Taraxacum kok-saghyz (TKS) carries great potential as alternative natural rubber source. To better inform future breeding efforts with TKS and gain a deeper understanding of its genetic diversity, we utilized de novo sequencing to generate novel genomic simple sequence repeats markers (gSSRs). We utilized 25 gSSRs on a collection of genomic DNA (gDNA) samples from germplasm bank, and two gDNA samples from historical herbarium specimens. PCR coupled with capillary electrophoresis and an array of population genetics tools were employed to analyze the dataset of our study as well as a dataset of the recently published genic SSRs (eSSRs) generated on the same germplasm. Our results using both gSSRs and eSSRs revealed that TKS has low- to- moderate genetic diversity with most of it partitioned to the individuals and individuals within populations, whereas the species lacked population structure. Nineteen of the 25 gSSR markers cross-amplified to other Taraxacum spp. collected from Southeastern United States and identified as T. officinale by ITS sequencing. We used a subset of 14 gSSRs to estimate the genetic diversity of the T. officinale gDNA collection. In contrast to the obligatory outcrossing TKS, T. officinale presented evidence for population structure and clonal reproduction, which agreed with the species biology. We mapped the molecular markers sequences from this study and several others to the well-annotated sunflower genome. Our gSSRs present a functional tool for the biodiversity analyses in Taraxacum, but also in the related genera, as well as in the closely related tribes of the Asteraceae. This important crop is threatened by the South American leaf blight pathogen Microcyclus ulei (Henn.) Arx (syn. Pseudocercospora ulei ) and is losing competition for the land and the manpower against the economically favored African oil palm Elaeis guineensis Jacq4.The growing human population has generated an increased demand for resources, including rubber, a substrate used for over 40,000 commercial products1. Several thousand plant species from across the world were screened for laticiferous properties, especially at times of increased rubber demand, e.g., WWI or WWII5 , rubber ficus (Ficus elastica Roxb. ex Hornem.), and Russian dandelion . The molecular properties of rubber from each of these plants differ from those of the Hevea product6 and point toward specialty uses on species basis. For instance, the guayule rubber could be used for medical products because of the lower content of allergenic proteins2. The TKS rubber is of particular interest to the tire industry due to its high molecular weight (polymer index) and fast generation time (six months in TKS vs. seven years in Hevea), albeit with a comparatively higher content of allergenic proteins3. Moreover, each of these species could be grown in areas complementary to the Hevea palm (24°S through 23°N7) with latitudes reaching as high as temperate zones .Natural rubber from plants outperforms that from petroleum in several aspects: the polymer of the natural rubber has much higher molecular weight compared with the synthetic rubber and the sustainable and renewable production of the plant  rubber is considered superior to processing the non-renewable petroleum8. The tire industry reported in their very first uses of TKS rubber that the tires “differed but little, according to their mechanical characteristics, from those made from imported natural rubber9” (citing10). In addition, it offers an accessory gain of inulin used in the manufacturing of numerous commercial products14. Both biosynthetic pathways are linked interchangeably within the TKS metabolism15. Several establishments devoted to TKS rubber production were founded in the United States (US) and Europe  emanating from the major research projects .TKS is of particular interest for the industry due to the proven success in production of tires17 and Western Xinjiang, China18, and is currently grown in Western Europe and North America alike . The plant was a major crop and model plant for rubber studies during the times of the Soviet Union of Socialist Republics (USSR). As hypothesized in other studies, likely due to the governmental pressure on performance, the TKS germplasm was profusely confused with the common dandelion species . As a result, the world’s germplasm and botanical gardens collections were annotated as TKS for over 50 years despite being the common dandelions23. Recent United States Department of Agriculture - Agricultural Research Service (USDA-ARS) and European expeditions helped remedy this issue and provided new properly identified germplasm24. TKS is obligatory out-crossing, self-incompatible, diploid herbaceous plant (2n = 16), and morphologically resembles common dandelions, which exhibit mostly clonal reproduction due to polyploid genomic architecture25.TKS is native to Kazakhstan21, molecular26, biochemical14, physiological15, and breeding26 approaches. The outcrossing nature of TKS was regarded when devising the plant genome linkage map27, followed by its genome sequence assembly to contigs4 and transcriptome sequencing28. All of this helped elucidate the TKS latex biosynthesis pathways29. In addition, a number of physiological and developmental studies on currently available germplasm provided data that was helpful in maximizing the rubber/inulin yield in both years the plants were grown19. TKS also proved amenable to genetic transformation and tissue culture26, indicating the potential for its breeding engineering and increases in yield of rubber31 or inulin15.A recent spike in the TKS research confirmed the USDA-ARS collected species identity using the morphologicalet al.32 utilized the USDA-ARS TKS germplasm24 to estimate the genetic diversity of the species using 17 expressed-sequence tags/simple sequence repeats markers  mined from the available GenBank EST libraries, across 17 TKS populations from the species native area24. They concluded that the majority of the species diversity is captured within each population32. Similar conclusions were drawn from a study of Russian, American (USDA-ARS), and wild Chinese accessions of TKS using 23 eSSRs33. None of these studies utilized nuclear genomic short-sequence repeats markers (gSSRs) to infer the population structure and genetic diversity of this economically important plant species. Moreover, the available TKS genome assembled to the contigs level only4 is lacking an extensive annotation or higher-level organization, despite providing insights into the TKS rubber/inulin biosynthetic pathways.Although some progress has been made regarding TKS biology, physiology, and genetics, until recently only limited information was available regarding the species diversity and inheritance/interplay of traits of interest. McAssey de novo sequencing to generate novel TKS gSSRs and to estimate the genetic diversity and spatial structure of the USDA-ARS TKS germplasm. We hypothesized that the majority of the species diversity would be captured in each examined population, as found in prior studies that utilized eSSRs33. The specific research objectives included the following: (1) identifying and characterizing polymorphic gSSR loci using de novo sequence of the TKS genome and mapping of the useful polymorphic gSSRs and other marker sequences onto the well-annotated genome of the related species Helianthus annuus32; (2) estimating the genetic diversity and inferring the population structure of the USDA-ARS TKS germplasm24 and two available historical herbarium TKS samples using gSSRs; and (3) comparing the gSSR data with the published eSSR data of McAssey et al.32 and Yushuang et al.33 to reach better-informed conclusions on TKS genetic diversity. We then deployed those gSSRs in a cross-amplification study with the local US dandelion samples , including their molecular identification. Information provided here will be useful in advancing future TKS studies, in the current and future breeding efforts of this potential crop for renewable rubber, and in augmenting the currently available resources for analyses of Taraxacum spp. and related plants.The goal of our study was to infer the TKS population structure, information of high value for breeding of this potential industrial crop. We utilized de novo genome sequencing yielded 45,804,966 paired-end reads of 275 bp. After trimming, 42,367,598 reads with a mean length of 265 bp were masked and used for de novo assembly on ABySS. The resulting de novo assembly contained 8,077,494 unitigs, from which 99,429 SSRs were identified on 95,692 sequences. From these, 11,259 were compound SSRs, meaning that two SSRs were separated by at most 15 bp. Primers were computed for 22,764 perfect SSRs. The number of SSRs with primers were 15,760 for the di-, 4,893 for the tri-, and 2,111 for the tetranucleotides, respectively.The TKS et al.4 containing the markers used for the construction of TKS linkage map27 and those of the gSSR and eSSR32 population genetics studies were mapped to the sunflower genome. The markers analyzed were located across all eight TKS linkage groups based on the mapping back to the sunflower genome  networks, and population structure: Our results suggest no significant deviations from the Hardy-Weinberg equilibrium (HWE) across the 25 gSSR markers used to analyze the TKS populations . Collectively, the data indicated rather low genetic differentiation among the TKS datasets analyzed, despite high allelic diversity of the obligatory outcrossing TKS. In agreement with the spatial fixation indices accrued, AMOVA for the gSSR dataset indicated the majority of the molecular diversity partitioned among the individuals and not among populations .The amplified gSSR markers yielded from 3 to 13 alleles per locus, averaging about 6 across the TKS germplasm pool Table . The 25 ST ranged from 0.018 to 0.355 for the gSSR, and from −0.024 to 0.261 for the eSSR datasets, respectively (data not shown). The pairwise population FST distance matrices  for the gSSR and eSSR datasets provided similar results , we generated pairwise matrices of population genetic distances for both. Values of the population pairwise distance matrix of FST population-wise trees  supported the F-statistics conclusions with no evidence of population structure in the TKS germplasm analyzed with gSSRs Fig. . The lacees Fig. , with th32, the significant deviations from HWE presented a locus-wise pattern and were much more common in occurrence than in the gSSR dataset , and an inferred Nm = 2.02. Partitioning of the molecular variance with AMOVA for the eSSR dataset yielded results similar to the gSSR dataset . Differences occurred in the variance partitioned among individuals within populations, and the gSSR dataset showed higher value of this parameter than the eSSR dataset. The FST distance matrix for the eSSR dataset of TKS showed different population-wise clustering from the gSSR dataset  networks, and population structure: In the re-analyzed eSSR TKS dataset32 of TKS lacking well-defined population structure. Analysis of networks from both gSSR and eSSR datasets resulted in similar Bruvo’s genetic distance ranges, and congruently implied lack of TKS population structure. Similar to the gSSR dataset, the DAPC analysis of the eSSR dataset , Hypochaeris spp. , Krigia spp. , Lactuca , Pyrrhopappus , and Erigeron .Species genotyping and assignment and ITS phylogeny of the plant materials: Species identity of the samples collected from Tennessee, Georgia, Alabama, and Mississippi Tables  and S1 wces Fig.  and coulTaraxacum officinale spatial fixation genetics indices, Multi-locus genotype (MLG) networks, and population structure: The majority of the TKS-derived gSSRs cross-amplified the gDNA of the related US native dandelions  and of the outgroup specimens . Overall, the cross-amplification was broad and proved effective even in the specimens of related genera and tribe  developed for TKS. Our results indicated violations of HWE in both locus- and population-manner . Linkage disequilibrium range for this dataset was similar to that of the gSSR study of TKS . To gain access to that data, we coded the whole dataset as tetraploid with occasionally missing alleles and corrected the ploidy with the R package polysat before analyses.The analyses of the species diversity and population structure included 74 samples of T. officinale dataset displayed between 5 and 16 alleles per locus . Several analyses indicated the presence of population structure in this dataset. The MSN analyses populations are more diverged from one another than in TKS. Bruvo’s distance-based tree of individuals  based on our de novo sequencing of TKS and utilized them for evaluating the genetic diversity of TKS germplasm. We then carried-out an array of comparative population genetics analyses, re-analyzing the recently published genic SSR (eSSR) dataset generated on the same TKS germplasm32, and an expanded cross-amplification study with the local US dandelions using those gSSRs.In this study, we aimed to gain a deeper understanding of the genetic diversity of TKS, a potential alternative, sustainable rubber cropde novo gSSRs were distributed across the TKS genome, based on the linkage disequilibrium data, as were the eSSRs32. We mapped both types of SSRs (gSSRs and eSSRs) along with the TKS markers used for the linkage map, back to the related and well-annotated H. annuus genome, based on the TKS contigs4. This is very likely to be helpful for the future breeding efforts. We chose not to use the TKS genome assembly4 or the closely related Lactuca sativa genome assembly35 because both are more fragmented and have fewer scaffolds anchored to chromosome locations in comparison to the H. annuus genome. To further underscore the need for improved TKS genome resources, the gSSR Tara003 sequence could not be found in the TKS contigs published4. Moreover, only 15 markers out of the 65 that constructed the TKS linkage map27 were mapped back together (in pairs or in threes) to six TKS scaffolds of Lin et al.4. Also, only one of the SSRs analyzed (gSSR Tara026) co-localized with two other map markers of Arias et al.27 within a single TKS contig of Lin et al.4. Several studies independently reported the TKS genome size as ~1,420 Mb based on flow cytometry (1.45 pg/1C31). Other studies estimated the diploid plant genome size at 2,400 Mb28. Comparatively, the draft TKS genome estimates at 1,040 Mb by 19 mer, 1,140 to 1,210 Mb by flow cytometry, or the 1,290 Mb assembly  represent an underestimation, which signifies room for improvement in the TKS genome completeness and assembly. As H. annuus is related, but somewhat distant to TKS, we expected mis-localizations and/or ambiguities in the marker placement due to genome rearrangements and/or sequence diversity. It is noteworthy though, that many chromosome regions in the map , which could stem from targeting the parts of genome different in character, duplications of the non-coding regions targeted by gSSRs37, or differences in the genomes of TKS (2n = 16) and H. annuus (2n = 34).Our 27. Seedling growth characteristics were also studied38. The first attempt at estimating the species genetic diversity using molecular methods was focused on a wide collection of TKS materials and allowed for a genetic distinction of the Russian/Kazakh and Chinese TKS germplasms33. A milestone in the TKS molecular diversity research was the study of the Kazakhstan-originating USDA-ARS germplasm using a set of eSSRs32 with which we compare the statistics of our gSSR dataset. Despite our sampling scheme being lower in number than in the previous eSSRs study of TKS, our study yielded very similar results and provided significant correlation of the population distances/indices. This result was possibly accrued by employing ~50% more gSSRs at lower population sampling, yet, ensured reliability of our results. This also confirmed the general observation on the TKS diversity formulated before32 that the overall low species diversity resides mainly within populations. This observation is in agreement with our research hypothesis for this outcrossing, self-incompatible dandelion species. Comparison of the HWE violations in the gSSR and eSSR datasets shows much lower occurrence in the former dataset. This could be intrinsically related to the sequences targeted by either SSR type, or variable mutational frequency of the targeted loci40. This is further substantiated by the patterns of HWE violations detected. The (sub-) population violations in gSSR dataset could stem from the limited sampling, whereas locus-wise HWE violations in the eSSR dataset suggest a different underlying reason, with abundant (sub-) population TKS sampling32.Several studies addressed the TKS diversity at various levels; agronomic performance and rubber/inulin production was of primary concern due to the industrial potential of the plant40. Owing to the fact of differences in parts of the genome targeted, in their conserved character, and in cross-amplification rates, both types of SSRs provide slightly different but complementary information42. Thus, inferences made from both types of SSRs together will provide more substantiated conclusions on the species diversity (or other studies for which they were used). Diversity of several economically important crops was analyzed using both types of SSRs, and in almost all cases led to similar results, which could also be taken as a confirmation study. For instance, deployment of both types of SSRs on the cucumber germplasm provided consistent positioning of most of the accessions analyzed on dendrograms and detected higher polymorphism rates using the gSSRs43. Similarly, high similarity was found between the gSSR and eSSR dendrograms among the tomato germplasms with higher polymorphism rate for the gSSRs, albeit slightly lower polymorphic information content44. The authors of that study postulated that combining both marker types in tomato would be effective for the species genetic diversity analyses. In contrast, studies of soybean indicated comparatively lower agreement between the gSSRs and eSSRs46. Authors argued for use of the eSSRs in soybean diversity studies for direct access to the population diversity in genes of agronomic interest but concluded that the species diversity was effectively estimated by both types of SSRs46. Analyses of the genetic diversity in wheat repeatedly indicated higher polymorphism of the gSSRs over eSSRs, but the authors of the studies argued that use of the eSSRs allowed for a more accurate delineation of the genetic relationships49. Studies in other cereal species observed the highest proportion of trimeric eSSRs, especially those encoding for neutral bulky amino acids50. Both studies also stated that the lower level of polymorphism detected by eSSRs compared to gSSRs might be due to the more conserved character of the targeted regions with selection acting against variation, a feature that could drive the relatively higher transferability of the eSSRs and a comparatively superior genotypic identification. Another conclusion emerged from the studies of the Prunus species. Although both types of SSRs resulted in similar dendrograms, combination of both datasets increased the genotypic discrimination44 and indicated a higher polymorphism and more effective resolution by the gSSRs than by the eSSRs51. The emerging conclusion from those and other studies is that similar levels of genetic diversity between populations or species may be recorded by using either SSR type with eSSRs often detecting lower variation, but performing more reliably at species differentiation54.Developing eSSRs is generally achieved faster and easier than the gSSRs due to comparatively more conserved character of the transcriptomeTaraxacum genus, the 14 gSSRs tested extensively in this study also cross-amplified to four independent gDNA samples of T. brevicorniculatum , but the Erigeron specimens belong to a distant tribe Asterae. This indicates a possible broad application of our gSSRs in the Asteraceae crops analyses. The TKS eSSRs also cross-amplified with four gDNA samples of local dandelions32. Thus, our gSSRs present additional resources to the classical (GA/CT)n gSSRs identified by restriction digest, hybridization, and Sanger sequencing55.Cross-amplification with the TKS gSSRs proved very successful and our markers transferred to other genera of the Asteraceae Fig. . Within 56. In contrast, results of the US dandelions are in agreement with the previous studies58 that provided evidence of both sexual and asexual modes of reproduction present in T. officinale with a broad cross-amplification to related species. The retrieved ITS sequences remained largely indiscriminate as to the species identity of the local US dandelions, co-localizing with the T. officinale ITS sequence consensus and the historical Herbarium specimen. Yet, previous research indicated predominance of only three Taraxacum species in North America . Including in the phylogenetic analyses the respective ITS consensus sequences of those three species, of the historical T. officinale specimen, and of T. officinale used for previous research26 (and data not shown) suggested the bulk of the US local dandelions could belong to T. officinale, if the microspecies of Taraxacum are disregarded60. Notably, the obligatory sexual diploid TKS was segregated with high confidence from the bulk of the US dandelions, as was the Central Asia-frequent T. brevicorniculatum.Both eSSR and gSSR datasets of TKS confirmed its sexual reproduction as observed in nature25. The majority of our dataset was tri- or tetra-ploid, and it is possible that we used too few markers to capture the higher levels of ploidy of the remaining several local dandelions samples classified as diploid based on the allele counts alone. In contrast to TKS, the US T. officinale presented evidence of population structure. This is in agreement with the biology of both species, especially considering the postulated clonal reproduction of the alloploid apomictic T. officinale in North America60. The higher frequency of sampling the outgroup specimens belonging to distant genera in the Southeastern US may be worth investigating in regard to the species range.The results of our gSSR analyses of this collection of US dandelions are in agreement with the recent ploidy analyses of the North America common dandelionsTaraxacum are notorious for hybridization, which often results in genome rearrangements, regional gDNA duplications, and/or polyploidization57. Cross-amplification of the TKS gSSRs (this study) and eSSRs (confirmed on four samples32), could help invigorate the molecular and genomic analyses of the more demanding polyploid dandelions57. Our study distinguishes the local US populations of T. officinale from TKS in several aspects. First, higher frequency of HWE violations indicated a difference in the US dandelions dataset. Second, the higher ploidy in this dataset inferred from the number of alleles detected indicated the possibility of clonal/asexual reproduction, which was further supported by the Index of association (IA). Third, several analyses indicated presence of population structure in this dataset contrary to the outcrossing diploid TKS. Overall, our gSSRs present a useful analytical tool for Taraxacum spp., due to cross-amplification in related species, even in distant genera.Species of Results on the genetic diversity of TKS accrued in the course of this study may help current and future breeding efforts of this potential crop for renewable rubber. Complementary and congruent data obtained from both gSSR and eSSR study on the same germplasm provided thorough insights into the species biology. Although the TKS well-annotated genome is still to come, the combined marker map located on the related sunflower genome may help advance future TKS studies. Furthermore, cross-amplification of our gSSRs into related species of dandelions and even other genera augments the currently available resources to analyze their biodiversity and provides a platform for their further research.24 was obtained from USDA-ARS and identified in a previous study15  extraction. We extracted three to five independent plant specimens per population for population diversity study (Tables TKS germplasm (seeds) collected in Kazakhstan15 Table . Plants y Tables . In addiT. officinale Weber (n = 74) accessions from the Southeastern US and plants morphologically very similar were collected across different geographical regions  and from eight distinct populations, as well as from historical herbarium specimens (Table Taraxacum spp. (n = 23) were set as a multiple outgroup. Leaf samples were collected in January and February of 2017, before the majority of the plants set bloom. No specific permissions were required for these locations/activities, as the materials are considered common weeds and regarded as neither endangered nor protected. Collected plant tissue was placed in ziplock bags containing silica gel . gDNA was isolated from the freshly collected tissues with the DNeasy Plant Mini Kit  as per the manufacturer’s protocol. Samples of the historical T. officinale were provided to us by the University of Washington Herbarium  and Oregon State University  for destructive sampling and analyses . The optimized thermal profile used included an initial denaturation at 94 °C for 2 min, 40 cycles of 95 °C for 30 s, 60 °C for 1 min, 72 °C for 90 s, and the final extension at 72 °C for 7 min. For each PCR, 5 µl of products were electrophoresed in 2% agarose-TAE buffered gels stained with ethidium bromide to confirm the amplification, and the rest was purified with ExoSAP-IT (Thermo Fisher Scientific) according to the kit manual. Analytical sequencing was done at McLab  or University of Tennessee – Knoxville Genomics Core . Sequences were assembled using LaserGene SeqMan version 7.0.0 , manually inspected and corrected, and identified using BLAST at NCBI. The obtained sequence matrix was enriched for published TKS ITS data34 (Genbank: KF437406 and KF 437407) and the ITS consensus sequences of T. ceratophorum (n = 3), T. erythrospermum (n = 12), and T. officinale (n = 53) from NCBI, respectively  Gblocks function with all the ‘less stringent selection’ options65. This sequence matrix was then submitted for phylogenetic analyses using RAxML GUI version 1.566 for Maximum Likelihood using 100 runs, with thorough bootstrap of 10,000, bootstrap branch lengths activated, and General Time Reversible (GTR) substitution model67. Multiple outgroup was set by selecting the 23 samples identified as not Taraxacum spp.  and ITS4 (Rv: 5′-TCCTCCGCTTATTGATATGC-3′)24; the detailed lineage is a proprietary information of ESKUSA GmbH, Parkstetten, Germany; chosen owing to abundant plant growth and thus availability of fresh leaf material) was isolated with the method described by Stein et al.69 and submitted to the UT Genomics Core for Illumina MiSeq sequencing at 275 bp, paired-end, on a v3,600 cycle flow cell. The gDNA library was prepared using the Nextera XT kit  following the manufacturer’s protocol with minor modifications, that included doubled incubation times and omission of the Normalizing step.Genomic DNA from the leaf sample E55/12 ; optimum annealing temperature of 60.0 °C (in the range of 55 to 65 °C); primer GC content in the range of 40 to 60%.Illumina sequencing adapters, low quality bases , and short reads (<30 bases) were trimmed off with Skewer version 0.2.24 was used in combination with the TKS linkage map information27 to infer the genomic locations of the SSR markers in this study. We used the marker sequences published therein, those obtained from our de novo sequencing gSSR search, as well the marker information and/or primer sequences of the published TKS eSSRs32 for comparison. The marker sequences were compared to the TKS genome contigs assembly of Lin et al.4 using gmap with default scoring settings . For each best sequence match to the TKS genome, a ~1 kb region containing the marker (500 bp on either side) was selected. The resultant contig fragments were used to BLAST the genome of related species, sunflower Helianthus annuus L., HA412-HO bronze assembly75. Best-hit sequences were then drawn on a map, respective to their physical locations on the sunflower chromosomes. If multiple best-hits had the same e-value, all were retained.The TKS genome sequencede novo sequencing was used for an initial genotyping screen with 50 primer pairs (25 di- and 25 tri-nucleotide repeats) with the PCR procedure described below. The results were visualized by capillary electrophoresis using Qiaxcel (Qiagen) and analyzed by using 25 to 500 bp DNA size marker and internal 15/600 bp alignment marker. We screened the results of genotyping with the 50 gSSRs for specificity on this gDNA sample, and the best-performing 25 gSSRs were selected for the analysis of the TKS gDNA collection  DMSO, 4 ng gDNA, 1 µM of each primer, and 1 U of AmpliTaq Gold DNA Polymerase (Fisher Scientific). The experimentally optimized thermal profile used included an initial denaturation at 94 °C for 3 min, 15 touch-down cycles of 95 °C for 40 s, 63-0.5 °C/cycle for 40 s, 72 °C for 30 s, 25 cycles of 95 °C for 40 s, 55 °C for 40 s, 72 °C for 30 s, and the final extension at 72 °C for 4 min.PCR reactions of 10 µl were composed of 1 × PCR buffer, 2 mM MgCl76). In addition, the published dataset of TKS-eSSR study was retrieved32 and binned to allow comparison of the datasets. Lastly, the dataset of T. officinale collected in the US (n = 74) and genotyped using 14 gSSRs was also binned, following the same procedure as the two datasets mentioned above. The binned datasets were analyzed separately for an array of population genetics parameters. To estimate the fixation and differentiation indices , we used packages: poppr79, hierfstat81, and polysat83 in R version 3.4.384. Due to the detected variation in ploidy levels in the US dandelions dataset, the data was corrected for ploidy in R version 3.4.3 using the package polysat and then recoded as tetraploid with occasionally missing alleles when samples were actually di- or tri-ploid. The mixed ploidy of that dataset limited the scope of the indices accrued, notably the differentiation index F’ST77; we resorted to GenoType/GenoDive85 to calculate the respective T. officinale dataset-wide FST and D’ST indices. As per convention, the FST bins considered were low (FST < 0.05); moderate (0.05 < FST < 0.15), and high (FST > 0.15). Deviations of Hardy-Weinberg equilibrium (HWE) were calculated using package pegas version 0.1086 in R version 3.4.3, using the exact test based on Monte Carlo permutations of alleles  and α = 0.05. The results were depicted as a probabilistic heatmap for HWE deviation in a locus- and subpopulation-manner. The multi-locus genotype (MLG) networks were constructed using the Bruvo distances, using the minimum-spanning networks (MSN) reticulation algorithm in the package poppr in R version 3.4.3. POPTREE287 was used to calculate the population-wise distance matrices using either FST or DST indices (both standardized and unstandardized). Mantel tests were performed in R version 3.4.3 using the package MASS88. Analysis of the molecular variance (AMOVA) was performed in R version 3.4.3 using the package poppr, and the resulting Φ indices are reported as [%] values, after 1,000 permutations, at. the three levels of each dataset hierarchy: within individuals ΦIT, within individuals between subpopulations ΦIS, and among subpopulation and ΦST. The mixed-ploidy T. officinale dataset did not lend itself to the ΦIT calculations using AMOVA. Discriminant Analysis of Principal Components (DAPC) was performed in R version 3.4.3 using the package adegenet version 2.1.190.A total of 62 TKS gDNA samples were genotyped using 25 gSSRs and binned using FlexiBin (an MS Excel macroResearch involving Human Participants and/or Animals: This article does not contain any studies with human participants or animals performed by any of the authors.Supplementary InformationSupplementary Table S1


Aegilops biuncialis, a valuable source of agronomical useful genes, may significantly facilitate the introgression breeding of wheat. The genetic diversity and population structure of 86 Ae. biuncialis genotypes were investigated by 32700 DArT markers with the simultaneous application of three statistical methods— neighbor-joining clustering, Principal Coordinate Analysis, and the Bayesian approach to classification. The collection of Ae. biuncialis accessions was divided into five groups that correlated well with their eco-geographic habitat: A (North Africa), B , C (Kosovo and Near East), D , and E (Azerbaijan and the Levant region). The diversity between the Ae. biuncialis accessions for a phenological trait (heading time), which is of decisive importance in the adaptation of plants to different eco-geographical environments, was studied over 3 years. A comparison of the intraspecific variation in the heading time trait by means of analysis of variance and principal component analysis revealed four phenotypic categories showing association with the genetic structure and geographic distribution, except for minor differences. The detailed exploration of genetic and phenologic divergence provides an insight into the adaptation capacity of Ae. biuncialis, identifying promising genotypes that could be utilized for wheat improvement.Understanding the genetic diversity of  Triticum aestivum L.) can be considered as a potential reservoir of gene variants for wheat improvement (Aegilops), which comprise 11 diploid, 10 tetraploid, and 2 hexaploid species, are the closest relatives of Triticum  is largely autogamous and belongs to the tertiary gene pool of bread wheat , sequence-specific amplified polymorphism, random amplified polymorphic DNA, and inter-simple sequence repeat molecular markers were used to reveal the genetic variability within the genome of Ae. biuncialis accessions , so do not represent the wide distribution of the species and do not allow an objective picture to be obtained of the genetic diversity existing within Ae. biuncialis  was originally developed as a hybridization-based microarray platform to detect polymorphism at the recognition sites of methylation-sensitive restriction enzymes . More retauschii , Triticunococcum , and hexnococcum .Ae. biuncialis genotypes to obtain a better understanding of the background of its genotypic and phenotypic diversification. To achieve this goal, the genetic relationships between the accessions were analyzed using three statistical methods after the DArTseq genotyping of the plants. Furthermore, the phenological variation pattern was compared with the genetic structure and eco-geographical distribution to obtain information on possible correlations between them. The comprehensive study of how changes in phenological traits relate with genetic diversity will facilitate the utilization of the enhanced adaptability of Ae. biuncialis, especially in the light of climate change.In the present work, genetic diversity and its association with the phenotypic variation in heading time were studied in a collection of 86 Ae. biuncialis Vis.  accessions, collected from 64 sites in 16 countries from Libya to Azerbaijan, were genotyped together with the Mv9kr1 wheat accession on a DArTseq® platform  , Wheat Genetics Resource Center , United States Department of Agriculture Agricultural Research Service , and Institute of Plant Genetics and Crop Plant Research . The A. biuncialis accessions were maintained and grown in the Department of Plant Genetic Resources of ATK Mezőgazdasági Intézet. To avoid hybridization and intercrossing between the genotypes, the isolated accessions were multiplicated by self-fertilization.Eighty-six wild Ae. biuncialis accessions was grown under field conditions in three consecutive seasons . The seeds were planted in fall in fields belonging to the Agricultural Institute of ATK . The soil type at each location was chernozem. Each genotype was grown in a 6 m2 plot with 6 × 3 m rows, 50 seeds per row, and a row distance of 0.15 m, as previously described by The collection of 86 Ae. biuncialis genotype was determined twice  during the grain-filling period using a Soil Plant Analysis Development 502 cholorophyll meter . At least 10 leaves were measured from randomly selected plants for each genotype on each occasion. For each leaf, the average of three SPAD readings around the midpoints of the flag leaf was taken.Additionally, to monitor the stay-green ability of the plants, the leaf chlorophyll content of each Ae. biuncialis genotypes together with hexaploid wheat (Triticum aestivum L.) genotype Mv9kr1 using Quick Gene-Mini80  with a QuickGene DNA tissue kit  according to the manufacturer’s instructions . The “wheat DArTseq™ 1.0” genotyping by sequencing service was optimized for wheat. This method used the combination of complexity reduction methods developed initially for array-based DArT and multiplex sequencing in Illumina HiSeq2500 instrument  using the admixture model of STRUCTURE software 2.3.4 with correlated allele frequencies .The phenotypic values of the Ae. biuncialis subpopulations in each year at the P < 0.05 significance level. In addition, the significant differences in the heading time of each subpopulation were examined among the 3 years by means of one-way ANOVA (SPSS 16.0). Bivariate correlation of the heading times of the Ae. biuncialis accessions among the 3 years was analyzed pair-wise by means of Pearson coefficient (SPSS 16.0). Linear regression analysis was carried out for each subpopulation among the years.One-way analysis of variance (ANOVA)  was conducted to compare the heading time of the Ae. biuncialis accessions by visualizing the grouping pattern of phenotypic variations.The principal component analysis of the Statistica 6 software was used to study associations between the heading times of the The stay-green trait (delayed foliar senescence) was determined by calculating differences between the SPAD values measured on the two dates. Pearson and rank correlation  coefficients were applied to analyze the dependence and relationship between the heading time and the differences in SPAD values at the P < 0.01 significance level (SPSS 16.0).Ae. biuncialis accessions originating from different eco-geographical habitats . The accessions clustered in accordance with their origin: the accessions in cluster A originated mainly from North Africa; cluster B represented genotypes from the Central Balkans, North Greece, and West Turkey; the three genotypes in cluster C originated from different geographic regions ; cluster D contained accessions from Asia Minor, the Crimean Peninsula, and Southern Greece (Peloponnese); while cluster E comprised genotypes from the Levant region  and Azerbaijan . The phylogenetic dendrogram split the Ae. biuncialis collection into five separate branches, which had a good fit with the subpopulations (A-E) revealed by STRUCTURE analysis , where genotypes from common areas share similar genetic ancestry and show high genetic similarity , and also discriminated the accessions of clusters A, B, D and E, confirming the STRUCTURE result  and the heading time was also investigated. At the first measuring date (May 12), the average SPAD values of the genotypes ranged between 42 and 61, indicating that these plants were still green  could not be classed into any of the distinct subpopulations based on their membership probability and these showed the lowest genetic similarity to the other accessions, indicating that this mixed group was the most distant genetically from the other genotypes in the population.The present study is the first report on the use of the DArTseq platform to estimate the intraspecific genetic diversity of Ae. biuncialis accessions from Transcaucasia were classed by Ae. biuncialis subpopulations from the central and southern parts of the Iberian Penninsula were clearly discriminated by AFLP markers that correlated with their geographical distribution . When comparing the genetic diversity of various Aegilops species, Aegilops speltoides (0.656) and Ae. tauschii (0.75) than in Aegilops sharonensis (0.84), Aegilops searsii (0.825), and Aegilops longissima (0.812). Species in the Sitopsis section, except for Ae. speltoides, have limited distribution in the Levantine, representing a smaller geographical area in the eastern Mediterranean Basin  estimated between the genotypes  under the H2020 framework program of the European Union and by the Hungarian National Research, Development and Innovation Office—NKFIH 116277, 112226, and 119387 and by the ERDF project „Plants as a tool for sustainable global development” (no. CZ.02.1.01/0.0/0.0/16_019/0000827).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Rc. Many pigmented rice landraces are still grown by ethnic communities for their nutritional and cultural value. This study assesses the genetic diversity in a collection of pigmented rice accessions from the Philippines.The wild ancestors of domesticated rice had red seed, white rice being the result of a mutation in the rice domestication gene japonica and indica sub-species. No genetic structure was observed in the core collection based on geographic origin. A pairwise comparison of accessions by region indicating that both local and long-distance exchange of rice accessions had occurred. The majority of the genetic variation was within regions (82.38%), rather than between regions (10.23%), with the remaining variation being within rice accession variance (7.39%). The most genetically diverse rice accessions originated from the Cordillera Administrative Region (CAR) in the far north of the Philippines, and in the regions of Davao and Caraga in the southeast. A comparison with pigmented rice accessions from the neighbouring countries Taiwan, Laos, China and India revealed a close relationship between accessions from Taiwan, supporting the hypothesis of southward diffusion of Austronesians from Taiwan to the Philippine. The 14-bp deletion within the gene Rc, known to result in loss of red pigmentation, was found in 30 accessions that still had coloured pericarps. Multi-spectral phenotyping was used to measure seed geometric and colour-appearance traits in 197 accessions from the core collection. The purple and variable purple rice accessions had the lowest values for the seed colour parameters - lightness (L*), intensity, saturation, a*  and b* .We undertook an analysis of the genetic and colour variation in a collection of 696 pigmented rice accessions held at PhilRice in the Philippines. The collection was reduced to 589 genotypes after removal of accessions with limited passport data or with low SNP marker call rates. Removal of duplicate genotypes resulted in a final, core collection of 307 accessions, representing all administrative districts of the Philippines, and composed predominately of These pigmented rice accessions represent a diverse genetic resource of value for further study and nutritional improvement of commercial rice varieties.The online version of this article (10.1186/s12284-019-0281-2) contains supplementary material, which is available to authorized users. Oryza sativa) is a staple food for over half of the world’s population. While rice can provide up to 50% of the daily calorie intake, white rice is a poor source of vitamins and minerals, and is associated with malnutrition among communities where rice is the predominant food  within exon 6 of the Rc gene; a basic Helix-Loop-Helix (bHLH) protein  or with a SNP marker call rate of < 0.90 were removed from the collection, leaving 589 accessions for subsequent analysis, along with 1536 SNP markers that had passed quality control.Cryptic relatedness analyses of the 589 rice accessions, using the 1536 SNP markers that had passed the quality control checks, revealed the presence of duplicate rice genotypes (accession sample genotypes with an estimated Probability of Identity (estimated PI) = 1; Additional file indica and japonica rice types, results being consistent between the model-based clustering analysis implemented in ADMIXTURE  and japonica (He = 0.21) sub-species . A similar pattern was observed between the groups for allele richness (Table Assessment of genetic diversity indicators within the 307 unique Philippine pigmented rice accessions (He = 0.35) revealed a higher level of genetic diversity within the admix group (He = 0.35) compared to the  He = 0.2 sub-spec He = 0.2 sub-specindica and japonica accessions were analyzed separately, the japonica accessions exhibited a lower level of polymorphism than the indica accessions, with a mean He value of 0.18 across regions versus a mean He value of 0.22 for the indica accessions  (He = 0.21) exhibited the least diversity. There was greater variation in genetic diversity across regions in the japonica group, He values ranging from 0.15 in Visayas to 0.2 in Central Luzon, MIMAROPA and Mindanao1 (Northern Mindanao and ARMM).When the indica and japonica, respectively .The inbreeding coefficient values (FIS) were 0.16, 0.91 and 0.95 for admix, FST test was used to assess genetic differentiation between all possible groups based on allele discrepancies . Genetic differentiation was high between the indica and japonica groups (FST = 0.50). High structuralization was also observed between the indica and admix groups (FST = 0.21), and between the japonica and admix groups (FST = 0.21). When considering the geographic origin of the rice accessions  to high .An  FST = 0.0. High sFST: 0.17 to 0.24) from accessions originating from Mindanao 2, Mindanao 3, Visayas, Calabarzon and Central Luzon, and even higher for accessions collected from Mindanao1, MIMAROPA and the Cagayan Valley (FST > 0.28), indicating genetic separation of rice accessions from these regions. A similar observation was reported between rice accessions from NCR versus Mindanao1, Mindanao 2, Mindanao 3, Central Luzon, Calabarzon, MIMAROPA, Cagayan Valley and Visayas (FST: 0.15 to 0.23). However, the average genetic differentiation over all regions was 0.09, showing that a high proportion of the variation was due to within region variation.Accessions from the Ilocos Regions were highly differentiated ), Mindanao 2  and Mindanao 3 (Davao Region/Caraga), and the neighboring regions of Calabarzon and Central Luzon (FST = 0.0099) indicating seed exchange between these regions. However, long distance transfer of rice seed was apparent between Calabarzon and Mindanao 1 (FST = 0.0099), Mindanao 2 (FST = 0.0099) and Mindanao 3 (FST = 0.0077), and between MIMAROPA and Mindanaol 1 (FST = 0.0054). Rice seed movement was also apparent between Bicol and the regions of Central, Eastern and Western Visayas (FST = 0.0084). Seed from Bicol may have found its way to Mindanao 3 (FST = 0.0059), via Visaya (Visayas – Mindanao 3 FST = 0.0024). In addition there was very little genetic differentiation (FST = 0.0006) between rice accessions from the regions of Ilocos and NCR.Low indica group of Philippine pigmented rice accessions, pairwise FST comparisons between regions ranged from little or no genetic variation (FST = − 0.04), to reasonable levels of genetic variation (FST = 0.19). Significant differences were seen between indica accessions from the Ilocos region, and accessions from the MIMAROPA (FST = 0.15) and Mindanao1 regions (FST = 0.17) . Considering only the japonica rice accessions, greatest genetic differentiation was seen between accessions from Visayas and Cagayan Valley  than the average genetic differentiation when all rice accessions were analyzed together, again indicating that most of the genetic variation was due to within region variation.Looking just at the indica/japonica/admix groupings, while within group differences accounted for 46.96% of the variation. Variation within the rice accessions, representing residual heterozygosity, accounted for 5.91% of the total genetic variation. After removing the admix accessions, the between group molecular variance increased to 50.14%, while the within group variation fell to 46.31% . When indica and japonica accessions were analyzed separately, the AMOVA results again showed that most of the variation occurred within regions, with the japonica and indica rice groups showing 87.18% and 84.05% variation, respectively within geographical regions  and Laos (FST = 0.08). However, when compared with pigmented rice accessions from India (FST = 0.16) and China (FST = 0.23) the accessions were quite distinct. The average FST value among these countries was 0.14, indicating that 14% of the genetic diversity can be explained by population sub-structuring . AMOVA indicated that the majority of genetic variation (79.03%) occurred within country populations, with 14.87% of the variation being between populations, and only 6.10% of genetic variation being within rice accessions, representing residual heterozygosity (Table FST = 0.04) and the close phylogenetic relationship between the majority of the accessions from Taiwan with accessions from the Philippines . This deletion is known to result in the loss of red pericarp  was preponderant among the accessions screened. Of the 202 accessions for which a deletion was not observed, 84.16%, 9.41%, and 3.96% of the samples were reported as red, variable purple and purple, respectively, 1.49% of the samples had mixed colour, while 0.99% were white. A total of 29 rice accessions carried the Rc 14-bp deletion but still had pigmented pericarps  accessions)), while two white (japonica) rice accessions did not carry the 14-bp deletion within the Rc locus. We recorded eight accessions, all with red pericarps, with ambiguous profiles, i.e. with more than one distinct band.A total of 241 genotypes  and 1 mix (red and variable purple)) from the 307 Philippine pigmented rice accessions were screened for the presence of the 14-bp deletion in the P < 0.05) for roundness, a*, b*, hue angle, intensity, saturation and lightness, while seed area, length and width showed no significant deviation from normality (P > 0.05) . Hue angle was the less dispersed (CV = 0.07) trait, while saturation showed the highest dispersion (CV = 0.43). The highest lightness, a* and intensity values were reported in the red rice accessions, while the lowest lightness, a* and intensity were reported in the purple rice accessions. The lowest saturation and b* values were detected within variable purple accessions, while the highest values were found in a red accession , intensity, hue angle, saturation, a*  and b* . The Shapiro-Wilk test indicated departure from a normal distribution  seed colour. A few discrepancies were noted; four accessions reported as variable purple on the basis of visual classification now clustered closer to red accessions. The first principal components (PC) explained 45.0% of the phenotypic variation, while PC2 explained 20.5% Fig. a. The va4, 192 = 0.568, p = 0.686), area , roundness  or width , substantiating the weak correlations reported between geometric and colour traits. ANOVA analysis, followed by post-hoc comparisons using the Tukey HSD test, indicated a difference in colour intensity , saturation  and lightness  between rice accessions with different pigmentation  reflecting the distinctiveness and barriers to crossing that normally prevents gene flow between these two sub-species , while the rice accessions from NCR showed the least diversity (He = 0.26). Genetic diversity is considered a function of a population’s ecological and evolutionary history , indica types predominated in CAR (48.9%). Indica rice is normally grown in low altitude and latitude areas, while japonica rice is favored in mountainous regions  is indicative of regular seed movement between local farmers. We would also expect farmers to maintain genetic diversity, choosing to grow genetically diverse varieties. However, low genetic differentiation between rice accessions grouped by region is evidence of seed exchange between regions. Long distance transfer of rice seed was apparent between the regions of Calabarzon and Mindanao 1, Mindanao 2 and Mindanao 3, and between MIMAROPA and Mindanaol 1. Rice seed movement was also apparent between Bicol and the regions of Central, Eastern and Western Visayas. In addition there was evidence for movement of rice accessions between the regions of Ilocos and NCR. Barter trading for goods is still frequent among rural farmers. Likewise, social and cultural practices, including giving rice as a gift, offerings as part of religious and spiritual practices, and during community events such as festivities could all have contributed substantially to movement of rice seeds  in rice pericarp pigmentation  or other genes, that effect pericarp pigmentation  in rc that reverted white rice pericarp to red. The Rc-g mutation was caused by a 1-bp deletion, 20 bps upstream of the rc 14-bp deletion in exon 6, that restores the reading frame to a functional allele. Considerable genetic variation has been found in the Rc gene, with Singh et al. , Cui et al. , redness (a*) and yellowness (b*) values of pigmented rice are strong indicators of bioactive components  . Twenty pigmented rice accessions from Taiwan, including 17 red and 3 purple accessions, were retrieved from the IRRI Genebank and genotyped using the Illumina Infinium 7 K SNP chip  . Genotypic data of red pigmented accessions from China, India and Laos were extracted from the Rice SNP-Seek Database  were retrieved.Pigmented rice accessions, collected from all administrative regions of the Philippines, were provided by the Genebank at PhilRice Fig. . Accessi Thomson . Genotyp Thomson . SubsequControl quality of the genotypic data was performed using SNP & Variation Suite (SVS v8.6.0) . Markers with call rates lower than 0.95  or minor allele frequencies (MAF < 0.05) were removed from the data set of 4551 SNP markers. The remaining SNPs were screened for redundant markers using the software’s default parameters , resulting in a final, usable data set of 1536 evenly distributed markers.Rice accessions with low genotype call rates  were removed from further analysis, leaving 589 rice accessions. The relatedness between rice accessions was also calculated using SNP & Variation Suite. Where rice accessions had similar genotypic SNP marker profiles (i.e. estimated probability of identity = 1) one accession was removed from further analysis (282 accessions removed), leaving 307 distinct rice accessions subsequently referred to as the core collection .The genetic distance between all possible pairs of rice accessions were calculated using the bitwise.dist function implemented in the R package Poppr  using the R package rrBLUP Endelman . PCA wasThe 1536 SNP markers were used to assess genetic diversity among the 307 accessions of the core collection. The following metrics were estimated by rice subspecies and region of origin; the number of alleles (Na), observed heterozygosity (Ho), expected heterozygosity (He), and the inbreeding coefficient (FIS). FIS significance was assessed using a 95% confidence interval and a 1000 bootstrap replicates estimate of genetic diversity. Estimates of genetic diversity were performed with the R package diveRsity using the divBasic function  was performed with the aim of determining the partition of genetic variation among populations and/or groups. These analyses were carried out using the R package Poppr  that utilizes a genetic algorithm  using the following PCR profile: An initial denaturation step at 94 °C for 5 min followed by 35 cycles of, a denaturation step at 94 °C for 1 min, annealing at 72 °C for 5 min, and extension at 72 °C for 2 min, ending with a final extension at 72 °C for 5 min. PCR products were separated in a 8% non-denaturing polyacrylamide gel ran at 100 V for 2.5 h. The gel was stained with SYBR Safe DNA gel stain for 10 min and visualized using an AlphaImager HP gel imaging device . PCR products were scored as binary data, with the presence of the deletion being scored as 0 and the absence of the deletion as 1.The rice gene https://videometer.com), calibrated with respect to intensity and geometric parameters, followed by light set up. Randomly selected seeds, representing the darkest and the lightest rice accessions were used for the light set up. Twenty seed from each accession were placed evenly across a 90 mm Petri dish. The multi-spectral image of each individual seed is captured at 19 different wavelengths from 365 to 970 nm, each with a resolution of 2056 × 2056 pixels  in 1976 and colour-appearance attributes; a* (green to red shade), b* (blue to yellow shade), L , intensity, saturation  and hue  were extracted and quantified, as well as geometric traits; area, length, width and roundness (an estimate of how closely the shape of the seed resembles a circle).Seed of 197 rice accessions from the core collection were dehulled and used for multi-spectral phenotyping  for each accession were calculated and used in further analyses. Descriptive statistics for each trait , standard deviation (SD)) were obtained using the R package pastecs describe function Additional file 2:Table S1. The 307 unique Philippine pigmented rice accessions used to assess genetic diversity. Table S2. Pairewise FST comparison between rice varietal types and regions among the 307 unique accessions. Table S3. Pairewise FST comparison between regions considering only the  indica accessions among the 307 unique accessions. Table S4. Pairewise FST comparison between regions considering only the japonica accessions among the 307 unique accessions. Table S5. Descriptive statistics of colour parameters and geometric-related traits. Table S6. Data: Intensity by Colour. Table S7. Passport information on the 696 Philippine traditional pigmented rice accession collected. Table S8. Accessions from neigbouring countries used in this study. (XLSX 171 kb)Additional file 3:Table S9. Philippine pigmented rice accessions allocated to redefined geographical regions. Table S10. Summary statistics of genetic diversity indicators across regions comparing the Philippine pigmented rice accessions that belong to the indica and japonica accessions. Table S11. Analysis of molecular variance of the Philippine pigmented rice accessions after removing the admix accessions. Table S12. Unique set of SNP markers that distinguishes individual accessions within the 307 core collection of Philippine pigmented rice accessions. Table S13. Summary of Philippine pigmented rice accessions carrying the 14-bp deletion within the rice Rc locus. (DOCX 43 kb)


To determine the impact of ecological and environmental histories on the evolution of coral reef damselfishes at two adjacent marine biogeographic suture zones.Indo‐West Pacific, notably including two suture zones: Socotra and Christmas and Cocos/Keeling Islands.Chromis dimidiata, Chromis margaritifer, and Chromis fieldi.We utilized a combination of nuclear and mitochondrial genetic markers in addition to visual abundance survey data of these fishes.Chromis species, there is evidence of hybridization between C. margaritifer and C. fieldi at Christmas Island based on molecular and visual identification. Introgression appears to be spreading westwards to other C. fieldi populations based on COI haplotype comparison. Moreover, the genetic distance between C. margaritifer and C. fieldi suggests that Pleistocene sea‐level fluctuations may have contributed to allopatric divergence and secondary contact between these two closely related species.Despite genetic patterns consistent with incomplete lineage sorting and relatively low genetic differentiation among the three studied Our study highlights that evolutionary processes in coral reef fishes operate differently between suture zones, possibly due to different ecological and environmental predispositions regulating secondary contact of sister species. While secondary contact likely led to hybridization and introgression at Christmas and Cocos/Keeling Islands, none of those processes seem present at Socotra for the chocolate‐dipped damselfish. This difference is likely due to an environmental barrier caused by hydrodynamic regimes in the Gulf of Aden. Within this family, there is evidence of hybridization at biogeographical borders between species  and identified in the field according to morphology and coloration pattern  at 37°C for 60 min, followed by 15 min at 85°C. Final products were sequenced in the forward  direction with fluorescently labeled dye terminators following the manufacturer's protocols  and on an ABI 3130XL Genetic Analyzer (Applied Biosystems).vers. 4.8.4 . COI fragments from each species were queried using the BLAST tool on GenBank; all displayed 99% to 100% similarity to existing voucher sequences from each species . The listed voucher sequences were included in analyses, as indicated, for comparison. Details on the methodology for sequence analyses are in Supporting Information Appendix Sequences were aligned, edited, and trimmed to same length using Geneious Pro 2.3N in parentheses), nuclear microsatellite fragments were amplified using nine fluorescent labeled primer sets  developed by Underwood  were discarded due to low amplification success in >90% of the samples, and loci CmA115 was also discarded due to high null allele frequency at several sampled locations. The remaining five loci  met Hardy–Weinberg Equilibrium (HWE) assumptions, and did not show signs of linkage disequilibrium. Moreover, five out of 189 samples had data from more than one locus missing; those samples were also excluded from further microsatellite analysis. Nonetheless, in addition to the results based on those five neutral microsatellite markers, we also present results based 8 loci , we evaluated the assignment of specimens to one or the other parent species, or alternatively as putative first‐ or second‐generation hybrids . At COC, also within this suture zone, similar patterns in abundance were recorded for C. margaritifer and C. fieldi , and intermediate colored individuals (putative hybrids with C. fieldi) were absent during our surveys. Here, only C. fieldi specimens were observed and collected.At another recognized hybrid zone for reef fishes, SOC, 3.2Chromis spp. were apparent, with all C. dimidiata haplotypes separated from C. fieldi haplotypes by at least six base pair substitutions , JF434879 , and JF434880  however grouped with the C. margaritifer lineage, despite those being morphotypically and biogeographically C. fieldi, which we interpret as evidence of introgression. The other two GenBank voucher sequences, JF434855 and JF434856 , morphotypically and genetically grouped with the C. margaritifer lineage , which may be due to a recent population expansion of C. margaritifer at XMS compared to other sampling locations, or due to introgression  was measured for the XMS population of 3.44 was C. dimidiata and C. margaritifer/C. fieldi was 1.8%, whereas it was 0.3% between C. margaritifer and C. fieldi. To roughly estimate divergence time between C. margaritifer and C. fieldi, a molecular clock of 1% divergence per million years (MY) was applied .The K2P genetic distance between 3.3Chromis samples contained 14 variable sites and 14 haplotypes, with a haplotype diversity of 0.478. For the network‐based haploweb, none of the Chromis lineages were distinct  and one C. fieldi sample (ID: COVEXCD02) from XMS shared an identical haplotype with C. dimidiata samples from the Red Sea  collected at XMS was discarded due to repeated amplification failure despite multiple PCR attempts. The remaining C. fieldi sample (ID: COVEXCD02) from XMS was genetically more similar to C. margaritifer from XMS than other C. fieldi populations. , we observed similar genetic structuring patterns with NewHybrid  than that between C. dimidiata and C. margaritifer/C. fieldi (1.8 MYA). However, both estimates correspond to repeated glacial cycles of the Pleistocene Epoch (2.7 ~ 0.12 MYA). Indeed, Pleistocene ice ages played a key role in the formation of temporary barriers to larval dispersal, ultimately leading to speciation within many coral reef fish families. Both the Socotra and Cocos–Christmas suture zones are adjacent to shallow areas that represented putative barriers to gene flow during ice ages because these likely formed land bridges separating species on either side when sea levels dropped  and genetic analysis of mitochondrial, nuclear, and to some extent microsatellite markers for most samples. The ability to detect incomplete lineage sorting among these lines of descent was limited by the low phylogenetic resolution of the RAG2 nuclear marker; but all markers displayed low genetic divergence between the three species and the presence of ongoing or historical introgression between C. fieldi and C. margaritifer was also supported with the remaining genetic markers, which was quite strong when using eight microsatellite markers as opposed to solely the five neutral microsatellites. Further, the genotypes of six individuals  exhibited incongruence for their species assignment depending on the methodology applied. We interpret this incongruence as further evidence of hybridization.Introgression at Cocos–Christmas is consistent with an active suture zone, where hybridization occurs among closely related species due to secondary contact between regional biotas from the Indian Ocean and Pacific Ocean , and the more microsatellite markers (eight vs. six loci) we included the clearer the segregation. The nuclear RAG2, however, has the slowest mutational rate among our genetic markers  but no evidence of more recent divergence between species . By combining results from these three complimentary markers, along with biogeographical and phenotypic information, we were able to posit testable hypotheses for inconsistent assignment of the six aforementioned samples:C. fieldi collected from Christmas Island (COVEXCD01) had a typical C. fieldi mitochondrial COI haplotype but its nuclear microsatellite loci grouped with C. margaritifer. This mismatch strongly suggests ongoing hybridization at Christmas Island, where the distributions of these two Chromis species overlap. This sample is probably an F1 hybrid, or a backcrossed individual, meaning the offspring of a male hybrid parent and a female C. fieldi. Due to the fourfold, slower evolutionary rate of the nuclear RAG2 compared to COI, there was no separation between C. margaritifer and C. fieldi at this marker.The C. dimidiata from the Red Sea suggests incomplete lineage sorting among C. fieldi and C. dimidiata, despite the substantial geographic separation between these sites .In the case of COVEXCD02, the similarity of its RAG2 haplotype with those of C. margaritifer ETHELXCM14.Similar to (2), evidence of incomplete lineage sorting was detected at Christmas Island based on the C. fieldi (RS7187) indicates putative introgression caused by hybridization between the two species, C. margaritifer and C. fieldi. This hypothesis of introgression is further supported by previously collected C. fieldi samples from Reunion (JF434880) and Madagascar (JF434879), which also carried a C. margaritifer COI haplotype. Hence, these putative hybrids or backcrossed individuals likely dispersed from Christmas Island and introgressed with C. fieldi at the Maldives. Furthermore, C. margaritifer COI haplotypes appear to have introgressed or backcrossed into C. fieldi populations at the western edge of their range . This pattern of introgression in the eastern Indian Ocean (XMS/COC) and a westward pattern of dispersal and introgression has already been reported among other hybridizing reef fishes  could not be genetically confirmed with the markers used in this study (only RAG2 provided genetic evidence). This, however, is not necessarily evidence against hybridization. The detection of an intermediate colour morph has proven itself a reliable indicator for hybridization of several coral reef fish taxa (DiBattista et al., Indeed, difficulties in distinguishing recently diverged species that are hybridizing are likely, given that some parts of the genome may show signs of gene flow, whereas other parts may not (Roux et al., Evidence of introgression and the confirmation of hybridization between closely related reef fish species highlights the importance of suture zones as natural evolutionary laboratories. Indeed, even though many reef fish species are widely distributed, it is the interactions at edges of their range that have a disproportionally large effect on a species’ genotype (Budd & Pandolfi, The authors declare no competing interests.J.D.D., J‐P.A.H., and S.H. conceived the idea for this study. J.D.D., J‐P.A.H., and M.J.T. collected tissue samples; J‐P.A.H. conducted abundance surveys; S.H. and J.D.D. produced DNA sequences; S.H., J.D.D., J‐P.A.H., V.R.S., and D.L.C. analyzed the data. S.H. and V.R.S. interpreted and discussed the results leading to the final construction of the manuscript. J.D.D., J‐P.A.H., V.R.S., M.J.T., and M.L.B. also made the significant contributions to the manuscript. Click here for additional data file. Click here for additional data file.


When cities develop rapidly, there are negative effects such as population expansion, traffic congestion, resource shortages, and pollution. It has become essential to explore new types of urban development patterns, and thus, the concept of the “smart city” has emerged. The purpose of this paper is to investigate the links between smart city policies and urban green total factor productivity (GTFP) in the context of China. Based on panel data of 200 cities in China from 2007–2016 and treating smart city policy as a quasi-natural experiment, the paper uses a difference-in-differences propensity score matching (PSM-DID) approach to prevent selection bias. The results show: (a) Smart city policies can significantly increase urban GTFP by 16% to 18%; (b) the larger the city, the stronger and more significant this promotion. Urban growth is a global trend, the world’s urban population will be close to 70% by 2050 . FollowiMany countries around the world have launched policy projects about the concept of Smart City. In November 2008, IBM first proposed the idea of “Smart Planet” and officially introduced the concept to the US federal government in January 2009, suggesting investment in building a new generation of intelligent information infrastructure. Singapore is one of the first countries in the world to establish a “smart country” construction strategy. Moreover, in 2006 and 2016, the “Smart Country 2015” plan and the “Smart State 2025” plan were launched in Singapore, respectively. In 2016, the Japanese cabinet first proposed the concept of “social 5.0” which is the ultra-smart society and the six necessary measures to achieve a super-intelligent society in the “Science and Technology Basic Plan”. These countries have made fruitful achievements in the construction of smart cities. China has begun to implement smart city policy in 2012.Since the reform and opening up policy was implemented, China’s economy has maintained moderate-to-rapid growth. However, this growth has led to high resource consumption and severe pollution. China is an important case where increasing human activities are leading to environmental degradation . To chanWith the intensification of environmental protection and resource conservation, people are becoming more aware of GTFP. The public advocate the environmentally friendly economic growth mode and the concept of sustainable development. In-depth analysis of GTFP under environmental constraints is of considerable significance to find solutions to alleviate the contradiction between economic growth and environmental pollution .Smart cities have become a popular concept because they have the potential to create a sustainable and livable urban future . Given tThis paper aims to study the impact of smart city policies on GTFP from the perspective of an urban development pattern. Treating smart city policy as a quasi-natural experiment, the paper uses a difference-in-differences propensity score matching (PSM-DID) approach. The propensity score matching (PSM) method was first proposed by Heckman  to elimiThe paper is structured as follows. In The concept of a smart city stems from the idea of the smart earth proposed by IBM in 2008. The smart earth is the product of digital cities and the Internet of Things. The construction of smart cities uses modern information technology to promote the interconnection, efficiency, and intelligence of urban operating systems, thereby creating a better life for citizens ,27. In sIn 2012, China officially issued the “Notice on the Pilot Work of National Smart Cities.” The first batch of smart city pilots involved 90 cities and county-level cities. In 2013, the Ministry of Science and Technology and the National Standardization Management Committee identified 20 cities including Qingdao and Jinan as “smart cities” technology and standard pilot cities . In 2014, with the approval of the State Council, the eight ministries and commissions including the National Development and Reform Commission, the Ministry of Public Security and the Ministry of Finance issued the “Guiding Opinions on Promoting the Healthy Development of Smart Cities”, requiring all regions and relevant departments to implement the tasks proposed in this guidance and ensure smart cities healthy and orderly advancement. The opinion also suggested that by 2020, several smart cities with distinctive characteristics will be built, and the gathering and radiation belts will be significantly enhanced.Since the implementation of the smart city policy, it has penetrated all aspects of human production and life, such as smart medical care  and inteGTFP is an indicator obtained by adding some factors such as environment and energy to TFP. GTFP can more comprehensively reflect the quality of national economic growth from the aspects of environmental protection and rational use of resources. In recent years, kinds of literature have reported the factors affecting GTFP ,31,32.There are three common types of measurement methods on total factor productivity as follows: Solow residual method (SRM), stochastic frontier production function method (SFA), and data envelopment analysis (DEA). The SRM  was propncy part ,35. Thouncy part  to measuThe measurement of GTFP includes two types of elements: Input factors and output factors. The input factors include labor input, energy input, and capital investment. The output factors include expected output and unexpected output. The expected output is generally measured in terms of GDP, and the unexpected output is the environmental output that measures the degree of environmental pollution.A smart city is an efficient and technologically advanced emerging city that combines green ecological development and economic development . Since tOn the one hand, the smart city policy is conducive to optimizing and upgrading of industrial structure, promoting the efficiency of resource allocation, thereby reducing energy waste and improving GTFP. Under the smart city policy, the industrial structure achieves a transformation of industrial structural factors from labor-intensive to capital-intensive, and then from capital-intensive to technical-intensive . Smart cOn the other hand, the smart city policy promotes the development of advanced monitoring and governance technologies, monitoring pollutants and pollution sources in real time, reduce urban environmental pollution , and impIn summary, as an efficient, technologically advanced, green, and socially inclusive city , the smaOur paper uses the data of China’s prefecture-level cities and removes the following unqualified urban samples: (a) Provinces, municipalities or autonomous regions that are smart city pilots, such as Hainan Province, Beijing, Shanghai, Xinjiang and Uygur Autonomous Region; (b) prefecture-level cities with administrative changes within the study period, such as Chaohu City, Bijie City, Tongren City, etc.; (c) prefecture-level cities with severe data loss, such as Lhasa City; and (d) prefecture-level cities containing counties or districts that are smart city pilots, such as Tangshan City and Handan City. The first batch of smart cities that emerged in 2012 was not eligible, so the second batch of smart cities that emerged in 2013 was regarded as the experimental group, and other cities in the provinces where the pilot cities were located were regarded as the control group. The third batch of pilot cities that appeared in 2014 was also removed but later used to test the robustness of the results. Finally, this paper selects the panel data of 200 cities in China from 2007–2016 as the research sample. The data are mainly from the China Statistical Yearbook (2006–2016), Shandong Statistical Yearbook (2006–2016), China Environmental Statistics Yearbook (2006–2016), the National Bureau of Statistics and provincial statistical bureaus, and missing data are estimated by the annual average growth rate method. For the specific experimental group and control group sample, see The explanatory variable of this paper is green TFP. Referring to the practice of Fare et al. , our papThe Malmquist TFP index model is as follows:n et al. , this pat and period t − 1, respectively, and δ represents the capital depreciation rate. Based on the practice of Zhang et al. [The input indicators selected in this paper include the three elements of capital, labor, and energy. We use the classic perpetual inventory method to measure capital investment. The basic formula is as follows:g et al. , the fixIn this paper, the key explanatory variable is smart city policies represented by The control variables of this paper include the level of economic development (ECO), the degree of government intervention (GOV), industrial structure (IND), traffic conditions (TRAF), and technological innovation (TECH). The level of economic development is expressed by per-capita GDP, the government intervention capacity is expressed by the proportion of fiscal expenditure to GDP, the traffic condition is expressed by the per-capita road area, and the industrial structure is expressed by the ratio of secondary industry to tertiary industry.The PSM method was first proposed by Rosenbaum and Rubin  in 1983.The DID method has become increasingly popular in policy analysis . It studIn this paper, we use statistical software Stata 15.0  to implement analysis and illustrate table or graphic results.Descriptive statistics were captured on the main variables of the treated group, the control group, and the whole sample. The sample size, mean and standard deviation are shown in We assess the impact of smart city policies on GTFP by using linear regression analysis, and the results are shown in To eliminate the influence of sample selection bias, we should select those non-smart cities whose characteristics are similar to those of the treated group as the control group. In this paper, the PSM method is used to perform logit regression on the control variables, and a control group is determined based on those cities whose propensity score is closest to that of the treated group is determined. The results are shown in Before using the PSM-DID method, a joint support hypothesis test is necessary to prove its applicability. As shown in Before the regression analysis, we tested the matching effect between the treated and control groups using the kernel density propensity matching method see . The proWe now offer a further robustness test using the third batch of smart cities that emerged in 2014 as the new treated group. As before, we implement a regression analysis and obtain some valuable results, as shown in To determine whether the increase in GTFP is affected by “smart city” or other policies, The above results show that the smart city policy can significantly improve the GTFP. Next, we will further answer the following two questions: Does the GTFP increase significantly for different city sizes? Is there a difference in the degree of improvement?As we know, compared with small cities, larger cities have more complete functions, stronger innovation capabilities, and higher resource allocation, and utilization efficiency. However, overpopulation will lead to urban congestion and more serious environmental pollution. The smart city policy can not only improve the efficiency of urban resource allocation but also solve urban problems by technological innovation. As a result, GTFP has also be improved. So it is necessary for us to further verify the impact of GTFP on cities of different sizes. The division of the city in this paper is based on the latest standard in the “Notice on Adjusting the Dividing Standards of Urban Size” issued by the State Council in 2014. Due to the limited sample size, there are fewer small-scale cities, resulting in unreliable regression results, so only the results of cities above medium-sized scale are reported. The regression results are shown in From an empirical perspective, we take the panel data of 200 cities in China from 2007–2016 as the research object and regards the smart city policy as a quasi-natural experiment, using the PSM-DID method to estimate the impact of “smart city” policies on GTFP. From the regression results, the smart city policy has a significant positive effect on GTFP. To verify the result accuracy, the robustness test is carried out by jointly supporting the hypothesis test and changing the control group. These tests have passed. Ultimately, we conclude that smart city policies can significantly improve urban GTFP.To enrich research conclusions, we analyze the heterogeneity of urban scale to test whether the impact of different city-scale smart city policies on GTFP is different. Regression results indicate that the larger the city size, the higher the promotion of smart city policy to GTFP, and the more significant the promotion effect. It may be because the larger the city, the more complete the city’s functions, and the better the conditions for implementing the smart city policy.Our contribution includes two aspects. First, from the perspective of the urban development pattern, we offer the first study of the impact of smart city policies on GTFP. It is a theoretical supplement to smart city policies and enriches the research on the factors affecting GTFP. Second, the resource and environmental problems stemming from the process of economic development appear not only in China but also in other countries. The environmental problems in developing countries have received very little attention. At present, research on GTFP is mainly concentrated on China. Certainly, this paper will also be useful for other countries.With a deep expansion and integration of information technology and the Internet of Things, smart cities are realizing intelligence in transportation, energy utilization, resource allocation, and urban pollution discharge through a large number of innovative technologies. As a new urban development pattern, smart cities establish operational command centers and use the Internet of Things technologies (such as intelligent transportation and modern information communication) to collect information and analyze data. The green concept and advanced technology are perfectly integrated into urban planning, which promotes the improvement of urban resource allocation efficiency , the upgIn addition, the city heterogeneity analysis shows that the key to the city development is not its large scale, but whether the model of the city management and development pattern is innovative. Therefore, in the process of implementing the new urbanization strategy, the introduction of smart technology is conducive to improving urban innovation capacity and resource allocation efficiency and reducing pollution. On the one hand, big cities give full play to their powerful urban functions and vigorously develop smart technologies. Therefore, we should apply advanced information technology to all aspects of production and life to alleviate and eliminate urban problems. On the other hand, small and medium-sized cities make full use of the advantages of smart city policies to develop smart projects that are suitable for the characteristics of the city and maximize urban governance and operational efficiency.There are some implications, limitations, and future directions: (a) Due to the limited sample size, the potential limitation may affect regression results, so we can extend the evidence scope from China to the whole world in order to show spatiotemporal effects; (b) the smart city policies can significantly improve urban GTFP but it does not mean that we need to invest as much as possible in smart cities, so we can pay attention to the appropriate investment scale and the crowding-out effect of smart city policies; (c) we should pay attention to spillover and siphon effects in smart city construction.


In general, cultured dairy products consistently contained higher levels, up to 109/mL or g. Although few specific recommendations and claim legislations for what constitutes a relevant dose exist, the findings from this survey revealed that many fermented foods are a good source of live lactic acid bacteria, including species that reportedly provide human health benefits.The popularity of fermented foods and beverages is due to their enhanced shelf-life, safety, functionality, sensory, and nutritional properties. The latter includes the presence of bioactive molecules, vitamins, and other constituents with increased availability due to the process of fermentation. Many fermented foods also contain live microorganisms that may improve gastrointestinal health and provide other health benefits, including lowering the risk of type two diabetes and cardiovascular diseases. The number of organisms in fermented foods can vary significantly, depending on how products were manufactured and processed, as well as conditions and duration of storage. In this review, we surveyed published studies in which lactic acid and other relevant bacteria were enumerated from the most commonly consumed fermented foods, including cultured dairy products, cheese, fermented sausage, fermented vegetables, soy-fermented foods, and fermented cereal products. Most of the reported data were based on retail food samples, rather than experimentally produced products made on a laboratory scale. Results indicated that many of these fermented foods contained 10 Lactobacillus, Streptococcus, and Leuconostoc are predominant in fermented foods, but other bacteria as well as yeast and fungi also contribute to food fermentations. Commercially-produced fermented foods also frequently serve as carriers for probiotic bacteria. Despite this interest and the potential public health benefits of these foods, there is still considerable confusion about which fermented foods actually contain live microorganisms, as well as understanding the role of these microbes on the gut microbiome  or are physically removed by filtration or other means (in the case of wine and beer). Moreover, many fermented foods are heat-treated after fermentation to enhance food safety or to extend shelf-life. Thus, fermented sausages are often cooked after fermentation, and soy sauce and sauerkraut and other fermented vegetables are made shelf-stable by thermal processing. Some products, such as many of the commercial pickles and olives, are not fermented at all, but rather are placed into brines containing salt and organic acids. Even non-thermally processed fermented foods may yet contain low levels of live or viable organisms simply due to inhospitable environmental conditions that reduce microbial populations over time. It is important to note, however, that the absence of live microbes in the final product does not preclude a positive functional role. For example, food fermentation microbes may produce vitamins or other bioactive molecules Streptococcus thermophilus and Lactobacillus delbrueckii subsp. bulgaricus. However, frozen yogurt, kefir and other cultured dairy products also claim the presence of live and active cultures, even though the microorganisms may be different than those found in yogurt. In the U.S., there is no regulatory requirement to state microbial levels, thus these label declarations are strictly voluntary.Yogurt, kefir, and other cultured dairy product manufacturers have long promoted the presence of live cultures. Indeed, the “live and active” seal was created by the National Yogurt Association (NYA), for yogurt products in the United States containing at least 100 million cells or cfu per gram at the time of manufacture . If other organisms are indicated on the label, they must be present at 106 cfu per g. Nonetheless, in Europe, to make a claim for yogurt containing live cultures for improving lactose digestion, the European Food Safety Agency requires a minimum of 108 cfu per g of live bacteria  focused on those studies that enumerated microorganisms exclusively in fermented food products. Keywords for these searches included, but were not limited to, the type of fermented food analyzed and, “commercially produced,” “commercial product,” “enumerated,” “lactic acid bacteria,” “microbial characterization,” “probiotic,” and “culture.” Food products that served only as vehicles for delivery of probiotic microorganisms were not included. Thus, studies that reported counts for frozen yogurt were included, but studies on ice cream containing probiotic microorganisms were not. In general, results were only included for commercial products, bought at retail locations, or those experimentally-produced under industrial manufacturing conditions. Thus, strictly experimental products  were not considered. The only exceptions were for products for which little or no data from retail or industrially manufactured sources was available. In those cases, lab- or pilot-scale-produced products were included, provided they were made using traditional manufacturing methods. No restrictions for date, location, or language were applied.For most products, quantitative data relied on cultural methods using well-established types of differential, selective, and general purpose media, as well as appropriate incubation conditions. LAB were the main group described, although other bacterial groups were occasionally reported. Some studies reported single microbial counts, whereas other reported ranges. Although papers reported counts either as log or as actual values, all of the data described in this review are shown as logs. For some products, values were estimated from graphs or figures. When products were held for shelf-life or aging studies, the counts from multiple times points are shown. Otherwise, single time-point data was reported. The region or origin of product manufacture was also noted.Approximately 400 published studies were reviewed in which fermented foods were characterized for the presence of live microorganisms. However, about three-fourths were excluded and not used in our results. Several excluded studies focused on development of selective methods for distinguishing between different species of LAB, determining ratios , or for enumerating only probiotics organisms. Although most studies reported data based on traditional plating methods, many of the more recent studies reported abundance data . Because the latter 16S-based methods also detect non-viable cells, these studies were excluded unless total counts were also reported. Ultimately, more than 140 studies were included in our survey. Although the literature from which the results were assembled covers a 50 year period and a range of different regions and methodologies, the results are remarkably consistent. As summarized below, nine groups of fermented foods were reviewed in this survey. These included yogurt and other cultured dairy products, cheese, fermented meats, fermented vegetables, traditional fermented Asian products, fermented cereals, beer, and fermented tea (Kombucha).S. thermophilus and L. delbrueckii subsp. bulgaricus, at levels ranging from < 104 to 109 cfu/g or ml. In general, counts for S. thermophilus were somewhat higher than for L. delbrueckii subsp. bulgaricus. In several studies, other microorganisms, including Bifidobacterium spp. and Lactobacillus spp., were also enumerated. Levels of the latter ranged from undetectable (< 10 cfu/g) to 108 cfu/g. The addition of these probiotic bacteria did not appear to have any effect on levels of the yogurt culture organisms. Although most studies reported counts at only a single time point, other studies reported initial counts as well as at a second time point, usually considered end-of-shelf-life. In such cases, counts were generally similar at both time points (>106 cfu/g), provided samples were stored at refrigeration temperatures  has also been studied  survived beyond the designated shelf-life period (1 year), with less than a 0.5 log reduction for most samples.In addition to fresh yogurt, frozen yogurt was also examined for bacteria. Results from several studies indicates that when these products were assessed for the relevant yogurt LAB, levels were generally similar to fresh yogurt, with counts ranging from 105–109 cfu/g range.The number and type of live microorganisms in other cultured dairy products have also been reported Table . These i3 cfu/g) to 109 cfu/g, with the highest levels found in Tilsit cheese . In contrast, Grana Padano aged 1 year, Parmesan aged greater than 1 year, and Swiss Gruyere aged greater than 1 year all showed no detectable microorganisms (< 103 cfu/g). As noted for other products, the methods used by the investigators may have influenced the reported data. Thus, enumeration of selected organisms  was only possible if the appropriate medium and growth conditions were used.Although considerable microbiological data for cheese exists, most of these reports are concerned with microorganisms having public health or cheese quality implications. Still, levels of lactic acid and related bacteria were reported for more than 30 types of cheese from 18 countries including the United States, Italy, France, Germany, Mexico, Ireland, and South Africa (Table 2 cfu/g) to 1010 cfu/g. Data were reported as either within the product shelf life or after ripening or maturation of the sausage. Counts of viable microorganisms in sausages from the United States were generally lower (< 107 cfu/g) compared to sausages from other countries. In particular, LAB levels were all < 106 cfu/g. In contrast, several of the European sausages contained high levels of LAB (>108 cfu/g.). European sausages were more often artisan sausages from smaller manufacturers, although similar microorganisms are used in comparison to sausages from the United States.Microbial counts for fermented sausages are shown in Table Microbial counts for fermented vegetables, including sauerkraut, olives, mustard pickles, pickles, and kimchi are summarized in Table 3 to 108 cfu/g. Reported samples were for sauerkraut originating from the United States, Finland, and Croatia. Levels of LAB and Lactobacillus were reported for olives produced in Italy, Greece, Portugal, Spain, and the United States. These products contained 104 to 108 cfu/g and were between 30 and 200 days.Microbial counts for sauerkraut were generally reported as LAB with counts ranging from 101) to 108 cfu/g. For several of these products, levels of species  were reported. Species of Leuconostoc, Weissella and Lactobacillus were also reported for Korean kimchi, where they were generally present between 107 and 108 cfu/g.Other products for which quantitative data were reported included mustard pickles and kimchi from Taiwan and pickled cucumbers from China, India, and the United States. Microbial counts ranged from undetectable  have considered fermented foods alone. Nonetheless, several reports provide evidence that fermented foods, such as kimchi, fermented soy products, and yogurt, can improve relevant biomarkers. For example, kimchi consumption improved fasting blood glucose and other metabolic syndrome symptoms in overweight and obese adults  consistently having the lowest counts.8 lactic acid bacteria per g  would be ingested. These are the doses noted above that can influence the gut microbiota and provide a potential health benefit (Derrien and van Hylckama Vlieg, In many cultures, fermented foods containing live microorganisms are consumed on a regular or even daily basis Hutkins, . Based o g Table , and givRecently, the concept of “shared core benefits” was introduced to explain how and why phylogenetically related organisms could deliver similar health benefits (Sanders et al., In part, this is why several prominent groups have recommended that health care professionals should promote fermented foods containing live microbes as part of public health policy (Ebner et al., SR, CK, and RH each contributed 30% to data collection. MH contributed 10% to data collection. SR, CK, and RH wrote the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.