ST statistics computed among different categorical variables showed little genetic differentiation (< 0.05) among breeding periods and breeding programs, but a diverse level of genetic differentiation among wheat classes and predicted groups. Diversity indices were the highest and lowest among cultivars registered from 1970 to 1980 and from 2011 to 2018, respectively. Using two outlier detection methods, we identified from 524 to 2314 SNPs and 41 selective sweeps of which some are close to genes with known phenotype, including plant height, photoperiodism, vernalization, gluten strength, and disease resistance.Previous molecular characterization studies conducted in Canadian wheat cultivars shed some light on the impact of plant breeding on genetic diversity, but the number of varieties and markers used was small. Here, we used 28,798 markers of the wheat 90K single nucleotide polymorphisms to (a) assess the extent of genetic diversity, relationship, population structure, and divergence among 174 historical and modern Canadian spring wheat varieties registered from 1905 to 2018 and 22 unregistered lines (hereinafter referred to as cultivars), and (b) identify genomic regions that had undergone selection. About 91% of the pairs of cultivars differed by 20–40% of the scored alleles, but only 7% of the pairs had kinship coefficients of < 0.250, suggesting the presence of a high proportion of redundancy in allelic composition. Although the 196 cultivars represented eight wheat classes, our results from phylogenetic, principal component, and the model-based population structure analyses revealed three groups, with no clear structure among most wheat classes, breeding programs, and breeding periods. F Hexaploid spring wheat (Triticum aestivum L.) is the dominant type of wheat accounting for ~ 72% of the total wheat production in 2020 in the country, followed by 23% of durum wheat [Triticum turgidum L. ssp. durum (Desf.) Husn.], and 5% of winter wheat (https://www150.statcan.gc.ca/). A total of 363 spring wheat varieties (cultivars) have been registered in Canada by 34 representative institutions and companies (https://inspection.canada.ca/active/netapp/regvar/regvar_lookupe.aspx) of which 260 spring wheat varieties had regional registration, including 172 registered for production in the British Columbia and the three prairies provinces. Currently, spring wheat varieties in Western Canada have been classified into eight wheat (market) classes based on grain characteristics , baking quality (dough/gluten strength), milling quality, grain protein concentration, and their end-use . The eight classes are Canada Northern Hard Red (CNHR), Canada Prairie Spring Red (CPSR), Canada Prairie Spring White (CPSW), Canada Western Extra Strong (CWES), Canada Western Hard White Spring (CWHWS), Canada Western Red Spring (CWRS), Canada Western Soft White Spring (CWSWS), and Canada Western Special Purpose (CWSP). Several varieties have been reclassified recently, which could have a significant financial impact on seed growers who have been multiplying pedigreed stocks to sell them as certified seed. We were interested to assess the extent of genetic differentiation (divergence) among these wheat classes, which forms one of the bases of this study.Canada is one of the top ten wheat producers and exporters globally with nearly 92% of its wheat produced in the three prairies provinces of Alberta, Saskatchewan, and Manitoba  generated through different genotyping platforms10. Results of the various molecular diversity studies shed some light on the impact of plant breeding on crops’ genetic diversity, but the results are inconsistent with the general perception that modern plant breeding reduces crop genetic diversity. Some studies reported a wider genetic base and an increase in genetic diversity11, while others reported a narrow genetic base and reduced genetic diversity in most modern varieties13. For example, researchers at Wageningen University studied the genetic diversity of 90 tomato varieties introduced in the Netherlands from 1950 to 2016 using 7661 polymorphic SNPs. The authors found out that the current genetic diversity among the tomato varieties was eight times higher than those varieties grown from 1950 to 1960. The main reason cited by the authors was the introgression of many new disease resistance genes from wild relatives in modern varieties, which has occurred across the whole tomato genome9. Using meta-analyses of 44 published papers on genetic diversity of eight crops (of which 25 papers were in wheat), researchers at the Wageningen University have also demonstrated the lack of substantial reduction in genetic diversity of crop varieties released since the 1970s11. However, the authors reported a significant reduction in genetic diversity between 1950 and 1960 as have also been reported in 75 Nordic spring wheat cultivars adopted by farmers from 1901 to 19932 and 511 winter wheat varieties that were widely grown in Central and Northern Europe between the 1940s and 1990s3. The study in the Nordic spring wheat cultivars demonstrated an increase in genetic diversity from 1901 to 1940 and again from 1960 onwards, but a loss of diversity in between the two periods.The impact of plant breeding on the genetic diversity of crops has been studied using diverse types of molecular markers, including simple sequence repeats (SSRs) or microsatellites12 and then 370 SSRs14. The authors found a significant reduction in allelic diversity in every part of the wheat genome associated with long-term wheat improvement, but the extent of reduction in diversity differed based on the breeding periods and breeding programs. They also reported that selection for some traits, such as early maturity introduced more new alleles, but other traits resulted in the loss of alleles1. However, the number of varieties and markers used in their studies was small, which forms another basis in this study. Positive selection increases the frequency of beneficial alleles to reach fixation in the population, which leads to reduction of genetic variation among nucleotide sequences that are near the selected genome known as selective sweeps17. Such events increase the fitness of the individuals carrying it but reduce overall genetic diversity at the specific regions that have undergone selection20. Detecting traces of positive selection in the genome has been achieved by searching for loci that show reduced genetic variation, a shift in the site frequency spectrum, and particular linkage disequilibrium (LD) patterns in a particular region21. Different studies used a wide range of statistical methods of varying complexity to identify loci and genomic regions that have undergone selection, including the F-statistics based hierarchical island model in Arlequin22, the total variance FST-based outlier detection method in Lositan23, the Bayesian genome scan method in BayeScan24, RAiSD15, SweeD25, SweepFinder26, SweepFinder227, and OmegaPlus28. The first three methods  identify loci that have undergone selection by computing summary statistics on individual markers as compared with the other five methods that utilize advanced algorithms to scan the whole genome using physical information. The objectives of this study were, therefore, to (i) assess the molecular diversity, population structure, and genetic relationship among historical and modern Canadian spring wheat cultivars; (ii) compare the extent of molecular diversity indices and genetic differentiation (divergence) among different categorical variables , and (iii) detect SNPs and genomic regions that have undergone selection using two contrasting outlier detection methods and explore if some of those regions are physically close to known genes that regulate phenotypic traits.In Canada, Agriculture and Agri-Food Canada (AAFC) researchers conducted multiple studies to assess the changes in the genetic diversity of 75 Canadian hard red wheat cultivars released from 1845 to 2004 using 31 SSR and 37 expressed sequence tags30. Briefly, the cultivars were genotyped at the University of Saskatchewan wheat genomics lab, Saskatoon, Canada, with the wheat 90K iSelect array, which consisted of a total of 81,587 SNPs31. We filtered the genotype data using a minor allele frequency (MAF) of 0.01, maximum heterozygosity of 50%, and missing data points of 20%, which resulted in 28,798 polymorphic SNPs for statistical analyses  were retrieved as described elsewhere33 and the start position was used to represent the physical position of each marker. Some of the SNPs did not either return hit or returned ambiguous hits that didn’t meet the blast search criteria; these SNPs were considered as unmapped.We used a total of 196 genotypes consisting of 174 spring wheat varieties (cultivars) registered in western Canada between 1905 and 2018 (174) and 22 unregistered lines (Supplementary Table es Table . For eac36. Population structure was examined using the Bayesian model-based methods implemented in STRUCTURE v2.3.437. STRUCTURE was run by varying the number of clusters (K) between two and eight using an admixture model, a burn-in, and a Markov Chain Monte Carlo cycle of 50,000 for three replications. We used STRUCTURE HARVESTER38 for identifying the optimal number of clusters (groups) based on the ΔK method39. Cultivars with a probability of membership of ≥ 60% were assigned to the same group, while those with < 60% probability in any cluster were assigned to a ‘‘mixed’’ group36. We used Structure Plot40 v2 to render STRUCTURE bar plots. We assessed the extent of genetic differentiation among the different categorical variables (see below) using an analysis of molecular variance41 implemented in Arlequin version 3.5.2.222. We also computed fixation index (FST)-based pairwise genetic distance matrices42 across the different categorical variables using Arlequin. FST values are indicative of the extent of genetic divergence (differentiation) among groups, with < 0.05, 0.05–0.15, 0.15–0.25, and > 0.25 indicating little, moderate, great, and very great genetic differentiation43. We also computed different diversity indices among different categorical variables using the Tajima’s neutrality test44 implemented in Molecular Evolutionary Genetics Analysis (MEGA) X45, which included the number of polymorphic (segregating) sites (S), the proportion of polymorphic sites (Ps), Theta (θ), and nucleotide diversity (π) as described in the previous studies46.Most statistical analyses were performed as described in previous studies4 and the US10. The first period represents the cultivation of primarily introduced and old cultivars before the 1980s, followed by the second period that consisted of cultivars developed by breeders in Canada using conventional breeding methods between 1981 and 2000. The third period (2001–2010) corresponds to the first era of molecular breeding using low-density, low throughput, and laborious molecular markers, while the fourth period (2011–2018) corresponds to the availability of genotyping by sequencing technologies, which generates relatively low cost, high throughput, and high-density molecular markers for germplasm characterization and molecular breeding. One hundred seventy-four of the 196 cultivars were also assigned into six breeding programs based on the institution (companies) that registered/developed them, which included Agriculture and Agri-Food Canada (74 cultivars), University of Saskatchewan (29), University of Alberta (19), Secan Association (30), Syngenta Canada Inc. (14), and Nutrien AG Solutions Inc. (8). The remaining 22 cultivars came from ten breeding programs (institutions) and were assigned to “others” due to their small sample size. We computed relative kinship using TASSEL v4.2.0 and both identity by state (IBS)-based genetic distance and principal component analysis using TASSEL v5.2.7247. We constructed phylogenetic trees from the IBS-based distance matrices using the neighbor-joining method implemented in MEGA X45. The first three principal components from PCA were plotted for visual examination of the population structure in CurlyWhirly v1.19.09.04  using the different categorical variables.We used multiple categorical variables for comparisons, including predicted group membership based on the model-based STRUCTURE, wheat classes effective as of 1st August 2021, representative breeding programs (institutions) that developed or registered the cultivars, and the breeding periods (year of registration or development) of the cultivars. We first divided the breeding periods into six corresponding to the pre-1970s, 1971–1980, 1981–1990, 1991–2000, 2001–2010, and 2011–2018. We also tried to assess the extent of genetic variation and divergence across four breeding periods by taking into account some of the rationale used in previous similar studies in CanadaST and heterozygosity under a hierarchical island model implemented in Arlequin22 using the different categorical variables. Markers were declared under selection by examining the distribution of FST values that are significantly (p < 0.05) different among categorical variables and greater than a threshold (target mean FST). Second, we used RAiSD15 to detect selective sweeps that may have undergone selection without a priori knowledge as described in a previous study15. We used a chromosome-wise threshold score to select the top outliers as candidate selective sweeps, which account for about 0.1% of the initial number of markers used for analysis per chromosome. The threshold values for declaring selective sweeps varied from 6 on chromosome 1D and 4D to 35 on 3B. The start and end physical positions of each candidate selective sweep region were used to search for candidate genes at Ensembl Plants using Triticum aestivum database (https://plants.ensembl.org/index.html). The gene ID retrieved from Ensembl Plants was then used to search for predicted gene functions at the Triticeae Toolbox (https://triticeaetoolbox.org/wheat/). We computed the squared regression coefficient (r2) as a measure of LD between pairs of genome-wide SNPs and outlier SNPs identified by the hierarchical island model using a sliding window size of 35 markers in TASSEL v5.2.74. Locally Weighted Scatterplot Smoothing (LOESS) curves were fitted by plotting r2 values on the Y-axis against physical distance in mega base pairs on the X-axis using JMP v16 (https://www.jmp.com/). Chromosome-wise r2 values were visualized using both TASSEL.To detect loci that may have undergone selection, we first examined the joint distribution of FOf the 81,587 SNPs used for genotyping the association mapping panel, only 35.3% were used in the final analysis, each with a minor allele frequency of ≥ 0.01; other SNPs were either monomorphic or had > 20% missing data. The number of polymorphic SNPs per chromosome varied from 323 on chromosome 4D to 2319 on 2B Table .We used the model-based population structure and PCA to determine how cultivars tended to cluster into groups. The LnP(D) sharply increased between K = 1 and K = 4, and mostly reached a plateau between K = 5 and K = 8, while the ad hoc statistic ∆K declined between K = 2 and K = 4, suggesting up to four possible groups  among breeding programs and breeding periods  and the eight wheat classes (0.022–0.451). Of the 28 pairwise comparisons of FST values among the eight wheat classes, FST was the smallest (0.022) between CNHR and CWRS and the largest (0.451) between CPSW and CWHWS. The extent of genetic differentiation (divergence) among predicted groups and wheat classes is also very evident in the PCA plots , and nucleotide diversity (π) values computed for the different categorical variables. When all 196 cultivars were used for analyses, Ps, θ and π were 1.0, 0.171, and 0.267, respectively. We then compared these three diversity indices by assigning cultivars into different categorical variables. Of the 28,798 SNPs that were polymorphic across the 196 cultivars, the proportion of segregating SNP among the eight wheat classes varied from 39.7% in the CWHWS to 91.1% in the CWRS, with an overall average of 69.1%. θ and π values across the eight wheat classes varied from 0.162 to 0.278 and from 0.158 to 0.297, respectively. For both parameters, the lowest and highest values were observed within the CWHWS and CWSP classes, respectively. Varieties registered between 1971–1980 had similar θ and π values. Both parameters decreased since the 1980s until they reached the lowest among cultivars registered from 2011 to 2018  with the lowest polymorphism but the highest θ.Supplementary Table 018 Fig. . Howeverhttps://www.secan.com/) and 15% of them were from the AAFC.Kinship coefficients between pairs of the 196 cultivars varied from 0 between Springfield and AC Elsa to 1.97 between CDC NRG003 and AC Vista , with an average of 0.62  different FST values among the three predicted groups, eight wheat classes, and six breeding periods, respectively (Supplementary Table Gli-A3 (at 3.85–4.03 Mb), Pm3b (4.49 Mb), Glu-A3 , Yr10 (6.16 Mb) and Glu-A1 (510.21 Mb) on chromosome 1A; Gli-B1 (4.97–5.00 Mb) and Glu-B3 (6.89 Mb) on 1B; Glu-D1 (414.59 Mb) on 1D; Ppd-A1 (41.5 Mb) and Ptr ToxA (185.12 Mb) on 2A; Ppd-B1 (63.35–63.47 Mb) and Glu1B (782.5–783.2 Mb) on 2B; Ppd-D1 (at 36.21 Mb) and GLU1C (648.24 Mb) on 2D; Rht-B1 (30.86–33.62 Mb) on 4B; Vrn-A1 (587.41–590.40 Mb) and Vrn-A2 (699.47–700.48 Mb) on 5A; Vrn-B1 (573.80–577.20 Mb) on 5B, and Vrn-D1 (467.18–470.05 Mb) on 5D . The physical position of Rht-B1 differed depending on the version of the physical maps, which corresponds to 30.86 Mb on the IWGSC RefSeq v1.0 and at 33.62 Mb on RefSeq v2.0. A selective sweep on chromosome 5A was located from 573.28 to 574.05 Mb and harbors eleven candidate genes, including NPH3 domain-containing protein (TraesCS5A02G375500), AA_permease_C domain-containing protein (TraesCS5A02G375600), and Aspartic peptidase (TraesCS5A02G376300). The second selective sweep on 5A was located between 697.82 and 705.03 Mb, which harbors 113 candidate genes, including the vernalization response Vrn-A2 gene at 700.48 Mb. On chromosome 5B, we identified three selective sweeps located from 87.30 to 99.11 Mb, from 221.41 to 222.50 Mb, and from 255.48 to 265.61 Mb, which harbors seventy, six, and forty-nine candidate genes, respectively. Protein kinase domain-containing protein (TraesCS5B02G079200 and TraesCS5B02G080100), F-box domain-containing protein (TraesCS5B02G079500 and TraesCS5B02G079700), Dirigent protein (TraesCS5B02G080000), Hexosyltransferase (TraesCS5B02G080300), and RNA helicase (TraesCS5B02G080500) are among some of the candidate genes that fell within the 87.30–99.11 Mb interval. The selective sweep on 5B located between 221.41 and 222.50 Mb harbors six candidate genes, including acyl-[acyl-carrier-protein] desaturase (TraesCS5B02G123200), and Glycos transferase domain-containing protein (TraesCS5B02G123400).One of the selective sweeps on chromosome 4B is located between 30.3 and 34.2 Mb and harbors a cluster of 44 candidate genes, including One of the selective sweeps on chromosome 6A (6A:107534467-107781133) spans 0.25 Mb and harbors three candidate genes, including peroxidase (TraesCS6A02G136500) that plays a role in response to environmental stress, oxidation of toxic reductants and their removal, biosynthesis, and degradation of lignin, suberization, and auxin catabolism. On chromosome 7A, we detected three selective sweeps of which one region (7A:361090422-371085384) harbors ten candidate genes. Some of the candidate genes include Superoxide dismutase (TraesCS7A02G292100) that destroys radicals that are produced within the cells and toxic to biological systems, an intracellular protein transport (TraesCS7A02G292400) that is required for vesicular transport between the endoplasmic reticulum and the Golgi apparatus, transmembrane ascorbate ferrireductase 2 (TraesCS7A02G292700), and protein kinase domain-containing protein (TraesCS7A02G292800). The other selective sweep on 7A spans 2.13 Mb and harbors four candidate genes, including Diacylglycerol kinase (TraesCS7A02G297200), chloramphenicol acetyltransferase-like domain (TraesCS7A02G297300). On chromosome 7D, we identified two selective sweep regions at 311.16–329.14 Mb and 347.69–351.95 Mb, which harbored 21 and 7 candidate genes, respectively. The latter region consisted of P-loop containing nucleoside triphosphate hydrolase (TraesCS7D02G291100), a protein associated with somatic embryogenesis receptor kinases (TraesCS7D02G291200) that play roles in brassinosteroid signaling and regulation of plant architecture, KH type-2 domain-containing protein (TraesCS7D02G291300), P-loop containing nucleoside triphosphate hydrolase (TraesCS7D02G291500), diacylglycerol kinase (TraesCS7D02G291600), and chloramphenicol acetyltransferase-like domain superfamily (TraesCS7D02G291900).2 ≥ 0.19, which is the overall mean of all pairs. The latter includes 80 and 253,609 inter-and intra-chromosomal LD pairs, respectively. Genome-wide LD declined to 0.19 within 10 Mb , < 0.25 for half-sib, 0.5–1.0 for full-sib and > 1.0 highly similar cultivars. We found only ten pairs of cultivars with kinship coefficients below 0.05, 7.1% of the pairs with kinship values ranging from 0.05 to 0.25, 34.6% with kinship ranging from 0.251 to 0.500, and the remaining 58.3% of the pairs with greater than 0.50 kinship , USA (133), and Mexico 90), the former authors reported lower genetic diversity and high coefficients of parentage among cultivars belonging to the CWRS class, which is comparable to a group of full sibs or sister cultivars. One of the reasons cited by the authors was the adoption of a rigid classification and quality standard, which precluded breeders from incorporating diverse sources of genetic diversity using different breeding methods other than the backcrossing schemes.Knowledge of relative kinship coefficients and population structure is essential for multiple purposes, including understanding the degree of relatedness in the germplasm, monitoring the pedigree structure of progenies, correctly mapping the association between molecular markers and phenotypic traits, and assessing the accuracy of different genomic selection modelship Fig. . Hence, 55 and AAC Brandon56 differed by just 1.1% of the alleles of the 28,798 SNPs (Supplementary Table 56. AC Cora showed high kinship coefficients (1.77–1.89) and very low genetic distance (0.023–0.049) with Katepwa, Benito, AC Michael, AC Minto, Katepwa, Kenyon, and Alikat. These eight cultivars are linked by common parents. AC Cora and AC Minto were derived from Katepwa/RL450957 and Columbus/BW63//Katepwa/BW55258, respectively, with Katepwa selected from Neepawa*6lRL2938/3/Neepawa*6//CI8154/2 *Frocor59. AC Michael, Kenyon, and Columbus were selected from Park/Neepawa60, Neepawa*5/Buck Manantial61, and Neepawa*6/RL 413162, respectively.We compared the kinship and genetic distance values for a few representative spring wheat varieties. AAC Elie63. The kinship value between Carberry and Superb was 1.52  is responsible for the registration of new varieties (cultivars) and demands the candidates possess a combination of 30-40 target phenotypic traits depending on the market class . To avoid redundant registration of highly similar cultivars, we strongly suggest to include molecular markers data as part of the application package for the registration of new wheat cultivars in Canada.Carberry is a doubled haploid cultivar derived from a cross between Alsen and Superb29, but we were not able to explain why the CWRS and CNHR cultivars were divided into 2–3 subgroups. Although individuals that cluster together to the same group likely shared ancestral cultivars in their pedigree, different studies have reported the lack of clear patterns of relationship based on wheat classes67.Using phylogenetic analysis, PCA, and the model-based structure analyses, we examined the population structure of the cultivars across breeding periods, breeding programs, and wheat classes. All these methods revealed three major groups Fig. . CultivaST values showed very great genetic differentiation among wheat classes with only CWRS and CNHR classes showing very little genetic differentiation (FST < 0.05), which suggests a close relationship between these two classes. The moderate to a very great level of genetic differentiation among most pairs of the wheat classes  and abiotic  stresses75.The inconsistent results reported in the literature regarding the impact of plant breeding on the genetic diversity of crops can be caused by several factors, including technical limitations associated with genetic diversity assessment, sample size, the type of molecular marker , marker density and genome coverage, and the level of genetic purity of the germplasm. Many breeders still struggle to balance the level of heterogeneity in the germplasm  vis-a-vis the need in developing genetically uniform (pure) germplasm to meet the variety registration/release requirements and retaining end-use quality traits. The development of genetically pure cultivars with heterogeneity of < 1% would help breeders not only meeting the distinctness, stability, and uniformity requirementsST-statistics based outlier detection method and a priori known categorical variables , we identified from 524 to 2314 SNPs showing evidence of selection  and Glu-3 loci 85, and gliadin Gli-B1, Gli-A387 genes. Similar results have been reported in other studies that identified several candidate selection regions containing genes that regulate diverse traits, including flowering time7.Using the FRht-B1 and Vrn-A2 were common among the known genes identified using both the FST-based and RAiSD outlier detection methods. The F-statistics method detects loci under selection by examining the joint distribution of FST values and heterozygosity under a hierarchical island model without using any marker position. On the other hand, the RAiSD method computes μ statistics using a fixed-size SNP window for a significant reduction in genetic diversity and is highly dependent on marker density and physical information. It relies on three factors that account for the expected reduction of variation in the region of a sweep, the shift in the site frequency spectrum toward low- and high-frequency derived variants, and high LD on each side of a beneficial mutation and low LD between loci that are located on different sides of the beneficial allele15. Hence, results among methods differ substantially, which have been described in different studies90. Using the total variance FST-based outlier detection method in Lositan23, the hierarchical island model in Arlequin22, the Bayesian genome scan method implemented in BayeScan24, N'Diaye et al.88 identified 403, 397, and 144 outliers out of the initial 4235 SNPs; however, only four SNPs were common in all three methods and 397 SNPs were common between Lositan and Arlequin. Crisci et al.91 and Alachiotis and Pavlidis15 compared SweepFinder, SweeD, and OmegaPlus and found highly variable results among these methods. Multiple factors affect the identification of SNPs and genomic regions under selection, including genotyping errors, marker density, allele frequencies, population structure, variation in mutation rate, and sensitivity to both false positives (Type I) and false negatives (Type II) error rates89. Combining results from two or more outlier detection methods could minimize the false positives and improve the reliability of the identified regions92. In our study, we think the outlier detection system based on the FST-based hierarchical model is more convincing than the RAiSD, but the former method has been frequently criticized for its high Type I and Type II error rates93.Using selective sweep analysis in RAiSD, we uncovered 41 candidate selective sweep regions across all the 21 wheat chromosomes, with each region harboring between one and one hundred thirteen candidate genes Table . OverallPpd-A1, Ppd-B1, and Ppd-D1) and vernalization , glutenin strength , gliadin , stripe rust resistance due to Yr10, powdery mildew resistance due to Pm3b, and insensitivity to Ptr ToxA host selective toxin. Further studies are needed to understand if the same regions would be detected using genomewide association studies and to identify haplotype blocks and their effect on different phenotypic traits. Our results provide valuable information and baseline data in spring wheat genetics and breeding studies, including understanding the degree of relatedness in the germplasm, selecting parents for new breeding starts, and mapping the association between molecular markers and phenotypic traits.We characterized the extent of molecular variation, population structure, and genetic differentiation of diverse historical and modern Canadian spring wheat cultivars and identified genomic regions that have undergone selection. Overall, the spring wheat cultivars were clustered into three groups with no distinct patterns of clustering across most wheat classes, breeding programs, and breeding periods. We observed a continuous decline in the genetic diversity indices since the 1980s until it reached its lowest estimates within cultivars registered from 2011 and 2018. Using two outlier detection methods, we identified several SNPs and/or selective sweeps that were physical close to known genes that regulate response to photoperiod (Supplementary Figures.Supplementary Table S1.Supplementary Table S2.Supplementary Table S3.Supplementary Table S4.Supplementary Table S5.Supplementary Table S6.Supplementary Table S7.Supplementary Table S8.


The aim of this study was to estimate genetic parameters for superovulatory response traits in order to explore the possibility of genetic improvement in Japanese Black cows. We analyzed 19 155 records of the total number of embryos and oocytes (TNE) and the number of good embryos (NGE) collected from 1532 donor cows between 2008 and 2018. A two-trait repeatability animal model analysis was performed for both. Because records of TNE and NGE did not follow a normal distribution, the records were analyzed following no, logarithmic, or Anscombe transformation. Without transformation, the heritability estimates were 0.26 for TNE and 0.17 for NGE. With logarithmic transformation, they were 0.22 for TNE and 0.18 for NGE. With Anscombe transformation, they were 0.26 for TNE and 0.18 for NGE. All analyses gave similar genetic correlations between TNE and NGE, ranging from 0.60 to 0.71. Spearman’s rank correlation coefficient between breeding values of cows with more than 10 records was ≥0.95 with both transformations. Thus, the genetic improvement of TNE and NGE of donor cows could be possible in Japanese Black cattle. TNE was defined as the sum of the number of embryos and unfertilized oocytes collected in a single flush; NGE was the number of embryos morphologically classified as grade 1 according to the International Embryo Transfer Society criteria . We analAfter their first calving, all cows received superovulation treatment every ≥70 d (81.4 ± 27.2 d in average). As a basic program, first, a total of 20 AU of FSH  was administered intramuscularly in the neck twice a day for 3 d. At the fifth treatment, PGF2α  was administered. The day after estrus was observed, cows were inseminated artificially. One week later, embryos were collected by washing of the uterine horns. Because embryo production performance from the same cow declines with the number of collections performed , the dosAs the distributions of TNE and NGE differed from normal distribution , logaritThe following two-trait repeatability animal model was used to estimate genetic parameters:i is the vector of phenotypic records ; ei are the vectors of fixed effects , breeding values of cows, permanent environmental effects of cows, and errors; i, i; i; A is the additive genetic relationship matrix constructed from pedigree information of 3,521 individuals; and I is the identity matrix. As preliminary check, we confirmed the significance of all the fixed effects in the model (P < 0.0001) by least squares analysis. The effect of service sire was not included because previous studies reported the impact of service sire was estimated to be negligible (where ygligible . We did −12). The SEs of the estimated heritability, repeatability, and genetic correlation were calculated by using the se_cover_function option  for cow i, and i.where Bos taurus and Bos indicus breeds, but they did not describe the treatment conditions. Genetic parameter estimates of TNE and NGE and their standard errors (SE) are listed in The estimated genetic correlation between TNE and NGE was 0.71 without transformation. With logarithmic transformation, heritability estimates were 0.22 for TNE and 0.18 for NGE, and repeatability estimates were 0.32 and 0.25 . With Ann = 805) with ≥10 records are listed in In this study, genetic parameters of TNE and NGE were estimated as traits relating to response to superovulation treatments, the first such report in Japanese Black cattle. The heritabilities of both were moderate and the genetic correlation between them was positive and high. Both had higher heritabilities than other female reproductive traits such as calving interval, non-return rate, and conception rate , and theThe data analyzed here differed from those in previous studies in terms of the greater number of records. The average number of repeated records per cow in previous studies was 1 to 3 , whereasThe superovulatory response decreased with age . The proWe estimated genetic parameters for TNE and NGE in Japanese Black cattle. Estimated heritabilities were moderate and estimated genetic correlation was high. The estimates of heritability and genetic correlation differed little between untransformed and transformed data. The values of rank correlation coefficients of EBVs between with and without transformation were very high. These results suggest that genetic improvement of both by selection is possible in Japanese Black cows.


We performed SNP genotyping of indigenous goats of Mongolia to explore their demographic history in the global context and to estimate their genetic risks. Recently, these risks have become a subject of concern due to recent climatic disasters and uncontrolled massive breeding. Various clustering methods demonstrated close genetic relations among Mongolian, Russian, Chinese, and West Asian breeds. Mongolian goats themselves exhibited low to moderate estimates of genetic differentiation. We identified genetic features highlighting the distinct origin and breeding history of Mongolian goat breeds, as well as traces of artificial selection and geographic isolation. However, none of them met formal criteria to be considered as endangered.Mongolian goats are of great interest for studying ancient migration routes and domestication, and also represent a good model of adaptability to harsh environments. Recent climatic disasters and uncontrolled massive breeding endangered the valuable genetic resources of Mongolian goats and raised the question of their conservation status. Meanwhile, Mongolian goats have never been studied on genomic scale. We used Illumina Goat SNP50 to estimate genetic risks in five Mongolian goat breeds  and explored phylogenic relationships among these populations and in the context of other breeds. Various clustering methods showed that Mongolian goats grouped with other Asian breeds and were especially close to some neighboring Russian and Chinese breeds. The Buural breed showed the lowest estimates of inbreeding and exhibited the shortest genetic distances within the other Mongolian breeds, especially, to Ulgii Red and Gobi GS. These three breeds formed a single core group, being weakly differentiated from each other. Among them, Gobi GS displayed obvious signs of inbreeding probably resulted from artificial selection pressure. Dorgon and especially Erchim goats stand apart from the other Mongolian breeds according to various types of analyses, and bear unique features pointing to different breeding histories or distinct origins of these breeds. All populations showed strong decline in effective population size. However, none of them met formal criteria to be considered as endangered breeds. The SNP data obtained in this study improved the knowledge of Mongolian goat breeds and could be used in future management decisions in order to preserve their genetic diversity. Although the conservation of biodiversity is now recognized as one of the main focuses in genetic research in domesticated animals, the great efforts made here still cannot fix the rate of loss in local animal breeds . Goat brIn Mongolia, goats have been the main source of food and clothing for several thousand years. Some authors believe that independent goat domestication could have happened in Mongolia . Local gMongolian goats have experienced various perturbations over the past decades. With the fall of the Soviet Union, the Mongolian market became open to the rest of the world. Therein, the goat population has increased several times as a result of the global demand for cashmere . This inMeanwhile, Mongolian goats’ genetic diversity, phylogeny among local breeds, and their relations with breeds found in the rest of the world are still largely unknown. The largest unified dataset of goat genotypes provided by the Adaptmap project covers many areas all around the world, but completely lacks samples from Central and East Asia . These rUsing SNP genotypes, we examined demographic history and estimated genetic diversity of indigenous goats of Mongolia and explored their phylogenetic relationship with goats of the rest of the world.Blood was sampled from 244 animals in five Mongolian goat populations located in five geographical areas of the country with different climatic conditions during a research expedition of the Vavilov Institute of general genetics in 2017, one breed per location. The geographical locations of sampled populations and brief sample descriptions are summarized in One of the peculiarities of Mongolian goats is the lack of deep specialization of their productivity: dairy, downy, or meat ones, which makes them versatile. All the breeds except for the Gobi Gurvan Saikhan (Gobi GS) were formed mostly by natural selection and adapted to round-year grazing and nomadic breeding style. Gobi GS was developed by crossing local cashmere goats in the Gobi area with Don breed bucks up to the F2, followed by pure and selective breeding . Buural DNA was extracted from the whole blood samples containing EDTA as an anticoagulant using Magna Prep 200 kit  to the manufacturer’s instructions. Quality of the DNA was measured using the Nanodrop 8000 spectrophotometer. Genotyping and preliminary data analysis were performed by Federal Center for Animal Husbandry (Russia). Genotyping was performed with Goat 50K BeadChip  containing 50,347 SNPs. Quality control was performed by setting a cutoff of 0.5 for the GenCall and GenTrain scores. The SNP dataset was filtered to remove poorly genotyped individuals, loci genotyped in <90% of individuals, and rare alleles using PLINK 1.9  with --gTo explore relationships between Mongolian populations and breeds from the rest of the world, we added genotype data obtained in other studies  to our dRaw datasets were combined using --merge PLINK command and filtered with --geno 0.01 --mind 0.2 --maf 0.001 parameters. --mind rate was set to be much more relaxed than usual to keep relatively poorly genotyped Mongolian, Russian, and Chinese goats in the analysis. To ensure that analyses would not be distorted by the presence of SNPs in a strong linkage disequilibrium (LD), the --indep-pairwise 50 5 0.1 command in PLINK was used to prune the SNPs that passed the initial filtering step.To obtain datasets with different resolution, we further created four subsets, namely, Worldwide, Asian, Local, and Mongolian. The Worldwide dataset included all available samples. The Asian dataset included goats from China, Mongolia, West Asia, and all Russian samples, except the transboundary Saanen (SAAN) breed. The Local dataset included Mongolian, Nanjiang, Qinggeda, and Orenburg samples, whereas the Mongolian dataset consisted exclusively of the SNP data obtained in this study. The three additional breeds included in the Local dataset were chosen because of their geographic proximity and genetic closeness revealed by principal component and phylogenetic analyses.To evaluate within-population genetic diversity, we calculated the observed heterozygosity (Ho), unbiased expected heterozygosity (He), and inbreeding coefficient (Fis) with 95% confidence intervals (CI 95%) using the R package “diveRsity” .Trends in effective population size were estimated by linkage disequilibrium (LD) using SNeP tool  with defp-value cutoff = 0.05) for multiple testing from dunn.test R package [ROH analysis was performed for the Mongolian dataset without LD and minor allele frequency pruning. To calculate ROH, we used the methodology of Meyermans et al.  with set package  were useROH) was computed as the sum of the length of all ROH per goat as a proportion of the total autosomal SNP coverage.The genomic inbreeding coefficient based on ROH  and phylogenetic analyses. PCA was performed using PLINK and was further visualized in R. The individual-level phylogenetic analysis of the Local dataset was performed using the aboot function from the R ‘poppr’ package . DendrogGenomic clustering of Worldwide and Local datasets was performed using fastSTRUCTURE software . The proST values [ST values was visualized as a Neighbor Net group genetic network using SplitsTree 4.14.5 software [Pairwise FT values  were calT values . The matsoftware .We generated a set of 45,665 SNPs from 167 animals from five Mongolian breeds . To analyze relationships of these goats with other breeds we added data from three previously published datasets from Russia, China, and other countries ,16,17. TTo estimate genetic diversity, we calculated the observed (Ho) and unbiased expected heterozygosity (He) . AccordiAncestral and recent effective population sizes (Ne) for five Mongolian goat populations are shown in To estimate the level of inbreeding, we calculated the ROH lengths for each Mongolian goat. Every recombination event reduces the ROH length, thus the longer ROHs mark recent inbreeding events. The most variable ROH lengths were found in the Gobi GS breed  with somp-value < 0.05). Furthermore, significant deficit in ROH frequency was observed in this breed compared to Dorgon, Erchim, and Ulgii Red breeds. The average ROH length in Erchim goats was significantly higher compared to other breeds except Dorgon. All breeds display low average FROH values ranging from 0.007 in Buural to 0.023 in Gobi GS , and one Russian (Orenburg) breed . For easST) (The genetic distances (FST)  and the ST)  showed ETo explore recent admixture events, we analyzed haplotype sharing in Mongolian goat populations between each other  and in cAccording to When plotted within the 20 most closely related breeds, all of the Mongolian goats had the highest proportion of shared haplotypes with Altai Mountain (ALTM), Altai White Downy (ATLW), Soviet Mohair (SOVM), Dagestan Downy (DAGD), and Angora  goats. The Angora breed originated in Turkey and further became a transboundary breed and thus was involved in improvement of many local breeds; its impact on ALTM, SOVM, and DAGD breeds is well documented . In contGenotyping by SNP arrays is an accurate and cost-efficient tool to analyze various indicators of genetic diversity. While large efforts are dedicated towards studying commercial high-yielding breeds, there is a growing interest in genetics of local populations due to their specific traits allowing them to adapt to local environments and resist pathogens, starvation, and harsh climatic conditions. There is a global trend to improve indigenous goats with transboundary breeds while trying to preserve useful local traits, thereby helping these admixed goats to remain adapted, especially, in such extreme regions as Mongolia. Moreover, some local breeds need to be conserved to maintain goat biodiversity and provide support to nomadic traditions and local communities, which are very typical for Mongolia. Whole genome SNP analysis allows exploring of genetic diversity and may help to achieve these goals.Additionally, goat genotypes from Central and East Asian regions could provide valuable information about ancient migration routes and domestication events. Meanwhile, these regions are covered unevenly by just few projects with low to moderate numbers of samples.We performed the first whole genome SNP analysis of populations of Mongolian goats and integrated these data with other local and global datasets. Our study complemented the results obtained in previous works that used STR ,31 and mAll of the five goat populations of Mongolia showed a tendency in reducing effective population sizes, but none of the populations had Ne values decreased below the generally accepted limit of 100 or even 50 individuals to be considered as endangered . HoweverTo put Mongolian goats in a broader context, we compared our results with previously reported data from other breeds and populations of the rest of the world. PCA partitioning of all Asian breeds into two clusters (Pakistani vs. others) probably resembles southern and northern goat dispersal routes in Asia . After tST) and various clustering methods showed the Erchim breed inhabiting the Darkhat Valley to be the most autonomous population among the Mongolian goats in our study. Origin of Erchim goats remains unclear. According to the PCA, they were more closely related to Chinese goats than to the rest of Mongolian or other Asian samples. Neighbor joining and Nei-distance trees also clustered them together with Chinese Nanjiang and Qinggeda goats. However, analyses of shared haplotypes revealed recent admixture events from the Altai goats. The fastSTRUCTURE analysis demonstrated two separate subpopulations within this breed. One of them looked very similar to the rest of the Mongolian breeds, but the another one bore genetic material unique to the local population. This partition could be explained by demographic reasons and may potentially have affected the results of the tests performed for the breeds as a whole .For Erchim goats, estimates of genetic distances  were the most closely related breeds and clustered together in the most types of analyses. Together with the Gobi GS, they formed a core cluster of the studied Mongolian breeds with a weak genetic differentiation.Our analysis of Mongolian goats revealed their close relationship to neighboring Chinese and Russian breeds. Our study demonstrated low genetic differentiation within Mongolian breeds, especially among Buural, Ulgii Red, and Gobi GS. These breeds formed a single core cluster, whereas Dorgon and, especially Erchim goats, were more autonomous. We identified genetic features highlighting distinct origin and breeding history in Mongolian goats and also indicating long-term preservation of specific allele combinations of ancestor breeds. All breeds showed strong tendency in reducing effective population sizes, but none of them yet meet the criteria for endangered breeds. Taken together, our analysis suggests Mongolian goats to be a valuable reservoir of genetic resources and provide useful information for the future investigations of goat diversity.


This program is about creating the environment and opportunities that enable older people to be and do what they value throughout their lives. Most of the elderly population lives in urban spaces, and aging represents a challenge as well as opportunities to the cities all over the world. Recently, only 16 Brazilian cities have received the seal of international certification by meeting the requirements stipulated by the WHO. In the State of Sao Paulo, only two cities have been qualified for this seal. Therefore, the aims of this article are (a) to provide a brief history of this important initiative taken by the WHO and (b) to urge the decision-makers of Brazilian municipalities to develop effective initiatives for their cities to be prepared for this demographic modification.The world population is aging fast and not all cities are prepared to cope with the needs of the elderly people. Cities need to develop strategies for senior citizens including the aspects of health, nutrition, consumer protection, housing, transportation, environment, social welfare, income, employment, safety, and education. The World Health Organization (WHO) created a program dedicated to older adults called the  The evolution of these programs is described in detail below.The aging of the world population is a factor for social transformation in this century. According to the United Nations (UN), about 71 million elderly people existed in the world in 2000, and this number had increased to 600 million by 2017. The elderly population in the world is expected to reach 2 billion in 2050.2 However, it was not until 1982 that the UN convened its members to the first UN General Assembly at which aging was discussed. On that occasion, the Vienna International Plan of Action on Aging was outlined (Resolution 37/5).3 In this plan, 62 recommendations were established for senior citizens, involving health, nutrition, protection of consumers, housing and transportation, environment, family issues, social welfare, income, and employment security and education. Research data involving different areas of knowledge had been collected and analyzed to enable a better understanding of the aging process. In that document, the UN emphasized that longevity was an achievement of the biological process and the improvement of populations’ health, considering “elderly” to be a person aged 60 years or above.3In 1948, the rights of the elderly people were first discussed at the UN. Almost 30 years later, in the 1970s, this issue was again raised, highlighting the need for urgent measures since life expectancy was increasing.4The establishment of a “seal” for age-friendly cities around the world came as an advance through the challenges and achievements of Resolution 37/5. Cities, certified as age-friendly, need to have demonstrated their provision for social participation and inclusion, employment, community support, healthcare, housing, transportation, open spaces, communication, and information for their elderly citizens.6During September 2006 to April 2007, 35 cities from developed and developing countries were enrolled in the WHO project leading, including six mega-cities  with over 10 million inhabitants, “almost mega-cities” such as Istanbul, London, and New York, as well as national capitals, regional centers, and small cities. 6A total of eight domains were established as a guide where the cities and communities can address to better adapt their structures and services to the needs of elderly people. The process begins with a commitment letter issued by the city applying for the seal. The proposed plan of the city for meeting the conditions for receiving the seal is followed up by the WHO or its representative in the region where the city is located. After a baseline assessment, strategies are drawn up and developed. An evaluation of the city is then carried out and the seal of an age-friendly city will be given, depending on the achievements and fulfillment of requirements for such certification. 6At present, approximately 1,215 cities and communities have received the seal as an age-friendly city from 44 countries. In the Latin America, only nine countries received the seal from the WHO from a total of 20 countries that belong to this geographical area, about 20% of the cities that are in the program worldwide. https:/extranet.who.int/agefriendlyworld/search-assessments/?sft_countries), and it is based on the resident feedback and annual reporting to the council. According to the elderly population, the cities did not reach the goal as an age-friendly city, approximately 55% of the complaints are related to the health services, outdoor spaces , and lack of intergenerational interaction. However, the beneficial effects of this program are certainly expected but it takes time. In fact, a recent study showed a significant slowdown in medical spending growth among the elderly people mainly for cardiovascular diseases, dementia, and aftercare for people with acute illnesses.7The progress and evaluation reports of each city are on the website of the WHO , such as personal, social, and family data, in addition to the health conditions of the elderly individuals and their life habits.Aging is not the same for everyone. There are determinants of individual characteristics , macroeconomic policies , and/or historical processes . However, cities can and should outline goals and strategies to become age-friendly cities, through planning healthcare expenditures and attracting investments to the municipality or its metropolitan region. The larger the population of elderly people who remain active and independent for their daily activities the lower the costs will be for long-term treatments for both families and society. Therefore, making cities more friendly to the elderly people is a necessary and logical response, promoting their well-being and keeping these cities prosperous; thus, it is an imperative task force to engage more cities in the WHO program at all levels of government/administration and the participation of all the citizens in the societies.


Yet few studies have examined patterns of population exposure and investigated the predictors of PM2.5 across the rapidly growing cities in lower- and middle-income countries.Exposure to particulate matter  study, a multi-country assessment of the determinants of urban health in Latin America, to characterize PM3 annual average. We also found that larger cities, cities with higher GDP, higher motorization rate and higher congestion tended to have higher PM2.5. In contrast cities with higher population density had lower levels of PM2.5. In addition, at the sub-city level, higher intersection density was associated with higher PM2.5 and more green space was associated with lower PM2.5. When all exposures were examined adjusted for each other, higher city per capita GDP and higher sub-city intersection density remained associated with higher PM2.5 levels, while higher city population density remained associated with lower levels. The presence of mass transit was also associated with lower PM2.5 after adjustment. The motorization rate also remained associated with PM2.5 and its inclusion attenuated the effect of population density.We found that about 172 million or 58% of the population studied lived in areas with air pollution levels above the defined WHO-AQG of 10 μg/m2.5 exposures remain a major health risk in Latin American cities and suggest that urban planning and transportation policies could have a major impact on ambient levels.These results show that PM  •2.5.Cities in Latin America with higher GDP, motorization rate, and congestion have higher PM•2.5.Cities in Latin America with higher population density and green space have lower levels of PM•Intersection density and mass transit infrastructure also impact pollution levels.•Urban planning and transportation policies may have a major impact on air pollution. It is aRecent estimates from the World Health Organization (WHO) indicating that nine out of 10 people in the world breathe air containing high levels of pollutants  reinforcThis growth of urban residents is predicted to be particularly pronounced in lower- and middle-income countries (LMIC) . TherefoThe Latin America and the Caribbean (LAC) region is one of the most urbanized areas of the world. Approximately 80% of the region's population resides in urban areas  and 2/3 10  and PM2.5  in most measured sites in LAC were significantly higher than WHO Air Quality Guidelines (WHO-AQG), with less than 5% of cities (among 117 studied) complying with the guidelines  study , a multi2departamentos, municipios, comunas depending on the country).SALURBAL encompasses all cities with population over 100,000 in 11 countries in Central and South America: Argentina, Brazil, Chile, Colombia, Costa Rica, El Salvador, Guatemala, Mexico, Nicaragua, Peru, and Panama . SALURBATogether, the SALURBAL cities in the 11 countries include approximately 300 million residents, representing more than half of the total population in Latin America, and comprise some of the largest cities in the world, such as Sao Paulo, Mexico City, Buenos Aires, Lima, and Rio de Janeiro. Nicaragua was not included in these analyses because of missing data on several key variables.2.12.5 global estimates available from: http://fizz.phys.dal.ca/~atmos/martin/?page_id=140). Ground-level fine particulate matter (PM2.5) was estimated using multiple satellite-based aerosol optical depth datasets combined with a chemical transport model, and subsequently calibrated to global ground-based observations using geographically weighted regression (3) in a gridded format with each grid cell representing 0.01 × 0.01 degrees, equivalent to 1.1 km × 1.1 km at the equator. We converted the value of each grid cell into points assigned to the geometric center of each cell (centroids). We calculated annual mean PM2.5 value in the year 2015 for each sub-city unit by averaging the values of each centroid contained within the unit boundary. We used all composition PM2.5 to better reflect the exposure of the population.Air pollution data were obtained from the Atmospheric Composition Analysis Group of the Dalhousie University (surface PMgression . Data we2.22.5. We determined whether the factor should be included at the city or sub-city level based on (1) theoretical understanding regarding at what level the construct is defined , and (2) whether significant and meaningful within-city variation was likely .We examined several city and sub-city factors related to urban form and transportation based on our hypotheses. We also included the gross domestic product (GDP), population size and city growth as they may also affect PM2 of area). At the sub-city level, we examined intersection density  and area median greenness measured by the normalized difference vegetation index (NDVI). NDVI was calculated using MODIS satellite-based observations from the MODIS vegetation product, MOD13Q1.006 for 2015 at a 250 m spatial resolution. We computed the maximum NDVI value for the year at 250 m resolution to present the ‘greenest’ condition of each grid cell, then calculated the median across grid cells contained within each sub-city unit.Urban form factors at the city level included population density (population divided by built-up area), and a measure of urban fragmentation (patch density: the number of contiguous patches of urban development per kmkability  per km2 Transportation at the city level was characterized based on the presence of mass transit infrastructure such as subway or Bus Rapid Transit (defined as present or absent) and an indicator of gas cost (the cost of 100 l/monthly minimum wage). We also calculated a travel delay index which measures the average congested travel time from a set of 25 random origin-destination points relative to the uncongested time using the street network. This indicator was constructed based on the largest built-up urban cluster within each administratively defined city, as this spatial definition is most relevant to characterizing congestion. The urban clusters were identified using the Global Urban Footprint Dataset as described in We also gathered data on motorization rate in 2015 for a small subsample of cities for which these data were available. The motorization rate (per 1000 population) is the total number of registered motor vehicles in 2015 which includes light and heavy-duty vehicles.Indicators of city size and growth included total city population in 2015 and population growth (%) between 2010 and 2015. We also examined the GDP per capita  of each city in 2015 using estimates from the first subnational administrative level  as described by 2.32.5 levels were analyzed at the sub-city level in order to capture any variations within cities composed of multiple sub-city units. Descriptive statistics, boxplots, and variance components from multilevel models were used to describe variability in PM2.5 levels among countries, cities and sub-cities. Because of the relatively smaller number of cities within Costa Rica, Guatemala, El Salvador, and Panama, we pooled data from these countries into one region of Central America for the analysis. We also described the population exposed to levels of PM2.5 above the WHO-AQG.PM2.5 for sub-city units, and to estimate variance at each level . Each predictor was first examined separately. We adjusted the measure of urban fragmentation (patch density) by the percentage of the administrative area that is built-up (% built-up), in order to account for differences in the amount of development in each area and thus, correctly interpret the coefficient of patch density as an indicator of fragmentation. In a second phase we included all predictors in a single full model. To facilitate comparisons, all city and sub-city factors were standardized to standard deviation (SD) units.We used linear mixed models with random intercepts at the country and city level and an unstructured covariance structure to examine the association of selected sub-city and city level factors (the predictors) with mean PM3Of the 371 cities in SALURBAL, five were excluded because they were in Nicaragua. A total of 366 cities and 1425 sub-cities were included in the analyses describing the distribution of the population above the WHO-AQG. For the multilevel analysis, we excluded cities missing city and sub-city level predictors; thus, the final sample included 343 cities and 1310 sub-cities in 10 Latin American countries. Forty-eight percent of cities were composed of only one sub-city unit. The median number of sub-cities within cities with more than one unit for the full sample was four but ranged from two in Argentina to 18 in the pooled Central America countries .Table 1CDescriptive characteristics of cities and sub-cities are displayed in 2.5 varied between and within countries  had annual mean concentrations of PM2.5 above the defined WHO-AQG of 10 μ/m3 , age groups traditionally considered at higher risk of health effects due to air pollution exposure  lived in areas (sub-city units) with PMexposure . The proountries .Table 2P2,5 greater than WHO-AQG. These 10 cities alone host more than 100 million people. In these 10 cities there were over seven million children under five and nearly nine million elderly adults over 65 living in areas with concentrations of PM2.5 above 10 μg/m3.A closer look at the 10 largest cities in our sample  showed that 95% of the population in these megacities live in areas with PM2.5 levels .When each predictor was examined separately, several city and sub-city characteristics were significantly associated with annual PM2.5 levels, while population density was inversely associated with levels of air pollution. Also, the presence of mass transit infrastructure was associated with lower PM2.5. When motorization rate was added to the full model, other estimates remained similar, although the association with population density was attenuated .When all exposures were examined adjusted for each other . The lack of extensive monitoring of air quality in Latin America makes it difficult to routinely assess the impact of the concentration of pollutants on health. It also hinders the population's perception and awareness of this important problem, and consequently, discourages efforts to ensure cleaner air for the population.A previous study  examined2.5 data currently available at a global scale  are related to levels of PM2.5. When compared to other contributing factors, there are indications that GDP per capita is the factor that offers the largest proportional contribution to PM2.5 concentrations , we estimate that the presence of this infrastructure is associated with 15% lower PM2.5 . This is consistent with prior research showing that public transport tends to have an impact on air pollution emissions through a decrease in individual road transport  was also an independent predictor of PMollution  and effoollution . Interes2.5 in cities, and Latin America. Research shows that major contributors for the deteriorating air quality in the region are the vehicle fleet, industrial sources and biomass burning (Overall, the associations we observed are consistent with what is known about what are the likely main sources of PM burning . Some of2.5 levels. Cities were defined based on administrative areas and may not coincide with other functional definitions such as metropolitan areas. Because some of the administrative units were large, they included urbanized and less urbanized areas. This might have implications for some of the urban measures we evaluated, such as measures of fragmentation, for which no statistically significant associations were detected.An important strength of our study is the investigation of the universe of Latin American cities with 100,000 people or more using a wealth of harmonized variables characterizing built environment and other features that could affect PM2.5 in the univariate model, but the association was weaker in adjusted analysis. Greenness and intersection density were both statistically significant in univariate models, but only intersection density remained significant in the multivariate model. Hence, all factors associated with PM2.5 whether in unadjusted or adjusted analyses, deserve further research and policy attention as important contributors to city levels of PM2.5.Our descriptive, cross-sectional analyses cannot be used to prove causality and the intercorrelation between variables makes isolating their independent associations difficult. For these reasons we show associations both before and after adjustment, as both are informative. For example, travel delay showed the highest correlation with population density and was significantly associated with PM2.5. We also found considerable variation across countries and cities. Informing decision-makers about the quality of the air in cities is an important step towards meeting air quality guidelines that guarantee greater health protection. Furthermore, PM2.5 was associated with distinct built environment features including population density, the presence of mass transit infrastructure, intersection density, motorization rate, and GDP per capita. Increasing knowledge about drivers of air pollution in urban areas will help devise interventions towards creation of healthy urban environments that will promote better quality of life for all urban residents.In conclusion, we found that 58% of residents in the Latin American cities examined live in areas that exceed WHO guidelines for PMNelson Gouveia: Conceptualization, Writing – original draft, Writing – review & editing. Josiah L. Kephart: Formal analysis, Writing – review & editing. Iryna Dronova: Writing – review & editing. Leslie McClure: Writing – review & editing. José Tapia Granados: Writing – review & editing. Ricardo Morales Betancourt: Writing – review & editing. Andrea Cortínez O'Ryan: Writing – review & editing. José Luis Texcalac-Sangrador: Writing – review & editing. Kevin Martinez-Folgar: Writing – review & editing. Daniel Rodriguez: Conceptualization, Writing – review & editing. Ana V. Diez-Roux: Conceptualization, Writing – review & editing.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Centaurea aspera ssp. scorpiurifolia is a threatened taxon endemic to Andalusia (Spain), which coexists in most of its distribution area with similar taxa. Because of the difficulty to identify it using morphology alone, we aimed to sample all the populations cited as ssp. scorpiurifolia as exhaustively as possible, morphologically characterise them, and analyse their genetic structuring using microsatellites, to better understand difficulties when conserving subspecies. Three different Centaurea species were found which were easily identified. Within C. aspera, two genetic populations and some admixed individuals were observed, one including ssp. scorpiurifolia individuals and the other including individuals identified as subspecies aspera, stenophylla, and scorpiurifolia. A morphological continuum between these two genetic populations and a wide overlapping of their biogeographic distribution were also found. This continuum can affect the conservation of ssp. scorpiurifolia because of potential misidentifications and harmful effects of subspecific hybridization. Misidentifications could be partly overcome by using as many different traits as possible, and conservation priority should be given to populations representative of the ends of this continuum.Subspecies are widely included as conservation units because of their potential to become new species. However, their practical recognition includes variable criteria, such as morphological, genetic, geographic and other differences.  Since then, several concepts of species have been proposed although all of them share the correspondence of species with metapopulation lineages or gene pools2.One of the most popular definition of species is that proposed by Mayr4. The potential that each intraspecific unit has to become reproductively isolated, cumulate ecologically relevant adaptations and finally become a new species justifies conservative efforts to protect them against extinction5. Furthermore, species with numerous intraspecific units that have alternative responses to environmental change may be less prone to extinction6. The contribution of the intraspecific biodiversity has led modern plant conservationists to consider intraspecific units as valuable conservation units. Policy frameworks include different names for these intraspecific units depending on legislation, although all of them recognize “subspecies” as a valid entity for protection and listing4. The International Union for the Conservation of Nature (IUCN), the Convention on International Trade in Endangered Species of Wild Flora and Fauna (CITES), and red lists of threatened species of many countries around the world nowadays include subspecies7.Species can display great genetic diversity that is often partitioned in local populations forming intraspecific units, even if these groupings can form hybrid zones at their geographical boundaries8, evolutionary divergence9, common and diagnosable phylogenetically acquired phenotypic characters, unique natural history5, genetic adaptation to local habitats6, and behavioural, physiological and phenological differences6. Thus, the practical implementation of conservation programs has been hindered by a lack of a uniform definition of subspecies. Criteria mentioned above commonly do not occur at the same time or in a regular order2. For example, in some cases molecular genetic characterization contradicts morphological descriptions, because neutral genetic markers and phenotypic traits are influenced by different evolutionary forces10. Delimitation of subspecies can also be especially problematic when hybridization among them occur, or when taxonomical adscriptions are not accurate or stable5. In such cases, genomic analysis combined with morphological and reproductive studies may be needed to provide a clear taxonomic frame.However, criteria for practical subspecies recognition largely vary among taxonomists. They include the existence of a partial reproductive isolation, differentiated geographical distributionsCentaurea aspera L. (Asteraceae) is endemic to Western Mediterranean. It comprises five subspecies: ssp. aspera, ssp. stenophylla (Dufour) Nyman, ssp. pseudosphaerocephala (Shuttlew ex. Rouy) Gugler, ssp. scorpiurifolia (Dufour) Nyman, and ssp. gentilii12. Of these subspecies, ssp. scorpiurifolia is a threatened taxon, classified as “Endangered” according to IUCN criteria13. It is included in the red lists of Andalusia14 and Spain16.C. aspera spp. scorpiurifolia (hereinafter ssp. scorpiurifolia) differs from C. aspera ssp. aspera (hereinafter ssp. aspera) in the leaf shape and size and in the stem branching, although the differentiation is frequently difficult in the field18.Morphologically, scorpiurifolia grows in shrublands or pine and cork oak forests on coastal sandy soils, between 0 and 1400 masl18. Some other morphologically similar Centaurea taxa, such as C. aspera ssp stenophylla (hereinafter ssp. stenophylla) and ssp. aspera, C. pullata, and C. sphaerocephala, have similar ecological requirements, and sometimes they may be found growing together . However, this is an arbitrary cutoff which varies in different studies from 60% [i.e. 35] to 90% [i.e. 36]. Consequently, estimating admixture is a somewhat subjective task, and percentage of admixture may differ depending on thresholds. In relation with morphology, we also found some overlapping of individuals, even between those that were genetically considered as belonging to different subspecies but resulted to be morphologically very similar. In general, subspecies are recognized based on morphological observations alone, as was the case of ssp. scorpiurifolia until now. Usually, few distinct traits that show no overlap with other subspecies are used. Our results showed that this can be tricky, because of the presence of a morphological continuum. A highly variable morphology in well genetically defined subspecies was also found in other Asteraceae, such as Arnica montana33. In fact, Wiens37 argued that very large sample sizes may be required to be reasonably certain that a given trait is diagnostic at the desired level. The same occurred with the biogeographical distribution, which has also been suggested to delimit taxa boundaries32. In the Gaditano-Onubo-Algarvish province we could only find ssp. aspera and one admixed individual, while in the Murciano-Almeriense province, which is characterized by drier ombroclimates19, we could only find ssp. scorpiurifolia and some admixed individuals. However, both subspecies clearly coexisted in the Baetic province.However, although we observed individuals clearly adscribed to these different subspecies using genetic and morphological traits, we also found some intermediate individuals causing a continuum in both traits. In relation to the genetic traits, it has been shown that only few molecular loci are enough to delimit few lineages and estimate the proportion of admixture among populations38, the continuum among subspecies may persist39 as in our case. This overlapping in the different traits used may lead to different important issues in conservation. First, due to the presence of a continuum, some mistakes can be inevitable when determining the C. aspera subspecies, as has been shown in other plant groups40. Although genetic analyses can enhance their morphological identification, they do not provide a complete solution because of the presence of arbitrary thresholds when determining the number and nature of clusters38. Consequently, integration of morphology, genetics and biogeography should be applied to identify as much accurate as possible the ssp. scorpiurifolia populations. Second, taxonomic errors may inflate species distribution41. Here, we did not find ssp. scorpiurifolia in many cited locations especially in more continental areas, but other morphologically similar taxa. The fact that we could not locate the cited population is possible, but if this subspecies does not exist in these locations, its geographic range would be much narrower than expected. This issue is particularly sensitive in endangered taxa like ssp. scorpiurifolia, which has been classified as “endangered”13 because of its narrow areas of occupancy and occurrence, small population sizes, and continuing decline in the area and number of mature individuals16. Third, an overlapping also exists in the distribution areas of ssp. aspera and ssp. scorpiurifolia and this can lead to the presence of admixed populations. This is also the case with other plant subspecies that grow in sympatry3 and an increasing number of studies indicates that speciation can occur with gene flow and without geographical isolation in populations provided that these populations still display distinct morphology and genetic divergence42. Whether gene flow should be prevented to avoid genetic pollution of the endangered taxa or facilitated to increase its genetic diversity has long been debated in conservation biology43. At the intraspecific level, a beneficial effect of hybridization may be an increased genetic variation while avoiding types that might cause outbreeding depression as subspecies generally share alleles, whereas a harmful effect may be the loss of local adaptations44. In our case, we found populations with different degrees of admixture, many pure populations of ssp. aspera, and only one pure population of ssp. scorpiurifolia. This may indicate the presence of hybrid swarms that can extend to all populations in a relatively short time lapse. The geographically restricted remaining non introgressed ssp. scorpiurifolia population from Alhamilla should therefore be preserved from hybridization with the highest priority, although admixed populations should also be protected in the hope that they will fill the ecological role of the threatened subspecies44.Even when using morphological and genetic analyses, which is unanimously suggested to infer taxa boundaries in combination with other traitsCentaurea ssp. aspera and ssp. scorpiurifolia, a continuum in morphology, genetic composition, and biogeographical distribution may be present. This can lead to difficulties in conserving threatened subspecies, which may be related with misidentifications and hybridization. Misidentifications could be partly overcome by using as many different traits as possible, and conservation priority should be given to populations that are representative of the ends of this continuum.Our results support that even between well differentiated subspecies, like scorpiurifolia was performed based on published literature (Table https://www.gbif.org), Anthos (Spanish Plants Information System: http://www.anthos.es), SIVIM (Iberian and Macaronesian Vegetation Information System: http://www.sivim.info), and REDIAM . In spring 2015, we performed an exhaustive sampling of the cited populations, considering especially the geographical coordinates and details of their locations, and we recorded the presence of spp. scorpiurifolia and related taxa  that are morphologically similar. We visited the coast and the mountain ranges near the sea, in the Andalusian political provinces of Cádiz, Málaga, Granada, Córdoba, and Almería .Prior to the field sampling trips, a detailed list of citations of ssp. re Table  and websaspera because of their entire upper leaves, although they were more than 5 mms wide (which is characteristic of ssp. scorpiurifolia), 10 ssp. aspera from Granada, 54 ssp. scorpiurifolia from Granada and Almería, 7 ssp. stenophylla from Almería, and 13 C. sphaerocephala from Cádiz and Málaga were included. Leaves were transported in a cooler and frozen plant tissues were stored at -80ºC.Young leaf samples of 95 individuals separated by at least 5 m were collected for further genetic analysis Table . Eleven aspera and ssp. scorpiurifolia, 7 ssp. aspera, 24 ssp. scorpiurifolia, 5 spp. stenophylla, and 7 C. sphaerocephala : VALA 9581 (from Bédar), VALA 9582 (Alhamilla), VALA 9583 (La Parra), and VALA 9584 (Órgiva). Voucher specimens were identified by Alfonso Garmendia, Hugo Merle and María Ferriol.Furthermore, 50 of these sampled individuals were morphologically characterized in the field to not damage the plant, including 7 that displayed traits of both ssp. la Table . VoucherC. pullata individuals found were not sampled because of its clear taxonomic adscription based on capitula and leaf traits.All the Centaurea were performed following Ferriol et al.45. Separation of the amplified fragments was carried out using a QIAxcel DNA High Resolution Kit (1200) (QIAGEN), which provides up to a 2 bp resolution when used with the OM700 method on fragments that range 100–500 bp in size, and analysis was performed using the BioCalculator software for the QIAxcel system, following Dean et al.46. To confirm the reproducibility of microsatellite fragments, PCR reactions were replicated in approximately 70% of the individuals for each locus, which were selected to maximize the genotypic diversity. Reamplified fragments were separated on polyacrylamide gels following Ferriol et al.45.Genomic DNA isolation and amplification of nine microsatellite loci specifically developed for Centaurea aspera subspecies as can be found in determination keys and floras, as well as several that are potentially useful for distinction of taxa of the sect. Seridia. A total of 28 quantitative variables were evaluated: 9 corresponded to reproductive traits and 19 to vegetative traits. Seven qualitative characters were also evaluated  was accomplished in the field during spring to reveal differences between taxa, or any other morphological variation patterns. Table C. sphaerocephala and C. aspera was investigated by testing significance of the index of association, Ia, and of its standardized alternative rbarD47, with 999 randomizations using the R package Poppr48. Frequency of null alleles in C. sphaerocephala and C. aspera was estimated in R using package PopGenReport49.Linkage disequilibrium between microsatellite loci in Centaurea individuals was estimated using software STRUCTURE v2.3.450, with the admixture model and the correlated allele frequencies between populations options. To estimate the number of populations (K), we ran STRUCTURE with varying K values, ranging from 1 to 9. Each run consisted of one million burnin iterations and 500 000 data collection iterations. Each value of K was evaluated using 20 independent Markov chain Monte Carlo replicates. The number of clusters was inferred following Evanno et al.51, based on the values of ΔK for each value of K (except for K = 1 and the maximum K tested). We attributed a plant to a given cluster when the proportion of its genome in the cluster (qK) was higher than an arbitrary cutoff value of 0.8. Otherwise, the plant was classified as admixed.The population structure of the sampled 52 in R. A hierarchical partition of the genetic variation among species and populations from different biogeographical provinces was determined with analyses of molecular variance (AMOVAs) with 1000 permutations using the pegas package53. As AMOVA is not able to detect admixed populations in contrast to STRUCTURE, those admixed individuals found in STRUCTURE between genetic clusters were assigned to a given cluster according to its highest qK. In addition, genetic differentiation among populations for the highest levels of the hierarchy was also estimated following the suggestions of Whitlock54 for within species purposes when using microsatellite markers, i.e. FST, and HS (the heterozygosity within populations) that can be used for other calculations.A Principal Coordinates Analysis (PCoA) was performed in order to analyse relationships among the three observed genetic clusters using the adegenet package55 and agricolae56. As a first step, reproductive and vegetative characters of the genetic clusters were analysed separately. Qualitative vegetative traits were transformed into binary characters. Descriptive statistical measures  were computed for the quantitative variables. ANOVAs and posthoc Tukey HSD comparisons among genetic clusters were calculated for all the quantitative variables. Bonferroni correction was applied to the ANOVAs significance to correct the effect of several repeated analyses.Analyses were carried out in R using the packages massp < 0.01)  was performed using the variables that showed significant differences in the previous ANOVAs (1) Table  online. 57 in R to estimate the correlation between the pairwise Euclidean distances obtained from morphologic and genetic data considering the 50 individuals that were morphologically characterized and running 999 permutations.A Mantel test was performed using package ade4C. aspera ssp. scorpiurifolia is not under protection in any legislation. All the samples have been collected in non-protected areas, excepting the location of Alhamilla, included in the “Paraje Natural Sierra de Alhamilla”. However, the Natural Resources Ordinance Plan  , was addressed on 23rd December 2016, more than one year after our field trip, so no permission was needed. All the samples were collected following the IUCN Policy Statement on Research Involving Species at Risk of Extinction. We used non-lethal sampling methods , we morphologically characterize plants in the field, and we took a single branch of a well-developed plant for herbarium specimens.Although included in the IUCN Red Lists from Spain and Andalusia as stated in the Introduction, Supplementary Information.


Sargassum thunbergii to test this hypothesis, by comparing present population genetic variability with inferred geographical range shifts from the LGM to the present, estimated with species distribution modelling (SDM). Projections for contrasting scenarios of future climate change were also developed to anticipate genetic diversity losses at regional scales. Results showed that S. thunbergii harbours strikingly rich genetic diversity and multiple divergent lineages in the centre‐northern range of its distribution, in contrast with a poorer genetically distinct lineage in the southern range. SDM hindcasted refugial persistence in the southern range during the LGM as well as post‐LGM expansion of 18 degrees of latitude northward. Approximate Bayesian computation (ABC) analysis further suggested that the multiple divergent lineages in the centre‐northern range limit stem from post‐LGM colonization from the southern survived lineage. This suggests divergence due to demographic bottlenecks during range expansion and massive genetic diversity loss during post‐LGM contraction in the south. The projected future range of S. thunbergii highlights the threat to unique gene pools that might be lost under global changes.Glaciation‐induced environmental changes during the last glacial maximum (LGM) have strongly influenced species' distributions and genetic diversity patterns in the northern high latitudes. However, these effects have seldom been assessed on sessile species in the Northwest Pacific. Herein, we chose the brown alga  Historically, this species was common along the entire Pacific coast of China  S. thunbergii survived in southern glacial refugia and expanded northwards following the glaciers retractions, causing homogeneous low diversity regions across the wide northern range and leaving behind most endemic diversity at rear edges. To address these hypotheses, microsatellite markers were used to detect fine‐scale phylogeographical structure and latitudinal diversity patterns along the Northwest Pacific. Additionally, independent species distribution modelling (SDM)  period  S. 22.1Sargassum thunbergii is found in the Northwest Pacific, as far north as Hokkaido, Japan and south to Hong Kong and Guangdong, China  as detailed in a previous study , the effective number of alleles (Ae), observed (Ho) and expected heterozygosity (He). FSTAT 2.9.3.2 , allelic richness (α^) and private gene diversity (π^). Allelic richness and private alleles were standardized to the smallest sample size with 104 randomizations. Pairwise FST and Jost's D were estimated with GENALEX. Genetic differentiation among 35 geographical populations was also explored by factor correspondence analysis (FCA) of multilocus scores using GENETIX , the analysis ran with 5 independent simulations using a full‐length of 8×106 iterations and a burn‐in of 106 iterations, which were determined to be sufficient to obtain consistent results. The estimation of K used the log probability of the data Pr(x/K) and the ΔK criterion , the Ocean Biogeographic Information System (OBIS) and scientific journal articles  were developed to test whether past climate‐driven range shifts explain the observed phylogeographical structure. The models were developed with the machine learning algorithm Boosted Regression Trees (BRT) using occurrence records against biologically meaningful environmental predictors for intertidal marine brown algae  and forecasted (two contrasting emission scenarios RCP26 and RCP85) with information about phylogeographical structure  as implemented in DIYABC 2.1.0  it survived in a single northern refugium during glaciation and expanded southward postglacially; (iii) the northern and southern groups survived as separated glacial relics, respectively, from an extinct common ancestor. The divergence between the two groups could have occurred before or after the LGM, thus generating six competitive scenarios  was set to t1 (post‐LGM) or t2 (pre‐LGM). The mutation rate of microsatellites (10−4–10−3) was inferred from the phylogenetically close species Fucus 2 distance for two sample statistics.The ABC analysis generated 1033.1Na = 3.64/3.73, Ae = 2.28/2.36). Gene diversity (He) ranged from 0.06 in Tateyama Bay, Chiba, Japan to 0.52 in Esashi, Hokkaido and Ama, Shimane, Japan, respectively  in Ama, Shimane, Japan relative to other localities (π^ ≤ 2.61). The Yellow‐Bohai Sea samples, particularly 9–12, 14, 16 and 18–27, exhibited relatively fewer private alleles  inbreeding coefficients (FIS) were significantly positive.No evidence of null alleles nor linkage disequilibrium was found between the 11 microsatellite loci. Consequently, the 11 selected microsatellite loci yielded 111 alleles (6–18 per locus). The mean percentage of polymorphic loci in all 35 populations was 86.75%, with the lowest value (27.27%) found in the samples from Tateyama Bay and Chita, Japan. The samples from Esashi, Hokkaido and Ama, Shimane, Japan exhibited the largest number of alleles (FST and Jost's D estimates were significantly differentiated (p < 0.01). FST and Jost's D revealed lower genetic differentiation within the northern and southern limits  genetic group is that comprised by Tateyam Bay and Chita, from the Japan‐Pacific coast, which were clearly highly differentiated from all other populations and also among themselves  was found in G3, which was as rich as c. 2‐fold higher than in G1 and G2 and c. 3‐ to 5‐fold larger than in G4, G6 and G7 (the southern limit)  to the centre (39°N < 28°N), and to the southern limit (<28°N) of the species range .At the first hierarchical level, allelic richness was lower (5.73) in the southern than the core‐northern range (7.91), and the number of private alleles in the core‐northern limit (35.41) was over 3‐fold larger than in the southern cluster (11.37) Figure . Allelic) Figure . The Amaa Figure . The cure Figure . No isoli Figure , becauseDifferentiation among populations within genetic clusters was greater in the range centre, followed by the northern and southern range limits, respectively Tables .3.2S. thunbergii have different inferred tolerance limits to salinity and nitrates . Suitable habitats were available along the coast of China and northward reached the north of the Pacific coast of Japan (c. 38°N)  expansion to Severo‐Kurilsky, Russia (c. 50°N)  Figure . From th) Figure . Compari) Figure .S. thunbergii. They indicated that the northern group was mainly restricted to the Japanese archipelago, the Korean peninsula and the Yellow‐Bohai Sea, whereas the southern group predominantly occurred in East China Sea , corresponding to a timeframe around 5–10 ka . The evaluation confidence in scenario 3 showed that the posterior predictive error computed over 1000 datasets was 0.058, indicating a reliable model selection.Model‐based inference at the first hierarchical level pointed to the southern group having persisted during the LGM, from which the northern group was derived through stepping stone colonization  and southern range contractions, with the risk of erosion of the current levels of genetic diversity. Projecting the distribution of S. thunbergii populations under contrasting scenarios of future climate change further showed unique gene pools in imminent risk of disappearing.This study combining empirical genetic data with independent theoretical modelling shows how climate‐driven distributional shifts in the Northwest Pacific structured present 4.1S. thunbergii. Rather, cryptic refugia, long‐distance dispersal and postglacial expansion seem to have created a complex pattern of distribution range shifts and genetic diversity. Such processes are common in seaweeds . While significant global sea‐level rise had an earlier start at ~14.5 ka  and imposed drastic range shifts in the extant coastal species along their geographical ranges Wang, . Generalr Figure , Table 1r Figure .S. thunbergii implies that it might not have persisted the harsher environments (e.g. ice scouring and freezing) of the Bohai Sea during the LGM, but suitable habitats might still have been available in the Yellow Sea and the west Pacific (e.g. the Ryukyu archipelago)  in the second hierarchical analysis Figure . The int) Figure . The clu) Figure  and/or t) Figure , 2015, d) Figure . The subS. thunbergii in glacial refugia during the LGM, as reported in the Northwest Pacific ; (ii) pUmezaki, . During 4.3S. thunbergii highlights the importance of niche conservation under climate change. This is particularly important for species like S. thunbergii that survived past climate extremes in habitats that remained suitable in the long‐term, but are currently in risk of disappearing, and with it so will those unique genetic pools, as species shift distributions poleward  have already experienced range‐wide distribution losses since the 1980 s  even further isolated from the core‐northern limit, owing to fragmented habitats and environmental pressures, particularly under the greenhouse gas emission scenario of RCP85  (Tanaka et al., S. thunbergii in the northern range may provide additional information to implement conservation plans, as our results provide guidance for consideration of gene pools in management under climate changes.The ongoing range shifts create multiple challenges for conservation and resource management (Lima & Wethey, None declared.Z.‐M.H., J.A. and E.A.S. conceived the project. Z.‐M.H., X.‐H. S., X.G. and H.‐G.C designed sampling and collected specimens, X.‐H. S. and J.Z. conducted molecular experiments, X.‐H. S., J.A. and J.Z. analysed molecular data, Z.‐M.H., X.‐H. S., J.A., E.A.S. and D.‐L.D. interpreted the data and wrote the manuscript. All authors approved the final version of the manuscript.Supplementary MaterialClick here for additional data file.