Based on the panel data of 82 cities in the Yellow River Basin (YRB) during 2008–2017, this paper calculated the urban ecological carrying capacity (UECC) index by means of the entropy method, drew a spatiotemporal evolution map using ArcGIS10.3 software, used a spatial cold–hot spot model to explore the spatial characteristics of the UECC index, and used the revised gravity model to construct the spatial network of the UECC. In addition, through social network analysis, we obtained the spatial network correlation characteristics of the UECC of 82 cities in the YRB. The study found the following: (1) The UECC index of the cities in the YRB increased steadily, and showed strong non-stationarity in space. The cold and hot spot patterns both changed greatly. Overall, the changes of the hot and cold spots were very significant. (2) The spatial correlation and linkage effects of the UECC in the YRB were not significant. The central cities with higher point centrality and closeness centrality showed the same spatial distribution, and most of them are located in the midstream and downstream of the YRB. The central cities in the midstream and downstream of the YRB had high betweenness centrality, and stood in the center of the association network. (3) The four plates in the spatial correlation network of the UECC in the YRB all showed their advantages and functions. The first plate was the net spillover plate, which was principally allocated in the upstream and midstream of the YRB. The second plate was the broker plate, which was principally located in the midstream and downstream of the YRB, and a few cities in the upper reaches. The third plate was the net inflow plate, which was distributed sporadically in the upstream and downstream of the YRB. The fourth plate was the broker plate, which was scattered in upstream, midstream, and downstream of the YRB. Therefore, it is necessary to shorten the gap of and promote the improvement of the UECC in the YRB. As the second-longest river in China, the Yellow River stretches across the eastern and central regions; it is a significant ecological safety shelter of China and a key region carrying population activities and economic development. The Yellow River plays an irreplaceable role in national strategic planning and socialist modernization construction. However, with the economic and social development mode of energy, the chemical industry, agriculture and animal husbandry in the YRB in recent years, the phenomena of the excessive exploitation and utilization of water resources, an increasingly fragile ecological environment, and a weak carrying capacity of resources and the environment in the YRB have become increasingly prominent; this has been a constraint on the subject of the development of society and the economy of the YRB. Since the 18th party congress of China, president Xi Jinping, has inspected the ecological protection and economic and social development of the YRB many times. On the morning of 18 September 2019, the President of China hosted a discussion on ecological environment protection and high-quality development in the YRB in Zhengzhou, He-nan Province. He stressed that ecological protection and high-quality development in the YRB were major national strategies. Therefore, we should strengthen the governance of ecological and environmental protection in the YRB, and we should promote high-quality development in the whole watershed.The YRB is a strategic support belt for China’s economic development in the new era, and its high-quality development is of great significance to the promotion of the high-quality development of China’s economy. In order to promote the ecological protection and high-quality development of the YRB, and to strictly observe the ecological red line, it is necessary to scientifically evaluate the UECC in the basin, and to analyze the relationship and influence of the UECC.Subsequently, experts and scholars have paid increasingly more attention to the ecological protection of the YRB. In the research on the ecological environment of the YRB, scholars have focused on single-perspective research, such as the effects of water resource management , sedimenIn order to further expand the research on the ecological conservation and economic growth of the YRB, it is necessary to construct a comprehensive evaluation index system to measure the development of the YRB ecosystem. The existing comprehensive measurement of the YRB mainly focuses on several aspects. One aspect is the comprehensive evaluation index system. Some scholars took the three perspectives of the resource-carrying capacity, natural environment and socioeconomic factors as the starting points to analyse the ecological environment quality of the YRB . In ordeWe summarize the above process in Based on the above existing studies, many scholars have explored the YRB from multiple perspectives, such as ecological protection and economic development, the comprehensive measurement of ecosystem levels, spatiotemporal analyses and spatial evolution. Compared with the existing literature, the possible contributions of this article consist in focusing on research on the YRB in the context of the national strategy, constructing a comprehensive evaluation index system for the UECC from a spatial perspective, and systematically analysing the relationship and influence of the UECC in the YRB.The article establishes the assessment indicator system of the UECC in the YRB. The system consists of a subsystem layer composed of ecological resilience, ecological support capacity and ecological pressure; a criterion layer composed of the resource supply, environmental protection, social progress and economic development; and an index layer composed of the per capita water resources and garden green space. The entropy weight method is used to measure the panel data of 82 cities in eight provinces of the YRB from 2008 to 2017; on this basis, the spatiotemporal evolution is analysed. The social network analysis method analyses the spatial network structure and its linkage effect among cities, and the mutual influences of the UECC are objectively analysed.The UECC is usually defined as the ability to provide natural resources for socioeconomic development, and to absorb human waste . First, Regarding ecological resilience, some scholars believe that the expansion of space will first affect land and biological resources . MoreoveRegarding ecological support, some scholars have introduced life cycle assessment methods to estimate the level of ecological deficits in a region. The assessment factors include the environmental impacts of the products produced by human activities, including raw materials and waste . TherefoRegarding ecological pressure, some scholars have proposed that there is a close relationship between social systems and ecological functions within a region. Therefore, it is feasible to evaluate the pressure on the ecological environment of a region by considering the pressure of human activities on the ecosystem in the region . China iBased on the data consistency, availability and operability, the samples of this paper are from 82 cities in the YRB from 2008 to 2017. There are 37 indicators for each city. The research data mainly came from the China City Statistical Yearbooks. In order to ensure data continuity and facilitate the analysis, we chose the interpolation method to fill the individual missing data and eliminate Haidong city due to it possessing too much missing data.The entropy method provides a basis for multi-dimensional comprehensive evaluation, which is more objective and avoids the influence of human factors in subjective evaluation . Here, tFor the elimination of the effect of different dimensions of indicators, we adopted data standardization using the range method, and the indicators were divided into positive indicators and negative indicators. The calculation equation are as follows:Positive indicators:Negative indicators:i-th index in year j is expressed as In Equations (1) and (2), the original value of the j-th index value in year i asCalculate the proportion of the Compute the index information asCompute the entropy of information redundancy asCompute the indicator weight asCompute the evaluation score of the indicators asj-th evaluation index in the i-th year; j-th evaluation index in all years, respectively, m is the number of evaluation years and n is the number of indices.In Equations (3)–(7), Exploratory spatial data analysis (ESDA) is a method to reflect the special dependence or spatial heterogeneity of geographical phenomena. It generally takes spatial data distribution characteristics as the breakthrough points and reflects the regional adjacency relationship through the spatial weight matrix . It is gFirst, in order to research the spacial correlation and difference of the UECC, and to analyse the spatial agglomeration trend of 82 cities in the YRB, this paper selected the global Moran’s I index. The equation is as follows:Gi* index to describe the special distribution features of hot spots and cold spots, and explored the local spatial correlation and degree of difference of the ECC. The specific calculation equation is as follows:This paper selected the Getis-Ord I is the global Moran’s I; n is the number of cities;i and city j separately; and Gi*. If the value of Z(Gi*) is positive, the value around position i is greater than the average value, which indicates a high-value spatial concentration (hot spot area); otherwise, it indicates a low-value spatial concentration (cold spot).In Equations (8) and (9), In the construction of a spatial association network, the establishment of a spatial association network is the premise and foundation of social network analysis. Therefore, we seek to set up a spatial association network of the UECC in the YRB. According to spatial correlation network theory, in this paper, the spatial association network of the UECC in the YRB is based on the collection of regional relations. In the network, cities are the “points”, and the spatial correlation between cities is the “line”. The composition of these points and lines is the spatial correlation network of the UECC in the YRB. Simultaneously, in order to prevent the problem of the VAR model being too sensitive to the selection of the lag order, which leads to the imprecise characteristics of the network structure, this paper considers the factors of the economic development and geographical distance of each city. The gravity model is introduced into the spatial correlation network in order to objectively reveal the evolutionary characteristics of the spatial correlation. Furthermore, with the purpose of enhancing adaptability, the gravity model is corrected. The corrected gravity model refers to related literature . With thi and city j. iR, iGDP, and iagdp denote the UECC, GDP, and per capita GDP of city i. ijD means the distance between city i and city j, and denotes the rate of contribution of city i to the relationship of the UECC between city i and city j.The type represents the UECC relationship between city In the feature analysis of the spatial correlation global network of the UECC in the YRB, the analysis of the spatial correlation network characteristics usually requires the overall network density, degree of network correlation, network diameter, and average path length. In the spatial correlation network analysis, on the basis of the data of the global network density, the degree of closeness of the spatial correlation network of the UECC in the YRB can be obtained. The larger the overall network density is, the closer the relationship between the cities, and the greater the impact on the UECC network structure in different cities. The degree of network association shows the vulnerability and robustness of the spatial correlation network of the UECC in the YRB. If there is a direct correlation path between one of the cities and many other cities, the overall degree of correlation of the network will increase. However, at the same time, the spatial network is highly dependent on the city, and the stability of the overall spatial network is low. The degree of correlation in the spatial correlation matrix is usually represented by the network diameter and average path length.i in the network. The greater the degree of point centrality is, the greater the number of cities that are associated with city i, and the higher the central position of city i in the network. Betweenness centrality reflects the degree to which city i is an “intermediary” in the spatial network, which is the extent to which the city plays the role of an “intermediary” and a “bridge”. The greater the degree of betweenness centrality is, the closer the location of city i is to the network center, and the greater the bridge’s role in communicating with each city. The closeness centrality is used to reflect the distance between them. The higher the closeness centrality, the closer city i is to other cities.For the analysis method of network individual centrality characteristics, in social network analysis, the central characteristics of individual networks are usually reflected in the main network indicators, such as the point centrality, closeness centrality, and betweenness centrality. Point centrality reflects the number of cities directly associated with city Regarding the module analysis method of the spatial correlation network, module analysis can describe the internal structure for different spatial network roles, and can determine the relationship and linkage between different plates. Therefore, this paper divides the network space of the UECC in the YRB into three types: the net inflow plate, the net spillover plate, and the broker plate. The first type is the net inflow plate. The plate members come from the relationships between the members within the plate; this plate also accepts the relationships between members from other plates, and the number of relationships sent by this plate is far less than that received from other plate members. The second type is the net spillover plate. The number of relations of the net spillover plate to other plates is significantly greater than that accepted by this plate from other plates. The last type is the broker plate. The plate members accept the relationships from other plate members and the relationships from internal members. However, the number of relationships coming from within the plate is less, and the plate mainly plays the role of an intermediary and a bridge.Using the entropy method, this paper calculated the four indices of the ecological resilience, ecological pressure, ecological support and UECC of 82 cities in eight provinces of the YRB from 2008 to 2017. Using ArcGIS10.3, this paper generated a spatiotemporal evolution map using the natural segment grading method . The natIn order to compare the distribution of the high and low values of the ecological resilience index of the cities in the YRB, as well as the changes of the index over time, the following analysis was made according to From the perspective of changes in the ecological resilience index of cities in the YRB over time, the temporal dimension distribution shows that the ecological resilience index of 54 cities spans several intervals and presents a scattered distribution trend in space. The specific cities include Jiayuguan, Dingxi, and Yinchuan upstream; Datong, Chifeng, and Xianyang midstream; and Shangqiu, Xinxiang, and Jinan downstream. Most of them show an increasing trend of ecological resilience, indicating continuous improvement and optimization.In order to compare the distribution of the high and low values of the ecological pressure index of the cities in the YRB and the changes of the index over time, the following analysis was made according to From the perspective of changes in the ecological pressure index of cities in the YRB over time, the ecological pressure index of 52% of the cities spans several intervals, and there is a certain time nonstationary effect. These cities include the upstream cities of Lanzhou, Baiyin, Zhongwei, and Longnan; the midstream city of Ulanqab; and the downstream cities of Zhengzhou, Heze, Weifang, and Jining. This phenomenon may be due to the acceleration of urbanization in recent years, the rising urban population, and the pressure on the local ecological environment having had different impacts.In order to compare the distribution of the high and low values of the ecological support capacity index of the cities in the YRB, and the changes of the index over time, the following analysis was made according to From the perspective of the changes in the ecological support capacity index of cities in the YRB over time, the ecological support index of 66 cities changed greatly from 2008 to 2017. Among them, nearly 80% showed an increase in ecological support. The ecological support indices of some cities  crossed more than three intervals to achieve leapfrog improvements. This phenomenon reflects the fact that, in recent years, with the implementation of a series of environmental protection measures—such as national returning farmland to forests, returning grazing lands to grasslands, and the 3-North Shelter Forest Program, as well as the further consolidation of the status of major grain-producing areas with the North China Plain, Fenwei Plain and Hetao Plain as the carriers and the energy-rich areas dominated by Shanxi and the Ordos Basin—the ecological environment has been continuously improved.In order to compare the distribution of the high and low values of the UECC index of the cities in the YRB, and the changes of the index over time, the following analysis was made according to From the perspective of changes in the UECC index of cities in the YRB over time, there are approximately 34 cities with a UECC index across multiple intervals, and the majority of the cities show an increase in their UECC. Specifically, these cities include upstream cities such as Jiayuguan, Jinchang, Dingxi and Longnan; midstream cities such as Jinzhong; and downstream cities such as Ankang, Datong, Zhoukou, Qingdao and Heze. This indicates that China has achieved remarkable results in various ecological restoration and protection measures in the YRB. Important agricultural and animal husbandry production and energy bases in China have been further consolidated. People‘s living has undergone the significant improvements, and the UECC in the YRB is steadily improving.In order to quantitatively analyse the spatial and temporal evolution characteristics of the UECC in the YRB, the global Moran’s I index of the UECC in the YRB from 2008 to 2017 was calculated using the spatial autocorrelation model. The values were 0.0657, −0.0819, 0.0207, 0.1327, 0.0759, 0.0164, −0.0215, −0.0585 and −0.0347, respectively, which are significant at the 1% significance level. The results showed that the UECC in the YRB was negatively correlated with spatial autocorrelation, and there was a geographical dispersion effect. Although the global spatial autocorrelation model can judge the whole spatial correlation, it cannot differentiate the spatial autocorrelation in different cities. Therefore, in order to further study the evolutionary characteristics of the local spatial agglomeration of the UECC in the YRB, and to consider the differences in the sample time length and information changes in different years, we used ArcGIS10.3 to calculate the local Gi* indices of the cities in 2008, 2012 and 2017. According to the results, the natural fracture method was adopted to divide the spots into hot spots, cold spots, sub-hot spots and sub-cold spots. We drew cold–hot evolution diagrams of the UECC in the YRB, as shown in Overall, the pattern of cold and hot spots of the UECC in the YRB changed greatly from 2008 to 2017, and the change in cities with different cold and hot spots was considerable. Specifically, regarding the evolution of hotspots and sub-hot spots, most of these spots were located in the midstream and downstream of the YRB in 2008, and there was a sporadic distribution of cities in the upper reaches of the YRB. Specifically, these cities include downstream cities such as Jinan, Taian, Laiwu, Heze, Kaifeng and Shangqiu, and cities in the middle reaches such as Bayannaoer, Baotou, Ulanqab, Hohhot, Hulunbeir, Ordos, Chifeng and Tongliao. This phenomenon was due to the Shandong Peninsula in the lower reaches of the YRB relying on location, policy advantages, and transportation hubs. Due to the area’s leading economic foundation, its infrastructure construction is relatively perfect, and the overall UECC occupies a leading position, which plays a leading role in the surrounding cities. In 2012, the number of hot spots and sub-hot spots of cities in the upper and middle reaches gradually increased to 37 cities, including upstream cities such as Zhangye, Jinchang, Wuwei, Lanzhou, Dingxi, and Longnan; midstream cities such as Hulunbeir, Hanzhong, Ankang, Ulanqab, Datong, and Shuozhou; and downstream cities such as Binzhou, Dongying, and Weifang. The hot spots and sub-hot spots expanded further in 2017, including upstream cities such as Dingxi, Guyuan, Longnan and Pingliang; midstream cities such as Baoji, Taiyuan and Yangquan; and downstream cities such as Jiaozuo, Nanyang and Zhumadian. This phenomenon shows that urban agglomerations—such as the Zhongyuan Urban Agglomeration, the Guanzhong Plain Urban Agglomeration, the Lanxi Urban Agglomeration, the Jinzhong Urban Agglomeration, and the Hohhot-Baotou-Ordos-Yulin Urban Agglomeration—have begun to take shape. The economic development and environmental protection of the central cities have led to the development of the surrounding cities, and the level of the UECC has increased.Regarding the evolution of the cold and sub-cold points, in 2008, these points included 38 cities primarily allocated in the upstream and midstream of the YRB, including Jiuquan, Jiayuguan, Zhangye, Longnan, Tianshui, Hanzhong, Nanyang, Xinyang, Zhumadian, and Linyi. By 2012, the cold and sub-cold points began to transfer to the downstream of the YRB. The upstream YRB cold points changed to sub-cold points. Compared with 2008, the range of cold points and sub-cold points in the YRB slightly expanded to 45 cities, including upstream cities such as Wuzhong and Qingyang; middle reach cities such as Yanan, Weinan and Yuncheng; and downstream cities such as Luoyang, Zhengzhou, Zhoukou, Linyi and Heze. This phenomenon is due to the impact of the global financial crisis in 2008. The level of social and economic development will have a certain impact on the UECC . TherefoFirst, according to Equation (10), the gravity matrix of the UECC in the YRB is calculated, and then it is transformed into an asymmetric matrix of the spatial correlation network. The specific conversion standard is as follows: the mean of each row in the gravity array is calculated, and then other values are compared with the average value. If the gravity value is greater than the average value of the row, it is denoted by 1, indicating that the row city is correlated with the column city. If the gravity value is lower than the average value, it is denoted by 0, indicating no correlation between the row cities and column cities. Simultaneously, we took 2017 as an example, imported the asymmetric matrix of the spatial correlation network into the UCINET software and the spatial correlation network diagram of the UECC in the YRB, which shows the structure plotted by using the visualization tool NetDraw  . This fiIn order to further explore the spatial correlation network features of the UECC in the YRB, the average path length, number of relationships, average number of relationships, network density and degree of network correlation were analysed . First, In order to study each member’s status and role in the network, we mainly studied individual networks’ centrality. Centrality characteristics need to be measured by three indicators: point centrality, closeness centrality and betweenness centrality. However, after calculating the centrality index of the network, it was found that the index of each city had no significant change, and the relative positions did not change significantly. Considering the accumulation of ecological environmental governance and economic construction achievements in cities, this paper only analysed the results from 2017.The measurement and calculation show that the average point centrality was 21.319, and 16 parameter values were higher than the mean value, indicating that the cities represented by these parameters are more related to other cities, which are relatively concentrated. Furthermore, the stability of the overall network structure is also highly dependent on these cities. Among these cities, cities with point centrality exceeding 80% included upstream cities such as Jiayuguan; midstream cities such as Hohhot, Ordos, and Baotou; and downstream cities such as Dongying, Weihai, Qingdao, and Zibo. There were a total of 16 cities. These cities had linkage effects with other cities, indicating that they are at the center of the linkage of the UECC in the YRB. The reason may be that they are mostly provincial capital centers or important energy-rich areas with abundant resources, obvious location advantages, and high levels of urbanization. They are the main carriers of the population and productivity distribution in the YRB. Moreover, in upstream cities such as Qingyang, Xining, and Yinchuan; midstream cities such as Bayannaoer, Ulanchabu, and Shuozhou; and the downstream city of Weifang, the degrees of the centralities in the associated network are relatively low. They are less related to other cities. The reason may be that these cities are restricted by geographical and economic conditions, and their ECCs are not closely related to other cities in space.The spatial network of the UECC in the YRB was further investigated using the point-out-degrees and point in-degrees. The mean value of the out-degree and in-degree is 11.329, and the out-degrees of 41 cities were greater than the mean value. The top cities were the upstream cities of Jinchang and Jiayuguan, the midstream cities of Baotou and Ordos, and the downstream cities of Zhengzhou and Xuchang, indicating that the spillover effects of these cities are obvious. The remaining cities will be affected by these cities. Additionally, there are 18 cities with a point in-degree greater than the average, and the corresponding point centrality rankings of these cities are also relatively high. These cities are mostly located in areas with good industrial foundations and developed economies. Due to the siphoning effect in the development and construction of these cities, there are different degrees of deprivation in the economies, resources and population of the surrounding cities, such that these cities play a beneficiary role in the spatial correlation network of the UECC in the YRB.The average closeness centrality of the spatial correlation network of the UECC in the YRB is 56.262, and 11 cities have numbers higher than the average. This indicates that these cities, as the central ones, have better and faster connections with other cities. Among these cities, there are eight cities, including midstream cities such as Baotou and Ordos, and downstream cities such as Dongying, Qingdao, Zibo, Weihai, Jinan and Yantai, with closeness centralities greater than 90%. These eight cities are the closest to other cities in the correlation network, and they occupy the center of the overall correlation network. Additionally, it is worth noting that the spatial distribution of 11 cities of which the closeness centralities are higher than the average value are quite similar in their spatial distribution to the point centrality. Most of them—such as Ordos, Hohhot, Dongying, Qingdao, Zibo, Weihai and Zhengzhou—are in the midstream and downstream of the YRB. The high closeness centrality of these cities may be attributed to the improvement of the infrastructure, superior geographical location, high economic development level, and good environmental awareness. Moreover, the upstream cities of Lanzhou, Qingyang, and Jiayuguan, and the downstream city of Linfen near the center of the ranking are relatively backwards, indicating that the most remote or edge cities are in the network. These cities are limited by water shortages, land desertification, and serious soil erosion; their UECCs are not high, and they are located at the edge of the network.The average value of the spatial correlation network of the UECC in the YRB is 1.004; there are nine cities, such as the midstream cities of Baotou and Ordos, and the downstream cities of Dongying, Qingdao, Weihai, Zibo, Yantai, and Jinan, which are higher than the average. These cities are located in the center of the key nodes in the network, and they play the roles of a “bridge” and an “intermediary”. They have strong control over the entire network. Once these key nodes have problems in the network, the “structural hole” phenomenon will occur, and the network will break. The upstream cities of Lanzhou, Xining, and Yinchuan; the midstream cities of Bayannaoer, Linfen, Shuozhou, and Ulanqab; and the downstream city of Binzhou rank behind the intermediary center, are vulnerable to the intermediary center of the larger cities, and have difficulties controlling and dominating the UECCs of other cities. The betweenness centrality of cities in the network shows obvious unbalanced characteristics. Most of the central cities of urban agglomerations are located in a significant central position in the network and have a good controlling effect. The remaining cities are basically in marginal positions, and are vulnerable to the influence of cities with large betweenness centralities.In order to further understand the structural features and interaction relations of the association network of the YRB, taking 2017 as an example, an in-depth analysis was carried out through the block model. Here, 82 cities in the YRB were divided into four plates, and the CONCOR method in UCINET 6.0  was used for the specific operation. This method is also called the iterative correlation convergence method. The block model analysis can be performed through multiple iterations to find the equivalent points of the location structure in the network. The largest cutting depth was set as 2, and the centralized standard was set as 0.2. Then, ArcGIS10.3  was applied to the map, and the spillover effects of the four plates were calculated. According to According to The relationship and spillover path between the urban plates of the UECC was further analysed based on the network density matrix calculation. The network density of the spatial correlation of the UECC in the YRB in 2017 was 0.1399. If the value of the plate’s density is higher than 0.1399, it is higher than the overall average, indicating that the UECC of the plate’s cities shows a centralized trend. The density matrix was transformed into an image matrix to intuitively illustrate the correlation and transmission mechanism between the UECC plates . The speOverall, each plate can play to its advantages and reflect its function. Specifically, the first plate is mostly the central cities of the YRB or the surrounding cities of provincial capital cities. This plate can provide energy, sites and other conditions for ecological protection and sustainable development, and can undertake the transfer of some high-pollution industries. The first plate plays the role of the first engine, and provides the impetus for other plates. Therefore, the first plate is the “net spillover” plate in the network. The third plate has an internal pointing relationship, and receives the spillover relationships of the other plates. This phenomenon occurs because part of the cities contained in the third plate are the central cities or provincial capital cities in the lower reaches of the YRB with a large population and a developed economy, which deprives the surrounding cities of different degrees of energy consumption, ecological environment and economic development. Therefore, the third plate plays a beneficiary role in the network and is the “net inflow” plate. The second plate and the fourth plate play the role of bridges. The main reason is that these plate cities played to their respective advantages in improving the overall UECC of the YRB, greatly promoting the linkage development of ecological environment protection and economic development among the cities in the YRB, and promoting the improvement of the UECC of the entire basin. Therefore, they can become the link and support of the network as the “broker” plate.Based on the panel data of 82 cities in the YRB from 2008 to 2017, the entropy method was adopted for the calculation of the UECC index. The spatial characteristics of the UECC index of the cities was explored using spatial, global and local autocorrelation models. Furthermore, the spatial correlation network of the UECC index was constructed using the modified gravity model, and the structure features of the spatial correlation network of the UECC of the cities were analysed using the social network analysis method. The findings were as follows.First, in general, in terms of the temporal and spatial evolution of the UECC index in the YRB, from 2008 to 2017, the UECC index of 82 cities in the YRB showed a steady upward trend, showing strong spatial non-stationarity. This shows that the various ecological restoration and protection measures, and the various measures to enrich the people implemented by China in the YRB have achieved remarkable results in recent years, and the UECC index in the YRB is steadily improving. Regarding the spatiotemporal characteristics, the global Moran’s I index of the UECC in the YRB from 2008 to 2017 indicates that the UECC index of the YRB was negatively spatially autocorrelated, and that there was a geographical dispersion effect. It shows that, on the whole, the spatial relevance of the UECC index of the YRB is poor. The value of a city’s ECC index will be negatively affected by the surrounding cities. The hot spots are concentrated in the midstream and downstream of the YRB, including some central cities and provincial capital cities; the cold spots are distributed in the upstream. This shows that the spatial agglomeration of the UECC index in the middle and lower reaches of the YRB is more significant. The cold/hotspot pattern changed greatly.Second, the total number of spatial network correlations of the UECC in the YRB rises according to the overall network structure characteristics. The degree of network correlation is between 0.8794 and 0.9637. The spatial correlation and linkage effect are not obvious, and the network density is low, indicating that there is a great deal of room for improvement regarding individual network structure characteristics. Cities with higher point centrality and closeness centrality have a consistent spatial distribution, and are mostly distributed in the midstream and downstream of the YRB. The betweenness centrality of each city in the UECC of the YRB shows obvious nonequilibrium characteristics. The central cities of most city agglomerations in the midstream and downstream of the YRB area have a significant central role in the network, and have a strong control role in the network. The remaining cities are basically at edge positions in the network, and are vulnerable to the influence of cities with a larger betweenness centrality.Third, the analysis of the block model shows that the four plates of the spatial network of the UECC in the YRB are quite different. The number of relationships between the plates is significantly greater than the number of relationships within the plates, and each plate has advantages and functions. The first plate is principally distributed in the midstream and upstream of the YRB, and plays the role of the first engine by providing the power to develop other plates. This plate is the net spillover plate. The third plate has a sporadic distribution in the upstream and downstream of the YRB, and accepts the spillover relationships of the first, second and fourth plates; however, the number of relationships is relatively small. This plate is a net inflow plate. The second plate is principally distributed in the midstream and downstream. The fourth plate is dispersed and scattered in the upstream, midstream and downstream. The second plate and the fourth plate have spillover relations with the third plate while receiving other spillover relations, which means that these plates play pivotal roles in the entire spatial network, and are typical broker plates.Based on the above research, in order to comprehensively improve the level of the UECC in the YRB and lay a good ecological foundation in cities in the YRB, the following suggestions are proposed.First, according to the research conclusions, spatially, the UECC and ecological plasticity in the YRB had strong spatial heterogeneity during the research period, while the spatial heterogeneities of ecological support and ecological pressure were not obvious. The level of the UECC and ecological plasticity of cities in the downstream of the YRB are relatively better than those in the midstream and upstream. Over time, the UECC of most cities has steadily increased, and their subsystems have also changed to varying degrees. Some cities upgrade faster and across multiple intervals. Therefore, in order to promote the overall improvement of the UECC in the YRB, attention must be paid to the various natural environments of the cities in the upstream, midstream and downstream of the YRB, and ecological environmental conservation and governance must be effectively promoted. Therefore, the diverse natural and economic factors in the upper, middle and lower reaches should be fully considered in the ecological conservation and high-quality development of the YRB. Adhering to local conditions, classifying policy, improving the economic and population carrying capacity, and intensifying the high-quality development in the entire watershed are important strategies. For upstream cities, the main objectives are to follow the laws of nature, restore the ecosystem, and reduce human activities’ destruction. First, it is necessary to promote major ecological protection and restoration projects, such as the strengthening of the protection of the main water supply areas in the upstream of the Yellow River, returning farmland to forest, and returning grazing land to grassland. Second, we must address the relationship between the ecological environment and people’s lives and production, abide by the ecological red line in various regions, and reduce the damage caused by overgrazing, excessive tourism and the overexploitation of resources. For midstream cities, we need to strengthen the protection of the soil and water loss, and cultivate characteristic agricultural industries with efficient water saving. For downstream cities, it is essential to enhance the administration of wetland ecosystems in the Yellow River Delta, coordinate flood control safety and high-quality land protection, enhance the economic and population carrying capacity of urban agglomerations, and promote the efficient flow of land, funds, talent and other production factors in the entire basin so that the ecology and environment of various cities in the YRB enter a virtuous circle.Second, according to the research conclusions, the overall network density of the spatial correlation network of the UECC in the YRB is relatively low, and there is a great deal of room for improvement. The intermediary centrality of the UECC at various levels in the YRB shows obvious non-equilibrium characteristics. In order to solve this problem, it is necessary to improve the connectivity level between the cities in the upstream, midstream and downstream of the YRB, strengthening the entire basin’s coordination and cross-domain linkages, and accelerating network layout construction. First, the government should consider the impact of the urban spatial correlation on the improvement of the environment of the entire YRB, and should commit to improving the density and robustness of the overall spatial network. Second, in the spatial correlation network, with the purpose of realizing the harmonious development of the locality’s UECC, it is necessary to strengthen the ecological linkage between cities in the spatial correlation network from the global perspective. First, we need to focus on promoting the infrastructure construction of cities; realizing interconnections; improving transportation, energy, internet and other important infrastructure constructions; and promoting the convenient flow of population materials and information in the region. By strengthening the construction of the high-speed railway and the Yellow River channel, the level of interconnection of resources and energy is improved. Second, we should build high-quality urban agglomerations along the Yellow River, enhance the carrying capacity of the economy and population of large cities, prevent the unlimited expansion of cities, create a high-quality growth pole of economic development, fully display the demonstration and guidance of big and medium-sized cities in the ecological environment protection and economic development model, and promote the overall improvement of the UECC in the YRB.Third, we need to formulate differentiated ecological protection policies according to the different plates, and promote cities to open and cooperate in the YRB. Given the different statuses of each city in the interconnected network and the different functions of each plate, more accurate and differentiated ecological protection construction policies are introduced according to local conditions. First, For the net inflow plate, it is necessary to solve its core ecological problems, such as the orderly and effective development of the Shanxi and Ordos Basins as the main energy-rich areas, the Hetao Plain and the Huang-Huai-Hai Plain. The plain is the main carrier of the grain production areas. Second, we must give full play to the spillover effect to help the surrounding cities solve problems related to environmental pollution and insufficient resources, and lay the foundation for other cities to improve their UECC by consolidating food and energy security. For the net spillover plate and broker plate, while making full use of the “engine” and “intermediary” roles, the two-way spillover effect between them and other plates should be strengthened, and the co -protection of the ecological environment and environmental pollution should be strengthened. Therefore, the state should strengthen the construction of high-quality development cooperation zones in Zhengzhou, Luoyang and Xian; jointly strengthen the ecological protection cities and the carrying capacity of resources and the population; promote the overall improvement of the ecological environment system; It also turns the YRB into a significant ecological barrier and a more stable economic zone in China.


Moreover, we found that genetic differentiation was related to geographical regions with an overall PhiPT  value of 0.130. We also detected subspecies-specific markers to classify rice (Oryza sativa L.) into indica and japonica. Additionally, we detected five marker-trait associations and rare alleles that can be applied in future breeding programs. Our results suggest that rice landraces in northern Vietnam have a dynamic genetic system that can create different levels of genetic differentiation among regions, but also maintain a balanced genetic diversity between regions.Northern Vietnam is one of the most important centers of genetic diversity for cultivated rice. Over thousands of years of cultivation, natural and artificial selection has preserved many traditional rice landraces in northern Vietnam due to its geographic situation, climatic conditions, and many ethnic groups. These local landraces serve as a rich source of genetic variation—an important resource for future crop improvement. In this study, we determined the genetic diversity and population structure of 79 rice landraces collected from northern Vietnam and 19 rice accessions collected from different countries. In total, 98 rice accessions could be differentiated into  Oryza sativa L.), which is one of the most important components of the human diet, is a staple food for almost half of the world’s population  denote the proportion of the variance among subgroups relative to the total variance between subgroups. VAP and VWP are the estimates of variance among and within subgroups, respectively. The higher PhiPT values are indicative of the greater differences between subgroups. To complement the above results of population structure, the AMOVA analysis was also performed among these six geographical regions to check whether the genetic differentiation among accessions was explained by their geographic regions.Genetic differentiation between the two subgroups was estimated by AMOVA based on the NEI distance matrix obtained from POWERMARKER software to justify the suitability of group classification using GenAlEx, version 6.503 ,46,47 wiIn addition, the two-sided t-test was performed on 11 agro-morphological traits to compare means between the two subgroups using MINITAB 16.2.4.p-value was less than 0.01.Marker-trait association was performed based on the phenotypic data, genotypic data, and Q matrix of AGI collection using the software TASSEL 5 v.5.2.72 ,52. The http://visualization.ritchielab.psu.edu/ (accessed on 3 July 2021)), and each chromosome was manually rearranged from bottom to top based on a ruler.The physical locations of the significant markers identified by association analysis were mapped on the 12 rice chromosomes using the web-based tool PhenoGram  into indica or japonica. By analyzing marker-trait associations, we detected five marker-trait associations and rare alleles that can be applied in future breeding programs and molecular approaches. Our results suggest that rice landraces in northern Vietnam have a dynamic genetic system that can create different levels of genetic differentiation among regions while maintaining balanced genetic diversity between regions.In the present study, we analyzed genetic diversity, population structure, and marker-trait associations in rice landraces from northern Vietnam. The rice landraces could be differentiated into


Voids are comment defects generated during the manufacturing process and highly sensitive to moisture in the hygrothermal environment, which has deleterious effects on the mechanical performances. However, the combined impact of void content and water-absorbed content on mechanical properties is not clear. Based on the random sequential adsorption algorithm, a microscale unit cell with random distribution of fibers, interfaces and voids was established. The quantitative effects of voids content on strength and modulus under the loading of transverse tension, compression and shear were investigated by introducing a degradation factor dependent on water content into the constitutive model, and the different failure mechanisms before and after hygrothermal aging were revealed. Conclusively, before hygrothermal aging, voids induce the decrease in mechanical properties due to stress concentration, and every 1% increase in the void content results in a 6.4% decrease in transverse tensile strength. However, matrix degradation due to the absorbed water content after hygrothermal aging is the dominant factor, and the corresponding rate is 3.86%. Voids are the most common type of defects induced by the residual air during the manufacturing process of composites , which sVarious characterization techniques were reported for identifying the microscopic void structures and voids content, including densities measurement method , opticalPA6 matrix can absorb about 10 wt% water , and theThe presence of void defects has deleterious effects on the service performance of composites. Several manufacturing parameters including molding pressure, curing temperature and resin viscosity can induce the formation of void defects in the matrix, as shown in iM) and the diffusivity coefficient DeffTo introduce the water absorption parameters of voids to the Halpin–Tsai equation, the three-phase composite model can be divided into two two-phase composite models (submodel-1 and submodel-2). Submodel-1 comprises the matrix and voids, and voids are embedded in the matrix to form the porous matrix phase. In Submodel-2, the porous matrix phase and the fiber consist of the composite. By substituting the parameters of component materials in submodel-1 and submodel-2 into Equation (3), the transverse diffusivity of three-component composites can be expressed as:As shown in As shown in Residual stresses occur in composite structures during the curing process and subsequence service in the hygrothermal environment, which play an important role in composite deformation and mechanical properties. However, the experimental measurement of the residual stresses is often costly and complicated. Alternatively, a numerical simulation method is employed to predict the residual stresses during the process of cooling contraction and hygroscopic expansion, respectively.−6/°C) and the PA6 matrix (4 × 10−6/°C) results in a significant difference in deformation behaviors. As shown in The PA6 matrix is highly sensitive to the moisture under the hygrothermal environment due to the presence of amide groups -CO-NH-, and the absorbed water content can reach 10%. Moreover, the retention rate of mechanical properties for the PA6 matrix decreases exponentially as the water content increases. Thus, water content has deleterious effects on the dimensional stability and mechanical properties in the service environment. The residual stress of hygroscopic expansion in the water absorption process can accelerate mechanical aging.As shown in To study the effects of voids content on the residual stress in the cooling contraction and hygroscopic expansion process, the same material properties and boundary conditions are applied into the RVE models with the different volume fractions of voids.Moreover, to avoid duplication and redundancy, only the evolution of Mises stress is selected to quantize the effects of voids content. As shown in For the models with 5% voids content before hygrothermal aging, the transverse tensile strength Water diffusion is significantly affected by the presence of voids, which significantly accelerate the water absorption rate and increase the water saturation content. Moreover, the modified Halpin–Tsai equation can effectively predict the transverse diffusivity of composite with different volume fractions of voids.(2)Tensile stress arises for the matrix in the cooling contraction of the molding process, and the stress change from tension to compression after hygrothermal aging. The stress state in CFs is just the opposite of the matrix. Stress distribution can be changed, and the residual stress can be greatly weakened by the presence of voids. Voids have beneficial effects on the relief or decrease the residual stress to some extent.(3)For the unaged composites, voids induce the decrease of mechanical properties due to stress concentration, and every 1% increase in the voids content results in a 6.4% decrease of transverse tensile strength. However, matrix degradation due to the absorbed water content after hygrothermal aging is the dominant factor, and the corresponding rate is 3.86%. This indicates that the negative effects of voids content are diminished, and hygrothermal aging is the dominant reason for the failure mechanism.(4)Compared with the failure images of unaged composites, the number and density of cracks increase after hygrothermal aging, which indicates the presence of voids can accelerate the matrix damage and interfacial debonding.To reveal the effects of voids content on the moisture absorption performances and mechanical properties under the loading of transverse tension, compression and shear, RVE models with different voids contents are established and analyzed. The following conclusions can be drawn:


Microtus arvalis) during a high-density phase in North-Western Spain. We compared genetic patterns from 15 paired plots bisected by three different barrier types, using linear mixed models and computing effect sizes to assess the importance of each type, and the influence of road features like width or the age of the infrastructure. Evidence of effects by roads on genetic diversity and differentiation were lacking. We speculate that the recurrent (each 3–5 generations) episodes of massive dispersal associated with population density peaks can homogenize populations and mitigate the possible genetic impact of landscape fragmentation by roads. This study highlights the importance of developing spatially replicated experimental designs that allow us to consider the large natural spatial variation in genetic parameters. More generally, these results contribute to our understanding of the not well explored effects of habitat fragmentation on dispersal in species showing “boom-bust” dynamics.Although roads are widely seen as dispersal barriers, their genetic consequences for animals that experience large fluctuations in population density are poorly documented. We developed a spatially paired experimental design to assess the genetic impacts of roads on cyclic voles ( In Europe, for example, almost half of all continent is located within 1.5 km of the nearest transport infrastructure, with impacts that expand over most of the land and important reductions projected for birds and mammals2. Most empirical evidence suggests that this improvement of connectivity among human populations involves worsening of connectivity in wild animal populations7. In this sense, road networks can prevent animal movement, either through behavioral avoidance9 or mortality during crossing attempts11. These physical barriers may shape the dispersal behavior of species12, causing the isolation of populations into habitat fragments for many taxa15 with severe consequences for individuals and populations18. Research on this topic has shown that increasing habitat fragmentation and population isolation by roads can reduce locally the population size, the individual exchange among fragments and the gene flow, can have a significant impact on population genetic structure20 and often leads increased genetic drift and/or inbreeding, which can further reduce effective population sizes and genetic diversity over time. All this can have, in turn, potential implications for long-term population persistence21 due to the combined effects of inbreeding, genetic drift, and demographic and environmental stochasticity23, specially of those already small and inbred populations (but see Ref.24).In today's globalized economy, the number of motor vehicles and the volume of transportation has reached unprecedented levels, increasing the ecological impact of the world’s human population during the last decades. Globally, roads currently cover more than a 20% of the earth’s land surface with large differences in occurrence, distribution and size of roadless areas among continents28. Specific road features, such as fences, median strips, drainage culverts, adjacent gravel and grassy verges, and structural passages beneath or above the paved surface influences road permeability for terrestrial species29. The severity of the barrier effect is often modulated by road width30 as the wider the barrier the higher the traffic intensity25, and the more reluctant or unable an individual will be to cross32. This effect is especially important in low-vagility species like small mammals or amphibians33. In addition, the time elapsed since road construction also affects the number of generations exposed to the putative barrier, influencing our ability to genetically measure the severity of the barrier effect36. However, species with high dispersal ability and abundance38, and large fluctuations in population size through time39 can mitigate the genetic effects of barriers, and our ability to detect them. Even so, studies on species that display large fluctuations in population size are particularly scarce41, limiting our understanding of the full impact of roads on animal populations.The barrier effect of roads varies largely with respect to road types, adjacent habitat quality, and traffic volume43.Our ability to detect genetic effects from barriers depends also on the approach used. Studies conducted without some level of spatial replication and control groups make difficult to infer causal relationships since the results are subject to high variance caused by the interaction between species characteristics  and site-specific factors , which subsequently influences dispersal, relatedness and spatial genetic patternsMicrotus arvalis , a small-bodied rodent with short dispersal distances that reaches high local abundance44, as a model to test for the road effects on genetic diversity and structure in highly fragmented landscapes. The high colonization ability reported for the species in the last decades45 make it especially suitable to understand the dispersal mechanisms of small mammals over short time periods. Additionally, common vole populations reach high local abundances and may experience extreme fluctuations in density 46 through 3–5 years population cycles with well-known phases  should experience the highest levels of genetic drift51. As a result, demographic bottlenecks should have a strong effect on genetic variation, reducing diversity within and increasing differentiation among populations54. However, the high population size and gene flow during the peak phases could minimize the effects of drift and bottlenecks, although the system might take several generations to approach new equilibrium values56. In that context, investigating the effect of roads as barriers to gene flow, which can rapidly re-shuffle alleles among population demes, is particularly interesting in vole species, considering the short time lags between peak and low phases and its short generation time57.Here, we use the common vole In the present study, we used a spatially paired-plot design to test whether roads may influence genetic diversity and structure of populations in cyclic voles. We used study plots (2.5 × 3.0 km each) bisected by different types of paved roads  and spatially-matched plots bisected by an unpaved rural tracks to control for the impact of the road. We used paired t-tests, Linear Mixed-effect Models (LMMs) and estimated associated effect sizes to explore the potential barrier effect due to roads. We additionally tested if highways of different width and age (time since road construction) might influence any barrier effect. If the road is not an effective barrier, individual movements ending at the same side of the road should be equally frequent as those across the road, and therefore, genetic parameters within plots will remain unchanged. We further discuss other possible explanations to this pattern based on the characteristics of the study species, such as that the large population size reached during peak phases was marginally affected by genetic drift and there is no spatial variation in genetic structure independently of gene flow. The alternative scenario (road barrier effect) would generate—given enough generations—observable differences in the distribution of genetic variation within plots bisected by roads but not in plots without roads.A total of 1757 common voles were sampled at 1790 different sampling points across the 30 plots Fig. , S2, allA summary of genetic diversity parameters of study plots is presented in Table p value > 0.1 in all cases, with negligible to small effect sizes; Table 2 ≤ 0.15; Supplementary Tables Age of the barrier was positive and the effect size was large (Cohen’s f2 = 1.176) while the relationship with Ar was negative (Cohen’s f2 = 1.094), indicating greater differences in observed heterozygosity and lower differences in allelic richness between plots with highways and their controls over time. However, both effect sizes were found to have 95% confidence intervals (95% CI hereafter) that includes zero, suggesting the predictors may contribute very little to the relationship. We observed no substantial effect of Width on these parameters (Supplementary Table DIF) while greater differences in allelic richness (ArDIF). Highways (H), however, presented lower differences in allelic richness (ArDIF) for experimental plots, all with large effect sizes. The strength of the relationship between Age and ArDIF was large and negative, and between Width and HoDIF was large and positive .No diversity parameters showed strong differences between experimental and control plots (paired t-test IT = 0.003–0.077) and extremely small genetic variance between individuals from opposite sides of the putative barrier . The proportion of variance explained by the presence of the barrier was statistically significant in six out of the 30 (20%) study plots, but significant results were not associated with any particular type of barrier, nor were more prevalent in experimental than in control plots. Plots bisected by highways (H) and combined barriers (CB) tended to have variance ratio values between both plot sides (σBetween/σWithin) lower than their respective controls, showing a large effect for CB  performed in each plot showed low values of genetic variance among individuals within the same side of the putative barrier  and highways (H) compared to their controls. As expected, the Mantel’s r computed with the relatedness estimator (rLrm) displayed the opposite pattern  and for rDps and rLrm in highways (H), showing lower values for rGd and rDps and greater values for rLrm in plots bisected by the infrastructure than in their respective controls  for experimental and control plots are shown in Fig. After accounting for the “natural” variation in genetic parameters occurring in our study system through control plots, we could not detect significant evidence to confirm that fragmentation by roads impacts the distribution of genetic variation. We detected a very small, almost null, deviation of genetic parameters from sites without barriers with respect to sites with putative barriers, suggesting either that subpopulations have recently been separated by roads and have not accumulated large differences, that the potential for voles to disperse overcomes the strength of the putative barrier to restrict gene flow, or that vole populations are marginally affected by genetic drift independently of gene flow.60. Generally, by dividing habitats into small fragments, roads act as barriers to gene flow of the isolated of populations. This can leads to adverse genetic effects, especially in species with low dispersal abilities61 or in species with low effective population size and thus more vulnerable to loss of genetic variation through drift62. A frequent limitation on these studies is that we cannot always possible to directly attribute the observed values of genetic parameters to anthropogenic changes without knowing the situation prior to fragmentation, because such values can result from natural subdivision of populations or may be due to other features present in the landscape, such as the abundance and distribution of habitats or the presence of elements in the landscape that influence individuals’ movement63. Unfortunately, we do not have samples available to represent conditions prior to road fragmentation for comparison, so we used a spatially paired design, using comparison between sites with and without paved roads to partially solve this problem64.Anthropogenic habitat fragmentation due to road construction is a ubiquitous feature that significantly alters modern landscape structure65, and not all methodological approaches are equally effective in detecting a recent barrier. Our studied barriers are relatively recent (time lag from 8 to 61 years-old) and therefore, our ability to detect genetic fragmentation with commonly used genetic indices, such as FST, could be compromised by a short time since fragmentation66 or by the analytical approach used. However, here we used multiple analytical methods to detect the presence of a linear barrier dividing populations. In addition to population-based approaches that generally requires a longer lag time to detect barriers, we used partial Mantel tests67 with individual‐based genetic and geographic distances, which can usually detect the signal of a new barrier within a shorter spatio-temporal scale36. Additionally, the study model is a short-lived species whose average lifespan is no longer than 4–5 months68 and a short-generation time (1 year70) so the expectation that genetic differences accumulate each generation since fragmentation makes it a suitable species for detecting barriers in the time frame considered71. Hence, the lack of evidence for population fragmentation by roads observed in the present work is less due to absence of evidence than evidence of absence. However, our findings do not agree with theoretical and empirical studies that suggest that landscape barriers can have rapid genetic effects that are detectable almost immediately or after a few generations74, but matches with expectations given the characteristics of the study species, such as its cyclical abundance, the large population size that it reaches in the peak phases and the severe population bottlenecks during the low phase of the cycle, the massive dispersal events associated with population peaks, or the short time lag between these two extremes .The genetic effects of fragmentation are expected to become more pronounced through time62. Therefore, a lack of evidence of road barrier effect could be derived from a marginal effect of drift alone. However, vole population size does not remain high for a long time; the species is well known to suffer “boom-bust” dynamics76, with a peak every 3–5 generations in which population can fluctuate dramatically from 1000 voles/ha in high-density peaks to 10 voles/ha during population crashes, so it is unlikely that genetic diversity and structure would not be affected by bottleneck-associated drift during low phases.One could argue that the lack of a barrier effect on genetic diversity and structure could be simply due to a large population size, since the amount of change in allele frequencies due to drift decreases as the effective population size increases77 and would explain the apparent lack of local genetic structure attributable to roads, matching with previous studies performed on the same species78.The analyses of genetic variation in the 30 study sites also revealed an apparent lack of local genetic structure attributable to roads, and the differences in the distribution of variance within and between barrier sides do not seem currently affected by road type. Altogether, these results point to compensating mechanisms, probably related to gene flow and frequent dispersal, that could outweigh the overall impact of roads in this species.It is more probable that substantial gene flow occurs across barriers, at least temporally, and that dispersal ability of the species outweighs any influence of the studied features. This is consistent with the lack of strong heterozygote deficiencies, which would be expected if there were an increase in mating events between relatives due to a dispersal decline. Dispersal would increase the geographic scale of genetic structuring, as previously suggested for other cyclic species like Siberian lemmings8. In cyclic voles, data on dispersal distances are scarce, but the most frequent seem to be short-distance dispersal events carried out mainly by females79, which can reach 50–160 m in the case of short-range natal dispersal, but occasionally up to 400 m in a few days44. This can lead to significant genetic differentiation even at spatial scales of a few hundred meters80. However, common voles are also capable of dispersing on the order of a few kilometers79 being these occasional long-distance dispersal events more balanced between genders80. The species also shows conspicuous and recurrent episodes of massive dispersal45 that may genetically homogenize populations. These episodes, although far from clear, are widely understood in the cyclic rodent literature as presaturation dispersal83 and occur under positive growth rates when the patch’s carrying capacity has not yet been reached81. Such dispersal events, together with the high effective population size reached during peak phases, may help us to explain the high genetic diversity that common vole maintains in the region. High levels of genetic diversity are well documented for other cyclic small mammals84, and suggest that population dynamics and dispersal movements play a key role in configuring the genetic diversity of open populations85.The question remains as to whether the lack of barrier effect observed is due to permanent high dispersal rates over time (i.e. high gene flow across the barrier remains constant from one generation to the next) or if the barrier effect varies through time following changes in Ne. Unfortunately, we do not have temporally replicated data to test this, something that needs to be addressed. However, the well-known cases of barrier effect of roads in similar species and the cyclical nature of the common vole suggests barrier effect varies through time. Roads with similar characteristics to those of this study have been proven effective in reducing the gene flow on a local scale of other mammals similar in size and vagility to common voles, but lacking strong abundance cyclesIn conclusion, our study points out a scenario in which, although there may be periods of relative isolation by roads for a few generations, they regularly alternate with a relatively high panmictic-density phase in which mixing and accumulation of genetic material across barriers occurs, probably due to the cyclic nature of the common vole. These repetitive (cyclic) pulses of gene flow might homogenize populations and erase the possible differences that may have accumulated during the short time elapsed between the “boom” phases. Nevertheless, we cannot conclusively discard that our findings can be explained by other factors independently of gene flow, such as for example a low drift effect and/or the high effective population size during the peak phases. Further research on populations that have been in a low population phase for some time, testing for potential effects of roads over short time scales, would be particularly valuable in that context. We also urge caution in the interpretation of these results to avoid over-emphasizing the low ecological significance of these local barrier effects on populations without larger sample sizes and better temporal sampling coverage, since we detected large effect sizes and trends attributable to roads but which we could not confirm with certainty. A larger number of replicates would give the design the ability to detect smaller differences  than in the present study, thus reducing the risk for Type-II errors.Last but not least, the present work underlines the importance of having a robust experimental design and sufficient spatial replication in studies of barrier effects, given the natural spatial variation in genetic parameters. For example, the proportion of genetic variance explained by the putative barrier was statistically significant in the 20% of our study plots, but significant results were not associated with any particular type of barrier, nor were more prevalent in experimental than control plots. This emphasizes the fact that studying the relationship between landscape fragmentation and genetic diversity/structure in single sites can be problematic because the observed genetic parameters are often site-specific and a basis for comparison is lacking, and therefore single-case studies will not allow firm conclusions.86) characterized by a landscape generally flat with few trees and subject to a moderate-to-high agricultural exploitation87. The region combines cities and rural areas, with a well-developed road network that has been expanded and upgraded in recent decades and that includes a wide variety of road types , which includes the Duero River basin, a central agricultural plain  finding plots bisected by a single road, with no other nearby infrastructures, (ii) having dirt tracks that crossed control plots in similar way to roads in experimental plots, and (iii) ease of simultaneous access to multiple sites during mammal trapping. We also avoided sites with any obvious signs of disturbance such as burning, human settlements and other constructions, that could confound analysis of the effects of roads on genetic differentiation. Therefore, we located sampling sites on flat terrain and avoiding any field with streams that were parallel to the road, thus avoiding possible additional barriers to common vole gene flowThe two plots in each pair (treatment and its control) were spaced 2–4 km apart, and each set of pairs was separated by 6–26 km to promote independence. We considered three treatments (types of road) in the experimental design, each one having intrinsic features  in 26–96 capture points per study plot, with 10 traps per point, deployed through the entire plot (260–960 traps per study plot). We tried to ensure that trap locations encompassed the diversity of habitats , but > 500 m from plot boundaries to reduce potential edge effects92. Traps were open for 24 h, or until captures reached a minimum of at least 20 individuals per infrastructure side, which usually took no more than 48 h. However, we were not always able to reach the desired number of 20 samples per plot side , were within the guidelines of the European and Spanish and policy for animal protection and experimentation and in compliance with the ARRIVE guidelines for how to report animal experiments.93, and diluted to a working concentration of 25 ng/μL. 9 microsatellite loci isolated in common vole95 were amplified in a total volume of 10 μL containing 1 × QIAGEN Multiplex PCR Master Mix, 0.2 μM of each primer (forward and reverse) and 20–50 ng of DNA. The 9 primer pairs were divided into two multiplexes using four dyes : Ma09, Mar012, Ma54, Ma66, (Multiplex 1); MM6, Mar063, Mar003, Mar016, Mar102 (Multiplex 2). Electrophoresis of PCR products was performed by Universidad Complutense de Madrid, Unidad de Genómica, Spain (https://www.ucm.es/gyp/genomica). Electropherograms were scored using Geneious 10.2.3 (https://www.geneious.com) and approximately 10% of all genotypes were re-amplified and re-scored to estimate the genotyping error rate.Total genomic DNA was extracted from tissue samples using a standard ammonium acetate protocol96. Hardy Weinberg equilibrium and Linkage disequilibrium were checked across all loci in the R package ‘pegas’97. We checked for the presence of null alleles following Brookfield98 implemented in the R package ‘PopGenReport v3.0.4′99. We used COLONY 2.0100 to identify and discard full and half siblings at the same sampling point from the dataset, which may indicate the presence of litters and bias the allele frequencies at a given spatial location. COLONY was run under a non-restrictive criterion considering a polygynandrous scenario . Analyses were performed according with the full-likelihood method with high precision, three runs of medium length and without priors. Since COLONY provides the uncertainty estimates (in probabilities) of a particular substructure, we assess, for each of our sampling points, the probability that the captured individuals share both (full siblings) or one parent  using a threshold of substructure probability p ≥ 0.95. Then, we randomly discard all but one full sibling from each sampling location. Siblings sampled at different sampling points were retained for analysis.Prior to statistical analysis, microsatellite error rate was calculated to check for allelic dropout and false alleles using Pedant v1.0’ v1.1.9101 R package. We first calculated observed (Ho) and expected (He) heterozygosity, unbiased expected heterozygosity (uHe), allelic richness (Ar) and inbreeding coefficient (FIS). We used ‘GENHET’ v3.1102 R package to calculate five measures of genetic diversity at the individual level, as described in Coulon102: the proportion of heterozygous loci (PHt), standardized heterozygosity based on the mean observed heterozygosity (Hs_obs), standardized heterozygosity based on the mean expected heterozygosity (Hs_exp103), internal relatedness (IR104), and homozygosity by locus (HL105). All metrics of genetic diversity were calculated (i) as a mean value per plot, using all samples collected in each plot, and (ii) as a mean value in each side of the putative barrier within each plot (arbitrarily coded as A and B). We then computed an absolute difference across the barrier to obtain a single value per plot . To verify that the observed values for these estimates (differences in diversity) were not just a consequence of sample size, we computed a Pearson’s correlation coefficient between our diversity parameters and the sample size per plot. In a possible scenario of subtle isolation due to roads, we would expect that although there were no differences in the overall diversity between an experimental plot and its control, we found differences in the way that diversity was distributed in relation to the position of the barrier (and absence of differences among sides in the control plot).We estimated genetic diversity through ‘diveRsity106 R package. Regardless of the outcome of the AMOVA tests computed in each plot, our aim was to identify an overall difference in the variance explained by the putative barrier between the experimental plots and their paired controls. We then computed the ratio of the genetic variance between different sides of the barrier to the variance on the same side of the barrier (σbw = σBetween/σWithin). This parameter was used as a response variable in subsequent analysis (see below), which was expected to be greater in the presence of a barrier effect. GENALEX v6.5107 was also used to assess pairwise genetic differentiation (FST) between groups of individuals at each side of the putative barrier within each plot, therefore testing genetic structuring by the barrier, and statistical significance based on 999 permutations. Recent analyses suggest that this standard measure of differentiation may be poorly suited for data sets in which allelic diversity is high110. Given the high variability of the markers used here, we also computed other measures of differentiation: GST111, G”ST112, G’STH108 and D109 following the recommendations and formulae of Meirmans and Hedrick110.Based on AMOVA and Phi-statistics, genetic variation between the two roadsides was estimated for all plots using three hierarchical levels: roadside, sampling site (plot) and individual. For the assessment of potential barriers, individuals where assigned to their respective roadside. AMOVA was performed with 999 permutations to assess significance in ‘poppr’114 have suggested that metrics based on genetic differentiation among groups of individuals may have low power to detect isolation, we also computed individual-based genetic distances within each plot. Pairwise individual genetic distances were estimated using the proportion of shared alleles (Dps113) and codominant genotypic distance (Gd115). For Dps, we subtracted values from one to obtain a distance measure and make it comparable with Gd. We additionally computed three relatedness indicators per plot: Qgm116, Ri117 and Lrm118 that measure genetic similarity in terms of probabilities of identity-by-descent including the overall population allele frequencies as a parameter . We multiplied Lrm by two to make it comparable with the other relatedness estimators, but we did not subtract values in this case since negative values are informative for these metrics. Gd and relatedness estimators were calculated in GENALEX and Dps in “adegenet” v2.1.1119 R package.Because some authors120, we also calculated pairwise Euclidean geographic distances among individuals. We then used genetic, geographic, and a third “barrier” matrix to compute a partial Mantel test per plot. The elements of our barrier matrix were binary . Under the null hypothesis (H0), one would expect that the genetic distances among samples are unrelated to the corresponding treatment distances (while controlling for corresponding geographic distances). Under the alternative hypothesis (H1) we expected to find significant correlation between genetic and treatment distances. Therefore, Mantel r coefficient was expected to be greater in the test carried out with the experimental plots than with the control plots.As we were aware of the potential spatial autocorrelation of individuals in our plots and that spatial dependence can interfere with our ability to make statistical inferences121 to be used as response variables. These included Fst and G”st as differentiation metrics, σbw as a measure of genetic variation, Ho, uHe, Ar and their homologous computations of differences between plot sides  as genetic diversity estimators, and partial Mantel r . A detailed explanation of each estimator is given in Table Due to the large number of metrics employed in the study, we retained only those which provided non-redundant information based on a correlation matrix with a Pearson’s coefficient threshold < 0.80Treatment effect. Then, we constructed Linear Mixed-effect Models (LMMs) to analyze variation in genetic metrics according to the type of putative barriers, including barrier Type as a fixed factor with the three treatments  and the control (T) as factor levels. Because of the nature of our sampling design, each experimental plot and its paired control were coded with the same identification number, which was included in the models as a random factor (GroupID) to make appropriate paired comparisons. We also conducted separate analyses to examine the effect of road Width and Age on genetic metrics. We restricted this analysis to highways, as they had larger contrast relative to the control plots than roads, which had all the same age, and lacked infrastructures with different age and width (CB), which potentially complicates interpretation. Therefore, we performed simple linear regressions after verifying that there was no significant correlation between Age and Width of the barrier for this data subset . Since this is a matched pair study we used the differences in genetic parameters between each experimental plot and its paired control plot as response variable in the linear regressions.First, we performed paired t-tests between experimental and their respective controls for each group of metrics to assess a 122, corrected for small sample sizes, with ‘bootES’123 R package, and used the threshold provided in Ref.124 to assess the magnitude of the effect in paired t-tests and LMMs, i.e. |g|< 0.2: negligible; 0.2 ≤|g|≤ 0.5: small; 0.5 ≤|g|≤ 0.8: medium; |g|> 0.8: large. We bootstrapped 999 times on effect sizes to get the 95% confidence intervals, which can inform inferences about the replicability and generalizability of the results. In the same way, we computed Cohen’s f2 for simple regression models as described in Ref.125 for the highways (H) subset: 0.02 ≤|f2|< 0.15: small; 0.15 ≤|f2|< 0.35: medium; |f2|≥ 0.35: large.Due to the low sample size in both sets of models we cannot rule out the possibility of insufficient statistical power. Therefore, we estimated the effect size by computing Hedges’s gSupplementary Information.


To conduct a long-term birth cohort study that includes genetic analysis, it is crucial to understand the attitudes of participants to genetic analysis and then take appropriate approaches for addressing their ambiguous and negative attitudes. This study aimed to explore participants’ attitudes toward genetic analysis and associated background factors among mothers who were enrolled in a large Japanese birth cohort. A questionnaire was sent to participants’ households, and the responses of 1762 mothers (34.0%) were used for the study. The majority of mothers recognized genetic analysis for themselves and their children and sharing of genetic data as beneficial. A low knowledge level of genomic terminology was associated with ambiguous attitudes toward genetic analysis and data sharing. Education level was positively associated with the recognition of the benefits of genetic analysis. Concern about handling genetic information was associated with the unacceptability of data sharing. Trust was associated with the approval of genetic analysis. Most mothers preferred that genetic analysis results be returned. These findings suggest the need for multiple efforts to maximize participants’ acceptance of genetic analysis, such as utilizing an educational approach to encourage familiarity with genetics/genomics, optimizing explanations for different educational levels, and explicitly disclosing the handling policy for genetic information. A large number of birth cohort studies have been planned and conducted as important epidemiological research to understand the impact of environmental factors on children’s health and development , 2. SomeIn birth cohort studies, which collect biological samples and data longitudinally, it is essential to gain participants’ understanding of the study and their trust in research institutions. Moreover, approval for genetic analysis must be obtained from as many participants as possible to explore gene–environment interactions. To maximize the number of participants who give their approval for genetic analysis and data sharing, it is crucial to understand the attitudes toward genetic analysis and related factors in mothers who are both participants and, at the same time, proxies for their children. Previous studies on the attitudes of public and research participants toward genomic cohort studies have reported that most people have positive feelings toward genetic testing, data sharing to a biorepository, and the return of individual genomic results, and attitudes toward genomics varied by education, nationality, gender, age, and marital status , 8. SomeMothers participating in birth cohort studies have commonalities in gender, age, having children, and donating biological samples, but they may have diverse attitudes toward genetic analysis. Moreover, studies involving children must address the ethical issue of protecting children’s privacy –16. A syDetails of the JECS can be found in Kawamoto et al. . A totalThe questionnaire included items for the following content areas: approving attitudes toward genetic analysis, concerns about handling genetic information, trust level in research institutions, preference for receiving results, self-rated knowledge level of genomic terminology, history of chronic disease or disability in the family, and the preference for receiving an explanation about genetic analysis. Data on the mothers’ age at delivery, educational background, and annual household income were collected from medical records or self-administered questionnaires completed during mid-to-late pregnancy ; the queTo investigate the relationship between environment and children’s health, genetic analysis on children and parents is beneficial,” for attitude toward data sharing, the item was “If individuals are not identified, it is better to share the anonymous genetic information with other medical studies for the development of medicine,” for concern about the security of genetic information, the item was “I have a concern about the secure management of genetic information to avoid leakage,” for concern about the secondary use of genetic information, the item was “I have a concern that genetic information may be provided to other institutions without notice,” for trust in research institutions, the item was “The credibility of research institutions is one of my motivations for participating in the study,” and for preference for receiving results, the item was “I want to know the results of the genetic analysis.” Answers were measured on a five-point Likert-type scale: “definitely agree,” “agree,” “unsure (not agree nor disagree),” “disagree,” and “definitely disagree.” Subsequently, “definitely agree” and “agree” were reclassified into “agree,” and “disagree” and “definitely disagree” were reclassified into “disagree.”Participants’ attitudes were assessed through several statements for which participants indicated their level of agreement. The items were as follows: for attitude toward genetic analysis, the item was “To examine participants’ basic knowledge of genetics, their self-rated knowledge level was assessed for the following five terms: “genome,” “gene,” “DNA,” “chromosome,” and “recombinant DNA technique,” following the procedure used in a previous study . ParticiParticipants’ preferences for receiving an explanation about the genetic analysis plan in the JECS was asked as follows: “We will explain the genetic analysis plan in the JECS by a written document. What other ways do you want to receive explanations?” The answer options were “no need except for documents,” “briefing session,” “individual face-to-face meeting,” “consulting corner in study-related events,” and “information service via telephone.” Multiple answers were allowed. The latter four answers were summarized as “verbal explanations” in the analysis.P < 0.05 was considered statistically significant. All analyses were conducted using SPSS ver. 25 .Multivariate multinomial logistic regression analyses were performed to identify factors associated with approving attitudes toward genetic analysis. The mother’s age, education, knowledge level of genomic terminology, trust in the research institutions, concern about the security of genetic information, and history of chronic disease in the family were included in the analysis models as independent variables. Concern about the secondary use of genetic information was not included in the analysis models because of its high correlation with concerns about security (Spearman’s correlation coefficient = 0.83). Structural equation modeling was used to explore the association of approving attitudes toward genetic analysis and related factors. A smaller Akaike’s information criterion (AIC) value was used to select the better-fitting model . Model fOf the 5176 participants in the Chiba Regional Center, 1845 responded to the questionnaire (response rate 35.6%). After excluding the responses of non-mothers and mothers transferred from other regions and those with missing data for the variables used in the analysis, the remaining 1762 responses (34.0%) were used for the study. The children’s ages ranged from 4 to 7 years. The mothers’ ages ranged between 20 and 50 years, averaging 37.7 ± 4.8 years. Of the respondents, 67% were 30 years old or above, 69% had a college-level education , 67% were higher-income households (4 million yen per year and above), and 29% had family members with chronic diseases or disabilities  expressed a preference to receive the results of genetic analysis. Although there were some differences in the ratio depending on the group of background factors, over 84% of all groups expressed a preference for the results  ambiguous attitudes toward genetic analysis among mothers with low familiarity with genetics/genomics, (2) denial of the benefit of genetic analysis among mothers with low educational backgrounds, (3) unacceptance of data sharing if mothers expressed concern about the handling of genetic information, and (4) approval of genetic analysis/data sharing if mothers had a high level of trust in research institutions. In addition to maintaining participants’ trust, approaches for enhancing familiarity with genetics/genomics, optimizing explanations for different educational levels, and explicitly disclosing privacy policies of genetic information will be needed to maximize participants’ acceptance of genetic analysis in birth cohort studies.Supplementary Table 1