Geographical Correlation Research Articles

Triple exposure: the geographic correlation between flood risk, climate skepticism, and social vulnerability in the United States

Abstract This study investigates the geographic correlation between flood risk, climate skepticism, and social vulnerability across the United States. Our results reveal a systematic underestimation of flood risk in the Federal Emergency Management Agency’s (FEMA) Flood Insurance Rate Maps, especially in Appalachia, parts of New England, and the Northwest. These three regions face two additional risks: high levels of social vulnerability and skepticism about climate change. Nationally, there is a statisically significant correlation (0.19, p < 0.005) between flood risk and climate change skepticism, which increases (0.28, p < 0.005) in regions with high FEMA undercounts and elevated flood risk. Climate change skepticism manifests as distrust in science, an underestimation of property and community risk, and a resistance to mitigation and adaptation efforts. Indicators of social vulnerability, such as poverty rates, physical disabilities, unemployment, households in mobile homes, and lack of vehicle access, are especially pronounced in Appalachia. Addressing this geographically-embedded triple exposure—flood risk, social vulnerability, climate change skepticism— requires strategies to enhance local resilience. These include revising the 100-year floodplain categorization in FEMA’s National Flood Insurance Program to better reflect climate change, conducting public education campaigns in vulnerable communities, and scaling-up financial assistance for flood mitigation and adaptation projects.

Europa’s H2O2: Temperature Insensitivity and a Correlation with CO2

H2O2 is part of Europa’s water-ice radiolytic cycle and a potential source of oxidants to Europa’s subsurface ocean. However, factors controlling the concentration of this critical surface species remain unclear. Though laboratory experiments suggest that Europa’s H2O2 should be concentrated in the coldest, most ice-rich regions toward the poles, Keck adaptive optics observations have shown the strongest H2O2 signatures in comparatively warm, salt-bearing terrain at low latitudes. As a result, it was suggested that the local non-ice composition of these terrains—particularly hypothesized enrichments of CO2—may be a more dominant control on H2O2 than temperature or water-ice abundance. Here we use observations of Europa from the NASA Infrared Telescope Facility, Keck Observatory, and JWST to disentangle the potential effects of temperature and composition. In order to isolate the effect of temperature on Europa’s H2O2, we use the ground-based observations to assess its response to temperature changes over timescales associated with Europa’s daily eclipse and diurnal cycle. We use JWST Cycle 1 data to look for any geographic correlation between Europa’s H2O2 and CO2. Changes in Europa’s 3.5 μm H2O2 absorption band both from pre- to post-eclipse and across a local day suggest minimal effects of the local temperature on these timescales. In contrast, the JWST observations show a strong positive correlation between Europa’s H2O2 and CO2 bands, supporting the previously suggested possibility that the presence of CO2 in the ice may enhance H2O2 concentrations via electron scavenging.

A geographic-semantic context-aware urban commuting flow prediction model using graph neural network

Urban commuting flow prediction is crucial for urban planning, transportation optimization, and supply chain management. Traditional models and machine learning solutions often fall short in capturing the complex dynamics of urban mobility, overlooking factors like spatial correlation and human mobility patterns. Recent deep learning advancements have shown promise by integrating spatial correlation through incorporating geographical adjacency. However, this approach may not fully capture spatial correlation, particularly the semantic adjacency effect of zones connected via public transportation lines, such as metro networks—a novel dimension overlooked in prior research. The metro station serves as a major transit hub, attracting passengers from various origins and destinations and correlating the flow between the connected zones and other zones within the metro network. Devising an optimized model to encapsulate these spatial correlations is another crucial area of research where scientists are actively seeking the most effective solutions. We propose a novel sequential hybrid approach leveraging graph-based learning, focusing on the underexplored impact of metro networks on commuting flow prediction. Our model integrates two types of adjacency—geographic and semantic—using Graph Convolutional Networks (GCNs) to embed geographic correlations among proximate zones and Graph Attention Networks (GATs) to incorporate semantic adjacency defined by metro line connectivity. Furthermore, our approach innovatively incorporates features directly from online maps, eliminating reliance on third-party resources and enhancing scalability and efficiency. We validate our model using real-world datasets from Montreal, Canada, comprising travel surveys and GPS trajectory types, totaling nearly 900,000 trip records. Through evaluation using established performance metrics such as MAE, RMSE, MAPE, and CPC, we demonstrate significant improvement in predictive accuracy compared to existing state-of-the-art models. Our research has practical implications for urban planning and infrastructure development, aiding policymakers and urban planners in analyzing the impact of new urban infrastructure on commuting flow dynamics and facilitating informed decisions regarding transportation and infrastructure planning.

Spatial Epidemiology and Its Role in Prevention and Control of Swine Viral Disease

Spatial epidemiology offers a comprehensive framework for analyzing the spatial distribution and transmission of diseases, leveraging advanced technical tools and software, including Geographic Information Systems (GISs), remote sensing technology, statistical and mathematical software, and spatial analysis tools. Despite its increasing application to swine viral diseases (SVDs), certain challenges arise from its interdisciplinary nature. To support novices, frontline veterinarians, and public health policymakers in navigating its complexities, we provide a comprehensive overview of the common applications of spatial epidemiology in SVD. These applications are classified into four categories based on their objectives: visualizing and elucidating spatiotemporal distribution patterns, identifying risk factors, risk mapping, and tracing the spatiotemporal evolution of pathogens. We further elucidate the technical methods, software, and considerations necessary to accomplish these objectives. Additionally, we address critical issues such as the ecological fallacy and hypothesis generation in geographic correlation analysis. Finally, we explore the future prospects of spatial epidemiology in SVD within the One Health framework, offering a valuable reference for researchers engaged in the spatial analysis of SVD and other epidemics.

Performance of a negative binomial-GLM in spatial scan statistic: a case study of low-birth weights in Pakistan.

Spatial cluster analyses of health events are useful for enabling targeted interventions. Spatial scan statistic is the stateof- the-art method for this kind of analysis and the Poisson Generalized Linear Model (GLM) approach to the spatial scan statistic can be used for count data for spatial cluster detection with covariate adjustment. However, its use for modelling is limited due to data over-dispersion. A Generalized Linear Mixed Model (GLMM) has recently been proposed for modelling this kind of over-dispersion by incorporating random effects to model area-specific intrinsic variation not explained by other covariates in the model. However, these random effects may exhibit a geographical correlation, which may lead to a potential spatial cluster being undetected. To handle the over-dispersion in the count data, this study aimed to evaluate the performance of a negative binomial- GLM in spatial scan statistic on real-world data of low birth weights in Khyber-Pakhtunkhwa Province, Pakistan, 2019. The results were compared with the Poisson-GLM and GLMM, showing that the negative binomial-GLM is an ideal choice for spatial scan statistic in the presence of over-dispersed data. With a covariate (maternal anaemia) adjustment, the negative binomial-GLMbased spatial scan statistic detected one significant cluster covering Dir lower district. Without the covariate adjustment, it detected two clusters, each covering one district. The district of Peshawar was seen as the most likely cluster and Battagram as the secondary cluster. However, none of the clusters were detected by GLMM spatial scan statistic, which might be due to the spatial correlation of the random effects in GLMM.

Analyzing the impact of planning schemes on the neighborhood-scale PM2.5 distribution through Pix2PixGAN and crowd-source data

The distribution of PM2.5 concentrations at the neighborhood scale is interfered with not only by spatial morphology, but also by spatial functions, and this effect shows heterogeneity in different urban locations due to differences in spatial dynamics. Taking the area within the Fourth Ring Road of Beijing in 2020 as an example, this paper provides urban planners with an evaluation framework for fine-grained prediction of neighborhood-scale PM2.5 distribution based on the Pix2PixGAN model and the multi-source data with crowd-source data at its core. Through the crowd-source data, the spatial functions and corresponding spatial dynamics in the neighborhood scale can be characterized in detail. Then the framework utilizes Geographically weighted poisson regression (GWPR) to screen spatial functions with significant geographic correlation with PM2.5 distribution and their heterogeneity coefficients. Based on the filtered functions, heterogeneity coefficients, and PM2.5 distribution raster, the dataset of Pix2PixGAN model is composed, and the optimal model with the most accurate PM2.5 distribution prediction is trained and selected. Finally, the paper illustrates how to analyze the qualitative and quantitative impacts of planning proposals on the distribution of PM2.5 through the optimal model, using six planning scenarios as examples.

Graph-based spatial co-location pattern mining: integrate geospatial analysis and logical reasoning

ABSTRACT Spatial co-location patterns reflect the inherent correlations among geographical elements. Mining co-location patterns of POIs can provide valuable insights for urban planning and resource management. Generally, co-location mining comprises two steps: proximity relationship determination (geospatial analysis) and frequent pattern recursion (logical reasoning). Previous methods often separate these two steps: serializing proximity relationships to enumerate frequent sequences. However, this approach suffers from limited flexibility and intuitiveness: as continuous spatial contexts are segmented into numerous small parts, it fails to adequately represent geographic correlations and hinders the effective visualization of logical reasoning. Facing these challenges, this study proposes a novel graph-based spatial co-location mining method (GSCM), which leverages graphs to integrate geospatial analysis and logical reasoning. Initially, to establish adjacency relationships, GSCM constructs the adaptive neighborhood graph, which dynamically adjusts proximity thresholds to accommodate geographic heterogeneity. Subsequently, the Apriori logical recursive process is realized on the graph structure. By leveraging graph searching, pruning, and growing, the potential growth directions of co-location patterns are identified, enhancing both the efficiency and intuition of frequent pattern recursion. Through experiments conducted on large-scale POI datasets from Wuhan, GSCM is compared with existing baseline methods, verifying its potential to uncover co-location patterns in complex spatial contexts.

Analysis of the Characteristics of Patients Visiting the Tokyo 2020 Olympics Polyclinic.

To evaluate the characteristics of patients who visited the Polyclinic during the Tokyo 2020 Olympics and analyze geographical and economic correlations with the number of clinic visits. Cross-sectional study. Polyclinic during the Tokyo 2020 Olympics. Patients who visited the Polyclinic. Data from the electronic medical record system of the Polyclinic were extracted. The number of visits for each athlete or team official was calculated by country. Relationship between number of visits per patient and total number of team members, total health expenditure per capita, density of medical doctors, life expectancy at birth, and education expenditure per gross domestic product (GDP) were investigated. Independent variables related to medal tables were also investigated. The average number of visits per athlete was 0.67, and it was higher in athletes from non-high-income countries compared with high-income countries for both male and female athletes. Number of visits per athlete was higher in countries with low life expectancy at birth (95% CI, -0.16 to -0.02, P = 0.012) and education expenditure per GDP (95% CI, -0.17 to -0.04, P = 0.003). During the Tokyo 2020 Olympics, the number of visits to the Polyclinic per athlete was higher in countries with low life expectancy at birth and education expenditure per GDP.

Can low-carbon transportation construction improve urban green total factor productivity? A quasi-natural experiment based on 282 prefecture-level cities in China.

Total factor productivity in the green economy is a major step forward in fostering robust growth in the economy. Urban green total factor productivity (GTFP) may be increased and carbon emissions decreased through the low-carbon transformation of the transportation sector. To analyze how the pilot policy of low-carbon transportation system construction affects urban GTFP, this study builds a DID model and a spatial Durbin model using panel data from 282 Chinese cities between 2006 and 2021. The findings show that low-carbon transportation systems boost urban GTFP. In addition, the mechanism test shows that low-carbon transportation development increases urban GTFP by optimizing urban industrial structure, expediting economic agglomeration, and encouraging technological innovation. Lastly, the geographical correlation test shows that GTFP is geographically associated and that spatial spillover reduces the impact of low-carbon transportation growth on GTFP. The findings affirm the need to vigorously promote low-carbon transportation systems to aid in the achievement of the carbon neutrality target.

RETRACTED: A semi-arid climate's use of exploratory data analysis (EDA) as a reliable non-parametric method for geochemical mapping

In this study, exploratory data analysis (EDA) methods, specifically boxplots, were employed to examine the composition of stream sediments in the Collo area of Northeast Algeria. The region's diverse lithological formations, interactions between permanent and temporary watercourses, intermittent flash floods, rugged topography, and fluctuating climatic conditions contribute significantly to data variability. Utilizing the boxplot's capabilities, our analytical approach successfully identified outliers, recorded the actual distribution of data, and determined skewness without the need for prior data processing, often required in conventional statistical studies. The boxplot proved to be an effective tool, offering insights into the geographical distribution of elemental concentrations, including iron (Fe), zinc (Zn), copper (Cu), arsenic (As), chromium (Cr), and lead (Pb). Geochemical mapping, driven by a robust class selection mechanism developed from the boxplot, revealed strong geographical correlations. A notable northeastern anomaly, characterized by elevated concentrations of Pb, Zn, Cu, and As, aligned with known base-metal sulfide and arsenopyrite mineralization sites. Additionally, outlier data for Cr indicated proximity to a plagioclase-lherzolite intrusion and known chromite deposits. The geographical distribution of iron, corresponding with known magnetite and hematite resources, was effectively highlighted using robust class selection based on boxplot analysis. This comprehensive investigation underscores the value of EDA methods in identifying mineralization trends amid the complex variability of the Collo area.

Decoding and Unravelling Mpox, Herpes, and Syphilis Infections: A State of Art Review.

As the world recovers from the COVID-19 pandemic, a resurgence in MPXV cases is causing serious concern. The early clinical similarity of MPXV to common ailments like the flu and cold, coupled with the resemblances of its progressing rash to other infections, underscores the importance of prompt and accurate diagnosis. Among the infections, smallpox is clinically closest to MPXV, and rashes similar to MPXV stages also appear in syphilis and varicella zoster. A comprehensive review of MPXV, herpes, and syphilis was carried out, including structural and morphological features, origins, transmission modes, and computational studies. PubMed literature search on MPXV, using MeSH key terms, yielded 1904 results, with the analysis revealing prominent links to sexually transmitted diseases. More in-depth exploration of MPXV, Herpes Simplex Virus (HSV), and Syphilis revealed further disease interconnections and geographical correlations. These findings emphasize the need for a holistic understanding of these interconnected infectious agents for better control and management.

Machine learning and genetic algorithm for mapping soil available phosphorus in coastal provinces in Southeast China

Soil available phosphorus (SAP) is a crucial nutrient for sustaining plant growth, and accurate prediction of its spatial distribution is vital for agricultural production and environmental management. Here, we comprehensively integrate multiple environmental variables impacting the phosphorus cycle, including climate, remote sensing imagery, vegetation indices, topography, and anthropogenic factors. Employing two geostatistical methods, three machine learning algorithms, and incorporating genetic algorithm (GA), the research aims to precisely forecast SAP content and generate spatial distribution maps. The GA successfully identifies the optimal indices of environmental variables, comprising Band3, Brightness Index, land use type, distance to water, and mean annual precipitation. In performance evaluation, the combination of GA with Gradient Boosting Decision Tree exhibits the best results (R2 = 0.60, MAE = 2.79, RMSE = 3.64). High-value SAP areas are predominantly located in the western region, demonstrating a correlation with land use patterns. Validating the model’s applicability in coastal provinces in Southeast China, covering Fujian, Zhejiang, and Guangdong, underscores the profound insights of this study into the dynamics and geographical correlations of SAP. Our findings demonstrate that GA exhibit outstanding stability and performance in mapping SAP, contributing to a better understanding of SAP dynamics, supporting improved soil management practices and sustainable agriculture.

Study on the spatial network structure of energy carbon emission efficiency and its driving factors in Chinese cities

How to adapt to climate change while achieving sustainable economic and social growth has become a major topic of concern worldwide. With the constraints of the dual‑carbon strategy, the integration of regional environmental governance and energy carbon reduction governance is a prevalent focus. This study focuses on analyzing 282 prefecture-level cities in China. The SNA approach and modified gravitation model are utilized to estimate the energy carbon emission efficiency of Chinese cities from 2006 to 2021. The spatial correlation network and the QAP model are ultimately utilized to investigate the factors. The study presented the following findings: ① There are notable temporal and regional discrepancies in the energy carbon emission efficacy of Chinese cities. Generally, defined by high values in the east and low values in the west. ② The efficiency of energy carbon emissions in networks connecting urban areas in China is multidimensional, complex, and organic and has improved stability. ③ The developed regions in the east exert a dominant influence on the geographical network, while the central and western parts of the country, which are distant, are considered peripheral. ④ There are few connections within each segment of the geographic correlation network for energy carbon emission efficiency in Chinese cities; however, there are substantial correlations between segments, indicating the presence of a substantial spillover effect. ⑤ The formation of energy‑carbon emission efficiency correlation networks in Chinese cities is significantly influenced by disparities in economic development and government intervention. Conversely, the level of science and education exerts a significantly negative impact on this phenomenon. It is advisable to encourage the development of a spatial correlation network that connects urban energy carbon emission efficiency. This can be achieved through the implementation of specific measures, the establishment of a regional coordination mechanism, leveraging the strengths of energy-efficient regions in the east, maximizing the potential of each sector, and considering the factors that influence the outcomes. Compressing the driving factors and attributes of the spatial correlation network of energy carbon emission efficiency holds substantial practical importance for facilitating the ongoing expansion of the regional low-carbon energy network space and establishing a regional low-carbon synergistic energy governance system.

Infrastructure Bottlenecks as Opportunity for Local Development: The Case of Decentralized Green‐Hydrogen Projects

AbstractEnergy geography has not scrutinized the role of energy infrastructures and market design for decentralized energy production and local development. The paper addresses this research gap by looking at the emerging green‐hydrogen market in Germany. We argue, first, that the capacity and geography of infrastructures and associated bottlenecks determine how supply and demand can be matched at different scales. Second, market design and associated controversies over infrastructure bottlenecks are important for understanding the green‐hydrogen market and its geography and financing. Conceptually, we draw on the geography‐of‐markets literature. Empirically, we show how infrastructure bottlenecks and market design link the green‐hydrogen to the renewable‐electricity market. As long as the market design requires green‐hydrogen production in geographical and temporal correlation with renewable‐electricity generation, the geography of green‐hydrogen production follows the geography of renewable‐electricity generation. Moreover, bottlenecks in transportation infrastructures offer a window of opportunity for decentralized production and local development.

Emerging Trends in Information-Seeking Behavior for Alpha-Gal Syndrome: Infodemiology Study Using Time Series and Content Analysis.

Alpha-gal syndrome is an emerging allergy characterized by an immune reaction to the carbohydrate molecule alpha-gal found in red meat. This unique food allergy is likely triggered by a tick bite. Cases of the allergy are on the rise, but prevalence estimates do not currently exist. Furthermore, varying symptoms and limited awareness of the allergy among health care providers contribute to delayed diagnosis, leading individuals to seek out their own information and potentially self-diagnose. The study aimed to (1) describe the volume and patterns of information-seeking related to alpha-gal, (2) explore correlations between alpha-gal and lone star ticks, and (3) identify specific areas of interest that individuals are searching for in relation to alpha-gal. Google Trends Supercharged-Glimpse, a new extension of Google Trends, provides estimates of the absolute volume of searches and related search queries. This extension was used to assess trends in searches for alpha-gal and lone star ticks (lone star tick, alpha gal, and meat allergy, as well as food allergy for comparison) in the United States. Time series analyses were used to examine search volume trends over time, and Spearman correlation matrices and choropleth maps were used to explore geographic and temporal correlations between alpha-gal and lone star tick searches. Content analysis was performed on related search queries to identify themes and subcategories that are of interest to information seekers. Time series analysis revealed a rapidly increasing trend in search volumes for alpha-gal beginning in 2015. After adjusting for long-term trends, seasonal trends, and media coverage, from 2015 to 2022, the predicted adjusted average annual percent change in search volume for alpha-gal was 33.78%. The estimated overall change in average search volume was 627%. In comparison, the average annual percent change was 9.23% for lone star tick, 7.34% for meat allergy, and 2.45% for food allergy during this time. Geographic analysis showed strong significant correlations between alpha-gal and lone star tick searches especially in recent years (ρ=0.80; P<.001), with primary overlap and highest search rates found in the southeastern region of the United States. Content analysis identified 10 themes of primary interest: diet, diagnosis or testing, treatment, medications or contraindications of medications, symptoms, tick related, specific sources of information and locations, general education information, alternative words for alpha-gal, and unrelated or other. The study provides insights into the changing information-seeking patterns for alpha-gal, indicating growing awareness and interest. Alpha-gal search volume is increasing at a rapid rate. Understanding specific questions and concerns can help health care providers and public health educators to tailor communication strategies. The Google Trends Supercharged-Glimpse tool offers enhanced features for analyzing information-seeking behavior and can be valuable for infodemiology research. Further research is needed to explore the evolving prevalence and impact of alpha-gal syndrome.

A Study on Geographic Correlation between Indoor Radon Exposure and Leukemia Incidence in Canada.

In Canada, leukemia is diagnosed at a rate of 15 cases per 100,000 persons and accounts for about 3% of all new primary cancers. In this study, geographical correlation between residential radon exposure and leukemia incidence was investigated at a provincial level with more accurate long-term radon measurement data in 21,330 homes and 10-y (2000-2009) age standardized incidence rates per 100,000 population for various subtypes of leukemia. The analyses showed that the incidence rate of non-Hodgkin lymphoma is statistically significantly correlated with average indoor radon (222Rn) concentration for Canadian females (p = 0.01210) but not for males. At a provincial level, the association between average indoor radon level and chronic lymphocytic leukemia incidence rate is statistically significant (p = 0.0167), and the correlation is somewhat stronger for females (p = 0.0043). No correlation was found between indoor radon exposure and any other subtypes of leukemia evaluated in this study.

Improving multi-site photovoltaic forecasting with relevance amplification: DeepFEDformer-based approach

The present research on photovoltaic (PV) forecasting is devoted to the use of spatial information about PV sites to improve the accuracy of the models, but most of the models have to increase their network complexity to learn the spatial dependence. In this paper, we propose to take advantage of the known geographic location information of PV sites and embed them directly into the input information of Decoder, which makes it easier for the model to focus its attention. We refer to this process as relevance amplification. Based on this, this paper proposes Relevance Amplification based DeepFEDformer (RAD-FEDformer), where DeepFEDformer adds multiple Multi-Layer Perceptron (MLP) layers to FEDformer to improve the perception of deep features. The Relevance Amplification Module (RAM) is designed to receive geographic correlation information as a way to enhance its influence on the Seasonality component of the Decoder input and improve the performance of the attention mechanism in the model. Using the power generation data from PV plants distributed in 11 regions of Belgium as our case study, we evaluated the performance of RAD-FEDformer in predicting PV data at 48/96/192 time steps into the future for each plant. Compared to other Transformer family models, RAD-FEDformer achieved SOTA results by demonstrating significant R2 improvements of 16.70%, 51.45%, and 23.50% over FEDformer, along with an average MSE reduction of 16.9%. We designed Ablation Experiments to validate and compare the performance of MLPs of different sizes based on the ETTm1 dataset and discussed the performance of RAM and its effect on the attention mechanism based on the PV dataset. The results show that our model enhances the performance of the attention mechanism in Multi-Site PV prediction scenarios, and conclude that the optimization of RAM is more effective in longer sequence predictions.

Spatiotemporal Dynamics of CO2 Emissions in China Based on Multivariate Spatial Statistics

With China’s rapid industrialization and urbanization in the process of socio-economic development, the extensive use of energy has resulted in a large amount of CO2 emissions, which puts great pressure on China’s carbon emission reduction task. Through multivariate socio-economic data, this paper proposes an extraction and screening method of multivariate variables based on land-use types, and the downscaled spatial decomposition of carbon emissions at different scales was carried out by using the spatial lag model (SLM). This paper makes up for the shortcomings of previous studies, such as an insufficient modeling scale, simple modeling variables, limited spatio-temporal span of spatial decomposition, and no consideration of geographical correlation. Based on the results of the spatial decomposition of carbon emissions, this paper explores the spatial and temporal dynamics of carbon emissions at different scales. The results showed that SLM is capable of downscaling the spatialization of carbon emissions with high precision, and the continuity of the decomposition results at the provincial scale is stronger, while the differences of the decomposition results at the municipal scale are more obvious within the municipal units. In terms of the spatial and temporal dynamics of CO2 emissions, carbon emissions at both scales showed a significant positive correlation. The dominant spatial correlation types are “Low–Low” at the provincial level, and “Low–Low” and “High–High” at the municipal level. The smaller spatial scope is more helpful to show the geographic dependence and geographic differences of China’s carbon emissions. The findings of this paper will help deepen the understanding of the spatial and temporal changes of carbon emissions in China. They will provide a scientific basis for the formulation of feasible carbon emission reduction policies.

Distribution, diversity and genetic structure of alders (Alnus lusitanica and A. glutinosa) in Spain

Alders (Alnus spp., Betulaceae) are keystone pioneer species in riparian habitats. In the Mediterranean Basin, where the diploid Alnus glutinosa coexists with the recently discovered tetraploid A. lusitanica, the distribution, diversity and genetic structure of alder species is unknown. The main objectives of this study were to report the distribution of A. glutinosa and A. lusitanica, quantify the local genetic diversity of populations and explore the presence of putative hybrids resulting from the coexistence of A. lusitanica and A. glutinosa individuals in Spain. We assessed 627 individuals from 22 natural alder populations by combining chromosome counts, flow cytometry and microsatellite markers (SSRs) analyses. The ploidy analysis, validated through chromosome counts, revealed the presence of 92 diploid and 535 tetraploid trees from three A. glutinosa and 18 A. lusitanica pure populations, respectively. Notably, no hybrids were detected, not even in Assa, the only population where both species co-occur. The occurrence of mixed ploidy level in alder is reported for the first time in Spain (Assa, Logroño, within the Ebro basin). SSR analysis revealed robust genetic differentiation between the two species and distinctive patterns of variation within A. glutinosa. The high level of differentiation observed was supported by the most probable population clustering determined through pca and structure analyses, along with the identification of a high number of private alleles. For A. lusitanica, the results revealed three distinct genetic groups, geographically distributed. Positive genetic and geographical distance correlations indicated that differentiation could have been driven by isolation by distance. In light of the results, A. lusitanica is the most representative, widespread and genetically variable alder species in Spain, and A. glutinosa is restricted to the north-east of the country. The Ebro riverbank is a contact zone between A. lusitanica and A. glutinosa species, in which no evidence of hybridization was detected yet. Finally, with focus on management, our findings may suggest considering A. lusitanica as a new species, which should be regulated by the forest reproductive material legislation.

Genetic background of lily germplasm resources based on SSR markers

To study the genetic background of lily (Lilium spp.) germplasm resources, and accurately evaluate and select excellent germplasm for genetic improvement of lily, we analyzed the genetic background of 62 lily germplasm accessions from 11 provinces of China by using simple sequence repeat (SSR) molecular markers. The results showed that 15 out of 83 pairs of lily SSR primers were polymorphic. A total of 157 allelic loci were amplified, with the number of alleles per locus ranging from 5 to 19 and the average number of effective alleles per locus being 4.162 8. The average observed heterozygosity and expected heterozygosity were 0.228 2 and 0.694 1, respectively. The average polymorphic information content was 0.678 8. The average Nei's diversity index and Shannon's information index were 0.694 1 and 1.594 9, respectively, indicating that the tested lily germplasm had high genetic diversity. The 62 germplasm accessions were classified into 5 groups by the unweighted pair group method with arithmetic mean (UPGMA) and into 3 groups by the principal component analysis. The two analyses revealed a geographic correlation among different groups. The majority of lily germplasm accessions from the same source tended to cluster together. The population structure analysis classified the lily accessions into 4 populations and 1 mixed population. The above results provide a theoretical basis and genetic resources for the precise identification and breeding of lily germplasm resources.