Cactoblastis cactorum, a species of moth native to Argentina, feeds on several prickly pear cactus species (Opuntia) and has been successfully used as a biological control of invading Opuntia species in Australia, South Africa and native ruderal Opuntia species in some Caribbean islands. Since its introduction to the Caribbean its spread was uncontrolled, invading successfully Florida, Texas and Louisiana. Despite this long history of invasion, we are still far from understanding the factors determining the patterns of invasion of Cactoblastis in North America. Here, we explored three non-mutually exclusive explanations: a) a stepping stone model of colonization, b) long distance colonization due to hurricanes, and/or c) hitchhiking through previously reported commercial routes. Genetic diversity, genetic structure and the patterns of migration among populations were obtained by analyzing 10 nuclear microsatellite loci. Results revealed the presence of genetic structure among populations of C. cactorum in the invaded region and suggest that both marine commercial trade between the Caribbean islands and continental USA, as well as recurrent transport by hurricanes, explain the observed patterns of colonization. Provided that sanitary regulations avoiding human-mediated dispersal are enforced, hurricanes probably represent the most important agent of dispersal and future invasion to continental areas. This has helped to identify source populations, frequency of invasion events, and the geographical patterns and demographic consequences of invasive species spread5. Reconstruction of past events of invasion can help in the recognition of the mechanisms of dispersal to prevent the prevalence of invasion or further spread to additional areas8. However, understanding local dispersal remains a central challenge to prevent and control the economic and biodiversity costs of biological invasions.During the last decades, biological invasion studies have strongly benefited from the use of neutral molecular markers to disentangle routes of invasion9. Whereas in some cases, these patterns explain the lower genetic variation of invasive species relative to native areas12, combination of migration events from different source populations have sometimes increased genetic variation within invaded regions, potentially increasing the risk of fast adaptation to novel conditions16. Once invasive species are established within non-native areas, understanding further local dispersal is a major challenge to identify potential environmental barriers and recommend control management programs17. It is possible to use highly variable molecular markers for understanding local scale dispersal and the entangled nature of the species invasion phenomena.Initially, molecular markers were used to identify sources and frequency of dispersal events of invasive species to non-native regionsCactoblastis cactorum, is an oligophagous herbivore during its larval stage that consumes the inner tissue of cladodes of plants within the genus Opuntia, negatively affecting the plant survival18. This moth, native to South America, was used in 1924 for biological control purpose against exotic Opuntia species with successful results in Australia. Later, was intentionally introduced to South Africa (1933), New Caledonia (1933) and Hawaii (1950). In 1957 moth larvae, from South Africa and Australia, were introduced in the island of Nevis, seeking to control the exponential growth of native Opuntia populations caused by deforestation and cattle ranging20. According to a recent study, this has been the single introduction event of C. cactorum to the Caribbean area21. Since its introduction in Nevis and latter in Saint Kitts in 1957 and until 1987-1989, the moth was detected in other areas as far as Florida22, increasing the risk of extinction of native species23. The entire Florida peninsula is now invaded and the moth has spread through the Gulf of Mexico coasts20. According to the Texas Invasive Species Institute the moth has been detected in Brasoria County  in 2018 (around 800 km from the Northern Mexican border). Following the Atlantic coast of North America, it has dispersed up to Charleston County in South Carolina26. The rate of dispersal of the moth according to females flight distances was 3-6 km and 16-24 km in 2.5 years in South Africa and Australia respectively28. Hence the rapid spread throughout the Caribbean and North America probably involved additional factors. Previous reports suggest that spread among Hawaiian Islands probably occurred through island-hopping29 and tropical storms30. In turn, human-mediated dispersal and climatic events like hurricanes have been proposed as sources of dispersal in the Caribbean32. The threat for North American deserts is that they have the highest diversity of Opuntia cacti species, which may suffer from this oligophagous moth18. Furthermore, domesticated Opuntia in Mexico have a high cultural and economic value as a food resource33. Thus, given the risk of further spread to continental areas and the ecological, social and economic associated costs, the present study examined possible routes of local dispersal in the Caribbean and Florida to better understand the factors that may favor additional introductions of this invasive species to the continent.The cactus moth, C. cactorum to Florida31. However, a more recent study with the same marker and a more extended population survey suggested that hurricanes may have also contributed to the dispersal of the moth within the region32. Whereas commercial transportation of ornamental cacti has been controlled ever since the detection of this invasive species18, further introductions within the continent may occur via climatic events. Accordingly, the goal of this study was to further advance in the comprehension of the patterns of dispersal of C. cactorum by using nuclear microsatellites and computational Bayesian approaches. This approach will allow us to add a new piece of evidence to determine whether dispersal through trade, climatic events (hurricanes) or both, better account for the current genetic structure of C. cactorum in North America.A previous study using a mitochondrial gene (COI) reported that commercial transportation of ornamental cacti from Dominican Republic and Puerto Rico was the most likely route of invasion of C. cactorum, were collected from 10 invasive sites within the Caribbean region and Florida (USA) (N = 185) . We removed loci that did not amplify among the 29 primers pairs tested. We chose 14 potential polymorphic loci  following the manufacturer protocol. Population genetic variation was determined using nuclear microsatellites developed by Genetic Marker Services (34 and observed heterozygositiy (Ho), the Fixation Index (Fis) and estimation of FST35 between each pair of sampling site using FSTAT 2.9.3.36. Mean allelic richness (AR) was calculated using the rarefaction method of Leberg37. Exact test for deviation from Hardy-Weinberg equilibrium were performed with GENEPOP38. We used FreeNA to determine the frequency of null alleles using EM algorithm39. Pairwise population differentiation was tested using only those loci that were in H-W equilibrium in more than 50% of the populations.Genetic variation within sampled populations was characterized using the mean number of alleles per locus (NA), the allelic richness (AR), the mean expected- (He)40 (v 2.3.3). We chose the admixture model with correlated allele frequencies. Two analyses were performed, one including all native and invaded population samples, and one including only populations from the invaded area. The first analysis examined whether genetic differentiation has occurred after the human-mediated journey of C. cactorum from its native Argentina to the Caribbean (1957). The second analysis was performed to explore genetic structuring within the invaded Caribbean only. Because groups of larvae were collected from discrete sites, sampling locations were used as prior information41. Each run consisted of a burn-in period of 105 Markov Chain Monte Carlo (MCMC) iterations, followed by 106 iterations. 20 replicated runs were carried out for each value of the potential number of clusters (K) set, between 1 and 9. STRUCTURE HARVESTER42 (available at http://taylor0.biology.ucla.edu/structureHarvester/) was used to collating output results from STRUCTURE and to determine the uppermost level of structure using the method of Evanno et al.43. Because STRUCTURE assumes the absence of null alleles and H-W equilibrium for all loci, analyses were performed using 10 out of 14 microsatellites that fulfilled the required assumptions.Genetic clustering of populations was examined using STRUCTUREFST/ (1-FST)] and geographic distances were estimated as the logarithm of the Euclidean distances (blog) using GenAlEx44 (v.6.4). Isolation was also tested considering that hurricanes may also shaped the genetic structure of populations in this area. To explore the relationship between genetic distances and the frequency of hurricanes incidence across pairs of populations, a resistance matrix was constructed using the CIRCUITSCAPE program45 . This rationale was applied to all pairs of populations to construct a resistance matrix . Since moths were taken from Argentina to Australia, and then to South Africa, the analysis considered a “ghost” population representing a non-sampled population that may represent this step. Given the location and date of introduction to the Caribbean, the sampled population of Antigua was considered the source population that initiated the invasion in the region. Antigua was preferred over Nevis because of the larger number of individual larvae in our collection, the high genetic similarity between both islands  and the short difference in time of introduction between them (1 year). The following step in the invasion process was characterized by the population La Romana (Dominican Republic), given that the moth was detected in the island of Desecheo, located between Puerto Rico and Dominican Republic in 1963. After this step, hypotheses differ in how the cactus moth reached Florida. In the first hypothesis, Cuba, represented by the sampled population of Santiago, is the source of the Floridian invasive population  was the result of the admixture of genotypes belonging to Cuba and Dominican Republic , an Approximate Bayesian Computational Analysis was performed with DIYABCned Fig. , and parned Fig. . In all C. cactorum usually has two generations per year51, this generation time was used to scale coalescent time in all scenarios. Dates of introduction to Australia (Tg) and Antigua (T2) were given as fixed values based on historical data of the intentional introduction events18. In the other areas (T1 (Argentina), T3-5 ; Table 49. The selected scenario was chosen as that with the highest posterior probability value. To evaluate the robustness of the analysis, we used pseudo-observed simulated data sets to quantify the type I error rate  and the type II error rate 49. All simulations and ABC analyses were carried out in DIYABC (v.2) sofware52.Given that No evidence of linkage disequilibrium was detected for all 14 microsatellite loci. Four loci were excluded from the analysis because they had more than 20% of null alleles and were not at Hardy-Weinberg equilibrium . With the remaining ten microsatellites loci we found that the native population from Yuquerí (Argentina) had on average the highest number of alleles (3.8), while the lowest average number of alleles (1.6) was observed in one of the most recent invasive population  . Within the invaded region, genetic differentiation ranged from almost no differences to rather high levels of genetic differentiation  presented lower levels of differentiation with the rest of the populations in the region than recently invaded populations. The Jamaican population  presented the greatest level of genetic differentiation within the Caribbean  distinguishing native from all invasive populations . On the other hand, the isolation by resistance hypothesis based on hurricane and wind currents was significantly supported .Clustering genetic analyses using STRUCTURE on the whole dataset revealed the presence of two main groups , (2) non-overlapping confidence intervals, and (3) low Type I and mean Type II errors . This dispersal scenario considers that the invasion to Florida (represented by the Highlands population) was funded by insects belonging to at least two sources of dispersal, one from Dominican Republic and the other from Cuba  support quiet well the hypothesis described in the third scenario Fig. , due to 0.7798),  non-overOpuntia plants infested with C. cactorum. Seizures occurred in plant shipments sent from the Dominican Republic to Miami and in luggage to Dallas Texas International Airport53. Commercial trade of ornamental cacti from the Dominican Republic to the United States of America coupled with the constant influx of American tourists to the Caribbean islands lead to the proposal that human-mediated dispersal has been the major agent of migration to the continent. Similarly, the comparison of the mitochondrial haplotypes from eastern populations of C. cactorum of Florida and those of Dominican Republic supports this idea32. Despite other potential mechanisms of dispersal as island-hopping and tropical storms or hurricanes30, the present study support the genetic similarity and likelihood of migration between La Romana (Dominican Republic) and Highlands (Florida) populations. However, the distribution of genetic variation and ABC analyses of invasion routes within the Caribbean region also suggest the presence of other nonexclusive routes of dispersal.During the 1981–1991 decade, North American customs authorities intercepted 54. Wind has been identified as one of the most important factors promoting the spread and long-distance dispersal of invasive insect species55 and to enhance distance and/or speed of dispersion as in the case of the monarch butterfly56, the cattle egret57 or the mealy bug, Oracella aculata58. Also, the grasshopper Eumetopina flavipes, vector of the sugarcane virus Ramu, entered Australia from New Zealand favored by wind currents60. Wind is also responsible for long-distance dispersal of the wasp Megastigmus schimitscheki in France7. Although previously suggested, the role of hurricanes on local dispersal of C. cactorum within the Caribbean was not examined until recently32. Our previous results using mitochondrial COI32 coupled with the findings of this study using nuclear microsatellites, are consistent and provide new pieces of evidence supporting the role of hurricanes on the spread of C. cactorum toward mainland North America. In comparison with the isolation by distance model, the isolation by resistance migration approach was a much better predictor of the current pattern of genetic variation of C. cactorum in the Caribbean and Florida. This finding suggests that hurricanes were one of the main drivers of dispersal from the Caribbean to mainland North America as previously shown using a more conserved mitochondrial marker32. In addition, ABC analyses of the invasion routes supported our hypothesis that moths entered Florida both through commercial transportation from the Dominican Republic and from Cuba as a consequence of climatic events (hurricanes); whereas trade with Cuba was suspended as a result of the embargo. Moreover, this finding suggests that hurricanes may have also dispersed C. cactorum from its initial introduction in Nevis, Antigua and Montserrat to other islands onto the Caribbean region. Historical records of hurricanes in the region (https://www.nhc.noaa.gov/) further support this statement. After the initial human-mediated introduction of C. cactorum, three hurricanes connected the Lesser Antilles and the Dominican Republic between 1963 and 1967, four impacted the south of Cuba and the Dominican Republic between 1975 and 1980, and two passed from Cuba and impacted the USA in 1985 and 1987. Nevertheless, the presence of the moth in the lower Bahamas in 198361, and the possibility of island hopping, as recorded in the Hawaiian archipelago, can also represent a possible route of dispersal towards Florida. During the last decades at least three hurricanes impacted the Bahamas before reaching the Atlantic coast of Florida . Hence a more extended survey of populations in this area of the Caribbean will provide further insight on the dispersion routes across the region.In the Caribbean and North Atlantic region, hurricanes are a ubiquitous temporary phenomenon with consistent wind current patterns. Studies of genetic variation have shown that hurricanes and marine currents influence the patterns of dispersal and constitute a source of variation in ecological and demographic processes62. This process can be counteracted if repeated invasion events occur in a given location, thus increasing the amount of genetic variation to equivalent, or larger levels of variation, than that observed on the native region63. Our analyses revealed that populations of C. cactorum from the Caribbean and Florida, sustained less genetic variation than in the native region, likely as a consequence of genetic drift. Given that present populations of C. cactorum outside the Caribbean region are located more that 8,000 km away in other biogeographical areas and continents, the genetic structure results support previous analyses using mitochondrial markers suggesting that the insect entered the Caribbean probably once in 195721. Similar to the Lepidopteran tomato pest, Tuta absoluta, in Africa and the Mediterranean Basin62, the occurrence of just one introduction of the cactus moth was enough to allow its spread throughout the Caribbean despite the lower genetic variability than the source population from South America. Unlike the tomato pest and other invasive insect species9 significant genetic differentiation was detected in the invaded region suggesting again a possible effect of drift. A more complete survey of invaded populations in Australia, South Africa and in other continents will help to decipher whether the magnitude of reduction in genetic variation will affect the adaptive potential of the moth into other continental areas with different climatic conditions as those of the Caribbean and Florida.The process of invasion is generally associated with the loss of genetic variation due to successive demographic bottlenecks as the invasion of new locations proceedsThe combined effect of hurricanes and genetic drift reducing genetic variation through space will likely affect the evolutionary potential of the moth during future introductions to the continent. Hence, monitoring programs of climatic events like hurricanes in the region and environmental genomics surveys will add new insight to better understand possible barriers to the expansion of the moth into continental areas.


Elmis aenea  and Limnius perrisi  in the springs and streams of the W Carpathians using the mitochondrial DNA barcoding fragment of the cytochrome c oxidase subunit I gene (COI). The relatively stable thermal and chemical conditions of springs throughout unfavourable climatic settings make these highly specific lotic systems potentially ideal for a long-term survival of some aquatic biota. Populations of both elmid species were relatively homogeneous genetically, with a single dominant haplotype. However, we revealed that E. aenea significantly dominated in the springs, while L. perrisi preferred streams. Relative isolation of the springs and their stable conditions were reflected in significantly higher molecular diversity of the E. aenea population in comparison to L. perrisi. The results of Bayesian Skyline Plot analysis also indicated the exceptional position of springs regarding maintaining the population size of E. aenea. On the other hand, it seems that streams in the W Carpathians provide more effective dispersal channels for L. perrisi, whose population expanded much earlier compared to E. aenea. Present study points out that different demographic histories of these two closely related elmid species are manifested by their different habitat preference and molecular diversity.The Western Carpathians are a particularly interesting part of the Carpathian Arc. According to recent molecular data upon aquatic and terrestrial taxa, this mountain area is an important biodiversity hotspot of Europe. Moreover, the W Carpathians include rich systems of karst springs inhabited by specific fauna, where molecular diversity and phylogeographic patterns are yet to be fully explored. Our study aims to compare population genetic structure and molecular diversity of two related and commonly co-occurring riffle beetles,  Prolonged isolation of populations influences their genetic diversity and can be considered as the main force shaping genetic structure of aquatic species in Europe . IsolateThe W Carpathians are considered a biodiversity hotspot for a wide range of aquatic and terrestrial taxa . HoweverSprings support unique macroinvertebrate communities that are found nowhere else in a catchment . They arElmis aenea  and Limnius perrisi  in springs and streams of the W Carpathians. Said species are relatively closely related and commonly co-occur, yet in terms of population genetics represent a generally understudied family of freshwater beetles. Limited dispersal abilities, high habitat specificity, and more or less fragmented distribution make Elmidae an ideal taxon for studying genetic diversification through many geographic regions. Both studied species are rheophilic, oligo-stenotherm, and typical inhabitants of epirhithral streams at higher altitudes  Are the spring subpopulations genetically more variable when compared to subpopulations in the streams? (b) Does genetic structuring of populations reflect population size change? (c) Are there interspecific differences in the population genetic structure among these related beetle species?The Carpathians form an arc of mountains stretching across Central and Eastern Europe, with its main geomorphological units being the Western and Southeastern Carpathians . For thiInvestigated localities (36 springs and 37 streams) are situated mainly on the territory of the Slovak Republic, partially in the Czech Republic and Poland; in the geomorphological units/subunits of the Inner and Outer Western Carpathians. The exceptions are Vihorlat Mts (VM) being part of the Inner Eastern Carpathians and Poloniny Mts (PM) belonging to the Outer Eastern Carpathians .A qualitative sampling of benthic invertebrates took place in 2016 and 2017. The sampling was performed in the framework of a broader research, which was permitted by The District Office, Department of Environmental Care Trenčín (Slovakia) No: OU-TN-OSZP1-2015/001937-12/Du. A sampling of macrozoobenthos was carried out by a multi-habitat kick-sampling technique  using a E. aenea; 263 individuals of L. perrisi) using the Chelex protocol , 0.5 μl of each primer (concentration 5 mM), 0.5 μl of dNTP Mix (20 mM), 0.125 μl (0.625 U) DreamTaq TMDNA Polymerase, 11.875 μl ultra-pure H2O and 5 μl of DNA template. The PCR cycling consisted of a 2-min initial denaturation at 94 °C, followed by 40 cycles of 94 °C (40 s) denaturation, 46 °C (40 s) annealing and 72 °C (1 min) extension and termination at 72 °C (10 min) for a final extension. A 4 μl aliquot of the PCR products were visualized by GoldView (Solarbio) in electrophoresis on a 1% agarose gel and GelLogic imaging equipment to check PCR product quality and length. The PCR products were purified with Exo-FastAP Thermo Scientific and were sent for sequencing to Macrogen Europe Inc., Amsterdam.Total DNA was extracted from the legs or abdominal tissue of 560 individuals . It was performed for testing the independence of rows  and columns  in a 2 × 2 contingency table. Odds ratio and p-value were computed. P-values <0.05 were considered statistically significant. We used analysis of variance (ANOVA) to test significance of the influence of altitude on the presence of species in different habitats . ANOVA test was performed in R v4.0.2 (http://www.r-project.org). All R analysis were carried out using RStudio  between the studied species is statistically significant, we used Fisher’s exact tests using the fisher.test function in R v4.0.2  and 24 of L. perrisi  outside of W Carpathians for haplotype networks. Sequences from Germany, Finland and France were downloaded from BOLD (http://www.boldsystems.org) and are included in datasets DS-SKLIMPER (DOI: dx.doi.org/10.5883/DS-SKLIMPER) and DS-SKELMAEN (DOI: dx.doi.org/10.5883/ DS-SKELMAEN). For the purposes of the paper, individuals (sequences) from each locality of W Carpathians are defined as subpopulation regardless of whether the locality is a spring or stream , number of polymorphic sites (S) and average number of nucleotide differences (K) per subpopulation of both species using DnaSP v5.10. Subsequently, a statistical comparison of molecular genetic indices between species, based on p-values, was computed with the Wilcoxon signed rank test for paired data in R v4.0.2 (http://www.r-project.org). The results are presented by boxplots with p-values.The haplotype data files and the diversity indices were generated in DnaSP v5.10 . We alsoE. aenea and L. perrisi in the broader context of the investigated localities.Haplotype networks were reconstructed using the median-joining method (MJN) in PopART v1.7 . The netST) using Arlequin v3.5  and fixation indices  and 136 L. perrisi sequences  were included to calculate the FST index. Localities with 1 sequence were excluded from the calculation. To test the significance of covariance components and fixation indices, 1,000 permutations were performed.FTo test if spatial distance is structuring the molecular diversity we run two types of isolation by distance tests: Mantel test  and geneFurther, the demographic and spatial dynamics of studied beetle populations were examined by the mismatch distribution analysis in Arlequin v3.5. The recent demographic expansion in both species was tested with Tajima’s D , Fu’s FsE. aenea and L. perrisi in the W Carpathians over time were identified with the extended Bayesian Skyline Plot (eBSP) in BEAST v2.6.2 software package (http://www.r-project.org). Both plots for each species were identical therefore only one is presented.The fluctuations of demography of  package . The str package . The mod package . For com package  and all L. perrisi (DOI: dx.doi.org/10.5883/DS-SKLIMPER) and DS-SKELMAEN for E. aenea (DOI: dx.doi.org/10.5883/DS-SKELMAEN).All analysed sequences with GenBank accession numbers are available within two BOLD datasets: DS-SKLIMPER for E. aenea and L. perrisi suggests statistically significant different habitat preferences between these species in the W Carpathians . E. aenea has a rather wide distribution in karst springs (31 sites), while it is less widespread in streams (16 sites). On the contrary, L. perrisi was found only in eight springs, but in 30 streams. L. perrisi was also found in four streams of VM (Inner Eastern Carpathians) and in one stream of PM (Outer Eastern Carpathians), while E. aenea was not recorded in these geomorphological subunits. Both species co-occurred only in three springs and 14 streams from the total of 73 sites sampled in the W Carpathians (The distribution of pathians . There wπ), number of polymorphic sites (S), and average number of nucleotide differences (K) showed significant differences between the studied species. The population of E. aenea was significantly more diverse than L. perrisi , Lp10 in a stream of WB (CZ06) and Lp11 from one spring of geomorphological unit FTA (V070). Besides that, two more haplotypes  were present in the FTA. Lp13 was shared with the locality of the different geomorphological unit SMC (SEL1). Lp3 was also present in unit WB, in addition to FTA (BEL3). The haplotype Lp2 was detected in a stream (CZ01) of WB and occurred also in one stream (KRV1) located in VM  compared to L. perrisi (12.86%) . However, in (12.86%) . The sam(12.86%) .ST values indicate different levels of genetic differentiation between the E. aenea localities 0–0.8 , V038, and V048 (SOM1 - Slovak Karst) have relatively high pairwise differences in allele frequency, but some level of genetic connectivity cannot be refused. Pairwise comparisons of differences in the frequency of alleles that include mentioned springs are largely significant (P < 0.05). On the other hand, none of L. perrisi FST values are statistically significant.The Fes 0–0.8  comparedh 0–0.38 . The FSTE. aenea - r = 0.313, P = 0.000; L. perrisi - r = 0.4122, P = 0.039). Although, only marginally positive but statistically significant correlations suggest a slight structuring effect of the geographical distance among springs in both species. Additionally in case of E. aenea, the spatial autocorrelation was also significant .Tests of isolation by distance between springs of both species revealed a positive correlation . The E. aenea occurred predominantly in karst springs and was rarely found in streams of the W Carpathians, while the distributional pattern of L. perrisi was opposite. This contradicts the previous claims about their common occurrence and similar biotope preference , a thermal maximum was recorded, which probably enhanced the expansion of species  and one locality in the Fatra-Tatra area (FTA: V009) had remarkably high FST values, suggesting that some W Carpathian springs could constitute Pleistocene refugia. According to several studies, the role of the W Carpathians as a glacial refugium . This indicates that there are some well-pronounced differences in genetic composition among most of the spring subpopulations of E. aenea within each geographical unit. Similar results emerged from the study on the black fly Prosimulium neomacropyga in the US Southern Rockies ecoregion with alpine tundra streams, where the differences among streams within the region were 24.58%  the studied area within the Carpathian Arc and (B) the 73 sampling sites (36 springs and 37 streams) divided into seven river basins represented by different color fill.The grey line represents the state borders of Slovakia (SK), Hungary (H), Ukraine (UA), Poland (PL), Czech Republic (CZ) and Austria (AU).Click here for additional data file.10.7717/peerj.10039/supp-2Supplemental Information 2x-axis between 0 and 413 km, and pairwise genetic distances (Ay) are given on the y-axis.The horizontal dotted line indicates the average genetic distance for the observed data set. Values above the horizontal line suggest a correlation between distance class and genetic distance.Distance classes (10) are given on the Click here for additional data file.10.7717/peerj.10039/supp-3Supplemental Information 3Click here for additional data file.10.7717/peerj.10039/supp-4Supplemental Information 4The subpopulation is defined as individuals of one locality within the drainage basins, Click here for additional data file.10.7717/peerj.10039/supp-5Supplemental Information 5MT357256 is located in line 302 (> SKELM304-19 —Elmis aenea —MT357256). The sequence MT357555 is found in line 592 (> SKELM579-19 —Elmis aenea —MT357555).The sequence name consists of the working ID I species I Accession number. The sequence Click here for additional data file.10.7717/peerj.10039/supp-6Supplemental Information 6MT357997 is found in line 504 (> SKELM203-18 —Limnius perrisi —MT357997). The sequence MT358260 is found in line 426 (> ELMEU020-18 —Limnius perrisi —MT358260).The sequence name consists of the working ID I species I Accession number. The sequence Click here for additional data file.


In this paper, we present the AlphaPart R package, an open-source implementation of a method for partitioning breeding values and genetic trends to identify the contribution of selection pathways to genetic gain. Breeding programmes improve populations for a set of traits, which can be measured with a genetic trend calculated from estimated breeding values averaged by year of birth. While sources of the overall genetic gain are generally known, their realised contributions are hard to quantify in complex breeding programmes. The aim of this paper is to present the AlphaPart R package and demonstrate it with a simulated stylized multi-tier breeding programme mimicking a pig or poultry breeding programme.http://CRAN.R-project.org/package=AlphaPart. We demonstrate the use of the package by partitioning the nucleus and multiplier genetic gain of the stylized breeding programme by tier-gender paths. For traits measured and selected in the multiplier, the multiplier selection generated additional genetic gain. By using AlphaPart, we show that the additional genetic gain depends on accuracy and intensity of selection in the multiplier and the extent of gene flow from the nucleus. We have proven that AlphaPart is a valuable tool for understanding the sources of genetic gain in the nucleus and especially the multiplier, and the relationship between the sources and parameters that affect them.The package includes the main partitioning function AlphaPart, that partitions the breeding values and genetic trends by pre-defined selection paths, and a set of functions for handling data and results. The package is freely available from the CRAN repository at AlphaPart implements the method for partitioning breeding values and genetic trends and provides a useful tool for quantifying the sources of genetic gain in breeding programmes. The use of AlphaPart will help breeders to improve genetic gain through a better understanding of the key selection points that are driving gains in each trait. Population improvement can be measured with a genetic trend calculated by averaging estimated breeding values by year of birth , 2. Whil1/2ad+wi , where \ividuals , 5 and \s et al.  uses a pThe partitioning method has been used in a number of cases. Gorjanc et al. , 7 estimThe aim of this paper is to present the AlphaPart R package that implements a method for partitioning breeding values and genetic trends. We demonstrate the package with an example in which we partition the genetic gain of a small population for which genetic material is imported. Next, we demonstrate the package by partitioning the genetic trends of a stylized multi-tier breeding example usually applied in pig and poultry breeding.https://CRAN.R-project.org/package=AlphaPart and thus easily installed via install.packages(“AlphaPart”) and loaded with library(AlphaPart) command inside R. We developed the package with the aim to create a user-friendly implementation of the method for partitioning breeding values and genetic trends. The only input required from the user is an initial data frame. All the subsequent functions in the analysis pipeline accept the output of a preceding function.AlphaPart is an R package available from the CRAN repository at https://git.ecdf.ed.ac.uk/HighlanderLab_public/jobsteter_alphapart.First, we demonstrate the standard AlphaPart analysis pipeline in an example session by analysing genetic gain in a breeding programme that imports genetic material from another population. Next, we demonstrate the usefulness of AlphaPart by analysing the dynamics of genetic gain in a multi-tier breeding programme. The simulation code for the generation and analysis of both datasets is available in the GitLab repository Here, we demonstrate the functionality of AlphaPart on a simulated example. Consider a case of a small population (Population 1) that imports genetic material from two larger populations (Populations 2 and 3) that have higher genetic gains per unit of time. Populations 2 and 3 achieve higher genetic gains with higher accuracy of selection (h2 = 0.9 vs. h2 = 0.7 in Population 1). In addition, Population 3 benefits from a higher intensity of male selection (2.89 vs. 2.67 in Populations 1 and 2). In this simulation, we assume the presence of genotype-by-environment interactions and genetic correlations of a trait in Population 1 with a trait in Population 2 and a trait in Population 3 of 0.9 and 0.8, respectively, and we assume 20 generations of selection of males on phenotypic values. In generations 1 to 10, we perform selection within these populations without import and in generations 11 to 20, Population 1 imports 10% of semen from Population 2 and 10% of semen from Population 3. To optimize the breeding strategy in Population 1, we would like to quantify how much genetic gain between generations 11 and 20 stems from each of the populations. Hence, we partition the breeding values by variable “population of origin”. To analyse this situation, we used stochastic simulation to generate the dataset. Details of the simulation are provided in Additional file The main input for the analysis is a data frame that holds pedigree information with individual/sire/dam or individual/sire/maternal-grandsire identification, a time-ordering variable such as year of birth, partition variable (path), and breeding values for one or multiple traits. The package provides functions to pre-process the input data, such as for correcting missing or incorrect years of birth and setting the base population of the pedigree. For this example session we prepared the input data frame PedEval containing the year (generation) of birth (Generation), identifications for individuals and their parents , population or origin (Population), and breeding values (Bv) for the trait expressed in Population 1  into contributions of selection in Population 1 (domestic source), Population 2 (imported source) and Population 3 (imported source) we use:The function AlphaPart requires the input data frame as described above Fig. . FollowiThe output of the AlphaPart function is an object of either a data frame with partitioned breeding values (AlphaPart class) or partitioned and summarized breeding values (summaryAlphaPart class). In either case, the output is a list with an info element and an element with partitioned breeding values for each of the traits. The info element is a list with information on the path variable (path), number of paths (nP), path labels (lP), number of traits (nT), trait labels (lT), and putative warnings (warn). The trait element is a data frame, named after the analysed trait Fig. , and holIn Fig. To summarise the individual partitions of breeding values, the package includes functions to summarize and/or combine paths by a grouping variable, subset the partitioning results and visualise these summaries.An interesting measure is the trend of mean breeding values (genetic trend) and path partitions  through time. To summarize the breeding values and the partitions by generation, we use the summary.AlphaPart. Since we are interested in the genetic gain of Population 1, we use the subset option to filter out Population 1 individuals:The input for the summary.AlphaPart is the output of the partitioning analysis (AlphaPart class). The function also requires a variable that allows us to summarize the breeding values and their partitions via the by argument. In the case above, we are summarizing by generation, but depending on the analysis this could be any variable . By default, the function summarizes the mean of the breeding values of the analysed trait, but the user can specify any R function via the FUN argument. The function can also summarize only a subset of the object by specifying which individuals to keep. The user passes the condition as a vector of logical values via the subset argument as shown above.The output of the summary.AlphaPart function is a data frame that holds summarized breeding values and their partitions for the trait (summaryAlphaPart class). It is a list with an info element and one element for each partitioned trait. The summary element, named after the analysed trait Fig. , containWe can use package functions to further manipulate the partitioned or summarized results. If we are only interested in the contributions of Populations 1 and 2, we can filter out the summary results with the AlphaPartSubset function:The AlphaPartSubset function takes the output of either the partitioning (AlphaPart class) or summarizing (summaryAlphaPart class) analysis, and a character vector of paths to keep via the paths argument. The output of the function is a subset of the dataset of the input class.We could also be interested in only the contribution of domestic vs. imported sources and not of specific populations. To this end, we can combine the contributions of imported sources, i.e. from Populations 2 and 3, with the AlphaPartSum function and compare the contributions of Population 1 (named Domestic) vs. the combined contributions of imported sources (named Import):The input for AlphaPartSum can be the output of either the partitioning (class AlphaPart) or summarizing (class summaryAlphaPart) analysis. The function also takes a list of paths to sum via the map argument. Each element of this list contains the name of the newly created combined path followed by the names of the paths to combine.Lastly, we can plot the summarized partitions with the plot.summaryAlphaPart function:> plot(sumPartByGen)The input for the plotting function must be a summarized dataset (class summaryAlphaPart), which could have been further subsetted or in which some of the paths have been combined. The output is a list (plotSummaryAlphaPart class) containing one plot for each partitioned trait. We show the output plot in Fig. We applied the AlphaPart R package on a simulated example of a multi-tier pig breeding programme. Our aim was to examine the gene flow between nucleus and multiplier and the contribution of nucleus and multiplier selection on genetic gain in each tier. Breeders select in the nucleus and multiply this improvement in the multiplier to supply a large number of breeding animals for commercial purposes. The multiplier generally has a lower genetic mean than the nucleus due to a time-lag in the gene flow. However, animals with very high breeding values for some traits can be observed in the multiplier tier. We simulated a multi-tier breeding programme and used AlphaPart to partition the genetic trend of true breeding values by a tier-gender variable to quantify sources of genetic gain in the nucleus and the multiplier.We used the AlphaSimR package  for stocIn the burn-in period, we simulated only the nucleus and we selected animals based on the index of phenotype values for both traits. We selected 25 males and 500 females in each generation and randomly mated them to produce a new generation of 6000 progeny (12 per mating). At the end of the burn-in, we generated 5000 females to seed the multiplier.In the evaluation, we simulated both the nucleus and the multiplier and selected animals within each tier based on the index of estimated breeding values for both traits Fig. . In the We estimated the breeding values for each trait independently before each nucleus or multiplier selection decision. We ran a pedigree-based model implemented in blupf90  and usedFinally, we partitioned the true breeding values and genetic trends with AlphaPart as demonstrated above. We used the AlphaPart function to partition standardized true breeding values from the 20 evaluation generations by the tier-gender variable and the summary.AlphaPart function to summarize the partitions by generations to quantify the contribution of each tier-gender level to genetic trend in the nucleus and the multiplier.We repeated the simulation 10 times and measured the genetic trend separately in the nucleus and multiplier. We present standardized true breeding values and genetic trends, as well as their partitions with the mean set to zero and the genetic standard deviation set to one in generation 20. We chose to present true (instead of estimated) breeding values to assess the true sources of genetic gain.The results show partitions of true breeding values and genetic trends in the nucleus and multiplier obtained with AlphaPart for the two simulated stylized multi-tier breeding scenarios. Partitioning showed that we can explain the situation with very high breeding values in the multiplier by the extent of nucleus-multiplier gene flow as well as accuracy and intensity of multiplier selection. For each scenario, we first describe the distribution of true breeding values in the nucleus and multiplier in generation 40 of one replicate. Next, we explain the sources of the observations by partitioning the nucleus and multiplier genetic trend and averaging the results across ten replicates. The distributions of partitioned true breeding values for one replicate are shown in Additional file In the MaleFlow100 scenario, the multiplier had a higher genetic merit on average than the nucleus for trait 1 and trait 2 as shown in Fig. The partitioning showed that the higher genetic gain in the multiplier compared to the nucleus for trait 1 was due to an additional contribution from selection of multiplier females as shown in Fig. In the multiplier, the average genetic gain for trait 1 was higher than in the nucleus. This increase was driven by two sources. First, nucleus males made a larger contribution to the multiplier than to nucleus genetic gain, since they contributed directly by fathering the multiplier animals and indirectly through subsequent selection of their genes in future generations. Second, multiplier female selection made a non-zero contribution. The mean genetic gain at generation 40 in the multiplier for trait 1 was 10.00 with nucleus males contributing 5.75, nucleus females 4.09, and multiplier females 0.14. The mean genetic gain and its path partitioning at generation 40 for trait 2 were similar in the multiplier and the nucleus. The distributions of partitioned true breeding values for one replicate are shown in Additional file In the MaleFlow20 scenario, the genetic merit was higher in the multiplier than the nucleus for trait 1, but lower for trait 2 as shown in Fig. The partitioning revealed, that selection of multiplier males and females further increased the genetic gain for trait 1 in the multiplier compared to the nucleus, but decreased the genetic gain for trait 2 as shown in Fig. In the multiplier, the genetic gain was again higher than in the nucleus, but only for trait 1. This higher genetic gain was a result of non-zero contribution of multiplier female and male selection and a reduced contribution of nucleus females. In MaleFlow20, we reduced the use of nucleus males in the multiplier, which reduced the contribution of nucleus females via reduced nucleus-multiplier gene flow. For trait 2, the genetic gain in the multiplier was lower than in the nucleus due to a small average negative contribution of multiplier females and multiplier males and reduced contribution of nucleus females and nucleus males via reduced gene flow. The mean genetic gain at generation 40 in the multiplier was 10.36 for trait 1 and 8.14 for trait 2, with nucleus males contributing 5.70 for trait 1 and 5.09 for trait 2, nucleus females contributing 4.21 for trait 1 and 3.13 for trait 2, multiplier males contributing 0.15 for trait 1 and -0.03 for trait 2, and multiplier females contributing 0.30 for trait 1 and -0.05 for trait 2. The distributions of partitioned true breeding values for one replicate are shown in Additional file In this paper, we present AlphaPart, a freely available R package that implements the method for partitioning breeding values and genetic trends. We demonstrate the package on a simulated stylized multi-tier breeding example with a higher genetic trend for some traits in the multiplier compared to the nucleus. Following this, we organized the discussion into two parts: (i) advantages and disadvantages of the AlphaPart R package and (ii) partitioning results of the breeding example.AlphaPart is a free implementation of the method for partitioning breeding values and genetic trends. The method and the package are valuable for deciphering and quantifying the sources of genetic gain in breeding programmes. The package is easy to use, since it streamlines the partitioning analysis into a few lines of R code. AlphaPart presents a holistic tool to perform a partitioning analysis, from preparing the input data—such as manipulating the pedigree data—to handling of results and plotting. The partitioning step is fast, even for large pedigrees, since the main partitioning function is recursive and implemented in C++.AlphaPart is aimed at researchers who are interested in quantifying the sources of genetic gain in their breeding programmes in order to understand the dynamics of genetic gain, improve selection efficiency in certain partitions, assess the performance of different breeding actions, or to optimize investment. The accuracy of partitioning depends on the accuracy of the estimated breeding values and their Mendelian sampling terms, which are driven by the genetic parameters of the trait, the information available in the breeding programme structure used, and choice of the prediction model.Future development of AlphaPart will include an extension of the partitioning method in three areas. The first extension will use genomic information to inform which genome regions and which specific haplotypes or alleles drive genetic change. The second extension will use the partitioning method to analyse contributions to changes in genetic variance in addition to the genetic mean. The third extension will simplify handling of uncertainty of path contributions when working with samples from posterior distributions , 9.The multi-tier breeding example illustrated the investigative power of the partitioning method and the free AlphaPart implementation. Here, we discuss the sources of genetic gain in the two tiers of a breeding programme.By partitioning the genetic trend in a simulated multi-tier breeding programme, we disentangled the observation that some multiplier animals have higher breeding values for some traits compared to the nucleus animals. While larger numbers of individuals and therefore recombinations in the multiplier can potentially reveal more variation and occasional outlying animals (due to an outstanding recombination), we expect lower breeding values in the multiplier due to time-lag between the nucleus and multiplier. In this study, we analysed a situation with no or limited genetic lag on the male side. We showed with partitioning that the gene flow from the nucleus into the multiplier was expectedly the main source of genetic gain in the multiplier.However, the results also showed that selection in the multiplier can contribute genetic gain in addition to the gene flow from the nucleus. The multiplier outperformed the nucleus for trait 1, because with the 10,500 recorded multiplier animals there was a substantial amount of information for accurate multiplier selection that generated additional genetic gain. We emphasise that this result is also due to a limited time-lag between the nucleus and multiplier as we used the nucleus males in the nucleus and multiplier concurrently assuming artificial insemination. The partitioning of genetic trend for trait 1 showed that when we used only the nucleus males in the multiplier , the multiplier generated additional gain from two sources. First, compared to the nucleus, the contribution of the nucleus males increased because they contributed through the immediate gene flow and through the long-term gene flow contributions that influenced the selection of multiplier females. Second, the selection of multiplier females contributed as well. When we used both the nucleus males and the multiplier males in the multiplier , the multiplier generated further gain through a combination of the sources—the contribution of selection in multiplier females and males, and the decreased contribution of nucleus selection due to the reduced gene flow. This decrease was due to a smaller number of progeny per nucleus male in the multiplier compared to the MaleFlow100 scenario. In both scenarios, we observed a trend of decreasing contribution of multiplier selection over generations, although the average multiplier contribution was always above zero. Since we partitioned breeding values with generation 20 as a base generation, the parent average and Mendelian sampling terms for multiplier animals in generation 20 were assigned to the multiplier path. Over the generations, the nucleus and multiplier contributions converged since the pedigree used in next generations accounted for the origin of the nucleus males. This shows the importance of proper base population specification (including unknown parent groups) for meaningful partitioning. This long-term dynamic of contributions is related to the dynamics of “long-term genetic contributions” in the context of genetic gain and inbreeding , 13, butOn the contrary, trait 2 was not measured in the multiplier and had a comparable or smaller genetic trend in the multiplier than in the nucleus. For trait 2, the multiplier animals were selected only on estimated parent average, which resulted in low accuracy selection. In the MaleFlow100 scenario, this low accuracy selection resulted in a null contribution of multiplier females to the genetic trend for trait 2 and comparable genetic trends between the nucleus and the multiplier. In the MaleFlow20 scenario with a reduced nucleus-multiplier gene flow, this low accuracy selection resulted in the reduced genetic gain for trait 2.AlphaPart R package is a freely available software for partitioning breeding values and genetic trends. Use of AlphaPart will help breeders to better understand sources of genetic gain and improve their breeding programmes.Additional file 1: Figure S1. Example session of analysing a breeding programme using import—simulation details.Additional file 2: Figure S2. Distribution of true breeding values and their partitions by trait, year, and tier in the MaleFlow100 scenario. We show scaled densities of partitions in years 23 and 40 of one simulation replicate. MalerFlow100 uses only nucleus males in the multiplier. Trait 1 is measured in the nucleus and the multiplier, while trait 2 is measured only in the nucleus. Black vertical lines represent the nucleus mean breeding value for a trait in a year.Additional file 3: Figure S3. Distribution of true breeding values and their partitions by trait, year, and tier in the MaleFlow20 scenario. We show scaled densities of partitions in years 23 and 40 of one simulation replicate. MaleFlow20 uses nucleus and multiplier males in the multiplier. Trait 1 is measured in the nucleus and the multiplier, while trait 2 is measured only in the nucleus. Black vertical lines represent the nucleus mean breeding value for a trait in a year.


Triticum turgidum ssp. durum modern germplasm and landraces, along with a selection of emmer and primitive tetraploid wheats to maximize diversity. GDP accessions were genotyped using the wheat iSelect 90K SNP array. Among modern durum accessions, breeding programs from Italy, France and Central Asia provided the highest level of genetic diversity, with only a moderate decrease in genetic diversity observed across nearly 50 years of breeding (1970–2018). Further, the breeding programs from Europe had the largest sets of unique alleles. LD was lower in the landraces (0.4 Mbp) than in modern germplasm (1.8 Mbp) at r2 = 0.5. ADMIXTURE analysis of modern germplasm defined a minimum of 13 distinct genetic clusters (k), which could be traced to the breeding program of origin. Chromosome regions putatively subjected to strong selection pressure were identified from fixation index (Fst) and diversity reduction index (DRI) metrics in pairwise comparisons among decades of release and breeding programs. Clusters of putative selection sweeps (PSW) were identified as co-localized with major loci controlling phenology (Ppd and Vrn), plant height (Rht) and quality (gliadins and glutenins), underlining the role of the corresponding genes as driving elements in modern breeding. Public seed availability and deep genetic characterization of the GDP make this collection a unique and ideal resource to identify and map useful genetic diversity at loci of interest to any breeding program.Representative, broad and diverse collections are a primary resource to dissect genetic diversity and meet pre-breeding and breeding goals through the identification of beneficial alleles for target traits. From 2,500 tetraploid wheat accessions obtained through an international collaborative effort, a Global Durum wheat Panel (GDP) of 1,011 genotypes was assembled that captured 94–97% of the original diversity. The GDP consists of a wide representation of  Triticum turgidum L. ssp. durum (Desf.) Husn.] is the 10th most important crop worldwide with an annual production of over 40 million tons (T. turgidum ssp. dicoccum (Schrank ex Schübl.) Thell., which originated from wild emmer wheat, T. turgidum ssp. dicoccoides (Körn. ex Asch. & Graebn.) Thell. in the Fertile Crescent approximately 10,000 years ago (i) domestication (from wild to domesticated emmer wheat), (ii) continued evolution under domestication (from domesticated emmer wheat to durum wheat landraces) and (iii) improvements achieved by modern breeding (from landraces to modern durum wheat varieties)  . As a corieties) . Modern rieties) . Geneticrieties)  and ofterieties) . Howeverrieties) . The limrieties)  and the rieties)  are the rieties) , and it rieties)  and CIMMrieties) , and by rn Italy , Iran . This panel was designed with the aim of capturing most of the readily exploitable genetic diversity, sharing it freely to facilitate research discoveries, and ultimately providing a rapid mean to exchange useful alleles worldwide. This article describes the germplasm composition and genetic structure of the GDP to provide the basic knowledge needed to support its international phenotypic characterization and exploitation.With this aim, the international durum wheat research community met in Bologna, Italy, in 2015 under the umbrella of the Expert Working Group on Durum Wheat Genomics and Breeding, as part of the Wheat InitiativeT. turgidum ssp. durum modern breeding accessions  from 49 countries/programs, an evolutionary population set from INRA France of 180 entries . Each entry was planted in two rows of 2 m in length under supplemental irrigation. Fungicide and fertilizer were provided in-season, following optimal local management practices. From each plot a single tiller was selected and tagged at flowering based on spike size, phenology and shape to be representative of most plants within the same plot. From this tiller, a leaf sample was collected for initial molecular screening. At maturity, the spike of the tagged tiller was harvested and used for advancement. In the 2016–2017 season at the same field station, 10 seeds from each spike were planted in rows of 0.5 m in length. Irrigation and chemical treatments were used to maximize productivity. Using the initial molecular data, a subset of approximately 1,000 entries were selected and defined as the Global Durum wheat Panel (GDP). The whole row was bulk-harvested and used for further advancement. In the 2017–2018 season, each entry was planted in plots of 6 m2 at the American University of Beirut (AUB) experimental farm in Lebanon. Fungicide, irrigation and fertilizer were applied in order to maximize productivity. Plots were visually inspected for homogeneity and off-types were manually rouged.A total of 2,503 accessions of tetraploid wheat were obtained from 25 worldwide partners representing institutions, universities, gene banks and private companies , all excT. durum lines  , which substantially included all rmplasm) . In the rmplasm) . Unfortuiversity .® markers .The initial molecular screening of the DWRC was performed by sending one leaf from each selected tiller to LGC Genomics (United Kingdom) for DNA extraction and subsequent analyses. Ninety-four KASP markers  were sel markers , includid = 3, to call samples only within three standard deviations from a known cluster position, and r = 0.8, minimum confidence score that the sample belonged to the cluster to which it was assigned versus the next closest cluster. Stepwise data curation was conducted on polymorphic SNP markers. First, markers with minor alleles present in fewer than three genotypes were discarded. Second, the remaining markers were filtered to retain SNPs with a unique map position in the available genetic maps (r2 (above a 0.3 threshold) with the putatively contiguous markers. SNP imputation was performed using Beagle 5 software using default parameters  were grouped based on the breeding program of origin and decade of release . Because the year of release was not available for elite lines included in the GDP, the year in which the cross was performed was used to estimate the year of release by adding 10 years. Polymorphic SNP datasets were selected according to the set filtering for minor allele frequency (MAF) > 5% and pruning at r2 < 0.99.Genetic diversity and population differentiation within the GDP, both at the genome-wide and at the single-locus level, were assessed within and between populations defined according to passport data provided by contributors or retrieved from GRIS  through Fst, P < 0.05. Population differentiation was assessed based on Nei’s genetic distance  was calculated using the modified ROD formula presented in Furthermore, single locus analyses of genetic diversity across the whole genome were conducted to identify genomic regions putatively affected by human-driven selection sweeps. Signals of putative selection sweeps were assessed using a hapmap pruned for (R) markers. The GDP set was then re-stratified using the Illumina 90k SNP genotyping data and three possible pruned hap-maps  were considered in order to optimize the trade-off between uniformity of genomic sampling and informativeness. Based on the analysis results, the pruned SNP-set at r2 = 0.5 was used for all subsequent population structure analyses. For both the DWRC and GDP, the population structure was estimated by the model-based likelihood method ADMIXTURE optimized using the block relaxation algorithm and the quasi-Newton convergence acceleration method and q = 3 secants  with any ancestry in the model-based likelihood analysis. Because the GDP is a selected sub-set of the initial DWRC panel, the population stratification was first used to define the most representative DWRC entries to be included in the GDP, and secondly to define what degree of genetic diversity was lost because of the sub-sampling process. Pairwise similarity estimated as identity-by-state (IBS) was also calculated for the DWRC population to filter for duplicated/highly similar entries using TASSEL5 software  were also chosen. The genetic diversity level present in the two collections was compared to confirm that no major genetic diversity losses occurred after sampling the GDP from the DWRC. The Shannon-Wiener’s diversity index, Nei’s expected heterozygosity, allelic evenness  were calculated on the SNP dataset of the GDP for each chromosome using TASSEL5 (r2 = 0.3 and 0.5 were extrapolated from the fitted regression curves. The r2 of unlinked markers (background noise) was estimated as the 95th quantile of r2 values of markers on different chromosomes (unlinked set). To estimate the local LD value along chromosomes, each marker LD was calculated using the mean r2 with the 50 nearest markers, and then smoothed as one value using the step-sliding window.Pairwise marker correlations  defined in Detection of putative selection sweep (PSW) signals was based on genome-wide (R) markers ((R) marker profiles. Population structure assessed by ADMIXTURE (k = 3: (i) a group including T. turgidum spp. dicoccum and dicoccoides, (ii) a second group including modern durum wheat germplasm and (iii) a third group comprising modern North American germplasm together with most durum landraces and accessions of the primitives T. turgidum spp. turgidum, turanicum and polonicum as durum-related sub-species. At k = 4 the North American modern germplasm was separated from landraces and the mentioned primitive subspecies. Finally, at k = 5 the group of the modern durum wheat germplasm was further subdivided in two groups: the first one tracing its ancestry to the CIMMYT breeding program, and the second one composed of the Southern European germplasm and those entries with ancestry from the ICARDA breeding program. The structure of the population was confirmed using bootstrapped Ward’s clustering  CIMMYT- and ICARDA-derived genetic materials, and modern semi-dwarf and vernalization-insensitive lines mostly adapted to the Mediterranean environment for a total of 288 genotypes; (ii) 96 elite semi-dwarf durum wheat lines with photoperiod and/or vernalization sensitivity mainly developed in Canada, France, Italy, and Central Europe; (iii) 96 non-semi-dwarf durum wheat lines of different origins. Three additional groups were selected to incorporate more genetic diversity including; (iv) 96 EPO lines (v) 192 durum wheat landraces representing the geographical distribution of the original collection (vi) a final group including domesticated emmer lines  was also included. The final GDP selection consisted of 1,028 accessions, 976 of which were multiplied in sufficient quantity and quality for seed re-distribution by ICARDA, while 42 among European varieties and accessions with Fhb1 introgressions are available from University of Bologna and Boku University, respectively  CIMPO lines ; (v) 192llection ; and  modern germplasm, (ii) landraces and (iii) emmer (T. turgidum ssp. dicoccum) accessions, for a total of 861 genotypes. AMOVA highlighted a moderate level (23%) of genetic variance distinguishing the three groups  with respect to the comparison landraces vs. modern lines (Fst = 0.1378) , AMOVA results across the three main groups  showed that the highest proportion of molecular variance (86.94%) was observed within clusters rather than among clusters (13.06%) , followed by modern germplasm (0.292) and EPO (0.288) (landrace vs. modern comparisons (Fst = 0.127), while an Fst of 0.1 was calculated for the EPO vs. modern comparison  . The EPOiversity . Conside(13.06%) . Landrac (0.288) . As to amparison . This relandraces (282) were grouped into 14 sub-populations according to the country of origin. This clustering process accounted only for 10.1% of the variance, while the vast majority of diversity still remained unclustered within sub-populations  decade of release from 1970 to 2018; and (ii) country of registration/release, which roughly defines the main groups of breeding programs. Thus, five decades  and 12 breeding program groups    were con CIMMYT) , attribu CIMMYT) , and pai history .modern germplasm, clustered according to breeding groups. AMOVA attributed the highest proportion of molecular variance . Among the remaining unique alleles, none was a frequent allele in the target breeding group, and most (64%) had frequency from 5 to 10%. However, 53 SNPs showed higher frequency, suggesting a role in a specific breeding target or for adaptation to the corresponding environmental conditions.Considering the durum selective sweep), and therefore divergence in allele frequency in the proximity of the selected loci. Low-resolution genomic scans can be used to identify regions containing loci and causative genes with a putative major influence on breeding processes. Scans for PSW between modern and landraces (Fst only (24) or on both indices, Fst and DRI (8). Most clusters (73%) extended for less than 50 Mbp, but three extended for >150 Mbp. All chromosomes were found to carry PSW clusters, with chromosome 1B being the most targeted by breeders’ selection. Promising putative candidate genes were found to co-locate with eleven PSW clusters, for instance the genes Rht1-B and Ppd-A1 on chromosomes 4B and 2A, respectively , while 18 PSW clusters were detected in a single comparison. PSW clusters physical size extended from 11 Mbp for cluster Cls-chr3B.1 to 386 Mbp for Cls-chr6A.4, with an average of 52 Mbp  extending for less than 50 Mbp. Clusters were found in two or more comparisons (54), and only five were pair-specific. For 19 clusters a possible correspondence with a putative candidate gene could be proposed. The North American breeding group had the lowest number of PSW clusters (79), followed by CIMMYT with 88 clusters and the French breeding program with 100 PSW clusters. ICARDA and the Italian breeding programs had the highest numbers, 105 and 110, respectively. Considering pair-specific PSW clusters, CIMMYT and French groups showed the lowest number of specific PSW clusters (9), while Italy and ICARDA presented 12 and 11, respectively, and North America showed the highest number of specific PSW clusters (18).Further pairwise comparisons were carried out for breeding groups that contributed more than 30 entries to the GDP . This inentified , 59 of tk = 2 up to k = 20 based on the SNP dataset pruned at r2 = 0.5. Results of these analysis are shown in K value based on the two analyses. Applying SNP pruning with r2 = 0.8 outperformed the other two in terms of cross-validated group assignment , dicoccoides (98%), carthlicum (92%) and turgidum (77%) clustered together , European domesticated emmer (subp. 12) and wild emmer (subp. 13).Population stratification was conducted based on both Ward’s clustering and admixture sub-population membership from signment , althougoup size , stabilik values  and meank = 2, the second mega-cluster included most T. turgidum ssp. durum (96%), T. turgidum ssp. turanicum and most of T. turgidum ssp. polonicum (67%). Separation between durum modern and landraces started at k = 3. At k = 6, durum landraces and primitive tetraploids were split into two main groups: Asian and North African landraces. Further meaningful landrace sub-groups were split at higher k values. The group including mainly Ethiopian accessions was split in two sub-groups: the first one contained accessions of T. turgidum spp. carthlicum, polonicum and durum landraces, while the second one was mainly T. turgidum ssp. dicoccum accessions, which might represent the founder group of Ethiopian durums.At T. turgidum ssp. carthlicum (subp. 8), Central Asia durum landraces and all T. turgidum ssp. turanicum (subp. 10). Notably, sub-population 9 included a group of ICARDA founder cultivars belonging to the Om Rabi set, which were derived from crossing the Syrian landrace Haurani to the CIMMYT cultivar Jori , a mixed group of other Mediterranean landraces and old Italian cultivars such as the breeding germplasm founder Cappelli, and (subp. 6) more recent Italian cultivars directly related to landraces (subp. 7), Ethiopian durum landraces and emmers plus var Jori .k = 4 separating photoperiod sensitive accessions from northern countries  and Mediterranean-adapted photoperiod insensitive accessions. K = 10 was the minimum k value at which both Ward’s clustering and ADMIXTURE clearly separated the modern durum entries originating from the two main CGIAR (CIMMYT and ICARDA) breeding programs. At k = 13, modern durum entries were already divided in four sub-sets corresponding to French origin and EPO (subp. 1), CIMMYT (subp. 2), ICARDA (subp. 3), North American and Austrian (subp. 4). At k = 18 the group containing mainly CIMMYT durum wheat modern lines was further split in three sub-groups: the first one contained CIMMYT and other modern lines with different origins, the second one included CIMMYT and Egyptian germplasm, and the third one only modern germplasm from the Mediterranean countries. Only at k = 20 was the EPO set split into two groups.The modern durum germplasm was first split at i) wild and domesticated emmers and T. turgidum ssp. carthlicum, (ii) durum landraces including the founders of modern germplasm and (iii) modern durums. Among durum landraces, one of the two sub-branches included North African/Southern European landraces and pioneering durum cultivars obtained from landrace selection and landrace intercrossing, such as Senatore Cappelli (selection from a landrace) and Capeiti8 (cross between Cappelli and a Syrian landrace selection). The second group included durum landraces from West Asia including Haurani, well-known as the most widely cultivated landrace population in its area of origin, showing developmental and morphological traits relevant for adaptation to low water availability and high temperatures, widely exploited by the ICARDA durum program since its inception , with the strongest reduction affecting some rare alleles.landraces and 0.28 for modern lines and ranging from 0.09 to 0.38 (Table 2B) indicated a generally higher or similar level of genetic diversity captured within the GDP compared to previously studied collections. Recent studies reported PIC values of 0.26 for durum modern germplasm  and Italy (99%), is consistent with extended selection for a particular environment. Studies aiming to describe allele fixation and genetic diversity are of great importance to guide breeders in planning their crosses and introgressions  and Fst metrics. Overall putative selection signals were found throughout the entire genome, including the centromeric regions. The average signal size of 50 Mbp suggested strong selection pressure. Several PSW clusters identified in this study co-located with known loci relevant to durum wheat breeding, thus demonstrating the predictive validity of the genome-wide search method. Expected signals associated with the transition from landraces to modern were related to the control of traits strongly selected in the post Green Revolution period causing the almost complete fixation of such loci in the modern subpopulations. As an example, Cls-chr4B.2 included the widely used Rht1-B  overlaps with TaSus2 (sucrose synthase), a main driver of starch accumulation in wheat found to be associated with strong changes in haplotype frequency in bread wheat  , Cls-chr1B.4 (Glu-B1), Cls-chr6A.1, Cls-chr6A.2 and Cls-chr6A.3, with the last three PSW partially overlapping and co-locating with Gli-6A  , S9, whi. CIMMYT , S9. Theh Gli-6A , S9.Cls-chr7B.3, centromeric and Cls-chr7B.12, distal) and in chromosome 5B (Cls-chr5B.5) and that are putatively related to grain quality. The two signals in chromosome 7B were associated to a strong QTL for grain yellow pigment content  vs. French and ICARDA breeding groups  can be found in the online repositories GrainGenes (see text footnote 3), and T3/Wheat (see text footnote 4). The genetic characterization of this panel increases the knowledge of genetic relationships and population structure of worldwide durum wheat, while facilitating the identification of the optimal sources of genetic diversity for a given target locus. The entire durum community is now empowered to use this panel to discover novel and useful alleles via GWAS. Finally, since the GDP is an open resource available to the whole community, the discovery of useful alleles can be immediately incorporated in breeding activities irrespective of the country or research group that makes the discovery. This is particularly true now that a number of genomic resources are available for wheat, including the reference sequence of the durum wheat genome (In the present study, a very large and diverse durum wheat panel referred to as the GDP has been assembled and made publicly available to drive further discovery and deployment of beneficial alleles. The GDP is maintained and distributed by ICARDA Genbankt genome . We belihttps://wheat.pw.usda.gov/GG3/global_durum_genomic_resources, and T3/Wheat https://wheat.triticeaetoolbox.org/breeders_toolbox/protocol/158.The datasets presented in this study can be found in online repositories: GrainGenes FB, RT, MM, LC, JA, KA, and SX designed this initiative. EM, DM, SC, SX, JF, and MH produced the genotypic data and all authors supported the genotyping. EM, GS, AM, FD, GP, MM, RT, LC, and FB analyzed the data. EM, GS, AM, MM, and FB developed the first draft. All authors reviewed and approved the final version of this 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. The reviewer MR declared a past co-authorship with several of the authors DM and FL to the handling editor.


Daphnia populations from three Arctic lakes in West Greenland through the past 200–300 years to test the impact of environmental change on the temporal and spatial population genetic structure. The contrasting ecological state of the lakes, specifically regarding salinity and habitat structure may explain the observed lake‐specific clonal composition over time. Palaeolimnological reconstructions show considerable regional environmental fluctuations since 1,700 (the end of the Little Ice Age), but the population genetic structure in two lakes was almost unchanged with at most two clones per time period. Their local populations were strongly dominated by a single clone that has persisted for 250–300 years. We discuss possible explanations for the apparent population genetic stability: (a) persistent clones are general‐purpose genotypes that thrive under broad environmental conditions, (b) clonal lineages evolved subtle genotypic differences unresolved by microsatellite markers, or (c) epigenetic modifications allow for clonal adaptation to changing environmental conditions. Our results motivate research into the mechanisms of adaptation in these populations, as well as their evolutionary fate in the light of accelerating climate change in the polar regions.Climate and environmental condition drive biodiversity at many levels of biological organization, from populations to ecosystems. Combined with paleoecological reconstructions, palaeogenetic information on resident populations provides novel insights into evolutionary trajectories and genetic diversity driven by environmental variability. While temporal observations of changing genetic structure are often made of sexual populations, little is known about how environmental change affects the long‐term fate of asexual lineages. Here, we provide information on obligately asexual, triploid  Daphnia populations with the intriguing result that the dominant clone in two lakes has persisted for the past ~250 years. Our results motivate research into the mechanisms of adaptation in these asexual populations, as well as their evolutionary fate in the light of accelerating climate change in the polar regions.Temporal observations of population genetic structure are often made for sexual populations, but little is known about how environmental change affects asexual populations. Applying a “time machine” approach, we recorded up to centuries‐long time series of population genetic structure in Arctic asexual  In addition to higher levels of biological organization , these environmental drivers affect species at the population level, including their temporal and spatial genetic structure and diversity  and across trophic levels  since deglaciation. Research has focussed on a range of biophysical aspects of the ice‐land‐water continuum, including glacier mass balance, terrestrial vegetation and herbivore dynamics, and aquatic biogeochemistry .Short sediment cores (4 replicates per lake) were taken in July 2015 with a 9.5 cm diameter Hon‐Kajak sediment corer from the deepest part of each lake. Replicate cores were taken at one location in the basin, approximately within a 30‐m radius. The boat was moved after each core was taken to avoid unnecessary sediment disturbance. Recovery was around 25‐cm at each lake and at all sites included an intact sediment water interface Figure . Cores wPreviously, the changing abundance of purple sulfur bacteria (PSB) at Braya Sø has been inferred from the variable concentration of the carotenoid okenone in the lake sediment , and clustered with lineages of the Polar Daphnia pulicaria clade based on the mitochondrial ND5 , n = 12 (SS1590), n = 23 (SS1381)).Ephippia were removed from 1‐cm‐sections of sediment and enumerated for each sediment section as described in Frisch et al. . Preferehttps://www.geneious.com) for peak calling and binning. Called peaks were visually inspected and manually adjusted when necessary.Microsatellite loci were amplified in single, 12.5 µl multiplex reactions , using an Eppendorf Nexus Thermal Cycler with thermal cycle conditions recommended in the Type‐it PCR kit manual. Ten microsatellite primers Table  represenpoppr 2.8.3  with alternating warm and cold periods, and the most recent warm period beginning at the end of the 20th century Figure . As examD Figure , indicat3.2Daphnia populations were studied in three lakes near Kangerlussuaq, SW Greenland . None of the MLGs included a locus with more than three alleles. Of the eight loci used for genotyping, six  had three different alleles in at least one individual, while two  had a maximum of two alleles.Genetic diversity and population structure of Daphnia population in SS1590 was more diverse than the former two lakes with seven MLGs in a total of 10 isolates, of which only one MLG was found during two (nonadjacent) time periods. These differences in clonal diversity and dominance structure are also reflected in the rarefied estimate of clonal diversity  was remarkably stable. Importantly, we found strong dominance of a single, lake‐specific asexual clone throughout the past two to three centuries in each of these two lakes.The results of this study indicated lake‐specific genotypic composition of the three s Figure , and des4.1Daphnia populations in an area about 250 km north of Kangerlussuaq . Not only are littoral and benthic habitats more diverse in SS1590, but the lake also has a more dynamic response to evaporative driven lake level changes than many of the lakes in the area. These short‐term lake level changes also exacerbate habitat heterogeneity more at this site than others, due to its morphometry. SS1381 has a simpler morphometry, essentially with one deep basin. SS4, the largest of the lakes can be considered a pelagic system and while lake levels are variable here as well, the contribution of the littoral zone to whole lake production and diversity is probably more limited.The overall greater clonal diversity of the SS1590 4.2Daphnia). But as well as these broader, regional responses which can be traced across lakes, each individual lake has its own ecological trajectory in environmental space , which Daphnia may exploit as a direct or indirect food source, as observed in other studies  abundance  and SS1381 (MLG10), across all sample depths, over the past 200–300 years. In SS4, two other clones were detected in two different time periods, both with very low abundance. In contrast, the third lake (SS1590) had a much shorter temporal record but a higher number of clones. While it is obvious that clonal diversity in this lake overall was much higher, the temporal pattern of genetic structure and diversity cannot be identified with certainty due to the low numbers of isolates; overall ephippial density in this lake was much lower  with a smaller amount of well‐preserved eggs suitable for genetic analyses.(i)The persistent clone may be a general‐purpose genotype that successfully exploits a variety of historic environments that are not (yet) within a harmful rangeThere are several tenable explanations for the apparent dominance and persistence of single clones in two lakesDaphnia clone have been observed in several lakes throughout a wide geographic range. For example, an asexual Daphnia pulex clone invaded the population in Lake Naivasha, Kenia in the 1920s, and then displaced the local sexual, genetically diverse Daphnia population  in the metalimnion at SS4 (a distance of some 8–12 m) would expose animals to a temperature change of >10°C, considerably more than the temperature change during the recent millennia. Moreover, the annual temperature range in the epilimnion is also in the order of 10°C Undetected by the applied genetic resolution, a single MLG may comprise several distinct clonal lineages, each adapted to a specific historic environmentIn two of our studied Arctic populations, the persistence of Daphnia pulex that had invaded Japanese lakes and ponds. Interestingly, these MLGs comprised 21 mitochondrial haplotypes, indicating a higher genotype diversity than that resolved by the microsatellite loci Epigenetic modifications explain clonal adaptation and allow clones to persistThe possibility of cryptic genetic variation must be considered due to the limited resolution offered by microsatellite markers which cannot fully account for possibly existing genome‐wide variation. For example, using 12 microsatellite loci, So et al.  detectedDaphnia, whose gametes are derived from almost fully matured tissue , or whether local  adaptation to different conditions can be observed. If possible, hatchlings of eggs from older sediments should be included to compare phenotypic and transcriptomic responses of ancient and contemporary isolates. In addition, the role of epigenetic modifications should be considered in further studies ; writing‐original draft (supporting). Nicholas John Anderson: Formal analysis ; visualization ; writing‐original draft . Christopher L. Osburn: Formal analysis (supporting); writing‐original draft (supporting). John K. Colbourne: Formal analysis (supporting); writing‐original draft (supporting). Dagmar Frisch: Conceptualization (lead); formal analysis (lead); funding acquisition (lead); investigation (lead); supervision (lead); visualization ; writing‐original draft .Supplementary MaterialClick here for additional data file.