This genotypic characterization was undertaken to know the genetic similarity among the parental lines to be used in developing a set of chromosome segment substitution lines. In all, 253 alleles were identified using 77 polymorphic SSRs, and polymorphism information content ranged from 0.31 to 0.97 with a mean of 0.79. Cluster analysis grouped the genotypes into three clusters at a genetic similarity of 0.26–0.75. Wild accessions grouped together in cluster-I, indica cultivars formed cluster-II, and aromatic, japonica and aus types came under cluster-III. Principal component analysis also showed similar results. The genotypic data was analyzed using STRUCTURE, and genotypes were grouped into four populations. RM1018 on chromosome 4, RM8009 on chromosome 7, and RM273 on chromosome 12 amplified alleles specific to wild accessions. The information obtained from core set markers would help in selecting diverse parents including wild accessions and for tracking alleles in mapping or breeding populations.Genetic diversity among 23 rice genotypes including wild species and cultivars of The online version of this article (doi:10.1007/s13205-016-0409-7) contains supplementary material, which is available to authorized users. Oryza sativa L.) is one of the most extensively cultivated cereal crops in the world spreading across a wide range of geographical, ecological and climatic regions. High genotypic and phenotypic diversity exists, and about 1,20,000 different accessions are reported in rice as a consequence of varied adaptations . PCR cycles were programmed as follows: initial denaturation at 94 °C for 5 min followed by 35 cycles of 94 °C for 45 s, 55 °C for 30 s, 72 °C for 45 s, and a final extension of 10 min at 72 °C. Amplified products were resolved on 4 % metaphor agarose gels prepared in 0.5 X TAE buffer and electrophoresis was conducted at 120 V for 2 h. Gels were stained with ethidium bromide and documented using gel documentation system . Only clear and unambiguous bands of SSR markers were scored. Amplified fragments were scored for the presence (1) or absence (0) of the corresponding band among the genotypes, and formed a data matrix comprising ‘1’ and ‘0’.165 SSR core set primers  of each SSR marker was computed according to the formula: PIC = 1 − ΣPiindica, aus, japonica, aromatic rice and wild accessions. Among the 165 markers, 38 SSRs were monomorphic, and 50 SSRs were not considered for analysis because of ambiguous amplification. 77 (46.6 %) clearly amplified polymorphic SSRs distributed on all chromosomes were selected for the study (ESM_1). These 77 SSRs showed a total of 253 alleles at polymorphic loci. All the polymorphic loci revealed PIC values between 0.31 (RM12923) and 0.97 (RM3805), and more than 56 % loci showed PIC values in the range of 0.8–0.97. Allelic richness per locus varied from 2 (RM6464) to 9 (RM3805) with an average of 3.3 alleles. The allelic richness observed was compared to reports of Singh et al.  were found to be polymorphic, exhibiting high PIC values (>0.7) followed by tri-nucleotide and tetra-nucleotide repeats. In this study, an average PIC value of 0.79 was observed which is higher than reported in previous studies of Ravi et al.  (0.57); O. rufipogon (Acc No CR100309) followed by Nipponbare and O. nivara (Acc No CR100008). RM1018 on chromosome 4, RM8009 on chromosome 7 and RM273 on chromosome 12 amplified a unique allele each in the wild accessions. RM8131 on chromosome 1 was able to delineate wild accessions from O. sativa cultivars except N22, an aus type of O. sativa cultivar. RM425 could help differentiate O. rufipogon (Acc No CR100309) and O.nivara (Acc No CR100008) accessions clearly. RM8004 on chromosomal and RM5543 on chromosome 7 could help distinguish the 2 O. rufipogon accessions from the 2 O. nivara accessions. The popular rice variety MTU1010 was uniquely identified using RM7075, Swarna with RM3805 and Pusa Basmati1 with RM5752.Rare alleles  were identified at 11 % loci, while the frequency of the most common allele at each locus ranged from 14 % (RM5748) to 95 % RM295). Among the markers used, 23 SSRs amplified 28 unique genotype specific alleles  or multiple bands  yet conserved in all the genotypes. Two SSR loci RM3805 and RM6314 showed double bands in most of the cultivars, but not in the wild accessions (ESM_2) indicating a locus duplication event during the domestication of rice.It is interesting to note that many of these conserved monomorphic loci are reported to be associated with traits such as leaf senescence, seed dormancy, plant height, panicle number, pericarp color . The analysis revealed wide genetic variability among the genotypes. Cluster I consisted of wild accessions with genetic similarity ranging from 30 to 50 % which indicates their diversity in the rice gene pool. Similar findings of separation of wild accessions from cultivars were reported by Ravi et al.  using 38Cluster III included two aromatic rice genotypes Basmati370 and Pusa Basmati1 which are closely related with a genetic similarity of 75 %. The unrooted neighbor-joining tree (UNJ) constructed using DARwin was in agreement with the UPGMA analysis, where the genotypes grouped into three major clusters. The boot strap analysis was done for 100 permutations and the boot strap values above 50 were considered and depicted in Fig. japonica genotypes Nipponbare, Moroberekan and aus type N22 grouped with indica genotypes Jaya, IR64, Pusa Basmati-1 and Basamti370. This grouping of indica- japonica- aus- aromatic might be due to an introgression of alleles from different sources during the course of evolution. The grouping of japonica genotype Moroberekan with indica type Tellahamsa and of aus type N22 with Basmati370 was reported previously as well  values increased up to K = 4, with the highest log likelihood score at the same position. The population structure using SSRs showed that likelihood reached a sharp peak when the number of populations was set at four, suggesting that these rice accessions can be grouped into four subpopulations.Model-based program STRUCTURE 2.3.4 was used to determine the genetic relationship among individual rice accessions. Simulations were conducted based on admixture model with K = 4; population 1 had all wild accessions with 3 pure and 1 admix genotype, population 2 had 13 Indian cultivars with four admixes, population 3 consisted of popular cultivars Jaya and Nagina 22 and aromatic genotypes. In population 4, japonica cultivars were present with admixes from indica genotypes and many subgroups appeared within the major groups when K was increased from 4. The population grouping through structure analysis also showed similar result as that of the distance-based clustering. Studies on genetic diversity and population structure reported that domestication, geographical location and breeding objectives influenced the genetic structure of rice genotypes significantly  was conducted to understand the genetic relationships among the elite breeding lines and with the wild accessions. Among the 23 genotypes, 4 wild accessions formed a distinct cluster in the upper-right corner of 3rd quadrant, showing a clear distinction from cultivars in the two-dimensional dispersion of all genotypes Below is the link to the electronic supplementary material.


Aedes aegypti, yet nothing is known about the population history and genetic structure of this insect in Indonesia. Our aim was to assess the spatio-temporal population genetic structure of Ae. aegypti in Yogyakarta, a densely populated region on Java with common dengue outbreaks.Dengue fever, the most prevalent global arboviral disease, represents an important public health problem in Indonesia. Control of dengue relies on the control of its main vector, the mosquito via Restriction-site Associated DNA sequencing) to analyze 979 Ae. aegypti individuals collected from the Yogyakarta city and the surrounding hamlets during the wet season in 2011 and the following dry season in 2012. We employed individual- and group-based approaches for inferring genetic structure.We used multiple marker systems  support the hypothesis that Ae. aegypti in Yogyakarta originated from a small or homogeneous source and has undergone a relatively recent demographic expansion.We found that Wolbachia-infected mosquitoes.We report the first insights into the spatio-temporal genetic structure and the underlying processes in the dengue fever mosquito from Yogyakarta, Indonesia. Our results provide valuable information on the effectiveness of local control measures as well as guidelines for the implementation of novel biocontrol strategies such as release of The online version of this article (doi:10.1186/s13071-015-1230-6) contains supplementary material, which is available to authorized users. Aedes aegypti. This mosquito has a close association with human habitation, preferentially exploiting domestic environments for its feeding and breeding requirements [Dengue fever is the most prevalent arboviral disease in the world and Indonesia boasts one of the highest numbers of dengue infections amongst the most endemic nations . Moreoveirements –6.Ae. aegypti populations around the world, including Indonesia [Wolbachia pipientis that has been artificially transferred into Ae. aegypti [Wolbachia infection induces a number of pathogen-resistant phenotypes that reduce the mosquito’s vectorial competency [Wolbachia-infected Ae. aegypti into the field to replace the vector competent populations with mosquitoes that have a significantly diminished capacity for pathogen transmission [Given that an effective vaccines or treatments against dengue viruses are not yet available, the control of dengue transmission is mostly dependent on the control of the vector populations . Insectindonesia , 8. In tndonesia . One suc aegypti , 11. Wolmpetency . This stsmission –15.Wolbachia releases in northern Australia and Vietnam [http://www.eliminatedengue.com/indonesia). Given that the establishment and spread of Wolbachia infection is influenced by the dynamics of local populations as well as migration among them [Ae. aegypti throughout Yogyakarta.Following the successful  Vietnam , 16, theong them , 18, it Ae. aegypti from Indonesia and samples from Vietnam, Australia and Brazil [Apart from information on the strong genome-wide differentiation between a small sample of d Brazil , there id Brazil . Howeverd Brazil ).Ae. aegypti in the city of Yogyakarta and the surrounding hamlets. This large and densely populated urban environment on the island of Java with an approximate population of 3.4 million people has common dengue outbreaks [Ae. aegypti collected during one wet and one dry season. We considered the following questions: 1) what is the level of structuring at the nuclear and mitochondrial genomes among sites in Yogyakarta? 2) Is there a seasonal variation in genetic structuring? 3) Is the level of gene flow different among urban and suburban areas (i.e. Yogyakarta city and hamlets)?Here, our aim was to assess the spatio-temporal population genetic structure of utbreaks . For exa2 and 1.0 km2.Thirteen collection sites within three districts were established in the Special Region of Yogyakarta for this study Fig. , Table 1Aedes aegypti larvae and adults were collected from ovitraps and backpack aspirators (BPA) positioned inside or very close to the houses (i.e. front- or backyards). Eggs collected from ovitraps were reared to the third larval instar to facilitate identification of species. A single larva from each ovitrap was used for analysis to minimise sampling of family groups [Within each collection site, y groups , 23, 24,Individuals were genotyped at nine microsatellite markers previously described . TheNlaIII and MluCI . Illumina adapters with customized barcode sequences  were ligated to the genomic fragments using 100 units of T4 ligase at 16 °C overnight . Size selection of 300–450 bp long fragments was completed with the Pippin Prep protocol . For the final library enrichment, five repeated PCR reactions with 1 nM standard Illumina primers included the following cycling conditions: 98 °C for 30 s, 12 cycles of 98 °C for 10 s, 60 °C for 30 s, 72 °C for 90 s, and the final elongation at 72 °C for 5 min. The independent reactions were pooled and purified to create a final library. Four libraries were sequenced in four lanes of Illumina HiSeq2000 using the 100 bp paired-end chemistry .Genome-wide single nucleotide polymorphism (SNP) genotyping was done using a customized double-digest RADseq method  as descrAe. aegypti nuclear and mitochondrial reference genomes [Raw Illumina reads were filtered and trimmed based on a minimum phred score of 20. We used the short read aligner program Bowtie  to align genomes , 33. Bec genomes ), we all genomes . DefaultBasic population genetic analyses such as observed and expected heterozygosity and tests for departure from Hardy-Weinberg equilibrium were carried out using GenAlEx v6.5  and FstaFST comparisons, treating each sampling location as an independent sample nested within season. Statistical significance was determined from 999 permutations using the program GenAlEx v6.5 [To estimate the level of spatio-temporal genetic structuring, we performed the hierarchical analysis of molecular variance (AMOVA) and post-hoc pairwise lEx v6.5 .Genetic structure was also inferred using the individual-based analysis implemented in the program STRUCTURE v2.3.4 , 39, thaFST values and the natural log of pairwise geographic distance between sites. IBD among individuals was tested using the matrix of unweighted squared genetic distances between individuals [Ne, was obtained using the two-sample temporal analysis method [F statistics and, in this case, the estimator Fs [Ne for multiple types of data sets including large panels of SNPs.Presence of isolation by distance (IBD)  between ividuals  and the ividuals . The spaividuals  with 999s method  as applis method  for boths method  uses mommator Fs  was applNh), nucleotide diversity (π), haplotype diversity (Hd), as well as the spatio-temporal AMOVA were done in the R package pegas [R2 test [R2 test has greater power than a classical mismatch distribution test for smaller sample sizes [The analyses of the number of mitochondrial haplotypes  indicated strong spatial genetic structure that was temporally stable Table . Three pon Table . Also, aon Table .Table 2HSTRUCTURE analyses of adult mosquitoes suggested four genetic groups based on the highest likelihood of data, and two groups based on the deltaK method , and individual larvae in the season . Spatial autocorrelation analysis indicated that greater relatedness between pairs of larvae can be found at distances of 8–9 km, even though it is low for larvae from different sites (i.e. autocorrelation coefficient is significantly negative at distances 3–6 km)  that were present in more than 80 % of individuals. We first checked for the presence of premature stop codons or heterozygous loci that would suggest paralogous/nuclear copies of mitochondrial sequences [R2 test for recent population expansion .We found 16 RAD tags in eight mitochondrial genes  showed that mosquitoes from some hamlets exhibit greater genetic isolation, regardless of their geographic position (e.g. sites 2 and 6). Also, mosquitoes from inner city sites and one hamlet (site 8) seem to interbreed freely, constituting a single genetic cluster. Mitochondrial variation suggests relatively recent population expansion of Ae. aegypti in this region.Multiple marker systems and temporal sampling of Aedes aegypti is thought to have invaded southeast Asia from the New World as recently as the 20th century [Ae. aegypti in Yogyakarta, but the presence of a single haplogroup supports the hypothesis of a small or genetically homogeneous source that underwent population expansion. This is in sharp contrast to Ae. aegypti from Rio de Janeiro, Brazil, where several highly divergent mitochondrial lineages have been found in one continuously distributed large population [Ae. aegypti populations from southeast Asia when compared with those in the New World. For example, Bosio et al. [ century . Mitochopulation . Our reso et al.  found thAe. aegypti from Yogyakarta. Genetic clustering did not clearly correspond to the spatial distance among sampling locations, resulting in a very weak isolation-by-distance. Generally, hamlets showed greater genetic isolation from each other and from the inner city sites. However, one hamlet (site 8) exhibited unrestricted interbreeding with mosquitoes from the inner city, forming a single genetic cluster. The spatial genetic structure of Ae. aegypti in Indonesia parallels the examples from Thailand, where mosquitoes show overall weaker differentiation within urban areas such as Bangkok or Hat Yai than in suburban or rural areas [Analyses of nuclear markers suggest stronger influence of human-assisted gene flow than active dispersal in shaping the spatial pattern of genetic structure in al areas , 61. ExcAe. aegypti across Yogyakarta. Diminishing efficacy of insecticide-based mosquito control is seen globally due to rapidly evolving resistance to all classes of insecticides [Ae. aegypti demonstrated that pyrethroid insecticides are likely to be losing efficacy in Yogyakarta, urging the need for the employment of resistance management tactics [A recent entomological study in Yogyakarta found that numbers of adult mosquitoes tended to fluctuate by season in sites 2 and 6, but were stable throughout a year in sites 7 and 8 . We did cticides . Recent  tactics . Our insAe. aegypti from Yogyakarta obtained through our study is also useful for implementation of Wolbachia–based strategies in this region. Replacement of local mosquito populations with Wolbachia¬infected mosquitoes could be more challenging in hamlet 8 and within the city sites due to high migration of uninfected mosquitoes into the release area. This could require a process of “adaptive management”, where an increased rate of releases is undertaken in areas with slower Wolbachia invasion [Knowledge of the spatio-temporal genetic diversity and structuring in invasion .Aedes aegypti, in Indonesia where dengue is endemic. Seasonal stability of population genetic structure and diversity suggests limited effectiveness of traditional control measures to reduce numbers of breeding mosquitoes. Finally, knowledge of the genetic connectivity/isolation of mosquitoes in some parts of Yogyakarta will be useful for optimizing the strategy that involves the releases of Wolbachia-infected mosquitoes to suppress dengue transmission.Our study provides the first insight into the genetic diversity and structure of the dengue fever mosquito,


Trypanosoma cruzi, the Fusarium solani complex, Aspergillus fumigatus, Blastocystis subtype 3, the Leishmania donovani complex, Candida albicans and Candida glabrata. We first analyzed phylogenetic relationships among genotypes within each dataset. Then, we examined different measures of branch support and incongruence among loci as signs of genetic structure and levels of past recombination. The analyses allow us to identify three types of genetic structure. The first was characterized by trees with well-supported branches and low levels of incongruence suggesting well-structured populations and PCE. This was the case for the T. cruzi and F. solani datasets. The second genetic structure, represented by Blastocystis spp., A. fumigatus and the L. donovani complex datasets, showed trees with weakly-supported branches but low levels of incongruence among loci, whereby genetic structuration was not clearly defined by MLST. Finally, trees showing weakly-supported branches and high levels of incongruence among loci were observed for Candida species, suggesting that genetic exchange has a higher evolutionary impact in these mainly clonal yeast species. Furthermore, simulations showed that MLST may fail to show right clustering in population datasets even in the absence of genetic exchange. In conclusion, these results make it possible to infer variable impacts of genetic exchange in populations of predominantly clonal micro-pathogens. Moreover, our results reveal different problems of MLST to determine the genetic structure in these organisms that should be considered.The model of predominant clonal evolution (PCE) proposed for micropathogens does not state that genetic exchange is totally absent, but rather, that it is too rare to break the prevalent PCE pattern. However, the actual impact of this “residual” genetic exchange should be evaluated. Multilocus Sequence Typing (MLST) is an excellent tool to explore the problem. Here, we compared online available MLST datasets for seven eukaryotic microbial pathogens:  Trypanosoma cruziThe Predominant Clonal Evolution (PCE) model LeishmaniaIt is clear that some recombination occurs or has occurred in pathogenic microeukaryotes. Ancient, strict clonal lineages seem to be rare in nature Multilocus Sequence Typing (MLST) Most studies in eukaryotic micropathogens define trees and clusters based on classical phylogenetic methods. These trees are based on the concatenation of the sequences of different loci. This approach is susceptible to biases caused by recombination and incongruence among loci. These biases are not considered in most papers dealing with MLST in eukaryotic micropathogens.Trypanosoma cruzi, the Fusarium solani species complex, Aspergillus fumigatus, Blastocystis subtype 3, the Leishmania donovani complex, Candida albicans and Candida glabrata. Then, we propose classification criteria of MLST datasets according to the degree of genetic structuring and the impact of genetic exchange in the populations under survey. Then, we evaluate the efficiency of MLST in the different datasets to determine clusters. Lastly, we propose criteria to define clusters or “near-clades” In the present study, we first analyze the genetic structure in MLST datasets of seven eukaryotic microbial pathogens: Candida glabrata dataset (Cg) using the MLST scheme proposed by Dodgson et al. Candida albicans dataset (Ca) using the MLST scheme proposed by Bougnoux et al. http://cglabrata.mlst.net/andhttp://calbicans.mlst.net respectively). In addition, a search in the available genomes of C. albicans showed that the 7 used housekeeping fragments are single-copy. The fragments for C. glabrata are also single-copy with the exception of FKS gene . This gene has one paralogous; however, this paralogous gene has low pairwise identity (78%) and it has not annealing sites for the used primers. The Aspergillus fumigatus database (Af) using the MLST scheme proposed by Bain et al. Blastocystis ST3 dataset (B3) using the scheme proposed by Stensvold et al. http://pubmlst.org/afumigatus/andhttp://pubmlst.org/blastocystis/). Six out of seven gene fragments of A. fumigatus were single-copy housekeeping genes according the BLAST search. Just one fragment  had a paralogous copy but with low pairwise identity (69.2%) and without annealing sites for used primers. All fragments used for typing Blastocystis ST3 dataset were single-copy regions of the mitochondrion-like genome and there were no paralogous copies in the nuclear genome. It is important to note that those regions used in Blastocystis ST3 were non-coding regions. Sequences for the Leishmania donovani complex (Ld) Trypanosoma cruzi (Tc) and Fusarium solani complex (Fs) T. cruzi are available at Genbank under KF889442–KF889571 accession numbers. The MLST scheme used for T. cruzi proposed by Lauthier et al. GTP) had paralogous in BLAST search for CL Brener strain genome but with low pairwise identity . In addition, Just Superoxide dismutase (SODB) had two copies with high pairwise identity (99%) and both with annealing sites for the used primers. All housekeeping gene fragments used for F. solani species complex and Leishmania donovani complex were single-copy. Only one strain for each sequence type (ST) was used in the analysis unless otherwise specified. Seq-Gen v1.3.2 C. glabrata. The length of the simulated alignments was set to 500 nucleotides. The number of fragments per dataset was set to 6. The evolutionary model was set to Kimura two-parameters with a transition/transversion ratio of 3 and a proportion of invariable sites of 0.66. The model was selected based on the best model that fit the T. cruzi dataset published by Lauthier et al. C. glabrata tree. Then, labels of taxa were permuted in order to simulate random combinations of alleles as expected under panmictic assumptions. Datasets simulated under panmixia are labeled here with the term RND (from RaNDom associations). For each micropathogen, we also analyzed datasets of 24 STs in order to make comparisons. These 24 STs were randomly selected (selection without reposition). These reduced datasets were made 10 times using different random seeds. Different analyses described below were made for each replica. The number of STs (24 STs) was selected because it is the number of STs for T. cruzi, which is the smallest dataset analyzed. In addition, we analyzed reduced datasets of 24 strains (instead of 24 STs) to determine possible bias of using just one representative strain per ST. In this case, more than one strain per ST is analyzed in the reduced dataset and the probability of repeated STs depends on the frequency of such STs in the full strain dataset.The Relationships among genotypes were analyzed with MLSTest Additionally, the number of individual fragment trees that support each branch was calculated. We used the term “consensus support” (CS) for this measure, because it is the support that a given branch would have if it appeared in a majority-rule consensus tree. In order to make comparisons, CS was arbitrarily considered moderate for datasets with 30%–60% of branches supported by at least two fragments, and high for datasets with more than 60% of branches supported by at least two fragments. In addition, the mean CS for each dataset was calculated. The mean CS was standardized for datasets with <7 loci. The last measure was calculated dividing mean CS by the number of loci in the dataset and multiplying by 7. In this way, the standardized mean CS is showing the mean CS if the dataset had 7 loci.n loci having from 20% to 40% of branches with n-1 fragments topologically incompatible with the clade in the concatenated tree. Moreover, TI was considered high for datasets with more than 40% of branches with n-1 fragments topologically incompatible.Overall incongruence in each dataset was assessed by using the Incongruence Length Difference test relying on the BIO-Neighbor Joining method (ILD-BIONJ) proposed by Zelwer and Daublin T. cruzi, which is the smallest dataset analyzed.The significance of the localized incongruence was evaluated using the NJ-Localized Incongruence Length difference test C. albicans datasets and the Mantel test were evaluated using the software CADM Candida albicansCongruence among distance matrices (CADM) of Frequently, data (sequences) had inconsistent information suggesting different and incompatible clusterings. Homoplasy  is a cause of contradiction. Another cause of inconsistency is the existence of different evolutionary stories  of the DNA fragments analyzed. The best tree is generally the one that minimizes the level of inconsistency. ILD-based tests analyze whether the inconsistencies with the concatenated tree are distributed at random among the different fragments (random homoplasy) or whether they are concentrated in certain fragments (incongruence produced by these fragments). Consequently, the null hypothesis of ILD-based tests is the random distribution of homoplasies, or congruence. Incongruence (nonrandom distribution) is the working hypothesis. On the other hand, the null hypothesis of CADM and Mantel tests is random correspondence (lack of correlation) among distance matrices. This means that the null hypothesis (H0) implies full incongruence  among distance matrices (and consequently trees). The working hypothesis is a statistically significant degree of correlation among distance matrices.Consequently, a significant p value in the ILD-based test (H0 is rejected) means that at least one fragment produces incongruence. However, a significant p value in a Mantel test means that the hypothesis of full incongruence among distance matrices should be rejected, which means that some level of congruence is recorded among matrices. In this sense, a significant Mantel test  is compatible with a significant ILD-based test (significant incongruence) when there is at the same time some degree of congruence and some degree of incongruence in the dataset. However, as it is the case for any statistical test, a non-significant p value for the Mantel test does not mean that the null hypothesis is corroborated. As a matter of fact, lack of significance could be due to the low power of the test due to insufficient data .T. cruzi to 1,000 STs for C. albicans. A. fumigatus and L. donovani complex showed the lowest numbers of polymorphic sites relative to other datasets. The typing efficiency (Number of ST/number of polymorphisms) was variable among datasets  were not supported by any fragment tree  branch was not supported at all. However, both datasets are not directly comparable because they have different number of STs. Because of this, we analyzed random subsamples of 24 STs for each dataset. The C. albicans dataset still had low support, since about 53% of the branches were not supported at all and only 5% of the branches were supported by three or more fragments . This value –although higher– was relatively close to the observed one for the simulated panmictic dataset . Instead, T. cruzi had a mean CS = 3.4 fragments per branch (CI95% = 2.35–4.32). Moreover, the simulated dataset CONG  was similar to T. cruzi with a mean CS = 3.08 (CI95% = 2.81–3.36). F. solani complex showed moderate branch support (33% of the branches with CS >2 and a mean CS = 2.7) . The CS was variable among datasets. The less supported dataset was ragments . In addiS = 2.7) . In the T. cruzi, F. solani complex and the simulated CONG) had at least 50% of the branches with a bootstrap value higher than 80%. On the other hand, datasets with low CS  had less than 30% of their branches supported by bootstrap values >80%. These results favor the hypothesis of a strong structuration for the T. cruzi and F. solani complex datasets.We also analyzed bootstrap values for the branches . BootstrT. cruzi. The last dataset has non-significant BIONJ-ILD . Moreover, BIONJ-ILD was still significant in subsamples of 24 STs . Blastocystis spp had variable p values for different subsamples (ranging from <10−4 to 0.068). We then analyzed how incongruence is distributed across the trees. Particularly, C. albicans and C. glabrata showed high levels of Topological Incongruence (TI) . Similar results were obtained using subsamples of the full set of strains for each micropathogen instead of the set of STs , with the exception of nce (TI) . Both da complex . This let of STs . These rC. glabrata and C. albicans showed 33% and 82.8% of the branches with significant localized incongruence (after Bonferroni correction), respectively. These results favor a weak structuration of these two datasets relative to the others. Other datasets showed either none or fewer than 30% of the branches with significant incongruence after Bonferroni correction  Three different genetic structure types (GST) may be proposed according to the analyzed data :T. cruzi and F. solani complex. This degree of structuration is explained by PCE. This should be the case for T. cruziT. cruziT. cruzi near-clades have a hybrid origin T. cruzi near-clades. We also observed low levels of incongruence and moderate support within the near-clade TcI from a restricted geographical area (data not shown) suggesting low frequency of genetic recombination. This suggests that PCE operates within this near-clade too, according to the “Russian doll model”: within each near-clade, one observes a miniature picture of the whole species, with linkage disequilibrium and lesser near-clades GST 1: datasets with moderate to well-supported branches and relatively low levels of incongruence. They may be considered as the best-structured datasets. These include F. solani complex dataset was clearly attributed to GST 1 too. This is in agreement with another multilocus analysis with high support for branches F. solani is a complex of species F. solani complex The A. fumigatus, L. donovani complex and Blastocystis ST3. The level of genetic structuration of the dataset is therefore not clearly defined by MLST. There are two possible explanations for this structure type. First, it may obtain if the dataset corresponds to a population of recent origin having undergone a radiation process. Although major clusters can be identified in the Blastocystis spp dataset, the majority of the STs corresponding to SSU clade 1 GST 2 corresponds to datasets with weakly-supported branches, but with low levels of incongruence. These include A. fumigatus dataset also falls into this structure type. Although it has been proposed that A. fumigatus exhibits two clusters by MLST A. fumigatus will require very polymorphic markers in order to analyze in depth its genetic structure and to determine the impact of its sexual cycle in natural populations.The second explanation for the GST 2 is that the markers used for typing have a low level of informative polymorphisms. The L. donovani complex dataset is referred to as GST 2. A low number of polymorphic sites was observed for the dataset despite the fact that long fragments  were analyzed. For example, quite low support was observed for the group L. infantum. However, other markers strongly suggest that the group is monophyletic Leishmania spp. The Candida datasets exhibit this pattern. However, not all weakly-structured datasets by MLST imply that the species under survey is poorly structured. At least 5 near-clades were evidenced in C. albicans with strong  correspondence between different markers C. albicansC. albicans MLST dataset: a role of recombination in blurring the C. albicans genetic structure and certain limitations of MLST used alone to define clusters. Odds et al. C. albicans. Recombination may cloud the structure of the species in MLST datasets, because some markers (such as MLST targets) could be more “sensitive” to the “clouding impact” of genetic exchange. This clouding is understood here as the effect of occasional recombination among strains or group of strains, which impedes that trees show well-supported clades by a little blurring of the limits between them. Although there are explanations for incongruence in diploids other than genetic exchange, they should not have sufficient impact. For example, events of Loss of Heterozygosity (LOH) may produce incongruence. However, similar levels of incongruence are observed in the relative C. glabrata dataset where LOH is not expected because is mainly haploid, suggesting that this mechanism is not the only cause of incongruence in C. albicans. The second important finding is that MLST could lack sufficient resolution to resolve certain branches in large datasets (particularly important for deep branches). Therefore, the cloud effect in Candida albicans MLST could be a combination of both factors, sensitiveness of MLST analyses to low levels of genetic exchange and lack of resolution to resolve certain near-clades.GST 3: weakly-supported branches and high and significant levels of topological incongruence. They should be considered as weakly-structured datasets. C. glabrata, it is clear that genetic exchange has had an impact in the evolutionary story of the species. Evidence of recombination has been shown for C. glabrata in 165 isolates among which 14 phylogenetic incompatibilities were found Candida dataset  available online showed a very similar structure to C. albicans (data not shown) suggesting that weak structuring by MLST is characteristic of the genus. Altogether, our results are compatible with the PCE model in Candida. However they bring additional information to it, which makes it possible to propose that genetic exchange was rare but enough to generate a blurring in particular regions of the tree-like population structure of this yeast.While it is true that no sexual cycle is known for Neisseria meningitidisIt is tentative to compare with the same tools radically different species, because this implies to operate at different scales; that is to say, different sampling strategies, different evolutionary times and different variability levels. Our tests deal with datasets, not species, and should be extrapolated cautiously to whole species. In this sense, scale is a crucial factor when whole species rather than only datasets are considered. This can be illustrated by the following example. If we analyze a mammalian dataset , we will conclude that it is a well-structured dataset, which is right (well-supported branches and low incongruence). This result obviously is not due to PCE. However, the conclusions about the dataset are important in themselves because they will orientate the following analyses and generate questions about the organism. Lastly, other GSTs, apart from the ones exposed here, are possible. For example, datasets with overall significant bootstrap but high incongruence have been observed in It is clear that near-clades, with the definition proposed by Tibayrenc and Ayala The PCE model of pathogenic microorganisms, as defined by Tibayrenc and Ayala Figure S1Candida albicans using uncorrected p-distances. Branches are colored according to the Consensus Support (CS). Red branches have CS = 0. Orange branches have CS = 1. Yellow branches have CS = 2. Green branches have CS>2. Grey branches are terminal branches.Neighbor Joining tree for 1000 ST from (TIF)Click here for additional data file.Figure S2Standardized Mean Consensus Support for each dataset. The mean consensus support was standardized at 7 loci. The error bars represent the 95% confidence interval for the standardized mean.(TIF)Click here for additional data file.Figure S3Consensus support (A) and Topological Incongruence (B) distribution for datasets of 24 randomly selected strains. The values are calculated as the mean of 3 replications. See legends of (TIF)Click here for additional data file.Figure S4Standardized Mean Topological Incongruence for each dataset. The mean Topological Incongruence was standardized at 7 loci. The error bars represent the 95% confidence interval for the standardized mean.(TIF)Click here for additional data file.Figure S5Candida albicans dataset of 18 strains previously identified, belonging to the near-clades I, II, III and SA . Bootstrap values over 60% , topological incongruence  and NJ-LILD p values for the near-clades  are shown.Neighbor Joining tree for (TIF)Click here for additional data file.Figure S6Candida albicans. The first tree is based on concatenated fragments (concatenated) and the following trees correspond to fragments trees (the fragment name is indicated at the top-left of each tree). Red boxes represent STs from MLST clade 1, blue boxes represent STs from MLST clade 2, and green boxes represent STs from MLST clade 3.Multiple topological incongruences in a random dataset of 60 STs of (TIF)Click here for additional data file.Figure S7Percentage of wrong branches for 50 datasets simulated along the showed tree. Branches in red were observed in less than the 50% of the replications of tree inference based on NJ method. Branches in orange, less than the 75%. Branches in green, more than 75%.(TIF)Click here for additional data file.Figure S8Concatenated tree for a simulated dataset of 7 congruent fragments. Topological incongruence is shown above the branch. Only values higher than 3 are shown.(TIF)Click here for additional data file.


Social behavior has long been known to influence patterns of genetic diversity, but the effect of social processes on population genetics remains poorly quantified – partly due to limited community-level genetic sampling (which is increasingly being remedied), and partly to a lack of fast simulation software to jointly model genetic evolution and complex social behavior, such as marriage rules.To fill this gap, we have developed SMARTPOP – a fast, forward-in-time genetic simulator – to facilitate large-scale statistical inference on interactions between social factors, such as mating systems, and population genetic diversity. By simultaneously modeling genetic inheritance and dynamic social processes at the level of the individual, SMARTPOP can simulate a wide range of genetic systems  under a range of mating systems and demographic models. Specifically designed to enable resource-intensive statistical inference tasks, such as Approximate Bayesian Computation, SMARTPOP has been coded in C++ and is heavily optimized for speed and reduced memory usage.SMARTPOP rapidly simulates population genetic data under a wide range of demographic scenarios and social behaviors, thus allowing quantitative analyses to address complex socio-ecological questions. Often studied in isolation, interest is now increasingly focused on how non-genetic factors, such as social behaviors, influence population genetic diversity. The pioneering social anthropologist Claude Lévi-Strauss  exhaustiLimited progress in this field can in part be attributed to a paucity of appropriate simulation tools. Coalescent theory, the workhorse of modern population genetics, makes the strict assumption of random mating (a necessary condition of ‘exchangeability’). Because marriage rules automatically impose non-random mate choices, coalescent approaches (and other simulation programs that make this assumption) cannot be employed. Some forward-in-time simulators do possess the required flexibility to accommodate complex social rules – simuPOP being an excellent example . HoweverSimulating Mating Alliances as a Reproductive Tactic for Populations – implements a forward-in-time simulation framework. Each individual carries a complete set of DNA, comprising sequences of unlinked loci on the autosomes, X chromosome, Y chromosome and mtDNA, which are inherited in the appropriate biological manner. Populations are defined by the user and evolve forward-in-time. The number of loci and their lengths can be chosen by the user.SMARTPOP – Each simulation can be considered as containing three features: •A demographic model, such as changes in population size.•A set of mutation rates for different loci. By default, SMARTPOP implements Kimura’s two-parameter mutation model.•A set of marriage rules – currently monogamy, polygamy, polygyny, polyandry and close-relative inbreeding avoidance, although a wider range of models are under active development.The challenge of all forward-in-time simulators is how to define the initial state of the simulation ,13, as nS, Watterson’s theta θw, the mean pairwise distance and its related diversity index θπ, the number of haplotypes h, allelic heterozygosity HA and Nei’s mean heterozygosity per site HN. Summary statistics (or DNA sequences) can be returned for the entire deme, or for a user-defined sample .SMARTPOP reports a battery of summary statistics and/or full DNA sequences both at the end of the simulation, and if requested, at set time intervals during the run. Summary statistics include the number of segregating sites Boost library for random computation and optimized array structures, iii) a DNA representation that packs 32 nucleotides into every 64-bit integer, iv) manipulation of DNA sequences by optimized bit operations, and v) code parallelized under the Message Passing Interface (MPI) framework. For most scenarios representative of real human communities, the resulting runtime is less than one second per simulation – often more than an order of magnitude faster than comparable forward-in-time simulators.A key feature of SMARTPOP, compared with other forward-in-time simulators, is its speed. Simulating DNA sequences for every individual within a population requires substantial computational resources, and runtime often increases linearly with the length of the locus. Benchmarking against other forward-in-time software shows that SMARTPOP can simulate datasets of a few thousand nucleotides within seconds, whereas alternative simulators may take minutes to hours . The code is under active development to address a range of ongoing anthropological and ecological questions. For instance, population structure and inter-deme migration are currently being implemented to explore mating systems that depend on spousal exchange between communities. Additional features are planned for subsequent implementation.SMARTPOP is a dynamic, open source project that aspires to provide an extendable statistical tool base for modeling the effects of social behavior on population genetic diversity. It is released with a supporting website containing exhaustive documentation about the source code and model implementation  modeled 1 Mb of the Y chromosome with a mutation rate of 3×10−8 mutations/site/generation in constant sized populations of 200 individuals. Leveraging the buffering phase, we mimic the founding of these two populations from a larger source group with much higher genetic diversity (θπ=25). Figure First, we model genetic diversity on the paternally-inherited Y chromosome through time in two small communities Figure A – the fn=104) modeled 1 Mb of the Y chromosome with a mutation rate of 3×10−8 mutations/site/generation in constant sized populations of 100 individuals under a switch from monogamy (generations 0–300) to polygyny (generations 301–600). Figure θw) for the Y chromosome through time.Second, we model a shift in mating systems. Simulations n=04 modelen=104) modeled 1 Mb of the X chromosome with a mutation rate of 3×10−8 mutations/site/generation. The population size is initially constant (n=100) for 500 generations and reaches an equilibrium state. The population then grows by two individuals per generation for 50 years, after which it evolves for 500 generations with a larger constant size of 200 individuals (consequently reaching a second equilibrium state). Figure θπ) of the X chromosome through time.Third, we model genetic diversity in a population experiencing demographic change. Simulations n=04 modelen=104) modeled 10 fully unlinked autosomal loci, each of 3200 nucleotides, with a mutation rate of 3×10−8 mutations/site/generation in constant sized populations of 100 individuals. Leveraging the buffering phase, we mimic the founding of these two populations from a larger source group with higher genetic diversity (θπ=25). Figure θw) through time in two polygamous populations that allow (red) or prohibit (black) sibling matings.Finally, we model the impact of sibling mating avoidance in small populations. Simulations n=04 modelehttp://smartpop.sourceforge.net).SMARTPOP runs from the command line with user-defined parameter flags. All parameters, except population size, have default values. If desired, parameters can be read from a command file. Given the complexity of the models that SMARTPOP is able to model, the interface is relatively simple and should rapidly become familiar to users of coalescent simulators such as MS . Full do−6 mutations/site/generation, with θπ (=Neμ) reaching 25 in the buffering phase, the following command line would be used:To simulate 500 instances of a 16 kb mtDNA sequence in a population of 200 monogamous individuals (mating system 1), for 100 generations, with a mutation rate of 2×10In the following example, an equivalent set of simulations parallelized under MPI would be distributed across four processors:−6 mutations/site/generation in a constant sized population. The programs were all executed on a Linux system running Ubuntu v. 13.04 with a 3.07 GHz Intel Xeon CPU X5675 processor. Simulations were not memory or I/O constrained. Runtimes for SMARTPOP varied from 2 to 153-fold (mean 41-fold) faster than other software applications . Table Figure Example 1B explores the effect of a switch in mating systems from monogamy to polygyny. Genetic diversity first reaches an equilibrium under monogamy. After switching to polygyny at 300 generations, genetic diversity decreases to a new equilibrium state of lower diversity. Such shifts between mating systems have also been documented in human communities. A particularly well-known example are the Mormons who practiced polygyny during the early history of the western US .Figure Figure Although these examples are relatively simple for didactic purposes, SMARTPOP can be used to explore far more complex social rules. We emphasize that this software is not specifically designed for humans, and as shown above, can be used to model a much wider range of biological systems in which social behaviors are thought to impact on patterns of genetic diversity.Developed to tackle the issue of computational speed when modeling interactions between genetic diversity and social behavior, SMARTPOP simulates complex social and demographic scenarios on a large range of genetic markers .The examples presented here illustrate the capacity of SMARTPOP to quantify the impact of social constructs, like mating systems, on population genetic diversity. They also highlight the importance of modeling the dynamics of population genetic diversity through time, emphasizing non-equilibrium outcomes of rapid shifts between social and demographic states over short timescales.SMARTPOP is well suited for studying human social systems, but is equally applicable to other species that exhibit complex social rules ,19,20. SProject name: SMARTPOP Project home page: http://smartpop.sourceforge.netOperating system: Linux, Windows, OS X Programming language: C++ Other requirements: 64 bit machine; C++ compiler; Boost v. 1.50 or higherLicense: GNU GPL v. 3.0 Any restrictions to use by non academics: NoneThe authors declare that they have no competing interests.EGG designed and developed SMARTPOP, and drafted the manuscript. MPC contributed to software design and analyses, and drafted the manuscript. Both authors read and approved the final manuscript.Implementation and validation. An extended discussion of implementation choices and a complete description of the software validation process.Click here for file


Lynx rufus were reintroduced to Cumberland Island (CUIS), Georgia, USA, from which they had previously been extirpated. They were monitored intensively for 3 years immediately post‐reintroduction, but no estimation of the size or genetic diversity of the population had been conducted in over 20 years since reintroduction. We returned to CUIS in 2012 to estimate abundance and effective population size of the present‐day population, as well as to quantify genetic diversity and inbreeding. We amplified 12 nuclear microsatellite loci from DNA isolated from scats to establish genetic profiles to identify individuals. We used spatially explicit capture–recapture population estimation to estimate abundance. From nine unique genetic profiles, we estimate a population size of 14.4 (SE = 3.052) bobcats, with an effective population size (Ne) of 5–8 breeding individuals. This is consistent with predictions of a population viability analysis conducted at the time of reintroduction, which estimated the population would average 12–13 bobcats after 10 years. We identified several pairs of related bobcats (parent‐offspring and full siblings), but ~75% of the pairwise comparisons were typical of unrelated individuals, and only one individual appeared inbred. Despite the small population size and other indications that it has likely experienced a genetic bottleneck, levels of genetic diversity in the CUIS bobcat population remain high compared to other mammalian carnivores. The reintroduction of bobcats to CUIS provides an opportunity to study changes in genetic diversity in an insular population without risk to this common species. Opportunities for natural immigration to the island are limited; therefore, continued monitoring and supplemental bobcat reintroductions could be used to evaluate the effect of different management strategies to maintain genetic diversity and population viability. The successful reintroduction and maintenance of a bobcat population on CUIS illustrates the suitability of translocation as a management tool for re‐establishing felid populations.In 1988–1989, 32 bobcats  Lynx rufus Schreber 1777 are currently the most broadly distributed native felid in North America, with a continuous distribution extending from Canada to Mexico  to multiple bottlenecks of fewer than five bobcats. Such small population size could facilitate inbreeding depression and genetic drift that would further reduce the viability of this population.Following the CUIS bobcat reintroduction, Diefenbach  modeled Although post‐reintroduction research on the CUIS population of bobcats examined behavioral and ecological processes such as social organization , scrub‐shrub thickets, freshwater wetlands, and salt marsh.Cumberland Island is a coastal barrier island, 25 km long, located 0.5 km north of the Georgia–Florida border. Its eastern shores face the Atlantic Ocean, and to the west, it is separated from mainland Georgia by 2–4 km of salt marsh and open water. Little Cumberland Island lies to the north, separated from the main island by 0.25 km of salt marsh and a tidal creek. The combined area of upland habitats on both islands is approximately 6935 ha. The climate is warm temperate to subtropical. Both islands contain a variety of habitats, including sandy beach and interdune meadow, interior maritime forests , we scraped a sample of the exterior surface using a scalpel blade and stored it at room temperature in a vial containing silica desiccant beads. Handlers replaced latex gloves and sampling tools after each collection to prevent contamination between samples. In most instances, individual scats were readily identifiable, but where multiple scats were deposited in close proximity, we collected samples only from scats likely coming from a single deposition; otherwise, no sample was collected.Canis latrans are present on CUIS, and both species produce scats similar in size and appearance that cannot always be distinguished morphologically. We used a mitochondrial DNA (mtDNA) control region fragment analysis test for species identification  in a laboratory dedicated to low‐quality DNA samples. We included negative controls to monitor for contamination. Coyotes Once we identified scats to the species‐level, we amplified DNA from each bobcat scat at a suite of 14 microsatellite loci described by Reding et al. . These lWe initially screened bobcat scats at two separate PCR multiplexes (multiplexes 1 and 2), each of which contained primers for three loci (Table S1). Each screening multiplex was amplified in at least three independent PCRs to minimize genotyping errors. Many scats failed to amplify at any locus and were subsequently excluded. Only scats that amplified at four or more loci were included in further analyses. After this initial screen, we amplified successful bobcat scats at an additional eight loci, within five multiplexes (Table S1). Once again, each locus was amplified a minimum of three times in independent PCRs.We calculated amplification and genotyping rates on a per locus basis using data for multiplexes 1 and 2, because those were the only loci amplified for all scats. The remaining multiplexes were selectively applied to samples that were successfully amplified at the first two multiplexes; thus, their rates are likely positively biased.P < 10−6) of identifying siblings as the same individual.We accepted a genotype at an individual locus only if that genotype was observed in three independent PCRs. For putative homozygous genotypes, a single allele had to be observed in all PCRs in order to be accepted as homozygous. Once genotypes were identified at each locus, we generated a consensus multilocus genotype for every scat. Only scats that produced consensus genotypes at six different loci were included in further analyses, which minimized the probability  and inbreeding coefficients because we expected many of the individuals in this isolated, restricted population to be related. For relatedness we used two methods: the maximum‐likelihood method employed by ML‐Relate  based on allele frequencies. The inbreeding coefficient, F, is the probability that an individual is homozygous  at any particular gene locus because its parents were related, that is, the alleles are identical by descent from a common ancestor. In these analyses, we utilized true allele frequencies with 100 simulated reference individuals and 100 bootstrapping replicates to estimate confidence intervals. We used GENALEX to estimate population level inbreeding (FIS) to measure the extent of homozygous excess relative to allele frequencies, where FIS is the proportion of the genetic variance in a subpopulation that is contained within an individual, and high FIS values imply inbreeding.We estimated pairwise relatedness (We estimated the abundance of the present‐day bobcat population on Cumberland Island (23 years post‐reintroduction) using the spatially explicit capture–recapture population estimation program, secr  to provide an indication of the effective number of bobcats that have contributed to the current gene pool. We used two methods implemented in program NeEstimator 2.01 . The average number of alleles observed per locus  was 3.67 (range 2–5). Combined across all loci, observed heterozygosity (HO) was 0.742 (±0.074 SE), and unbiased expected heterozygosity (HE) was 0.631 (±0.053 SE). The FIS was −0.255 (±0.065 SE), indicating an excess of heterozygotes.None of the loci deviated from Hardy–Weinberg equilibrium (r) values from ML‐Relate and COANCESTRY (Pearson's correlation coefficient between estimators was 0.785). The average value of r from ML‐Relate was 0.155 (SD = 0.248), and the average r from COANCESTRY was 0.077 (SD = 0.161). Of the 36 potential pairwise comparisons between individuals using ML‐Relate 27 (75%) produced r‐values close to or equal to zero, indicating the individuals were unrelated  were <0.03 and 95% confidence intervals overlapped 0, indicating no inbreeding  and was likely inbred.Overall, we found low levels of inbreeding and obtained similar relatedness . For effective population size (Ne), the heterozygous excess method produced an estimate of 5.0 effective breeders (95% CI = 2.7–∞) with a mean weighted D of 0.11. The coancestry method estimated 8.4 effective breeders (95% CI = 3.8–14.7).We used the 21 scat locations from nine different bobcats Fig. . We estiNe) size varied, the ratio of effective population size to total or census population size (Ne/Nc = 0.36–0.60) is relatively high for a vertebrate, which is typically Ne/Nc = 0.10–0.11  but lower allelic richness, AR (3.67) compared to bobcat populations sampled in continental‐wide surveys  sampled by Reding et al.  suggest a genetic bottleneck has occurred on CUIS.The CUIS bobcat population has similar levels of observed heterozygosity, g et al. . The higr = 0.155) was higher than that previously reported for other populations of bobcats  exist as part of a metapopulation, they have nevertheless experienced significant loss of genetic diversity over time. It is likely, therefore, that the small, isolated island bobcat population will follow a similar trajectory. Continued monitoring of CUIS bobcats could determine when genetic diversity declines, and additional introductions of individuals may be effective in maintaining genetic diversity and increasing population viability.Genetic rescue, in which the purposeful introduction of new individuals provides an input of genetic diversity, can improve the viability of a population .Microsatellite genotype data, GPS location data for each scat, locations of the search transects, shapefiles of the island boundary, and the secr code used to estimate abundance have been uploaded to the Dryad digital repository (doi: None declared.Table S1. PCR multiplexes and the corresponding loci associated with each reaction.Click here for additional data file.


Tetrapturus georgii), a recently validated species, has been historically misidentified as the morphologically very similar and severely overfished white marlin  (IUCN listing: Vulnerable). In effect, no information exists on the population status and evolutionary history of the enigmatic roundscale spearfish, a large, highly vagile and broadly distributed pelagic species. We provide the first population genetic evaluation of the roundscale spearfish, utilizing nuclear microsatellite and mitochondrial DNA sequence markers. Furthermore, we re-evaluated existing white marlin mitochondrial genetic data and present our findings in a comparative context to the roundscale spearfish.Misidentifications between exploited species may lead to inaccuracies in population assessments, with potentially irreversible conservation ramifications if overexploitation of either species is occurring. A notable showcase is provided by the realization that the roundscale spearfish  DNA markers provided mixed evidence for roundscale spearfish population differentiation between the western north and south Atlantic regions, depending on marker-statistical analysis combination used. Mitochondrial DNA analyses provided strong signals of historical population growth for both white marlin and roundscale spearfish, but higher genetic diversity and effective female population size (1.5-1.9X) for white marlin.The equivocal indications of roundscale spearfish population structure, combined with a smaller effective female population size compared to the white marlin, already a species of concern, suggests that a species-specific and precautionary management strategy recognizing two management units is prudent for this newly validated billfish.The online version of this article (doi:10.1186/s12863-014-0141-4) contains supplementary material, which is available to authorized users. Identifying genetic conservation units of large-bodied, marine pelagic fishes remains challenging as a result of their often large population sizes, typically strong dispersal ability  and few apparent physical barriers to gene flow. These parameters are generally associated with shallow levels of genetic differentiation across large geographic regions -3. More Tetrapturus georgii) and its routine misidentification as the morphologically very similar and overfished, sympatric white marlin, Kajikia albida , a third morphologically similar species , 72°C for 1 min, and a final 20 min extension step at 72°C. Electrophoresis was performed on an AB 3130 (Applied Biosystems Inc.) genetic analyzer. All fragments were sized using LIZ 600 as the internal allele size standard and scored using the software GENEMAPPER 3.7 (Applied Biosystems Inc.).The 13 microsatellite loci used for genotyping were those developed for roundscale spearfish by Bernard et al.   and observed (HO) heterozygosities, and tests for Hardy-Weinberg (HWE) and linkage equilibrium (LE) were performed using GENEPOP on the web (v.4.0.10)  as implemented in Arlequin 3.1 [D statistic [Snn) [Snn test statistic was estimated using 10 000 permutations ].To test for western Atlantic population subdivision with both mitochondrial and nuclear markers, we estimated divergence between WNA versus WSA samples. For mtCR sequence data, divergence was estimated using Фquin 3.1 , Jost’s tatistic  as impletatistic  (10 000 ic . Two disparate Structure analyses (see below) were performed both consisting of ten replicates for the values K = 1 - 5 , assuming correlated allele frequencies  were obtained from jModelTest. The piecewise-constant skyline model was selected and runs were fixed at 10 groups. The fixed substitution rate was set to correspond to the mutation rates utilized in the previous analyses (1.8 – 4.5% per site per million years). MCMC tests were run for 50 million generations and sampled every 5000th step (10% burn-in). Convergence was assessed using the program Tracer and estimates were assumed to have converged once ESS scores exceeded 200 for all parameter estimates.To assess the historical trajectories in female effective population size for roundscale spearfish and white marlin, we constructed coalescent-based Bayesian skyline plots (BSP) using the program BEAST v1.5.2 . Plots wtge105, tge23 & tge54; WSA: tge135 & tge139) after sequential Bonferroni correction (P < 0.05). We note, however, that where significant departures from LE were detected; they were not widespread, being restricted to only one of the two a priori defined populations . Furthermore, no evidence of linkage disequilibrium (LD) was found when all roundscale spearfish samples were pooled, suggesting that the surveyed microsatellite loci do sort independently. The frequency of null alleles estimated by FreeNA across all genotyped loci was < 5.0% and therefore considered negligible for analysis purposes , and variances associated with likelihood estimates increased at K > 1 (not shown). Geneland derived posterior distributions of the estimated number of populations (K) also produced a clear mode at K = 1 for all 10 runs. Log likelihoods ranged from −8805 (run 4) to −9002 (run 9) and no evidence of genetic subdivision among samples was detected (not shown). Coordinate uncertainty had no effect on the estimated number of populations (not shown). Individual-based Mantel tests revealed a lack of significant correlation between pairwise genetic and geographical distance among individuals for all comparisons .Results from the 13- and 10-locus microsatellite data sets were congruent for all individual-based analyses. h) and nucleotide (π) diversities were 0.993 ± 0.004 and 0.024 ± 0.012, respectively, and were similar between the WNA and WSA samples. In comparison, white marlin mtCR sequences (n = 99) resolved 91 haplotypes consisting of 21.69% cytosine, 28.84% thymine, 30.48% adenine, and 18.99% guanine. A total of 225 polymorphic sites were identified, consisting of 208 transitions, 10 transversions, and 22 indels. Overall, mtCR genetic diversity estimates in white marlin were higher than roundscale spearfish .Sample sizes and population-level mitochondrial diversity indices for both billfish species are listed in Table I) and a gamma distribution (Γ) of rate heterogeneity across variable sites provided the best fit to the roundscale spearfish mtCR data set (jModelTest). The estimated parameters under this model were Γ = 1.1990, and I = 0.51. For white marlin, jModelTest identified the HKY + I + G model  and sailfish, [Istiophorus platypterus]), have also found little, if any, support for population structure within the Atlantic [FST of 0.0041 (P = 0.017) between WNA and WSA collections, which is very similar to the level of differentiation we found for roundscale spearfish (FST = 0.0037). Furthermore, as was found with the roundscale spearfish, the white marlin mtCR data did not detect significant differentiation between the WNA and WSA samples based on the ФST statistic [Snn statistic did not differentiate white marlin from the WNA and WSA, despite both species having similarly high haplotype diversities. These contrasting Snn results may be due to the small white marlin sample sizes used for the mtCR analyses (n = 20 per collection), which likely reduced the power to detect shallow differentiation [Previous surveys of the genetic population structure of other istiophorid species , raises the question of whether these populations should be treated as separate management units . While s, sensu  - and ths, sensu , leads uHri and SSD in Suppl. Online Additional file With one exception (see below), all statistical tests Table  and BayeNfE of white marlin may be larger (1.5 to 1.9 times) than roundscale spearfish. However, it is important to note additional caveats related to this inference.Estimates of roundscale spearfish nucleotide and haplotype diversity fell within those reported for the mtCR of other billfishes ,43,81,82To date, no information is available on the generation time and growth of the roundscale spearfish , and smaNfE derived from the BSPs  for white marlin were approximately 1.5 to 1.9 times greater than for roundscale spearfish , suggesting that historically white marlin population size has been larger than that of the roundscale spearfish.Coalescent-based estimates of the female effective population size also suggested a higher effective size for white marlin. Final median estimates of h Figure , althougNfEs should be considered highly provisional. Moreover, it is important to note that coalescent-based analyses incorporate numerous sources of error when estimating both the genealogy and population history from a small sample of individuals [NfEs for white marlin compared to roundscale are preliminary, they are concordant with recent species observations of catch proportion data from fisheries which suggest that white marlin census population size is higher than that of roundscale spearfish, at least in the WNA and Caribbean waters [NfE estimates suggest that white marlin census size may also be close to two times higher than that of roundscale spearfish. What are the implications of a roundscale spearfish population size that is much lower than the white marlin population size? It is well established that white marlin have been severely overfished [As stated above, because the estimates of female effective population size generated herein are based on a single mitochondrial region, and the assumption of equal generation times and mutation rates for the two species, the absolute and even relative ividuals , and thaividuals , suggestn waters ,86,87. Aerfished  and thaterfished . Since ierfished ,86,87.To examine the effects of misidentification of roundscale spearfish on population trends of both roundscale spearfish and white marlin, Beerkircher et al.  conducteThe population structure and comparative demographics results presented here underscore the importance of changing from the current international management model for “white marlin” as a two species complex to management of the roundscale spearfish and white marlin as distinct evolutionary lineages. We recognize that a shift to species-specific management will be challenging due to misidentifications. However, there are now morphological identification tools available to distinguish the two species ,17, and Finally, we recognize that our conclusions pertaining to roundscale spearfish population structure and demographics are based on analyses of samples from only the western Atlantic, which may represent just part of this species’ potential Atlantic-wide distribution. However, our findings on these parameters placed in context of general billfish overfishing, almost no information on the life history of the roundscale spearfish, and the difficulty of enforcing pelagic fishery regulations on the international level, call for an aggressive precautionary management policy for this enigmatic species. Individual species management and conservation attention, including management on a two stock precautionary basis, are prudent to avoid inadvertent, drastic reductions in what appears to be a lower abundance species, and unrecognized population collapse of either potential roundscale spearfish hemispheric stock.


Aristeus antennatus is an ecologically and economically important deep-water species in the Mediterranean Sea. In this study we investigated the genetic variability of A. antennatus sampled from 10 sampling stations in the Western and Central Mediterranean. By comparing our new samples with available data from the Western area, we aim to identify potential genetic stocks of A. antennatus and to reconstruct its historical demography in the Mediterranean. We analyzed two regions of mitochondrial DNA in 319 individuals, namely COI and 16S. We found two main results: i) the genetic diversity values consistent with previous data within the Mediterranean and the absence of barriers to gene flow within the Mediterranean Sea; ii) a constant long-term effective population size in almost all demes but a strong signature of population expansion in the pooled sample about 50,000 years B.P./ago. We propose two explanation for our results. The first is based on the ecology of A. antennatus. We suggest the existence of a complex meta-population structured into two layers: a deeper-dwelling stock, not affected by fishing, which preserves the pattern of historical demography; and genetically homogeneous demes inhabiting the fishing grounds. The larval dispersal, adult migration and continuous movements of individuals from “virgin” deeper grounds not affected by fishing to upper fishing areas support an effective ‘rescue effect’ contributing to the recovery of the exploited stocks and explain their genetic homogeneity throughout the Mediterranean Sea. The second is based on the reproduction model of this shrimp: the high variance in offspring production calls for a careful interpretation of the data observed under classical population genetics and Kingman’s coalescent. In both cases, management policies for A. antennatus will therefore require careful evaluation of the meta-population dynamics of all stocks in the Mediterranean. In the future, it will be particularly relevant to sample the deepest ones directly. Accurate estimates of genetic variation in marine populations can support adequate strategies for the management of severely exploited fishery resources. Although many of the scientific recommendations for fisheries management are based on outputs of stock assessment models, better integration of genetic information and conventional methods of fisheries stock assessment could significantly improve the quality of management advice . PopulatAristeus antennatus and Aristaeomorpha foliacea, both representing the main target species of Mediterranean deep-sea trawling, have been carried out, and data on their population genetic structure have been reported mostly in the Western Mediterranean [Despite the increasing exploitation of the Mediterranean deep-sea resources, considered highly vulnerable to anthropogenic impact , the generranean ,5,6,7 whAristeus antennatus  is the main target species of deep-sea trawling and one of the most valuable marine resources in the Mediterranean, where thousands of tonnes of these species are landed along the coasts of the western and central basin [A. antennatus is widely distributed in the Central area of the East Atlantic, in the Mediterranean Sea, with the exception of the North Adriatic Sea [The blue and red shrimp  Risso, 116 is theatic Sea , in the atic Sea . In receThe blue and red shrimp is an eurybathic species, with a wide depth distribution between 80 and 3300 m  althoughA. antennatus catches according to the season and area have been reported in recent decades [A. antennatus [A. antennatus, also considering its peculiar life history and its extensive migrations [Although large fluctuations in the  decades , a large decades ,18,19. Mtennatus ,19,20. Agrations .A. antennatus indicated the absence of population differentiation in the Western and Central Mediterranean Sea using mtDNA markers [Previous genetic studies on  markers ,21,22. T markers . However markers  while noA. antennatus in the Western and Central Mediterranean and to test for the presence of barriers to gene flow, which may have shaped its genetic variability; (ii) to reconstruct the historical demography of A. antennatus in the Mediterranean by comparing our new samples from the Western and Central Mediterranean to previous samples collected from Westernmost Mediterranean and the Atlantic Ocean (AO) [A. antennatus in the new study areas as well as a better characterization of its evolutionary history will contribute to the development of the Mediterranean-wide management strategies for this important commercial species.The aim of the present study is: (i) to identify potential genetic stocks of ean (AO) . The knoA total of 319 individuals were collected between 530 and 750 m in depth from 10 sampling stations located in 3 basins of the Western and Central Mediterranean  during the 2010–2011 MEDITS experimental surveys .Specific permission was required from the Maritime Authority  to carry out the survey at sea and for the locations or activities. The field studies did not involve vulnerable or endangered species. The specific location of the study has been reported in A. antennatus distribution in the Adriatic Sea  , 6 stations in the North-Western Ionian Sea , and 2 in the Southern Adriatic Sea which represents the northern limit of rranean) , Table 12 (50 mM), 5 μl dNTP (2 mM), 1.5 μl of each primer (10 μM), 0.3 μl of 5 units EcoTaq DNA polymerase and 2 μl template. The 16S region was amplified and sequenced using the primers 16SARLpan-T: 5’-TGCCTGTTTATCAAAAACAT-3’and 16SBRHpan: 5’-CCGGTCTGAACTCAAATCATGT-3’ [A portion of about 10 mg of muscular tissue was excised from the second pairs of frozen-upon-capture sample pereiopods and was stored in 95% ethanol. DNA was extracted using the Quiagen DNeasy Blood and Tissue Kit. Polymerase chain reactions of the COI and 16S fragments were carried out following the procedures outlined in : 50 μl oCATGT-3’ . The COICATGT-3’ .The thermal cycling profile began at 94°C for 2 min as a hot start, followed by 35 cycles of 94°C (60 s), 53°C (60 s) and 72°C (60 s), with a final step of 7 min at 72°C for the termination of PCR.http://www.bmr-genomics.com). The sequencing primers were the same as those used for the PCR amplifications.Amplified fragments were screened on 1% agarose gel and the bands were cleaned for sequencing with Exonuclease I and Shrimp Alkaline Phosphatase (SAP) . PurifieAristeus antennatus obtained by Roldan et al. (2009). Each distinct haplotype has been submitted to GenBank (accession numbers KF768029 to KF768043 for 16S rDNA haplotypes and KF768044 to KF768067 for COI haplotypes).Chromatograms were edited, assembled and aligned using MEGA 5 employing as reference the 16S rDNA and COI sequences of ϕst and hierarchical analysis of molecular variance (AMOVA) [ϕst takes molecular distance between haplotypes into account and it is therefore more appropriate when dealing with sequence data. The two genes were analyzed separately (COI 500 bp and 16S 447 bp) and concatenated in a single stretch of 947 bp.Haplotype diversity, nucleotide diversity, neutrality tests (Tajima’s D and Fu’s Fs), pairwise  (AMOVA)  were com (AMOVA) . ϕst taϕ-statistics were assessed by a permutation test with 1,000 bootstrap replicates [K (the number of groups) from 2 to 5.AMOVA was applied to partition genetic variance either according to the geographic location or to the depth of the sampled populations. The significance of variance components and plicates . The genplicates . We ran ϕST was plotted in two dimensions by means of a non-metric multidimensional scaling algorithm [isoMDS function in R [The matrix of pairwise lgorithm  as impleion in R .A median-joining network (MJ) was built using NETWORK 4.5.1.6  with defI), as selected by means of the Akaike Information Criterion implemented in the program jMODELTEST 0.1.1 [We performed demographic inferences using the Extended Bayesian Skyline  coalesceST 0.1.1 . Each anST 0.1.1 .A total of 319 concatenated 16S rDNA (447 bp) and COI (500 bp) sequences was obtained from ten sampling station in the Western and Central Mediterranean.As mtDNA is inherited as a single locus, we preferred to concatenate the two genes but to account for the different in the mutation rate of the two regions we also provided summary statistics for each of them sepatately in the Supplementary Materials (Table A in h±SD = 0.750±0.139) while the lowest was in the Central-Southern Tyrrhenian, station 70 (h±SD = 0.340±0.093). The values for the remaining Mediterranean samples were h = 0.371 to 0.675. All summary statistics are presented in Maximum haplotype diversity values was found in the North-Western Ionian Sea, at station 65 . Second, when we tested whether geographical areas  or the fishing depth range , as a good predictor of the genetic variability, we found that ϕCT  was not significant in both cases  ϕCT values . However, populations within these groups did not display any clear geographic or depth pattern , meaning there are no large difference in alleles distribution among populations see , even th pattern , confirmϕCT value for K = 5, populations from the same area or depth were located in different groups, suggesting that also in this case no clear geographic or ecological pattern is present  (using COI only because the concatenate sequences were not available), the highest percentage of genetic variability (86%) was still present within samples . Despite present . This is present . Consist present .D and Fu’Fs in the pooled samples, but we detected no departure from equilibrium at the single population level (with the execption of stations 55 and 70) (The Extended Bayesian Skyline Plot (EBSP) for the concatenated sequences suggests a strong signature of expansion around 50,000 years B.P., in the pooled Mediterranean, Western-Central Mediterranean and WM samples . Consist and 70) .A. antennatus throughout the Western and Central Mediterranean were consistent with previous data within the Mediterranean [h±SD = 0.941±0.026; π±SD = 0.0040±0.0004) [Genetic diversity values of erranean ,7 but loThe absence of a clear geographic structure suggests that no barriers to gene flow exist within the Mediterranean at the mtDNA level. This confirms the results obtained on the same species by Maggio et. al (2009) and Roldan et al. (2009) with mtDNA, and Cannas et al. (2011) with microsatellite markers, but questions the results presented by Fernandez et al. (2011) who identified the Strait of Sicily as a barrier to gene flow using the same mtDNA markers. However, the latter study inferred the role of the Strait of Sicily in preventing gene flow on the basis of the geographical distribution of genetic variants rather than explicitly testing for it. Here, we have performed rigorous statistical analyses  on an extended sample and found no evidence of restricted migrations between the two sides of the Strait.A. antennatus populations. The topographical and hydrographical characteristics of this Strait would make it difficult for Mediterranean adults and larvae of this species to cross to adjacent Atlantic waters [The only phylogeographic discontinuity we found is represented by the Straits of Gibraltar, which effectively reduces the gene flow between c waters . The Strc waters  with a cc waters ,38.A. antennatus around 50,000 years B.P., which may be related to environmental changes which occurred in the Mediterranean Sea. Although different species within the Mediterrenean basin do not show a uniform phylogeographical pattern, three main timings of expansion seem to be common to many of them, namely the Late Pleistocene, the Mid-Pleistocene and the Early Pleistocene [Paracentrotus lividus [Thunnus thynnus [Pomatoschistus minutus [A. antennatus. It is difficult to clearly identify the main ecological forces that can explain the present results, but changes in the trophic conditions with productive environments for many marine species and a general enrichment of the benthic ecosystems at the beginning of the last glacial period  could be hypothised [We did not find a signature of a genetic bottleneck, although a reduction in population size due to fishing pressure and physical disturbance has been reported in the WM and an overfishing condition has been observed in various Mediterranean stocks ,19,20. Cistocene ,39. The istocene ,41. Inde lividus , the Atl thynnus  and the  minutus  dated bapothised .A. antennatus in the Mediterranean Sea leads to three main observations. First: heterozygosity and nucleotidic diversity levels are similar to those described in many decapod crustaceans characterized by an “off shore” life cycle [Fst correlates neither to geographic distances nor to the depth of the sampling stations. Third: there is generally a constant demography in the single demes with only a few of them showing a recent and slight increase while there is a strong expansion around 50,000 year B.P. in the pooled sample.To summarize, the analysis of the mitochondrial genetic variability of fe cycle . Second:A. antennatus.Based on these evidences, the present results can be explained considering both the ecological pattern and the specific population genetic of the deep-sea shrimp A. antennatus have been more recently proposed by [A. antennatus distributional range and therefore they are probably recently recolonized units. The shape of the gene genealogy of a deme of a meta-population depends on its effective population size and the migration pattern between demes (Nm) [Nm values, intra-deme genealogies, particularly for recently occupied demes, will have a mixture of recent and more ancient coalescent events, typical of the constant size demography of an unstructured population [A. antennatus in the Mediterranean Sea, apparently not affected by recent fishing pressure or overexploitation conditions. However, we also notice that the exploitation of deep-sea stocks only started in the first few decades of the last century due to the development of the fishing technology in deep waters. In particular, the deep-sea shrimps A. foliacea and A. antennatus became the target of deep-water bottom trawl fishing firstly in the Ligurian Sea in the 1930s and in the Catalan Sea in 1940s [Nm values. We therefore performed some indirect inferences using contrasting single deme and pooled deme samples [According to  an exchaposed by . Therefoposed by , and demposed by  contribuposed by . Moreoveposed by , togetheposed by ,49 increposed by . These mposed by ,51,52 Thmes (Nm) ,54. For pulation . This expulation . Therefoin 1940s , followein 1940s . Thus, tin 1940s . These min 1940s , such as samples  and planA. antennatus present more challenge when analysed at the population genetic level. The large variance in the reproductive success determines that gene genealogies are better described by the so called multiple mergers models, where multiple coalescent events per generation can occur, unlike in the Kingman’s n-coalescent [Fst than more distant demes. In these cases, gene genealogies are often expected to be starlike [Species characterized by a large skew in the offspring production as alescent . As a coalescent  and “chastarlike  but othestarlike ) overcomstarlike . TherefoA. antennatus. Ecological and life history traits make the reconstruction of the evolutionary history of the species particularly challenging. We envisioned for the future both to extend the sampling  and to screen more independent genetic markers in order to achieve a good estimation of the demographic parameters. We also highlight the importance in the next future to extend and apply model based on multiple mergers coalescent to check if they can provide a better interpretation of the data observed. Comparing models based on Kingman’s coalescent and the new extension of coalescent theory [We observed a very complex pattern in the genetic variability of t theory  would beS1 FileST values. A) Samples from Western and Central Mediterranean (present study), Western and Central Mediterranean and Atlantic Ocean ; B) Samples from Western and Central Mediterranean Sea (present study). The green rectangles are samples station from Adriatic Sea; the red ones from Ionian Sea; the blue are from Tyrrhenian Sea. The purple rectangles represent samples from Central and Western Mediterranean and the orange one is the Atlantic Ocean sample. Fig. B, Median-joining network of haplotypes detected for 16S rDNA and COI genes from the sampling locations of the Western and Central Mediterranean Sea. The area of each circle is proportional to the number of individuals exhibiting that haplotype. Each line in the network represents one mutational step, and red vertices represent missing or undetected haplotypes. Fig. C, Extended Bayesian Skyline Plot (EBSP) for COI sequences. A) Western and Central Mediterranean (samples from present study); B) Western Mediterranean ; C) All Mediterranean basin . X axis: calendar years. Y axis: effective population size (assuming a generation time of one year). The red lines show the 95% HPD limits and the black line is the median estimate. Table A, Diversity measurement for 16S rDNA (447 bp) and COI (500 bp) sequences. In the table are report the summary statistics for each locus gene analyse separately. Number of haplotypes (Nh); number of polymorphic sites (Np); haplotype diversity (h); nucleotide diversity (π). Tajima’s D and Fu’s Fs neutrality tests. * p≤0.05, **p≤0.005. Table B, Results of hierarchical analysis of molecular variance (AMOVA) in Central-Eastern Mediterranean. The significance of variance components and Φ-statistics was assessed by a permutation test with 1,000 bootstrap replicates. Locality region as in Fig. A, MDS Plot based on pairwise F(DOC)Click here for additional data file.