The appearance of Slavs in East-Central Europe has been the subject of an over 200-year debate driven by two conflicting hypotheses. The first assumes that Slavs came to the territory of contemporary Poland no earlier than the sixth century CE; the second postulates that they already inhabited this region in the Iron Age (IA). Testing either hypothesis is not trivial given that cremation of the dead was the prevailing custom in Central Europe from the late Bronze Age until the Middle Ages (MA).To address this problem, we determined the genetic makeup of representatives of the IA Wielbark- and MA Slav-associated cultures from the territory of present-day Poland. The study involved 474 individuals buried in 27 cemeteries. For 197 of them, genome-wide data were obtained. We found close genetic affinities between the IA Wielbark culture-associated individuals and contemporary to them and older northern European populations. Further, we observed that the IA individuals had genetic components which were indispensable to model the MA population.The collected data suggest that the Wielbark culture-associated IA population was formed by immigrants from the north who entered the region of contemporary Poland most likely at the beginning of the first millennium CE and mixed with autochthons. The presented results are in line with the hypothesis that assumes the genetic continuation between IA and MA periods in East-Central Europe.The online version contains supplementary material available at 10.1186/s13059-023-03013-9. The decline of the Western Roman Empire under the pressure of barbarian tribes led to the emergence of new political and ethnic structures –3. WhileData collected thus far indicate that at the end of the Late Neolithic period, 3700–1800 BCE, the genetic structures of the populations occupying Central Europe were stabilized and remained to a large extent unchanged until the end of the Bronze Age, 1800–700 BCE , 26. ThrIn the studies presented here, we took advantage of the fact that for a limited period of time, inhumation became the dominant funeral practice in the region of contemporary Poland within the population associated with the Wielbark culture –33. ThisOur recent analyses of mitochondrial genomes of IA groups associated with the Wielbark culture , 38 showTo reconstruct the biological history of the population living in East-Central Europe before and after the Migration Period, we analysed DNA samples taken from 474 individuals buried in 27 cemeteries in contemporary Poland , 39–62 aTo compare the genetic structures of the studied ancient populations and contemporary populations living in Europe, genome-wide data obtained for the individuals from the IA and MA groups were compared with the corresponding datasets describing the genetic variation of present-day West Eurasians (HO dataset) . We found that the major genetic component present in the DNA of IA individuals was the one maximized in the present-day Northwestern European genomes, whereas the DNA of MA individuals had, on average, the highest contribution of the genetic component maximized in the genomes of contemporary East-Central Europeans  Fig. d. For evTo increase the resolution of the PCA, the genomic data obtained from the IA and MA individuals were compared with analogous data generated for the following 5 present-day East-Central European and Scandinavian populations: Eastern European, Northwestern European, Baltic, Finnish, and Norse (ancestry-restricted dataset) Fig. a. The reK = 2)  , where IA_X is IA_Pruszcz_Gdanski, IA_Kowalewko, or IA_Maslomecz, MA is the studied MA group, and test population is one of the 459 earlier identified ancient populations preceding or contemporary to the MA group  can be generated with Denmark_IA, Sweden_LN or Scotland_Orkney_IAs as a source  or could be genetically not related with the local medieval population (genetic discontinuation hypothesis). To verify these hypotheses of genetic continuity or discontinuity within the territory of contemporary Poland from the IA to medieval times, we examined whether and how the individual populations forming the MA group  one-way models with the IA_X populations. As shown in Additional file Further, we used qpAdm to test if the MA_X populations form valid  for all tested MA_X populations (8) with ~ 78–92% of the ancestry attributed to the IA_Maslomecz. IA_Kowalewko and IA_Pruszcz_Gdanski also produced valid two-way models but not for all MA_X populations . In the IA group, the Y-hg I1/I1a was the most frequent, whereas in the MA group, R1a predominated  belonged to the following major branches of the Y-chromosome phylogenetic tree: E, F, G2a, I1, I2a, J2a, J2, N1a, R1a, and R1b  between the IA and MA groups. The frequency of the dominant matrilineal lineage, mt-hg H, remained nearly constant in populations from both the IA and MA periods   also could not explain the genetic makeup of all MA_X populations. The valid two-way models for all MA_X populations were obtained only for Poland_EBA_Unetice or Czech_EBA_Unetice and IA_Maslomecz. The latter population represents the north incomers with the highest admixture of the autochthon IA population since IA_Maslomecz was formed at the last stage of migration of the Wielbark Culture-associated peoples through the territory of contemporary Poland. It further means that the autochthonous IA genetic component was indispensable to model the whole MA group and that it is possible to explain the genetic makeup of the MA group without a need for separate migration from east in sixth CE or later. f4 statistics demonstrated also that neither the MA nor the IA group was statistically significantly more shifted towards Poland EBA Unetice or Czech EBA Unetice. This observation indicates that the latter two could be a common ancestral component for the IA and MA populations. However, qpAdm models showed that the EBA related ancestry in the IA and MA populations was minor.The above results are consistent with the hypothesis assuming migration from north and genetic continuity in the region of contemporary Poland from the IA to the MA. Accordingly, we observed that majority of the ancestry of the MA populations could be derived from the preceding IA populations in particular IA_Maslomecz. The MA populations, compared with the IA populations, had lower genetic affinities to ancient northern Europeans and higher to ancient Eastern Europeans . Thus, a small number of individuals with Y-hg R1a in the IA group does not necessarily mean a low frequency of this haplogroup in the autochthonous IA population. It may, however, indicate a natural phenomenon of excluding local men from communities dominated by male immigrants.Here, we provide several pieces of evidence that the ancestors of the medieval populations lived in the region of present-day Poland during the IA. There are, however, several aspects that need further elucidation. Firstly, how and when the ancestors of the MA populations with Y-hg R1a appeared. The times when Y-hg R1a-M417 dominated in this territory are associated with the spread of the Corded Ware culture (from 3000 to 2300 BC) . Later, Although they seem less likely one cannot exclude the alternative scenarios that do not assume the presence of the ancestors of the medieval populations in the region of contemporary Poland during the IA. One possibility is the numerous waves of migration from northern Europe in both IA and medieval times. According to this scenario, the medieval populations could have arisen as a result of the migration of individuals carrying genetic components similar to those possessed by the autochthonous IA populations. In this case, the immigrants would not be direct descendants of the autochthonous IA populations. Another scenario may assume the occurrence of a bottleneck effect that could make the IA and MA populations similar to each other under the conditions of reduced genetic diversity. There is also possibility that both the IA and medieval populations shared a common ancestral population that has not yet been studied. This common ancestry could explain the genetic similarities between the two populations without the direct presence of the ancestors of the medieval populations in the area of present-day Poland in the IA. Certainly, the basic limitation hindering the verification of the scenarios presented above is the lack of aDNA samples from people living in the period between the disappearance of the Unetice culture and the beginning of the IA and from people belonging to the Przeworsk culture.DNA extraction from skeletal samples and preparation of barcoded next-generation sequencing libraries were performed in a clean laboratory dedicated to ancient DNA analysis. All DNA samples were initially submitted to screening sequencing with the use of Illumina GAIIx or NextSeq 550 instruments to obtain 5–10 million 75-bp single reads per sample. The obtained sequences were mapped  to the hLibraries selected as the most promising as a result of screening sequencing were enriched in sequences overlapping the mitochondrial genome, Y chromosome, or whole human genome and sequenced with an Illumina HiSeq 4000 instrument and 100-bp-long paired-end reads. The data were processed as described above with additional trimming of 5 nucleotides from 5′ and 3’ ′ends. Then, the data from different libraries prepared with DNA of the same individual were merged. DNA authenticity was further evaluated by estimating X-chromosome contamination in males based on the rate of heterozygosity  and mtDNhttp://www.phylotree.org/) [mtDNA haplogroups were assigned based on complete mtDNA sequences using HaploFind  with resee.org/) . Only saThe Y-chromosome haplogroup nomenclature of the International Society of Genetic Genealogy (ISOGG) database ver. 14.111 was applied. Haplogroup prediction was performed with Yleaf v2.1  with theFor the comparative genome-wide analyses, several publicly available datasets were applied. The first dataset contained genomes of 983 present-day West Eurasians genotyped with the Human Origins Array (HO dataset). The second dataset, consisting of 694 human genomes, was extracted from the Population Reference Sample (POPRES) project  by randoFor each ancient and present-day individual, each heterozygous SNP was assigned as pseudohaploid by randomly sampling one of two alleles detected at a particular position.PCA was performed on the HO, POPRES, and ancestry-restricted datasets using the ‘smartpca’ program in EIGENSOFT . PCA wasK = 2 to K = 10 clusters, with 20 replicates for each K value. For each K value, the replicate with the highest log likelihood was retained. Finally, we chose the K = 9 value, as it was the lowest value for which components of ancestry related to Northwestern and East-Central Europeans were maximized. Supervised ADMIXTURE was run on the dataset restricted to the present-day Northwestern and East-Central Europeans.Model-based clustering analysis was performed using unsupervised ADMIXTURE  on the Hf-Statistics were computed using the HO dataset (present-day populations) or AADR v54.1 1240 panel (ancient populations) in ADMIXTOOLS with default parameters. We used qpDstat with f4mode:Yes for f4 statistics. Standard errors were calculated using a weighted block jackknife over 5-Mb blocks.One-way and two-way qpAdm modelling was done for each IA and MA sites separately. As left populations, we used West Eurasian ancient populations preceding or contemporary to the IA and MA groups. All models were tested under the same set of right outgroup populations consisting 17 ancient populations previously described by . For detAdditional file 1. Supplementary Figs. S1-13 and Supplementary Materials.Additional file 2. Supplementary Tables.Additional file 3: Fig. S14. PCA embedding of the studied ancient individuals and present-day West-Eurasians. Ancient individuals were projected onto the first two eigenvectors of a PCA based on contemporary West Eurasians.Additional file 4: Fig. S15. Genetic distances between studied ancient individuals and present day West Eurasians. Colour coding reflects the genetic distance as measured by the Fst coefficient between ancient individuals and present-day ones separately for the IA and MA group.Additional file 5: Fig. S16. Estimated Eastern-European admixture proportions in ancient populations. Shown is the result of unsupervised ADMIXTURE run at K=9 corresponding to the genetic components presented on Fig. 1D and fig. S17.Additional file 6: Fig. S17. Estimated ancestry proportions for present-day populations from Human Origins dataset. Shown are unsupervised ADMIXTURE results for K=9.Additional file 7: Fig. S18. Geographical distribution of Eastern-European admixture in present-day European populations. Shown is the result of unsupervised ADMIXTURE run at K=9 corresponding to the genetic components presented on Fig. 1D.Additional file 8: Fig. S19. Geographical distribution of North-Western admixture in present-day European populations. Shown is the result of unsupervised ADMIXTURE run at K=9 corresponding to the genetic components presented on Fig. 1D.Additional file 9: Fig. S20. Spatiotemporal distribution of samples and their genetic affinities to ancient populations. (a), PCA embedding of the studied samples and other ancient post-LN samples from Western Eurasia. Polygons highlight the space occupied by the samples from each group. (b), supervised ADMIXTURE (K=3) for IA and MA individuals modelled as mixtures of Neolithic Anatolian Farmers (orange), Western Hunter-Gatherers (blue) and Yamnaya (green).Additional file 10. Author and affiliation list for members of the Polish Archaeogenomic Consortium.Additional file 11. Peer review history.


Morphological divergence between Siamese and other crocodiles has been identified by size, number of scales, and patterns of cervical squamation with post-occipital scutes (P.O.). However, a large variation of P.O. has been observed in captive Siamese crocodiles in Thailand, leading to questions about possible crocodile hybrids. The genetic diversity and population structure of Siamese crocodiles were studied using mitochondrial DNA D-loop and microsatellite genotyping. The STRUCTURE plot revealed numerous distinct gene pools, indicating that the crocodiles in each farm descended from distinct lineages. Researchers also discovered evidence of introgression in several individual crocodiles, implying that Siamese and saltwater crocodiles may have hybridized. A schematic protocol for screening hybrids was proposed based on patterns observed in phenotypic and molecular data.Crocodylus siamensis) have severely declined because of hunting and habitat fragmentation, necessitating a reintroduction plan involving commercial captive-bred populations. However, hybridization between Siamese and saltwater crocodiles (C. porosus) has occurred in captivity. Siamese crocodiles commonly have post-occipital scutes (P.O.) with 4–6 scales, but 2–6 P.O. scales were found in captives on Thai farms. Here, the genetic diversity and population structure of Siamese crocodiles with large P.O. variations and saltwater crocodiles were analyzed using mitochondrial DNA D-loop and microsatellite genotyping. Possible crocodile hybrids or phenotypic variations were ascertained by comparison with our previous library from the Siam Crocodile Bioresource Project. Siamese crocodiles with <4 P.O. scales in a row exhibit normal species-level phenotypic variation. This evidence encourages the revised description of Siamese crocodiles. Moreover, the STRUCTURE plot revealed large distinct gene pools, suggesting crocodiles in each farm were derived from distinct lineages. However, combining both genetic approaches provides evidence of introgression for several individual crocodiles, suggesting possible hybridization between Siamese and saltwater crocodiles. We proposed a schematic protocol with patterns observed in phenotypic and molecular data to screen hybrids. Identifying non-hybrid and hybrid individuals is important for long-term in situ/ex situ conservation.Populations of Siamese crocodiles ( Crocodylus siamensis, Schneider, 1801) ,,21,20,21ulations ,25,26,27The mtDNA D-loop sequences of DNA fragments were amplified using the primers mtCytbf2 (5′-TGCCATGTTCGCATCCATCC-3) and mt12srRNAr2 (5′-CCAGAGGCTA GGCGTCGTGG-3) . We usedF values, but Siamese crocodiles from CN and NP populations exhibited positive F values, similar to saltwater crocodiles from NR. The PIC of all captive populations ranged from 0.057 to 0.932 and I ranged from 0.153 to 3.031 : 0.629 ± 0.033) and the He values ranged from 0.058 to 0.936 (mean ± SE: 0.718 ± 0.037)  . Welch’sve pairs . The AR r values were calculated for a total of 13,530 combinations of crocodiles, which included all 165 sampled individuals, including Siamese and saltwater crocodiles, were −0.018 ± 0.033 . No pairs showed r < −0.25. There were 13,525 pairs with −0.25 < r < 0.25 and 5 pairs with 0.25 > r , 202.2 (95% CI: 87.7–113.3) for the NR population, 115.5 (95% CI: 69.5–88.1) for the CN population, 45.2 (95% CI: 37.1–136.1) for the NP population, and 103.7 (95% CI: 66.7–74.8) for saltwater crocodiles in the NR population  were observed in the estimates of FST between captive populations after 110 permutations. The AMOVA showed that genetic variation was 84% among individuals crocodiles within a population and 11% between populations  based on Evanno’s ΔK, while the mean ln P(K) also revealed one peak (K = 16) (Significant differences (ulations . Nei’s ge others . The dise others . Differe(K = 16) .C. rhombifer) (GenBank accession number: NC_024513); whereas CP01, CP05, CP06, CP11, CP12, CP13, CP15, CP20, CP23, CP25, CP27, CP28, CP29, and CP30, categorized with the saltwater crocodile, were placed as a sister clade to Siamese crocodile  from CB, CN, NR, and NP were grouped in the same gene pool, while saltwater crocodiles were part of a new group, with a small part shared with Siamese crocodiles. At higher K levels, saltwater crocodiles  became identifiable, whereas Siamese crocodiles from different farms were separated from each other. Gene pool structuring from both species or each farm showed admixture at higher K levels; however, P.O. scale number variation also appeared in Siamese crocodiles with mixtures of specific gene pools of Siamese crocodiles. This suggests that Siamese crocodiles with fewer than four scales in a row are part of normal phenotypic variations at the species level. Similarly, the first version of the species identification guideline was revised after DNA analysis and proved various characteristics under the same species such as in fighting fishes [As shown in the STRUCTURE plot and PCoA, Siamese crocodiles from CB, CN, NR, and NP, and saltwater crocodiles from NR, were likely clustered into different groups. We analyzed the clustering order and gene pool pattern from ocodiles . For K =g fishes ,49. This misunderstanding of widespread hybridization in Thailand has probably resulted from personal communication among experts from IUCN/SSC/Crocodile Specialist Group and other NGOs who visited farms and followed the CITES Identification Guide based on morphological characteristics in Charette , leadingK levels of the STRUCTURE plot. Although Siamese crocodile individuals from this study grouped in the same gene pool at K = 3, and Siamese and saltwater crocodile individuals from our previous study [K = 5–25. In the NP population, two subpopulations of different gene pools were found, consistent with the positive fixation index value. It can be inferred from this that Siamese crocodiles in each farm had different ancestral original lineages.Using data generated from microsatellite loci derived from Siamese and saltwater crocodiles by Lapbenjakul et al.  and thisus study  were shoAR, and Ne in Siamese crocodiles from the four captive sites in this study. The pairwise FST value was statistically significant among the four populations, implying genetic structure differentiation between the farms. The differentiation in genetic makeup reflects the accumulation of variations in allelic frequencies, providing crucial insights into the evolutionary history, genetic drift, and selection of distinct populations [K = 5–25. This was also observed in the saltwater crocodile population, which might have resulted from the historical genetic exchange of parental stocks between farms. Regarding mtDNA D-loop sequences, positive Nm, low FST, and sharing haplotypes of crocodiles between farms also confirmed the presence of crocodile genetic exchange in the market farms. The exchange of Siamese crocodile parental stocks has been conducted to impede inbreeding within each captive site, which may provide a negative inbreeding level. These findings collectively suggest that large captive populations of Siamese crocodiles held on farms represent a good potential source for reintroduction programs. Crocodile farms under TCFA are willing to donate Siamese crocodiles for this purpose [Historically, Siamese crocodiles were widespread in Central Thailand. The current captive Siamese crocodile populations might reflect differences in the original sources brought into farms in Thailand. This result agreed with the genetic diversity parameters showing high values of heterozygosity, ulations ,54. Howe purpose . i) Consideration of genetic admixture at different K levels to examine the trend of clustering, separation of allelic signals and the majority of allelic pattern, although the best K level might be predicted from different algorithms; (ii) sharing a gene pool between the two species might be possible, but should not have more than a posterior probability of 0.05 at the K level, which shows the trend of separation between the two species; (iii) clustering by PCoA should be considered together with the STRUCTURE plot to test the group of crocodile specimens; (iv) determination of maternal lineage by mtDNA D-loop sequences may be added to confirm; and (v) external morphological observation with updated phenotypic variations in the P.O. should be scored together with genetic screening.Captive crocodiles must be genetically identified at the species level before release ,56,57. H1 hybrid or backcross shows diverge chromosome constitution from the parental species [2, F3 or backcross) might escape detection of chromosome number. More research utilizing genome-wide scans with single nucleotide polymorphisms (SNPs) is necessary to enhance our comprehension of selection signatures in diverse populations and species. However, genome-wide SNP analysis might not be reliable for multiple processes with small crocodile numbers in each reintroduction program. From this state, we found suspected hybrids with CB11 (P.O. 4), CB22 (P.O. 3), CB27 (P.O. 4), CP01 (P.O. 0), CP05 (P.O. 4), CP06 (P.O. 4), CP11 (P.O. 4), CP12 (P.O. 4), CP13 (P.O. 4), CP15 (P.O. 2), CP20 (P.O. 0), CP23 (P.O. 4), CP25 (P.O. 2), CP27 (P.O. 4), CP28 (P.O. 4), CP29 (P.O. 0), CP30 (P.O. 0), CSI05 (unidentified P.O.), CSI06 (unidentified P.O.), and CPO09 (unidentified P.O.)  , which sC. rhombifer) as a result of human-mediated movement has also been observed in Southeast Asia [The Thai government and TCFA have never promoted hybridization in crocodile farms under Appendix I captive breeding operation ; howeverast Asia ,39,44,45ast Asia  or in thIdentifying purebred individuals from captive populations is a significant challenge for restocking and reintroduction efforts. The CSG has proposed various recommendations to support the conservation of Siamese crocodiles, including legislative and regulatory measures, compliance with CITES obligations, appropriate management of captive populations, conducting surveys and conservation initiatives, controlling illegal trade, promoting regional conservation initiatives, and exploring restocking options. These recommendations are aimed at supporting the current efforts of Thai national agencies to conserve the species, including the Department of Fisheries under the Ministry of Agriculture and Cooperatives and the Department of National Parks, Wildlife and Plant Conservation (DNP) under the Ministry of Natural Resources and Environment. To restore, protect, and create habitats for the Siamese crocodile, initiatives for public–private partnerships and sustainable financing will be launched. Prioritizing the management of key threats and conducting large-scale assessments of Siamese crocodile conservation status will be crucial for addressing challenges related to populations, protected areas, and other conservation initiatives.These results indicate that a P.O. variation of 2–4 is within the species-level variation of Siamese crocodiles. In short, this is a phenotypic variation and not the result of hybridization with saltwater crocodiles. This baseline information on the association between genetic status and phenotypic variation of Siamese crocodiles in captive populations in Thailand is important for future conservation. Large differences in gene pools were observed in Siamese crocodiles, suggesting different historical origins of Siamese crocodiles in the wild population before massive capture and collection. Recently, a call was made to redefine the role of admixture in species conservation. It emphasized that crocodiles that have undergone gene flow and introgression during their evolutionary history or have been impacted by anthropogenic issues require protection measures. Ultimately, hybridization presents a management problem for Siamese crocodiles and complicates species identification based on morphology alone . Adequat


Euphorbia resinifera is a melliferous, medicinal, and endemic plant to Morocco. Nevertheless, its ecological and genetic diversity still unknown. The objective of this study is to analyze the diversity and genetic structure of Moroccan wild populations of E. resinifera using ISSR markers. Twelve natural populations collected from its geographical range in Morocco were analyzed using 14 ISSR primers.r = 0.507; P < 0.0001).A total of 125 bands were obtained, with polymorphism of 74.81%. The polymorphic information content (PIC), resolving power (Rp), Shannon’s information index (I), and total genetic diversity (Ht) were 0.33, 2.8, 0.35, and 0.21, respectively. The analysis of molecular variance showed that 75.56% of the total variability is present within populations and that 24.44% exists among populations. Also, the analysis showed a very low genetic differentiation between groups of mountain range type (FCT = 0.066), mountain versant type groups (FCT =  −0.024), and altitude groups (FCT =  −0.022). Moreover, the geographical distances between populations are correlated with their corresponding genetic distances according to the Mantel test (These results suggest that the population structuring follows a model of isolation by geographical distance. Indeed, the genetic structuring of populations into two groups obtained from PCoA and structure analysis revealed a dependence on the geographical origin of the populations. By contrast, the genetic distances are not correlated with the altitude. Euphorbia is the largest of the Euphorbiaceae family and the second largest genus of flowering plants in the world [Euphorbia resinifera O. berg is one among them, growing naturally in the Atlas Mountains, particularly in Beni Mellal and Azilal provinces [E. resinifera employs both sexual and vegetative means of reproduction to ensure the survival and expansion of its population [E. resinifera populations during the flowering season. Thus, with an annual honey production of about 300 tons, it is an important plant of the solidary agriculture in the region. It is well known as a melliferous plant for its high therapeutic and nutritional quality honey [With nearly 2000 species, the genus he world , 2. Desphe world , the genhe world . In Morohe world . Euphorbrovinces . The plarovinces . It prespulation . The yelty honey , 9, whicty honey .E. resinifera was recognized as having a vast therapeutic properties and benefits. In folk medicine, it is used to treat some types of complicated dermatoses, cancer treatment and control glycaemia in type II diabetics [E. resinifera was identified [Additionally, iabetics –13. In 1entified . This moentified . Moreoveentified , 17, antentified , 19, an entified , 21.E. resinifera populations may help to identify different genotypes and rationalize conservative treatments. Therefore, it has become imperative to establish a research program aiming at the evaluation of the genetic diversity of this species. Our first study on morphological characters showed high phenotypic diversity in twelve Moroccan populations [Nevertheless, the genetic resources of this species underwent important genetic erosion caused by various factors including the overgrazing, the deforestation provoked by a demographic pressure, voluntary or natural fires, and some fungal diseases attacking the plant. Consequently, emergency measures should be taken to safeguard this wild species. Thus, the safeguard and valorization of these local plant genetic resources constitute not only an imperative but also a major component of the stability of natural ecosystems and their valorization in agro-economic, therapeutic, and environmental perspectives. For that, the description of Moroccan ulations . HoweverSeveral types of molecular markers are employed for assessment of genetic diversity and relationships in plant species, including RFLPs (restriction fragment length polymorphisms) , RAPDs . Firstly, global analysis of AMOVA was done to apportion the total genetic variation into two hierarchical levels: among populations (FST) and within populations. Secondly, hierarchical AMOVA analysis was used to partition the variation among:Two mountain range groups of populations: Middle Atlas  and High Atlas Three mountain versant groups of populations: North versant , Southwest versant , and South versant Two altitude groups of populations: very low  and low  Table The band profiles of each gel were scored visually and recorded as presence (1) or absence (0) of bands leading to the construction of data binary matrix . For each primer, the percentage of polymorphic band (PPB), the polymorphic information content (PIC), and resolving power (Rp) were determined. Also, the POPGENE software was used to measure the following parameters: numbers of alleles (Na), effective number of alleles (Ne), genetic diversity within populations (Hs), total gene diversity (Ht), coefficient of gene differentiation (Gst), and Shannon’s information index (I). Partition of the observed genetic variation and calculation of the corresponding em) was approximated estimated through Wright’s island model: Nem = 0.25 (1/FST - 1) [Population’s specific FST indices were also calculated to investigate which population is more divergent from the remaining. The average gene diversity over loci was calculated to estimate intra-population variability. These evaluations were performed using the package ARLEQUIN version 3.01 . The paiFST - 1) . A ManteFST - 1) . To idenFST - 1) , which iFST - 1) . FurtherFST - 1) .E. resinifera populations, of which 101 band were polymorphic. The number of bands ranged from 4  to 13 (UBC840), with an average of 7.21  varied from 0.12 (UBC834) to 0.44 (UBC836) with an average of 0.32, indicating that 32% of total genetic variability was distributed among populations and the remaining (68%) accounted for within populations. These results were congruent with that revealed by FST value, accounting for 0. 244 (Table em = 0.77) which provides information on amount of number of exchanged individuals between populations studied.Estimates of genetic diversity of studied populations are summarized in Table .12 UBC83 to 0.44 When AMOVA was performed, at three hierarchical levels, with two mountain range type, very low genetic differentiation was observed between groups (FCT = 0.066) even though that it is slightly significant Table . This inST values and geographic distances between the 12 populations are presented in Table r = 0.507; P < 0.0001), the geographical distances between populations are correlated with their corresponding genetic distances. These results suggest that the populations’ structure follows a model of isolation by geographical distance. By contrast, no significant association was obtained between the genetic distance and difference of altitude between populations . Thus, the populations “AAS” and “FMJ” with a low difference of altitude (2 m) have a high value of genetic distance (0.376), and the populations “BZO” and “WAW” with a high difference of altitude (743 m) have obtained the low value of genetic distance (0.195). Between 66 pairwise FST values, 54 values are significant, meaning that the populations are widely different from each other. The significant values varied from 0.084  to 0.466 , which mean that populations FMJ/BZO are the most genetically similar and OUZ/KSB are the most divergent. The result indicated that the Bin El Ouidane (BIN) and Bzou (BZO) populations have the lowest value of specific FST index (respectively 0.209 and 0.212), and Elksiba (KSB) population has the highest value of this index (FST = 0.289), implying that this latter population is the most divergent from the others studied. Regarding the intrapopulation variability assessed by gene diversity over loci (data not shown) which oscillated from 0.148 for Elksiba (KSB) to 0.261 for Bin El Ouidane (BIN) and 0.257 for Bzou (BZO), reflecting consequently for any in situ and/or ex situ conservation strategy should aim to include those later populations.The pairwise FK = 2 (H′ = 0.947), indicating the stability of the result for this model. Considering the genotypes as pure when the membership coefficient was greater than 0.80 and as a hybrid or admixture when the membership coefficient was lower than 0.80, 41 individuals among the 60 analyzed (68.33%) were assigned to one of the model’s defined groups. The first group (red) was formed by the individuals from High Atlas Mountain populations, namely Bzou (BZO4), Ouled Ayyad , Bin El Ouidane (BO3 and BO4), all individuals from populations of Ouzoud , Imi n’Ifri (IMI5), and four from Wawla , with a membership coefficient oscillated between 0.815 to 0.990, while the rest of this group were originating from Middle Atlas Mountain populations: Afourer (AFO2 and AFO4), Modj (MOD1), with a membership coefficient ranging from 0.953 to 0.980. Nevertheless, the other seven individuals, namely  from Tagzirt population,  coming from Afourer population, MOD3 from Modj population, IMI1 belonging to Imi n’Ifri population, and WAW3 from Wawla population could be considered as admixed (coefficients ranged from 0.507 to 0.644). The second group (green) contained individuals coming from High Atlas Mountain populations:  of North versant, with a membership coefficient ranging from 0.954 to 0.975, all individuals of Foum Jemaa population , and one individual (IMI2) from Imi n’Ifri population belonging to Southwest versant (coefficients between 0.840 and 0.959) and  collected from South versant having a membership coefficient between 0.922 and 0.986, while the rest of group include bushes coming from Middle Atlas Mountain populations: (AAS3 and AAS4) of North versant with an assignment coefficient of 0.888 and 0.86 respectively,  of Southwest versant with a membership coefficient from 0.932 to 0.969, and all bushes of Elksiba population  from South versant mountain, having a coefficient of assignment oscillating between 0.929 and 0.976. Finally, 12 remaining bushes which could be considered as admixture belonged to the OAY, AAS, AFO, MOD, IMI, and BZO populations (coefficients between 0.508 and 0.775).The structure analysis based on the ΔK method showed that the best number of genetic clusters (K) was 2, suggesting that all individuals fell into two clusters  in Iran by Pahlevani et al.  [Euphorbia prostrata Aiton, Euphorbia peplus L., and Euphorbia terracina L.)  [Euphorbia species by El-Hawary et al.  [Euphorbiaceae (Croton urucurana Baill) (89%) based on ISSR markers [E. telephioides (80.7%) revealed by allozyme markers [E. resinifera. These values are comparable with these obtained by Reginaldo et al. for Brazilian Euphorbiaceae (Croton urucurana Baill) using ISSRs markers (PIC = 0.29 and Rp = 3.4) [I = 0.35). This value is higher than that obtained by Reginaldo et al. (I = 0.26) for Brazilian Euphorbiaceae using ISSRs markers [E. resinifera populations is in agreement with the general trend for allogamous and long-lived woody perennial species (Ht = 0.28) and for angiosperm species (Ht = 0.28) [E. resinifera were largely differentiated  which could be due to restricted gene flow (Nem = 0.77) between populations. This finding is in concordance with that observed for other herbaceous outcrossing perennial plant species (Gst > 0.20) [Euphorbia telephioides (0.10) using allozyme markers [Euphorbia fauriei in Korea  and Euphorbia jolkinii in Taiwan  [E. resinifera populations.DNA markers have become a useful tool for evaluating the genetic diversity of many different plant species. In this work, ISSR markers were used to assess the genetic diversity of ctively) , in Saudctively) , and for= 57.7%) . However markers  and by D markers . In addip = 3.4) . Moreove markers . Besides = 0.28) . The wil > 0.20) . The Gst markers . Similar = 0.77) . The indThe analysis of molecular variance (AMOVA) showed that 75.56% of the diversity accounted for within population leaving 24.44% for among populations. The existence of high genetic variability within population should help the population to cope with local environmental changes. This result implies that sampling from a small number of populations, particularly those with high intrapopulation variability, is sufficient for species in situ and/or ex situ conservation. Bin El Ouidane (BIN) and Bzou (BZO) populations with high intrapopulation variability are more convenient for this purpose.E. resinifera populations. Strangely enough, genetic distances were not correlated to the difference of altitude between the populations . A similar result was reported for Moroccan walnut using SSR marker [r = 0.507; P < 0.0001). This result suggests that the population structure follows a model of isolation by geographic distance. Consequently, our finding suggests that more closely situated populations tend to be more genetically similar to one another.Moreover, hierarchical AMOVA revealed a very low genetic differentiation between the two groups of mountain range (FCT = 0.066), even though that it is slightly significant, which suggests that mountain range type have had little influence on structuration’s populations. These findings give choice to sampling from populations of Middle or High Atlas Mountains for conservation and breeding species. Likewise, when assembling the populations according to their mountain versant type and altitude, a very low genetic differentiation was obtained between respective groups , indicating that mountain versant type and altitude did not have an effect on population structure of the Moroccan R marker . Also, oR marker , 59, proE. resinifera populations in Morocco using ISSR markers. The results of this study confirmed that ISSR markers could be powerful tools for detecting genetic diversity among and within E. resinifera populations. The level of genetic diversity was high, and the genetic variation mainly existed within populations. The results led to structure of populations in two gene pools independently of mountain range type, mountain versant type, and altitude. Based on these results, it could be sufficient to sample from a few populations, particularly those most genetically diversified, for any in situ and/or ex situ conservation strategy.The present study is the first work aiming to evaluate the genetic diversity and structure of


AP) was developed to facilitate the comparison of SIDTA with AMOVA. These analyses show that the genetic population structure seen by AMOVA is notably different in many ways from that provided by SIDTA, and the extent of this difference is greatly affected by the stability of the markers employed. Negative among group values are lacking with SIDTA but occur with AMOVA, especially with allopolyploids. To provide more focus on measuring allelic differentiation among populations, additional measures were also tested including Bray–Curtis Genetic Differentiation (BCGD) and several expected heterozygosity-based indices . Corrections, such as almost unbiased estimators, that were designed to work with heterozygosity-based fixation indices  are problematic when applied to differentiation indices .AMOVA is a widely used approach that focuses on variance within and among strata to study the hierarchical genetic structure of populations. The recently developed Shannon Informational Diversity Translation Analysis (SIDTA) instead tackles exploration of hierarchical genetic structure using entropic allelic diversity. A mix of artificial and natural population data sets  is used to compare the performance of SIDTA (a ‘q = 1’ diversity measure) vs. AMOVA (a ‘q = 2’ measure) under different conditions. An additive allelic differentiation index based on entropic allelic diversity measuring the mean difference among populations (Ω ST, GST) and allelic differentiation measures   scaled differentiation measure representing the mean allelic diversity among populations expressed as a proportion of the grand total diversity for a given data set (DT) (Equation (12)). The range of values and expected behavior for Ω and the other indices used in this study are presented in As noted above, AMOVA has both ‘MDAP’ formatted  and (17)). Thus, these three equations (Equations (2), (16) and (17)) all yield the same result.In addition to Equation (2), when all populations have a balanced number of samples, the TDAP index D′AP can, in turn, be apportioned among populations to yield the MDAP-formatted ΩAP. For a data set without a regional stratum, and when all populations (k) have an equal number of samples, ΩAP can be directly derived from D′AP by Equation (18):D′T to refer to the grand total variance. I also use FWI and FAI to refer to the proportion of FT represented by variance within individuals and by variance among individuals within populations, respectively. Na stands for the grand total number of different alleles in a data set (or subset) and is a q = 0 diversity index. Na-MIN is the theoretical minimum (Na-MIN = 1 = DT-MIN) for a given data set (or subset) and Na-MAX stands for the theoretical maximum Na possible (Na-MAX = DT-MAX). For data sets I–III, each subset has (Na-MAX = 40 = DT-MAX). Finally, N′a is the proportion of Na-MAX present in a given data set (or subset). In addition to the traditional F values, I use FAP was 50% for each subset, and the corresponding TDAP-formatted indices were at the theoretical maximum . The N′a for the subsets ranged from 10% to 90% of Na-MAX . Two additional subsets were created, one having Na-MIN and one having Na-MAX. This data set is available online in Nine pairs (subsets) of artificial diploid populations were created, each representing two populations and based on one ‘marker’. Each subset had the following properties: ten samples per population; no allelic overlap between populations, and with equal AMD (Δ), equal heterozygosity, and equal variance within each population. By design, ΩTen (10) pairs of artificial populations (subsets) were created based on the same parameters as the subsets in DS-I with the exceptions of (1) allelic overlap occurring between the two populations in each subset, and (2) TDAP indices were not at the theoretical maximum. This data set is available online in a for the subsets ranged from 0.05–0.95 . All of the changes in N′a were limited to one population (population A) across the subsets having N′a = 0.90–0.50, with the second population (population B) being unchanged across these subsets. The change in N′a for subset N′a = 0.40 resulted from changes in Na made in both populations. For this subset, there was no variance and heterozygosity in the population A . For subsets N′a = 0.3–0.05, change in N′a was limited to population B. This pattern yields an infection point at N′a = 0.5. Change in Na was required in both populations to achieve N′a = 0.95. This data set is available online in Eleven (11) pairs of artificial populations (subsets) were created based on the same parameters used for the subsets in DS-II except that heterozygosity, variance, and Δ are not balanced between the two populations in each subset. The N′Sphagnum (peat moss) gametophytes: haploid data set  index could be created for SIDTA:AMOVA also yields the  MDAP-formatted PHiPT index (an estimate of Φof FSTv) . PHiPT cSTv, FSTh, GST, G′STN), which measure fixation, the MDAP-formatted ΩAP index yields an assessment of differentiation among populations based on allelic diversity. The addition of ΩAP provides SIDTA with MDAP-, TDAP-, and ENP-based indices. Additionally, the suite of MD-formatted differentiation indices  allows for SIDTA-based analyses of the hierarchical genetic structure of populations to be clearly and effectively presented, as shown in Unlike the heterozygosity- and variance-based MDAP-formatted indices  vs. focus on the total difference among populations (TDAP)). For instance, a recent paper notes that Jost’s D is often mistakenly used as an estimator of GST . Such a AP and Jost’s D (and DEST) clearly measure different parameters. They only yield the same value when (1) two populations have identical alleles and allele frequencies (DEST = Jost’s D = D′AP = 0.0), and when (2) there are no shared alleles between two populations (DEST = Jost’s D = D′AP = 1.0). Between these two extremes, and based on DS-II and DS-III, D′AP is typically both greater than, and also tracks closer to N′a than either Jost’ D or DEST do , Jost’s D, DEST) focus on the probability of alleles being in a heterozygous pairing, F′STv measures variance among genotypes. Not a part of the Hill-number family of diversity measures [ST and FST, this study unequivocally shows that BCGD yields results close to those of the q = 1-based SIDTA D′AP and that they are highly divergent from those of the q = 2 indices (both expected heterozygosity based and variance based).The TDAP indices examined in this study all measure allelic differentiation, but they differ in the parameters that they measure: D′measures ,34, BCGDmeasures  to have AP and BCGD in one group (subclass I) and all of the q = 2 indices in a second grouping (F′STv (=G″ST), G′STH, Jost’s D, DEST) (subclass II). Subclass I indices track ‘q = 0’ far more closely than the ‘q = 2’ subclass indices do. This reflects how the subclass I indices are related to ‘q = 0’ diversity values, where each allele has the same weight (1). BCGD is based on simple allelic frequency , which is related to q = 0 data  by ‘1∙pi’, where pi is the relative frequency of an allele in a population. BCGD differs from the q = 1 SIDTA indices, which are related with q = 0 data by ‘1∙pi∙(ln pi)’. The SIDTA approach tends to dampen  the differences in frequency among the alleles in a population. Thus, these two subclasses I indices typically remain relatively close to q = 0 diversity data. Although BCGD closely tracks changes in allelic diversity among populations with DS-I and DS-II, it fails to always do so with DS-III. This indicates that BCGD does not track changes in allelic diversity in some cases. In contrast, the SIDTA-based D′AP closely tracks changes in allelic diversity across all three of the stress test data sets.Although all of the TDAP-formatted indices are equal to 1.0 with DS-I, that is what they were designed to do at the maximum values of the respective parameters that they measure. However, there are striking differences among their overall respective performances in DS-II and DS-III B. The moi2’, with the squaring of relative frequency data typically greatly increasing the difference between q = 0-based data and q = 2-based data, particularly when compared to that associated with the subclass I indices. Squaring the relative frequency of each allele results in more weight being given to alleles having high frequency and very little weight being given to uncommon alleles. This results in q = 2 indices measuring notably different parameters than those measured by the subclass I indices. The heterozygosity-based TDAP indices, which include Jost’s D, DEST, G′STH. and G″ST (=F′STv), are unequivocally shown to be tracking changes in the probability of expected heterozygosity by their performance in DS-II and DS-III. Another difference between the two subclasses is that subclass I indices are positive across the three stress test data sets while the adjusted heterozygosity-based indices and the variance-based indices of the subclass II have both positive and negative values.In contrast, the incorporation of q = 2 data with q = 0 data is achieved by ‘1∙pSTh and Jost’s D, which are restricted to lie within the interval from 0 to 1, are both positive across all three stress test data sets. In contrast, negative values occur in the ‘adjusted’ expected heterozygosity indices ; TDAP: G′STH, G″ST (=FSTv), DEST). The ‘adjusted’ expected heterozygosity indices are all positive in DS-1, all negative in DS-II, and both negative and positive with DS-III. The negative values result from the adjustments for small population size and inbreeding in the calculations made by GenAlEx for these indices. The adjustment leads to increases in both HS and HT relative to the corresponding unadjusted HS and HT values, but with HS being increased more than HT. The imbalance between the adjusted HS and HT values becomes greater with increases in N′a and negative values for estimated heterozygosity occur when HS > HT. With DS-II, this adjustment results in the adjusted estimated heterozygosity values always being negative. The occurrence of negative ‘among population’ values in the ‘adjusted’ heterozygosity indices closely tracks the occurrence of negative values in FSTv and F′STv. Consequently, one of the notable outcomes of ‘adjustment’ made by GenAlEx to several of the heterozygosity-based indices is that it allows for heterozygosity-based indices to more closely match the entire spectrum of FSTv and F’STv based values. Indeed, a bit of additional tweaking results in two heterozygosity-based indices being equivalent to variance-based indices .The two unadjusted heterozygosity indices, Fa is lower than when that same incremental change occurs when N′a is very high, given their sigmoidal nature. This is problematic for a differentiation measure to have. Another drawback to the q = 2 differentiation measures is that they were primarily designed for diploid genotypic data. Thus, their application to allopolyploids is problematic, especially with gametophytic data. For instance, negative variance values are considered as representing excess heterozygosity [WI), represents the allelic differentiation (ΩWI) among/between the ancestral monoploid genomes, not heterozygosity [Although it is not an issue for q = 1 differentiation measures, one drawback for q = 2-based differentiation measures is that an incremental change in allele number and frequency has a much smaller change on the resulting value of a given index when N′zygosity  and/or tzygosity . Howeverzygosity . In suchMarker variability has a major effect on the genetic structure of populations and the nature of this influence differs markedly when based on SIDTA versus AMOVA. Some of these effects are discussed in the section on negative F statistics below.WI) decreases with increasing marker variability while the reverse is the case for the proportion of variance within individuals (FWI). This pattern also holds true for ΩWI in allopolyploids, and, in the absence of negative values, it is also true for FWI. However, the frequent occurrence of negative values with AMOVA-based F statistics both greatly confuse this pattern for allopolyploids, and, also, notably distort the genetic structure of a population. The negative values are often associated with, but not limited to, FAI  represented by variance among individuals within populations). The most negative values occur with highly stable markers and then decline with increasing marker variability and, in some cases negative F values may be lacking with hypervariable markers. In contrast, negative values are lacking altogether in SIDTA-based indices.With both haploids and diploids, the proportion of total allelic diversity within individuals (ΩCareful consideration needs to be given to the variability of markers selected to study genetic structure as well as their influence on the method(s) of analysis used. In terms of marker variability, a strong focus on highly variable markers leads to a high degree of divergence among individuals within a stratum, and this has the potential to minimize, or completely mask, strong evolutionary signals that may be found in markers having lower variability ,19,37. IAI. As noted above, the most negative ‘F’ values occur with highly stable markers and then decline with increasing marker variability. The following is a brief discussion about the negative ‘F’ statistics associated with allopolyploids.Although lacking with SIDTA, negative F statistics are ‘part and parcel’ of AMOVA and are notably particularly an issue with allopolyploids. Negative values notably distort the interpretation of the genetic structure of a population, making interpretation difficult. They are often associated with, but not limited to, FWI is directly closely tied to N′a , and these particular values are largely independent of marker variability. For instance, based on SIDTA, D′WI for the allodiploid data is (D′WI ≥ 0.97) across the STAB, MOD, and HYPE subsets, and that for allotriploid data is (D′WI ≥ 0.87). This implies that the corresponding estimated variance within each individual is close to maximum. Furthermore, negative estimated variance among individuals augments both FWI and FWI*. Both ΩAI and FAI in contrast, are highly affected by marker variability. In allopolyploids, markers having low to moderate variability (such as the STAB and MOD subsets) frequently yield mean square among individuals within populations values that are less than the corresponding high mean square within individual values with AMOVA, which results in negative FAI values. Markers having a sufficiently high variability (such as the HYPE subset) may yield mean square among individuals within populations values that exceed the high mean square within individual values, and thus provide positive FAI values in allopolyploids, as is the case for the allotriploid data , while there is no variation among individuals (FWI = 0.0) with AMOVA. The second influence occurs primarily at diploid and higher ploidy levels where high divergence occurs among the respective component monoploid genomes. In such cases, AMOVA and the foundationally adjusted heterozygosity measures often yield negative values. Negative among group F values can occur with haploid data, but they occur more frequently at higher ploidy values, particularly with allopolyploids. Although this aspect is primarily a result of the extent of divergence among the component monoploid genomes, it is also influenced by ploidy level. Ploidy level has two major influences in a comparison of AMOVA and SIDTA. One is at the haploid level, where there is allelic diversity within individuals (DST (both FSTv and FSTh) and GST as measuring differentiation have the major problem that their conclusions unequivocally do not measure differentiation. Based on marker variability one can gain some general information to partially mitigate this issue. Generally, but not always, highly stable markers are likely to have FST and GST values exceeding the corresponding allelic differentiation between/among populations (ΩAP). When highly variable markers are used, there is a high probability that the FST values are much lower than corresponding ΩAP values. Additionally, when moderately stable markers are used it is possible that their associated FST values may be somewhat comparable to ΩAP, despite measuring different things. The above is based on haploid and diploid ploidy levels. This pattern does not always apply to allopolyploids, where the frequent occurrence of negative F values, particularly FAI, distorts the interpretation of population structure.Studies misinterpreting FEST), and (2) yielding both positive to negative values while the unadjusted heterozygosity-based indices are always positive. However, the adjustments affect these fixation and differentiation indices differently. All three of the stress test data sets show the adjusted heterozygosity-based fixation indices  to closely follow FSTh across all subsets. In contrast, the heterozygosity-based differentiation indices show a more variable pattern. This is most clearly shown in EST (as well as G″ST and G′STH) values closely track Jost’s D when N′a ≤ 0.5, but they greatly diverge from Jost’s D when N′a > 0.5, and ultimately become highly negative. These findings indicate that corrections, such as almost unbiased estimators, that are designed for fixation indices are problematic when applied to differentiation indices, particularly at high levels of allelic diversity. Given that the differentiation and fixation indices greatly differ in what they measure, it is not surprising that this is the case. Exploring why such adjustments result in such a complex relationship between Jost’s D and the adjusted heterozygosity-based differentiation indices  is beyond the scope of this study.For both the fixation and differentiation heterozygosity-based indices, adjustments for the almost unbiased estimators of heterozygosity  and for EST, F′ST) should be used [AP, ΩAP) would be the clear choice. Finally, if the presence or absence of alleles is what matters, then a q = 0 differentiation measure should be used. In addition, there are other aspects to consider, including: (1) when the focus is on the hierarchical genetic structure of populations and not simply on differentiation among populations; (2) avoidance of the problem of negative values; (3) when studying allopolyploids. For these issues, the q = 1-based SIDTA suite of indices clearly fits the bill.As the various allelic differentiation indices measure notably different parameters, it is clear that one size does not fit all. Thus, it is important to select the index (or indices) that match the focus of research being undertaken. If heterozygosity or variance are important causal variables in the given application, then heterozygosity-based q = 2 differentiation measures  differentiation measures (1) can decline with increases in allelic diversity and (2) can be negative, they unequivocally have limitations in the measurement of differentiation. Given this, studies focused primarily on how different two or more groups  are would most likely be best served by subclass I differentiation measures . This is especially true when comparing groups above the population level, where allelic diversity likely plays a bigger role than either heterozygosity or variance do.


QST–FST comparisons). Subsequently, a redundancy analysis (RDA) can facilitate identification of putative agents of natural selection on the trait. Here, we employed both a QST–FST comparisons approach and RDA to infer whether natural selection shaped traits of invasive populations of Solidago canadensis in China and identify the potential environmental drivers of natural selection. We addressed two questions: (1) Did natural selection drive phenotypic trait variation among S. canadensis populations? (2) Did climatic, latitudinal, longitudinal, and altitudinal gradients drive patterns of genetic variation among S. canadensis populations? We found significant directional selection for several morphological and reproductive traits  and stabilizing selection for physiological traits . The RDA showed that stem biomass of S. canadensis was strongly positively correlated with longitude, while leaf width ratio and specific leaf area were significantly positively correlated with the mean diurnal range. Stem biomass had a strong negative correlation with annual precipitation. Moreover, height of S. canadensis individuals was strongly positively correlated with altitude and precipitation of the wettest quarter. A longitudinal shift in precipitation seasonality likely selected for larger stem biomass in S. canadensis. Overall, these results suggest that longitudinal and altitudinal clines in climate exerted strong selection pressures that shaped the phenotypic traits of S. canadensis.Trait evolution in invasive plant species is important because it can impact demographic parameters key to invasion success. Invasive plant species often show phenotypic clines along geographic and climatic gradients. However, the relative contributions of natural selection and neutral evolutionary processes to phenotypic trait variation among populations of invasive plants remain unclear. A common method to assess whether a trait has been shaped by natural selection or neutral evolutionary processes is to compare the geographical pattern for the trait of interest to the divergence in neutral genetic loci (i.e.,  Solidago canadensis populations leading to genetic divergence among the populations.There was significant directional selection for several morphological and reproductive traits and stabilizing selection for physiological traits. Populations at higher latitudes expressed larger mean values of morphological traits, while populations in higher altitudes expressed smaller mean values of the traits. Climatic conditions associated with latitudinal, longitudinal, and altitudinal clines likely exerted differential selection pressures on  Second, if QST = FST , then the effects of natural selection and neutral evolutionary forces on the trait are not distinguishable and the trait is considered selectively neutral. Third, QST < FST  indicates that a trait is uniform across populations as a result of stabilizing selection  was mainly associated with adaptation to temperature oscillations , loci involved in adaptation to climate were identified using RDA , which find significant associations between genetic polymorphism, phenotypic variation, and environmental variables  is a native of North America that is presently invasive in China. In China, the perennial forb was first introduced into the southeastern region for use in ornamental horticulture in 1935 and subsequently escaped to the wild  Did natural selection drive phenotypic trait variation among S. canadensis populations? (2) Did climatic, latitudinal, longitudinal, and altitudinal gradients drive patterns of genetic variation among S. canadensis populations?Here, we employed both a 22.1S. canadensis populations  that contained a mixture of field soil, sand, and peat in the ratio of 6:3:1, respectively. The field soil was collected from Linhai City, Zhejiang Province, China. The soil mixture had the following properties: pH 6.8, organic matter 27.66 g/kg, total nitrogen 361.0 mg/kg, available phosphorus 8.0 mg/kg, and available potassium 12.0 mg/kg.To reduce environmental carry‐over effects, the field‐collected 2.3S. canadensis individuals from the 14 populations. We obtained three similar‐sized (ca. 15 cm) plantlets for each of the 12 families in the 14 populations from the pre‐experiment cultivation of plants described above. The plantlets were grown in a common garden in the field in three blocks. Thus, the total number of experimental plants was 504: 14 populations × 12 individuals  per population × 3 blocks. Within a block, the 12 individuals were planted in 12 separate plots that each measured 1.5 m × 1.5 m. The individual plants were grown 30 cm apart from each other. Throughout, the experimental plants received only rain‐fed water and were not fertilized.In April 2013, we established a common garden experiment on Linhai campus of Taizhou University  to assess quantitative trait variation among 2.4−2 s−1, leaf temperature of 25°C, CO2 concentration of 400 ppm, and relative humidity of 70%. For each plant, we recorded net photosynthetic rate (Pn), intercellular CO2 concentration (Ci), stomatal conductance (Gs), and transpiration rate (Tr). These measurements were taken six times on different dates, and the average of the six rounds of measurements was used in the statistical analyses described below. Seven months after transplant (November 2013), we measured various morphological, physiological, and reproductive traits of the S. canadensis individuals. Individual plant height, leaf length (L), and leaf width (L) for the third leaf per plant were measured to a precision of 0.1 cm. We used the L and W dimensions to compute the L/W ratio. Chlorophyll content of the third leaf was measured using a portable chlorophyll meter. We also measured basal stem diameter to an accuracy of 0.01 cm. After 97% of the plants had flowered, we counted the total number of inflorescences per individual plant. Then after the plants had matured, we obtained a total seed count and 1000‐seed weight per plant. We also measured the total leaf area per plant using a WinFOLIA computer image analysis system  for use in the computation of specific leaf area (SLA). The above‐ground plant parts were then harvested and separated into shoots, leaves, and seeds. Biomass of the stem, leaf, and seed was measured after the samples had been dried at 80°C for 72 h. We measured biomass using an electronic balance to an accuracy of 0.01 g. To obtain the total vegetative biomass per S. canadensis individual, we summed up the oven‐dried stem and leaf biomass of the individuals. We computed SLA as the ratio of leaf area to leaf dry biomass.One month after transplant (21–27 May 2013), we took in situ measurements of photosynthesis on the third fully expanded leaf from the top using a portable photosynthesis meter . The measurements were taken between 9:00 and 11:00 AM under the following conditions: a photosynthetically active radiation of 1400 μmol mS. canadensis individuals that were not harvested. After the measurements had been taken, the whole root system for each S. canadensis individual was dug up and washed free of soil particles under running water. The roots and shoots were then dried at 80°C for 72 h and weighed individually to the nearest 0.01 g.A year later (November 2014), we repeated measurements of the same traits above from the same 2.52.5.1S. canadensis individuals per population in the same 14 populations that we sampled for rhizomes as described above . We performed PCR amplifications using a PTC 220 Peltier Thermal Cycler  with the following settings: denaturation at 95°C for 5 min, followed by 34 cycles of 30 s at 95°C, 30 s at 58°C (−1°C per cycle), 45 s at 72°C, with a final elongation of 7 min at 72°C. We analyzed the PCR products using a Fragment Analyzer™ Automated CE System  with an 80 cm‐long capillary column. We used a DNF‐900 35–500 bp ds DNA Reagent Kit in the analysis. We genotyped DNA fragments using PROSize® 2.0 Data Analysis Software based on the elution time compared with a size standard.In November 2012, genomic DNA was extracted from 0.1 g of each leaf sample using a modified sodium dodecyl sulfate protocol on a FastPrep‐24 Automated Lysis and Homogenization System . Total DNA concentration was determined with a NanoDrop 2000 Lite Spectrophotometers . The DNA samples were then diluted to 10 ng/μL and stored at −20°C until use in simple sequence repeats (SSR) analyses. Five primer pairs  were used in SSR amplifications Table  Wieczor. We ran 2.72.7.1S. canadensis populations exhibited significant variation in phenotypic trait expressions in the common garden experiment. In the models, identity of S. canadensis population was included as a fixed term, while family and plot were treated as random terms. We specified morphological, physiological, and reproductive traits of S. canadensis individuals as response variables. Statistical significance of the factors was tested using F‐statistic. The models were run separately for measurements that we took in year 1 and year 2 of the experiment to assess whether the plant traits would be expressed consistently during the lifetime of S. canadensis. The statistical analyses were performed in SPSS v. 19.0.We ran general linear models to test whether the different 2.7.2N), percentage of polymorphic loci (P %), number of different alleles (Na), number of effective alleles (Ne), Nei's gene diversity per locus (HS), and Shannon's information index (I). We performed an analysis of molecular variance (AMOVA)  and neutral molecular marker (FST) differentiation among the 14 populations to infer the relative contributions of natural selection and random genetic drift to clinal variation in quantitative traits of S. canadensis. We computed the among‐population coefficient of gene differentiation (Fst) based on microsatellite DNA using GenAlEx v. 6.501 , wherein Vpop and Vind represent variation in the quantitative traits among and within populations, respectively  (see results below on population genetic structure) to infer the effects of natural selection vs random genetic drift.We compared quantitative genetic ), BIO3 = Isothermality (BIO2/BIO7) (×100), BIO4 = Temperature Seasonality (standard deviation ×100), BIO5 = Max Temperature of Warmest Month, BIO6 = Min Temperature of Coldest Month, BIO7 = Temperature Annual Range (BIO5‐BIO6), BIO8 = Mean Temperature of Wettest Quarter, BIO9 = Mean Temperature of Driest Quarter, BIO10 = Mean Temperature of Warmest Quarter, BIO11 = Mean Temperature of Coldest Quarter, BIO12 = Annual Precipitation, BIO13 = Precipitation of Wettest Month, BIO14 = Precipitation of Driest Month, BIO15 = Precipitation Seasonality (Coefficient of Variation), BIO16 = Precipitation of Wettest Quarter, BIO17 = Precipitation of Driest Quarter, BIO18 = Precipitation of Warmest Quarter, and BIO19 = Precipitation of Coldest Quarter) were downloaded from the WorldClim database (http://www.world‐clim.org/current) using DIVA‐GIS v. 7.2.1.1. The 19 bioclimatic variables were based on historical climate data for the years 1950–2000, which were interpolated at 30 arc‐seconds resolution (ca. 1 km2 resolution)  to ensure that the variable units were comparable , while the lowest genetic diversity  occurred within the WZ population (Table S. canadensis populations (92.186%) (Table S. canadensis populations (FST = 0.078). Estimated gene flow (Nm) among the populations was 2.955.We scored a total of 49 bands from the five SSR primers. The populations exhibited variability in the genetic diversity indices  I Table . The hig3.3QST values of all the morphological and reproductive traits and two physiological traits  were larger than the common FST value of 0.078 , while QST values of the other quantitative traits were lower than FST  and each of the three environmental variables  that were included in the model were significant (p < .05). The three environmental variables explained 4.5% of the variation in phenotypic traits of S. canadensis. The first axis (RDA 1) was significant  and primarily associated with annual precipitation (BIO12), while longitude and mean diurnal range (BIO2) similarly drove variation along the axis but in the opposite directions  and driven by the mean diurnal range (BIO2) and annual precipitation (BIO12) . The stem biomass of S. canadensis was positively correlated with longitude, while the L/W ratio and SLA were positively correlated with mean diurnal range (BIO2)  and each of the three environmental variables, including altitude, BIO3, and BIO16, that were included in the model was significant (p < .05). The three environmental variables explained 10.9% of the variation in phenotypic traits of S. canadensis. The first axis (RDA 1) was significant  and primarily driven by altitude and precipitation of the wettest quarter (BIO16)   .In the RDA for traits that were measured in the first year of the experiment, the global RDA model was significant (ns Table . The sec) Figure . However) Figure . The thr) Figure . For the) Figure . Plant h) Figure . The thi4S. canadensis in China   in both years of the experiment  Figure , which st Figure , which iFST = 0.078) among S. canadensis populations despite divergence in growth and reproductive traits among the populations. A possible explanation for this finding is that there was a high degree of gene flow, which led to admixed populations. Solidago canadensis reproduces both clonally and sexually . Studies on other plant species have found evidence of diversifying selection and local adaptation in spite of strong gene flow among populations of those species      developed larger stems when grown under drought conditions  Figure  indicate) Figure . We foun) Figure . TherefoCynodon dactylon (Poaceae) had a significant negative relationship with longitude   Figure  supportsS. canadensis at maturity was strongly positively correlated with precipitation of the wettest quarter (BIO16)  Figure  are in a) Figure . These re Figure  contradie Figure . As plane Figure , the pre4.3S. canadensis traits. Apart from climatic factors, soil biota can drive natural selection on plant traits , annual precipitation (BIO12), and precipitation of the wettest quarter (BIO16),acted as dynamic selective agents that promoted diversification of morphological and reproductive traits across the populations. Future field experiments evaluating the role of biotic interactions and ecophysiological function in a wide range of climatic conditions will be necessary to confirm the selective agents that produced the clines in phenotypic traits observed here.Our complementary use of Leshan Du: Data curation ; formal analysis ; investigation ; methodology ; writing – original draft . Ayub M.O. Oduor: Conceptualization (supporting); methodology ; writing – original draft ; writing – review and editing . Wei Zuo: Data curation ; formal analysis ; investigation ; methodology . Haiyan Liu: Data curation ; formal analysis ; investigation ; methodology . Junmin Li: Conceptualization (lead); data curation ; formal analysis ; funding acquisition (lead); investigation ; methodology ; project administration (lead); resources (lead); writing – original draft ; writing – review and editing .This work was supported financially by the National Natural Science Foundation of China (No. 31850410484 & No. 31270461), the Ten Thousand Talent Program of Zhejiang Province (2019R52043), the National Key Research and Development Program of China (2016YFC1201100), and Taizhou city 500 Talent Program.


Diceros bicornis) faces an uncertain future after an intense poaching caused a 98% decline in wild populations from 1960 to 1995. Although numbers are currently increasing, the animal remains critically endangered. The historical range of the black rhinoceros covered vast swaths of sub-Saharan Africa, but today's remaining individuals inhabit just a handful of protected areas. The survival of the black rhinoceros within the fragmented remains of its natural habitat relies on dedicated conservation efforts. A new study published in Molecular Biology and Evolution, “Historic Sampling of a Vanishing Beast: Population Structure and Diversity in the Black Rhinoceros” (The iconic African black rhinoceros (noceros” , reshapeThe study characterizes the population structure and genomic diversity of the black rhinoceros, both before and after its range-wide collapse in the last century, providing a model for how genetic diversity is shaped during population contractions. “The only way to really explore this is to use species with well documented, temporal collections that are also tied to good demographic records,” says Thomas Gilbert, one of the study's lead authors. “Sadly, species like the black rhinoceros are a perfect example, given their long-term appeal to big game hunters and poachers.” The motivation for the study, however, extended beyond mere scientific curiosity according to cofirst author Binia De Cahsan Westbury: “Studying the genetic history of the black rhinoceros through time provides crucial insights into its evolutionary trajectory and aids in developing effective conservation strategies for its remaining populations.”With this goal, the authors sequenced the genomes of 63 museum specimens collected from 1775 to 1981, as well as 20 individuals from modern black rhinoceros populations, compiling the most comprehensive genetic dataset of the species to date and significantly advancing earlier research efforts. “Whole genome sequences have revealed much more conservation-relevant population structure in the black rhinoceros than expected from traditional markers,” notes the study's other lead author, Yoshan Moodley, emphasizing the transformative power of cutting-edge genomic techniques.Analysis of the data revealed the presence of six major black rhinoceros populations historically as well as four subpopulations, offering more precise delineation of population borders than ever before. Notably, the results suggested that tectonic rifts in Africa during the Pleistocene had “driven the evolution of several hitherto unknown populations, many of which probably still exist within the present day Kenyan metapopulation,” highlights Moodley.In addition to geographical barriers, the evolutionary history of the black rhinoceros was shaped by secondary contact when these barriers to gene flow were temporarily removed. “The interplay of these events has resulted in a significant pattern of isolation by distance across the sub-Saharan territory of the species,” says De Cahsan Westbury, referring to a trend in which populations that are farther apart geographically also show greater genetic differences from each other.The researchers further evaluated levels of inbreeding among historical and modern populations of the black rhinoceros, an essential consideration for species that have suffered severe population bottlenecks. “Modern samples underscore the profound impact of population contractions and subsequent genetic drift,” notes De Cahsan Westbury, “with southern African individuals experiencing the most severe effects and the highest inbreeding among all populations.” Some populations showed evidence of inbreeding that predated the colonial period, which highlights the long-standing impact of human activity on this species according to the study's authors.Altogether, the study offers a resounding call to action to improve the conservation and management of the black rhinoceros. “For too long, wildlife conservation authorities have struggled to incorporate and implement urgent genetic recommendations, to the detriment of the biodiversity concerned,” notes Moodley. “It is absolutely crucial that the new populations identified in East Africa be given the highest conservation priority,” he emphasizes, echoing the study's urgent plea for comprehensive genetic testing of black rhinoceroses in Kenya and Tanzania. In addition, the distinct evolutionary groups identified in the study, such as the Ruvuma, Maasai Mara-Serengeti, and possibly Chyulu National Park subpopulations, should be the focus of separate management to maintain their unique genetic lineages.The study pays homage to the late Professor Mike Bruford of Cardiff University, a prominent figure in conservation genetics and a coauthor of the project. His death “was a tragedy for not only his family but conservation biology in general,” note the authors. Bruford's legacy continues to influence the field, an enduring testament to the pursuit of knowledge and the protection of Earth's genetic heritage.


Bufo gargarizans, to study local adaptation and genomic offset  under climate warming scenarios. We obtained high‐quality SNP data for 94 Asiatic toads from 21 populations in China to study spatial patterns of genomic variation, local adaptation, and genomic offset to warming in this wide‐ranging species. Population structure and genetic diversity analysis based on high‐quality SNPs revealed three clusters of B. gargarizans in the western, central‐eastern, and northeastern portions of the species' range in China. Populations generally dispersed along two migration routes, one from the west to the central‐east and one from the central‐east to the northeast. Both genetic diversity and pairwise FST were climatically correlated, and pairwise FST was also correlated with geographic distance. Spatial genomic patterns in B. gargarizans were determined by the local environment and geographic distance. Global warming will increase the extirpation risk of B. gargarizans.Genomic signatures of local adaptation have been identified in many species but remain sparsely studied in amphibians. Here, we explored genome‐wide divergence within the Asiatic toad,  First, SNP datasets are cost‐effective, allow sampling schemes to cover a broader geographical distribution range, and more reliably evaluate genetic patterns and variation at different spatial scales  to extract total genomic DNA from each sample. The quality of genomic DNA was assessed by 1% agarose gel electrophoresis. DNA templates were stored at −80°C for later use.2.2MseI , 0.8 U PstI‐HF , and 10× cut smart buffer. The reaction mixture was incubated at 37°C for 6 h and 65°C for 90 min. Second, the ligation reaction between fragments and adaptors was conducted in a reaction volume of 40 μL at 22°C for 2 h, containing 0.1 μM MseI adaptors, 15 μM common adaptors, and 200 U/μL T4 DNA Ligase . Third, each sample was amplified by PCR in a 25‐μL reaction volume containing 50–100 ng of adaptor‐ligated DNA fragments as template, 5 μL of 5× Master Mix , and two overhang primers (5 μM). PCR amplification was performed using the following program: 95°C for 30 s; 16 cycles at 95°C for 30 s, 60°C for 20 s, and 68°C for 15 s; and a final extension at 65°C for 5 min. The PCR products were run on a 1.5% agarose gel, and 300‐ to 500‐bp fragments were recovered from the gel. All samples were combined in a single pool for library construction. The quantity of DNA in the libraries was determined using a Qubit fluorometer (Invitrogen), and libraries were considered qualified when the DNA concentration was greater than 5 ng/μL. The qualified libraries were sequenced on the Illumina Novaseq PE150 platform using 150‐bp pair‐end reads. A total of 5 Gb of raw sequencing reads were obtained for each sample in this study. These raw sequencing reads have been deposited in the National Genomics Data Center (NGDC) GSA database (accession number CRA005915).We used the double‐digest restriction site‐associated DNA sequencing method to develop genome‐wide SNP markers . We used SAMtools 1.11 to convert SAM files to BAM files  based on the high‐quality SNP data and thereby analyzed population genetic structure  was calculated using the high‐quality SNPs in VCFtools. Genetic diversity, including nucleotide diversity (π), average expected heterozygosity (He), average observed heterozygosity (Ho), and Wright's inbreeding coefficient (FIS), was measured using the program Populations in Arlequin 3.5.2.2 , and mean annual solar radiation . These environmental variables were selected based both on their perceived biological relevance for the distribution of this toad species and the low degree of collinearity between variables  based on 20% of the occurrence localities . We used the resistance surface layer to calculate the least cost‐weighed distance (LCD) and circuit distance (CD).We used the eight environmental variables and 356 occurrence records as input data to predict the habitat suitability across the range of 2.5We used three pairwise distance matrices  to estimate the degree of geographic isolation between populations. Euclidean distance is by definition the straight‐line distance between localities without accounting for the effect of landscape characteristics. LCD and CD account for the heterogeneity of landscapes. LCD is the path that minimizes the total cumulative cost between two points through heterogeneous landscapes  in the habitat suitability model to estimate the environmental distance between populations. We used ArcMap 10.4 to extract values for every environmental variable of each population. We used R to calculate environmental distance by determining the absolute differences between populations and thereby produce eight pairwise environmental distance matrices  for partial Mantel test A (RPM‐A) and B (RPM‐B) between the genetic matrix and two geographic/environmental matrixes as reciprocal causal models. Comparing the values of RPM‐A and RPM‐B, if RPM‐A > RPM‐B, the variable form partial Mantel test A was better supported, and vice‐versa. A heatmap summarized results of RPM‐A – RPM‐B.We used reciprocal causal modeling (RCM) with partial Mantel tests, generalized dissimilarity modeling (GDM), and linear mixed effects models with maximum likelihood population effects (MLPE) to evaluate the effects of geographic distance and environmental dissimilarity on genetic differentiation. RCM was used to evaluate relationships between genetic distance and geographic distance and between environmental distance and geographic distance matrices while accounting for either environmental or geographic distance. A total of 110 tests were organized into 55 reciprocal causal models and the causal relationships were predicted following the methods of MacDonald et al. . We firsFST between localities caused by pairwise geographic and environmental differences. We performed GDM using the pairwise FST matrix for high quality SNP dataset as the response variable in the R package “gdm” 1.5.0  derived from 2081 to 2100 to predict genomic offset at the population level. These future bioclimatic variables were derived from six global circulation models  and the shared socio‐economic pathway [SSP245 (an average scenario) for 2081–2100]. Future climate variables were obtained by extracting average values from the WorldClim database at a 2.5‐min spatial distribution  on chromosome 11 and highest  on chromosome 1 Figure . A ML trFST values differed significantly from zero in most population pairs and ranged from 0.020 to 0.598, with an overall mean of 0.311 , and then the populations in the second terrain step  were further divided into two clusters along the altitudinal gradient . A Mantel test revealed a positive correlation between pairwise FST with Bio5 , NDVI  and longitude . However, all other geographic and environmental variables were not correlated with pairwise FST .Pairwise 11 Table . The res11 Table . The crot Figure . We consA Figure . The resHO did not differ from HE within each of the 21 populations , 0.452 ± 0.014 (0.370–0.614), and 0.083 ± 0.006 (0.040–0.123), respectively , and the reciprocal partial Mantel test indicated that genetic distance was positively correlated with Bio5 . Moreover, partial Mantel tests revealed that there were nonsignificant relationships between genetic distance and geographic/environmental distances when either Bio5 or Euclidean distance  was removed including LCD , CD , Bio2 , Bio6 , Bio12 , Bio13 , and Bio15 . However, there were significant correlations between genetic distance and geographic/environmental distances after partialling out either Bio5 or Euclidean distance  including NDVI  and Bio7 . GDM showed that the best supported variables affecting genetic distances ranked in order of importance were Bio13 (p = 0.040), Euclidean distance (p < 0.0001) and Bio5 , high‐latitude populations (northern populations) and populations along the Yangtze River and east coast (southeastern populations) compared with populations in the middle of the distributional range in China according to GDM analysis  clusters based on mtDNA, nuclear, and allozyme data of 166 specimens from 56 localities  among different populations. Genetic diversity is related to the evolutionary potential of species, their ability to respond to local and global environmental changes, and current population fitness  and FIS related to climatic variables were significantly correlated with climate, suggesting that the direction and strength of climate‐driven selection on populations of B. gargarizans vary throughout its range. Environmental variables have been reported to drive local adaptation in amphibians. For example, altitude drives variation in genomic diversity among Tibetan frog (Nanorana parkeri) populations (Wang et al., B. gargarizans. The effects of local temperature and precipitation on amphibians have been demonstrated in many species, such as P. guentheri (Cummins et al., R. temporaria (Muir et al., B. gargarizans.Both genomic diversity (e.g., 4.5B. gargarizans. RCM, GDM, and MLPE linear mixed effects models all indicated that environmental and geographic variables were the main factors affecting the spatial distribution of genomic variation in B. gargarizans (Figures Euptelea might be more vulnerable to extinction due to climate warming (Cao et al., B. gargarizans predicted that populations along the Yangtze River and in the western, northern and eastern coastal parts of the species' range might face higher climate change‐induced extirpation risk compared with other populations (Figure This study and a previous one (Pan et al.,  Figures  and 3. T Figures . For exas Figure . Previous Figure . The slos Figure . Complex5B. gargarizans in the western, central‐eastern, and northeastern parts of the species' range in China. Asiatic toads may have experienced an expansion both from west to east and from south to north. Spatial genomic patterns in B. gargarizans were determined by the local environment and geographic distance, as revealed by the fact that both IBE and IBD were the main drivers of spatial genomic differences in B. gargarizans. Future global warming will increase the risk of extirpation in populations along the Yangtze River and in the western, northern, and eastern coastal parts of the range of B. gargarizans in China.Population structure and genetic diversity analysis based on high‐quality SNPs revealed three clusters of The authors declare no conflicts of interest.Figure S1.Click here for additional data file.Figure S2.Click here for additional data file.Figure S3.Click here for additional data file.Figure S4.Click here for additional data file.Figure S5.Click here for additional data file.Figure S6.Click here for additional data file.Table S1.Click here for additional data file.Table S2.Click here for additional data file.Table S3.Click here for additional data file.