance was reduced approximately 50 to 60% by 1975 F. p. pealei), abundance remained relatively stable during this time period Historic estimates of peregrine falcon abundance range from 400 to 500 pairs in Greenland F. p. anatum and F. p. tundrius on the precursor of the federal Endangered Species List in 1970 (35 FR 16047), and extensive propagation and release efforts made by many conservation groups F. p. tundrius; 59 FR 50796), and 1999  in the U. S., and reassigned Special Concern status in April 2007 in Canada. Current estimate of total breeding population size for both F. p. tundrius and F. p. anatum is between 4,300 and 10,400 In the past two decades, peregrine falcons in the U.S. and Canada, including Europe F. p. anatum and tundrius. This was accomplished using temporally segregated samples from both migratory and breeding peregrine falcons sampled during fall and spring migration through Padre Island, Texas over a 21 year period (∼7 generations) and from southwestern Greenland over a 12–14 year period (∼4 generations), respectively. We assume that the sampled migratory individuals from Padre Island used in this study possess high latitude breeding distributions throughout Alaska, Canada and Greenland as supported by band recovery this study; see below), and therefore represent the northern peregrine falcon breeding population in North America. Our temporal sampling allowed the investigation of overall population stability by assessing allele frequency change over time and the estimation of Ne for both the migrant population and a focal breeding population in Greenland. These results are useful for conservation monitoring purposes and for making decisions that may influence future peregrine falcon population viability in North America.The primary aim of this study was to determine the overall stability and effective size of high latitude peregrine falcon populations in North American and Greenland, F. p. cassini (n = 25) from South America and F. p. macropus (n = 15) from Australia, were included in the analyses for comparative purposes.All samples were obtained from birds caught and bled under government permits, and all birds were released after processing. A total of 292 peregrine falcons were sampled for genetic analyses during migration through Padre Island, Texas. Blood samples were taken during both autumn and spring migration for each of the following temporal subsets: 1985/86, 1988/89, and 2006/07, with additional samples from spring 2001 migration period see ; Table 1Eleven microsatellite loci originally developed for the peregrine falcon  and observed (Ho) and expected (He) heterozygosity values were calculated using GDA. Measures of allelic richness (AR) were calculated using the program Fstat version 2.9.3.2 AR estimates control for uneven sample sizes among populations FIS and its significance for each sampled population was calculated using Fisher's exact test within Genepop v. 4.0.10 (http://genepop.curtin.edu.au) after adjusting the p-value to account for multiple simultaneous comparisons Microsatellite genotypes were tested for linkage equilibrium and departure from Hardy-Weinberg equilibrium within each population at each locus using the computer program GDA tructure version 2.1. The number of genetically distinct clusters (K), or populations, was identified based on allele frequencies across loci while minimizing linkage and violations to Hardy-Weinberg equilibrium. The most likely value of K is determined by comparing the likelihood of the data for different values of K. To determine the number of clusters, we also calculated the rate of change in the log probability of the data between successive K values (ΔK) plotted against K following Evanno et al. F. p. pealei, F. p. cassini and F. p. macropus samples to determine the influence the latter three subspecies have on the overall approximation of K. Calculations were conducted with a burn-in period of 105 iterations followed by an additional 106 iterations. Each simulation from K = 1 to 8 was performed four times using an ancestry model allowing admixture where individual α was inferred from the data for each cluster  and geographic distance of breeding sample locations of anatum and tundrius. A second set of analyses that included pealei samples were also performed. Geographic distance between sample locations was measured as the euclidean distance (km) using the ruler implemented in Google Earth version 5.2.1.1329.Estimates of genetic differentiation based on pairwise Ne) were calculated using different methodological approaches to assess the robustness of our results. Two general approaches to estimating Ne were explored: 1) analyzing single time period population samples, and 2) analyzing multiple temporal samples from the same population. Although the temporal approach has been shown to outperform single-sample methods when assuming a closed population Ne estimates from individual population samples . We used a method originally based on linkage disequilibrium  between at least two sampling periods  to calculate Ne. Because we do not have a precise estimate of N for the peregrine falcon population, we calculated Ne using a range of values from 1,000 to 100,000 individuals to determine if uncertainty in N influences our estimate of Ne. Two additional estimates of Ne were obtained for the Padre Island migrant population using a coalescent-based method as employed in the program TM3 Ne of the migrant population while assuming an Ne-MAX of 10,000, no immigration, and a generation time of three years.To estimate Ne based on multiple temporal sampling periods for the Greenland population, we used MLNE 2.3 while assuming 1) a closed population (NeCLOSED), and 2) accounting for immigration from a potential source population (NeOPEN). Estimates of Ne from temporal data when mistakenly assuming a closed population are likely to be incorrect because, in addition to the effects of genetic drift, immigration will influence allele frequencies of the population to an extent that is related to the amount of differentiation between populations Ne. Using MLNE, two temporally spaced datasets from Greenland  were used to estimate both NeCLOSED and NeOPEN to assess the potential influence of immigration on our estimate of Ne and for comparative purposes with our estimates from the migrant Padre Island population Ne. Similar to the migrant dataset, 10,000 was used as our NeMAX. We used the pooled allele frequencies from contemporary tundrius and anatum breeding locations as the potential source population for immigrants into Greenland for estimating NeOPEN. Additional estimates were also calculated using the spring migrants from Padre Island, TX as the source population to evaluate the choice of source population on NeOPEN.To estimate Ne, we used simulated data representing multiple populations of specified size. This was done primarily to determine our ability to estimate Ne in populations of large size where drift is not likely to play a strong role influencing allele frequency change over a short time periods, e.g., seven generations (as with this study). Using the empirical data from spring 1986 as our initial sampling period (T0), we used the program BottleSim T7) at population sizes Ne = 50, 100, 200, 300, 500, 1000, 2000, and 5000. For each simulation based on 1000 iterations, we used the settings for maximum generation overlap (100%), random mating, three years for age at first breeding, 12-year longevity, and equal sex ratios Ne were calculated similar to the empirical data, and their deviations from the specified Ne were then determined and directly compared to results obtained using the migrant Padre, TX temporal dataset. We also assessed levels of differentiation among ten populations from each of the simulated datasets of known size using similar samples sizes (n = 46). This was done to investigate the development of genetic differentiation relative to Ne after seven generations had passed similar to our empirical dataset.To assess the utility and precision of methods used to estimate F. p. tundrius Northwest and Ungava Bay, F. p. anatum Alberta and Northeast, F. p. cassini, and Padre Island spring 2001 migrant sampling locations. Loci Fp54 and Fp92-1were both monomorphic for F. p. macropus. After adjusting for multiple comparisons, significant departures from Hardy-Weinberg equilibrium in the form of heterozygote deficiencies were observed in one locus (Fp92-1) among four sampling locations . Similarly, significant FIS values were observed with five sampled locations  of the five remained significant after excluding locus Fp92-1 from the analysis. No pairwise comparisons testing for linkage disequilibrium were significant after correcting for multiple comparisons.Eight of the eleven microsatellite loci were polymorphic in all peregrine falcon sampling locations. Locus Fp5 was monomorphic in p>0.05) between geographic sampling locations in North America or between temporal sampling periods from Padre Island, Texas or Greenland  and Greenland  and the anatum population in Ontario . Expected heterozygosity (He) ranged from 0.485±0.092 in Ungava Bay F. p. tundrius from northeastern Canada to 0.557±0.083 in F. p. anatum from eastern Canada. A significant difference in He was observed between F. p. tundrius populations in Ungava Bay and Nunavut . Genetic diversity estimates for F. p. cassini and macropus from Argentina and Australia, respectively, were significantly lower in all comparisons of AR when compared to all sampling locations for the North American subspecies anatum, tundrius, and pealei (p<0.02); F. p. macropus He was significantly lower than He estimates from all anatum, tundrius, and pealei populations (p<0.04), with the exception of Ungava Bay F. p. tundrius . F. p. cassini He was not significantly different (p>0.06) from any of the He estimates from anatum, tundrius, and pealei populations.The majority of microsatellite genetic diversity estimates do not differ significantly  values from multiple runs at K = 3 (SD = 2.2) and K = 4 (SD = 3.6) being more consistent across runs compared to K = 5  indicated the highest posterior probability values for K = 2 (Ln P(D) = −15843.5), while K = 1 and K = 3 had lower posterior probability values . We are unable to evaluate between K = 1 and K = 2 using the ΔK method . For K = 2 in this second analysis, F. p. tundrius and anatum sample locations possessed a relatively high proportion of membership to the same inferred cluster  and the majority of the pealei samples were assigned to the second cluster at a lower proportion (proportion of membership  = 0.661). Samples collected from Padre Island across all years clustered with high support  with samples collected from breeding grounds identified as F. p. tundrius/anatum.The posterior probability values for each value of tructure. The minimum DIC value (30669) was achieved with K = 4  and plateaus with higher values of K. The average value for the 20% best runs when K = 4 showed that the tundrius and anatum individuals clustered in a single group, which also included all of the migrant samples collected from Padre Island, whereas the remaining three subspecies, pealei, macropus and cassini, were in separate clusters each with high support (data not shown).The results from TESS corroborated those found with SFST and Dest largely agreed with the results from Structure and TESS, indicating strong genetic differentiation between subspecies with the exception of those comparisons between F. p. tundrius and anatum which showed much lower levels of differentiation (FST comparisons were significant (p<0.001) after correction for multiple comparisons between F. p. tundrius and anatum sampled breeding territories, the values were low (FST = 0.006 to 0.050) and similar in magnitude to those obtained from within subspecies comparisons  of which some were also significant , with only one comparison being significantly different from zero (Ontario & Greenland_1990). Dest values between F. p. tundrius and anatum were similar to those obtained from pairwise comparisons between sample locations within F. p. anatum (Dest = 0.003 to 0.029), while pairwise comparisons between tundrius subspecies locations were consistently low (Dest = 0.000 to 0.012) and not significantly different from zero  and Dest  measures. Similarly, significant isolation by distance was observed among F. p. tundrius and anatum sample locations when southwest Ontario samples were excluded from the analyses . However, isolation by distance was not supported when we included F. p. pealei genetic distance measures with tundrius and anatum breeding locations .Estimates of ntiation . Althougrom zero . After eFST and Dest comparisons were significant with those made with F. p. tundrius  than with anatum samples .When comparing migrant peregrines from Padre Island with samples collected on breeding territories, far fewer pairwise Ne for the migrant peregrine falcon population varied depending on the choice of method, but in all cases, the values were high and ranged from 509 to >10,000 breeding individuals (Ne-MAX). The choice of the census population size  and 516 (121-infinity) using an N of 1,000 or 100,000, respectively. Estimates of Ne from the method LDNe using spring migratory single time periods ranged from 187.8 in the 2001 dataset and >10,000 in the remaining three periods  with 95% CIs ranging between 59.5 to infinity.Point estimates of ividuals . ReporteNeOPEN estimate of 122.7 (95% CI 55.3-590.7) with a joint migration estimate of 0.103 (95% CI 0.024-0.234). The choice of source population did not substantially affect our estimates of NeOPEN. When we defined the source population for potential immigrants as Padre Spring 2007, our estimates of NeOPEN was 102.5 (95% CI 44.7-470.7) with a joint migration estimate of 0.221 (95% CI 0.056-0.734). Estimate of NeCLOSED for the Greenland peregrine falcon population was 158.9 (95% CI 75.7-804.5).MLNE analyses on the Greenland population while allowing for immigration see  producedNe decreased  in southern Scandinavia Nearly 7,000 peregrine falcons were released in the United States and Canada between the years 1974 and 1999 F. p. tundrius and anatum samples from Brown et al. (tundrius and anatum) and Greenland (tundrius), our results indicate little if no genetic population differentiation  among sampling locations of these two subspecies. The significant values that were observed were those comparisons that included F. p. anatum samples from eastern Canada or geographically distant locations, including three other subspecies (FST) and geographic distance matrices suggests an isolation by distance model of population differentiation  that included other subspecies pealei, cassini and macropus were significant.Using n et al. ; n = 140bspecies . A signilocation , and simon e.g., . In contpealei and tundrius/anatum was not as strong with Structure and TESS compared to FST and Dest, the lower proportion of membership for a few individuals from the two groups was likely due to pealei also being used in the captive breeding program for which some post release individuals subsequently possessed mixed ancestry  and/or the lack of power to detect population structure in cases with relatively low levels of differentiation , we remain cautious and resist equating reported population differentiation estimates with actual relative degree of differentiation between populations . Peregrine falcons in North America, particularly those on the East Coast and Midwest in both Canada and the U.S., have experienced dramatic changes in population abundance over the past half-century With the exception of those comparisons made between high latitude populations of tructure, TESS, FST and Dest do indicate, however, no genetic differentiation between F. p. tundrius and high latitude anatum sample locations in Alaska and northwest Canada. The high latitude sampling locations used in this study are all geographic regions that have not experienced any direct supplementation from translocated or captive bred individuals Haliaeetus albicilla) populations suggesting that their long life span (∼17 years) has helped buffer against the effects of genetic drift and loss of genetic variability [see also 102–104]. In comparison, maximum longevity of banded wild peregrine falcons ranges from 16 to 20 years Our results from SFIS values, or heterozygote deficiencies  between breeding areas across their North American high latitude distribution, the more plausible explanation for significant FIS values is due to sampling multiple subpopulations within the three areas (1990 & 2001-04 Greenland F. p. tundrius and Northwest F. p. anatum). For example, the samples for Greenland 2001-04 were obtained from both Thule (n = 15) and Kangerlussuaq (n = 27), two geographic areas separated by >1,000 km, and the samples from Northwest F. p. anatum were obtained from a large geographic area throughout Alaska and northwest Canada . Estimates of Ne ranged from 509 to infinity , with extremely wide 95% confidence intervals , a variety of methods, some of which were employed in this study, do exceptionally well in inferring how strong genetic drift was or how large the Ne of the population must be to cause the observed change in allele frequencies over time when assuming no mutation, selection and migration during the sampled time period Ne becomes much more difficult. As was observed with our estimates of population differentiation measures  while accounting for immigration, suggesting that a much larger migratory population must exist when extrapolated to the remainder of its breeding distribution across North America.Our inability to obtain a precise estimate of ntervals . The powNe. Either we increase the sample size of each time period and/or increase the number of loci characterizing allele frequency change over time. In a recent study investigating Ne of an Australian tiger prawn (Penaeus esculentus) population, Ovenden et al. Ne of about 8,000 breeding individuals with eight microsatellite loci using similar temporal methods as this study. By increasing the time between sampling periods to four generations, their simulations suggested that the same sample size would produce accurate estimates for Ne of 10,000; however, by decreasing the sample size to 1,000 individuals, their ability to obtain finite estimates dropped from 100% to 65% S/Ne ratio (where S is the sample size) be approximately 0.10 for adequate sampling for the temporal approach for estimating Ne with genetic data. With a sample size of 46 individuals, we should be able to provide reliable estimates of Ne∼460. Therefore, the wide 95% confidence intervals, or low precision, we obtained from all of the temporal methods for estimating Ne in this study suggests that the actual Ne is of larger size.There are ways to improve the precision of our estimate of Ne from large populations using genetic data; however, the extent of its improvement will depend on the variability of the loci with increased polymorphism required Ne that can be used to make future management decisions for this species.Alternatively, increasing the number of loci would also increase the power to estimate F. p. anatum) and those further north, which includes both F. p. tundrius and northern F. p. anatum subspecies. Peregrine falcons (F. p. anatum) south of 54°N latitude possess reduced migratory behavior compared to those further north, producing a “leap-frog” breeding/migratory peregrine distribution tundrius and anatum subspecies, are estimated for the northern population (≥54°N latitude) based on non-genetic methods. The southern populations (<54 °N latitude) east of 100°W longitude (F. p. anatum) is estimated at ∼450 pairs, while west of 100°W longitude  consist of ∼1,400 to 1,800 pairs The above results can inform future management decisions impacting the full recovery of peregrine falcons breeding in North America. Recently, the U.S. Fish and Wildlife Service allowed the take in 2009 of up to 36 first-year (FY) autumn migrant “passage” peregrine falcons east of 100°W longitude for use in falconry Ne<500), the methods that we employed should have had sufficient power to provide precise estimates of Ne; yet, our inability to obtain such an estimate suggests this population is of larger size. The estimate of Ne as measured in this study  of the population  can outnumber the actual breeders in some areas by severalfold Ne/N ratio is further reduced due to a large number of individuals not producing offspring relative to fall census estimates.Therefore, even if we assume that Ne in this study, we can conclude that the Ne for the migrant peregrine falcon population is unlikely to be smaller than 500. This agrees with field data suggesting a much larger breeding population size Although we were unable to provide a precise point estimate of Table S1FST  and Dest  based on 11 microsatellite loci between regional peregrine falcon sample locations.Pairwise estimates of (0.09 MB PDF)Click here for additional data file.