Dioscorea alata (L.), also referred to as water, winged, or greater yam, is one of the most economically important staple food crops in tropical and subtropical areas. In Côte d’Ivoire, it represents, along with other yam species, the largest food crop and significantly contributes to food security. However, studies focusing on better understanding the structure and extent of genetic diversity among D. alata accessions, using molecular and phenotypic traits, are limited. This study was, therefore, conducted to assess the pattern of genetic variability in a set of 188 D. alata accessions from the National Agronomic Research Centre (CNRA) genebank using 11,722 SNP markers (generated by the Diversity Arrays Technology) and nine agronomic traits. Phylogenetic analyses using hierarchical clustering, admixture, kinship, and Discriminant analysis of principal component (DAPC) all assigned the accessions into four main clusters. Genetic diversity assessment using molecular-based SNP markers showed a high proportion of polymorphic SNPs (87.81%). The analysis of molecular variance (AMOVA) showed low molecular variability within genetic groups. In addition, the agronomic traits evaluated for two years in field conditions showed a high heritability and high variability among D. alata accessions. This study provides insights into the genetic diversity among accessions in the CNRA genebank and opens an avenue for sustainable resource management and the identification of promising parental clones for water yam breeding programs in Côte d’Ivoire. Dioscorea alata (L.) yam, commonly known as water yam, winged yam, or greater yam, is one of the most widespread root and tuber crops in tropical and subtropical areas of the world. It is produced mainly for its starchy underground tubers [Dioscorea alata represents, along with other yam species, such as D. rotundata (Poir.) and D. cayenensis (Lam.), the second most important edible root and tuber crop after cassava in West Africa, where yam plays an essential role in the nutrition, food security, income generation, and socio-cultural lives of more than 300 million people [d tubers ,2. Waterd tubers ,4. Dioscn people . In Westn people ,6,7.D. alata is dominant and accounts for ~60–70% of the total yam production [D. alata cultivation in the country stems from many factors, including ease of propagation, rapid growth allowing it to suppress weeds, stable yield under ranges of soil fertility and ecologies, long post-harvest tuber storage, and high nutritional values for human consumption and livestock feed [In Côte d’Ivoire, yam is the most economically important food crop and is mainly grown in the central and northern savannah areas ,9,10. Amoduction . In 2018ock feed ,12,13,14Despite the importance of water yam as a staple food, youth employment, and income generation, many biotic and abiotic constraints threaten its production worldwide ,15. Yam D. alata in Côte d’Ivoire to allow the sustainable and effective exploitation and management of existing diversity for breeding purposes to produce improved varieties [Phylogenetic relationships of water yam in Côte d’Ivoire have not been well established among yam accessions because of limited morphological markers and flow of planting materials across areas  . Consequarieties ,20. For arieties .D. alata accessions in Côte d’Ivoire, and existing information is still based on morphological data and marker systems with low reproducibility.Several Next-Generation Sequencing (NGS) techniques, including Genotyping-by-Sequencing (GBS) and Diversity Array Technology (DArT), have recently been developed. They have enabled the discovery and generation of genome-wide high-throughput markers, such as SNPs, for genetic mapping, quantitative trait loci (QTL) identification, and molecular plant breeding and genetic diversity studies in yam ,22,23,24D. alata accessions of the National Agronomic Research Centre (CNRA) using SNP molecular-based DNA markers and nine agronomic descriptors to update the information on the yam genetic diversity in Côte d’Ivoire. The results should provide research institutes and plant breeding programs with reliable data for the choice of plant materials to be used for the creation and selection of new varieties that are more productive and resistant to diseases, pests, and environmental constraints and meet growers and consumers’ tuber quality needs.The main objective of the present study was to assess the genetic structure and diversity pattern of the in vivo collection of p < 0.001) among accessions for agronomic traits across years. The Fisher least significant difference (LSD) test showed that the variances for all traits were significantly different (p < 0.05) between 2018 and 2019 (−1) and plant vigor were recorded in 2018 compared to 2019 (7.43 t ha−1). In contrast, anthracnose disease severity score (ADSS), tuber length, tuber width, tuber circumference, dry matter content, and weak vigorous plant rate (WVPR) were higher in 2019 than they were in 2018  based on Wilks’ lambda test revealed highly significant differences . The variances across the years showed highly significant differences among genotypes for all agronomic traits. However, the genotype × year interactions indicated highly significant differences among genotypes for five of the nine traits evaluated, including anthracnose disease severity (ADSS), tuber length, tuber width, tuber circumference, and rate of weak vigorous plants (WVPR). The interaction effects were not significant for tuber yield, vigorous plant rate, medium vigorous plant rate (MVPR), and dry matter content. In addition, mean square values of genotypes were higher than that of the genotype × year interaction for all agronomic traits (2 > 0.50). The highest value (H2 = 0.80) of broad-sense heritability was obtained with VPR, while tuber length had the lowest H2 value (0.56).The analysis of variance (ANOVA) revealed that the agronomic traits of c traits . The broD. alata accessions were generated by the Diversity Arrays Technology (DArT) platform. The transformation of these allelic sequences into genotypic data resulted in a raw data file of 22,506 SNPs, and after quality control analysis (SNP filtering), only 11,722 high-quality SNPs were retained for further analyses.A total of 45,012 SNP markers from 188 p = 0.61) for all assessed traits  methods assigned the 188 accessions into four main genotypic clusters a. Kinshid traits .Population analysis based on cross-validation suggested four clusters as the optimum number of genetic groups within the accessions. Through the Admixture analysis, most of the accessions were properly assigned to a genetic group, and only a few were considered as admixt . The memThrough principal component analysis (PCA), the first and second components explained only 29.2 and 18.7% of the total variance, respectively, and account for 47.90% of the total observed variation . The anaDAPC analysis and based on the Bayesian Information Criteria (BIC) detected a maximum of K = 4 to be retained with clearly distinct clusters .The membership probability of each accession to be assigned into different clusters was 100% for all accessions, and no admixture or accession with multiple affiliations was detected by DAPC analysis. Cluster membership estimates showed that cluster 2 accounted for the largest number of accessions (69), followed by cluster 3 with 42 accessions, cluster 1 (41 accessions), and cluster 4 was found to be the smallest group with 36 accessions .p < 0.001) between the agronomic performances of clusters defined according to Wilks’ lambda test. The Fisher LSD test carried out following an analysis of variance for each of the quantitative traits revealed that cluster 1 consisted of accessions with the highest agronomic performance, including high yield (7.17 t ha−1), high resistance to anthracnose disease (2.77), high dry matter content (28.65%), and the production of tubers with the highest circumference (23.25 cm), length (17.88 cm), and width (10.47 cm)  .However, the weak vigorous plant rate (WVPR) was the lowest at 18.11% . This clIt is noteworthy that the phylogenic tree and DAPC analysis only affiliated 48 accessions  into the same clusters.The first linear discriminant function (LD1) clearly separated clusters 1 and 3 from clusters 2 and 4. Clusters 1 and 3 were presented in the negative area of LD1, while clusters 2 and 4 were located in the positive area a. The see was higher than that of Ho  were only detected in cluster 2, and none of the clusters contained all the identified alleles. Cluster 2, with the largest number of alleles, only accounted for 23,442 alleles, representing 99.99%. The average number of alleles per locus in each cluster ranged from 1.81 (cluster 1) to 2 (cluster 2). The Shannon’s information index, which measures the diversity pattern within a population, was generally low (<0.50) in three clusters with an average of 0.34 . Clusterat of Ho .2 equilibrium test indicated that 37.82% of the markers were not able to make differences (p > 0.05) between the observed and expected heterozygosity, while 62.18% showed significant differences. Clusters 1, 3, and 4 showed high proportions of markers for which the difference between Ho and He was significantly different from 0, and only cluster 2 recorded a high proportion of markers with no significant differences (The Hardy–Weinberg Chiferences .p = 0.001). It was higher among clusters (1825.43) and lower (1152.90) within clusters (ST) showed a low genetic distance and FST among clusters, and the values ranged from 0.24 to 0.61, and 0.20 to 0.42, respectively.The analysis of molecular variance (AMOVA) revealed that the genetic variance was significantly different among and within clusters  between clusters 3 and 4 and lowest (0.20) between clusters 2 and 3. The genetic differentiation index between clusters 1 and 3 was 0.34, while it was 0.23 and 0.29 between the pairs  and , respectively  [The knowledge of the genetic background within existing germplasm is important for proper conservation, management, and use for crop improvement through breeding . Geneticts (SSR) ,28,29,30D. alata accessions from Côte d’Ivoire. Cluster analysis using the molecular-based SNP data categorized the 188 assessed accessions into four main genetic groups. Bayesian-based clustering method, kinship, and principal component analysis used as complementary approaches classified the accessions into four groups as well. The kinships and PCA showed higher relatedness of accessions within clusters. Shannon’s information index was low in general, but cluster 2, with the largest number of accessions, was more diverse than the others, showing that this cluster may be divided into several subgroups. These findings suggested that the accessions within clusters were genetically similar in their genome, and the current set of material could be considered highly valuable for genetic improvement in D. alata. Furthermore, a moderately low genetic diversity within the clusters could be used to form potential heterotic groups and new breeding populations by crossing individuals among the four clusters, thereby broadening the genetic base of the breeding programs [High throughput markers (SNPs) developed by the Diversity Array Technology (DArT) were used in this study to elucidate the structure and genetic diversity in the CNRA programs ,31.D. rotundata [D. alata domestication revealed that this plant species was introduced to Africa from the southern border regions between Asia and Oceania in the 16th century, where natural outcrossing with its wild relatives, such as D. nummularia, was reported [However, analysis of molecular variance (AMOVA) showed that genetic differentiation within and among identified clusters was statistically significant. It was lower within clusters and higher among clusters. This is also confirmed by the low proportion of admixture in the present study. In this study, private alleles found only in one cluster associated with the high genetic variability among clusters could be interpreted as a lack of gene flow between accessions from different groups or that varietal groups are grown in geographically-distant areas with low seed exchanges among farmers. The low diversity within clusters could also mean that there are regional preferences for some dominant varieties, and thus farmers do not perceive the need to adopt several varieties simultaneously. This result contrasts findings by Agre et al.  in Beninotundata , to incrreported ,32.D. alata accession showed significant differences between observed (Ho) and expected (He) heterozygosity not only in the total population but also in the four groups identified. The high difference between He and Ho, as revealed by the lower negative value of inbreeding coefficient (Fis) in the population, would reflect an excess of heterozygous genotypes at the SNP loci analyzed [D. alata germplasm from Côte d’Ivoire, most of the accessions are landraces that rarely flower. When they flower, there are more male plants than females. In addition, flowering in female plants is sparse and irregular such that natural cross-pollination between accessions is a rare event [D. alata yam breeding and improvement programs.The Hardy–Weinberg equilibrium test indicated that most of the SNP markers used to assess the patterns of genetic diversity in analyzed ,34. Thisanalyzed ,35. In tre event . It is are event . The tubre event . In contD. alata accessions was significantly different across years. The yield for vigorous plant rate (VPR) and medium vigorous plant rate (MVPR) was higher in 2018 compared to 2019. In 2018, planting was delayed (July) due to a lack of rainfall during the ideal planting periods . This led to low disease severity compared to the year 2019. Egesi et al. [D. rotundata accessions using minisett. The lower dry matter content observed in 2018 compared to 2019 could be due to a decrease in length of the growing season, resulting in shortened crop cycle by late rains  . We alsoD. alata accessions indicated that the variability among molecular-based clusters only was highly significant among clusters defined by DAPC analysis. This finding would suggest a high variability among clusters at the agronomical level, which was in agreement with molecular results. However, phylogenic tree and DAPC analyses’ accession assignments only agreed for 25.41% accessions. The results indicated potential correlations between molecular data and agronomic traits in D. alata accessions, signifying that the relevant genomic regions involved in the variations of phenotypic traits expressions were efficiently detected by the SNP markers. In D. alata accessions, a low correlation between the genotypic and phenotypic distance matrices has also been observed [D. rotundata accessions with SNPs. The low correlation between the phenotypic and the genotypic data could have resulted from the natural and artificial selections on phenotypic variables, as these are under selection and influence of environmental factors. In contrast, the variation detected by molecular markers is commonly non-adaptive, and hence, not subject to natural and or artificial selections. This currently explains the interest in combining molecular and phenotypic analyses simultaneously instead of separating them to correct the level of mismatch observed in previous yam studies [Generalized linear models or MANOVA performed with the nine quantitative agronomic traits of observed  reported studies . Phenoty studies . Harness2 based environment may have revealed the true value of the trait inheritance. Thus, plant breeders have a high probability of selecting promising genotypes for used traits when targeted in breeding programs [Broad-sense heritability estimate in plant breeding is usually exploited for understanding the probability of inheriting target genes from parental lines after hybridization. It indicates the degree to which traits are expected to be inherited and provides an indication of the amount of genetic progress that would result in selecting the best individual based on the desired traits. Furthermore, the genotypic causes of genotype × year interaction are important indicators to estimate the variability of traits and make the promising genotypes possible to be selected based on the agronomic or phenotypic performance. Even though high broad-sense heritability was obtained for all agronomic traits despite the importance of genotype × year interaction, the environment was similar and estimation of the Hprograms ,41.The experiment was conducted at the Research Station for Food Crops (SRCV) of the National Centre of Agronomic Research (CNRA) located in the Bouaké City, in central Côte d’Ivoire, at 7°44′ N latitude and 5°04′ W longitude. The Bouaké region constitutes a transition zone between the humid forests with short dry seasons and the dry savannas with long dry seasons . Bouaké A collection of 188 water yam accessions were selected from the core collection maintained at the National Centre of Agronomic Research (CNRA—Bouaké) and used for molecular-based diversity assessment. These accessions were partly the result of countrywide prospecting and collection missions carried out between 2008 and 2013 in major yam-producing areas of Southern, Central, and Northern Côte d’Ivoire. They were split into different variety groups, including landrace varieties (Betebete (56 accessions), Brazo (10 accessions), Douoble (16 accessions), Florido (41 accessions), and Nza (56 accessions)), and hybrid lines  .For agronomic data collection, the experiments were carried out under field conditions on a plot of 60 m length and 42 m wide, i.e., a surface area of 0.56 ha. The field experiment comprised two completely randomized Fisher blocks with 2 m apart. In each block, the elementary plot containing an accession consisted of five plants at a spacing of 1 m between and within rows. After plowing and clearing the experimental plot, planting was conducted using either tuber fragments or whole tubers, and accessions were planted for two consecutive years, 2018 and 2019, at the beginning of the rainy season. Data displaying the meteorological variations during the two years of study is provided in Nine most discriminative quantitative traits were selected and used to evaluate the agronomic performance of the 188 CNRA accessions. These traits were selected based on recommendations by Agre et al.  and Assa®, Canberra, Australia, for DNA extraction, library construction, whole-genome resequencing, and SNP marker development.Three (3) months after planting in 2018, young, healthy leaves of each of the 188 accessions were harvested in the morning hours and stored on dry ice. Ten (10) leaf discs of 5 mm diameter were sampled from the leaf blade of each accession using a biopsy curette. The leaf discs were then put into two 96-deep well PCR plates with the accession codes written on the wells of each plate. The plates were covered with silica gel and sent to Diversity Array Technology (DArT)https://kdcompute.seqart.net/kdcompute, accessed on 22 August 2021). The markers were first filtered based on the call rate of raw data [Single row format data received from DArT were firstly converted into HapMap and VCF formats using KDcompute  library package [k-means clustering method, tends to reduce the variance among accessions within clusters and maximize the variance among clusters [D. alata accessions. Following a cross-validation test using the ‘xval’ function, the optimum number of principal components (PCs) and discriminant functions to be retained and able to accurately predict the group membership was estimated with 100 repetitions, and the PCs retained was found to be associated with the lowest root mean square error (RMSE) [o), expected unbiased heterozygosity (He), Shannon information index (I), inbreeding coefficient index (Fis), polymorphism information content (PIC), and minor allele frequency (MAF). For each locus, the Hardy–Weinberg equilibrium test was assessed to compare the observed heterozygosity (Ho) with the expected heterozygosity (He) values. As the conditions of normality, independence, and heterogeneity of the residual values were not met for an analysis of variance (ANOVA), the Mann–Whitney U test was carried out to compare the mean ranks of Ho and He of each locus in each grouping pattern and the total population. The genetic differentiation among and within groups was estimated using the analysis of molecular variance (AMOVA), and the significance was tested with a non-parametric approach with 999 permutations. Differences between the groups defined were measured by computing PhiPT probability at the threshold of 0.05 and Nei’s genetic distance between paired groups [2 is Broad sense heritability, σG: genetic variance, σP: Phenotypic variance, and r: Number of replicationsThe structure and pattern of genetic diversity in  package . As comp package  and Fact package . Discrim package . DAPC, bclusters . The Bayr (RMSE) ,51,52. Ar (RMSE) . Geneticd groups . GanAlExd groups . The corD. alata accessions from CNRA genebank can be structured into four (4) main groups based on molecular SNP data. The genetic diversity within each cluster was relatively low. However, genetic variability among accessions was high among clusters. The genetic differentiation index (FST) and Nei’s genetic distance between pairs of identified groups was low, while the FIS, which measures the difference between observed and expected heterozygosity, showed a very low inbreeding coefficient (Fis < 0) in the total population. This study provided reliable information and useful insights at molecular level into D. alata accessions from the CNRA genebank to guide plant breeding programs and sustainable management of existing genetic resources in Côte d’Ivoire. Furthermore, a highly significant variation in genotypes as revealed for all tested agronomic traits across the years allows the selection of promising parental genotypes for breeding activities to develop and supply more suitable varieties to farmers and other end-users in the region.The results from this study showed that


The highly migratory Atlantic bluefin tuna (ABFT) is currently managed as two distinct stocks, in accordance with natal homing behavior and population structuring despite the absence of barriers to gene flow. Larval fish are valuable biological material for tuna molecular ecology. However, they have hardly been used to decipher the ABFT population structure, although providing the genetic signal from successful breeders. For the first time, cooperative field collection of tuna larvae during 2014 in the main spawning area for each stock, the Gulf of Mexico (GOM) and the Mediterranean Sea (MED), enabled us to assess the ABFT genetic structure in a precise temporal and spatial frame exclusively through larvae. Partitioning of genetic diversity at nuclear microsatellite loci and in the mitochondrial control region in larvae spawned contemporarily resulted in low significant fixation indices supporting connectivity between spawners in the main reproduction area for each population. No structuring was detected within the GOM after segregating nuclear diversity in larvae spawned in two hydrographically distinct regions, the eastern GOM (eGOM) and the western GOM (wGOM), with the larvae from eGOM being more similar to those collected in the MED than the larvae from wGOM. We performed clustering of genetically characterized ABFT larvae through Bayesian analysis and by Discriminant Analysis of Principal Components (DAPC) supporting the existence of favorable areas for mixing of ABFT spawners from Western and Eastern stocks, leading to gene flow and apparent connectivity between weakly structured populations. Our findings suggest that the eastern GOM is more prone for the mixing of breeders from the two ABFT populations. Conservation of this valuable resource exploited for centuries calls for intensification of tuna ichthyoplankton research and standardization of genetic tools for monitoring population dynamics. Thunnus orientalis  as one stock, Southern bluefin T. maccoyii  also as one stock, and Atlantic bluefin tuna (ABFT) T. thynnus  as two, Western and Eastern, stocks. Each of these stocks is considered a distinct population , Madrid, Spain. The 2017 assessment results from the virtual population analysis (VPA) indicated that the spawning stock biomass (SSB) exhibited a substantial increase from the late 2000s , which hto 0.012 . The anato 0.012 . Other dto 0.012 , 2008a; to 0.012 , and a fto 0.012 . A recento 0.012 . There iLarval cooperative studies have provided insights into multiple aspects of tuna larval ecology and biology, particularly growth and food web dynamics . IntensiManagement and conservation of ABFT requires continuous research concerning the population dynamics and exchange between Western and Eastern stocks that can be achieved through tagging studies  or genetWe conducted larval ABFT collections during the peak of the reproductive season in the two main spawning areas for ABFT, the GOM and the MED . The GOMLarvae were identified as ABFT following morphological, meristic and pigmentation characters. Standard length (SL) was measured to the nearest 0.01 mm using Image J 1.44a . Developmental stage was determined following A total of 112 ABFT larvae spawned contemporarily in the GOM  and MED (50 larvae) were genotyped . DNA extT. thynnus based on sequence similarity searches performed with the nucleotide Basic Local Alignment Search Tool (BLAST) (https://blast.ncbi.nlm.nih.gov/). Quality trimmed sequences left a fragment of 361 bp aligned detecting 53 haploid binary sites with SNPs. All sequences have been submitted to GenBank (accession numbers MT912036–MT912078).We analyzed eight highly polymorphic microsatellite loci selected according to their moderate-to-high allele count and compatible amplification in two multiplex PCR reactions . Allele O) and expected (HE) heterozygosity. Software Cervus v3.0.7 , and ST analogous PhiPT was obtained through Analysis of Molecular Variance (AMOVA) of genetic distances. Spatial genetic analysis of both nuclear and mitochondrial diversity was studied performing a Mantel test with GenAlEx software v6.5 , proportionally assigned individuals to population clusters inferred from the microsatellite genetic data. In the Bayesian approach, STRUCTURE v2.3.4 software  was usedarvester . Clumpakarvester  was usedadegenet  to studyDuring the 2014 spawning season, approximately half of the stations explored were positive for ABFT larvae , 43% forIS) varies between −1 and +1, measuring the difference between expected heterozygosity (HE) and observed heterozygosity (HO) according to allele frequencies. Significant deviations from HW proportions and homozygote excess (positive FIS) were obtained for loci Tth16-2 and Ttho1 . Partitioning of the total expected genetic diversity between subpopulations or groups under the assumption of equilibrium was quantified through several statistics ranging from 0 to +1. We calculated the general or most frequently used fixation index Fparisons . GeneticUnique individuals were completely discriminated with four loci according to a genotype accumulation curve . We obtaPairwise comparison of MED and GOM larvae haplotypes at 53 mtDNA control region variable sites (SNPs) through AMOVA resulted in a PhiPT value of 0.029 (with a significant associated data randomization probability of 0.019 with 999 permutations). Phylogenetic analysis of mtDNA sequences to infer evolutionary relationships from a common ancestor failed to separate larvae spawned in the MED and in the GOM (data not shown), as previously reported .ST, we report other genetic diversity estimators, G″ST  and D  or almost certain (juveniles) origin  Table 3Table 3. s) and D , arguingp 0.001) and mtDNA control region binary haplotypes . Genetic structuring within different distance classes (within the same or different spawning areas) was explored through multivariate spatial autocorrelation analysis. Autocorrelation for microsatellite genotypes resulted in a positive r value of 0.024 (with confidence limits of 0.005 and −0.004) for distance classes within the same spawning area and a negative r value of −0.020 (with U of 0.003 and L of −0.004) for distance classes belonging to different spawning areas, in agreement with a weak but significant spatial structuring of genetic diversity.We assessed whether genotypes were more similar when comparing larvae collected in the same spawning area than when comparing larvae from different spawning areas. Spatial genetic analyses were performed by analyzing the autocorrelation between geographic and genetic data through a Mantel test. Close to zero autocorrelation statistics resulted from analyzing genotypes for six loci  proposed by K values, and that for our data is necessarily only obtained for K = 2. A different clustering approach is multivariate DAPC analysis that is applied to discriminate clusters through discriminant analysis performed on data previously transformed through principal components analysis  used to calculated the statistic of Evanno ∆K (deltaK) that is plotted in the lower panel, and can only be obtained for K = 2.Plots obtained with arvester  from allE v2.3.4  shown inClick here for additional data file.10.7717/peerj.11568/supp-3Supplemental Information 3Discriminant Analysis of Principal Components (DAPC) shows differences between clusters through one discriminant function representing diversity in ABFT larvae collected during 2014 in the GOM (grey) and MED (black) spawning areas. Fifty three single nucleotide polymorphisms were genetically characterized in a fragment of the mitochondrial DNA (mtDNA) control region .Click here for additional data file.10.7717/peerj.11568/supp-4Supplemental Information 4DNA was extracted from ABFT fish larvae using the NucleoSpin® Tissue XS (Macherey–Nagel). Multiplex PCR reactions were performed with a Q5® High-Fidelity PCR kit  including 50 ng template DNA and 25 pmol primer mix. The forward primer for each locus was fluorescently labeled for laser detection as indicated. Multiplex PCR conditions were: 97 °C 3 min; 10 cycles , 25 cycles , and final extension at 72 °C for 3 min. Alleles were separated by capillary electrophoresis on an Applied Biosystems 3730xl Genetic Analyzer , and scored after double manual inspection using GeneMapper® software v4.0 (Applied Biosystems).Click here for additional data file.10.7717/peerj.11568/supp-5Supplemental Information 5Click here for additional data file.10.7717/peerj.11568/supp-6Supplemental Information 6For each area in which ABFT larvae were collected (Pop), pooled by collection area and all together, the number of samples (N) genotyped, number of alleles found for each locus (Na), Polymorphic Information Content (PIC) and null allele frequency (Null) are indicated.Click here for additional data file.10.7717/peerj.11568/supp-7Supplemental Information 7max) and minimum (Nmin) individuals reported per cluster. Inferred mothers, fathers and families are indicated together with number of full siblings (Full sib) and average associated probability (Full sib p).For each spawning area replicate datasets excluding missing data were analyzed with COLONY software. Average number and standard deviation are shown for each parameter. N indicates the number of multilocus genotypes  included in the sibship analysis. Linking unrelated offspring through pedigree results in a number of clusters with average maximum (NClick here for additional data file.10.7717/peerj.11568/supp-8Supplemental Information 8MT912036–MT91207.GenBank entries for nucleotide sequences: Click here for additional data file.


Aedes mosquitoes are important invasive species contributing to the spread of chikungunya, dengue fever, yellow fever, zika virus, and other dangerous vector-borne diseases. Aedes albopictus is native to southeast Asia, with rapid expansion due to human activity, showing a wide distribution in the Korean peninsula. Aedes flavopictus is considered to be native to East Asia, with a broad distribution in the region, including the Korean peninsula. A better understanding of the genetic diversity of these species is critical for establishing strategies for disease prevention and vector control.Ae. albopictus and 166 specimens of Ae. flavopictus in Korea, and amplified two mitochondrial genes (COI and ND5) to compare the genetic diversity and structure of the two species.We obtained DNA from 148 specimens of COI and a 423-bp sequence of ND5 from both mosquito species. We found low diversity and a nonsignificant population genetic structure in Ae. albopictus, and high diversity and a nonsignificant structure in Ae. flavopictus for these two mitochondrial genes. Aedes albopictus had fewer haplotypes with respect to the number of individuals, and a slight mismatch distribution was confirmed. By contrast, Ae. flavopictus had a large number of haplotypes compared with the number of individuals, and a large unimodal-type mismatch distribution was confirmed. Although the genetic structure of both species was nonsignificant, Ae. flavopictus exhibited higher genetic diversity than Ae. albopictus.We obtained a 658-bp sequence of Aedes albopictus appears to be an introduced species, whereas Ae. flavopictus is endemic to the Korean peninsula, and the difference in genetic diversity between the two species is related to their adaptability and introduction history. Further studies on the genetic structure and diversity of these mosquitos will provide useful data for vector control.The online version contains supplementary material available at 10.1186/s13071-021-04873-5. Aedes, Anopheles, and Culex are the main vectors of mosquito-borne diseases [Aedes are the main vectors for spreading fatal diseases such as chikungunya, dengue fever, yellow fever, and Zika virus, that often occur in Asian countries [Arthropod-borne viruses are transmitted by blood-sucking insects to animals and humans. Most of them are transmitted by mosquitoes , 2. Therdiseases , 4. In pountries –7. As moountries .COI and ND5 as markers, for example, to determine whether a species has been introduced or to determine the genetic diversity of a population [Mitochondrial genes are widely used in research on molecular evolution and population genetics of vector insects. Because they have a relatively high mutation rate and high levels of polymorphism and divergence due to their inherent sensitivity, they are highly useful as molecular markers –12. Manypulation , 16. Poppulation . An undepulation .Aedes albopictus, originally from Southeast Asia, has recently spread throughout all parts of the world except Antarctica, and is considered one of the most dangerous alien species [Ae. albopictus in South Korea was in 1940, and its distribution has recently expanded throughout the Korean peninsula [Aedes aegypti, substantial research attention has been paid to Ae. albopictus as major players in the transmission of vector-borne diseases [Ae. albopictus have been shown to tolerate cold weather, and have the potential to expand its distribution in colder regions [ species –21. The eninsula , 23. Togdiseases –26. The diseases , 28. Adddiseases , 30. Egg regions , 32.Aedes flavopictus is known to be native to East Asia, and is divided into three subspecies depending on the region. The subspecies are morphologically and genetically distinct [Ae. flavopictus, Ae. flavopictus downsi, and Ae. flavopictus miyarai. Among the three, Ae. flavopictus is distributed in the Korean Peninsula, and records show that they have existed here for a long time, but there have been few molecular studies on this species, so the extent of its genetic diversity is not fully known [Aedes flavopictus eggs have been found to survive in colder environments than Ae. albopictus eggs [Ae. flavopictus is not high [Aedes flavopictus is not known to act as a vector like Ae. albopictus and other Aedes species, but it has previously been shown that it may propagate dengue fever [distinct , 34: Ae.ly known , 35, 36.tus eggs , and thetus eggs –40. Acconot high , 41–43. ue fever , 44, 45.Ae. albopictus and Ae. flavopictus are closely related species and have similar ecological roles and habitats, they can be compared to each other.Since the two species are distributed over a wide area in Korea and Japan and share a common habitat , 46, 47,Aedes. The presence of Ae. albopictus and Ae. flavopictus was recorded in Korea in the past [Aedes has not been thoroughly investigated [Aedes; therefore, a preemptive control strategy needs to be established [The Korean Peninsula has various climates and geographical environments, and the diversity of arthropods that transmit arthropod-borne viruses is also high , 55. Thethe past , 56, 57.stigated –60. Therablished , 62.Aedes mosquitoes living in Korea using two mitochondrial genes, with the aim of monitoring mosquito populations. With this work, we intend to create genetic data that infer the genetic state of vectors, to establish a vector control strategy.This study compares the genetic diversity and genetic structure of two species of Ae. albopictus from 19 locations and 166 individuals of Ae. flavopictus from 14 locations  were amplified by PCR using the following primer pairs: albCOIF (5′-TTTCAACAAATCATAAAGATATTGG-3′) and albCOIR (5′-TAA ACTTCTGGATGACCAAAAAATCA-3′) for COI [2 (25 mM), 0.7 μl of dNTP solution (2.5 mM each), 0.5 μl of each primer, and 0.3 μl of Taq DNA polymerase . The PCR cycling conditions were as follows: an initial denaturation step at 95 °C for 5 min; followed by 35 cycles of denaturation at 95 °C for 30 s, annealing at 45 °C for 30 s, and elongation at 72 °C for 45 s; with a final extension at 72 °C for 7 min. For ND5, the PCR mixture was the same as that used for COI. The amplification conditions were as follows: initial denaturation at 98 °C for 5 min; followed by 10 cycles at 95 °C for 1 min, 45 °C for 1 min, and 72 °C for 1 min 30 s; 30 cycles at 95 °C for 1 min, 46 °C for 1 min, and 72 °C for 1 min 30 s; and a final extension at 72 °C for 3 min. PCR products were separated by 2% agarose gel electrophoresis  and sequenced by Cosmo Genetech  using the ABI 3730xl DNA Analyzer .Two regions of mitochondrial genes  and prepared as concatenated sequences. DnaSP 6.12.03 [H), number of segregating sites (S), haplotype diversity (Hd), nucleotide diversity (π), and average number of nucleotide differences (k) were examined.The sequences of the two mitochondrial genes were aligned using the ClustalW plugin on Geneious Prime 2020.1.2  were performed using Arlequin 3.5  with theTo better understand the genealogical relationships, the haplotypes were constructed using the TCS method as implemented in PopART 1.7 .D [FS [To investigate the demographic history of populations, deviations from selective neutrality were tested by Tajima’s D  and Fu’sD [FS  metrics D [FS . To confD [FS .CO1 sequence of 658 bp and an ND5 sequence of 423 bp in the 19 populations  of Ae. albopictus, and sequences of the same length were obtained for the 14 populations  of Ae. flavopictus . In the two mitochondrial DNA concatenated sequences, there were 25 haplotypes in Ae. albopictus and 107 haplotypes in Ae. flavopictus.Mitochondrial gene sequence analysis resulted in a Ae. albopictus compared to the total number of individuals, with relatively low haplotype diversity (0.396) and nucleotide diversity (0.00075) in the entire population. The highest haplotype diversity was found for the 2018 Anyang and Gyeongju populations, and the lowest values were 0 for six populations. The 2018 Anyang and Gyeongju populations also exhibited the highest nucleotide diversity. Ae. flavopictus showed a relatively high number of haplotypes compared to the total number of individuals, and high levels of haplotype diversity (0.990) and nucleotide diversity (0.00894) were found in the entire population. Analysis of the two species revealed that Ae. flavopictus exhibited higher levels in various genetic diversity indices  and Fu’s FS (−0.44574) in the whole population. Negative values of Tajima’s D (−0.34726) and Fu’s FS (−1.37746) were also found for the entire population of Ae. flavopictus. In both species, Ae. flavopictus showed negative Fu’s FS values. For the mismatch distribution, the result of Ae. albopictus was nonsignificant, whereas Ae. flavopictus showed a large unimodal shape, indicating the possibility of sudden expansion of the population (Fig. With respect to demographic history, ’s FS −0.4574 in tAe. flavopictus than in Ae. albopictus, and the two species of mosquitoes generally showed low levels of genetic structure except for some populations.Based on the above results, two conclusions can be drawn. Higher genetic diversity was observed in Ae. albopictus in Korea. The first concerns the spread of Wolbachia to the mitochondria: in many arthropods, selective sweeps arising from widespread Wolbachia infections often lead to low diversity, therefore posing a high possibility of unreliable results [Wolbachia was detected in 17 populations in Korea, and these groups are known to have low mitochondrial diversity [Wolbachia in many samples, and further analysis of the nuclear genes, as well as the mitochondrial genes, will be needed to reveal the impact of Wolbachia on genetic diversity in the population [Ae. albopictus may have been affected by Korea’s harsh winter climate, which is different from that of the country of origin. Ae. albopictus is considered to be an invasive species that has recently spread abroad from its origin in Southeast Asia [Ae. albopictus, is hot and humid, facilitating the spread of Ae. albopictus [There are two hypotheses addressing the overall low diversity of  results . Previouiversity , 73. Howpulation , 74. Theast Asia , 20. Theast Asia , 15. Thebopictus . Howeverbopictus , 76. In bopictus , and thebopictus .Ae. flavopictus have high genetic diversity and a complex haplotype network. There are two hypotheses regarding their high diversity. The first is based on the fact that Ae. flavopictus is endemic to East Asia. Studies on mosquitoes of the genus Aedes show that genetic diversity in the original population is much higher, which supports the contention that the original population of Ae. flavopictus is Korean [Ae. flavopictus is an Asian species that does not exist in tropical regions, and lives in subtropical areas throughout the cool-temperate region [Aedes flavopictus has excellent environmental adaptability, and its eggs can survive in cold and dry conditions for long periods [Aedes galloisi, a northern mosquito species in the same genus [Korean s Korean , 80. Thee region . This moe region , 40. Aed periods , 81. Stume genus .Ae. albopictus and Ae. flavopictus can be explained in several ways. A small mismatch in distribution and a single haplotype shared by various populations indicate that the patterns observed in Ae. albopictus may have been affected by a decrease in effective population size, human introduction, and natural environmental changes [Aedes flavopictus, an endemic species, shows high genetic diversity, a large unimodal mismatch distribution, and a complex haplotype network. However, the unimodal form of its mismatch distribution indicates that the Ae. flavopictus population may have recently experienced a large population expansion. This successful distribution and increasing population of the endemic Ae. flavopictus may have been affected by human demographics [Ae. albopictus can survive for up to 24 h at −10 °C in the form of diapause eggs [Ae. flavopictus can survive for a longer period [Ae. flavopictus appearance was not high, but the reason for the large potential population size in this study lies in the difference between the location and the collection method [Differences in diversity between  changes , 82, 83. changes . Aedes fgraphics ; furthergraphics , 86. Theuse eggs , and ther period , 37, 53.r period , 89. Thir period , 90, 91.n method , 41–43. Ae. albopictus and Ae. flavopictus populations revealed in this study suggest that the continuous monitoring of these species with multiple possibilities as vectors is essential. To understand the genetic diversity of Aedes mosquito species in Korea, sampling in more diverse regions and the use of different genetic markers will be conducted in further studies.The differences in the genetic diversity of Aedes mosquito species inhabiting Korea. The results showed that Ae. albopictus, which is considered to be an introduced species, has lower genetic diversity than Ae. flavopictus, the endemic species. The low diversity of Ae. albopictus suggests that these mosquitos were introduced by humans, but did not fully adapt to the environment of the Korean Peninsula. The high diversity of Ae. flavopictus could be due to its greater adaptability to the environment of the Korean Peninsula as an endemic species, but may also be influenced by an increase in population and resistance to pesticides. However, in light of the rising temperatures caused by climate change, the domestic inflow of patients, and the population density, the Korean Peninsula will continue to face the threat of mosquito-borne diseases. Studies of the genetic status of potential vector species will provide useful data for inferring effective population sizes and monitoring and managing mosquito populations.This is the first paper comparing genetic diversity and the genetic structure of two Additional file 1: Figure S1. Representative picture of a forest area surveyed in this study in Korea. Figure S2. Representative picture of a rural area surveyed in this study in Korea. Figure S3. Representative picture of a park surveyed in this study in Korea.Additional file 2: Table S1. Details of the mosquito populations used in this study, with GenBank accession numbers for CO1 and ND5 gene sequences.Additional file 3: Table S2. Pairwise FST values obtained using two mitochondrial DNA concatenated sequences from Aedes albopictus. Table S3. Pairwise FST values obtained using two mitochondrial DNA concatenated sequences from Aedes flavopictus.


Fiber-reinforced composite structures are used in different applications due to their excellent strength to weight ratio. Due to cost and tool handling issues in conventional manufacturing processes, like resin transfer molding (RTM) and autoclave, vacuum-assisted resin transfer molding (VARTM) is the best choice among industries. VARTM is highly productive and cheap. However, the VARTM process produces complex, lightweight, and bulky structures, suitable for mass and cost-effective production, but the presence of voids and fiber misalignment in the final processed composite influences its strength. Voids are the primary defects, and they cannot be eliminated completely, so a design without considering void defects will entail unreliability. Many conventional failure theories were used for composite design but did not consider the effect of voids defects, thus creating misleading failure characteristics. Due to voids, stress and strain uncertainty affects failure mechanisms, such as microcrack, delamination, and fracture. That’s why a proper selection and understanding of failure theories is necessary. This review discusses previous conventional failure theories followed by work considering the void’s effect. Based on the review, a few prominent theories were suggested to estimate composite strength in the void scenario because they consider the effect of the voids through crack density, crack, or void modeling. These suggested theories were based on damage mechanics (discrete damage mechanics), fracture mechanics , and micromechanics (representative volume element). The suggested theories are well-established in finite element modeling (FEM), representing an effective time and money-saving tool in design strategy, with better early estimation to enhance current design practices’ effectiveness for composites. This paper gives an insight into choosing the failure theories for composites in the presence of voids, which are present in higher percentages in mass production and less-costly processes (VARTM). Composite materials have significant properties like their high strength to weight ratio ,2,3. TheInitially, the VARTM cycle’s advancement was introduced by Williams, Summerscales, and Gove in their noteworthy review of resin injection under flexible tooling (RIFT) . In 1950However, because of the mass and cost-effective production environment, the VARTM manufactured products contain voids and micro-voids as ordinary deformities which influence the composites’ applications ,26,27. TIt is an important topic to determine failure state and components’ effectiveness due to growth in the advanced application of composite structural materials in current years. When the final processed composites present flaws, then it is necessary to incorporate manufacturing defects in failure theories. There are many failure criteria for composites which have been developed without consideration of voids. These are summarized in Void-free manufacturing is impossible, but to work safely at the application level, reliable design is necessary ,27,32. TThe vacuum-assisted resin transfer molding (VARTM) process has been developed to use effectively during the past two decades. This closed-mold technique can produce high performance and various types of composites, especially fiber reinforced polymer structures, at low cost . The metAfterward, the laminate is healed, and both injection and cure can occur at either ambient or elevated temperature. In this process, it is possible to use any fibers, and the stitched materials work well since the gaps allow for the rapid transport of resin . This isBroadly, the VARTM technique can be separated into three processing steps, including material and tool preparation, viz. pre-infusion, infusion, and post-infusion . Each prThere are different names to describe this process, viz. vacuum-assisted resin infusion molding (VARIM), vacuum-assisted resin infusion (VARI), and vacuum bag resin transfer molding (VBRTM). Further, many other developments have been in the VARTM process, such as Seeman composites resin infusion molding process (SCRIMP), vacuum-assisted process (VAP), and controlled atmospheric pressure resin infusion (CAPRI).The VARTM procedure was formerly produced to fabricate superior and huge composite parts, such as infrastructure and transport structures ,39. ThisThe main benefit of VARTM is that it lies in the low injection pressures (approx. 1 atm). At the time of manufacturing, less movement is required for reinforcement, which has been achieved by this little pressure to get better quality products . Due to Over the VARTM process’s advantages, there are different types of defects, like fiber misalignment and voids, whereby defect-free manufacturing is impossible. In mass production, voids formation is the primary defect. Many factors like resin-flow pressure variation or temperature changes initiate voids formation in this process ,42. FiguLinear models, including maximum normal stress, maximum strain, and maximum shear stress criterion, were the earliest failure models for composites and brittle/ductile materials. Failure theories for the composite are different from ductile and brittle materials. Norris presented a simple anisotropic failure model for plywood . Hill exTill now, the above-described failure prediction theories have not considered any manufacturing defects such as voids. As discussed, voids are well-known defects in VARTM processed composites, so the understanding of failure with the void’s effects is necessary to access good design. This was started earlier to consider manufacturing defects in the failure mechanism. The next section covers a precise survey on failure theories with defects (voids).The above-presented theories did not take the voids defects into account, so this section covers the works that consider the voids’ effect. Chamis introduced a two-level failure theory with voids for unidirectional filamentary composites, matrix strain magnification, and introduced a rule of mixture for the first level and modified distortion energy for the second level . These tIn this section, the discussed survey took the effects of voids on failure mechanism. Although the above-discussed survey worked with different kinds of composites, it showed how the researcher took voids in failure theories. These researchers signify the reason to take manufacturing defects into account for a better pre-estimation of design. The different kinds of approaches were discussed. Some belong to experiments and others to analytical and numerical observations. When the load is applied on parts, if components cannot resist it, failure initiation starts, and when there are already voids, this failure initiation happens on less stress. A strain-based criterion was proposed for the primary level check. The drawback in stress-based criteria is that scale effects associated with crack length with similar stress fields cannot be modeled correctly . Also, dƐ2 to check matrix cracking, but it works to check fiber failure in the complementary lamina, which can be built and model into the laminate precisely to constrain the transverse deformation and strain (Ɛ2). This criterion is well-established and straightforward to apply for researchers and industrialists within the FEM domain. More details about this criterion and its effectiveness can be found in the literature [In the aircraft industry, maximum strain criteria are well-established. On that basis, Hart-smith made advancement in maximum strain criteria by truncating its tension-compression coordinate for shear and compared with different direction laminate. These criteria made better failure prediction and also followed by aerospace industries . Truncatterature . This wa1, D2, D6, etc., for orthotropic damage [Continuum damage mechanics studied by Kachanov for stress conditions in damaged metals under creep scenario; then this approach got researcher’s attention and developed for brittle, ductile, and composite materials. This approach represents the cracks indirectly by their effect on stiffness, using a damage variable, e.g., Dc damage . These dc damage . AlthougcG . Finite element implementation of DDM is mesh insensitive such that is most beneficial for computational study. The damage activation function is the function of strain and cracks density for the evolution of cracks or voids’ stage as shown in the below equations [In DDM, the state variable represents a measurable quantity, i.e., the crack density lambda  λ= IcG and IIcG are the fracture toughness in mode I and mode II, respectively which are the material properties of given materials, IG and IIG are energy release rates in mode I and mode II, respectively, and r is the constant term which is the ratio of IcG and IIcG. IG and IIG are calculated as the function of crack density (IcG and IIcG) and energy release rates (IG and IIG) [In Equation (4), and GII) . In DDM,g = 0), but when crack initiates, crack density and strain increase, then energy release rate (IG and IIG) decrease, thus damage activation (g) will be less than zero.Equation (5) shows that when there is no crack  based on energy release rate at normal and shear crack-tip deformation mode and compares the energy release rate for interlaminar fracture. Strain energy release rate is the driving force for void (crack) growth to delamination. VCCT assumes that the energy required to separate the surface is equal to the energy required to close the surface. VCCT calculates energy release rate by the function of the crack yielding direction. This method is based on energy release when a crack goes from one position to another . These ayF,xF, and zF are the reaction forces in the opening, sliding, and shearing action for modes I, II, and III, respectively. v and w is the relative displacement for the action and reaction of mode I, II, and III conditions, respectively, in the local coordinate system and F = IG/ICG = 1, when an opening is dominant than sliding and shearing), delamination is started. In the above Equation (6), As discussed above, voids/crack lead to delamination that’s why it is required to study delamination, and modeling in FEM makes it time and money-saving. Zhuang et al. modeled delamination front void and studied its effect on mode I and II, and it was observed that mode II is profoundly more affected by the voids than mode I . GliszczTo provide more physical insight into the void’s effect on manufactured components’ failure, micromechanical approaches can be an advantageous because they can cover micro-level manufacturing details. This approach is applied by researchers to obtain the failure behavior. In that direction, Hyde et al. studied both interfiber and matrix voids used in the microstructural model of composites . As per RVE (unit cell) can be modeled based on scan images or statistical or understanding of fiber, and void fraction in a unit cell, any of the discussed algorithms need well understanding. For FE analysis voids can be modeled as a blank space or elements if the element model it, then assigning low properties are feasible. Elnekhaily and Talreja modeled RVE with defects by using shaking algorithms to make a degree of non-uniformity of fiber distribution depending on their fiber volume fraction . Sudhir One strain and micromechanics-based approach is presented below, named strain invariant failure theory (SIFT), which has good capability to predict failure. The beneficial point of SIFT is micro-mechanical magnification like the micro-strain of laminates amplified by a strain amplification factor which can be predicted by microlevel (RVE) FEM.Strain invariant failure theory (SIFT):Strain invariant failure theory (SIFT) proposed by Gosse in 2001 is another precise strain-based failure criterion . VolumetSIFT delivers that strain and critical material parameters are related to each deformation mode. These deformation states are related to invariant quantities of the strain tensor, viz. strain and stress correspond in a linear material is Ɛ1,2,3 is the principal elastic strain tensor, through that effective strains are calculated. Failure occurs if any of the strain invariants  exceeds the critical value, as shown in Equation (9). For each strain invariant, critical material allowable is obtained for failure estimation [The first invariant of the elastic strain tensor and the square root of the second invariant of the strain deviator tensor illustrate the dilatational and distortional strain, respectively. This criterion covers volumetric and distortion deformations, as shown in timation .(9)maxεM) is used to connect micro and macro-structure levels. Here, M is the amplification factor, This is also the promising failure criterion at the microlevel, and the strain amplification factor  conditions. After analyzing the proposed criteria, the below example demonstrates how manufacturing defects (cracks/voids) affect failure. The current example showed the effects of voids in delamination. This presented example shows the effect of voids on the failure mechanism by the suggested VCCT method. As it is discussed, that void leads to make early delamination. An example is shown here, which takes the voids into account and estimates the delamination by proposed (VCCT) criterion. This example shows that additional voids affect early delamination between the bonded layers. For that, two cases are studied, first with single voids and second with an additional void near the previous void, to check the effect of voids in the failure mechanism.2, and due to the additional void, it increases to 16 j/m2. Also, CC ERR increase from 0.91 to 4.45 j/m2, For mode II. In the first case, a single void created less delamination, which increased in the second case due to an additional void. Hence, voids support easy debonding and delamination.Two layers were modeled as a two-dimensional plate with unidirectional carbon fiber reinforced polymer composite (CFRP). The mechanical properties used for CFRP are E11 = 121 GPa, E22 = E33 = 8.60 GPa, G12 = G13 = 4.70 GPa, G23 = 3.10 GPa, ν12 = ν13 = 0.27, ν23 = 0.4. The plane 182 element was used for meshing the model; as per the VCCT recommendation void region is modeled with pre-meshed crack with a smaller size , as shown in This example was presented to show a voids’ effect on delamination and effect on failure after taking them into account by proposed criterion. It was obtained that additional voids increased delamination length, as shown in This paper discussed the failure theories and suggested for VARTM processed polymer matrix composite in mass production environments. The VARTM process is a cost-effective and highly reliable process, but due to void contents, it is necessary to consider void’s (crack) effects in failure theories as a design strategy. It is known that in the case of defects, failure occurred earlier than the calculated strength without void conditions, so voids are important to consider in the design, as illustrated in Although some old conventional failure theories are well established, they do not take process defects into account. Recently, researchers have worked on failure theories with process defects. This paper considers void conditions from the manufacturing stage. From the survey, some failure approaches were suggested for failure estimation. These suggested approaches are applicable for primary to advance level design. These well-established selected theories, such as discrete damage mechanics, fracture mechanics (VCCT), and micromechanics, are suggested to design composites. These verified approaches have better capability to capture voids growth and failure. It is suggested to use strain-based theory like TMSC for primarily level design to get the first estimation because it is in a simple form and the extension of well-known maximum principal strain criteria, after passing from it, can be checked by any of advance level discussed theories. In these cases, VCCT is suggested for delamination due to voids. Discrete damage mechanics is concluded for the advanced level as damage mechanics because its calculation is based on the crack (void) density and its function. Further, it is independent of mesh details.Micromechanics is also an excellent approach to get micro to macro-level failure, so it is presented that RVE (unit cell) can model with void consideration. This approach is discussed because it takes the small level manufacturing details and analyses the failure at the micro-level. This estimation supports the precaution strategy and preunderstanding for macro-level safety. Finally, one example is presented for delamination because it is the most known cause of failure in composite laminates and estimated the effects of voids on delamination by proposed criterion. One more criterion is suggested, named SIFT, which accounts for strain and micromechanics to cover details with accuracy. Overall, it can be obtained that this paper gives a better idea to choose the failure theory for composites. Researchers and industrialists can choose the theory according to the manufacturing and practical conditions.The failure theories with manufacturing defects enhance the reliability of the composite design. It so happens that a cost-effective VARTM process is possible for mass production with enhanced failure understanding due to process conditions. If voids have undesirable effects, then the process should be revised according to the void’s conditions. After estimating the strength with voids, it can be understood that components are useful or not. It is good to use a reliable failure theory for designing. It can be valuable for each composites’ application. Design with manufacturing defects needs a less costly process, so FEM is the best tool for predesign to check approximate conditions of manufactured components under application loading, and suggested theories are well-recognized in FEM. This review gives a better vision into choosing failure theories for composite materials with the manufacturing process and practical conditions, i.e., defects (voids). After understanding the selected known technique, new criteria can be proposed to obtain all the conditions’ premises at the application level with process defects.2) in mode I with additional voids. Although the composite manufacturing field has reached an advanced stage, optimizations are still underway as it is implausible to manufacture defect-free composites. So, defects should be considered in failure theories to estimate the design conditions accurately. VARTM is a good technique for polymer composite fabrication with a high production rate and good quality. However, it suffers from voids defects in fabrication. Also, high cost and sophisticated accessories techniques cannot manufacture completely void-free composites. Therefore, it is necessary to consider voids defects in the failure approach for a durable design. This review presented the previous work on failure theories that do not consider the effect of voids. Then, due to the essence of void consideration, we reviewed the researches that considered void effects in failure. It was obtained from a survey that strength is diminished by the increment of void content, as 1% and 2% percent increases in voids, accompanied by decreases of 2.5% and 12.7% in tensile and flexural strength, respectively ,90. The It is concluded from this review that for the reliable and durable design of VARTM processed composites, a consideration of process defects (voids) is necessary. This paper suggests failure theories in void states and gives a future idea for the better design and prediction of strength at a quality level. A few meaningful points are highlighted for an accurate design of cost-effective VARTM processed composites:Voids are significant defects in the composite manufacturing process (VARTM). Readers will understand the selection of failure theories in a cost-effective environment (mass-production) at the application level (working environment because different loading makes a different kind of failure) with the presence of voids.Voids to delamination should be considered at the time of design for composite laminates. Fracture mechanics-based VCCT failure estimation was proposed for the delamination study because it can consider the voids’ effect, enabling the energy release rate.Preliminary and damage level failure prediction is essential. Checking for preliminary level design viz. preliminary failure theories (TMSC) without considering damage behavior is enough then consideration of damage and crack behavior by DDM will be a good practice for precise understanding because DDM can consider voids by their density.Micromechanics with failure theory is also an excellent option to model defects (voids) for failure prediction at the micro-level and supporting a relation from micro to macro-level.


Ta). At Tas requiring heating of indoor living spaces, the energy-Ta plot of a city contains information on city-wide thermal insulation (I), making it possible to quantify city-wide I by use of the city as the unit of measure. We develop methods for extracting this insulation information, deriving the methods from prior research on mammals. Using these methods, we address the question: in North America, are cities built in particularly cold locations constructed in ways that provide greater thermal insulation than ones built in thermally more moderate locations? Using data for 42 small and medium-size cities and two information-extraction methods, we find that there is a statistically significant inverse relationship between city-wide I and T10-year, the average city Ta over a recent 10-year period (range of T10-year: − 11 to 26 °C). This relationship represents an energy-conserving trend, indicating that cities in cold climates have greater built-in thermal insulation than cities in warm climates. However, the augmentation of insulation in cold climates is only about half as great as would be required to offset fully the increased energy cost of low Tas in a cold climate, and T10-year explains just 5–11% of the variance in measured insulation, suggesting that cities in North America vary greatly in the extent to which thermal insulation has been a priority in city development.Previous research demonstrated that cities are similar to individual mammals in their relationship between the rate of energy use for heating and outdoor air temperature (The online version contains supplementary material available at 10.1007/s00360-021-01411-8. For analysis of continent-scale human energy use, a key question is whether people build better-insulated cities in places where superior thermal insulation would have the greatest effect. We argue in this paper that, for quantifying city thermal insulation, it may be possible to treat the city as the unit of measure by applying methods developed for the analysis of insulation in animal homeotherms. Our specific interest is in insulation as a defense against heat loss in cold environments.At present, there is great multidisciplinary interest in the thermal insulation of the built environment in cities. Cities are where much of human energy use takes place  in much the same way as the metabolic rate of a homeothermic animal varies with Ta  as the units of measure, estimate I for each, and synthesize. Such an approach would be fraught with privacy concerns and expensive. An alternative approach—far less fraught and expensive—is to treat the entire city as the unit of measure and calculate city-wide I from the city energy-Ta curve.The thermal insulation, Ta curve of an individual mammal  is constant regardless of Ta. This range, the thermoneutral zone (TNZ), is bounded at its lower limit by the lower critical temperature (TLC) and at its upper limit by the upper critical temperature (TUC). The rate of heat production in the TNZ is the basal metabolic rate, which we call the resting metabolic rate (RMR) in application to cities. Within the TNZ, an animal varies its value of thermal insulation, I, as Ta varies. However, according to the standard model, an animal’s I is maximal and constant at Tas below the TLC. Because of this constancy of I below the TLC, the animal’s rate of heat production increases linearly as Ta decreases below the TLC. The absolute value of the slope of the energy-Ta curve at Tas below the TLC is equal to conductance, C, which is the inverse of I (I = 1/C) , which formally refers to this resistance, is defined operationally by the following equation, analogous to Ohm’s law:H represents the rate of internal heat production required to replace heat lost. In an animal, H is the animal’s metabolic rate , the quality of structural maintenance over time, and other structural features. When I is measured using Eq. . We recognized six categories of published annual average temperature: < − 4 °C, − 4 °C to + 2 °C, 2–8 °C, 8–14 °C, 14–20 °C, and 20–26 °C. Then, as we sought cities for inclusion in this study, we maintained two objectives: (1) obtain data on as many cities as possible, but also (2) include, to the extent possible, approximately equal numbers of cities in the six temperature categories. We limited our search to mid-sized cities because large cities often have indefinite physical and functional boundaries, and because the inclusion of large cities would have substantially increased among-city heterogeneity in the urban-heat-island effect  to request daily, city-wide usage data for an entire year, and here we report on all 42 cities in which the primary energy sources for home heating are electricity and natural gas, and for which the utilities agreed to provide data. In the process of gathering these data, we sought cities with widely different exposure to low fect Oke , 1982.Prior to approaching the utilities in a city, we used publicly available satellite images and narratives  to assess whether large, energy-intensive factories—or other industrial or military facilities—were present in the city. We did not approach cities in which we were able to identify such facilities. We also focused on city boundaries and did not pursue cities that we became aware were seamlessly connected to adjacent cities. If we approached the utilities in a city, we queried utility personnel about the possible presence of heavy industrial or military customers and withdrew our request if we learned of their presence.Approached candidate cities were often unusable because one of the utilities could not or would not provide the data needed. If the utilities expressed willingness to cooperate but could not provide the exact types of energy-use data we sought, we sometimes modified our request. Most notably, in two far-northern cities  we obtained data on energy use at weekly or monthly resolution because data at daily resolution were unavailable.Our target year for data was 2013, and for 35 cities we report data for that year. We also include  the six cities we earlier studied . Some cities (particularly in relatively warm regions) do not use natural gas, and in those cases we calculated daily heat production from electric data alone. To produce an energy-Ta plot, daily Ta must be known for each calendar day. To obtain data on the average Ta during each day, we used MapServer at the Utah Climate Center (Utah State University) to access temperature records for cities in the United States. For cities in Canada, we used either the Government of Canada Historical Climate Data database or the Environment Canada database. For a typical city, using these methods, we obtained 365 x, y data points , where x is daily average Ta and y is daily city heat production, Hcity. In Utqiagvik (formerly Barrow), AK, we obtained 49 weekly data points. In Inuvik, NWT, we obtained 36 monthly data points by combining data for three adjacent years (2013–2015).For each city, we calculated city-wide, within-building heat production during each 24-h calendar day—symbolized All statistical methods were planned in detail a priori. After data were obtained, the data were processed strictly according to the a priori plan.I, we use two methods, which differ in how effectively they address two types of limitations in the data available /Hcity, where TLS is the air temperature in the living spaces of buildings. As already described, for each city we have daily data on Ta and Hcity for an entire year. Accordingly, we can calculate city-wide I for each day , limiting ourselves to days when Ta ≤ TLC because, as described earlier, I is expected to be constant at such Tas, meaning all such estimates of I are estimating the same value. The obvious problem with the application of this method is the lack of empirical knowledge of the temperature maintained in living spaces, TLS. However, we do know that people in North America tend to set thermostats near 20 °C in the cold seasons of the year  /Hcity(10) (see “Tas chosen). In addition to being unitless, this ratio provides a measure that is easily understood by nonspecialists.The slope method explicitly addresses a different limitation: the problem that city population size, see Fig. . C itselTLC, of each city (SPSS lacks a procedure for segmented regression). To keep the calculation of TLC as straightforward as possible and minimize the chances of confounding factors  to determine which estimate of TLC to use.All statistical calculations were carried out in IBM SPSS Statistics (version 26) except that we used the Segmented package  and resolved ambiguities using AIC. To estimate the regression of the energy-Ta relationship above thermoneutrality, we calculated a lower Ta limit for the data to be used as TUC plus 1 SE. We then carried out linear least-squares regression, using the data for all days when Ta was equal to or higher than this lower limit.Although the energy-Ta was (1) more than 1 SE above TLC (using the SE of the estimate for TLC) and (2) more than 1 SE below TUC (using the SE of the estimate for TUC). There were many cities for which Segmented detected no TUC . In such cases, we calculated the mean daily energy consumption in the TNZ using the data for all days meeting criterion (1).To calculate RMR, the mean daily energy consumption in the TNZ of each city, we averaged the data for all days when the Ta was “below the TLC,” in fact the days were those with Ta below TLC minus 1 SE of the TLC. Similarly, when we speak of data “above the TUC,” the data were actually above the TUC plus 1 SE of the TUC.Throughout the rest of this paper, we use a short-hand terminology for referring to data boundaries. Where we speak of data gathered on days when T10-year. For cities in the United States, we obtained population size from the US Census Bureau (2010 census); for cities in Canada, we used Statistics Canada. To obtain the number of electric accounts, we asked the electric utility.For each city, we obtained three additional types of information used in our analysis: population size, number of electric accounts, and T10-year we used data acquired through MapServer (Utah Climate Center), the Government of Canada Historical Climate Data database, or the Environment Canada database. Barring lapses in data records, our target 10-year period was typically 2004–2013, and we averaged the Ta data for all days in that period. If data were missing for some of those years, to obtain 10 years of data, we included data (if available) from the adjacent years for which data were available and that were closest in time to the target 10-year period. For cities in which long-term Ta records have not been maintained, we used data for the closest cities, of similar size and altitude, where data are available. For three cites, we could not obtain 10 years of data near our target years and used 8 years (Fort Macleod) or 5 years ; for Inuvik, the only suitable data were “climate normal” data for 1981–2010.To calculate Ta curves for all 42 cities are presented in Fig. T10-year, and population P. Although the zones of average annual air temperature in Fig. T10-year (Table The energy-T10-year and P for the 42 cities. The Pearson correlation coefficient between the two variables is 0.04 (p > 0.8), indicating no relationship. A scatterplot of the two variables likewise suggests that T10-year and P are completely unrelated in our dataset.We analyzed the potential correlation between I = (TLS − Ta)/Hcity for each day when Ta was below TLC, assuming in three sequential analyses that TLS was 20 °C  were not always met. Thus, following our a priori strategy, we calculated the average P-normalized I [see Eq. (TLS − Ta) by Hcity/P for each day and then averaging the data over all N days at each assumed TLS. Given that thermostats are commonly set near 20 °C during the cold seasons of the year in North America  as a function of Ta, at Ta < TLC, for each of the 42 cities . In all cases, the regression had a statistically significant slope different from 0 .To implement this method, we first calculated the linear regression for the total rate of heat production /Hcity(10), to obtain a unitless index of city insulation  is significantly different from 0 , although the adjusted r2 is 0.048, indicating that the regression explains just 5% of variance. According to the standard model  /Hcity(10) and T10-year (p = 0.95).Using the Hcity at a specific low Ta could also serve as an index (inverse) of city resistance to heat loss. Thus, for each of the 42 cities in our full dataset, we calculated Hcity at Ta = − 20 °C using the Hcity-versus-Ta regression (Supplementary Table 1). Recognizing that city population size, P, helps determine Hcity, we carried out an analysis of covariance to examine the potential relationship between Hcity at Ta = − 20 °C and T10-year. P explains a highly significant part of the total variance (p < 0.005). However, there is no evidence of a significant relationship between Hcity at Ta = − 20 °C and T10-year (p = 0.18).Related to the slope method is the concept that the predicted value of TUC in the energy-Ta curves for 25 cities, but not the other 17 cities. Supplementary Table 2 summarizes the estimates of TUC, estimates of RMR/person (mean Hcity/P in the TNZ), and—for cities with a defined TUC—the regression coefficients for regression between Hcity and Ta at Ta > TUC.The Segmented routine in R identified a breakpoint corresponding to Hcity in the TNZ) as a function of T10-year was examined with P as a covariate. Although the effect of P was significant (p < 0.001), that of T10-year was not (p = 0.61).For statistical analysis of RMR, the regression of mean total city-specific RMR  by use of heating systems as Ta decreases below TLC. However, southern Florida is so warm that Ft. Pierce and Key West barely express segment ①. Conversely, cities in the Far North such as Inuvik and Utqiagvik (previously Barrow) experience such cold environments that they do not express segment ②, the part of the TNZ at Tas immediately above the TLC; that is, these cities do not express the TNZ. Cities in high mountains , city-wide I tends to be higher, the colder the climate.Considered in its entirety, this research indicates that in North American cities built in a wide range of thermal climates (TLS (living-space temperature). An underlying assumption is that TLS is independent of Ta at Ta < TLC, an assumption supported by the data for the heating seasons in Huchuk et al.  and TLS = 20 °C (p = 0.014), although moderately short of statistical significance at TLS = 23 °C (p = 0.062)  /Hcity(10). The regression for all 42 cities . Although analysis with just cities that adhere to the x-intercept expectation of the standard Hcity-Ta model does not yield a significant regression  /Hcity(10) is an instructive, accessible metric in its own right, reflecting the extent to which a city’s energy use must be elevated for thermoregulation at Ta = − 20 °C relative to Ta = 10 °C. Thus the full set of data on Hcity(-20) /Hcity(10) is pertinent , we can address a significant question: Is the slope of the regression between I and T10-year  that the average daily low Ta in December-February at our five warmest cities (average T10-year = 21.0 °C) is 7 °C, whereas at our five coldest cities (average T10-year = 3.1 °C), the average daily low Ta in December-February is − 15 °C. Combining the energy-Ta plots of the five warmest cities, we calculate a combined city-wide Iwarm cities of 4200 °C·person/MW and estimate the energy cost (W/person) at Ta = 7 °C for those cities taken as a group. We then ask what the value of I in the five coldest cities, Icold cities, would need to be for the energy cost in the cold cities (W/person) at Ta = − 15 °C to be the same; this is the I in the cold cities that would be required to completely offset the increased cold there so they would have no higher energy cost than the warm cities. By this analysis, Icold cities would need to be 2.7 times higher than Iwarm cities to achieve this end. In actuality, the regression we calculated from our city energy-Ta curves  than our warm cities (excluding Key West). The trend we detect is thus only about half as great as would be required for increased city-wide I to offset fully the effects of thermal climate.Focusing on our estimates of city-wide ear Fig.  great enPrior to this research, there were already reports in the literature that, taking a reductionistic approach, focused on particular variables in home construction that affect insulation. For example, Healy  reportedI—as we use the concept in this report—is a holistic measure of resistance to heat loss that takes all these considerations into account. Thus, city-wide I is the operationally relevant property that energy analysts need to know for understanding how efficiently energy consumed for thermoregulation is used to create desired temperatures in living spaces. Our methods estimate this city-wide I.However, for energy analysis on regional or continental spatial scales, it is important to know holistically the resistance to heat loss of the large assemblages of houses and other occupied buildings in entire cities. Moreover—and just as important—this resistance does not depend solely on individual reductionistic properties . Resistance to heat loss at city scale depends on many factors, including the totality of materials used to build dwellings at various stages in a city’s history, dwelling geometry, and maintenance. As explained in the Introduction, city-wide I, of considerable potential importance for understanding city energy use and the effectiveness of energy-conservation efforts. Our approach to measuring city-wide I evades intrusive house-by-house insulation measurements that would be complex, expensive, and politically tenuous. Because of its practicality and relatively low cost, our approach provides perhaps the only immediately available and realistic means of measuring city-wide I on regional or continental scales.Our methodological approach has two great strengths. It (1) uses publicly available information to (2) quantify a hard-to-measure property, city-wide P /Hcity. The method has two limitations: (1) TLS is unknown, although it is constrained by normative thermostat-setting behavior  is probably, in general, the more reliable measure of city size for purposes of energy analysis.Population size, l et al.  used thal et al.  states tTa plots is more indirect than the point-by-point method because, rather than being based directly on the definition of insulation [Eq. , thereby expressing heat production in a unitless way. Scholander et al. then demonstrated the unique usefulness of the unitless approach for comparing species of widely different body sizes: their unitless method allowed them to define major trends in body insulation across species and environments. Our unitless measure, although different from that of Scholander et al., shares some of the same virtues. For our purposes in this paper, the unitless measure has three signal advantages. First, the estimation of I as the ratio of two regression-calculated values of Hcity removes the simple, direct effect of city size (P), permitting cities of many different sizes to be compared straightforwardly. Second, as a corollary, the ratio remains the same regardless of how Hcity might be normalized for city size; whether city size is measured using P or the number of utility accounts, the ratio stays the same. Third, recognizing that the present report is intended to be read by non-specialists in thermoregulatory energetics as well as specialists, the unitless measure Hcity(-20) /Hcity(10) has straightforward meaning for any reader . Thus, the calculated ratio reflects all the data available for the city, and a city did not actually need to experience a Ta as low as − 20 °C for the ratio to be calculated. These are assets of the method. On the other hand, the calculations (by not taking within-city variance directly into account) lose information, and they provide just a single value for each city, thereby reducing the sample size for statistical analysis to equal the number of cities.A limitation of the unitless slope method is that the LC Table . We chosHcity/P in the TNZ) to be a function of thermal climate, cold-climate cities having higher size-normalized values than warm-climate cities. Now that we have data on 42 cities (see Supplementary Table 2), it seems clear that that apparent trend was an artifact of the small sample size. We find no evidence for a relationship between RMR/P and T10-year.Hill et al. , in theiTa in which body insulation increases as Ta decreases between TUC and TLC —would seem to be far less poised to vary their insulation than a mammal. Nonetheless, two mechanisms exist by which city-wide I might, in fact, be variable in the TNZ. First, people in traditional houses do things like close the windows to an increasing extent as Ta decreases and often ultimately add extra window protection . Second, people vary their personal clothing insulation (e.g. switch tee-shirts for sweaters). One way to think of the TNZ for a city is that it is the range of outdoor temperatures in which people in their dwellings feel no need to use energy-demanding devices to feel comfortable. This formulation helps emphasize that human behavior is an important factor Below is the link to the electronic supplementary material.