Asclepias syriaca) populations for phenotypic traits. Using seeds from 52 populations along three urban-to-rural subtransects in the Greater Toronto Area, Canada, one of which followed a green corridor, we grew ~ 1000 plants in a common garden setup and measured > 20 ecologically-important traits associated with plant defense/damage, reproduction, and growth over four years. We found significant heritable variation for nine traits within common milkweed populations and weak phenotypic divergence among populations. However, neither urbanization nor an urban green corridor influenced genetic divergence in individual traits or multivariate phenotype. These findings contrast with the expanding literature demonstrating that urbanization promotes rapid evolutionary change and offer preliminary insights into the eco-evolutionary role of green corridors in urban environments.Urbanization is altering landscapes globally at an unprecedented rate. While ecological differences between urban and rural environments often promote phenotypic divergence among populations, it is unclear to what degree these trait differences arise from genetic divergence as opposed to phenotypic plasticity. Furthermore, little is known about how specific landscape elements, such as green corridors, impact genetic divergence in urban environments. We tested the hypotheses that: (1) urbanization, and (2) proximity to an urban green corridor influence genetic divergence in common milkweed ( In turn, these alterations can impact populations and communities directly by affecting species richness4, abundance6, and spatiotemporal distribution8. These environmental conditions can also impose indirect impacts by restructuring species interactions such as antagonisms  and mutualisms 11. In response, individuals and ultimately populations have experienced trait changes associated with growth, reproduction, defense/damage, morphology, and behavior, a process indicative of intraspecific phenotypic divergence16. Despite the recent rise of studies documenting phenotypic divergence among urban and rural populations15, few have distinguished the degree to which evolution and phenotypic plasticity drive these patterns.The intense environmental changes associated with urbanization can profoundly alter the ecological processes of urban-dwelling populations. Several biotic and abiotic factors differentiate urban from non-urban environments including higher pollution, habitat fragmentation, and temperature due to the urban heat island effect17 and was shown to account for the majority of the increased heat tolerance exhibited by urban water fleas (Daphnia magna) in a common garden experiment18. Conversely, genetic divergence happens via evolution and is, in urban environments, most commonly attributed to changes in natural selection, genetic drift, and gene flow21. Specifically, the widespread habitat fragmentation characteristic of urban environments is frequently implicated in reports of heightened genetic drift within populations23, restricted gene flow among populations25, and natural selection26.Phenotypic divergence can be explained by two general mechanisms: phenotypic plasticity and genetic divergence. Phenotypic plasticity occurs when individuals with identical genotypes exhibit different phenotypes in different environments30, yet few included multiple traits, or traits from diverse ecological functions such as reproduction, defense/damage, and growth30. Thus, we have a poor understanding of how urbanization affects divergence in the multivariate phenotype. Individual responses to environmental change often involve multiple correlated traits32, and excluding traits from major categories  complicates the detection of shifts in life history strategies, including trade-offs, or the lack thereof. Thus, surveying a broad range of phenotypic traits is essential for determining the drivers of phenotypic divergence in urban environments.Several studies have investigated the basis of phenotypic divergence in plants35 such as forest fragments, frequently by facilitating gene flow37 and alleviating effects of genetic drift38 (but see Orrock39). In turn, these processes can accelerate adaptation through the introduction of beneficial alleles or slow it through the introduction of deleterious or neutral alleles43. However, virtually nothing is known about how these corridors impact phenotypic trait evolution in urban areas. Broadly, urban green corridors frequently impact dispersal and gene flow45, such as in the white-footed mouse (Peromyscus leucopus) in New York where vegetation corridors facilitate dispersal46. Investigating how urban green corridors influence genetic divergence in phenotypic traits is essential for discerning whether aspects of urban landscapes shape the phenotype of urban-dwelling taxa. This knowledge would also offer conservation agencies important context about the evolutionary consequences of corridors primarily utilized to remedy the ecological costs of habitat fragmentation in urban environments47. Evaluating how urban green corridors impact the evolution of phenotypic traits represents an important step towards understanding evolutionary dynamics in heterogeneous urban landscapes.Individual elements of the urban landscape are thought to impact the evolution of phenotypic traits. Long, connected habitat patches—henceforth, green corridors— have been shown to impact evolution in non-urban areasAsclepias syriaca), as well as its pollinators and herbivores, suggests that urbanization may be a powerful agent of natural selection in this system49. In an observational survey, urbanization influenced reproductive success and pollinator community structure, while in urban populations, proximity to a green corridor inconsistently affected reproductive success, but uniformly decreased pollinator diversity and richness49. Likewise, another study highlighted the complexity with which nine specialist herbivore species were impacted by urbanization and season across six cities48. Urban common milkweed populations showed higher herbivore species richness yet less leaf herbivory than rural populations, with these effects varying by season and city. Additionally, the milkweed leaf-mining fly (Liriomyza asclepiadis) was 90% more abundant in urban areas and the milkweed stem weevil (Rhyssomatus lineaticollis) was 41% more abundant in rural areas, although the effect for the milkweed stem weevil varied by city. Consequently, differential ecological stressors within urban areas could drive the evolution of defense and reproductive traits in common milkweed.Prior research demonstrating how urbanization influences ecological change in a native plant of conservation importance, common milkweed (50. (Q2) To what extent is urbanization associated with genetic divergence in phenotypic traits among populations? If common milkweed has experienced genetic divergence in response to urbanization, then population-level phenotypic trait estimates will correlate with local urbanization levels. (Q3) Is proximity to an urban green corridor related to the levels of genetic divergence among urban populations? If so, there will be distinct differences in phenotypic trait estimates between populations near to and far from the corridor. This study builds upon work investigating how urbanization and a green corridor influence phenotypic divergence in reproduction in common milkweed49 by uncovering the extent to which evolution and phenotypic plasticity shape phenotypic divergence in reproductive, growth, and defense-related traits. These results provide insights into how plants are likely to respond to rapid environmental change in heterogeneous urban environments.Here, we used a common garden experiment to test the hypotheses that: (1) urbanization, and (2) proximity to an urban green corridor drive genetic divergence in phenotypic traits among populations of common milkweed. We asked three questions: (Q1) Is there heritable variation for phenotypic traits within populations and genetic divergence between populations? These are two necessary conditions if common milkweed is to evolve in response to urban environmental change51, urban populations tend to be smaller and inhabit public parks, railway and transmission rights-of-way, and roadsides, as well as private lawns and gardens. Plants can reproduce vegetatively through rhizomes that generate clonal ramets, or sexually through the cross-pollination of hermaphroditic flowers, each with five pollen sac pairs collectively called pollinaria53. Fertilization following successful pollination by insects, such as the western honey bee (Apis mellifera), bumblebees (Bombus spp.), and Halictidae spp. (Hymenoptera) in urban areas55, yields follicles  filled with wind-dispersed seeds56. Common milkweed is also recognized for its conservation significance because milkweeds (Asclepias spp.) provide essential resources for the threatened Monarch butterfly (Danaus plexippus)57.Common milkweed is an herbaceous perennial plant native to eastern North America. Although common milkweed grows in discrete patches of one to thousands of ramets (stems), often in abandoned agricultural fields60. Herbivores that can overcome this barrier must also tolerate cardenolides, a suite of secondary metabolites which can disrupt sodium–potassium pumps (Na+/K+-ATPases) required for maintaining membrane potential61. At least 16 herbivores have coevolved with milkweeds and developed tolerance to these defenses62, and at least nine, including the Monarch butterfly, can enhance their own toxicities by sequestering cardenolides63. Compared to growth and reproduction, heritabilities of defense traits  are often high68, indicating an increased likelihood for traits in this category to evolve.Multiple traits protect common milkweed against herbivores. Milkweeds contain a pressurized, milky sap called latex that physically interferes with herbivores chewing tissue by gumming their mouthparts49. This gradient was split into two parallel urban subtransects ca. 5 km apart, and a third rural subtransect, all approximately the same length (30–35 km). The northern urban subtransect  crossed through the urban matrix while the southern urban subtransect  followed a green corridor: a mostly continuous vegetated strip of land, ca. 50–350 m wide, that ran adjacent to linear features like trails, rights-of-way, and railways. Sampling sites were located within or near the corridor. The rural subtransect  extended westward from the suburban termini of the urban subtransects. Sampling sites (henceforth “populations”) of isolated patches of common milkweed were spaced > 500 m apart. We then divided the population into five equally-sized sections and collected seeds from one follicle  per ramet per section, aiming to separate ramets by > 3 m to avoid resampling the same clone. Seeds were stored in small paper (coin) envelopes and preserved at − 20 °C until germination. We performed the seed collection in accordance with the relevant guidelines and regulations .To evaluate whether urbanization affects the evolution of ecologically important phenotypic traits, we sampled plants at 52 sites along a 67 km urban–rural gradient in the Greater Toronto Area, Ontario, Canada Fig.  as descr49. Briefly, we first measured the distance from each population to the Toronto urban center , as a proxy for the degree of urbanization. Distance from the urban center is correlated with numerous environmental factors associated with urbanization69, and, for this city specifically, has been shown to influence the ecology and evolution of other plant, herbivore, and pollinator communities71. We also calculated an urbanization score for each population with the UrbanizationScore software74, which ranged from − 3.56 (least urban) to 3.37 (most urban). For each population, the software downloaded a 1 km radius aerial at 100 m resolution from Google Maps . The amount of vegetation, buildings, and roads was quantified and used to calculate landscape-cover variables, which were finally combined with principal component analysis into an urbanization score. Urbanization metrics were highly correlated  = 122.952, p < 0.001, R2adj = 0.709)  in May 2019. Seedlings were transplanted into 3.79 L circular pots filled with field soil and topped with 2.5 cm Triple Mix, then randomized into 4 contiguous spatial blocks with 1.5 m spacing between rows and columns  in King City, Ontario, Canada  was quantified by snipping the tip (~ 0.5 cm) off the youngest fully expanded intact leaf and collecting the latex exudate on a pre-weighed 1 cm filter paper disc as described in Agrawal et al.64. Discs were placed in a pre-weighed microcentrifuge tube on dry ice, then transferred to a − 80 °C freezer until weighed to the nearest 1 µg.We assessed five ecologically important traits related to plant defense/damage .In mid-July and mid-August 2020, we surveyed the abundance of ten specialist herbivore species located on all aboveground parts of the plants  and dry mass/wet mass, respectively.To assess specific leaf area (SLA) and leaf dry matter content (LDMC), we collected the youngest fully expanded intact leaf and measured wet mass, dry mass, length, and width. SLA and LDMC were calculated as leaf area/dry mass . For the three oldest inflorescences per plant, we recorded the number of flowers per inflorescence once at least half the flowers in an inflorescence became fully reflexed. From three flowers per inflorescence, we assessed the number of pollinaria removed and flower size. Flower size was measured as the length and width of each flower’s corolla , hood (nectar-containing structure), and distance from one hood tip to the opposite side of the reproductive whorl  to fit the following general linear models using restricted maximum likelihood:We calculated three quantitative genetic parameters to understand how urbanization could affect divergence between populations and genetic variation within populations. To estimate the variances explained by population and family for each trait, we used the In these models, data was restricted to the last year of measurement to minimize impacts of maternal effects. Block was treated as a fixed effect while Population and Family were random effects, with Family nested within Population.lme4 package80 (version 1.1.27.1), and residual variances with the “sigma” function from the stats package78 (version 4.1.1).We extracted population and family-level variances with the “VarCorr” function from the H2), the ratio of genetic variance to total phenotypic variance50, for each phenotypic trait as:ST, a standardized measure of the genetic differentiation of a quantitative trait among populations81, as:We calculated estimates of full-sibling broad-sense heritability (G), a dimensionless measure of evolutionary potential that is closely related to the coefficient of additive genetic variation (CVA) and is useful for comparing the magnitude of genetic variation among traits82, as:We calculated the coefficient of genetic variation , then divided p-values by 2 because these were 1-sided tests 84. We then obtained percent variance explained (PVE) for each random effect after extracting variances with lme4 and calculating:For models with Gaussian distributions, we tested the significance of Population and Family using the “ranova” function from the pbkrtest package85 (version 0.5.1) with 1000 simulations, then divided p-values, which were calculated assuming that the likelihood ratio test has a χ2 distribution, by 2 because these were 1-sided tests 84. Results for ten models analyzed with 1000 simulations were identical to those analyzed with 100, so we used 100 simulations for the remaining models. We refitted models to Gaussian distributions, then extracted variances and calculated PVE for each random effect as described above. We inspected model diagnostics with the DHARMa86 (version 0.4.3) and performance87 (version 0.7.3) packages and transformed response variables to meet the assumptions of normality and homogeneity of variance when necessary . Because we set alpha to 0.05 for the previous hypothesis tests of individual traits, we set the probability of success to 0.05. The tests were one-sided  to identify scenarios wherein the proportion of “successes” (traits with heritable genetic variation) out of all “trials”  was significantly higher than the expected proportion. Since this test functions under the assumption of independent trials, and there are inherent correlations among traits , then these p-values should have been higher in Question 2 models’ results. To test the significance of fixed effects , we adjusted the models to use maximum likelihood and then computed χ2 and p-values from a type II sums-of-squares ANOVA with the car package91 (version 3.0.11). We used type II sums-of-squares because this method is unbiased by the order of effects, especially for unbalanced data, unlike type I. In contrast to type III, type II has more statistical power and is based on the assumption that interactions are minimal or absent92. If urbanization contributed to genetic variation within and/or between populations, then some variance should have shifted from Population and/or Family to urbanization and the p-values for urbanization should have become significant.To test if urbanization caused genetic divergence among populations, we fitted linear mixed effect models with two metrics of urbanization added as fixed effects using the same packages described above. For both general and generalized linear mixed effect models, urbanization was added to models as both Distance to the City Center and Urbanization Score, separately. To test the significance of random effects , we repeated the remaining steps as described for Question 1 above. If urbanization contributed to heritable genetic variation within and/or between populations  to fit general linear models and examine how multivariate phenotype varied with urbanization (both Distance and Urbanization Score) using one-way ANOVA.To test for differences associated with urbanization in overall phenotype, as opposed to differences in individual traits that could reflect specific selection pressures, we performed a multivariate phenotype analysis. This analysis incorporated all traits in the dataset and accounted for non-independence between correlated traits , we added Subtransect and Urbanization:Subtransect interaction terms to the linear mixed effect models described for Question 2. For both general and generalized linear mixed effect models, we tested the significance of the random effects  by repeating the steps as described for Question 2 above. To test the significance of the fixed effects , we fitted reduced models without the interaction term, ranked full and reduced models based on Akaike's information criterion (AIC)We also fitted models with multiple years of data and found that the effects of urbanization and a green corridor were qualitatively identical to those reported in Tables 51). Moreover, since our common garden was located in a rural area, we could only measure selection in one environment. It would have been more appropriate to measure selection if we had comparisons among urban and rural environments, but this would have required planting multiple common gardens.We did not perform phenotypic or genotypic selection analyses because our experiment was only designed to quantify quantitative genetic variation. We could not obtain accurate estimates of variation in lifetime fitness because common milkweed is a long-lived perennial , pollinaria removed , and milkweed stem weevil damage (binary) , while nine traits, at least one per category, exhibited near-zero heritabilities. Milkweed stem weevil damage (binary) , herbivory after flowering (binary) , and Monarch butterfly abundance  exhibited the highest, statistically significant coefficients of genetic variation, while seven traits exhibited near-zero values. It was unlikely that the high frequency of heritable genetic variation within populations was due to chance . Three traits associated with plant defense and reproduction exhibited statistically significant genetic divergence among populations: latex exudation , flowering success  , and no. of inflorescences  (Table ST ranged from 0 to 1 (mean ± SE: 0.241 ± 0.068) with seven traits exhibiting near-zero values. The relatively few instances of genetic divergence between populations could be due to chance . Thus, these results suggest moderate heritable genetic variation within populations and little phenotypic divergence among populations, the first of which is a prerequisite for adaptation.There was heritable variation for multiple phenotypic traits within populations and evidence of genetic differentiation between populations for some traits. At least one trait per category exhibited significant heritable genetic variation within populations . Heritabilities and coefficients of genetic variation ranged from 0–0.588 (mean ± SE: 0.110 ± 0.031) and 0–4.456 (mean ± SE: 0.467 ± 0.207), respectively Table . The hig5) Table . QST ran2 = 4.029, p = 0.045, R2m = 0.037, N = 699 individuals & 51 populations) and herbivory before flowering (quantitative) . When adjusted for false discovery rates , our results did not provide strong evidence that urbanization influenced genetic divergence in phenotypic traits between populations  or urbanization score . Consistent with our results that urbanization had little effect on genetic divergence, the variation explained and statistical significance of the effects of population and genetic family did not substantially change once urban metrics were included in models  and urbanization score . We detected an interaction between urbanization (quantified as an urbanization score) and proximity to a green corridor for latex exudation , flowering duration , follicles , date of first follicle , inflorescences , and milkweed stem weevil damage (binary) . These interactions suggest that the impact of corridors may depend on the intensity of urbanization. However, when adjusted for false discovery rates, our results did not provide strong evidence that proximity to a green corridor influenced genetic divergence in phenotypic traits between populations as none of these effects were statistically significant  or urbanization score . Consistent with our results that proximity to a green corridor had little effect on genetic divergence, the variation explained and significance of the effects of population and genetic family did not substantially change once urban metrics and proximity to a green corridor were included in models  and a local scale . Similarly, genetic divergence for growth-related traits was detected among common milkweed populations sampled from a > 1500 km latitudinal gradient from New Brunswick, Canada, to North Carolina, USA68. In our common garden experiment, we found moderate heritable genetic variation within populations and weak phenotypic divergence among populations sampled from an urbanization gradient. Our results, which indicate that these populations have met the prerequisites to evolve in response to urbanization and/or an urban green corridor, are mostly consistent with previous findings from non-urban contexts67.Genetic divergence of phenotypic traits along an urbanization gradient necessitates that populations exhibit genetic divergence, which is more likely to occur when those populations contain heritable phenotypic variation. These criteria have been observed in previous studies of common milkweed, although not in an urban context67. For example, heritability estimates for plant height after flowering and the number of ramets (range: H2 = 0.132–0.161) were close to estimates by Vannette et al.67 (aboveground biomass: H2 = 0.11) and Agrawal et al.64 (vegetative biomass: H2 = 0.12). We found significant and relatively high heritability estimates for herbivory after flowering (H2 = 0.588) and milkweed stem weevil damage (H2 = 0.418) when measured on a presence/absence basis, but not quantitatively when measured as percent leaf area removed. In comparison, Agrawal et al.64 found moderate heritability for herbivory when measured as the percentage of leaves with foliar damage due to chewing herbivores (H2 = 0.284), but not for milkweed stem weevil damage when measured as the length of stem scars (H2 = 0.037). We also found that two reproductive traits had moderate to high heritabilities , suggesting a higher evolvability of these traits in response to environmental change. Overall, these results confirm the capacity for these common milkweed populations to evolve in response to ecological disturbances, such as urbanization.Our estimates of genetic variation for phenotypic traits within populations are comparable to previous studies30, size29, fecundity29, defense16, and competitive ability98. Additionally, in both native and introduced ranges, common milkweed exhibits clines for growth and leaf physiology traits that correspond with a defining feature of urban environments: temperature96. Yet despite surveying several suites of traits in common milkweed, we found low support for genetic divergence along an urbanization gradient. Furthermore, we detected only small effect sizes (range: 0.01–0.037) for the few traits associated with urbanization even with the large scale and replication afforded by our experimental design. Thus, multiple lines of evidence suggest the lack of such divergence at present, though we do not, and cannot, rule out the possibility of genetic divergence emerging in the future.Many taxa exhibit genetic divergence between urban and nonurban populations for various traits. In plants, this has been documented for traits associated with phenology99. The life history traits of common milkweed could also slow evolutionary change. For example, the vegetative reproduction inherent to common milkweed can lead to clonal growth and the loss of genotypic diversity within populations101, which is compounded by the species’ long-lived nature. Long-distance pollen and seed flow could yield high gene flow among populations and low genetic drift within populations, as common milkweed is largely self-incompatible  , seeds are wind-dispersed, and flowers are frequently pollinated by insects that can travel > 1 km 104.The relative rarity of this outcome in the existing urban evolution literature presents a valuable opportunity to explore circumstances that could prevent urbanization from influencing genetic divergence among populations. In this case, evolutionary change may have been precluded by urban ecological pressures that were possibly too small or brief, as the city of Toronto has contained ≥ 50,000 residents for only ca. 150 years105, and environmental heterogeneity functions across temporal and spatial scales107. However, our methods account for both temporal and spatial processes. Distance from the city center is highly correlated with percent impervious surface and numerous other environmental variables associated with urbanization69, and in Toronto reflects urban expansion in concentric zones, with each zone’s environment correlated with the time since development. By including urban and rural environments as transect endpoints and densely-sampled transitional areas in between, distance from the urban center captures extensive multivariate environmental change associated with urbanization— an important feature, since we did not know which environmental factors might impact common milkweed a priori. A complementary metric is urbanization score, which quantifies the immediate land use/land cover surrounding each population, without consideration of the site’s proximity to the urban core. Therefore, despite this limitation, we argue that our estimates of urbanization are credible and reliable.We acknowledge that urban-nonurban environmental gradients are complex and that our data cannot fully capture the heterogeneity of the landscape mosaic. Urbanization is a multifaceted process that involves complex change along axes including, but not limited to, environmental, economic, and sociological dimensions. There are manifold ways to define “urban” vs. “rural” vs. “natural” landscapes49. Specifically, as we observed relatively little phenotypic divergence among populations within the common garden, our results suggest that the previously observed in-situ phenotypic divergence was mostly consistent with phenotypic plasticity as opposed to genetic divergence. This finding underscores the importance of investigating the genetic basis of phenotypic divergence observed in cities and reiterates the role of phenotypic plasticity in shaping how populations respond to human-caused change. Future studies could investigate the specific conditions that promote phenotypic plasticity within urban environments.This study lends valuable context to prior work demonstrating how urbanization impacts the reproductive success of common milkweed populations110. Limited research in urban environments suggests that green corridors often increase gene flow among animal populations113 , and that urban features such as railways can function as corridors among plant and animal populations116. In our study, proximity to a green corridor did not strongly influence genetic divergence in phenotypic traits among common milkweed populations. Despite finding low support for genetic divergence along an urbanization gradient in Question 2, we proceeded with this analysis because we believe that once research questions or hypotheses have been decided, it is important to address each, so as not to change the hypothesis based on the results.Very little is known about how urban green corridors influence genetic divergence among plant populations. In non-urban environments, green corridors are predicted to increase gene flow among plant populations by facilitating pollen flow and seed dispersal, which is expected to decrease genetic divergence between populations114. Conversely, our data also suggest that proximity to a green corridor does not inherently facilitate genetic divergence either. It is also possible that urban green corridors actually impact genetic divergence but that our results reflect our inclusion of a single corridor, or the specific environmental conditions associated with our chosen corridor. For example, the efficacy of the corridor could have been impeded by a highly hostile external matrix or edge effects generated by the narrow shape118. Relatedly, the corridor we sampled would have provided minimal connectivity to plants or pollen vectors if it were not perceived as a functional corridor to either group–the plants or the pollinators. The manner in which common milkweed established in this particular corridor also influences our results, as plants growing in the corridor since establishment many generations ago are more likely to exhibit genetic divergence than plants descending from relatives in nearby non-corridor environments. Regardless, these findings contradict the expectation that habitat fragmentation impacts the evolutionary processes of urban populations and invite further research into how and when corridors impact genetic divergence of phenotypic traits in urban populations.As discussed above for Question 2, urbanization may not restrict gene flow among populations. If true, there would be little opportunity for the green corridor to significantly enhance gene flow and diminish any hypothetical genetic divergence in common milkweed, as proposed for butterflies in Angold et al.Urbanization is associated with phenotypic trait divergence for many species, yet in most cases, the genetic basis of these trait differences is unresolved. Additionally, the extent to which specific aspects of the urban landscape impact genetic divergence in phenotypic traits among plant populations is virtually unknown. Here, we show that neither urbanization nor an urban green corridor impacted genetic divergence in phenotypic traits in common milkweed, a native plant of conservation importance. These results demonstrate an example in which our measures of urbanization have not substantially contributed to genetic divergence among populations. To our knowledge, this study is also the first to investigate if urban green corridors impact genetic divergence in phenotypic traits in plant populations and ultimately suggests the absence of such effects in our study system. To further understand how urban environments and urban green corridors impact eco-evolutionary dynamics in plant populations, future research should verify the consistency of these findings by testing similar questions in plants with diverse life history, and in a range of cities with heterogeneous landscapes.Supplementary Information 1.Supplementary Information 2.


The nucleotide diversity (π) and Tajima’s D (TD) per chromosome for the populations ranged from 0.29–0.36 and 3.46–5.06, respectively, with genome level, mean π values of 0.33 and TD values of 4.43. Genomic scan using the stF outlier test revealed 85 loci under selection signatures, with 65 loci under balancing selection and 17 under directional selection. Putative candidate genes co-localized with regions exhibiting strong selection signatures were associated with grain yield, plant height, host plant resistance to pathogens, heading date, grain quality, and phenolic content. The Bayesian Model (STRUCTURE) and distance-based  methods grouped the genotypes into five subpopulations, where landraces from geographically non-adjoining environments were clustered in the same cluster. This research provides further insights into population structure and genetic relationships in a diverse set of durum wheat germplasm, which could be further used in wheat breeding programs to address production challenges sustainably.Yanyang Liu, Henan Academy of Agricultural Sciences (HNAAS), China; Landraces are an important genetic source for transferring valuable novel genes and alleles required to enhance genetic variation. Therefore, information on the gene pool’s genetic diversity and population structure is essential for the conservation and sustainable use of durum wheat genetic resources. Hence, the aim of this study was to assess genetic diversity, population structure, and linkage disequilibrium, as well as to identify regions with selection signature. Five hundred (500) individuals representing 46 landraces, along with 28 cultivars were evaluated using the Illumina Infinium 25K wheat SNP array, resulting in 8,178 SNPs for further analysis. Gene diversity (GD) and the polymorphic information content (PIC) ranged from 0.13–0.50 and 0.12–0.38, with mean GD and PIC values of 0.34 and 0.27, respectively. Linkage disequilibrium (LD) revealed 353,600 pairs of significant SNPs at a cut-off ( The LD https://ebi.gov.et/resources/). In addition, only scanty research has previously analyzed the within-population genetic variation of Ethiopian durum wheat using recent genomic tools  after confirming their DUS  , which n2.2For each genotype , five healthy seeds were randomly selected and planted in a square-shaped pot with a size of 10 cm × 10 cm × 11 cm in the greenhouse of the Swedish University of Agricultural Science (SLU) Alnarp, southern Sweden, for two weeks. Ten discs of young leaf samples pooled from five plants per genotype were harvested in 96-well deep well plates and freeze-dried using CoolSafe ScanVAC Freeze Dryer following the instruction of TraitGenetics. The freeze-dried leaf samples were sent to Trait Genetics  for genomic DNA extraction and subsequent genotyping. The genomic DNA was extracted using a standard cetyltrimethylammonium bromide (CTAB) method from the leaf samples using TraitGenetics’ lab protocol.2.3https://sgs-institut-fresenius.de/en/gesundheit-und-ernaehrung/traitgenetics/genotyping. The SNP loci were filtered by removing those with a missing value above 5% and a minor allele frequency (MAF) below 5% using TASSEL v 5.2.67 software (The samples were genotyped using a high-density Illumina Infinium 25k wheat single nucleotide polymorphism (SNP) array by TraitGenetics Gmbh . This SNP array contains most SNPs from the earlier 90k Infinium array, 35K Wheat Breeders array, 135K Axiom wheat array, and SNPs within genes associated with specific importance in durum wheat breeding. SNPs accurately matching the A and B genomes were selected based on a cluster file of hexaploid wheat and the details of these SNPs can be found at software . These f2.42.4.1I) and Hardy-Weinberg equilibrium (HWE) test were performed using the GenAIEx v.6.5 software (Ho), expected heterozygosity (The nucleotide diversity (π)  and Tajisoftware . The polsoftware  and genesoftware . The obszygosity , and thezygosity .Triticum databases including GrainGene, T3/wheat, and Wheat URGI were used to determine the potential genes associated with loci under selections that are controlling important traits .The nucleotide position extending from 1–8.56 Mbp up and downstream from the SNP position was used for searching the potential candidate genes, as previously reported for wheat ((Triticum turgidum (Svevo.v1)) reference genome  for SNP pairs was calculated as described in 2 = r0.20, p<0.01) for A and B genomes separately as well as for the whole genome. The association of genome-wide LD decay and the physical distance was plotted by fitting a locally weighted linear regression (loess) line using the R function ‘loess` distribution and heterozygosity were analyzed as described in Principal Coordinate Analysis (PCoA) based on Nei’s standard genetic distance was also performed to investigate further the association between the populations using GenAIEx v.6.5 . A Bayesdmixture . Nei’s sdmixture  based undmixture  to deterdmixture  was useddmixture  using thdmixture . Arlequi33.1). The average marker density was 0.72, 0.73, and 0.72 markers per Mbp for the A and B genomes and the whole genome, respectively. In total, the distribution of these SNP markers covered 9.85 Gbp regions of the durum wheat genome, with chromosomes 1A and 2B having the least (582.20 Mbp) and largest (788.36 Mbp) regions covered  and 3658 (44.7%) have known map positions on the A and B genomes, respectively Table 1.He) value across all loci ranged from 0.02 to 0.18.The minor allele frequency (MAF) of the 8,178 SNP loci ranged from 0.07 to 0.5 with a mean of 0.24. The levels of polymorphism measured in terms of gene diversity (GD) ranged from 0.13 (at 505 loci) to 0.50 (at 2345 loci) with a mean gene diversity of 0.34. At a chromosome level, GD ranged from 0.32 to 0.36 with a mean value of 0.33 across the genome Table 1.3.2I) for the landraces ranged from 0 (for EH2) to 0.33 (for WSH8), with a mean of 0.11. The observed heterozygosity (Ho) values were from 0 for landrace EH2 to 0.07 for landrace BL1, with a mean value of 0.011. The expected heterozygosity (He) of the landraces varied from 0 for EH2 to 0.21for NSH8, with a mean value of 0.07. The gene diversity for the landraces ranged from 0 for nine of the 46 landraces to 0.22 for WSH8, with a mean value of 0.07  and the effective number of alleles (Ne) per locus of the landraces varied from 1.00 (EH2) to 1.75 (WSH8) and from 1.00 (EH1) to 1.31 (WSH8), respectively. The mean Na and Ne values were 1.30 and 1.10, respectively. The highest percentage of polymorphic loci  was found in cultivars population, followed by landrace WSH7 , WSH3 (71.51%) and WSH8 (64.67%) (I) ranged from 0.02 to 0.26, with a mean value of 0.12 (He) and loss of heterozygosity (increased Ho) were recorded for 99.9% and 0.1% of the loci, respectively. The fixation indices showed wide variation between the SNP loci. The fixation indices’ minimum, maximum, and mean were -0.66, 1.00, and 0.84 for ISF, 0.04, 1.00, and 0.96 for ITF, and 0.39, 0.96, and 0.76 for STF, respectively (There was a wide range of variation of molecular diversity of the SNP loci. The Shannon Information Index (p<0.01). Almost all their loci (99%) significantly deviated (99.9%), thus showing heterozygote deficiency, which is in agreement with the inbreeding reproductive system of durum wheat. Only 0.1% (8 loci) had excess heterozygosity (p<0.05).The Hardy Weinberg Equilibrium (HWE) test was carried out for all SNP loci for each landrace (population) as well as for all landraces. Almost all of the SNP loci (99.9%) significantly deviated from HWE across landraces (3.32r), measured as the squared correlation of alleles at two loci, was estimated based on 7,129 SNPs in durum wheat genotypes since 1,049 SNPs do not have known positions on the durum wheat chromosome. Considering the whole genome, 353,600 pairs of SNPs were in LD and 107,471 (30.4%) were significant marker pairs at p<0.01  to 0.26 on chromosome 3B  of pairs of markers happened within the range of 3.65 Mbp on chromosome 4A to 22.90 Mbp on chromosome 3B, with a mean of 8.56 Mbp across the genome  to 22 × 10-2 (for populations AR4 and cultivars), with the overall population π, a mean value of 34 × 10-2, which indicated a wide genetic variation among the populations (Genome-wide variation and selection signature in Ethiopian durum wheat were examined with nucleotide diversity and Tajima’s D. The mean nucleotide diversity (π) per chromosome varied from 0.29 on chromosome 3B to 0.36 on chromosome 1B, with an average π value of 0.33 Table 2.The highest (5.06) and lowest (3.5) mean Tajima’s D were recorded for chromosomes 1B and 3B, respectively, with Tajima’s D mean of 4.4 across the whole genome Table 2.The number of segregating variants at different levels of allele frequency in a population was estimated based on the site frequency spectrum (SFS) to infer the joint distribution of observed and expected allelic frequencies. The SFS analysis revealed considerable variation in the minor allele frequency (MAF) distribution of all SNP loci across the landraces (3.5p<0.01) were regarded as loci under selection, covering all 14 chromosomes of the durum wheat genome , TRITD2Bv1G029100 (heat shock transcription factor), TRITD3Av1G181000 (E3 ubiquitin-protein ligase SDIR1 G), TRITD5Bv1G162250 (sugar transporter ERD6), TRITD5Bv1G162180 (disease resistance protein (TIR-NBS-LRR class) family), TRITD3Bv1G028390 , TRITD5Bv1G155770 , TRITD5Bv1G198940 (photosystem II protein), TRITD5Bv1G236030 (high affinity nitrate transporter), TRITD7Bv1G197270 (MADS-box transcription factor G), TRITD6Bv1G138770 (MYB transcription factor 1), and TRITD7Bv1G165520 (zinc finger CCCH zinc-finger proteins) , UPGMA, and model-based Bayesian Inference were used to determine the population structure and genetic relationship between the landraces. The first three principal components (PCs) of PCoA explained 67.3% of the total variation, with the first two PCs  capturing 59.29% of the total variation. The PCoA grouped the landraces into five major clusters  revealed that 177 genotypes (149 from landrace landraces and 28 cultivars) were admixed   divergence among the subgroups inferred by STRUCTURE showed that the highest allelic divergence (0.47) was observed between clusters 1 and 3, whereas the lowest (0.24) was observed among clusters 4 and 5. The average genetic distance between genotypes in the same clusters ranged from 0.01 (Cluster 5) to 0.26 (Cluster 2). The mean expected heterozygosity between genotypes in the same clusters for cluster 1, cluster 3, and cluster 4 was 0.19, 0.10, and 0.13, respectively. The mean 3.7stF = 0.77, p<0.001), which accounted for 76.68% of the entire genetic variation. Genetic variation among individuals within populations accounted for 20.18% of total genetic variation. The genetic differentiation between groups of populations that were grouped according to their Regional States of origin accounted for 1.18% of the total genetic variation , 76.66% among populations within the Regional States  and 22.17% among individuals within populations , indicating high genetic variation among populations and individuals within the Regional States and absence of genetic differentiation among Regional State-based groups. AMOVA carried out by grouping the populations according to their geographical locations of origin revealed that 75.42% of the entire variation exists among populations within geographical regions of origin , 23.04% among individuals within populations  and 1.54% among geographical regions of origin . According to the AMOVA for the five STRUCTURE-based subpopulations, 44.50% of the total genetic variation was found between the five subpopulations and 52.62% among individuals within the subpopulations  was used to infer hierarchical genetic differentiation and estimate genetic variation within individuals, within populations, and among populations. The analysis revealed highly significant genetic differentiation among populations . Wide variation and significant Nei’s mean number of pairwise differences between populations (πxy) were revealed for all population pairs, except for NSH1 vs. BL2, WH vs. NO, NSH7 vs. AR3 and WH vs. NSH7  and PIC (0.41), indicating the high genetic variation in Ethiopian durum wheat, which might have arisen due to crucial evolutionary forces such as mutation rate, natural selection, linked selection, population history, and demographic history. Previous research  reportedThe Ethiopian durum wheat gene pool exhibits high mean gene diversity and PIC values at the A subgenome, B subgenome, and whole genome levels. These results are in line with previous research that showed high genetic diversity in Ethiopian durum wheat germplasm . There i4.2IS, FITF, and stF) suggest a high degree of genetic differentiation among the landraces and limited gene exchange, as reported previously (Genetic diversity parameters mean of GD (0.10), I (0.11), %P (30.00%), and He (0.07) of the loci recorded low variation within the durum wheat landraces and is by far below those reported previously . The difeviously . Low witeviously .A Hardy Weinberg Equilibrium (HWE) test is a widely used approach to estimate allelic and genotype frequencies in populations, thereby providing crucial information regarding reproductive mechanisms as well as the different evolutionary forces shaping their genetic makeups. The HWE test for individual landraces revealed that the vast majority of the loci are not in HWE. This is not surprising, as durum wheat reproduces primarily through self-fertilization . Several4.32r ≥ 0.2, p<0.01) significant SNP pairs across the durum wheat genome, a considerably higher percentage in comparison with the 13.4% (p<0.01) reported by p<0.01) by p<0.01) by Determining the extent, pattern, and distribution of LD throughout the durum wheat genome provides crucial information necessary to define inherited genomic regions . Further2 = r0.21  was estimated for the entire durum wheat set used in this study, including both landraces and cultivars. These results demonstrate the influence of significant elements of LD because of genetic linkage and the residual LD that might arise due to factors such as selection, rate of genetic recombination, and evolutionary history, leading to high genetic diversity  declined within the physical distance varying from 3.65 Mbp (chromosome 4A) to 22.90 Mbp(chromosome 3B), with a mean of 8.56 Mbp across the genome is comparable with previous research, i.e., 11.8 Mbp by The LD decay (at cut-off 4.4The high nucleotide diversity (π) and Tajima’s D revealed in this study suggest substantial genetic variation in Ethiopian durum wheat populations. The mean π and Tajima’s D values across the whole genome of 0.33 and 4.43, respectively, are high compared to several previous reports . Reduced4.5)Fst outlier test is suitable to identify genomic regions subjected to selection signatures because it is not strongly influenced by ascertainment bias  that separated them into different gene pools. UPGMA tree cluster 1 (Cl-I) comprised 25 landraces grouped together with all modern cultivars. This could have be caused by the fact that some farmers practice planting mixed genotypes, allowing cross-pollination between cultivars and landraces. Another probable reason could be that cultivars were be mistakenly classified as landraces during the germplasm collecting mission or that they are admixed germplasm. 4.7AMOVA indicated significant genetic differences among landraces, showing that genetic variation between populations is more significant than genetic variation within populations. Observed genetic variation among individuals within landraces might have occurred during domestications or might have been caused by seed exchange among farmers and local traders from adjoining and nonadjacent regions. 4.8Genetic characterization of the diverse set of durum wheat germplasm provided a sound insight into the population structure and genetic diversity of Ethiopian durum wheat gene pool as well as the genetic linkages between the SNP markers along its chromosomes. The information provided here facilitates the identification of beneficial loci and useful alleles that will aid in the development of more resilient durum wheat cultivars capable of coping with climate change challenges and ensuring durum wheat’s significant role in sustainable food security. These accumulated beneficial genetic variants of Ethiopian durum wheat could also help breeders to exploit available genetic variation more efficiently, optimizing future yield potential in more sustainable production systems and driving further discovery and deployment of beneficial alleles. The genetic analyses based on LD, GD, ND, Tajima’s D, and loci under selection revealed key genomic information, including apparent differences among the landraces. This provides a basis for future conservation of the crop’s genetic resources and breeding efforts to improve the crop.5The Illumina Infinium 25k wheat SNP array was used for genotyping 528 Ethiopian durum wheat to assess genetic diversity and population structure, determine LD, and uncover selection signatures related to domestication and breeding. High nucleotide diversity and Tajima’s D were observed at distal regions than pericentromeric regions (nearly zero diversity) of the chromosomes except for 1A, 1B, 6A, and 6B, which showed high diversity across their entire regions indicating the influence of selection during domestication by farmers and breeders for specific traits. Loci found under balancing selection spanned over all 14 durum chromosomes, whereas those under directional selection were distributed across 2A, 3A, 5B, 6B, and 7B chromosomes. Interestingly, genomic regions previously reported to impact grain yield, days to heading, grain quality, and disease resistance have been confirmed in this study. Hence, our results showed Ethiopian durum wheat germplasm’s high genetic diversity and untapped potential, which can be explored to discover novel genes for broadening the gene pool to develop climate-resilient cultivars. We recommend that Durum wheat breeders should strive to use these genetic materials to develop improved cultivars through fine mapping of genetically complex traits like grain yield and end-use quality traits, thereby maintaining yield stability, genetic gain, and adaptation to specific biotic and abiotic factors.The original contributions presented in the study are included in the article/Conceptualization: BM, MG, KT, RO, and TH, Methodology: BM, MG, KT, and RO, Data curation: BM, Formal analysis: BM, Visualization: BM, Investigation: BM and MG, Resources: KT, RO, MG, and TH, Funding acquisition: KT, RO, MG, and TH, Project administration: KT, RO, MG, TH, and CH, Supervision: KT, RO, MG, TH, CH, and FH, Writing original draft: BM, Writing-review, and editing: BM, RO, MG, KT, TH, CH, and FH. All authors contributed to the article and approved the submitted version.


The application of technology supported by cyber infrastructure has emerged as a critical factor influencing city management. This study aims to investigate whether the development of cyber infrastructure can enhance cities’ confidence in responding to potential epidemic threats in the context of COVID-19.China serves as a good example for both COVID-19 management and smart city construction. We take advantage of a special time point, the 2022 Chinese New Year, to observe cities’ precautionary epidemic policies. We utilize choice models and data from 188 Chinese cities to examine the impact of internet coverage on the degree of policy relaxation.We found that cities with higher internet coverage tend to adopt looser policies. In the benchmark regression, for every 1 percentage point increase in internet coverage, the likelihood of implementing loose measures increases by 0.9 percentage points. This result remains robust across different classifications of policies. We also addressed potential endogeneity issues by using the instrumental variables method.Our study indicates that effective management of epidemics in the modern era requires not only the utilization of traditional medical resources but also the incorporation of new city features, such as information technology infrastructure. Since tEach outbreak of a pandemic urges lessons to be learned. Existing literature has predominantly focused on intervention policies –4. For tAn important factor influencing city development in recent years has been the application of information and intelligence technology supported by internet infrastructure . Just asSo does the improvement of cyber infrastructure also help to increase the confidence of cities in dealing with potential epidemic outbreaks? China serves as a good example to observe this issue. China adopted a strict management policy aiming at achieving zero cases during the COVID-19 epidemic, which allowed the country to maintain a very low number of infections during the prevalence of the Delta variant . During This paper examines the impact of internet infrastructure on COVID-19 preventive policy choices based on data from 188 cities in China. We take advantage of a special time point, the 2022 Chinese New Year , established by the State Council on January 20, 2020, led by the National Health Commission and composed of 32 ministries and commissions, is at the core to coordinate the national epidemic prevention and control efforts. It issues national guidelines for epidemic management measures. The decisions are made based on the Protocol on Prevention and Control of COVID-19 issued by the National Health Commission. This medical technical guidebook has been updated ten versions by the end of 2022 and provides definitions, treatment methods, and management criteria for infected and high risk-exposed populations. According to the medical protocol and combined with the experience found in practice, JPCM provides guidance at the nation level on the management of key population groups, locations and organizations. It also establishes binding rules that must be followed by all local regions, such as specifying the exact quarantine measures and duration for close contacts with the virus. Local authorities at the provincial, municipal and county levels are responsible for their local epidemic prevention and control respectively, setting localized measures under the requirements of the central authorities and bearing the responsibility for any local epidemic outbreak  travel unless necessary” is required. Both areas are subject to measures distinct from those applied to general low-risk areas. Local authorities also follow these guidelines to further classify the population and arrange control measures. In addition to controlling the populations at risk within a specific region, it is equally important to cut off potential cross-regional transmission routes as part of the management policy. According to the JPCM, regions are classified into three risk levels based on a comprehensive evaluation of factors such as local population and the number of infected cases within a given period. These risk levels include high-risk, medium-risk and low-risk areas. Correspondingly, regions at each level adopt differentiated control measures. The region (area) was initially defined as the county level or above during the emergency stage, but gradually contracted to towns, blocks, and later building units in the regular management stage . For the cross-regional travel, strict restrictions are imposed by JPCM for the outflow from high- and medium-risk areas. Accordingly, local authorities at the destination usually impose control measures on these people. For people coming from low-risk areas, further distinctions are made considering the proximity to medium- or high-risk regions, as there is still a possibility of exposure to the epidemic. For instance, JPCM in its 2.2.As new tools for urban governance, the internet and information technologies have played a broad role in epidemic control during COVID-19. It can be summarized into the following four aspects.Epidemiological investigation. The tracing of cases and identification of contacts have transitioned from the traditional manual approach to locating them with cell phone signals. The holders of mobile phones that presented at the time and geographical range of risk exposure can be quickly reached by CDC personnel and their risk level can be categoried automatically. This greatly improves the efficiency of epidemiological investigations. Based on the traceability of mobile phones, China has employed QR code in epidemic management with authorization from the public. Each person is assigned a Health QR Code displayed through a mobile phone program. The code’s color  corresponds to the individual’s risk level, which is calculated based on their public visits. The code is used to access public venues. The form and functions of the codes vary across cities, determined by the municipal authorities. Some are integrated with additional features such as displaying virus test results and vaccination status, as well as binding with electronic transportation cards for automatic identification on public transport. In addition to the health QR codes for intra-city use, for cross-city travel, the China Academy of Information and Communication Technology in collaboration with China’s three major mobile communication operators, has established Digital Travel Records. These records identify and track the mobile phone users’ paths through base stations, making it easy for people to check whether they have recently passed through risk areas. This system helps filter people at risk and avoids misreporting of travel information. As of June 2022, digital trip records have served 1.6 billion cell phone users in China, processing over 55.6 billion inquiries.Medical treatment capacity. Internet-based remote diagnosis and treatment has become a powerful complement to on-site hospital services. Firstly, online platforms that support video consultations and surgical assistance break the previous geographical limitations of medical facilities, thereby expanding the emergency mobilization capability to deal with large-scale outbreaks  reached 421 million, equivalent to 30% of the national population  14 days of centralized quarantine, 7 days of centralized quarantine, 3 days of centralized quarantine, 14 days of home quarantine, 7 days of home quarantine, 3 days of home quarantine, 14 days of home monitoring, 3 days of home monitoring, and self-monitoring . Recognizing that centralized quarantine requires individuals to stay in designated places for isolation, which significantly impacts their daily lives, we further classified these measures into a dummy variable indicating centralized or non-centralized management, where The independent variable, cyber infrastructure, is represented by the city’s internet coverage following existing literature . It is cper capita as a proxy for the fiscal space for healthper capita, China Integrated City Index ranking), and degree of urbanization . Considering the possible demonstration effect of provincial capital cities, a dummy variable of provincial capital city is also added. Fiscal revenue per capita, population, population density and GDP per capita are in logarithmic form in regressions.Control variables include local healthcare resources and other city characteristics. Healthcare resources are represented by the number of hospital beds per thousand population and the number of doctors per thousand, which measure the city’s medical infrastructure from the perspectives of hardware and software, respectively. Moreover, as most of the epidemic-controlling activities are led by the local authorities and thus funded through local public finance, we also incorporate the city’s fiscal revenue 3.2.China City Statistical Yearbook. CICI ranking was sourced from Yunhe City Research Institute. In order to mitigate the endogeneity problem, we used data from the year prior to the outbreak of COVID-19, i.e., the year of 2019, as the epidemic itself may influence social and economic performance.Data on the precautionary policies of cities were collected from a manual search on the official websites of local authorities and from official media reports on the internet. City characteristics were obtained from the The original dataset contains all cities in the Yearbook. We exclude those of special status to form our regression sample. Cities excluded are: cities from ethnic autonomous regions, as well as Qinghai, Yunnan, and Guizhou provinces, which are classified as ethnic regions by the Ministry of Finance for public finance funding purposes; cities from provinces that are adjacent to other countries; province-level municipalities directly under the Central Government; and Hebei province, which was preparing to host the Winter Olympics. Additionally, eight cities that could not obtain the specific policies were also excluded, as well as six cities with missing variables in the Yearbook. Our final sample contains 188 cities.per capita, higher GDP per capita, a larger proportion of urban population and a higher CICI ranking.4.4.1.Since the dependent variable is in discrete form, we also change the LPM setting and use binary choice models for regression. Columns 4 and 5 of Among the control variables, it is noteworthy that the number of hospital beds and the number of doctors act in a different way. Although the number of doctors has significantly positive effect on cities’ choosing loose policies, the effect of hospital beds is negative. This suggests that increase in physical facilities alone does not directly lead to a boost in confidence in epidemic prevention and control; even in the case of medical resources, the confidence depends on the corresponding capability building. This supports the idea that software improvements on cities’ epidemic responding capability may play an important role in precautionary decision making.4.2.4.2.1.In our benchmark regression, cities’ management measures are grouped into centralized management and non-centralized management to construct the dependent variable Policy-making in cities can be influenced by the opinions of their provinces. Some provinces have even unified the policies over their cities, which makes the policy choice of these cities unable to reflect their own characteristics. We then control for provinces and categorize the policy leniency of cities by comparison within provinces. We exclude those provinces with completely uniform policies and take only those provinces with differed policies within its boundary, and divide the cities into strict group and loose group based on comparison with other cities within the province. The grouping method follows the rationale of the previous classifications. Specifically, if within the province cities take either centralized or non-centralized management measures for our targeted people flow, those cities with centralized management are grouped as being strict while those with non-centralized management being loose; if all the cities within the province take non-centralized measures, those with home management are sent to the strict group, while those with no measures to the loose group; if all the cities within the province take centralized measures, those with centralized management for 14 days are considered being strict, and those for 7 days and below are considered being loose. If the measures for our targeted people flow  are completely unified within the province, the policies for “other counties within the city where a county with medium-risk areas is located” is taken and used to divide cities following the above method. In this way we obtain binary variable 4.2.2.The support of cyber infrastructure for epidemic prevention and control is not solely generated by broadband internet; mobile network also makes its contribution. Therefore, we also use mobile phone coverage as an alternative measure of cyber infrastructure, and put it into the regression. The mobile phone coverage of the city is calculated as: mobile phone coverage = number of mobile phone subscribers at year-end /total population at year-end *100. Data is from the China City Statistical Yearbook. Column 4 of 4.3.Decisions on precautionary policies may not be entirely based on objective conditions, but also involve subjective factors of the authorities such as cautious or bold city governance styles , which mFirst, we use the instrumental variable method. The geographic distance from each city to Hangzhou was used as an instrument. Hangzhou is among the most developed cities in China in terms of digital economy. The health QR code widely used during COVID-19 period in China was originally invented by Hangzhou, and then promoted to its local province and the whole country. In Chinese literature, the geographic distance to Hangzhou is commonly used as an instrument to represent the level of digital economy development of the city . We use Second, we add on control variables that may contribute to the formation of subjective styles of cities. In the context of epidemic management, we use two variables indicating the city’s COVID-19 experience, namely the severity of the city’s current outbreak and the severity of its past outbreaks. The former is measured by a dummy variable which takes a value of 1 if the number of current existing cases is above zero and 0 otherwise. The latter is measured by a dummy indicating “whether the city’s cumulative number of cases is high “, which takes a value of 1 if the city’s cumulative number of cases exceeds the median and 0 otherwise. Case data are obtained from the Baidu website epidemic statistics column as of January 28, 2022. Columns 3 and 4 of 5.We examined the precautionary epidemic policies of 188 cities in China in the face of COVID-19 and found that cities with higher levels of internet coverage tended to develop looser policies when faced with unknown risk exposures. This result remains robust across different policy classifications. Our study shows that cyber infrastructure gives confidence in cities’ public health management, a role that may exceed that of the traditional medical infrastructure such as hospital beds. Therefore, to prepare for future pandemics, cities need to look beyond the healthcare system and prioritize the application of technology to establish comprehensive capabilities for epidemic response.Our study confirms that cyber infrastructure, as a new foundation for development, also plays a role in the health sector. As described in the background section, the internet-enabled techniques applied to epidemiological investigation, treatment capability expansion and dissemination of epidemic knowledge, feature in ultra-high analytical speed and efficiency, which enable them to achieve effective epidemic control at the early stage of an outbreak. This greatly transforms the epidemic management practices from the traditional manual approach. The effectiveness of this new model gives cities the confidence to respond to epidemics, which further translates into the confidence to adopt loose precautionary policies that entails less interference to social activities in the face of an epidemic threat. This correlation is underpinned by the fact that a larger network coverage corresponds to a greater abundance of technological management tools and a superior new management model. Consequently, the adoption of looser precautionary policies is not a subjective decision, but rather a consequence of objective, superior cyber-infrastructure conditions.Our paper also suggests that the smart city development can help empower cities to respond to public health emergencies. A limitation of our study is that we only focus on China. Different countries may have distinct social mores and cultures that can influence policy decisions, suggesting that the factors influencing policy-making may vary. Nevertheless, the management of epidemics in all countries should be grounded in certain objective conditions. Therefore, the findings of this paper hold instructive value even for countries with different socio-economic backgrounds. Specifically, we have focused on the role of networks as city infrastructure rather than the specific applications. Future research could further investigate the role of cyber-infrastructure in different countries and against different public health events.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.QG led the overall study. She designed the study, led the data collection and analysis, and wrote the manuscript. YH contributed to the data analysis and literature review, as well as assist in manuscript writing. ZL contributed to the study design, reviewed the manuscript and put inputs in the writing of the first manuscript. LY contributed to the data interpretation and manuscript edits. All authors contributed to the article and approved the submitted version.This paper was funded by Program for Young Innovative Research Team in China University of Political Science and Law.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Burgundy truffles are heterothallic ascomycetes that grow in symbiosis with trees. Despite their esteemed belowground fruitbodies, the species' complex lifecycle is still not fully understood. Here, we present the genetic patterns in three natural Burgundy truffle populations based on genotyped fruitbodies, ascospore extracts and ectomycorrhizal root tips using microsatellites and the mating‐type locus. Distinct genetic structures with high relatedness in close vicinity were found for females (forming the fruitbodies) and males (fertilizing partner as inferred from ascospore extracts), with high genotypic diversity and annual turnover of males, suggesting that ephemeral male mating partners are germinating ascospores from decaying fruitbodies. The presence of hermaphrodites and the interannual persistence of a few males suggest that persistent mycelia may sporadically also act as males. Only female or hermaphroditic individuals were detected on root tips. At one site, fruitbodies grew in a fairy ring formed by a large female individual that showed an outward growth rate of 30 cm per year, with the mycelium decaying within the ring and being fertilized by over 50 male individuals. While fairy ring structures have never been shown for truffles, the genetics of Burgundy truffle populations support a similar reproductive biology as those of other highly prized truffles. Tuber aestivum Vittad.) is an ascomycetous soil fungus that forms hypogeous fruitbodies, which are coveted by chefs around the world as a culinary delicacy. As most other economically important truffle species, such as the Italian white truffle (Tuber magnatum Picco), the bianchetto truffle (Tuber borchii Vittad.) and the Périgord black truffle (Tuber melanosporum Vittad.), they belong to the True truffles of the genus Tuber. This genus contains at least 180 species, most of which are naturally distributed in the northern hemisphere and all of which live in ectomycorrhizal (ECM) symbiosis with trees and shrubs exchanging nutrients and water for sugars  show the presence of both, many rather small genets often forming only one fruitbody and a high genet turnover, but also a small number of individuals that persisted over several years and formed genets over 100 m in size for both T. aestivum  the spatial distribution of mating types, (ii) the fine‐scale distribution, interannual persistence and genetic diversity of maternal and paternal individuals and (iii) the spatial correlation of T. aestivum soil mycelium amount with fruiting production. To assess this, fruitbodies were sampled in three natural T. aestivum populations located in northern Switzerland (WSL) and southern Germany (BB and Ue) over 4 , 3  and 2  years. In two of these sites, ECM root tips were sampled and genotyped, and in one site, soil samples were collected to quantify T. aestivum mycelium.The few previous studies addressing genetic structures of T. aestivum sites located in southern Germany  was calculated for each triplicate. The absolute amount of soil mycelium (μg of dried mycelium/g of dried soil) was calculated based on the values obtained by Gryndler et al. , applying the same bin set used by Molinier et al. , with thHe)] and assignment of MLGs based on the nSSR profiles and the mating‐type locus were assessed for haploid and diploid (zygotes) datasets with GenAlEx v.6.512b implemented in Excel 2016  was calculated using MLGsim with 1000 simulations  (uAc) of mating types was calculated on the complete and clone‐corrected datasets for all years combined and for the year 2019 separately in the BB site using GenClone v.2.0 /(Nb. of ramets − 1), ranging from 0 to 1, where 0 means that there is a single clone of the same MLG and 1 means that all samples belong to an individual MLG. We used GenClone v.2.0 to calculate (i) the adapted Simpson index for genotypic diversity (D*), also ranging from 0 to 1, where 1 represents maximal diversity, (ii) the Simpson evenness index (ED*), which ranges from 0 to 1, where 1 means that all MLGs show equal frequencies, and (iii) the spatial aggregation index (Ac) of maternal and paternal MLGs. The clonal‐specific range, defined as the farthest distance between two samples belonging to the same MLG, was measured in ArcGIS Pro v.2.4.0. For zygotes, that is fruiting bodies for which maternal and paternal genotypes were identified, inbreeding coefficients Fis (i.e. 1 − observed heterozygosity Ho/mean expected heterozygosity He) were calculated in GenAlEx v.6.512b using a diploid dataset, estimating the proportion of inbreeding under the assumption of random mating. Fis values are ranging from −1 to 1, with values close to zero representing random mating. Positive values suggest inbreeding, while negative values are an indicator of excess heterozygosity  described by Loiselle et al. , where b represents the slope of the regression of Fij values on the natural logarithm of spatial distance and F1 is the average Fij value of the first distance class.To investigate the distribution of MLGs in relation to geographic distance, we performed an IBD and a spatial autocorrelation analysis using the clone‐corrected datasets for both maternal and paternal genotypes from the two southern Germany populations. IBD analysis was conducted with the haploid data option using GenAlEx v.6.512b. Genetic and geographic distance matrices were generated and a Mantel test was performed with 999 permutations  was conducted on a subset of the clone‐corrected dataset containing maternal and paternal genotypes originating from southern Germany and on the complete clone‐corrected dataset including all fruitbody samples from all three sites using the K = 1–10, under the admixture model with correlated allele frequencies  values as visualized in Structure Harvester v.0.6.94  across the full datasets  of genetic groups in the BB site using the clone‐corrected dataset.Pairwise genetic differences among the three populations  and among the three genetic groups (sites Ue and BB) were calculated on the clone‐corrected datasets with the distance measure ΦTo visualize spatial patterns, ArcGIS Pro v.2.4.0 was used to describe the sampling site and to map distribution of MLGs.T. aestivum site WSL, 291 T. aestivum fruitbodies were harvested over 4 years . Their spatial locations revealed a fairy ring structure similar to those of some epigeously fruiting fungi [Figures T. aestivum ECM morphotypes [Figure In the natural  Figures  and 2. Fs Figure  and to qT. aestivum and 66 could be included in the genetic analyses as all markers were successfully genotyped.On three intensive truffle hunts with four trained truffle dogs, 74 (43 in 2013 and 31 in 2019) and 75  fruitbodies were harvested and georeferenced in the BB and Ue sites, respectively Figure . Of the MAT 1‐1. Mating type characterized on soil samples by PCR revealed that even in the soil mycelium, MAT 1‐1 was dominating over MAT 1‐2. Only in 22% of the MAT‐PCR positive samples, both MAT loci were detected by non‐quantitative PCR (qPCR) analyses  and all ECMs were genotyped as es Table . These lia Table . One disAc = 0.74 with p = 0.001 and Ac = 0.47 with p = 0.02, respectively) and for the complete , but not the clone‐corrected dataset  at the WSL, Ue and BB site, respectively (Table All nuclear simple sequence repeats (nSSR) markers were polymorphic across all sites, except for 15 Table . The numly Table .Psex value, all at WSL  and Ue  and one hermaphrodite out of the four MLGs  at the BB site were present over several years  were detected at WSL , 63 in the BB  and 53 in the Ue site  showing a new allele for the locus aest18, which so far was monomorphic for this site. All other nSSR markers of this new MLG showed the same allele as the dominant maternal individual.All ECMs of Psex value, meaning that MLG D cannot be considered as a true clone. Therefore, only maternal and hermaphroditic genotypes were present at ECM root tips.At the Ue site, comparing MLGs present in fruitbodies  and ECMs collected in 2016, 68 MLGs were exclusively detected in fruitbodies, 21 only in ECMs, and six in both  than for males  and higher aggregation of females  compared to males  calculated for zygotes, that is fruitbodies for which maternal and paternal alleles are available, was negative (Fis = −0.026), indicating random mating within this site.Genotypic diversity and evenness at the WSL site was rather low  and Ue . As at the WSL site, paternal diversity was higher than maternal diversity  than in the BB  site  and paternal  individuals in the Ue, but only for paternal ones in the BB site  was significant up to 6 m for maternal and paternal individuals in both BB and Ue sites  site, coinciding with the IBD analysis  and low estimates for paternal individuals (Sp = 0.05 for both BB and Ue sites), suggesting a strong genetic structure within the first 5 m for females, but not males  and Ue  but not for paternal ones . Soil mycelium of T. aestivum ranged from 0 μg dried mycelium per gram dried soil to a maximum of 592.7 μg g−1 soil  populations revealed a strong mating‐type aggregation with spatial structuring of maternal individuals carrying the opposite MAT locus , although the markers were established as highly polymorphic  spoe et al.  hypothesT. melanosporum, confirming that (i) T. aestivum is hermaphrodite with usually forced unisexual behaviour with paternal, but also many maternal genotypes being short‐lived and generally propagating over short distances by sexual spores (ii) a few, but often dominating maternal individuals spread by vegetative growth and produce most of the fruitbodies, in our case more than half of all truffles in each of the sites and (iii) hermaphroditism is possible but rare, in our case only 3%–4% of all characterized genotypes, as similarly found for the Périgord truffle  which would be required for a saprophytic lifestyle . At locations where the clusters spatially overlapped, we found three fruitbodies that arose by mating of individuals belonging to two different clusters. This indicates that no intrinsic genetic barrier exists and gene flow is possible, but rarely happens, most likely because the clusters are spatially separated. This was similarly concluded for T. melanosporum, where mating of unrelated genotypes was occasionally found in zygotes . Interestingly, as reported for other fairy‐ring forming fungi  might explain the absence of fruitbodies. Plantations of Burgundy truffle‐inoculated host trees require 5–10 years to produce fruitbodies (Stobbe et al., The strong correlation between the amounts of soil mycelium, the presence of ECM root tips and the production of fruitbodies has been observed previously (Chen et al., Our study revealed that the Burgundy truffle has a similar life cycle to its better‐studied cousin, the Périgord truffle. In both species, generally few, long‐lived maternal mycelia produce most of the fruitbodies after mating with short‐lived paternal individuals that grow out from closely related ascospores. These findings provide a scientific foundation for the common practice in truffle farming of distributing spores under inoculated trees to increase productivity by providing paternal partners. Although rare, the ecological niche of perennial paternal individuals remains open and requires further research with intensive ECM sampling and monitoring of both the spatial distribution and timing of the presence of mating types in soils of productive sites. Furthermore, the presumed life cycle leading to severe inbreeding and restricted gene flow seen in natural Burgundy and Périgord truffle populations raises concerns about the adaptability of these economically and ecologically important ECM species to the rapid climate change we are already facing.The authors declare that they have no conflict of interest.APPENDIX S1 Supporting InformationClick here for additional data file.APPENDIX S2 Supplementary TablesClick here for additional data file.


Panax notoginseng production in China. However, the genetic diversity of its etiological agent Mycocentrospora acerina has yet to be studied. In this work, firstly, we developed 32 M. acerina polymorphic microsatellite markers using MISA and CERVUS 3.0 and selected 14 for further analysis. Then, we studied the genetic diversity of 187 isolates collected from P. notoginseng round spot using simple sequence repeat markers and polyacrylamide gel electrophoresis. The genetic diversity ranged from 0.813 to 0.946, with 264 alleles detected at the 14 microsatellite loci. The expected average heterozygosity was 0.897.Round spot is a destructive disease that limits of  Panax notoginseng (Burk.) F. H. Chen], a member of the Araliaceae family, is widely distributed throughout the Yunnan Province, China. This species is a valuable traditional Chinese medicinal herb with multiple pharmacological applications  > 0.5) and were used as SSR markers. According to the SSR primer data , the aveM. acerina using 14 SSR primers. The PIC ranged between 0.813 (MP56) and 0.946 (MP114), with an average PIC value of 0.885, all highly polymorphic  and Shannon’s information index (0.1712) were higher in the Honghe population (HH), followed by those in the Puer population (LC) (Nei’s genetic diversity = 0.0893 and Shannon‘s information index = 0.1676). They were the lowest in Lijiang (Nei’s genetic diversity = 0.0842 and Shannon‘s information index = 0.143) .The observed allele number (Na) averaged 2.00, while the effective allele number (Ne) averaged 1.11; Nei’s gene diversity (h) was calculated to be 0.0908, and the average value of the Shannon diversity index was found to be 0.1761. The total genetic diversity (Ht) was estimated at 0.0909, with an intra-population genetic diversity (Hs) of 0.0884 and a genetic differentiation index (Gst) of 0.0277, indicating low inter-population genetic variation at only 2.77%. Notably, the estimated level of gene flow (Nm) stood at a substantial value of 17.5757, suggesting that gene flow has significantly contributed to the observed changes in genes across different regions; however, it is not considered the primary factor influencing the population’s genetic diversity. Among the six populations, Kunming and Honghe exhibited the highest genetic similarity (0.9988) and the smallest genetic distance (0.0012). Furthermore, Lijiang and Lancang displayed the lowest genetic similarity (0.9931) and the most significant genetic distance (0.007) . OverallCluster analysis revealed that the similarity coefficient ranged between 0.83 and 0.97 .M. acerina focused mainly on host diversity and transmission methods [M. acerina. Sébastien Louarn studied the influence of M. acerina on the polyacetylenes and 6-methoxymellein in organic and conventionally cultivated carrots (Daucus carota) during storage [M. acerina, and much research has been done on the trait of spread in the field. It was found that M. acerina is a low-temperature-loving fungus. Its conidia will lyse when the temperature exceeds 32 °C. Its optimum growth temperature is 14–22 °C. The latest measured length of M. acerina is (137.36~486.24 μm) × (4.35~16.46 μm) (n = 100), and single conidia can cause infection . Using the technology of facility cultivation to build a rain-proof film in the rainy season can prevent P. notoginseng round spots and reduce the use of chemical pesticides (M. acerina and ease the burden of prevention and treatment.In the 20th century, many studies on  methods ,17. In c storage . Our labnfection . M. acersticides . This reM. acerina genome than in some eukaryotes [M. acerina could enhance our understanding of its genome structure, especially the composition of non-coding regions, its mechanisms of pathogenicity, and its heredity at the genome level. Among all SSR types, A and T are abundant, which is consistent with the SSR loci results in most eukaryotic genomes, probably due to the transformation of methylated C residues into T residues [M. acerina, suggesting this fungus had a relatively high mutation frequency or a relatively short evolutionary time [A total of 8250 repeats were obtained from the screened SSR sequences, indicating that the number of SSRs was higher in the karyotes ,47. The residues . Accordiresidues , many shresidues . Many shFusarium graminearum genome, SSR sequences obtained accounted for 0.27% of the whole-genome sequences [Sphacelotheca reilianm genome [With advancements in genome sequencing technologies, molecular marker studies have become more cost-effective . Based oequences  and 0.21m genome . In the m genome ,55.Dactylis glomerata L. [Magnaporthe oryzae [Panonychus citri [M. acerina populations. The PIC of screened primers was higher in these loci. However, after population analysis, the genetic diversity of M. acerina does not reflect geographic specificity. The potential reason is that the SSR primer loci are within the coding regions of the genome, which have high degrees of conservation [In the current study, the PIC of polymorphic loci ranged from 0.53 to 0.8, which was high compared to the PIC in other eukaryotes. For example, PIC ranged from 0.3 to 0.4 in erata L. , from 0 e oryzae , and frous citri . Accordiervation . JudicioPyricularia oryzae Cav. and Puccina striiformis f. sp. tritici in Yunnan Province have been reported to be high [M. acerina in the study could be due to its background as a quarantine pest in China [M. acerina strains with a moderate population. The mode of reproduction of M. acerina in the field is asexual reproduction [M. acerina population. These factors partly explain that the isolates from the same region cannot be clustered into a group, and some isolates from different regions have very high genetic similarity coefficients. Another possible explanation is that the host P. notoginseng is only grown in Yunnan and Guangxi, China, and originates from Wenshan in Yunnan. The rest of the sampling points in the paper have been from P. notoginseng planted in the past 5–6 years. Current research shows that, due to the low genetic diversity of sample populations, we can effectively prevent diseases in this area through timely cleaning of diseased leaves, rain-proof cultivation, and alternate use of chemicals [The genetic diversities of  be high ,61. Therin China  or its sin China ,63,64,65oduction , which, oduction . In the hemicals .M. acerina with a good polymorphism that can be used in diversity analysis and identification of M. acerina and its closely related species. This result proved that the genetic diversity level of M. acerina was relatively low.In this study, we developed 14 SSR primers of