Although urban greening is universally recognized as an essential part of sustainable and climate-responsive cities, a growing literature on green gentrification argues that new green infrastructure, and greenspace in particular, can contribute to gentrification, thus creating social and racial inequalities in access to the benefits of greenspace and further environmental and climate injustice. In response to limited quantitative evidence documenting the temporal relationship between new greenspaces and gentrification across entire cities, let alone across various international contexts, we employ a spatially weighted Bayesian model to test the green gentrification hypothesis across 28 cities in 9 countries in North America and Europe. Here we show a strong positive and relevant relationship for at least one decade between greening in the 1990s–2000s and gentrification that occurred between 2000–2016 in 17 of the 28 cities. Our results also determine whether greening plays a “lead”, “integrated”, or “subsidiary” role in explaining gentrification. The relationship between new greenspaces and gentrification is an important one for urbanization. Here the authors show a positive relationship for at least one decade between greening in the 1990s–2000s and gentrification that occurred between 2000–2016 in 17 of 28 studied cities in North America and Europe. Greenspaces and other forms of public places also create or reinforce community ties, civic engagement, and sense of place12.The value of urban green infrastructure and greenspace in particular is widely recognized for healthier, livable, low-carbon and climate-resilient cities. Some of the benefits include critical ecosystem services for climate adaptation, such as urban cooling and stormwater management14, France15, Germany16, Spain17, and Australia18. Empirical research on the topic ranges from an early study of Milwaukee, which pointed at a significant positive correlation between residential tree canopy cover and median household income19, to recent research demonstrating that place-based race, ethnicity and poverty factors are important correlates of poor spatial access to parks and other greenspaces25. These inequities in access to green infrastructure and greenspace have been linked with uneven negative ecological and climate impacts in cities26 and attributed to a historical and social context that produced and entrenched patterns of exclusion, segregation, or unequal urban development more generally27.Yet, large inequalities exist and persist in the distribution of and access to greenspace, especially so by race and income, as the ample literature on urban environmental justice demonstrates. Numerous studies have identified inequities by race and class when it comes to area of accessible parks, park quality, and park maintenance and safety in the Global North, including in the United States (US)30, by which seemingly laudable municipal strategies of restoring degraded urban environments, creating greenspace, or deploying climate-adaptive green infrastructure improve an area’s attractiveness while resulting in increased property values, housing prices, and physical displacement of working-class residents and racialized groups and cultures—ultimately serving as a gentrifying force through a process known as green gentrification, environmental gentrification, or climate gentrification35. Urban greening initiatives might thus become “disruptive green landscapes”36 or what some have called Green Locally Unwanted Land Uses for historically marginalized groups37.Most recently, scholars have pointed at the so-called greenspace paradox39. To the extent that interventions become embedded in processes that contribute to the displacement of the very residents urban greening was often meant to benefit, new green infrastructure can undermine municipal goals of shifting toward climate-responsive cities for all43. They can also compromise calls for transformative, equity-based climate action focusing on vulnerable groups45. In short, urban greening can become a “wicked” problem for climate and sustainability initiatives, highlighting both the presence of strong competing interests underpinned by unequal power structures46 and the need to examine its social impacts beyond climate adaptation or mitigation benefits47. This quality generates the general hypothesis of the green gentrification literature: Urban greening in a neighborhood during a given time period contributes to or accelerates gentrification in that neighborhood during the period immediately following.Consequently, green gentrification concerns have folded new planning conundrums into already complex environmental and climate justice challenges58. However, other than a few exceptions50, these analyses do not proceed from multivariate measures of gentrification and thus do not necessarily conceptualize or operationalize the complex nexus of changes happening across social, cultural, and economic arenas within gentrification. Rather, most quantitative studies examine single characteristics of social change as the outcome, such as changes in the percentage of wealthier residents (or residents’ income), racial or ethnic characteristics, housing prices, and, in some cases, residents’ occupation. Findings reveal that changes in the percentage of white residents are often revelatory indicators of green gentrification , while other studies identify increases in housing prices58; in median household income48; in college-educated residents51; loss of African-American residents51; of Hispanic residents54; or of residents from the Global South50 as key indicators reflecting gentrification. Meanwhile, gentrification is sometimes removed as a concept in favor of “neighborhood change” with new parks being shown to impact change variables such as residential composition, in- and out-flows of residents, housing costs, housing vacancies, and an area’s reputation53.Thus far, all quantitative studies of green gentrification in the Global North find substantial support for the green gentrification hypothesis49. These studies also point to the importance of a critical density of greenspaces. In New York City, the number and percent area of greenspaces in a census tract are identified as having a strong positive impact on property values56 and as positively correlated with gentrification28 while this trend is not necessarily as clear with per capita income57. Another recent study56 conducted in New York City included area of greenspace within census tracts as a control. Other studies, such as the US-wide study conducted by Rigolon and Németh48 found that new parks located close to city centers tend to trigger gentrification regardless of their size and function (function is related to offering infrastructure for active transportation). Interestingly, their finding holds true for small parks, which contrasts with other studies in the US and China in which small parks are not linked with gentrification55. Last, the effect of new greenspaces has been linked with the quality of housing stock and park design. New parks in Barcelona located in post-industrial neighborhoods with rehabbed or new housing are more associated with gentrification than those located in neighborhoods with mass housing built from the 1950s–1970s50, as are greenspaces with more esthetic and recreational value to users59.Beyond the conceptualization of gentrification, one important tension that remains unresolved within the green gentrification literature is the question of where and when greening plays a primary versus a subsidiary role relative to other factors fueling gentrification. Some studies expose this tension by examining the context of greening through the introduction of control variables at the (a) neighborhood level  and at the (b) citywide level . These studies, for example, point to the importance of greenspaces linked with gentrification being concentrated near existing gentrifying neighborhoods. In Philadelphia, neighborhoods with new public greenspaces located in proximity to other gentrified neighborhoods are found to be more susceptible to gentrification than those located farther awayIn this study, we address several limitations in the existing literature through our test of the green gentrification hypothesis across 28 cities in 9 countries of Western Europe and North America Fig. . Our dat2 and H3). The results, shown in Table We employ a Bayesian linear mixed model (in which city and country were considered as random effects) to analyze global relationships across all 28 cities in our dataset. This global model implies that, in general, greenspaces are relevant in the presence of covariates for explaining gentrification across the entire sample during the first two decades of the 2000s  growth (where included) in explaining gentrification, and a negative and relevant role for closer distance from the city center. While they may have dual roles, we operationalize these variables as drivers, not consequences, of gentrification. For example, regarding GDP growth, we assume that higher economic growth may induce gentrification. It is true that it may also follow from gentrification, but we do not test this potential as we considered it outside our study framework given the hypotheses (Methods section). While these are not standardized covariates, which limits direct comparison, these results point toward a likelihood that gentrification between 1990 and 2016 across all cities studied here was linked with new greenspace and growth initiatives in areas within the city proper and relatively close to, but not usually within, the historic city center. This greening and growth link in gentrifying areas seems to have started to be present in the cities studied here during the 2000s and into the 2010s, while prior to this time the role greening played relative to gentrification was not aligned with growth and development in a general sense.While the global model offers an important overview of the overall relationships between the data in our models, a shift toward city-by-city analysis allows us to deploy a more precise Bayesian model with a spatial weight added as a conditional autoregressive term to understand the potential role of greenspace in explaining gentrification over time. Importantly, this analysis also allows us to identify areas where gentrification took place but greening was not indicated as a relevant variable in explaining it. In all, 17 out of the 28 cities show city-level results that indicate greenspace additions from an earlier time period are relevant for explaining gentrification that occurred during the period immediately following for at least one decade of the 1990s, 2000s, or 2010s. A total of 4 of the 17 cities showing green gentrification trends are European, 2 are Canadian, and 11 are in the US. In all, among the cities studied, we find that green gentrification is more prevalent in North America than Europe, but it is not a solely North American phenomenon, and tests for the effect of region and nation show that the variation is best explained at the city-level. Overall, these results that examine the effect of all new greenspaces within 28 cities show a robust occurrence of likely green gentrification outcomes for at least one decade .Figure For our remaining 11 cities, the associations we explore show negative or non-relevant relationships between greening and gentrification throughout the study period. Figure For our final result, we summarize the role played by greening relative to the other potentially relevant covariates in the city-level models. The 17 cities where we find at least one decade of citywide green gentrification can be classified differently according to the extent to which greening is indicated as a stand-alone driver of gentrification or it is enmeshed with other growth-oriented drivers, such as new residential development or new transit infrastructure. In Fig. In our first type, “Lead Green Gentrification” cities, which include 8 of the 17 identified as experiencing citywide green gentrification, greening is the standout improvement of the built environment playing a relevant and sustained explanatory role over a long period of time in our models. While other qualities of the built environment like density and distance to center might be relevant, other changes to the built environment like new development and new transit are not, and this is the case throughout the entire study period. In short, the green gentrification hypothesis is affirmed most strongly in these cities as it appears that greening, and greening alone, is the main intervention in the built environment that explains gentrification across the city.In our second type, “Integrated Green Gentrification” cities, which include 6 of the 17 identified as experiencing citywide green gentrification, greenspace is indicated as having an explanatory role that is roughly on par with that of new development and/or new transit in terms of the relevance and temporal extent of association. Here, green gentrification is certainly a real phenomenon, but it is not greening alone that explains gentrification. Rather, greening is mixed up within a set of local interventions working together towards neighborhood redevelopment and, often, broader sustainability initiatives.Finally, in our third type, “Subsidiary Green Gentrification” cities, which include 3 of the 17 identified as experiencing citywide green gentrification, greenspace is indicated as having a relevant role in explaining gentrification, yet it is a more secondary role, with other built environment interventions seemingly more prominent based on strength and duration of association. For example, in Detroit, greening is a relevant positive explanatory variable for gentrification during the 2010s, but new residential development demonstrates a more relevant and sustained explanatory role throughout the study period. In these cities, we would say that green gentrification is present, but likely plays a subsidiary or nascent role in fueling gentrification processes in the city.Importantly, these three types of green gentrification all indicate cities where greening plays a likely role in explaining tract-level variation of gentrification results. While one might be tempted to discount integrated and subsidiary forms of green gentrification as less impactful, this interpretation would ignore the relevant role our model indicates for greening across all three types. In other words, it is not the case that because other potential explanatory variables, like transit and new real estate development, are also relevant that green gentrification is not occurring. Rather, it is possible that without greening as part of the mix in these cases, gentrification would not occur at all. Thus, in delineating these three types, we are highlighting the fact that greening drives gentrification in a manner that is not monolithic (and it should be emphasized that it sometimes is not associated with gentrification). However, even subsidiary green gentrification shows a likely relevant role for greening, meaning greening is implicated in the mix of forces that drive gentrification to some degree and needs to be considered on those terms. Thus, all three forms of green gentrification are conceptually important when testing the overall hypothesis.It is also worth noting that, while the spatial patterns of green gentrification within each city have to be considered relative to the city’s local context, there are grounds for assuming that some common spatial patterns exist and that these patterns could inform future development of the typology described in Fig. 60. While one might reasonably focus within our results on what we call “lead” green gentrification cities, the other types of cities wherein greening is indicated as having a less central but still relevant role in explaining the variation in gentrification trends are conceptually as important. All of these three types of green gentrification cities considered together form a heterogeneous affirmation of the overall hypothesis in many cities and point toward important nuances that need to be considered.Our modeling accounting for spatial effects allows us to analyze the role of greening vis-à-vis other built environment changes in explaining citywide gentrification trends; and to assess the different role of greening across time periods. Our global model results indicate that a general association between greening and gentrification is likely a somewhat more recent overall trend in the cities under study, emerging with increased strength over time and with some lag as it unfolds, at least in a general sense when considered citywide across the cities we analyze. Moving to individual city models, in 17 out of 28 cities greening from an earlier time period is relevant for explaining gentrification for at least one period immediately following  given the presence of other covariates. Those results support the overall green gentrification hypothesis within our sample given the high rate (majority of cities) and our conservative citywide analytical approach. This citywide approach offers a more robust indication than a site-level analysis examining specific greenspaces, even though those analyses offer important insights into the pathways through which green gentrification manifests62. Overall, it seems green gentrification is one possible outgrowth of approaches to urban sustainability that put initiatives ostensibly focused on ecological and public health improvements in service to economic growth agendas through visibly rebranding cities as green and livable areas ripe for investment 63.Overall, citywide green gentrification is mostly evident across our North American cities, but not only. In European cities with more recent green gentrification, including Copenhagen, Nantes, and Barcelona, an emerging pattern in this direction could be explained by recent context. These cities have seen rounds of investment-oriented greening and climate-responsive livability initiatives (such as the Barcelona Green and Biodiversity Plan or the Nørrebro climate resilience initiative) attract higher-income skilled workers, especially those in recently redeveloped areas. At the same time, those cities have also seen social protection and housing affordability policies—potentially protective tools against gentrification—dismantled to various degrees , or such tools were almost absent until nascent efforts by more progressive mayors put housing rights for vulnerable groups at the center of urban policies, as was the case in Barcelona since 201660. Often, these 11 cities show in our models that gentrification is mostly explained at the citywide level by new development (D) or transit (T) infrastructure. This is the case in Baltimore (D), Bristol (D), Cleveland (DT), Portland (D), Sheffield (DT), and Valencia (D). This finding resonates with events on the ground. For example, in Valencia, an epicenter of grandiose interventions during the Spanish economic bonanza of the 1990s and 2000s, gentrification has been linked to regeneration programs and the arrival of high-speed railway64. In other cities with negative or non-relevant relationships between greening and gentrification, other factors have been identified as strongly contributing to gentrification on a citywide scale, such as large-scale urban regeneration and high technology investment and related real estate development in places like Dublin65. In addition, in other cases, housing affordability and rights policies , such as 30% or more of the housing stock being allocated to social housing, might be playing an important protective role against green gentrification and its associated displacement66. Those anti-displacement policies are known to be more absent in North American cities, which exhibit relatively limited housing rights laws and municipal budgets heavily dependent on property taxes—and thus on increased neighborhood economic value and growth62. This policy context could also explain why the majority of sustained citywide green gentrification cities are located in the USA.When examining the 11 cities that showed negative or non-relevant relationships between greening and gentrification throughout the study period, an important caveat is that data was not always available to examine all time periods for most of these cities. It should be noted as well that in some of these 11 cities, gentrification is shown in the existing literature to be associated with broader sustainability initiatives, potentially indicating that the citywide analytic approach used here is not capturing some important specific manifestations or that the narrower operationalization of greening as one of five types of greenspace used here may not be capturing all relevant trends 67 that draw new large-scale market-price housing and associated higher-income residents. Atlanta is the host of the emblematic 53 km Beltline which will eventually link 45 neighborhoods and is accompanied by new and rejuvenated parks built since the early 2000s 68. Nantes with a series of green axes along its two rivers, bordering small and large parks, has since the early 2010s branded itself as “The City in a Garden”69, building on its 2013 European Green Capital Award, with some of the most emblematic spaces located within the greened former shipyard site Ile De Nantes in the center-west area of the city, but not only.Looking more deeply at the types of green gentrification cities identified in Fig. 28. For example, Barcelona’s more recent green gentrification has taken place in Sant Martí (eastern part of the city), a post-industrial area partially rebranded as the tech- and innovation-oriented 22@ district. Greening and gentrification has also taken place in the 2010s in the regenerated Old Town (Ciutat Vella)70 and further intensified in the upper-income district of Sarrià-Sant Gervasi. In Boston, another integrated green gentrification city, several large-scale so-called resilient development projects alongside greening, which started in the mid-2000s, are located along the self-branded climate-adaptive waterfronts of East Boston, Seaport District, South Boston, and North and South Dorchester.In other cities exhibiting green gentrification—Barcelona, Boston, Denver, Edinburgh, San Francisco, and Seattle—greening plays an explanatory role roughly on par with that of new development and/or new transit. Those are “integrated green gentrification” cases. There, greening has indeed been part of an economic growth-driven redevelopment strategy, rather than a less visible, small-scale, community-oriented approachMuch of our study’s value lays in its large-scale and international scope across 28 cities in the global North, as well as our ability to assess green gentrification through a composite gentrification score, which includes both demographic and real estate change, over diverse time periods, and at a high spatial resolution. It also lays in the high analytic robustness of the study since it examines citywide, aggregate trends, allowing us to look at whether greening on the whole, as opposed to in specific sites, is relevant for explaining gentrification processes. In that sense, it is a high-bar for attaining a positive relationship between greening and gentrification and represents a conservative approach to testing the green gentrification hypothesis.72.Our study also presents some limitations. First, the most recent period (2010–2016) was shortened due to data availability at the time of study, which might have affected our ability to assess the extent of gentrification in the most recent time period, thus effectively reducing its scope. While accounted for as much as possible, data availability and variables differed somewhat between the United States and Europe, which might also explain result differences. Further, some of our measures  can be conceptualized as a consequence of gentrification, as well as a driver, which introduces some complexity to interpretation. However, the temporal structure of the model makes clear the prior position of greening before gentrification, allowing us to at least be certain that greening is wrapped up in the generative processes producing the demographic and real estate changes observed together. Finally, our study analyzes trends within formal city boundaries with the potential for modifiable areal unit problems that may come with such boundary delimitations. Future analyses must also include green gentrification trends and variations by types and size of greenspaces and by operationalizing greening in more expansive ways. Global South cities as well as smaller towns are also worthy of examination, especially so as greening is increasingly used in a variety of contexts and has become a type of global planning orthodoxy across numerous places, including cities such as Medellin or Cape Town73, which—taken together—would ensure the construction of green and climate-responsive cities starting from an equity lens focused on long-term health equity41, rather than green cities triggering or embedding dynamics of unequal urban development.In sum, our study shows that in most of the mid-sized cities from the Global North we evaluated, while urban greening has diverse climate, health, and socio-economic benefits, it also contributes to green gentrification processes in a number of circumstances and thus to new social, racial, and health inequalities that eventually undermine climate equity and justice. Our findings call for accompanying anti-displacement and inclusive greening policies74, and an interest in comparing similar outcomes across different urban contexts in order to increase generalizability. The relevance of medium-sized cities is linked to the reality that such places compete for visibility in the context of transnational investment that can flow past places lacking a bold urban identity. Neighborhoods in these cities are often intense laboratories for urban regeneration, innovation, and green livability initiatives61.This study was designed around an international, comparative analysis of mid-sized cities  in North America and Western Europe with diverse greening trajectories. Our focus on North America and Western Europe derives from a recognition of green planning traditions and trajectories that have recently influenced both regions, even if not always to the same extentFrom the 99 mid-sized cities in North America and Western Europe that we initially identified, we sought to develop a final sample for analysis accounting for important differences between cities that may affect the likelihood and form of green gentrification. Our selection is not necessarily representative of all cities, but it is based on a city-by-city analysis designed to enable us to maximize geographical diversity; diversity in urban development pathways (e.g. growing or shrinking city); and diversity across various urban forms. We prioritized this diversity approach as it provided the best means of understanding whether the dynamics we observe are simply reflective of a certain city type or region. While this does not guarantee our results apply beyond our sample, it does help to maximize generalizability. We included Northern, Continental, and Southern Europe; Western and Eastern Canada; West and East Coast US; Southern  US; and Midwest  US cities. In terms of urban development pathways, we included cities whose economy and population were mostly shrinking since the 1990s , while others economically and demographically recovered in more recent years . We also selected cities with a stable, prolonged growth and economic vitality over the last 30 years . Some cities in our final selection are compact and dense while other cities are sprawling, with important implications for the size and quantity of new greenspace as well as for the density and magnitude of real estate (re)development projects.63. This resulted in a classification of cities by intensity and length of their greening agenda, ranging from those seeking to be the “greenest city” or a so-called “green capital” to those for whom greening was a more muted but still highly visible goal61. Using this baseline of 99 cities, we then selected a final subset of 28 cities , and the 1997 Habitat Agenda rapidly catalyzed the start of sustainability and climate planning in cities.Our goal in completing an extensive three-decade long  analysis stemmed from the fact that the 1990s are widely considered as the starting point for formal urban sustainability and later climate planning programs in many cities around the world before their explosion in the 2000s and 2010sThe broad temporal, geographic, and urban typological scope highlights the intent of our study to indicate the general degree to which green gentrification is occurring in the Global North. This intent justified the use of a conservative city-level measure wherein we only report green gentrification when the cumulative effect of all greenspaces across a city has a relevant role overall in explaining gentrification trends, using the census tract as the unit of analysis. Requiring that it be reflected citywide is a high-bar for testing the green gentrification hypothesis because there could easily have been instances of gentrifying tracts around one or a few greenspaces without registering a city-level trend if there were enough sites where green gentrification did not occur in the rest of the city. We deemed it important to maintain this conservative, citywide approach because we sought a fundamental test of the overall green gentrification hypothesis. An alternate approach focused on individual sites would almost certainly expose specific instances of green gentrification within a greater number of cities, but would not necessarily test the overall effect of new greenspaces.24). In some cases, the boundaries and sizes of greenspaces represent a manually augmented version of publicly available files when missing spaces were identified through other sources. To identify the year of inauguration, we used a combination of direct communications with city or non-profit organization staff, city land records, published reports, media reports, historic imagery, and city archival searches. We defined inauguration as the year the space was acquired for public use as a greenspace. If this year was not available, then the year the space was opened to the public was used instead.In order to build the models for this analysis, for each city we gathered data on greenspaces, gentrification, and covariates  variables. The data on greenspaces includes the spatial boundaries for all parks, greenways, preserves, gardens , or recreation areas in place as of 2016, and the year of inauguration of the spaces—starting in 1990. Acknowledging that there are multiple definitions and understandings of greenspace across disciplines , for the purpose of this study we define it as the creation of any of our five classifications of urban greenspace—parks, greenways, preserves, gardens, or recreation areas  (~5–10 min) estimate of a standard catchment for a greenspace. For this reason, we calculated the number and area of greenspaces in a given small-area geography by first creating a 400-m Euclidean distance buffer around the area. We then used the formal tract boundary plus the 400-m buffer for all greenspace calculations by tract (see ref. Connolly and Anguelovski (2021)62 for a more detailed discussion of this procedure).In order to calculate the amount of greenspace added over time to a given small-area geography, it was first assumed that every greenspace has a catchment area wherein nearby residents are easily drawn to it. We employed the widely used measure of 400 m as an easily walkable  are also includedIn order to test our main hypothesis—greenspace in a prior period explains gentrification in the period immediately following—we developed a gentrification score for each of three time periods based on a diversity-weighted sum of demographic change plus change in rent values. As change in rent is an indicator of market dynamics and prices, rather than of ability of the population to pay, change in rent and change in income are distinct measures. To develop the score, we used data from each city that coincides with a generalized list of five potential social change indicators chosen to reflect variables of importance that capture some of the complexity in the gentrification literature  variables and one rent variable suitable for comparison across cities.Our formula calculates 81. Thus, in our view—and as other studies of green gentrification posited50—a greater diversity of change across multiple variables is more indicative of gentrification than changes within only one or two variables. We added a weight for this type of diversity in change by calculating Shannon’s Equitability Index (HE) for the five social variables—a unique theoretical and methodological contribution of our study in the analysis of gentrification.Having generated the foundation of our gentrification score, we next created a weight for the diversity of change occurring. This weight is meant to respect the complexity of gentrification processes as expressed in the literature wherein we consider it to be most acute and most accurately identified in neighborhoods where there are multiple social, economic, and cultural changes happening at onceHE) is a variation of Shannon’s Index developed in prior studies and typically used to provide a measure of both the abundance and the evenness of a group of observations in species diversity studies79. The Shannon Index is calculated by summing the product of the proportional increase in each indicator in relation to the increases in all social change indicators at that location and the negative natural logarithm of that increase for each indicator variable at each location across the city. The “equitability” variation on the original index allows the number of variables measured per indicator to vary, which was necessary for us as not all cities had all five social variables available. Thus, in our case, we used this approach to develop a weight that gives greater importance to small-area units that have change across many variables at once, rather than only having change across one or two, in order to better represent the complexity and diversity of gentrification as a process of urban change81.Shannon’s Equitability Index (hrent). A higher value from this equation indicates that more demographic and real estate changes are occurring at once in the area, which we use as a proxy measurement for the degree of gentrification. Thus, we use here a unique scale of gentrification activity in small-area geographies that can be compared across national and municipal contexts.Finally, with the standardized and normalized version of the variables weighted by the Shannon’s Equitability Index, we calculated a final gentrification score. For our analysis, the extent to which gentrification is occurring is measured as the sum of the standardized and normalized changes in social variables weighted for the diversity of change occurring plus the standardized and normalized change in our rent indicator  are relevant in the presence of covariates for explaining gentrification that occurred during time period 3.With the final aim of incorporating the underlying variability between the different cities and countries, a linear mixed model (in which city and country were considered as random effects) was used for the global analysis. The model also included all covariates previously mentioned.BayesVarSel)82 was used to show which covariates were the ones that better explained the response (in presence of the others). Owing to the importance of some variables (such as greenspace), this selection process started from the null model that incorporates them.As performing a complete model selection for linear mixed models can be computationally challenging, we used a two-step procedure to identify the (best) final model. First, after removing those variables with substantial missing data, a Bayesian model selection procedure for multiple linear regression models with fixed effects .Second, starting from the selected model for each hypothesis, we selected the best model among those with the covariates already included, plus the two random effects, n = 28 cities:Inference on the final global model of each hypothesis was performed within the Bayesian framework and so prior elicitation for all the parameters and hyperparameters of the model was required. Inference is performed within the Bayesian statistical approach as it allows us to deal in a natural way with the uncertainty of both the parameters and the models. In particular, we chose vague prior distributions to emphasize our lack of knowledge about them. The complete global model can be expressed as follows (Eq. ), where BayesVarSel82 was used to show which covariates were the relevant ones (in presence of the others). Next, we used the Moran index83 to test whether or not the residuals of the model presented spatial autocorrelation. When they did, our third step was to add a spatial random effect that aimed to explain the underlying spatial effect. The spatial component was included as a conditional autoregressive term, reinforcing the idea that the value of an area depends on the values of its neighbors. In order to avoid confounding of the spatial effect and the covariates, the model was expressed as a spatially restricted regression where the spatial random effect is orthogonal to the covariates space.With respect to the city-level analyses for each hypothesis, a three-step procedure was used to check which variables of interest were relevant and whether there was any spatial effect (showing possible hidden geographic drivers resulting from a wide range of possible dynamics within the underlying urbanization process). Again, as a first step, As before, inference was performed within the Bayesian framework and prior elicitation for all the parameters and hyperparameters of the model was again required. In particular, we chose vague prior distributions to emphasize our lack of knowledge about them. The complete model can be expressed as follows (Eq. ):3\docum84. An important note is that Bayesian probabilities are not traditional p-values and should not be considered as interchangeable.As the posterior distributions of the parameters and hyperparameters do not have an analytical expression, all the resulting models were fitted using the Integrated Nested Laplace Approximation methodology, which is a computationally fast software implementation that provides accurate approximations to posterior distributionsFurther information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary InformationSupplementary DataReporting Summary


Introduction: Viral hemorrhagic septicemia virus (VHSV) is the most lethal pathogen in aquaculture, infecting more than 140 fish species in marine, estuarine, and freshwater environments. Viral hemorrhagic septicemia virus is an enveloped RNA virus that belongs to the family Rhabdoviridae and the genus Novirhabdovirus. The current study is designed to infer the worldwide Viral hemorrhagic septicemia virus isolates’ genetic diversity and evolutionary dynamics based on G-gene sequences.Methods: The complete G-gene sequences of viral hemorrhagic septicemia virus were retrieved from the public repositories with known timing and geography details. Pairwise statistical analysis was performed using Arlequin. The Bayesian model-based approach implemented in STRUCTURE software was used to investigate the population genetic structure, and the phylogenetic tree was constructed using MEGA X and IQ-TREE. The natural selection analysis was assessed using different statistical approaches, including IFEL, MEME, and SLAC.Results and Discussion: The global Viral hemorrhagic septicemia virus samples are stratified into five genetically distinct subpopulations. The STRUCTURE analysis unveiled spatial clustering of genotype Ia into two distinct clusters at K = 3. However, at K = 5, the genotype Ia samples, deposited from Denmark, showed temporal distribution into two groups. The analyses unveiled that the genotype Ia samples stratified into subpopulations possibly based on spatiotemporal distribution. Several viral hemorrhagic septicemia virus samples are characterized as genetically admixed or recombinant. In addition, differential or subpopulation cluster-specific natural selection signatures were identified across the G-gene codon sites among the viral hemorrhagic septicemia virus isolates. Evidence of low recombination events elucidates that genetic mutations and positive selection events have possibly driven the observed genetic stratification of viral hemorrhagic septicemia virus samples. Novirhabdovirus  belongs to the order Mononegavirales, the family Rhabdoviridae, and the genus bdovirus . VHSV isper year . The smaper year . The higper year . The VHSper year . VHSV vind birds .Oncorhynchus mykiss) farms for five decades since the disease was originally described in Germany in the 1930s. In the 1980s, VHSV emerged in the Pacific Northwest region of North America, causing fatal disease in fish in marine and brackish environments of Washington, Alaska, and California. Additional epizootiological investigations in Europe revealed the widespread presence of VHSV in the marine environments of the Baltic Sea, Scotland, the English Channel, Kattegat, Skagerrak, and the North Sea, as well as in Japan and Korea. VHSV has become established in the North American Great Lakes and caused extensive losses to aquafarming. Additional fish mortality episodes appeared during 2006–2008 at several locations in lakes Michigan, Erie, St. Clair, and connected waters (8). The clinical signs and outcome of the infection vary depending on VHSV genotype, fish species, age, stress level, temperature, and other environmental factors  reported VHSV as a worldwide fish pathogen . Schäper factors . VHSV plVHSV is an enveloped, bullet-shaped, and non-segmented virus. It possesses a single-stranded negative-sense RNA genome comprising 11,158 nucleotides. The linear genome contains six genes encoding a non-virion protein (NV) and five structural proteins, namely, the nucleoprotein (N), phosphoprotein (P), matrix protein (M), glycoprotein (G), and RNA polymerase (L), which are organized as 3-N–P–M–G–NV–L-5 . The genG-gene, is one of the key components responsible for VHSV cross-species transmission and infection emergence. The G-gene has a complete coding length of 1,524 bases, encoding 508 amino acids. The nucleotide sequence encoding the G-protein has been widely used as the preferred molecular marker to assess the VHSV genetic diversity and evolution  and several sub-lineages of genotypes I and IV  , encoded by the volution . Few stuvolution . The G-pvolution . The G-pIVa–IVb) . The genIVa–IVb) .G-gene. We speculate that VHSV G-gene might be under the influence of natural selection, due to its role in the host immune response. Therefore, the possible role of genetic natural selection and recombination, to shape the VHSV genetic structure, was examined. Additionally, the enormous amount of complete G-gene sequences available in the public domain repositories from different continental regions motivated us to undertake the current study. The outcomes of this study may implicate in future G-gene-based vaccine development against VHSV.The current study aims to elucidate the genetic distinction and spatiotemporal distribution of VHSV based on G-gene sequences of VHSV isolates were retrieved from the virus pathogen resource database of NCBI  was generated via MUSCLE, implemented in MEGA X software , namely, r2 and |D′| were computed over the entire alignment using the DnaSP v6.0 program  approaches.The G-gene. This clustering program assigns individuals probabilistically to one or more populations based on allele frequency at each locus  method with default parameters and 1,000 permutations.The genetic composition results obtained from STRUCTURE v2.3.4 were validated by the Fst (fixation index) value. Arlequin v3.11 was used to calculate the Fst value  via the G-gene sequences of VHSV was used for the generation of the phylogenetic tree based on the neighbor-joining (NJ) method (The MSA of 710 ) method . In addi) method . The inf) method . The trevia the prcomp (stats) function in R.PCA is a multivariate statistical method employed to explore the relationships between variables and samples. PCA is a powerful approach to reduce the dimensionality of complex datasets . The appG-gene, seven different methods were used in the RDP4 package. These methods are RDP  method  method  was empl) method .G-gene, a Bayesian skyline plot (BSP) model  model , implemeP) model , was useP) model . The GTR program .via the STRUCTURE program, the data need to be tested for the hypothesis of whether the loci are in a linkage equilibrium state. The standardized index of association ISA value obtained for VHSV was 0.0000 , indicating a signal of weak linkage disequilibrium (non-random associations at different polymorphic sites). In order to further investigate the existence of the LD, |D′| and r2 were calculated. D′ is the function of LD measurement. The average value of |D′| and r2 were 0.8783 and 0.0627, respectively, indicating the existence of low LD and weakly linked polymeric loci. This justifies the usage of STRUCTURE to examine the genetic structure of VHSV data.To study the population structure of VHSV via the admixture model implemented in the STRUCTURE v2.3.4 program. Five independent simulation runs were pursued for each distinct K  to identify the Kopt value (highest peak in the plot K vs. ΔK). The analysis inferred a major peak at K = 3 and a minor peak at K = 5 , acquired for the three major clusters. This suggests the statistically promising subpopulation genetic structure existence among the VHSV isolates.Cluster C1 comprises samples from genotypes II, III, IV, IVa, and IVb. Among the members of C1, the genotypes IV, IVa, and IVb majorly are of Asian origin, while the samples from genotypes II and III have been isolated from Europe. Cluster C2 corresponds to members of genotypes Ia and Ic. All the samples from genotypes Ia and Ic isolated from Denmark are grouped in cluster C2, except a Turkish isolate that belongs to genotype Ie, i.e., observed as admixture, holding membership scores of 0.422 and 0.378 from C2 and C3 clusters, respectively. The third cluster, i.e., C3, holds samples from Ia, Ib, Id, and Ie genotypes. The negligible genetic distinction in C3 among different genotype samples might be due to low sample sizes of Ib, Id, and Ie genotypes. The genetic component distinction at G-gene observed several admixed strains from subpopulation C2a at K = 5, holding major membership scores from C1a and minor membership scores from C3a ancestries. Additionally, these admixed strains were observed to constitute a distinct clade in the phylogenetic tree.The peak at K = 5 unveils additional diversification of VHSV into five genetic components  . The memG-sequences (n = 710) to construct NJ and ML trees with 1,000 replicates. The tree topologies were compared with the clustering pattern acquired from STRUCTURE (at K = 3 and 5). The preliminary delineated K = 3 and K = 5 results assented with the phylogenetic tree . However, the United States samples clustered closely with the Asian samples . The genp < 0.00001) (DA) and mean pairwise differences (πxy) between C1 (genotypes IVa and IVb) and C2 (genotype Ia) were observed among the proposed population’s clusters, whereas the least pairwise net number of nucleotide differences (DA) and mean pairwise differences (πxy) were observed between C2 (genotype Ia) and C3 . The highest within-population genetic differentiation (π) was observed for C1 samples belonging to genotypes IVa and IVb, while the least π value was calculated for the samples from genotypes Ia and Ib  . AMOVA calculates the genetic variance at single or multiple loci due to variation within the population as well as between populations. The locus-by-locus AMOVA analysis inferred that 60.25% of genetic diversity in VHSV samples was attributed due to differences among the population groups (0.00001) . A highe0.00001) . The sig0.00001) . The hig and C3) . During  and C3) .G-gene sequences at a significant p-value = 0.00001 threshold. The analyses were repeated with loose p-value thresholds, i.e., 0.0001, 0.001, 0.01, 0.1, and 0.5; however, no recombinant sequences were identified. Later on, 50 complete genome sequences of VHSV were examined for recombination events based on set criteria with a p-value = 0.00001 threshold. Among these, only two potential recombinant VHSV isolates were identified. The same two recombinant isolates were acquired during subsequent analyses performed at loose p-value thresholds, i.e., 0.0001, 0.001, 0.01, 0.1, and 0.5 for complete VHSV genome data. A potential recombinant sample (VHSV-I*), with accession number MK598848, was isolated from China according to the database sample collection record. This recombinant isolate is speculated to have a major parent (accession number MN038328.1) and minor parent (accession number MN038333.1) reported to be isolated from Japan and France, respectively. Another potential recombinant isolate (VHSV-II*), submitted under accession number KC778774, is isolated from Denmark, having a major parent with accession number MN856666 from France and a minor parent with accession number KY359356 isolated from the United States.There was no recombinant VHSV isolate identified based on p = <0.05) for C1 samples, three codons for C2 samples, and eight codons for C3 samples  (Fewer recombination events were evident among VHSV samples in the current study. Recombination is reported to occur less frequently in negative single-stranded RNA viruses (RNA) compared to positive single-stranded RNA viruses (RNA+) . Patiño-+) .G-gene sequences and observed a substantial increase in population size during the years 2000–2001 and 2001–2003  substitution. This suggests purifying selection as the principal evolutionary force acting upon the VHSV G-gene. In general, positive selection sites may responsible for the immune pressure, leading to an escape mutation, and maintain the sites that increase virus fitness, whereas negative selection or purifying selection is responsible for removing mutations that are lethal and cause reduced fitness. Epitopes (short peptides) are the minimal structural feature recognized by the immune system and are the principal components of subunit vaccines. Characterizing the positive selection features of the pathogen proteins holding epitopic features is valuable to identify vaccine candidates worthy of experimental validation. The sites evolving under strong purifying selection may suggest targets for vaccine or antiviral development with improved resistance profiles. However, identifying sites evolving under negative selection is challenging, as it is often impossible to distinguish from sampling bias. Therefore, designing the vaccines based on the information on positive selected sites is more appropriate than that on negative selection sites (G-gene-based DNA vaccination strategy could be promising against VHSV. However, the genetic distinction and homogeneity of this gene in different continental regions, as addressed in the current study, might be considered to devise an effective vaccination strategy worldwide.Additionally, the signature of negative selection was observed in the VHSV on sites . DNA vacon sites . Howeveron sites . The G-gG-gene sequences. The isolates were initially clustered into three subpopulations based on genetic heterogeneity, congruent with the clustering pattern of phylogenetic tree analysis. The VHSV samples were additionally stratified into five subpopulations at K = 5, speculating the possible emergence of new VHSV genotypes and lineages. The genetic structure analyses inferred that the majority of genotype Ia isolates collected from Denmark constituted a separate cluster. Later on, these samples were clustered into two lineages. The evidence of episodic positive selection events was observed in VHSV G-gene. Some of the positive selection sites were located within the conformation-dependent neutralizing epitope regions of the coding protein, suggesting an adaptation to immune response-related genetic variation across different host fish species. The analyses of the current study unveiled that most of the observed genetic diversification is based on mutation rather than recombination. The current analyses unveiled that genetic mutation might be an important factor driving observed selection as well as genetic stratification among the VHSV samples. The molecular epidemiological information may implicate in the prevention and control of VHSV infection in aquafarms.The population hierarchical study of the VHSV was pursued on their available complete


Drosophila melanogaster remains poorly understood. In particular, how genetic variation is partitioned within its putative ancestral range in Southern Africa remains unresolved. Here, we study patterns of population genetic structure, admixture, and the spatial structuring of candidate incompatibility alleles across a global sample, including 223 new accessions, predominantly from remote regions in Southern Africa. We identify nine major ancestries, six that primarily occur in Africa and one that has not been previously described. We find evidence for both contemporary and historical admixture between ancestries, with admixture rates varying both within and between continents. For example, while previous work has highlighted an admixture zone between broadly defined African and European ancestries in the Caribbean and southeastern USA, we identify West African ancestry as the most likely African contributor. Moreover, loci showing the strongest signal of introgression between West Africa and the Caribbean/southeastern USA include several genes relating to neurological development and male courtship behavior, in line with previous work showing shared mating behaviors between these regions. Finally, while we hypothesized that potential incompatibility loci may contribute to population genetic structure across the range of D. melanogaster; these loci are, on average, not highly differentiated between ancestries. This work contributes to our understanding of the evolutionary history of a key model system, and provides insight into the partitioning of diversity across its range.Despite a century of genetic analysis, the evolutionary processes that have generated the patterns of exceptional genetic and phenotypic variation in the model organism  Drosophila melanogaster remains one of the most powerful genetic systems to understand the molecular underpinnings of phenotypic and fitness variation since its development in the early 20th century . RegardD. melanogaster, with particular focus on population differentiation within the presumed ancestral range in Southern Africa. We also implement a new linear discriminant analysis (LDA) approach to quantify the frequency of nine common inversions across Southern Africa. Lastly, we describe the geographic distribution of previously identified candidate incompatibility alleles and their contributions to admixture and population structure. Our results help to clarify the demographic history of D. melanogaster and provide some insights into the persistence of genetically unique clades within D. melanogaster.Here we address the extent of genetic differentiation and gene flow across a global sample of D. melanogaster, we combined whole genome resequence data from 803 lines of D. melanogaster sampled globally, including 223 newly sequenced genomes, 190 of which originate from previously unsampled or undersampled rural locales within the presumed ancestral range in Southern Africa.To understand the global distribution of diversity and population structure of D. melanogaster is mainly structured between flies from Southern Africa and Out of Africa (OOA), as reflected by PC1 (which explains 10.2% of the variation). While somewhat intermediate along PC1, flies collected from East and West Africa, are much more distinct from OOA and Southern Africa along PC2  analysis based on collinear regions of the genome revealed that genetic variation within NGSAdmix (PCAngsd (K) and assign ancestry to each individual. These methods varied significantly in the number of inferred ancestry types, with NGSAdmix inferring K = 3 as most likely , Central Africa , and all OOA lines. At K = 14 we identify eight ancestries that predominantly occur in Africa: five of which are most common in Southern Africa, and one that is most common in each of East Africa (excluding Ethiopia), West Africa, and Ethiopia. The OOA lines comprise three ancestries, one ancestry type that is more common in Europe, Egypt, and Tazmania, one that is more common in North America and the Caribbean, and one ancestry that is most common in Beijing (K = 14) did not have a single ancestry >50%, and so were excluded from further analyses.To explicitly compare diversity within and divergence among ancestry types, we used NGSAdmix  and PCAn and new Beijing  online.  Beijing  online. K-means clustering and PC analyses largely agree with our consensus phylogeny of these lineages (A). The genome-wide consensus phylogeny was based on individual ML phylogenies of 811 fly lines  in non-overlapping 100 kb windows across collinear regions of the genome. This phylogeny predominantly groups samples by geography and the ancestries we have identified (A). When using K = 3, each majority ancestry type is largely monophyletic (A). An interesting exception to this pattern is a small group of individuals with a majority Central African ancestry  that are sister to the OOA clade. When using K = 14 ancestries, monophyly of each ancestry is variable. Of these 14 ancestry types, nine are commonly the majority of ancestry . Of these nine majority ancestries, seven are largely monophyletic across the autosomal genome. These include three ancestries that are most common in or unique to flies collected in Southern Africa; one each for flies collected from West Africa, East Africa, and Ethiopia; and one ancestry group that is most common in flies collected from Beijing. The remaining two major ancestries are most common in OOA lines, with one being more common in flies collected from North America and the Caribbean and one being more common in flies collected from Europe, Tasmania, and Egypt , we next sought to assess global differentiation, divergence, and diversity to further clarify evolutionary relationships and histories among ancestries. When using K = 3 ancestries, FST ranged between 0.076 and 0.161, with the lowest global FST found between AFR1 and AFR2 (FST = 0.076), and OOA being relatively more differentiated from both AFR2 and AFR1 . Variation in FST was linked to both geographic distance and variation in within-species diversity. In general, geographically proximate ancestries were less differentiated and ancestries with lower diversity had higher levels of FST . Second, we find much reduced DXY between all OOA lineages . Clades from OOA all showed low diversity, with Beijing individuals showing the lowest diversity , while Ethiopia and all three OOA ancestries showed the opposite pattern  and Tajima's quencies . Under belection  online. election . For theelection A and 2. Y values  online, ine with ). In coniversity . Similarcestries  online.  pattern  online. election  online. e lowest  online, D. melanogaster, we evaluated the extent of gene flow among distinct lineages within a global sampling of D. melanogaster. While we calculated Patterson's D statistics for all possible trios , we focus on three potential cases of gene flow: (1) between Southern Africa and other African lineages, (2) between African and OOA lineages, and (3) the source(s) of African ancestry in the southeastern USA . Similarly, several lines collected in South Africa cluster with the OOA clade. As in above, these mismatches in clustering may represent more recent examples of admixture between ancestries, including evidence of back migration of OOA ancestries into South Africa . We note, however, that West Africa and Ethiopia also exhibit significant introgression . Therefore, we find evidence of more recent as well as likely historical gene flow between several African ancestries and OOA lineages. Although multiple shared signals of introgression likely indicate non-independent incidences of introgression (either via post-introgression migration or introgression to a common ancestor), a lack of shared introgression in other sets of comparisons indicates that rates of gene flow are likely unequal among ancestries .In contrast to patterns in Southern Africa, we find that some Central African ancestries show significant introgression with several OOA ancestries. In particular, both West Africa and Ethiopia show evidence of introgression with both Beijing and OOA2  online. fig. 1gression  online, ene flow C. TherefD statistics with windowed analyses using fdM, which quantifies admixture rates and is better suited to a window approach than Patterson's D (fdM is significantly higher with OOA1 than OOA2 (C). These results indicate that while West Africa likely experienced some amount of gene flow with the common ancestor to all OOA lineages , elevated introgression levels between OOA1 and West Africa suggests that OOA lineages have experienced at least partially independent introgression events with West Africa or differential loss of introgressed alleles between OOA ancestries.It's long been recognized that African ancestry is present in the Caribbean and USA , includirson's D , 2021. Wrson's D  online, han OOA2 C. These K = 3 and K = 14 ancestries, we find significant latitudinal clines in West African ancestry, but no significant clines in other African ancestries , where X denotes a collection site from the Caribbean or the USA. We note that when all samples are included this trend is not significant , as trios where samples from Georgia, North Carolina, or Ithaca were used as P3 exhibit elevated fdM with West Africa. When samples from Georgia, North Carolina, and Ithaca were omitted, we recover a marginally significant negative cline in fdM across the Caribbean and southeastern USA that mirrors the cline observed in West African ancestry . Together, our results indicate that the Caribbean and USA have either experienced independent gene flow from West Africa relative to other OOA ancestries or there has been less loss of introgressed West African alleles in flies from the Caribbean and southeastern USA than in flies from other OOA ancestries. Clines in West African ancestry may then have been formed via neutral diffusion with northward migration, selection maintaining clines in ancestry, or some combination . We find that the X chromosome exhibited significantly elevated fdM relative to the autosomes, while differences among the autosomes were much less apparent t, In(2R)NS, In(3L)OK, In(3R)K, and In(3R)P are all fairly common throughout Africa, particularly in Southern and West Africa, consistent with African origins (In(3L)OK was most common in samples from Southern Africa, and our results are consistent with findings that this inversion is both common in and unique to Southern Africa, and more common in rural locales than urban locales (In(3R)Mo, In(1)Be, and In(1)A), for others this may be because the relative frequencies of inversions differ between genetic lineages and many are found in heterozygous form ((2L)t inversion, individuals that are homozygous for the inverted haplotype appear paraphyletic, with one large cluster representing inverted lines from West and East Africa and one representing inverted lines from Southern Africa. Assuming each inversion only evolved once, in the absence of gene flow we would predict individuals that were homozygous for the inversion to be monophyletic, and not cluster by geography. Lastly, we find that inversion regions tend to have higher π, DXY, and Tajima's D, and lower FST than the collinear regions of the genome  for the loci identified by fig. 4A; FST for candidate incompatibility loci than the genome-wide average  exhibit elevated FST at candidate incompatibility loci identified by To understand if and how alleles with negative epistatic fitness effects are structured throughout the range of K = 3 or the nine major ancestries, and thus we only present the latter below . Given D analyses do not strongly support a scenario of historical gene flow between most Southern African ancestries and other ancestries, mismatches between major ancestries and geographic sampling locations may provide insight into contemporary gene flow. When the major ancestry does not correspond with geographic location we infer that these individuals represent recent migration events which can potentially provide insight into the directionality of contemporary gene flow. These mismatches were quite common- particularly between flies collected in South Africa and both OOA and Ethiopia. In line with this observation, recent ancestry-based analyses have found evidence of a single, recent pulse of admixture from cosmopolitan flies into several African D. melanogaster populations, including in Ethiopia and South Africa . Further, West African ancestry is the only ancestry type to show a clinal gradient of prevalence across the Caribbean and North America, which is an important pattern structuring admixture in these populations.It has long been recognized that flies from the Caribbean and southeastern USA possess both European and African ancestry . Here weegh, dlg1, btv, CaM, and lov. Expression of egh and dlg1 both regulate the amount of time spent courting, with expression of egh in particular being required for male courtship to occur at all  of 30, a minimum quality (minQ) of 20, and a genotype information for at least 90% D. melanogaster individuals. Then, using PCAngsd, we inferred K (the number of distinct ancestry types) for each inversion region and performed an LDA analysis on the ancestry proportions. As inversions suppress recombination in heterozygotes, they can create long blocks of linkage disequilibrium in natural populations NS, In(3L)OK, In(3L)P; Large chromosomal inversions are known to segregate within ome arms . We ther phylogenies in windows across the genome using iqtree version 1.6.12  defined as the number of significant PCs + 1. In contrast, NGSAdmix functions similarly to a typical K-means clustering program, wherein we computed the likelihood for a range of values of K. In our case, we estimated the likelihood of five replicate runs of K for each value from 2 to 15. We then used CLUMPAK to estimate the best K . Additionally, when individuals are defined based on these major ancestries, most major ancestry types are largely monophyletic. Given these observations, we defined nine major ancestries from the K = 14 analyses. Three ancestries are most common in Southern Africa: one is largely found in individuals from Harare, Zimbabwe (we refer to this as HD), the other two include individuals from many collection locales (we refer to these as South1 and South2), but we note that South1 tends to include more rural samples and is sister to all other lines studied herein. Three major ancestries largely include lines from Out of Africa (OOA). One of these is mainly restricted to Beijing, one largely contains individuals from Europe, Egypt, and Tasmania (OOA2), and one largely contains individuals from the southeastern USA and the Caribbean (OOA1). The final three ancestries are mainly found in Central Africa, with a unique ancestry being most common in Ethiopia, all of West Africa, and East Africa (minus Ethiopia). All subsequent analyses were performed on populations defined by ancestry, with each analysis performed for K = 3 ancestries and the nine major ancestries groups described above.pixy . For this analysis we used an all-sites VCF to include invariant sites. We also calculated Tajima's D using VCFTools v.01.15 , Ethiopia)}, HD], Southern). Because the OOA lines represent three distinct and non-monophyletic major ancestries, “OOA” in the phylogeny above could represent OOA1, OOA2, or Beijing. Similarly, “Southern” in the phylogeny above could represent either the South1 or South2 major ancestry group. In total, we present results for 65 unique and phylogenetically accurate trios. Significance of Patterson's D was determined using a standard block jackknife procedure (To estimate broad patterns of gene flow between genetic lineages of gression ) using D [Dsuite  with D. rocedure , we thenrocedure  online.fdM in non-overlapping 20 SNP windows. fdM is an f statistic derivative that is more appropriate for windowed analysis and provides a more accurate estimate of the proportion of the genome that has experienced introgression than Patterson's D , where X could denote each of the three Southern African ancestries . To assess differences in the extent of derived allele sharing between West Africa and each Southern African ancestry, we performed an ANOVA with Type III SS using fdM as the response variable and P3 , chromosome, and their interaction as independent variables. We also used twisst to assess heterogeneity in phylogenetic relationships across the genome, with  being the consensus tree. Because only West and HD exhibited a significant Patterson's D and our goal was to assess potential signals of introgression between West Africa and HD, we only performed this analysis with HD as sister to the Central African clade.The first set of analyses sought to assess differences in allele sharing among West Africa and various Southern African ancestries. For the fdM in 20 SNP windows across the genome for the following phylogeny , where X could denote OOA1 or OOA2. We then asked if the value of fdM differed between these two comparisons using an ANOVA with type III SS, with fdM as the response variable and P3 , chromosome, and their interaction as the independent variables. We also completed these analyses with ET as P1, but as these results of these analyses did not differ, we present only the findings in which East was used as P1. To bolster these results, we then tested the following phylogenetic relationship:  in twisst, and asked if there was a surplus of windows in which West Africa and OOA1 were sister, relative to West Africa and OOA2 using an ANOVA with type III SS, with the number of windows supporting each relationship as the response variable and the comparison , chromosome, and their interaction as independent variables. Under a model of either equivalent introgression or ILS, there should be no difference in either the number of windows which show West Africa and either OOA ancestry as sister or the values of fdM with either OOA ancestry as P3. In both cases, we find that West Africa and OOA1 exhibit significantly more allele sharing than West Africa and OOA2. To further assess clines in West African ancestry across the Caribbean and the southeast USA, we calculated fdM again, but instead of grouping all collection locales into one population based on major ancestry, we used each collection locale as P3.For the second set of analyses, we sought to further explore potential signals of introgression between West Africa and OOA ancestries. For these analyses, we used South1 as the outgroup to better polarize SNPs. We calculated fdM windows between West Africa and each OOA ancestry, then parsed the windows that were unique to the West Africa-OOA1 comparison. We then performed an overrepresentation test using PANTHER v.17.0 with an FDR cutoff of 0.05 . Using a similar premise, but in a naturally admixed population, Lastly, we characterized patterns of differentiation for loci that have previously been implicated in two studies of genetic incompatibilities with nogaster . First, D. melanogaster. Here, we aim to differentiate two potential scenarios: First, potential incompatibilities between Europe and Africa may have arisen with or after the Out of Africa expansion. Under this scenario, we predict that differentiation at potential incompatibility loci should be low between genetic lineages in Africa, but high between Europe and all African populations. Second, it is also plausible that candidate incompatibilities between Europe and West Africa originated in Africa, with shared ancestry or subsequent introgression explaining allele sharing between Europe and Southern Africa. Under this scenario, we expect that differentiation should be high between West Africa and both Europe and Southern Africa, but relatively low between Europe and Southern Africa.Elevated differentiation of these candidate incompatibility alleles between West Africa and France may stem from multiple evolutionary scenarios, and differentiating these scenarios can help elucidate the geographic distribution and potential origins of candidate incompatibilities within FST windows from above for all pairwise comparisons between our nine major ancestries. We also performed these analyses using K = 3 ancestries for comparison. These two scales of analyses broadly agree, and so we focus on the nine major ancestries for clarity's sake. We first ask if potential incompatibility loci have elevated divergence relative to the whole genome for any comparison using an ANOVA with Type III SS using the car library in R , as well as pairs of interacting loci that were highly differentiated between OOA2 and each of West Africa and Southern Africa . For these analyses, we define highly differentiated loci as those with FST values within the top 2.5% of FST for that population pair.To differentiate these scenarios, we used the ary in R  for eachmsac223_Supplementary_DataClick here for additional data file.


The genetic diversity and population structure of breeding germplasm is central knowledge for crop improvement. To gain insight into the genetic potential of the germplasm used for potato breeding in a Nordic breeding program as well as all available accessions from the Nordic genebank (NordGen), 133 potato genotypes were genotyped using the Infinium Illumina 20 K SNP array. After SNP filtering, 11 610 polymorphic SNPs were included in the analysis. In addition, data from three important breeding traits – percent dry matter and uniformity of tuber shape and eye – were scored to measure the variation potato cultivars and breeding clones.The genetic diversity among the genotypes was estimated using principal coordinate analysis based on the genetic distance between individuals, as well as by using the software STRUCTURE. Both methods suggest that the collected breeding material and the germplasm from the gene-bank are closely related, with a low degree of population structure between the groups. The phenotypic distribution among the genotypes revealed significant differences, especially between farmer’s cultivars and released cultivars and breeding clones. The percent heterozygosity was similar between the groups, with a mean average of 58–60%. Overall, the breeding germplasm and the accessions from the Nordic genebank seems to be closely related with similar genetic background.The genetic potential of available Nordic potato breeding germplasm is low, and for genetic hybridization purposes, genotypes from outside the Nordic region should be employed.The online version contains supplementary material available at 10.1186/s12870-022-03726-2. Solanum tuberosum L.) originates from the Andes in South America, where it was domesticated 8 000–10 000 years ago [The cultivated potato  later than the rest of the countries in Europe. It was introduced to Sweden in 1658; i.e., about 50 years after other European sites , 53, 55.Sweden was the first country in the Nordic region to start a formal potato breeding program through work at the Swedish Seed Association  in 1903 , 38. Thiin vitro, and metadata such as morphological characteristics, collecting site, release year, and pedigree information are kept in their database GENBIS. Their collection of farmer’s cultivars was characterised by Veteläinen et al. [Since 1979, the Nordic Genetic Resource Center (NordGen) has collected released potato cultivars, old farmer’s cultivars, and breeding clones from the Nordic countries for future conservation . The potn et al.  studyingn et al. .In our research that we report herein we studied the diversity of 133 potato genotypes from SLU and NordGen. Genotypic data was collected using the GGPv3.0 array , and theP < 0.001) difference was found for percent dry matter and tuber shape uniformity. According to Tukey’s range test the accessions from NordGen displayed, on average, significantly different percent dry matter and tuber shape uniformity than the two other groups (SLU and cultivar grown today). An alternative division of genotypes was by grouping them by clonal type – breeding clones, released cultivars or farmer’s cultivar. There were significant differences for all measured phenotypic traits –percent dry matter, tuber eye depth, and tuber shape uniformity among these three groups (P < 0.001) (Table The sample of genotypes included in this study is of the table potato type grown in the Nordic region of Europe – particularly Sweden. The genotypes were grouped based on two criteria 1) source of the germplasm , or 2) clonal type . Phenotypic characteristics of importance to potato marketing – percent dry matter in the tuber flesh, tuber eye depth and tuber shape uniformity are shown in Table 3 and T4) separating from the remnant accessions  was not observed, neither when studying the genotypes separately  negative trend (-0.27) towards lower levels of heterozygosity over time  are unevenly distributed among the groups being compared throughout all analysis in this study. The largest difference in population size was found when comparing the three groups based on source of germplasm , the group cultivars grown today contain only nine accessions , and NordGen contain 75 accessions. There are about 25 cultivars commonly grown for the potato market today in Sweden . Among these 25 cultivars are ‘King Edward’, ‘Bintje, ‘Connect’ and ‘Carolus’, which we have included in this study. The most commonly grown early maturing cultivar in Sweden by far is ‘Solist’. Both the phenotypic and genotypic diversity could be limited to the number of individuals, which must be considered when discussing the results from this study. The grouping based on clonal type has a more even distribution of population size, spanning from 29 (farmer’s cultivars) to 57 (breeding clones) genotypes.1-T4). A larger proportion of the genotypes from NordGen has a higher percent dry matter content compared to the other two groups, a character that is correlated to mealy potato tubers [The phenotypic data from the NordGen accesssions has been extensively described in a previous publication . We combo tubers . When stIn general, potato exhibits a low degree of population structure , 37, witK ranging from 0 to 10 , NordGen (n = 75), and a selection of nine released cultivars popularly grown in Sweden today and commonly used as parents for the breeding germplasm at SLU (Supplementary Table S3) were selected, and clonal generation were defined for each genotype until five cycles of selection (T6+). The information about the nine released cultivars regarding both country of origin (breeder or breeding company) and release year were available through the Potato Pedigree Database [n = 8), cultivars (n = 38) and farmer’s cultivars (n = 29). The origin countries of the accessions from NordGen were defined as the Nordic country in which the genebank did the collecting. Regarding cultivars, there are two, ‘Early Rose’ and ‘Marius’ that are non-Nordic (the former released in the USA and the other in Poland) but in the database they are assigned to Sweden and Norway because of their former extensive cultivation in both.In total, the sample of our study included 133 genotypes from the germplasm collections at the potato-breeding program at SLU n = 9, NordGeDatabase . The accth May and harvested on 11–12th October. Tuber shape uniformity was recorded using a scale ranging from 1 (non-uniform) to 9 (uniform), while tuber eye depth was recorded on a scale ranging from 1 (deep) to 9  following Selga et al. [Phenotypic information was available for 109 of the total 133 genotypes , Tukey’s range test [To detect variation of the means of each of the groups – either by source of genotypes  or type of clone , an analysis of variance was conducted for each of the three breeding traits. If the analysis of variance was significant  for DNA extraction, genotyping and allele calling. The GGPv3.0 array  was usedWASpoly’ . The impWASpoly’ .stamppConvert from the R package ‘StAMPP’ [stamppNeisD from the same R package. To investigate population structure, the genetic distance matrix was displayed in two ways. A heatmap was used to visualise the genetic kinship among the genotypes based on Nei’s genetic distance. In addition to the heatmap, a principal coordinate analysis (PCoA) [pcoa from the R package ‘ape’ [Population structure was investigated using the set of 11 610 SNPs. The allele format was converted from numeric format (0–4) to an AB format, where A represents the major allele and B represents the minor allele at each given locus. The function ‘StAMPP’  was used‘StAMPP’  using ths (PCoA)  was carrge ‘ape’  without ge ‘ape’ .set from the R package ‘dendexted’ [colored_bars from the same package.The genetic relationships among the genotypes from the NordGen population were further investigated with a hierarchical clustering analysis. The Euclidian distance was calculated among the 75 genotypes from NordGen using 11 610 SNPs in numeric format (0–4). Subsequently, a neighbour joining dendrogram was generated using the function ndexted’ . The coun = 133) was also determined using the ‘STRUCTURE’ software [K) ranging from 1 to 10 with 50 000 replicates of the Markov Chain Monte Carlo after 15 000 burn-ins. The optimal K value was determined as the log-likelihood of K, and visualized using the ‘STRUCTURE HARVESTER’ web application [K were permuted using the software ‘CLUMPP’ [FullSearch. The output was subsequently visualised using the software ‘DISTRUCT’ [The population structure for all genotypes  and clonal type. *Origin for the NordGen population means Nordic country the genotype was collected from, not country of breeding program. **Breeding cycle for breeding clones from the SLU breeding program, Tn. Where n denotes the number of cycles of selections in the breeding program.Additional file 4:Figure S1. The 25 NordGen accessions included in Veteläinen et al. (2005), dendrogram based on our 15 000 single nucleotide polymorphism (SNP) markers.