Hepatocellular carcinoma (HCC) is a major global health problem and its treatment options have been limited. Signal transducer and activator of transcription 3 (STAT3) is a transcription factor important for various cellular processes. Overexpression and constitutive activation of STAT3 have been frequently found in HCC and associated with poor prognosis. Ample evidence has shown that STAT3 plays pivotal roles in the initiation, progression, metastasis and immune suppression of HCC. Thus, STAT3 has attracted attention as a novel therapeutic target in HCC. Clinical trials have investigated STAT3-targeted therapeutics either as monotherapy or in combination with chemotherapeutic agents, immune checkpoint inhibitors and alternative targeted drugs. Some of these studies have yielded encouraging results. Particularly, napabucasin—a cancer stemness inhibitor targeting STAT3-driven gene transcription—has stood out with its promising clinical efficacy and safety profile. Nonetheless, clinical investigations of STAT3-targeted therapies in HCC are limited and more efforts are strongly urged to evaluate their clinical performance in HCC. Here, we provide a comprehensive review of the roles of STAT3 in HCC and follow by comprehensive analysis of STAT3 targeted strategies. Primary liver cancer is the sixth most prevalent cancer and the second leading cause of cancer mortality worldwide . The mosSignal transducer and activator of transcription 3 (STAT3) has recently emerged as a potential therapeutic target for HCC due to its crucial roles in oncogenesis. STAT3 was initially determined to control acute-phase genes in response to interleukin-6 (IL-6) and epidermal growth factor (EGF) during inflammation . It beloIntensive investigation has been done on STAT3 since its discovery, revealing its physiological roles in early embryonic development, growth and differentiation of various adult tissues . In addifos, cyclin D, c-Myc, pim1 and anti-apoptotic genes such as B-cell CLL/Lymphoma-2 (Bcl-2), Bcl-xL, survivin and X-linked inhibitor of apoptosis protein (XIAP) . I. In = 58 stated) . The DCR stated) . Thus, s1563302) . The DCRIn general, AZD9150 is deemed safe and effective in advanced cancer patients especially those with lymphoma. There have been few unacceptable toxicity events, with the most common adverse events being elevated levels of aspartate and alanine aminotransferase and thrombocytopenia. Many phase I/II trials of AZD9150 are now underway in numerous cancer types including solid tumors and hematological malignancies, either as a single agent or in combination with chemotherapeutic, targeted or immunomodulatory agents. Nonetheless, like napabucasin, clinical investigations of AZD9150 in HCC are still in its infancy and further efforts are urgently needed.n = 14) (NCT00955812) [In the realm of SH2 domain inhibitors, OPB-31121 is one of the earliest oral drugs developed by Otsuka Pharmaceuticals. It prevents STAT3 dimerization upon phosphorylation by binding with high affinity to the SH2 domain of STAT3 . Preclin0955812)  Table 3n = 14 (Nn = 33) (NCT01942083) [n = 145) (NCT01711034) [In the same phase I study, OPB-111077, the primary metabolite of OPB-31121, has been found to be accumulated at higher tissue levels . In vitrn = 51) (NCT01184807) [n = 20) (NCT02058017) [n = 9) (NCT02058017) [On the other hand, OPB-51602 has also demonstrated inhibitory effects against different cancer models  but into1184807) . Modest 2058017) . However2058017) . There hWhile OPB-111077 is probably the most promising OPB candidate by far, pyrimethamine and TTI-101 are two other STAT3 SH2 domain inhibitors currently undergoing early phase clinical trials in relapsed chronic lymphocytic leukemia (CLL) or small lymphocytic leukemia (SLL) (NCT01066663) and other advanced cancers including HCC (NCT03195699) respectively . Both ofn = 32) and reported that intratumoral injection of STAT3 decoy abrogates expression of STAT3 target genes including cyclin D1 and Bcl-xL without toxicities [STAT3 decoy is a 15-bp double-stranded oligonucleotide that competitively inhibits STAT3 binding to the response element within the c-fos promoter. It has been shown to effectively induce apoptosis, suppress growth and downregulate STAT3 target genes in in vitro and in vivo cancer models, including liver , lung 1,120, heaxicities . AlthougThe existing preclinical and clinical evidence strongly justifies the use of STAT3-targeted drugs as a promising therapeutic approach against HCC. To date, napabucasin is considered the most potent STAT3 inhibitor with an acceptable safety profile in the clinic. Although the STAT3-targeted antisense oligonucleotide, AZD91150, have demonstrated antitumor activities, its efficient delivery and stability in vivo remain technically challenging. For SH2 domain inhibitors, the development of OPB compounds is somewhat limited by tolerability issues, whereas others are still under early clinical investigation. Proof of concept for DNA-binding domain inhibition by STAT3 decoy has sufficed to warrant further clinical studies but it shares the same concerns as antisense oligonucleotides. Despite these limitations, direct STAT3 inhibitors are better options than indirect inhibitors targeting upstream regulators of STAT3, as the latter lack specificity to the STAT3 pathway and may produce a range of undesired pleiotropic effects. While many direct STAT3 inhibitors have not been clinically well explored in HCC, current clinical data in various other cancers suggest their potential benefit in HCC patients.Importantly, a combinational approach for STAT3-targeted agents may be more effective than STAT3-targeted monotherapy. It has been elucidated that STAT3 is involved in extensive crosstalk with other signaling pathways and that single activating mutation of STAT3 is rare in HCC. Combination of STAT3-targeted drugs with other anticancer therapeutics may address these issues by simultaneously targeting different mechanisms of action, thus eliciting more powerful antitumor responses. While targeted agents specifically block molecular pathways that promote oncogenesis, chemotherapeutic and immunotherapeutic agents respectively inhibit growth of all dividing cells and stimulate immune responses to attack tumor cells. So far, napabucasin is the only STAT3-targeted drug that has been clinically studied in combination with other therapies. As discussed before, early-phase trials have revealed promising antitumor efficacy when combining napabucasin with standard chemodrugs including paclitaxel ,92,93,94Apart from combinational strategies, novel STAT3 inhibitors and better biomarker strategies may improve the therapeutic efficacy of STAT3-targeted agents. It is expected that advancement in technology, for instance, high-throughput screening platforms of protein-protein interaction inhibitors, would boost the discovery of novel STAT3-targeted drugs. As STAT3 is a pivotal regulator of cellular metabolism under physiological conditions, an ideal STAT3-targeted agent should minimize its toxicities in normal cells while preserving its specificity and efficacy against tumor-associated components. Besides, a more robust biomarker strategy should be established for patient stratification. Although overexpression of STAT3 pY705 is the typical definition of STAT3 activation, it may not be broadly representative. Further investigations on biomarkers of STAT3 activation may be helpful in identifying cancer patients for STAT3-targeted therapies and thus improve clinical outcomes.HCC is an extremely deadly tumor and the search for innovative treatment strategies is never ending. Overexpression and constitutive activation of STAT3 in HCC tumors has been found to associate with disease development and patient prognosis. The oncogenic functions of STAT3 have also been well established in numerous HCC models. These preclinical and clinical findings provide rationale for the use of STAT3 as a novel therapeutic target in HCC. Various types of STAT3 inhibitors have since been developed. Currently, clinical trials evaluating different STAT3-targeted strategies as monotherapy or combination therapy are ongoing. Although encouraging results have been shown in various cancer types, clinical trials on STAT3-targeted therapies in HCC are still limited. This stresses the need for more assessments of STAT3 inhibitors in HCC patients, especially in combination with other anticancer therapeutics. Future development of novel STAT3 inhibitors with lower toxicities and higher efficacy is anticipated. Besides, more efforts are required to delineate a comprehensive STAT3-associated biomarker profile, which helps define a subset of HCC patients that are more susceptible to STAT3 inhibition.


The history of Ecuador was marked by the arrival of Europeans with Africans, resulting in the mixture of Native Americans with Africans and Europeans. The present study contributes to the knowledge of the Ecuadorian mestizo population by offering information about ancestry and ethnic heterogeneity. Forty-six AIM-InDels (Ancestry Informative Insertion/Deletion Markers) were used to obtain information on 240 Ecuadorian individuals from three regions . As a result, the population involved a significant contribution from Native Americans , followed by Europeans  and Africans . Furthermore, we compared the data obtained with nine previously reported scientific articles on autosomal, mitochondrial DNA and Y chromosomes. The admixture results correspond to Ecuador’s historical background and vary slightly between regions. Specifically, in “Ecuatorial Andinoamérica” (known today as Ecuador), there is evidence of settlement from twelve thousand years ago1. American history continues with the arrival of Europeans (Spanish) to the continent, in which African descendants were brought as slaves2. Consequently, the Spaniards took control of the Panama Isthmus and then started to travel south. The first conquer and colonization expedition to Ecuador was in 1526. They arrived at the coast where they disembarked to go inland1.The origin of men in America begins with their arrival from Asia by the Bering Strait, which started forty or fifty thousand years before Christ4.In addition, slaves arrived from Africa to Europe due to the slave trade, in addition to Muslim and Portuguese as merchants, since the Middle Ages. As a result, the slaves who came to America constituted a heterogeneous group from different societies and cultures5.For approximately twelve thousand years, the lands that are now known as Ecuador have been populated by indigenous people from Asia and Oceania. Nonetheless, after the conquering, the Spanish brought their language and customs, and the population started mixing between the natives and the Spanish and the African slaves, resulting in diversity in the Ecuadorian population7. The most recent census was in 2010, and according to the results, the population projection for 2019 was an estimated 17 267 986 Ecuadorians, with 24 provinces distributed in 4 different regions: the Coast (8 523 453), the Highlands or the Andes (7 733 725), Amazonia (937 406), and the insular region (32 320)8. Moreover, in the census, Ecuadorians self-identified as “mestizos” 71.9%, “montubios” 7.4%, Afro-Ecuadorians 7.2%, “Indígenas” 7% and “blancos” 6.1%9.The census in Ecuador that began in 1950 classified people by asking for certain characteristics, such as language, and tried to predict the behavior of the indigenous groups12, Y chromosome13 or autosomes18. Some of these studies are focused on Native American or Afro-Ecuadorian groups12, and others are focused on mestizo populations but with a sampling that did not consider the Ecuadorian regions18. Moreover, those reports emphasize different genetic markers, such as AIMs (Ancestry Informative Markers), that have been used to fully understand the differentiation  across populations, which can also infer migration, admixture, colonization, and/or invasion events19. Here, we report genetic data of the Ecuadorian mestizo population using AIMs-InDels. In addition, we analyzed and compared the data with previously reported data of the genetic ancestry of the Ecuadorian population.The genetic population structure of Ecuador has been previously studied using different genetic markers, such as mitochondrial DNAA total of 240 unrelated self-identified mestizo samples were randomly selected . All individuals signed the informed consent form for population genetic studies. Blood samples were collected on FTA paper  at the Genetic Laboratory of Centros Médicos Especializados Cruz Roja Ecuatoriana.20 protocol, with a modification in the concentration of Chelex used (20%).DNA was extracted using Chelex 100 (Bio-Rad) (20%) according to the standardized method of the Genetic Laboratory based on the method published by the President´s DNA Initiative21. Moreover, the experimental protocols were approved with the number 2018-127E by “Comité de Ética de Investigación en Seres Humanos Universidad San Francisco de Quito”.All methods were carried out in accordance with relevant guidelines and regulationet al.22. Fragment separation and detection were executed on the ABI PRISM 3100, 3130 and 3500 Genetic Analyzers (Applied Biosystems). The results were collected with Data Collection v2.0 and v4.0 and analyzed by Gene Mapper v3.2 and v5 (Applied Biosystems). Along with the samples, to evaluate the amplification efficiency during the PCR and the genotyping steps, positive controls were used , and one negative control was used to test for contamination. Short alleles were coded as 1, while long alleles were coded as 2.PCR amplification of the 46 AIM-InDels was performed with the same primers and one multiplex reaction, according to Pereira ST genetic distances) were estimated using Arlequin v3.5.2.226. With STATISTICA v.1327, an multidimensional scaling (MDS) scatterplot was constructed to visualize the genetic distances between Ecuador and each reference population from HGDP-CEPH  subset H95229. Once the allele frequencies were obtained, the allele frequency differentials (δ) were estimated by comparing the frequencies reported by Pereira et al.22 from Native Americans, Europeans, and Africans due to the historical background of Ecuador.The population genetic parameters  interactions. The option used was the admixture model (“Use population information to test for migrants”). Moreover, three repetitions were performed to estimate the cluster used, and the k value was tested from K = 1 to K = 20 to evaluate the Ln probability of data (LnP(D)) by plotting it according to Evanno et al.31. Finally, principal component analysis (PCA) was performed to visualize the population structure: the relationship between the Ecuadorian population and the reference population used32. RStudio v1.0.4433 was used to obtain the PCA plot and the variance in the two first principal components.Ancestry inferences were made using STRUCTURE v2.3.4An exhaustive study was performed to compare the present study with the results obtained in previous reported studies that used autosomes, Y chromosome or mitochondrial DNA related to the origin of the Ecuadorian population. Table Allele frequencies for short alleles (1) and long alleles (2) were estimated for the 240 samples from the three regions (Amazonia (AM), Highlands (HL), Coast (CO)) and joined as Ecuador (ECU). Moreover, observed and expected heterozygosities were calculated for Ecuador, in which the marker with the highest variability was MID-397 (0.551). Allele frequency differentials (δ) were estimated for Ecuador against each reference population , resulting in a differential average of 0.320 with Africans, 0.196 with Europeans, and 0.12 with Native Americans. As expected, Ecuador has a similar level of diversity with the reference Native American population. Furthermore, allele frequency differentials (δ) were also estimated between regions, showing a low allele frequency differential mean  that increased when the geographical distance increased. Table 34.Once the Bonferroni correction (p > 0.001) was applied, no significant deviations from Hardy-Weinberg equilibrium were found for the 46 loci in the population under study. Moreover, linkage disequilibrium (α > 4.6 × 10-5) did not show any significant associations between the markers, except for MID-225 and MID-94 in the Coast population, but not in the other two populations. That result suggests that there is not a real association between the markers, allowing the use of the migration model in STRUCTURE analysisST . Values between 0–0.05 indicate little genetic distance, 0.05 and 0.15 indicate moderate differentiation and 0.15 and 0.25 indicate great differentiation, and values up to 0.25 indicate very great differentiation35. In the present study, there is a small genetic distance between all the Ecuadorian regions . When compared with the reference population, there is a moderate differentiation between all Ecuadorian regions and the Native American population with values < 0.073, and between the Highland and Europe populations (0.15) and the Coast and Europe populations (0.118), there is a great differentiation between African and all the Ecuadorian regions and between Europe and Amazonia. The pairwise FST is represented in the multidimensional scaling plot in Fig. The pairwise genetic distances showed significant differentiation between all Ecuadorian samples and the reference population and between Ecuadorian regions. To interpret the genetic distances obtained, we used the suggested values classification of Ballaux ST was performed to obtain the genetic distances between the Ecuadorian population and available published data from the South American population. The evaluations were obtained from Colombia36 , which is located in the north border of Ecuador and Brazil37 , which is located further away. Those results showed little differentiation with Colombia and were moderately differentiated with Brazil  by Evanno methodThe admixture analysis results are concordant with the genetic distances. Native American ancestry is the main composition of the mestizo Ecuadorian population, with values higher than 51%. Moreover, the African contribution is the least predominant (<16.3% in the Coast) in the analysis for the three regions Fig. .Figure 3Principal component analysis (PCA) allowed us to visualize the variance in the Ecuadorian data regarding the three reference populations . The two first principal components represent a total of 31.88% of the variance in the dataset and permitted a clear spatial separation of the reference population in three clusters and one cluster in the middle of Europeans and Native Americans (mainly in Native American group) representing the Ecuadorian regions  Fig. .Furthermore, because we applied a random sampling with a self-identification as a mestizo, we found some individuals with a different prevalent ancestry. Figure After the bibliographic revision of previously reported data on the origin of the Ecuadorian population, there were nine scientific articles that used different genetic markers applied to elucidate it.The review was based on the public scientific information, and there were a limited amount of published papers about the ancestral characterization of Ecuadorians, but while searching, we saw that there were more papers available regarding genetic markers for forensic identification purposes. Figure el al.15, Santangelo et al.14 and the present study have the closest proportions reported for African (SD = 0.039), Europeans (SD = 0.008) and Native Americans (SD = 0.031).When analyzing the ancestral composition percentages, the results are heterogeneous, as shown in Fig. 39; (b) the InDels approach is easy to use and is time and cost effective, reducing the genotyping time compared with AIM-SNPs due to its potential of multiplexing by amplifying it in a single reaction39; (c) the workflow reduces the manipulation of samples, minimizing the number of variables that affect the results, for example, contamination risks or sample mix-ups39; (d) the potential of use in forensic genetics, such as a tool in criminal investigation, because its results could indicate the ancestry of the donor and help direct the case under analysis39.The main objectives of this study were to infer the ancestry proportions of 240 Ecuadorian samples divided into three continental regions and to compare the results with previously reported data. To achieve this goal, we used a panel of 46 Ancestry Informative InDel markers to corroborate the advantages of their use over other commonly used genetic markers, such as SNPs: (a) the length of the InDel polymorphisms can be genotyped by fragment size separation, while SNP detection requires more complex sequencing methods45, the study of ancestral identification markers in the Ecuadorian population is underrepresented in scientific papers.While there are various studies on genetic markers commonly used in forensic identification analysis in Ecuador47.InDels showed low allele frequency differentials between Ecuadorian regions: δ < 0.178 among Amazonia- Highlands, δ < 0.194 among Amazonia-Coast and δ < 0.14 among Highlands-Coast. Moreover, Hardy-Weinberg analysis did not show any significance, which suggests that there is no process that affects the conditions of Hardy-Weinberg equilibrium48.Additionally, the pairwise linkage disequilibrium test detected one significant association within MID-2256 and MID-94 in the Coast population. Hence, while looking at the physical distance between them, it was observed that a significant distance does not exist: MID-2256 is located on chromosome 22, position 41.04 Mb; and MID-94 is also on chromosome 22, but in position 42.55 Mb. This linkage may occur due to the segregation caused by ancestry blocks of the mestizo population that do not allow for adequate recombinationST between Ecuadorian regions are low, but they are statistically significant. The FST among Ecuadorian regions and European and African reference populations increase, while the geographic distance from the Coast increases, and the genetic distances with Native American decreases. Moreover, the ancestral characterization percentages are in accordance with the genetic distances for each region: Amazonia , the Highlands  and the Coast . These results are concordant with the history of Ecuadorian settlement, where the Europeans arrived with African slaves along the coast to later enter the Ecuadorian territory5.The genetic distances F49. Moreover, the Coast has greater proportions of African ancestry (16.3%) than the other Ecuadorian regions, which was elucidated by a study about an important Afro settlement in Esmeraldas that has its origins based on the arrival of African slaves in 1553 to the coast of that province and due to the group of African slaves that was brought from Colombia in the XVIII century49.Comparing the ancestral proportions among regions, there are differences that are concordant with the history: Amazonia exhibits greater proportions of Native American origin (66.7%) than the other two regions, which is explained by the number of different indigenous nationalities  and because until the 1950s, Amazonia was habited principally by indigenous populations50, which is consistent with the historical records of these populations. Other countries, such as Puerto Rico and the Dominican Republic, reported greater African ancestry 18. In Colombia, which is located on the northern Ecuador border, heterogeneous ancestral proportions were reported depending on the region and the population under analysis, but the results of the present study show low differentiation 36. In Brazil, the results are also mixed, depending on the population, and when compared with the Ecuadorian population, it showed moderate differentiation 19 )19 Fig. . The dif13;The analysis of the Y chromosome in mestizos showed a greater proportion of European ancestry (61%), followed by Native American ancestry (34%) and African ancestry (5%). The results are because of male inheritance patterns of the Y chromosome15, 65.8%14, 71.2%16, 50.10%17 and 38.8%18; the different percentages (SD 11.78%) could be due to the sampling procedures because some of the reported studies did not specify the region or the sample where the population under study was from16. For instance, Santangelo et al. (2017) reported that the mestizo population samples were collected at a university located in Quito without specifying the geographic origin of the individuals14; another study by Poulsen et al. (2011) specified that the samples were collected in Quito, yet no individual origin is provided16.Autosome analysis always shows that the Native American composition is the highest in all the studies: 59.6% (present study), 63.10%15. There were two studies using SNPs in 19 and 6 samples in which the percentage of European origin was 40.8%17 and 53.90%18, which is more than a 10% difference than the other publications (SD 10.78%), 38.8% (present study), 30.3%15, 30.1%14, 28.8%16, which could also be explained by the sample size used of the studies because a study with a small sample size is going to estimate parameters with poor precision or be unable to detect differences between groups51. Lastly, African ancestry is similar between the studies (SD 2.7%): 7.30% in the present study, 6.60%15, 4%14, 6.80%17 and 7.30%18. Moreover, taking into account only the papers with more than 100 individuals16, the standard deviation is 4.8%.Thus, the sampling procedure could explain the differences in percentages with the present study because we take into account all the Ecuadorian regions while they specified sampling only in Quito, which is located in the Highland region, but the studies reported an approximation with other previously reported studies of the mestizo highland population12.By using mitochondrial DNA as markers for mestizo and indigenous ancestral characterization, the results correspond to haplogroups A, B, C and D from Native American ancestry, again due to the inheritance patternsAdditionally, comparing the results of the present study with other available data in the Ecuadorian population, it showed the following:In conclusion, our study demonstrated the use of 46 AIMs-InDels as an alternative method to measure the ancestry proportions in Ecuador and its relevant implications for understanding and corroborating the history and demography of Ecuador. The population admixture in Ecuador started with the arrival of Europeans along with African slaves; however, the Native American ancestry represents the prevalent genetic composition with more than 51%. The autosomal AIM-InDels, mtDNA and Y chromosome information presented support the historical records with a prevalence of Native American but influenced by European  and African composition.Figure S1, Table S1


Genetic erosion is a major threat to biodiversity because it can reduce fitness and ultimately contribute to the extinction of populations. Here, we explore the use of quantitative metrics to detect and monitor genetic erosion. Monitoring systems should not only characterize the mechanisms and drivers of genetic erosion  but also its consequences . Technological advances in genomics now allow the production of data the can be measured by new metrics with improved precision, increased efficiency and the potential to discriminate between neutral diversity (shaped mainly by population size and gene flow) and functional/adaptive diversity (shaped mainly by selection), allowing the assessment of management‐relevant genetic markers. The requirements of such studies in terms of sample size and marker density largely depend on the kind of population monitored, the questions to be answered and the metrics employed. We discuss prospects for the integration of this new information and metrics into conservation monitoring programmes. The maintenance of genetic diversity is also included in the UN's Sustainable Development Goals (https://sustainabledevelopment.un.org/). For the purpose of population monitoring, many metrics have been proposed to assess changes in genetic diversity and possible genetic erosion, including the coancestry coefficient, population allelic diversity, population differentiation and diversity of domesticated breeds and varieties .The conservation of genetic diversity is one of the priorities of the Convention of Biological Diversity  equally at both neutral and adaptive loci in small populations. However, selection acts at different levels: as a driver of genetic erosion, it will affect the number and productivity of successful breeders, thereby indirectly impacting inbreeding and drift. As a mechanism, directional selection is also expected to decrease variability at target genes and adjacent genomic regions that are linked by lack of recombination. Historically, the limitations of molecular markers available to conservation geneticists meant that it was difficult to obtain data for both marker types and evaluations of genetic diversity at neutral loci were used as a proxy for genetic variation at adaptive loci when evaluating adaptive potential and the relationship between genetic diversity and fitness , which measures the rate of inbreeding , and variance effective size (Nev), which measures the rate of drift , are equivalent for a single population of constant size  can increase locus‐specific variation, whereas directional selection can decrease it Wright, . The typIn genetic monitoring for conservation, the focus may be less on the detection of loci under selection and more on identifying genetic variants of interest for fitness and population persistence. For instance, in a study on a transmissible cancer affecting Tasmanian devils, Epstein et al.  identifi2.1.3Introgression refers to the flow of alleles/genes from one species into another by repeated backcrossing of interspecific hybrids with one of the parental species. It is a natural evolutionary process that can have positive impacts on biodiversity, such as an increase in genetic diversity and fitness in hybrid individuals (hybrid vigour), adaptive radiation and the creation of new species  or the general level of admixture in terms of the proportion of the targeted population belonging to genotype clusters identified. For example, Monzón, Kays, and Dykhuizen  can be estimated using a variety of nongenetic tools, from tracking individuals using natural markings, to line‐transect studies, to counting an entire population using satellite imagery. Genetic and genomic tools can however be used to estimate census population size indirectly in a variety of ways including enumeration of the number of genotypes , population growth, (λ), survival (Φ) or abundance , Creel–Rosenblatt estimator (CRE) and rarefaction analysis (R) approaches  fishery in the world. Fluctuations in production and catches of populations are however highly variable over time, which is challenging for sustainable population‐based management. In order to detect migratory trends in stock populations, a marker set of 38 SNPs was used to perform mixed‐stock analysis and determine stock composition. Data from genetic analyses provided information on relative abundance within 4 days of capture, allowing managers to shift fishing effort among districts in anticipation of the distribution of the total return to the various stocks of origin.The several dozen populations that spawn in Bristol Bay support the largest sockeye salmon .2.2.2F‐statistics , which are relevant for management , the population inhabiting the region may also become fragmented, with limited migration. Over time, drift and inbreeding will increase the differentiation among populations and deplete the genetic variation in each. The differentiation between populations can be measured and monitored by  Wright,  calculat Nunney, . Kinship Nunney,  have als Nunney,  used pat2.32.3.1Inbreeding depression (ID) is defined as the reduction in fitness due to inbreeding, and it has been shown to affect any trait under selection . Most recently, Rogers and Slatkin .2.3.4We define maladaptation as the increase in deleterious phenotypes, occurring through direct or indirect effect of selection. The implication of selection differentiates maladaptation from the random emergence of deleterious phenotypes. In large populations, such traits are rare because of purifying selection. However, anthropogenic pressures can reduce population sizes to the point where genetic drift and inbreeding increase the frequency of such maladaptive phenotypes. Such human‐induced pressures include over‐exploitation, habitat destruction and artificial selection. For example, size selective harvest can lead to the evolution of smaller body sizes in fish, which leads to maladaptive traits such as fewer vertebrate, slower larval growth, high larval mortality, smaller and fewer eggs . However, a meta‐analysis by Wood, Yates, and Fraser  the effects of inbreeding on phenotypic plasticity, (ii) increased magnitude of inbreeding depression under stressful conditions and (iii) reduced genetic variation for evolutionary adaptation  were the marker of choice in the 1990s and 2000s, more recently the focus has increasingly switched to the more abundant single nucleotide polymorphisms (SNPs). For population monitoring, study requirements in terms of sampling and marker density should be carefully considered, including in relation to the time scale being considered in the analysis.3.1Ne, census size, selection, introgression and fragmentation) in population monitoring can be accomplished using sample sizes below 100 individuals  because the signal of drift is weak relative to sampling noise, requiring increased marker density  and random mistyping. Genotype based metrics are, however, sensitive to typing errors when they are inadequately accounted for  and GD² (http://gd2.pierroton.inra.fr/). The project also developed a protocol for monitoring and measuring forest genetic diversity at pan‐European level. Under this protocol, monitoring should be conducted every 10 years, with at least 100  adult individuals and at least 100  saplings sampled per monitoring plot, for 20–50 plots per species. It was recommended to use SNPs, less prone to genotyping and scoring errors than microsatellites. The marker set should contain more than 150 SNPs, including adaptive and non‐adaptive ones. In terms of metrics of genetic diversity, it was recommended to use effective number of alleles, unordered number of genotypes, genetic distance among adults and seedlings and effective population size.With the increasing need to adapt both forest and forest management practices to climate change, several countries in Europe have developed monitoring system of their forest genetic resources. The FORGER project (towards sustainable management of forest genetic resources) aimed at providing, to actors of the European forest sector, integrated knowledge and information resources for enhanced conservation and use of forest genetic resources. Among other outputs, the project provided linkage between existing monitoring databases, like EUFGIS .3.2Timescale is an important consideration for genetic metrics as (i) genetic erosion is a function of time and (ii) monitoring requires an assessment of changes occurring over different periods  or marker stretch length (ROH metrics) is potentially valuable because interpolation of the historical events can be made without genotyping historic specimens. This bridges the gap when historical samples are not available and where coalescent‐based methods may be unreliable, and reduces the sampling costs involved in processing ancient or historical DNA.The possibility of considering variable timescales according to physical marker linkage , especially when changing from microsatellite to SNPs  is often complex, sometimes requiring advanced bioinformatics skills and resources. This can represent a serious limit to the translation of non‐neutral markers into routine management practice. Also, the necessity of phenotyping may also increase the cost of routine implementation.Pearse  has argu5Recently, there has been debate on the lack of use of genomic tools by field practitioners  is the subspecies of the endemic Hector's dolphin (C. h. hectori) that is restricted to a small segment of New Zealand's North Island (A and B). Ranked Nationally Critical under the New Zealand Threat Classification System , and an instance of biopsy sampling of a Māui dolphin for genetic monitoring (C). As shown by Table D, the population initially maintained a high Nc:Ne ratio, as expected by a population that has recently undergone a genetic bottleneck. The most recent estimate, however, shows an erosion of Ne that was expected after an initial lag period. With restrictions on set nets in place across much of the current Māui dolphin range, it is hoped that Nc will increase, decreasing the speed and scale of genetic erosion. B is from Hamner et al. . Relative to the threats caused by climate change, Hoegh‐Guldberg et al.  proposedConsidering how marker sets and metrics should be developed and chosen, it is important to differentiate implementation needs for genetic monitoring from the development phase, identification of loci and testing of indicators (Fussi et al., 6Ne, and Willoughby et al. (Ne and relative levels of genetic diversity. It is not in the scope of this review to take a position on those thresholds, but our underlying arguments support the inclusion of metrics that describe the drivers and consequences of genetic erosion in population monitoring programmes.Frankham, Bradshaw, and Brook  recentlyy et al.  made simClassical molecular tools in conservation genetics have provided useful insights into the drivers and, to some extent, the mechanisms of genetic erosion from a neutral perspective. Modern genomics approaches now offer a more complete view on the phenomenon, investigations into functional variation, as well as providing more accurate estimations of the consequences of genetic erosion on fitness and adaptation in populations. In the latter case, it is important to underline that most metrics will need to be combined with phenotypic information related to the traits relevant for fitness.The views expressed in this information product are those of the authors and do not necessarily reflect the views or policies of FAO.


Prunus domestica commonly known as European plum is a hexaploid fruit tree species cultivated around the world. Locally it is used for fresh consumption, in jams or jellies, and the production of spirits while commercially the fruit is primarily sold dried (prunes). Despite its agricultural importance and long history of cultivation, many questions remain about the origin of this species, the relationships among its many pomological types, and its underlying genetics. Here, we used a sequence-based genotyping approach to characterize worldwide plum germplasm including the potential progenitor Eurasian plum species. Analysis of 405 DNA samples established a set of four clades consistent with the pomological groups Greengages, Mirabelles, European plums, and d’Agen (French) prune plums. A number of cultivars from each clade were identified as likely clonal selections, particularly among the “French” type prune germplasm that is widely cultivated today. Overall, there was relatively low genetic diversity across all cultivated plums suggesting they have been largely inbred and/or derived from a limited number of founders. The results agree with P. domestica having originated as an interspecific hybrid of a diploid P. cerasifera and a tetraploid P. spinosa that itself may have been an interspecific hybrid of P. cerasifera and an unknown Eurasian plum species. The low genetic diversity and lack of true wild-types coupled with the known cultivation history of Eurasian plums imply that P. domestica may have been a product of inter-specific cross breeding and artificial selection by early agrarian Eurasian societies. Prunus domestica). The plants clustered genetically into four groups that corresponded with known plum varieties, such as greengages and mirabelles, but not with others, including damsons. Overall, the cultivated plums harbored a low level of genetic diversity, suggestive of repeated inbreeding from a small number of founder plants. The data also point to the European plum originating from a hybrid cross between the cherry plum (Prunus cerasifera) and blackthorn (Prunus spinosa).The plums grown today for dried prunes likely originated from hybrid crosses and artificial selection by early agrarian societies, a genetic analysis shows. Chris Dardick from the US Department of Agriculture’s Appalachian Fruit Research Laboratory in Kearneysville, West Virginia, and coworkers sequenced more than 100,000 single DNA letters scattered across the genomes of 405 different samples of the European plum ( Prunus domestica L. is a polymorphic allopolyploid (hexaploid) species (2n = 6x = 48) commercially grown worldwide for a variety of uses including fresh fruit, prunes, distilling, and as processed additive ingredients. This plum species, commonly referred to as “European plums” or “prune plums”, is distinct from the large round diploid “Japanese plums”  which are widely grown for fresh market consumption. P. domestica plums are critical components of human diets as prunes have been shown to have a broad range of health promoting activities including protection against cardiovascular disease, diabetes, digestive disorders, and osteoporosis2. Historical evidence suggests P. domestica and other Eurasian plum species including Prunus cerasifera (Cherry plum) and Prunus spinosa (“Sloe” or “Blackthorn” plum) were important to the development of early European societies. Stones from all three species have been uncovered from Neolithic archeological sites dating back to 4000–6000 BC in Germany and the Ukraine3.European plum 3. Around the birth of Christianity, the Roman author Pliny gave the first clear account of “Damascus plum” where he described a wide variety of cultivars with different fruit types and colors, their uses, and grafting onto “sorb” rootstocks (probably Prunus cerasifera or Prunus spinosa), which is still a common practice today. Roman depictions of plum fruit provide further evidence that at least some of the P. domestica varieties grown at that time were very similar or even possibly the same as those still grown today. References to different types of plums being imported from Syria or Persia and the fact that plums were not native to some of the Neolithic sites indicates that they were under human selection and cultivation in the Caucasus region long before their introduction into Europe. The propensity of P. domestica to form root suckers would have provided a simple and efficient way to distribute clonal material across long distances even before grafting techniques had been developed.The plum or “prumnon” was first recorded in Archilochus’s “Pollux” written in the 7th century BCP. domestica is thought to have originated from the Middle East, specifically, in the area south of the Caucasus between the Black Sea and the Caspian Sea that encompasses Georgia, Armenia, Azerbaijan, and the northern plateau of Iran4. Vavilov5 placed the center of its origin in the region south of Caucasian Mountains through the Caspian Sea, in the area overlapping the distribution of P. cerasifera L. and P. spinosa L; which were likewise concurrently distributed across Europe for cultivation, consumption, and use as rootstocks. Widespread feral growth, the apparent lack of natural stands in forests, and human movement of both cultivated and wild type plums across the continent has made determining the origin of P. domestica extremely difficult. The existence of wild forms of P. domestica in the Caucasus region was recurrently reported in the 1930s based on morphological traits but failed validation from cytogenetic analyses6. Recent discovery of wild P. domestica stands in Xinjiang province in northwestern China prompted the hypothesis that European plum may be of Chinese origin8. However, narrow molecular diversity of botanical material from Xinjiang found that the Chinese P. domestica was likely feral resulting from escape and naturalization9.P. domestica has remained a matter of debate for nearly a century. A major complication is the wide range of intraspecific variations and transitional forms10. Traditionally, plum cultivars have been divided into a number of different pomological groups; small fruited mirabelle plums, damsons, small wild plums or bullaces, greengages, prune plums, and large-fruited European plums 14. It is now generally accepted that P. domestica is most likely an interspecific hybrid of P. cerasifera and P. spinosa. However, defining the precise genetic history of these events has been complicated by the fact that P. cerasifera and P. spinosa are found as diploid and polyploid individuals, sometimes having morphologies very similar to P. domestica. In fact, hybridization experiments and restriction site analysis of Ribosomal RNA genes indicated that P. spinosa itself may be an inter-specific hybrid having P. cerasifera as one of the parents19. Crane and Lawrence20 and Rybin6 performed direct interspecific hybridization experiments between P. cerasifera (2x), and P. spinosa (4x) and found that, while the vast majority of the resulting offspring were infertile, a very small number of fertile hybrids that were morphologically similar to P. domestica could be obtained6. Still, many questions remain unresolved as to the origins of the various pomological plum groups and whether they represent independent inter-specific hybridization events or arose from the same or limited number of events20.Molecular phylogenetic analyses have more recently confirmed the close relationships among P. domestica germplasm across Europe in several geographically distant countries24, in Turkey25 and China26. This approach enabled a reconstruction of parental lineages among related European plum cultivars27 and molecular characterization of “Reine Claude” types in Spain28. Using the allelic variants of the S-RNase gene, groups of cross-compatible and incompatible cultivars were generated for Latvian plums29, and selected cultivars propagated in Hungary and Slovakia31. In several studies cytoplasmic (chloroplast) and nuclear SSR markers were used to reveal genetic diversification within P. domestica and to attempt to resolve its origin. Based on chloroplast markers it was shown that P. cerasifera was a likely progenitor, at least in the maternal lineage32. Two main cpDNA haplotypes were identified, however, major haplotypes were not clearly associated with pomological groups21. Nuclear DNA markers, on the other hand, indicated a potential contribution of P. spinosa to the P. domestica genome21. Unfortunately, the limited number of molecular markers used for characterization of plum germplasm to date, have not yet provided a clear picture of the genetic relationships among Eurasian plum germplasms.More recently, genetic diversity of Eurasian plum varieties and species has been studied using bi-parentally transmitted nuclear and maternal cytoplasmic  markers. Microsatellite (SSR) and Inter-simple Sequence Repeat (ISSR) markers were used for characterization of 34. In the present study, we generated a set of sequence-based SNP markers densely distributed across the Prunus genome in order to: (1) investigate genetic relationships among cultivated plums from different pomological groups that originated under the influence of different ecological factors and were propagated in different geographical regions; (2) estimate the extent of variation among P. domestica germplasm and identify diagnostic molecular markers; and (3) estimate the potential contributions of other plum species P. cerasifera and P. spinosa to the nuclear genome of hexaploid P. domestica. The resulting phylogenetic relationships along with our understanding of the origins, history, and future of plum cultivation are discussed.Restriction site-associated sequencing has been an effective method for identifying and screening high-resolution polymorphisms within and between populations, ecotypes, and speciesP. domestica and other Eurasian Prunus species including the potential P. domestica progenitors P. cerasifera and P. spinosa  raw reads. Of these, 97.8% (2005 mln) reads were retained after filtering for base call accuracy, barcode match, and presence of the Pst1-restriction site (CTGCA/G). After demultiplexing, the average numbers of reads per DNA sample was 4.93 million reads. Only 5.6% (23 out 405 accessions) had fewer than 1 mln reads. All sequences were aligned against the P. persica genome assembly v2.1 for SNP identification. The average depth of stacks for variant calling was 221 and varied from 6 to 778. A total of 178,403 unfiltered sites were identified including monomorphic SNPs across all plum accessions. Filtering for SNPs present in more than 80% of accessions resulted in a dataset of 129,110 SNPs that were evenly distributed throughout the P. persica pseudochromosomes with little or no large-scale bias  genome were also identified. Of these, 14 SNPs were present in more than 80% of accessions Table . After fWe investigated genetic relationships among all 405 plum accessions by performing a hierarchical clustering analysis  on a dissimilarity matrix calculated in R. In total, 103,382 polymorphic SNPs out of 129,110 sites exported from Stacks were used for phylogenetic classification , Mirabelles (MIR), Greengages (GRG), and d’Agen prune plums (DAP) Fig. . Differeon Table . Using tld Table .P. domestica clades are given below.Due to the large number of samples used in this study, a detailed analysis of individual cultivars and their genetic relationships is not reported here. General results from the four distinguishable 36. Other smaller clonal clades consisted of varieties of French origin with mixed but often related names and the so-called “Italian” plums which produce large, oblong shaped fruit  consisting of cultivars named as “Reine Claude” types. These findings suggest that a significant portion of the commercial “Greengage” germplasm is clonal.The name “Greengage” comes from Sir William Gage who supposedly imported these small, often green-fruited types from France in the early 1700sDAP is the most economically important group of plums and includes the vast majority of commercially growth prunes. The DAP clade (144 accessions) was composed of commercial d’Agen prunes from different geographical regions, progeny from self-pollination of the d’Agen prune “Improved French”, cultivars with known d’Agen types in their pedigree and a few unclassified cultivars. Nearly all individuals named as d’Agen types were identified as clones, with the exception of three d’Ente varieties collected from INRA that fell into the EUP clade and d’Ente-jaune which is likely a self-pollinated d’Agen seedling Table . These rP. simonii (native to China), the interspecific hybrid rootstock P. cerasifera × P. munsoniana (‘Marianna 2634’) (note: P. munsoniana is native to North America), several diploid P. cerasifera selections, P. brigantina, and a hybrid of P. cerasifera × P. brigantina. Tetraploid P. spinosa accessions were collected from the U.S., Sweden, and Portugal. Accessions recorded as P. insititia included the rootstocks “Saint Julien” as well as several varieties collected from INRA.Several diploid plum species were examined including accessions of P. simonii formed a distinct out-group and, to a lesser extent, so did the P. cerasifera × P. munsoniana hybrid “Marianna 2624”. In contrast, all P. cerasifera, P. spinosa, and P. brigantina selections displayed relatively short genetic distances to P. domestica. These species did not form discrete clades and three potentially clonal groups were identified  to explore the degree of differentiation between pomological groups and Eurasian plum species. FST estimates the level of reduction in heterozygosity when compared to the expected Hardy–Weinberg equilibrium39. Consistent with random mating, the inbreeding coefficient FIS was low among P. cerasifera and negative within P. domestica pomological groups. The overall fixation index (FST) for P. domestica groups was estimated to be 0.005–0.033 . Overall the FST index was largely in the range of 0.05–0.15 for pairwise comparisons between DAP, GRG, MIR, or EUP vs. potential progenitor species P. cerasifera and P. spinosa. This range represents moderate to low differentiation according to Hartl and Clark40 indicating that P. spinosa is less differentiated from P. domestica groups than P. cerasifera ; suggesting that EUP is less diverged from the P. spinosa ancestor. This difference became more obvious after removing diploid Prunus species as well as the interspecific hybrids “Marianna 2634” and the P. brigantina × P. cerasifera hybrid from the dataset  to estimate the genetic stratification of the germplasm. We compared the patterns of genetic variation using the entire dataset of 129,110 SNPs vs. filtered datasets generated with different minimum minor allelic frequencies (MAF) and missing rates  (data not shown). Optimal resolution of plum species and pomological groups was achieved at MAF = 5% and a call rate >80% (missing rate = 20%) resulting in an optimized set of 24,978 SNPs which were used for further genetic analysis.Principal components analysis (PCA) of the 24,978 SNP dataset identified 32 significant principal components (PCs) . The first six PCs  were selected for classifying/clustering accessions into groups Fig. .Fig. 4PrST statistics, DAP and potential progenitors of hexaploid plum, P. cerasifera and P. spinosa, were well differentiated along the first two principle components PC1 and PC2. Diploid Prunus species (P. cerasifera and P. brigantina) and P. spinosa were differentiated along PC4 (2.5% of variance). Plotting PC1 against PC3 and PC6  gave only weak differentiation of GRG and MIR from EUP. This was in contrast to the relatively strong differentiation observed for DAP under all combinations.A large proportion of the genetic variation remained unstructured. In agreement with pairwise FRandom Forest (RF) is a machine-learning approach that allows classification of individuals into groups and the ranking of the relative importance of each SNP based on classification error. Each SNP is essentially removed one-by-one and the significance of its contribution evaluated. This approach was used to confirm whether the GRG and DAP groups can be effectively discriminated from the rest of the Eurasian plum germplasm. First, we applied the RF algorithm and constructed a multidimensional plot for the dataset composed only of three plum groups—DAP, GRG, and combined plums (cPLM) representing the rest of accessions, i.e., EUP, MIR, autochthonous plums, and related species Fig. . We geneP-value < 0.001 by Monte Carlo permutation procedure, Figure STo extract a subset of the most significant genetic markers for differentiating these groups, we ran a RF analysis for DAP or GRG vs. the rest of the germplasm. After each round of calculations, we extracted the top 20 most significant SNPs and then combined them, retaining only unique marker names. This resulted in a list of 76 significant markers for DAP plums. In a similar manner, we generated a list of the 85 most significant markers for discriminating GRG from other plum groups. Next, we combined these SNPs and verified that they would reproduce the scattering pattern for GRG and DAP on a PCA plot Figures S. Lastly,41. The Permutation test (P < 0.001) confirmed a significant association with a subset of nuclear markers. These markers were scattered along the CCA1-axis, potentially reflecting their simultaneous introgression into plum germplasm along with the T haplotype , so these were removed from the dataset. In total, 371 samples were retained for analysis. A C/T substitution in the large subunit of the RUBISCO gene was found in 29.9% of the accessions, while 61.7% of cultivars had the cp haplotype C. Interestingly, the haplotype T was almost exclusively found in DAP, among which only 6.50% of samples had haplotype C. BLAST searches against available cp genomes in NCBI revealed that this residue is highly conserved among Angiosperms, suggesting the T haplotype is rare (data not shown). To uncover potential associations between nuclear markers and the cp haplotype we conducted Canonical Correspondence Analysis (CCA) using the vegan package in RP. domestica as well as among seedlings derived from self-pollinated cultivars prompted some early authors to propose that subdivision of P. domestica cannot be justified from a genetic point of view43. Based on the analysis of worldwide germplasm presented here, we report clear genetic evidence that at least some pomological groups of P. domestica have distinctive genotypic signatures that can be used for assignment of unknown accessions and paves the way for future genome-enabled prediction of agronomic traits in breeding material.Overlapping distribution of morphological traits among pomological groups of P. domestica germplasm. GRG and DAP formed individual clades on the dendrogram derived from the complete dataset of 129,110 polymorphic SNPs and were readily separated by PCA analysis using a representative dataset of 24,498 SNPs. Accordingly, the highest fixation index between all pomological groups (FST >  0.03) indicated that DAP and GRG are the most differentiated among Eurasian plum germplasm. The relatively strong differentiation of DAP and GRG was also supported by RF analysis, an advanced machine-learning approach44. Recently, successful implementation of RF for site-by-site classification of Atlantic and Chinook salmon species allowed the establishment of a relevant, non-redundant and maximally reduced panel of genetic markers45. Identification of informative DNA markers useful for differentiating populations was also achieved in cattle breeds46 and perennial ryegrass47. This report provides support for the efficiency of RF in analyzing and unmasking NGS datasets.Two pomological groups, DAP and GRG, were clearly separated from each other and from the rest of the We were able to computationally identify two subsets of SNPs that were most informative for assigning unknown accessions to the GRG and DAP groups . Based on the uneven distribution of these SNPs across the genome, we hypothesize that these genomic regions may harbor important genes for fruit type or other agronomic traits that are distinct between the DAP and GRG groups. Historically, these pomological groups were selected for different fruit usage such as drying vs. fresh consumption. In many cases these SNPs were within or proximal to orthologues of proteins known to have key roles in sugar metabolism and transport which could underlie important fruit quality differences. Thus, these SNPs may serve as good markers for screening hybrid material in breeding programs aimed at dried or fresh types of fruits.P. domestica pomological groups, the results revealed relatively small genetic distances between them. Thus, P. domestica was likely derived from a limited set of founders where breeding consisted primarily of self-pollination and/or hybridization among selected siblings. This conclusion was made possible by the inclusion of siblings derived from a self-pollinated “Improved French” individual. This also allowed us to estimate branch length cutoffs to distinguish clonal accessions from siblings. Clonal subclades were identified within all pomological groups and Eurasian plum accessions, consistent with the historical practice of making clonal selections from vegetative buds or sports. Importantly, nearly all commercially grown DAP varieties from France, USA, Argentina, and Australia were found to be clonal, establishing that the plum industry worldwide is predominantly a monoculture. A surprising finding was the unique cp haplotype T that prevailed in DAP plums but was not detected in related Prunus species. This suggests that DAP types were historically not cross hybridized to EUP, GRG, or MIR plums and were derived from a distinct hybridization event. Interestingly, DAP plums are very old varieties as they date to before the 12th century where they were reportedly brought to France directly from Syria37. Thus, their differentiation from other P. domestica cultivars despite the relatively short genetic distances between them raises the possibility that this cp haplotype is a remnant of one of the inter-specific events that gave rise to P. domestica in the Caucasus. Given the lack of evidence for this haplotype in all Eurasian accessions tested, it is possibly derived from a theoretical 3rd Eurasian genomic contributor. Such a possibility is supported by CCA analysis that found sets of nuclear markers were associated with the cp T haplotype. Future genome sequencing of P. domestica and its progenitors will be necessary to confirm these hypotheses.Despite the ability to clearly resolve some P. persica genome for reference assembly of the P. domestica sequences. As shown for Sugarcane and Saccharum Complex, the quality of the reference assembly strongly influences GBS performance in polyploids48. Consequently, we likely missed an opportunity for discovery of polymorphic markers derived from intergenic regions, as well as SNPs that can discriminate between subgenomes (homoeoSNPs). Despite this limitation, the results of our analyses were highly consistent with known genetic relationships and support the robustness of the SNP calling pipeline.The sequence-based genotyping method used here has limitations caused by the lack of a plum reference assembly and the reliance on the P. domestica have been studied and debated for over a century. The wide range of phenotypic diversity observed in P. domestica and its Eurasian relatives along with their overlapping distributions has made resolving these questions particularly difficult. A handful of competing theories have been proposed that are not all mutually exclusive: [1] P. domestica is entirely of P. cerasifera origin and represents an autopolyploid event of this species, [2] P. domestica is a hexaploid of P. spinosa which is a discrete species, [3] P. domestica is an interspecific hybrid between a diploid P. cerasifera and a tetraploid P. spinosa, and [4] P. domestica is a hybrid of interspecific hybrids having a hexaploid chromosome complement composed of P. cerasifera, P. spinosa, and possibly contributions from other Eurasian plum species. The results presented here all but rule out models [1] and [2] but do not definitively resolve between [3] and [4]. Consistent with the prevailing theory proposed by early researchers, our data are most consistent with model [4] whereby one subgenome was contributed by diploid P. cerasifera and the other from a tetraploid P. spinosa that itself is an interspecific hybrid between diploid P. cerasifera and a second, as of yet, unknown Prunus species such as Prunus ramburii Boiss as suggested by Reales et al. in 201032. The endemic species P. ramburii was not included in our study but mixed grouping of P. brigantina and its hybrid P. brigantina × P. cerasifera with P. spinosa and P. cerasifera accessions agrees with results reported by these authors and Shi et al. in 201349. Likewise, the lack of the DAP cp haplotype in the P. cerasifera or P. spinosa accessions tested imply that this haplotype may be derived from a distinct ancestral species. Irrespective of whether a 3rd species contributed to the P. domestica genome, the relatively close genetic distances among the germplasm supports the hypothesis by Eryomine, 1990 that some or all of the interspecific hybridization events that gave rise to P. domestica did not occur naturally but were artificially selected for by early Eurasian societies15. Such a scenario is consistent with the lack of known wild P. domestica, the low genetic diversity across the germplasm reflecting a more recent origin, and the low pollination and fertility rates among experimental interspecific hybrids which would presumably have had tremendous difficulty naturally establishing themselves50. In addition, historical evidence suggesting widespread simultaneous cultivation of P. domestica, P. cerasifera and P. spinosa is consistent with the current state of wild Eurasian germplasm that appears to be comprised by feral populations with relatively low genetic diversity. Whole genome sequencing, accompanied with additional genotyping using a broader range of Eurasian plum germplasm will be necessary to fully resolve these possibilities.Questions regarding the origin of P. domestica held at the germplasm repository at INRA , 66 cultivars from germplasm collections maintained at University of California at Davis and two USDA Agricultural Research Service (ARS) sites—National Clonal Germplasm Repository (NCGR) and the Appalachian Fruit Research Station . Also, 31 accessions representing related Eurasian plum species and cultivated varieties were obtained from Argentina, Australia, Portugal, Sweden, and Ukraine. A complete list of accessions, their origin and pomological characteristics is provided in Supplemental Materials , Mirabelles, primitive forms and autochthonous forms. Finally, 34 genotypes derived from self-pollination of the cultivar “Improved French” maintained at AFRS  were used to aid in distinguishing self-pollinated genotypes from clonal material.Plant material for this study included samples from 251 cultivars and forms of ls Table . ClassifTo verify the reproducibility of genotyping and dendogram construction, we randomly selected and sequenced 23 of the DNA samples twice. Technical replicates were included in analyses under cultivar names followed by “.1” and “.2”. Sixteen sets of biological replicates were also included, consisting of independently extracted DNAs from the same cultivar present in different locations, and listed under cultivar names followed by numbers 1 or 2 without a period.51. the quality and integrity of the DNA was examined using a NanoDrop ND-8000  followed by electrophoresis on 1% agarose gels. Quantification of DNA samples was done using QuantiFluor® dsDNA kit  and a Synergy H1 microplate reader .Genomic DNAs were isolated using either the Qiagen DNeasy DNA extraction kit  following manufacturer’s instruction or a modified CTAB protocol by Kubisiak et al.52 with a few modifications. Briefly, 100 ng of genomic DNAs were digested in the presence of PstI . Custom barcoded adapters were ligated to the PstI-compatible overhang, and samples were pooled and then purified with a commercial PCR purification kit , and finally quantified with a NanoDrop ND-1000. For test-amplification, 46 ng of DNA was amplified with common primers and NEBNext® High-Fidelity 2X PCR Master Mix  as outlined in Elshire et al.52. The total volume of the PCR mix (50 µl) was divided into five tubes (10 µl each). Typically, 12, 14, 16, and 18 cycles were set up for testing against a non-amplified control. DNA fragments were resolved on 1.5% agarose gels against the Hi-Lo™ DNA marker  to select optimal number of cycles for fragment enrichment in the range of 200–700 bp. Final libraries for genotyping-by-Sequencing (GBS) were amplified in two replicates (combined volume 100 µl) and purified using Mag-Bind® Total Pure NGS magnetic beads . Two-step size selection with 0.4× and 0.8× volumes of magnetic beads depleted fragments larger than 1.5 kb and smaller than 121 bp, respectively. Quality checks for GBS libraries were done using a 2100 BioAnalyzer . Fragment size distribution agreed with that predicted from “in silico” digestion of the P. persica reference genome with the Pst1 restriction enzyme (not shown). Amplified GBS libraries were quantified using a Qubit® 2.0 fluorometer  and were submitted for sequencing. Four 96-plexed and one 48-plexed sets of libraries were pair-end sequenced (2 × 125 bp) on a single lane each using the Illumina HiSeq 2500 instrument at the core facilities of the Medical University of South Carolina . Raw reads for all 405 accessions were deposited into NCBI’s SRA database .Libraries for sequencing were constructed following protocols by Elshire et al.53. Briefly, raw paired-end (PE) sequences were de-multiplexed according to barcodes, trimmed to remove adapters and low quality (<phred33) sequences, and filtered for the presence of the PstI restriction sites. Typical success rate was in a range of 95–97%. Using the GSNAP software package54 reads were aligned to the P. persica v 2.1 genome56. An inclusive catalog of tags and SNP genotypes  were generated using the “ref_map.pl” command with default parameters. The SNP genotypes were generated in the population mode of Stacks, recorded in the Variant Call Format (VCF) and filtered using VCFtools57. To simplify analyses and limit errantly called variants, all indels and multi-nucleotide variants were deleted, leaving only bi-allelic SNPs and invariant sites. Individual genotypes were filtered to remove calls supported by less than 5 reads. Only SNPs present in more than 80% of all accessions (330 out of 405) were kept in the dataset for genetic analyses.Data processing and SNP genotyping were performed using Stacks v.1.44-v.1.45P. persica pseudochromosomes was done using the SnpEff 4.3e (build 2016-11-19) variant annotation and effect prediction tool58. The physical positions (bp) of reference-based SNPs were correlated with the GFF file of the peach genome annotation v2.1 deposited at the Genome Database for Rosaceae (GDR)59. The genomic distributions of SNPs across all eight peach chromosomes (pseudomolecules) were plotted individually based on their physical positions (100K screening window) and visualized using the Circos visualization tool60.Structural and functional annotation of SNPs in coding and non-coding regions on the P. persica cp sequence deposited at GenBank (HQ336405.1). The cp-derived polymorphic sites were called using “ref_map” command adjusted for ploidy level 153.The same analytical pipeline was used for isolation of genomic fragments derived from the peach chloroplast (cp) genome. Demultiplexed reads were aligned against the 61 was used to investigate genetic diversity of the plum germplasm. A distance matrix derived from the Euclidian metric was calculated and used in hierarchical cluster analysis. A dendrogram was constructed using the between-group average linkage method, or UPGMA. The confidence interval of the genetic relationships among the accessions was determined by performing 5000 bootstrap replicates. A graphical representation of the resulting tree (in Newick format) was visualized in Geneious R1035. Wright’s fixation index (FST) was used to explore the degree of differentiation between pomological groups and species. FST values and standard errors for all pairwise comparisons were calculated using a “population” mode in Stacks39.The SNPRelate package in R61. The VCF file with genotypic data was converted into the genomic data structure (GDS) file format, filtered for MAF ≥ 0.05 and missing rate ≤0.20 and used for PCA analysis with the snpgdsPCA function. The percentage of explained variation was calculated for the first six PCs used for plotting the genotypes on a two-dimensional scale. All plots were generated using ggplot2 included with the library.Principal component analysis (PCA) was performed using the R package SNPRelate44 was executed using the R package Random Forest with 1000 permutation trees62. The variable  importance was estimate based on the Mean Decrease Accuracy (MDA) metric that measures contribution of individual SNPs to an accuracy of prediction when all other SNPs are constant. Higher MDA values indicate large changes to the prediction power, and hence, the importance of the SNP. In total, 7 output datasets of the most significant SNPs were generated with the “ntree = ” parameter settings of 250, 500, 1000, 2000, 5000, 8000, and 15,000. The distribution of accessions on a PCA plot (with most significant SNPs) and RF plot was compared using the Procrustes technique implemented in the R package vegan 2.4-441. Association between nuclear markers and cp haplotypes was estimated by the CCA using vegan in R. Significance of constrain was estimated with the anova.cca function by permutation test under reduced model (n = 999).The nonparametric tree-based ensemble random forest (RF) analysisSupplemental materialsFigure S1Figure S2Figure S3Figure S4Figure S5Table S1Table S2Table S3Table S4Table S5


Sitobion avenae (Fabricius). We found that clones of S. avenae from western provinces  had significantly higher genetic diversity than those from eastern provinces , suggesting their differentiation between both areas. Based on genetic diversity and distance estimates, biotype 1 clones of eastern provinces showed high genetic divergence from those of western provinces in many cases. Western clones of S. avenae also showed higher genetic divergence among themselves than eastern clones. The Mantel test identified a significant isolation-by-distance (IBD) effect among different geographic populations of S. avenae, providing additional evidence for a critical role of geography in the genetic structure of both S. avenae biotypes. Genetic differentiation  between the two biotypes was low in all provinces except Shaanxi. Surprisingly, in our analyses of molecular variance, non-significant genetic differentiation between both biotypes or between barley and wheat clones of S. avenae was identified, showing little contribution of host-plant associated differentiation to the divergence of both biotypes in this aphid. Thus, it is highly likely that the divergence of the two S. avenae biotypes involved more geographic isolation and selection of some form than host plant affinity. Our study can provide insights into understanding of genetic structure of insect populations and the divergence of insect biotypes.Host plant affinity and geographic distance can play critical roles in the genetic divergence of insect herbivores and evolution of insect biotypes, but their relative importance in the divergence of insect populations is still poorly understood. We used microsatellite markers to test the effects of host plant species and geographic distance on divergence of two biotypes of the English grain aphid,  Acyrthosiphon pisum), Hessian fly (Mayetiola destructor), black currant leaf midge (Dasineura tetensi), and soybean aphid (Aphis glycines) [Many insect species are serious pests on various horticultural plants and agricultural crops. In order to develop ecologically based management programs, it is of great importance to understand the genetic diversity and population structure of insect pests. Genetic diversity and divergence among populations are essential in enabling insect species to respond rapidly to evolutionary challenges, thus having significant impacts on the adaptability of insects under constantly changing environmental conditions ,2,3. Durlycines) ,18,19,20Buchnera, Regiella and Hamiltonella) [A. pisum), greenbug (Schizaphis graminum), and Russian wheat aphid (Diuraphis noxia) [Different insect biotypes can be distinguished by their characteristic response patterns  on different plants or different varieties of the same plant ,22. In ptonella) ,30,31. Atonella) . Indeed,s noxia) ,33,34,35s noxia) ,37,38,39s noxia) ,37,38. Sitobion avenae (Fabricius) (Hemiptera: Aphididae), as a model to address this issue. This aphid can use and survive on various cereal crops and numerous wild grasses [S. avenae were found to be the most common in China [S. avenae biotypes, which can be critical for relevant crop breeding and integrated management programs. Specifically, our objectives are: (1) to examine genetic diversity and structure of the two S. avenae biotypes; (2) to explore genetic differentiation and relationship between both biotypes of S. avenae; and (3) to assess the importance of geography and host plant use in explaining differentiation of S. avenae biotypes.Here, we use the English grain aphid,  grasses ,40,41. M grasses . Biotypein China . Microsain China ,42, hostin China , and hosin China ,44, and in China . In thisS. avenae were collected on wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.) from nine provinces of China (S. avenae samples from more than five wheat or barley fields (cultivars and pest control measures unknown) at the same location. In order to minimize the probability of collecting identical clones, apterous aphid individuals were taken at a distance of >10 m [Apterous adults of of China . We randof >10 m ,46. A miS. avenae  with a temperature program as follows: 94 °C for 2 min, followed by 30 cycles , then 8 cycles , then the last step at 72 °C for 10 min. The length of all PCR products was determined with the automated DNA sequencer ABI3730XL . Genotypes of S. avenae were distinguished with GENCLONE 2.0 [Six microsatellite loci  were used to genotype all collected individuals of . avenae  [7,47,48LONE 2.0 . DetaileS. avenae by using life-history bioassays. All collected S. avenae genotypes were then tested for their response profiles on resistant barley/wheat varieties identified in the above-mentioned screening life-history tests and three susceptible controls . Based on these tests, it was found that five wheat/barley  varieties could be used to distinguish between S. avenae biotypes. Biotype 1 was non-virulent on the five wheat/barley varieties mentioned above, whereas biotype 2 was characterized by its virulence on the barley (Hordeum vulgare L.) cultivar Zaoshu No.3. The identification of biotypes 1 and 2 in these provinces was conducted as described in . BrieflyS) and allelic richness (AR) for biotypes 1 and 2. Using this software, Hardy–Weinberg equilibrium (HWE) was also tested for S. avenae populations. In all the above-mentioned analyses, we used data sets with only a single representative of each multi-locus genotype (MLG) per population following [The software GenAlEx version 6.5 was utilized to analyze the following parameters: number of alleles (Na), number of effective alleles (Ne), observed heterozygosities (Ho), expected heterozygosities (He), and Shannon’s information index (I) . We usedollowing . FST) and its significance with 9999 bootstraps. Effects of isolation by distance (IBD) were evaluated (9999 permutations) with the Mantel test in the program GenAlEx (version 6.5) [STF/(1-STF) and geographic distances between different populations of S. avenae were used. Based on Nei’s standard genetic distance (Ds), we used MEGA 5 to establish a neighbor-joining (NJ) tree of S. avenae clones [S. avenae was calculated by using the microsatellite analyzer (MSA) version 3.15 [S. avenae clones, a principal coordinate analysis (PCoA) was conducted with GenAlEx (version 6.5) [S. avenae biotypes was evaluated with the Bayesian clustering program in STRUCTURE version 2.3.3 [S. aveane clones [The software ARLEQUIN (version 3.5.1.2) was used to conduct the analysis of molecular variance (AMOVA) . The sofion 6.5) , for whie clones . Ds betwion 3.15 . In ordeion 6.5) . The genon 2.3.3 . In thison 2.3.3 ,58. The e clones . Based oe clones .Cones of biotype 1 were distributed over all provinces sampled in this study, whereas those of biotype 2 occurred only in western provinces  . The nump < 0.05) was found for all geographic populations but Anhui and Henan  ranged from 1.6 for biotype 2 collected from Gansu (GS2) to 11.33 for biotype 1 of Qinghai (QH1) , whereasnd Henan .t = 2.542, p = 0.027), indicating higher genetic diversity for biotype 1 compared to biotype 2. A significantly higher I was also detected for barley clones (1.48 ± 0.11) compared with wheat clones (1.20 ± 0.06) . Aphid clones of western provinces (0.76 ± 0.01) showed a higher gene diversity (Hs) than those of eastern provinces (0.69 ± 0.02) . This result might be in part attributed to larger sample size in western provinces than eastern provinces. Biotype 1 on both plants , and biotype 2 on barley showed significant deviation from HWE (Biotype 1 showed a significantly higher I (1.51 ± 0.06) than biotype 2 (1.01 ± 0.17)  between the two biotypes were detected in some cases, such as between AH1 (biotype 1 of Anhui) and GS2 (biotype 2 of Gansu), between HB1 (biotype 1 of Hubei) and GS2, between HN1 (biotype 1 of Henan) and GS2, between ZJ1 (biotype 1 of Zhejiang) and GS2, between GS1 (biotype 1 of Gansu) and SX2 (biotypes 2 of Shaanxi), and between XJ1 (biotype 1 of Xinjiang) and SX2 (FST) between the two biotypes in each province ranged from low to moderate (FST = 0.001–0.078). Moderate differentiation (0.05 < FST ≤ 0.15) between the two biotypes was found only in Shaanxi (FST = 0.078).Based on pairwise  and SX2 . IndicesS. avenae biotypes from nine locations . Another major clade included both biotypes from Xinjiang (XJ1 and XJ2) and Gansu (GS1 and GS2), as well as biotype 1 of Qinghai. Biotype 1 of Shaanxi and biotype 2 of Qinghai formed a third clade. Biotype 2 of Shaanxi alone formed a separate clade.A neighbor-joining tree based on Nei’s standard genetic distance (Ds) was created to show the genetic relationships of both ocations . Clones S. avenae clones fell in the lower right quadrant, and they included biotype 1 of Shaanxi (SX1), and both biotypes of Xinjiang (XJ1 and XJ2), Gansu (GS1 and GS2), and Qinghai (QH1 and QH2). Principal coordinate analyses (PCoA) showed that the first two axes accounted for 76.76% of the total variation (S. avenae in this study could be grouped into two genetic clusters. The majority of S. avenae clones for biotype 1 of Henan (98.0%), Hubei (96.2%), Anhui (95%), Jiangsu (96.6%) and Zhejiang (98.5%) belonged to cluster 1 , indicating a significant effect of IBD (isolation-by-distance) among geographic populations of S. avenae , showing no significant genetic differentiation between biotypes 1 and 2 of S. avenae. Another AMOVA analysis showed that 9.70% of the overall molecular variation could be explained by geographic location (p < 0.001), suggesting significant variation between S. avenae clones from eastern  and western  provinces. AMOVA analyses of the host plant effect showed that it could contribute to only 0.48% (p = 0.950) of the overall molecular variation, suggesting that the overall variation between wheat and barley clones of S. avenae was non-significant in our sampling areas of this study. AMOVA analyses showed that only 0.26% of the overall molecular variation was explained by variation between the two biotypes  of western provinces. Low rates of migration were also found from biotype 1 of eastern provinces to biotype 1 (m = 0.0258) and biotype 2 (m = 0.0823) of western provinces.Substantial gene flow (m) was found between the two biotypes , as well as between eastern and western provinces . In westBayesAss analysis was also used to estimate gene flow between the two biotypes from barley and wheat . SignifiS. avenae clones in different areas of China [S. avenae is still not well understood. In this study, in addition to differences in pairwise FST values, clones of S. avenae from western provinces had significantly higher gene diversity than those from eastern provinces, indicating differentiation between both areas. In addition, based on other genetic parameters like number of effective alleles (Ne), allelic richness (R), and Shannon’s information index (I), clones of western provinces for biotype 1 were also shown to have much higher genetic diversity than those of eastern provinces for the same biotype. It is generally believed that genetic differentiation between populations is low to negligible when FST ≤ 0.05, moderate when 0.05 < FST < 0.15, and high when FST ≥ 0.15 [S. avenae clones from eastern provinces, which were all found to be biotype 1, showed little genetic divergence with pairwise FST values ranging from 0.010 to 0.054. However, some clones from western provinces showed high levels of genetic differentiation among them. For example, the pairwise FST value between XJ1 (biotype 1 of Xinjiang) and SX2 (biotype 2 of Shaanxi) was 0.221, and it was 0.186 between SX2 and GS2 (biotype 2 of Gansu). Clones from western provinces showed more scattered distribution in the PCoA plot than those from eastern provinces, suggesting higher levels of genetic divergence for clones from western provinces. Biotype 1 clones of both Xinjiang and Gansu showed high genetic differentiation from those of all five eastern provinces. A similar pattern was also found between GS2 and biotype 1 clones of eastern provinces. In addition to estimates of genetic diversity and distance , analyses of genetic structure of S. avenae clones and AMOVA  also indicated significant genetic divergence between eastern and western populations of this study. Significant geographic divergence of S. avenae populations could be attributed to limited long-distance dispersal from eastern to western provinces. Indeed, movements of biotype 1 of S. avenae were found to be very limited between eastern and western provinces in both directions. In China, the Qinling Mountains are clearly an important genetic barrier between populations to the east or west of the mountains [p < 0.001), indicating a significant isolation-by-distance (IBD) effect among different geographic populations of S. avenae. Our results indicate that geographic distance can play a significant role in the genetic structure of both S. avenae biotypes.Geographic structure and isolation of populations can play critical roles in the diversification of herbivorous insect populations. Despite that, a few studies have focused on the genetic differentiation of of China ,6,59, thT ≥ 0.15 ,59,60. IS. avenae clones from eastern and western provinces can be closely related to geographic differences in their life cycles. In Europe, a majority of holocyclic clones of this aphid were discovered at locations with harsh winters , but more clones of obligate parthenogenesis were found in places with mild winters  [S. avenae in China could occur in western provinces, but probably not in all the eastern provinces of this study except Jiangsu. Thus, one possible explanation for high genetic differentiation between S. avenae clones from eastern and western provinces in China was that western provinces had lower winter temperatures, which could lead to more occurrences of sexual reproduction  for S. avenae in western provinces than in eastern provinces [S. avenae in both areas might contribute significantly to the abovementioned divergence and biotype development of this aphid. Sexual and asexual clones of S. avenae can be distinguished by rearing them under short day conditions in the lab [S. avenae clones that we did not include in this study [S. avenae in different areas) on the biotype development of S. avenae. High genetic differentiation between  France) ,61. Acco France) , overwinrovinces ,65,66,67 the lab . Furtheris study ,70,71. FS. avenae) was non-virulent on barley, whereas biotype 2 was characterized by its high virulence on barley  [S. aveane clones. In the present study, clones of biotype 1 in western provinces had higher genetic diversity than those of biotype 2 in the same area, based on estimates like number of effective alleles (Ne), allelic richness (R), and Shannon’s information index (I). When all samples were combined, S. avenae biotype 1 showed a higher Shannon’s information index (I) than biotype 2  ,33,34,35hu No.3) , clearlyiotype 2 B. In addS. avenae was not significant in our analyses of molecular variance (AMOVA) with samples from nine provinces . Similarly, substantial gene flow was also found between wheat and barley clones in our previous study [FST values and genetic structure analyses, significant levels of genetic divergence between barley and wheat clones were identified in Jiangsu and Zhejiang in our previous study [S. avenae biotypes. Some studies have proposed several mechanisms that may promote the maintenance of genetic structure of insect geographic populations and evolution of insect biotypes, such as differential composition of secondary endosymbionts, phenotypic plasticity, habitat isolation, and habitat persistence [S. avenae. However, the genetic divergence between the two biotypes  of rovinces . Geneticus study . Thus, gsistence ,30,31,73S. avenae clones is still not well understood. In this study, in terms of comparisons on genetic diversity between groups of S. avenae clones , the results of Nei’s gene diversity (Hs) were different from those of Shannon information index (I). The meaning of these differences is still difficult to interpret [S. avenae, since the plant factor contributed little to the total genetic differentiation of different clones of this aphid. In our previous study limited in eastern provinces, we found evidence for significant differentiation of barley and wheat clones of S. avenae [S. avenae showed significant structure and differentiation between eastern and western populations, and biotype 2 only occurred in western provinces. Therefore, it is very likely that the divergence of the two S. avenae biotypes [S. avenae genetic structure suggests that levels of host plant specialization in this aphid are still low, or that host shift is still recent in the evolutionary history, consistent with our previous life-history studies on this aphid [In summary, host plant use and geographic distance are some of the factors that can influence the genetic divergence of insect herbivores and development of insect biotypes. So far, the relative importance of both factors in the divergence of nterpret ,74,75,76biotypes  involvedis aphid ,15. Alth


Agelaius tricolor) has suffered severe losses during the previous century largely due to anthropogenic impacts on their habitat. Using a dataset composed of a whole genome paired with reduced representation libraries (RAD‐Seq) from samples collected across the species’ range, we find evidence for panmixia using multiple methods, including PCA (no geographic clustering), admixture analyses (ADMIXTURE and TESS conclude K = 1), and comparisons of genetic differentiation (average FST = 0.029). Demographic modeling approaches recovered an ancient decline that had a strong impact on genetic diversity but did not detect any effect from the known recent decline. We also did not detect any evidence for selection, and hence adaptive variation, at any site, either geographic or genomic. These results indicate that species continues to have high vagility across its range despite population decline and habitat loss and should be managed as a single unit.Habitat loss and alteration has driven many species into decline, often to the point of requiring protection and intervention to avert extinction. Genomic data provide the opportunity to inform conservation and recovery efforts with details about vital evolutionary processes with a resolution far beyond that of traditional genetic approaches. The tricolored blackbird ( Using multiple demographic modeling approaches to distinguish between recent and historical events, we assess genetic diversity at multiple temporal scales. Finally, we investigate evidence for local adaptation using outlier and genotype–environment association (GEA) analyses. Our primary objectives are to (a) assess gene flow and genetic diversity, both neutral and adaptive , (a) estimate current and long‐term effective population sizes (Ne), and (c) provide management recommendations based upon our results.22.1n = 1) and, (b) restriction‐site associated sequencing .We obtained tissue samples from breeding tricolored colonies throughout their range Figure . Detaile2.2We prepared DNA for whole genome sequencing using the Illumina TruSeq DNA PCR‐Free LT kit (Illumina). After fragmenting 1 µg of DNA to 400 bp using a Diagenode sonicator and cleaning with magnetic beads at a ratio of 105 µl of beads/79 µl of water to select for >400 bp fragments, bioanalyzer traces were collected by the University of California, Los Angeles GenoSeq Core to verify library quality. We sequenced a final library with fragments averaging ~500 bp using a 250 bp paired‐end run on an Illumina HiSeq2500 at the University of California QB3 Vincent J. Coates Genomics Sequencing Laboratory. Scaffolds were assembled from resulting sequence data via the Discovar DeNovo assembler (Broad Institute), and those <5,000 bp were removed. We used BUSCO  and expected (HE), of detected variants and the inbreeding coefficient (FIS) were estimated with the STACKS POPULATIONS module. We calculated global Tajima's D that is bias‐corrected for missing data and tested for significance with 1,000 simulations in the R package “r2vcftools”  using the POPULATIONS module and tested for isolation by distance (IBD) with a Mantel test.Population structure is in part a consequence of recent gene flow, and hence is indicative both of a species’ natural and recently developed changes in movement and dispersal patterns. Since closely related individuals can bias signatures of population structure and genetic diversity, we used KING , via ∂a∂i and fastsimcoal2, we used an SFS generated via a modification of easySFS (https://github.com/isaacovercast/easySFS) to smooth over missing data and maximize the total number of SNPs using a hypergeometric projection of a dataset filtered to remove loci with >75% heterozygosity. Focusing on historical population size changes for a single population (see results), we compared multiple demographic models: a nested “one epoch” model with no size changes, a “two epoch” model with a single size change, and a “three epoch” model with two size changes  of 4.6 × 10–9  of 60,429,389 bps. This L is based on sites that had at least 190X coverage  across a merged BAM file composed of the individuals that passed quality filters with and with no close relatives or PCA outliers (see results). Meanwhile, g is based upon a robust estimation from another Passerine  for each replicate's parameters was inferred using ∂a∂i, and scaled by L and μ to calculate the ancestral size in diploids (Neanc):In ∂a∂i were then scaled by Neanc, and times were scaled by 2*Neanc*g. After determining the best‐fit model, we used a grid‐search approach to refine the bounds of our inferred parameters. We examined a 100x100 grid of values of nu (contraction size relative to Neanc) and T (contraction duration in terms of 2*Neanc*g) spaced evenly along a log10 scale. We obtained the expected SFS for each of the 10,000 parameter pairs using ∂a∂i and calculated the multinomial log‐likelihood. We then plotted the delta log‐likelihood between each parameter pair and the MLE as a heatmap.Population sizes inferred in Fastsimcoal2 offers an alternative analytical framework for using the SFS for demographic inferences using coalescent simulations. For each of the previously described demographic models, we ran 100,000 coalescent simulations and used 50 Expected/Conditional Maximization (ECM) parameter‐optimization cycles to estimate the expected SFS for each set of parameters.We conducted ABC analyses in a sequential, hypothesis‐testing manner with at least 100,000 simulations of each scenario Figure . First, Estimations of contemporary, short‐term Ne using the LD method are downwardly biased by the presence of overlapping generations , and surface water measurements .The presence of adaptive variants would be critical information for conservation planning. We used both outlier analyses and genotype–environment association (GEA) methods to detect loci potentially under selection. For the former, we used PCAdapt  to maximize quantity and quality  and PCA outliers (N = 11), a dataset with 153 tricoloreds genotyped at an average of 68,366 SNPs was used for analyses except where indicated. Additional data assessments are provided in Figures We obtained 389 samples from throughout the tricolored's breeding range, including many colonies sampled along range periphery Figure . The geny Figure  and remo3.2K = 1—suggesting high gene flow across the species’ range. This pattern is supported by similar levels of heterozygosity  and inbreeding (FIS: −0.012 to −0.002) across sample sites (Table p < .001) and positive , which is a pattern usually attributed to a population expansion after a decline. Average pairwise FST between sampled colonies is quite low at 0.029 (0.02–0.049) and there was no correlation with geographic distance . This along with a lack of significant differentiation anywhere in the examined genome  model in favor of a two‐epoch contraction . Additional size changes did not significantly improve the fit to the data beyond the two‐epoch model . ABC results were similar. The expansion and two‐contraction scenarios were sequentially rejected with high confidence . Specific parameter estimates for the two‐epoch model from the grid search in ∂a∂i suggest ~50% population size decline occurred 21,317  g/ago from an Neanc of 174,455  to a Nelt of 91,315  results in Nelt and g since the contraction are 10X as large; meanwhile, a higher rate such as that used for the rhinoceros hornbill  results in estimations that are 50% smaller . Meanwhile, gradient forest analyses revealed both average r2 and numbers of correlated SNPs from randomized environmental datasets were higher than those detected from the empirical data  is less well resolved than most others in an Icteridae phylogeny  and recent Ne  estimated here are surprisingly low given the 400,000 birds reported in a 2008 census Kelsey, . Long‐te4.3While full whole genome sequences are indisputably better for assessing subtle genetic variation patterns, our results illustrate no major selective sweeps affecting large regions of the tricolored genome. It is notable that such evidence has been reported with less sequencing effort in other species with much larger genomes  were generally inconclusive about gene flow in the species, reporting at the same time a lack of genetic differentiation but also differences in genetic diversity among sample sites. Our data allow us to conclude that tricoloreds may be managed without concern for gene flow, directed preservation of unique genetic variation, or a focus on recovery of any particular local aggregation anywhere in their range. Genetic diversity, while seemingly low overall, is homogenous across breeding colonies. These results indicate the species as a whole may justifiably be considered a single management unit.FIS , agreement #2017‐073–03.None declared.KRB, ACB, PK, RAB, KR, and TBS helped design the research and write the paper. RAB designed the bioinformatic pipeline for processing raw data, and both assembled and assessed the quality of the de novo genome. KRB and JR conducted fieldwork and laboratory work. ACB and PK ran SFS inferences. KRB completed all remaining analyses.Supplementary MaterialClick here for additional data file.