In the era of Precision Medicine the approach to disease diagnosis, treatment, and prevention is being transformed across medical specialties, including Cardiology, and increasingly involves genomics approaches. The American Heart Association endorses genetic counseling as an essential component in the successful delivery of cardiovascular genetics care. However, with the dramatic increase in the number of available cardiogenetic tests, the demand, and the test result complexity, there is a need not only for a greater number of genetic counselors but more importantly, for highly specialized cardiovascular genetic counselors. Consequently, there is a pressing need for advanced cardiovascular genetic counseling training, along with innovative online services, telemedicine, and patient-facing digital tools, as the most effective way forward. The speed of implementation of these reforms will be of essence in the translation of scientific advancements into measurable benefits for patients with heritable cardiovascular disease and their families. It fBK1116/) . Until r2.TMEM43 p. S358l in Newfoundlanders; MYBPC3 c.927-2A > G in Icelanders)  .Furthermore, at the pre-testing stage cardiovascular genetic counselors provide psychosocial support, discuss genetic testing procedures as appropriate, and obtain informed consent . This laPost-testing, genetic counselors are involved in result interpretation and result disclosure, as well as patient and family support. Once a molecular diagnosis is made in the proband, the genetic counselor proceeds to support cascade genetic testing for relatives. Cascade testing aims to identify those at increased risk who might benefit from surveillance while discharging those who do not. Pre-test counseling for at-risk individuals should include specific discussion items. The collective experience of this author group has been distilled into a suggested genetic counseling agenda as outlined in Genetic counseling aims to facilitate family communication about the option of pursuing testing which can help predict risk of heart disease . The comWhen testing at-risk family members for a known disease-causing variant, a negative result can be interpreted with certainty, however, awareness about the value of genetic testing for unaffected at-risk relatives has created new challenges for the genetic counselor. A common presenting challenge is that of an unaffected patient who knows of an affected relative who is otherwise not available for genetic testing. In an individual without evidence of cardiovascular disease but with a family history of genetic heart disease, genetic testing may be an option to consider. However, it's important to interpret the results with care because a negative test result does not necessarily rule out the risk of developing the disease. This scenario requires a careful approach for results interpretation and a dedicated discussion to communicate that a molecular diagnosis can guide cardiac surveillance. While this screening approach is not incorrect, this assessment assumes that the affected relative unavailable for testing carried the same variant. The risk, of course, is that the affected relative had a different diagnosis for which the unaffected patient was not tested.Similarly, a negative result in this setting is uninformative because the detection rate of diagnostic genetic testing for cardiovascular disease is not 100%. Therefore, it is impossible to determine if the patient tested negative because they did not inherit a variant in a testable gene that would have been detected in the proband, or if the disease-causing variant in the family has not been discovered. In the latter scenario, testing on any family member, affected or not, would be negative.3.The advent of next generation sequencing (NGS) enabled not only an unprecedented high speed of investigation and discovery of new CVD-related genetic variants, but also the development of new, significantly more advanced genetic testing tools . These iThe introduction of NGS panels radically and permanently changed the complexity and direction of cardiogenetics. With the ability to test for a larger number of genes, novel insights into the genetic architecture of cardiovascular disease, such as the presence of multiple variants in a minority of probands, emerged –19. AcknDSC2, PKP2, and TMEM43 have a definitive association with arrhythmogenic cardiomyopathy of autosomal dominant inheritance, while variants in the genes ACTC1, MYBPC3, and TPM1 have a definitive association with hypertrophic cardiomyopathy  . Similarly, several definitive gene-disease associations have been established for dilated cardiomyopathy , long QT syndrome , and other CVD . However, different variants in a gene can have very different effects at the molecular and clinical level, while in many instances variable expressivity and reduced penetrance for a given gene variant is observed. For example, over 170 variants have been reported in MYH7, with 29 being classified as pathogenic. Some of those have been associated with hypertrophic, dilated or arrhythmogenic cardiomyopathy, and in some cases incomplete penetrance has been reported  . In other instances, such as in the case of PLN R14del, incomplete penetrance and a highly variable clinical phenotype ranging from normal to heart failure have been reported, with extensive ongoing research work aiming at deciphering pathogenesis . Through the close collaboration of experts from a broad range of different fields, including genetics, medical, informatics, academia, and industry, valuable tools for determining gene-disease validity, dosage sensitivity, variant pathogenicity and clinical actionability are being determined. The continuous enrichment of ClinGen and possibly other large-scale international clinical genetic resources, offers the means for fast, easy and centralized access to the latest scientific evidence that has been curated by expert panels, with consistent widely agreed criteria  was established by the US National Institute of Health (NIH), primarily funded by the National Human Genome Research Institute (NHGRI), to aggregate relevant data about genes, genetic conditions, and the genetic variants that cause them, and curate them towards the establishment of a public resource of clinically relevant genes and variants to support both the clinical and research settings , specific clinical areas , or specific phenotypes . Genetic testing targeting the appropriate set of genes ensures that results have the highest possible degree of informativeness while decreasing the number of variants of uncertain significance (VUS) or other irrelevant results. In its review of cardiogenetics guidelines and statements, the American Heart Association (AHA) endorsed the genes that should be considered during a genetic evaluation of patients presenting with specific phenotypes . In addiThe results of these tests and their interpretation are significantly more complex than those of previous genetic test generations. Although some genetic variants have been directly linked to specific CVD, many others may have partial, indirect, disease modifying, or unknown roles. In each of these cases a personalized genetic counseling approach that includes careful consideration of the clinical findings and the family history is required , 32. AnoFor the VUS in specific, extra caution in the communication of the results is warranted, and a long-term follow-up strategy needs to be implemented, especially in pediatric populations , 35. VagBeyond diagnostic and cascade testing, an increasing number of healthy individuals seek genetic testing out of concern for a known risk in the family. Meanwhile, others may be unaware of a genotype that puts them at risk for heart issues that may be unmasked upon exposure to stressors, such as pregnancy or athletic training. These scenarios provide a rationale for the idea of population genetic screening for cardiac disease, which has been discussed for some time now . New iniRecent evidence supports the utility of wellness cardiac testing. For example, in a study of 10,478 individuals who underwent genetic screening, 608 (5.8%) had variants identified in cardiac genes, including those associated with arrhythmia, aortopathy, cardiomyopathy, and FH . This co4.The significant technological advances have enabled faster and expanded analysis, at a fraction of the original cost, along with improved data analysis and interpretation abilities –45. TogeDriven by phenotype and family history, single gene testing, panel testing, WES or WGS are increasingly being ordered clinically as genetic testing options continue to expand. In 2020 there were 166,703 genetic tests available for ordering . GenomicMeanwhile, a growing number of individuals worldwide receive genetic test results outside of the purview of their healthcare team or research . An arti5.Anecdotally, leaders in cardiogenetics describe the latest developments and the future ahead as exciting, while mainstream providers still grapple with the ever-changing landscape of this field. Illustrating this point, a recent survey of cardiologists and electrophysiologists showed that cardiogenetics providers are not confident about ordering genetic testing or leading appropriate follow-up, expressing the need for more education in cardiogenetics . PreviouCardiogenetic conditions have reduced penetrance, variable expressivity and marked allelic and locus heterogeneity. These factors complicate variant interpretation because of the challenges in identifying proper case, control and family data to feed lines of molecular evidence. Because of this, interpretation discrepancies occur . Most coThe AHA has therefore taken initiative to combat the lack of education, which has kept cardiogenetic testing underutilized , 62. TheFor patients, the thought or pursuit of genetic testing can trigger a broad range of emotions including anxiety, fear, privacy concerns, shame, and confusion, while the risk of misunderstanding the results and making inappropriate medical or daily life decisions is high in the absence of specialist healthcare provider guidance , 65.In light of these challenges, the AHA endorses genetic counseling as an essential component in the successful delivery of cardiovascular genetics care , 64, and6.Along with the dramatic increase in the number of available cardiogenetic tests, the increasing test result complexity, and demand, there is a need not only for a greater number of genetic counselors but importantly, for highly specialized cardiovascular genetic counselors that will be continuously updated on the developments of the field .N = 95) of all respondents  selected cardiology as their primary area of practice. A formal workforce study conducted by the 2015 Genetic Counselor Workforce Working Group indicated a shortage of clinical GCs in North America  and concluded that supply may not reach equilibrium until 2030  Professional Status Survey ascertained the percentage of genetic counselors who specialize in various areas of practice in the United States . Only 3%til 2030 . Arguabltil 2030 . Though til 2030 . A similtil 2030  showing 6.1.While an evaluation of how genetic counseling training programs are addressing the challenges outlined is out of scope for this review, it is noteworthy that many programs are taking measures to address these issues. It is essential to recognize that challenges surrounding cardiovascular genetic counseling are applicable to other subspecialties. Training programs are on the right track to overcome these crucial challenges. Meanwhile, despite AHA recommendations to incorporate genetic counseling and genetic counselors into cardiology practice, cardiogenetic testing remains underutilized. Service delivery models integrating cardiologists and genetic counselors have been successfully implemented, with collaboration between genetic counselors and general cardiologists most often reported . Still, Increase in the capacity of ongoing training programs, establishment of new ones, and creating flexible opportunities for certification (including an on-demand option) are some of the steps that could help combat the genetic counselor shortage and address the evolving clinical needs of CVD patients in a timely manner. At the writing of this paper, there were 54 accredited genetic counseling training programs and six emerging programs in various application stages in the U.S. , speakinThe harmonization across established programs, as well as new genetic counseling training curricula and certification processes internationally is important for the high-level clinical care of CVD patients. Parameters adding to this necessity include the increasingly available options to patients for cross-border genetic testing, genetic testing at different laboratories or different times, communication with multiple genetic counselors at the same or different locations over time, as well as genetic counselor mobility and online genetic counseling services.In some countries, addressing the national genetic counseling needs may require establishing genetic counseling as a new allied health profession, along with genetic counseling specialty programs and formal certification procedures. These efforts can capitalize on the existing, strong foundations set by well-established curricula and experienced genetic counselors, while learning from reported shortcomings or challenges historically faced. However, the steps towards this direction can be strikingly slow and challenging in certain countries/cultures where long-standing practices can hold back the advancement of clinical practice. International genetic counseling awareness campaigns targeting health authorities, healthcare professionals, patient groups and lay public in those countries, along with closer collaboration of internationally certified genetic counselors with local healthcare professionals and patient groups could be instrumental towards this end.6.2.Another strategy to meet patient needs involves harnessing service delivery models beyond the traditional, scheduled in-person care. Many medical institutions and insurance companies in the US offer online, immediate access to providers for both urgent and non-urgent indications. This trend for rapid or on-demand service is starting to permeate genetics care delivery.Telehealth is another example of a service delivery model that has the potential to improve access to genetic counselors. For several years companies in the US have helped fill a gap in the services within certain institutions and geographic regions by offering a telehealth genetic counseling model. Further, the COVID-19 pandemic increased access to telehealth services, which in turn increased the amount of patients providers could care for , withoutp < 0.001). The authors reported that the FST promoted more FH cascade testing than what has been reported in the literature (59% vs. 23%), demonstrating that electronic cascade testing tools can complement traditional approaches used by genetic counselors to assist with family communication , design and coordination of cascade testing, follow-up of patients with VUS, along with patient support at multiple levels, are only some of the skills needed in the Cardiology clinic of the Precision Medicine era. Specialized cardiovascular genetic counseling programs, expanded continuing education opportunities, revised training and certification procedures, curricula harmonization, along with innovative online services, telemedicine, and patient-facing digital tools appear to be the most effective way forward. The speed of implementation of these reforms will be of essence in the translation of scientific advancements into measurable benefits for CVD patients.


US) in origin. The US breeds were composite breeds developed between the 1849 and 1935. Animals (n = 24–31 per breed) were sampled for DNA analysis from 2 or 3 hatcheries per breed and a total of 8 hatcheries. The hatcheries were assumed to maintain and breed their own populations of the studied breeds. Effective population sizes ranged from 47 to 145 and used to estimate probabilities of extinction for a 50-generation timeline. It was determined that Crevecoeur and Aseel had a probability of extinction that exceeded 40%, the remaining 8 breeds had probabilities of <28%. ADMIXTURE analysis indicated the minimal CV corresponded to 9 populations. In that analysis New Hampshire and Rhode Island Red were classified as the same population, which was not unusual given that New Hampshire was developed as a subpopulation of Rhode Island Red. Crevecoeur and Buttercup were the 2 most genetically divergent breeds based on pairwise Fst among the breeds and principal component analysis, which was supported by the ADMIXTURE results. Inbreeding coefficients computed from genomic information was lowest for Crevecoeur, Rhode Island Red, Buttercup, and Andalusian (0.8–2.6%), while New Hampshire, Buckeye, and Aseel were highest (12.8–14.3%). Within breed Fst among hatcheries supplying animals for sampling generally indicated a genetic structure was present on a breed-by-breed basis. Genetic relationships within hatchery were also computed for each breed. Several of the hatcheries had sent samples that suggested genetic relationships as high as half-sibs while several others had genetic relationships closer to first cousins. We conclude that the chicken breeds evaluated have substantial genetic variability within the in situ populations and the gene bank has captured this diversity for future use.Genetic differences among heritage or fancier breeds of chickens have not been quantified in the United States. Gene banks collecting germplasm for conserving these breeds need this information as do breeders and companies raising them. Our goal was to evaluate genetic diversity of 10 heritage/fancier chicken breeds that are a component of the national collection and to use this information to establish a baseline of their genetic diversity and future conservation efforts. Breeds could be broadly classified as European, Asian, Mediterranean, and United States ( In this study, we use the US collection to quantify the genetic variation that has been acquired in the collection and genetic differences among noncommercial chicken breeds which have not been addressed previously.The importance of genetic resources for industry and scientific use has been discussed as a component of USDA's 2018 to 2027 genome to phenome blueprint . Since 2HF) chicken breeds have not been widely performed. The breeds studied were either imported or developed as new composite breeds after entering the United States. Those breeds representing the original imported breed were derived from countries in Europe or Asia. Domestication, historic routes of dispersal, and early breed formation have been discussed , and the smaller HF breed sector that does not employ high levels of selection nor industrial concentration. Reduced numbers of breeders for HF breeds have created a marketing niche for commercial hatcheries that maintain multiple lines or breeds and then sell eggs or day-old chicks regionally and/or nationally. The populations maintained by such hatcheries may represent lines bred by the hatchery over time and/or the sourcing of breeding stock from other breeders and then multiplying those genetics. It is not known if multiple hatcheries source breeding stock from the same source.Given the importance of this hatchery system to the maintenance and distribution of HF breeds it is of interest to quantify the genetic diversity they maintain and offer for sale. Therefore, in the present study we report an evaluation of 10 HF chicken breeds all from nonindustrial populations but sourced from 8 commercial hatcheries and 1 university. The goal is to better understand the genetic diversity of the breeds evaluated, differences among hatchery populations, and assess the use of the information in conservation activities.https://www.fao.org/dad-is/data/en/) are provided in RIR) is thought to have been formed by using Malay (South Asia), Brown Leghorn , and potentially Java (Java) and Shanghai (China). While Buckeye is a 3-way cross that used Black Breasted Red Game X (Buff Cochin X Barred Plymouth Rock). Breeds, country of origin, number of hatcheries sampled, number of animals sampled, and conservation classification (NAGP) as part of its breed conservation efforts. No animals were sampled specifically for this study. Eight different hatcheries and 1 university were used as sources of samples for this evaluation. Each breed is represented by sourcing samples from 2 to 3 hatcheries. Each hatchery provided samples from 1 to 5 breeds. Typically, no one hatchery maintains and sells all chicken breeds. Hatchery geographic distribution extended from the Midwest to California and from Wisconsin to the southern USA. The Wisconsin source was the University of Wisconsin that only supplied New Hampshire samples. This source and population were selected because they were highly inbred and therefore could serve as an additional benchmark to compare other lines and breeds.DNA for this study was extracted from cryopreserved tissues that were collected by the National Animal Germplasm Program (MAF) <0.05, sample call rate <0.95, and Hardy-Weinberg equilibrium (HWE) <0.001. Additionally, the sex chromosomes and ambiguous marker information were removed from the dataset. After filtering 76,760 SNP were used in the analysis. Admixture 1.3 (LD) therefore in running ADMIXTURE no additional LD filtering was performed. In preliminary ADMIXTURE runs, it became apparent that 14 chickens  were crossbred and were removed from the dataset. This preliminary analysis also showed 2 Buckeyes where Phoenix or Rhode Island Red they were therefore reassigned to Phoenix and Rhode Island Red. The software used cross-validation (CV) error for values of K from 1 through 10 with 10 repetitions for each value of K being run. The lowest CV error represented the appropriate number of subpopulations for evaluation. StructurePlot  genomic relationship matrix, Fst, and baseline genetic parameters of observed and expected homozygosity, minor allele frequency, were performed using SNP & Variation Suite . Additional pruning of the data was performed to adjust for linkage disequilibrium using a window size, step size, and threshold of 50, 5, 0.2, respectively, which left 75,558 SNP in the analyses. Additionally, inbreeding coefficients were calculated for the breeds and hatcheries within breed.Among and within breeds and hatcheries, principal component analysis  after 50 generations could be calculated as described by Effective population size and its trend over generations were computed with SNeP software as described by in situ management of breeds. Of course, with samples secured in the gene bank extinction of a breed has been rendered something of an obsolete concept.This equation provides insight into the potential extinction based only upon genetic factors  and not social or market perspectives. Therefore, it provides information that is useful in current GRM) available in the Golden Helix SVS software package.Within breed analysis of inbreeding and genetic relationships (by hatchery) were computed using the genomic relationship matrix  on K for evaluation . The closest pairings (Fst < 0.10) suggesting a weaker genetic structure between New Hampshire and Rhode Island Red and Plymouth Rock.Structure among breeds was tested by computing pairwise Fst . Most paNe) was computed for each breed . The remaining breeds’ Ne ranged from 79 to 110 animals. While Aseel, Crevecoeur, and Buttercup had the lowest Ne their curves had the slowest rate of decay . The remaining breeds showed varying levels of subpopulation differentiation (0.01–0.27). Highest Fst (0.26 and 0.27) were associated with the university hatchery that supplied New Hampshire.Genetic diversity within breeds was evaluated using  to 0.26 . No popuGRM; Golden Helix,) then averaged by inbred animals measured within breed, and all animals within a breed. Andalusian, Buttercup, Crevecoeur, and Rhode Island Red had inbreeding levels of less than 3%  was used to differentiate population structure of the breeds evaluated and ranged from 0.06 to 0.30, similar ranges have been reported for British, Rwandan, and French chicken breeds  which could explain the reported differences. The levels of Fst for US developed composite breeds, Rhode Island Red, Plymouth Rock, New Hampshire, Jersey Giant, and Buckeye, suggest a level of distinctness and structure has been obtained since breed formation. Fixation index  their genetic diversity is larger than might be expected. The large Ne for Rhode Island Red, New Hampshire, and Plymouth Rock suggest substantial levels of genetic diversity exists among these breeds thereby warranting the “not at risk” rating  to be more intensively managed from a genetic conservation perspective. Despite Ne > 50 for Jersey Giant, Phoenix, Buckeye, and Andalusian their probabilities of extinction after 50 generations ranged from 0.22 to 0.27 suggesting they may need increased efforts to maintain genetic diversity. However, given the general structure of heritage/fancier breeding programs concerted efforts may not be possible. Given this situation having adequate tissues collected in the gene bank will help maintain a broader range of diversity for use if the population continues to contract or the need arises to reconstitute any of these breeds.Generally, within breed effective population size suggests sufficient genetic diversity exists among these breeds. Only Aseel and Crevecoeur fell below the ” rating . The remh breeds . Among bdiffered . Aseel, ntain Ne . Using NFst) was used to compare subpopulations within sampled breeds. Generally, some substructure existed within most breeds as demonstrated by Fixation index  research population and Aseel from hatchery 5 . The hatFst analysis. While some inbreeding levels are relatively high, they most likely are not at a level that will depress performance on a breed wide scale. Furthermore, inbreeding levels are such that they can be reversed or slowed as demonstrated with Hereford cattle , while the status of Crevecoeur in France and globally is unknown . We found Buttercup, Phoenix, and Crevecoeur in the United States genetically distinct from the other breeds evaluated. These breeds were imported at or over a century ago, as a result they may provide untapped potential for reintroducing genetic variability into their respective countries of origin, as suggested with goats (Buttercup and Phoenix are reported to be “at risk” in Italy and Germany, respectively, and globally (FAO, th goats . That saA basic premise of our study was that relatively small commercial hatcheries would be maintaining or sourcing breeding chickens from different elite breeders, and these subpopulations facilitate maintenance of within breed genetic diversity. While the results generally support this assumption it must be recognized that the hatcheries, and breeds sampled from them, are not a full sampling of the breeds studied. We further assume there are more purebred flocks within the breeds studied and therefore genetic diversity could be greater than demonstrated in the present study. That said, this report provides a baseline for future monitoring of the breeds evaluated.The samples used in this study were all sourced from the national gene bank  which cu


Leonurus japonicus, a significant medicinal plant known for its therapeutic effects on gynecological and cardiovascular diseases, has genetic diversity that forms the basis for germplasm preservation and utilization in medicine. Despite its economic value, limited research has focused on its genetic diversity and divergence.petN-psbM and rpl32-trnL(UAG) spacers, which can be used for genotype discrimination. These accessions divided into four clades with significant divergence. The four subclades, which split at approximately 7.36 Ma, were likely influenced by the Hengduan Mountains uplift and global temperature drop. The initial divergence gave rise to Clade D, with a crown age estimated at 4.27 Ma, followed by Clade C, with a crown age estimated at 3.39 Ma. The four clades were not showed a clear spatial distribution. Suitable climatic conditions for the species were identified, including warmest quarter precipitation 433.20 mm ~ 1,524.07 mm, driest month precipitation > 12.06 mm, and coldest month min temp > -4.34 °C. The high suitability distribution showed contraction in LIG to LGM, followed by expansion from LGM to present. The Hengduan Mountains acted as a glacial refuge for the species during climate changes.The avg. nucleotide diversity of 59 accessions from China were 0.00029 and hotspot regions in L. japonicus and the identified hotspot regions could facilitate the genotype discrimination. The divergence time estimation and suitable area simulation revealed evolution dynamics of this species and may propose conservation suggestions and exploitation approaches in the future.Our findings reflected a clear phylogenetic relationships and divergence within species The online version contains supplementary material available at 10.1186/s12870-023-04284-x. Leonurus japonicus Houtt. is an annual or biennial herbaceous flowering plant that belongs to the Lamiaceae family. This species was first described in “Shennong Bencao jing,” a famous herbal work from the Han dynasty of China, and has been utilized in traditional Chinese medicine for over two thousand years. The center of distribution for L. japonicus is located in China, where it is commonly referred to as “Yi Mu Cao,” which translates to “beneficial to mothers.” This literal meaning implies its function: it has therapeutic effects in gynecological and cardiovascular disease [L. japonicus in traditional Chinese medicine, much of the existing research on this species has focused on its chemical constituents, pharmacological activity, and clinical applications, with comparatively little attention paid to the genetic diversity of its germplasm resources [ disease . Despiteesources –5.L. japonicus may face the same situation; therefore, assessing its genetic diversity and population divergence are the first steps to determining the extent of its germplasm degradation. These steps are also prerequisites to deciding how to use, as well as how to conserve, the germplasm resources in order to stop or slow down the process of germplasm degradation. Recently, most germplasm protection or utilization measures have mainly been intuitively based on morphological or chemical traits, moving forward with little information on the genetic diversity and population divergence. However, this situation needs to be rectified.Genetic diversity is considered to be the amount of species’ genetic variability among individuals or populations . MultidiL. japonicus, it is essential to integrate genetics and climate data.Understanding the habitat and climate dynamics of species is also indispensable, since the genetic diversity of species is significantly shaped by geographic changes and climate oscillations . DispersOwing to the development of next-generation sequencing technologies, the high L. japonicus sampling work, newly sequencing and assembling plastomes of 59 L. japonicus accessions from almost all its habitats in China. Combined with the geographical and climatic data from the Last Interglacial (LIG) to the present in China, we aimed to (1) evaluate the genetic diversity and population divergence of L. japonicus, (2) elucidate its evolutionary history and (3) simulate its suitable distribution dynamics from past to present. We are attempting to propose long-term conservation strategies and more reasonable exploitation approaches for this historic medicinal plant by merging the results of these aspects.In this study, we conducted nationwide L. japonicus accessions were obtained using a genome skimming approach and deposited in GenBank with the accession numbers listed in Table L. japonicus accessions. The plastomes possessed 113 unique genes, including 80 protein-coding genes, 29 transfer RNA genes, and four ribosomal RNA genes.Plastomes of 59 L. cardiaca accessions as outgroups. The topologies and cluster of the accessions calculated by the two algorithms were almost consistent  revealed similar conclusions, indicating that most of the genetic diversity existed among subclades  , 1025 bp in size  obtained from the 59 accessions. This analysis employed a combination of STRUCTURE analysis, principal component analysis (PCA), and network analysis. The results of the PCA revealed a marked separation of accessions, which clustered into four distinct subclades  from high to low were 49.7 × 104, 122.3 × 104, 149.5 × 104, and 125.7 × 104 km2, respectively  was found to be similar to that of its close relative, L. cardiaca, with an average Pi of 0.00042. Interestingly, a relatively low level of genetic diversity seems common among medicinal plants with a long history of cultivation and use, such as Coptis chinensis, Ziziphus jujuba, Angelica sinensis, and Panax ginseng [L. japonicus. The genetic structure of this species provides ideas for measures to be taken; the phylogeny and population structure results in Figs. L. japonicus in China can be divided into four clades with significant divergence between each other and rare gene flow, and we can treat these clades as four genotypes. Compared to other clades, accessions within Clade D diverged earlier and had a longer evolutionary time, which may account for the highest genetic diversity of Clade D and the second highest genetic diversity of Clade C.In this study, the genetic diversity of  ginseng , 27–29. L. japonicus were initially employed by the inhabitants. If it remained in this stage, it seemed likely that there would be some correlation between genetic clustering and geographic distribution. However, with the widespread commercial production of medicinal plants, a considerable number of seedlings have been frequently exchanged between different regions. Subsequently, these seeds have spread beyond the plantation and escape into the wild. Notably, these escaped accessions exhibit a greater genetic affinity with their origins. While it appears that geographically these accessions disrupt the existing differentiation pattern among the original populations, it is challenging to trace their initial origin given their recent occurrence. Overall, the findings of this study firmly establish that despite the frequent trading of seedlings, L. japonicus in China originates from four fundamental sources.Interestingly, the genetic clusters were not correlated with their geographical distributions. As a medicinal plant, local resources of L. japonicus and its related species. The results showed that the accessions of L. japonicus form a monophyletic group and, within the species, divergence events took place between 7.36 Ma during the late Miocene period, leading to the formation of four subclades. This period of diversification coincided with the rapid uplift of the Hengduan Mountains after the Miocene and reached its peak before the Late Pliocene [L. japonicus was most likely distributed in close proximity to and south of the Tropic of Cancer, and in the Hengduan Mountains. The refuge effect was strengthened in the Hengduan Mountains as the temperature dropped, leading to an expanded distribution range in the LGM. As the climate stabilized, the species spread to other regions in China. Overall, the results of the phylogeny and suitable distribution dynamics of L. japonicus highlight the complex and dynamic history of this species and the importance of continued study to gain a deeper understanding of its evolutionary history and genetic structure.In this study, we conducted a comprehensive and meticulously resolved phylogenetic analysis of Pliocene . In factPliocene . ContinePliocene , 33. In Pliocene . This isL. japonicus is heavily reliant on a better understanding of its genetic diversity. This understanding is critical for making informed decisions about what to conserve and how to use the species [L. japonicus in China faces two problems: genetic bottlenecks and germplasm mixing. Clarifying the genetic background (genotypes) is a prerequisite for solving these two problems. Although the cost of plastome acquisition has largely decreased with the development of next-generation sequencing, it is still cost-prohibitive work if applied to large sample sizes. The hotspot regions (two spacer regions: petN–psbM and rpl32–trnL) identified by sliding window analysis in L. japonicus plastomes will help us clarify different genotypes in a more cost-effective way, and could be accessed through two polymerase chain reactions (PCR). Moreover, these two markers were capable of forming accessions into four subclades with high support value. The sample composition of each subclade was identical to whole-plastome sequences, which indicated that these two fragments could be taken as efficient markers for L. japonicus genotype discrimination. These two fragments have also been used in phylogenetic analyses of Cistus, Bambusa, and Juniperus [L. japonicus. This study has comprehensively demonstrated the adverse ramifications of unregulated seedling exchange. From the standpoint of long-term species protection, the government should also be aware of this as soon as possible and take suitable measures.The conservation and utilization of medicinal plant species like  species . From thuniperus –37. ClarL. japonicus in terms of precipitation and temperature  can contribute to the selection of germplasm nursery sites and will also be useful for guiding cultivation and introduction. Moreover, the genotype and chemotype of a medicinal plant are undoubtedly closely related, and the chemical composition of the medicinal plant directly affects its clinical effectiveness [L. japonicus for better utilization.Additionally, the suitable conditions for L. japonicus in China should be clustered into four subclades represented four genotypes. Comparative analysis revealed that accessions from these four genotypes were highly differentiated and the identified hotspot regions could be used for genotype discrimination. Within the species, divergence events started during the late Miocene, and may be related to the rapid uplift of the Hengduan Mountains. In Quaternary glaciation, the Hengduan Mountains played the role of a refuge for L. japonicus. By MaxEnt modeling, we visualized the habitat dynamic changes and clarified the optimal precipitation and temperature range for L. japonicus. Overall, our findings provide insights into the genetic diversity, divergence, and evolution of L. japonicus and suggests suitable conservation and exploitation approaches.In this study, the phylogeny suggested that the L. japonicus accessions were collected from 24 provinces in China during the Fourth National Survey of Chinese Materia Medica Resources, and only four of which were cultivated accessions. The voucher specimens were identified by Jiahui Sun and deposited at the herbarium of CMMI  with deposition number listed in Table L. japonicus in China were collected from the Global Biodiversity Information Facility  and the National Plant Specimen Resource Center . After removing or correcting synonyms, unresolved names , and invalid distribution points (checked by ArcGis 10.8), 417 distribution records of L. japonicus were obtained for further analysis.59 de novo assembly with settings: -R 15 and -k 105 [After fragmenting the extracted DNA into 300–350 bp by sonication, a pair-end library was constructed using the NEBNext Ultra™ DNA library prep kit . PE150 sequencing was performed on the Illumina HiSeq XTen platform at Novogene Co., Ltd . The clean data were generated from the PE150 raw data by using Trimmomatic 0.39 software for quality control and filtering (with settings: ILLUMINACLIP:TruSeq3-PE. fa:2:30:10:1:true LEADING:20 TRAILING:20 SLIDINGWINDOW:4:15) [d -k 105 . Plastomd -k 105 .A total of 59 plastomes were aligned using MAFFT online service in auto strategy , and manhttp://taylor0.biology.ucla.edu/struct_harvest/) and aggregated by running the CLUMPP program [A matrix containing 291 SNPs was generated in DnaSP v5.10, and sites with alignment gaps were excluded . The SNP program , and (4) program .Leonurus cardiaca species from GenBank taken as outgroups (MZ274168 and MZ274153). The program ModelFinder was employed to find the best-fit model based on the Bayesian information criterion [http://emmahodcroft.com/TreeCollapseCL.html) [D of L. japonicus subclades were also calculated using DnaSP v5.10. AMOVA and pairwise Fst calculations were performed in Arlequin v3.5 with default settings to assess the degree of divergence among subclades [A total of 61 plastomes were used for phylogenetic inference, including two sequences of riterion . Two metCL.html) . The BI CL.html)  under a ubclades .Stachys clade was constrained at 13.8 Ma based on the oldest reliable lamioid fossils discovered from the Serravallian Age of the Middle Miocene flora of Germany [http://beast.bio.ed.ac.uk/Tracer). Divergence time with 95% HPD intervals was displayed using FigTree v1.3.1 and modified in AI CS6.We selected four accessions from each subclade as representatives to create a dataset with other plastomes from Nepetoideae and Lamioideae for divergence time estimation Table . The dat Germany , (2) the Germany . We ran https://www.worldclim.org/) and applied for distribution prediction. All the climatic layers were converted to ASCII format using ArcGIS 10.8. To assure the accuracy and minimize overfitting, a Pearson correlation analysis of the 19 climate variables was conducted using the R package “ggpairs.” After removing the correlation pairs with |r| > 0.7, nine climate variables remained  value, which ranged from 0 to 1, with the following evaluation criteria: poor (0.6–0.7), fair (0.7–0.8), good (0.8–0.9), and excellent (0.9–1) . The conBelow is the link to the electronic supplementary material.Supplementary Material 1Supplementary Material 2Supplementary Material 3Supplementary Material 4Supplementary Material 5Supplementary Material 6Supplementary Material 7Supplementary Material 8Supplementary Material 9Supplementary Material 10


Theories of urban scaling have demonstrated remarkable predictive accuracy at aggregate levels. However, they have overlooked the stark inequalities that exist within cities. Human networking and productivity exhibit heavy-tailed distributions, with some individuals contributing disproportionately to city totals. Here we use micro-level data from Europe and the United States on interconnectivity, productivity and innovation in cities. We find that the tails of within-city distributions and their growth by city size account for 36–80% of previously reported scaling effects, and 56–87% of the variance in scaling between indicators of varying economic complexity. Providing explanatory depth to these findings, we identify a mechanism—city size-dependent cumulative advantage—that constitutes an important channel through which differences in the size of tails emerge. Our findings demonstrate that urban scaling is in large part a story about inequality in cities, implying that the causal processes underlying the heavier tails in larger cities must be considered in explanations of urban scaling. This result also shows that agglomeration effects benefit urban elites the most, with the majority of city dwellers partially excluded from the socio-economic benefits of growing cities. The authors use large-scale data on urban productivity, innovation and social connectivity, as well as extensive mathematical modelling, and show that power-law urban scaling laws arise out of urban inequalities. Cities’ total outputs increase more than proportionately with increases in city size, suggesting that inhabitants of larger cities are, on average, better off economically. This relationship has been described by a power-law function of the form Y~Y0Nβ, where Y represents a city-aggregated socio-economic quantity, N is population size, Y0 is a normalization constant and β is a scaling exponent capturing the non-linear change in Y as a function of N. Estimates of β > 1 indicate greater socio-economic output per capita with increasing city size.In recent years, researchers from across disciplines have identified striking and seemingly universal relationships between city size and various urban quantities5. This interpretation meets earlier descriptions of cities as ecosystems of social exchange9 and, remarkably, simple formalizations of cities as interconnected networks provide predictions that map very well onto empirical observations of superlinear scaling12. More recent research has added economic complementarities and the higher industrial complexity found in larger cities to the list of crucial drivers of urban scaling phenomena15.To explain such superlinear scaling relations, reference has been made to increasing levels of social interconnectivity in dense urban environments16. Prior research therefore implicitly painted a picture in which scaling effects are driven by a homogeneous shift of the whole city distribution as the population grows larger (see 17 that represents the state of the art).However, the main tenet of the scaling paradigm assumes strong levels of homogeneity. It assumes that the residents of a city have roughly equal numbers of network contacts and that the companies in a specific urban industry have similar levels of economic complexity, and thus—as implied by the theory—have approximately equal levels of productivity. Empirical studies have built on this assumption in their use of city sums and means to capture agglomeration effects, as well as in their interpretations, which focus on the ‘average’ resident or firm19 or highly successful individuals21 contribute large proportions to city totals. Power laws are common in nature and society, present not only as scaling laws between cities but also as extremely skewed distributions within agglomerations22. Consequently, sums and means are poor and potentially misleading indicators of the relevant quantities of cities25.The homogeneity assumption is attractive because it renders mathematical models tractable and empirical analyses straightforward. But—as literature from both the social sciences and complexity research has documented—human networking and productivity show heavy-tailed distributions in which small fractions of extremely well-connectedAcknowledging the extreme skewness of urban indicators implies a discrepancy between observed distributions and the assumptions made by theoretical models, and it suggests an inadequacy of the measures used to test their predictions. A number of questions that go beyond the scope of current theoretical models naturally follow from this. Does within-city tailedness—which we define as the relative contribution of the top ten percentiles in a city, and which therefore also reflects urban inequality—differ systematically by city size? If so, how much of the previously reported superlinear scaling can be attributed to differences in cities’ tails, defined here as individuals or firms in the top (≥90th) percentiles of within-city distributions, as opposed to differences in their mass, which we define as being represented by the typical resident or firm (50th percentile) in a given city. Do phenomena that have heavier tails scale more or less than those with smaller tails? If they do, how much of the variance in scaling exponents across complexity categories  can be explained by differences in the tails? And, importantly, if within-city tails turn out to be essential to between-city scaling, what mechanisms underlie the emergence of tail differences by city size? These are the questions that we aim to answer in this article.We use micro-level data from Sweden, Russia and the United States that provide detailed information of within-city distributions of interconnectivity, productivity and innovation. First, we call attention to urban indicators’ heavy tails, particularly in larger cities. Second, we quantify the implications that differences in city tails have for urban scaling. Our findings show that cities’ tails—and, crucially, their growth as cities become larger—disproportionately contribute to superlinear scaling between cities. While we obtain scaling coefficients for city means that are in line with prior results, we find that cities’ tails are responsible for 36–80% of the observed superlinearities across indicators. Additionally, we find that tails explain most of the differences in scaling coefficients between indicators of various levels of complexity. This implies, for example, that once within-city tails have been discarded, average productivity differences by city size are similar for starkly different sectors, such as information technology firms and restaurants. Third, we provide a formal description—in the form of a computational model—of the positive link between the size of tails of within-city distributions and scaling exponents. The model marks out the conditions that give rise to a city size-dependent cumulative advantage mechanism, according to which large cities provide for some people novel opportunities for sustained growth, and it shows how tail differences by city size are brought about at the macro level. The model reproduces our main results, and micro-level data on the earnings trajectories of 1.4 million Swedes confirm the model’s prediction of greater cumulative advantage effects for tail units in larger cities, and thus of their disproportional contribution to superlinear scaling.These results have ramifications for the dominant mean-field interpretation of urban scaling. In revealing the crucial role of within-city tails, our findings point towards a different understanding of between-city scaling, where agglomeration effects operate on and intensify urban inequality. Our research implies that the causal processes underlying heavier tails in larger cities constitute an indispensable element of urban scaling, and that any theory seeking to explain urban scaling—whether it be through interconnectivity, complexity or other factors—must also explain the emergence of tail differences by city size.27. The insets plot the degree, d, to which heavy tails dominate city outputs in cities of different size; for a given city, d equals the proportion of an indicator’s sum that is contributed by the top 10% as compared with the bottom 90%; i = 1, 2, ..., N and y representing individual-level output. We find that, on average across the different indicators, the top 10% within cities account for 50% of city totals, and that d is 44% higher in large (>1 million) compared with small cities . For (1) interconnectivity, we use two measures: First, the number of online friendships in 177 Russian cities using data from the social media platform VKontakte. Second, we construct inter-firm networks that trace employee mobility between all private companies within each of Sweden’s 70 labour market areas, and we measure each firm’s degree in the constructed networks. These mobility networks transmit economically relevant information between local workplaces29. (2) We measure productivity using firms’ revenue per employee and the annual gross wages of all full-time workers in Sweden. (3) We approximate urban innovation on the basis of the number of patents filed per inventor and the US dollar sum of grants awarded to researchers in the US Metropolitan Statistical Areas , implying superlinear scaling when β > 0 (refs. 31)  capturing how strongly heavy tails dominate each indicator across cities. The marker sizes signify each indicator’s slope of d (the degree to which its tails grow heavier with city size), which we compute by regressing d on the logarithm of city size N . As a result of these tail differences, highly skewed indicators have disproportionally more extreme outcomes in larger cities than in smaller ones, and these extreme outcomes contribute crucially to the commonly observed superlinearity of urban scaling relations. Completely removing any tail differences between cities by focusing on city medians (empty circles) renders the association between β insignificant (grey line), and decreases the variance of β across indicators by 76%. Consequently, the great majority of the differences in superlinear scaling between urban indicators can be accounted for by differences in the tails.To generalize the importance of city tails for superlinear scaling, Fig. mentclass2pt{minim14. Our results corroborate this link between complexity and steeper scaling coefficients. Importantly, however, our micro-level data also provide a detailed account of previously overlooked heterogeneities within different levels of complexity. We subgroup the wage indicator /Var(βmean)). This result implies, for example, that most of the difference in scaling that is observed between the finance, law and consulting sector (β = 0.159 ± 0.109 and R2 = 0.154) and the hospitality sector (β = 0.010 ± 0.025 and R2 = 0.012) is due to a minority of very successful finance firms in the largest cities. Once these are removed from the picture, scaling relations become similar for both industries . The insets illustrate our core argument: while an indicator’s mass (50th percentile) does not differ much between smaller and larger cities, larger cities have heavier tails when the economic activity in question is complex.Previous work has demonstrated that activities of higher complexity show greater scaling coefficientstor Fig.  and firmtor Fig.  by diffeThe central role played by tail differences in explaining superlinear scaling warrants a deeper understanding of their emergence. Here we show that tail differences by city size Figs.  and 2 an34. Owing to the greater diversity, specialization and matching in larger cities36, scarcer skills tend to concentrate in such cities37. This concentration implies that the skilled and the specialized are more likely to find others whose skills are complemented by their own, allowing for higher levels of productivity in economic activities39. Those whose productivity depends less on whom they interact with—typically the less specialized and the less skilled—do not reap similar returns to complementarity.The first strand shows how individuals’ productivity depends on the local social environments in which they are embedded, and how this dependency affects agglomeration effects41. However, this line of research has yet to consider how learning effects vary as a function of the properties of local social environments in which the inhabitants of a city are embedded , and how stochastic selection processes and path dependencies affect individual life courses.The second strand concerns the dynamic benefits of living in larger cities, showing how big-city life facilitates greater learning opportunities and thus, on average, steeper wage trajectories compared with smaller cities43. An individual’s current labour market position, for example, may importantly shape their opportunities in the future44. When processes are stochastic and exhibit path dependency, small differences, if accumulated over time, can produce substantial inequality at the collective level46. It has recently been shown that such dynamics might explain both urban growth and inequality17. However, this research does not account for contextual effects, and it disassociates the dynamics that bring about inequality from the processes that give rise to superlinear scaling.The third strand focuses on the stochastic and path-dependent nature of life coursesβ becomes superlinear because small cities do not provide similar opportunities (and thus inhibit growth); d, on the other hand, increases because not everyone can access big-city opportunities to a similar degree, and because opportunities and their effects on individual outcomes accumulate over the life course. By contrast, for the low-skilled and non-specialized, large cities offer few additional interaction opportunities . Brought together, the three perspectives give rise to what we call the city size-dependent cumulative advantage mechanism, according to which large cities provide novel but heterogeneously distributed opportunities needed for sustained growth and, at the same time, produce the tail differences by city size that account for a substantial proportion of the overall differences in urban outputs between cities.Bringing together these perspectives—the contextual, the temporal and the stochastic—it follows that, for those who are specialized and skilled, (1) large cities provide complementary social environments and novel interaction opportunities that facilitate individual learning and growth, but that (2) such opportunities are accessed to varying degrees by different individuals and (3) that differences accumulate over time due to the stochastic and path-dependent nature of the process. As it pertains to urban scaling and within-city inequality, this implies that, among the specialized and skilled, d and β can be derived from synthesizing the temporal, contextual and stochastic perspectives. The computational model combines a selection model , specifequation ), specifequation  and 2)  d and β i of type j interacts with another agent of type k in city c at time t asFkc represents the fraction of type k agents present in city c, Cjk the complementarity between agents of types j and k, and Dikt−1 is a distance in actor-type space between the agent type that i interacted with at t − 1 and the currently considered option k , past interactions do not affect (or negatively affect) future interactions. For a given timepoint t, we use equation  i, we let their output yit update between timepoints asCit is the complementarity of the realized interaction of agent i at time t (Cit is short for Cijkt where agent i is of type j and they interacted with an agent of type k at t). Si is an integer between 1 and 10 qualifying i’s specialization rank . Note that we define i uniquely across cities and therefore drop the index c here to simplify notation. ω controls the returns to complementarity, and increases with larger returns. When ω = 0 (or < 0), complementarity has no effect (or a negative effect) on agents’ productivity. τ specifies how returns to complementarity are moderated by agent specialization, with larger values of τ indicating a stronger dependency of returns to complementarity on specialization. When τ = 0 (or < 0), returns to complementarity are independent of specialization . The constant α0 defines the maximum learning rate or, more precisely, the maximum increase in productivity at t.Second, we simulate individual-level productivity conditional on the complementarity of realized interactions. For an agent yit  and updequation ) for allmentclass2pt{minim48,50,ω > 0, that is, agents yield positive returns to complementarity51,τ ≥ 0, that is, returns to complementarity are positively moderated by specialization and skill52,δ > 1, that is, specialized agents disproportionately locate in large cities36.53, we assign the fraction Fj of different agent types j in the entire urban system based on a log-normal distribution, reflecting much larger fractions of some agent types  while others  have a considerably smaller representation. We calibrate the standard deviation of the log-normal distribution to the Swedish labour market data  to each alternative agent type k, Rjk, such that Rjk = 1 if j and k have the most similar overall population fractions, Rjk = 2 for the second most similar fractions and Rjk = K − 1 for the least similar fractions (where K is the number of agent types). Then, we define complementarity as an exponential function of Rjk, in which complementarity is decaying rapidly with skill distance. We empirically calibrate the exponential function to match the complementarity concentration found in the Swedish labour market . In line with results from past researchω > 0, τ ≥ 0 and δ > 1  categories and firms in more complex industries, experience both greater β and β on specialization rank S yields a slope coefficient of −0.0378 ± 0.001 and R2 = 0.826). Another important empirical pattern, which is closely reproduced by the computational model, is that deviations in tailedness explain a large proportion of the deviations from scaling predictions, the so-called scale-adjusted metropolitan indicators  or typical (40–60th percentile). Agents who from an early stage draw on big-city benefits  achieve sustained growth; they increasingly distance themselves from both the agents within their own city who are not able to access such opportunities (40–60th percentile) and also from everyone in smaller cities where such opportunities do not exist (≥90th and 40–60th percentiles). By contrast, typical agents in big cities—who fail to draw substantial benefits from novel social environments—yield outputs that are almost identical to those of their counterparts in small cities .Configurations satisfying the general conditions stated above produce the pattern in Fig. β by age; red line). To quantify the impact of differential cumulative advantage effects on tail differences, we estimate counterfactual wage trajectories under the assumption that tail earners’ wages grow at the same rate as those among the subset of median earners in their respective cities who held similar educational degrees . We identify ‘tail earners’ in each city who early in their career had outstanding wages (≥ 90th percentile), or ‘median earners’ in their respective 40–60th percentiles. We trace the wage developments and mobility patterns for all annual cohorts that reached age 30 in 1990–2007 over 10 years. For Fig. z = 38.786 and P < 0.001) up to age 40, and that those who relocated tended overwhelmingly to move to the largest cities /0.089). In terms of promoting tail differences by city size, the disproportionate out migration of the most successful individuals from smaller cities results in a reinforcement process that trims the tails in less populous regions while thickening them in larger cities. Together, the two mechanisms account for approximately half of the observed tail differences ((0.119–0.060)/0.119).Considering that tail earners in small cities are incentivized to tap into agglomeration benefits just like their big-city counterparts do, leads to the expectation of selective migration, that is, the relocation of promising workers into larger urban areas. Indeed, we find that those classified as tail earners at age 30 are more than twice as likely to have left a small city (18.9%) compared with a large city (8.7%; 12. In contrast to this work, we have shown that the outliers identified by Pareto are disproportionately located in larger cities (Fig. Vilfredo Pareto demonstrated a long time ago that small fractions of society often account for large shares of population totals, giving rise to the widely known ‘80/20 rule’, according to which 20% of people own 80% of the output. This observation remains overlooked by the main tenet of urban scaling theories, which instead assumes that inhabitants within the same cities have similar interconnectivity, productivity and innovativenessWe presented a computational model that provides a formal description of how within-city distributions and scaling exponents are positively linked. Building on the assumption that large cities provide novel opportunities of interaction and learning to individuals with varying degrees, the model predicts city size-dependent cumulative advantage at the micro level and tail differences by city size at the macro level, and it marks out the conditions that reproduce our empirical results Fig. . An analWhile reflecting higher-level empirical patterns, the agent-based model we have used surely abstracts away many details and particularities observed in the real world. We have assumed highly stylized agent types and complementarity spaces (for robustness checks and alternative specifications of complementarity, see Supplementary Note 14. Our work supports this thesis, but also elucidates it further, showing that higher complexity promotes heavier tails, and that it is these tails that explain a large part of the scaling differences between complexity levels (Fig. The observation that tails systematically grow by city size has implications not only for how we understand any given quantity to change by city size, but also for our understanding of why some quantities scale more than others. Past research has documented scaling relations for a wide spectrum of urban indicators, and theories have attempted to make sense of the variation in magnitude between them. One prominent explanation for this variation is provided by complexity research, which postulates that more complex activities show greater scaling coefficientsels Fig. . Additio54.Together, our results indicate that the processes arising from urban density—and from social interconnectivity more generally (Supplementary Fig. 44, a process commonly referred to as cumulative advantage. In this article, we found evidence for a strong moderator of cumulative advantage: the size of the relevant social environment. Larger cities provide more opportunities and, as a result, sustain longer cumulative chains. This effect, which we have dubbed the city size-dependent cumulative advantage mechanism, helped to explain approximately one-third of the tail differences that emerge between cities with respect to wages. When simulated, the mechanism also helped to reduce a large proportion of deviations from scaling predictions to deviations in tailedness (Supplementary Fig. 56.Robert K. Merton famously observed that, over the course of individuals’ lives, resources tend to diverge in such a way that ’the rich get richer’25 and to ensure the robust estimation of scaling exponents in the light of heavy-tailed urban indicators57. Second, a recently proposed theoretical framework17 has acknowledged heterogeneity and inequality in urbanization processes. Still, that framework remains predicated on a traditional type of urban scaling theory with mass shifts at its core, and where the process underlying inequality and the process underlying scaling are disassociated from one another. By incorporating the size-dependent cumulative advantage mechanism identified here, however, these processes become interlinked and the predictions can be brought into accordance with our empirical observations (Supplementary Note Recent research has taken important steps along the lines argued for here. First, empirical methodology has been developed to capture within-city variation in scalingFrom a policy perspective, our analyses underscore that urbanization is no panacea against social inequality. Agglomeration effects are instead particularly beneficial to the city elites that dominate urban hierarchies. Their networks and their affluence depends, more than others, on the local social environments that the largest cities provide. At the same time, the higher-than-expected outputs of larger cities critically depend on the tail outcomes of these successful few. Ignoring this dependency, policy makers risk overestimating the stability of urban growth, particularly in the light of the high spatial mobility among urban elites, their movement to ‘where the money is’ and their dependence on specific industries and on these industries’ long-term growth trajectories.31. Dominant positions in the urban hierarchy thus give an advantage to larger cities58. This path dependency also limits the self-similarity of growth paths of cities, implying that cities that at a certain time have very different sizes are not self-similar ‘scaled versions of one another’2, predicted to experience similar growth paths. Instead, they are cities with very different relative status and thus provide the social environments that the successful few strive on to very different degrees.Zooming out to the system level, the size-dependent cumulative advantage mechanism operating on the micro level accumulates into a city-level rich-get-richer process, where the largest cities benefit from strong path dependencies in the composition of individuals and firms, and from the attraction of further tail units from beyond the cityWith respect to urban inequality, our findings draw attention to the partial exclusion of a majority of city dwellers from the socio-economic benefits of growing cities. Their lifestyle, different than among the urban elite, benefits less from geographical location. When accounting for the cost of living in larger cities, many big-city dwellers will in fact be worse off as compared with similar people living in smaller places. In light of the extreme inequalities that exist within urban populations, our results stand in stark contrast to a mean-field interpretation of superlinear urban scaling that is derived from, and dependent on, homogeneity assumptions, and they raise questions about the sustainability of urbanization against the backdrop of rising inequality in cities.Further information on research design is available in the Supplementary InformationSupplementary Figs. 1–15 and Table 1.Reporting Summary


Mytilisepta virgata, we continuously recorded environmental temperatures in both tidal pools and emergent rock microhabitats and then assessed genetic differentiation, gene expression patterns, RNA editing level, and cardiac performance. Our results showed that the subpopulations in the tidal pool and on emergent rocks had different genetic structures and exhibited different physiological and molecular responses to high-temperature stress. These results indicate that environmental heterogeneity across microhabitats is important for driving genetic differentiation and shed light on the importance of post-settlement selection for adaptively modifying the genetic composition and thermal responses of these intertidal mussels.Rapid genetic selection is critical for allowing natural populations to adapt to different thermal environments such as those that occur across intertidal microhabitats with high degrees of thermal heterogeneity. To address the question of how thermal regimes influence selection and adaptation in the intertidal black mussel   •Intertidal species are subject to rapid selection under environmental stress•Summer high temperature is essential in promoting rapid genetic selection•Post-settlement selection can modify genetic structure across microhabitats Marine organism; Environmental science; Aquatic science; Zoology A fundamental focus of ecology and evolutionary biology is to determine if and how natural populations adapt to rapid environmental change.O-phosphate-beta-ethyl-esterase-cytidine-3′-monophosphate-deaminase (APOBEC), respectively.Drosophila.,Among various molecular alterations that could play significant roles in enhancing environmental tolerance, one noteworthy process is RNA editing.,,,,Certain environmental features that may be important in driving adaptive genetic variation are also inadequately understood. An important but easily overlooked aspect in assessing the role of adaptive genetic selection is the ecological impact of spatial variation across microhabitats.,,,,,The rocky intertidal zone presents an opportune system for elucidating the adaptive strategies employed by organisms to mitigate the challenges posed by thermal extremes.Mytilisepta virgata.,,,An excellent study organism for these purposes is the black mussel, 99 for tidal pool and rock microhabitats were 33.16°C and 51.69°C, respectively.Thermal environments on the shore varied considerably among different microhabitats  and showed a high degree of spatial and temporal heterogeneity. Thermal environments on the shore showed distinct daily fluctuation from August to September 2020 . During To understand the population genetic relationships among all individuals from different microhabitats, the number of subgroups  was preset to 1–10 for clustering; the clustering results were cross-validated, and the optimal number of clusters was determined according to the lowest cross-validation error (CV error) rate. In this study, CV error ranged from 0.5699 to 0.8255. We mainly focused on two cases , because they clearly showed the major structure of two populations. It can be clearly shown that the tidal pool individuals and the rock individuals were divided into two groups A, when tTo better understand the genetic structure, a principal-component analysis (PCA) was applied based on identified SNPs among all 63 individuals. PCA analysis generally confirmed the pattern of the genetic structure obtained with K = 2. Our results showed two principal components accounted for 76.84% (PC 1) and 11.93% (PC 2) of the total variability C. The reTo better visualize the relationship between sample distribution and genetic structure, a phylogenetic tree of 63 individuals was constructed D. The inThe genetic structure of the neutral loci showed that mussels from the two microhabitats were genetically more similarly distant A. NotablThe heterozygosity of non-neutral loci was higher than that of neutral loci . The hetIntegrated analysis of DEGs was analyzed using the R project Deseq2 package (p < 0.05 and |log2FC| > 1.0). Significantly differentially expressed genes between different microhabitats were generated as shown in the volcano plot; the red dots represent significantly upregulated genes, whereas the blue dots represent significantly downregulated genes A and 4B.In immersion temperature ramps, the expression of 172 genes was significantly altered between tidal pools and rock A. ComparIn aerial treatments, a total of 231 genes as DEGs were identified; compared to the tidal pool subpopulation, 133 genes and 98 genes in the rock subpopulation were upregulated and downregulated, respectively. Among the upregulated genes, heat shock protein beta-6 (HspB6), heat shock protein Hsp-16.1 (Hsp-16.1), alpha-crystallin B chain (CRYAB), adenylate cyclase, Ribosomal proteins , NADH dehydrogenase (ubiquinone) Fe-S protein 4 (Ndufs4), ubiquinol-cytochrome c reductase subunit 6 (QCR6), NADH dehydrogenase (ubiquinone) 1 alpha subcomplex subunit 5 (NDUFA5), Type III collagen alpha (COL3A1), and Ras-related C3 botulinum toxin substrate 1 (RAC1) exhibited notable increases in expression. Conversely, downregulated genes encompassed solute carrier family 2 (Slc2a1). The genes exhibiting differential expression within tidal pools and rock across the temperature were enriched in “Longevity regulating”, “Ribosome,” and “Diabetic cardiomyopathy” pathways D.M. virgata, and the percentage of A-to-I RNA editing varied among microhabitat groups, different temperatures, and different treatments. We did not examine the amounts of recoding due to RNA editing, albeit this could be an important contributor to rapid adaptive change.Given that A-to-I RNA editing exists in all metazoans,In the immersion treatment, the percentage of A-to-I RNA editing sites was significantly higher for individuals on rocks than those from tidal pools at 25°C. The percentage of A-to-I RNA editing sites was lower at 30°C and 35°C than at 25°C in the rock subpopulations; the percentage of A-to-I RNA editing sites was higher at 30°C than 25°C and 35°C in the tidal pool subpopulations A.Figure In the aerial treatment, the percentage of A-to-I RNA editing sites was significantly higher for individuals in the tidal pool subpopulation than the rock subpopulation except at 30°C. For the mussels on the rocks, the percentage of A-to-I RNA editing sites was significantly higher at 30°C, 35°C, and 43°C than at 25°C; similarly for the mussels in the tidal pool, the percentage of A-to-I RNA editing sites was significantly higher at 35°C and 43°C than 25°C and 35°C B. In bot−1 (mean ± SE), respectively. In contrast, mussels on rocky substrates at the same temperatures exhibited heart rates of 26.31 ± 4.22, 36.46 ± 5.80, and 60.26 ± 7.05 beats min−1, respectively. In the aerial treatment, mussels in tide pools at 25°C, 30°C, 35°C, and 43°C had heart rates of 4.20 ± 1.10, 7.24 ± 1.96, 8.12 ± 2.34, and 10.87 ± 0.95 beats min−1, respectively. Meanwhile, mussels on rocky substrates at the corresponding temperatures displayed heart rates of 6.08 ± 0.57, 7.65 ± 1.04, 10.11 ± 2.50, and 12.74 ± 2.10 beats min−1, respectively.In the immersion treatment, the heart rates of mussels in tide pools at 25°C, 30°C, and 35°C were 31.33 ± 3.24, 41.24 ± 6.80, and 67.25 ± 7.22 beats minM. virgata in tidal pools and rocks exhibited different patterns in the face of temperature changes. Firstly, in both the rocks and the tidal pools, the heart rate of M. virgata in the immersion treatment was significantly higher than that in the aerial treatment under equivalent temperature conditions  being significantly lower in the tide pools than in the rock microhabitat.,Considerable adaptive variability in cardiac thermal tolerance was found between tide pool and rock microhabitats in black mussels. Individuals on the rocks had higher cardiac heat tolerance when exposed to high temperatures in the air, whereas individuals in the tidal pools had higher cardiac heat tolerance at high temperatures during immersion. This implies physiological adaptation to the local thermal environment. The structured rocky shores of the intertidal zone create a large difference in environmental temperatures between the two microhabitats, with extreme heat stress  editing of mRNAs. Protein-recoding mRNA editing plays an important role in helping organisms adapt to environmental changes,Environmental heterogeneity is commonly associated with genetic heterogeneity, as shown by Antonovics,Analysis of neutral loci revealed no excessive genetic variation between microhabitats, and neutral loci remained largely similar in both populations, suggesting that the two subpopulations, tide pools and rocks, share common parental populations. However, adaptive genetic differences were observed between the tide pool and rock microhabitats of black mussels in the non-neutral loci. Rock individuals subjected to high temperature selection possessed a clustered population structure compared to the tide pool subpopulation with a mild environment. This may be due to the extreme environment of the rock microhabitat selecting for non-neutral loci, and the death of heat-intolerant rock individuals after being exposed to high summer temperatures, allowing non-adapted alleles to be removed from the survivors on the rocks, which may increase the frequency of heat-tolerant genes in the entire population. This suggests that natural selection plays a major role in the construction of neutral genetic diversity patterns.,,,,Tidal pools can provide relatively unstressed environmental conditions and shelters for organisms in the intertidal zone.,Another advantage of tide pool microhabitats in bolstering organismal resilience to environmental stresses lies in the uninterrupted availability of oxygen. In rocks microhabitats, mussels adopt a strategy of shell closure during air exposure to mitigate water loss. However, the seawater retained within the mussel’s shell contains only limited dissolved oxygen, depleting rapidly and necessitating a transition to anaerobic metabolism.Environmental complexity across microhabitats must be taken into consideration when predictions are made of how biota will respond to future climate change. Relatively benign microhabitats can provide a buffer against extinction or range shifts due to climate change. Rapidly generated differences at the genetic level in conspecifics occurring across small-scale microhabitats with different physical conditions can lead to different molecular responses, e.g., gene expression and RNA editing, and physiological phenotypes, e.g., cardiac thermal sensitivity. These genetic and physiological adaptations across microhabitats are apt to be highly important for preventing catastrophic biological events in the face of climate change. Therefore, when using temperature sensitivity to predict the effects of climate warming, it is essential to take into account all aspects of adaptive variation across microhabitats.While our results showed that the subpopulations in the tidal pool and on emergent rocks had different genetic structures and exhibited different physiological and molecular responses to high-temperature stress, several limitations should be acknowledged. Firstly, our study employed RNA-seq to assess genetic differentiation in two microhabitats, which can only capture genetic disparities at coding genes. Secondly, it is important to note that genetic structure, molecular responses, and physiological traits within microhabitats may exhibit seasonal variations. Finally, the differences among microhabitats could also be influenced by a multitude of environmental factors  and biotic factors .dongyw@ouc.edu.cn).Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Yun-Wei Dong (This study did not generate new unique reagents.•The RNA-seq raw data have been deposited at the Sequence Read Archive (SRA) and are publicly available as of the date of publication. The accession number is listed in the •All original code has been deposited at FigShare online and is publicly available as of the date of publication. The url link is listed in the •Any additional information required to reanalyze the data reported in this work paper is available from the Mytilisepta virgata, and no experimental models were used.This study was based on wild mussel Mytilisepta virgata exposed to different environmental conditions were continuously recorded using biomimetic thermal loggers, Robomussels, under both immersed and emersed conditions.M. virgata mussel to more accurately estimate the temperature changes observed by individuals observed in this study. These Robomussels were deployed in tidal pools (30–50 cm in depth) and the emergent south-facing rocks in the intertidal zone of Dongshan , Fujian Province, China from July to September 2020. The measurement accuracy of the iButton was set to 0.5°C with a sampling interval of 20 min. As described in a previous study,99) was considered as the acute thermal stress temperature of M. virgata in that month; T99s were calculated for tidal pool and rock microhabitats separately. To characterize further the different thermal properties of tidal pool and emergent rocks microhabitats, an infrared thermal image analysis was conducted by using a Fluke TiX660 Infrared camera .The operative temperatures of (1)Aerial temperature ramps: Mussels were heated from 25 °C at a rate of 0.1°C min-1 in the air until the designated temperature  was reached. To remove the effect of the length of heating time on the results, for the 25°C groups, mussels were held at 25°C for a whole exposure period (∼3.5 h). For the 30, 35°C and 43°C groups, mussels were maintained at the designated maximal temperatures for different durations to maintain the same exposure periods (∼3.5 h) that simulated thermal stress in the natural environment.(2)Immersion temperature ramps: Animals were placed into beakers with seawater . The water temperature was increased to the designated temperatures at a rate of 0.1°C min-1 and then maintained for different durations to maintain the same exposure periods of 3.5 h.The mussels with a similar size  were randomly collected in tidal pool and rock microhabitats in Dongshan, Fujian, China, and were transported to the laboratory at Dongshan Swire Marine Station (D-SMART) within 1 h. The mussels were collected from both tidal pools  and emergent rocks within 20 cm . All specimens were acclimated under immersion for 2 days at a water temperature of 25°C and salinity of 28 psμ prior to use in the experiments described below. The temperature of 25°C was chosen as it represents the average annual temperature in Dongshan where the mussels were collected. The salinity level of 28 psμ was selected to align with the typical seawater salinity range of the mussel’s habitat. Additionally, a two-day acclimation period was established, to remove the effects of sampling and transportation.A non-invasive method was used to measure individual heart rates.An infrared sensor (CNY70) was fixed directly above the heart using Blue-Tack . Variations in the light-dependent current produced by the heartbeat were amplified, filtered and recorded using an infrared signal amplifier  and PowerLab AD converter . Data were viewed and analyzed using LabChart v7.2 . Real-time heart rates were recorded every minute.After the heating treatment, the gill tissue of each sample was dissected and placed into a 2 mL tube with 1 mL of RNAlater solution (Thermo Fisher Scientific). Tubes were stored overnight at 4°C to allow the RNAlater solution to inhibit RNase. Samples were then transferred to a −80°C freezer for storage for transcriptome sequencing experiments.Total RNA was extracted with an EZNA Mollusc RNA kit  following the manufacturer’s instructions. The concentration and integrity of column-purified RNAs were determined using NanoDrop spectrophotometry (ND-1000) and 1% agarose gel electrophoresis, respectively. The RNA samples used in the present study showed high purity  and integrity . A total of 63 RNA samples (two microhabitats × seven treatments × three-five replicates) were subjected to standard library construction procedures using the NR604-VAHTS Universal V6 RNA-seq Library Prep Kit from Illumina  according to the manufacturer’s protocol. Library quality was assessed using a 2100 Bioanalyzer (Agilent Technologies). The library preparations were sequenced on an Illumina HiSeq 6000 platform  and 150-bp paired-end reads were generated.M. virgata genome (BioProject: PRJNA910323) by HISAT2http://ccb.jhu.edu/software/stringtie/dl/prepDE.py3). Differential expression analysis was performed using DESeq2https://cran.r-project.org/web/packages/pheatmap/).The RNA-seq data are available in the NCBI short-read archive database . The quality control assessment was performed using FastQChttps://github.com/broadinstitute/gatk). The GATK VariantFiltration standard hard filtering option (‘QUAL <30.0 || QD < 2.0 || FS > 60.0 || SOR >4.0’) was used to filter SNPs. The VCF file containing high-quality SNPs was filtered based on the following criteria using VCFTools version 0.1.16Raw reads were trimmed and assessed for quality control using fastp,To assess whether non-neutral genomic variation occurs within the microhabitat, the loci with Fst >0.15 (indicating significant population differencehttps://itol.embl.de/). Evidence for population genetic structure was assessed using Analysis of Molecular Variance (AMOVA) that was conducted with the poppr.amova function as implemented in the poppr package.Population structure analyses were performed using ADMIXTURE,The data employed for the SNP analysis was also used for the RNA editing analysis. We quantified RNA editing levels of a total of 63 individuals across the two microhabitats. Editing levels of a site were calculated by determining the fraction of reads with a ‘G’ nucleotide at that site. Clean data were obtained by quality control of the raw data using FastQCThe differences in heart rate of mussels from different microhabitats and the difference in RNA editing level of mussels in different microhabitats were analyzed using generalized linear model (GLM) with a Gaussian model in SPSS 22 . In light of the nonnormal distribution inherent in our factor data and the imperative to account for potential interactions among factors, the adoption of the GLM emerges as a judicious choice.All quantification and statistical analyses were performed as described in the