Poor public health is always associated with the mismanagement of municipal solid waste (MSW). Many cities are besieged by MSW in the world. It is essential to do a good job in MSW management (MSWM). In order to improve the efficiency of MSWM, the Chinese government has intensively implemented relevant policies. There are still few studies on MSWM efficiency in China. The research aims to comprehensively analyze MSWM efficiency, find high-efficiency MSWM policy implementation routes and the breakthrough on improving MSWM efficiency. To measure Chinese MSWM efficiency accurately, this paper introduced the three-stage data envelopment analysis (DEA) model into the research. According to the results of DEA, Fuzzy c-Means algorithm was used to the cluster analysis of 33 typical cities. After eliminating the interference of the external environment and random disturbance, the mean value of MSWM efficiency declined from 0.575 to 0.544. The mean of pure technical efficiency (PTE) was declined from 0.966 to 0.611, while the mean of scale efficiency (SE) increased from 0.600 to 0.907. The PTE of central and northeastern cities was relatively low. The SE of western cities was comparatively high and the efficiency distribution of the eastern region was relatively scattered. In general, MSWM efficiency is low and expected to be improved. Regional differences in MSWM efficiency have been shown. The management effectiveness of eight pilot cities (MSW classification) is affirmative but not that significant. To improve MSWM efficiency, differential management for four types of cities should be carried out. Many cities are besieged by municipal solid waste (MSW) in the world, particularly in the cities of developing countries . MSW is The formidable rise in MSW generation requires suitable management systems . The misChina Urban Construction Statistical Yearbook, China’s per capital MSW collection and transportation was 1.19 kg/day in 2016 [MSWM is one of the major problems that affect China’s environmental quality and the sustainable development of cities. Severe environmental issues have aroused due to improper and inefficient disposal of MSW ,16. In 2Notice on the Publication of Pilot Cities for Classified Collection of Municipal Solid Waste [Classification and Evaluation Standard of Municipal Solid Waste [Measures for the Management of Municipal Solid Waste [Environmental Protection Tax Law of the People’s Republic of China [Notice on Promoting Classified Management of Municipal Solid Waste in Schools [Opinions on Innovation and Improvement of Price Mechanism for Promoting Green Development [Recently, Chinese government has intensively issued relevant policies to strengthen MSWM. In June 2000, the Ministry of Housing and Urban-Rural Development of People’s Republic of China (MOHURD) published id Waste . Eight cid Waste , Measureid Waste , Environof China , Notice  Schools , Opinionelopment  and so oelopment , mixed celopment , lack ofelopment . It is aelopment .Number of Containers, Total Number of Vehicles, Total Number of Direct Workers were inputs. Tons of Refuse of Organic Material Collected was output [Costs of Personnel, Current Transfers were input indicators. Number of Tons of Domestic Refusal Collected, Number of Tons of Industrial or Commercial Refusal collected, Number of Industries, Commercial Establishments and Houses in which Refuse is Collected Daily were output indicators. Compared with police, culture, sports, green areas and water supply, the efficiency of waste collection was at an intermediate level [Capital investment for MSW maintenance and construction, Fixed Assets Investment in the Public Facilities of Municipal Environmental Sanitation as inputs. Quantity of MSW Transported by Airtight Vehicle and MSW Harmless Treatment Rate were outputs. The results showed that MSWM efficiency should be improved [Waste Collection Coverage, Waste Captured by the MSWM and recycling systems as quantitative indicators. The research revealed that the MSWM was not efficient with regard to collection, recycling, financial sustainability [Measuring MSWM efficiency has gradually become a research hotspot. Bosch et al. (2000) used various methods to analyze the technical efficiency of the waste collection services in 75 municipalities located in Spanish, such as DEA , FDH . s output . Benito te level . Zhou etimproved . Cristóbimproved . Ferronanability . In addinability ,37,38,39The review above shows that the researchers have done some research on the efficiency of MSWM previously. However, further research is still necessary. DEA has been rarely used to evaluate the whole process management efficiency of MSW ,31,33,35The research aims to comprehensively analyze the management effectiveness of MSWM, find high-efficiency MSWM policy implementation routes and the breakthrough on improving MSWM efficiency. The paper introduces the three-stage DEA model into the research. On the basis of previous research, this paper establishes a reasonable indicator system for evaluating MSWM efficiency. Taking 33 cities in China as samples, the paper measures the real efficiency after eliminating external environment and random interference in 2016. The influence of external environment on the efficiency of MSWM is analyzed. And then, the status of MSWM in an all-round way is carried out. The analyses of the management effectiveness of the pilot cities (MSW classification) and regional differences are included. Furthermore, we find the crux of low efficiency and put forward the effective countermeasures to improve MSWM efficiency.SFA of parametric method  and DEA With the deepening of scholars’ research on the DEA model, the methods of one-stage DEA, two-stage DEA, three-stage DEA and so on have been appeared ,45,46. OIn 2002, three-stage DEA was first presented by Fried et al. It removes the influence of external environmental factors and random errors based on the traditional DEA method. Therefore, the measured efficiency value is more realistic and reliable . The basStage 1: Using traditional DEA model to measure the efficiency value of decision-making unit (DMU).X denotes the input indicator variable matrix of each city, and Y represents the corresponding output indicator variable matrix. K is the number of DMU. i represents the ith DMU weight of the input variable i, whereas W is the efficiency [× SE. TE is a comprehensive measure and evaluation of the resource allocation ability and the resource efficiency in the case of variable returns to scale (VRS) [The controlling of input is easier than controlling the output. Hence, the paper adopts traditional input-oriented BCC model to analyze the initial input and output data of 33 cities. BBC model is described as:ficiency . The effle (VRS) . PTE refle (VRS) . SE is ale (VRS) .Stage 2: Using SFA regression model to adjust the Inputs.I inputs and p observable external environment variables, the multiple linear regression model is constructed as follows:ith item of the kth DMU, + algorithm to analyze whether there was regional difference in the efficiency of MSWM. FCM algorithm is a partition-based clustering algorithm, which can promote the maximum similarity between objects divided into the same cluster, and the smallest similarity between different clusters. In FCM, the degree of membership is used to determine the extent of each data point belongs to a certain cluster. It overcomes the defect of common C-means algorithm in which the data points are hard-divided.kth data point in the ith cluster,d is the Euclidean distance between the data point and the data center. FCM algorithm divides N data points into C clusters. To cluster data, it minimizes the objective function by iteratively updating the membership and cluster centers. The objective function is as follows: centers . ClusterFormulas (5) and (6) are iterated until the optimal membership and cluster center are obtained .Scientific selection of input and output indicators is the key to an efficiency measurement. Based on the scientific-nature of indicators and the feasibility principle of data indicator, this paper not only drew on previous research, but also considered the characteristics of MSWM to select relevant indicators. The selected indicators are shown in According to the comprehensive, scientific and representative principles in indicator selection, input indicators for measuring the efficiency of MSWM should cover the manpower, physical and financial resources in the four links of delivery, collection, transportation and disposal. Manpower resources can be measured by the number of relevant practitioners in MSWM, such as the number of sanitation workers, MSW sorters, the number of refuse collection and transportation personnel . PhysicaNumber of Vehicles and Equipment Designated for Municipal Environmental Sanitation was taken as the physical resources invested in each city [Fixed Assets Investment in the Public Facilities of Municipal Environmental Sanitation was used to measure the financial input. Therefore, considering the feasibility principle, Number of Vehicles and Equipment Designated for Municipal Environmental Sanitation and Fixed Assets Investment in the Public Facilities of Municipal Environmental Sanitation were selected as input indicators to measure MSWM efficiency.At present, there are no authoritative data on the personnel related to MSWM in many cities. We cannot get the manpower input . Based oach city . Due to Quantity of MSW Collected and Transported is the amount of MSW that can be transported to waste transfer or treatment stations, which is affected by the collection rate and the transportation rate. It was selected as the first output indicator, which correspond to the first input indicator to measure the efficiency of MSW collection and transportation [MSW Harmless Treatment Rate as the second output indicator to evaluate MSWM efficiency, and it objectively reflected the level of MSW treatment.In China, the efficiency of delivery is low . Quantitortation . We not Quantity of Patent Authorization, Total Retail Sales of Consumer Goods and Excellent Rate of Urban Air Quality as the environmental variables. Previous scholars have found that the level of science and technology have a positive impact on the efficiency of MSWM, and Quantity of Patent Authorization better represents the scientific and technological level of city [Total Retail Sales of Consumer Goods can measure the consumption level of urban residents, and the increase of consumption ability promotes the increase of MSW generation, which in turn affects the MSWM efficiency [Excellent Rate of Urban Air Quality is closely related to the environmental condition [Environmental indictor is the factor, which affects the efficiency of MSWM but is not subjectively controlled by the sample. Considering the characteristics of MSWM, the paper selected  of city . Total Rficiency ,64. Exceondition . With thondition .Total Retail Sales of Consumer Goods. Eventually, 33 cities were chosen. To fully understand the status of MSWM in China, we analyzed the MSWM efficiency of 33 typical cities. At the same time, eight pilot cities were compared with other 25 cities to evaluate the pilot effect of MSW classification. From In this paper, we selected 34 cities as DMUs. Pilot cities, municipalities and provincial capital cities were included. The eight pilot cities for MSW classification, namely, Beijing, Shanghai, Guangzhou, Shenzhen, Nanjing, Hangzhou, Xiamen and Guilin, were identified by MOHURD in June 2000. The reason why we chose municipalities and provincial capitals was because they had typical significance in economic, social and ecological development. We didn’t analyze Lhasa due to the lack of data on the Number of Vehicles and Equipment Designated for Municipal Environmental Sanitation, Fixed Assets Investment in the Public Facilities of Urban Environmental Sanitation, Quantity of MSW Collected and Transported and MSW Harmless Treatment Rate were all from China Urban Construction Statistical Yearbook. Not only Quantity of Patent Authorization but also Excellent Rate of Urban Air Quality came from the corresponding urban statistical yearbooks. Total Retail Sales of Social Consumer Goods was from the statistics database of China Economic and Trade Network. The descriptive summaries of inputs and outputs are shown in In the research, DEAP 2.1 , Frontier 4.1 , MATLAB R2017b  were employed .DEAP 2.1 is the computer program designed for DEA methods: CCR , BCC, the extension of CCR and BCC models, Malmquist . The proFrontier 4.1 is a computer program written by Tim Coelli . It is aMATLAB (matrix laboratory) has become a strong functional mathematic software tool for numerical analysis, engineering and scientific drawing, digital image processing, and so on. And it is widely used in technical computing . It needIn 1984, MathWorks officially released MATLAB 1.0  . Subsequently, the version of MATLAB is continually upgraded. In September 2017, MathWorks released MATLAB R2017b. MATLAB R2017b is an upgraded version of MATLAB R2017a. It adds important deep learning capabilities that simplify how engineers, researchers, and other domain experts design, train, and deploy models. With the MATLAB R2017b employed, FCM algorithm was applied to cluster analysis of MSWM efficiency.With DEAP2.1 and FRONTIER 4.1 employed, taking 33 typical cities as samples, we developed three-stage DEA model to measure the TE, PTE and SE of MSWM. The results of the efficiency analysis were available in From Among the 33 representative cities, Nanning had the lowest TE. The PTE of other cities was higher than the SE of themselves except Lanzhou. The PTE of the other 31 cities was greater than 0.9, apart from Harbin and Lanzhou. Besides, the SE of 25 cities was less than 0.85. It all signaled that the low SE was the main reason for restricting the MSWM efficiency in China. The value of PTE was not 1, which indicated cities cannot effectively integrate and use inputs . The TE Quantity of Patent Authorization, Total Retail Sales of Consumer Goods and Excellent Rate of Urban Air Quality. The results of SFA regression were obtained by Frontier4.1, as shown in For the purpose of analyzing the influence of three environmental variables on the slacks of two input variables, the slacks of Stage 1 were the dependent variables. Three environmental variables were explanatory variables, namely, γ values of the two input slack variables were both 0.999, which indicated that the influence of management factor was dominant. The γ value passed the significance test at 1%. So, the impact of environmental factors and random errors on efficiency was significant. It is necessary to strip out and analyze the environmental factors and random errors by employing SFA model. The results of the impact of environmental factors were as follows:As we can see from 1Z and the slacks of two input variables were significantly negative, indicating that an increase in scientific and technological level will reduce the input and improve the efficiency of MSWM. The regression coefficient between the slacks of two input variables and household consumption, the slacks of two input variables and environmental condition were both positive and passed the 1% significant test, which confirmed the previous analysis.The correlation between According to the analysis of SFA, the MSWM efficiency was affected by external environmental factors and random errors, and the efficiency of Stage 1 was not real efficiency. Consequently, to obtain true and reliable management efficiency, the influence of the environmental variables and random errors should be removed, and 33 typical cities should be placed under the same objective conditions.Based on the data obtained in the Stage 2, the raw input data was adjusted by using Formula (3). The DEA model was adopted again to analyze the MSWM efficiency. And then, we obtained the adjusted TE, PTE and SE.It can be seen from In terms of the TE, there was little change in 33 typical cities. Among eight pilot cities for the classified collection of MSW, only Hangzhou and Guilin had a TE of 1. Shenzhen’s TE decreased from 1 to 0.917. The TE of Beijing and Shanghai was still at a low level. The mean TE of the eight pilot cities for the classified collection of MSW was higher than the mean of the other 25 cities. From a national perspective, the TE of six cities has increased, and the average growth rate was more than 10%. Among the six cities, Haikou had the largest growth rate of 59.52%. It showed that the poor environment of MSW was one of the reasons for the low efficiency of Stage 1 in Haikou . After tAs for PTE, in eight pilot cities, the PTE of Guangzhou, Nanjing and Xiamen decreased by 39.21% on average. The reduction of PTE indicated that the efficiency of using input elements was lower than it seemed. In Stage 1, the higher PTE was closely related to the favorable environment. And the remaining five cities was 1. That is, 5 cities can fully integrate and use input factors . What isWith regard to the SE, the mean SE of the eight pilot cities for the classified collection of MSW was 0.815, which was smaller than 0.937 (the average of the other 25 cities). Obviously, the SE of eight pilot cities needed to be improved. Among eight pilot cities, there was a gap between the actual scale and the optimal production scale of the remaining six pilot cities. The 6 cities must explore the optimal scale except Hangzhou and Guilin . AccordiIn the aspect of the changes in returns to scale, among the eight pilot cities, Hangzhou and Guilin were in a state of constant returns to scale. Xiamen was increasing returns to scale. While the remaining five cities were at the decreasing stage of the scale return, the scale of input can be appropriately expanded. The returns to scale of the 33 representative cities before and after adjustment were quite different. In the Stage 1 of the traditional DEA model, seven cities were in a state of constant returns to scale, accounting for up to 21.21% of the total, and the remaining 26 cities were decreasing returns to scale. After the adjustment, 18 cities were decreasing returns to scale, the proportion decreased from 78.79% to 54.54%. Six cities were in a state of constant returns to scale. And the remaining nine cities were at the increasing returns to scale. It can be explained that the main restriction on the efficiency of MSWM was not the scale reduction .To better distinguish the efficiency of MSWM in each city, cluster analysis was conducted on 33 typical cities using the data obtained in Stage 3. With the MATLAB R2017b employed, FCM algorithm was applied to cluster analysis of MSWM efficiency. The clustering diagram was shown in According to the clustering results, the efficiency of MSWM in the sample cities can be divided into four types. Ten cities, including Nanning, Wuhan, Urumqi, Tianjin, Zhengzhou, Harbin, Guiyang, Hefei, Taiyuan and Lanzhou were the first type named “low-high”. The central coordinate of the first cluster was . Jinan, Yinchuan, Nanjing, Chengdu, Guangzhou, Changchun, Shenyang, Changsha, Shijiazhuang, Nanchang, Kunming, Haikou, Xi’an, Chongqing and Xiamen were the second type named “medium-high”. The cluster center coordinate was . Hangzhou, Guilin, Hohhot, Fuzhou, Xining and Shenzhen belonged to the third type named “high-high”, and the cluster center coordinate was . While Beijing and Shanghai were the fourth type named “high-low”, the cluster center coordinate was . Hence, among the eight pilot cities, Nanjing, Guangzhou and Xiamen belonged to the “medium-high” type. Hangzhou, Guilin and Shenzhen were the “high-high” type, while Beijing and Shanghai were the “high-low” type.Total Retail Sales of Social Consumer Goods [In 1990s, the concept of MSW classification came into China. Since then, Chinese government has always emphasized MSWM to avoid economic and environmental problems caused by MSW. Identifying the status of MSWM, finding high-efficiency MSWM policy implementation routes and the breakthrough on improving MSWM efficiency were China’s important tasks . The reser Goods . Then, ter Goods . The worAfter excluding the environmental factors and random errors, the TE average value of 33 typical cities declines to 0.544 from 0.575. The efficiency of MSWM in China was not high, and the effectiveness of MSW classification was not obvious. The mean PTE value in Stage 3 is 0.611. The low efficiency of MSWM is constrained by low PTE. The main reasons may be that the implementation of MSW classification was insufficient, the level of MSW disposal technology was not high, and the quality and quantity of classification facilities in China were insufficient. China should attach importance to improving technological level and managerial skills for the purpose of improving the efficiency of MSWM.At present, the eight pilot cities for classified collection of MSW, including Beijing and Shanghai and so on, have been trialed for 18 years. But the results showed that the effectiveness was not significant. Only Hangzhou and Guilin were at production frontier, indicating only two of the eight pilot cities took the leading position in China in terms of MSWM. The average value TE of the eight pilot cities was bigger than the remaining 25 cities. Moreover, the mean of SE was smaller than the remaining 25 cities. The results indicated that the TE of the eight pilot cities was subject to the lower SE. Management skills and technical level of 25 cities need to be improved.Comparing the results of the Stage 1 with the Stage 3, Haikou had the largest growth in TE, which indicated that Haikou was in bad environment. Haikou municipal government should increase the investment in MSWM and increase the publicity of MSW classification. It is necessary to improve the classification awareness of residents and further create a good atmosphere for MSWM. PTE was declined in 24 cities, indicating that the surface PTE was higher than the real PTE. And the PTE of MSWM in 24 cities benefited from the environment. In terms of SE, the SE of other 23 cities was increased. The economies of scale of 23 cities were adversely affected by environment. Therefore, it is necessary to introduce three-stage DEA model.Regional distribution of four types of MSWM efficiency is available in As we can see from The efficiency value calculated in Stage 3 was more realistic. So, the countermeasures to improve the efficiency of MSWM in China were put forward based on the efficiency value of Stage 3 . It is sAs for the first “low-high” type and the second “medium-high” type of cities with low PTE, Improving PTE is a top priority. Not only the scientific and technological level of MSW classification but also the management skills and decision-making level should be improved.As for the first and second type of cities, improving the scientific and technological level is the first key solution. Improving the technical level of the relevant staff  and streIn terms of the first and second types of cities, advocating multi-subject governance is the second key solution. All subjects are encouraged to make joint efforts. The MSWM is a common career of the whole society. According to China actual situation, MSWM should be led by the government . SpecifiAs for the fourth “high-low” type of cities with low SE of MSWM, paying attention to narrowing the gap between actual scale and optimal scale is required . BeijingIn addition to ensuring leading position, the third type of “high-high” cities with high PTE and TE should keep pace with the times. Moreover, it is essential that improving the efficiency of the pilot cities and encouraging the eight pilot cities to play a demonstration role in MSWM. For the pilot cities of MSW classification, Hangzhou, Guilin and Shenzhen should constantly introduce new management skills and technology. The governments of these cities should innovate the propaganda model of MSW classification to strengthen the awareness of the people’s MSW classification. Management subjects should actively learn from the international advanced management skills. And constantly explore the model of enterprises participating in MSWM is essential, such as BT (Build-Transfer), ROT (Renovate-Operate-Transfer), BOO (Build-Own-Operate). As for Beijing and Shanghai, it is necessary to enhance the SE by optimizing allocation of resources. Guangzhou, Nanjing and Xiamen should pay great attention to promote the PTE with new technology and higher management capabilities. Paying attention to explore and give full play to the leading role of the eight pilot cities in MSW classification is of great significance for China to improve the MSWM efficiency.For cities with decreasing returns to scale, the allocation of resources should be optimized. The cities have two important things which are increasing the investment in technology and adjusting the scale of investment . In ordeThis paper innovatively introduced the three-stage DEA model into the research, and the MSWM efficiency of China typical cities in 2016 was accurately evaluated. The paper is helpful for decision-makers to identify the status of MSWM and put forward effective countermeasures. Furthermore, it is beneficial to improve public health.This research method can be adopted to measure the efficiency of MSWM in many years so that the MSWM efficiency of China in long period can be reflected more accurately. And the model can be used to evaluate MSWM efficiency of cities in other countries. This research has potential for further developments. Firstly, owing to the limitations of data acquisition, the research on the recycling, harmlessness and reduction effects of MSW classification in China cannot be carried out. Therefore, our next step is to dig deeper into the currently available data so that we can find the best substitution indicators. Secondly, our research is unable to cover all considerations due to the unavailability of data. We will quantify the social factors in the concept of sustainable development and incorporate them into the evaluation system of MSWM. Thirdly, we can analyze the efficiency of MSWM from the smaller perspective, such as the status of MSWM in enterprises and social organizations.Poor public health is always associated with the mismanagement of MSW. In China, more importance has been attached to MSWM to improve public health. However, there are still some problems in every link of MSWM. The research shows that China’s MSWM efficiency is low. The effectiveness of policy implementation is expected to be improved. The management effectiveness of eight pilot cities (MSW classification) is affirmative but not that significant. There are regional differences in the efficiency of MSWM in China. The PTE of the central and northeastern regions is relatively low, while the SE of the western region is higher than other regions. In addition, the efficiency distribution of the eastern region is scattered.In order to improve the efficiency of MSWM, differential management should be implemented according to the actual situation of each city. Different types of cities have different breakthroughs on improving MSWM efficiency. As for the first “low-high” type and the second “medium-high” type of cities with low PTE, the breakthrough to improve efficiency is improving PTE. In the process of policy implementation, these cities should pay attention to improving the technical level of the relevant staff, introducing new technology for MSW classification, advocating multi-subject governance. Because the fourth type of “high-low” cities with low SE are at the decreasing stage of the scale return, the key point for high-efficiency MSWM policy implementation routes is narrowing the gap between actual scale and optimal scale. To optimize the allocation of resources, enterprises and social organizations should be encouraged to participate in MSWM. The third “high-high” type of cities is the most ideal. The outputs are larger than other types with given inputs. New methods and management skills should also be constantly explored. Innovating the propaganda model of MSW classification is vital. It is feasible to enhance the publicity effect by developing on-the-spot presentation, thematic activities, and so on. Management subjects should actively learn from the international advanced management skills. Enterprises are supposed to actively explore the model of participation in MSWM, such as BT, ROT, BOO. Moreover, we should also keep exploring and playing the role of “leading-sheep” in the pilot cities.


Triticum aestivum L.). Our MAGIC population is composed of 394 F6:8 recombinant inbred lines lacking significant signatures of population structure. The linkage map included 5435 SNP markers distributed over 2804 loci and spanning 5230 cM. The analysis of population parameters, including genetic structure, kinship, founder probabilities, and linkage disequilibrium and congruency to other maps indicated appropriate construction of both the population and the genetic map. It was shown that eight-founder MAGIC populations exhibit a greater number of loci and higher recombination rates, especially in the pericentromeric regions, compared to four-founder MAGIC, and biparental populations. In addition, our greatly simplified eight-parental MAGIC mating design with an additional eight-way intercross step was found to be equivalent to a MAGIC design with all 210 possible four-way crosses regarding the levels of missing founder assignments and the number of recombination events. Furthermore, the MAGIC population captured 71.7% of the allelic diversity available in the German wheat breeding gene pool. As a proof of principle, we demonstrated the application of the resource for quantitative trait loci mapping analyzing seedling resistance to powdery mildew. As wheat is a crop with many breeding objectives, this resource will allow scientists and breeders to carry out genetic studies for a wide range of breeder-relevant parameters in a single genetic background and reveal possible interactions between traits of economic importance.Multiparent advanced generation intercross (MAGIC) populations were recently developed to allow the high-resolution mapping of quantitative traits. We present a genetic linkage map of an elite but highly diverse eight-founder MAGIC population in common wheat ( In plant breeding, the detection of quantitative trait loci (QTL) is no longer limited by the availability of genetic marker information and genotyping throughput , but ratThrough intensive genetic analysis, we evaluated the impact of a MAGIC mating design with a greatly reduced number of overall crossings. First, we explored the genetic structure of our Bavarian MAGIC wheat population (BMWpop) for the validation of the crossing procedure. Second, we constructed a genetic linkage map, which we evaluated through population parameters and congruency with other maps. For comparative analysis, we used two other wheat MAGIC maps , one bip1 seed, one ear per plant was used in four two-way crosses  . Out of 32 available four-way crosses, sixteen independent plants were selected and crossed, to obtain eight F1 populations that involved four reciprocal cross combinations (ABCDEFGH and EFGHABCD). The four eight-way crosses were further hybridized with the four reciprocal eight-way crosses for establishing 16 eight-way intercross combinations (ABCDEFGH/EFGHABCD). The term ‘MAGIC group 1–16’, as used hereinafter, refers to these 16 subpopulations. The F1 seeds of BMWpop were progressed to the F6 generation via single seed descent followed by two generations of bulk propagation in the field. The crossing procedure started in June 2009, and was completed in December 2010 followed by 5 years it took to self the plants to F6:8 recombinant inbred lines (RILs).Eight winter wheat lines ‘Event’ (A), ‘Format’ (B), ‘BAYP4535’ (C), ‘Potenzial’ (D), ‘Ambition’ (E), ‘Bussard’ (F), ‘Firl3565’ (G), and ‘Julius’ (H) were selected as founders of an eight-way MAGIC population based on the following criteria: (i) variation for disease resistance, quality, and agronomic traits, (ii) derivation from diverse breeding programs, and (iii) importance within the respective quality group Table . All fou6:8 RILs and all eight founders were harvested, pooled and freeze-dried. Genomic DNA was extracted according to the procedure described by ® iSelect® array containing 17267 single nucleotide polymorphism (SNP) markers provided as a service by the company TraitGenetics . The array combines markers from the 90K iSelect array . The population structure was investigated with a principle coordinates analysis (PCoA) based on Roger’s distance matrix in R/ape V5.0 . The kinp V2.0.2 . The thrp V2.0.2 , the foup V2.0.2 , and thep V2.0.2 . Informap V2.0.2 , whereasp V2.0.2 . This ge1,2 . Unless otherwise stated, the functions used were from the mpMap package. Heterozygous calls were set to missing. A recombination fraction (rf) matrix between all pairs of markers was calculated using the function ‘estimateRF’ in the package mpMap2 at default settings. The estimation of the rf matrix was based on identical by descent probabilities given in The construction of the genetic map was based on the two R packages mpMap V2.0.2  and mpMaChromosome length and number of markers and loci were compared to the wheat genetic maps of NIAB 2015, 9kMAGIC, and L19 BC1 populations. The congruency of marker order was assessed in relation to the three above-mentioned genetic maps and to the physical marker positions of IWGSC RefSeq v1.0  employinr2 value was determined at the 95th percentile of the square root transformed r2 value distribution for unlinked markers. r2 values above this threshold were expected to be caused by genetic linkage. A least square regression (loess) curve was fitted to the LD decay estimation using a smoothing span parameter of 0.10  seedling resistance in the glasshouse under controlled conditions with a mean relative humidity of 67%, a temperature of 18°C, and 16 h of light per day. For each experiment, the population and the eight founders were sown in multi pot plates in a randomized complete block design with two replicates each. After the primary leaf emerged up to a length of 5 cm, the plants were inoculated with a  and Pm8 . DiseasePhenotypic data were adjusted in R/lme4 V1.1-14  based onijky is the trait observation, μ is the overall mean, ig is the fixed effect of genotype ji, l is the random effect of the trial ijj, gl is the random interaction effect of genotype i with trial kjj, r is the random effect of replication k nested within trial j, and ijke is the random residual error. To obtain variance components, the genotype was fitted as random. Heritability was estimated on a progeny mean basis according to where R2) by individual QTL. The QTL support interval (S.I.) was defined as the map distance in cM surrounding a QTL peak at a -log10(p) drop of ±1.0. The designation of QTL followed the recommended rules for gene symbolization in wheat . The function ‘mpIM’ was used for mapping. QTL were detected at a genome-wide significance threshold of α < 0.001. The threshold was derived from an empirically null distribution with 1000 simulation runs similar to in wheat .http://doi.org/10.14459/2018mp1435172. Seed of all 394 RILs of BMWpop and the eight founders are available for non-commercial use upon request from the Bavarian State Research Center for Agriculture .All raw genotypic data and the complete pedigree traceable to two-way crosses are publicly available at 6:8 generation and a number of 394 lines were selected based on sufficient seed availability for field trials and suitability for experiments in the agricultural environment.The crossing design of the MAGIC population was greatly simplified compared to the maximum number of possible crosses ; however® iSelect® array, 11426 markers (66.2%) were segregating in the population. After marker filtering 6738 SNPs (58.8%) remained. Since founder lines were genotyped twice, a genotyping error rate of 0.1% could be observed. Twenty-two markers were found to be polymorphic in the population but not in the founders. These markers were also excluded from the data set, and a total number of 6716 codominant SNP markers and one dominant PCR marker remained for further analysis. The percentage of missing marker data per line was in the range of 0.0–9.5%, with an overall mean of 0.3%  to 18.5 cM (chromosome 7D). To consider LD between loci due to genetic linkage the r2 critical population-specific threshold was 0.017. Thus for the genome, markers were assumed to be genetically linked within a distance of 68.6 cM.Plots of LD showed a clear pattern of intra-chromosomal LD decay in BMWpop for the genome and for the individual chromosomes  from the parental mean  values and accounted for 17.4 and 18.3%, respectively, of the variation for PM. The alleles reducing disease severity at QPm.lfl-6B and QPm.lfl-7A were contributed by ‘Event’ and ‘Ambition’, respectively. The QTL QPm.lfl-1B explained 4.5% of the phenotypic variance with all founder effects being negative relative to the parent Julius, whereas four parental lines decreased disease severity for QPm.lfl-4A.The repeatability of PM trials was 81.3 and 81.9% with a correlation between the trials of 0.8 (Supplementary Table A full eight-founder MAGIC crossing design is built up of 28 two-way crosses, 210 four-way crosses, and 315 eight-way crosses . StartinOur greatly simplified MAGIC design and the small population size of 394 RILs did not cause a considerable structure in the population as indicated by population structure and kinship analyses Figure . This noIt appears that a greatly reduced MAGIC mating design including an additional eight-way intercross step is equivalent to an almost full design. This refers to the total number of recombination events, the ability to construct an appropriate genetic map, and the proportion of missing founder assignments. Furthermore, we could show that QTL mapping for seedling resistance to powdery mildew was successful in the background of this simplified eight-founder MAGIC design. These findings are of great importance for researchers and scientists working on multiparental populations as the effort of developing such a population can be significantly reduced. The eight-founder BMWpop and its genetic linkage map is a valuable genetic resource that could allow scientists and breeders to carry out genetic studies for a wide range of breeder-relevant parameters in a single genetic background.VM and LH conceived the study. MS conducted the experiments and analyzed the data. VM supervised the research. MS and VM drafted the manuscript. All authors read, edited, and approved the final manuscript.The 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.


Uncaria guianensis is native to the Amazon and is used traditionally as an anti-inflammatory. Natural populations of the species have declined markedly in recent times because of strong anthropic pressure brought about by deforestation and indiscriminate collection. The aim of the present study was to assess the genetic and chemical diversity among eight natural populations of U. guianensis located in the Brazilian states of Acre, Amapá and Amazonas. A set of four primer combinations was employed in sequence-related amplified polymorphism (SRAP) amplifications of leaf DNA, and the fragments were analyzed in an LI-COR model 4300 DNA Analyzer. Genetic variability within the populations (81%) was substantially greater than that detected between them (19%). The highest percentage of polymorphic loci (90.21%) and the largest genetic variability were observed in the population located in Mazagão, Amapá. Genetic differentiation between populations was high (Fst = 0.188) and the studied populations formed three distinct genetic groups (K = 3). The population located in Assis Brasil, Acre, presented the highest average content of the mitraphylline . However, mitraphylline and isomitraphylline not detected in most individuals in the studied populations, and it is questionable whether they should be considered as chemical markers of the species. The genetic data confirm the urgent need for conservation programs for U. guianensis, and for further studies aimed at ascertaining the genetic basis and heritability of alkaloid accumulation. Uncaria guianensis (Aubl.) J.F.Gmel. (Rubiaceae), commonly known as uña de gato, is endemic to the Amazonian regions of Bolivia, Brazil, Colombia, Ecuador, Guiana, French Guiana, Peru, Suriname and Venezuela [U. guianensis [enezuela . The plaenezuela . Pharmacenezuela . These pianensis –6.U. guianensis have declined dramatically in recent times because of strong anthropic pressure brought about mainly by deforestation and indiscriminate extraction of the bark for the commercial production of phytotherapeutic preparations [Natural populations of arations ,8. In tharations . Furtherarations ,11. The arations .U. guianensis, investigations on the genetic and chemical diversity of the species would be of significant interest. Thus, the objectives of this study were to analyze the genetic diversity of natural populations of U. guianensis using SRAP markers and to determine the concentrations of mitraphylline and isomitraphylline in leaf extracts using high performance liquid chromatography (HPLC). The results of our study will contribute to the selection of individuals that could be considered elite in respect of POA concentrations and to ascertain the need for immediate conservation strategies.Considering the ethnopharmacological and industrial importance of U. guianensis for use in the study was authorized by the Conselho de Gestão do Patrimônio Genético/Ministério do Meio Ambiente  through the offices of the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq). A total of 157 specimens of U. guianensis were collected from eight populations located at different sites in the Amazonian region of Brazil, namely Boca do Acre in Amazonas state, Assis Brasil, Cruzeiro do Sul, Feijó, Rio Branco and Xapuri in Acre state, and Mazagão and Oiapoque in Amapá state. The identification codes of the samples are presented in The collection of specimens of Young healthy leaves were collected from each specimen and either stored in labeled test tubes in the freezer at -20°C until required for DNA extraction and SRAP analysis, or dried at 45°C in a forced-air oven  for HPLC analysis. All experiments were performed in the Molecular Biology and Phytochemical Laboratories of the Biotechnology Department at UNAERP.(CTAB) method [Genomic DNA was extracted from leaf samples (100 mg) using the cetyltrimethylammonium bromide ) method . The int) method . Four co2 (25 mM), 1 μl of dNTP mixture (2.5 mM), 0.4 μl of forward primer (5 μM), 0.4 μl of reverse primer (5 μM), 0.2 μl of Taq DNA polymerase (5 U/μl), 1 μl of DNA template (5 ng/μl) and deionized water to a final volume of 10 μl. Amplification procedures involved 5 cycles of denaturation at 94°C for 1 min, annealing at 35°C for 1 min and extension at 72°C for 1 min, followed by 35 cycles of denaturation at 94°C for 1 min, annealing at 50°C for 1 min and extension at 72°C for 1 min, with a final extension step at 72°C for 7 min.For all four primer pairs, polymerase chain reactions (PCR) were performed with reaction mixtures containing 1 μl of 10 X reaction buffer, 0.8 μl of MgClMX automated analysis software version 3.3.Amplicons were denatured at 95°C for 5 min and an aliquot (0.6 μl) of each sample was applied to a KB Plus 6.50% Gel Matrix  together with a 50–700 bp DNA ladder (0.8 μl). Electrophoresis was performed at 1.500 V and 40 W for 2:30 h at a constant temperature of 45°C in an LI-COR model 4300 DNA Analyzer. Primers were labeled with LI-COR 700 and 800 nm infrared dyes to allow collection of fluorescent images in real time during electrophoresis. Image data were viewed, analyzed and converted into numerical data files using LI-COR SAGAU. guianensis that had been reduced to a fine powder in a Marconi MA048 cutting mill fitted with a 40 mesh sieve. Powdered leaf material (100 mg) was mixed with 1 ml of methanol  in an amber flask and submitted to static maceration at room temperature (22 ± 1°C) for 24 h, following which the mixture was filtered and the filtrate reduced to dryness in a fume cupboard. Triplicate extractions were performed for each of the studied specimens.A modified version of the method of Bertol et al.  was emplv/v) mixture of methanol (J.T. Baker HPLC grade) and Milli-Q Ultrapure water  and applied to Supelco LC-18 solid-phase extraction (SPE) tubes  that had been previously eluted with 1 ml of methanol followed by 1 ml of 80:20 (v/v) methanol:water mixture. Tubes were subsequently eluted with 3 ml of 80:20 (v/v) methanol:water mixture and 20 μl aliquots of the eluents (5 mg/ml) were analyzed by HPLC on a Shimadzu  model LC-10ADvp instrument coupled to an SPD-M10Avp diode array detector (DAD). Separations were carried out at room temperature (22 ± 1°C) on a Zorbax Eclipse XDB-C18 column  protected by a Zorbax Eclipse XDB-C18 pre-column . The mobile phase comprised 10 mM aqueous ammonium acetate (pH adjusted to 6.9 with triethanolamine)  and acetonitrile  and was supplied at a continuous flow rate of 0.8 ml/min according to the program: 35% B between 0.01 and 18.00 min, 50% B between 18.01 and 25.00 min, 35 to 100% B from 25.01 to 40 min, and 35% B between 40.01 and 45 min. The detection wavelength was set at 245 nm and the acquired data were processed using Shimadzu LabSolutions Multi LC-PDA software.Samples (15 mg) of dried extracts were redissolved in 1 ml of an 80: 20 , the effective number of alleles (Ne), Nei’s genetic diversity index (H) and Shannon's diversity index (I). The unweighted pair group method with arithmetic mean (UPGMA) was used to group populations according to genetic divergence estimated from Nei’s genetic distances [The sequence data were used to perform analysis of molecular variance (AMOVA) in order to decompose the total genetic variance to within and between population components. Descriptive analysis of total variability was obtained by calculating the percentage of polymorphic loci, the observed number of alleles  using the software packages GenAlEx version 6.5  and STRUvegan and ecodist R packages [vegan, fields and ecodist R packages.Data relating to the accumulation of mitraphylline and isomitraphylline within and between populations were submitted to analysis of variance (ANOVA) and, when significant differences were detected, mean values were compared using the Scott-Knott test at 5% probability. A dendrogram was constructed using the UPGMA clustering method to establish the organization of chemical variables among the evaluated populations. The matrix-based cophenetic correlations produced by the UPGMA dendrogram were calculated using the packages . The Eucigarapé  located in a non-anthropized area  correlation between geographical and genetic distances among the eight U. guianensis populations . On this basis, the conservation of U. tomentosa should prioritize the collection of as many individuals as possible within populations that presented the highest genetic variability. For allogamous species, the within population genetic variability is expected to be high [U. guianensis is allogenic, although the mechanism of fertilization of this species has yet to be described. It is important to emphasize that during the collection of specimens of U. guianensis for the present study, we observed extensive areas of fragmented forest, a situation that will certainly lead to the diminution of species diversity in the medium term.The results from AMOVA  revealed be high , hence iU. guianensis (Fst = 0.188) was high but slightly lower than that observed for U. tomentosa (Fst = 0.246) [Psychotria ipecacuanha (Rubiaceae), which presented high Fst values ranging from 0.355 to 0.457 [The genetic differentiation between populations of Fst = 0.18 was hig Fst = 0.6 [PsychoU. guianensis tended to form three genetic clusters (K = 3). The formation of three genetic clusters clearly demonstrates that the environment was fragmented by human interference, rendering the species vulnerable to genetic erosion and indicating the need to implement conservation strategies.The UPGMA dendrogram , the PCoU. guianensis, the concentrations of these POAs varied considerably within the populations studied . An association between altitude and POA content has also been observed for ve 230 m .U. guianensis to be characterized. It was possible to detect the presence of three genetic groups, indicating the need to develop conservation strategies in order to preserve such variability. Geographic distance did not explain the distribution of genetic variability between the studied populations. Since genetic variability was higher within populations, we suggest that the best conservation strategy would involve the collection of large numbers of individuals from populations presenting the highest variability. Although a few POA-rich individuals were detected, the majority of specimens did not accumulate mitraphylline and/or isomitraphylline. This finding suggests that these two POAs are not the best chemical markers for U. guianensis and that further studies are required in order to understand the heritability of chemical traits in this species for domestication and breeding purposes. The scarceness of POA-rich specimens of U. guianensis means that commercial production of phytotherapeutics based on this species may be problematic since it depends on the collection of specimens from natural environments without previous selection. We conclude that there is an urgent need for conservation projects involving U. guianensis, with particular emphasis on the creation of germplasm banks.Analyses employing polymorphic SRAP markers enabled the genetic variability within and between eight natural populations of S1 FigA) mitraphylline and (B) isomitraphylline, along with extracts from specimens collected in (C) Boca do Acre, AM (BC) in which peaks labeled a correspond to mitraphylline, (D) Assis Brasil, AC (AB) in which the peak labeled b corresponds to isomitraphylline, (E) Xapuri, AC (XA) in which peaks labeled a and b correspond to mitraphylline and isomitraphylline, respectively, and (F) Rio Branco, AC (RB) in which peaks a and b are absent.The chromatograms show the standards ((DOCX)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.


Pomacanthus maculosus) across the Northwestern Indian Ocean (NIO). We then discuss our results with respect to the above abiotic features in order to understand the contemporary distribution of genetic diversity of the species. To do so, restriction site‐associated DNA sequencing (RAD‐seq) was utilized to carry out population genetic analyses on P. maculosus sampled throughout the species’ distributional range. First, genetic data were correlated to geographic and environmental distances, and tested for isolation‐by‐distance and isolation‐by‐environment, respectively, by applying the Mantel test. Secondly, we used distance‐based and model‐based methods for clustering genetic data. Our results suggest the presence of two putative barriers to dispersal; one off the southern coast of the Arabian Peninsula and the other off northern Somalia, which together create three genetic subdivisions of P. maculosus within the NIO. Around the Arabian Peninsula, one genetic cluster was associated with the Red Sea and the adjacent Gulf of Aden in the west, and another cluster was associated with the Arabian Gulf and the Sea of Oman in the east. Individuals sampled in Kenya represented a third genetic cluster. The geographic locations of genetic discontinuities observed between genetic subdivisions coincide with the presence of substantial upwelling systems, as well as habitat discontinuity. Our findings shed light on the origin and maintenance of genetic patterns in a common coral reef fish inhabiting the NIO, and reinforce the hypothesis that the evolution of marine fish species in this region has likely been shaped by multiple vicariance events.Ocean circulation, geological history, geographic distance, and seascape heterogeneity play an important role in phylogeography of coral‐dependent fishes. Here, we investigate potential genetic population structure within the yellowbar angelfish ( Metapopulation genetics of coral reef fish. Phylogeography Northwestern Indian Ocean. Biogeographic studies of marine fishes have demonstrated the existence of numerous endemic species around isolated oceanic islands and in peripheral areas , suggesting that larval retention can occur in a myriad of ways even in short spatial scales. Indeed, in addition to distances and heterogeneous seascapes, historical and oceanographic barriers are also thought to be important factors affecting larval dispersal and consequently population structure , a common coral‐dependent fish found throughout the NIO. We first examine whether intraspecific genetic variation has been driven by either IBD or IBE, and if not, we examine whether genetic variation within P. maculosus aligns with contemporary species distribution patterns by investigating the role of the putative marine barriers in the NIO.In this paper, we investigate population genetic structure of the yellowbar angelfish 22.1The study region comprised the Northwestern Indian Ocean (NIO): northeast of Kenya on the eastern African coastline (hereafter referred to as Kenya), and the seas surrounding the Arabian Peninsula, that is the Red Sea, the Gulf of Aden, the Arabian Sea, the Sea of Oman, and the Gulf  were collected from 27 locations that together covered an extensive part of the distributional range of the species, including the Red Sea, the Gulf of Aden, the Sea of Oman, the Arabian Gulf and Kenya  Fluorometer and checked for high‐molecular‐weight bands on a 1% agarose gel.The sampling scheme was designed to maximize the geographic and environmental distances covered, so as to best investigate the importance of these factors for population genetic variation within 2.3RAD tag libraries were constructed by Floragenex from two 96‐well plates, following the protocol outlined by Baird et al. , Hohenloprocess_radtags, denovo_map.pl and populations.Raw reads obtained from 100 bp single‐end Illumina sequencing were assessed for sequence quality, AT/GC content, and duplicate or overrepresented sequences using FastQC v.0.11.5. After initial quality assessment, reads were filtered and detection of single nucleotide polymorphism (SNP) was performed in Stacks v.1.42 pipeline  between them, and using only sequences with a minimum read depth of three (m = 3) were required to create a stack. After building of loci at the individual level, cstacks was used to match loci across samples and build a catalog, which allowed a maximum of one base pair mismatch . Subsequently, RAD tags for all individuals were used to detect only the first SNP (‐‐write_single_snp) by identifying at the same locus a marker that was present in one set of individuals but absent in another.As a reference genome was not available for s et al. . This wa2.4r2 threshold of 0.2. Finally, markers that did not meet the Hardy–Weinberg Equilibrium (HWE) assumptions were excluded.A second filtering step was performed in Plink 1.9. Individuals with more than 10% missing genotypes were excluded and only SNPs with a 90% genotyping rate (10% missing) and a minor allele frequency (MAF) higher than 5% were included. We also filtered data in respect to linkage among SNPs using a window size of 100 bp and a pairwise 2.5FST statistic ] values and geographic distances (in kilometers). Geographic distances between sampling locations  were determined in Google Earth Pro v.7.3, and the Mantel test was executed in GenoDive v.2.0.Correlation between genetic divergence and geographic distances was tested for IBD by applying the Mantel test to the linearized raster  data for a total of nine water environmental variables  from Bio‐ORACLE , we ran TreeMix using the five sampled regions, and rooting the tree at the Red Sea, a suggested glacial refuge for many fish species during the last glaciation  with the highest values observed in comparisons between the Arabian Gulf and the Kenyan coast, while the lowest values occurred between the Red Sea and the Gulf of Aden. Grouping the samples into three populations based on the clustering analyses (see ADMIXTURE and PCA results below), FST ranged from 0.043 between the Eastern Arabian Peninsula (EAP) and the Western Arabian Peninsula (WAP) populations, to 0.135 between Kenya and the EAP  with pairwise genetic distances , the clusters were comprised by a Western  and an Eastern (the Arabian Gulf and the Sea of Oman) cluster. At K = 3 (CV = 0.57138), individuals from Kenya composed a distinct group, and therefore suggested a division into three distinct clusters; a Western Arabian Peninsula cluster (Red Sea and Gulf of Aden), an Eastern Arabian Peninsula cluster (Arabian Gulf and Sea of Oman), and an eastern Kenya cluster. Although the Sea of Oman consistently clustered with the Arabian Gulf, this area is a very clear admixture zone  pointed to two values of s Figure . At K = e Figure .3.5The top two principal components (PCs) explained 6.74% of the total of genotypic variation. PC1 (4.3% of the variation) distinguished individuals sampled on the east side of the Arabian Peninsula from those obtained on the west side of the peninsula. Whereas PC2 (2.44% of the variation), to a higher extent, discriminated between individuals sampled off the coast of Kenya and those collected around the peninsula. Thus, one cluster included individuals from the western Arabian Peninsula , the second cluster comprised eastern sampling sites (Sea of Oman and Arabian Gulf), and the third cluster consisted only of individuals from the African coast off Kenya Figure .3.6w = 0.006) from Kenya to the Sea of Oman  being environmentally similar to each other  requires biological information  that are not available for the majority of species inhabiting the NIO. Biophysical models would become oversimplified without sufficient biological information, and will thus likely fail to represent the characters of a single target species  could provide greater resolution for particular taxa of interest. On the other hand, biogeographic studies in the WIO are still hindered by socioeconomic and political restraints in some of the countries bordering the region, which have created a situation of limited access for scientists. For example, the sea off Somalia coincides with the major faunal change between the Arabian Peninsula and the WIO, but is largely unstudied, much like the understudied region off the coast of the Arabian Sea between the Gulf of Aden and southern Oman. These areas are not well characterized, and not solely in terms of species distributions, but also in terms of habitats.None declared.FT, PR, RB and PRM conceived the ideas; all authors conducted the fieldwork and collected the data: Red Sea ; Gulf of Aden ; Sea of Oman (AM); Qatar ; Abu Dhabi ; Kenya . FT conducted laboratory work and performed statistical analysis. FT led the writing with assistance from all authors; all authors contributed to revisions of the manuscript.


For infectious diseases, the probability that an animal gets infected depends on its own susceptibility, and on the number of infectious herd mates and their infectivity. Together with the duration of the infectious period, susceptibility and infectivity determine the basic reproduction ratio of the disease (GEBV for GEBV for  Infectious disease transmission in a host population is a dynamic process. The probability that an animal is infected depends on its own susceptibility, on the number of infected contact animals (“group mates”), and on the infectivity of those group mates. The number and composition of the infectious contact animals in a population varies over time, since some animals get infected while others recover. Whereas state-of-the-art epidemiological models take this variation into account, it should probably also be taken into account to make optimal genetic inference. However, most genetic studies use linear mixed models that ignore the dynamics of the transmission process in the population, such as the variation in exposure of susceptible individuals to infectious contact individuals. Moreover, studies on host genetic variability commonly connect an individual’s disease status to its own pedigree or genotype only, and therefore capture genetic effects related to susceptibility (or resistance) only , 2. HoweGenetic inference on infectious diseases can probably be improved by using quantitative genetic models that are founded on epidemiological theory. Moreover, such models would give estimates of genetic variation and breeding values for fundamental epidemiological parameters, such as the basic reproduction number An animals’ infectivity affects the disease status of other animals, rather than its own disease status. Thus, if the observed variation in infectivity has a genetic component, then infectivity is a so-called indirect genetic effect (IGE). IGE can have a considerable effect on the rate and direction of evolution by natural selection, and on response to selective breeding –12. HencTogether, susceptibility, infectivity, and the duration of the infectious period determine the basic reproduction ratio . DD is a claw disorder that affects (mainly) the hind feet of (dairy) cattle , 15. TypThe prevalence of DD is affected by many factors, such as herd management, lactation stage, flooring system, climate, and breed . OptimizThe objective of our work was to quantify the genetic variation in host susceptibility, host infectivity, and Phenotypes for DD were collected on 12 dairy cattle farms in the Netherlands, between November 2014 and April 2015. Two observers (author FB being one of them) visited these farms 11 times with a 2-week interval between visits . On each−15), the missing rate was lower than 0.05, and the minor allele frequency was higher than 0.02. In total, 75,904 SNPs passed the quality control and were included in the analysis.The cows were genotyped with the Eurogenomics 10 K chip. A first round of quality control was performed before imputation, following the standard procedure of the breeding company CRV. A marker  was included only when its call rate was higher than 0.85, the deviation of the observed frequency from the expected Hardy Weinberg equilibrium frequency was less than 0.15, and the minor allele frequency was higher than 0.025. Furthermore, inconsistent genotypes between parents and offspring were set to missing. SNPs that passed the quality control were imputed to a set of 76,438 SNPs based on the Illumina BovineSNP50 chip and a custom chip from the breeding company CRV, with a reference population of more than 1000 animals with genotypes on both chips and the combination of Beagle  and PHASIn this section, we develop a generalized linear mixed model (GLMM) founded on epidemiological principles, to estimate genetic parameters for susceptibility, infectivity and s et al. , Lipschus et al. , Anche es et al. , and Bies et al. .DD is an endemic infectious disease with infections reoccurring in the same animals. DD is transmitted via the environment , where tIn the SIS-model with an environmental route  rate with which a single susceptible claw of cow number of previously infected claws, but these were not weighted according to their infectivity. Thus, our estimates of genetic variation in infectivity use only part of the variation in the infection pressure. This issue is further addressed in the “In the entclass1pt{minimaThe probability that a susceptible claw becomes infected in an observation interval varies among observation intervals, because it depends on the number of infected herd mates, the infectivity of those herd mates, and on the infection pressure coming from the environment. To estimate this probability, we assumed that the transmission rate (probability per unit of time) is constant within the interval, which is the default assumption in disease transmission models. With this assumption, transmission follows a so-called Poisson process, where the number of transmissions within the interval has a Poisson distribution with a mean equal to the product of the rate and the length of the interval; entclass1pt{minimaThe number of cases for each interval (counting process in discrete time) follows a binomial distribution with a probability that follows from a Poisson process within the interval (counting process in continuous time). Therefore, the complementary log–log is the appropriate link function to connect the explanatory variables to the expected value of the observed variable . Thus, tEquation ean  of cow We tested four models Table . Model 1Model 2 included genetic effects for both susceptibility and infectivity:We expected that the interaction between farm and period could be partly confounded with the genetic effect for infectivity, because previous IGE studies showed that omitting group-effects may substantially inflate estimated genetic parameters for infectivity . To inveThe VanRaden , with thVanRaden . We fittVanRaden . Model fWe validated the GEBV to investigate their bias and accuracy with a 12-fold cross-validation on all four models. In each analysis, all the cases  farm. Subsequently, we validated the models using a weighted correlation and a regression of observed records on predicted probabilities , we calculated the genetic variance and GEBV (The additive genetic variance of lated as :\documenThe GEBV for the basic reproduction ratio is the product of the GEBV for susceptibility (ent}1/α) :7\documese farms  and the GEBV are prone to overestimation, as illustrated by regression coefficients of validation phenotypes on predicted GEBV, which are often less than 1 e.g. . To avoiAfter this correction, the regression coefficient of the observed ^R0/σAR0 . We usedThe farm effect was significant (P < 0.05) in Models 1 and 3, but not in Models 2 and 4. For all models, there was a significant effect for period, parity, and months in milk. The probability of becoming infected during an interval increased during the first six periods and stabilized thereafter. The transmission rate parameter increased with increasing parity; it was 21% higher for parity 2 compared to parity 1, and 69% higher for parities greater than 2 compared to parity 1. For months in milk, the transmission rate parameter decreased by 4% with every month in milk. A table of fixed effect estimates and standard errors on the log scale from Model 1 is presented in Additional file Table t}x = 1) . For ModFigure Figure entclass1pt{minimaThe genomic estimated breeding values (GEBV) for susceptibility corrected for bias were approximately the same for Models 1, 2 and 4, and ranged from 0.49 to 2.81; the GEBV from Model 3 ranged from 0.50 to 2.53. A cow with a susceptibility GEBV of 0.50 is genetically about two times less susceptible than an average cow, whereas a cow with a susceptibility GEBV of 2.50 is genetically about 2.5 times more susceptible than an average cow. The GEBV for infectivity showed substantially more variation than the GEBV for susceptibility.We chose to illustrate the variation in GEBV for ur farms , \docume515 Fig.   were lower for Model 3 that included neither a genetic effect for infectivity nor an interaction between farm and period. Anacleto et al.  showed tIn the cross-validation, we estimated a weighted correlation of about 0.2 between the observed and predicted number of cases over the number of susceptible feet. This value can be used to approximate the accuracy of the GEBV  ,8\documedel used . Assumindel used , the accin principle feasible. Further work is needed to quantify the benefits of GLMM based on epidemiological theory over simpler linear models, to better account for the potential genetic variation in infectivity via the environment, and to include genetic variation in the duration of the infectious period.In general, studies on genetic variability of infectious diseases commonly focus on individual differences in susceptibility only, and those differences are estimated with linear models that ignore variation in exposure between individuals. In this study, we used a GLMM founded on epidemiological theory to estimate genetic variability in susceptibility and infectivity. An important advantage of models founded on epidemiological theory is that they provide estimates for epidemiological parameters, such as Genetic variance components for susceptibility and infectivity for digital dermatitis were estimated with four generalized linear mixed models. We managed, only partly, to account for genetic effects on infectivity. We estimated GEBV for the basic reproduction ratio from a (conservative) model including genetic effects for susceptibility only. GEBV for Additional file 1. Fixed effect estimates. Table of fixed effect estimates and standard errors on the log scale from Model 1.


Broussonetia papyrifera (L.) L’Hér. ex Vent. (Moraceae), a dioecious species, was transported by humans from Taiwan to the islands of Remote Oceania. Its introduction and cultivation in Remote Oceania was intentional due to its cultural importance as a fiber source for barkcloth textiles. The aim of this study was to explore the genetic diversity and structure of paper mulberry populations within Remote Oceania in order to infer dispersal patterns that may reflect past human interaction among island groups. We present the integrated analysis of 380 samples (313 contemporary and 67 herbarium specimens) collected in Near and Remote Oceania. Genetic characterization was based on a set of ten microsatellites developed for B. papyrifera and complemented with the analysis of the ribosomal internal transcribed spacer ITS-1 sequence, a sex marker and the chloroplast ndhF–rpl32 intergenic spacer. Microsatellite data identify a total of 64 genotypes, despite this being a clonally propagated crop, and show three major dispersal hubs within Remote Oceania, centered on the islands of Fiji, Tonga, and Pitcairn. Of 64 genotypes identified, 55 correspond to genotypes associated to female-sexed plants that probably descend from plants introduced by the prehistoric Austronesian-speaking voyagers. The ratio of accessions to genotypes between herbarium and contemporary samples, suggests recent loss of genetic diversity. In addition to the chloroplast haplotypes described previously, we detected two new haplotypes within Remote Oceania both originating in Taiwan. This is the first study of a commensal species to show genetic structuring within Remote Oceania. In spite of the genetic bottleneck, the presence of only one sex, a timespan of less than 5000 years, and asexual propagation of this crop in Remote Oceania, we detect genetic diversity and regional structuring. These observations suggest specific migration routes between island groups within Remote Oceania.Paper mulberry,  The last stage of human colonization of uninhabited regions of our planet, involving the settlement of the remote islands in the Pacific Ocean, has fascinated many, from early European explorers to historians, archaeologists, and, more recently, geneticists and Pacific communities. Archaeological research has allowed for the construction of a chronological framework for the main migratory waves and associated cultural history . The humColocasia esculenta) [Musa spp.) [Artocarpus spp) [Dioscorea spp) [Piper methisticum) [(Ipomea batatas) [Broussonetia papyrifera (L.) L’Hér. ex Vent. (Moraceae)), a dioecious plant native to China, Taiwan and Indochina [B. papyrifera is reproduced asexually in the Pacific islands, a common practice for many crops in Oceanian arbori- and vegeculture [While linguistic studies have determined that the origin of the Austronesian languages spoken in the Pacific region is in Taiwan , geneticculenta) , bananassa spp.) , 12, 13,pus spp)  yams (Direa spp) , kava (Pisticum)  and sweebatatas)  have beendochina , where indochina , was alsndochina , 21. Papeculture . Its sinB. papyrifera, using different molecular markers, to infer human movements in Oceania. Using the internal transcribed spacer (ITS) ribosomal region, it is possible to distinguish between Asian and Oceanian samples through a single nucleotide polymorphism [ndh-rpl32 hypervariable chloroplast DNA marker showed that the most common haplotype (cp17) of paper mulberry found in the Pacific islands from New Guinea to Easter Island, and the most likely to have been introduced by the early colonizers, showed a tight phylogeographic link to Taiwan, providing the first genetic proof based on plants of the “Out of Taiwan” hypothesis of the Austronesian expansion [We have previously analyzed contemporary specimens of morphism , 24. Anamorphism  that indmorphism . Recent xpansion . Also, uxpansion . A fundandh-rpl32 cpDNA region [Here, we report the combined analysis of a total of 380 samples (313 contemporary and 67 herbarium accessions) based on the ITS-1 region , 24, a sA region  and nineA region  previousThe use of microsatellites is warranted because of their abundancy, robustness, high reproducibility, codominant nature and randomly dispersed polymorphisms in the genome. Therefore, they are efficient markers for population genetic studies , 29, 30.A total of 380 specimens, of which 313 correspond to contemporary plants and 67 to herbarium specimens, were analyzed. Samples were collected from 39 different localities, 34 from Pacific islands, four from the native range in Asia, and one from South America , S1 Fig.Genomic DNA from contemporary samples was extracted following a CTAB extraction protocol as reported previously , 31, 27 ndhF-rpl32 chloroplast DNA region using the primers and protocol as described in Chang et al. [B. papyrifera  as described in Peñailillo et al. [Samples were analyzed using four different molecular markers: i) the nuclear ITS-1 region, following the protocol described by González-Lorca et al. ; ii) theg et al.  with a so et al. . Primerso et al. , using rndhF-rpl32 regions. The amplified samples were sequenced at Macrogen Inc. . Electropherograms were checked using BioEdit 7.1.3.0 software [https://blast.ncbi.nlm.nih.gov) and identified as B. papyrifera to avoid possible misidentifications [B. papyrifera ITS-1 sequences were analyzed for the single nucleotide polymorphism (SNP) located at position 203 previously identified by our group [ITS-1 and software  and polysoftware  in CLC Ssoftware , 27. Theur group , 24, 27.The amplified samples were analyzed by visual inspection using 1.5% agarose gels. As described in Peñailillo et al. , female ndhF-rpl32 region were obtained as described by Chang et al. [cpDNA haplotypes using the g et al. . Alignmeg et al.  and confg et al. .The amplified samples were analyzed by capillary electrophoresis at the sequencing services from the Pontificia Universidad Católica de Chile  and electropherograms were visualized and analyzed with Peak Scanner v1.0 software (Applied Biosystems). Allele sizes were registered in an Excel spreadsheet, as described by Payacán et al.  and alleK), a burn-in period of 5,000 was used in ten independent runs, and data were collected over 50,000 Markov Chain Monte Carlo (MCMC) replications from K = 1 to K = 15. The probability values were averaged across runs for each cluster. This procedure clusters individuals into populations and estimates the proportion of membership in each population for each individual [K value was determined by the log probability of data (Ln P(D)) based on the rate of change in LnP(D) between successive K values. The optimum K value was predicted following the simulation method of Evanno et al. 2005 [K = 1 to K = 8.In order to establish the number of real populations and the genetic admixture in the data obtained from the microsatellite markers, we performed a Bayesian population structure analysis using the Structure v 2.3.4 software . To detedividual . The K val. 2005  using thal. 2005  with a bTo determine the relationships among genotypes, we constructed a Minimum Spanning Tree (MST) using BioNumerics v.7.6 (Applied Maths NV) as described Payacán et al.  using thA second network was built using EDENetworks  to constGenomic DNA was successfully extracted from all contemporary leaf and herbarium samples using the Lodhi protocol  modifiedUsing the sex marker developed by Wang et al.  and modindhF-rpl32 cpDNA marker. Haplotype cp17 was found in 84 samples , in concordance with previously described results [The 57 herbarium and 35 contemporary samples from Oceania were selected for analysis with the  results . Haploty results , enabledper SSR marker and populations see All 380 samples (313 contemporary and 67 herbarium samples) were successfully analyzed using nine SSR markers. The SSR marker Bro 07 produced erratic amplification with herbarium samples and was therefore excluded from the population structure analyses. In spite of the vegetative propagation of this species in Oceania, a total of 104 different genotypes were identified in the 380 samples based on the nine SSR markers. In the 313 contemporary samples 65 genotypes were identified, while 31 genotypes were identified in 67 herbarium samples. We found eight shared genotypes between contemporary and herbarium samples. Within 339 samples from Remote Oceania, a total of 64 genotypes and 32 private alleles were found. Each SSR presented between one and 16 private alleles . In thisvia Europe. Some of the Taiwanese samples that group in the first cluster exhibit genetic properties of the second cluster. In contrast, within the introduced range, the sole island group that presents samples of both clusters is Hawaii. However, in these samples no significant mix of clusters is observed within any individual, as observed in samples from Taiwan. In particular, specimens that group in the first cluster correspond exclusively to male individuals, while all those grouping in the second cluster are female samples, revealing a different genetic structure within the B. papyrifera population of Hawaii, as opposed to the plants from all other Pacific islands.The Bayesian analysis of genetic populations indicates two genetic clusters . All samA Minimum Spanning Tree (MST) analysis revealed a complex structure among the 64 genotypes found in Near and Remote Oceania and a close relationship between three central nodes , each connected through one mutation .The first node (N1) corresponds to genotype G049  corresponds to genotype G048 and is found in Fiji (one contemporary sample), Pitcairn (three herbarium and two contemporary samples) and Tonga (one contemporary sample). From N2 a total of 23 genotypes are divergent by one to eight mutations. Of these, two genotypes are found in Near Oceania (New Guinea) and ten in WRO . Ten genotypes were found in contemporary specimens from ERO , and one genotype (G084) is found in contemporary samples from Tonga and Hawaii.The third node (N3) is genotype G063, found in Tonga (one contemporary sample), Rapa (one herbarium sample), Hawaii (12 herbarium and eight contemporary samples) and the Marquesas (nine herbarium and one contemporary samples). From N3, a total of 25 genotypes found only in ERO , are derived. N3 presents a complex pattern where 16 genotypes are separated from the central node by only one or two mutations. One of these genotypes is found in Tahiti (four herbarium samples) and Niue (two herbarium samples). Nine genotypes are divergent by four to 15 mutations from the N3 node. This node includes 18 Hawaiian genotypes, where female, male and non-sexed samples are shown in different colors. All five male genotypes from Hawaii (light blue) group on a single branch, separated by 11 to 15 mutations from this central genotype. Most of the non-sexed samples share the same haplotype as or are closely related to female samples. However, one of the Hawaiian samples that could not be sexed differs by at least 11 mutations from the closest female sample. Interestingly, branching from the N3 node, we identified genotype G104, which corresponds to the male plant from the Marquesas. The relationship between the male Hawaiian plants and Asian samples is shown in Analysis using EDENetwork  software based on the Goldstein distance resulted in a network where nodes representing islands or “localities” are linked based on genetic data and geographic coordinates . The anak) is k = 4, with China identified as a central node linking directly to Taiwan and indirectly to Vietnam, all in the native range. The latter branch also includes plants from outside the native range that represent historic (Japan) and modern introductions, corresponding to plants from the Solomon Islands and the male samples from Hawaii. On a separate branch we find samples from New Zealand and Santiago (Chile), which also represent introductions within the last 150 years.In the resulting network we observe that the maximum degree of connectivity (k = 3) that establish a link to Remote Oceania. One of these links is to a herbarium sample from Fiji (Yacata Island) in West Polynesia and the other link connects New Guinea through Pitcairn to all other samples in East Polynesia. The maximum degree of connectivity found in the introduced range is k = 5 present in Pitcairn and in Fatu Hiva (Marquesas Islands). Samples from Pitcairn are connected directly with samples from New Guinea, Fiji, the Cook Islands and two islands from the Marquesas. Samples from Fatu Hiva connect a subgroup of ERO islands, including other islands from the Marquesas, plants from most of the Hawaiian Islands, the Austral Islands and Niue.Samples from Taiwan are the sole link between the native and introduced range in the Pacific. Taiwan is connected directly to samples from New Guinea, in Near Oceania. New Guinea shows two connections (k = 4). These are Tongatapu (Tonga), the island of Hawai’i  and Tahuata in the Marquesas. Four other localities present connectivity of k = 3, corresponding to New Guinea, Wallis, Vava’u in Tonga and Viti Levu in Fiji. The branches grouping localities from WRO have less clonal diversity than branches with localities from ERO, as seen by node shape and the number of branches of the network  and through several steps with other islands in Fiji (Taveuni and Vatulele), Tonga (Vava’u and ‘Eua), Futuna, Wallis, Western Samoa (Savai’i and Upolu) and New Caledonia. Samples from Savai’i and Upolu islands are derived from those from Wallis. New Caledonia plants derive from Savai’i.k = 4) includes several derived genotypes that are associated to other Marquesan islands, to Tahiti, to several islands in Hawaii, to Rapa and Rurutu in the Austral Islands and to Niue.The three other branches from Pitcairn connect to all genotypes from ERO and two genotypes from WRO, found in American Samoa and Niue. One of these branches connects Pitcairn with the Cook Islands. The second and third branches connect Pitcairn with two islands in the Marquesas. One of these branches links Pitcairn to Hiva Oa and to derived genotypes from Rapa Nui and American Samoa. The other branch that links Pitcairn to the Marquesas via Tahuata Island  to a k = 5 node in Fatu Hiva. Fatu Hiva links to islands in Hawaii, Niue, Rurutu  and the Marquesas. One sub-branch from Fatu Hiva connects to Niue. A second sub-branch connects to Rurutu. The third sub-branch connects to Ni’ihau and derived genotypes of female samples from Molokai and non-sexed specimens from Oahu, all from Hawaii. The fourth sub-branch links Fatu Hiva to derived female samples from Ua Huka and also three genotypes in Hawaii  , three islands from the Hawaiian archipelago  and in American Samoa, located in WRO. However, the diversity of the node from American Samoa is misleading since this island is represented by a single sample, and therefore node shape is not representative of greater clonal diversity. One sub-branch from Tahuata connects with the island of Nuku Hiva from the same archipelago and to a derived genotype from non-sexed samples from Lana’i (Hawaii). The second sub-branch links Tahuata to Tahiti. The third sub-branch connects to the female samples from the island of Hawai’i and presents Hawai’i) .B. papyrifera specimens with the aim of understanding its dispersal history in Remote Oceania and inferring the associated human movements. Paper mulberry is unique among the commensal species studied, because it is not primarily a food item, but a source of fiber for making barkcloth textiles which are of great cultural importance [In this work, we present the analysis of portance . This paThe analysis of the samples with the sex marker confirmed that contemporary plants found today from New Guinea to Rapa Nui are all female plants, with the exception of those from Hawaii, where male plants are also present. Male plants in Hawaii share haplotypes with the Asian mainland and Japan, suggesting an independent introduction to this archipelago, probably during historic times . Among tB. papyrifera (NCBI Reference Sequence: NC_035569.1) are available, but not so a nuclear reference genome. The closest genome published is from Morus notabilis (2n = 14) [B. papyrifera  [New sequencing technologies such as Next Generation Sequencing (NGS) have been used to study the dispersal patterns of other species through Remote Oceania, such as rats  and dogs2n = 14) , a Morac 503 Mb)  or a 2C  503 Mb) . The use 503 Mb)  that areUsing microsatellites with a standard PCR approach, genetic diversity within Remote Oceania was revealed, despite the asexual propagation of paper mulberry and the short time scale since its human-mediated introduction into the Pacific . This genetic diversity is evidenced by two kinds of independent analyses, i.e. network analysis based on Bionumerics and Goldstein distance. The first analysis highlights the internal structure of genotypes from paper mulberry populations within Remote Oceania revealing three main dispersal hubs, while the latter analysis shows the degree of genetic closeness of plants from each sampled geographic locality and their connections with the Asian homeland.The inclusion of a large number of contemporary accessions and additional herbarium samples to the ones published in Payacán et al. , shows aThe derived genotypes of N2 include the same regions or island groups that were linked in the former study of herbarium specimens, such as New Guinea and Rapa Nui. Genotype G070, shared by two samples from Tahuata in the Marquesas and several samples from Rapa Nui, is derived by one mutation from the central N2, and gives rise to several genotypes connected to Fiji and Samoa and shared with the Marquesas. This suggests that the islands Tahuata and Rapa Nui have maintained ancestral Pacific genotypes until today. Some of these derived genotypes are also found in New Caledonia, Samoa and Fiji, suggesting that G070 is an ancient genotype. The presence of samples from Rapa Nui in this node may reflect the relatively early arrival or translocation of paper mulberry plants to this island, in spite of its geographic location on the southeastern margins of the Polynesian triangle.Node 3 (genotype G063) includes genotypes from Tonga and localities from ERO. Tonga is the only island group that is represented in the three nodes, reflecting its importance as a dispersal center or interaction hub between West and East Remote Oceania. The derived genotypes from N3 show a large genetic diversity of female plants within the Hawaiian archipelago, and a connection between the genotypes found on the Austral and Cook Islands. In this node one genotype links a group of plants collected at the Musée de Tahiti et des Îles which share a common genotype with plants from Niue (herbarium sample). The most distant samples (by at least 11 mutations) in this node cluster all male specimens and one non-sexed sample from Hawai’i, indicating again that their genetic signature reflects a different history . This noOur results show a loss of genetic diversity in contemporary plants when herbarium and contemporary samples from Remote Oceania are compared. Integration of results from herbarium and contemporary samples was possible with nine microsatellites. One marker (Bro 07) was excluded because it did not amplify DNA from herbarium samples consistently. The analysis of the collection with nine microsatellites supports diversification of clones within Remote Oceania. Our data show that contemporary paper mulberry plant samples are less diverse than herbarium samples, indicative of a loss of genetic diversity over time. Our relatively large collection of 279 contemporary samples from Remote Oceania exhibits 40 genotypes, while the smaller collection of 60 Remote Oceania herbarium samples exhibits 32 genotypes. The ratio between the number of contemporary samples to genotypes is seven to one, while this ratio for herbarium samples is close to two to one (1.9), meaning that in relative terms the smaller herbarium collection exhibits higher genetic diversity. It is difficult to assess the reasons for this loss of diversity, which may be explained by the recent decline of tapa culture during the twentieth century on some islands, replacement of plants on others, local disappearances and, finally, incomplete sampling on some of the most remote islands.ndhF-rpl32 region we found two new haplotypes (cp49 and cp50) which derive from the cp17 haplotype found in all other analyzed samples  colonization between the Marquesas, Pitcairn, the Society Islands, Hawaii and Rapa Nui  with intntury AD . The linntury AD , 63. Larntury AD , in a phntury AD  comparedGenetic data on human dispersal routes within Remote Oceania are scarce, as most human genetic studies have focused on the origins of Pacific peoples from East or Southeast Asia to Near Oceania, rather than on their regional variation or dispersal within Remote Oceania. According to Chapman  the neglR. exulans) mitochondrial DNA from archaeological bones suggests an expansion from the Austral Islands to Rapa Nui [A recent re-analysis of the genetic relationships and colonization routes inferred from the analysis of Pacific rat  and Fiji represent the genetic link between WRO and ERO. As for the genetic structure within ERO, we distinguish dispersal events that mirror migration and interaction networks as observed from archaeological data, such as the repeated relationship between the Marquesas and Hawaii, Marquesas and Central Polynesia (Tahiti) and with Marginal Southern Polynesia . In this way, the genetic information confirms that the Marquesas archipelago is an important connecting hub within East Remote Oceania, consistent with archaeological and linguistic data. Despite the short time span in which the dispersal of paper mulberry occurred and the clonal nature of its propagation in the Pacific, the genetic study of the dispersal of this fiber plant is a useful proxy for the Austronesian expansion, from its native region (Taiwan) throughout introduced range in the Pacific. The genetic connections detected in contemporary and herbarium samples from paper mulberry reflect prehistoric human movements between multiple islands in Remote Oceania and, to date, provide a more comprehensive picture than other model species. In contrast, textiles made of the bark from this plant may represent evidence of exchange networks of prestige objects, which are unlikely to persist in the archaeological record. Therefore, the genetic analysis of ethnographic textiles from museum collections represents the closest approach to past plant material from this species and could add complementary insight focused on eventual exchange networks.S1 Fig(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 FigMap constructed based on data from the Minimum Spanning Tree using Goldstein distance. The black dotted line divides Near and Remote Oceania. Lines indicate genotype connections and not directionality. The red line shows connections between the native range and the introduced range. Green lines indicate connections among ERO islands and /or island groups. Purple lines indicate connections among WRO islands and their connection to ERO. The blue dotted lines indicate the connection between Hawaiian Islands and the native range in recent times.(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S6 Table(DOCX)Click here for additional data file.S7 Table(DOCX)Click here for additional data file.S1 Text(DOCX)Click here for additional data file.