The ongoing SARS-CoV-2 pandemic has been holding the world hostage for several years now. Mobility is key to viral spreading and its restriction is the main non-pharmaceutical interventions to fight the virus expansion. Previous works have shown a connection between the structural organization of cities and the movement patterns of their residents. This puts urban centers in the focus of epidemic surveillance and interventions. Here we show that the organization of urban flows has a tremendous impact on disease spreading and on the amenability of different mitigation strategies. By studying anonymous and aggregated intra-urban flows in a variety of cities in the United States and other countries, and a combination of empirical analysis and analytical methods, we demonstrate that the response of cities to epidemic spreading can be roughly classified in two major types according to the overall organization of those flows. Hierarchical cities, where flows are concentrated primarily between mobility hotspots, are particularly vulnerable to the rapid spread of epidemics. Nevertheless, mobility restrictions in such types of cities are very effective in mitigating the spread of a virus. Conversely, in sprawled cities which present many centers of activity, the spread of an epidemic is much slower, but the response to mobility restrictions is much weaker and less effective. Investing resources on early monitoring and prompt ad-hoc interventions in more vulnerable cities may prove helpful in containing and reducing the impact of future pandemics. Initially, the response toolbox of public health authorities worldwide has strongly relied on non-pharmaceutical interventions, such as social distancing, travel bans and stay-at-home orders14.The SARS-CoV-2 virus, believed to have originated in the Hubei province of China around the end of 201925 as it was the case for the first wave that hit China following the inter-city mobility from Wuhan27. In a connected planet, rapid world-wide spread is enabled by long-distance air-, land- and sea-transportation among countries and continents, which subsequently expand the disease to new and susceptible area of the world32. While early travel restrictions contribute to delayed disease spread, their utility is much reduced if the disease has incubation periods longer than a day or if there is asymptomatic transmission39. Long-range mobility is mainly driven by air transportation, but restrictions to international flights have shown limited utility in mitigating the propagation of infectious diseases unless they are applied very early and in a massive manner38.Human mobility is known to play a central role in the spreading process40. In particular, the mixture of different transportation modes at an urban level, coupled with the spatial organization of socioeconomic activities in residential areas, creates unique mobility fingerprints47 in which stations, office buildings and stores are continuously packed and close contact is inevitable. Historically, epidemics have influenced planning of cities48. In the history of urban planning, e.g. after the black plague or cholera, ventilation of streets, indoor daylight, large public squares and parks became architectural recipes to mitigate the spread of disease, in particular respiratory ailments such as tuberculosis. Such measures helped to reduce the density of cities, created safer and less infectious residential areas and increased the opportunities for physical distancing in public spaces, while allowed for better isolation of affected individuals from the larger urban population51.The majority of work on the role of mobility in regulating the spread of pandemics has focused on long range trips between countries, states and cities. Once restrictions on land, sea and air travel are imposed, and long range transportation networks cease to play a role, the core of contagion keeps developing in the main center of human activity: the densest and most crowded cities of the globe. In urban areas, the predominant transportation modes include vehicular traffic, pedestrian, bike, metros and buses52. A vigorous debate on how to redesign urban spaces to respond to future challenges stemming from the post-pandemic era has emerged53. While there has been prior work on the role of mobility on viral spread in urban systems30, showing how dense urban areas exhibit more diffuse epidemics of influenza54 and higher attack rate peaks of SARS-Cov230, scarce attention has been devoted to the intrinsic spatial structure of intra-urban mobility and its effect on the evolution of urban outbreaks. The way in which human mobility affects an epidemic progression is by regulating the population mixing patterns. This is not only an important factor at international and national levels, but it is critical in urban environments where most close contacts happen. This is particularly important in the case of airborne diseases such as COVID-19 or influenza, given that contagions may occur at fortuitous encounters due to shared transportation media, crowded areas, and business activities. Hence, understanding how the spreading of a disease depends on the structural organization of a city mobility and its population mixing patterns is of fundamental importance to devise adequate and tailored containment strategies and surveillance protocols55.Currently more than half the world’s population lives in urbanized areas, while forecasts see this number growing to 56. The level of centralization can be encapsulated in a metric Here we investigate how the spatial structure of urban mobility shapes critical features of the local spreading patterns and the efficiency of the potential mitigation measures. We showed in a previous analysis that when it comes to the internal organization of city-wide mobility patterns, urban areas can be classified in a spectrum between centralized-hierarchical and decentralized-sprawledhttps://github.com/google/s2geometry) of approximately 5 km56. To provide strong privacy guarantees, all trips were anonymized and aggregated over one week using differential privacy57. The research presented here is carried out on the resulting weekly flow data. No individual user data was ever manually inspected, only heavily aggregated flows of large populations were handled. To further confirm the model results, we have additionally used commuting data at zip code level from the Longitudinal Employer-Household Dynamics surveys of the US census office. In particular, the information comes from the LODES dataset concerning commuting in 2016–2017 (see Data Data availability statement for the download link).The mobility flows are sourced from the Google COVID-19 Aggregated Mobility Research Dataset, containing anonymized trips weekly aggregated over users who have turned on the Location History setting, which is off by default. The dataset aggregates flows of populations between S2 cells  establishes a threshold. Those cells over the threshold are classified as level-1 hotspots58. The process is applied iteratively with the remaining cells until all of them have an assigned level. Once every cell i is assigned a level m are levels and 56:56. The metric is also well correlated with considerably more complex composite measures  such as so-called sprawl indices59, and outperforms them as a predictor of urban indicators.To calculate The epidemic data has been downloaded from two sources (USAFacts and NY Times) listed in the Data availability statement below. Despite, the fact that we have global mobility data, in what is to follow, we focus on cities in the United States. This is done primarily for two reasons: first, US cities show a wide spectrum of configurations from hierarchical to extended or sprawled, with New York city and Atlanta as archetypal examples. Second, due to inherent biases and noise in collection of epidemic data, it is problematic to compare between different countries. We also restrict our analysis to the time-period corresponding to the so-called first wave of infections. For the purposes of our analysis we collected data on the 22 largest metropolitan areas in the United States, in terms of the number of counties and with populations exceeding 2 million inhabitants. The full list of cities and their corresponding values of j of city u at day t. These incidence curves can be aggregated at full city scale by summing the cases in the counties forming the urban area and dividing by the total population, u, The epidemic evolution is studied using the incidence curve as basis, which corresponds to the number of new cases detected in a day per capita. In the case of the data, the number of detected individuals depends on the testing policy, while in the models these are all the new infected individuals in each simulated day. The incidence can then be represented by curves 60 and the code available at61. This method relies on a Bayesian dynamic inference of the reproductive number based on a stochastic epidemic model informed by an estimation of the serial interval mean. The meaning of Besides the direct height of the incidence peak, we are interested in how quick is the spreading process initially in every population. To quantify this, we calculate P is distributed among the S2 cells i according to their mobility, j runs over all cells including i and T corresponds to the total trips of the city. Note that while this is not a realistic configuration the goal here for the model is to uncover the effect of mobility hierarchy, and not necessarily to forecast epidemic evolution, which would require more precise data on the population mixing. Usually, only a fraction of people, M, leave the residence area, as for instance in the case of commuting between zip codes, a value of 62. Unless otherwise specified, the particular parameters selected for the model are shown in Table The simplest version of our model is structured in a meta-population framework with Susceptible (S), Exposed (E), Infected (I) and Recovered (R) compartments and taking as basic spatial units the S2 cells provided by the mobility data. For a given city, the population mentclass2pt{minimi moves and selects their destination proportional to outflow i to the other cells j . These cells have to be aggregated to county or city levels to allow for a fair comparison with the results obtained with the epidemiological data. A sketch with the different geographical scales involved and the type of data available at each of them can be found in SI Fig. S37.i for each of the scenarios are then To model the effect of lockdowns on mobility, we need to consider that mobility flows change in both magnitude and destination in response to restrictions, with reductions that can reach up to 66, where a fraction During stay-at-home orders, part of the population remained isolated or only interacted with individuals in their household; not participating in the global spreading process directly. This effect has been modeled following the literaturementclass2pt{minimi to j, k) while maintaining the link-weights. In case a link already existed between the original area i and k, the weights of the links To produce instances of a single city with different values of mentclass2pt{minimThe primary response to the early onset of the epidemic were mobility restrictions corresponding to stay-at-home measures, modifying the spatial patterns of urban mobility and disrupting most of the routes used by the virus to propagate. In Fig. The resolution of the epidemic data allows us to run a granular analysis of the impact of lockdowns by investigating the extent to which specific areas of the city drove the epidemic spreading to other parts. To do so, we calculated the average Transfer Entropy, entclass1pt{minimaIn Fig. entclass1pt{minimaentclass1pt{minimaentclass1pt{minimaThese patterns generalize beyond the three cities considered. In SI Fig. S4 we plot the equivalent of Fig. 67, and a relation between mobility reduction and This increased transmissibility is also reflected in the extent of the spread of the disease as measured by plotting The connection between mobility reduction and transmission mitigation is also far more pronounced: plotting res Fig. d. That iThe empirical results Figs.  and 3 suFor this, we check if simulating disease spread on multiple idealizations of a city while tuning their mobility structure confirms the empirical observations of the pandemic trends. As representative examples, we consider Atlanta, Chicago and New York and simulate spread while changing lower in cities with higher On the other hand, Fig. M as shown in SI Figs. S13 and S14.To confirm the qualitative trends presented thus far, we systematically explore the effects of different parameters on the model results, as well as run our model on a different dataset. A key parameter is the infectivity The mobility data considered here has information on both commuting and non-commuting flows, all of which can serve as potential infection routes. However, one can repeat the analysis using different types of mobility data, and here we do so by using the publicly available commuting data from the United States census bureau’s LODES database for the year 2016 collected at the zip code level. In this case, the number of residents, commuting flows and fractions of mobile population can be directly measured. In SI Figs. S15–S18, we use Atlanta as a representative example and plot 63 that adds age diversification of agents, age-dependent contact patterns for the individuals (POLYMOD matrices), an expanded set of health state compartments and a set of parameters fit by the authors of63 to mimic the real data of daily occupancy in the hospitals of Paris. We employ the same rewiring procedure to scan over multiple values of Thus far, we presented a systematic analysis of the parametric landscape of a minimal model that is valid in characterizing the relation between mobility hierarchy and epidemic spreading in cities. In order to focus on the characteristics unique to SARS-CoV-2 we next test the robustness of our findings with a realistic modelWe note that while in our simulations we considered a homogeneous confinement  which reports cases curves for 210 Upper Tier Local Authorities (UTLA), both for reporting and specimen date. What we see in SI Fig. S34 is that the daily estimation of The epidemiological data used so far does not report cases by onset of symptoms, but by reporting date. This could introduce some differences70, such as the amount of work force employed in essential services. In Supplementary Fig. S37 we provide a multivariate analysis with socio-economic factors available at the scale of metropolitan areas . We do not have a specific variable for the fraction of service sector employed workers, however we have the GDP per capita, which seems to represent a good and reliable proxy for this socio-economic variable (see https://ourworldindata.org/grapher/gdp-vs-services-employment). As shown in SI Fig. S37, the GDP per capita only explains the Finally, different lockdowns experienced in cities with diverse hierarchies may depend even on socio-economic variablesIn summary, we studied how mobility restrictions in urban areas affect the propagation of an infectious disease. We leveraged a dataset that captures aggregate flows of populations around the world, in a consistent way from the period before the pandemic to a few months after the first wave. In a previous work, we had shown that hierarchical cities have better indicators for the use of public transportation, walking, emissions per-capita and health indicators. However, their mobility structure favors spreading of infectious diseases in terms of speed and extent of the contagions. At the same time, the empirical analysis shows that lockdown and travel restriction measures can lead to better outcomes in more hierarchical cities.47. We include these effects in more elaborate versions of the model and find in all the cases that the relation between hierarchy and spreading speed and the possibility of a better response of hierarchical cities to lockdowns still hold, which means that those factors induce changes of higher order in the propagation patterns. These results have been confirmed using a set of US cities as basis for the simulations, their randomized clones, and also data from London and Paris. Still the relation between mobility hierarchy and spreading is robust and holds too in other cities and even in theoretical settings such as lattices.Besides empirical observations, we corroborate our findings with a family of metapopulation epidemic models operating at sub-urban scales. The results of the main manuscript are obtained with the simplest version of the models, in which we include the distribution of the population and the mobility information only. This is enough to be able to generate a dependence between the speed and virulence in terms of maximum of incidence and total disease size, and the mobility hierarchy in cities. Such relation originates from the mobility configuration regarding hotspots, where the most diverse contacts concentrate and thanks to which the contagions can spread faster across the city. Hierarchical cities are characterized by intense and better connected hotspots with respect to decentralized ones. Mitigation measures in the form of mobility reductions impact mainly the hotspot and this is why hierarchical cities respond better to lockdowns. This is the main order effect, other factors such as age structure or socio-economic differences across neighborhoods can play a role as well as observed at coarser scalesSeveral implications present themselves from a policy making point of view: Given the limited resources for testing and surveillance in much of the developing world (and for that matter in parts of the developed world as well), it seems prudent to classify cities that are more vulnerable to spread ahead of time and deploy those resources there first. Furthermore, the analysis provides clues on the extent to which non-pharmaceutical interventions are effective. It is effective to enforce mitigation measures as early and as thoroughly as possible, given that the time-to-response is particularly crucial in hierarchical cities within or near an epidemic outbreak. Sprawled cities, with their distributed mobility and less connected outbreaks, have a larger time window within which to enforce policy measures, yet even so, the intensity of response is important to reduce the final number of cases. Similar lessons may be employed in terms of vaccination strategies, given its even more limited availability. While well established graduated procedures for vaccinations have been followed worldwide, in the face of exponential growth as has being seen in waves in India and Brazil, it may be more effective to deploy vaccines considering mobility hotspots , motivating new research in optimal spatial vaccination strategies. We note that our findings, while presented in the context of COVID-19, are generally applicable to other respiratory infectious diseases. In terms of a post-pandemic urban policies, just as outbreaks of tuberculosis and other diseases shaped the form of the modern urban space in the past, our results also shed new light on how to protect the different type of cities from infectious disease spreading.Supplementary Information 1.


Polygala lewtonii, an amphicarpic, perennial species endemic to fire-prone Florida sandhill and scrub, where fire kills plants but subsequently increases recruitment and population sizes. Polygala lewtonii produces three flower types: above-ground chasmogamous flowers and above-ground and below-ground cleistogamous flowers, with previous research demonstrating chasmogamous flowers produce a much greater proportion of seeds than cleistogamous flowers. We quantified outcrossing in seeds produced by chasmogamous flowers to determine whether it differed from the 100 % self-fertilized below-ground seeds. Approximately 25 % of seeds from chasmogamous flowers showed evidence of cross-pollination. Assuming that chasmogamous flowers produce the majority of the above-ground seeds, as was shown previously, this indicates it is possible to differentiate between germination by above-ground versus below-ground seeds in post-fire colonization. We next compared genetic diversity, admixture, inbreeding and population genetic structure pre- and post-fire. If fire promoted germination of chasmogamous seeds, heterozygosity and admixture would increase, and genetic structure and inbreeding would decrease. Instead, inbreeding and genetic structure increased and admixture decreased, suggesting that the below-ground selfed seeds  increased their contribution to the population after fire, possibly because fire reduced above-ground seed viability. Additionally, new alleles not found previously in range-wide analyses emerged from the seed bank post-fire. These results suggest that amphicarpy is a powerful adaptation to preserve genetic variation, maintain adaptive potential and promote rapid post-fire colonization.Amphicarpic plants produce both above-ground and below-ground seeds. Because below-ground seeds are protected in the soil and may maintain viability when above-ground conditions are stressful, they were proposed as an adaptation to recolonize a site after disturbance. However, whether below-ground seeds are the main colonizers after a disturbance remains unknown. Our goal was to understand whether recolonization by an amphicarpic species after fire was accomplished primarily through germination of seeds produced above-ground or below-ground. We investigated  Polygala lewtonii, a species endemic to fire-prone Florida sandhill and scrub. Polygala lewtonii has three flower types: above-ground chasmogamous (open-pollinated) and cleistogamous  flowers and below-ground cleistogamous flowers, with fruits produced both above-ground and below-ground. We used a genetic approach to test whether post-fire recolonization was accomplished primarily through germination of above-ground or below-ground seed. The below-ground selfed seeds showed an increased contribution to the population and new alleles emerged from the seed bank post-fire. These results suggest that amphicarpy is an adaptation to promote rapid post-fire colonization and preserve genetic variation and adaptive potential.We examined the mating system’s response to fire in  Understanding how reproductive strategies evolve in disturbance-maintained ecosystems connects basic evolutionary theory to conservation applications. Episodic mortality from regular disturbance tends to reduce population genetic variation and increase inbreeding . Adaptivt ≤ 0.8  is a federally endangered , smoke stimulated seed germination  CH flowers, (ii) above-ground CL flowers and (iii) below-ground CL flowers. In P. lewtonii, open-pollinated CH flowers also have a delayed selfing mechanism  is a short-lived (2–10 years) perennial, reaching ~20 cm tall  in the spring of 2017 see . We alsoP. lewtonii within the site, yielding an estimated area of ~0.28 km2. Within this study area, four blocks were created, and within each block, eight collection plots were established that ranged from 1 to 4 m in radius containing a minimum of nine P. lewtonii individuals, with a minimum of 10 m separating plots in the same block. The closest plots in neighbouring blocks were separated by larger distances . By design, plots were placed at a very fine spatial scale to understand how genotypes and genetic clusters were distributed spatially, with the goal of providing insight into the patterns of reproduction and the dispersal of selfed versus outcrossed seeds. In the 2014 sampling, leaves were collected from nine individuals per plot in each of eight plots per block, totalling 72 individuals per block, or 288 individuals total across the four blocks. A prescribed fire the following year (2 May 2015) burned and killed all plants in the blocks and plots except block 1 . To analyse whether seeds produced by CH flowers were derived from inbreeding versus outcrossing, we counted the number of non-maternal alleles in the offspring, which were presumably contributed by the pollen donor. Seeds were placed into three categories: individuals having zero non-maternal alleles, those having only one non-maternal allele and those having more than one non-maternal allele. Individuals with zero non-maternal alleles were products of either selfing or biparental inbreeding involving highly genetically similar individuals. Individuals with one non-maternal allele were either the product of cross-pollination between close relatives or genotyping error. Individuals with multiple non-maternal alleles were likely products of outcrossing.For seed data, we compared the heterozygosity of seeds from CH flowers to that expected under pure selfing by conducting a simulation of pure inbreeding in R version 3.4.1 . For eacA) and number of private alleles per population (AP). We used FSTAT v. 2.94 (HO and HE); if HO increased post-fire, it would indicate that a greater proportion of outcrossed seeds from CH flowers preferentially germinated in response to fire. If HO decreased or remained at pre-fire levels , it would indicate that seeds that germinated were predominantly produced by selfing or biparental inbreeding.For each block both pre- and post-fire, we used GenAlEx version 6.503  to calcu v. 2.94  to calcuF). Because technical artefacts such as genotyping errors and null alleles  inflate inbreeding coefficients, we used INEST version 2 (FB). We used the Bayesian Markov chain Monte Carlo (MCMC) approach with 5 000 000 iterations, a burn-in of 500 000, sampling every 250th iteration. If F and FB increased post-fire, this would indicate that fire stimulated the germination of seeds produced by inbreeding, whereas a decrease would indicate that a greater proportion of outcrossed seed germinated post-fire.To detect whether fire stimulated germination of selfed versus outcrossed seeds, we also used GenAlEx to measure the inbreeding coefficient  analyses as implemented in GenAlEx version 6.5 . Analysea priori population designations, we utilized InStruct version 1.0  and the −ln likelihood values from InStruct and selected the value where they plateaued and showed clear patterns of genetic structure.To estimate genetic structure independently of To quantify how admixture and genetic structure changed after a fire, we identified the predominant genetic cluster to which individuals were assigned, then calculated the percentage of individuals in each plot both pre- and post-fire that showed their greatest assignment to the predominant cluster. We also calculated average admixture proportions at the predominant InStruct cluster across all individuals in a plot, both pre- and post-fire. If below-ground selfed seed predominantly germinated after a fire, then we would expect post-fire plots to have more individuals and greater average admixture proportions assigned to the predominant genetic cluster. If above-ground seeds preferentially germinated after a fire, then because these seeds are capable of dispersal, we would expect post-fire plots to show fewer individuals assigned to a predominant genetic cluster. Provided that above-ground seeds showed some outcrossing, we would also expect lower average admixture proportions assigned to the predominant genetic cluster.Finally, we repeated analyses described above with individuals grouped into the predominant genetic cluster assigned by INSTRUCT. Analyses included estimates of genetic diversity, AMOVA and average admixture proportions at the majority cluster.[see].Seeds produced by CH flowers showed evidence of limited outcrossing. After removing 10 seeds from the analysis because of genotyping failure or genotyping errors, the final data set contained 40 seeds from nine maternal individuals, of which 30 out of 40 (75 %) had only maternal alleles present, 4 of 40 (10 %) differed from their mother by only one allele and 6 of 40 (15 %) differed from their mother by three or more alleles. When comparing observed inbreeding metrics to those simulated under 100 % selfing, the IR among seeds from open-pollinated flowers (0.718) was well below the range of values simulated for pure selfing , observed heterozygosity (HO) and inbreeding coefficients increased slightly after fire. The mean pre-fire HE averaged across loci was 0.311, whereas it was 0.322 post-fire  reached values close to one. Inbreeding coefficients increased slightly from pre-fire values  had only maternal alleles present, indicative of self-fertilization, biparental inbreeding or geitonogamy. Given the low seed set previously observed in P. lewtonii when pollinators were excluded  would still be greater than that expected for the 100 % selfed below-ground seed, indicating that it would be possible to detect whether above-ground or below-ground seed predominantly germinated after a fire.F and FB), indicating high rates of selfing or inbreeding. After fire, both observed and expected heterozygosity increased slightly, likely due to a slight increase in allelic diversity post-fire reflecting new alleles emerging from the seed bank. However, post-fire expected heterozygosity increased slightly more than observed heterozygosity, resulting in greater inbreeding coefficients. If seeds produced by above-ground flowers preferentially germinated after a fire, then given the greater seed set of CH flowers , the AMOVA-derived FIS decreased after fire. The apparent discrepancy likely reflects differences in how different methods quantify the effects of genetic drift on inbreeding. GenAlex and INest measure inbreeding based on the probability of identity by descent ) curves for InStruct analysis of Polygala lewtonii as presented in K = 1–10.plab067_suppl_Supplementary_MaterialClick here for additional data file.


The prevalence of autism spectrum disorders (ASD) in Taiwan has been increasing, and genetic testing for ASD has been available and provided to parents of children diagnosed with ASD in Taiwan. However, there is still limited understanding of Taiwanese parents’ knowledge of and attitudes toward such testing. Therefore, the present study addressed this gap by assessing the attitudes toward as well as actual and perceived knowledge of ASD genetic testing among Taiwanese parents of children diagnosed with ASD. A sample of 443 parents of children with ASD recruited from 236 public schools in Taiwan completed a paper-and-pencil survey. Although parents generally held favorable attitudes toward ASD genetic testing, they had deficient knowledge of such test . Tailored health education materials should be developed to improve the knowledge of ASD genetic testing among parents with affected children in Taiwan. Autism spectrum disorders (ASD) are characterized by deficits in social communication and interactions, as well as the presence of restricted, repetitive, and stereotyped patterns of behaviors and interests . The worGenetics is an important risk factor for ASD, with an estimated heritability of 80% based on a recent study with a sample of two million people across several countries . Over onThe current medical guideline for genetic diagnosis of ASD recommends both CMA and Fragile X testing as the first-tier genetic testing for ASD ,18 as suResearch has shown that parents’ intention to pursue genetic testing for their children is significantly associated with their knowledge of ASD genetic testing and their attitudes toward ASD genetic testing . However(1)What is the level of knowledge of genetic testing for ASD among Taiwanese parents of children with ASD?(2)What are the attitudes toward ASD genetic testing among parents of affected children in Taiwan?(3)What are the factors associated with these parents’ knowledge of and attitudes toward genetic testing for ASD?While ASD genetic testing has been available in Taiwan for many years ,35, therThis study was part of a larger study designed to examine views regarding genetic testing among parents of children with ASD in Taiwan ,36,37. TA paper-and-pencil survey was developed based on previous literature ,40,41,42Actual knowledge of ASD genetic testing: Parents’ actual knowledge was measured using seven multiple-choice questions. One point was assigned for each correct answer to compute an overall knowledge score. The theoretical score for actual knowledge is 0–7.Perceived knowledge of ASD genetic testing: To measure parents’ perceived knowledge of ASD genetic testing, we used a single item, “how much do you know about ASD genetic testing?” rated on a 5-point Likert-type scale ranging from very little, a little, average, a lot, to a great deal.Attitudes toward ASD genetic testing: A 5-item scale with a 4-point Likert-type rating ranging from 1 (strongly disagree) to 4 (strongly agree) was used to measure parental attitudes toward ASD genetic testing .Attitudes toward the cost of ASD genetic testing: To examine attitudes regarding the cost of ASD genetic testing, we used the item, “Undergoing ASD genetic testing is costly” rated on a 4-point Likert-type scale ranging from 1 (strongly disagree) to 4 (strongly agree). In addition, we asked parents who they think should pay for ASD genetic testing with three options including national health insurance, self-pay, and other insurance. Parents were asked to fill out a dollar amount in the following question, “Suppose you had to pay out-of-pocket for undergoing ASD genetic testing, what is an acceptable amount for you? NTD_______.”Other variables: Sociodemographic variables  and information regarding the numbers of children with ASD, type of ASD, severity level of ASD, and parents’ family history of ASD were also collected in the survey.Descriptive statistics were used to examine the central tendency, variability, percentages, and frequency distribution of the variables. Psychometric testing  was conducted for the construct of attitudes. Bivariate correlations were also conducted to assess the parental attitudes and knowledge’ associations with the continuous variables  and binary variables  examined in this study. Analyses of variance were conducted to test the relationship between attitudes/actual knowledge and the categorical variable . Only the statistically significant variables were then included in the multiple regression analyses that were utilized to evaluate factors associated with parents’ actual knowledge and attitudes pertaining to ASD genetic testing.p < 0.05 as the criterion for significance. Because the chi-square test is sensitive to large sample sizes [All statistical programming was conducted with STATA Version 15.0 with le sizes , construle sizes ,48.SD = 5.4; range = 28–63). The majority of the respondents were female (77.4%), married (88.7%), and employed (61.9%). About two-thirds of the participants (67.2%) had a below-college education level. Furthermore, a large portion of the sample was affiliated with traditional folk religion (29.4%), Buddhism (27.1%), Christianity (9.0%), or more than two religions (13.7%). Most of the participating parents reported no family history of ASD (65.4%), and 94.1% of the participants had only one child diagnosed with ASD. Over half of the participants reported the severity of their child’s ASD as mild (52.9%). Moreover, the child’s type of ASD included autistic disorder (30.5%), Asperger’s syndrome (25.6%), pervasive development disorders not otherwise specified (PDD-NOS) (20.2%), and more than two disorders (5.1%).SD = 1.5; range = 0–6) with a theoretical range of 0–7. As shown in n = 415, 95.8%) provided incorrect answers for the question about the availability of ASD genetic testing in Taiwan.The mean score of the parents’ actual knowledge of genetic testing for ASD was 2.2  reported that they knew little or very little about ASD genetic testing. Only 10 participants (2.3%) reported that they knew a lot or a great deal about ASD genetic testing; the remaining perceived an average level of knowledge of ASD genetic testing (13.6%).p < 0.01) and education level ; it was negatively related to the severity level of their children’s ASD diagnosis .As presented in 2 = 2.14, df = 3, p = 0.544, RMSEA = 0, CFI = 1.000, SRMR = 0.002). The sum score of attitude scale was 13.4 . The mean score for attitude scale was 2.7 .p < 0.05) and education level . Neither the actual nor perceived knowledge of ASD genetic testing was significantly related to the parents’ attitudes in the binary correlation analyses.As presented in More than half of the parents (61.9%) either “somewhat agree” or “strongly agree” that undergoing ASD genetic testing is costly for them. The majority of participants (88.1%) believed that the cost of ASD genetic testing should be covered by Taiwanese national health insurance. On average, they were willing to pay NTD 1033.5 (USD ~37.5) for such testing.Despite the fact that genetic testing for ASD has been available in Taiwan for many years ,35, littNotably, the participating parents’ attitudes toward ASD genetic testing in this study differed depending on the recipient of the test. Specifically, although the majority of the parents believed that children diagnosed with ASD or with ASD characteristics should undergo ASD genetic testing, only about half of the participants thought the siblings of the child with ASD should also undergo the testing. This finding was different from a study conducted in the United States, in which most of their participants (80%) with a younger undiagnosed child wanted their undiagnosed child tested if it was available . MoreoveIn addition, we also found that the severity level of the children’s ASD was negatively and significantly associated with the parents’ knowledge of genetic testing for ASD. Parents of children with a more severe level of ASD may spend more time and energy on taking care of their children instead of searching for information on ASD genetic testing. Therefore, more targeted health education materials and programs are needed in Taiwan to improve these parents’ awareness and knowledge of ASD genetic testing. These could help the parents have a correct understanding of the ASD genetic testing and potential benefits for siblings of children with a high severity level of ASD.Interestingly, we found that participating parents with higher education levels showed better actual knowledge of ASD genetic testing but held less favorable attitudes. Besides the potential benefits of ASD genetic testing, parents with higher education levels might have more concerns about the implications of ASD genetic testing, such as privacy and discrimination issues raised by genetic testing ,51. ThisOur results showed that the majority of parents in our sample believed that ASD genetic testing was expensive, and most believed that the cost should be covered by national health insurance. If not covered, the average cost that they were willing to pay was NTD 1033.5 (USD~37.5). In Taiwan, however, although national health insurance covers a large number of health services and care , it currOur study had some limitations. First, most of the parents in this study were recruited from urban areas of Taiwan, and 77.4% of the participants were mothers. Though mothers are often the main caregivers of children with ASD, future research needs to prioritize the recruitment of more fathers of children with ASD as their attitudes toward genetic testing may be different from those of mothers of children with ASD. Furthermore, similar to other survey research, we were unable to obtain a 100% response rate and study the entire target population . Thus, tDespite the limitations, this study provides insights into knowledge of and attitudes towards ASD genetic testing among parents of children with ASD in Taiwan. In line with other studies in the United States and Europe, our findings suggest that parents generally hold positive attitudes toward ASD genetic testing. Nevertheless, most parents lack knowledge on this matter. To improve parental knowledge and awareness regarding ASD genetic testing and assist parents in making informed medical decisions, health education materials and programs on the topic of ASD genetic testing should be developed and implemented in Taiwan. Meanwhile, as physicians are the most important source of information regarding ASD genetic testing for parents in Taiwan , more coThis survey study examined knowledge and attitudes toward ASD genetic testing among parents of children with ASD in Taiwan. Our findings suggest that Taiwanese parents had low levels of knowledge about ASD genetic testing, but generally held positive attitudes regarding such testing. In addition, the majority of our participants believed that ASD genetic testing was currently unaffordable and that national health insurance should cover it. Accordingly, it is important to provide health education to Taiwanese parents of children with ASD to improve their knowledge about ASD genetic testing. We further recommend that future discussions be held to explore the possibility of including ASD genetic testing coverage as a part of national health insurance.


Carcinoscorpius rotundicauda Latreille, a less mobile mangrove species, has finer population structure and lower genetic diversity compared with the dispersive deep‐sea Tachypleus gigas Müller. Even though the source populations of both species during the last glacial maximum exhibited comparable effective population sizes, the less dispersive C. rotundicauda seems to lose genetic diversity much more quickly because of population fragmentation. Contra previous studies’ results, we predict that the more commonly sighted C. rotundicauda faces a more uncertain conservation plight, with a continuing loss in evolutionary potential and higher vulnerability to future climate change. Our study provides important genomic baseline data for the redirection of conservation measures in the face of climate change and can be used as a blueprint for assessment and mitigation of the adverse effects of impending sea‐level rise in other systems.Impending anthropogenic climate change will severely impact coastal organisms at unprecedented speed. Knowledge on organisms’ evolutionary responses to past sea‐level fluctuations and estimation of their evolutionary potential is therefore indispensable in efforts to mitigate the effects of future climate change. We sampled tens of thousands of genomic markers of ~300 individuals in two of the four extant horseshoe crab species across the complex archipelagic Singapore Straits.  We aimed to measure the dispersal ability and genetic diversity of the two horseshoe crab species as a novel type of baseline data for the implementation of conservation and mitigation measures against the effects of impending rising sea levels. The results of this research are already being implemented by some local authorities.2C. rotundicauda and 116 T. gigas were collected across 46 locations  and performed eight PCR cycles on the size‐selected fragments for final library preparation before submission for next‐generation sequencing .Our sampling covered the Singapore Straits at a radius of 100 km around Singapore island . A total of 188 s Figure . All sams Figure  with fewFastQC (Babraham Bioinformatics) and demultiplexed all sequences using process_radtag as implemented in Stacks v2.4  and 2,779,046 SNPs for T. gigas (average stack depth 21.7x) without prior population assignment (per sample coverage weighted by sample size is 8.98–54.89 for C. rotundicauda and 7.85–31.33 for T. gigas). We did not assign individuals to populations based on their sampling locality because our sampling focused on maximizing spatial coverage to guarantee as much differentiation as possible in pairwise spatial distances for increased robustness of the IBD‐related analyses (see below), leaving uneven numbers of individuals at each site. Therefore, we excluded some subsequent analyses that require population assignment, for example, the calculation of private alleles per population. Moreover, as the area of study is relatively small compared with the mobility of horseshoe crabs, analyses based on sampling locality determined population assignment may not be informative. We used PLINK v1.9  .We checked sequence quality using 7.15 Li, . Reads wC. rotundicauda with 0% missing data and no linked loci ; (C0L) C. rotundicauda with 0% missing data while retaining linked loci ; (C1) C. rotundicauda with ~10% missing data and no linked loci ; (T0) T. gigas with 0% missing data and no linked loci ; (T0L) T. gigas with 0% missing data while retaining linked loci ; and (T1) T. gigas with ~10% missing data and no linked loci . Detailed scripts outlining data quality control are appended in the supplement.To accommodate different requirements of downstream analytical programs, we generated six SNP datasets, three for each species: (C0) C. rotundicauda and T. gigas in our study area, we performed AMOVA, as implemented in the R package poppr , which is considered one of the superior ways of computing effective population sizes with single time point sampling , requiring us to convert the unit of original locus positions from bp to cM using 106 bp = 1 cM as an approximation based on the estimated genomic recombination rate of the Atlantic horseshoe crab   and the minimum allele frequency at >0.05. We used a generation time estimate of 14 years based on life‐history data  between probable ancestral populations using VCFtools v0.1.16  based on the SNP genotype data, as implemented in diss.dist) as implemented in the R package poppr   0.0529, (C1) 0.1003, (T0) 0.0324, and (T1) 0.0988. When applying equal regimes of missing data filtering , C. rotundicauda has a lower genetic diversity, as indicated by higher F coefficients . A relatively low genetic diversity in C. rotundicauda as compared to T. gigas is also indicated by pronounced differences in estimates of contemporary LDNe (age = 0), 395.8 in C. rotundicauda and 4237.6 in T. gigas  in C. rotundicauda but none in T. gigas. A total of 39 out of these 45 pairs were sampled at the southeast end of the Singapore Straits; 24 of them were collected at overlapping sites; and 19 of them at one particular pair of sites ~10 km from Bintan Island  for both horseshoe crab species across the Singapore Straits, while variation in individuals between sampling localities contributes ~2% for as Table . C. rotu0.0529, C 0.1003, nd Table .C. rotundicauda are geographically arranged along a northwest–southeast genomic cline  in the ADMIXTURE analyses (Table K = 1). The second likely number of ancestral populations is two, with the second lowest cross‐validation error across all four datasets, for both species. When enforcing K = 2  failed to reveal major subdivisions within the two horseshoe crab species Figure . Populate Figure . In contes Table , the mos2 Figure , individu Figure , a smalls Figure . Even th Figures . The PCAC. rotundicauda significantly so  but T. gigas not . Results from spatial autocorrelation analyses . In C. rotundicauda, DResD analysis detects significantly high resistance to dispersal around the Singapore Deeps  in the study area, s Figure  indicates Figure .4C. rotundicauda as compared to the more dispersive, open sea‐adapted T. gigas. Based on our study, we predict further steep declines in the genetic diversity of local C. rotundicauda with the impending human‐caused sea‐level rise. Further significant losses of genetic diversity directly translate into losses in evolutionary potential (Frankham et al., C. rotundicauda populations in the Singapore Straits during the periods of intense environmental change expected for the coming decades and centuries. In contrast, the genetic diversity of T. gigas may be maintained or even be favored by a rapid sea‐level rise of up to 0.7 m (Stocker et al., We detected a relatively lower genetic diversity and finer population structure in the less dispersive, depth‐limited, mangrove‐adapted C. rotundicauda (Figure C. rotundicauda as compared to the larger T. gigas suggest that active benthic dispersal of adults, rather than passive phytoplanktonic dispersal of larvae, better explains the differences in dispersal distances of the two species. This is consistent with observations of limited larval dispersal in the other two horseshoe crab species from the Atlantic and East Asia (Botton & Loveland, Our population genetic analyses with a comprehensive geographic coverage yield the first firm evidence on differences in dispersal ability between the two horseshoe crab species Figure , particua Figure . Our appa Figure . Given sa Figure , our finT. gigas has lost almost half of the genetic diversity during the expansion from the source population whereas the less dispersive C. rotundicauda has lost more than 95% (Figure T. gigas was able to maintain genetic exchange across fairly distant populations, keeping diversity loss in check (Berthouly‐Salazar et al., C. rotundicauda branched out into increasingly fragmented and isolated populations across the Sunda Shelf. Besides differences in dispersal ability, differences in their ability to adapt to novel coastal habitats created by rapid sea‐level rise may also have contributed to the difference in genetic diversity. However, this can be only tested with exclusive sampling beyond our study area to verify whether populations of C. rotundicauda from across the Sunda Shelf display the same level of low genetic diversity.Our sampling of horseshoe crabs from randomly mixed cohorts may underestimate LDNe (Waples et al., % Figure . Loss of% Figure , which bC. rotundicauda over the last 10,000 years stands in stark contrast to the massive expansion and coastal domination of its preferred mangrove habitat in the region (Bird et al., C. rotundicauda populations on Batam and Bintan islands to the south of the Singapore Straits. Individuals sampled around the latter two islands exhibit the region's lowest genetic diversity, emerging as isolated from all other regional populations across the deep‐water barrier of the Singapore Straits (Figures C. rotundicauda across the Singapore Straits, restoration of mangrove habitats may not be as cost‐effective as conserving extant mangrove habitats in shallow parts of the Straits. Habitat restoration should primarily focus on creating corridors to provide increased connectivity between isolated populations characterized by low diversity. In the immediate future, further loss of genetic diversity in the local C. rotundicauda population unfortunately appears inevitable—intense conservation efforts notwithstanding—so long as sea levels continue to rise or persist at the current level. As for T. gigas, rapid future sea‐level rise may facilitate a slight increment in the level of genetic diversity. However, the precise fate of local populations also depends on small‐scale habitat dynamics and human disturbance and development, all of which need to be taken into consideration by conservation planners (Di Nitto et al., Our results provide important baseline data for the conservation of horseshoe crabs in the Singapore Straits and for mitigation against the future effects of climate change. These baseline data, which allow insights into both genetic diversity and dispersal ability, illustrate how dispersal ability may have contributed to present‐day patterns of genetic diversity and therefore may offer superior conservation recommendations as compared to the conventional baseline data of census population size or effective population size alone. The gradual and continuous drop of effective population size in  Figures . To ensua concentration in this area (Siswanto & Tanaka, Our results identified the southeastern end of the Straits of Malacca as a stronghold featuring the highest genetic diversity for both horseshoe crab species Figure , so thisT. gigas may even be the more endangered species because of fewer detections (Cartwright‐Taylor, T. gigas, with its panmictic population structure and strong recolonization potential, is of much lesser concern than the poorly dispersive C. rotundicauda, which suffers from a long‐term trend of decreasing effective population size throughout the Holocene. This dichotomy highlights the importance of studies such as ours in identifying the right conservation targets in our efforts to mitigate the effects of future climate change. While limited to horseshoe crabs, our study provides a blueprint for impending research to help safeguard the Earth's coastal and marine organismic communities.Recent conservation concern for horseshoe crabs in the Singapore Straits has revolved around both species at equal measure, with suggestions that The authors declare no conflicts of interest.Supplementary MaterialClick here for additional data file.Supplementary MaterialClick here for additional data file.Supplementary MaterialClick here for additional data file.Fig S1‐S5Click here for additional data file.Table S1Click here for additional data file.Table S2Click here for additional data file.Table S3Click here for additional data file.Table S4Click here for additional data file.


Ascaphus truei  is an indicator species of the health of forests and perennial streams in the Coastal and Cascade Mountains of the Pacific Northwest of North America. We used two genetic techniques—microsatellite and genotype‐by‐sequencing (GBS)—to compare the within‐region genetic diversity of populations near the northern extent of the species’ range  to two geographic regions in British Columbia and two in Washington, USA, moving toward the core of the range. Allelic richness and heterozygosity declined substantially as latitude increased. The northernmost region had the lowest mean expected heterozygosities for both techniques  and the southernmost region had the highest . The northernmost regions (NC and MC) clustered together in population structure models for both genetic techniques. Our discovery of reduced diversity may have important conservation and management implications for population connectivity and the response of A. truei to climate change.Reconstruction of historical relationships between geographic regions within a species’ range can indicate dispersal patterns and help predict future responses to shifts in climate.  Ascaphus truei's range  to two geographic regions in British Columbia and two in Washington, USA, moving toward the core of the range. We discovered a strong reduction in genetic diversity that may have important conservation and management implications for population connectivity of A. truei.We used two genetic techniques—microsatellite and genotype‐by‐sequencing (GBS)—to compare the within region genetic diversity of populations near the northern extent of  Longitudinally, British Columbia, Canada, is partitioned into the Cascade and Coast Mountains in the west, the Northern Rocky Mountains in the east, and the Central Interior in between. Several studies have compared the genetic relatedness of amphibian species in the Cascade and Coast Mountains to species in the Rocky Mountains, incidentally providing information on the genetic diversity of those species that have dispersed north of the southern periphery of the LGM  clustering into a single group. RAPD markers were ultimately dropped from genetic research due to their lack of reproducibility among studies, as the quality of DNA template and the competing effects of coamplifying loci affected the merit of the results. Nielson et al.  region, around Terrace BC, in 2014. The “Midcoast” (MC) region, around Bella Coola BC, and the “Southcoast” (SC), around Chilliwack BC, were sampled during the summer of 2015 Figure . We also2.22.2.1We used ten polymorphic microsatellite DNA markers for the microsatellite analysis . Fifteen samples were sent for sequencing in duplicate and triplicate to determine the efficacy of the genotyping method. SNPsaurus converted genomic DNA into nextRAD genotype‐by‐sequencing (GBS) libraries as in Russello et al. . GenomicFragmented DNA was then amplified for 27 cycles with 74°C extension, with one of the primers matching the adapter and extending 10 nucleotides into the genomic DNA with the selective sequence GTGTAGAGCC. Thus, only fragments starting with a sequence that could be hybridized by the selective sequence of the primer were efficiently amplified. SNPsaurus sequenced the nextRAD libraries on a HiSeq 4000 with two lanes of 150 base pair (bp), single reads (University of Oregon), and 20x depth of coverage.2.32.3.1eH) and observed (oH) heterozygosity were used to determine the genetic diversity of each region, calculated in POPPR v.2.8.0 for R v.3.4.0 . Genotype calling was done using SAMTOOLS v1.8 and BCFTOOLS v1.8  Li, , generatThe vcf file was filtered to remove alleles with a population frequency of less than 3% . Loci were removed that were heterozygous in all samples or had more than 2 alleles in a sample . The presence of artifacts was checked by counting SNPs at each read nucleotide position and determining that SNP number did not increase with reduced base quality at the end of the read. From the vcf file provided, we removed loci that were variable due to base insertions or deletions using VCFTOOLS v0.1.14 , including those genotypes in triplicate and duplicate. We expected to have some reduction of genetic diversity in the northern geographic regions and were concerned about genotype call errors, missing data, and paralogous loci in the nextRAD dataset presenting as genetic diversity in these geographic regions . We tested for significant deviations from Hardy–Weinberg equilibrium in each geographic region using GENALEX. HST for both molecular marker datasets (ADEGENET relies on HIERFSTAT v0.04.22 for F‐statistics). We used 10,000 bootstraps with a lower and upper quantile for confidence intervals of 0.25 and 0.975 to determine pairwise FST between sets of geographic regions (boot.ppfst).We used ADEGENET v2.1.1 package for R v.3.4.0 Jombart,  to calcuΘ, per geographic region with the microsatellite dataset. We calculated Θ using mean allele frequency per microsatellite locus under a stepwise mutation model  ranging from 2 to 10, without prior sample site location and with admixture. It performed 10 iterations with a burn‐in of 100,000 and a running length of 100,000 steps. We included K's based on Ln P(D) and ΔK values, scored using STRUCTURE HARVESTER .For the DAPC, we used find.clusters in ADEGENET to determine the placement of genotypes within clusters. We used spline interpolation α‐score, also in ADEGENET, to determine the number of principal components to retain. All discriminant functions were retained  of ≤30.We used the previously published We removed duplicate sequences and merged files using PICARD, retaining a single sample from those sent in triplicate or duplicates for sequencing. Files were sorted and indexed using SAMTOOLS. SNPs were called using FREEBAYES v0.9.10 . The number of on Table .De novo assembly and initial filtering of nextRAD sequences produced 8690 polymorphic loci. The dataset was reduced to 8213 loci after we removed SNPs based on insertions and deletions. The number of loci was further reduced to 4249 after we removed loci with excessive heterozygosity and excessive missing data per region. We eliminated additional loci because of significant deviations from Hardy–Weinberg equilibrium in two or more regions, yielding a final dataset of 4228 loci.3.3Fifteen samples were processed from library construction to SNP genotyping in triplicate or duplicate; seven samples in triplicate from the NC region and three samples in triplicate and one in duplicate from both the MC and SC geographic regions Table . A phyloIn the MC and NC regions, mean pairwise genetic distance for triplicates was either greater than or similar to the mean genetic distance for nontriplicates Figure . One tri3.4oH was 0.60 and ranged from 0.19 (NC) to 0.86 (CM).For the microsatellite analysis, mean allelic richness across loci provides a measure of genetic variability as loci can have many alleles. The mean allelic richness for 9 loci across geographic regions was 12.27 ± 1.54 and ranged within regions from 3.33 (NC) to 23.56 (CM) (Table o ranged from 0.028 (NC) to 0.18 (CM) , followed by the OP region with 61.49%. Mean H3.5ST was 0.26 for the microsatellites and 0.63 for the nextRAD  clustered together even as the allotted number of clusters increased for both analyses. STRUCTURE plots with clustering from 2 to 5 were included as informative patterns emerged from the data with increasing total clusters  had the highest ΔK and Ln P(D) values for the microsatellite analysis  suggested four . The low number of mtDNA haplotypes throughout geographic regions sampled in the north suggests the combined effect of limited dispersal ability and a single refugium, or a single founding population recolonizing the Coast Mountains following the LGM  for the microsatellite markers was between the northcoast and midcoast regions. This was the smallest genetic distance in the nextRAD dataset. Conversely, the smallest distance for the microsatellites was between the Cascade Mountains and Olympic Peninsula regions of Washington, USA; the greatest for the nextRAD markers.Microsatellite genotypes  and nextRAD genotypes  showed similar spatial genetic patterns for Microsatellite markers have the potential for many alleles per locus , where the genetic signal is difficult to differentiate from potential biases in the data. Checking the noise to signal ratio will ensure the accuracy and efficacy of any population, landscape, or spatial genetic study using GBS.5New methodologies and technologies in genetic and genomic research may deliver vastly more data; however, they also deliver more noise. Our research shows that GBS requires a check to ensure that the genetic diversity within the studied species is great enough to overpower the noise in the data. However, if operating at the appropriate geographic scale, GBS data provide a more accurate picture of the genetic distance among populations compared to microsatellites.A. truei's recent dispersal ability in the southern and central portions of its range. It is likely that A. truei had a very low dispersal ability during northern range expansion yet their range extends far north of the LGM, around 25,000 years ago. The long‐term implications of the dramatic reduction in genetic diversity are not known; however, we recommend that managers consider the potential implications considering ongoing climate changes.Our results reflected a northern range expansion into British Columbia following the late Pleistocene glaciations. The northern portion of the range may not reflect the total number of refugium nor Cherie M. Mosher: Data curation (lead); Formal analysis (lead); Investigation (lead); Methodology (lead); Visualization (lead); Writing – original draft (lead). Chris J. Johnson: Conceptualization (lead); Funding acquisition (lead); Investigation (supporting); Methodology (supporting); Supervision (lead); Validation (supporting); Writing – review & editing (lead). Brent W. Murray: Conceptualization (lead); Data curation (supporting); Formal analysis (supporting); Funding acquisition (lead); Investigation (supporting); Methodology (supporting); Supervision (lead); Visualization (supporting); Writing – review & editing (lead).Appendix S1Click here for additional data file.Table S1, S2, S4, S6Click here for additional data file.


ODFM is a data management system that integrates comprehensive omics information for microorganisms associated with various fermented foods, additive ingredients, and seasonings . The ODFM archives genome, metagenome, metataxonome, and (meta)transcriptome sequences of fermented food-associated bacteria, archaea, eukaryotic microorganisms, and viruses; 131 bacterial, 38 archaeal, and 28 eukaryotic genomes are now available to users. The ODFM provides both the Basic Local Alignment Search Tool search-based local alignment function as well as average nucleotide identity-based genetic relatedness measurement, enabling gene diversity and taxonomic analyses of an input query against the database. Genome sequences and annotation results of microorganisms are directly downloadable, and the microbial strains registered in the archive library will be available from our culture collection of fermented food-associated microorganisms. The ODFM is a comprehensive database that covers the genomes of an entire microbiome within a specific food ecosystem, providing basic information to evaluate microbial isolates as candidate fermentation starters for fermented food production. These microbial entities are all essential in determining the microbial signature and thus, the inherent characteristics of a given ecosystem. In this context, a comprehensive database covering all genomes of a microbiome within a specific ecosystem would aid in improving our understanding of the complex interactions among the microbial populations.Advances in next-generation sequencing technology have led to the rapid expansion of microbial genome sequence data. Easy access, as well as convenient analytical tools, have enabled the exploration of microbial communities in various environmental samples. However, efficient resource usage is becoming increasingly difficult because of the rapid accumulation of sequencing data. Environmental microbiomes in fermented foods, the mammalian gut, and soils comprise not only bacteria, but also archaea, eukaryotic microorganisms, and viruses5. Given that consumers and manufacturers alike are interested in tasty, high-quality foods6 as well as the reliability of geographic origins 1, providing standardised microbial profiles and/or genome information for key microorganisms during the fermentation process is important for ensuring the high quality of fermentation products.Fermented foods are an integral part of the global human diet. Microbial entities in fermented foods include bacteria, archaea, yeasts, and viruses. Microbial activities, as well as the type of raw materials, ultimately determine the nutritional and organoleptic properties, quality, and safety of the fermentation product1. Like other fermented foods, kimchi shows the presence of a distinct microbial community5. Taxonomic studies using culture-dependent and -independent  approaches have revealed that lactic acid bacteria (LAB), including Leuconostoc, Lactobacillus, and Weissella, are mainly responsible for kimchi fermentation9.Kimchi is a traditional Korean food prepared by fermentation of vegetables, such as kimchi cabbage, along with various added ingredients and seasonings. The global annual consumption of kimchi is 1,500,000 tonsWe have developed the Omics Database of Fermentative Microbes (ODFM), a data management system that integrates comprehensive omics information for fermentative microorganisms at the World Institute of Kimchi funded by the Korean government. The ODFM offers not only curated omics sequences of fermented food-associated bacteria, archaea, eukaryotic microorganisms, and viruses, but also several analytical tools that enable gene diversity and taxonomic analyses of an input query on the database at the whole genome level. Our knowledgebase is valuable to researchers who are interested in the functions and spatiotemporal dynamics of microbiomes in fermented foods. In particular, it provides basic information to evaluate microbial strains isolated from fermented foods as candidate starters in terms of food safety and sanitation.The ODFM is a web-based application developed in compliance with the HyperText Markup Language (HTML) 5 web standards and, thus, is supported by most web browsers. The program was designed based on the Representational State Transfer (REST) service architecture to support use on various devices, including desktop computers and mobile devices. To support stable web service in a cloud-based service environment, the ODFM is hosted on four servers . Key specifications for each server are summarised in Table https://www.oracle.com/technetwork/java/index.html)-implemented Spring framework (https://spring.io) and is additionally equipped with the open-source programs Python (https://www.python.org/) and FastQC (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/). The database module uses MySQL (https://www.mysql.com) to manage the database, while the ODFM user interface architecture consists of Registration, Data Search, Tools, Our Projects, Statistics, and Q&A  as a front-end framework to support cross browsing. The server module operates on JAVA species to each of the genera Pediococcus and Weissella20, three to the genus Lactococcus, two (sub)species to each of the genera Brachybacterium, Clostridium, Corynebacterium, and Pseudomonas, and one species to each of the genera Alishewanella21, Bacillus23, Brevibacterium, Dietzia, Escherichia, Glutamicibacter, Hafnia, Halomonas, Lentibacillus, Listeria, Megasphaera, Microbacterium, Morganella, Mycetocola, Oceanobacillus, Paracoccus, Pectinatus, Pistricoccus, Propionibacterium, Salimicrobium24, Streptococcus, Tetragenococcus, and Vibrio. As for archaea, 38 complete/draft genome sequences of extremely halophilic archaea (19 genera with 36 species) are available, including seven species belonging to the genus Haloarcula25, four belonging to the genus Halorubrum26, three belonging to each of the genera Haloferax and Natronomonas, two belonging to each of the genera Halapricum27, Halobacterium28, Halolamina29 and Haloplanus, and one species belonging to each of the genera Haladaptatus30, Halalkalicoccus, Halarchaeum, Halobellus31, Halococcus, Halogeometricum, Halopenitus, Halorhabdus, Halostella, Haloterrigena32, and Natrinema33. As for eukaryotic microorganisms, 28 genome sequences are currently available, including five undesirable white colony-forming yeasts of the species Candida, Hanseniaspora, Kazachstania, Pichia, and Yarrowia. These spoilage yeasts can grow on the surface of the kimchi and affect its odour, appearance, and texture34. Genome sequences of Brettanomyces-, Penicillium-, and Saccharomyces-belonging species isolated from beer, cheese, and sake, respectively, are available. The database also contains 70 metagenomes, 113 bacterial metataxonomes, nine (meta)transcriptomes, and seven metabolomes for various fermented foods species, of which 24 (sub)species belong to the genus 7. To cover the entire microbial populations involved in food fermentation, since 2018, we have been constantly isolating and sequencing fermented food-associated microorganisms, and updating the database with new data to expand the ODFM archive.The primary purpose of the ODFM is to provide integrative functional omics information on fermented food-associated microorganisms. The ODFM and online resource provide omics information for microbial isolates from food materials . Recent microbial community analyses based on metataxonomics have revealed that hundreds of bacterial operational taxonomic units/amplicon sequence variants can be detected in fermented foods, and that the number of species varies according to the fermentation processFor easy access of omics information, several search tools with simple (i.e. exact-match keyword) and lexical  search options are available on the front page Fig. . These thttps://www.bioinformatics.babraham.ac.uk/projects/fastqc/) to facilitate simple quality control checks of raw sequence data . The QC report tab utilises FastQC 112. In our projects tab on the front page, a list of fermented food-associated microbial studies is provided 114, preliminary screening by means of comparative genomics analysis between query and subject genome resources can be a practical way to select strains for the production of fermented foods. Accordingly, several open-source analytical tools for comparative genomics analysis are integrated in the ODFM.The microbial resources included in the ODFM are all candidate culture starters for fermented foods. Given that metabolic capabilities  and resistance to bacteriophages, but not antibiotic resistance and virulence potential, are desirable functions for a candidate starter115 search-based local alignment function. Once nucleotide/amino acid sequences are provided by users, the system aligns the query sequences with the local DNA/protein database ase Fig. . The sysase Fig. , and proase Fig. . Given t116 was integrated into the ODFM. The ANI value is calculated from two genome sequences. Users can create comparative datasets by selecting FASTA files from the registered data in the ODFM or by uploading their own sequences -based genome clustering and genetic relatedness measurement, the dRep toolces Fig. . The queage Fig. .Fig. 4ANhttps://odfm.wikim.re.kr. This easily accessible online-browsable resource facilitates rapid and functional explorations of genomes of fermentation-associated microorganisms. Genome sequence and annotation results for reference microorganisms, as well as analytical results, are directly downloadable. All microbial strains registered in the ODFM will be made available. Our team operates a culture collection of fermented food-associated microorganisms, the Microorganism and Gene Bank (https://mgb.wikim.re.kr), at the World Institute of Kimchi.We developed the ODFM, a web-based knowledgebase featuring archival and analytical functions for genome data for bacteria, archaea, eukaryotic microorganisms, and viruses associated with fermented foods. The ODFM is freely available on the website Leuconostoc and Lactobacillus species are the major mannitol- and gamma-aminobutyric acid-producing LAB, respectively119. Lactococcus and some Lactobacillus species are homo-fermentative LAB responsible for the production of lactate from pyruvate by lactate dehydrogenase120. Leuconostoc mesenteroides, Lactobacillus sakei, and Weissella koreensis convert pyruvate to diacetyl/acetoin by using acetolactate synthase, acetolactate decarboxylase, and diacetyl reductase and thus contribute to the flavour of kimchi121.By using the omics resources combined with the search tools, users are able to evaluate microbial strains isolated from fermented foods as candidate starters, and/or select microbial strain(s) among the deposited resources for use as starters. However, the process of fermentation is difficult to control because the fermentation phenotypes of different isolates are influenced by environmental conditions  and interactions with other, pre-colonised microbial communities. Previous studies have reported findings that allow linking of certain fermentative microbes with expected key features/metabolites in kimchi fermentation. We expect the ODFM to provide a framework for the analysis of genome characteristics of microorganisms isolated from various fermented foods. To increase the usage of the data and information contained in the ODFM knowledgebase, we will continuously improve the features and performance of each function. By adding categories of fermented foods based on global consumer preferences and encompassing microbial resources, our long-term goal with the ODFM is to facilitate the genomic characterisation of food microorganisms and their application as fermentation starters, as well as further functional probiotics and biological agents.Supplementary Information