Genetic introgression between divergent lineages is now considered more common than previously appreciated, with potentially important consequences for adaptation and speciation. Introgression is often asymmetric between populations and patterns can vary for different types of loci (nuclear vs. organellar), complicating phylogeographic reconstruction. The taxonomy of the ecologically specialized Abert’s squirrel species group has been controversial, and previous studies based on mitochondrial data have not fully resolved the evolutionary relationships among populations. Moreover, while these studies identified potential areas of secondary contact between divergent lineages, the possibility for introgression has not been tested.S. aberti kaibabensis, found only on the north rim of the Grand Canyon, is highly divergent from other populations. However, we also detected introgression of S. aberti kaibabensis DNA into other S. aberti populations, which likely accounts for the previously inferred close genetic relationship between this population and those south of the Grand Canyon.We used RAD-seq to unravel the complex evolutionary history of the Abert’s squirrel species group. Although some of our findings reinforce inferences based on mitochondrial data, we also find significant areas of discordance. Discordant signals generally arise from previously undetected introgression between divergent populations that differentially affected variation at mitochondrial and nuclear loci. Most notably, our results support earlier claims  that Overall, the evolutionary history of Abert’s squirrels appears to be shaped largely by divergence during periods of habitat isolation. However, we also found evidence for interbreeding during periods of secondary contact resulting in introgression, with variable effects on mitochondrial and nuclear markers. Our results support the emerging view that populations often diversify under scenarios involving both divergence in isolation and gene flow during secondary contact, and highlight the value of genome-wide datasets for resolving such complex evolutionary histories.The online version of this article (10.1186/s12862-018-1248-4) contains supplementary material, which is available to authorized users. The evolutionary histories of many species are characterized by repeated range contractions and expansions, leading to periods of isolation and subsequent secondary contact between divergent lineages , 2. InteTamias [In the past, phylogeographic reconstruction has relied extensively on the use of mitochondrial DNA. More recently, the inclusion of nuclear loci has become more common, particularly with the advent of next-generation sequencing technologies that enable genome-wide sampling –9. As moTamias . CompletTamias , 13, 14.Sciurus aberti) are endemic to the Rocky Mountain region of Colorado, Arizona, and New Mexico in the United States, and the Sierra Madre Occidental, in northern Mexico. In the Rocky Mountain region, they are considered habitat specialists, relying on ponderosa pine (Pinus ponderosa var. scopulorum) for nesting sites and feeding almost exclusively on hypogeous fungi associated with tree root systems, seeds, and inner bark of terminal twigs [P. arizonica). Within the range of Abert’s squirrels, pine forests are mainly restricted to montane environments (between ≈1800–2600 m), forming a patchwork of “islands” isolated by broad barriers of unsuitable habitat. Current habitat isolation, coupled with past range contractions and expansions associated with glaciation cycles, has resulted in considerable genetic and morphological divergence among populations [Abert’s squirrels , supported some, but not all, of these designations [S. aberti kaibabensis and populations of S. aberti aberti south of the Grand Canyon, with minimal genetic divergence between these populations evident at mitochondrial loci. Based on these data, it was reasonably concluded that S. aberti kaibabensis likely colonized the north rim of the Grand Canyon only recently from source populations on the south side of the canyon [S. aberti kaibabensis and S aberti aberti from Arizona) and populations further east in New Mexico and Colorado. Interestingly, S. aberti chuscensis, found only in the Chuska mountain range on the border of New Mexico and Arizona, included individuals carrying mitochondrial haplotypes from both western and eastern clades [S. aberti barberi and S. aberti durangi) and the populations in the United States. The study based on cytb sequences suggested these populations were the first to diverge from the others [S. aberti initially expanded from southern glacial refugia. However, the study based on mitochondrial restriction profiles suggested that these populations were more closely-related to members of the eastern clade than to those from the western clade [The Abert’s squirrel complex has been the subject of numerous taxonomic debates resulting in several revisions over the years. For example, the population on the Kaibab Plateau of Arizona is separated from the closest neighboring populations by the Grand Canyon, and was once considered a textbook example of allopatric speciation due to vicariance –25. Sciuge color . While pl of six . Subsequgnations , 22. Note canyon , 22. Then clades , 22. Thee others , as mighS. aberti species.Abert’s squirrels represent an exciting system in which to investigate how patterns of divergence and admixture resulting from historical range shifts impact genetic diversity and differentiation. In this study, we use genome-wide single nucleotide polymorphisms (SNPs) generated from Restriction Associated DNA sequencing (RAD-seq data) to further resolve the evolutionary history of the complex of S. aberti . Population structure analysis using Weir and Cockerham’s FST revealed substantial genetic structure among S. aberti populations collected from different localities with the FST overall being 0.339 (CI: 0.336–0.342). Pairwise FST values were generally quite high with the exception the comparison of S. aberti chuscensis samples carrying the different mitochondrial haplotypes , and S. aberti barberi and all other populations (range: 0.228–0.413). Among other comparisons, the lowest value was between S. aberti ferreus from San Juan and Carson-SFW (0.098) and the highest values were between the S. aberti chuscensis groups and S. aberti aberti from San Juan (0.431 and 0.383).We used RAD-seq to identify SNPs for genetic analyses of samples collected from across the range of rti Fig.  and one pes Fig. . The larS. chuscensis  and eastern  samples, and distinguished groups within the western samples. The third eigenvector separated the eastern samples, although there was some overlap among samples from San Juan and Carson-SFW. Samples from Carson E were approximately intermediate between San Juan/Carson-SFW and Pike.The first three eigenvectors from the Principal components analysis (PCA) explained 7.7, 4.1, and 3.3% of variation, respectively, and largely separated all the groups except for the samples carrying divergent mitochondrial haplotypes in sis Fig. . In linein Figs. , and d. K = 5 being chosen as optimal by the CV procedure of ADMIXTURE and K = 6 identified as optimal for the STRUCTURE analysis based on Evanno’s method  since the source population was not sampled. These tests generally supported the validity of the migration edges inferred by TREEMIX. The one exception involved the introgression from S. aberti kaibabensis into the common ancestor of S. aberti aberti MT-Zuni and S. aberti chuscensis, which was only partially supported. While the four-population test for introgression of S. aberti kaibabensis into S. aberti aberti MT-Zuni was supported, the test for introgression into S. aberti chuscensis was marginally non-significant , and what were previously considered western and eastern haplotype lineages . In this alignment, S. aberti barberi and S. aberti kaibabensis were about equally divergent from other populations (e.g. 53.1 and 51.7% identity between these respective populations and the S. aberti aberti Coconino-Gila population). In contrast, pairwise identity between S. aberti aberti Coconino-Gila and all other populations was uniformly high (e.g. 98.6% between this population and S. aberti ferreus Pike). Thus, while the mitochondrial data indicate that representatives from eastern and western haplogroups (e.g. S. aberti ferreus Pike and S. aberti aberti Coconino-Gila) are as divergent from one another as they are from Mexican samples, the genome-wide data reveal much less divergence between eastern and western samples than exists between either of these groups and Mexican samples.As expected based on the previous study that generated the ges Fig. . The MexCytb haplotyping of additional samples suggested that haplotypes from the western lineage are fixed in the S. aberti aberti Coconino-Gila (N = 44) and Cibola (N = 11) populations and in S. aberti kaibabensis (N = 47), as all additional tested samples had western haplotypes . The fact that mitochondrial haplotypes from the western haplogroup are as divergent from eastern haplotypes as they are from the S. aberti barberi haplogroup is therefore most easily explained if ancestral haplotypes originated in S. aberti kaibabensis. Under the alternative scenario, the high level of divergence between eastern and western haplotypes, on par with divergence from S. aberti barberi, would be difficult to explain. Finally, introgression of S. aberti kaibabensis haplotypes into other S. aberti populations would provide an explanation for the presence of divergent mitochondrial haplotypes in S. aberti chuscensis and S. aberti aberti MT-Zuni populations. The genome-wide data suggests a low level of introgression of S. aberti kaibabensis alleles into the ancestors of these populations, which appears to have been accompanied by some introgression of mitochondrial haplotypes, but without complete mitochondrial capture. Under the alternative scenario, introgression of eastern mitochondrial haplotypes into S. aberti chuscensis and S. aberti aberti MT-Zuni would have needed to occur, which is not supported by genome-wide data. The only piece of evidence seemingly at odds with the direction of introgression we infer is that current S. aberti kaibabensis haplotypes might be expected to show characteristics associated with assumed ancestral haplotypes in the haplotype network  since presumably all S. aberti aberti and S. aberti chuscensis haplotypes would have been derived from ancestral S. aberti kaibabensis haplotypes. However, if introgression occurred in the distant past, ancestral haplotypes may have since been lost from the relatively small S. aberti kaibabensis population, while being retained in relatively large S. aberti populations. Interestingly, the haplotype with the most connections in the western portion of the network is found in both S. aberti aberti Coconino-Gila and S. aberti chuscensis, which would be consistent with ancient introgression and subsequent retention of S. aberti kaibabensis mitochondrial haplotypes into the common ancestor of these populations, a scenario supported by the TREEMIX analysis of the genome-wide data. Altogether, while we cannot completely rule out the alternative scenario, the weight of evidence strongly suggests introgression of S. aberti kaibabensis mitochondrial DNA into other western populations rather than the other way around.Putting together these results with those from earlier studies, we propose that the previously inferred close relationship between  Arizona , 22 likeComplete mitochondrial capture has been observed in other systems, sometimes even with no evidence for substantial nuclear introgression , 30. SevS. aberti kaibabensis and western S. aberti populations, we also detected introgression in three additional cases: (1) a population equally related to S. aberti aberti Carson-SFW/San Juan and S. aberti ferreus Carson E into S. aberti aberti Carson-SFW, (2) S. aberti ferreus Pike into S. aberti ferreus Carson E, (3) S. aberti barberi into S. aberti aberti Coconino-Gila. While the first case is difficult to evaluate since the source population of introgressed DNA was not sampled, the other cases are generally supported by four population tests and genetic clustering analyses. Introgression involving the two S. aberti ferreus populations makes sense geographically, given that these populations are currently connected by corridors of suitable habitat. Introgression of S. aberti barberi DNA into S. aberti aberti Coconino-Gila highlights the apparent dynamic history of range expansions and contractions of S. aberti populations, as these subspecies are currently isolated by substantial areas of unsuitable habitat. Interestingly, although mitochondrial introgression might not be obvious in the case involving S. aberti ferreus given the close relationship of the populations, there was no evidence for mitochondrial introgression for the admixture event involving S. aberti barberi, which should have been detectable had it occurred. A pattern of nuclear introgression without evidence for mitochondrial introgression is somewhat atypical, as mitochondrial DNA is generally assumed to introgress more easily than nuclear DNA [In addition to post-divergence gene flow between arson E,  S. abertlear DNA , 11. Howlear DNA , 31, so lear DNA , future S. aberti barberi from Mexico is the earliest branching lineage in the complex, and the most genetically diverse. This suggests that population sizes have been more stable in the most southern regions occupied by S. aberti. A similar split is seen in pines, as P. arizonica from the Sierra Madre in Mexico is now considered to be a separate species rather than a southern variety of ponderosa pine [S. aberti barberi and other populations occurred approximately 1–1.5 million years ago [S. aberti kaibabensis appears to have separated from other northern populations around the same time. It is not clear, however, when S. aberti kaibabensis reached the north rim of the Grand Canyon. Based on the paleoecological record, ponderosa pine was previously assumed to be absent from areas north of the 36th parallel  during the last glacial maximum [S. aberti arrived on the north rim relatively recently. Given the amount of genetic divergence that has accumulated since S. aberti kaibabensis split from other S. aberti, this would suggest that substantial divergence would have had to occur prior to reaching the north rim. However, recent genetic analyses of ponderosa pine indicate that glacial refugia were likely to have been present in more northern regions as well [P. ponderosa var. ponderosa) during the last glacial maximum  in the area currently occupied by S. aberti kaibabensis [S. aberti is not currently associated with this variety of ponderosa pine, this does not rule out the possibility that such associations existed in the past. If so, S. aberti kaibabensis may have persisted in areas north of the Grand Canyon for longer than previously assumed. Altogether, our data support earlier interpretations of S. aberti kaibabensis as a highly distinct lineage endemic to a remarkably small area of habitat  on the north rim of the Grand Canyon [S. aberti kaibabensis implicate genetic drift in playing a large role in facilitating divergence between this subspecies and other S. aberti populations.While we found evidence for gene flow during periods of secondary contact for some groups, overall our data suggest that the evolutionary history of Abert’s squirrels has been largely shaped by divergence during periods of habitat isolation. This is evidenced by the fact that all sampled populations were monophyletic in the phylogenetic analysis, except for the Carson-SFW and San Juan populations, which were intermingled. Based on these data, osa pine . Previouears ago , 22. Bas maximum –35. If t as well . Moreovebabensis . Althougd Canyon . Low levS. aberti populations, although some caution is warranted in interpreting the relationship between S. aberti aberti Coconino-Gila and other populations (see results), the bulk of the evidence suggests a subsequent split between western and eastern groups followed by further splits within each clade. The overall significant level of differentiation among all S. aberti populations of Colorado, New Mexico, and Arizona stands in contrast to relatively lower levels of differentiation among ponderosa pine populations from these same areas, at least as revealed by studies using a handful of nuclear and mitochondrial genetic markers [S. aberti ferreus and S. aberti aberti), the magnitude of this differentiation was relatively low compare to that observed in S. aberti. Areas of unsuitable habitat separating S. aberti populations thus appear to be more formidable barriers to current gene flow among squirrel populations, as divergence even among neighboring populations is relatively high. This is somewhat surprising as previous studies have documented the rapid spread of introduced Abert’s squirrel populations into new areas even when separated by patches of unsuitable habitat [S. aberti ferreus Carson E population.For the remaining  markers , 57. Alt habitat . In Colo habitat . Future S. aberti subspecies designations. Although there is no formal genetic metric for distinguishing subspecies, our data confirm that S. aberti barberi and S. aberti kaibabensis are the earliest diverging lineages in the group and are highly distinct from other S. aberti. Nevertheless, evidence for gene flow between these and other S. aberti populations since divergence suggests that reproductive isolation between these and other populations might be absent or incomplete despite considerable genetic divergence. While challenging, future studies assessing the potential for reproductive isolation between the subspecies (S. aberti barberi and S. aberti kaibabensis) and the rest of the group, would be necessary to determine whether these populations are isolated enough to be considered different species, as has been argued in the past (e.g. earlier schemes designated S. kaibabensis as a separate species). Moreover, future studies genetic studies aimed at determining when gene flow from S. aberti kaibabensis and S. aberti barberi into other populations occurred would be informative. Our data conflicts to some degree with current subspecies designations for the rest of the group, with the most obvious discrepancy involving S. aberti aberti. According to the last formal taxonomic revision of S. aberti [S. aberti ferreus (Pike and Carson E in this study) [S. aberti aberti MT-Zuni is more closely related to S. aberti chuscensis than S. aberti aberti from Arizona. It is thus clear that S. aberti aberti, as currently described, does not accurately reflect the evolutionary relationships among the included populations. Our data also provide strong support for an eastern clade that includes populations from Pike, Carson E, San Juan, and Carson-SFW, which is consistent with previous suggestions that the range of S. a ferreus should be expanded to include populations in western Colorado and northern New Mexico [S. aberti barberi and S. aberti durangi might not be genetically distinct [S. aberti durangi to evaluate the relationship between these subspecies.Like previous genetic studies, our results provide support for some, but not all, currently . aberti , this sus study) , 22. Furw Mexico , 22. Witdistinct , 22, we S. aberti kaibabensis, current range sizes for S. aberti subspecies do not correlate strongly with observed patterns of genetic diversity. For example, despite the small range size of S. aberti chuscensis this population harbors relatively high levels of genetic variation. Moreover, despite large areas of contiguous ponderosa pine habitat in Colorado, levels of genetic diversity in these populations were relatively low. In general, genetic diversity in S. aberti declines moving from the south to the north and east. This is consistent with initial expansion from southern glacial refugia, as has been proposed by previous authors [Consistent with inferences from mitochondrial data , 22, wit authors , followe authors –38. If tIt is unclear to what degree more recent human-based activities such as timber harvesting, fire suppression, grazing, and hunting have influenced observed levels of genetic diversity. The impact of such practices on squirrel populations is potentially substantial, as previous studies have demonstrated that forest structure has important effects on the quality and suitability of Abert’s squirrel habitat –45. VariS. aberti is likely to be greatly impacted by future climate change. Predictive models suggest that a large portion of the area currently occupied by Abert’s squirrels will be unsuitable habitat for ponderosa pine in the coming decades [S. aberti kaibabensis is a highly distinct genetic lineage currently occupying an area encompassing only about 89,000 ha [As a highly specialized herbivore dependent almost entirely on ponderosa pine,  decades . Given t9,000 ha . Abert’s9,000 ha , and pos9,000 ha –49. More9,000 ha . ImpactsThe results or our study suggest that Abert’s squirrels have a complex evolutionary history involving divergence in isolation with subsequent gene flow occurring between some populations during periods of secondary contact. This interbreeding resulted in discordant patterns of introgression across mitochondrial and nuclear genomes, with mitochondrial introgression being higher than nuclear introgression in some cases and lower in another. These variable outcomes suggest that large genome-wide datasets may be necessary to accurately assess the extent of mito-nuclear discordance. Overall, our results highlight the utility of large genome-wide datasets for inferring the evolutionary history of species that have diversified under complex scenarios involving divergence with gene flow.S. griseus tissue sample from The Museum of Vertebrate Zoology at Berkeley for use as an outgroup. We follow subspecies designations as proposed by Hoffmeister & Diersing [S. aberti durangi from Mexico, as only one sample was available for this subspecies, which would preclude population genetic inferences. Approximate collecting locations and sample sizes are shown in Fig. S. aberti chuscensis [cytb sequences [HphI, which detects different polymorphisms in cytb haplotypes. Prior to library preparation, all genomic DNA samples were run on agarose gels to verify the presence of abundant high molecular weight DNA with little degradation. A total of 95 genomic DNA samples were sent to Floragenex, where Restriction Site Associated DNA (RAD) Illumina libraries were prepared using SbfI as the restriction enzyme and sequenced on an Illumina Hiseq2000 with a 100 bp single end protocol.We used DNA samples originally collected as part of previous genetic studies of the Abert’s squirrel species complex , 51–53, Diersing  to descrDiersing . We did uscensis , 22, we equences . We usedprocess_radtags script with default settings, which resulted in a total of 137,353,563 clean reads across the 95 libraries . The sample with the lowest number of reads was removed from subsequent analyses due to large amounts of missing data, and we also removed two additional samples that had been mislabeled  all S. aberti samples and the S. griseus outgroup sample, and (2) all S. aberti samples. We used the default minimum stack depth (−m) of three, which is the number of identical reads required to initially form a stack . We ran a range of different parameter settings for distance allowed between stacks (−M 2–3) and distance between catalog loci . The distance allowed between stacks parameter (−M) corresponds to the maximum number of nucleotide differences between stacks allowed for them to be merged into a single locus . Once loci are built in all individuals, the data from each individual is used to generate a catalog representing all the alleles and loci in the dataset . The distance between catalog loci parameter (−n) sets the maximum number of mismatches between catalog loci for them to be merged into a single locus. For example, if -n is set to two then loci from different individuals differing by two or fewer nucleotides would be merged into a single locus. We ran all resulting datasets through the stacks corrections module (rxstacks) using the following parameter settings: --lnl_limit − 10, −-prune_haplo, −-model_type bounded, −-bound_high 0.05, −-conf_lim 0.2. We compared F-statistics computed by the STACKS populations program for each of the parameter set combinations to assess the impact of different parameter settings on the resulting datasets. In general, we did not find major differences across the range of parameter settings although the total number of identified loci varied. Considering this, we chose to use -M 3, −n 5 for the dataset with the outgroup, and –M 2, −n 3 for the dataset with no outgroup.Sequencing yielded a total of 144,307,453 reads across the 95 samples. Data were processed using STACKS version 1.37 , 55 . Sepopulations program in STACKS. We used the write_single_snp option to include only one SNP per RAD locus since most downstream analyses assumed loci are unlinked. We included loci that were present in at least 50% of individuals in 7/10 groups , which yielded a total of 67,430 loci. Population structure was analyzed in several ways. Pairwise FST values were estimated between all groups using the R package ASSIGNER [FST values [populations program in STACKS was used as input for this analysis. We also performed Principal Component Analysis (PCA) using the R package SNPRelate [K) present in the data and produce estimates of individual ancestry from each cluster. These programs differ in that ADMIXTURE uses a maximum likelihood approach while STRUCTURE relies on Bayesian methods. We used the full dataset for ADMIXTURE, but because STRUCTURE is less efficient with large datasets, we randomly selected 6000 SNPs for this analysis. For both programs, we ran 20 replicates for K values ranging from 1 to 10. For the STRUCTURE analysis, we used the admixture model with correlated allele frequencies, and ran the MCMC simulations for 50,000 generations after a burnin of 10,000 generations. Convergence was assessed by analyzing the values of summary statistics to ensure estimates had stabilized. To choose the optimal K value we used the cross-validation (CV) procedure available in ADMIXTURE [K method [K, which resulted in highly variable cross-validation (CV) errors or data likelihoods for different modes of a given value of K. This made the usual practice of averaging over replicates of K problematic. To deal with this issue, we used the software PONG [K using a similarity threshold of 0.97 to group replicates into the same mode. Once different modes were identified, we retained only the best mode for each value of K for further analysis (i.e. lowest CV error for ADMIXTURE or highest likelihood for STRUCTURE). In ADMIXTURE, we then chose the optimal K based on the lowest average CV error across replicates. For structure, we used Evanno’s ΔK method [To extract SNPs for population genetic analyses we used the ASSIGNER , which cT values  and theiNPRelate . We usedNPRelate  and STRUNPRelate  to inferDMIXTURE  and EvanK method  for the are PONG  to formaK method  as impleK method .π, and number of polymorphic loci) were obtained using the –fstats option in the populations program in STACKS. Because analyses of population structure failed to detect any structure between samples carrying the divergent mitochondrial lineages present in the S. abert chuscensis subspecies (see results), we combined these samples into one group for genetic diversity estimates.Genetic diversity estimates  was used for phylogenetic analysis. Given computational demands and to reduce the amount of missing data, we included 3–4 individuals from each of the sampled localities  and the one outgroup sample  in the analysis. The individuals from each population were chosen because of their relatively high sequencing coverage. We used the populations program in STACKS to generate an alignment file including concatenated loci present in at least 30/40 individuals . We used JMODELTEST2 [The dataset with the outgroup  for this analysis. We designated S. aberti barberi as the outgroup given the results of phylogenetic analysis by RAXML-NG, which confidently assigned S. aberti barberi as the earliest branching lineage in the S. aberti complex. We ran TREEMIX with 0–6 migration edges using the –global option to perform a global rearrangement. We performed five runs for each migration model using a different random seed to assess the consistency of the results. We used a function available in the RADpipe package [p-values for migration edges, the authors caution that these values may be unreliable estimates of significance since they are generated under a heavily parameterized model [f4 statistic should be zero. However, a positive f4 value would indicate introgression between populations A and C or B and D, while a negative f4 value would indicate introgression between A and D or B and C. This test is highly robust to different demographic scenarios and is therefore a powerful test of introgression. We conducted four-population tests using the program F4 [f4 values using coalescent simulations to assess the probability that the observed results could be produced by incomplete lineage-sorting alone. Since positive and negative f4 values do not necessarily discriminate between two possible scenarios of introgression , we set-up our tests with trees that made one of the two scenarios unlikely and, therefore, provide a more direct test for gene flow between a focal population pair.Given that genetic clustering analyses suggested a history of admixture between some populations, a bifurcating phylogenetic tree may not fully represent the evolutionary history and/or relationships of the sampled populations. We therefore used the program TREEMIX ver. 1.12  to inves package  for R toed model . As sugged model . Assuminogram F4 , which tcytb haplotypes generated by a previous study [Sciurus. Given this discrepancy, we excluded these nucleotides from all sequences in our analysis. In previous studies, all samples from Coconino-Gila and S. aberti kaibabensis had haplotypes from the western lineage, all samples from MT-Zuni had eastern haplotypes, while S. chuscensis had representatives from both lineages [S. aberti Coconino-Gila, N = 44; S. aberti Cibola, N = 11; S. aberti kaibabensis, N = 47; MT-Zuni, N = 7; S. aberti chuscensis, N = 51). These DNA samples were from previous collections or were taken from tissue samples supplied by the Museum of Southwestern Biology Pairwise FAdditional file 2:cytb haplotyping results. Tab one includes detailed information on the collecting locations of each sample and museum identifiers when applicable. Tab two includes the results of cytb haplotyping. (XLSX 20 kb)Collecting information and


Interestingly, populations with the highest malaria intensity can be identified by the increased occurrence of endemic Burkitt Lymphoma (eBL), a pediatric cancer that affects populations with intense malaria exposure, in the so called “eBL belt” in sub-Saharan Africa. However, the effects of intense malaria exposure and sub-Saharan populations’ genetic histories remain poorly explored. To determine if historical migrations and intense malaria exposure have shaped the genetic composition of the eBL belt populations, we genotyped ~4.3 million SNPs in 1,708 individuals from Ghana and Northern Uganda, located on opposite sides of eBL belt and with ≥ 7 months/year of intense malaria exposure and published evidence of high incidence of BL. Among 35 Ghanaian tribes, we showed a predominantly West-Central African ancestry and genomic footprints of gene flow from Gambian and East African populations. In Uganda, the North West population showed a predominantly Nilotic ancestry, and the North Central population was a mixture of Nilotic and Southern Bantu ancestry, while the Southwest Ugandan population showed a predominant Southern Bantu ancestry. Our results support the hypothesis of diverse ancestral origins of the Ugandan, Kenyan and Tanzanian Great Lakes African populations, reflecting a confluence of Nilotic, Cushitic and Bantu migrations in the last 3000 years. Natural selection analyses suggest, for the first time, a strong positive selection signal in the ATP2B4 gene (rs10900588) in Northern Ugandan populations. These findings provide important baseline genomic data to facilitate disease association studies, including of eBL, in eBL belt populations.Populations in sub-Saharan Africa have historically been exposed to intense selection from chronic infection with  falciparum malaria transmission and high endemic Burkitt Lymphoma (eBL) incidence. These populations are from different ethnolinguistic groups and are located 2400 miles apart in sub-Saharan Africa. We characterized genetic composition of these populations in the context of 22 additional African populations and present evidence for gene flow events that occurred in the last 3000 years, possibly related to regional migrations in Western Africa and major migrations involving Nilotic, Cushitic, and Bantu groups. We identified in Northern Ugandans a strong signal of malaria-driven selection in the ATP2B4 gene coding for a calcium transporter expressed in erythrocytes. Characterization of biological relationships between the ATP2B4 gene and malaria may inform the investigation of complex genomic disease associations in eBL belt populations.We present a genome-wide analyses of genetic structure, gene flow, and natural selection in Ghana and Northern Uganda populations, both residing in the Sub-Saharan eBL belt, a region with intense  Plasmodium falciparum (Pf) malaria (for 7–12 months in the year), eBL burden provides a novel way to identify populations under strong malaria selective pressure. Pf malaria is one of the most important selective pressures that have shaped the African genetic diversity , ou, ou22], c models –S17 FigsRARB found in Ghana and in UNC (different index SNPs), and six genomic regions within/adjacent to KLHL20, ATP2B4, NIT2, TENM3, GPHN and HERC2, are found in both UNW and UNC (five of six regions share the same index SNP)   Tables.ATP2B4 gene in UNW  and UNC  (ATP2B4 gene for both UNC and UNW (ATP2B4 encodes the plasma membrane Ca2+-ATPase type 4 protein (PMCA4), the main calcium pump of the human erythrocyte [ATP2B4 gene  were detected as PBS outliers in both UNW and UNC. These six SNPs are located within two adjacent linkage disequilibrium  blocks of 6 and 12 Kb  , but not 0.0021)  Tables.  and UNW  Tables. throcyte . Six SNPnd 12 Kb . The intnd 12 Kb . This SNnd 12 Kb . Consistnd 12 Kb .KLHL20 , NIT2 , TENM3 , GPHN  and HERC2 . None of these genes have clear relationship with malaria pressure and are likely related to other selective pressures in Northern Uganda, which are not explored in the current study.The other five shared signals of candidate selection in Northern Ugandans (UNW and UNC) for the following genes: ATP2B4 gene.Our study highlighted how the combined effects of demographic history and likely malaria-driven natural selection have shaped the genetic structure of populations in the eBL belt. We found evidence of gene flow events across the eBL belt in the last 3000 years, possibly related to regional migrations in Western Africa and major migrations involving Nilotic, Cushitic, and Bantu groups. Importantly, we identified for the first time in Africa a Northern Uganda-specific strong signal of malaria-driven selection in the Our results showed that historical migrations (denoted as i-vi in the Introduction) have left signals in the genome of eBL belt populations. The historical interactions of diverse linguistic groups  along lush migratory corridors in the Lake Victoria basin plateau  is refleOur three dozen Ghanaian tribes showed high genetic homogeneity, but also evidence of gene flow from Gambian tribes in West Africa. Historically, the West and West-Central African regions have experienced extensive interactions between local kingdoms and tribes in the last 2000 years ,9. For Gfalciparum malaria transmission in the eBL belt  [2+ transporter in human erythrocytes [ATP2B4 intronic SNPs (not present in our data) have been reported to be associated with multiple blood cell-related traits in African American, East Asian, European and Hispanic populations: mean corpuscular volume and hemoglobin concentration [ATP2B4 SNPs  were associated with resistance against severe falciparum malaria in Western African populations in Ghana and Gambia [r2 = 0.94) with the minor allele (as defined in non-Nilotic populations) of our strongest signal of selection (ATP2B4 rs10900588-G) in the Luhya population (LWK) from the 1000 Genomes Project.To search for natural selection driven by malaria in the eBL belt, we used eBL burden as an indicator of populations exposed to high sustained eBL belt . By compulations  Tables.  (PMCA4) , which ihrocytes . We notentration –41, lympntration . Furtherd Gambia , and rs1d Gambia . In an ad Gambia . ImportaATP2B4 gene were described as protective against severe malaria in Ghana and Gambia [ATP2B4 as a candidate selection gene in Ghanaians and Nigerians , i.e., the minor allele rs10900588-G is associated with significantly reduced ATP2B4 expression. Recently, in a search for eQTLs enriched in human erythroblasts, Lessard et al. identified an erythroid-specific enhancer region just proximal to exon 2/alternate exon 1 of ATP2B4 [et al. demonstrated functional effects of the enhancer region through genome editing and in vitro cell culture, suggesting a Ca2+ homoeostasis defect as one possible pathway for the ATP2B4 associations with malaria. The core haplotype we defined in the Northern Ugandan population extends from just proximal to exon 2/alternative exon 1 into intron 2/alternative exon 1. This haplotype overlaps with a minor ATP2B4 haplotype in a European population  that results in reduced erythrocyte PMC4A expression and reduced Ca2+ export [et al. and Zámbó et al. have suggested mechanisms by which reduced Ca2+ export may be related to reductions in malaria risk: Lessard et al. suggests erythrocyte dehydration as a resistance factor, while Zámbó et al. suggests that reduced Ca2+ export into the invaginated extracellular membrane reduces Ca2+ concentration, which is required for Pfa maturation. Supporting the suggested mechanism, the most recent report [falciparum malaria parasitemia and the homozygous genotype for the ATP2B4 rs1541255-G allele (not present in our data). Importantly, this allele is in perfect LD (R2 and D’ = 1) with our most important ATP2B4 signal (rs10900588-G) in Kenya.The biological relationship between parasite . In an eparasite , the allf ATP2B4 . Lessardt report  showed aHBB, ABO, DARC and G6PD genes [HBB and ABO, that are evolving under a balancing selection regime [DARC, that despite being under positive selection, is almost fixed and with low differentiation among African populations [G6PD, found on the X chromosome, was not investigated in the present study.There are extensive reports in the literature regarding selection pressure driven by malaria in the PD genes , 56. It ulations . Also, aOCA2/HERC2 and NIT2 genes . These results provide important baseline genomic data to facilitate disease association studies, including of eBL, in eBL belt populations.After characterizing the genetic structure of the Ghanaian and Ugandan populations in the eBL belt, we showed that (i) historical interaction between West and West-Central Africa involved episodes of gene flow from West to West-Central Africa; (ii) the documented cultural interaction between the local kingdoms of West-Central Africa, specifically in Ghana, were accompanied by an homogenization of the gene pool of these populations, independently of their linguistic diversity; (iii) the pattern of genetic diversity of the eBL belt populations show the signature of migrations across the Sahel that include Nilotic expansion into West Africa; (iv) the genetic composition of Great Lakes African populations is the result of the interactions between Nilotics, Cushitics and Bantu groups in the last 3000 years; and, (v) the Ethical approval for EMBLEM was obtained from the Uganda Virus Research Institute Research and Ethics Committee, the Uganda National Council for Science and Technology (H816), and the NCI Special Studies Institutional Review Boards (10-C-N133). The Ghana Prostate Health Survey was approved by the Noguchi Memorial Institute for Medical Research Institutional Review Board (001/01-02) and by the NCI SSIRB (02CN240). Participants in both the EMBLEM and Ghana Prostate Healthy Study gave informed written consent.Epidemiology of Burkitt Lymphoma in East-African Children and Minors (EMBLEM) study in two regions of Northern Uganda . The healthy children were enrolled from 100 randomly selected villages in these regions  using the PLINK 1.07 software [ij threshold ≥ 0.1 was used to create family networks  and the kinship coefficients  with public African genome-wide datasets, creating a Pan-African dataset (PA dataset) of 1,287,642 SNPs for 3102 individuals, from 9 countries, and 11 ethnolinguistic groups in Sub-Saharan Africa  Tables. Since ADMIXTURE software  assumes We phased the PA dataset using SHAPEIT . Using tƒ3 statistic to infer events of gene flow and their possible directions, as implemented in the software ADMIXTOOLS [ƒ3 statistics with Z-score ≤ -3 were considered as highly significant evidence of gene flow. For the ƒ3 statistic and GLOBETROTTER analysis of historical gene flow events, we described contributing ethnic groups or populations with the suffix “-like”, representing present day surrogates of the real sources [http://ldgh.com.br/scientificworkflow/, [We also estimated the MIXTOOLS , for all sources . Mastersrkflow/, ).To search for genomic footprints of selection in Ghana and Uganda, we explored allele frequency differentiation using Population Branch Statistic (PBS) using all the data, i.e., without LD pruning as done during the PCA and ADMIXTURE analysis , but excIn addition to PBS, we performed Extended Haplotype Homozygosity (EHH)  analysiset al. [et al. high and low divergence inference), and current Dinka and Luhya Ne of 3x104 and 3x104, respectively [4. We also used the French population as a European proxy, 40 to 60 kya for an inferred divergence time and 3x104 for current Ne. Considering that the study populations were involved in gene flow events, we introduced migration parameters between study populations and Southern Bantu considering the ancestry proportions inferred by ADMIXTURE Click here for additional data file.S1 FigThe eBL belt is shown in red shade and the incidence of eBL is denoted by the red color intensity.(TIF)Click here for additional data file.S2 Figij) estimates by the probabilities of IBD = 0 estimates for all pairs of individuals. The colored dots are the theoretical relatedness degree probabilities of Φij and IBD = 0. (B) The distribution of individual inbreeding coefficients estimated for all individuals.(A) Kinship coefficients (Φ(TIF)Click here for additional data file.S3 Fig(TIF)Click here for additional data file.S4 Figij) that represents the relatedness among the individuals. In this network, the nodes are the individuals and the edges are kinship relationships between individuals. Here, we linked only pairs of individuals with Φij ≥ 0.06 (A) or ≥ 0.1 (B), which means we consider as related only individuals with relatedness ≥ third or second degree, respectively. The size of nodes is proportional to the absolute value of individual inbreeding and the shape of the node serves to signal whether inbreeding is positive (square) or negative (circle). The colors of the nodes represent the Uganda individual’s tribe Click here for additional data file.S5 FigIn each network, the nodes are the individuals and the edges are kinship relationships between individuals. We represented only the samples with proportion of identity by descent (Plink PI_HAT) > 0.05.(TIF)Click here for additional data file.S6 Fig(Top) The proportions of individual ancestry values were calculated using ADMIXTURE unsupervised mode with the number of ancestral K = 2 to K = 15. (Bottom) ADMIXTURE cross-validation errors as a function of K.(TIF)Click here for additional data file.S7 FigThe populations are: Ghana; Uganda North West; Uganda North Central; and three populations from Uganda South West . The Pan-African populations are from West Africa , West Central Africa , Southern Africa , Horn of Africa  and Great Lakes Africa .(TIF)Click here for additional data file.S8 Fig(TIF)Click here for additional data file.S9 FigWe represented the unsupervised ADMIXTURE analysis with the number of ancestral clusters K = 10. This K captured the six African clusters represented in (TIF)Click here for additional data file.S10 FigWe compared the following PC combinations: PC1 vs PC2, PC3 vs PC4, PC1 vs PC3 and PC2 vs PC4.(TIF)Click here for additional data file.S11 FigWe compared the following PC combinations: PC1 vs PC2, PC3 vs PC4, PC1 vs PC3 and PC2 vs PC4.(TIF)Click here for additional data file.S12 Fig(A) fineSTRUCTURE tree and (B) heatmap of the length of the chunks shared by individuals. Each row of the heatmap represent a copyvector of a recipient individual and each column represent the proportions of haplotypes that a donor shares with a recipient. Dark regions of the heatmap represent the long haplotype segments shared between individuals. Dark regions outside on the diagonal indicate more recent gene flow events. We highlight the inferred clusters using the colors of the ADMIXTURE ancestries.(TIF)Click here for additional data file.S13 FigAncestry profiles and admixture dynamics were inferred using non-local donors. Donor populations were selected based on fineSTRUCTURE results. In the mixture model and event sources, the bars show the contribution of each African population to the recipient populations. The plot on the left represents the most likely estimated admixture dates inferred by GLOBETROTTER. The plot shows two admixture events, except in the West Africans and North West Uganda where only one admixture event was found. Inferred date(s) and 95% CIs are represented by the dots and horizontal lines in the graph. Bars corresponding to the event sources represent the inferred admixing sources for each estimated admixture event and the proportion of contribution of the African donor populations.(TIF)Click here for additional data file.S14 FigThese curves are informative for date estimation and the genetic composition of the sources of the admixture event. Each curve describes the probability to find two chunks of two donor populations along the genome of the target population. Curves with decreasing probability indicate that the two donor populations describe the genetic composition of one source population. Increasing probability indicates that the two donors could describe different sources. One date admixture (North West and Ghana) is characterized by a uniform curve that decreases or increases its probability. On the other hand, multiple date admixture  is characterized by a curve that changes its behavior, for example, from increasing (indicating different donor for the earlier event) to decreasing (both donor populations describe one source for the recent event).(TIF)Click here for additional data file.S15 Figm = migration rate. We used the migration rates following the current ancestry profile estimated by ADMIXTURE, as 4Nemij, where 4Ne is the population effective size and mij the fraction of population i that is made up of migrants from population j. The simulated genotypes were obtained by the ms program [ij−em 0.002 3 1 NMigrantsji -ej 0.025 3 1 –en 0.06 1 gPop1 at time2 –en 0.06 1 gPop2 at time2 –ej 0.06 2 1 –en 0.1 1 gPop1 at time3.Ne = effective population size, kya = thousand years ago and  program  with the(TIF)Click here for additional data file.S16 FigThe dotted line demarcates the 99.9th percentile. The red dots represent genes that also have a selection signal by the xpEHH test (>2).(TIF)Click here for additional data file.S17 FigPBS neutral values were generated by 10,000 simulations of plausible neutral demographic models  for UNW (TIF)Click here for additional data file.S18 FigWe presented the LD table of the Uganda North West (UNW) population that showed the highest signal of selection.(TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Tableƒ3) statistic evaluates if the allele frequencies of a target population are intermediate of two sources which is interpreted as a result of admixture. Evidence of admixture is represented by negative ƒ3 values; significant evidence of admixture is inferred with a Z-score <-3. All populations evaluated in the global ADMIXTURE analysis were included. In addition, several populations were combined and evaluated as sources or target to assess broader groups . Only combinations that resulted in Z score ≤-3 are tabulated.The three-population test ((XLSX)Click here for additional data file.S5 TableThe SNP with the highest PBS value for each candidate gene is tabulated.(XLSX)Click here for additional data file.S6 TableThe SNP with the highest PBS value for each candidate gene is tabulated.(XLSX)Click here for additional data file.S7 TableThe SNP with the highest PBS value for each candidate gene is tabulated.(XLSX)Click here for additional data file.S8 Table(XLSX)Click here for additional data file.


Investigating adaptive potential and understanding the relative roles of selection and genetic drift in populations of endangered species are essential in conservation. Major histocompatibility complex (MHC) genes characterized by spectacular polymorphism and fitness association have become valuable adaptive markers. Herein we investigate the variation of all MHC class I and II genes across seven populations of an endangered bird, the crested ibis, of which all current individuals are offspring of only two pairs. We inferred seven multilocus haplotypes from linked alleles in the Core Region and revealed structural variation of the class II region that probably evolved through unequal crossing over. Based on the low polymorphism, structural variation, strong linkage, and extensive shared alleles, we applied the MHC haplotypes in population analysis. The genetic variation and population structure at MHC haplotypes are generally concordant with those expected from microsatellites, underlining the predominant role of genetic drift in shaping MHC variation in the bottlenecked populations. Nonetheless, some populations showed elevated differentiation at MHC, probably due to limited gene flow. The seven populations were significantly differentiated into three groups and some groups exhibited genetic monomorphism, which can be attributed to founder effects. We therefore propose various strategies for future conservation and management.  Bottlenecked species are prone to genetic drift and depletion of adaptive variation , which mThe major histocompatibility complex (MHC), an essential component of the vertebrate immune system, is an ideal fitness-relevant marker . This hiThe remarkable level of polymorphism in MHC is thought to result from sexual selection, maternal–fetal interactions, pathogen-mediated selection (PMS), and demographic processes such as genetic drift ,8,9,10. Previous studies of MHC variation were mainly focused at the allele level. However, particularly in birds and reptiles, alleles from different MHC loci are sometimes highly similar and even identical to each other because of frequent gene duplication, conversion, and recombination ,19,20,21Nipponia nippon, Pelecaniformes; hereafter referred to as Nini), which is listed as “Endangered” according to IUCN criteria . Th. ThUBA aNini-MHC haplotypes combining all class II loci and the major class I locus in the Core Region. Linkage disequilibrium (LD) between polymorphic loci (gametic phase unknown) was tested by Arlequin 3.5 [DAB and/or UAA). For individuals with ≥2 heterozygous loci , most haplotypes were deduced using pedigree information provided by reserves (assuming Mendelian inheritance), and the remainder lacking parentage data were phased according to the most frequent haplotypes in their respective populations. Notably, although the crested ibis is strictly monogamous, captive cages are sometimes occupied by >1 breeding pairs (2–3 in most cases), creating the potential for recording false pedigrees. Thus, we verified the pedigrees using microsatellite data, and corrected the paternities of three suspect individuals among candidate parents. Moreover, individual haplotypes were estimated by Haplotype Inference (gametic phase unknown) implemented in Arlequin 3.5 using both Excoffier-Laval-Balding (ELB) and Expectation-Maximization (EM) algorithms , and by PHASE 2.1 using a Bayesian statistical method credited with higher accuracy than the EM algorithm [In this study, we mainly analyzed the quin 3.5 . Individlgorithm . LD coefCOL11A2–BRD2” fragment) for each haplotype, we randomly selected twelve homozygotes from different populations as follows: four HT02/HT02, three HT03/HT03, one HT07/HT07, two HT04/HT04, and two HT05/HT05 (there were no homozygotes of HT01 and HT06). The BAC clone containing HT01 [To determine the genomic structure of the class II region . The selection parameter ω, which estimates the ratio of the nonsynonymous substitution rate (dN) to the synonymous substitution rate (dS), was calculated, and the Z-test with Jukes-Cantor correction was performed in MEGA 6 [Sequences of the n MEGA 6 . We usedrom DBB3 , we inten MEGA 6 . Given tn MEGA 6  to detecp ≤ 0.05 for multiple comparisons. Recombination events identified by at least two methods were then verified and refined by several methods according to the manual, including examination of the plots, matrices, and phylogenetic trees. Recombination on the full-length sequences of IIβ genes was detected by the RDP beta 4.16 package  using seOH) and expected (EH) heterozygosities were obtained from Arlequin 3.5. Allelic richness (AR) was calculated using FSTAT 2.9.3 [Haplotype/allele frequencies, and observed  was used to detect the population structure. The parameter α was inferred from the data and λ was set to 1. An MCMC chain of 1,000,000 generations was run with an initial burn-in of 100,000 generations. Seven independent replicates of K = 1–7 were performed, and the final number of clusters was determined by simultaneously evaluating posterior probability and the delta K using the online Structure Harvester program [STRUCTURE 2.3.4  was used program . The est program . In addi program  using chRST may suffer from higher sampling variances under a random mutation process [FST values for both MHC and microsatellites in Arlequin 3.5. Analysis of molecular variance (AMOVA) was conducted on 17 potential groupings with 50,000 permutations in Arlequin 3.5.As expectations for  process , we firs process  to checkG’ST was adopted to compare the population structures inferred by MHC and microsatellites because it can control differences in variation among genetic markers [G’ST was calculated in R code and 95% confidence intervals (CIs) for pairwise microsatellite G’ST values were generated from 1000 bootstrap replications. Then we compared MHC-G’ST to the 95% CIs of microsatellite-G’ST, and the outliers represent population structures that are significantly different from that expected under neutrality. The correlation between MHC-G’ST and microsatellite-G’ST was tested using Pearson product-moment correlation in SPSS 20.0. markers . G’ST waUAA, UBA, and UCA2 were dimorphic whereas UCA1 and UDA were monomorphic among the crested ibis studied. UBA*02 and UCA2*02 were newly discovered in this study, but differed from UBA*01 and UCA2*01, respectively, by only a single silent nucleotide substitution on exon 3. Thus, only UAA was dimorphic at the protein level. The allele frequencies of UAA*01/*02, UBA*01/*02, and UCA2*01/*02 were 0.604/0.396, 0.914/0.086, and 0.500/0.500, respectively. Notably, UBA*02 was only present with UCA2*01, and these two loci showed significant linkage disequilibrium (p = 0.000). However, this linkage was not significantly supported between UAA and UBA–UCA2 (p = 0.083), in accordance with the reported physical distance [The MHC class I genotyping showed that distance . Six claDAA , 1 sequence for DBAs , 4 alleles for DAB , and 2 sequences for DBBs  . Three DBA genes share the same exon 2; DBB2 and DBB3 share the same exon 2. Accordingly, all the class II loci except DAB were monomorphic. We also found that DBAs and DBBs were absent in individuals with DAB*04, whereas DBB1*01 was only present with DAB*01. In the class II exon 2 genotyping, we obtained 1 allele for DAB and UAA were polymorphic, and they were in significant linkage disequilibrium . The manually inferred haplotypes were consistent with the estimates from PHASE and Arlequin (intra-species level) for each individual. When estimating at intra-population level in Arlequin, three individuals (2 from SD and 1 from BJ) showed inconsistent results with the above-inferred haplotypes. However, given their unusually low phase-frequencies , these results should be considered invalid and were likely biased by the relative frequencies of HT04 and HT05. We detected nine recombination events between class II genes and UAA in parentages: two HT06 were recombined from HT02, three HT07 from HT03, two HT04 from HT05, and two HT05 from HT04. The multilocus MHC haplotype of the Core Region consisted of all class II loci and the major class I locus . Among tilibrium . Accordiilibrium  from 177 alleles . We furtDBB2 and DBB3 were differentiated by their downstream sequences, and two full-length DBB2 alleles varying in exon 3 were found, in HT01 and HTs02/06  in HT01, there are only three  in HTs02/06, two (DA and DB3) in HTs03/07, and one (DA) in HTs04/05  by the first 13 residues  and significant positive selection on DBB . All the substitutions on ABS of DBB were nonsynonymous. When considering the recombination, omegaMap revealed a mean value of ω = 14.04 per codon and identified fourteen codons under positive selection on the exon 2 of DAB .RDP analysis on the eight full-length IIβ sequences detected five significant recombination events with the most likely parental sequences of  DBB3*01 . This ro DBB3*01 a. Along  DBB3*01 . AccordiEach of the seven populations carried 5 or 6 haplotypes . In genep = 0.015). For both markers, allelic richness (AR) was highest and lowest in Wild and HN, respectively, and decreased progressively in more recently established populations . There was a significant correlation  between the AR values of MHC and microsatellites. The BOTTLENECK analysis supported a significant historical bottleneck in all populations (p < 0.005).Of the eleven microsatellite loci, eight were dimorphic and three were trimorphic . No popuClustering analysis using STRUCTURE revealed that both MHC and microsatellite captured the best population genetic structure when K = 3 . AccordiFST values between pairwise populations ranged from −0.036 to 0.199 for MHC and −0.014 to 0.099 for microsatellites  (YX) (BJ) ” for MHC and “ (BJ) ” for microsatellites   (tellites . AMOVA atellites . Notably. 0.005) . For bot. 0.005) . Similar= 0.050) . G’ST for MHC haplotypes (0.381) was significantly higher than that for microsatellites . Meanwhile, 10 of the 21 pairwise MHC-G’ST values fell above the 95% CIs of the microsatellites-G’ST . The mantel test indicated significant correlation between pairwise MHC-G’ST and microsatellites-G’ST estimates . The global tes-G’ST , and 6 pNini-MHC haplotypes are characterized by different copy numbers of IIαβ units. Evidence for CNVs in MHC class II regions between individuals has been increasingly reported in birds [Nini haplotypes, we inferred an evolutionary model using the unequal crossing over mechanism. The previous study on HT01 proposed that DAA/DAB and DBA3/DBB3 pairs belonging to the two ancestral avian IIαβ lineages might represent the two primitive Nini-IIαβ dyads [DAB*03 and DBB3*01 on HTs03/07 as the two parental sequences. The other five haplotypes all contain 1–2 recombinant IIβ sequences. Accordingly, we strongly suggest that HTs03/07 represent the ancestral Nini haplotypes. Based on the RDP results, we hypothesized that HTs02/06, HTs04/05, and HT01 were successively generated from HTs03/07 through two rounds of unequal crossing over events between DAB and DBB3  were recombined from parental HT02. In contrast, most cases of HT07  can be traced back to one founder (HT03/HT07) of BJ, born in the wild in 1985. Thus, we suggest an earlier generation and/or higher probability of presence in the initial seven birds for HT07 than HT06. Nevertheless, in view of the higher genetic diversity found in historical samples [The in birds ,23,24,25in birds . In birdin birds . The evoin birds . Generalin birds . Given tαβ dyads . This spand DBB3 b. In add samples , other uNini-MHC haplotypes, most loci were monomorphic. The class II genes showed higher polymorphism than class I genes, and this is thought to be associated with the stronger selection and gene conversion at class II genes (in non-passerines) [Nini-UAA, the crested ibis is predicted to be at high risk for infections by intracellular pathogens, such as bird flu and Newcastle virus [Nini-IIβ genes (as suggested by MEGA and omegaMap) indicated that the ancestral MHC variation should be high [G’ST values between MHC and microsatellites, as well as the consistent population structures at MHC and microsatellites in most differentiation analyses. The only exception is that AMOVA supported the differentiation of one more group with MHC than with microsatellites; however, this result was not informative as there was also a pronounced support for an identical grouping between the two markers  . Given tle virus , and thile virus . Concord be high ,66, and  markers . A previgression . AlternaG’ST values suggested elevated differentiation at MHC relative to microsatellites. However, this trend appears to be a by-product of strong MHC differentiation between only a few populations. This claim can be strongly supported by the comparisons of pairwise G’ST between two markers . This might be associated with the unusually high frequency of HT03 (60.0%) and low frequency of HT02 (14.3%) in BJ compared to those in other six populations , may be the most immunologically competitive haplotype, whereas HTs04/05 may be most susceptible. Nevertheless, the immunity can be confounded by various factors, especially when gene copies function at different levels. A small-scale analysis on the correlation between Nini-MHC haplotypes and offspring mortality by our laboratory found a potentially higher mortality in HT03/HT04 heterozygotes [Nini-MHC haplotype–pathogen relationships should be undertaken for improved conservation. Besides, the MHC haplotypes are ideal markers for studying mate-choice mechanism. Associations between certain MHC haplotypes and infections have been increasingly identified in birds ,77,78. Iozygotes . HoweverHere we demonstrated an application of multilocus MHC haplotype in population genetic analysis, which particularly suits to populations with low polymorphism, such as bottlenecked or isolated populations ,40. Loci


Many tilapia species or varieties have been widely introduced and have become an economically important food fish in China. Information on the genetic backgrounds of these populations is deficient and requires more research, especially for red tilapia strains.Oreochromis ssp., Taiwan (TW), Israel (IL), and Malaysia (MY) strains and other populations, including O. aureus (AR), O. niloticus (NL), O. mossambicus (MS), and the GIFT strain of O. niloticus, were collected and analyzed in this study.In the present study, displacement loop (D-loop) sequences were used to evaluate the genetic relationship and diversity of seven tilapia populations that are widely cultured in China; this was done specifically to speculate on the maternal ancestry of red tilapia strains. Three red tilapia varieties of π) of each population ranged from 0.234 to 0.826, and 0 to 0.060, respectively. The significant positive Tajima’s D value of neutral test were detected in the NL, IL, and MY populations (P < 0.05), which indicated these populations might have not experienced historical expansion. According to the pairwise F-statistics, highly significant genetic differentiations were detected among populations (P < 0.01), with the exception of the IL and MY populations (P > 0.05). The nearest K2P genetic distance (D = 0.014) was detected between the MS and TW populations, whereas, the farthest (D = 0.101) was found between the GIFT and AR populations. The results from the molecular variance analysis (AMOVA) showed that there was an extremely significant genetic variation observed among the populations (P < 0.01), which contained 63.57% of the total variation. In view of the genetic relationship of red tilapia strains with other populations, TW and IL were detected with more similar genetic structures related to MS, and MY was more genetically similar to GIFT (or NL), which could provide more genetic evidence for the red tilapia strains maternal ancestry.A total of 146 polymorphic sites and 32 haplotypes of D-loop sequences were detected among 332 fish and four major haplotypes were shared among the populations. The TW and NL populations had a greater number of haplotypes . The haplotype diversity (Hd) and nucleotide diversity ( Oreochromis, Sarotherodon, and Tilapia , with a high rate of evolution and no recombination, which becomes one of the most commonly used mtDNA sequences for addressing the evolutionary relationship of close relatives and/or subspecies . At presOreochromis spp., Chinese Taiwan (TW), Israel (IL), and Malaysia (MY) strains, and other populations of tilapia, including the GIFT strain of O. niloticus, O. aureus (AR), O. niloticus (NL), O. mossambicus (MS). The TW and IL populations were transferred from Fujian Province, China in 2014, and the MY population was introduced from Malaysia in 2009  of the Chinese Academy of Fishery Sciences (CAFS) . Experimental fish were sampled from seven populations, including the three red tilapia strains of  in 2009 . All tilGenomic DNA was extracted using the phenol-chloroform method . The intNC_014060) from the National Center for Biotechnical Information (NCBI). The D-loop sequences of 867 bp was amplified using a primer pair , which was synthesized by Sangon Biotech (Shanghai) Co.Ltd. The PCR amplification was performed on an Eppendorf Mastercycler Pro 384 PCR thermocycler . Amplifications were performed in a volume of 50 µL, containing 5 µL 10 × PCR Buffer, 3 µL MgCl2(0.25 mM), 4 µL dNTPs (2.5 mM), 1 µL Taq polymerase (2.5 U/µL), 1 µL of each primer (10 µM), 2 µL genomic DNA (20 ng/µL), and 33 µL DNase/RNase-free deionized water. PCR amplification was performed under the following conditions: pre-denaturing for 2 min at 94 °C, 35 cycles of denaturing for 40s at 94 °C, annealing for 55s at 55 °C, prolonging 1 min at 72 °C; final prolonging for 10 min at 72 °C; and then held at 12 °C. Subsequently, the reaction products were detected using 1% agarose gel electrophoresis, and the bidirectional sequencing was carried out with the ABI3730XL sequencing instrument of the Shanghai Majorbio Company.Primers of the D-loop were designed according to the complete sequences of tilapia mtDNA (Accession NO: The sequences were edited using the BioEdit version 7.0.9 software . To ensuπ), average number of nucleotide differences (k) and Tajima’s D. For phylogenetic analysis, MEGA 5.05 software (F-statistics (FST) and the analysis of molecular variance (AMOVA) among seven tilapia populations. The Network 4.6 software . Different numbers of haplotypes (from 1 to 20) were detected among the populations  was higher than in C + G . By sequence alignment, a total of 32 haplotypes were found in the D-loop sequences and deposited in the GenBank database under the accession numbers ulations . Four ofK = 0 − 47.32) and nucleotide diversity (π = 0 − 0.060) were determined. Among them, the AR population had the lowest genetic diversity , the TW and NL populations had a higher haplotype diversity , and the highest nucleotide diversity (π) was detected in the NL population (π = 0.060). Tajima’s test indicated that the Tajima’s D value of the TW, GIFT, and MS populations were negative, and other populations were positive. Among them, NL and MS reached a significant level (P < 0.05), GIFT, IL and MY populations reached extremely significant levels (P < 0.01).The genetic diversity parameters of the tilapia populations based on D-loop sequences are shown in The pairwise genetic distance was calculated using the Kimura 2-parameter (K2P) model among seven tilapia populations . The seven populations were divided into two groups consisting of wild-type or breeding populations, and red tilapia. The genetic differentiation index among populations within groups accounted for 0.633 (P < 0.01). The pairwise F-statistics values (Fst) of the seven tilapia populations (P < 0.01), except among the MY and IL populations (P > 0.05).The results of the analysis of molecular variance (AMOVA) were shown in The median-joining (MJ) network diagram of D-loop haplotypes was described in According to the genetic distances among the tilapia populations (π >0.005), which was consistent with other research using microsatellite markers , which was found to be similar to the results of the previous reports assessed by RAPD , indicating that its population may have recently experienced a bottleneck or founder effect produced by minority populations  in tilapia D-loop sequences was higher than the content of G+C (35.7%), which was in line with the distribution characteristics of the base content in the D-loop (control region) of many fishes . In D-lo markers , and iso markers . It was  by RAPD , microsa by RAPD , and mtD by RAPD . This maulations . The higO. mossambicus, O. aureus, or O. niloticus (The red tilapia stocks were reported with different genetic sources that originated from iloticus . In thisChanna fishes. The fixation index (Fst) was commonly used to examine the genetic variation of populations and the contribution of this variation to genetic differentiation  were found in this study, which demonstrated that a higher level of genetic differentiation exists among tilapia populations except for red tilapia. The results indicated that red tilapia populations may have evolved independently after separating from a common ancestor, but that those three strains of red tilapia were closed.The analysis of molecular variance (AMOVA) by grouping and non-grouping showed that the main genetic variation was derived from inter-population, which is similar to the results of ntiation . SignifiOreochromis mossambicus with other species including O. aureus, O. niloticus, and O. hornorum , speculating that MY population was probably bred with the GIFT population or that MY and GIFT populations might come from the similar, artificially selected NL population. In addition, the degree of genetic differentiation between red tilapia  and the breeding source populations  were relatively small, demonstrating that a close genetic relationship was maintained between the breeding varieties and breeding source populations; this highly homology was related to the characteristics of maternal inheritance and non-recombination of mtDNA  populations, respectively. The TW and IL populations were closely related to each other and were clustered with MS, which was confirmed that two strains of red tilapia were produced from local crossbreeding of the rare, mutant-colored (reddish-orange) female sambicus . The GIF in Asia . The K2Pof mtDNA .O. mossambicus population, whereas the MY red tilapia was probably derived from GIFT or O. niloticus.In this study, we used the D-loop sequences to estimate the genetic structures of seven tilapia populations mainly cultured in China. Furthermore, we analyzed the maternal ancestry of three strains of red tilapia, which provides more basic data for the reasonable protection and further utilization of tilapia populations in the future. In brief, the IL and TW red tilapia strains may derive from the 10.7717/peerj.7007/supp-1Data S1Raw sequences exported from the ABI3730XL sequencing instrument applied for data analyses and preparation for Click here for additional data file.


Neochetina bruchi and N. eichhorniae (Coleoptera: Curculionidae) have been introduced globally to control the invasive floating aquatic weed, Eichhornia crassipes, with variable outcomes. Here, we use the importation history and data from polymorphic microsatellite markers to examine the effects of introduction processes on population genetic diversity and structure. We report the first confirmation of hybridization between these species, which could have important consequences for the biological control program. For both species, there were more rare alleles in weevils from the native range than in weevils from the introduced range. N. eichhorniae also had higher allelic richness in the native range than in the introduced range. Neither the number of individuals initially introduced nor the number of introduction steps appeared to consistently affect genetic diversity. We found evidence of genetic drift, inbreeding, and admixture in several populations as well as significant population structure. Analyses estimated two populations and 11 sub‐clusters for N. bruchi and four populations and 23 sub‐clusters for N. eichhorniae, indicating divergence of populations during and after introduction. Genetic differentiation and allocation of introduced populations to source populations generally supported the documented importation history and clarified pathways in cases where multiple introductions occurred. In populations with multiple introductions, genetic admixture may have buffered against the negative effects of serial bottlenecks on genetic diversity. The genetic data combined with the introduction history from this biological control study system provide insight on the accuracy of predicting introduction pathways from genetic data and the consequences of these pathways for the genetic variation and structure of introduced species.The intentional introduction of exotic species through classical biological control programs provides unique opportunities to examine the consequences of population movement and ecological processes for the genetic diversity and population structure of introduced species. The weevils  While regulations vary by country  Is there evidence of hybridization between these species, and (2) how do introduction processes affect the genetic variation and structure of these species? More specifically, (2a) are there indications of decreased heterozygosity and allelic diversity in the introduced populations relative to the native range, (2b) do increases in the number of individuals initially released or genetic admixture from multiple introductions result in increased genetic diversity, (2c) do populations with more introduction steps between them and the source population in the native range exhibit greater loss in genetic variation compared to populations with fewer introduction steps, and (2d) despite originating from the same initial populations, have introduced populations differentiated from the native range and from each other?Neochetina bruchi and N. eichhorniae Hustache (Coleoptera: Curculionidae) from their native and introduced ranges. These two weevils are the most widely used biological control agents of water hyacinth, Eichhornia crassipes  began in the early 1970s, with initial and subsequent releases in 30 and 32 countries, respectively. These weevils have contributed substantially to the control of water hyacinth in at least 13 countries  On a global scale, regarding the number of serial introductions, we hypothesized that populations with more introduction steps away from the native range would harbor lower genetic diversity than those with fewer steps. (2d) As the initial releases occurred over 40 years ago, we hypothesized that despite originating from the same initial populations that most introduced populations would have diverged genetically from the native range and each other.Based on the importation history and documented releases of these two biological control agents, we proposed several hypotheses addressing our five study questions in turn. (1) We hypothesized that hybrids of these two species would occur, as they have frequently been co‐introduced to the same geographic regions , unpublished quarantine records , and published quarantine records . However, these reports indicated potential nematode and fungal infestation in this later shipment, and again it was not clear whether or not offspring from these weevils were included in augmentation of laboratory colonies or released.From the shipment letters and quarantine reports pertaining to the initial exports from Argentina to USA: Florida , it appears that samples from at least two populations of N. bruchi adults were released from USA: Florida in Wallisville Reservoir, Texas, USA  the native range in the Uruguay River, Soriano, Uruguay, (b) the Sacramento–San Joaquin River Delta, California, USA; (c) Gainesville, Florida, USA; (d) Wallisville, Texas, USA; (e) Jilliby, Australia; (f) the Western Cape, Eastern Cape and KwaZulu‐Natal regions of South Africa, (g) Lake Victoria, Port Bell, Uganda; (h) Heping, Shantou, China; and (i) the Sungei Buloh Wetlands, Singapore  and an internal control of aliquoted DNA from an individual weevil that was used on every plate for the respective species. PCR multiplex reactions were run separately for the two species to avoid cross‐contamination. Pig‐tails Table  was adde® 600 Size Standard v. 2.0 (Life technologies) and genotyped on an Applied Biosystems 3730XL DNA Analyzer (Life Technologies) at the DNA Sequencing Facility at the University of California Berkeley. Fragment lengths were manually scored and binned using the Microsatellite Plug‐in for Geneious Pro v. 5.6.2  and expected (He) heterozygosity, deviations from HWE (exact test), and the average “inbreeding coefficient” (FIS) for each collection site across all loci with the R package “diveRsity”  on genetic diversity by fitting linear mixed models (LMM) with the lmer function in the lme4 package  affects genetic diversity, we examined the number of weevils imported to each Florida, Texas, and California in the USA (described in the importation history) and the ratio of allelic richness retained from the native range in those states. To examine the influence of the number of introduction steps on the genetic diversity in populations of these two biological control agents, we combined the documented importation history  and to assign individuals to their appropriate genetic cluster. We validate the results from these analyses by using information gained from the documented importation history. We used: (a) a discriminant analysis of principal components (DAPC)  and then reassigns the genotypes at each iteration to the group with the highest probability of belonging, using the multilocus method of maximum likelihood described by Paetkau, Calvert, Stirling, and Strobeck . A second individual from Uruguay yielded 63% amplification of the markers designed for N. bruchi and 100% of markers designed for N. eichhorniae. Amplification of loci from both species in other individuals from populations in Texas and Uganda suggested possible later generation hybrid backcrosses (F2 or later), with 100% amplification of loci for one species and 30%–40% amplification of markers designed for the other species. As discussed in the methods, none of the species‐specific microsatellite markers developed for N. bruchi cross‐amplified on individuals with species‐specific morphological characteristics of N. eichhorniae and vice versa. Based on the morphological characteristics, we also noticed potential hybrids from the SA: George population, but these individuals did not amplify well for either set of markers likely due to poor DNA extractions. We could not analyze the prevalence of hybrids due to the sampling bias from collectors that selected individuals for each species based on distinct markings that separate the species.We confirmed hybridization between a Figure . One ind3.2N. bruchi . In contrast, there was a significant effect of collection site on the allelic richness of N. eichhorniae  and expected heterozygosity . The lowest allelic richness and heterozygosity were found in George in the Western Cape, South Africa (SA: George), with the highest allelic richness occurring in Uruguay, and the highest heterozygosity in USA: Florida (Table p < 0.05). SA: George had significantly lower expected heterozygosity compared to USA: Florida, Texas and SA: Wolseley and Uruguay .Allelic richness and expected heterozygosity did not differ significantly among populations of Neochetina spp. Evidence of increased homozygosity, based on FIS > 0.2, was found in the SA: Enseleni and Uruguay populations of N. bruchi and in the SA: Wolseley population of N. eichhorniae. Additional potential evidence of genetic drift was found in the California population of N. bruchi with FIS = 0.15   was 14% that of the number of N. bruchi released, the retention of allelic richness from Uruguay in populations in USA: Florida was very similar  or for N. eichhorniae   or for N. eichhorniae   Figure a. Simila) Figure b. For ex) Figure a,b.3.3FST estimates were generally low for N. bruchi (<0.2), and the highest FST values (≥0.09) occurred in pairwise comparisons of N. bruchi genotypes from each Australia, USA: Florida, SA: Wolseley and Uganda against N. bruchi genotypes from USA: Texas, and between genotypes from Enseleni compared to those from Uruguay  demonstrated one genetic cluster with Texas and Uruguay, and the other cluster consisting of Australia, USA: Florida, SA: Wolseley and Uganda. In addition to these two main genetic clusters, further FLOCK runs demonstrated significant subpopulation structure. Three genetic sub‐clusters were found in the USA: Texas site and two genetic sub‐clusters for each of the Uruguay, California, USA: Florida, and SA: Enseleni sites.The results from the FLOCK runs are visualized in Figure p < 0.05). Genetic sources to the three USA: Texas sub‐clusters were identified as USA: Florida, Uruguay (subpopulation UR2), or both, but with significant differentiations occurring between all three sub‐clusters from USA: Texas compared to their allocated genetic source (p < 0.001). Of the USA sub‐clusters, we determined one of the genetic sub‐clusters in USA: California (CA1) as genetically sourced from USA: Florida and the other sub‐cluster in USA: California (CA2) as sourced from USA: Florida and two sub‐clusters in USA: Texas (TX1 and TX3). Contrary to the FLOCK allocation table, additional DAPC analysis (Supporting Information Appendix p < 0.001).Allocation tables from FLOCK runs determined UR1, a sub‐cluster in Uruguay as the genetic source of the two USA: Florida sub‐clusters, but also indicated significant differentiation occurred between USA: Florida and the native range . We determined that the genetic sub‐cluster (SA: Enseleni‐1) was mostly allocated to an untested source, with more similarity to Uruguay than to USA: Florida or Australia , but clearly genetically distinct from the population in Uruguay . In contrast, analyses clearly pointed to the population from FL2, a genetic sub‐cluster in USA: Florida, as the source for the sub‐population, SA: Enseleni‐2 , compared to the other populations .The population in SA: Wolseley was genetically sourced  from populations in USA: Florida and Australia, with no genetic differentiation between the weevil population in SA: Wolseley and the populations from Australia and Florida  and Uruguay. Additionally, Jost's D indicated distinct structure (>0.2) between SA: George and Texas and SA: George and Uganda (Table The highest K = 4 to K = 6). The initial analysis separated the genetic cluster consisting of China and Singapore from the populations including: Australia, all three sites in the USA , all three sites in South Africa , Uganda, and Uruguay. Separate FLOCK runs with just the China and Singapore sites determined that these two populations were genetically distinct from one another, each as a distinct genetic cluster . After removing China and Singapore populations from the main analysis, further FLOCK runs resulted in a plateau analysis that indicated the potential of two additional genetic clusters . The majority of N. eichhorniae from California, Florida, SA: Wolseley, SA: Kubusi River, Texas, and Uganda formed one genetic cluster, and the majority N. eichhorniae from Uruguay and all of the weevils from SA: George formed the second genetic cluster, with weevils from Australia split equally between these two genetic clusters. We found additional subpopulation structure in FLOCK for populations of N. eichhorniae, indicating two genetic sub‐clusters within SA: George, SA: Wolseley, Singapore, USA: Texas, and Uruguay; two to three genetic sub‐clusters within the USA sites: California and Florida; and three sub‐clusters within the China, SA: Kubusi and Uganda sites  was determined to be genetically sourced from and identical to the population from USA: Florida and a USA: Texas subpopulation (TX2) , and this was further supported by DAPC (Supporting Information Appendix p < 0.001). The two USA: Texas sub‐clusters (TX1 and TX2) were each determined to be genetically sourced from USA: Florida . As DAPC analyses indicated the two USA: Florida sub‐clusters were not very different, we used the whole USA: Florida population in allocation tables in FLOCK to determine the genetic source. The population in USA: Florida was determined to have genetic sourcing from UR2, a Uruguayan sub‐cluster rather than from UR1 .Results from FLOCK allocation tables clarified the genetic sources of several of the examined populations of p = 0.02). The population in Australia was most likely genetically sourced from USA: Florida, but still significantly differed in genetic composition . DAPC supported these FLOCK analyses and demonstrated that genetic sources from the Uruguayan subpopulation UR2 went to USA: Florida and to Australia. In South Africa, N. eichhorniae from SA: George had the highest genetic similarity to the weevils from Australia compared to the other potential source populations, but still differed significantly indicating additional genetic contribution from an untested source . Weevils from SA: Wolseley were most likely genetically sourced from the population from USA: Florida, but the genetic structure between these two populations still differed significantly . The weevil population from SA: Kubusi River was determined to be genetically sourced and identical to the populations from both USA: Florida and Australia . Populations in China and Singapore were not genetically sourced from any of the tested populations . Furthermore, the weevils in these populations significantly differed from each other, thus indicating different importation histories and pathways to these populations .The population in Uganda was most likely genetically sourced from Australia, but still differed in genetic composition  to F2 and later generations. Based on the morphological characteristics, we also noticed potential hybrids from South Africa, but these individuals did not amplify well for either set of markers likely due to poor DNA extractions. Interspecific hybrids are likely present in all regions based on the fact that we found a hybrid from a population in Uruguay in the native range, and they may have been introduced from the original collections and releases. However, we could not accurately assess the percent of hybrids per site due to the strong likelihood of sampling bias against hybrids during the collections. Future studies should conduct in‐depth surveys to examine the prevalence of hybrids in the native and introduced regions and perform hybrid crosses. As these two weevils are used across the globe for the biological control of water hyacinth, it is important to investigate the effect of hybridization on the performance and growth of these weevils. Interspecific hybrid crosses can result in hybrid vigor or hybrid breakdown   Table . Althougy) Table .N. bruchi or N. eichhorniae. However, initial propagule sizes in this study system may have been higher than a specific threshold required for an effect to have taken place. Our initial hypothesis that populations with more introduction steps away from the native range would harbor lower genetic diversity than those populations with fewer steps was not supported. For example, we found intermediate levels of genetic diversity in Uganda for both species even though the populations in Uganda had the highest number of steps away from the native range. Overall, N. bruchi had similar allelic richness and heterozygosity across the eight collection sites. Although not significantly higher, the populations of N. bruchi in the native range (Uruguay) and in SA: Enseleni harbored the most alleles. In contrast, there was significant variation in allelic richness and expected heterozygosity across populations of N. eichhorniae, with the highest allelic richness in the population in Uruguay. Populations of each N. bruchi and N. eichhorniae from Uruguay also exhibited the highest number of private alleles, with eight and 10 private alleles, respectively. The high allelic richness and many private alleles found in the population in Uruguay supports the general trends that introduced populations typically undergo a loss in genetic diversity  had lower allelic richness, the sample size for that population was only six individuals. FLOCK allocation tables helped demonstrate that SA: Enseleni was a composite population with two genetic sub‐clusters. One sub‐cluster was mostly allocated to the Australian genetic cluster, and the other cluster appeared somewhat related to the Australian and Ugandan genetic clusters. Based on the importation history, the latter sub‐cluster was likely derived from Zimbabwe, a population that we did not test. This finding supported the multiple introductions documented in the importation history, and the notion that genetic admixture can increase genetic diversity  and FLOCK allocation tables found that one of these populations (SA: Kubusi River) had two genetic sources . Interestingly, FLOCK analyses demonstrated that only one population from USA: Florida contributed to the genetic composition of SA: Wolseley, which also demonstrated high allelic richness. This population also had indications of genetic drift and inbreeding, which supports the contrasting forces of genetic admixture and inbreeding, with the latter sometimes selected for when a population is adapted to the local area , due to a low abundance of N. eichhorniae in the Campana Lagoon. Although we sampled populations from Uruguay, we sampled them from the Uruguay River, in‐between Argentina and Uruguay, and speculate that the sample is likely genetically similar to those weevils in Santa Fe, Argentina. Thus, rather than due to multiple introductions, the higher allelic richness in Florida, Texas, and California, USA, may have been due to the temporal proximity of these populations to the initial imported population from the native range .In contrast, the populations of N. bruchi and N. eichhorniae. In the case of N. bruchi, FLOCK and DAPC indicated two main genetic clusters and 11 sub‐clusters For N. eichhorniae, FLOCK and DAPC signified four to six main genetic clusters and 23 sub‐clusters. In comparison, the STRUCTURE program detected two to six distinct broad populations for each weevil species, but did not detect sub‐clustering within these populations (Supporting Information Appendix In addition to clarifying the introduction pathways, our population genetic analyses demonstrated the presence of several distinct and broad genetic clusters for each One caveat that we acknowledge is that the genetic divergence between the introduced and native range may have been due to the fact we sampled from Uruguay rather than Argentina, where the actual initial source populations were exported from. However, based on the DAPC and FLOCK analyses, the populations from Uruguay for both species appear to be genetic sources for several of our populations. Thus, we feel confident that the genetic composition from weevils in Argentina compared to those in Uruguay is not very different.N. eichhorniae under cool temperature conditions simulating the fall season in Sacramento–San Joaquin River Delta, California. Reddy et al. (N. eichhorniae used in the present study and found that weevils from the population in Australia had a higher fecundity under these cool temperature conditions compared to weevils from California and Uruguay, SA: Kubusi River. These results were surprising as the population in Australia had lower genetic diversity than the other populations, thus suggesting that populations can still adapt to local areas even with moderate levels of genetic diversity. Furthermore, the present study combined with that of Reddy et al. (In addition to the results demonstrating that genetic drift and inbreeding occurred in several populations, we speculate that divergence has also occurred due to local adaptation to some of the regions of introduction. Rapid local adaptation has been observed in invasive species (Sotka et al., y et al.  tested ty et al.  demonstrWe support the recommendation that population genetic analyses be performed prior to the selection and release of biological control agents (see Rauth, Hinz, Gerber, & Hufbauer, None declared.Microsatellite allele calls used for this study are available in Supporting Information Appendix N. bruchi and N. eichhorniae, are found in a public Google Drive folder: https://drive.google.com/drive/folders/0B6mDY592nSW9NWJLWDh5MVR3djA?usp=sharing.Photographs of the weevils,  Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.