Scomberomorus concolor population in the Gulf of California was confirmed using nine nuclear microsatellite loci in combination with mitochondrial cytochrome b gene sequences. Samples were collected from the upper and central Gulf areas, representing the two main biogeographical regions of the Gulf. The analyses support the existence of a single panmictic population of S. concolor inhabiting the Gulf of California which in terms of fishery management represents a single genetic stock. Additionally, the contemporary effective population size estimated for the S. concolor population (Ne = 3056.9) was high and similar to another pelagic species. The gene flow seems to be bidirectional between the upper and central Gulf, which coincides with the seasonal movements between both regions related to spawning and feeding activities. A population expansion event was detected, which agrees with a colonization-expansion hypothesis of the S. concolor population in the Gulf.The genetic homogeneity of the Monterey Spanish mackerel  The Gulf of California (GC) is a long, narrow, semi-isolated basin which was formed by tectonic rifting, separating the peninsula of Baja California from the mainland approximately 5–12 million years ago . Based oScomberomorus concolor  is an epipelagic fish species inhabiting these two main biogeographical regions of the Gulf of California  sequence data from S. concolor, suggesting the existence of a mobile population migrating between the upper and central Gulf. However, the mtDNA is sensitive to past fluctuations in population size, which may obscure the signal of divergence (mtDNA-CR) may result in complex and unclear phylogeographic patterns. As evidence of population expansion events and a high genetic variability for S. concolor has been previously reported, the use of a less variable region as the mitochondrial cytochrome b gene (mtCyt-b) may be useful to clarify the species’ phylogeography. This, besides the inclusion of nuclear microsatellites markers may offer a more complete scenario as it gathers information from both, maternal and paternal lineages within a more contemporary context. This approach might be useful to test the genetic homogeneity hypothesis of S. concolor population or alternatively, whether its migratory capability is insufficient to cross the MAR separating the upper and central Gulf.Previous mitochondrial DNA  and 2008 , Santa Clara in 2006 , Puerto Peñasco in 2006 , 2007  and 2008 , and Puerto Libertad in 2006 . The central Gulf locations were Bahía Kino in 2005 , in 2006 , and 2007 , Bahía Guaymas in 2005 , and 2006  and Huatabampo in 2005   . The sam n = 28) . It is iScomberomorus cavalla  and RNase-free water. The high specificity of the kit components allowed multiplex PCR reactions. For this reason, loci were organized into four multiplex sets per PCR reaction that permitted to separate loci with overlapping allelic size ranges (® 4.1 software (Life Technologies). Genotype determination was based on the allelic size range for each locus  . PCR reactions were performed with DreamTaq DNA Polymerase  in 15 µL reactions containing 10–100 ng of DNA, DreamTaq Buffer (10X) 2SO4 and 20 mM MgCl2), 0.2 mM dNTPs, 0.2 µM of each primer, 0.38 U of DreamTaq® DNA polymerase and deionized water. Cycling conditions consisted of an initial denaturation step at 95 °C for 5 min, followed by 35 cycles of denaturation at 95 °C for 1 min, annealing at 56 °C for 1 min and an extension at 65 °C for 3 min, and a final extension at 72 °C for 7 min. The resulting PCR products were purified and sequenced in an ABI 3730xl (Applied Biosystem) automatic sequencer using primer CytB-I.The entire GTTG-3′)  and revTGTTG-3′)  and sequThe software Micro-Checker 2.2  was usedHo) and expected (HE) heterozygosity were estimated with Arlequin 3.5  between GST and HS (expected heterozygosity) across all loci and test its significance , Puerto Peñasco (PP), Bahía Kino (BK) and Bahía Guaymas (BG), in order to increase the statistical power for testing significance in spatial variation through FST pairwise sample comparisons.As software  was perfificance . Afterwak = 1 to 7, where k = 1 means no population differentiation and k = 7 corresponds to the maximum number of sampled populations. Each run consisted of a 500,000 MCMC after a burn-in period of 50,000. We tested with and without admixture model, both with the correlated allele frequency model. The posterior probability LnP(D) was used to infer the most probable number of populations. Finally, to evaluate genetic differences between major Gulf regions, localities were grouped into upper  and central Gulf  and an AMOVA was performed in Arlequin.A structure analysis was performed using STRUCTURE 2.3.4 . We condNe) was estimated for all pooled samples using the Linkage Disequilibrium (LD) method  method , and by ) method  as imple) method . As highthod , segregating sites (S), nucleotide (π) and haplotype (h) diversities, were calculated using Arlequin. A haplotype network was constructed using the software http://popart.otago.ac.nz).A ividuals . Sequencividuals  implemenividuals . The numST in Arlequin. The existence of spatial genetic differences was examined through an AMOVA in Arlequin, pooling the samples into upper and central regions.In order to select the best evolutionary model fitting the data, jModelTest 2.1.6  was usedmtCyt-b was calibrated using the sequences of S. concolor (n = 199) and S. sierra (n = 13) in BEAST 2  were estimated in Arlequin. The parameter τ was translated into years using the equation: T = τ∕2μ, where μ is the mutational rate of 1.8% per site per million years for the mtCyt-b and a generational time of four years for S. concolor was used  to 10.92 (SC). The mean observed heterozygosity was 0.721 ± 0.024 and ranged from 0.675 (SC) to 0.744 (HT), whereas the mean expected heterozygosity was 0.736 ± 0.019 and ranged from 0.709 (SC) to 0.766 (BG). All diversity measures were very similar among the locations see .mtCyt-b. The mean haplotype diversity was 0.940 ± 0.012 and ranged from 0.924 (BK) to 0.966 (SF), whereas the mean nucleotide diversity was 0.0048 ± 0.0027 ranging from 0.0043 (PL) to 0.0051 (BG) , 69 transitions and 9 transversions were obtained for the 873 bp fragment of 051 (BG) .GST and HS estimates among the nine microsatellites was positive (r = 0.35), and not significant P = 0.356, implying that the use of FST is suitable for the succeeding analyses.The correlation between FST among the temporal collections were not significant  =  − 15838.4 and −15838.2, respectively), suggesting no population structure  nor the AMOVA for spatial structure showed no significant differences , being consistent with a panmictic S. concolor population in the GC.The most probable number of genetic populations obtained from the STRUCTURE analysis, either with or without admixture model, was ture see . AccordiST estimates varied from zero to 0.012 (PL-HT comparison) and none of the comparisons between locations exhibited significant genetic differences .Based on the distance matrix calculated with the HKY + I model  as estimferences . SimilarNe) as a single population using the Linkage Disequilibrium method (LD) and a Pcrit = 0.02, was 3056.9. The upper and lower bound of 95% CI for the Ne estimates were (95% CI jackknife = 1344.9-infinite).The estimated contemporary effective population size , supporting deviations from neutrality. Harpending’s raggedness index was low and non-significant , as were the values for the sum-of-squares deviations (SSD), which did not deviate from the unimodal distribution . The τ obtained under the demographic model was 3.5 and for the spatial model was 2.3. Based on these results obtained from the analysis of mismatches from the pairwise differences for all sequences  occurred between 111,366  and 73,183  years before present (YBP). Additionally, and giving support to the previous results, the skyline plot  was observed similar to those of congeneric species based on mtDNA-CR sequences  was observed than reported for the mtDNA-CR of other Scomberomorus species  was 3056.9. This large population size is similar to that observed in other pelagic fishes  following by the number of each individual. For example: SF0601 corresponds to the individual number 1, sampled in the location of San Felipe in 2006. Total sample size:n = 482. Sample size by location: SF06 = 37, SF08 = 32, SC06 = 29, PP06; n = 29, PP07 = 15, PP08 = 32, PL = 30, BK05 = 50, BK06 = 60, BK07 = 31, BG05 = 70, BG06 = 39 and HT = 28. * Month not available.Click here for additional data file.10.7717/peerj.2583/supp-3Table S2Click here for additional data file.10.7717/peerj.2583/supp-4Table S3S. concolor for each location and for the entire population in the GC.Summary statistics for nine microsatellite loci amplified for Click here for additional data file.10.7717/peerj.2583/supp-5Table S4FST pairwise sample comparisons over temporal collections. (A) San Felipe (SF) collections. (B) Puerto Peñasco (PP) comparisons. (C) Bahía Kino (BK) comparisons and (D) Bahía Guaymas (BG) comparisons. None FST value showed significant differences (p < 0.05).Results of Click here for additional data file.


Prionace glauca, among the most common and widely studied pelagic sharks, is a top predator, exhibiting the widest distribution range. However, little is known about its population structure and spatial dynamics. With an estimated removal of 10–20 million individuals per year by fisheries, the species is classified as “Near Threatened” by International Union for Conservation of Nature. We lack the knowledge to forecast the long‐term consequences of such a huge removal on this top predator itself and on its trophic network. The genetic analysis of more than 200 samples collected at broad scale  using mtDNA and nine microsatellite markers allowed to detect signatures of genetic bottlenecks but a nearly complete genetic homogeneity across the entire studied range. This apparent panmixia could be explained by a genetic lag‐time effect illustrated by simulations of demographic changes that were not detectable through standard genetic analysis before a long transitional phase here introduced as the “population grey zone.” The results presented here can thus encompass distinct explanatory scenarios spanning from a single demographic population to several independent populations. This limitation prevents the genetic‐based delineation of stocks and thus the ability to anticipate the consequences of severe depletions at all scales. More information is required for the conservation of population(s) and management of stocks, which may be provided by large‐scale sampling not only of individuals worldwide, but also of loci genomewide.The blue shark  Many pelagic shark species exhibit philopatry , the Mediterranean Sea  and the Pacific Ocean , the Indian Ocean could unfortunately not be sampled for this study. Samples were preserved in 96% ethanol and stored at room temperature.DNA was isolated from fin tissue or muscle samples collected between 2010 and 2014 in three oceanic basins  gene, 1,000 bp on average, was amplified with Glu14314L (5′‐CCAATAACTTGAAAAACTATCG‐3′) and Thr15546H (5′‐TCTTCGACTTACAAGGTC‐3′). Reaction volumes were 50 μl, containing 0.4 μl of Taq (at 5 U/μl), 5 μl of MgCl2 (at 25 mM), 5 μl of dNTP (at 8 mM), 5 μl of buffer (5×), 23.6 μl of pure water and 3 μl of each primer (at 10 μM). Cycling conditions were 94°C for 3 min, 35 cycles of 94°C for 30 s, 48°C for 1 min and 72°C for 1 min 15 s, finishing with a final extension at 72°C for 5 min. Paired‐end sequencing was performed using the classical Sanger method. The paired‐end sequences were assembled and aligned.Genetic variation at two kinds of putatively neutral genetic markers  was used to test the hypothesis of population differentiation and bottlenecks. A fragment of the mitochondrial cytochrome Taq (at 5 U/μl), 2 μl of MgCl2 (at 25 mM), 2 μl of dNTP , 4 μl of buffer (5×), 6.1 μl of pure water, 0.4 μl for the reverse primer and 0.2 μl for the forward counterpart (both at 10 μM).Ten microsatellites loci were used: A2ASY, BEF94, CY92Z, D0MST, DZOXN, EWU1E and FV6T5  and scored manually.The loci D0MST presented critical amplification failures and was eventually removed from data, leaving nine loci for further data analysis.2.3h) diversities  and haplotype  under Hardy–Weinberg expectations, observed heterozygosity (HO) and estimators by Weir and Cockerham  was calculated using FSTAT , batches and iterations per run, for a total of 1,000 runs each.We used the program POWSIM 4.1 from Ryman and Palm  to deterST Nei,  “modifieST Nei,  implemenST Nei,  to deter2.4K = 1–8 under an admixture model. This model is consistent with the fact that blue sharks are able to migrate over long distances. All runs were executed with 50,000 burn‐in periods and 200,000 MCMC (Markov chain Monte Carlo) repetitions, using the eight regions defined earlier as prior information. STRUCTURE HARVESTER displays results allowing to assess K, the number of genetic populations that best fit the data, based on maximum likelihood .We used NeEstimator 2.01  of 10,000 and 100,000 and 1,000,000 with per‐generation numbers of migrants (Nem) of 0, 1 and 10. Considering variations in Nem may also result from a change in Ne at constant m  of 1,000,000 and with per‐generation numbers of migrants (Nem) of 100. At generation 500, we reduced effective sizes either to 100,000 (bottleneck of 90%) or to 10,000 (bottleneck of 99%). The migration rate (m = 0.0001) was kept constant in both scenarios. FST, sub‐FST and proportion of significant sub‐FST were computed every 50 generations before the bottlenecks and every 20 generations after.Simulations were performed to characterize the genetic differentiation among two increasingly diverging populations, in order to illustrate the number of generations required to overcome the “population m Figure , simulat33.1b was variable for 25 polymorphic sites, leading to the discrimination of 22 distinct haplotypes. Haplotype (h) and nucleotide (π) diversities ranged from 0.62 to 0.86  and from 0.17 to 0.26, respectively.From a total of 201 sequences . No tendency to an unbalanced amount of missing loci was observed in relation to the geographic area where the samples were caught. The mean number of alleles per loci ranged from 3.78 for Greece to 12.11 for the Gulf of Lion . As for microsatellites  or the number of migrants exchanged (Nem), the FST and the sub‐FST values are very similar , to 1,400 (Nem = 1), and to 1,600 (Nem = 10).For an effective size of 10,000 individuals, the number of migrants exchanged has nearly no influence on the “grey zone” range: it takes an average of 200 generations to obtain a detection capacity of a significant sub‐FST index ranges from at least 160 (bottleneck of 99%) to 2,200 generations (bottleneck of 90%).As for the bottleneck results Figure , the numAdding simulations with larger effective population sizes would be extremely computationally time‐consuming for an iterative process. The estimates given here are thus only lower bound in terms of generation time required for the divergence to be detected. The comparison of results obtained when switching effective population size thus delivers rather conservative estimates of the number of generations during which the “grey zone” effect applies. These results suggested such “grey zone” effect for effective population size of hundred millions individuals is likely even more pervasive then illustrated in Figure 4FST overall, in samples across the three oceanic basins included in this study . These results reflect large‐scale panmixia, that is, random mating at the worldwide scale, or a departure from random mating too recent to have left a signature on the genome, depending on the population size(s). Whether this genetic homogeneity reflects the existence of a single demographic entity and/or the demographic interdependence of all groups of individuals worldwide is essential information to manage the population(s). The low sample size and limited geographic coverage out of Europe, however, call for a cautious interpretation of results. Large‐scale panmixia is not entirely supported here for two reasons. First, the occurrence of a handful of significant pairwise FST values , although low and distributed with no geographic coherence, does not allow to entirely reject the hypothesis of subtle pattern of very low genetic differentiation, difficult to grasp with the limited statistical power of our data set. Second, and more importantly, even in the presence of confirmed large‐scale panmixia based on a more robust data set, the scenario of widespread genetic interdependence can hide a wide range of demographic situations, as we will develop and illustrate below, leading us to propose the concept of “population grey zone.”Widespread genetic homogeneity of one of the most widely distributed species worldwide, the blue shark, is supported here by the similar levels of allelic richness and lack of significant Sphyrna lewini, Ovenden et al., Rhizoprionodon acutus, Ovenden et al., Galeorhinus galeus, Hernández et al., Cetorhinus maximus, Hoelzel, Shivji, Magnussen, & Francis, FST values, however, suggested at least very mild and randomly distributed pairwise departure from panmixia, as also observed in many of the other shark species exhibiting a similar pattern of large‐scale panmixia  and the per‐generation number of migrants (Nem). In the absence of genetic split of populations , the rate of per‐generation number of migrants (m) ensuring genetic homogeneity may be largely insufficient to lead to demographic interdependency or to ensure a rescue effect  often lead to weak or no genetic differentiation  individuals of Indo‐Pacific and Atlantic could be explained by the hypothesis of recent dispersal as proposed in Díaz‐Jaimes et al. article  in the Atlantic  while Kumar and Kocour review  recorded with microsatellites  mostly showed no structure until Carlsson, McDowell, Carlsson, and Graves (Beyond the “population McDowell , the oppd Graves  analysedd Graves  sampled grey zone” concept.The effective management of fisheries requires a clear definition and understanding of the stock structure of the target species (Begg & Waldman, 5Elasmobranch recovery is possible but requires in situ management actions (Ward‐Paige, Keith, Worm, & Lotze, grey zone” does not reject the possibility that the blue sharks all belong to a single worldwide population. On the one hand, such global panmixia may fit a very large global population size, providing robustness to the species in cases of local depletion. On the other hand, depending on the spatial dynamics of the species, it may also imply that any serious impact in any part of the world might have serious consequences at a global scale. In both cases and as suggested by Theisen et al. (grey zone” problem likely responsible for the frequent mismatch between high expectations and inconclusive results obtained thus far when applying population genetics to detect differentiated stocks of fisheries' targets.The case study used here to illustrate the “population n et al.  who repon et al. . A consen et al. , as welln et al. . All emehttps://doi.org/10.5061/dryad.k302gData available from the Dryad Digital Repository:  Click here for additional data file. Click here for additional data file. Click here for additional data file.


Nacella mytilina is broadly distributed along the southern tip of South America and at the Falkland/Malvinas Islands. Considering its distribution, abundance, and narrow bathymetry, N. mytilina represents an appropriate model to infer how historical and contemporary processes affected the distribution of intraspecific genetic diversity and structure along the southern tip of South America. At the same time, it will be possible to determine how life history traits and the ecology of the species are responsible for the current pattern of gene flow and connectivity across the study area. We conducted phylogeographic and demographic inference analyses in N. mytilina from 12 localities along Pacific Patagonia (PP) and one population from the Falkland/Malvinas Islands (FI). Analyses of the mitochondrial gene COI in 300 individuals of N. mytilina revealed low levels of genetic polymorphism and the absence of genetic differentiation along PP. In contrast, FI showed a strong and significant differentiation from Pacific Patagonian populations. Higher levels of genetic diversity were also recorded in the FI population, together with a more expanded genealogy supporting the hypothesis of glacial persistence of the species in these islands. Haplotype genealogy, and mismatch analyses in the FI population recognized an older and more complex demographic history than in PP. Demographic reconstructions along PP suggest a post-LGM expansion process (7.5 ka), also supported by neutrality tests, mismatch distribution and maximum parsimony haplotype genealogies. Migration rate estimations showed evidence of asymmetrical gene flow from PP to FI. The absence of genetic differentiation, the presence of a single dominant haplotype, high estimated migration rates, and marked signal of recent demographic growth, support the hypothesis of rapid post-glacial expansion in N. mytilina along PP. This expansion could have been sustained by larval and rafting-mediated dispersal of adults from northernmost populations following the Cape Horn Current System. Marked genetic differentiation between PP and FI could be explained through differences in their respective glacial histories. During the LGM, Pacific Patagonia (PP) was almost fully covered by the Patagonian Ice Sheet, while sheet coverage in the FI ice was restricted to small cirques and valleys. As previously recorded in the sister-species N. magellanica, the FI rather than represent a classical glacial refugium for N. mytilina, seems to represent a sink area and/or a secondary contact zone. Accordingly, historical and contemporary processes, contrasting glacial histories between the analyzed sectors, as well as life history traits constitute the main factors explaining the current biogeographical patterns of most shallow Patagonian marine benthic organisms.Glacial episodes of the Quaternary, and particularly the Last Glacial Maximum (LGM) drastically altered the distribution of the Southern-Hemisphere biota, principally at higher latitudes. The irregular coastline of Patagonia expanding for more than 84.000 km constitutes a remarkable area to evaluate the effect of Quaternary landscape and seascape shifts over the demography of near-shore marine benthic organisms. Few studies describing the biogeographic responses of marine species to the LGM have been conducted in Patagonia, but existing data from coastal marine species have demonstrated marked genetic signatures of post-LGM recolonization and expansion. The kelp-dweller limpet  The associated probabilities of these analyses were estimated using 25 000 permutations. We didn’t detect any significant correlation between genetic and geographic distance along PP .Mean general values of differentiation measured over 13 populations of ape Horn . In cont PP ones . Considenn) in N. mytilina (Snn = 0.09) showed low but significant levels of phylogeographic signal (P < 0.00001) for the whole COI data set. However, when this analysis was performed considering the pattern of genetic structure recorded in the species (PP vs FI) Snn became very high (Snn = 0.9) showing the elevated degree of phylogeographic signal recorded between these two areas. This pattern of genetic structure was further supported by the model based on Bayesian clustering algorithm, which detected two main clusters (K = 2). The first cluster includes all the PP localities from the Strait of Magellan to Cape Horn , the expansion process in N. mytilina would have been hampered or delayed. As observed in N. mytilina, molecular phylogeographic analyses in Patagonian populations of macroalgae including Macrocystis pyrifera (n = 144) [G. skottsbergii (n = 166) [N. mytilina’s distribution in PP, both Macrocystis and Gigartina, exhibited the presence of a broadly distributed dominant haplotype and very low levels of genetic diversity suggesting a recent expansion processes in Patagonia. In the case of M. pyrifera the single Patagonian haplotype was also detected in several geographically distant subantarctic islands  suggesting a very recent colonization of the subantarctic region. However, in the Falkland/Malvinas Islands (n = 4), M. pyrifera individuals carried a different haplotype separated by a single substitution from the Patagonian one. In the particular case of S. skottsbergi, even when the dominant PP haplotype was also highly abundant (> 70%) in FI, no population level analyses were performed in this species in order to evaluate population differentiation [N. mytilina. At the same time, several studies have demonstrated that kelp-specialized Subantarctic invertebrates show very low levels of genetic diversity and structure along their distribution and also across geographically distant areas [Nacella [N. mytilina along PP  would be sustained by effective population sizes (Ne) between 73.500 and 148.000 individuals. These estimates are lower than those recorded for other Patagonian Nacella species [S indices and the presence of L-shaped mismatch distribution in PP are the result of an excess of low frequency haplotypes commonly explained by a very recent demographic expansion process. Population expansion along PP would have occurred ~ 7.5 ka according to the sudden growth model and this estimate is consistent with analyses of other Patagonian species of Nacella [Nacella, beginning at this time [N. mytilina showed higher levels of genetic diversity comparable to those recorded for N. magellanica and N. deaurata in the Atlantic [Levels of mtDNA genetic diversity in stropods ,95 and istropods ,24,25. Iconcinna ,97. In s Zealand  and Mari Zealand . Extremen = 144)  and G. sn = 166)  recordedntiation . Considentiation  the highnt areas ,102. Suc[Nacella ,21,24,25 species . Low lev species ,103 and  species . Further Nacella ,25, as w Nacella  the timi Nacella . Major a Nacella ,18,105.  Nacella ,106. A phis time –110. In Atlantic ,21 and PN. mytilina along PP from the Strait of Magellan to Cape Horn. For benthic species the primary dispersal phase is associated with the earliest life history stages  and therefore considerable focus is placed on the processes that influence developmental modes, type of larvae, larval development and lifespan [Nacella exist but it is expected that their development should be similar to the Antarctic relative N. concinna [N. mytilina larval lifespan could extend for at least six weeks. Gene flow mediated by larval dispersal and/or rafting in this kelp-dweller species may have been enhanced by oceanographic conditions in southern South America. Off southern Chile, the West Wind Drift divides in two major branches: i) the Peru or Humboldt Current that flows northward and transports subantarctic water of relatively low temperature and salinity towards lower latitudes and ii) the Cape Horn Current that flows southward around Cape Horn [N. mytilina populations across Pacific Patagonia. Strong genetic differentiation between PP and FI recorded in N. mytilina supports a recent phylogeographic study in the sister-species N. magellanica [Serolis [N. mytilina showed higher levels of genetic diversity than PP ones that are comparable to those recorded for N. magellanica and N. deaurata populations from the Atlantic [M. pyrifera even when the single Patagonian haplotype was also detected in different geographically distant subantarctic islands  it was absent in the Falkland/Malvinas Islands (n = 4). Macrocystis pyrifera individuals from FI carried a different haplotype separated by a single substitution from the Patagonian one. In the particular case of S. skottsbergi, even when the dominant PP haplotype is highly abundant (> 70%) in FI, no population level analyses were performed in this species in order to evaluate population differentiation [10Be and 26Al) surface exposure dates [In spite of the geographical and oceanographical complexity along the southern tip of South America we recorded a single genetic unit in lifespan ,111. Henlifespan –114. Abslifespan ,115–117 lifespan ,35,118, lifespan ,100,119.concinna –54. Consconcinna ,121 it wape Horn ,123. As ellanica  and in i[Serolis  recognizAtlantic ,21 and tAtlantic ,25 coastAtlantic , while tntiation . Evidencntiation ,125,126.ntiation ,128. Morre dates . Studiesre dates ,131 provre dates .N. mytilina. In fact, these islands have been previously proposed as refugial areas for several plant species during the LGM [N. concinna, as well as in its sister-species N. magellanica, support the resiliency of some near-shore marine benthic invertebrates in FI during the LGM. At the same time, our migration analyses in N. mytilina do not support an scenario of posterior colonization from FI to PP.Traditional genetic models of postglacial recolonization and refugia include the prediction that formerly ice-covered areas should exhibit low levels of genetic diversity and a small number of haplotypes dominating large areas . Simulta the LGM  and as a the LGM . MtDNA dN. mytilina is strongly supported by the general oceanographic circulation in the study area. After rounding the southern tip of South America, the Cape Horn Current is divided into two minor branches. The first one flows eastward to the South Georgia [An asymmetrical dispersal pattern from PP to FI recorded in  Georgia ,123 whil Georgia ,136. As  Georgia , FI seemN. mytilina indicate that historical and contemporary processes, as well as life-history traits played a major role in the current pattern of genetic diversity, structure, and connectivity. This study corroborates previous analyses in the genus showing the important role of oceanography in explaining the asymmetry in gene flow across the southern tip of South America. Even in the presence of gene flow from PP to FI, the migration between both areas is not enough to homogenize populations and they constitute different genetic units. According to the general pattern of genetic diversity, neutrality tests and demographic inference analyses, N. mytilina populations in FI are older than those from PP. However, haplotype identities and gene flow analyses indicate that FI did not participate in the postglacial recolonization of PP. In this context, more studies in other shallow marine benthic organisms are required to determine if the pattern recorded in two species of Nacella constitute a general rule of the Quaternary biogeography of southern South America.Molecular-based phylogeographic analyses in


Piaractus brachypomus , is a fish from the Amazon basin and is considered to be one of the main native species used in aquaculture production in South America. The objectives of this study were: (1) to perform liver transcriptome sequencing of pirapitinga through NGS and then validate a set of microsatellite markers for this species; and (2) to use polymorphic microsatellites for analysis of genetic variability in farmed stocks. The transcriptome sequencing was carried out through the Roche/454 technology, which resulted in 3,696 non-redundant contigs. Of this total, 2,568 contigs had similarity in the non-redundant (nr) protein database (Genbank) and 2,075 sequences were characterized in the categories of Gene Ontology (GO). After the validation process of 30 microsatellite loci, eight markers showed polymorphism. The analysis of these polymorphic markers in farmed stocks revealed that fish farms from North Brazil had a higher genetic diversity than fish farms from Southeast Brazil. AMOVA demonstrated that the highest proportion of variation was presented within the populations. However, when comparing different groups , a considerable variation between the groups was observed. The FST values showed the occurrence of genetic structure among the broodstocks from different regions of Brazil. The transcriptome sequencing in pirapitinga provided important genetic resources for biological studies in this non-model species, and microsatellite data can be used as the framework for the genetic management of breeding stocks in Brazil, which might provide a basis for a genetic pre-breeding programme.The pirapitinga,  Piaractus brachypomus) is a native fish from the Amazon and Orinoco Rivers and can reach up to 20 kg of weight , and patinga  farmed stocks of pirapitinga have lower genetic diversity in relation to wild stocks; (2) farmed stocks of pirapitinga in Brazil are genetically structured; and (3) gene-linked microsatellites can be associated to economic traits of pirapitinga, such as growth and disease resistance. These analyses will support the creation of a breeding programme to increase the productivity of pirapitinga, by directed matings which lead to the formation of families, avoiding the problems of bottlenecks and inbreeding in the base population . The present study was performed under authorization N° 33435-1, issued through ICMBio . No animal was housed or cared for in the laboratory. Fish were euthanized by benzocaine anesthetic overdose for collection of liver tissue for transcriptome sequencing. For microsatellite validation and genetic variability analysis, fin fragments were collected from each fish under benzocaine anesthesia and all efforts were made to minimize suffering.n = 3); Projeto Peixe fish farm, Sales Oliveira, SP (n = 1); Fazenda São Paulo fish farm, Brejinho de Nazaré, TO (n = 5); and Tocantins River, Lajeado, TO (n = 1). Individuals from different origins were used in order to achieve the highest genetic variability in microsatellite discovery analysis. Liver samples were selected for transcriptome studies because the liver plays a critical role in coordinating various physiological processes, including digestion, metabolism, detoxification, and endocrine system immune response  and TO2 (n = 26), from Tocantins State (North Brazil); and SP1 (n = 36) and SP2 (n = 20), from São Paulo State (Southeast Brazil). To maintain the confidentiality of these fish farms, the names of and information on the fish farms have been preserved.Analyses of microsatellite validation were performed in 22 individual pirapitinga collected from the Tocantins River (TO) from Lajeado City, Tocantins State, Brazil. We then used the microsatellite markers to study the genetic variability in samples collected from four commercial fish farms: TO1  and Cytochrome b (mt-cyb); and the nuclear genes, α-Tropomyosin (tpm1) and Recombination Activating Gene 2 (rag2), according to the protocols and methods of Hashimoto et al. . Each sample was quantified by spectrophotometry using NanoDrop ND-1000 equipment and the quality (integrity) was checked by 2100 Bioanalyzer equipment. It succeeded the preparation of an equimolar pool of total RNA samples  to mRNA enrichment with μMACS mRNA Isolation Kit (Miltenyi Biotech).TM—ST fluorimeter (Promega). Titration of emPCR (emulsion PCR) was performed with the GS FLX Titanium SV em PCR Kit (Lib-L) (Roche), according to the emPCR Amplification Method Manual—Libl SV, GS FLX+ Series, to identify the optimal number of DNA molecules per bead (cpb = copies per bead). After emPCR titration, the emPCR was performed with GS FLX Titanium LV emPCR Kit (Lib-L) (Roche), according to the emPCR Amplification Method Manual—LibL LV, GS FLX+ Series. The transcriptome sequencing was conducted using the Roche/454 technology (GS FLX Titanium Sequencing Kit XL +) from HELIXXA company , which has been used for transcriptome analysis of non-model fish species . The High Sensitivity DNA LabChip Kit (Agilent Technologies) with 2100 Bioanalyzer was used for quality analysis of the cDNA library. The concentration of sample (molecules/μL) was obtained by QuantiFluorde novo assembly, contigs <200 bp were also discarded and the default local alignment settings were used to rank potential matches . The highest scoring matches that shared ≥50% of their length with ≥80% of similarity were included in the alignment. The assembled transcripts were subjected to cd-hit-est programme with an identity threshold of 90% to remove redundancy . The raw reads were cleaned by trimming low quality sequences with quality scores of <20. Terminal nucleotides (five nucleotides at each extremity 5′ and 3′), ambiguous nucleotides, adapter sequences and reads <15 base pairs (bp) were discarded. For National Center for Biotechnology Information (NCBI) non-redundant protein database (nr)  using BLAST2GO software  regions. These approaches allowed us to locate microsatellites in coding sequences (CDS) or untranslated regions (5′UTR and 3′UTR) through graphical sequence viewer Tablet , 0.1 μM of each primer, 0.5 units of Taq Polymerase (Invitrogen) and 10-50 ng of genomic DNA. The reactions were performed in a thermocycler  following initial denaturing for 10 min at 95°C; 35 cycles of 30 s at 95°C, 30 s at 55–60°C (adjusted for each primer set), 20 s at 72°C; and a final extension at 72°C for 20 min.DNA was extracted from fin fragments using the Wizard Genomic DNA Purification Kit (Promega), according to the manufacturer's protocol. Microsatellite validation was performed in 30 loci, selected according to the motif and functional annotation of the contigs. Amplifications were performed by polymerase chain reaction (PCR) in a total volume of 25 μl containing 100 μM of each dNTP , 1.5 mM MgCl2, 1X Taq DNA buffer, 0.1 μM of each primer (F and R), 0.01 μM of the CAGtag primer, 0.5 units of Taq Polymerase (Invitrogen), and 10–50 ng of genomic DNA. The cycling programme for amplification consisted of: nine cycles at 95°C for 30 s, 55–60°C for 30 s (adjusted for each primer set), 72°C for 20 s; then, 30 cycles at 95°C for 30 s, 50°C for 30 s, and 72°C for 20 s. During the first nine cycles, the annealing temperature of 55–60°C allows incorporation of the primers (F and R) from the microsatellite loci. Then, in the following 30 cycles, the temperature of 50°C facilitates the annealing of the fluorescent dye-labeled CAGtag primer. PCR products were analyzed by capillary electrophoresis with a 3130xl genetic analyzer, using the DS-30 matrix, with the GeneScan 500 ROX dye Size Standard (Thermo). The programme GeneMapper 3.7 (Applied Biosystems) was used to determine the allele sizes.Microsatellites that showed polymorphism in 6% polyacrylamide gels were analyzed in a 3130xl sequencer (Life Technologies) to get better accuracy of allele determination. The sequencing strategy adopted in this study was according to protocols described by Schuelke , using tHo) and expected (He) heterozygosity, Hardy-Weinberg Equilibrium (HWE) and Analysis of Molecular Variance (AMOVA)  was performed using Genepop 4.0.11  was calculated using FSTAT 9.3.2 software ; moderate genetic differentiation (0.05–0.25); high level of genetic differentiation (> 0.25). The programme Cervus v.3.0.7  was predefined from 1 to 5. The analysis was performed in 25 replicated runs using 200,000 iterations after a burn-in period of 50,000 runs. The K value most likely to explain the population structure is the modal value of this ΔK. The outputs of STRUCTURE analysis were visualized through the STRUCTURE HARVESTER programme , as suggested by Evanno et al. , using tme Earl, .Analysis for population bottlenecks was tested using BOTTLENECK . The size characteristics of the contigs are presented in Table The results of liver transcriptome sequencing in pirapitinga yielded a total of 192,373 reads, which were deposited in the Short Read Archive (SRA) of NCBI under the accession number nr), protein RefSeq of zebrafish and fugu. A total of 2,568 unique protein accessions (69.4% of transcripts) had significant similarity in the nr database. In relation to the protein RefSeq of zebrafish and fugu, we found similar numbers of annotated genes, which were of 2,498 (67.6%) and 2,419 (65.4%), respectively. No sequence showed homology with known pirapitinga protein sequences deposited in NCBI database, because the available sequences database is still limited to mostly mitochondrial sequences.Non-redundant sequences were annotated by BLASTx algorithm against the NCBI databases: non-redundant protein  were annotated in the categories of Gene Ontology (GO). A total of 1,831 assignments to Biological Process (88.2%) were found, followed by 1,757 to Molecular Function (84.6%) and 1,378 to Cellular Component (66.4%). In relation to the GO subcategories, the most abundant terms were related to: metabolic process, cellular process, and single-organism process of the Biological Process category; binding, catalytic activity, and transporter activity of the Molecular Function; cell, organelle, and membrane of the Cellular Component  in the 3,696 contigs resulted in the discovery of 130 microsatellite markers distributed in 95 contigs. In total, 75 pairs of primers were designed adjacent to the microsatellite loci, including the following sequence repeats: 56 di, 13 tri, 4 tetra, and 2 pentanucleotide. Among the dinucleotide motifs, the main repeats were the types AC (48.28%), AG (39.65%), AT (10.35%), and CG (1.72%). In relation to the trinucleotide motifs, we identified seven types . The tetranucleotide  and pentanucleotide (ACTAT and ATAGT) sequences were described with the presence of four and two types of motifs. In relation to the gene position, 26.76% of the microsatellite markers were found in the 3′UTR (untranslated region), 19.71% in the 5′UTR, and 29.58% in the cds (coding sequence).MG595996—MG596003), revealed by the presence of different fragment sizes (Table He) and observed (Ho) heterozygosity in the wild population had an average of 0.466 ± 0.061 and 0.355 ± 0.076, respectively. Most of the loci showed positive values for FIS, except the locus C64. Three microsatellite loci  showed significant deviation from the Hardy–Weinberg Equilibrium (HWE) after Bonferroni correction (adjusted p = 0.00625).In the process of microsatellite validation, 30 markers were evaluated in 22 samples of pirapitinga collected from the wild. Of these markers, eight microsatellite loci showed polymorphism  and Ho  and Ho . The majority of the markers were in concordance to HWE, after Bonferroni correction, with the exception of C25 , C64 (SP1 and SP2) and C1376 (SP1) (Table p < 0.00625). Although molecular markers on linkage disequilibrium were not applied in genetic variability studies, this information can be useful in future analysis of genetic mapping.The results of genetic variability in farmed stocks revealed that North fish farms TO1 and TO2 had higher diversity than the wild population, demonstrated by number of alleles  and average values of p = 0.027).In bottleneck analyses, evidence for recent reductions in population size (bottleneck) using TPM was not found, except for the wild population of Tocantins River , Group 2 (TO1 and TO2), and Group 3 (wild) . Pairwise FST detected a higher genetic differentiation between the wild population and all farmed stocks, particularly when compared to SP1  and SP2 . Additionally, high genetic structure was found between the populations from North and Southeast Brazil, as observed between TO2 with SP1  and TO1 with SP2 , Group 1/Group 3 , and Group 2/Group 3 .The global FCT = 0.291, p < 0.001) occurred between groups (according to STRUCTURE clustering), while the variation among individuals within populations was only 8.21%  and among populations within groups presented 6.06% of genetic variation .The results of AMOVA showed that the majority of genetic variation  is linked with faster growth rate, especially the alleles 250 and 254, which can be used for breeding management and genetic selection for this trait , which plays a role in viral infection pathology  (C25), which contributes in the production of red blood cells and its mutation can cause hemolytic anemia and influence on the immune system , rainbow trout (Oncorhynchus mykiss), salmon , Nile tilapia (O. niloticus), and cod (Gadus morhua), investigating the resistance to pathogens , which is associated with the central nervous system and the senescence process  ranged from 0.000 to 0.545 and an average of 2.750 alleles per locus. These values confirm the low genetic variability when compared with related species, such as pacu P. mesopotamicus , and tambaqui C. macropomum , which was also observed in studies with other related species  and two from the North (Tocantins State: TO1 and TO2). In general, farmed stocks were expected to have low genetic variability as a result of genetic decline, genetic drift, selection and inbreeding . This genetic pattern has also been reported in studies carried out with pacu (Calcagnotto and DeSalle, In general, our study of genetic characterization in piratininga farmed stocks provides important insights which can lead to better management of this species in aquaculture. Our results are fundamental to beginning a breeding programme, since the genetic structure should be taken into consideration when composing an initial base population, where matings between farmed individuals from North and Southeast Brazil are shown to result in higher genetic variability in the families. Moreover, the data suggested levels of genetic diversity which were higher in farmed stocks than in wild fish, discarding the occurrence of inbreeding. In general, lack of knowledge on genetic variability of stocks can result in inbreeding and fixation of deleterious genes, reduced growth rates, disease resistance problems and reduced ability to adapt to new environments (Arkush et al., The prospection of genetic data for pirapitinga is one of the priority issues for aquaculture, since this species is of high economic importance in national and global fish farming. The identification of gene-associated microsatellites by NGS is fundamental to understanding the genetic structure of wild and farmed populations, providing support for further management programmes and genetic pre-breeding programmes. Moreover, the microsatellites described herein are interesting targets used to find QTL markers, specifically related to the immune system of pirapitinga.PJ: Acquisition, analysis and interpretation of data, draft of the work, final approval of the version; VM-F, RA, and MdF: Draft of the work, development of intellectual content, final approval of the version; MH: Analysis and interpretation of data, draft of the work, final approval of the version; NM: Analysis and interpretation of data, draft of the work, final approval of the version; MV: Analysis and interpretation of data, draft of the work, final approval of the version; FP-F: Acquisition, Interpretation of data, development of intellectual content, final approval of the version; DH: Draft of the work, development of intellectual content, writing of the manuscript, final approval of the version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Blastocystis subtype 3 (ST3) is a parasitic protist found in the digestive tract of symptomatic and asymptomatic humans around the world. While this parasite exhibits a high prevalence in the human population, its true geographic distribution and global genetic diversity are still unknown. This gap in knowledge limits the understanding of the spread mechanisms, epidemiology, and impact that this parasite has on human populations. Herein, we provided new data on the geographical distribution and genetic diversity of Blastocystis ST3 from a rural human population in Mexico. To do so, we collected and targeted the SSU-rDNA region in fecal samples from this population and further compared its genetic diversity and structure with that previously observed in populations of Blastocystis ST3 from other regions of the planet. Our analyses reveled that diversity of Blastocystis ST3 showed a high haplotype diversity and genetic structure to the world level; however, they were low in the Morelos population. The haplotype network revealed a common widespread haplotype from which the others were generated recently. Finally, our results suggested a recent expansion of the diversity of Blastocystis ST3 worldwide. Blastocystis  is a genus comprising parasitic protists that inhabit the digestive tract of several metazoans, such as fishes, amphibians, birds, reptiles, rodents, and humans [ Blastocystis is globally distributed, showing a high rate of infection from underdeveloped to developed countries [d humans –3. Blastountries , 5. Blastocystis infection range from diarrhea to flatulence, bloating, and abdominal discomfort [This parasite is often transmitted via the oral-fecal route to people who work directly with animals, such as those involved in intensive animal farming or industrial livestock production . In humascomfort , 8, withscomfort . Blastocystis [ Blastocystis ST3 has medical importance and high prevalence in humans, the real magnitude of its genetic diversity and geographical distribution remains so far unknown [ Blastocystis [ Blastocystis ST3 from Mexico.Molecular evidence based on the small subunit ribosomal RNA (SSU-rDNA) gene suggests that, at least, 17 genetic subtypes can be recognized withintocystis . Nine oftocystis –18. ST3 tocystis . Other stocystis . While B unknown , 17. Apptocystis , 21, but Blastocystis ST3 from a rural and asymptomatic human population in Mexico. To do so, we collected and targeted the SSU-rDNA region in fecal samples from this population and further compared its genetic diversity and structure with those previously observed in populations of Blastocystis ST3 from other regions of the planet.Herein, we aimed to provide new data on the geographical distribution and genetic diversity ofThe protocol used in this study was conducted under the ethical principles and approval of both the Mexican Commission on Ethics and Research of the Health Ministry of the State of Morelos  and the Commission on Ethics in Research of the Facultad de Medicina of the Universidad Nacional Autónoma de México (UNAM) . The guidelines of the committees are based on the Mexican Official Norm , which regulates the ethical principles of every research on humans and on laboratory animals, as well as on the Declaration of Helsinki, which set ethical principles regarding human experimentation developed by the World Health Organization (WHO).Based on the abovementioned guidelines, our study only used samples from volunteers, who were respectively informed about the objectives of this research, the potential risks (if any), and the sampling procedures. We obtained an informed consent letter from all the participants.Between May and November 2015, fecal samples were collected from 182 volunteers  from Puente de Ixtla in the community of Xoxocotla, State of Morelos (Mexico), ranging in age from 2 to 51 years old. The asymptomatic status was defined according to the ROME III criteria. Three fecal samples were collected from each volunteer on three consecutive days. The samples were maintained at 4°C and transported to the laboratory in Mexico City on the same day of collection. A subsample of each fecal sample was smeared, stained with 4% Lugol's iodine solution, and examined under a light microscope at 10x and 40x magnifications .μl : 20 μM of primers RD5 (5′-ATC TGG TTG ATC CTG CCAG T-3′) and BhRDr (5′-GAG CTT TTT AAC TGC AAC AAC G-3′) [ Blastocystis SSU-rDNA was purified and sequenced using a dideoxynucleotide-terminal method. Sequencing was carried out in a capillary sequencer . The sequences obtained were edited and/or analyzed with BioEdit, MEGA 5.0 software [DNA was extracted from fresh fecal samples using QIAamp DNA stool kit  and following the manufacturer's instructions. PCR protocol targeting the SSU-rDNA was conducted according to Scicluna et al. . In brieAC G-3′) , as wellsoftware , 25, and Blastocystis ST3 using the novel SSU-rDNA sequences reported in the present study and those previously reported within the literature. We provided an exhaustive list of the latter sequences (n = 169) and sources in the Supplementary Material (S), number of haplotypes (h), haplotype diversity (Hd), and nucleotide diversity (π) for each set of sequences according to their geographical region of origin. We built a global haplotype network using TCS network inference method [http://popart.otago.ac.nz/downloads.shtml) to investigate the global genealogical relationship between the different haplotypes of Blastocystis ST3. We also ran Tajima's D test in the software DnaSP [ Blastocystis ST3. We finally estimated pairwise FST statistics in the software Arlequin ver. 3.11 (http://cmpg.unibe.ch/software/arlequin3) to investigate whether the geographical populations of Blastocystis ST3 were genetically structured.We investigated the global genetic diversity  withinMaterial . We invee method  implemenre DnaSP  to inves Blastocystis, Chilomastix mesnili, Entamoeba coli, Hymenolepis nana, Iodamoeba bütschlii, Endolimax nana, the Entamoeba histolytica/Entamoeba dispar complex, and Giardia lamblia. Among the abovementioned parasites, Blastocystis had the greatest frequency, occurring in 109 (74%) out of 148 positive samples. It was also the unique parasite in 99 (67%) out of 148 positive samples, and 7% of the samples (10/148) were coinfected with other parasites  of the 182 fecal samples collected in Morelos (Mexico) exhibited at least some type of intestinal parasite. These 148 samples (positive samples hereafter) harbored different parasites, including representatives ofarasites . Blastocystis subtypes in 72 of the 148 positive samples collected in Morelos. These three Blastocystis subtypes (ST) were recorded according to the following frequencies: Blastocystis ST1, 9.7% (n = 7 samples); ST2, 15.3% (n = 11 samples); and ST3, 75% (n = 54 samples) (Further PCR and sequencing procedures successfully confirmed the presence of three differentsamples) .S) and 20 haplotypes (h), as well as a total haplotype diversity (Hd) of 0.563 and nucleotide diversity (π) of 0.019. Tajima's D test provided values ranging between −1.303 and −2.363  [ Blastocystis exhibits a frequency range of 0.5% to 62% [ Blastocystis has been linked to hygiene factors including the consumption of food/water contaminated with Blastocystis and exposure to domestic and peridomestic animals infected too with this parasite [In the present study, we analyzed the frequency and distribution ofn Mexico , 28, 29. to 67%) –32. Arouparasite . Blastocystis was considered as saprophytic yeast of the digestive tract, innocuous for the host [ Blastocystis although the participants were asymptomatic, suggesting tolerance to this parasite, as reported elsewhere [Many years ago,the host . Nowadaylsewhere , 35, 36.Blastocystis has a high worldwide genetic diversity, represented by 17 subtypes (ST1–ST17) [ Blastocystis ST3 is the most prevalent subtype in humans found in different geographic areas [T1–ST17) . It is pT1–ST17) , 5. Regaic areas . In our ic areas , 39. Blastocystis ST3 in this political region: 14% in Colombia, 36% in Brazil, 84% in Ecuador, 30% in Bolivia, 92% in Peru, and 63% in Argentina [In Latin America, the frequency of ST3 is high , 37, witrgentina . While S Blastocystis ST3 on the planet, we analyzed sequences of Mexico City, South America, France, Switzerland, Italy, Nepal, and Iraq and the sequences obtained in the present study . The genetic parameters calculated for these sequences suggested a recent population increase or directional/purifying selection. These results are supported by the haplotype network, which showed a star topology with the haplotypes distributed in a radial way supporting inferences of a recent geographical expansion in Blastocystis ST3. This behavior is similar to reporting by other parasites, such as Plasmodium falciparum [To improve our knowledge on the magnitude of the genetic diversity oflciparum .A total of 20 haplotypes were found on all sequences analyzed. Haplotype 1 was the most abundant and widely distributed, being detected in the majority of the studied countries, mainly in Latin America. These results are showing that haplotype 1 is perhaps an ancestral type from which all the other haplotypes have been generated recently. Haplotype 1 probably originated in Latin America and has recently colonized other regions of the world, probably via human migration . Blastocystis ST3 haplotype 1 spreading this parasite into the cities. Already in large cities, the transmission of Blastocystis ST3 haplotype 1 could happen from one person to another [In some studies, it has been observed that this subtype has been strongly related to rural populations , 43. It  another . Blastocystis ST3 could be colonizing different areas promoted by the human migratory phenomenon. This idea would explain the high representation of certain haplotypes throughout the American continent and others in European countries. For example, seven sequences of haplotype 9 correspond to South America and only one corresponds to Europe (Switzerland).Also, it is possible that the other haplotypes of Blastocystis could be related to the lack of symptoms that occurs in many cases, because asymptomatic Blastocystis infections are not treated and therefore there are a large number of carriers of this parasite. It is known that these factors can influence the distribution of parasites [However, to test both hypotheses, it will be necessary to have a larger number of sequences and complete genomes from around the world. It is known that migration causes mobility of parasites and it is important to have information of other sociodemographic parameters of the host as the nationality or antecedents of previous travels to endemic areas . In addiarasites , 46. Blastocystis ST3 were grouped into three haplotypes, which means that there is a low genetic diversity, a reduced rate of mutations, and little genetic differentiation; this suggests isolation and homogeneity in the population.With respect to Morelos, all the sequences of Blastocystis ST3 subtypes in different human populations. In addition, our work facilitates the vision of a global distribution in Blastocystis ST3. We provide evidence of a recent expansion of this subtype that may be related to the migration of humans to other regions of the world. However, it is necessary to continue studying this parasite, in order to generate a more complete knowledge that allows us to know the course of Blastocystis infection, its epidemiology, and the causal factors that contribute to its dispersion dynamics and distribution.To our knowledge, this is the first study analyzing the haplotype diversity and distribution of


Cephalophus dorsalis, C. callipygus and Philantomba monticola). Genetic data from geo-referenced feces were collected from a wide range of sites across Central Africa. Historical patterns of population genetic structure were assessed using a ~ 650 bp fragment of the mitochondrial control region and contemporary patterns of genetic differentiation were evaluated using 12 polymorphic microsatellite loci.This study aims to assess the role that Pleistocene refugia, rivers and local habitat conditions may have played in the evolutionary diversification of three central African duiker species  showed that environmental variation explains most of the nuclear genetic differentiation in both C. callipygus and P. monticola. The forest-savanna transition across central Cameroon and the Plateaux Batéké region in southeastern Gabon show the highest environmentally-associated turnover in genetic variability. A pattern of genetic differentiation was also evident between the coast and forest interior that may reflect differences in precipitation and/or vegetation.Mitochondrial analyses revealed that populations of Findings from this study highlight the historical impact of Pleistocene fragmentation and current influence of environmental variation on genetic structure in duikers. Conservation efforts should therefore target areas that harbor as much environmentally-associated genetic variation as possible in order to maximize species’ capacity to adapt to environmental change.The online version of this article (10.1186/s12862-017-1054-4) contains supplementary material, which is available to authorized users. The tropical forests of central Africa contain some of the most important areas of biological diversity remaining in the world and are important targets for conservation –3. SeverOf the many tropical biodiversity hypotheses proposed for central Africa, one that has attracted considerable attention is the Pleistocene refugia hypothesis. According to this hypothesis, tropical forests contracted into smaller fragments during the more arid phases of the Pleistocene, leading to the isolation and allopatric diversification of forest-associated taxa , 11. TheWhile data from contemporary vertebrate and plant species distributions support a single major refugium spanning much of Gabon and Cameroon –23, evidIn general, researchers recognize three major predictions associated with the Pleistocene refugia hypothesis. Firstly, that fragmentation of formerly contiguous forest into isolated blocks during periods of low rainfall led to the allopatric divergence of forest-associated taxa. Secondly, that forest expansion during moister periods left signatures of population growth in refugial taxa. Lastly, that signals of admixture are to be expected in zones of secondary contact between expanding refugial populations , 28, 29.Rivers have also been proposed to act as important drivers of allopatric divergence in tropical systems . Two of . Previous studies in central Africa have shown that the forest-savanna ecotone may represent an important driver of differentiation [.Lastly, proponents of the ecological gradient hypothesis have argued that biodiversity patterns formerly attributed to past Pleistocene refugia could have arisen as a result of divergent selection across major ecological gradients , 42. Prentiation –45. Simintiation . Whateventiation , 47.Cephalophus dorsalis) and Peter’s duiker  are restricted to dense forest, the blue duiker (Philantomba monticola) occurs in a much wider range of habitats [C. dorsalis and C. callipygus are both medium in size and have much larger home ranges than the smaller P. monticola [C. dorsalis is nocturnal and solitary whereas both C. callipygus and P. monticola are exclusively diurnal and exhibit different degrees of sociality: C. callipygus exhibits a polygynous mating system whereas P. monticola is strictly monogamous [Forest duikers  constitute an ideal group for testing mechanisms of evolutionary diversification in African rainforests. Duikers are small to medium sized artiodactyls that are endemic to sub-Saharan region of Africa. Most species are associated with tropical forests and are believed to have originated in the late Miocene , making habitats  and favohabitats . C. dorsonticola , 58. Altonticola , 59. Spenogamous , 60.The main objective of the present study is to assess the relative roles that candidate Pleistocene refugia, major rivers, and environmental gradients may have played in driving the evolutionary diversification of these three central African forest duiker species. To test these hypotheses, we used mitochondrial sequencing and nuclear microsatellite genotypes obtained from geo-referenced duiker feces. The specific aims of this study are to: (a) determine whether there is a common pattern of allopatric differentiation coincident with the location of hypothesized Pleistocene forest refugia; (b) test for a riverine barrier effect along the Ogooué and Sanaga rivers using a paired cross-bank sampling scheme and (c) examine the effects of geographic distance and environmental variation on genetic structure. We used a suite of methods to examine each of these aims including a generalized dissimilarity modeling (GDM) approach  that hasN, MOON, MOOS, LON, LOS, OIVN, OIVS, LAN, OLON, OLOS, HAON; where ‘N’ and ‘S’ denote North and South banks of the Ogooué River respectively) and the Sanaga . Together this sampling strategy encompassed a range of different ecosystem types and environmental gradients, allowing us to also explicitly test the role of environmental heterogeneity in shaping population structure.A total of 2074 samples  were collected from 46 sites across nine countries in Africa  as previous studies have shown that these two storage methods maximize nuclear and mitochondrial DNA extraction yields [At each site, fresh fecal pellets (< 24 h) were sampled opportunistically along a transect line that followed a different compass bearing each day. Replicate samples from the same individual were eliminated on the basis of their multi-locus genotypes (see below). One to four pellets were placed into a vial containing either 5 g of silica gel beads  or a vial containing 1.2 ml of RNAn yields , 61. ForMuseum specimens of known geographic origin were also obtained from the following sites, the majority of which could not be sampled otherwise: Diecke, Mount Nimba (DIE) in the Republic of Guinea; Cape province (CAP) in South Africa; Dja Reserve (DJA) and Bamenda (DIV) in Cameroon; Lefini Reserve (LEF), Brazzaville (BRA) and Odzala National Park (ODZ) in the Republic of the Congo; Mbaéré-Bodingué National Park (NGO) in Central African Republic; Mitzic (MTZ) in Gabon; Kisangani (KIS) in the Democratic Republic of the Congo  were collapsed into representative haplotypes using the program COLLAPSE v.1.2 (available from http://collapse.sharewarejunction.com/). An intraspecific Minimum Spanning Network (MSN) was created with the program NETWORK v.4.6.1.0 [A fragment of the mitochondrial control region of approximately 650 base pairs (bp) in length was amplified and sequenced following previously optimized conditions . Mitocho.4.6.1.0  in orderID ≤ 0.05). KINGROUP v.2 [Fecal samples from these three species were also genotyped at 12 polymorphic microsatellites assembled into three multiplex reactions of four loci each, following previously optimized conditions . A quantROUP v.2  was usedP. walteri described by Colyn and colleagues [P. maxwelli , P. monticola , P. walteri (GenBank HM144017–144019) and an outgroup species C. callipygus using the program MEGA7 [Fecal samples were identified to species level using an established mitochondrial control region diagnostic . We alsolleagues . To testlleagues . These sam MEGA7 . A neigham MEGA7  and a boP. monticola, C. callipygus, C. dorsalis, C. silvicultor, C. leucogaster and C. nigrifons). The identification of each NGO sample was then inferred by observing the group in which it clustered with an estimated membership coefficient (Q) of ≥ 0.9. In these cluster analyses, the total number of steps in the Markov Chain Monte Carlo (MCMC) and burn-in were set to 100,000 and 10,000 generations respectively, with 10 separate simulations for each value of K. The admixture model was used and allele frequencies among populations were assumed to be correlated. STRUCTURE HARVESTER was used to determine the most probable value of K using the ΔK statistics procedure [Although the majority of fecal samples could be identified on the basis of their mitochondrial DNA, fecal samples from NGO could not be sequenced due to their poor quality and were instead identified on the basis of their multi-locus microsatellite genotypes using a cluster-based analysis implemented in the Bayesian program STRUCTURE v.2.3.3 . To do trocedure . CLUMPP rocedure  was usedrocedure  was used, sequences from the following nearby sites (< 100 km) were pooled: (1) TAK, NIG and BAM in the Gulf of Guinea refugium; (2) MTA and MM within the Equatorial Guinea and northern Gabon refugium; (3) ND and CO within the Parc National de Nouabelé Ndoki. A Spatial Analysis of Molecular Variance (SAMOVA) of each species was carried out using the program SAMOVA v.1.0 [P. monticola, AMOVA analyses were conducted without sequences from P. walteri since mitochondrial analyses showed them to be highly divergent from P. monticola (see below). The effect of a major fluvial refugium within the Sanaga River basin was assessed by pooling all sites within the river basin into one group and placing all remaining sites into a second group.All mitochondrial DNA analyses only considered sites where three or more individuals had been sampled. In cases where sampling was relatively limitedVA v.1.0  where thThree measures of mitochondrial diversity were calculated for each sampling site using the program DnaSP v.5.1  namely: Patterns of nuclear genetic structure were investigated using STRUCTURE. The total number of steps in the Markov Chain Monte Carlo (MCMC) and burn-in were set respectively to one million and 100,000 generations with 10 separate simulations for each value of K. The admixture model was used and allele frequencies among populations were assumed to be correlated. Clustering of K populations within species was used to assess whether population differentiation was structured by putative refugia. As described above, STRUCTURE HARVESTER was used to determine the most probable value of K using the ΔK statistics procedure, CLUMPP v.1.1 was used to align all multiple runs for each K and DISTRUCT 1.1 was used to make graphical representations of STRUCTURE outputs.C. callipygus, we could not test the effect of the Sanaga River barrier due to insufficient sample sizes. STRUCTURE analyses of nuclear genetic data were also carried out to examine whether genetic variation was partitioned by either river.An AMOVA analysis of mitochondrial DNA sequence datasets was also carried out to test the effects of the Ogooué and Sanaga rivers on genetic structure of all three species. To do this, sites immediately flanking the north and south banks of each river were placed into two separate groups. To test the combined effects of both Sanaga and Ogooué River bodies, the entire dataset was divided into three groups made up of: (a) sites to the north of the Sanaga; (b) between both rivers and (c) to the south of the Ogooué. In the case of A generalized dissimilarity modeling (GDM)  approachST values). To do so, we took a two-step modeling approach, following an established protocol [In the present study, GDM was used to model the relationship between several predictor variables , and cells with a minimum suitability threshold were deemed ‘unsuitable’. Minimum suitability was defined by the minimum training presence threshold, which was equal to 0.45, 0.47 and 0.01 for C. dorsalis, C. callipygus, and P. monticola, respectively. A presence-absence map was thus generated for use in subsequent GDM analyses, where models of genetic differentiation were only projected into areas of species’ presence. The maps were trimmed to include the region where the majority of sampling for this study was carried out, namely: Cameroon, Gabon, Equatorial Guinea and the Republic of Congo.The MAXENT model was fitted on all occurrence records (locations were at least 50 km apart and based on GPS data), within a study area bound by 16° W, 31° E, 10° N, and 10° S, thus incorporating the West and Central African rainforests and adjacent savanna regions. We used a convergence threshold of 10GDM modeling of each species was implemented in ArcView 3.2 and S-plus 4.0, and results were visualized in ArcGIS 9.3 . The importance of each predictor variable was tested by iteratively adding and removing each variable to the model and comparing the variation explained to other such models. Only predictors that made a significant contribution to explaining variation in the response variable were retained. Overall model performance was evaluated relative to a null model based on random environmental variables. Competing models were thus constructed with the following sets of predictor variables: 1) environment + refugia + geographic distance; 2) environment + resistance distance + geographic distance; 3) environment + geographic distance; 4) environment; 5) refugia; 6) resistance distance; 7) geographic distance; 8) random environmental variables (i.e. null model).The resulting function from the best-fit model describing the relationship between genetic differentiation and the significant predictor variables was used in a subsequent step to interpolate patterns of genetic differentiation across the entire study area. For visualization purposes in the resulting maps, areas were color-coded, where the magnitude of the color difference between two locations represents the magnitude of genetic differentiation.C. dorsalis, C. callipygus and P. monticola. Five other species were also identified: the yellow-backed duiker C. silvicultor, the black-fronted duiker C. nigrifrons, the white-bellied duiker C. leucogaster, the water chevrotain Hyemoschus aquaticus and the sitatunga Tragelaphus spekii. However, the low sample sizes in these species precluded them from any further analyses , 27 for C. callipygus (sequences differing at ≤27 nucleotide positions), and 25 for P. monticola (sequences differing ≤31 nucleotide positions). Within C. dorsalis, most haplotypes were widespread across the study area except for haplotypes K-P which were restricted to single sites  that was highly differentiated from other sequences in the dataset  and highest when compared to P. monticola (average 0.111 base substitutions/site). These sequences were removed from any further mitochondrial DNA analyses. Other haplotypes within this species were also widespread with the exception of P, Q, S, U, W, X, and Y which were restricted to individual sites. Two haplotypes  restricted to south-eastern Gabon and Equatorial Guinea respectively both possessed a 77 bp indel. The highest mitochondrial haplotype diversity in C. dorsalis was found on the east bank of the Ivindo River in central Gabon (IVE), southeastern (LBK), central (MBJ) and south-western Cameroon (TAK), and the Ngotto region of the Central African Republic (NGO). The highest average nucleotide diversity (π) and highest polymorphism per site (θ) was found in LBK  and central D.R.C. (SA). In contrast, π and θ was greatest on BKO  and south-eastern Cameroon (LBK), whereas π and θ was greatest in south-western Cameroon (EBO) , 77 out of 310 , and 82 out 332 (P. monticola) samples were first-order relatives of other individuals in the dataset. These individuals were subsequently removed from all further analyses.With respect to the microsatellite data, we detected very few instances of deviations from HWE  for three clusters comprising a riverine site in Cameroon (NKO), Gabon (OLOS) and the rest of the dataset. SAMOVA analysis of C. callipygus identified a significant among group variance component  comprising samples from the Gulf of Guinea refugium , central D.R.C. (SA) and all other sites. Partitioning the dataset into two groups comprising only the Gulf of Guinea refugium and all remaining sites also revealed a marginally significant among group variance component  for three clusters comprising Gulf of Guinea refugium , Sanaga River mouth (EDEA) and elsewhere across the study region. Once more, partitioning the dataset into the Gulf of Guinea refugium versus all other sites revealed an among group component that was only marginally significant  when sites were grouped according to the Sanaga River basin  was observed in P. monticola, but only when sites were grouped according to both the Sanaga and the Ogooué rivers.There was no evidence for any obvious mitochondrial genetic structure within P. monticola, both π  and θ  was significantly higher in refugial versus non-refugial sites. A significant negative Fu’s Fs statistic was also observed in the largest SAMOVA grouping of sites for C. dorsalis , C. callipygus  and P. monticola  and is consistent with a history of demographic expansion across the entire study area. A significant signature of expansion was also observed in the Sanaga River basin grouping for C. dorsalis , C. callipygus  and P. monticola .In C. dorsalis indicated that the best estimate of K = 2 although there was no obvious geographic pattern in the dataset  for P. monticola  and tree cover provided unique information for C. dorsalis and C. callipygus respectively, whereas annual precipitation (Bio 12) and precipitation of the wettest quarter (Bio 16) provided unique information for P. monticola. Although all species were predicted to occur across much of the study area, the distribution of C. callipygus and C. dorsalis was patchy across Cameroon and Equatorial Guinea whereas for P. monticola, it was relatively uniform . Environmental variables from either WorldClim  and temperature variables  were all significant in explaining observed genetic differentiation in C. callipygus  and Sanaga  rivers are considerable [Findings from this study do not support a role for the Sanaga and Ogooué rivers as barriers to dispersal in duikers. The only possible exception is  species , 38, 105iderable , other fiderable  may haveiderable .C. callipygus and P. monticola. Remarkably, most of the genetic differentiation between sites can be explained by a handful of environmental variables associated with temperature, moisture and vegetation cover. Interestingly, previous GDM analyses of neutral variation in chimpanzee Pan troglodytes subspecies [Trachylepus affinis [GDM analyses reveal that environmental variation explains a large proportion of genetic differentiation in both bspecies  and the  affinis  have shoThe present study also indicates that the greatest turnover in environmentally-associated variation is found in the Gulf of Guinea and in the Plateaux Batéké region of southeastern Gabon. Both regions encompass important environmental and/or elevational gradients that would be expected to play a major role in diversification. Previous GDM studies on chimpanzees and skinks mirror elevated genetic turnover in the Gulf of Guinea region and also suggest a strong east-west pattern of genetic differentiation from the coast to the interior , 45. HowC. callipygus is likely to reflect the east-west gradient in annual precipitation (Bio 12) commonly experienced across this region [C. callipygus would appear to support this hypothesis. In P. monticola, we also observed a modest shift in genetic structure between the western portion of the study area and the interior of the Congo basin that mirrors shifts in the distribution of precipitation in the driest quarter (Bio 17). Taken together, these findings clearly demonstrate that a GDM approach can uncover cryptic genetic structure that would have otherwise been missed using more conventional landscape genetic approaches and identify which environmental variables might be the most important drivers of genetic differentiation.The narrow coastal to interior pattern of genetic differentiation observed in s region . Althougs region , findingC. callipygus and P. monticola populations, this pattern was not evident in C. dorsalis. This effect could be simply due to limited number of samples obtained for C. dorsalis within the Gulf of Guinea refugium or due its wider geographic distribution [C. dorsalis.Although mitochondrial data indicate that the Gulf of Guinea refugium has played a role in structuring ribution , larger ribution , and broribution  that mayribution  in orderP. monticola appears to be the species whose genetic structure is most likely to have been affected by fluvial refugia. P. monticola has a much smaller home range than either of the other two other species in this study [C. callipygus is the only species to show a pronounced pattern of genetic differentiation from the coast to the interior that could either reflect its greater sensitivity to rainfall gradients and/or be due to the fact that the distribution of its sister taxon C. ogilbyi is along the coast. Interpretation of these findings is complicated even more by the fact that genetic markers cannot distinguish C. ogilbyi from C. callipygus [Of the three focal taxa in this study, is study  and consis study . C. callllipygus , 93. Fut, high economic value [. However, our understanding of the causes of biological diversification and the landscape elements that promote these processes remains poor. This has been compounded by the fact that evolutionary research on cryptic rainforest vertebrates such as forest duikers has traditionally been very challenging, requiring geo-referenced non-invasive sampling and intensive genotyping technologies such as those employed here [The rainforests of central Africa have attracted considerable attention because of their remarkable species richness and endemism , 112, hiic value , and gloic value . Howeveryed here .Building on our previous phylogenetic analysis of the Cephalophinae , this stC. callipygus is likely to be much more sensitive to habitat changes than P. monticola. It therefore appears urgent that more studies are carried out in this region to better understand not only how historical climate change and environmental heterogeneity have shaped past and present population structure but also how future landscape modification and climate change will ultimately affect species’ capacity to adapt or move under different development scenarios [Although the taxonomic scope of the present study is still very limited, it is clear that as a forest interior specialist, cenarios .Additional file 1:Table S1. Sampling locations by country, site name, letter code, and corresponding sample sizes for identified species in our study area. Table S2. Summary of environmental variables used in MAXENT and GDM models. Table S3. Distribution of composite C. dorsalis haplotypes by site. Table S4. Distribution of composite C. callipygus haplotypes by site. Table S5. Distribution of composite P. monticola haplotypes by site. Table S6. Mitochondrial diversity indices for each species by site. Table S7.C. dorsalis expected heterozygosity and tests of deviation from Hardy-Weinberg Equilibrium. Table S8.C. callipygus expected heterozygosity and tests of deviation from Hardy-Weinberg Equilibrium. Table S9.P. monticola expected heterozygosity and tests of deviation from Hardy-Weinberg Equilibrium. Table S10. Significant associations between pairs of loci for C. callipygus and P. monticola. Table S11. Probability of identity (PIsibs) for each locus (within species) and cumulative probabilities of identity across multiple loci for each species. Table S12. The among group component of the total variance (FCT) for hypothesized C. dorsalis groupings. Table S13. The among group component of the total variance (FCT) for hypothesized C. callipygus groupings. Table S14. Among group component of the total variance (FCT) for hypothesized P. monticola groupings (DOCX 113 kb)Supplementary tables. Additional file 2:Figure S1. Graphical representation of the STRUCTURE output for the identification of unknown NGO samples for K = 2. Figure S2a. minimum Spanning Network (MSN) of C. dorsalis based on 16 composite haplotypes (collapsed haplotypes). Red circles indicate the median vector or hypothesized  haplotypes and the length of each branch connecting sampled haplotypes indicates the genetic distance between haplotypes. b. Minimum Spanning Network (MSN) of C. callipygus based on 27 composite haplotypes (collapsed haplotypes). C. minimum Spanning Network (MSN) of P. monticola based on 25 composite haplotypes (collapsed haplotypes). Figure S3. Cytochrome c oxidase subunit I neighbour-joining bootstrap consensus phylogeny based on Tamura-Nei distances. Bootstrap values of 75% or greater are indicated at the relevant node. Figure S4a-b. Graphical representation of the STRUCTURE output for C. dorsalis for K = 2. Figure S5a-b. Graphical representation of the STRUCTURE output for C. callipygus for K = 6. Figure S6a-b. Graphical representation of the STRUCTURE output for P. monticola for K = 2. Figure S7. Environmental variables used as predictors in MAXENT and GDM analyses. Figure S8. Jackknife of regularized training gain for C. dorsalis. Fig. S9. Jackknife of regularized training gain for C. callipygus. Figure S10. Jackknife of regularized training gain for P. monticola. Figure S11. MAXENT predicted species distributions for: a) C. dorsalis b) C.callipygus and c) P. monticola. Figure S12. GDM functional response curves for C. callipygus. Figure S13. GDM functional response curves for P. monticola (PPTX 2695 kb)Supplementary figures. Additional file 3:Matrix of DNA distances. (XLS 48 kb)