Trypanosoma brucei rhodesiense, which infects humans and livestock in East and Southern Africa, has classically been described as a host-range variant of the non-human infective Trypanosoma brucei that occurs as stable clonal lineages. We have examined T. b. rhodesiense populations from East (Uganda) and Southern  Africa using a panel of microsatellite markers, incorporating both spatial and temporal analyses. Our data demonstrate that Ugandan T. b. rhodesiense existed as clonal populations, with a small number of highly related genotypes and substantial linkage disequilibrium between pairs of loci. However, these populations were not stable as the dominant genotypes changed and the genetic diversity also reduced over time. Thus these populations do not conform to one of the criteria for strict clonality, namely stability of predominant genotypes over time, and our results show that, in a period in the mid 1990s, the previously predominant genotypes were not detected but were replaced by a novel clonal population with limited genetic relationship to the original population present between 1970 and 1990. In contrast, the Malawi T. b. rhodesiense population demonstrated significantly greater diversity and evidence for frequent genetic exchange. Therefore, the population genetics of T. b. rhodesiense is more complex than previously described. This has important implications for the spread of the single copy T. b. rhodesiense gene that allows human infectivity, and therefore the epidemiology of the human disease, as well as suggesting that these parasites represent an important organism to study the influence of optional recombination upon population genetic dynamics.African trypanosomes are unusual among pathogenic protozoa in that they can undergo their complete morphological life cycle in the tsetse fly vector with mating as a non-obligatory part of this development.  Trypanosoma brucei have evolved the ability to survive in human serum. This survival in human serum is conferred by the presence of one gene in the East African human-infective T. b. rhodesiense. How often trypanosomes exchange genetic material (they can mate in the tsetse fly) is debated, but will impact upon the spread of genes (e.g. that which confers human infectivity) through a population. We studied T. b. rhodesiense populations from different geographic locations , and over time (Uganda), to see if the populations are stable over time and space, using a panel of variable genetic markers enabling assessment of diversity. Our results suggest that there is significant difference in diversity between locations; those in Uganda are very closely related, increasingly so over time, whereas the Malawi population is very genetically diverse, consistent with the trypanosomes mating. These findings suggest that a greater understanding of T. b. rhodesiense population evolution will inform on sleeping sickness epidemiology.Trypanosomes are single-celled organisms transmitted by the biting tsetse fly, which cause sleeping sickness in humans in sub-Saharan Africa, but also infect livestock and other mammals. Most trypanosomes cannot infect humans as they die in human serum, but two mutants of  Pathogens that can adapt quickly to environmental change often pose the greatest challenge to disease control. A clear example of this is the generation of drug resistance and subsequent rapid spread through a population Plasmodium falciparum, which undergoes both asexual reproduction and an obligate sexual component of the life cycle, including out-crossing and self-fertilization. As sexual reproduction occurs in the insect vector, the frequency of out-crossing is a consequence of the transmission intensity, thus differences in transmission can result in a spectrum of population structures ranging from effective clonality (due to extensive self fertilization) to panmixia Plasmodium research demonstrates that sampling from a range of epidemiological situations is necessary to evaluate the role of recombination in shaping the population genetic structure of a particular parasite species.The evolutionary potential of pathogen populations is a product of a number of factors, including the system of reproduction, the potential for gene flow, the effective population size and the mutation rate. Protozoan parasites offer a particular analytic challenge in this regard as many have complex life cycles in both vector and host, with some life cycle stages that expand mitotically and others in which sexual recombination occurs, resulting in mixed reproductive systems. Analyses of pathogenic protozoan populations reveal that there is significant diversity between different species and populations of the same species in terms of the role of genetic exchange, with some species showing clear clonality Trypanosoma bruceiT. brucei is transmitted by tsetse flies (Glossina spp.) and in humans two subspecies, T. b. rhodesiense and T. b. gambiense, cause the often-fatal disease Human African Trypanosomiasis (HAT), also known as Sleeping Sickness. Sexual recombination in T. brucei occurs in the tsetse fly salivary glands and is well characterised under laboratory conditions While mating in Apicomplexan parasites is an obligatory part of their life cycle in the arthropod vector, this is not the case with African trypanosomes. This issue is probably central to the controversy that has surrounded the definition of population structure and the role of mating in natural populations of the zoonotic protozoan parasite, T. brucei isolates from tsetse flies in East Africa indicated a panmictic or randomly mating population structure in vitro or in vivo prior to analysis has also been suggested as another possible reason for the departures from expected genotype or allele frequencies Initial isoenzyme analysis of T. brucei population genetics is that T. brucei consists of three morphologically identical sub-species. T. b. brucei cannot infect humans but causes disease in a wide range of domestic and wild animals, whereas T. b. gambiense is responsible for HAT in West and Central Africa, a chronic disease, and T. b. rhodesiense causes HAT in East and Southern Africa, typically a more acute disease. T. b. gambiense has been subdivided into two groups consisting of a homogeneous group 1 and a less common more heterogeneous group 2 T. b. gambiense group 1 is a clonal organism that undergoes sexual recombination very rarely, if at all T. b. gambiense group 1 is clearly genetically distinct from both T. b. brucei and T. b. rhodesienseT. b. rhodesiense isolates from a range of foci in East and Southern Africa has shown that while isolates from different foci are broadly similar to each other, there is an association of the genotypes with their geographical origin T. b. rhodesiense is genetically very closely related to T. b. bruceiT. b. rhodesiense populations. The basis of human infectivity in T. b. rhodesiense has been understood for some time, and is due to the expression of a single gene, the serum resistance associated (SRA) gene SRA as a marker, the detection of T. b. rhodesiense parasites in non-human hosts has become more straightforward T. b. rhodesiense is clonal SRA gene amplified in the human population, resulting in an epidemic clonal expansion. However, these genotypes were also stable over time T. b. rhodesiense was not mating with the genetically more diverse sympatric T. b. brucei population, within which evidence for frequent mating was demonstrated. However it is clear that, unlike T. b. gambiense group 1, there do not seem to be biological barriers to T. b. rhodesiense mating with T. b. brucei, as this has been demonstrated in the laboratory in two separate crosses with different T. b. brucei strains T. b. rhodesiense and so examine populations in different epidemiological settings in order to rigorously address the question of clonality in this human infective sub-species. This will also allow a series of questions to be addressed, such as whether T. b. rhodesiense HAT foci in different geographical regions display similar levels of clonality; whether different foci are genetically distinct from each other, as well as from local T. b. brucei populations; and whether clonal populations of T. b. rhodesiense are stable over space and time.An additional confounding factor for the study of T. b. rhodesiense populations, we have employed microsatellite markers to determine allelic variation and multilocus genotypes from parasites isolated from three different foci of disease in East Africa, two in Uganda, and one in Malawi. The microsatellite loci were selected from a panel of genome wide markers, which had been used in the construction of the first genetic map of the parasite T. b. rhodesiense genetically distinct? (2) Are the population structures and the role of genetic exchange similar in different foci? (3) By analysing samples over a period of 45 years from in and around the clonal Tororo focus, are the multilocus genotypes stable over time?To clarify our understanding of This study was conducted according to the principles expressed in the Declaration of Helsinki. All patients recruited received written and verbal information explaining the purpose of this study and gave informed written consent. All protocols were approved by ethics committees in Uganda  and Malawi  as appropriate. Furthermore, the protocols, information forms and consent forms were reviewed and approved by the Grampian Research Ethics Committee . Ethical consent forms were designed in English and also translated into local languages. Consent was given as a signature or a thumb print after verbal explanation. For those under 16 years of age consent was given by their legal guardian, and for those whose clinical condition prohibited full understanding of the recruitment process, consent was gained from a spouse or other family member.T. b. rhodesiense transmission covering the Tororo, Iganga, Jinja and Busia districts T. b. rhodesiense population. Samples were identified as being T. b. rhodesiense if they were isolated from an HAT patient or if they were able to resist the lytic effects of human serum HAT patients presenting to local hospitals or identified during community surveillance were recruited in South-Eastern Uganda in 2002 and 2003 from an extensive focus of Twenty eight patients were sampled from the Central Malawi HAT focus and were recruited after admission to Nkhotakota General Hospital between 2002 and 2003. Suspect cases were initially identified by clinical surveillance teams in communities within and on the periphery of the Nkhotakota Wildlife Reserve.Patient recruitment protocol has been previously described in For samples isolated on FTA cards, discs of 2 mm diameter were cut from each blood spot using a Harris Micro-punch (Whatman). The discs were washed three times with 200 µl FTA purification reagent (Whatman), and twice with 200 µl 1 mM TE buffer pH 8.0, with incubation for 5 minutes at each wash. The washed discs were then used as substrate for multiple displacement amplification (MDA) whole genome amplification reactions. Whole genome amplification was carried out using the GenomiPhi DNA Amplification kit (Amersham) as described previously 4)2SO4, 4.5 mM MgCl2, 6.7 mM 2-mercaptoethanol, 4.4 µM EDTA, 113 µg.ml−1 BSA, 1 mM of each four deoxyribonucleotide triphosphates), 1 µM of each oligonucleotide primer, and 1 unit of Taq polymerase (Abgene) per 10 µl reaction. For nested reactions, 1 µl of a 1/100 dilution of first round product was used as template in the second round PCR. Microsatellite PCR products were resolved by electrophoresis on a 3% Nusieve GTG agarose gel (Cambrex), and gels were stained with 0.2 µg/ml ethidium bromide and visualised under UV light.One µl of each MDA product or purified DNA was used as PCR template in a volume of 10 µl. The seven microsatellite loci  have been described previously http://www.dnaseq.co.uk/) was included in the run, allowing accurate determination of DNA fragment size. Data were analysed using Peak Scanner v1.0 software (Applied Biosystems). A multilocus genotype (MLG) for each isolate was defined by the specific combination of alleles across the seven loci . A set of ROX-labelled size standards  generating a Phylip Drawtree string , which was converted into a dendrogram by Treeview (http://taxonomy.zoology.gla.ac.uk/rod/treeview.html) ST) between sample populations, were calculated using GenAlex http://eburst.mlst.net/default.asp) was used to analyse the clonal expansion of the Ugandan genotypes and identify putative ‘founder’ genotypes Analysis of MLGs used Clustering Calculator , and one in Malawi. In addition, 52 samples from the Tororo focus were collected between 1961 and 1997 (referred to as‘Ug/Ke 61–97’) and include 28 samples collected in the period 1988–90. During the period between 1990 and 2003, the Tororo focus is considered to have seeded the outbreak in Soroti, which has been linked to the restocking of cattle herds in the region T. b. rhodesiense. This has been achieved through the use of seven previously described single-locus microsatellite markers, which have been physically and genetically mapped to six different megabase chromosomes of T. brucei. Of the 198 samples, full multilocus genotypes (MLGs) were obtained for 176, with the remainder genotyped for at least four of the seven loci , to avoid possible temporal sub-structuring. There were more private alleles in the Malawi population (eight) compared to three in Tororo and three in Soroti. Of the private alleles in Malawi five were present at frequencies above 0.1 within the population, whereas only one was above this frequency in Tororo and none in Soroti  to that of Tororo 2003  that the populations are of low diversity with frequent identical genotypes and secondly previous studies have shown that T. b. brucei can be distinguished from T. b. rhodesiense by RFLP and minisatellite markers T. b. rhodesiense has lost the ability to undergo genetic exchange. This could be tested by attempting laboratory crosses with these strains. In contrast, our data support the occurrence of genetic exchange in Malawian T. b. rhodesiense and so one would predict that genetic exchange would also occur with local T. b. brucei with human infection occurring when the SRA gene is inherited. Unfortunately no viable Malawian T. b. brucei strains are available and so it is not currently possible to test this hypothesis.One question that these findings raise is why mating occurs in the Malawi focus but not in the Ugandan foci. The available laboratory data show that mating can occur between The genotyping of isolates from the two foci in Uganda not only provides important information about the role of genetic exchange in these populations but also information about the temporal genotypic stability in Tororo and the potential origin of the Soroti outbreak. Our data show that genetic exchange is limited or does not occur in these populations based on the lack of agreement with Hardy-Weinberg predictions, high levels of heterozygosity, linkage disequilibrium and the high frequency of identical genotypes. These findings lead to the conclusion that these populations are clonal, primarily evolving by mitotic division and mutation. This conclusion agrees with previous analysis of the Ug/Ke 61–97 population using minisatellite markers T. vivax and T. b. gambiense being clonal T. b. brucei and T. congolense can undergo frequent mating T. b. rhodesiense provides evidence for these differences being displayed within a sub-species. The identity of the trigger for whether mating occurs or not within these species or subspecies is obviously a key question to address, but it seems reasonable to assume that it is likely to depend upon certain epidemiological scenarios . This plasticity in the use of sexual recombination within a genus, and particularly within a species (T. b. rhodesiense versus T. b. brucei presenting a prime example), makes trypanosomes a unique paradigm for studying the evolution of sexual recombination, and the role that mating plays in shaping the responses to epidemiological selective pressures.These findings have implications for our understanding of recombination as an evolutionary driving force in trypanosomes. It is clear that mating plays different roles in different species, with Table S1Sample origin and multi locus genotype (MLG) data for the 195 single genotype samples. Genotype data lists allele size in base pairs with missing data represented by 0. MLG IDs have not been assigned to samples with missing data. * This MLG was observed in both the Soroti and Tororo populations.(DOCX)Click here for additional data file.Table S2Microsatellite loci and the primers used for their amplification. For each locus the first pair of primers were used for the primary reaction and the second pair for the subsequent nested reaction.(DOCX)Click here for additional data file.Table S3Allele frequencies for the seven microsatellite markers in all four trypanosome populations.(DOCX)Click here for additional data file.Table S4Linkage disequilibrium between pairs of loci for the two populations where analysis was warranted for ‘all samples/unique MLGs’, respectively. Allele combinations were preserved for loci showing significant disagreement with HWE predictions. *P<0.05 = Significant linkage disequilibrium, indicated in bold.(DOCX)Click here for additional data file.


The islands of North Maluku, Indonesia occupy a central position in the major prehistoric dispersal streams that shaped the peoples of Island Southeast Asia and the Pacific. Within this region a linguistic contact zone exists where speakers of Papuan and Austronesian languages reside in close proximity. Here we use population genetic data to assess the extent to which North Maluku populations experienced admixture of Asian genetic material, and whether linguistic boundaries reflect genetic differentiation today.Autosomal and X-linked markers reveal overall Asian admixture of 67% in North Maluku, demonstrating a substantial contribution of genetic material into the region from Asia. We observe no evidence of population structure associated with ethnicity or language affiliation.Our data support a model of widespread Asian admixture in North Maluku, likely mediated by the expansion of Austronesian-speaking peoples into the region during the mid Holocene. In North Maluku there is no genetic differentiation in terms of Austronesian- versus Papuan-speakers, suggesting extensive gene flow across linguistic boundaries. In a regional context, our results illuminate a major genetic divide at the Molucca Sea, between the islands of Sulawesi and North Maluku. West of this divide, populations exhibit predominantly Asian ancestry, with very little contribution of Papuan genetic material. East of the Molucca Sea, populations show diminished rates of Asian admixture and substantial persistence of Papuan genetic diversity. The islands of North Maluku, Indonesia  lie in a pivotal position on the migration routes of humans through the Indo-Pacific. Two major prehistoric movements of people are particularly important with respect to the linguistic, cultural, and genetic diversity of the region. First, the initial Pleistocene colonization of the western Pacific flowed from west to east through Indonesia to New Guinea and Australia, bringing the ancestors of modern Papuans and Australians into the region. Although the exact route(s) of this migration are not known, North Maluku lies along a probable pathway of inter-island travel between Sulawesi and New Guinea . Indeed,With respect to these prehistoric influences, North Maluku is particularly remarkable because it is one of only a handful of island groups in eastern Indonesia that maintains extant languages that likely reflect both the initial Pleistocene colonization event and subsequent immigration of Austronesians. Geographically, the most widespread languages in North Maluku are of Papuan origin and fall within the West Papuan language family; this family ultimately traces its ancestry back to the original Pleistocene colonization of Island Southeast Asia . The WesThe mechanism by which Austronesian languages became established in North Maluku remains unknown. The diversity of Papuan languages in the area suggests that they were well-established prior to the arrival of Austronesians ,7. Two sGiven the remarkable linguistic diversity of North Maluku, and the obvious importance of its geographic position for both Pleistocene and Austronesian migration, we have surprisingly little knowledge of the genetic composition of populations living in this region today. Because languages in North Maluku have both Asian and Papuan origins, our goal here is to test whether there is a genetic signature that also reflects this division.i.e., hundreds of thousands) of single nucleotide polymorphisms (SNPs) across a range of samples . However, from the perspective of information content, this approach is expensive. The vast majority of SNPs in a population are unlikely to show frequency differences between Asian and Papuan groups, and therefore have no statistical power to infer Asian-Papuan ancestry. Instead, information about ancestry proportions is carried by a relatively small number of SNPs with high frequency differences between the parental groups. Such SNPs are called ancestry informative markers (AIMs). AIMs are considerably cheaper to screen than genotyping with standard SNP chips, but they retain extremely high power to infer ancestry proportions. One key limitation is that AIMs only have statistical power to answer the questions they were designed to address - in this case, proportions of Asian versus Papuan ancestry. Nevertheless, this is the main axis of population history in the Indo-Pacific region today.One approach to this question would be to genotype large numbers . Nevertheless, the general pattern is indicative of female-biased gene flow into the region from Asia, as is consistent with earlier work [Each of the AIMs included in this study exhibited alleles of intermediate frequency in our total sample from North Maluku, thus suggesting strong Papuan ier work . There ii.e., performing an analysis of molecular variance, or AMOVA), we can begin to describe the genetic structure of human groups living in North Maluku. At both the X chromosome and autosomal loci, an average of ≥99.5% of the genetic variability can be found within populations .Generally speaking, there was very little variability among markers in terms of the AMOVA results , which also show a trend toward female-biased Asian admixture using the same AIMs we screened here [Populations in North Maluku have been substantially impacted by gene flow from Asia. At the ancestry informative loci tested here, we estimate the overall Asian admixture fraction to be ~67% (using two different methodologies). This fraction appears slightly higher on the X-chromosome, indicating a possible Asian female bias during the admixture process. In a regional context, these results mirror previously surveyed populations in eastern Indonesia. Several islands have broadly similar admixture profiles , which he described based on the region's non-human fauna. Our data suggest that differing rates of Asian admixture, likely mediated by the geographical expansion of Austronesian-speakers, may be responsible for Wallace's phenotypic boundary observed for human populations in the region. Within North Maluku, Wallace also noted that individuals displayed what he deemed to be a composite of Papuan and Malay characteristics [It is remarkable that the genetic divide we observe at the Molucca Sea (and which has been observed previously in Nusa Tenggara) precisely follows a breakpoint in human morphological phenotypes first observed by the nineteenth century biologist Alfred R. Wallace . Wallaceeristics . Similareristics -19. The eristics -25. GiveDespite the relatively strong contribution of Asian genetic material to the peoples of North Maluku, we observe no correlation between genes and language among our surveyed populations. Whether Papuan- or Austronesian-speaking, all populations shared similar levels of Asian admixture and showed no evidence of genetic differentiation. This result is not easy to reconcile with either the Replacement or Adoption Models. Unless swamped by subsequent gene flow, the Replacement hypothesis predicts some degree of genetic differentiation between populations speaking Austronesian versus Papuan languages. Likewise, the Adoption hypothesis predicts little genetic evidence of Austronesian admixture. Neither of these patterns is consistent with our observations. Instead, our results suggest a process of extensive Asian admixture with heterogeneous linguistic replacement. Austronesian languages made inroads in North Maluku, but this process only sporadically resulted in replacement of indigenous languages . Why some Papuan languages persisted in the face of widespread gene flow, and others did not, represents an intriguing open question.et al. [This outcome contrasts strongly with other nearby island systems, including many populations in coastal Melanesia, where replacement of indigenous languages with Austronesian ones is complete, despite the persistence of Papuan genetic variation ,26. Indeet al.  found noet al. -32. Regaet al. .One important caveat of our AIM-based analysis is that it is designed expressly to tease out patterns of population genetic variation that have been shaped by Asian introgression into predominantly Papuan regions. Our finding of a lack of differentiation could be caused by either high rates of gene flow among populations or uniformly high rates of Asian admixture into individual populations, but not necessarily both. In other words, it would be premature to infer from our results that there exists no genetic structure that differentiates populations within North Maluku. However, if such population structure exists, it has not been caused by differential rates of Asian admixture. Other genetic systems may reveal important patterns of subdivision that result from alternative historical processes . For insPopulations in North Maluku inhabit a remarkable linguistic contact zone reflecting deeply divergent Papuan and Austronesian languages. Our data suggest that the arrival of Austronesian languages was accompanied by extensive genetic admixture across North Maluku together with sporadic replacement of indigenous Papuan languages by Austronesian ones. In the broader context of the Austronesian expansion into this region, we have identified the Molucca Sea as an important phylogeographic and linguistic divide, east of which indigenous languages and genes resisted replacement more effectively than to the west. The cause of this pattern remains unclear, but likely reflects a geographical or social transition point in the expansion dynamics of the Austronesian dispersal .DNA samples were collected from unrelated adults of both sexes attending secondary school in the city of Ternate, North Maluku. Data on natal language and self-identified ethnicity were obtained from each individual. Because DNA collection occurred at a centralized location, the number of samples representing each population varied widely. For population-based analyses, we included in this study only those for which the sample size was at least five individuals. All samples were collected with informed consent by staff of the Eijkman Institute for Molecular Biology, using protocols approved by the Institute.ST between southern Han Chinese and PNG highlanders. Candidate markers were chosen from two sources: the HOMINID dataset, a collection of re-sequenced putatively neutral regions distributed across the human genome [i.e., they are evolutionarily and statistically independent).A total of 28 ancestry informative markers (AIMs)  at the University of Arizona Genetics Core facility  was calculated from the distribution of re-sampled admixture rates. Note that this approach explicitly accounts for variation in sample sizes and effective population sizes, even when they are quite small and/or differ widely between populations.Admixture estimates and confidence intervals were generated for each population using a variant of the admixture estimation method introduced by Chakraborty et al. , as modii.e., FST, FCT and FSC) was assessed using 1,000 random permutations of the data. We implemented the AMOVA using the program Arlequin version 3.5.1.2 [6 steps per run, varying K (the number of populations) from 1 to 5. We ran each condition multiple times to ensure similarity of results among runs. To improve the detection of structure in our data we allowed prior information about individuals to inform the clustering process, as described by Hubisz et al. [i.e., Papuan versus Austronesian) as prior information. Use of ethnicity produced very similar results (data not shown).Population differentiation was assessed using several methods. First, we employed a hierarchical analysis of variance (AMOVA), as described by Weir . For thi 3.5.1.2 . Our sec 3.5.1.2 . We impl 3.5.1.2 . We ran z et al. . All resAs a final examination of population structure, we performed an individual-based principal components analysis (PCA), implemented using the program SMARTPCA , to testJAW conceived of the study, assisted with data collection, performed data analysis, and drafted the manuscript. MPC performed data analysis, statistics and edited the manuscript. AWS, AP and RA assisted with data collection. AH and HS helped conceive of the study and oversaw sample collection. All authors have read and approved the final manuscript.AIM frequencies for all samples (Spreadsheet).Click here for fileHierarchical analysis of population differentiation by individual ancestry informative marker (Spreadsheet).Click here for fileSNP genotyping primer details and reaction conditions (Spreadsheet).Click here for file


Glossina fuscipes, a riverine species of tsetse, is the major vector of human African trypanosomiasis (HAT) in sub-Saharan Africa. Understanding the population dynamics, and specifically the temporal stability, of G. fuscipes will be important for informing vector control activities. We evaluated genetic changes over time in seven populations of the subspecies G. f. fuscipes distributed across southeastern Uganda, including a zone of contact between two historically isolated lineages. A total of 667 tsetse flies were genotyped at 16 microsatellite loci and at one mitochondrial locus.EST. Effective population size estimates using momentum-based and likelihood methods were generally large. We observed significant change in mitochondrial haplotype frequencies in just one population, located along the zone of contact. The change in haplotypes was not accompanied by changes in microsatellite frequencies, raising the possibility of asymmetric mating compatibility in this zone.Results of an AMOVA indicated that time of sampling did not explain a significant proportion of the variance in allele frequencies observed across all samples. Estimates of differentiation between samples from a single population ranged from approximately 0 to 0.019, using Jost's DG. f. fuscipes were stable over the 8-12 generations studied. Future studies should aim to reconcile these data with observed seasonal fluctuations in the apparent density of tsetse.Our results suggest that populations of  Glossina spp (Diptera: Glossinidae) transmit several species of pathogenic trypanosomes causing Human African Trypanosomiasis (HAT) and African Animal Trypanosomiasis (AAT). HAT affects human welfare directly through the chronic and acute forms of the disease caused by Trypanosoma brucei gambiense and T. b. rhodesiense respectively. AAT, on the other hand, stands as a major obstacle to the development of more efficient and sustainable livestock production systems in tsetse-infested areas [Tsetse flies, ed areas . A majorUnderstanding tsetse population dynamics is critical for determining which control strategy is most appropriate , for choosing the best method for enacting that strategy , and for determining the scale at which vector control activities must be implemented . DetermiGlossina fuscipes is one of the most important vectors of HAT, transmitting an estimated 90% of all disease cases [Glossina fuscipes is a member of the palpalis group of tsetse, which inhabit low bushes or forests at the margins of rivers, lakes or temporarily-flooded scrub land. In eastern Africa, populations of the subspecies G. f. fuscipes appear to respond to seasonal weather patterns, often disappearing during the bi-annual dry season from sites where they were previously abundant [G. f. fuscipes, we evaluated temporal changes in gene frequencies at one mitochondrial locus and 16 microsatellite loci in multiple Ugandan populations. Our sampling scheme included three populations situated at a zone of contact between two divergent lineages of G. f. fuscipes. These two lineages exhibit distinct mitochondrial DNA (mtDNA) haplotypes and strong differentiation at microsatellite loci, suggesting a long history of isolation, and providing a unique opportunity to monitor their interaction over time [Across Africa, se cases . Glossinabundant . If popuver time .Tsetse flies were collected using biconical traps  during tG. fuscipes, but varied somewhat in regard to the immediate environment. Sites at Busime (BU) and Junda (JN) were located in a transition zone between marsh and woodland on the edge of Lake Victoria and Lake Kyoga, respectively. Sites at Bunghazi (BN), Dokolo/Otuboi (OT) and Okame (OK) were situated along permanent streams in a region of mixed agriculture and pastureland. Sampling at Mukongoro (MK) was conducted at the margin of ephemeral wetlands associated with rice cultivation. Sampling at Masindi (MS) was conducted within a region of banana and sugar cane plantations.Tsetse collections were conducted at seven sites spanning central and southeastern Uganda Figure . These sG. fuscipes co-occuring in Uganda [The study sites spanned a zone of contact between two divergent groups of n Uganda ,10. SiteDNA was extracted from tsetse legs using NucleoSpin 96 Tissue Kits  or DNeasy kits  following the manufacturer's protocols.2 and 0.5 U Go Taq polymerase. The amplification involved a denaturation step at 95°C for 8 min, followed by 50 cycles each at 94°C for 30 s, 51°C for 30 s, 72°C for 45 s, with a final extension step at 72°C for 7 min. PCR products were sequenced using an ABI Model 3730 automated sequencer . Electropherograms were visually inspected and sequences were trimmed to remove poor quality data. The resulting sequences (530 bp) were aligned by eye using the computer program Sequencher 4.2.2 (Gene Codes Corporation).PCR was used to amplify a 570 bp fragment of mtDNA from a random subset of flies from each population using the primers COIF1 (CCT CAA CAC TTT TTA GGT TTA G) and COIIR1 (GGT TCT CTA ATT TCA TCA AGT A) as described by . We amplG. morsitans genome and optimized for use in G. fuscipes: GmmA06, GmmB20, GmmD15, GmmL03, GmmL11 [2 and 0.5 U Go Taq polymerase. PCR products were multiplexed in groups of two or three and then genotyped on the ABI 3730 automated sequencer. Alleles were scored using the program Genemarker v 5.0 (SoftGenetics) with manual editing of the automatically scored peaks.We genotyped individual flies at 16 loci. We used 11 of the 13 loci described by , excludi, GmmL11 . Amplifio) and expected (He) heterozygosity for each population. DnaSP version 5.0 [d) and nucleotide diversity (π).Microsatellite loci were evaluated for Hardy Weinberg equilibrium (HWE) and linkage disequilibrium (LD) using Genepop version 4.0 . Markov sion 5.0  was usedEST [EST provides a less-biased estimate of differentiation than FST and related statistics, especially when estimated using highly polymorphic microsatellite loci [EST were performed using the web-based program SMOGD [For microsatellite data, we used Jost's DEST  to quantite loci . Locus-sam SMOGD  and thenam SMOGD  to charae) is defined as the size of an ideal population , which would exhibit the same genetic characteristics as the population at hand [e, therefore, reflects the rate of change in gene frequencies due to random drift alone [e of 20,000.We estimated current effective population sizes based on temporal changes in microsatellite allele at all seven sites. The effective size of a population  in Guinea and Equatorial Guinea [For all analyses, we assumed that er year,  ~8.5 gener year,  and thosl Guinea . All popFor each temporal sample in all seven populations, we also tested for an excess of heterozygosity relative to observed allelic diversity, which may be indicative of a recent bottleneck . For eacIS (out of 288) that exhibited significant departures from HWE at p < 0.05  ranged from 0.418 to 0.609, (Table Microsatellite diversity was lowest in the samples from Mukongoro (MK) and highest in the samples from Bunghazi (BN). Allelic richness ranged from 3.0 to 4.4 and expected heterozygosity  and Otuboi .Variation in allele frequencies by population and locus are depicted in Figure Mitochondrial haplotype frequencies also exhibited little change over time Figure . We obseAn analysis of molecular variance using both microsatellite allele frequencies and haplotype frequencies suggested that differences between temporally-spaced samples explained an insignificant amount of the overall genetic variation Table . Differee were generated for the seven populations based on microsatellite allele frequency changes observed among samples collected at different times from the same population. Momentum-based estimates ranged from 216 to infinity, but only the estimate from OT was bounded by a 95% confidence interval that did not include infinity , Masindi (MS), Okame (OK) and Otuboi (OT)) exhibited significant signatures of a recent population bottleneck under the infinite allele model (IAM) model. Only one of these samples (MS generation 0) also tested positive for a bottleneck under the two phase model  must occur in waves of tsetse that are large enough to be representative of the refugia population. Large populations of pupa, which develop in the ground over a period of weeks, may also help to ensure the continuity of tsetse populations and would contribute to reducing the variance in genetic changes over time.Compared to other riverine species of tsetse, estimates of Nl Guinea  and 2 tof Guinea . Values in Kenya . The larpothesis  that sean Uganda . To recon Uganda ,37, we se were low for populations MS and especially OT, where both moment and likelihood methods produced values of only about 200. These values could be indicative of small populations. Ne may also be influenced by overlapping generations and temporal variance in reproductive success as well as the forces of selection, mutation and migration. In this study, however, the low values of Ne observed in these populations probably reflected small differences in the location of trapping sites used for the two temporal samples. Generation 13 from MS was sampled at a distance of about 4 km from the original site at which generation 0 was sampled. Likewise, generation 11 from OT was sampled at a single site that was 11-20 km from the relatively widely dispersed sites from which generation 0 was sampled. Thus, for these sites, which were the only two sites sampled at different locations across years, fine-scale spatial genetic variation could be responsible for the apparent temporal variation in gene frequencies, thus depressing estimates of Ne.In contrast to the other populations, estimates of NG. f. fuscipes [Wolbachia, a symbiont that may impose mating barriers due to cytoplasmic incompatibility between infected and uninfected tsetse individuals [Wolbachia. If present, the zone of contact in Uganda may provide a unique opportunity to monitor symbiont-induced population changes over time.Given that genetic variation in MS and OT samples can probably be attributed to microgeographic variation, the change in genetic composition of the population at JN likely reflects the only significant temporal change observed in this study. Although microsatellite allele frequencies were largely invariate, mtDNA haplotype frequencies here differed significantly between generation 0 and generation 13. Junda (JN), along with sites BN and MS, lies along a narrow zone of contact between two long-diverged and historically-isolated groups of fuscipes ,10. In 2fuscipes . Mating ividuals , therebyividuals . Given tThe authors declare that they have no competing interests.RE and JSB contributed to field collections, performed lab work, analyzed the data, and drafted an initial version of the manuscript. CH also performed lab work and data analysis. LMO, SA and AC helped design the study, coordinated fieldwork and provided guidance on the manuscript. All authors read and approved the final manuscript.Table S1. FIS values for the 16 microsatellite loci. Significance was assessed at p < 0.05 (*) and a Bonferroni-corrected value of p < 0.0028 (bold). Low variability precluded calculation of FIS in some populations (n/a).Click here for fileTable S2. Pairwise estimates of genetic differentiation (Jost's DEST) between samples taken from seven populations of G. f. fuscipes. Estimates of differentiation  and associated standard error  between populations of flies sampled at the same site but different times are shaded in grey.Click here for file


Anopheles lesteri is a primary vector of Plasmodium spp. in central China. A complete understanding of vector population structure and the processes responsible for the differentiation is important to the vector-based malaria control programmes and for identifying heterogeneity in disease transmission as a result of discrete vector populations. There is no adequate An. lesteri population genetic data available.An. lesteri from China.Polymorphism of sequence variations in mitochondrial COII and Cytb genes were assessed to explore the level of genetic variability and differentiation among six populations of ST Fvalues was from 0.101 to 0.655 by mtDNA-COII, and 0.029 to 0.231 by Cytb gene. The analysis of molecular variance (AMOVA) showed that the percentage of variation within populations  was greater than that among populations  using both genes. The Tajima's D and Fu's Fs values were all negative, except Tajima's D values of YN and HNB populations, which suggest a large number of low-frequency mutations in populations and the populations were in expansion proceeding.There were 30 (4.37%) and 21 (5.33%) polymorphic sites for mtDNA-COII and Cytb gene, respectively. Totally 31 COII and 30 Cytb haplotypes were obtained. The range of An. lesteri populations were higher than among them. While these results may suggest considerable levels of gene flow, other explanations, such as the effect of historical population perturbations can also be hypothesized.Levels of genetic variation within  Anopheles lesteri, which belongs to the Hyrcanus group of the genus Anopheles is a primary vector of malaria in central China ) value was less than one [An. lesteri pairwise populations was below one, except YN/HNB, YN/LN, HNB/LN and LN/GD using mtDNA-COII gene, but all more than one except SC/YN using Cytb. The shallow population genetic structure was showed by Cytb gene. But the results by COII gene suggested that there was an apparent segregation from LN with the other populations, which is in agreement with the previous investigations with RAPD markers [An. lesteri population genetic divergence using mtDNA-COII gene should represent wild populations.The pairwise genetic distance using mtDNA-COII gene (0.101-0.655) was higher than Cytb (0.029-0.231). In theory, it was hard to prevent genetic divergence caused by genetic drift if the gene flow [than one . The lev markers . So, theAn. lesteri was the ancestor and the other region populations spread from Yunnan in the late stage of glaciations. The haplotype network suggested that An. lesteri migrated and spread from Yunnan towards the North and the East China, and occurred colonization and expansion during migration proceeding. They were the same as the An. lesteri population patterns with An. dirus complex in Southeast Asia by mtDNA-COI and microsatellite DNA [An. jeyporiensis in South China by mtDNA-COII [An. lesteri samples in south of Yunnan should be increase to further investigation. An. lesteri is widespread in Palaearctic and Oriental region, and there is different climate, breeding habitation and blood preference, such as An. lesteri in southern and central China mainly is anthropophagic, but in Liaoning preferred animal's blood [An. lesteri in China.The factors responsible for population genetic structure should be analysed related with the climate, geography and the behaviour of mosquitoes. Yunnan is a highly complex region topographically due to its transitional position from tropical southern Himalayas to eastern Asia and from tropical Southeast Asia to sub-tropical China as well as at the junction of the India and Burmese plates, derived from Gondwanaland, and the Eurasian plate . It is alite DNA ,49, An. DNA-COII . If the 's blood . The aboAn. lesteri populations were higher than among them. There was an apparent segregation from Liaoning with the other populations using mtDNA-COII gene. The results of neutrality test suggested a large number of low-frequency mutations in populations and the populations were in expansion proceeding. While these results may suggest considerable levels of gene flow, other explanations such as the effect of historical population perturbations can also be hypothesized.Levels of genetic variation within The authors declare that they have no competing interests.YM conceived and designed the experiments. MY performed the experiments. MY and YM analyzed the data. MY and YM wrote the paper. JW Provided part of the material. All authors read and approved the manuscript.


Jumellea rossii, an epiphytic orchid endemic to Réunion growing in three different habitats. We found a significant genetic differentiation between populations that is structured by habitat heterogeneity rather than by geographic distance between populations. These results suggest that ecological factors might reduce gene flow among populations located in different habitats. This pattern of isolation-by-habitat may be the result of both isolation-by-ecology by habitat filtering and asynchrony in flowering phenology. Furthermore, data on floral morphology match these findings, with multivariate analysis grouping populations by habitat type but could be only due to phenotypic plasticity. Indeed floral scent compounds were not significantly different between populations indicating that specific plant-pollinator mutualism does not seem to play a major role in the population differentiation of J. rossii. In conclusion, the results from our study emphasize the importance of habitat diversity of small oceanic islands as a factor of population differentiation.Identifying factors that promote population differentiation is of interest for understanding the early stages of speciation. Gene flow among populations inhabiting different environments can be reduced by geographical distance (isolation-by-distance) or by divergent selection resulting from local adaptation (isolation-by-ecology). Few studies have investigated the influence of these factors in small oceanic islands where the influence of geographic distance is expected to be null but where habitat diversity could have a strong effect on population differentiation. In this study, we tested for the spatial divergence of phenotypes  and genotypes (microsatellites) among ten populations of  Genetic differentiation among populations of a species can be the result of limited gene flow and genetic drift followed by divergent natural selection acting on these different gene pools So far, population differentiation has been mainly studied in a geographical context in which initial divergence between populations depends on the level of migration between them Species exclusively found on small oceanic islands are excellent systems to study ecological isolation because geographical distances between populations are a priori less important on a small oceanic island than on the mainland. Moreover young oceanic islands are topographically complex and, as a result, a wide range of climatic and ecological conditions is found at small geographic scale Orchids are particularly interesting in this context because they produce numerous minute seeds capable of long-range dispersal Jumellea rossii, which is endemic to Réunion and widespread across several habitat types on this island J. rossii in Réunion.Here, we focus on the genetic differentiation among populations of an epiphytic orchid species, 2) and young (about two million years) oceanic island in the Mascarene archipelago. Two volcanic massifs shape the island; one of them is still active  is a small , mountain leeward rainforest (MLR), and submountain windward rainforest . Pollinators include at least one Sphingidae and two Noctuidae species . Plants form dense clumps of 20–50 cm high stems, each stem producing one to five flowers, white in colour, and each flower emitting a pleasant, sweet fragrance at dusk. The flowering period is between December and March during the rainy season, and variation in floral phenology remarkably correlates with altitude: highland populations flower earlier than lowland populations. A molecular phylogeny of the genus JumelleaJ. rossii.est SWR; , Fig. 1.J. rossii per habitat type were sampled between January and July 2012 in Réunion ; that is, a total of ten populations and 410 plant individuals  designed during this study. PCR multiplexes with fluorescently labelled primers were performed as in Three to four populations of AL) and the number of private alleles (AP) per population were computed using GenAlEx 6.5 AR, El Mousadik and Petit HE) over all loci, observed heterozygosity over all loci (HO), multilocus FIS estimated through the fixation index of Weir and Cockerham AR and HE) between habitats, a test of comparison among groups of populations using FSTAT 2.9.3 All pairs of loci were tested for linkage disequilibrium using a probability test in Genepop 4.0 FST) was computed among pairs of populations as in Weir and Cockerham RST  of each population and migration rates between populations were estimated. Ne of each population was estimated using a single-sample method . LDNE software Ne from linkage disequilibrium in the data set. For this analysis, random mating was assumed, and all alleles with frequencies lower than 0.05 were excluded from the analyses. To estimate recent migration rates among populations and to identify which factor  affect migration among populations, a Bayesian method based on microsatellite multilocus genotypes was applied, as implemented in BIMr Analysis of molecular variance AMOVA;  was perfAssignment of multi-locus genotypes to different clusters was examined using two methods. Following a Bayesian clustering method, we ran InStruct Flowers were sampled from eight out of the ten populations under study during the flowering season in 2012  and environmental distances (IBE) in genetic differentiation using Mantel tests. Significance of coefficients and R2 were estimated after 9999 random permutations completed in GenAlEx 6.5 Differentiation in floral morphology and scent between populations was estimated using pairwise Mahalanobis DJ. rossii revealed high levels of genetic variability within populations, with mean numbers of alleles per locus (AR) ranging from 5.57 to 9.27 and expected heterozygosities (HE) from 0.680 to 0.793 (AR and HE was higher in MLR than in SWR (P = 0.002 and P = 0.0005 respectively) but not significantly different for the other pairs of habitats. All populations showed private alleles (AP), from 1 to 8 according to the population . The global Hedrick’s standardized G’ST was 0.231 among all populations. Genetic differentiation was slightly but significantly higher  among populations of different habitat types (G’ST = 0.233±0.048 ranging between 0.154 and 0.315) than among populations of a same habitat (G’ST = 0.150±0.029 ranging between 0.112 and 0.201).According to the AMOVA, 93.5% of the total genetic variation was found within populations, only 4.4% among populations of a same habitat type, and 2.1% among habitat types. The average pairwise Ne) across populations was 32 individuals with a minimum of 6 in Mourouvain (95% CI: 3–8) and a maximum of 91 in Basse-Vallée (95% CI: 57–199). In addition to Basse-Vallée, only Cilaos had a Ne of more than 40 individuals . The results from five different runs with the software BIMr were concordant and estimated that the mean immigration rate between each pair of subpopulations was 3.91×10−3 with a minimum of 9.88×10−11 and a maximum of 1.05×10−1. The numbers of recent migrants per generation (Nm) between each pair of subpopulations were obtained by multiplying the effective size of the population of origin by migration rates , indicating that significant morphological differences exist among habitats and among populations within habitat. This variation could be effectively visualized using the two first canonical variables of the CDA for habitat comparisons (J. rossii are shown in The MANOVA was highly significant for habitats and populations within habitats (parisons . At the P = 0.91 and P = 0.70 respectively), indicating that there was no significant difference in the floral volatiles among habitats and among populations within habitat.In the analysis of floral volatiles, we identified ten major compounds across all 16 individuals examined : that isE = 0.476, P = 0.004) was about two times greater than the regression coefficient for geographic distances  suggesting that IBE explained the majority of genetic distance  compared to environmental distances, which did not have a significant role . Neither geographical distances nor environmental distances significantly explained the chemical distances between populations.Mantel tests between geographic distances and genetic distances and between environmental distances and genetic distances revealed that both IBD and IBE played a significant and independent role in genetic differentiation of populations. Indeed, environmental and geographic distances were both strongly associated with genetic distances but not significantly correlated with each other . Howeverdistance . ConcernJumellea rossii at relatively small geographic scale i.e. less than the total surface area of Réunion . Genetic clustering analyses showed that the general pattern of population differentiation in J. rossii was structured geographically in relation with the variation in forest type. Moreover, multiple matrix regression analyses also indicated that the three main forest types where J. rossii was found, and which were characterized by seven environmental variables in the analyses, primarily caused population differentiation in this orchid species compared to geography. In other words, gene flow seems to occur preferentially among populations of a similar habitat type. Conversely, gene flow seems to occur less frequently between populations of different habitat types, even if these are geographically close. This was well illustrated by the two populations growing on eastern slopes of the island at Plaine des Fougères (1220 m) and Dugain (770 m): although only 5 km separate them, we observed a significant genetic differentiation between these two populations (G’ST = 0.186) that mainly differed in respect to the forest type. The general pattern of isolation-by-habitat was also supported by a Bayesian analysis revealing that the best model explaining the migration rates among populations was the one based on the environmental variables only. Thus, migrations among populations whose environmental characteristics are dissimilar appear to be less frequent.We found a significant genetic differentiation between populations of the epiphytic orchid species J. rossii are the conditions under which genetic drift can occur J. rossii, one likely explanation for the observed pattern of isolation-by-habitat would be variation in the flowering phenology. Differences in flowering phenology between populations growing under different climatic conditions, e.g. in more or less elevated rainforests, should restrict gene flow through asynchronous pollination and ultimately cause genetic isolation. For instance, the two geographically close and genetically differentiated populations growing at Plaine des Fougères and Dugain blossomed in late December-early January and late February-early March respectively. Nevertheless, variation in flowering phenology alone cannot explain the overall pattern of genetic differentiation. Indeed, populations growing at the same altitude along the environmental gradient from east to west, i.e. in mountain windward rainforests or in mountain leeward rainforest respectively, flower synchronously. For example, the populations growing at Cilaos (1400 m) and at Bébour-Takamaka (1370 m) both blossomed in late December-early January. Moreover, as they are 8 km away from one another, they could have potentially exchanged genes through pollen flow. Despite the fact that these flowering populations coexist spatially and temporally, we observed a significant genetic differentiation (G’ST = 0.219) between them. In this particular case, as in several other cases, we believe that gene flow may be limited by IBE. Under IBE, local adaptation of populations to their habitat drives their ecological niche differentiation and thus limits the likelihood of gene flow between them Pattern of genetic differentiation obtained with microsatellites markers can be viewed as the result of stochastic evolutionary processes J. rossii but that the latter factor was more likely to explain it. Under IBE, the proximal cause of population differentiation is ecologically-based divergent selection J. rossii was concordant with genetic differentiation and was also structured by habitat. For example, lip width tends to decrease along the gradient from east to west, and spur length tends to increase with altitude. Even if such consistent patterns of morphological variation can theoretically arise through random processes J. rossii in each habitat type is too scarce to support the hypothesis of pollinator-mediated divergence. Moreover, in this species, floral volatiles did not differ significantly between populations or between habitats. Floral odour is highly relevant for pollinator attraction and is consequently expected to be under strong selection, especially in night pollinated flowers J. rossii. Moreover, spatio-temporal variation in pollinators of a generalist plant might likely result in inconsistent selective regimes that will greatly reduce the possibilities of local adaptation to particular pollinators Jumellea rossii has a generalist pollination system because three species of effective pollinators have been identified so far but their relative abundance in each habitat type, their influence on the orchid reproductive success and the dispersal distance of pollen remains to be investigated. Our results suggest that specific plant-pollinator mutualism does not play a major role in the population differentiation of J. rossii but that other factors are involved. In this case, phenotypic plasticity could be a likely explanation for the morphological differentiation of populations. Plasticity of floral traits has indeed been documented in response to variation in environmental factors such as water, light, temperature and nutrient availability We previously showed that both IBD and IBE contributed to the pattern of genetic differentiation among populations of J. rossii were genetically structured according to the forest types and interpreted it as the result of habitat filtering in combination with variation in flowering phenology. One question here remains: are populations of J. rossii undergoing speciation on Reunion Island? From a broader perspective, the relationship between population divergence and speciation is not always obvious. Indeed, some species can maintain high population differentiation without necessarily splitting into several lineages We found that populations of J. rossii are genetically differentiated, there is no clear evidence that this orchid species is undergoing speciation. However, genetic differentiation between populations has long been perceived as an early step during the speciation process PsiadiaDombeyaJumellea, we observe the same tendency in the phylogeny J. rossii that diverged from the lowland forest species J. fragrans through adapting to the cloudy mountain and submountain rainforests. Further research on the genetic differentiation among populations of J. fragrans and J. rossii will help understanding the mechanisms driving speciation in this orchid clade and perhaps answering the question on whether populations of J. rossii are undergoing speciation.Although populations of Figure S1Principal components analysis of environmental variation between populations of Jumellea rossii. Based on altitude (Alt), monthly mean minimum (Tmin) and maximum (Tmax) temperatures, annual rainfall height (ARH), annual number of rainy days (ARD), maximum daily rainfall (MDR) and Emberger’s pluviothermic quotient (EPQ). The colours correspond to the type of habitat, dark blue for the mountain windward rainforest, light blue for the submountain windward rainforest and orange for the mountain leedward rainforest.(TIF)Click here for additional data file.Figure S2Detection of the number of genetic clusters K. (A) Using K-means algorithm and the Bayesian Information Criterion (BIC) for each K with adegenet (TIF)Click here for additional data file.Table S1Mean relative abundances of major volatile organic compounds found in floral scent of Jumellea rossii populations.(PDF)Click here for additional data file.


Holcocerus hippophaecolus Hua) is a native species throughout the north of China and is considered the main threat to seabuckthorn, Hippophae rhamnoides L. We assessed the influence of outbreaks, environmental factors and host species in shaping the genetic variation and structure of H. hippophaecolus by using Amplified Fragment Length Polymorphism (AFLP) markers. We rejected the hypothesis that outbreak-associated genetic divergence exist, as evidenced by genetic clusters containing a combination of populations from historical outbreak areas, as well as non-outbreak areas. Although a small number of markers (4 of 933 loci) were identified as candidates under selection in response to population densities. H. hippophaecolus also did not follow an isolation-by-distance pattern. We rejected the hypothesis that outbreak and drought events were driving the genetic structure of H. hippophaecolus. Rather, the genetic structure appears to be influenced by various confounding bio-geographical factors. There were detectable genetic differences between H. hippophaecolus occupying different host trees from within the same geographic location. Host-associated genetic divergence should be confirmed by further investigation.Understanding factors responsible for structuring genetic diversity is of fundamental importance in evolutionary biology. The seabuckthorn carpenter moth ( Pests with fluctuating population size are of major concern for forest security. Knowledge of a pest's population dynamics and associated influential factors is crucial for forest management. Habitat, weather, natural enemies and heritable traits are considered to play roles in insect population dynamics Holcocerus hippophaecolus Hua (Lepidoptera: Cossidae) is the main pest of seabuckthorn, Hippophae rhamnoides L. (Elaeagnaceae). It usually occurs on seabuckthorn, but can also occur on Ulmus pumila L.  as well as a couple of species of Rosaceae H. hippophaecolus can lead to more than 70% mortality of seabuckthorn in plantations in the Inner Mongolia Autonomous Region The seabuckthorn carpenter moth, H. hippophaecolus was firstly reported as a pest of seabuckthorn in 1990 H. hippophaecolus is considered to be the main threat to seabuckthorn in China. It infests 133,000 ha of seabuckthorn and killed 67,000 ha during the 1990's. Most of the outbreak events occurred in Seabuckthorn monoculture plantations H. rhamnoides plantations in western and northern China, no outbreak events of H. hippophaecolus had been recorded. H. rhamnoides was introduced as a novel plant to Jianping County (Liaoning Province) in the 1950's. Prior to that, H. hippophaecolus mainly fed on U. pumilaSeabuckthorn is native to western and northern China, the northern Himalayas and northwestern Europe, through to central Asia and the Altai Mountains H. hippophaecolus to address the questions below.Molecular markers are widely used for insect population genetic research and are a useful tool to study population structure. Correlation with ecological factors, to identify causes of the observed genetic structuring, is often possible using these techniques Microtrous ochgasterEst-4) and protease (Pts-4) loci in Tortrix viridana L. were directly related to its population dynamics.Mormon crickets (Anabrus simplex) were found to consist of genetically distinct clusters that correspond with gregarious (outbreak) populations and solitary (non-outbreak) populations, respectively H. hippophaecolus populations may therefore consist of genetically distinct clusters with different propensities for outbreak. Here we describe the genetic variation of H. hippophaecolus populations from 10 areas across its range with contrasting historical patterns of outbreak events Natural selection, favoring certain genotypes at low densities and others at high densities, may contribute to the regulation of animal numbers H. hippophaecolus undergoes outbreak events that are followed by population decline when food plants become unavailable. Such population fluctuations may have large effects on genetic diversity within populations. Furthermore, non-genetic studies indicate recent outbreak events were related to a lack of rainfall H. hippophaecolus movement and genetic exchange within populations. Therefore, we might also expect lower genetic differentiation within populations during drought.Identifying the factors responsible for structuring genetic diversity is important for a better understanding of the insect's evolutionary history. Such fundamental studies help to infer ecological characteristics that are crucial for establishing management strategies. Geographical distance, outbreaks, and drought were considered in this study. First, in the case of species with restricted dispersal abilities, we expect to observe a positive correlation between the genetic and geographical distance. Moreover, in the historical outbreak regions, H. hippophaecolus populations feeding on different host trees in a common location? To answer these questions, we sampled larvae from sympatric populations of four host trees, sebuckthorn, U. pumila, Prunus armeniaca L, , Pyrus pyrifolia (Burman) Nakai , in Jianping.Herbivores and their host plants maintain an intimate relationship in feeding, oviposition, mate finding and predator avoidance. Distribution, availability, longevity and chemistry of host plants are major factors that affect the genetic differentiation of herbivorous populations U. pumila , Prunus armeniaca , Pyrus pyrifolia ). Individuals were transported alive to the laboratory, and then kept at −80°C. Prior to DNA extraction, insects were washed in 80% ethanol. Total genomic DNA was isolated using the SDS-method of Zhang and Hewitt Individuals (n = 217) were collected from 10 locations across the carpenter moth range during the summer of 2008  by direcH. hippophaecolus. The AFLP procedure followed Vos et al. EcoRI and MseI restriction enzymes (New England Biolabs) and double stranded adapters were ligated to the sticky ends of the fragments. After 4 h incubation at 37°C, each sample was diluted 1∶9 with H2O and a two-step amplification strategy was used. Pre-selective amplification was performed for 3 min at 94°C, then 30 cycles of 30 s at 94°C, 30 s at 56°C and 1 min 72°C. A 20-µl Pre-selective amplification PCR mixture consisted of 30 mM MgCl2, 4.5 mM dNTP, 0.6 U Taq DNA polymerase, 30 ng EcoRI-C and MseI-A primer. In the selective amplification, we used the following nine primer combinations selected from 100 tested combinations EcoRI-AAC/MseI-CAA, EcoRI-AAC/MseI-CAC, EcoRI-AAC/MseI-CCT, EcoRI-AAC/MseI-CTT, EcoRI-AAG/MseI-CCA, EcoRI-AAG/MseI-CTG, EcoRI-CA/MseI-CAA, EcoRI-CA/MseI-CAC, EcoRI-CA/MseI-CCT. The EcoRI primers were labeled with IRD-700. Selective amplification was performed with the following touchdown thermal profile: 3 min at 94°C; 12 touchdown cycles at 94°C for 30 s, 65°C for 30 s (decreasing the temperature by 0.7°C per cycle), and 72°C for 60 s; 30 cycles at 94°C for 30 s, 56°C for 30 s, 72°C for 1 min; 5 min at 72°C. The 10 µl PCR mixture contained 15 MgCl2, 1.5 ng MseI and EcoRI primer, 2 mM dNTP), 2 µl diluted (1∶9) Pre-amplified DNA. All PCRs were conducted on a GeneAmp PCR System 9700 (USA Applied Biosystems).Amplified fragment length polymorphism (AFLP) analysis was used to assess genetic diversity among sampled populations of Amplification products were separated on 6% polyacrylamide gels for 2.5 h on a LI-COR 4300 DNA Analyzer , using LI-COR 50–700 bp (Labeled with IRD-700) as a size standard. Fragments from 100–700 bp in size were scored as present (1) or absent (0) using SAGA MX , and exported for data analysis.A blank control was carried out along with each set of DNA extractions and PCR amplifications to monitor any possible cross contamination. Poor-quality DNA samples that did not amplify were excluded from further analysis.The diversity of geographic populations was assessed by estimating the percentage of polymorphic loci (%P) and Nei's heterozygosity. Percentage of polymorphic loci estimates were based on 99% criteria and heterozygosity estimates were made using the software TFPGA H. rhamnoides and among individuals across different host plants in Jianping. An additional analysis of individuals feeding on H. rhamnoides compared to the group combining three other host plants in Jianping was also performed. Genetic differentiation coefficients between populations (both geographic and host-associated) were calculated as STF, with 95% confidence intervals (CI) obtained by bootstrapping 1000 replicates over loci. The TFPGA software was also applied to calculate Nei's genetic distance (D) The genetic structure was examined by an analysis of molecular variance (AMOVA) performed by the software ARLEQUIN 3.1 http://popgen.eu/soft/mcheza/). Allele frequencies are estimated in Dfdist based on Zhivotovsky's Outlier loci were identified using the Dfdist approach P-values at FDR threshold of 50% were identified using the Benjamini and Hochberg method.Because of our particular interest in outbreak-associated divergence, the Dfdist was run for two groups of populations (outbreaking population vs non-outbreaking population). A total of 50000 realizations were performed and maximum allowable allele frequency was 0.99. We chose the 0.995 confidence interval and set the significance level at 99%. The Benjamini and Hochberg false discovery rate (FDR) correction method was used to correct for the occurrence of false positives in loci identified as under selection H. hippophaecolus populations. Secondly, outbreak patterns were scored with 1 indicating populations from areas where outbreaks had occurred and 0 representing populations in non-outbreaking areas. Finally, an index for the “degree of drought,” represented by the average annual rainfall collected over 50 years was obtained . Mantel tests were conducted with the software TFPGA to test the correlation between Euclidean distances for all the factors and genetic distances.The following analysis tested outbreaks and environmental factors that potentially influenced genetic population structure. The effect of geographical distance was assessed using linear map distances between P-value of <0.05 was used to indicate statistical significance. GLM was implemented using SPSS 16.0.The general linear models (GLM) method was also used to test the effect of outbreak and drought on the genetic differentiation between populations. In this analysis the factor “drought” was defined as locations with less than 400 mm average annual rainfall. Values of 1 were used for drought locations  and 0 for other locations . The outbreak factor was standardized, as previously, for an outbreak area of 1 and a non-outbreak area of 0. We performed a GLM analysis of the heterozygosity with outbreak and drought as fixed factors. A The nine primer combinations produced a total of 933 bands. The global 5∼0.2042 .STF = 0.2254, P<0.0001) (ST ranging from 0.0424–0.3663 (P<0.0001) with the exception of the YY and LX populations (P = 0.0182). This result indicates that most of the 10 sampled populations represent differentiated populations.AMOVA conducted on AFLP markers confirmed the presence of moderate genetic differentiation showing that 22.54% of total variability was due to the variation among geographic populations (<0.0001) . The pai4–0.3663 . Most ofThe Neighbor Joining phenogram  indicateST is plotted against heterozygosity in Examination of the AFLP data using Dfdist in Mcheza sought to determine whether there was evidence of any highly differentiated loci. Fr value of 0.0554 , indicating no correlation between geographic and genetic differences. The Nei's genetic distances between populations were not significantly correlated to outbreak differences in the Mantel test . The interaction between Euclidean distances for average annual rainfall and genetic distances was also not significant . GLM analysis showed that the factors of outbreak and drought, and their interaction, did not have a significant effect on heterozygosity .The Mantel test based on the 10 localities gave an STF statistics between JPS and each other location population ranged from 0.0856 to 0.2978  . Pairwiso 0.2978 , while to 0.2978 .H. rhamnoides had a great separation from individuals feeding on other host plants. When combined individuals feeding on U. pumila, P. armeniaca and P. pyrifolia as a group, compared to individuals feeding on H. rhamnoide, the variation among two groups rose up to 34.82% by AMOVA with ARLEQUIN. Pairwise comparisons of STF values between all host plant combinations further supported the pattern of genetic structure. STF values were much greater in comparisons between the H. rhamnoides feeders (0.3510–0.3773) and each other host-plant feeders (0.0527–0.1180) (H. rhamnoides feeders showed strongly significant differences (P<0.0001) with the moth on other host plants  . H. rhamt plants .H. hippophaecolus. NJ genetic population clusters contained a combination of populations from historical outbreak areas as well as non-outbreak areas  were identified as outlier loci. The false discovery rate test supported the conclusion that all identified outlier loci are under selection. These results do not allow us to reject the hypothesis that specific genome regions or genes are associated with outbreak events. Having indentified outlier loci in We found evidence of limited gene flow among samples collected from 10 locations. The poor correlation between genetic and geographical distances is an unexpected result given the previous assumption that populations are isolated-by-distance.H. hippophaecolus. Compared to other observations in insects, Nei's heterozygosity values for each population are moderate These results suggest that multiple factors other than simple geographic distance are influencing the genetic composition of populations. The Mantel analysis failed to support the idea that outbreak and drought are pertinent factors underlying genetic structure in Comparative studies of population structure in phytophagous insects show that genetic structure is mostly determined by the ability of the species to disperse H. hippophaecolus is a specialist, which only feeds on a few plants . Moreover, most of the principal host in China, H. rhamnoides, is in single species plantations which are suffering from human interference. Could this independent population structure of H. hippophaecolus be explained by low dispersal capacity and habitat fragmentation? Combining habitat landscape and population genetic analysis might answer this question in the future.Louy et al. ST values (0.3510–0.3773) between JPS and other non-seabuckthorn populations. It is indicated that H. rhamnoides constitutes a barrier to gene flow between H. hippophaecolus populations from other host plants in Jianping. H. hippophaecolus feeding on H. rhamnoides in Jianping are more genetically differentiated than those from other hosts in sympatric rather than other geographically distant populations of seabuckthorn. Host races might therefore exist in seabuckthorn and other host plant used by H. hippophaecolus. Factors favoring host race formation include correlations between host choice and mate choice. Although host fidelity and assortative mating has not been fully explored in H. hippophaecolus, tests using both artificial and natural methods suggest female host preferences may exist. Adult emergence from the seabuckthorn roots confirmed oviposition preference on H. rhamnoides, rather than on U. pumila and P. armeniacaHost races are genetically differentiated sympatric populations of parasites that use different hosts and between which there is limited gene flow H. hippophaecolus in Jianping is likely due to the introduction of H. rhamnoides. However, how did host shifting occur in H. hippophaecolus in Jianping? When did host-associated genetic divergence initially occur in H. hippophaecolus? Data from many host utilization systems gave rise to a possible scenario that host shifts occur as a result of host plant's increased abundance and availability as a potential resource following human-mediated plant community changes H. hippophaecolus following the introduction of seabuckthorn in Jianping. Though a rapid range expansion of H. hippophaecolus following human-mediated changes is possible, it does seem unlikely given the wide extent of genetic divergence observed during such a brief time. This scenario was also rejected by Sword et al in the Hesperotettix viridis host utilization system H. rhamnoides and other hosts, prior to the host shift. Feder et al. Rhagoletis pomonella L. pre-dating the introduction of the apple to North America. Given the long life history of H. hippophaecolus and brief planting history of H. rhamnoides in Jianping, we suppose the latter scenario is the case. Seabuckthorn is native to parts of western and northern China although records for the historical host plant use by H. hippophaecolus are lacking. Our results indicate that an H. hippophaecolus lineage might have adapted to utilize H. rhamnoides in China prior its spread. The possibilities of an ancestral host shift and stable host-associated genetic divergence in seabuckthorn carpenter moth are suggested.Seabuckthorn was an endemic perennial, sporadically growing in Inner Mongolia, Shanxi and areas of Liaoning province before it was widely promoted. The timing of host shifting of We found no fixed diagnostic differences in AFLP data between the different host-associated forms. Host-associated genetic divergence should also be further demonstrated by sampling additional populations feeding on different host plants in more locations. In future studies, more different genetic markers are recommended in this system. They should include co-dominant markers such as microsatellites (not currently available for this species) and incorporation of variable regions of the mitochondrial genome. Microsatellites are highly polymorphic, locus-specific and can show co-dominant inheritance. They may recover higher levels of variability than other markers, particularly if following a population bottleneck associated with host shift. Mitochondrial sequences can be analyzed to determine patterns of evolutionary relationships between different haplotypes. This may provide information on the historical evolution of host-associated forms in the seabuckthorn moth.


Pediculus humanus). This species is composed of two ecotypes: the head louse (Pediculus humanus capitis De Geer), and the clothing (body) louse (Pediculus humanus humanus Linnaeus). Hundreds of millions of head louse infestations affect children every year, and this number is on the rise, in part because of increased resistance to insecticides. Clothing lice affect mostly homeless and refugee-camp populations and although they are less prevalent than head lice, the medical consequences are more severe because they vector deadly bacterial pathogens. In this study we present the first assessment of the genetic structure of human louse populations by analyzing the nuclear genetic variation at 15 newly developed microsatellite loci in 93 human lice from 11 sites in four world regions. Both ecotypes showed heterozygote deficits relative to Hardy–Weinberg equilibrium and high inbreeding values, an expected pattern given their parasitic life history. Bayesian clustering analyses assigned lice to four distinct genetic clusters that were geographically structured. The low levels of gene flow among louse populations suggested that the evolution of insecticide resistance in lice would most likely be affected by local selection pressures, underscoring the importance of tailoring control strategies to population-specific genetic makeup and evolutionary history. Our panel of microsatellite markers provides powerful data to investigate not only ecological and evolutionary processes in lice, but also those in their human hosts because of the long-term coevolutionary association between lice and humans.Understanding the evolution of parasites is important to both basic and applied evolutionary biology. Knowledge of the genetic structure of parasite populations is critical for our ability to predict how an infection can spread through a host population and for the design of effective control methods. However, very little is known about the genetic structure of most human parasites, including the human louse ( Pediculus humanus) is thought to be an ancient parasite based on archeological remains, the worldwide co-distribution with humans, and coevolutionary studies Pediculus humanus capitis De Geer), and the clothing (body) louse (Pediculus humanus humanus Linnaeus) -type point mutations  in the voltage-sensitive sodium channel alpha-subunit gene kdr-type resistance has been reported in many countries among head louse populations There are hundreds of millions of head louse infestations every year affecting mostly children of 3 to14 years of age Rickettsia prowazekii), trench fever (Bartonella quintana), and relapsing fever (Borrelia recurrentis). In the last 30 years, several outbreaks of louse-borne diseases have occurred, such as epidemic typhus in Burundi, which infected over 45,000 people Bartonella quintana in Nepal Clothing lice affect predominantly homeless and refugee-camp populations c oxidase 1 gene (COX1) revealing the presence of three deeply divergent mtDNA clades or haplogroups, named A, B, and C Pediculus humanus genome to identify new target DNA sequences to develop as microsatellite markers. We genotyped 93 human lice from 11 geographic sites distributed throughout the globe using 15 newly developed microsatellite loci. This panel of microsatellites was successful in uncovering strong signals of genetic structure that corresponded to geography and ecotype.The genetic diversity of human lice has been widely studied using the mitochondrial (mt) cytochrome The Institutional Review Board of the University of Florida exempted the study from review (Exemption of Protocol #2009-U-0422) and waived the need for written informed consent of the participants. This exemption is issued based on the United States Department of Health and Human Services (HHS) regulations. Specifically, HHS regulation 45 CFR part 46 applies to research activities involving human subjects. Because louse removal was voluntary and no information was recorded that would allow patients to be identified directly or through identifiers linked to them, the University of Florida waived the need for written informed consent from the participants.Sampling details are provided in Pediculus humanus USDA strain  were obtained from VectorBase (http://www.vectorbase.org/) An overview of our primer development strategy and multiplex optimization is presented in Primer pairs that produced amplicons of the expected size and demonstrated allelic variation were selected for further testing and multiplex optimization . We usedO and HE), and mean FIS estimates  over loci per populations. Confidence intervals for FIS estimates were calculated by bootstrapping over loci using 1,023 random permutations. Genotypic disequilibrium among loci was estimated using FSTAT version 2.9.3.2 The final optimized four multiplexes were tested on the DNA extracted from 93 human lice from 11 sites . Evidenchttp://pritch.bsd.uchicago.edu/structure.html). This method uses the individual multi-locus genotypic data to evaluate models assuming different numbers of genetic clusters (K) based on the posterior probabilities given the data and model. All simulations used 50,000 Markov chain Monte Carlo (MCMC) generations in the burn-in phase and 100,000 generations in the data collection phase. Ten independent runs, using default parameters for each K, to ensure equilibration during burn-in and consistency in estimation of the posterior probabilities. Selection of the number of distinct clusters was based on the evaluation of the ΔK statistic K produced nearly identical individual membership coefficients. The run with the highest likelihood of the data given the parameter values for the predominant clustering pattern (i.e. the mode) at each K was used for plotting with DISTRUCT http://www.stanford.edu/group/rosenberglab/distruct.html). A series of STRUCTURE analyses were conducted: S1) the worldwide dataset for all 15 loci, S2) the worldwide dataset for 14 loci (we excluded locus M2-13 due to some missing data), S3) the worldwide dataset for 14 loci plus the mitochodrial haplogroup coded as another marker, and S4) local datasets for sites with 10 or more lice . We ran up to K = 15 for the global , and K = 5 for the local (S4) datasets. The mitochondrial haplogroup information was obtained from Ascunce et al. (in prep.).Population structure was inferred with a Bayesian clustering approach implemented in the STRUCTURE software http://biology.anu.edu.au/GenAlEx) to validate and further define genetic clusters for these lice. For sample sizes exceeding 10 lice, we also estimated population pairwise values of the Weir and Cockerham (1984) FST, ΘWC, in FSTAT 2.9.3.2 emN) based on the private alleles method http://genepop.curtin.edu.au/). These two estimates: ΘWC and emN were also evaluated considering the genetic clusters inferred from STRUCTURE simulations.Principal coordinate analysis (PCA) was conducted using pairwise genetic difference between individuals calculated in GenAlEx http://cmpg.unibe.ch/software/bayescan/). BayeScan is a hierarchical Bayesian method that assumes that allele frequencies within populations follow a multinomial-Dirichlet distribution FST coefficients, therefore allowing for different demographic histories and different amounts of genetic drift between populations. BayeScan incorporates the uncertainty on allele frequencies due to small sample sizes. The estimation of model parameters was automatically tuned on the basis of 20 short pilot runs of 5,000 iterations. The sample size was set to 5,000 and the thinning interval to 10, resulting in a total chain length of 100,000 iterations. Four independent runs were performed for each of the two datasets to account for the consistency of the detected outliers. The loci were ranked according to their estimated posterior probability and all loci with a value over 0.76 were retained as outliers. This corresponds to a Bayes Factor >3, which provides substantial support for acceptance of the model To detect outlier loci under selection we used the program BayeScan r) among lice was determined using the software Relatedness version 5.0 http://www.gsoftnet.us/GSoft.html) for the following groups of lice: R1) clothing lice from a single homeless person in Canada; R2) head lice from people in New York; and a single head louse from each child in one of two orphanages in: R3) Cambodia, and R4) Honduras.The average relatedness . Out of 519 tests for linkage disequilibrium, none of the tests were found to be significant after Bonferroni correction . We excluded M2-13 loci from further analyses because of its relatively high percentage of missing data (26%).From the approximately 150 primer pairs tested, 33% gave clear amplifications and showed allelic diversity. A final set of 15 microsatellite loci were thoroughly optimized and validated over 93 human lice from around the world , and S3.ectively . In popuectively . Values ulations . OverallNew York . MoreoveK = 4), one defined by clothing lice from Canada, the other head lice from North America and Europe, a third cluster was composed of head lice from Honduras, and the fourth cluster included Asian lice (both head and clothing lice) (Population structure was inferred with a Bayesian clustering approach implemented in the STRUCTURE software ng lice)  (STRUCTUng lice) . This STng lice) .K = 3), and in Cambodia (K = 3)  . AlthougFST and emN across human louse populations with more than 10 lice (Table S5). Both FST and emN produced results concordant with the STRUCTURE analysis. There were high values of genetic differentiation  between the clothing louse population (Canada) and any of the remaining head louse populations. However, these results should be taken with caution and more clothing louse populations should be analyzed to corroborate these results. Effective rates of nuclear gene flow (emN) using the private allele method yielded values less than 1.0. The highest value was found between Central America and Asia (0.7961), which may represent a more recent ancestry.In order to estimate gene flow, we calculated 10 (PO) of 2.4185. This corresponded to a Bayes Factor larger than 100, which provides ‘decisive’ evidence for selection The microsatellite data set was used in a global analysis for outlier detection using BayeScan r) among clothing lice from a single homeless person, and head lice from each population. These analyses can determine whether a few genotypes are responsible for the production of the majority of offspring. Although average population relatedness values (r) were negative suggesting outbreeding and unrelated populations r values lower than 0, thus they are very different from each other. The second largest group of lice had r values up to 0.4, showing some relatedness. All sites showed the presence of some full-siblings (r = 0.5) and the highest level of inbreeding with identical genotypic profiles was found in New York (r = 1). These high relatedness values found in the New York samples parallel the evidence of heterozygote deficits and high inbreeding, reflecting a biological phenomenon.For the fine scale genetic structure analyses we examined the patterns of relatedness  than in head louse populations. For example, clothing louse females lay up to 300 eggs along the seams or hems of clothes compared with 150 eggs laid (and glued) on human hair shafts by head louse females.Interestingly, we found a weaker genome-wide effect in terms of heterozygote deficits relative to Hardy–Weinberg equilibrium and null alleles in clothing lice than among head lice . Some likdr-allele typing for clothing lice from France Another factor that can contribute to the difference in heterozygosity is selection for insecticide resistance. Among head louse populations, intense selection by insecticides may have resulted in periodic population bottlenecks. These demographic events are expected to have genome-wide effects by reducing genetic polymorphisms in the louse genome. Whereas insecticide resistance is well known for head lice, it is less common in clothing lice and only recently has a study performed de novo and be selected for in separate populations kdr-like haplotype frequencies in each geographic area kdr-alleles were not correlated with treatment failure, thus other factors may be involved Gene flow can have broad implications in the evolution of drug resistance. For example, the widespread resistance to the most common antimalarial drugs (chloroquine and sulfadoxine-pyrimethamine) is thought to be the result of few drug-resistance alleles coupled with high gene flow in vectors (mosquitoes) and human hosts P. humanus (whether head and clothing lice represent one species or two) has been a topic for debate for over a century (see in situ) throughout the world from head louse populations P. humanus by analyzing head and clothing lice from doubly-infested individuals The species status of tury see . These rOne of the more exciting aspects of our understanding of nuclear diversity in lice is its application to human evolution. Bayesian clustering analyses assigned lice to four distinct genetic clusters: the first cluster consisting of clothing lice from Canada, the other cluster included head lice from North America and Europe, a third cluster was composed of head lice from Honduras, while the fourth cluster included Asian lice. How this geographic structure reflects human migrations requires greater sampling. Although preliminary, our study suggests that the Central America-Asian cluster is mirroring the (human host) colonization of the New World if Central American lice were of Native American origin and Asia was the source population for the first people of the Americas as has been suggested  [68], [6Homo sapiens. For example, previous studies Homo sapiens  and that they became associated with modern humans via a host switch during periods of overlap. If true, then examining nuclear markers for lice with haplotypes A (host: H. sapiens) and B  would permit a test for admixture in lice and could provide a time frame for when louse admixture occurred . This host switching would give us a time period during which H. sapiens and H. neanderthalensis co-occurred. Whether modern humans interbred with archaic hominids is a much-debated question This study provides preliminary evidence that microsatellites are effective in determining population genetic structure among human louse populations at a scale that could help us better understand patterns of human migration worldwide and might also provide insight into interactions between archaic hominids and anatomically modern humans,  sapiens . StudiesUnderstanding the processes that shape the genetic structure of human parasite populations is important to both basic and applied evolutionary biology. In human lice, knowing the processes and mechanisms that maintain and generate human louse genetic diversity within and among host populations is critical for our ability to design effective control methods and to predict how louse-borne diseases can spread through human populations. In this study, we showed that human louse populations are genetically structured based on geography . The higFigure S1Microsatellite abundance (counts): Data are shown for di- , tri- (grey), and tetra- (solid black) per number of repeat motifs.(PDF)Click here for additional data file.Figure S2Average pairwise relatedness (r) of lice within sites. Relatedness values can range from 1  to –1 .(PDF)Click here for additional data file.Methods S1This document includes: Supporting Information Methods and Supporting Information References.(XLS)Click here for additional data file.Table S1Characteristics of 27 microsatellite loci developed using the genome data of clothing louse .(XLS)Click here for additional data file.Table S2Master mixes of primers for each multiplex. All primers working solutions are 10 µM.(XLS)Click here for additional data file.Table S3Total PCR mix, quantities are indicated for individual tubes and for whole plates. Labeled tail primer concentration is 10 µM.(XLS)Click here for additional data file.Table S4Polymorphisms of the microsatellite loci used in this study.(XLS)Click here for additional data file.