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    Pronounced mito-nuclear discordance and various Wolbachia infections in the water ringlet Erebia pronoe have resulted in a complex phylogeographic structure

    Erebia pronoe exhibits highly structured and strongly differentiated mitochondrial lineages, which are consistent with the distribution of previously described morphotaxa and analyses of Dincă et al.10 These genetic lineages are also reflected to varying degrees in the nuclear markers. The observed mito-nuclear discordances can be explained by different evolutionary rates of genetic markers, the effects of Wolbachia infections, and introgression. These aspects are discussed in more detail in the following sections on the phylogeographic history of this species complex.Mito-nuclear discordance and the systematic status of Erebia melas
    Based on genital morphology and nuclear markers, E. melas represents a distinct group to E. pronoe. The common area of origin of both species was probably located in the eastern Alps, which is supported by a RASP analysis based on the nuclear markers. However, E. melas acts as an ingroup of E. pronoe based on the mitochondrial markers, and a RASP analysis indicates a common origin for both taxa in the Carpathian region. Since most Erebia species in Europe have at least parts of their distribution in the Alps21 and are adapted to Alpine environments and habitats22,23, we consider an eastern Alpine origin of the ancestor of E. pronoe and E. melas more likely. This hypothesis subsumes the assumption that the genetic proximity on the mitochondrial level was probably caused by hybridisation and introgression events, which could have occurred as a result of several eastward advances of E. pronoe to the Balkan Peninsula (see below). This seems plausible, because the ability and tendency of E. pronoe to hybridise with other Erebia species have been demonstrated repeatedly12,24,25.The existence of Wolbachia strain 2 in both species, and its distribution from the Pyrenees (in E. pronoe) to the Balkan Peninsula (in E. melas) also speaks for a common origin of both species. Thus, Wolbachia strain 2 might represent the ancient strain present in the common ancestor of this species group, surviving today at the geographic margins (i.e. Pyrenees, western Alps, Balkan Peninsula), but which at some time was replaced in the centre of the butterfly’s range (i.e. the eastern and central Alps) by strain 1. The link between co-occurrence in a common area and prevalence of one Wolbachia strain was also recently demonstrated in other Erebia species26 and might facilitate mitochondrial introgression27.Intraspecific differentiation and glacial refugia of Erebia pronoe
    The Pyrenean region is inhabited by one of the oldest and most differentiated intraspecific lineages of E. pronoe. The high genetic diversity in the Pyrenees speaks for large effective population sizes throughout time, enabled by mostly altitudinal shifts in response to climatic cycles, and a lack of major genetic bottlenecks. Compared to the Pyrenean group, the genetic diversity of the western Alpine populations, also well differentiated from all other groups, is lower. This lower diversity was probably the result of repeated cold stage retreat to a geographically more restricted refugium at the foot of the south-western Alps, a well-known refugial area for numerous species28.We cannot say conclusively whether the populations in the Pyrenean region or in the western Alps differentiated first, due to the contradictory genetical markers. The higher evolutionary rate of the mitochondrial markers, the allopatric distribution, and the hybridisation with diverse Erebia species may have led to a greater differentiation of the Pyrenees and/or a loss of the genetic link between the western Alps and the Pyrenees. Since a link between the western Alps and the Pyrenees is still well reflected in the nuclear data set and by the shared Wolbachia strain 2, we consider the most likely scenario to be an early Pleistocene or even Pliocene expansion from the western Alps to the Pyrenees, with subsequent isolation and differentiation. Thus, the Pyrenees-western Alps populations might first have separated as one group from an eastern Alps group s.l., as suggested by nuclear information, and not in two independent events, as suggested by mitochondrial genes.Simultaneously to the split between western Alps and Pyrenees, a separation of the eastern Alpine group s.l. into a southern Alpine subgroup and an eastern Alpine subgroup should have occurred. The southern Alpine subgroup displays a high genetic diversity in their nuclear markers, but a significantly lower diversity in the mtDNA. This might be explained by the existence of a cold-stage refugial area in the southern Alps or their margin, supporting the constant survival of large populations, but also a reshaping of the mtDNA patterns through introgression from the eastern Alpine subgroup during secondary contact when both subgroups expanded into formerly glaciated east-central Alpine areas. The isolated occurrence of Wolbachia strain 1 and mitochondrial haplotypes H29 and H30 (shared with the eastern Alps subgroup) in the southern Alps further support the hypothesis of gene flow from the eastern Alpine region into the southern Alpine populations and vice versa.The eastern Alpine subgroup probably survived glacial periods in a large, cohesive refugium at the eastern edge of the Alps, as has been demonstrated for numerous other species28. This area is also seen as a potential centre of origin of the entire taxon. From there, a recent (most likely postglacial) dispersal must have taken place, which should be responsible at least partly for the star-like pattern of this group in both mitochondrial and nuclear haplotype networks. However, further dispersal events out of the eastern Alps during previous interglacials and maybe even going back to the Pliocene have to be postulated to explain the entire range dynamics in E. pronoe.Apparently, multiple advances out of the eastern Alps into the Balkan mountain systems have taken place from several independent glacial refugia in the region, as indicated by the different mtDNA lineages in Slovenia, western Balkan mountains, and eastern Balkan mountains. A separation between the eastern and western Balkans, and hence also separate glacial refugia in both areas, was frequently observed for mountain taxa28,31. This pattern may have resulted from a succession of independent dispersal events from the eastern Alps throughout the younger Pleistocene, with subsequent regional extinction events and/or independent dispersal events across the Carpathians, as has been demonstrated for numerous other species29.A similar pattern of two independent colonisation events also applies to the Carpathians. Thus, the highly isolated populations in the south-eastern Carpathians must go back to an older expansion out of the eastern Alps. This probably took place during one of the last interglacial phases. The route most likely followed the Carpathian arc, but only a few populations survived at their south-eastern edge. This underlines the phylogeographic independence of this part of the Romanian Carpathians, which is also supported by studies on numerous other mountain species30,31,32. On the other hand, the Tatra mountains, as the northernmost part of the Carpathians, were colonised very recently, most likely postglacially, out of the eastern Alpine area. The strong and rather recent link between these two areas is also supported by phylogeographic studies on many taxa30,33,34.Because of the slower evolutionary rate of nuclear DNA and the resulting incomplete lineage sorting, nuclear markers can contribute little to the reconstruction of these presumably recent events. In line with that, the Valais lineage also has little nuclear differentiation but is clearly distinguished from the western and eastern Alpine lineages by the exclusive mtDNA haplotype H17 and Wolbachia strain 3. The presence of a single, highly differentiated mtDNA haplotype and an exclusive Wolbachia strain indicates a selective sweep. This lineage most likely represents a chronological relict of an interglacial expansion of the eastern Alpine subgroup to the western-central Alps surviving since then in this area, finding glacial refugia in nearby unglaciated areas and becoming infested by a Wolbachia strain not present in any other E. pronoe lineage, hence accelerating its differentiation.Another selective sweep was probably the cause of the mito-nuclear unconformity in the southern Alps lineage. The occurrence of the mtDNA haplotypes H29 and H30 and the Wolbachia strain 1 indicate mitochondrial hybridisation between the eastern and southern Alpine lineages during an expansive interglacial phase. As a result, Wolbachia infection probably occurred, which might have impoverished the mitochondrial diversity of the southern Alps lineage.Consequences for subspecific differentiation in Erebia pronoe
    In general, the support given by our data for the so-far described subspecies decreases from west to east. Erebia pronoe glottis Fruhstorfer, 1920, distributed in the Pyrenees, represents the best-supported subspecies. Fixed mitochondrial amino acid changes emphasize the distinctness of this taxon, which might be well advanced in the process of speciation; we cannot even exclude the possibility that it has already reached full species rank. The genetic separation of the western Alps from the Valais, geographically separated along the main Alpine ridge, justifies the recognition of the taxa E. pronoe vergy (Ochsenheimer, 1807) and E. pronoe psathura Fruhstorfer, 1920, respectively, and is supported by both marker sets as well as by the existence of two different Wolbachia strains. The eastern Alpine subgroup resembles the nominotypical E. pronoe pronoe. The existence of at least one lineage in the southern Alpine area is supported by both marker sets. A finer separation based on the mitochondrial markers is not possible, because of recent introgression events affecting east Alpine haplotypes, as also indicated by the existence of Wolbachia strain 1. This population group could be assigned to the taxon E. pronoe gardeina Schawerda, 1924, or to E. pronoe tarcenta Fruhstorfer, 1920, considering their ranges. Nevertheless, a final decision requires further regional studies. Erebia pronoe fruhstorferi Warren, 1933 was accepted to be widely distributed in the Balkan mountain systems. However, our data suggest independent lineages in the western and eastern Balkan mountain systems of which only the eastern populations can be assigned to this taxon. The lineage of the Slovenian Alps is primarily based on mitochondrial markers and morphological characteristics7. The existence of an independent lineage for the highly isolated populations in the southern Carpathians, justifies the subspecies status of E. pronoe regalis Hormuzachi, 1937. Both marker sets display a differentiation, which was more pronounced in the nuclear than in the mitochondrial DNA. More

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    Spatial and temporal evolution of ecological vulnerability based on vulnerability scoring diagram model in Shennongjia, China

    Spatial and temporal distribution of ecological vulnerabilityBased on the SPCA model, the temporal and spatial distribution of ecological vulnerability in Shennongjia is obtained, as shown in Fig. 3. From 1996 to 2018, the area of micro vulnerability areas continued to increase and occupied a dominant position. Moreover, their distribution pattern tended to be gradually integrated, indicating that the structure and function of the ecosystem in most areas of Shennongjia were relatively complete, and in a healthy and stable state. However, the ecological environment of the severely vulnerable areas in the northeast, south and southwest of Shennongjia is in a trend of continuous deterioration, and the risk of extreme vulnerability is gradually emerging. From the spatial distribution of ecological vulnerability in 2018, it can be seen that the extremely vulnerable areas have increased significantly, and exhibit a dense and continuous distribution trend in some areas, accompanied by the development of rapid urbanization and highway traffic construction. There are also high-risk ecological vulnerable zones and the extremely vulnerability areas.Figure 3Spatial and temporal distribution of ecological vulnerability in Shennongjia. Spatial and temporal distribution of ecological vulnerability for (a) 1996, (b) 2007, (c) 2018 in Shennongjia, China.Full size imageIt can be seen from the area proportion of different levels of vulnerable areas (Fig. 4) that the area proportion of micro and extremely vulnerable areas increased significantly. Specifically, the area proportion of micro vulnerable areas increased from 59.98% in 1996 to 71.02% in 2018, while the area proportion of extremely vulnerable areas increased from 1.23% in 1996 to 7.32% in 2018. This shows that the ecological vulnerability of Shennongjia exhibits a significant two-level differentiation trend.Figure 4Proportion of the area of vulnerable districts at all levels in Shennongjia.Full size imageDynamic change of ecological vulnerabilityDuring the study period, the areas with a positive fitting slope account for more than 90% of the total area of the study area, which indicates that the overall vulnerability of Shennongjia presents a downward trend. According to the natural discontinuity point method, the dynamic change results of ecological vulnerability in Shennongjia are divided into five levels (Fig. 5), in order to discern the spatial angle more intuitively and clearly. It can be seen that the ecological vulnerability of most regions exhibits a decreasing trend, while the ecological vulnerability of certain regions increases.Figure 5Dynamic changes of ecological vulnerability in Shennongjia. Changes in the ecological vulnerability of Shennongjia in different periods: (a) 1996–2007, (b) 2007–2018, (c) 1996–2018.Full size imageFrom 1996 to 2007, whether the spatial distribution trend of ecological vulnerability increased or decreased is not obvious. However, from 2007 to 2018, the areas with significantly increased ecological vulnerability were concentrated in Yangri and Songbai in the northeast and near the Hongping airport in Shennongjia in the midwest. During this same time period, in the areas around the main urban areas and along the roads that were seriously disturbed by human activities, ecological vulnerability also exhibited a decreasing trend.Change trend of comprehensive ecological vulnerability indexAnnual change of the comprehensive ecological vulnerability indexThe results of the comprehensive ecological vulnerability index of 1996, 2007, and 2018 are 2.77, 2.71, and 2.51, respectively. From the annual change of the ecological vulnerability index in Shennongjia (Fig. 6), it can be seen that the ecological vulnerability of Shennongjia showed a downward trend from 1996 to 2018, and the stability and health of the ecosystem were improved overall.Figure 6Annual change of the comprehensive ecological vulnerability index. CEVI, comprehensive ecological vulnerability index.Full size imageAmong them, the decline of ecological vulnerability is relatively small from 1996 to 2007, which may be ascribed to the preliminary implementation of restrictive policies, such as banning logging and returning farmland to forest, which reduced ecological exposure factors, such as illegal logging and deforestation. From 2007 to 2018, the comprehensive index of ecological vulnerability in Shennongjia decreased significantly, which is mainly due to the designation of national nature reserves and the implementation of various ecological protection projects36. While reducing the exposed ecological disturbance, it simultaneously markedly improved the adaptability of the ecosystem, and further reduced the overall ecological vulnerability of the region.Changes of the comprehensive ecological vulnerability Index in different townsAccording to the comprehensive index of ecological vulnerability of eight towns in the Shennongjia (Table 5, Fig. 7), the ecological vulnerability difference of each town is obvious. In 2018, the comprehensive index of ecological vulnerability of each town is lower than that in 1996 and 2007. The results show that the average value of CEVI is, from high to low, Yangri, Xiaguping, Songbai, Xinhua, Jiuhu, Hongping, Muyu, and Songluo. The maximum value of the CEVI appeared in Yangri in 1996, and the minimum value occurred in Songluo in 2018.Table 5 Comprehensive ecological vulnerability index of towns.Full size tableFigure 7Radar chart of the comprehensive ecological vulnerability index of towns.Full size imageDriving factors of spatial and temporal evolution of ecological vulnerabilityThe formation and evolution of ecological vulnerability in Shennongjia constitutes a dynamic process, which is the result of interactions of human and natural factors. Based on the principle of SPCA of ecological vulnerability, the transformed principal components are extracted, and the rotated factor load matrix is obtained to reflect the different effects of various factors on the evaluation results. Each principal component possesses a different ability to explain the original index factors, but it has similar rules in the first four principal components (Table 6). The cumulative contribution rate of the first four principal components in the three groups of data reached more than 80%, which can reflect the information of most factors, and thus it has good representativeness.Table 6 Principal component loading and score.Full size tableAmong the first principal component and the third principal component, the contribution of land-use type index (C9) is higher; in the second principal component, the contribution of population density (C1) is higher; among the fourth principal components, the contribution of vegetation coverage (C13) is higher. Moreover, the contribution of other factors in different years and main components is dissimilar.The influence of land-use type on ecological vulnerabilityWhether due to natural or human factors, the original properties of the ecosystem are altered by changing the surface cover. Therefore, land-use type is an important factor affecting regional ecological vulnerability. The difference of surface cover leads to the difference of ecological community, and then produces varied ecological environmental benefits. Forest land is the most important land-use type in the study area, and the ecological vulnerability of the distribution area is mainly micro degree and light. However, consider the important ecological value of the forest ecosystem, attention should be given to its vulnerability. The ecological vulnerability of the construction land is mainly severe and extreme, which is largely due to the expansion of construction land, which destroys the original ecological structure and ecological community. Furthermore, a large number of manmade patches replace natural patches in the construction land, and biodiversity decreases, leading to the decline of the stability of ecological structures and the increase of vulnerability.The influence of population density on ecological vulnerabilityPopulation density is one of the most direct exposure factors in the vulnerability of ecological environments. Population density is generally higher than that in high area, and it is also a region with a developed economy and high urbanization. In these areas, human activities are frequent, which usually impart a negative disturbance to the natural environment, including the rapid expansion of cultivated land and construction land area, as well as high discharge of production and domestic wastewater waste, which has caused great pressure on the ecological environment, leading to a significant increase in ecological vulnerability.The influence of vegetation cover on ecological vulnerabilityFrom 1996 to 2018, the vegetation coverage of the Shennongjia exhibited an overall upward trend, which is of positive significance to the reduction of the vulnerability of the ecosystem. Vegetation, as the main body of the land ecosystem, maintains the balance of ecological environment through interactions with climate, landform, and soil37. Extant literature shows that the change of vegetation coverage is an major factor of regional ecological environment change, and has a clear indication function for the change of regional ecological environment38. The spatial distribution trend of ecological vulnerability in the Shennongjia is markedly similar to that of vegetation coverage. The ecological vulnerability of regions with higher vegetation coverage is lower, exhibiting a significant negative correlation. In the Shennongjia, the change of vegetation coverage is also obviously influenced by human factors.Contribution of landscape pattern index to ecological vulnerabilityThe spatial distribution of each index in Shennongjia have been obtained from previous studies47. From the unary linear regression analysis, in the years of 1996, 2007 and 2018, the NP, LPI, AI, DIVISION and SHDI are all significantly correlated with the ecological vulnerability index (Fig. 8).Figure 8Scatter plot of linear regression of landscape pattern index and ecological vulnerability index. EVI, ecological vulnerability index.Full size imageIn the case of different independent variable combinations in 1996, 2007 and 2018, the multiple regression relationship between the independent variable and the dependent variable of each group is significantly correlated, and the multiple linear regression equation of the full model is obtained as follows:$$1996{:};;{text{ Y}} = 6.443 + 0.014{text{X}}_{1} + 0.006{text{X}}_{2} – 0.038{text{X}}_{3} – 0.066{text{X}}_{4} + 0.058{text{X}}_{5}$$$$2007{:};;{text{ Y}} = 4.497 + 0.016{text{X}}_{1} + 0.007{text{X}}_{2} + 0.793{text{X}}_{3} – 0.047{text{X}}_{4} – 0.305{text{X}}_{5}$$$$2018{:};;{text{ Y}} = – 1.980 + 0.037{text{X}}_{1} + 0.006{text{X}}_{2} + 0.703{text{X}}_{3} + 0.019{text{X}}_{4} – 0.123{text{X}}_{5}$$The contribution rate of landscape pattern index to ecological vulnerability in different years of 1996, 2007, and 2018 is shown in Table 7. The contribution of AI and NP to ecological vulnerability in 1996 was high; the contribution of NP and AI to ecological vulnerability was higher in 2007; and the NP in 2018 had the highest contribution to ecological vulnerability, reaching 95.77%.Table 7 Contribution of the landscape pattern index to the ecological vulnerability index.Full size tableBased on the analysis results from 1996 to 2018, the contribution of NP and AI to ecological vulnerability is relatively high. The main reason for this is that the forest coverage rate of Shennongjia is as high as 91%. Specifically, with the forest as the landscape matrix, the NP is small and the connectivity between patches is high, showing a trend of aggregation. The degree of landscape fragmentation is relatively low and decreases annually, and ecological vulnerability decreases with the decrease of the degree of landscape fragmentation, Therefore, the impact of NP and AI on ecological vulnerability is highly significant.The AI and ecological vulnerability index always exhibit a significant negative correlation in the study period. In the 1996 research results, the contribution of AI to ecological vulnerability is the most obvious. Combined with the spatial distribution of ecological vulnerability, it can be seen that most of the severe and extremely vulnerable areas are distributed in areas with low AI. Most of them are the distribution areas of artificial patches, such as rural living areas, airports, tourism centers, etc., which are obviously disturbed by human activities, resulting in low connectivity among various landscape types, which greatly reduces the aggregation degree of landscape and increases regional vulnerability.There is also a significant positive correlation between the NP and the ecological vulnerability index. This is especially the case in 2018, when the contribution of the NP to ecological vulnerability is as high as 95.77%, which is mainly attributable to the urbanization construction of Songbai town in Shennongjia. Combined with the land-use structure map, it can be seen that the number of construction land patches in the northeast region increased sharply. In this process, the renewal of patches aggravates the degree of landscape fragmentation and plays a key role in the aggravation of regional vulnerability risk.Although the impact of LPI, SHDI and DIVISION on ecological vulnerability always exists, the contribution is not very significant. Among them, SHDI contributed 10.38% in 2007, which was more sensitive to the unbalanced distribution of each patch type. In areas with high SHDI, landscape heterogeneity is high, the ecological pattern is unstable, and ecological vulnerability increases. More

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    An intergenerational approach to parasitoid fitness determined using clutch size

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    Water ecological security assessment and spatial autocorrelation analysis of prefectural regions involved in the Yellow River Basin

    Water ecological security evaluation results of Yellow River BasinIndex weight analysisThis study selects the index weights in 2009, 2014, and 2019 for comparative analysis. As shown in Table 3, in terms of space, in the pressure layer, indicator A6 (Water area) has the most prominent weight, and indicator A3 (Natural population growth rate) has the most negligible weight; in the state layer, indicator B6 (Proportion of wetland area to total area) has the most prominent weight, and B1 (COD emissions per 10,000 yuan GDP) has the most negligible weight; in the response layer, indicator C3 (Green area rate of built-up area) has the most prominent weight, and indicator C2 (Centralized treatment rate of urban domestic sewage) has the most negligible weight. In summary, water area, wetland area, and built-up green space are the key indicators affecting the water ecology of the Yellow River Basin, including natural factors and economic and social factors.Table 3 Water ecological security index weight.Full size tableIn terms of time, indicators A6 and B6 have equal weights in three years and have always been in an important position. The weight of indicator C1 (the rate of stable compliance of wastewater discharge by industrial enterprises) has fallen for three consecutive years, from 0.38 to 0.09. It shows that after years of environmental management in various cities, the rate of compliance with wastewater discharge standards of industrial enterprises has been continuously increasing. It plays a positive role in the construction of water ecological security. The weight of indicator C3 has increased significantly in three years, from 0.31 in 2009 to 0.90 in 2019, indicating that with the continuous development of urbanization, the built-up area has become larger and larger, which has a massive impact on water ecological security. Therefore, the green area in the built-up area is vital, which is the key to ensuring the urban ecological environment. It is also a critical factor in maintaining the water ecological security.Trend analysis of water ecological securityThis study is based on Eq. (4) to calculate the WESI of the nine provinces in the past ten years, as shown in Fig. 3. From the perspective of the changes in WESI from 2009 to 2019, the overall trend is slowly increasing. Compared with 2009, WESI increased by 5.96% in 2019, but the average annual growth rate was only 0.59%. The sharp rise stage was in 2009–2012, with an average annual growth rate of 1.84%. Since 2009, there has been no inferior V water in the main stream of the Yellow River, and the water quality has been improving year by year. During this period, the nine provinces implemented the Yellow River Basin Flood Control Plan under the guidance of The State Council. The plan calls for strengthening infrastructure construction in the Yellow River Basin and conducting work such as river improvement and soil and water conservation. Therefore, we will promote the restoration of water ecology in the river basin and improve the safety of water ecology. From 2012 to 2019, WESI showed a trend of ups and downs. This is because the provinces have gradually shifted their development focus to the economy after achieving significant results in restoring water ecology in the river basin. The rapid economic development has brought more significant pressure to environmental governance and hindered water ecological safety improvement.Figure 3Trend map of water ecological security index (WESI) of nine provinces.Full size imageCriterion layer quantitative resultsTo further study and appraise the water ecological security of the study area, this paper quantifies the criteria layers (i.e., pressure, state, response) on account of the SMI-P method. It selects 2009, 2014, and 2019 for comparative analysis. As shown in Fig. 4, the criterion layer has undergone specific changes over time. First of all, the distribution of pressure in 62 cities has not changed much in three years. The areas with more tremendous pressure on water ecological security are mainly concentrated in eastern cities, including Shuozhou, Taiyuan, Jinzhou, Luliang, Linfen, Jincheng, and Changzhi, Anyang, Hebi, Jiaozuo, Puyang, Liaocheng, and other cities. Areas with less pressure are mainly concentrated in western and eastern cities, including Guoluo Tibetan Autonomous Prefecture, Hainan Tibetan Autonomous Prefecture, Haibei Tibetan Autonomous Prefecture, Ordos, Bayannaoer, Yulin, and other cities. In 2009, the precipitation in spring and winter in Lanzhou is less, the degree of drought is serious, and the flood disaster is more severe in flood season, which brings tremendous pressure to the water ecological security. After 2015, Lanzhou continued to implement the Action Plan for Prevention and Control of Water Pollution and then the river chief system was implemented. In 2019, The Work Plan of Lanzhou Municipal Water Pollution Prevention and Control Action in 2019 was issued and implemented. All these measures and actions have laid a foundation for water ecological security. On the contrary, with the rapid development of urbanization and economy and society, the pressure of water ecological security in Jinan has increased.Figure 4Quantitative spatial distribution map of the 62 cities in the Yellow River Basin. Note This was created by ArcMap-GIS, version 10.5. https://www.esri.com/.Full size imageThe larger the value of the status layer, the better the aquatic ecological status. On the contrary, the worse the aquatic ecological security. The overall spatial distribution of the status layer has not changed significantly in the past three years, and the changes are mainly concentrated in some cities. For example, the water ecological security status of Wuhan and Ulan Chab has gradually deteriorated in three years. The reason is that the urban population is becoming denser and sewage discharge is increasing, but related management and measures have not been fully implemented. In Dongying, the water ecological security status improved in 2014 and 2019. According to the Environmental Status Bulletin, in 2014, Dongying deepened its drainage basin pollution control system, continuously strengthened the restraint mechanism to improve river water quality, and carried out a pilot wetland ecological restoration.In the three years of 2009, 2014, and 2019, the response layer has changed more significantly than the pressure and status layers. It can be seen that the degree of response scarcity has gradually shifted from western cities to eastern cities. The reason can be understood as that due to their superior natural conditions, western cities have relatively weak awareness of water ecological protection and governance, and their ability to respond to emergencies is insufficient. However, with the increasingly prominent ecological and environmental problems, the awareness of maintaining water ecological safety is increasing, and the protection and governance measures are constantly improving. For example, Guoluo Tibetan Autonomous Prefecture, Hainan Tibetan Autonomous Prefecture, and Haibei Tibetan Autonomous Prefecture. Eastern cities are densely populated, urbanization development is faster than western cities, and environmental problems occur more frequently. Therefore, the awareness of ecological and environmental protection is more substantial, the governance system is relatively complete, and responsiveness is relatively good. However, as time progresses, some cities have somewhat slackened their ecological environment governance, and therefore their responsiveness has also weakened. For example, Shuozhou, Jinzhou, Lvliang, Linfen, and other places.Final quantitative resultsIn order to show the water ecological security status of 62 cities more intuitively, this paper shows the water ecological status level in Table 2 through the GIS spatial distribution map (Fig. 5).Figure 5Distribution map of water ecological security status in 62 cities of the Yellow River Basin. Note This was created by ArcMap-GIS, version 10.5. https://www.esri.com/.Full size imageLooking at the overall situation in the past three years, the water ecological security status is relatively stable, with little overall change. The reasons mainly include natural geographical location and economic and social development. In terms of physical geography, the safer areas are concentrated in the upper reaches of the Yellow River Basin, all of which have the characteristics of large land and sparsely populated areas and relatively superior natural conditions. They provide good conditions and foundations for the construction of water ecological security. The moderate warning cities are primarily located in the Loess Plateau and the North China Plain, where water resources are scarce, and the dense population, posing a threat to water ecological security. In terms of economic and social development, relatively safe areas are located in remote areas with inconvenient transportation. The region is dominated by agriculture and animal husbandry, with relatively backward economic development and a low level of urbanization. In addition, the threat to water ecological security is relatively tiny. Residents in the moderate warning area have a significant living demand, and the over-exploitation and utilization of natural resources have led to the destruction of the ecological environment. Therefore, it poses a more significant threat to water ecological security.Combining Fig. 5 and Table A.2 of appendix, it can be seen that in 2009, there were 8 safer cities, 22 with early warning level, and 32 with moderate warning. Relatively safe cities are concentrated in the southwest and north of the Yellow River Basin; cities with moderate warning level are distributed in the central and eastern areas. In 2014, the number of safer cities increased to 10, and the number of cities with moderate warning level decreased to 30. The means that water ecological security has received more and more attention, and cities have consciously strengthened the protection and governance of water ecology to maintain water ecological security. In 2019, there are 11 relatively safe cities, 21 cities with warning level, and 30 cities with moderate warning level. The overall situation has not changed much, and some cities have changed significantly. For example, Erdos had increased from an early warning status in 2009 to a safer status in 2014, and its safety index has risen from 0.57 to 0.65. Wuzhong has been upgraded from the warning level in 2009 (0.39) to the relatively safe in 2014 (0.44), and the safety index (0.47) in 2019 has also increased. Binzhou had improved from its early warning status (0.60) in 2009 to a relatively safe level (0.64) in 2014, and its safety index (0.66) has also increased in 2019, but the increase is not significant. On the contrary, Jinan has deteriorated from the early warning level in 2009 and 2014 to the moderate warning level in 2019, indicating that the water ecological security of Jinan has been seriously threatened in the process of rapid development.Spatial autocorrelation analysis of 62 cities in the Yellow River BasinGlobal spatial autocorrelation analysisThis paper selects 2009, 2014 and 2019, and analyzes the global spatial autocorrelation based on GeoDa. Combining Table 4 and Fig. 6, the Moran index for these three years was 0.298, 0.359, and 0.334 respectively, which were all in the [0,1] interval, indicating the water ecological security of 62 cities in the past three years showed significant spatial autocorrelation. Moreover, there is a positive spatial correlation, and the spatial autocorrelation is strong. The four quadrants of the scatter chart are high-high (i.e., first quadrant) aggregation area, low–high (i.e., second quadrant) aggregation area, low-low (i.e., third quadrant) aggregation area, and high-low (i.e., fourth quadrant) aggregation area. After testing, z-value  > 1.96, p-value  More

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    Tracing the invasion of a leaf-mining moth in the Palearctic through DNA barcoding of historical herbaria

    Detection of archival Phyllonorycter mines in historical herbariaOnly 1.5% (225 out of 15,009) of herbarium specimens of Tilia spp. examined from the Palearctic contained Ph. issikii leaf mines. These 225 herbarium specimens occurred in 185 geographical locations across the Palearctic, with the westernmost point in Germany (Hessen; the herbarium specimen dated by 2004) to the most eastern locations in Japan (on the island of Hokkaido; 1885–1974) (Fig. 1).Figure 1The localities where herbarium specimens of Tilia spp. carrying Phyllonorycter mines were collected in the Palearctic in the last 253 years. The dotted line divides Ph. issikii range to native (below the line) and invaded (above the line). The map was generated using ArcGIS 9.3 (Release 9.3. New York St., Redlands, CA. Environmental Systems Research Institute, http://www.esri.com/software/arcgis/eval-help/arcgis-93).Full size imageMost specimens with leaf mines (90%; 203/225) originated from Eastern Palearctic, in particular from the Russian Far East (RFE) (67.5%, 137/203) (Fig. 2a). In some cases, leaves were severely attacked, carrying up to 12 mines per leaf (as documented in the Russian Far East in 1930s–1960s). On the other hand, we found only 22 herbarium specimens with mines (10%; 22/225) from the putative invaded region in Western Palearctic, with the majority of herbarium specimens with mines (7% 15/225) from European Russia (Fig. 2b).Figure 2The presence of Phyllonorycter issikii mines in the herbarium specimens collected in the putative native (a) and invaded (b) ranges over the past 253 years (1764–2016). The number of herbarium specimens with and without mines and the percentage of the specimens with mines in each region or country from all herbarium specimens examined in a region or country (in brackets) are given next to each graph. The total number of herbarium specimens, including those with and without mines, is given for Eastern (a) and Western Palearctic (b) separately and altogether (a + b).Full size imageThe average number of leaf mines per herbarium specimen found in native (5.68 ± 0.77) and invaded regions (6.09 ± 1.70) was not significantly different (Mann–Whitney U-test: U = 20,145; Z = 0.43; p = 0.43). However, the infestation rate by Ph. issikii, i.e. percentage of leaves with mines per herbarium specimen was statistically higher in the West than in the East: 35% ± 8.19 versus 23% ± 1.94 (Mann–Whitney U-test: U = 1339; Z = 2.30; p = 0.02).Leaf mines from the East were significantly older than those from the West (Mann–Whitney U-test: U = 81; Z =  − 4.4; p  400 bp) were obtained for 71 archival specimens that were between 7 and 162 years old (Fig. 4, the points in dashed frame) (Table S4). Nine of these 71 specimens were over one century old (106–162-year-old): eight originated from the Palearctic and one from the Nearctic (Fig. 4, the points in gray cloud).In the Palearctic, the oldest successfully DNA barcoded Ph. issikii specimen (obtained sequence length 408 bp) was a 162-year-old larva dissected from the leaf mine on Tilia amurensis from the RFE (village Busse, Amur Oblast, the year 1859), sequence ID LMINH119-19 (Fig. 5, Table S5). In the Nearctic, the oldest sequenced specimen (obtained sequence length 658 bp) was 127-year-old larva of Ph. tiliacella on T. americana from USA, Pennsylvania (Fig. 5, Table S5).Figure 5A maximum likelihood tree of 81 COI sequences of Phyllonorycter spp. Overall, 71 archival sequenced specimens were dissected from herbaria collected in the Palearctic and the Nearctic in 1859–2014 and ten specimens (highlighted in blue) originated from the modern range20. The tree was generated with the K2P nucleotide substitution model and bootstrap method (2500 iterations), p  More