Rhagoletis cerasi  (Diptera: Tephritidae). This tephritid is distributed throughout Europe and western Asia, and has adapted to two different genera of host plants, Prunus spp. (cherries) and Lonicera spp. (honeysuckle). Our data imply that geographic distance and geomorphic barriers serve as the primary factors shaping genetic population structure across the species range. Locally, however, flies genetically cluster according to host plant, with consistent allele frequency differences displayed by a subset of loci between Prunus and Lonicera flies across four sites surveyed in Germany and Norway. These 17 loci display significantly higher FST values between host plants than others. They also showed high levels of linkage disequilibrium within and between Prunus and Lonicera flies, supporting host‐related selection and reduced gene flow. Our findings support the existence of sympatric host races in R. cerasi embedded within broader patterns of geographic variation in the fly, similar to the related apple maggot, Rhagoletis pomonella, in North America.Elucidating the mechanisms and conditions facilitating the formation of biodiversity are central topics in evolutionary biology. A growing number of studies imply that divergent ecological selection may often play a critical role in speciation by counteracting the homogenising effects of gene flow. Several examples involve phytophagous insects, where divergent selection pressures associated with host plant shifts may generate reproductive isolation, promoting speciation. Here, we use ddRADseq to assess the population structure and to test for host‐related genomic differentiation in the European cherry fruit fly,  The general role of natural selection per se is largely undisputed, but the population genetic details, especially the role of gene flow, continues to be hotly debated  populations across environments with contrasting selection pressures  contemporary gene flow between populations in different environments; and (b) intrinsic forms of RI, such as Bateson‐Dobzhansky‐Muller incompatibilities, are largely lacking between populations. Direct evidence for migration and gene flow between habitat‐ or host‐associated populations can come from mark‐release‐recapture studies  (Diptera: Tephritidae), is an important agricultural pest distributed throughout Europe and western Asia. This tephritid fly is responsible for considerable economic loss due to the damage it causes to cherry production  and Lonicera spp. (Caprifoliaceae) , showed significant, although modest, allele frequency differences between Prunus‐ and Lonicera‐infesting flies  after accounting for the effects of geographic distance. This same locus has also been shown to be differentiated between the ancestral hawthorn‐ and derived apple‐infesting host races of R. pomonella  where Prunus spp. and Lonicera spp. host plants co‐occurred and were separated by distances of 0.9 to 3.2 km . A BluePippin System  was then used to size‐select fragments 400–500 bp in length from the pooled and purified PCR products. Final fragment size range of the library was assessed using a Bioanalyser 2100  and the DNA concentration of the library was measured on a Qubit 2.0 Fluorometer . Finally, the library containing all 192 samples was 100 bp paired‐end sequenced on a single lane of an Illumina HiSeq2000 machine .We used genomic DNA from Hoekstra  double dHoekstra . Briefly2.3stacks version 1.4  was rejected with α = .05 . Because this study relied on de novo fragment assembly, only forward reads were included in all analyses described below. In brief, we generated consensus sequences de novo using 2.4He, observed heterozygosity Ho, nucleotide diversity π and inbreeding coefficient FIS) and average pairwise FST values among geographic sites and between host plants, based on the populations module in stacks . Confidence intervals (0.95) were calculated from individual FST and FIS values for each SNP for each population using the groupwiseMean function of the rcompanion package .The second data set with 2,494 SNPs was used to calculate population genomic summary statistics  assessed from K = 2 to K = 15, with a burnin period of 100,000 and 100,000 Markov Chain Monte Carlo (MCMC) repetitions after initial burnin. To estimate the number of clusters within our data, we used the delta K approach described by Evanno, Regnaut, and Goudet  was performed using the R package Jombart, . The scaR. cerasi SNP data to generate phylogenetic trees. A bootstrapped maximum‐likelihood tree, with individual samples at its tips, was constructed using the SNPhylo pipeline with MAF < 0.05 and 1,000 bootstrap replicates , using the r package adegenet. Values of mean probabilities for correct assignment of individuals to populations in DAPC were compared with simulated values  to determine if they exceeded the upper 0.95 quantile of the randomised distributions. Randomisations were computed by randomly shuffling the host population labels and running DAPC. Finally, we ran DAPC on all four sympatric pairs of host populations (K = 8) to compare levels of geographic vs. host‐related clustering (see run details in Table S4). The advantage of DAPC is that this method works well with structured and nonparametric data and allows extraction of the highest contributing SNPs to the axes of greatest differentiation between groups.To assess the genetic relationship between 2.7Prunus and Lonicera flies across pairs of sympatric sites. Correlations were calculated for four different SNP sets representing: (a) all loci, (b) only loci showing significant  allele frequency differences between Prunus and Lonicera flies at both geographic sites being compared, (c) only loci showing significant allele frequency differences between Prunus and Lonicera flies in at least three of the four total sympatric sites, and (d) loci showing significant allele frequency differences between Prunus and Lonicera flies at all four sympatric sites.To examine the degree to which the same loci contributed to host‐related differentiation across sites, we calculated and compared correlations of local SNP allele frequency differences between Prunus and Lonicara flies at each site were estimated for the more inclusive, low coverage set of 23,964 SNPs (see above) using genotype probabilities as:n = number of individuals, g1:3 =  , and p(jg|iD) is the probability of genotype j given the sequence data D at locus i, as estimated in GATK , and the statistical significance of allele frequency differences between hosts was estimated as described above. Then numbers of significant loci shared among geographic sites were calculated. We performed 10,000 permutations to generate a null distribution of counts of shared loci.2.8bayenv2 , where Prunus = 1 and Lonicera = 0. The program produces Bayes factors for each SNP, which represent a ratio between support for H1  and support for H0 , and Spearman's rho values, which represents the magnitude and direction of the association. We selected the top 1% of SNPs that contained the highest Bayes factors conditioned that they were also within the top 10% of rho values as candidate loci.bayenv2 runs on the German and Norwegian populations to test whether the same outlier SNPs are associated with host plant in both these regions. bayenv2 was run as described above except on 2,294 SNPs in Germany and 2,094 SNPs in Norway, due to the exclusion of monomorphic markers after subsetting the data.As flies tended to cluster according to host affiliation within Germany, but according to geography when comparing Norwegian flies  Prunus flies, and (c) Prunus and Lonicera flies pooled together. Only those SNPs previously identified as being associated with host plant in at least two of the three analyses performed  were included in LD calculations. We also calculated the mean LD for all 2,336 called SNPs. To test whether candidate loci displayed higher LD than the background LD of all SNPs, a permutation test was conducted by randomly drawing 17  SNPs and calculating LD 1,000,000 times. This step was repeated for each host plant separately and for Prunus and Lonicera populations pooled together at each site. The LD.measures command from the ldcorsvr package  through either physical linkage  compared to those in Iran, Portugal, Italy or Norway  and were positively related to geographic distance between sites, supporting a pattern of IBD .Higher observed than expected heterozygosity was seen at all geographic sampling sites, whereas ns Table . German ay Table . Pairwis3.2structure implied the existence of five different genetic clusters (K = 5) of flies largely defined geographically as: (a) Iran, (b) Portugal, (c) Italy, (d) Norway, and (e) the German and Austrian sites . Individuals sampled from Germany formed a cohesive cluster under all K values . Additionally, there was some evidence for shared ancestral polymorphism or recent admixture between German sites and Austrian, Portuguese and Norwegian sites.Bayesian cluster inference implemented in s Figure a. AlthouThe same general pattern of geographic genetic differentiation was observed in the PCA, with Iran, Austria, Germany, Portugal, Italy and Norway forming separate clusters Figure b. In addThe ML phylogenetic tree further supported the clusters inferred from structure analysis and PCA. Most flies from Iran, Austria, Portugal, Norway, and Italy were on different and distinct branches from one another with high bootstrap support Figure c. German3.3FST values among geographic sites and flies affiliated to different host plants). Nevertheless, several lines of evidence supported the existence of significant host plant‐related genetic differentiation between Prunus and Lonicera flies. First, a partial Mantel test indicated a significant effect of host plant on genetic differentiation between Prunus and Lonicera flies, while controlling for geographic variation .The magnitude of host‐related genetic divergence was generally less than divergence among geographic sites  clustered by host plant rather than by geographic location  separated flies by geography (Norway vs. Germany), the second discriminant function (10.33% of the variation explained) separated flies by host‐affiliation  to 0.791 ± 0.056 SE . It is important to note that assignment probabilities and the identification of host plant‐associated SNPs were conducted in separate DAPC analyses. When running DAPC on all four sympatric sites, the first discriminant function reflects geography, while when running DAPC on each sympatric site separately the first discriminant function reflects host association.Third, DAPC including only the four pairs of Prunus and Lonicera flies for all 23,964 SNPs across the four sympatric field sites was r = .113 , 84% (range 74.63%– 92.54%) and 88% (range 75%–100%) of the time for SNPs displaying significant host‐associated variation in both populations being compared, and in at least three and all four sympatric sites, respectively . These SNPs were then compared to the top 1% (n = 24) of SNPs that loaded most strongly onto the host race discriminant function of the DAPC . Due to the small number of SNPs queried, whose BLAST hits also included uncharacterized loci, no gene ontology terms were found enriched using the DAVID gene functional classification tool  largely returned mRNAs for 3.5n = 2,336 loci) separately both within and between Prunus and Lonicera flies at each of the four sympatric sites  ranged from 0.213 to 0.548. Mean r2 values for simulated null distributions were very similar to values observed for all SNPs, while observed LD values of host‐associated SNPs in most cases lay far outside the range of the null distribution  that R. cerasi populations appear to be genetically diverging based on their use of either Lonicera or Prunus host plants, and (b) that ddRADseq has increased resolution compared to microsatellites previously used in this system for inferring signatures of host‐associated genetic differentiation.In the current study, we used ddRADseq on natural 4.1Rhagoletis cerasi flies sampled in Europe and Iran genetically grouped into five main geographic clusters, which exhibited an increase in genetic isolation with geographic distance. Several factors most likely contributed to the formation of this pattern. Adult R. cerasi flies have short dispersal distances  s Nosil, . We cannPrunus and Lonicera flies, including the same sympatric samples in Ober‐Ramstadt (DE), based on 13 microsatellite loci. That the highly polymorphic microsatellite loci failed to detect signatures of genetic divergence suggests: (a) that genomic divergence across geography and hosts may be limited to localized regions of the genome not surveyed by the microsatellites, and (b) that gene flow is most likely ongoing and sufficient to homogenise regions of the genome not under geographic selection or divergent selection between sympatric Prunus and Lonicera flies. Schwarz et al. . Such patterns of imperfect host fidelity suggest the potential of ongoing gene flow in the field. Finally, it is also possible that endosymbionts could restrict gene flow between the Prunus and Lonicera host races. If the endosymbiont, Wolbachia, infects Prunus flies at higher levels than it does Lonicera flies, which appears to be the case  largely returned mRNAs for t et al.  identifiPrunus‐ and Lonicera‐infesting R. cerasi flies, similar to the host races of R. pomonella. Furthermore, our results indicate that both shared standing genetic variation and new mutations specific to local sympatric sites may have contributed to this host shift. Many questions remain and much further work needs to be done on the system to fully understand the nature and interaction of factors generating the patterns of geographic and host‐related genomic differentiation observed in R. cerasi and their relationships to RI and speciation. Our results add to a growing list of potential examples of ongoing ecological divergence.In conclusion, we present genetic evidence supporting the existence of host races of C.S. and H.S. designed the study, and V.B. H.S. conducted the library preparation. V.B. generated and analysed the data with guidance from G.J.R. and J.L.F., V.B. led the writing of the manuscript. All authors contributed to interpretation of results and critically revising the manuscript. Click here for additional data file.


This paper addresses the question of how a sustainable urban development can be supported through simple measured quantities in the context of the specific provision of green space and open space. The specific provision of green space is analysed based on a combination of six indicators that describe, on the one hand, the access of inhabitants to green spaces and on the other hand, the settlement character as well as the strong anthropogenic imprint on the urban landscape. The indicators were calculated and combined in a 9-cell matrix for classifying the areas studied. The implementation was carried out at two scales for all German cities with at least 50,000 inhabitants as well as exemplarily for city districts of eight big cities. The calculated indicator values for representing green characteristics decrease with increasing number of inhabitants, whereas the opposite relationship was obtained for the indicators of the grey characteristics. We show how the approach provides an in-depth morphological assessment of German cities ranking their scores from low to the high presence of grey/green characteristics. In the past decades, the land use development in many parts of the world has mostly been characterised by a steady increase in settlement and transportation areas and is contrary to the principles of sustainability ,3,4,5,6.–promotion of building development in the inner area of settlements )–urban development funding programme ‘Zukunft Stadtgrün’ (‘Future Urban Green’), protection and development of functional urban ecosystems and their performance capacity ,16–reduction of the land consumption for settlement and transportation uses ,18There is a rising demand for residential space per capita, an increase in numbers of inhabitants due to migration, as well as the designation of industrial and commercial areas in the open space, sometimes despite existing urban fallow and converted areas, not only in Germany ,10,11. TThe promotion of building development in the inner area of the cities can lead to a local decrease in green spaces and also to a reduced interconnection of green spaces ,16. As aA sustainable urban development strives for an optimal relationship between grey and green characteristics in order to ensure the respective functionalities. In particular, buildings and streets that provide public service functions  are part of the grey areas. Spaces characterised by vegetation  and bodies of water form the green characteristics, which makes a contribution to biodiversity and provides multiple ecosystem services such as air filtering, recreation, flood retention  using the example of big German cities. The indicators are intended to be updatable (suitable for monitoring), but due to data availability and rising model complexity, changes over time are not treated in depth in the paper.Taking into consideration potential trade-offs between making the framework complex and keeping it simple as well as providing meaningful insights from a scientific and practical point of view is challenging. In this respect, the combined indicator approach for characterising cities/city districts with 3 sub-indicators for ‘green’ and 3 for ‘grey’ characteristics that are discussed in the paper differs from even more complex approaches such as the ‘Green Cities Index’. The Green City Index methodology was developed by the Economist Intelligence Unit (EIU) in cooperation with Siemens and measures cities regarding to their environmental performance on approximately 30 indicators across eight to nine categories depending on the region . Our mulIn practice, mostly indicators such as ‘accessibility of urban green space’  and ‘green space per inhabitant’ are used as quantitative measures (indicators) for characterising the provision of the population with public green spaces in the urban space ,47,48,49Complementarily, quantities are established that tend to represent the grey characteristics, such as ‘settlement density’ or ‘soil sealing’ . The strIn the following, a combination of six measures is proposed in order to characterise all German cities with at least 50,000 inhabitants and, exemplarily, city districts of eight big cities in their specific provision of green space and open space. Relevant indicators were selected that are available at the federal level or are easy to acquire and updatable ; Table 1(i1) ‘accessibility of urban green space’ captures the population that can reach both green spaces ≥ 1 ha within walking distance (accessibility of nearby green spaces) and green spaces ≥ 10 ha at medium distance (accessibility of larger green spaces at a medium distance) and places it in relation to the total population. For more methodological explanations, e.g., how the data about recreational areas were selected, see Grunewald et al. [The indicator d et al. . In gened et al.  for conc(i2) ‘green space provision—settlement’ captures all green spaces with recreational function  in the vicinity (300 m) of predominantly inhabited and contiguously built-up areas and relates them to the number of inhabitants of the respective municipality. On the other hand, (i3) ‘green space provision—total’ determines all green spaces with recreational function within a municipality and relates them to the total number of inhabitants [The provision of green space per inhabitant was implemented in two forms. On the one hand, abitants .Generally, in our approach higher indicator values i1, i2, and i3 indicate that the provision with green space is better ensured in the area under consideration.(i4) ‘soil sealing—settlement’, which describes the proportion of the area in which the soil is covered or sealed by partly permeable  or impermeable materials . The indicator (i5) ‘settlement density’ measures the number of inhabitants in relation to the inhabited settlement area ((i6) ‘hemeroby’ measures the distance between the current vegetation and an assumed final state of self-regulated vegetation in the complete absence of human intervention [For describing grey areas (or the provision of open space), our measurement approach draws on the indicator ent area . This inrvention ,53.For the indicators i4, i5 and i6, we assume in our measurement approach that the area analysed is more strongly characterised by grey areas the more sealed it is, the denser the settlement area is and the less it is close to nature ,55.ATKIS Basis-DLM  is the central data base for the national ecosystem services assessment in Germany . It contwww.eea.europa.eu/data-and-maps/data/urban-atlas). They are particularly suitable for European comparisons  represent an alternative to the Basic-DLM . The. Thewww.s  are additionally taken into account in the analysis. In addition, geometries are required as reference points for the calculation of indicators in the geographic information system (GIS). Data used in our analysis are listed in the following:–ATKIS Basis DLM: data set 2015, source: Federal Agency for Cartography and Geodesy (Bundesamt für Kartographie und Geodäsie—BKG)–Hemeroby 2012: indicator Hemeroby 2012 (grid 100 m), source: IOER Monitor, own calculations–Imperviousness 2012: Indicator Imperviousness 2012 (grid 100 m), source: IOER Monitor, own calculations based on Imperviousness Data 2012 of the European Environment Agency–Administrative city boundaries : administrative boundary geometry VG25, source: BKG–Administrative city district boundaries : OSM Boundaries Map 4.1, source: OpenStreetMap (https://osm.wno-edv-service.de/boundaries/)–Polygon features of basic raster geometries (INSPIRE grid 100 m), source: IOER, own calculations–Population raster of the 2011 census, size of raster cells 100 m, source: Federal Statistical Office –The municipal directory information system (Gemeindeverzeichnis-Informationssystem), source: Federal Statistical Office .Grid data on hemeroby and soil sealing from 2012 published in the IOER Monitor —(i6) described above are first calculated, and subsequently the areas studied are sorted in descending order according to the indicator values determined . In the n = 187), 3 classes of city size and their measurements are identified: cities > 500,000 inhabitants , cities > 100,000–500,000 inhabitants , cities ≥ 50,000–100,000 inhabitants .The calculation of the indicator values, carried out for all German cities from 50,000 inhabitants upwards with the same data basis and calculation steps, allows for a comparative characteristic at the city level (statistics in 1) and 87.55 (Q3). Norderstedt (min = 49.5), Erfurt (52.9) or Hanau (53.3) have particularly low values.The mean accessibility of urban green space (i1) amounts to approx. 80% of the German city population. The cities of Bayreuth, Dinslaken, Aschaffenburg, Dorsten or Siegen are representatives (median = 80.4). Cities with a particularly good accessibility of green space include some towns and medium-sized cities such as Wetzlar (99.1), Gießen (98.5), Schwerin (97.7), Ulm (96.0) or Bonn (95.4). 50% of German cities exhibit a value between 71.05 (QThe cities of Viersen or Aschaffenburg (median = 128.7) are characteristic for the provision of green space in the settlement area (i2). Gummersbach (max = 902.4) is an extreme positive example. It is followed by Stolberg (447.7), Passau (410.5) and Siegen (405.4). Cities with more than a million inhabitants like Munich (min = 36.1) or Berlin (41.9) but also Frankfurt am Main (36.5) exhibit rather low values for this indicator.2 per inh., the city of Munich also represents the minimum with respect to the total provision of urban green space (i3), followed by Herne (74.0) and Ludwigshafen (83.6). The cities of Bottrop (median = 346.8), Aachen (353.4) or Heidelberg (345.5) represent typical examples of green-space provision. The less densely built-up cities of Arnsberg (max = 1942.2), Brandenburg an der Havel (1926.5) or Baden-Baden (1924.4) are extreme positive examples.With 46.1 mCities with a particularly good supply with respect to the indicators i1 to i3 include Dessau-Roßlau, Wetzlar, Wesel, Goslar, Passau, Schwerin, Kaiserslautern, Brandenburg an der Havel or Baden-Baden. By contrast, big cities such as Frankfurt am Main, Leipzig, Mannheim, Krefeld, Berlin or Hanover exhibit a lower supply. Concerning green-space provision , one predominantly finds that the smaller the cities, the higher the green-space provision. On the other hand, the mean accessibility of urban green space (i1) is relatively similar for all classes of city size.The value range of the proportion of sealed area relative to the inhabited settlement area (i4) lies between 23.4% and 66.9% in German cities. In the mean, nearly 50% of inhabited settlement areas are sealed. Typical examples include the cities of Reutlingen (median = 48.4), Konstanz (48.3), Bonn (48.3), Mülheim an der Ruhr (48.5) or Wolfsburg (48.8). In Germany, Rüsselsheim (max = 66.9) has the highest proportion of sealed area in the settled areas, and Gummersbach (min = 23.4) the lowest.The mean settlement density (i5) is 148.6 inhabitants per hectare of predominantly inhabited settlement area. Cities in the Rhine-Main metropolitan region exhibit the highest values of settlement density: e.g., Frankfurt am Main (max = 317), Mannheim (291), Offenbach (290), Ludwigshafen (277). Minden (min = 73), Gummersbach (73), Detmold (74) or Celle (76) are examples of less densely built-up cities. The typical settlement density amounts to 138 inh./ha (median) and is represented by the cities of Leverkusen, Düren, Gladbeck or Landshut.1, examples: Wiesbaden, Detmold, Offenburg) and 5 .The values of hemeroby (i6) in German cities with ≥50,000 inhabitants vary between 3.5  and 5.5 . 50% of the German cities have a hemeroby value between 4.4  seems to be indicated. As expected, bigger cities have a higher proportion of soil sealing, have a stronger anthropogenic impact (hemeroby) and are more densely settled compared to cities with less than 100,000 inhabitants.n = 144) of eight selected big cities, are represented in The statistical parameters of the indicators, calculated for all city districts  as well as VII  . In the A categorisation of the cities at the federal level, underpinned by three examples of typical representatives of types I (Berlin), V (Paderborn) and IX (Passau), illustrates the results in maps . It can While the eight big cities studied are characterised by ‘dominance of grey characteristics’ , the disFor example, Botnang, a district of the city of Stuttgart, exhibits a high accessibility of green space (i1) and at the same time a very low provision with public green (i3). The degree of soil sealing (i4) there is comparable to that of the rather rural district of Hedelfingen, while the green space per inhabitant within the unit of investigation (i3) is comparable to that of Stuttgart Mitte. This shows that the city district of Botnang is a loose settlement with low density that is almost completely characterised by inhabited settlement areas and thus tends to exhibit a low need for public green spaces. This is also apparent from the classification result ‘balanced level’ (class V).A ‘green’ urban development figures prominently on the political agenda in Germany and other countries ,38,39. IPlanners and policy-makers need objective and comparable measures and indicators reflecting urban green space provision across multiple communities. Such indicators should be evidence-based, unanimously defined, and universally applicable across various populations and environmental conditions. Green space characteristics refer to type, size, and quality of green spaces and the use functions that they allow . UsuallyQualitative recommendations must complement quantitative benchmarks for urban green. Both are scale-dependent. Therefore, it has to be specified whether they concern an entire city , one or more quarters of a city (city-district level) or an individual green space (site level). At each level, quantitative and qualitative aspects have to be taken into consideration ,44.2/inh. green space in general and designates 50 m2/inh. as preferable [Often, (unspecified) green space proportion indicators such as i2 and i3 measure the share of vegetated area in comparison to the entire city . If theeferable .www.ioer-monitor.de/home/?L=1), a permanent scientific service of the Leibniz Institute of Ecological Urban and Regional Development.The six individual indicators, as well as the combined indicator used in this study, are rather simple, robust and reproducible measures. At a national level, they provide a comparison between the cities. Most of them are monitored via the IOER Monitor of Settlement and Open Space Development ((i1) ‘accessibility of urban green space’ shows that in 2015 nationwide 79.2% of the inhabitants of the cities studied were able to reach at least green spaces (>1 ha) at a linear distance of no more than 300 m  and larger green spaces (>10 ha) at a maximum linear distance of 700 m . The result is easy to interpret, since the closer the degree of provision comes to 100%, the higher the welfare effect for the dwellers [The indicator dwellers . This taThe distances and green space categories for (i1) were stipulated/chosen by the authors and planners in the framework of a research project ,56. In p2 per capita (median). Kabisch et al. [Wüstemann et al.  used theh et al.  show thaGreen space accessibility considers not only the available green space but also how to get there. It can be assessed using linear distance or actual path length ,59. ThesAdding three more indicators  complements the provision of green space with proxies for the grey characteristics and human impact on urban nature (hemeroby). An approximately linear relationship between cities or city districts characterised as ‘green’ and such characterised as ‘grey’ can be discerned . This apThe interpretation of the results is supported by deriving a ranking combined with statistical quantities. In this way, city types can be identified by analysing the indicators of ‘green’ . With respect to the indicators, all cities exhibit a certain profile of green-space provision and need situation, i.e., they can be assigned to a certain type of provision of green space. With the help of the indicators used, structural aspects and connections between provision of green space and ‘grey’ aspects  can be differentiated further. This reflects the spatial heterogeneity and properties of urban ecosystem service supply and demand ,42,44,47The approach used for categorising cities is a practical approach for a combined evaluation of several parameters and helps when discussing the calculated indicators. Thus, big cities sometimes exhibit high values for the accessibility of green space, whereas mostly cities with fewer inhabitants and a large area attain high indicator values for the provision of green space. This shows that the results for green-space provision (particularly for i3) are strongly influenced by the area size and implemented reforms of administrative regions. Cities which have incorporated surrounding municipalities are thus characterised by more green space per inhabitant, since newly added areas mostly exhibit lower population densities and large proportions of free space. By contrast, municipalities that mainly consist of a core city have a lower provision of green space. These problems do not occur with approaches for the accessibility of green space, since areas with high population density and green spaces in the surroundings influence the indicator value more strongly than e.g., areas outside the core city with a lower number of inhabitants.A representative example (Stuttgart) was used to compare how the different indicator values turn out at the levels of city and city districts . OverallIn the context of the selected methodology, the question of further opportunities for study arises with a view to, on the one hand, inspecting indicators of urban green provision in more depth with respect to their spatial, temporal, and statistical characteristics and on the other hand, conducting a multi-dimensional analysis and an assessment with the aim of gaining new and useful insights about the indicators and their relationship structures.The term ‘Urban Data Mining’ characterises a methodology developed for the urban context which serves to discover logical or mathematical and sometimes complex descriptions of patterns and regularities in data sets and to use them to derive and assess findings ,66. ClasIn addition to the applied rank-based classification approach it is possible to use extreme value standardization for each indicator . RegardiIn future, depending on the availability of further data, the set of variables used can be supplemented, the classification methodology can be compared , and changes in land use can be characterised in a multi-dimensional way (dynamic perspective). Moreover, a distinction is to be made between descriptive analyses that look for groups (cluster) and predictive analyses that are more interested in patterns of connections (link), temporal patterns (sequence), rules and dependencies as well as formulas and regularities.The Modifiable Areal Unit Problem (MAUP) needs to be considered when investigating the green space provision of cities and interpreting the results. In correspondence to any spatial analysis, we suggest computing the multi-indicator at different spatial scales. For example, in-depth studies can take place at much finer spatial scales in order to better understand the characteristics for cities of approximately the same sizes . We see Indicator-based assessments of the green spaces are necessary for supporting political decision-makers, planners and practitioners for ecologically sustainable urban development , or further classification methods could be tested .However, it is rarely possible to represent a topic completely using one indicator or even an index, as a compromise needs to be struck between the complexity of the representation of the object of study and the comprehensibility and communicability of the results . The genIt should be critically noted that the 3 selected indicators for green spaces essentially only characterise the provision of the population with public green spaces , but not the actual characteristics of all green spaces in the settlement area. It is obvious that small pocket gardens are also important for the connectivity of green spaces and the biodiversity. In case of better data for the entire of Germany it might be very interesting to take into account all green spaces and to judge about the size, characteristic and quality of public and non-public green spaces. It might be also interesting to analyse co-localisation patterns of green spaces that link the coexistence of a number of non-spatial features in a spatial neighbourhood. Such additional information is valuable both for the practitioner and the data analyst and forms the basis for broader indicator concepts in the future.The measurement approach presented allows a classification, in this study shown for the biggest German cities, according to their provision of green and grey characteristics (dominance green or grey or balanced level, cf. Spatial and urban planning should secure areas for both green and grey infrastructure to a balanced extent and define them in terms of planning. The individual measures of discussed green and grey characteristics can contribute to providing the municipal decision-makers and planners with a basis for argumentation in order to give weight to the goals of a need-oriented, interconnected provision of green space, relative to other goals of urban development ,44,54. T


Uncaria macrophylla Wall. is an important Chinese medicinal herb. Rhynchophylline (RIN) and isorhynchophylline (IRN) are its major active compounds. We investigated the influence of genetic differentiation and environmental factors on the RIN and IRN to find the main influencing factors of their contents and lay the foundation for the following cultivation and breeding. We used inter-simple sequence repeat (ISSR) markers to investigate the genetic diversity, and high-performance liquid chromatography (HPLC) to measure the contents of RIN and IRN in 200 samples of U. macrophylla obtained from nine natural populations, and then to analyze the correlation between genetic differentiation, environmental factors of sampling sites and the contents of RIN and IRN. We found that High intra-population (80.05%) and low inter-population (19.95%) genetic diversity existed in the samples of U. macrophylla. To some extent, genetic differentiation and the contents of RIN and IRN had correlation in individual populations . The RIN and IRN contents were significant negatively correlated with the precipitation in May  and June , indicating that precipitation was the main affecting factor of their contents. Interestingly, the analysis results showed that the RIN content had a significant positive correlation  with the IRN content (they are isomers); the proportion of RIN had a significant negative correlation with the sum of the two , while the proportion of IRN had a significant positive correlation . It meant that, with the total quantity of the two compounds increased, the proportion of RIN decreased and the proportion of IRN increased, illustrating that their conversion exist some regularity. Moreover, the content ratio of RIN and IRN was significant positively correlated with the January precipitation , implying that January may be the key period for the mutual transformation of RIN and IRN. Uncaria macrophylla Wall., a medicinal plant belonging to the family Rubiaceae, is one of the main original plant of Uncariae Ramulus cum Uncis (known as “Gou-teng” in China), as recorded in the Pharmacopoeia of the People’s Republic of China [U. macrophylla grows mainly in Yunnan and Guangxi Provinces [U. macrophylla from various regions and the difference between the highest and the lowest was nearly 10 times. Huang et.al also got the similar results [U. macrophylla is poor, which was bound to seriously affect its clinical efficacy. Therefore, seeking the main factors affecting the contents of RIN and IRN, and then purposefully improving their content and uniformity is necessary to solve the above problems.of China . In Chinrovinces . Gou-tenrovinces , 4. It irovinces –9. HowevU. macrophylla was analyzed by ISSR and the contents of RIN and IRN by HPLC in order to analyze the effect of genetic factors on the content of active components; Furthermore, the ecological factors  of sampling point were collected, and the correlations between the content of main active components (RIN and IRN) and various ecological factors were analyzed in order to screen key ecological factors affecting the contents of RIN and IRN.It was known that the content of active ingredients was influenced by both genetic factors and ecological environment. In this paper, the genetic diversity of By analyzing the relationships between population genetic diversit, environmental factors and the contents of RIN and IRN, we can confirm the influence of biotic and abiotic factors on the contents of RIN and IRN and propose appropriate methods for cultivated location selection and germplasm screening to improve the content of active ingredients and ensure the quality of“Gou Teng” medicinal materials.Uncaria macrophylla Wall. is not currently a protected species in China; therefore, no specific permission was required to conduct the fieldwork.All specimens were collected at locations for which specific permission for entry was not required. None of these locations were protected areas or private land. U. macrophylla from early September to early October, 2016 for chemical examination and genetic analyses  were used in the mobile phase according to a 55%:45% isocratic elution program.Individual specimens were dried in the shade at room temperature. Each dried sample was pulverized, and about 3 g power was weighed accurately and then extracted with 60 ml 100% methanol for 60 min in an ultrasonic bath, during which ice was added several times to keep the water temperature about 4 °C. After adding the loss weight, the extracts were filtered through a 0.45-μm membrane prior to HPLC analysis and determined within 24h. The chromatographic separation was performed on a Waters 600 HPLC system , equipped with a Waters 2998 photodiode array detector, Waters 600 pump, Waters 2707 autosampler, and Waters in-line degasser AF. A Waters XTerra RP-18 column  was also used. The column was kept at 35°C and the flow rate was 1 mL/min. The photodiode array detector was set at 245 nm. Methanol and HU. macrophylla according to their chemical concentration data   was used to examine the relative distributions of different populations of ulation) . Agglomeulation) .Total genomic DNA was isolated from silica gel-dried leaves following the manufacturer’s instructions for the Plant Genomic DNA Kit . The quality and quantity of the isolated genomic DNA was assessed by electrophoresis on 1.0% agarose gels, staining with ethidiumbromide and comparing with a set of known DNA concentration standards, and UV spectroscopy using a NanoDrop Spectrophotometer.Sixty ISSR primers were tested (published by Columbia University), from which only the ten primers that produced relatively more repeatable and distinct fragments were selected. ISSR amplification was performed in a 20 μl volume containing 10–30 ng of genomic DNA template, 1×PCR buffer with 1.5 mM MgCl2, 0.2 mM of each dNTP, 0.5 μM of each primer , and 1.5 U Taq DNA Polymerase . The amplifications were carried out for an initial 4 min at 94°C, followed by 30 cycles of 45 s at 94°C, 45 s at the respective annealing temperature (55–58°C), 2 min at 72°C, and a final 10-min extension at 72°C. The amplification products were electrophoresed in 2% agarose gels and stained with ethidium bromide. The gels were visualized and photographed using the UVItec system . To confirm the reproducibility of the banding patterns, the PCR experiments were repeated twice.Data were recorded in terms of the presence (1) or absence (0) of a band. Only distinct, well-separated bands were included in the analysis. To assess genetic diversity, Nei’s gene diversity index (H), the Shannon index (I), percentage of polymorphic loci (PPL), population diversity (HS), total gene diversity (HT) within populations, and inter-population differentiation (GST) were calculated using the POPGENE 1.32 program ,12. GeneTo eliminate the effects of different scales of measurement, the data were standardized using SPSS software . The contents of the two active components measured by HPLC, and the environmental data gathered from the meteorological stations and field work, were combined and analyzed using correlation tests in XLSTAT 2015 to detect the key factors affecting the contents of the active compounds. The genetic UPGMA tree constructed based on genetic distances among different populations, and the chemical dendrogram constructed according to the distance among the population chemical contents, were compared to determine the correlation between genetic and chemical differentiation.U. macrophylla of the nine populations, we discovered that this broad range was mainly within rather than among the populations ; the proportion of RIN had a significant negative correlation with the sum of the two compounds , while the proportion of IRN had a significant positive correlation   .U. macrophylla populations produced a total of 201 amplified bands, of which 195 were polymorphic (97.01%) . The survey of 200 individuals from the nine (97.01%) . The hig(97.01%) . The totTo represent the genetic relationship among populations, an UPGMA dendrogram was constructed based on their genetic distances . The denTo some extent, a few populations  had correlation between genetic differentiation and the contents of RIN and IRN, while the most populations had no correspondence . In othep = 0.038)  and June , while the content ratio of these two compounds was significant positively correlated with the January precipitation   , indicat= 0.030) . The RIN= 0.030)  Tables.U. macrophylla could reveal the genetic variation within and among populations, and illuminate the genetic structure of U. macrophylla. The value of Nei’s GST was 0.1995, indicating that 19.95% of the genetic variation was among populations and 80.05% was within populations, which meant that genetic differentiation mainly occurred within, and rarely among, the populations. U. macrophylla is a perennial woody liana with a wide distribution in the wild. Its seeds are very small and scatter easily and randomly after falling, which leads to frequent gene flow among populations. Therefore, genetic differentiation has rarely occurred among the populations. Furthermore, the high genetic variation within the populations indicated that U. macrophylla has strong adaptability to environmental changes and strong evolutionary potential, in turn indicating that many individual plants within each population should be collected as a germplasm resource for U. macrophylla.Genetic diversity studies of U. macrophylla was influenced by both genetic factors and environmental factors. The results suggest that we should not only consider the influence of germplasm resources on the quality of medicinal materials, but also the influence of environmental factors present at cultivation locations to obtain medicinal materials containing high levels of active compounds.The cluster graphs of genetic differentiation and chemical composition content showed that individual populations could be clustered into a class both in the two cluster graphs, while the clustering positions of the most populations in the two cluster graphs were not corresponding to each other, indicating that the contents of RIN and IRN in U. macrophylla, in which the greater the precipitation, the more conducive to its growth, making it in a non-adversity state, and further retarding the biosynthesis of RIN and IRN. This suggests that areas with low rainfall could harvest the high quality U. macrophylla materials. Besides, RIN and IRN is a pair of diastereoisomers that can transform mutually [The RIN and IRN contents of the samples in this study were negatively correlated with the precipitation in May and June, i.e., the higher the rainfall, the lower the contents of the two compounds. It is well known that alkaloids are secondary metabolites produced by plants to adapt to the adverse environment. The above results may be attribute to that May and June are the critical period for vegetative growth of mutually , 15. ThePrevious studies had shown that the targets and strength of pharmacological effect differ between RIN and IRN in Gou-teng: IRN has a relatively strong antihypertensive effect, while RIN has relatively strong effects on the nervous system –18. The 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.S8 Table(DOCX)Click here for additional data file.


The impact of climate change is causing challenges for the agricultural production and food systems. More nutritious and climate resilient crop varieties are required, but lack of available and accessible trait diversity is limiting crop improvement. Crop wild relatives (CWR) are the wild cousins of cultivated crops and a vast resource of genetic diversity for breeding new, higher yielding, climate change tolerant crop varieties, but they are under-conserved (particularly in situ), largely unavailable and therefore underutilized. Here we apply species distribution modelling, climate change projections and geographic analyses to 1261 CWR species from 167 major crop genepools to explore key geographical areas for CWR in situ conservation worldwide. We identify 150 sites where 65.7% of the CWR species identified can be conserved for future use. Holly Vincent et al. use species distribution modeling, climate change projections, and geographical analyses of more than 1200 crop wild relatives, identifying 150 key conservation areas most effective in preserving genetic diversity of crop wild relatives. Developing new crop varieties able to withstand climatic extremes, endure altered or increased exposure to pests and diseases, and be more resource efficient requires access to as broad a range as possible of plant genetic resources, and a far greater range than exists today4. Crop wild relatives (CWR), the wild and weedy plants closely related to cultivated crops, are a rich source of novel genetic diversity for crop breeding5. Despite their value for food and agriculture, globally CWR are poorly represented ex situ in gene banks6, although systematic effort to improve ex situ coverage has begun7. Further, only a handful of genetic reserves for active in situ conservation exist8, despite the generally accepted requirement for complementary conservation9 and the particular need to develop CWR in situ activities that enable the conservation of geographical partitioned genetic diversity which retains potential for local environmental-evolutionary adaptation10. Furthermore, existing in situ reserves do not meet the required management standards to maintain CWR populations and their genetic diversity long-term11. The most effective means of systematically ensuring in situ CWR conservation would be to establish a global network of in situ populations actively managed to maintain genetic diversity12. Here, we tackle the in situ CWR conservation deficit and, to our knowledge, for the first time address which sites and CWR populations might most effectively form the foundation for a global network of reserves for priority CWR in situ conservation. The selection of such sites and CWR populations needs to consider climate change resilience, maximize potential CWR taxonomic and genetic diversity inclusion and where feasible, use the existing global network of protected areas to avoid the expensive establishment of reserve sites and minimize the impact of human habitat modification associated for example with agriculture, forestry and urbanization. Addressing these challenges will contribute to achieving globally agreed goals on biodiversity and sustainable development. CWRs are explicitly mentioned in Target 13 of the Convention on Biological Diversity’s Aichi Targets13 and UN Sustainable Development Goal 2 – Ending Hunger, Target 2.5 “maintain genetic diversity of… cultivated plants... and their related wild species…”14.Ensuring global food security now and for the future is one of the greatest challenges of our time. One in nine people worldwide suffer from chronic hungerBrassica cretica Lam. belongs to the secondary genepool of both kale and oil seed rape. A total of 164 species (of the 1425 species—11.5%) had no occurrence records, leaving a total of 1261 CWR species related to 167 crops to analyze. In total, we gathered 136,576 CWR occurrence records with unique coordinates. We modelled the distributions of 791 CWR using MaxEnt, but 67 of these models did not meet our model adequacy criteria. We therefore produced a circular buffer of 50 km around occurrence records for such cases and for the remaining 537 CWR that had fewer than 10 occurrence records to produce an adequate distribution model.We identified a total of 1425 CWR species, related to 167 crops, as priority CWR for improving food security and income generation  Fig. . CWR spe17. Numbers of CWR species per crop type ranged from 15 for citrus fruits to 264 for root, bulb, or tuberous vegetables, which contains crops with large genepools, such as potato and cassava. The number of CWR projected to lose 50% or more of their current ranges by 2070 under 726 CWR/adaptive climate change scenarios were totaled for each crop type; the root, bulb, or tuberous vegetables have the most CWR facing potential substantial distribution loss, with 20 CWR facing over 50% current range loss, followed by cereals with 19 and leguminous crops with 17 CWR. No modelled CWR from grape crops or citrus fruits were found to lose more than 50% of their current distribution. Of CWR that are set to lose more than 50% of their current potential substantial distribution, those of spice crops are the most vulnerable, with 26.7% of all modelled CWR losing distribution by 2070, followed by sugar CWR (14.3%), cereals CWR (13.7%) and beverages (13.6%). Under the consolidated crop types, CWR are not well covered by the existing global protected area network, with grape CWR having the least coverage at 14.7% and CWR of leafy or stem vegetables having the most protected area coverage at 32.8% on average  diversity within the existing protected areas was recorded for each species, then summarized under each crop type. Figure Pennisetum glaucum (L.) R.Br., related to pearl millet; Prunus argentea (Lam.) Rehder, related to almond, and Prunus sibirica L., related to apricot. The top five CWR found to have the highest proportion of distribution in protected areas were: Coffea costatifructa Bridson related to coffee, Ficus glareosa Elmer related to fig, Manihot alutacea D.J. Rogers & Appan related to cassava, Beta patula Aiton and Beta nana Boiss. & Heldr. Both related to beet. If a threshold of 50% or more of CWR genetic diversity within protected areas is considered adequate for genetic conservation, then 112 of the assessed CWR are under-conserved and 91% of CWR are well represented by existing protected areas. However, this existing in situ conservation is likely to be passive, meaning that currently CWR populations located in protected areas are not being actively managed and monitored to maintain their diversity; more active conservation is recommended for these populations to ensure their genetic diversity is conserved18.Individual CWR in general were found to be well represented in current protected areas; only 35 (2.5%) of the studied species related to 28 crops were distributed exclusively outside of protected areas (supplementary data Vicia hyaeniscyamus Mouterde and Zea perennis (Hitchc.) Reeves & Mangelsd.—are likely to lose all their current predicted distribution by 2070. However, a further 83 species are predicted to lose more than 50% of their current range by 2070 and Arachis batizocoi Krapov. & W. C. Greg., Arachis appressipila Krapov. & W. C. Greg., Manihot gabrielensis Allem, Vigna keraudrenii Du Puy & Labat and Oryza nivara S. D. Sharma & Shastry are all predicted to lose over 80% of their current potential distribution. Regarding potential CWR genetic diversity in 2070 based on ELC zonation, 15 CWR are projected to lose over 50% of their current genetic diversity by 2070 through distribution loss and 39 CWR are expected to lose over 50% of their genetic diversity that is currently passively conserved in protected areas. Further details on the in situ gap analysis results for individual CWR are available  and in a complementary fashion considers additional sites outside protected areas where there are CWR/adaptive scenarios combinations not identified within protected areas. The top 10 sites listed in Table 2 globally and would protect 475 CWR species, and 1257 unique CWR/adaptive scenario combinations. Meanwhile, only 0.01% of the world’s total terrestrial area would be required to conserve the top 150 sites presented.While when identifying sites for global in situ conservation of CWR, the top 150 sites Fig.  covering2 worldwide where 829 CWR species related to 157 crops can be systematically conserved in situ. One hundred of these sites are in current protected areas, so theoretically are under some form of existing conservation management, though that management is unlikely to be focused on the genetic conservation of the CWR populations present. The active in situ management of these 150 sites to maintain genetic diversity and their incorporation into a global CWR in situ conservation network would substantially improve the genetic breadth of CWR conservation and substantially contribute to global food security and poverty alleviation as required by the international conservation policy12.Our results identify 150 sites covering ~2000 kmVicia hyaeniscyamus found on the Lebanese/Syrian border. Actions that can contribute to in situ CWR conservation including establishing new protected areas or less formal in situ management sites is required. Unlike existing protected areas managed to preserve unique habitats or rare and threatened taxa, these would be genetic reserves, where the goal is to maintain or enhance the genetic diversity of the priority CWR, rather than species presence alone, irrespective of levels of intra specific genetic diversity. If establishing genetic reserves in existing protected areas, existing site/population management plans would require amendment and/or preparation to specifically address the requirement to maximize genetic diversity maintenance19. Other less formal in situ conservation approaches may also be employed, such as conservation easements, that establish voluntary agreements between conservation agencies and landowners restricting or limiting the development of a site21. This approach could aid the conservation of many CWR, particularly ruderal legumes and grasses, which are often found in disturbed habitats, such as roadside verges, and are often absent from conventional protected areas established to preserve pristine environments.The approach presented here prioritizes sites for conserving multiple CWR within existing protected areas to optimize overall cost/benefit. Protected area management can be adapted for CWR genetic in situ conservation, but the current global grid has gaps and 35 CWR occur solely outside protected areas, such as Zea perennis and Vicia hyaeniscyamus, which are predicted to lose 100% of their current distribution by 2070, and Arachis appressipila, Vigna keraudrenii and Manihot gabrielensis, which are predicted to lose over 50% of their existing range by 2070, should be prioritized for ex situ conservation or even introduction to climate-proofed sites, such as those identified in our analysis. Further work is required to analyze the level of fragmentation CWR distributions are likely to face in the future as this would affect in situ conservation requirements and increase the need to plan for corridors or stepping stones between established reserves to promote migration and to maintain gene flow between populations. It should also be remembered, as recommended in the CWR genetic reserve quality standards11, that all CWR populations conserved in situ should be backed-up in ex situ collections, particularly given that until the global CWR in situ network is established, most CWR users will gain access to diversity via gene bank collections.Our results indicate that the predicted impacts of climate change on CWR distributions vary widely among CWR, even within crop gene pools; therefore, it is important conservation strategies are adapted to individual taxon requirements. CWR such as 4, but it seems unlikely such techniques will be practical in the near term for planning the conservation of as many as 829 CWR taxa throughout their global range. Incorporating actual genetic diversity and characterization data for individual occurrences into conservation planning on such a scale may be possible eventually, but in the meantime the approach taken here offers a practical methodology that can be applied widely in wild plant species conservation planning.The methodology applied in this analysis is innovative, prioritizing sites found within existing protected areas, maximizing overall environmental  diversity coverage and long-term site viability in view of climate change. Genetic diversity data per CWR in the analysis was estimated using environmental diversity as a proxy. Further study and experiments are required to test whether this approach is truly appropriate for such a wide range of taxa. It is noted that the increasing power and decreasing costs of direct measures of genetic diversity will be useful in refining conservation priorities5. The process of targeted global ex situ CWR collection which has begun7 will itself generate additional data for subsequent in situ CWR conservation planning, especially for rare and under-collected taxa. Some crop genepools are particularly under represented, such as citrus fruits, tea and tropical and sub-tropical fruit-bearing trees, possibly due to unresolved taxonomy in the case of Citrus or difficulty in collecting and conserving recalcitrant seeds in the case of tropical fruit trees. It may also be advisable in subsequent in situ gap analyses to weight target taxa, using relative gene pool level22 or IUCN Red List assessment23 of the CWR species to ensure those taxa easiest to cross with the crop or most threatened in the wild are prioritized during analysis.The occurrence dataset used in this analysis highlighted that many CWR are poorly represented in gene banks and herbaria and occurrence databases worldwide, with 164 CWR having no occurrence records at all and a further 470 CWR having fewer than 10; this reinforces the recommendations for increased CWR surveying and conservation24, the restriction to include only global major crops highlights Europe and the Middle East as the main centre of diversity, but the CWR diversity of minor crop of regional or local importance not included in this analysis also require active in situ maintenance. Therefore, analysis of these minor crop of regional or local importance and their CWR diversity is required to identify additional complementary to add to the 150 sites identified here.Our results identify 150 sites where active in situ management might be most effective in maximizing CWR species and intra CWR genetic diversity inclusion and offer the highest chance of persistence under climate change. However, these are not the only sites worthy of active in situ management globally. Here, the basis of the analysis were the priority list of CWR taxa related to 167 global major crops25. However, the establishment of such a network is complex. For example, several of the 150 prioritized sites identified are located on country borders: Israel-Lebanon-Syria, Lebanon-Syria, Armenia-Azerbaijan and China-Myanmar; and two of the highest priority sites are in current conflict regions: Syria and Crimea. Therefore, although we highlight here a global matrix of 150 priority sites for in situ CWR conservation, the establishment of a global CWR in situ conservation network will need to be an incremental effort, starting pragmatically with a few sites/populations, and building upon those with time. Previous published work has identified global priority CWR taxa and broadly where they are located24, and how populations should be managed in situ19, from our analysis, we now know which combination of sites and CWR populations can form an effective network to maximize in situ CWR diversity conservation. It is now an urgent priority to identify existing and novel mechanisms to finance and govern the proposed network, the network that will provide a fundamental basis for ensuring our future food security. Failure to address this challenge is likely to have a devastating outcome for food production and agriculture, further the implications for rural people on low incomes in developing countries could be catastrophic, therefore action to prevent these outcomes is required immediately.Finally, we propose that the CWR in situ conservation sites, along with additional CWR sites of minor crop importance, can most efficiently be managed as a global CWR in situ conservation network that coordinates practical in situ conservation management, fostering stronger partnerships at national, regional and global levels, demonstrates benefits that directly support the ultimate custodians of agrobiodiversity, the local communities found in and around the included sites, and ultimately safeguard for perpetuity this critical resource for use either directly by farmers or by plant breeders and other scientists in crop improvement. Catalyzing better linkages between conservation and sustainable use of agrobiodiversity for the benefits of current and future generations is required26 to determine the ability of CWR to successfully cross with crops and produce fertile offspring. Surrogate concepts were used where there was no hybridization data available24. Occurrence records for all target CWR species were obtained from a global CWR data set of ecogeographic records that is available online at http://www.gbif.org/dataset/07044577-bd82-4089-9f3a-f4a9d2170b2e27. CWR were recorded at the species level due to low availability of occurrence records for intraspecific levels. We removed non-target species, records of cultivated species, occurrence records outside their reported native range, records without coordinates and records with highly uncertain coordinates . Information of native species ranges were obtained from the GRIN-Global and the Harlan and de Wet portals. CWR taxonomic nomenclature in the occurrence record database was standardized to match GRIN nomenclature28.We selected the wild relatives of 167 crops of global importance for food security and farmer income generation, primarily based upon their ability to successfully cross with cultivated taxa and produce fertile offspring, and their known or potential use in plant breeding. We used the gene pool concept29. MaxEnt requires a background area and background points when absence data is not available. Moreover, defining the extent of the background area is key to reduce model overfitting and therefore to improve the performance of species distribution models produced with the algorithm29. We used the native geographic range of each CWR species to determine the background extent of each distribution model and produced ten thousand random background points within this. For environmental drivers, we selected an initial set of 27 edaphic, geophysical and climatic variables for input use in MaxEnt . For climate change projections, we selected thirty global circulation models (GCMs) produced by the Coupled Model Intercomparison Project Phase 5 (CMIP5)  logistic threshold, as this thresholding method has been found to consistently outperform other techniques30. For the CWR models that did not meet the adequacy criteria, or for species with fewer than 10 unique occurrence records, we opted to produce a 50 km circular buffer surrounding each individual georeferenced occurrence record to represent the potential distribution31. Only the current climate species models that met the validation criteria were projected onto each individual GCM. Then, we averaged all GCMs per species to produce a future ensemble model. The MAXTRSS threshold was again used to produce binary presence/absence distribution maps. We compared each future distribution against the current distribution model to obtain maps of geographical areas that are likely to remain climatically stable per species, and thus can be considered as suitable areas for long term in situ conservation of CWR.We projected each potential distribution model to baseline data for the period of 1960–1990 . We then overlaid the ELC maps with the current potential CWR distributions to determine the breadth of adaptive scenarios per species, and thus potential genetic diversity.The genetic diversity of individual populations must be considered to ensure maximum coverage in protected areas, prevent genetic erosion in the wild, and ultimately to effectively conserve CWR in situ for future utilisation. Given the limited availability of molecular data for the CWR species selected in the study, we created an ELC map for each CWR, using species distribution models as a proxy to estimate potential genetic diversitywww.protectedplanet.net (Downloaded 25/05/2016). Protected areas represented as point data were discarded and individual protected area polygons were transformed to produce a global presence/absence raster of protected areas. Then, we estimated the proportion of current and future potential distributions, including corresponding adaptive scenarios, within protected areas for each CWR and then summarized these per crop type. Finally, we compared the proportion of CWR area within protected areas and current adaptive scenarios that are likely to be lost due to climate change in the future.We estimated the number of current and future distributions of CWR that are encompassed within established protected areas worldwide, to estimate the status of CWR species under passive in situ conservation, and the likely geographical losses under a stringent climate change scenario. For this, we obtained a comprehensive spatial dataset containing the geographical location of the world’s protected areas from 33, resulting in a final species penalty factor of one for all species, to ensure equal chance of selection. (3) We created the planning unit versus species representation file by overlapping the distribution maps of each CWR/adaptive scenario combination with the planning unit file. We used the current distribution models for species with predicted full loss of current range due to climate change, or where a valid MaxEnt model was not produced. For species with a valid current and future MaxEnt distribution model, we used only the geographical distribution that is predicted to remain climatically stable in the future. (4) For the boundary file, we listed the numerical identifiers of the horizontal and vertical neighbours of each terrestrial planning unit cell. We added this file to improve the spatial clumping of selected sites, as it is often easier and more cost effective to conserve closely located sites rather than dispersed ones. (5) For the input parameters file, we set the Marxan scenario to perform 100 runs of 100,000,000 iterations. The boundary length modifier, which helps to produce spatially clumped network of potential conservation sites, was calibrated using the standard technique33 and set to 0.001.We used Marxan, a widely used conservation planning software, to determine the most effective global reserve network to conserve all target CWR species and their individual adaptive scenarios. To run Marxan, we prepared the following input files: (1) The planning unit file was created by producing a terrestrial global grid (5 km × 5 km grid cell resolution at the equator), where each grid cell was assigned a unique identifier. We assigned a planning unit cost of 10 to grid cells that overlapped protected areas, and 50 to grid cells that did not overlap with protected areas. Lower cost units are prioritized as conservation units. Furthermore, we prioritized every planning unit grid cell that overlapped a protected area to ensure the final network maximised the inclusion of existing protected areas. (2) The species file was created by listing all CWR species and adaptive scenario combination as a concatenated string and assigning each a unique identifier. We set Marxan targets to achieve at least one of every CWR species and adaptive scenario combination in the final network. We calibrated the species penalty factor, which allows prioritization of biodiversity elements for selection in Marxan, using a standard technique34 to maximise taxonomic and genetic diversity in the reserve network. This algorithm initially selects the site with highest richness count for CWR/adaptive scenario combination, and then chooses the second site that will be most complementary to the first, i.e. the site which will most increase the net number of CWR/adaptive scenario combinations. We used pragmatic scenario prioritizes sites containing protected areas (to maximise use of existing protection) and in a complementary fashion considers additional sites outside protected areas where there are CWR/adaptive scenarios combinations not identified within protected areas. It is advisable to identify site both inside and outside of existing protected areas because CWR taxa are commonly found in disturbed anthropogenic environments and less often found in the climax community often designated as conventional protected areas35. All CWR were given equal weighting in the algorithm and it was run until all CWR/adaptive scenario combinations were represented in the final solution at least once. The top 150 priority sites within the top ranked Marxan network were then mapped, the top 100 sites inside protected areas, along with the top 50 complementary sites outside protected areas.The resulting Marxan solutions were then ranked by fewest number of planning units followed by lowest cost. We chose the top ranked solution as the most suitable overall solution. We further prioritized the planning units in the top ranked Marxan solution by using the complementarity ranking algorithmFurther information on experimental design is available in the Description of Additional Supplementary FilesSupplementary InformationSupplementary Data 1Reporting Summary


Vicia sativa subsp. sativa L.) is a self-pollinating annual forage legume with worldwide importance. Here, we investigate the optimal number of individuals that may represent the genetic diversity of a single population, using Start Codon Targeted (SCoT) markers. Two cultivated varieties and two wild accessions were evaluated using five SCoT primers, also testing different sampling sizes: 1, 2, 3, 5, 8, 10, 20, 30, 40, 50, and 60 individuals. The results showed that the number of alleles and the Polymorphism Information Content (PIC) were different among the four accessions. Cluster analysis by Unweighted Pair Group Method with Arithmetic Mean (UPGMA) and STRUCTURE placed the 240 individuals into four distinct clusters. The Expected Heterozygosity (EH) and PIC increased along with an increase in sampling size from 1 to 10 plants but did not change significantly when the sample sizes exceeded 10 individuals. At least 90% of the genetic variation in the four germplasms was represented when the sample size was 10. Finally, we concluded that 10 individuals could effectively represent the genetic diversity of one vetch population based on the SCoT markers. This study provides theoretical support for genetic diversity, cultivar identification, evolution, and marker-assisted selection breeding in common vetch.Common vetch ( Vicia sativa L. subsp. sativa L.), known as an important self-pollinating forage legume [Common vetch  [Medicago sativa) [Elymus sibiricus) [Mangifera indica L.), grape (Vitis vinifera L.), peanut (Arachis hypogaea L.), Siberian wild rye (Elymus sibiricus L.), and chickpea (Cicer arietinum L.) [With the development of molecular marker technology, a variety of molecular markers have been used to evaluate genetic diversity in crops. Examples include the amplified fragment length polymorphism (AFLP) in yz Rodin , single scanthus , restricea spp.) , and sim sativa)  and in Sbiricus) . The stabiricus) ,17. SCoTbiricus) . SCoT mainum L.) ,20,21,22To determine the appropriate number of individuals that should be sampled to represent reasonable genetic diversity in a population, we designed an experiment to study the genetic variation patterns of different populations of common vetch with SCoT markers. The optimized sampling size could provide useful information for studies of genetic diversity, cultivar identification, evolution, and marker-assisted selection breeding in common vetch.PIC  and the Reserve Percentage (RP) of the four accessions increased along with the sampling size increase from 1 to 10 individuals but did not change obviously when the sample size exceeded 10 individuals. The RP value of the four germplasms in different sampling sizes represented more than 90% of the genetic variation in the total population when the sample size was 10 (I) of four accessions ranged from 0.67 to 0.69  values, we found that whether the sampling level was 10 or 60, the number of optimum groups was four (We used the Unweighted Pair Group Method with Arithmetic Mean (UPGMA) and STRUCTURE 2.3.4 software to investigate the relationships between the different sampling levels. According to the UPGMA dendrogram , based owas four . The twoGenetic diversity is one of the most important indicators of biodiversity and is the basic material for the survival and reproduction of species. The study of the genetic diversity of different accessions not only clearly elucidated the genetic structure and characteristics of their populations but also provided technical support for the utilization and protection of their germplasm resources, which has far-reaching implications for endangered or complex species .Common vetch is an annual and strictly self-pollinating forage legume. According to the biological principle on population genetics of species with different breeding systems, comparatively rich genetic diversity should be expected within this species, which may be caused by the trait of a strict closed pollination for the common vetch . PresentGlycine soja) populations, researchers studied the germplasms in several areas and determined that they only needed to sample 27 to 52 individuals for the representation of most of the genetic information of the accessions [Agropyron Gaertn. species, the genetic diversity of different germplasms and individuals were studied by random sampling, and the researchers concluded that at least 12 individuals can represent the majority of the genetic diversity of the species [Dactylis glomerata L.) and Trichoderma koningii using SCoT markers [EH, PIC and I values of four accessions also showed the high level of genetic diversity for common vetch accessions.The proposed statistical models to obtain a general estimation of the number of samples that should be collected to represent a population ,26 deter species . This fi markers ,30. FurtIn the present study, alleles and the RP values were used as the criterion for estimating the genetic diversity of the four common vetch populations, which include both cultivated varieties and wild accessions. As indicated in EH, PIC, Na, and RP , 1.0 μL of each primer and 2.0 μL double-distilled water. The PCR programs were set at 94 °C for 4 min, followed by 35 cycles of 1 min at 94 °C, 1 min at 50 °C and 2 min at 72 °C, with a final extension at 72 °C for 7 min. After PCR amplification, fragments were separated in 1.4% agarose gel containing 0.14 μg/mL of Goldview through electrophoresis in 1× TBE buffer solution at 129 V for 2 h 15 min. DNA fragments were visualized under the ultravlolet (UV) light Gel Doc (TM) XR System , and photo documentation was obtained for each gel.EH), Polymorphic Information Content (PIC), and the observed number of alleles (Na) were calculated as previously described [K [K = 2 to K = 8 (each with 10 independent runs). The FST values of the alleles of different sampling sizes were also calculated by the software ARLEQUIN version 3.11 [b [I) values between four accessions were analyzed using POPGENE 32 Version 1.31 [The amplified bands were scored as absent (0) or present (1), and only reproducible bands were considered. To evaluate the genetic diversity within the germplasm, the indexes of Expected Heterozygosity (escribed ,33. Usinescribed . The Resescribed , and theescribed . A clustescribed , and theescribed . A modelescribed . We usedribed [K  to estimion 3.11 . The Rp  3.11 [b . The Shaion 1.31 .In this study, four different germplasms including 240 individuals were used for the determination of a sampling size for common vetch. The results showed differing degrees of genetic variation on the various sampling levels. Based on the results, we concluded that a sample of 10 individuals is optimal for future studies of common vetch, as this sampling size can represent over 90% of the genetic diversity of the population. This sampling size could provide technical support for molecular breeding and the protection of germplasm of common vetch.


Within the frame of a collection program of the National Gene Bank of Tunisia (NGBT), durum wheat germplasm was collected from different Tunisian agro-ecological zones. The collected samples were studied using simple sequence repeats (SSRs) markers to explore the genetic diversity and evaluate the genetic structure in Tunisian germplasm. The results demonstrated significant diversity in the Tunisian durum wheat germplasm, with clear differentiation between traditional varieties and modern cultivars. The population structure analysis allowed the identification of five subpopulations, two of which appear to be more strongly represented in germplasm collected in central and southern Tunisia, where environmental conditions at critical development phases of the plant are harsher. Moreover these subpopulations are underrepresented in modern varieties, suggesting that traits of adaptation useful for breeding more resilient varieties might be present in central and southern germplasm. Moreover, our results will support, the activity of  Triticum turgidum L. subsp. durum, genome AABB, 2n = 4× = 28) and common  wheats are species with significant agricultural importance as cereal grains throughout much of the world. In particular, durum wheat is grown mainly in the Mediterranean basin, and is primarily used for pasta, couscous and bread. Durum wheat, which originated in the Fertile Crescent, spread west of the Mediterranean basin, reaching the Iberian Peninsula and North Africa ca. 7000 years BC [Durum , which in turn accounted for more than half of the cereal cultivated area  grouped Several descriptions exist to define a ‘landrace’ ,14. VillMolecular markers provide a satisfactory option for several genetic plant studies, since they are highly polymorphic and almost unlimited in number, do not vary among tissues or developmental stages of a plant and are not affected by environmental factors. Therefore, molecular markers can be used for genome analysis, and many of these have been applied with success for genetic mapping ,22, cultNowadays, thanks to the activity of the National Gene Bank of Tunisia (NGBT), a program to collect durum wheat germplasm has been started. This program includes the idea of fostering the cultivation of traditional Tunisian durum germplasm by on farm conservation . Within The socio-agro-ecological zones of Tunisia follow a distribution similar to the natural regions . In the In these three zones, the production of durum wheat shows a strong North-to-South decline along the cline determined by the bio-climate of the areas, as reported by Latiri et al. . The durXgwm193) to 2 (Xgwm154). The polymorphism information content (PIC) was used to evaluate the informativeness of SSRs. The PIC mean value of the 16 SSRs was 0.57 with a range from 0.82 (Xgwm193) to 0.28 (Xwmc406) as reported in r = 0.84***; p < 0.001) was observed between the number of alleles and PIC values. The Shannon’s information index (I) value ranged from 2.07 (Xgwm193) to 0.51 (Xwmc406). The expected heterozygosity (He), which corresponds to heterozygosity at a single locus, ranged from 0.28 (Xwmc406) to 0.82 (Xgwm193). The fixation index (F), which compares the expected heterozygosity (He) with observed heterozygosity (Ho) to estimate the degree of allelic fixation, ranged from –0.30 (Xgwm154) to 1.00   markers were used to study the genetic diversity in a durum wheat collection including 41 Tunisian samples and 13 cultivars . A totals fixed) . The F mBased on the dissimilarity matrix, a neighbor-joining dendrogram clearly showed the presence of two main clusters . The firQ) was > 0.60, as suggested by Chen et al. [For population genetic structure analysis, Bayesian clustering modeling was performed using STRUCTURE software with genotyping data generated according to 16 SSR markers. As the clustering model presumes the underlying existence of K clusters, an Evanno  test wasn et al. . The fivQ of the five subpopulations, detected by structure analysis, was calculated and showed a strong genetic stratification. In particular, two main subpopulations (in orange and red) were relatively abundant in Tunisian durum samples, but rare (<10%) in modern durum cultivars. Two subpopulations (in orange and green) were found the main genetic components (some 80%) of durum wheat samples collected in the South of Tunisia. A subpopulation (in yellow) was present at a frequency >60% in modern durum cultivars and almost absent (<1%) in durum wheat samples collected in the South of Tunisia. The AMOVA confirmed that 9% of the total variation was found among Tunisian durum samples split according agro-ecological zones, while the rest (91%) was within groups ; the fourth group included the modern durum cultivars . In eachn groups .During the second half of the last century, a significant increase in wheat yield was observed in Tunisia. This was a consequence of the introduction of modern high-yielding varieties able to optimize water and soil resources, as well as a result of improvements in agronomic techniques, such as better crop management ,8. In Tunisia, as in the whole Mediterranean region, rainfall is highly variable in its yearly distribution . The oveHe = 0.58) was similar to the values reported by previous studies involving durum wheat collections  [In our study, a set of 16 SSR markers with a good coverage of the A and B genomes of durum wheat were tested in the composed durum wheat collection. They allowed the identification of 95 alleles, with an average of 5,9 alleles/SSR locus. The considerable number of alleles was due to the multi-allelic nature of these markers that facilitates variability detection . Similarnd 0.68) ,35. To define the genetic distances among samples, the matrix obtained with a simple matching coefficient was also used to produce a neighbor-joining dendrogram . This deTo better investigate this result, an analysis of the molecular variance was performed to test different sources of variation. In the first instance, we tested the distribution of molecular variation among the CTV group and the rest of the analyzed germplasm. The results clearly indicate that the CTV group is unquestionably differentiated from the rest of the analyzed material, since 22% of the total variation is between the two groups of samples . In a seNevertheless, the above analyses did not provide any information on the distribution of genotypes along the geographic cline. To this end a Bayesian approach was utilized by analyzing genotypic data through the STRUCTURE software. Structure analysis assigns each genotype to a specific subpopulation and five different ones were identified after the Evanno test . The robThe approach described above allowed us to observe that some genotypes (belonging to the subpopulations indicated with the colors red and orange in This picture indicates that: (a) modern varieties are more fit for cultivation in the northern part of Tunisia, where the rainfall is adequate for new high-yielding varieties that develop their full potential only in the presence of moderate to high inputs; (b) there is an increased presence of traditional varieties and CTVs in the Center and South groups, which are characterized by reduced and more erratic precipitations. These findings indicate that the traditional varieties, originally developed from North African landraces, might possess traits of resistance to unfavorable climatic conditions that are absent in the modern cultivars. This is not surprising since landraces and derived traditional varieties are the result of a non-scientific process of selection driven by farmers’ cultural values and by natural adaptation to local environment . The fact that some old cultivars, such as AgiliAC2, are still called by the names of traditional varieties might be the result of different causes. In fact, the improved material of the 1970s–1990s might have been distributed using the names of the traditional varieties in order to overcome the diffidence of more traditional farmers against innovations. Another reason might reside in a phenotypic resemblance of some morphological traits of the new introductions with those of the CTVs, or by the misnaming of landraces during seed exchange among farmers or during the collecting missions undertaken by the NGBT .ex situ conservation efficiency, because SSRs allow the detection of redundant duplications within durum germplasm conserved in the NGBT. In situ on-farm conservation, in addition to the ex situ approach, could ensure the long-term conservation of genetically diverse Tunisian durum wheat available in local germplasm, which is a reservoir of genes for drought, heat and salinity tolerance and quality traits. The traditional varieties could help maintain the use of local knowledge and challenge ongoing genetic erosion due to the spread of modern pure line cultivars.In conclusion, our results showed that the molecular markers distinguished Tunisian durum wheat germplasm (named the CTV group) and detected putative synonymy and homonymy cases. These results could be useful to increase The population structure analysis suggests a strong genetic stratification along the North–South cline of Tunisia. This could be the result of breeding programs from the second half of the last century that produced high-yielding varieties for the northern highly productive areas rather than tackling the agro-ecological conditions of the Center and South.in situ conservation of agro-biodiversity on farms are essential, particularly for those species that are the food staple of Mediterranean populations and Tunisians in particular, such as durum wheat [The strategy of incorporating adaptation traits found in the genetic diversity pool of landraces and traditional varieties into modern breeding programs is widely supported . In thisum wheat . In factA durum wheat collection including 41 Tunisian samples  and 13 cThe data of monthly rainfall were obtained from the National Institute of Meteorology of Tunisia and derived from the mean of the last 30 years (1988–2018). The data were recorded at three stations in the North , three stations in the Center  and two stations in the South (Djerba and Tozeur).The genomic DNA was extracted from 3 g of fresh young leaves from a single plant for each sample using the cetyltrimethyl ammonium bromide (CTAB) method . The DNANa), Shannon’s information index (I), heterozygosity observed (Ho), heterozygosity expected (He) and fixation index (F = He − Ho/He) were calculated using the GenAlEx program version 6.5 [The genetic indices as number of alleles (sion 6.5 ,48. The sion 6.5  and Andesion 6.5 . The allsion 6.5 . This dision 6.5 . The conThe molecular data were processed using the STRUCTURE program version 2.3.4 . The num