Based on the results of a large-scale survey, we construct an agent-based network model for the China independent inbound tourism system and, by the approach of numerical simulation, investigate the responses of the tourist behaviors to perturbations in different scenarios, including the closure of a tourism city, the opening of a new port city in western China, and the increase of the tourism attractiveness of a specific city. Numerical results show that: (1) the closure of a single city in general will affect the tourist visitations of many other cities and, comparing to the non-port cities, the overall visitation volume of the system is more influenced by closing a port city; (2) the opening of a new port city in western China will attract more tourists to the western cities, but has a negligible impact on either the overall visitation volume or the imbalanced tourist distribution; and (3) the increase of the tourism attractiveness of a non-port (port) city normally increases (decreases) the overall visitation volume, yet there are exceptions due to the spillover effect. Furthermore, by increasing the tourism attractiveness of a few cities simultaneously, we investigate also the strategy of multiple-city-upgrade in tourism development. We find that the overall tourist volume is better improved by upgrading important non-port cities that are geographically distant from each other. The study reveals the rich dynamic inherent in complex tourism network, and the findings could be helpful to the development and management of China inbound tourism. On one hand, a tourism city acts not only as the destination, but also as the transition stops for the tourists to transfer to other cities. In this regard, the increase of the visitation of the destination will prompt the visitation of the transition city, i.e., their visitations are positively correlated and they form the collaborative relationship. On the other hand, due to practical concerns such as time limit and travel cost, a tourist can only visit a limited number of cities in the destination country. As a consequence of this, the increase of tourist visitation of a city could decrease the tourist visitation of another city. In this situation, the visitations of the cities are negatively correlated and they form the competitive relationship. The collaborative-competitive relationships between tourism cities render the dynamics of tourism system highly nonlinear, giving rise to many intriguing collective behaviors, e.g., a small, local perturbation added on a single city might trigger a large, global event over the whole system. The complicated relationships between tourism cities and the nonlinear feature of the system dynamics to perturbations pose unprecedented challenges for the development and management of tourism system, calling for the developments of new theoretical models and analyzing methods different from the traditional ones6.A well-known feature of tourism system is that cities inside the destination country form very complicated collaborative-competitive relationships7. However, comparing with the outbound tourism (China has been the largest resource of outbound tourists worldwide since 2009), the development of China inbound tourism is largely lagged behind8. Moreover, among the inbound tourists, a large fraction (about 81%) is contributed by Chinese tourists from Hong Kong, Macao, and Taiwan8. The dramatic imbalance between the inbound and outbound tourism markets makes the development of inbound tourism an economic imperative, which has been broadly interested and extensively studied by both the policy-makers and academic researchers in the past years13. Besides the overall tourist volume, another key challenge in developing the China inbound tourism is how to deal with the problem of imbalanced tourist distribution. According to the recent reports14, the tourism resources of China are concentrated in cites in the eastern part. These eastern cities, which are also densely populated and well developed in economy, attracted most of the inbound tourists (more than 80%)15. Despite stimulating efforts such as infrastructure investments and policy supports, the western cities are still lagging behind largely in terms of tourist visitation volume16. The imbalanced tourist distribution restricts seriously the performance of the whole inbound tourism, and how to eliminate the imbalanced distribution by attracting more tourists from the eastern to western cites has been one of the key issues for the development of a healthy China inbound tourism. To solve this problem, a common approach adopted currently in practice is to increase the tourism attractiveness of some western cities16, e.g., improving their service level and transportations. However, due to the complicated collaborative-competitive relationships between the tourism cities, the upgrading of a city may either prompt or deteriorate the whole market. To have a proper evaluation on the impacts of city upgrading, it is necessary to consider the networked tourism cities globally from the point of view of dynamical complex system and, more importantly, measure the impacts in a quantitative manner.There is an urgent need for China to develop the inbound tourism from the point of view of complex dynamical systems. As the 4th largest inbound-tourist destination in the world, the inbound tourism is an essential component of the China’s tourism industry17. Stimulated by the discovery of the small-world and scale-free features in many realistic networks19, in the past two decades great efforts have been paid to the exploration of the topological properties of various empirical networks, as well as their influences on the network functions20. As a typical example of complex social network, a variety of tourism network models have been constructed and studied in literature25. In the general model of tourism networks, the destinations are represented by nodes, and the interactions among destinations are represented by links. Depending on the specific problem that are interested, the network nodes and links may have different definitions. For instance, treating the tourism sectors  within a destination city as nodes and the business relationship between them as links, in refs29 the authors have constructed the micro-tourism-network models. At the macroscopic level, a node can be defined as a country or a group of cities inside an area, and links can be defined as the economic ties among the counties or areas31. In previous studies of complex tourism networks, a common finding is that the networks possesses the general features of many realistic complex networks, e.g., the small-world property and heterogenous degree distribution30. It is worth noting that the existing studies focus mainly on the topological properties of the tourism networks, whereas their dynamical properties have been largely overlooked31. Specifically, it remains not clear how the patterns of the tourist flows are emerged from the random motions of the tourists, to what extend the tourism market is influenced if a destination city is closed during a tourism crisis, and how large the overall tourism performance will be increased if a tourist city is upgraded by the given amount of resources38. These dynamical properties, which are rooted in the collaborative-competitive relationships between the cities, are crucially important for the stable functioning of tourism network, as well as for the development and management of modern tourism.Network science provides a powerful tool for exploring the structure and performance of complex tourism systems15, and, by the approach of numerical simulation, explore its dynamical responses to various external perturbations. In the constructed network model, each node stands for a specific tourism city in China, and two nodes are connected by a link if there is a direct transportation between the corresponding cities. We introduce a large number of agents into the network through only the port cities, and make the agents travel among the connected nodes in a random fashion. Based on the results of a large-scale survey, we obtain the key parameters characterizing the random movement of the agents, and then investigate numerically the responses of the tourist behaviors in different scenarios, including: (1) the removal of a city from the network (which simulates the closure of a city due to tourism crisis); (2) the opening of a new port city in western China (which models the adjustment of the tourism policy); and (3) the increase of city tourism attractiveness (which corresponds to city upgrading). Our main finding is that in a complex tourism network the performance of the tourism cities is strongly coupled with each other, and the change of a single city could affect the tourism of many other cities, resulting in a large variation in the overall tourism market. The study sheds lights on the dynamics of tourist flows in complex tourism system, and could be helpful to the development and management of China inbound tourism.In the present work, we construct an agent-based network model for the China inbound tourism systemn. The cities are divided into two groups: the port and non-port cities. Here, port cities are defined as those cities where tourists are allowed to enter or leave China with certain probabilities, and non-port cities are defined as those cities where tourists can only pass through them. Correspondingly, the network nodes are classified into port and non-port nodes. Secondly, we attribute each city (node) a weight, ai, which characterizes the overall tourism attractiveness of the ith city . Thirdly, a link is established between nodes i and j if the two cities are connected by either the direct flight or high-speed railway transportations. The link is weighted by the geographical distance, dij, between cities i and j. We thus name N, into the network through the port nodes, and let the agents travel among the nodes in a random fashion. The agents are inputted and allowed to leave the network through the port nodes only. To be specific, denoting i in the network, the probability for an agent to be hopping from node i to l is defined as (the gravity model)40γ a key parameter to be estimated from the surveys. We note that this model of agent-based complex tourism network is essentially different from the existing models in literature in that the tourists are moving over the network in a dynamic fashion, which is critical for mimicking the collaborative-competitive relationships among the tourism cities and the nonlinear responses of the network dynamics to external perturbations.The agent-based network model of China inbound tourism is constructed as follows. Firstly, we regard each destination city in China as a node. The network size, i.e., the total number of nodes in the network, is denoted as A the port city from where the agent enters the network, D the port city from where the agent leave the network, and B and C the transition cities the agent passes through. The length of the travel route is defined as m the number of transition cities. For the example mentioned, we have i and j, the tourist flow is denoted by wij. It is noted that as wij includes both the routes from i to j and from j to i, we therefore have i. Different from By numerical simulations, we monitor the movement of the agents on the network and keep records on their travel routes. When an agent arrives a port city, there is a predefined probability for it to leave the network. If the agent departs from this port city, the number of agents in the network will be deceased by one. The simulation is stoped once all agents leave the network. We then conduct analysis on the statistical characteristics of the travel routes (itineraries). We characterize each travel route by a chain of nodes, which are always started from and ended at the port nodes. For example, if an agent visited four cities in sequence, we denote its travel route by the chain: We concrete the conceptual model of tourism network by the results of a large-scale survey. In constructing the network model, we need to estimate from the surveyed data the following key parameters. First, we need to identify the set of cities contained in the network. Currently there are totally about 700 tourist cites in China, but only not all of them are included in our surveys. Second, as the behaviors of the tourists are very different at the port and non-port cities, we need to identify the set of port cities. Third, the fraction of tourists arriving a specific port city, as well as the probability for a tourist to depart from it. These information can only obtained from the questionnaires. With these concerns, in conducting the survey, we ask the surveyed tourists to provide the complete information of their itineraries, including the arrival, departure and transition cities , and the survey was conducted from November 2010 to August 2011. The tourists were intercepted and surveyed at the major attractions of eight popular tourism cities in China, including Guangzhou, Shanghai, Hangzhou, Suzhou, Beijing, Chengdu, Guilin, and Xi’an. A total number of 3,000 questionnaires were distributed, with 2,687 returned on-site for a response rate of 89.6%. As we are focusing on only the behavior of independent foreign tourists, we keep only surveys from tourists arranging the trips by themselves, or by their relatives, employers or other individuals, rather than by tourism companies or travel agencies. For the same concern, we also remove the surveys from the grouped tourists, since tourists within a group do not make independent decisions. In addition, we remove the surveys collected from Chinese visitors , as they behave very differently from the foreign visitorssi), together with their tourism attractiveness (ai).The itineraries contain totally mentclass2pt{minim42. For cities that are not connected by direct flight, we check further the high-speed railway connections between them. The railway information is obtained from the Service Center of National Railway Administration of China43. By the time the surveys were conducted, there are totally 326 flight and high-speed railway connections among the investigated tourism cities. The connectivity of the cities is characterized by the distance matrix i and j.We next define the links connecting the nodes. A link is established between two nodes if there is at least one direct flight between the corresponding cities. The flight information and flight distance between the connected cities are obtained from the Civil Aviation Administration of China (CAAC)i and j, wij, is represented by the thickness of the associated link. We see that: (1) in consistent with the results in previous studies30, the tourism network possesses complex topological structure; (2) the distribution of the tourists is heterogeneous and imbalanced 15; (3) there are a few hub cities, e.g., the four port cities, which are densely connected to other cities in the network; (4) the tourist flows between the hub cities are clearly larger than those between the non-hub cities, forming the backbone of the network. The distribution of the tourist volumes, si, are plotted in Fig. By the surveyed results, we plot in Fig. ai)] and links .Having fixed the properties of the network nodes  to what extent will the overall tourist volume be affected when a single city is closed? (2) will the opening of a new port city in western China attract more tourists from the eastern cities? (3) how to improve efficiently the overall tourist volume by increasing the tourism attractiveness of one or a few cities? These questions capture the essential features of the collaborative-competitive relationships between cities in the tourism network, and are also of direct implications to the development and management of tourismN the total number of agents and S is determined solely by i. The results in Fig. This scenario mimics the closure of a city during a tourism crisis, e.g., the outbreak of an epidemic disease. In numerical simulations, this is implemented by removing a city from the network, together with the links associated with it. If a port city is removed, its arrival agents will be distributed to other three port cities in proportion to their arrival volumes. We first investigate how the closure of a city will affect the overall tourist volume of the network, entclass1pt{minimaentclass1pt{minimaith city. The results for removing port cities are plotted in Fig. si to a few of other cities. By the descending order of Δs, the top 5 cities that are benefited most from the removal of Beijing are Shanghai  the overall performance of the network is more influenced by removing a port city than a non-port city; 2) while the removal of a port city will increase the visitation volumes of many other cities, the visitation volume of its adjacent city however could be significantly decreased, due to the spillover effect (Tianjin to Beijing and Suzhou to Shanghai); 3) though as a non-port city, Chongqing is crucially important to the global performance of the network, i.e., the removal of it will induce a large reduction to the overall tourist volume , a plausible approach would be upgrading one or a few of western cities, saying, for example, upgrading a non-port city to a port city. Before doing this, it is necessary to have a proper evaluation on the possible impact it will bring, e.g., the number of tourists that are attracted from the eastern cities and its impact on the overall visitation volume. As shown in Table S, induced by opening a new port city. For the original network, the overall visitation volume is si}, when different cities are upgraded. We see that, comparing to the results without upgrading, the visitation volumes of the cities are almost unchanged in all cases. To quantify the overall impact of opening a new port city on the tourist distribution, we calculate the change of the geographic concentration index. The geographic concentration index is defined as45G, the more heterogenous and imbalanced are the tourists distributed on the network. It is therefore desirable that by opening a new port city in western China the value of G could be significantly decreased, so that more tourists will be attracted from eastern to western cities. For the original network, we have G is changed to 33.01, 33.26 and 33.27, respectively. We see that by opening a new port city in western China, the value of G is only slightly decreased, indicating the infeasibility of this approach in balancing the tourist distribution.We first check the change of the overall tourist volume, In summary, the opening of a new port city in western China neither affect significantly the overall visitation volume, nor the tourist distribution. That is, the problem of imbalanced tourist distribution can not be solved effectively by opening a new port city in western China.In tourism development, instead of a global upgrading (due to the limited resources), a common approach adopted in practice is increasing the tourism attractiveness of some specific cities each time, saying, for example, opening a new scenic area in a tourism city. While this approach in general could attract more tourists to the upgraded city, it may either increase or decrease the visitation volumes of other cities, due to the complex collaborative-competitive relationships between the networked cities. Furthermore, for the limited amount of upgrading resources, to which city should one invests such that the overall visitation volume of the system is maximally increased? Finally, when a large amount of upgrading resources are available, should one invests all the resources into a single city or, alternatively, distributes the resources among a few cities? We next address these questions by numerical simulations, based on the proposed model of agent-based tourism network.S′. The variation of the overall visitation volume, S, the top 5 cities are Wuxi  To check the generality of the results, we increase the increment of tourism attractiveness to S with respect to the city index again. We see that the set of most influential cities are not changed with Δa, and a sharp decrease of ΔS is also observed at Beijing and Chongqing.We first evaluate the influence of upgrading a city on the overall visitation volume. Fixing the increment of the tourism attractiveness as mentclass2pt{minimmentclass2pt{minimS with respect to a over a wide range, saying increasing a from its current value to 300 for each city. The results for the port cities are plotted in Fig. a, the value of S is decreased monotonically for each case. This result is expectable, as by increasing the tourism attractiveness more tourists will be attracted to the port city, making the number of tourists departing from this city increased. Figure S with respect to a for some important non-port cities, including Hangzhou, Suzhou, Chongqing, and Tianjin. We seen that, except Tianjin, the value of S is monotonically increased with the increase of a, indicating the positive role of upgrading a non-port city in increasing the overall visitation volume. The abnormal behavior of Tianjin shown in Fig. To explore further the influence of tourism attractiveness on overall visitation volume, we investigate the variation of a, as the impact of a single node on the whole network has its upper limit. This raises the question of upgrading efficiency, i.e., the increment of overall visitation volume per unit of increase of tourism attractiveness. Conventionally, the upgrading efficiency can be measured by the quantity S is decreased by increasing a. In such a case, a smaller value of S). By scanning a over a wide range, i.e., from its current value to 1 × 103, we calculate the variations of S with respect to a by upgrading different cities , Jiuzhaigou-Lhasa-Lijiang , Xi’an-Guilin-Chengdu (within a broad area in western China), and Hangzhou-Suzhouo-Shanghai (within the Yangtze-River-Delta area in eastern China). For illustration purpose, we increase the attractiveness of the upgraded cities by the same amount. The changes of the city visitation volumes, i = 11, 12, 19), the visitation volumes of the other cities are almost unchanged. As the visitation volumes of the three upgraded cities are clearly increased, the overall visitation volume is still increased by a noticeable amount, as depicted in Table S among the three cities in terms of single-city-upgrade, as can be seen from Fig. The saturation point in the variation of overall visitation volume reflects the limit of upgrading a single city in boosting the tourism market. When a large amount of upgrading resources are available, e.g., Δi = 18, 30, 48), the visitation volume of Chongqing (which is not directly upgraded in this case) is also increased significantly. The results for Xi’an-Guilin-Chengdu configuration are plotted in Fig. i = 5, 6, 8), the visitation volume of Chongqing is also increased significantly. However, comparing with the results shown in Fig. The results for Jiuzhaigou-Lhasa-Lijiang configuration are plotted in Fig. The results in Fig. Summarizing the influence of increasing city tourism attractiveness on tourism, it is found that: (1) the increase of tourism attractiveness of a non-port (port) city in general will increase (decrease) the overall visitation volume; (2) the behavior of Tianjin is different from other non-port cities, due to its strong connection with Beijing; (3) each non-port city has a saturation point in the tourism attractiveness over which the overall visitation volume is hardly increased; (4) when a large amount of upgrading resources are available, the upgrading of several cities could increase the overall visitation volume more efficiently than upgrading just a single city.The closure of a single city in general will affect the visitation volumes of many other cities in the network, due to the collaborative-competitive relationships between the networked cities. Comparing to a non-port city, the overall visitation volume is more influenced by the closure of a port city. Whereas the closure of a port city could increase the overall visitation volume, there are cities in the network whose visitation volumes are significantly decreased due to the closure, such as Tianjin (when Beijing is closed) and Suzhou (when Shanghai is closed). The abnormal responses of Tianjin and Suzhou are attributed to the spillover effect of the port cities. Among the non-port cities, the behavior of Chongqing is very special in that it is strongly coupled to many important cities in the network, and the closure of Chongqing will generate a significant decrease in the overall visitation volume.The opening of a new port city in western China will attract more tourists to the western cities, but can not solve fundamentally the problem of imbalanced tourist distribution. As a matter of fact, the opening of a port city in western China will decrease slightly the overall visitation volume. To balance the tourist distribution, a more efficient approach would be increasing the attractiveness of several important cities in western China together. Numerical simulations suggest that with the same amount of upgrading resources, the latter is more efficient in attracting tourists to western China and, in the meantime, is able to increase the overall visitation volume.The increase of tourism attractiveness of a port (non-port) city in general will decrease (increase) the overall visitation volume. Cities inside the Yangtze-River-Delta area form a strong motif, and behave coherently to external perturbations. More importantly, while individually each city in this motif is not important , the importance of a city to the whole network is also valued by its structural properties, including the geographical location, node degree, and the set of adjacent cities. These features, which are absent in the model of static and regular tourism networks, manifest the necessary of treating tourism system as a dynamical, complex network.Providing a quantitative evaluation on the impacts of external perturbations on tourism performance. Whereas it is well noticed that in tourism system a local perturbation could induce a global response, a quantitative evaluation on the degree and scale of such a response is still missing. With the agent-based network model, we are able to quantify not only the degree of the global response , but also the detailed changes at the city level . In addition, by the agent-based network model, we are also able to quantify the degree of correlations between the cities, based on which the spillover effect of Beijing on Tianjin , the synchronous behavior of Guangzhou and Hong Kong, and the crucial role of Chongqing have been revealed.Proposing a new strategy for tourism development. Our numerical results show that neither the opening of a new port city nor the increase of the tourism attractiveness of an individual city in western China will change the imbalanced tourist distribution. Our results of agent-based simulations suggest that, to attract more tourists to western China, an efficient approach would be upgrading simultaneously several cities that are deliberately selected according to several factors, including the city importance, connectivity, and geographical location. This finding is of practical significance as it points out the fact that in developing the tourism industry, the tourism cities should be upgraded from the global point of view, instead of their individual interest.The above findings could be helpful to the development and management of inbound tourism), including:34, a large amount of foreign tourists were poured into Beijing. Besides, the number of inbound tourists is also varying with seasons and is affected by the performance of the global economy. Therefore, to make the model more realistic, the number of agents in simulations should be time- and perturbation-dependent.The number of inbound tourists should not be fixed as constant. For simplicity, we have assumed in the model that when a city is closed or upgraded, the total number of agents inputted into the system is keeping unchanged. In a realistic situation, e.g., the closure of a city due to tourism crisis, the total number of tourists arriving China in general will be decreased. The similar concern exists also when increasing the tourism attractiveness of a city, which generally attract more inbound tourists. For instance, during the period of the 2018 Summer Olympic Gamesγ in Eq. , i.e., by the method of brute-force search. This method, however, requires a significant amount of computing resources, especially for the large-size networks. We wish this question could be addressed in our further studies.Another question that has not been addressed in the present work is the impact of adding a new link. Recent studies in network science have shown that the introduction a shortcut link may change largely the network dynamics, which makes it interesting to identify the crucial link to be established between cities such that the system performance can be significantly improved. In practice, this scenario may correspond to the situation of opening a new flight or train between two cities. While the importance of a link in principle can be evaluated by its centrality, the current studies on link centrality are mainly focused on networks of homogeneous nodes. In our model of tourism network, the nodes are characterized not only but their attractiveness, but are also classified as port and non-port cities. These features render the current knowledge of link centrality can not be applied directly to tourism network. To find the crucial link, the only possibility seems to scan all the candidates  consisting of our tourism network model, there are totally 105 5A scenic areas and 783 4A scenic areas.The weights of the scenic areas are obtained by the Delphi method. Specifically, we surveyed all faculty members  in the college of the first author, and conducted four rounds of surveys in total. We adopted the integers for all rounds. In the final results, one 5A scenic area was assigned a value of 5 and one 4A scenic area was assigned a value of 3. The full list of the tourism attractiveness of the investigated cities is given in Table γ in Eq. (Ll}), the city visitation volumes ({si}), and the tourist flows ({wij}). The distribution of the route lengths as obtained from the surveyed results are plotted in Fig. L. To find the optimal value γ that fits this distribution best, we scan γ over the range γ the averaged difference between the the numerical and surveyed results, L obtained by simulations and Lmax the maximum travel length in the surveys. In Fig. γ. We see that ΔThe exponent γ in Eq.  is trainsi}, obtained from the surveys are plotted in Fig. γs, for this distribution, we calculate the averaged difference between the surveyed and numerical results, γ. It is seen that Δs is minimized at about 2.3. We therefore set wij}, as obtained from the surveys are plotted in Fig. γ based on numerical simulations. We see that Δw reaches its minimum at The distribution of the city visitation volumes, {mentclass2pt{minimThe questionnaire surveys were carried out in accordance with the guidelines and regulations of Shaanxi Normal University and approved by the Academic Committee of School of Geography and Tourism at Shaanxi Normal University. The informed consent was obtained from all participants and/or their legal guardians.Supplementary


Strix occidentalis lucida). The two resistance models represented the landscape under low- and high-fragmentation parameters. Under low fragmentation, the landscape had larger but highly concentrated habitat patches, whereas under high fragmentation, the landscape had smaller habitat patches that scattered across a broader area. Overall habitat amount differed little between resistance models. We tested eight scenarios reflecting a factorial design of three factors: resistance model (low vs. high fragmentation), isolation hypothesis , and dispersal limit of species (200 km vs. 300 km). Higher dispersal limit generally had a positive but small influence on genetic diversity. Genetic distance increased with both geographic distance and landscape resistance, but landscape resistance displayed a stronger influence. Connectivity was positively related to genetic diversity under IBR but was less important under IBD. Fragmentation had a strong negative influence on the spatial patterns of genetic diversity and effective population size (Ns). Despite habitats being more concentrated and less widely distributed, the low-fragmentation landscape had greater genetic diversity than the high-fragmentation landscape, suggesting that highly concentrated but larger habitat patches may provide a genetic refuge for the Mexican spotted owl. We evaluated how differences between two empirical resistance models for the same geographic area affected predictions of gene flow processes and genetic diversity for the Mexican spotted owl ( Globally, human-induced environmental change degrades habitats and drives biodiversity loss and species extinctions across nearly all taxonomic groups ,2,3. ThrLandscape genetics is an emerging discipline that integrates landscape ecology and population genetics to statistically analyze the relationship between population genetic structures and landscape features ,13. The Isolation-by-distance (IBD) is a common null model in landscape genetics that addresses whether genetic differentiation exists among populations as a function of geographic distances alone. IBD, however, does not consider the effects of heterogeneity in landscape features. One common approach that evaluates the effects of landscape heterogeneity on genetic connectivity is isolation-by-resistance (IBR) modeling ,29,30, wAlthough IBR may complement IBD to provide a more complete depiction of relationships between landscape features and spatial population processes, its predictions are sensitive to the assumptions, methods, and data sources of landscape resistance models. Quite often, multiple resistance models derived from different data sources and geographic areas are available for the same species. These models often depict different spatial configurations of habitat extensiveness and fragmentation, which strongly affect detectability of landscape genetics relationships . The valAccording to a recent review, birds are the most underrepresented taxa in the landscape genetics literature, and most bird species in these studies have relatively low dispersal ability . InteresStrix occidentalis lucida). This owl is highly selective for nesting habitats and the availability of those habitats appears to limit its distribution [In this study, we sought to understand how differences between landscape resistance models might affect predictions of gene flow processes and genetic diversity for a rare bird species of high conservation importance, the Mexican spotted owl , representing the relative permeability of landscape features to species movement. A high resistance value (near 10) indicated areas the species was unlikely to traverse, and a low resistance value (near 1) indicated areas the species was likely to traverse. Both models predicted fragmented areas of low resistance that were patchily distributed across the study area, but areas of low resistance were more aggregated as larger patches in the first model (hereafter low-fragmentation model), whereas the second model had areas of low resistance that were more fragmented and scattered across linear canyon terrain throughout the study area (hereafter high-fragmentation model). Thus, the two empirically based habitat models produced resistance surfaces that differed in habitat configuration, providing two plausible models describing current habitat connectivity in this area. These differences in connectivity in turn may drive future differences in gene flow across the landscape.To characterize habitat fragmentation, we transformed the probability maps of habitat suitability predicted by the two models into binary maps of habitat versus non-habitat using 0.2 as the cutoff point . We chose a relatively low value as the cutoff point  because the owl is a mobile species and can disperse through most habitats. Then, we used FRAGSTATS  to calcuIsolation-by-distance (IBD): This hypothesis serves as a null model and assumes that species movement decisions are affected purely by geographic distance, and genetic exchange occurs more frequently between proximate individuals than distant individuals.Isolation-by-resistance (IBR): This hypothesis posits that species movement decisions and resulting gene flow are influenced by landscape features and patterns associated with resource selection.We evaluated patterns of gene flow under two hypotheses:For each hypothesis, we used the Cost Distance Populations (CDPOP) simulation model  to simulIn our simulations, we considered two upper dispersal limits—high dispersal ability (300 km) and low dispersal ability (200 km). We selected these two dispersal limits because they were identical to the dispersal limits in the connectivity models of Wan et al. , which eThe model stipulated that individuals would only mate with the nearest neighbor to represent monogamous territorial mating. Fecundity was parameterized to a mean of 3.8 offspring per pair per generation with a Poisson distribution to approximate fecundity over ~5 successful breeding seasons (~0.76 young per pair per year across multiple studies) ,70,71. AWe tested eight scenarios, reflecting a full factorial design of three factors: (1) resistance model , (2) isolation hypotheses , and (3) maximum dispersal ability . For each scenario, we performed 100 Monte Carlo simulation runs. Each run simulated dispersal and breeding movements of 100 discrete and nonoverlapping generations. We used nonoverlapping generations to simplify and accelerate the genetic processes in the simulations, which allowed us to project forward into the future with fewer iterations.s) at each node using R package sGD [We tracked the number of extant individuals at each node at the 100th generation of each simulation run and calculated the mean effective population size , observed heterozygosity (Ho), and expected heterozygosity (He) within each node using R package sGD [To evaluate genetic diversity, at the end of each simulation run , we calculated standard indices including allelic richness  for each pair of nodes. Note, genetic distance, by definition, is a measure of the dissimilarity as a function of distance, whereas Dps is a measure of similarity. Therefore, Dps and genetic distance are inversely related.To assess the effects of the two isolation hypotheses on genetic divergence, we calculated pairwise resistance distance and genetic distance between each node. For IBR, resistance distance was quantified with the least-cost path approach, indicating the lowest cumulative path resistance between nodes. For IBD, resistance distance was the total resistance of the least-cost path on a resistance surface in which all pixels were assigned a value of one. Resistance distance was calculated with the age in R . To measage in R  and calcecodist package in R [After obtaining the matrices of resistance distance and genetic distance, we conducted two analyses. First, we calculated Mantel correlations  between age in R . Second,We evaluated whether relationships of landscape connectivity and genetic diversity differed between the two resistance models by using the connectivity models of Wan et al. . The con2) and 0.08 (74 km2), respectively. Mean habitat patch size was larger in the low-fragmentation model than in the high-fragmentation model  were patchily distributed throughout the southern half of the study area , are provided in ps and resistance distance in the high-dispersal scenario were moderately strong under both IBD and IBR. In the low-dispersal scenario, the correlation was also moderately strong under IBD but weak under IBR.In both the Mantel test and regression analysis, the DR2) for all IBD models, the linear model had the best fit for the low-fragmentation IBR models, and the logarithmic model had the best fit for the high-fragmentation IBR models  at describing relationships between population size and landscape connectivity for most scenarios  models that contained similar amounts of habitat but differed in degree of fragmentation of that habitat and (2) hypotheses based on IBD versus IBR. In contrast, our simulations suggested fewer differences in spatial genetic patterns in these landscapes between the two dispersal distances tested. ps decreased by ~4% between nodes with the greatest distance under IBD  began to stabilize when connectivity strength  debate ,86: are scenario . For a tThe isolation effect on genetic diversity has long been theorized  and empiFew previous genetic studies have featured the Mexican spotted owl and none have explicitly considered the context of landscape features ,99,100. Direct comparisons between our results and the empirical data in  are compDespite these complicating factors, our results are at least partly consistent with empirical results. For example, under the IBD hypothesis, the Mogollon Rim area (corresponding to the Upper Gila Mountains Region in ) appearsIn contrast, results under the IBR hypothesis  generallThis simulation study of landscape genetics of the Mexican spotted owl provides an analytical framework to identify relationships between landscape connectivity and genetic divergence and develop a spatial model of gene flow. We also identified potential effects of fragmentation on genetic processes by comparing two empirical resistance models. Our approach is replicable and can be used to help understand landscape effects on gene flow and genetic diversity for other species. In addition, this approach has relatively small data requirements, which makes it especially useful for analyzing landscape genetics of rare species, such as the Mexican spotted owl in this study. The spatial model may be valuable in designing conservation and management plans and in guiding monitoring efforts. For example, our results suggest that habitat fragmentation/configuration may be as or more important than habitat amount in promoting gene flow. The current Recovery Plan for the Mexican spotted owl does not explicitly incorporate habitat configuration in management recommendations , but futOur study approach can estimate variation in spatial patterns of genetic diversity and evaluate the effects of landscape features based on simulated genetic data but does not provide inferences regarding true genetic diversity and population structure. Rather, our results are useful for generating hypotheses. Those hypotheses cannot be directly tested at this time due to the limited empirical genetic data available and differences in time and spatial scales represented. Comparisons with the limited empirical genetic data suggest areas of both agreement and disagreement between those data and our results, however. Of particular importance, the empirical data suggests that, at the spatial scale and level of connectivity present in our study landscape, IBD may better describe spatial pattern in owl genetics than IBR. This is potentially an important finding with implications for understanding how dispersing owls perceive the landscape, but the strength of that conclusion is limited by the small samples and limited spatial coverage of existing genetic data for our study area. Comparisons with empirical data could be strengthened by gathering a more current, extensive, and spatially balanced sample of genetic material from owls within our study area. Better data on typical distances traveled and habitats used by dispersing owls also would aid in refining landscape simulation models.Because transforming habitat suitability into landscape resistance carries uncertainty for many species ,101, we


Epichloë. Recent studies on endophytes have shown the beneficial roles of host-endophyte associations as protection against insect herbivores in agriculturally important grasses. However, large-scale screenings are crucial to identify animal friendly strains suitable for agricultural use. In this study we analyzed collected populations of meadow fescue (Schedonorus pratensis) from 135 different locations across Europe, 255 accessions from the United States Department of Agriculture and 96 accessions from The Nordic Genetic Resource Centre. The analysis also included representatives of S. arundinaceus, S. giganteus, and Lolium perenne. All plants were screened for the presence of Epichloë endophytes, resulting in a nursery of about 2500 infected plants from 176 different locations. Genetic diversity was investigated on 250 isolates using a microsatellite-based PCR fingerprinting assay at 7 loci, 5 of which were uncharacterized for these species. Phylogenetic and principal components analysis showed a strong interspecific genetic differentiation among isolates, and, with E. uncinata isolates, a small but significant correlation between genetic diversity and geographical effect (r = 0.227) was detected. Concentrations of loline alkaloids were measured in 218 infected meadow fescue plants. Average amount of total loline and the proportions of the single loline alkaloids differed significantly among endophyte haplotypes (P < 0.005). This study provides insight into endophyte genetic diversity and geographic variation in Europe and a reference database of allele sizes for fast discrimination of isolates. We also discuss the possibility of multiple hybridization events as a source of genetic and alkaloid variation observed in E. uncinata.Species belonging to the Festuca-Lolium complex are often naturally infected with endophytic fungi of genus  Epichloë  or from interspecific hybridizations between sexual and/or asexual Epichloë species : E. uncinata  collected at various sites in Europe; (ii) screened meadow fescue accessions labeled as wild or landraces from the United States Department of Agriculture (USDA) and Nordic Genetic Resource Centre (NordGen); (iii) identified isolates from S. arundinaceus var. glaucescens as FaTG-5 through a phylogenetic analysis of tubB and tefA sequences, along with morphological examinations and microsatellites fingerprinting; (iv) tested the descriptive and discrimination power of 5 microsatellite markers on E. uncinata, E. coenophiala, E. siegelii, E. festucae, and E. festucae var. lolii; (v) investigated the genetic diversity of these species; and (vi) measured the amount of loline alkaloids in the isolates and tested their associations with endophyte microsatellite profiles.In this study we (i) determined the incidence of Schedonorus pratensis, Schedonorus arundinaceus, Schedonorus Giganteus, and Lolium perenne plants were collected during the summer 2016 and summer 2017 in Denmark, Sweden, Norway, Italy, Germany, and Austria. The 135 collection sites were meadows located near roadsides, in isolated environments, supposedly not contaminated by cultivated grasses. Whenever possible, approximately 15 to 20 plants were sampled from each location. Latitude, longitude, and altitude data of all collection sites were recorded. Plants were planted in pots (9 cm × 11 cm) with standard peat , watered regularly and grown in the greenhouse with 16 light hours at approximately 15–24°C.S. pratensis accessions were requested from USDA and 92 from NordGen. Only accessions labeled as wild or semi-wild were chosen. Seeds were germinated in the same conditions as described above. A map and a detailed database of the plant material used in this study are provided in A total of 255 Epichloë endophytes was determined in two tiller sections from each of the collected plants, 6 weeks after the transplant, using the immunoblot assay “Phytoscreen Field Tiller Endophyte Detection Kit”  described by in planta assay using microsatellite markers B10 and B11 described by The presence of 2 plugs of mycelium were placed at the center of PDA plates and grown in the dark at 24°C. Colony diameter was measured from 5 replicate plates after 14, 21, and 28 days. For microscopic examinations of conidia and conidiophores, agar plates were inoculated with a suspension obtained by macerating 2 mm3 of culture in 100 μL of sterile water and kept in a dark growth chamber at 24°C for 7 days until conidiation occurred. The examination was performed with an Olympus BH-2 microscope  and photographs taken with a Canon EOS 600D camera. Measurements of conidiogenous cells (length and width at tip and base) and conidia and conidiophores (length and width) were taken from 20 structures each in 3 different isolates using an ocular micrometer at 1000×.Pure cultures of endophytes were obtained from pieces of surface-sterilized pseudostems on potato dextrose agar (PDA) as described elsewhere . To studtubB) and translation elongation factor 1-alpha (tefA) were PCR-amplified from genomic DNA. Both genes have been widely used for phylogenetic analysis of broad taxonomic range of endophytes worldwide. The primers used for the PCR reaction were 5′-TGG TCA ACC AGC TCA GCA CC-3′ (forward) and 5′-TGG TCA ACC AGC TCA GCA CC-3′ (reverse) for tubB, and primers 5′-GGG TAA GGA CGA AAA GAC TCA-3′ (forward) and 5′-CGG CAG CGA TAA TCA GGA TAG-3′ (reverse) for tefA . Sequences were aligned in GENEIOUS version 6.1.3  along with sequences from representative Epichloë species using default alignment parameters, gaps were removed, and ambiguously aligned sites were checked manually and adjusted if needed. Sequences were deposited in GenBank: tefA: MK423914 and MK423915; tubB: MK423916 and MK423917.DNA extraction, PCR reaction, and gene cloning were performed using the method described by for tefA . IsolateMaximum likelihood (ML) trees were constructed with MEGA X  with def2, 0.25 mM of each deoxynucleotide triphosphate , 4 μM of each primer , 0.4 U of Taq DNA polymerase with 1× key buffer (Mg2+ free) . Each primer pair was fluorescently labeled either with DY-682 or with DY-782 . Reactions were carried out in a Mastercycler gradient 5331  programmed with 2 min of initial denaturation at 95°C followed by 34 cycles of 95°C for 20 s, 56°C for 30 s, and 72°C for 1 min, with a final extension of 72°C for 10 min.Genomic DNA was extracted from infected stems on a Quadra 96 Model 320 robotic system  using the method described previously by E. uncinata, E. siegelii, E. festucae var. lolii, E. coenophiala, FaTG-2, endophyte free plant and negative control) were used in each PCR reaction. The products were then electrophoresed using a LI-COR model 4200 automated fluorescent DNA sequencer  with standard peat , watered regularly and grown in the same greenhouse room with 16 light hours at approximately 15–24°C. In June 2018 samples from the basal part of the tiller were harvested and freeze-dried in the same day and subsequently powdered in a laboratory mill. About 50 mg of each sample were sent in duplicate to AgResearch Grasslands , were the analysis was performed using a gas chromatographic method described by In March 2018, 218 endophyte infected E. coenophiala samples, which present three alleles in 5 out of 7 microsatellites, could be included in the analysis. Null-alleles were regarded as missing values. Number of multi-locus genotypes (MLG), allelic richness, poppr package . Principle component analysis (PCA) was performed to complement model-based clustering methods and to test the ability to distinguish haplotypes. Matrixes comparison using Mantel test was performed between the genetic similarity matrix with the cophenetic matrix. The change in genetic similarity associated with increasing spatial distance between individuals was investigated by testing and plotting spatial autocorrelation at above distance intervals. A geographical distance matrix of Euclidean distances (km) was computed between all pairwise combinations sites from their GPS coordinates with the R package geosphere  was needed to calculate pairwise comparisons between haplotypes.Microsatellite data were coded with a tetraploid-like format so that  package  in R  were collected from 135 different locations at different altitudes (from 0 to 1740 m) and longitudes and screened for the presence of Epichloë endophytes with a tissue-print immunoblot assay. Infection rates (IRs), calculated for each location as the ratio of infected (E+) plants, and the number of analyzed plants, showed a large variation among the sites, spanning from 0 to 100%  were found in 42 of 42 sites. Also in Norway all the collection sites were found to host infected plants with high IRs, from 68 to 100% , but the occurrence of meadow fescue was lower and it was difficult to find isolated habitats due to the higher frequency of farms and grass cultivated fields in the Østfold area close to the border with Sweden. The average IR was slightly lower in the collections made in the Alpine regions, across the Italian and Austrian border where it ranged from 6.7 to 100%  and 25 to 95% , respectively. Regarding the collections made in Germany and in Denmark, the number of infected sites was 4 out of 11 for L. perenne and 14 out of 20 for S. pratensis. The scarcity of isolated wild and semi-wild habitats made collection trips rather inefficient in these countries, especially when the target is meadow fescue.A total of 2008 plants  and endophyte free (E-) stems. A clear and strong amplification was detected up to bulks with 14 E- stems and 1 E+ stem. This approach allows also to identify the presence of different species or strains in the mixture if they have different amplicon sizes at the chosen loci. Bulks of stems infected with E. uncinata and E. siegelii were tested and species distinction was possible up to 14:1 ratio. Further ratios were not tested because of the increasing amount of plant material which exceeded the threshold recommended in the DNA extraction protocol. Of the 255 USDA seedlots, 215 were able to grow enough seedlings to be analyzed: 159 were endophyte free, 12 had ambiguous results, and 44 had clear evidence of endophyte presence. Subsequent verification of the 56 (12+44) putative E+ accessions by immunoblotting only confirmed Epichloë infection in 12 accessions. The infection rates ranged from 2.5 to 77.5% . Regarding the 96 NordGen accessions, only 4 of them did not germinate. Of the remaining, 53 out of 92 (57.6%) were found infected with Epichloë endophytes both with the PCR assay and the immunoblot, the infection rates ranged from 3.1 to 100% . Twelve accessions had an infection rate of 100% while 4 of them had few weak seedlings that eventually died and could not be used for further analysis. The final outcome was therefore 49 E+ accessions, for a total of 1256 plants.A large-scale screening was performed analyzing 255 S. pratensis collected had 14 chromosomes, thus no S. pratensis subsp. apennina (2N = 4× = 28 chromosomes) was collected in the Alpine regions. S. arundinaceus ecotypes sampled in Denmark were hexaploid with a chromosome number of 2N = 6× = 42, as well as the S. giganteus ecotypes collected in Denmark and Germany. All the infected accessions from NordGen and USDA were confirmed to be S. pratensis with 2N = 4× = 14 chromosomes except for 3 USDA accessions, collected in Morocco , which were tetraploid, with a chromosomes number estimated to be 2N = 4× = 28, and a clearly different phenotype from typical meadow fescue. After detailed morphological analysis these accessions were identified as S. arundinaceus var. glaucescens.Two plants per each infected population/accession were analyzed with a flow cytometer to confirm the host ploidy. All the tefA and tubB  was placed in a clade with FaTG-5 among E. festucae and a E. festucae var. lolii strains. Gene copy 2 (GC2) formed a separate subclade with all the other S. arundinaceus isolates  nested within a larger clade including E. baconii and E. amarillans reference strains. In the tubB phylogram the GC1 formed again a separate subclade with all the other S. arundinaceus taxonomic groups next to E. festucae var. lolii. that seemed to be genetically close. The GC2 of tubB showed genetic similarities with those of FaTG-5 and E. festucae. By contrast, FaTG-2 strains in the tubB tree were more similar to E. baconii and FaTG-3/FaTG-4 more similar to E. typhina. Moreover, SSR profiles of the endophyte from PI347573 that has been previously identified as FaTG-5 showed close similarity with the isolate from PI347572. Taken together, evidence suggest that the newly characterized endophyte from S. arundinaceus var. glaucescens is correctly assigned to FaTG-5.The endophytes harbored in PI347571 and PI347573 were formerly classified as FaTG-2 and FaTG-5, respectively , therefoand tubB  genes. BE. uncinata, 9 E. coenophiala, 7 E. festucae, 6 E. festucae var. lolii, 1 E. siegelii, 1 FaTG-2, and 2 FaTG-5) was investigated using 7 microsatellites. The isolates were a representative subset of the screened populations. A detailed list of the samples and their allelic profiles at each locus is provided in E. festucae and E. festucae var. lolii. The number of alleles at the 7 loci spanned from 0 to 5, according to the endophyte species. Also, the information index at each locus varied among species  lacked this allele. B11 was not detected in E. typhina . E. coenophiala had 3 haplotypes which were characterized by three alleles at loci B10, E33, E36, and E39 and two at the remaining ones. The most abundant tall fescue haplotype collected in Zealand (Denmark) was Ec_H1, isolated in 5 different locations. Ec_H2 was isolated from two samples collected in a relatively small area around Stevns Klint  and it can be distinguished from the previous haplotypes for a different amplicon size at B11 . In comparison, isolates from Spain had a different profile at B10, B11, and E29. The most abundant E. festucae haplotype found in this study was Ef_H1 isolated both in Denmark and in Germany. Ef_H1 is very similar to Ef_H2 and Ef_H3, from which it differs at only one locus, but very different from Ef_H4 with differences at 4 loci. Interestingly, Ef_H4 was isolated from a single location in Fyn (Denmark) where the majority of the plants collected nearby were infected with Ef_H3. Isolates from E. festucae var. lolii collected in Denmark and Germany had the same genetic profile, named El_H1, and differed from isolates used as internal standard, collected in Spain (El_H2) and in Italy (El_H3), at one locus. E. uncinata showed an unbalanced distribution of the 8 detected haplotypes. The 224 E. uncinata isolates were grouped in 4 populations  and Eu_H7 (88 samples). Eu_H1 is the most abundant haplotype in Scandinavia, found in 73.9% of the isolates, represents 47.8% of the isolates from the population North-West and 16.7% of the isolates from East, but is completely absent in the Alps. Eu_H7 is the only haplotype shared among the four populations, it is the most abundant in the Alps (87.8%), it is as abundant as Eu_H1 in the population North-West (43.5%), and present with lower percentages in Scandinavia (23.1%) and East (22.2%). Eu_H2 and Eu_H3 were the only two haplotypes with a polymorphism at B11, they were isolated from a single population, respectively in Scandinavia and East. Eu_H4 was only found in two locations in Scandinavia. Eu_H5 was the most abundant haplotype in East (27.8%), isolated in a single population both in Scandinavia and North-West, but completely absent in the Alps. Eu_H6 was the only other haplotype isolated on the Alps (12.2%), fairly abundant in East (22.2%), present in only one isolate in North-West but completely absent in Scandinavia. Eu_H8 was only found in a single population in East. During the screening, genotypes from 47 locations were found infected with more than one haplotype. Among the Scandinavian collected samples, when two haplotypes were detected in the same location, one was Eu_H1 and the other was always Eu_H7, except for one case when a Eu_H4 was found. In the Alps, the second haplotype in addition to the common Eu_H7 was always Eu_H6.Unique combinations of alleles across the 7 different loci were called different haplotypes . A total typhina  thereforulations  accordinulations . SamplesR2 = 0.0034). Genotypic diversity indices (Alps) to 1.64  ranged from 1.25% (Scandinavia) to 26.44%  ranged from 0.22 (Alps) to 0.84 (East). Therefore East was clearly the population with the highest diversity and Alps was the population with the lowest diversity according to all indexes except for G′, but this is due to the different sensitiveness of the index to changes in abundant genotypes or in rare alleles. Overall, the populations were very similar to each other as shown by the pairwise comparisons (Scandinavia and the Alps. Evenness (E.5) ranged from 0.61 (Alps) to 0.90 (East) showing a relatively unbalanced distribution of haplotypes in the Alps.The number of multi-locus genotypes (MLG) detected span from 2 to 6 and incidence was independent of the population size .The relationship between isolates was investigated using the Provesti’s distance inferred from clone-corrected microsatellite data. The phenogram  generatex-axis and with their ploidy on the y-axis: on the left side E. uncinata haplotypes, whose ancestors are E. bromicola and E. typhina, group together; on the right side there are the E. festucae strictly related species, whereas at the center of the plot there are E. siegelii, whose ancestors are E. festucae and E. bromicola, and, in the upper part, the allotriploid-like E. coenophiala which share the same E. typhina ancestor as E. uncinata, the same LAE ancestor as FaTG-2 and FaTG-5. The Mantel correlation between pairwise Provesti’s genetic distance and pairwise geographic distances (measured in kilometers) between populations was equal to 0.226 (p < 0.0001). Thus, populations close to each other tend to be genetically more similar than expected by chance, and genetic differences increase with geographic distances. In order to study the relationship between genetic and geographic distances across space and its variations in the correlations a Mantel correlogram , this indicates that the haplotypes composition is more similar than they would by chance at the shortest distances. Mantel correlation decreased almost linearly up to -0.23 (p < 0.0001) for populations distant approximately 1500 km from each other. From 1800 km onward the correlation tends to stabilize on an average level of about -0.06 (p < 0.005) which can be related to patches of genetic variation such as those areas in Sweden and East where some atypical haplotypes (Eu_H2-3-4-8) were isolated. A negative correlation indicates more dissimilar haplotypes than expected by chance on farther distances. Mantel correlations were however, not high, therefore the spatial structure is not strong, and only 5% of the genetic divergence is explained by geographic distance.The correlogram, with five distance classes, showed an overall significance since 7 out of 8 correlation coefficients were significant. Populations distant by 164 km tend to be similar (SE = 3%); NANL = 16.2% ; and NFL = 73% . These proportions changed with the increase of the total amount of loline with a calculated P < 0.005  and NFL was 70.9% , but in the range 3000–4000 μg/g they increased to 18.1%  and 75.3% , respectively. By contrast, in the same ranges, the average percentage of NAL decreased from 15.2%  to 6.7% . Data suggested that the production of high levels of loline is correlated with a slight increase of the proportion of NANL and NFL (R2 < 0.1) and with a greater decrease of NAL (R2 < 0.54).Levels of NAL, NANL, and NFL were measured in 218 samples of endophyte-infected S. pratensis . The tot < 0.005 : in the P < 0.005) and percentage of NANL (P << 0.001) but a higher percentage of NAL (P < 0.005) compared to Eu_H5, Eu_H6 and Eu_H7. As it concerns the percentage of NFL, significant differences were found only between Eu_H1 and Eu_H7 (P = 0.01).Significant differences in the average amount of total loline and in the proportion of the loline alkaloids were also found among haplotypes . SpecifiTraces of NFL (30 μg/g) were detected in the FaTG-2 sample isolated from the accessions PI 347571, but no loline alkaloids were detected in the FaTG-5 isolates.Epichloë endophytes are a valuable resource, useful to bring new resistances in forage and turf grasses. Their use in agriculture is well-established and breeding companies are looking for new strains with improved characteristics to introduce in their varieties. Animal-friendly Epichloë endophytes are rare and substantial sampling effort is required to identify genotypes with an appropriate alkaloid profile. In this study we screened wild- and semi-wild grass ecotypes collected in different countries or provided by seed banks, after establishing a nursery of Epichloë infected plants.Epichloë endophytes in Europe is consistent with the results of previous screening , which increases the probabilities that the sampled plants were siblings, although efforts were made to collect plants as widespread as possible. Infection rates lower than 100% occur when E+ and E- plants coexist in the same population. This could happen if the population is in a transition period toward complete infection or, on the opposite side, toward a loss of infection. Another possible explanation is the occurrence of imperfect vertical transmission to seeds  sampled in each location and by the small sampling area  infestation and by a long and cold winter. Long exposure to sub-optimal temperatures can strongly affect the amount of endophyte mycelia in the plant, as described by Epichloë endophytes. For this investigation, only accessions labeled as “wild” or “semi wild” were chosen, but more endophytes can be found also in commercial varieties , translation elongation factor 1-α  and γ-actin (actA)  . Anothern (actA) . Microsa strains . The lev strains  so that E. coenophiala (6–15 μm). The microsatellite analysis was able to distinguish the two closely related taxonomic groups, their genetic profiles at B10 and B11 can be compared to the result of other studies . Conidia dimensions are consistent with the ones described by  studies  where th studies , which sequences  and SSR-equences .E. siegelii, at E33 in FaTG-2 and at E39 in three haplotypes of E. uncinata. According to Our analysis revealed 21 different haplotypes and amplified a number of amplicons consistent with the one provided in previous studies. Fewer alleles than expected were only observed at B10 in S. arundinaceus, haplotype Ec_H1 has the same profile as E. coenophiala profile 1-1   and 109 (Eu_H3), respectively. These polymorphisms are likely to arise by errors during the replication process, due for instance to DNA polymerase slippage. At B10 the haplotypes split into two groups, one with amplicons of approximately 164 and 200 bp and the other with amplicons of approximately 176 and 196 bp, which reflect the same allele sizes as the two strains isolated from Fp1 and Fp4 described by A direct comparison of the haplotypes is possible at loci B10 and B11 with the results reported in other studies using the same loci . In S. afile 1-1  at both file 1-1  and it wate Frc7 , which wype loA)  whereas E. uncinata ancestors E. typhina and E. bromicola. There are several variations in repeated structures between the two copies of the locus among the two haplotypes and this raises the question whether these variations occurred in E. uncinata or in its ancestors, assuming different independent interspecific hybridization events. If two independent anastomosis events occur in different locations an unbalanced geographical distribution of the haplotypes may be expected since E. uncinata is only vertically transmitted. Eu_H1 is the most abundant haplotype in the northern countries and it is completely absent in Italy and Austria, whereas Eu_H7 can be found throughout Europe, even in Russia and Kyrgyzstan. Its wide distribution might be anthropogenic if, for example, this haplotype was infecting one or more cultivars commercialized in several countries. Contamination of natural populations with plants from infected varieties could also explain the presence of two haplotypes in the same location. It can also be speculated that a possible explanation for the observed genetic variation in relation to the geographical distribution involves local adaptation to specific stresses which may positively select one haplotype over another. This could explain the absence of Eu_H1 on the Alps. The above mentioned uneven distribution of the haplotypes and the fact that 5 of the 8 haplotypes scored came from very diverse USDA accessions, reported to be collected in Russia, affected the relation between geographical distribution and genetic diversity investigated with the Mantel correlogram. Having on the shortest distance individuals which are genetically closer than random individuals is partially coherent with what it is expected from clonally propagated species. But the fact that on the longer distance they are more diverse than random individuals does not fit with the idea of a single strain vertically transmitted throughout Europe, it rather supports the hypothesis of different hybridization events.E. festucae and E. festucae var. lolii being closely related, E. coenophiala clustering in a different clade, FaTG-2 and FaTG-5 being genetically very similar and related to E. festucae.It has to be taken into account that the number of haplotypes strongly depends on the number and on the information content of the profiled loci, and the number of individuals and populations sampled. Using a wider panel of SSRs on more samples may lead to a deeper characterization of the isolates. The 7 microsatellites used in this study allowed a clear separation between species as shown in the phylogenetic tree and in the PCoA, but as it is clear from the latter, and genetic variation within isolates of vertically propagated species is usually very low due to the absence of sexual reproduction . For a dE. uncinata an endophyte appealing for agricultural uses is its production of high level of loline alkaloids. Measuring the alkaloids at a given time is like taking a screenshot on a state that it is known to vary greatly during the host lifespan. There are several factors that can directly affect the concentration of these compounds, which include the season of the year . Since our current knowledge of endophyte distribution is incomplete, population genetic studies on ancestor species from a wide geographic range are needed to understand the evolutionary origin of the hybrid endophytes of the Festuca-Lolium complex. Moreover, the different genetic background of the haplotypes may affect the production of loline alkaloids. Finally, sharing of the genetic profiles of screened isolates is crucial for identifications of new isolates that may have improved characteristics and could be used for grass breeding and future research.The Scandinavian northern regions and the Alps are a good source of https://www.ncbi.nlm.nih.gov/nuccore/MK423914.1, https://www.ncbi.nlm.nih.gov/nuccore/MK423915.1, https://www.ncbi.nlm.nih.gov/nuccore/MK423916.1, and https://www.ncbi.nlm.nih.gov/nuccore/MK423917.1.The datasets generated for this study can be found in NCBI: GC, NR, CJ, and TA designed the experiment. GC, NR, FF, and AL collected the data. GC and AL analyzed the data. All authors contributed to manuscript preparation, editing, and gave final approval for publication.GC, NR, and CJ are employees of DLF Trifolium A/S. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


An experiment was conducted to evaluate genetic diversity of 26 Chinese indigenous goats by 30 microsatellite markers, and then to define conservation priorities to set up the protection programs according to the weight given to within- and between-breed genetic diversity.Twenty-six representative populations of Chinese indigenous goats, 1,351 total, were sampled from different geographic regions of China. Within-breed genetic diversity and marker polymorphism were estimated calculating the mean number of alleles, observed heterozygosities, expected heterozygosities, fixation index, effective number of alleles and allelic richness. Conservation priorities were analyzed by statistical methods.A relatively high level of genetic diversity was found in twenty-four population; the exceptions were in the Daiyun and Fuqing goat populations. Within-breed kinship coefficient matrices identified seven highly inbred breeds which should be of concern. Of these, six breeds receive a negative contribution to heterozygosity when the method was based on proportional contribution to heterozygosity. Based on Weitzman or Piyasatian and Kinghorn methods, the breeds distant from others i.e. Inner Mongolia Cashmere goat, Chengdu Brown goat and Leizhou goat obtain a high ranking. Evidence from Caballero and Toro and Fabuel et al method prioritized Jining Gray goat, Liaoning Cashmere goat, and Inner Mongolia Cashmere goat, which agree with results from Kinship-based methods.Conservation priorities were determined according to multiple methods. Our results suggest Inner Mongolia Cashmere goat (most methods), Jining Gray goat and Liaoning Cashmere goat  should be prioritized based on most methods. Furthermore, Daiyun goat and Shannan White goat also should be prioritized based on consideration of effective population size. However, if one breed can continually survive under changing conditions, the straightforward approach would be to increase its utilization and attraction for production via mining breed germplasm characteristics. Goats, one of the most ancient livestock species, were domesticated ~10,000 years ago in Southwestern Asia [Recently, Chinese indigenous goat breeds have been threatened by the introduction of exotic goat breeds, which have typically been selected for optimal production of meat, wool, or other products. Because short-term profit maximization incentivizes the replacement or crossbreeding of Chinese indigenous goat breeds with more productive breeds, the indigenous gene pool is at risk. Both population size reduction and genetic variation dilution are major threats. There has been an increasing tendency to neglect the unique qualities and variety offered by indigenous breeds in favor of exotic breeds and their crosses. However, it is now recognized that it is important to establish conservation priorities and strategies to conserve genetic diversity within- and between-breeds, primarily to avoid further losses of genetic resources. Genetic diversity is one the most important factors that determines whether a breed survives and flourishes, or ultimately faces depression and extinction. The genetic diversity of domestic animals is crucial to meet current production needs in various environments, prospective and changing breeding objectives, and sustained genetic improvement. In response, various conservation strategies have been implemented. However, in order to make the best use of limited conservation funds, it is necessary to prioritize specific indigenous breeds for conservation .E). The strategy prioritizes a breed if its removal from a population results in a maximum loss of global average HE. However, the drawback to this method is that it is insensitive to between-breed genetic diversity. Alternative methods that consider between- and within-breed diversity, based on co-ancestry or kinship, have been developed to prioritize breeds for conservation [ST  can be used to adjust the between-breed diversity derived from the Weitzman method, and 1-FST to adjust the within-breed diversity derived from the HE change in a metapopulation [Several conservation approaches have focused on breeds with maximum conservation value  ,4. The Wervation ,9. Althoervation ,10–12 FSpulation . For exapulation . Ginja epulation  and Cañópulation  conducteConservation priorities have been studied using the methods described above for domestic animals, including cattle , pig 1010, sheepProtocols for blood and tissue sampling were approved by the Biological Studies Animal Care and Use Committee of the National Animal Husbandry Services, Beijing, People’s Republic of China. All experimental procedures followed guidelines established under the Law of Animal Husbandry in the People’s Republic of China . The experimental procedure was approved by the Institutional Animal Care and Committee at National Animal Husbandry Service.In total, 1,351 samples from 26 representative Chinese indigenous goat breeds  were useCSRD247, ETH10, MAF209, OARAE54, SRCRSP15, SRCRSP3, SRCRSP5, TGLA53, DRBP1, ILSTS087, INRABERN172, MAF065, MCM 527, OARFCB20, SPS113, SRCRSP8, ILSTS029, INRA023, INRA063, INRABERN185, P19(DYA), SRCRSP23, SRCRSP9, TCRVB6, BM6444, ILSTS011, ILSTS005, SRCRSP7, OARFCB48, and MAF70. DNA was isolated from cryopreserved blood samples using the DNeasy Blood and Tissue Kit . Polymerase chain reaction (PCR) amplifications were conducted using fluorescence-labelled primers. PCR products were analyzed by capillary gel electrophoresis using an ABI-PRISM 3130xl Genetic Analyzer  and LIZ 500 internal size standards . Data were collected and analyzed using GENEMAPPER 3.5 .Thirty microsatellite markers recommended by the International Society for Animal Genetics (ISAG)/Food and Agriculture Organization of the United Nations (FAO) were used to genotype the 1,351 samples. The markers are O) and HE. Allelic richness (Rt) over all loci for each breed was computed with FSTAT v 2.9.3.2 [E(0.05) were performed with linkage disequilibrium method and random mating model  using LDNe program [Within-breed genetic diversity and marker polymorphism were estimated using GENALEX  and the  2.9.3.2 . In this program .The methods of conservation analyses used in this study have been demonstrated previously ,14. To bThe Core Set method  is basedfm) using the colorRamps package in R.Pairwise kinship distances were computed from the MEK matrix following Eding et al . NeighboE. The partial contribution made by individual breeds was computed as the proportional variation in average internal heterozygosity of the complete data set after removal of each breed (PCHe).Within-breed contributions to diversity were computed using the average within-breed HWeitz), based on Reynolds genetic distance . This method estimates between-breed diversity and ignores within-breed diversity. Analyses conducted using the Weitzman method were computed using a FORTRAN program developed by Ollivier and Foulley [The Weitzman method was also used to calculate the partial contribution made by each breed to total genetic diversity (PC Foulley . Reynold Foulley .Fst) should weight the between-population diversity using the factor FST, and weight the within-population diversity using the factor 1-FST; ii) Piyasatian and Kinghorn proposed that the between-population diversity component should receive five times more weight than the within-population diversity (PC5:1) [When making decisions about conservation priority, both within- and between-breed genetic diversity should be taken into account . Three a (PC5:1) ; iii) Ca (PC5:1)  and Fabu (PC5:1)  assigned (PC5:1) . PairwisBM6444 showing the largest observed  and effective  allele number , indicating that there is genetic differentiation among the 26 Chinese indigenous goat populations. There are 23 loci exhibit positive FIS values, and the positive mean FIS is 0.082±0.029. This may indicate non-random mating, and these loci may be morphological or productive traits under selection.Summary statistics describing microsatellite marker polymorphisms and genetic diversity per breed are presented in e number . In contO were observed in the JNQ , whereas the values of HE and HO were 0.507±0.039 and 0.434± 0.013 in the DYS, representing the smallest heterozygosity , and the lowest in the Daiyun goat  . Howeverzygosity . All popO. Loss of genetic diversity among populations can be caused by genetic introgression [IS had a positive value in all breeds, demonstrating a deficit of heterozygosity  is also a valuable indictor for evaluating conservation priorities. Effective population size (NE0.05) were computed based on linkage disequilibrium with minor allele frequency 0.05  (NE0.05 = 47.5) should be paid more attention and considered in conservation program.The level of allelic diversity in this study was similar to that reported for 9 Chinese cashmere goats , but lowgression , environgression  and inbrzygosity . FIS is re goats . Effectincy 0.05 . NE0.05 The neighbor-net phylogeny of kinship distances shows the relationships among the 26 Chinese indigenous populations . The phyfm) obtained from allele frequencies. To aid in the analysis, contour plots were plotted to visualize both within- and between-breed kinships. Each population was sorted based on its genetic proximity defined in the phylogenetic neighbor-net graph (fm = 0.499), LLS (MEK = 0.242 and fm = 0.417), CDM (MEK = 0.229 and fm = 0.400), MGS (MEK= 0.223 and fm = 0.408), Guizhou White goat (MEK = 0.222 and fm = 0.424), Leizhou goat , and the FQS (MEK = 0.221 and fm = 0.436). The yellow shading represents goat breeds with intermediate kinship values: the XDH (MEK = 0.186 and fm = 0.398), LLY (MEK = 0.185 and fm = 0.368), YLS (MEK = 0.178 and fm = 0.378), MTS (MEK = 0.174 and fm = 0.381), Taihang goat , and the Yangtse River Delta White goat . The light blue and light yellow areas also reveal goat breeds with relationships. The two groupings consist of the LLS, MGS, YLS, and ZTS , and the DYS, FQS, XDH, YCB, and MTS .Within-breed diversity could also be estimated using kinship coefficients with either the MEK derived from genotypes or the average coancestries  were observed, based on the proportional contribution of each breed to the average HE for the whole population (E)  (He<0.363) based on PCHe value  and CDS (0.173<PCHe<0.227)  ; Table 1<−0.195) . The mosHe value . Breeds e<0.227) .Weitz<7). The HWS, Tibet goat, LBB and YCB (4.14<PCWeitz<5.6) made intermediate contributions to total diversity. The smallest contributions were made by the YLS, THS, and ZTS (1.58<PCWeitz<1.96)  .Fst) [ST value of the whole populations is used to weight PCWeitz (FST = 0.138 in this study) and 1-FST is used to weight PCHe. The PCFst method prioritizes breeds by kinship , and also prioritizes breeds that contribute highly to PCWeitz . The breed with the highest priority is the MGR . Although the Tibet goat makes an intermediate contribution to total diversity as estimated by various methods , it also has a relatively high PCFst value (PCFst = 0.951). However, the ranking derived from the PC5:1 method (as proposed by Piyasatian and Kinghorn) is similar to the priority estimated using PCWeitz, because it prioritizes the CDM, MGR, and LZS, due to the increased weight given to the between-breed component.Ollivier and Foulley  proposedFst) . In thisfii<0.499) and the relatively low distance from all the other populations (0.095<Dnei< 0.120). Although the DYS has a relatively high fii value (0.499), its mean genetic distance was also larger (Dnei = 0.147) and thus its contribution to f was relatively low (0.0119). The difference between fii and Dnei is responsible for calculating the contribution to global co-ancestry. The absolute contribution to total diversity prioritized the JNQ (0.0356), CD (0.0343) and Xijiang goat (0.0335). Ranking by absolute contribution yielded results similar to assigning priority based on proportional contributions to genetic diversity. Using absolute contributions, priority was assigned to the JNQ (4.808), CDS (4.632), and XJS (4.524). The ZWS has the lowest contribution (1.931), perhaps due to its relatively low sample size (n = 25). When the proportional contribution to genetic diversity was estimated without reference to sample size, only the JNQ (4.240) and CDS (4.078) maintain their higher priorities. The Tibet goat also obtained a high priority (4.051). In contrast, the DYS made the smallest contribution (PC1 = 3.052 and PC2 = 3.362). Finally, total genetic diversity was analyzed after removing one subpopulation at a time from the total population. Removal of the JNQ resulted in the largest reduction in total genetic diversity , followed by the MGR  and LNS .The results obtained from the combined approach proposed by Caballero and Toro  and FabuE contributions (0.321). The correlations between PC5:1 and other methods are similar to the correlation between PCWeitz and others because of the excess weight given to between-breed components in PC5:1. Compared to PCweighted, higher correlations were observed between PCunweighted and other methods. As there are too many null contributions in the three core set methods, it is not useful to calculate the correlation between these and the other methods, despite the higher correlation between core set methods and PCHe.Linear correlation coefficients calculated for pairwise contributions derived from different methods are shown in In conclusion, this study analyzed the extant genetic diversity of Chinese indigenous goats in terms of within- and between-breed components. Various approaches utilizing these factors were compared to evaluate their utility and shortcomings in a conservation program. Our results suggest MGR (most methods), JNQ and LNS  should be prioritized based on above results. Furthermore, DYS and SNB also should be prioritized based on consideration of effective population size. However, if one breed could survive in changing conditions all the time, the straightforward approach is to increase its utilization and attraction for production via mining breed germplasm characteristic.