Disease emergence is accelerating with global changes. Understanding by which mechanisms host populations can rapidly adapt will be crucial for management practices. Pacific oyster mortality syndrome (POMS) imposes a substantial and recurrent selective pressure on oyster populations, and rapid adaptation may arise through genetics and epigenetics. In this study, we used (epi)genome-wide association mapping to show that oysters differentially exposed to POMS displayed genetic and epigenetic signatures of selection. Consistent with higher resistance to POMS, the genes targeted included many genes in several pathways related to immunity. By combining correlation, DNA methylation quantitative trait loci, and variance partitioning, we revealed that a third of phenotypic variation was explained by interactions between the genetic and epigenetic information, ~14% by the genome, and up to 25% by the epigenome alone. Similar to genetically based adaptation, epigenetic mechanisms notably governing immune responses can contribute substantially to the rapid adaptation of hosts to emerging infectious diseases. Similar to genetically based adaptation, epigenetic mechanisms can contribute to hosts rapid adaptation to emerging diseases. There has been a substantial increase in the emergence of nonhuman pathogens (epizootics) resulting from human-linked activities, including anthropogenic-driven climate change, pollution, habitat fragmentation, overexploitation, local biodiversity impoverishment, and transport of living organisms (Crassostrea gigas) is the most widely farmed oyster worldwide and one of the main marine resources produced by aquaculture. However, since the emergence of the ostreid herpesvirus 1 microvariant (OsHV-1 μVar) in 2008, young oysters (less than 24 months old) living in high density [e.g., oyster farm and wild oyster beds that often co-occur in farming areas  and one component of the epigenetic variation . The genetic and epigenetic bases associated with natural variation in oyster resistance to POMS were detected by carrying out GWA and epigenome-wide association (EWA) mapping, respectively. Subsequent correlation analyses, methylation quantitative trait loci (MethQTL) mapping, and variance partition methods  allowed us to quantify the relative contribution of genetic and epigenetic variations underlying adaptation to POMS.P < 0.001; table S2). Resistance to disease reached > 94.9% in the farming area populations  and two populations from a “farming area” (B5 and B6) in the Rade de Brest, northwest France . Mortalities in recipient oysters  started at 72 hpc . The farming area oyster populations, which have been subjected to high pathogen pressure, have therefore become resistant to POMS disease, whereas the nonfarming area populations, which have not experienced the disease, were highly susceptible  started at 24 hours post-cohabitation (hpc). At 192 hpc, the survival rate dropped to 13.5 and 49.5% for H12 and NSI donors, respectively . Quantift 72 hpc  and did C. gigas genome, and the bisulfite conversion efficiency was 99.6 ± 0.1% (data S2). SNPs and DNA methylation calling resulted in 5,110,093 SNPs and 3,449,600 CpGs for the 240 oysters analyzed. After applying filtering criteria for GWA and EWA mapping, we obtained data for 102 susceptible and 118 resistant oysters, characterized by 214,263 SNPs and 635,201 CpGs (data S2). Analysis of multivariate homogeneity of group dispersions (variances) among the six populations did not reveal any signature of population structure at the genetic and epigenetic levels (fig. S3). A permutational multivariate analysis of variance (PERMANOVA) analysis confirmed this result with no genetic variance  and a small percentage of epigenetic variance  explained by differences among the six populations.We performed an exome-capture experiment using bisulfite-converted DNA to characterize genetic (SNP) and epigenetic (CpG) variation in both susceptible and resistant oysters. In total, 116 susceptible and 130 resistant oysters’ exome were captured and sequenced. On average, sequencing resulted in 0.5 million to 60 million paired-end (PE) reads per sample (mean ± SD = 26 ± 1 millions), and we discarded the six samples that displayed less than 7.8 million PE reads. On average, 59.3 ± 2.7% of PE reads were uniquely mapped to the P = 5.53 × 10−8 (P = 1.13 × 10−7 (UBA2 (CGI_10018487), and the SNP associated with the semiquantitative trait was mapped on chromosome 4 in a gene of unknown function (CGI_10022698). Given the low number of significant SNPs identified and the polygenic architecture of POMS resistance , which led us to the identification of 113 and 112 SNPs associated with the binary and semiquantitative traits, respectively (data S3 and S4). Among these 225 SNPs, 186 of which were nonredundant, 39 were in common, and 74 and 73 were specific to the binary and semiquantitative traits, respectively : scaffold1832_479264; A>T; PRMT5; aminoacyl tRNA synthetase complex interacting multifunctional protein 1, AIMP1; UBA2, and DC-STAMP domain containing 1, DCST1), the STING/RLRs pathway , the Toll-like receptor (TLR)/nuclear factor κB (NF-κB) pathway , the RNA interference (RNAi) pathway , and pathogen recognition  revealed the enrichment of four groups of biological process (BP) category: development, metabolism, cellular organization, and stress response . In thesTo investigate the epigenetic differences between susceptible and resistant oysters, we analyzed the EWA mapping that revealed that the methylation level of 240 (data S8) and 226 (data S9) CpGs were significantly associated with the binary and semiquantitative traits, respectively , the STING/RLRs pathway , the TLR/NF-κB pathway , pathogen recognition , and the autophagy pathway . We also used MethQTL mapping, which identified 5,151,194 and 5,152,611 significant SNP-CpG pairs (FDR < 0.05) when using the binary and the semiquantitative trait as a covariate, respectively. Removing redundant SNP-CpG pairs, we identified 160,325 (binary) and 160,220 (semiquantitative) SNPs associated with the DNA methylation rate of 557,703 (binary trait) and 557,850 (semiquantitative trait) CpGs  showed that genetic and epigenetic variation jointly explained the highest percentage of phenotypic variation, with 33.5 and 34.2% for the binary and semiquantitative traits . In addiThus, most of the genetic and epigenetic variations associated with POMS resistance were correlated with each other. However, some of the genetic and epigenetic variation remains independent, suggesting that changes in the epigenome, independent of genetic changes, have contributed to POMS resistance.Here, we show that populations of wild oysters exposed to POMS displayed an association that may reflect selection both in their genome (SNPs) and their epigenome (CpG DNA methylation). These signatures of selection, on genetic and epigenetic variation, targeted the same biological processes  but acted through different genes. Correlation analysis between genetic and epigenetic variation and MethQTL mapping showed that genetic and epigenetic variations are partially correlated and that genetic variation influences a large proportion of the epigenetic variation (88%). However, ~50% of CpGs significantly associated with oyster resistance are among the 12% of epigenetic variation not associated with genetic variation (MethQTL mapping), suggesting that epigenetic variation can occur independently of genetic variation, at least in our study context. These results were confirmed by RDA, showing that the expressed phenotypic variance was mainly explained (33.5 to 34.2%) by the interaction between genetic and epigenetic variation, a smaller fraction (17.3 to 26.1%) by epigenetic variation alone, and the smallest fraction (13.1 to 14.1%) by genetic variation alone. These results suggest that a host population facing recurrent and strong pathogen selection pressure selects genetic variants and selects or induces epigenetic variants during rapid adaption.In our study, we took advantage of the differential environmental selective pressure exerted by POMS on wild oyster populations, in farmed areas versus nonfarmed areas, to quantify the interaction and relative effect of genetic and epigenetic variations on phenotypic variation . GWA and EWA mapping carried out by grouping the resistant versus susceptible individuals irrespective of their population of origin to avoid the detection of signature linked to (epi)genetic drift enabled us to identify phenotype-genetic/epigenetic associations reflecting a putative selection both in the genome and the epigenome. We quantified the relative effect of the genetic and epigenetic variation using genetic/epigenetic matrix correlation tests , MethQTL mapping (control of CpG methylation level by SNPs), and variance partition analysis by RDA. These analyses demonstrated that most genetic and epigenetic variations are linked, given that methylation of ~88% of CpGs was significantly associated with one or more SNPs. In contrast, the remaining 12% of CpGs that were not significantly associated with SNPs, included ~50% of the associations detected by EWA mapping. We can conclude that the adaptive phenotype observed in response to POMS selection may involve both genetic and epigenetic variation, much of which is correlated. However, there is also independent genetic and epigenetic variation significantly associated with the selected phenotype of resistance to POMS.Gasterosteus aculeatus) (Ostrea lurida)   have previously been reported in various invertebrates (Coregonus clupeaformis) and the European whitefish (Coregonus lavaretus), both comprising sympatric benthic and limnetic specialists  further supported this conclusion, showing that 33.5 to 34.2% of the observed phenotypic variation was explained by the interaction between genetic and epigenetic variation, 26.1 to 17.3% by epigenetic variation alone, and 14.1 to 13.1% by genetic variation alone. Similar approaches had been used before to analyze gene expression variation between sister species of whitefish, the Lake whitefish , the RLR/STING pathway , the NF-κB pathway , RNAi (DICER), and autophagy (ATG4C), as well as several pathogen recognition receptors, such as DSCAM, mannose receptors, C1q, and PRGP  were collected in October 2018 from six natural populations referred to as B1 to B6 located in the Rade de Brest (France). A total of 356 oysters were collected with ~60 individuals per population . Length and width of sampled oysters were recorded, but it is important to note that these parameters can vary between oyster’s populations depending on the substrate that they grow on and are not indicative of their age (table S4). This sampling design enabled the collection of individuals from contrasting environments regarding POMS exposure with “nonfarming” and farming areas hosting a high proportion of susceptible and resistant oysters, respectively  that host a similar number of oysters from each population. Tanks were placed in a water bath to maintain the temperature at 21°C . In each tank, a water pump  and air bubbling maintained water motion and O7 genomic units) of OsHV-1 suspension originated from three different oyster production basins  into the adductor muscle of pathogen-free donor oysters. The virus was identified as the OsHV-1 μVar in previous studies performed on oysters originating from the same area at the same sampling period , and 24 hours later, the cohabitation started with the addition of the recipient oysters in each tank. Last, each tank hosted the same number of H12 and NSI donor oysters and the same number of recipient oysters from each population (B1 to B6). Disease progression in donor oysters  was monitored twice a day (dead donor oysters were removed) during the first 192 hpc, after which they were removed from all experimental tanks. Disease progression in recipient oysters started at 24 hpc and was performed every 2 hours for 360 hours .An oyster was classified as moribund or recently died when it could not keep close or close completely its valves after 30 s of emersion (movie S1). Oyster collection at a moribund (susceptible) status enabled the sampling of susceptible oysters before death to avoid DNA degradation. Resistant oysters corresponded to the individuals that were still alive at the end of the experiment . This phenotype was further coded either as a binary trait with the values 0 and 1 corresponding to susceptible and resistant individuals, respectively, or as a semiquantitative trait corresponding to the survival time (expressed in hours) of an individual after its exposure to the OsHV-1 μVar . Upon collection, the flesh of susceptible and resistant oysters was immediately snap-frozen in liquid nitrogen and stored at −80°C until DNA extraction.Differences in oysters’ survival among the six populations were investigated by Kaplan Meyer model with the “survfit” and “ggsurvplot” functions of the survival (v3.2-11) and survminer (v0.4.9) packages, respectively . Quantitative polymerase chain reaction (qPCR) was performed with 5 μl of DNA using the primers and qPCR parameters previously described . The resulting powder (~20 mg) was used for DNA extraction using the NucleoSpin Tissue Kit following the manufacturer’s instructions (MACHEREY-NAGEL GmbH & Co. KG). DNA quantity and purity were checked with a NanoDrop One spectrophotometer (Thermo Scientific), and quality was checked by 0.8% agarose gel electrophoresis (fig. S6). The extracted DNA was stored at −20°C.C. gigas exome was captured using the SeqCap Epi Enrichment System protocol (Roche Sequencing Solutions Inc.)   dedicated to SNP calling from population data and MethylExtract (v1.9) dedicated to SNP calling from bisulfite-converted sequences  was done using MethylExtract (same parameters as mentioned above). The methylation data of all samples were combined and used for EWA mapping (Supplementary Text).P < 1 × 10−6) and susceptible oysters (HWE P < 1 × 10−10) were excluded. (v) Samples with an SD of 3 units from the mean heterozygosity rate of all samples were discarded. (vi) If present, closely related individuals were excluded to remove cryptic relatedness.According to the best practices for GWA mapping, the following filtering criteria were applied under the PLINK environment (v1.9): (i) SNPs supported by a coverage of 8× to 150× were kept. (ii) SNPs and samples with a level of missing data above 5% were discarded. (iii) SNPs with a minor allele relative frequency below 0.05 were discarded. (iv) SNPs displaying a significant deviation from Hardy-Weinberg equilibrium (HWE) in resistant  CpGs supported by a coverage of 8× to 150× were kept. (ii) CpGs and samples with missing data levels above 5% were discarded.For both datasets, the absence of genetic and epigenetic structure between the six populations was checked using an analysis of multivariate homogeneity of group dispersions with the “betadisper” function of the vegan package (v2.5-7) (t test) under the PLINK environment (v1.9) (t test (“cpg.assoc” function) from CpGassoc package (v2.60)  and the semiquantitative trait . The following parameters were used for the adaptive clustering: largest = 0.4, smallest = 10, and clusterCutHeight = 0.25. A GO-term was considered enriched when FDR < 0.05. We used the REVIGO tool to visualize significant GO-terms in a semantic similarity relationship obtained from GO_MWU outcome, with the UniProt database and an aggregation value of 0.7 with SimRel as a semantic similarity measure.SNPs and CpGs significantly associated with phenotypic variation were subjected to Gene Ontology term enrichment to identify BPs enriched (data S16). This was done using a rank-based Gene Ontology analysis with adaptive clustering following the GO_MWU protocol (G ~ M + covariate), where G and M are the genetic and the DNA methylation level matrix and covariate is the phenotypic trait. This model was applied with the binary and the semiquantitative trait separately.Correlative (Mantel test) and association MethQTL approaches between genetic and epigenetic variation were adopted to investigate their relationships. On the basis of the Spearman correlation coefficient, Mantel test was applied to estimate the correlation between the genetic and epigenetic matrices of dissimilarity among samples. The association between SNPs and CpG methylation levels was identified using a linear regression implemented in GEM package (v1.24.0) according to the following “Gmodel”: lm(Variation partitioning is a method of using the coefficient of determination to fraction the variation of a response variable into four explanatory variables (To estimate the relative contribution of genetic and epigenetic variation to phenotypic variation, we used a method developed by and applied in (


Common ragweed is an invasive alien species causing severe allergies in urban residents. Understanding its urban invasion pathways is crucial for effective control. However, knowledge is limited, with most studies focusing on agricultural and natural areas, and occurrence record-based studies exhibiting uncertainties. We address this gap through a study in East China cities, combining population genetics and occurrence records. Leaf samples from 37 urban common ragweed populations across 15 cities are collected. Genomic and chloroplast DNA extraction facilitate analysis of spatial genetic patterns and gene flows. Additionally, international grain trade data is examined to trace invasion sources. Results indicate spatial genetic patterns impacted by multiple introductions over time. We infer the modern grain trade between the United States and China as the primary invasion pathway. Also, cities act as transportation hubs and ports of grain importation might disperse common ragweed to urban areas. Invasive species control should account for cities as potential landing and spread hubs of common ragweed. Examination of the spatial genetic pattern of common ragweed plants in urban areas in East China cities and the influence of grain trade between China and the U.S suggestions multiple invasion events. Ambrosia artemisiifolia L., Asteraceae) is native to North America but is now an invasive alien species (IAS) on all continents except Antarctica1. Besides causing significant losses in agricultural production, common ragweed pollen is a primary allergen source, causing seasonal allergic rhinitis and asthma, which result in substantial health costs and labor losses2. The negative health impact of common ragweed pollens is worsened in urban areas because the production of pollens and allergenicity of pollens are both enhanced in urban environments4. Therefore, control of the common ragweed is a priority in many cities worldwide5.Common ragweed  was first introduced to Buenos Aires and then spread to Mendoza13. The population structure of slender false brome (Brachypodium sylvaticum (Huds.) P. Beauv.) in Corvallis, the United States, showed that the species was introduced from multiple locations in Europe14. Studies have already shown the potential of population genetic methods for revealing the invasion pathways of common ragweed. The method has been used to trace the source of common ragweed populations in Asia, South America, and Western and Eastern Europe17. It has also been adopted to identify the invasion pathways in Australia18 and quantify the genetic patterns of common ragweed populations in North America and China20. The invasion sources of common ragweed populations in East China was explored in an earlier study21. However, several key questions have not been answered. How did the propagules of common ragweed enter China? Are invasion sources different for common ragweed populations in East China cities? Which city(cities) is the dispersal centers of common ragweed? The answers to the above questions are crucial for devising effective and targeted management measures for common ragweed.Population genetic methods provide a viable way to address the above limitations. The phylogenetic relationship among populations of IAS can be reconstructed using various genetic methods to interpret the invasion pathway. For example, in Argentina, the genetic analysis showed that the common sunflower  ranged from 0 to 0.68, and the values of nucleotide diversity (π) ranged from 0 to 0.00122  was obtained at K = 5, followed by K = 2, 4, and 3  of the populations in East China cities were not significantly different from those of the modern North American populations  of the populations in East China cities were significantly lower than those of the modern North American populations , Thailand, Kwangchowwan leased territory , the U.S., Myanmar, Korea, Canada, and Argentina during 1935-193941  were a minor source of common ragweed populations in cities in East China. Common ragweed was first recorded in China in 193521. This can be attributed to the less developed transportation networks in non-urban areas, where populations of non-urban common ragweed interact less with populations from other areas. Thus, more historical genotypes retained. We offered an potential explanation to the early findings.The minor role of the historical North American clusters as invasion sources could be attributed to the repeated modern introductions in East China cities and genetic exchange among cities. These findings support the possibility that modern genotypes have gradually replaced the genotypes introduced through historical introductions. Cluster F has the highest proportion of individual plants from the historical North American cluster. Individuals in cluster F came from Guiyang and Chongqing, which are in the inland region of China. The spread of modern genotypes of common ragweed to inland areas takes a longer time, resulting in a greater retention of historical genotypes. A previous study found that the proportion of historical North American clusters in the sources of non-urban common ragweed populations in China was three times that of modern North American clusters47. Although other studies have identified grain trade as an introduction pathway of IAS to cities through occurrence records48, our results provide new evidence from a population genetic perspective.These findings support the hypothesis that international trade introduced common ragweed into urban areas in East China cities. We inferred that modern grain exportation from the United States to China dominated the introduction process. While economic variables such as grain importation are not directly related to the establishment of IAS, they integrate the effect of variables that directly affect the outcome of invasion, such as propagule pressure and pathways of introductionIS, and rare bottleneck effects were observed in the populations in East China cities. Multiple introductions often lead to high genetic variability in invasive speciesIS = 0.342) suggests significant genetic differentiation among parental plants in most populations, which could be attributed to multiple introductions49. Rare bottleneck effects indicate that most populations did not experience a substantial reduction in population size. For species at the introduced range, the recovery of their gene pool may be replenished through multiple introductions15. These observations support the occurrence of multiple introductions.The existence of both the historical and modern North American clusters as invasion sources pointed to multiple introductions of common ragweed in East China cities that occurred in different periods. The spatial genetic pattern of common ragweed populations in East China cities showed that this possibility is highly likely. High genetic variability, low mean F50. In other words, the similar invasive source was introduced multiple times into those cities.Furthermore, we observed that populations from distant cities  were grouped together in the same genetic cluster. However, contemporary gene flows among these populations were low. The limited gene exchange between distant cities suggests that their assignment to the same genetic grouping is more likely a result of high genetic similarity among invasive source populations rather than recent gene flows52. Higher international trade activities in coastal areas than inland areas might explain this pattern because the trade increases the propagule pressure. Previous studies also found that common ragweed populations in Eastern and Western Europe were related to different regions of North America due to differences in trade patterns17.We also found that introductions occurred more frequently in coastal areas than inland. The co-existence of common and rare haplotypes from different clades in coastal cities indicated possible multiple introductions. The sole existence of the rare haplotype in inland cities suggested less frequent introductions. According to the literature, most areas of East China are considered suitable habitats for common ragweed54. The two cities are far from each other (1118 km), making natural dispersal less likely. Also, the result of the genetic analysis showed that common ragweed populations from Qingdao and Fuzhou belonged to different genetic clusters. These findings support the possibility that common ragweed invaded East China cities through multiple introductions at different times.Occurrence records also indicated a high likelihood of multiple introductions. For example, Qingdao first recorded common ragweed in 1964, and Fuzhou recorded the first observation in 199155. In this study, we observed high levels of gene flow from Shenyang to Changchun (0.1544), Qingdao to Guiyang (0.1149), and Fuzhou to Guangzhou (0.1461). Those cities are connected by dense road networks.In the spreading stage, the dispersal of common ragweed between cities in East China primarily might depend on stowaways associated with anthropogenic transportation. Studies have demonstrated that road networks and vehicle airflows promote the spread of common ragweed seeds1. Common ragweed seeds can remain viable in the soil for up to 40 years45, and a single common ragweed plant can produce up to 6000 seeds56. Once established in an area, common ragweed can produce a large number of seeds that contaminate the soil over an extended period. If the invaded area is a center of horticulture or agriculture, common ragweed seeds can inadvertently be transported to other areas through the movement of soil and mowing machines. Among the studied cities in East China, Nanjing is one of the most significant horticulture centers in China, which may contribute to the high gene flow observed between Nanjing and other cities, e.g., Changsha (0.1721), Wuhan (0.1651), Shanghai (0.0889), Fuzhou (0.06), and Chongqing (0.0518).Furthermore, the movement of soil and mowing machines may contribute to seed spread1. However, the barochory distance of common ragweed seeds in terrestrial habitats was found to be less than 1 meter per year36. Hydrochory has been identified as a secondary mode of natural seed dispersal for common ragweed within its native range56. Its role in the dispersal of common ragweed seeds among East China cities worth further studies. We detected significant gene flow from Nanjing to Changsha (0.1721) and from Nanjing to Wuhan (0.1651). Those cities are connected by waterways. However, for other cities with significant gene flows, no direct waterway connections exist among them.Lastly, natural dispersal may have played a role in the spread of common ragweed seeds between cities. Barochory is the primary way of natural dispersal of common ragweed seeds59. However, it is important to note that the pollination rate of common ragweed pollen originating from distant locations thousands of kilometers away would be extremely low, given that common ragweed populations were not widely distributed in the cities examined in our study.Some studies suggest that pollen may play a role in gene exchange among cities. For instance, research conducted in Europe has demonstrated that common ragweed pollen can be dispersed over several hundreds or even thousands of kilometers through the air60. The common ragweed populations in these cities exhibit a mixture of common and rare cpDNA haplotypes from different clades, and their inbreeding coefficients are low. However, as a result of effective control measures implemented in recent years2, the genetic diversity of Shanghai’s population is low. In contrast, the populations in Qingdao, Guangzhou, and Fuzhou maintain high levels of genetic diversity. Consequently, the potential risk posed by these latter populations are therefore greater.Shanghai, Guangzhou, Qingdao, and Fuzhou emerge as potential main sites of introduction. These cities, being port cities, have a substantial grain importation volume. Previous studies have suggested that port cities are more likely to serve as entry points for the introduction of alien species62, further strengthens its influence. In addition, both Nanjing and Shenyang serve as key transportation hubs in China64, contributing to their dominance in gene flows along the transportation network. Notably, Nanjing’s floriculture adds another pathway for seed dispersal65, as soil containing common ragweed seeds can be inadvertently transported to major cities through floriculture products.Nanjing and Shenyang are identified as potential primary centers for the spread of common ragweed. Our findings demonstrate significant gene flows originating from the metapopulations of common ragweed in these cities to other cities in East China. This can be attributed to the prominent roles of Nanjing and Shenyang in grain importation. Nanjing ranks first in imported grain volume among all the studied cities, while Shenyang surpasses other cities within the same genetic cluster in terms of grain imports. Shenyang’s proximity to Yingkou, a historically crucial grain importation port24. The gene flow pattern did not support the designation of Wuhan as a spread center. Wuhan’s spread potential may have been significantly reduced due to effective biocontrol programs applied in recent years66. Another possibility is the small sample size in Wuhan because only one urban population was located in Wuhan. In addition to the cities mentioned above, we inferred that Hangzhou, Jiujiang, Qingdao, Mudanjiang, Qinhuangdao, Laibin, and Fuzhou had high spread potentials of common ragweed in China based on the temporal information of occurrence data. Among these cities, samples collected from Fuzhou, Qingdao, Mudanjiang, and Qinhuangdao were analyzed, but the metapopulations in Mudanjiang, and Qinhuangdao had low gene flows to nearby sampling cities in East China. The results of the genetic analysis did not corroborate with the inferences based on the occurrence records. The discrepancy might be due to the subjectivity of the inference method and the uncertainties associated with historical occurrence data.Previous studies listed Wuhan, Nanjing, and Shenyang as cities with a high spread potential of common ragweedFindings from this study can contribute to the control of common ragweed in the following aspects: (1) Our results show that urban areas can serve as landing points for and spread hubs of common ragweed. In order to control common ragweed, controlling the spread among urban areas should not be left out of the overall control program. (2) There is plenty of room for improvement in quarantine work at Chinese port cities because the primary source of common ragweed might be from the modern North American clusters. Also, coastal cities have experienced more introduction events than inland cities. Strict inspection and quarantine of grains imported from regions that are known sources of common ragweed seeds should be practiced in ports that handle grain importation. Monitoring programs and awareness campaigns should be set up in these cities to monitor common ragweed populations. Waste from factories that process imported grains must be treated before final disposal.While our study contributes a new understanding of the invasion pathway of common ragweed in urban areas, several limitations should be addressed in future studies. First, although our field campaign covered the East China cities, more samples from more cities can further reduce uncertainty in the results. Second, we could only obtain genetic data for North America and two European countries after searching through the major international genetic databases and contacting authors who published papers on common ragweed’s genetics. Due to the lack of data, we could not explore the contribution of other potential sources to the genetic pattern of common ragweed populations in East China cities. For example, we could not confirm the bridgehead introduction passage from Argentina, Japan and Australia due to the lack of genetic data on common ragweed in these countries.Knowledge of the invasion pathway of common ragweed in urban areas is crucial for controlling this IAS. However, limited information is available because studies have not adequately addressed this topic. This study examined the spatial genetic pattern of common ragweed plants in urban areas in East China cities. We also explored the possible role played by the grain trade between China and the U.S. in shaping the genetic pattern. The results indicated that common ragweed in East China cities had experienced multiple introductions at different times. We inferred that the modern-day grain trade between the U.S. and China could be the primary source of propagule. Finally, we identified cities that might serve as spread centers by examining the gene flows among the cities and the temporal information in occurrence records. Our findings contribute new knowledge on the invasion pathway of common ragweed in urban areas. Also, the information gained in this study is useful for controlling common ragweed in urban areas in China and other countries. Nevertheless, the entire study should be viewed only as the beginning of efforts to reveal invasion pathways of common ragweed and other IAS in urban areas. More studies combining the strength of observations with population genetic methods are needed to understand the pathways and driving mechanisms better.25 and is the main suitable habitat for common ragweed51. To locate the current distribution of common ragweed populations in cities, we referred to the literature68 and consulted local experts. Also, we checked the geographic locations of occurrence records of common ragweed contained in the Chinese Virtual Herbarium (CVH)69. Based on the compiled occurrence records, we selected 19 East China cities with reliable records of common ragweed to conduct field surveys and were able to find urban populations of common ragweed in 15 cities.The study area includes cities in East China , FJ5950 (Ambart06), FJ5952 (Ambart17), FJ5953 (Ambart18), FJ595155 (Ambart24), and FJ595156 (Ambart27) .We sequenced chloroplast DNA (cpDNA) of the 37 East China populations. The two spacers (atpH–atpF and psbK–psbI) of chloroplast DNA spacer regions in common ragweed were chosen for analysis  .75. Genetic diversity indices, including the number of different alleles (NA), expected heterozygosity (HE), and inbreeding coefficients (FIS) were estimated in GenAlEx 6.51 and GENEPOP 4.7.076 to represent diversity at nuclear microsatellite loci, respectively. Deviations from Hardy-Weinberg equilibrium of sampled populations were assessed using FIS77, and their significance was tested using GENEPOP 4.7.075. The potential bottleneck of sampled populations was detected using BOTTLENECK version 1.2.0.278.Tests of genotypic linkage equilibrium and estimation of null allele frequency were performed in GENEPOP 4.7.079 with our haplotypes to determine the correctness of our sequence. The number of haplotypes (h), haplotype diversity (HD), and nucleotide diversity (π) of sampled populations were computed using DnaSP 680. Except for software packages specifically noted, all statistical analyses were carried out using the basic R 4.0.281.We aligned the atpH–atpF and psbK–psbI sequences of common ragweed from CBOL Plant Working Group82 and GENELAND version 4.0.383 were used to cluster common ragweed populations based on the nuclear microsatellite and the cpDNA haplotype . Pairwise population-specific fixation indices (FST) between sampled populations were calculated based on nuclear microsatellite markers. Pairwise population FST were calculated in the diveRsity package84.The Bayesian clustering algorithms contained in STRUCTURE version 2.3.3Ambrosia trifida L. as an outgroup was constructed using Geneious version 11.0.4  with a Tamura-Nei genetic distance model using 1000 replicates. A median-joining network of haplotypes was generated by using NETWORK 10.185. The tree and the network were used to explore the relationships among cpDNA haplotypes.The geographical distribution of cpDNA haplotypes was visualized using ArcGIS Desktop 10.2 . A rooted neighbor-joining tree with 86. A genetic graph was built using the gene flows among the sampled cities. The R packages POPGRAPH87 and IGRAPH88 were used to construct the graph and calculate graph centrality measures. More details of the analyses can be found in the supplementary materials.The gene flows among the sampled cities were estimated using BayesAss v3.0.4E and FIS of French and Italian, and North American populations, were estimated in GenAlEx 6.51 and GENEPOP 4.7.076, respectively. Analysis of variance (ANOVA) was used to test differences among the geographic ranges in HE and FIS using the stats package81. In the previous study, the source populations of common ragweed in East China was identified21. This study further investigated the primary source of propagule and dispersal centers for common ragweed in cities in East China. Each plant was assigned to French and Italian, modern North American, and historical North American populations. The North American samples were grouped by the genetic clusters identified by Martin et al.19. The historical clusters are comprised of herbarium specimens collected between 1873 and 1939, while the modern clusters are consisted of samples collected in 200919. The assignment was performed in the GENECLASS2 software89.Genetic diversity indices, including H19. Data collection and analysis were conducted using Excel.Because this study focused on exploring the role of the grain trade in the introduction of common ragweed to East China cities, we collected exportation and importation data. Exports of grain products from states in the United States to China from 1999 to 2009 were provided by the Foreign Trade Division, the U.S. Census Bureau. The exports were adjusted by using the Consumer Price Index. This time range was chosen because the U.S. Census Bureau has provided data on grain trade to China since 1999, and the genetic analysis of the invasion source was based on modern North American common ragweed genetic data collected in 2009http://microdata.sozdata.com/home.html). The time range was chosen because China’s customs data on grain imports were available in EPS CHINA DATA from 2000, and China’s customs began to implement stricter quarantine measures for common ragweed seed in 2007. Data on China’s imports of grain products from 1935 to 1939 were collected from Chinese Maritime Customs Historical Material: 1859-194839. This time range was selected due to the historical North American genetic data being based on herbarium specimens collected between 1873 and 1939. In addition, common ragweed was first recorded in China in 193522, and importation data prior to 1935 utilized a different statistical scope compared to the later years. Data collection and analysis were performed using the stringi and the dplyr package91.Data on grain importation from 2000 to 2006 in studied cities were collected from EPS CHINA DATA  The city first reported the occurrence of common ragweed in the region; (2) It has consistent occurrence records over a 20-year period; (3) Its adjacent cities are suitable habitats for common ragweed.We searched occurrence data of common ragweed from databases such as the Global Biodiversity Information Facility (GBIF)A, HE, FIS, h, HD and π) were estimated in GenAlEx 6.5176, GENEPOP 4.7.075 and DnaSP 680. The potential bottleneck was detected using BOTTLENECK version 1.2.0.278. STRUCTURE version 2.3.382 and GENELAND version 4.0.383 were used to cluster common ragweed populations. The rooted neighbor-joining tree was constructed using Geneious version 11.0.4. The median-joining network of haplotypes was generated by using NETWORK 10.185. The gene flows were estimated using BayesAss v3.0.486. The assignment was performed in the GENECLASS2 software89. Except for software packages specifically noted, all statistical analyses were carried out using the basic R 4.0.281. The specific details of the analyses and sample sizes are described in the methods above.Genetic diversity indices (NFurther information on research design is available in the Supplementary informationReporting Summary


Balaenoptera physalus were hunted unsustainably across the globe in the 19th and 20th centuries, leading to vast reductions in population size. Whaling catch records indicate the importance of the Southern Ocean for this species; approximately 730,000 fin whales were harvested during the 20th century in the Southern Hemisphere (SH) alone, 94% of which were at high latitudes. Genetic samples from contemporary whales can provide a window to past population size changes, but the challenges of sampling in remote Antarctic waters limit the availability of data. Here, we take advantage of historical samples in the form of bones and baleen available from ex-whaling stations and museums to assess the pre-whaling diversity of this once abundant species. We sequenced 27 historical mitogenomes and 50 historical mitochondrial control region sequences of fin whales to gain insight into the population structure and genetic diversity of Southern Hemisphere fin whales (SHFWs) before and after the whaling. Our data, both independently and when combined with mitogenomes from the literature, suggest SHFWs are highly diverse and may represent a single panmictic population that is genetically differentiated from Northern Hemisphere populations. These are the first historic mitogenomes available for SHFWs, providing a unique time series of genetic data for this species.Fin whales  Balaenoptera physalus, are the second largest species of baleen whale in the world. In the Southern Hemisphere (SH), fin whales have a broad circumpolar and subpolar foraging distribution )Ne); see . Howevery route) ,80; and 04–0.01) . Here, mene flow . Therefoene flow ). Alternene flow ,84.Megaptera novaeangliae, and southern right whale, Eubalaena australis, which both exhibit strong regional population structuring driven by maternal fidelity to breeding and calving grounds [Balaenoptera musculus intermedia, show limited, but significant, mtDNA FST population structuring on feeding grounds in the Southern Ocean [Nevertheless, the circumpolar population structuring of SHFWs that we observed here is weaker than that of other species, including humpback whale,  grounds ,38,85,86 grounds . Similar grounds , althougrn Ocean ,89. Limirn Ocean . Nuclearrn Ocean . Photo irn Ocean ,91,92, aSHFWs tend to undertake seasonal, movements between low- and high-latitude areas ,93. It iSimilarly to the genetic data, stock structure contradictions have been observed with acoustic data. SHFW acoustic pulses of 15–30 Hz are common and relatively uniform between various SH sites, potentially indicative of a single, stable fin whale population . HoweverIn this study, sex-based differences in the mitochondrial haplotype frequencies within ocean basins or patterns of genetic differentiation using biparental data  were not explored. Little is known about the mating systems of fin whales . HoweverB. musculus [Eubalaena spp. [B. borealis [Our results are in concordance with other genetic studies that show limited gene flow between fin whales across the equator ,17,18, wmusculus , humpbacmusculus ,102, rigena spp. ,104 and borealis ,106, sugborealis ,105,106.borealis .B. physalus patachonica, that occur predominantly at lower latitudes relative to its larger counterpart, B. physalus physalus, now known as Balaneoptera physalus quoyi [B. p. patachonica (up to 55° S), thus providing no further evidence to support the existence of B. physalus patachonica.It has been suggested previously that two subspecies of fin whales may exist in the Southern Hemisphere . Observaus quoyi , have beus quoyi . Previouus quoyi , and theSHFWs are wide-ranging generalist predators that feed on a variety of zooplankton and have varied foraging strategies ranging across multiple latitudes ,109,110.The SO whale feeding grounds are already undergoing rapid environmental change due to the climate crisis ,118,119.Southern Hemisphere fin whales are key predators in the Antarctic and Subantarctic ecosystems, and an understanding of their genetic identity, migratory behaviour and recovery potential is essential to inform ecosystem management in these regions. This study provides the most comprehensive genetic dataset of SHFWs to date. Genetic analysis supports previous findings that Southern Hemisphere fin whales are genetically distinct from the Northern Hemisphere populations. By using historic and contemporary datasets, our results suggest SHFWs may represent a single panmictic population at the circumpolar scale. However, samples from the Indo–Pacific are currently few, and therefore, additional genetic samples from this region alongside genome-wide assessments of genetic diversity are required to fully resolve the population structure.


There have recently been intensive efforts aimed at addressing the challenges of environmental degradation and climate change through the applied innovative solutions of AI, IoT, and Big Data. Given the synergistic potential of these advanced technologies, their convergence is being embraced and leveraged by smart cities in an attempt to make progress toward reaching the environmental targets of sustainable development goals under what has been termed “environmentally sustainable smart cities.” This new paradigm of urbanism represents a significant research gap in and of itself. To fill this gap, this study explores the key research trends and driving factors of environmentally sustainable smart cities and maps their thematic evolution. Further, it examines the fragmentation, amalgamation, and transition of their underlying models of urbanism as well as their converging AI, IoT, and Big Data technologies and solutions. It employs and combines bibliometric analysis and evidence synthesis methods. A total of 2,574 documents were collected from the Web of Science database and compartmentalized into three sub-periods: 1991–2015, 2016–2019, and 2020–2021. The results show that environmentally sustainable smart cities are a rapidly growing trend that markedly escalated during the second and third periods—due to the acceleration of the digitalization and decarbonization agendas—thanks to COVID-19 and the rapid advancement of data-driven technologies. The analysis also reveals that, while the overall priority research topics have been dynamic over time—some AI models and techniques and environmental sustainability areas have received more attention than others. The evidence synthesized indicates that the increasing criticism of the fragmentation of smart cities and sustainable cities, the widespread diffusion of the SDGs agenda, and the dominance of advanced ICT have significantly impacted the materialization of environmentally sustainable smart cities, thereby influencing the landscape and dynamics of smart cities. It also suggests that the convergence of AI, IoT, and Big Data technologies provides new approaches to tackling the challenges of environmental sustainability. However, these technologies involve environmental costs and pose ethical risks and regulatory conundrums. The findings can inform scholars and practitioners of the emerging data-driven technology solutions of smart cities, as well as assist policymakers in designing and implementing responsive environmental policies. Both of the leading global paradigms of urbanism—smart cities and sustainable cities—are facing unprecedented challenges pertaining to increasing urbanization, rapid urban growth, environmental degradation, and climate change. The escalating rate of urbanization is one of the special conundrums posed to all major cities worldwide, especially “mega-cities,” and has a strong connection to the most challenging and complex issues of our time. The 55% of the world's population living currently in urban areas is expected to rise to 70–75% by 2050 UN . As engiUrbanization and urban growth exacerbate the wicked problems characterizing cities, whether those badging and regenerating themselves as sustainable/smart or those planning to become smart sustainable/sustainable smart, respectively. Consequently, they may jeopardize the endeavors being undertaken to achieve the long-term goals of environmental sustainability, as they put a high demand on the world’s natural resources and ecosystem services. With this situation being compounded by the increased uncertainty of the world, it is becoming more challenging for both smart cities and sustainable cities to reach the environmental targets of the Sustainable Development Goal (SDG) 11—without the adoption of the applied solutions of the Internet of Things (IoT), Big Data  that are striving to become environmentally sustainable based on integrating data-driven technologies and solutions with green technologies and strategies. The latter concept, which is mainly used in this study for comparative purposes, refers to those sustainable cities  that are striving to improve and maintain their contribution to environmental sustainability on the basis of advancing their green technologies and strategies through data-driven technology solutions. Important to note is that these examples of cities are known for promoting certain aspects of environmental sustainability, and some of them are both smart and sustainable/sustainable smart. For example, Stockholm as a smart sustainable city aims to become climate positive by 2040 by using data-driven technologies to optimize or improve energy efficiency, air pollution, power grids, urban metabolism, and waste management , and environmental sustainability and the associated keywords retrieved from the Web of Science (WoS) serve as the source of data for the bibliometric analysis. The rationale for selecting WoS among the available pool of academic research databases lies in its reputability in regard to its track record of indexing high-quality peer-reviewed studies related to the topic on focus and related comprehensive bibliographic data. These elements are crucial for conducting accurate analyses based on the bibliometric tools of VOSviewer. For each article, the WoS provides full record details, including cited references, making it possible to obtain co-citation analysis results of precision.To retrieve relevant publications from the WoS, we developed a broad-based search string as a combination of different terms related to smart cities, sustainable cities, advanced ICT , and environmental sustainability (see the Appendix). The search string returned 5885 documents, and the initial search was conducted in early January 2022 using different citation indexes of the WoS . The articles, review articles, proceeding papers, book chapters, editorial materials, letters, and data papers published until the search date and written in English were considered for inclusion in the bibliometric analysis. Titles and abstracts of these documents were screened to select those focused on the relationships between smart city solutions/technologies and environmental sustainability. At the end, 2574 documents remained in the database. Bibliographic data of these documents, i.e., “Full Record and Cited References,” were downloaded from the WoS to be used for the bibliometric analysis. While the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Fig.  is desighttps://www.vosviewer.com. The website also provides free access to user manuals and demo projects. We refer interested readers to the manuals for detailed step-by-step description of bibliometric analysis using VOSviewer.Various software tools have been developed for bibliometric analysis since the turn of the century , citation , co-citation , and bibliographic coupling  . Other milestones before 2015 could have also been selected, but, as Fig. In addition, we combined bibliometric analysis with evidence synthesis. The latter brings together all relevant information on a research topic and high quality studies in a field. As Kirkevold  notes, “The results collected after running the data in VOS viewer are analyzed as well as informed and expanded by bringing together all relevant information on the topic under study. Section "Smart sustainable/sustainable smart cities are relatively new urban phenomena whose emergence has resulted from the convergence of a set of intertwined societal factors and technological trends, notably the rise of urbanization, the diffusion of sustainability, the dominance of ICT, and the shift in science and technology . These congeries of forces also shape and reshape their insertion, functioning, expansion, and continuation as currently predominant paradigms of urbanism. This has led to a plethora of streams of research, which have, since the mid-2010s, begun to rapidly evolve compared to the previous period starting from the early 2010s, as shown in Fig. The widespread popularization of the concept of smart cities due to the rapid rise of ICT since the early 2000s became associated with the rapid rise of urbanization since the early 2010s given the synergy between these two major global shifts. Townsend  portraysUnited Nations (UN)United Nations Human Settlement Programme (UN-HABITAT)United Nations Economic Commission for Europe (UNECE)European Commission (EC)Organization for Economic Co-operation and Development (OECD)United Nations Environment Program (UNEP)International Telecommunication Union (ITU)Environment Agency Austria (EAA)As regards sustainable cities, the second strand of urbanism that constitutes the notion of sustainable smart cities, they became prevalent and evolved into the leading paradigm of sustainable urban development during the period 1991–2010 ,” which is coordinated by UN-habitat, UNECE, and ITU and supported by a number of UN agencies and other international organizations, aim to guide sustainable cities and communities “to become smarter and more sustainable while accelerating their digital transformation” toward achieving the SDGs ITU .Urban politics and policy around the world are now infused with the language of sustainable smart/smart sustainable urbanism, and a complex tapestry of urban visions can be formed and a multitude of exemplary urban initiatives can be found. The subject of sustainable smart/smart sustainable cities has become very fascinating and enticing for scholars, practitioners, and policymakers, providing tremendous opportunities to address sustainable development challenges, spur groundbreaking research and innovation, and monitor progress in SDGs targets. As regards the latter, for example,  and themes . The main ideas are outlined in the analysis performed on the different publications associated with the period 1991–2021. These publications are categorized into, as shown in Fig. There is a long-standing criticism of the extreme fragmentation of the landscapes of smart cities and sustainable cities and its implications for hampering progress toward achieving the environmental targets of the SDG 11. Smart cities and sustainable cities are divergent on many aspects and hence not necessarily synonymous with each other  indicates that while the interlinked developments of environmental sustainability, advanced ICT, and urbanization have converged under smart sustainable cities before the end of this period, it is not until the publication of the report on the SDGs UNSDG  that thiEvolution of environmentally sustainable smart cities. As highlighted in Fig. In parallel, a plethora of smart-city initiatives were developed and implemented across different geographies (Cowley et al. On the rise of AI and AIoT technologies and solutions and related models and techniques in environmentally sustainable smart cities The year 2019 witnessed a dramatic increase in the use and adoption of AI or AIoT technologies and solutions in environmental sustainability, climate change, and smart cities. This followed the massive use and wide adoption of the IoT and Big Data technologies and solutions of smart cites during the period 2016–2018 (Ahmed et al. The computational capabilities of AI largely expand opportunities to understand, prevent, and handle environmental problems as well as transform decision-making processes. Therefore, AI models and techniques have gained traction and become widely applicable during the second period as part of AI and data-driven IoT solutions for environmental sustainability, climate change, and smart cities. For example, only during the period 2016–2019, there were more than 200 studies that applied AI to energy conservation and renewable energy (Nishant et al. The growing interest in the innovative technologies and applied solutions of smart cities, coupled with their potential to tackle the environmental challenges of sustainability, resulted in an increase in research on a range of new themes related to environmentally sustainable smart cities, as illustrated in Fig. Evolution of environmentally sustainable smart cities. Figure In addition, the event of COVID-19 resulted in a further increase in the findings on existing and new topics. These are outlined in the analysis performed on the different publications as categorized into the four clusters. The Green cluster comprises publications focusing on climate change, emissions, air pollution, air quality, COVID-19, economic growth, circular economy, policy, water resources, green infrastructure, urbanization, resilience, and others. The Red cluster consists of publications targeting the implementation of the data-driven technologies and solutions of smart cities that could have positive impacts on both environmental sustainability and COVID-19 response through optimization, efficiency, management, performance, behavioral change, and others. The Yellow cluster represents publications covering the underlying infrastructure of smart cities, with new appearing topics, such as electric vehicles and waste management. Lastly, the Blue cluster is dedicated to publications focusing on broad issues related to sustainable cities, smart cities, and smart sustainable cities and their dimensions and synergies, with new appearing topics, such as digitalization and environmental indicators. Worth pointing out is that smart cities and sustainable cities graphically moved closer to one another compared to the second period, signalizing the materializing patterns of environmentally sustainable smart cities. More to this, smart cities became an integral part of the Blue cluster, which covers the broad issues related to smart sustainable cities during the second period. In a similar vein, sustainable cities became an integral part of the Red cluster, which was dedicated to the technologies and solutions of smart cities that could have implications for environmental sustainability during the second period.Being associated with the rushed rollout and massive deployment of advanced technologies across major cities worldwide—and hence its ramifications on urban lives, urban dynamics, and urban futures, COVID-19 became a topic of high priority and impotence to address during the period 2020–2021. Against the backdrop of this study, this crisis attracted many scholars and practitioners in the fields of smart cities, environmental sustainability, and climate change, turning their attention to investigating or critically engaging with the topic from a variety of perspectives. In this regard, COVID-19 served as an opportunity to test the ability of smart city technologies and solutions to solve major societal issues, but also provides additional momentum to further develop and implement smart cities Kunzmann and can pOn the increasing adoption of AI or AIoT technologies and solutions and their expansion from environmental sustainability to sustainable smart cities. The third period witnessed a marked intensification of the adoption of AI or AIoT technologies and solutions in smart cities and thus the emergence of several integrated models under the umbrella term of environmentally sustainable smart cities. As shown in Fig. Several AI models and techniques were used as part of AI and AIoT applications for environmental sustainability in the realm of smart cities, including DL, ANN, NN, SVM, and random forest. The slight difference graphically shown in Fig. Compared to Fig. As to the amalgamation of smart cities and sustainable cities during the third period, the environmental dimension of sustainability became one of the key driving factors for the innovative technologies and applied solutions of smart cities. This is also illustrated in Fig. The prospect that AI, IoT, and Big Data technologies will instigate massive changes in smart cities is fast becoming the new reality due to the acceleration of their digitalization and digitization. The digital transformation being generated by the convergence of these innovative technologies is seen to hold great potential to tackle the problematicity surrounding the planning and development of smart cities in response to the decabornization and climate change emergencies. In parallel, the need to streamline the transition to environmental sustainability and mitigate the impacts of climate change has given rise to the phenomenon of environmentally sustainable smart cities. As natural resources have become highly on demand due to the escalating rate of urbanization and urban growth, smart cities are making huge efforts to find new and innovative ways to reduce their consumption of resources and thus lessen their impacts on the environment. This is reflected in the growth of knowledge of the relevant facets of sustainable smart cities from the mid-2010s onwards. This growth is manifested in the marked upsurge in the scholarly output on this multifaceted topic, with new terms being coined and new themes and areas being explored in response to the increasing need for more integrated approaches to sustainable urban development. The growing number of the academic works devoted to the topic of environmentally sustainable smart cities has coincided with the rise of the databases tracking this paradigm of urbanism in terms of its scientific, technological, environmental, and institutional dimensions.Academic interest in the topic of smart sustainable cities started to increase around 2014 to 2016, grew exponentially in 2017, and evolved rapidly afterwards. One of the main factors behind the knowledge growth in this area is the rapid advancement of AI, IoT, and Big Data technologies and their novel applications, in addition to the general consensus on the urgency to tackle the challenges of environmental sustainability and climate change. The first period witnessed the materialization of sustainable cities in the early 1990s in the wake of the widespread diffusion of sustainable development, the materialization of smart cities in the early 2000s following the acceptance of smart growth movement, and the materialization of smart sustainable cities around the mid-2010s in response to the SDGs agenda and the rising concerns over urbanization. The second period marked the materialization of sustainable smart cities and a few related integrated models of urbanism in light of the advancement of the IoT and Big Data technologies and solutions. The third period heralded the emergence of environmentally sustainable smart cities and several related integrated models of urbanism under different labels—due mainly to the convergence of AI, IoT, and Big Data technologies and its consequential datafied, algorithmized, and platformized penetrative patterns. The essence of this new paradigm of urbanism lies in leveraging the collective power and multifaceted potential of these advanced technologies in making progress toward achieving the goals of environmental sustainability. While research on the relationship between smart cities, sustainable cities, environmental sustainability, and data-driven technologies is still in its infancy, the convergence of these interlinked developments is rapidly gaining traction as a strategic approach to sustainable urban development.Beyond the shadow of a doubt, sustainable development and technological advancement have, at varying degrees, inspired different urban scholars, scientists, and practitioners in the pursuance of explorations and investigations to unlock numerous opportunities to improve the health of the city and the quality of life of its citizens. This has more often than not taken the form of integrating sustainability strategies and technology solutions in the field of urban planning and development in the attempt to slash energy consumption, reduce pollution, lower material use, and minimize waste. In particular, the notion of sustainable development has, since the early 1990s, motivated scholars and practitioners to device more integrated and holistic frameworks for urban development that can rise up to the challenges of environmental sustainability head on through enhancing the performance of urban systems, connecting urban infrastructures, coordinating urban domains, and revitalizing underdeveloped areas. In recent years, ICT-based innovations have undeniably become pivotal in facilitating the practical endeavors of urban sustainability and its transformative changes. This has led to the emergence of an alternative paradigm to sustainable cities and smart cities—sustainable smart cities. This paradigm further interplaying with the recent global factors, notably the appearance of COVID-19, the digital transformation of urban societies, and the accelerated transition to smart cities, has brought about the phenomenon of environmentally sustainable smart cities. While this phenomenon has many faces, this study is concerned with the integration of the emerging data-driven technologies and solutions of smart cities in the field of environmental sustainability with the green technologies and strategies of sustainable cities. As such, it expands on the previous works done on environmentally smart sustainable cities that emphasize the innovative and synergistic potential of IoT and Big Data in improving and advancing environmental sustainability (Bibri and Krogstie AI, IoT, and Big Data technologies have been instrumental in pushing smart cities to rethink their strategies for sustainable development. This is reflected in the increased interest among scholars to develop new approaches and solutions that could make smart cities environmentally more sustainable. In recent years, the rise of Big Data and computing power has empowered AI, and its new generation is rapidly expanding and becoming “an attractive topic for research” (Duan et al. Being at the core of advanced analytical systems, AI is also reshaping how the IoT infrastructure operates in terms of data collection and aggregation, big data analytics functions in terms of data management and processing, and decision-making is performed in terms of support, automation, and autonomization. The latter entails replacing human intellect instead of expanding it in future urbanism. AI systems can serve either to assist or replace the human decision makers (Duan et al. AI, IoT, and Big Data technologies and solutions are associated with huge environmental costs in smart cities. This is a daunting challenge to deal with and a special conundrum that should not be overlooked. According to a recent review study conducted by (Bibri et al. The ethical risks of AI are one of the key topics in academic and policy debates, and constitute the most difficult conundrum to solve. They include privacy, security, trust, bias, unfairness, lack of transparency, and social inequality (Ahmad et al. Regulatory policies and poly-centric governance systems are more needed than ever under the current circumstances of democratic decay and privacy loss. This is to ensure that AI systems take into account algorithmic fairness, transparency, accountability, and safety as core values and principles of their designs, thereby upholding procedural and algorithmic justices—instead of outsourcing these ethical and social values to the global technology sector. Incidentally, COVID-19 has worsened the effects of big tech companies in terms of their interference in data policy and data governance as a result of the accelerated adoption of digital technologies (Li et al. It would be helpful to conduct an in-depth exploration of the other AI models than those mostly applied in relation to IoT and Big Data in the field of environmental sustainability and their strengths and weaknesses. The deficiencies inherent in the existing ML/DL models with respect to decision-making processes in large-scale urban AI system warrant further research. Research might also explore why some areas of environmental sustainability are given more attention than others in AI research. It would be useful to investigate other driving factors behind the emergence of environmentally sustainable smart cities than those identified in this study.The trade-offs of the adoption of AI, IoT, and Big Data technologies and solutions in environmentally sustainable smart cities would be of high importance to examine for generating complementary and conflicting perspectives and insights. This would be best carried out through systematic reviews to deliver a clear and comprehensive overview of relevant, trustworthy empirical evidence and identify further gaps in the current understanding of this emerging paradigm of urbanism.It would be interesting to analyze in more depth how environmentally sustainable smart cities could balance between the environmental, economic, and social objectives of sustainable development by leveraging the convergence of AI, IoT, and Big Data technologies and their collective expertise.How regulatory frameworks and governance systems are responding to critical debates over the need for the responsible development and adoption of AI or AIoT systems.How the goals of environmentally sustainable smart cities and the opportunities offered by their converging IoT, Big Data, and AI technologies and solutions could be aligned.What bottlenecks and barriers exist in the development of holistic models for containing the environmental risks of AI, IoT, and Big Data technologies.Given that the convergence of AI, IoT, and Big Data technologies is still in its incipient stage with respect to generating transformative changes in environmentally sustainable smart cities, there is a need to critically engage with:More methodological work is needed on how to robustly capture the positive impacts and outcomes of emerging environmentally sustainable smart cities, including further enviro-economic and social critical analyses. These are of increasing importance to the field of green and public policy.It would be beneficial to carry out a full cost–benefit analysis of AI, IoT, and Big Data technologies. Despite being methodologically challenging, it would be highly useful to conduct some in-depth studies to quantify the impact of these advanced technologies on not only key environmental but also economic and social indicators in the realm of sustainable smart cities as a holistic approach to sustainable development.There are unique opportunities for advancing the planning and development of emerging environmentally sustainable smart cities to achieve better performance outcomes and thus the targets of the SDG 11. As this is an emerging model of sustainable urban development that is attempting to unlock and exploit the multifaceted potential of the converging IoT, Big Data, and AI technologies and solutions, there are many questions that need to be addressed to secure the expected benefits of environmental sustainability. This relates to a number of gaps in our knowledge on emerging environmentally sustainable smart cities that follow from our results and their discussion. Below are some of the recommendations we deem relevant for further research:The world has recently experienced unprecedented challenges and increased uncertainties, making it more challenging for smart cities to improve their environmental efficiency and to contribute to actual progress toward reaching the environmental targets of the SDG 11. This has, together with other global and technological factors, led to the materialization of environmentally sustainable smart cities as an umbrella term for several models of urbanism focusing mainly on the environmental dimension of sustainability. This new paradigm of sustainable urban development entails integrating the data-driven technologies and solutions of smart cities and the green technologies and strategies of sustainable cities. The idea revolves around leveraging the collective expertise in AI, IoT, and Big Data in tackling the challenges of environmental sustainability and climate change. The technologies and solutions of smart cities are creating many new opportunities for better understanding, preventing, and handling environmental problems as well as enhancing decision-making processes.This study explored the key research trends and driving factors of environmentally sustainable smart cities and maps their thematic evolution. In addition, it examined the fragmentation, amalgamation, and transition of their underlying models of urbanism as well as their converging AI, IoT, and Big Data technologies and solutions. The results showed that environmentally sustainable smart cities are a rapidly growing trend that markedly escalated during the second and third periods—due to the acceleration of the digitalization and decarbonization agendas—thanks to COVID-19 and the rapid advancement of data-driven technologies. The analysis also revealed that while the overall priority research topics have been dynamic over time—some AI models and techniques and environmental sustainability areas have received more attention than others. The evidence synthesized indicated that the increasing criticism of the fragmentation of smart cities and sustainable cities, the diffusion of the SDGs agenda, and the recent dominance of advanced ICT have significantly impacted the materialization of environmentally sustainable smart cities, thereby influencing the landscape and dynamics of smart cities. It also suggested that the convergence of AI, IoT, and Big Data technologies provides new approaches to tackling the challenges of environmental sustainability. This convergence is of crucial importance to make critical urban infrastructure resource-efficient, low-emission, and resilient by making progress toward reaching the environmental targets of the SDG 11 related to energy, transport, waste, water, biodiversity, and climate. However, the converging data-driven technologies involve environmental costs and pose ethical risks and special regulatory conundrums.While the analysis and synthesis has allowed us to achieve the overall aim of the study, there are some limitations that need to be highlighted so to be addressed in future research. One key limitation lies in the current study being based solely on peer-reviewed literature. It would be useful to consider grey literature to gain and provide more comprehensive and clear insights. Thus, future research may combine both categories of literature or should prioritize grey literature for better knowledge outcomes, as well as include other databases to have a sufficiently reasonable and representative form of results. Especially, the dynamics of smart cities in terms of their converging AI, IoT, and Big Data technologies and their recent advancements as shaping forces essentially occur as coordinated actions of local governments and international organizations. Therefore, research produced by these entities, including reports, working and evaluation papers, and planning and policy documents, would add value to knowledge in future research. In addition, given the marked effects of COVID-19 on the intensified digitalization of smart cities and thus the massive roll out of advanced technologies, it would be useful and relevant to conduct research to establish the post-pandemic landscape of publications on emerging environmentally sustainable smart cities. This would map out or trace how both COVID-19 and AI innovations have impacted on urban policy and governance in relation to environmental sustainability. A comparative study of these two influential factors would also be beneficial. Further research on the large-scale digital transformation of urban societies might serve to highlight the role of recent scientific and technological advances in the infusion of data-driven solutions as the new frontiers in urban environmental policy and governance systems.We anticipate this study will stimulate debates over the substantive effects of the converging AI, IoT, Big Data technologies on the future form of sustainable urban development, enhance the understanding of the multifaceted nature of the flourishing field of environmentally sustainable smart cities, and provides grounding for spurring groundbreaking research and large-scale implementations. Especially, this area is in infancy research and the early stages of its development, with great potential to bring about meaningful urban transformations based on the responsible development and implementation of the AI, IoT, and Big Data technologies and solutions of smart cities.


Strychnos spinosa Lam., commonly known as green monkey orange, is a highly valued indigenous fruit tree in South Africa with potential for domestication and commercialization. However, no study has reported on the molecular diversity of Strychnos spinosa morphotypes. Therefore, this study aimed to determine genetic variation among 32 Strychnos spinosa morphotypes using simple sequence repeat (SSR) markers. Fourteen amplified SSR markers produced 159 alleles, with a mean of 5.68 per locus. The polymorphic information content (PIC) values ranged from 0.22 (Ssp_1) to 0.84 (Ssp_6). Morphotypes were clustered in a biplot based on their genetic distances. The dendrogram chiefly discriminated morphotypes according to variation of pericarp texture. The population structure had the highest delta value K = 3, thus the 32 morphotypes were divided into three subpopulations based on the Bayesian approach. The affinities produced by the population structure agreed with the genetic distance of closely related morphotypes. This study is the first to report on SSR marker development and their successful use for genetic diversity and population structure studies of Strychnos spinosa. It provides insights into the molecular characterisation of Strychnos spinosa. This can lead to breeding programs and crop improvement programs, particularly in varietal developmental programs, which can contribute to alleviating food security challenges. Strychnos spinosa is one of the large-fruited Strychnos species native to sub-Saharan Africa, which has potential for domestication and commercialization for its edible fruit value [it value . Domestiit value . Studiesit value  have revit value . It provit value . Generalit value .Genetic progress in characteristics desired for domestication is partly determined by the existing phenotypic diversity in the genetic base of plant populations . TraditiStrychnos spinosa, and currently no study has been reported on the molecular diversity of Strychnos spinosa morphotypes. This study aimed to determine genetic diversity among Strychnos spinosa morphotypes using SSR markers, which is one of the prioritised tasks in a germplasm repository for fruit trees [Strychnos spinosa morphotypes identified by Mbhele et al. [All molecular marker systems that are developed and applied for analyses of genetic diversity and relatedness have their strengths and weaknesses . Moleculit trees . Determie et al.  will helStrychnos spinosa morphotypes  to 407 bp (Ssp_18_F), with an average size of 270.57 bp (In 70.57 bp . The fouStrychnos spinosa ranged from 0.00 (Ssp_13) to 0.88 (Ssp_11), with an average of 0.43, and the expected heterozygosity varied from 0.28 to 0.82 from loci Ssp_13, Ssp_6, and Ssp_11, respectively, with an average value of 0.60. The highest polymorphism (PIC = 0.84) was recorded in the forward marker Ssp_6, while the lowest (PIC = 0.22) was found in reverse marker Ssp_1.The forward marker Ssp_6 had the highest genetic diversity (GD = 0.82), whereas the reverse marker Ssp_7 had the lowest (GD = 0.24), with a mean of 0.62 . The obsThe genetic distance varied from 0.10 to 1.26 . MorphotStrychnos spinosa morphotypes into three sub-populations. The population structure grouped the genetic relationships of the morphotypes into sub-populations and admixtures as shown in K = 3  versus Delta . Thus, iin K = 3 . The higin K = 3 . The strStrychnos spinosa morphotypes were grouped based on the genotypic distance , distance . The firThe second quadrant was defined by morphotypes GRP-GRO, GSR-GEO, PRR-dGRF, PRXCP-dGEO, and GRR-GEF . The firThe dendrogram based on Euclidean distance classified the morphotypes into two major clusters and two sub-clusters . ClusterStrychnos spinosa. Many species lack sequence data that would enable them to be positioned on the plant tree of life, and this limits our understanding of diversity and evolution among the species, despite significant sequencing initiatives such as the DNA barcoding effort led by the International Barcode of Life (iBOL) consortium and supported by the African Centre for DNA Barcoding in South Africa [Strychnos spinosa is one of the important potential crops that essentially requires microsatellite markers. Additionally, this study is the first to document the wide genetic variation of Strychnos spinosa within the same geographical area, which may indicate that KwaZulu-Natal can be the primary, secondary, or even tertiary gene pool and centre of diversity for Strychnos spinosa. Areas with high species diversity need urgent conservation measures to secure this germplasm and ensure food security for future generations [The present study is the first to report on simple sequence repeat (SSR) marker development and their successful use for genetic diversity and population structure studies of h Africa . A lack h Africa . Any croh Africa . Thus, th Africa , and Strerations . Wild sperations . Geneticerations . If properations .Strychnos spinosa morphotypes was similar to a range from 140 to 550 among Psidium genotypes in New Delhi, which were developed from microsatellites-enriched libraries [Strychnos spinosa morphotypes  [Pisium sativum  [Solanum lycopersicum  [Strychnos spinosa morphotypes and its relative Strychnos cocculoides, with whom they share similar phenotypic, sensory traits, and the same common name in Zimbabwe [Strychnos spinosa, which are properties required for plant breeding programs [A range 0.26–0.86) and average (0.62) of genetic diversity among photypes  was grea guajava . The ave = 0.33) , Pisium = 0.323) , and Sol = 0.33) . The hig = 0.33) , in addi–0.86 and = 0.33) . This ef = 0.33) . The higZimbabwe . The polZimbabwe . This inprograms . Strychnos spinosa morphotypes  with GRXCR-dGEF. The genetic distance between GRXCP-GEO and GRXCR-GEF; GRXCP-dGEF and GRXCR-dGEF; PRR-dGEF and GRR-dGRO; and GRXCR-GEF and GRR-GEO, was also 1.26. This suggests that these morphotypes may be potentially good candidates for breeding. Breeding genetically distant morphotypes can result in a heterotic effect . HeterosStrychnos spinosa [Strychnos spinosa is resilient to unfavourable conditions.The population structure for K = 3  and the  spinosa . Knowled spinosa , which i spinosa , as StryMorphotypes GSR-GRO, GRP-GEO, GRXCP-dGEF, and GRP-dGEO, with narrow Nei’s genetic distance among each other  were groThe grouping of morphotypes in the dendrogram according to the variation of pericarp texture, using SSR markers  confirmsStrychnos spinosa plant material was collected across the study area at Bonamanzi Game Reserve in KwaZulu-Natal to assess the genetic variation among the 32 morphotypes identified in the area  according to the manufacturer’s instructions. Freeze-dried leaves were ground using a mortar and pestle. For every plant per morphotype, 20 mg of powdered leaf tissue was added to 1.5 µL Eppendorf Safe-Lock Tubes (microcentrifuge tubes) together with 400 µL of Buffer AP1 and 4 µL of a 100 mg/mL RNase A stock solution. This mixture was thoroughly vortexed to eliminate any tissue clumps that may have formed. In order to lyse the cells, the mixture was incubated for 10 min in a 65 °C preheated water bath, with mixing two or three times during incubation by inverting tubes.A representative set of the area . MorphotThe detergent, proteins, and polysaccharides were co-precipitated by adding 130 µL Buffer AP2 to the lysate, mixed, and incubated for 5 min in ice. The lysate was centrifuged (using Eppendorf Mini Spin plus) for 5 min at 14,000 rpm, and the supernatant transferred (by pipette) into the QIAshredder Mini spin column placed in a 2 mL collection tube and centrifuged for 2 min at 14,000 rpm. This was performed to eliminate the effect of the DNA shearing that can result from the extreme viscous lysate and large amounts of precipitates that can be generated during this step.The flow-through fraction was transferred to a new 1.5 mL microcentrifuge tube, without disturbing the cell-debris pellet. Usually, 450 µL of the lysate is recovered; therefore, 1.5 volumes (675 µL) of buffer AP3/E were added to the cleared lysate and immediately mixed by pipetting. During this stage, precipitation was produced. The 650 µL of the mixture, including any formed precipitate, was transferred using a pipette into the DNeasy Mini spin column in a 2 mL collection tube and centrifuged for 1 min at 8000 rpm, and the flow-through was discarded. The collecting tube was re-used, and this step was repeated with the remaining sample.g (8000 rpm). The flow-through was discarded while the collection tube was re-used. An additional 500 µL of Buffer AW was added and centrifuged at 14,000 rpm to dry the membrane. The DNeasy Mini spin column was transferred to a 1.5 mL or 2 mL microcentrifuge and 100 µL of Buffer AE was added onto the DNeasy membrane. This was incubated for 5 min at ambient temperature (15–25 °C), after which it was centrifuged for 1 min at 8000 rpm to elute. This process was performed in a separate microcentrifuge tube in order to obtain two identical sets of DNA with the same purity but differing concentrations.The DNeasy Mini spin column was placed into a new 2 mL collection tube, and 500 µL of Buffer AW was added and centrifuged for 1 min at 6000× ® in 50 ng/µL of DNA template in two separate 10 µL volume reactions. The reactions contained 4 µL of DNA template, 0.8 µL of deoxyribonucleotide triphosphate (dNTPs) (2.5 mM), 1.0 µL of 10 × buffer and 0.06 µL of Taq polymerase . In the first reaction, 1.0 µL of MgCl2 (50 mM), 1.0 µL of forward and reverse primers (5 µM), and 1.14 µL of ultrapure water were included. In the second reaction, a 1.2 µL of MgCl2 (50 mM) and 1.5 µL of both forward and reverse primers were added to make up the master mix. Forward primers were labelled with FAM (blue), ATTO565 (red), and ATTO550 (yellow), ATTO532 (green) fluorescent dyes. The PCR conditions consisted of denaturing at 94 °C for 2 min, nine cycles at 93 °C for 15 s, annealing at 65 °C for 20 s, and the extension at 72 °C for 30 s. The annealing temperature of each cycle was decreased by 1 °C with the final 30 cycles at 55 °C and the final elongation step at 72 °C for 5 min. The PCR products were separated by capillary electrophoresis analysis performed on an ABI3500 genetic analyser. Allele size was determined for each SSR locus using GeneMarker HID version 2.9.5. Of all primers tested, fourteen produced constituent amplification of well-defined allele sizes and were selected for further analysis (The polymerase chain reaction (PCR) amplifications were performed by the Eppendorf mastercycleranalysis . http://tayloro.biology.ucla.edu/struct_harvest/, accessed on 20 October 2022). Bar plots were generated with mean results of runs for the highest K value using STRUCTURE version 2.3.4. The principal coordinate analysis (PCoA) was performed using GenAlEx version 6.4 software. The dendrogram was obtained using Ward’s method of linking based on Euclidean distance in XLSTAT version 2022.1.2 and displayed genetic relations among the Strychnos spinosa morphotypes.Genetic diversity parameters, namely allele number and frequency, gene diversity, heterozygosity, and polymorphic information content (PIC), were calculated in PowerMarker software version 3.25. However, observed and expected heterozygosity were determined using Cervus version 3.0.7, where both the forward and reverse primers had one value for each marker. To clarify the gene differentiation between morphotypes, Nei’s genetic distance was evaluated. Values < 0.50 indicated closely related individuals, whereas ≥0.50 indicated distantly related morphotypes. The population structure analysis was determined using a Bayesian model-based clustering approach. The STRUCTURE version 2.3.4 program was applied to detect population genetic structure using a defined number of pre-set populations K, where each K is characterized by a set of allele frequencies at each locus. The Evanno test is recommended to identify the best-fitting number of populations within a sample. The structure program was set as follows: the analysis was run with 10 simulations per K value from K = 1 to 10, using a burn-in period length of 5000 and after burn-in 50,000 replicates. The most expected value of K for each test was detected by ΔK  using thStrychnos spinosa morphotypes. This paves the way for further molecular genetic investigations, further breeding programs, and crop improvement programs, particularly in the varietal developmental programs, which have the potential to contribute to alleviating food security challenges. Additionally, the findings of this study can also be used to inform conservation efforts and make informed conservation decisions for Strychnos spinosa, as well as contribute to the understanding of the evolutionary history and genetic diversity of this species in the region. This is the first study to report on genetic variation of Strychnos spinosa. Hence, this study was limited by the number of markers used (14 markers). Therefore, it is recommended that more markers be used in future studies.This study provides insights into the molecular characterisation of