Stipa grandis, and then distinguished which adaptive mechanism(s), phenotypic plasticity or local adaptation, was/were involved in this species to adapt to environmental changes. (1) All traits were significantly influenced by the interaction of population and growth condition and by population in each condition, and inter-population variability (CVinter) was larger in the field than in the common garden for 8/9 traits, indicating that both phenotypic plasticity and genetic differentiation controlled the phenotypic differences of S. grandis. (2) From a functional standpoint, the significant relationships between the values of traits in the common garden and the environmental variables in their original habitats couldn’t support local habitat adaptation of these traits. (3) Low CVintra, low quantitative differentiation among populations (QST), and low plasticity shown in the western populations indicated the very low adaptive potential of S. grandis to environmental changes. (4) From the original habitats to the common garden which is far away from S. grandis distribution region, positive phenotypic responses were found in several populations, indicating that some original habitats have become unfavorable for S. grandis.Whether plants are able to adapt to environmental changes depends on their genetic characteristics and phenotypic plastic responses. We investigated the phenotypic responses of 7 populations of an important dominant species in semi-arid steppe of China -  Whether a plant in terrestrial ecosystems is able to adapt to the environmental changes depends on its population genetic characteristics and phenotypic plastic responses3. The importance of genetic characteristics to predict distribution shifts is advancing4. However, the potential for phenotypic plastic responses has often been neglected even though understanding evolutionary potential of a species is limited without considering its phenotypic responses.Environmental changes, such as climatic changes or anthropogenic activities, would be expected to shift plants’ distributions as species expand in newly favorable areas or decline in increasingly unfavorable/hostile locations5. Likewise, because of the increasing impact of environmental changes on plants’ distribution shifts, there has been increasing interest in identifying which adaptive mechanism(s) of phenotypic responses, phenotypic plasticity, or local adaptation (adaptive genetic changes), or combination of these two mechanisms, help(s) them to adapt to environmental changes7. Phenotypic plasticity can be distinguished from genetic differentiation which includes local adaptation (adaptive genetic changes) and maladaptive or non-adaptive genetic changes, depending on whether quantitative traits differences among populations in situ disappear by raising individual plants from these populations under the same conditions (i.e. in common gardens)10. Moreover, if quantitative traits differences in situ show larger than those in common gardens, it might be controlled by the combination of phenotypic plasticity and genetic differentiation11. Furthermore, ecologically meaningful correlations between the values of quantitative traits in the common garden and the environmental variables in their original habitats could predict local adaptation (adaptive genetic changes) to the selection of environmental changes13. What’s more, how a trait varies within and among populations is critical to determine the potential of a species/population to perform along environmental gradients8.A deep understanding of phenotypic plastic responses of a species is necessary to forecast its full potential to adapt and/or evolve to changing conditions14. Comparing with tree species18, only a few studies paid attentions to phenotypic plastic responses of steppe species and local adaptation is less common20. Given steppe species could not adapt to the rapid environmental changes, their distributions will be greatly influenced. Therefore, more studies are needed to know about the phenotypic plastic responses of steppes species to environmental changes in order to protect the structures and functions of grassland communities.Due to climate changes and anthropogenic activities, steppes are becoming fragmented and degraded, especially in arid and semi-arid areasStipa grandis steppe is the most common, representative and stable community of typical steppe in Euro-Asian Steppe21. However, the distribution region of S. grandis has rapidly changed due to fragmentation and degradation by climate changes and anthropogenic activities in the past decades, showing a pattern of eastward migration22. Mode of reproduction can influence distribution shifts by affecting evolutionary potential and dispersal capacity3. S. grandis is self-compatible23, therefore, the fragmentation and degradation of habitats would enhance its inbreeding and enlarge population genetic drift, decrease population genetic diversity, then affect its evolutionary potential to environmental changes24. In our pervious study, amplified fragment length polymorphism (AFLP) markers were used to analyze its population genetic characteristics based on 7 populations across its distribution region in semi-arid steppe of China25. In this study, exactly the same 7 populations  and in a common garden, and the environmental variables including geographic and bioclimatic variables in their original habitats were collected to use for trait – environment correlation analysis. In order to test the evolutionary potential and distinguish phenotypic adaptive mechanism(s) of S. grandis to the environmental changes across its main distribution region, we tested reaction norms of these quantitative traits from their original habitats to the common garden, estimated traits differences among populations in each condition, calculated intra (inter) - population variability (CVintra and CVinter) for every trait, related the values of quantitative traits with the environmental variables in their original habitats in each condition, and calculated quantitative differentiation among populations (QST) for every trait examined in the common garden.The first 2 principal components summarized 89.86% of the overall variation among the 19 layers. PC-1 and PC-2 explained 53.56% and 36.12% variance, respectively  and growth condition (C) showed significant effects (P < 0.05) on all 9 traits, that is to say, there were significant different reaction norms among populations  among populations  positive relationships with longitude, height of reproductive shoot showed a significantly negative relationship with PC-1 score (precipitation component) (P < 0.05), and height of vegetative shoot and length of the maximum leaf showed significantly negative relationships with altitude (P < 0.05). No significant relationships were found between seed related traits and any environmental variable (P > 0.05)   by Mantel’s tests  Table . In addi02) Fig. , betweensts Fig. .Table 2Sintra ranged from 0.065 to 0.192 and CVinter ranged from 0.070 to 0.264. CVintra was a little lower than CVinter for all traits, with significant differences (P < 0.001) for 3 growth related traits and non-significant differences for 6 seed related traits (Table CVP < 0.05) among populations  negative relationships with latitude. Seven traits, excluding height of reproductive shoot and length of callus, showed significantly (P < 0.05) positive relationships with PC-2 scores (temperature component)    showed significantly (10) Fig. , and bet24) Fig. .intra ranged from 0.053 to 0.166 and CVinter ranged from 0.063 to 0.227. CVintra was a little lower than CVinter for all traits, with significant differences (P < 0.05) for height of reproductive shoot, length of the maximum leaf, seed, and the first segment of awn  different for all 9 traits as shown by the significance of the interaction of population and growth condition, traits differences among populations were significant (P < 0.05) in each condition  of S. grandis populations although some significant trait – environment relationships were found. For example, regarding  9 quantitative traits measured in the common garden, five traits showed significantly negative relationships with latitude and 7 traits showed significantly positive relationships with PC-2 score (temperature component) by Spearman’s correlation analysis  and the distribution region of S. grandis has eastward shifted22. In the present study, the common garden site was chosen as an unfavorable or a hostile condition because it is beyond the distribution region of S. grandis22. From their original habitats to the common garden, the eastern populations, such as Bayantuhai, showed negative phenotypic plastic responses for most traits , or under homogenizing selection, respectively. In this study, the result that QST of 8 traits was lower than FST value (0.2431) by AFLP markers25 indicated that homogenizing selection rather than directional selection played an important role in affecting the quantitative trait differentiation among S. grandis populations.Maladaptive or non-adaptive genetic changes could occur as a result of genetic drift or founder effect, or as a result of stress, nutrient limitationintra showed significantly lower than CVinter  played an important role in helping S. grandis is the most important dominant and constructive species of the climax community in semi-arid steppe of China, therefore, its distribution shifts and population changes have great effects on community structure and function. We have studied its genetic characteristics based on 7 S. grandis populations which covers its main distribution region , and in this study, we selected exactly the same 7 populations to analyze their phenotypic plastic responses from their original habitats to a common garden. A detailed description of sampling sites could be found in Wu et al.’s literature25.S. grandis distribution region and was thought as a hostile growth condition for S. grandis. Soil was collected from semi-arid steppe of China and filled in 0–30 cm in the experiment field, soil C, N, P contents showed similar values with the mean of their original habitats10, but average annual precipitation (550–680 mm) and annual temperature (12.3 °C) were higher than their original habitats  and between-population variance (σP), then calculated quantitative differentiation among populations (QST) by formula QST = σP2/(σP2 + 2σw2)32. Fourth, intra-population variability (CVintra) and inter-populations variability (CVinter) were calculated as the ratio of SDwithin population to population mean and the ratio of SDbetween population to overall mean, respectively. “SD” is the abbreviation of “standard deviation”. Significant difference between CVintra and CVinter was tested by one sample t-test, with CVinter as test value .Quantitative data meet assumption of normality and homogeneity of variance, therefore, they do not have to be transformed before data analysis. First, two-way analysis of variance  was conducted to investigate the effect of block on values of quantitative traits examined in the common garden, with block and population as fixed factors. Results showed that values were not influenced by block and population × block interaction. Therefore, we did not have to think about the block factor when we analyzed data examined in the common garden. Second, in order to examine the differences of phenotypic plasticity and reaction norms among populations, two-way analysis of variance  was conducted to investigate effects of population, growth condition and their interaction on quantitative data, with population and growth condition as fixed factors. Third, based on significant interactions of population and growth condition, we further analyzed trait differences among populations in each condition (field or common garden) by one-way analysis of variance , and got within-population variance  was used, and variable factor loadings, cumulative proportions of the total variance and scores of the first 2 principal components for each population were calculated . Furthermore, relationships between values of quantitative traits and the environmental variables in their original habitats were analysed by Spearman’s correlation analyses . It should be noted that environmental variables included geographic data  and climatic data  in this study.33.Population pair-wise distance matrix based on 19 bioclimatic variables or quantitative data collected in each condition were calculated by Euclidean’s distance coefficient after standardization of data, respectively . Population pair-wise geographic distance matric were estimated in Google Earth. Relationships between quantitative and geographic distances, and between quantitative and climatic distances were examined by Mantel’s tests (3000 permutations) in NTSYS-pc software


Oncorhynchus tshawytscha, 11,579 SNPs and resident Brook Charr Salvelinus fontinalis, 13,522 SNPs) with differing invasion success after introduction to new environments in South America relative to populations from their native range in North America. Estimates of genetic diversity were not significantly different between introduced and source populations for either species, indicative of propagule pressure that has been shown to maintain diversity in founding populations relative to their native range. Introduced populations also demonstrated higher connectivity and gene flow than those in their native range. Evidence for candidate loci under divergent selection was observed, but was limited to specific introduced populations and was not widely evident. Patterns of genomic variation were consistent with general dispersal potential of each species and therefore also the notion that life history variation may contribute to both invasion success and subsequent genetic structure of these two salmonids in Patagonia.Invasive species have become widespread in aquatic environments throughout the world, yet there are few studies that have examined genomic variation of multiple introduced species in newly colonized environments. In this study, we contrast genomic variation in two salmonid species (anadromous Chinook Salmon,  Extracted genomic DNA was quantified using Quantit PicoGreen dsDNA Assay Kits (Invitrogen) and a Victor2 microplate fluorometer (Perkin Elmer).SbfI‐HF  and individually barcoded using a six nucleotide barcode adapter sequence. Digested and barcoded samples of the same starting concentration were pooled into libraries that averaged 48 individuals, where no two samples within a library were assigned the same barcode sequence, and each barcode sequence within a library differed by at least two bases from another barcode sequence. Libraries were then mechanically sheared using a Bioruptor 300 sonicator (Diagenode) to generate DNA fragment lengths between 200 and 700 bp, and fragments were isolated using an Agencourt AMPure XP bead purification system (Beckman Coulter). Prior to sequencing, RAD libraries were quantified using real‐time PCR on an ABI 7900HT Sequence Detection System (Life Technologies). Each library was sequenced in one lane on a HiSeq 1500 sequencer at a single read length of 100 bp, across a total of 16 lanes.Restriction site‐associated DNA (RAD) libraries were prepared for Illumina HiSeq 1500 sequencing using a protocol similar to those previously published .SNP discovery was completed with the http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). The 25 bases on the 3′ end of sequence reads had reduced quality scores relative to the 5’ 75 base positions across our sequence data, so reads were trimmed to 75 bp. Reads were quality filtered and de‐multiplexed using the “process_radtags” program of STACKS, including options for cleaning the data by discarding any read with an uncalled base (‐c), discarding reads with low‐quality scores (‐q), and rescuing barcodes and partial restriction enzyme recognition sites (‐r). All other parameters and options were executed with the default values as outlined in the manual for the program (http://creskolab.uoregon.edu/stacks).For the STACKS pipeline of each species, raw Illumina reads were first checked for quality using the program FastQC  to represent genetic variation in each species. Individual samples were then aligned to the catalog using the module “sstacks,” and genotypes were exported using the “populations” module.After individual sample reads were quality filtered, trimmed, and de‐multiplexed, sequences for each sample were taken through the “ustacks” module of STACKS to identify loci. In “ustacks,” the deleveraging (‐d) and removal (‐r) algorithms were applied to filter out those sequences that were likely to be paralogous and highly repetitive. Parameters for STACKS were minimum depth of coverage at a stack (“‐m” = 2–5), maximum distance between stacks (“‐M” = 2), distance between secondary reads, and primary stacks (“‐N” = 4). SNP discovery was carried out using the default SNP model with a chi‐square significance level of 0.05. We created a ST, we only retained the first SNP marker per RAD tag. Individual samples were also filtered from the dataset if they were missing more than 30% of genotypes across all filtered loci.Genotypes were filtered with multiple steps as shown in Table Bowtie2 v.2.2.3 , allelic richness (AR), and private allelic richness (PAR) in HP‐RARE . Genetic relationships among populations within each species were estimated by pairwise FST . The Bayesian information criterion (BIC) values, which provide a metric of model selection similar to likelihood, were averaged across the 10 iterations and standard deviation was estimated for each value of K. The rate of change in BIC value was used to determine the most appropriate value of K  from the “adegenet” package  for more than a third of populations. Based on these criteria, we excluded one locus per pair that exhibited signs of physical linkage which resulted in the removal of eight loci for Chinook Salmon and 25 loci for Brook Charr. This revised dataset was used to generate estimates of Nb using the random mating model and exclusion of rare alleles  following procedures described in previous studies  by randomly selecting individuals for analyses. These sample size criteria resulted in the exclusion of one Chinook Salmon population  and two Brook Charr populations  from analyses to estimate Nb. Finally, we incorporated a correction for downward bias in estimates of “naïve” Nb . A total of 11,759 SNPs passed filtering criteria and were used for all further analyses. Table 3.2.2De novo SNP discovery was completed for Brook Charr with 12 collections representing seven native (North America) and five introduced (South America) populations. Our STACKS pipeline identified 13,522 SNPs that passed filtering criteria, of which 4,911 were assigned positions based on MapComp alignment with the Brook Charr linkage map  among collections  were similar to values among populations from the five native groups , but FST values were generally lower among introduced populations  and explained much of the variation  or AR (p = .10) than those from North America, PAR (p = .04) was statistically significant. The difference in private alleles for Brook Charr was largely driven by higher PAR (0.029) in the Los Lagos collection relative to all other collections . Estimates of Nb revealed that both native and introduced collections of Brook Charr had low Nb that were not significantly different with all individuals included  among collections  were similar to values among populations from North America locations , but FST values were generally much lower among introduced populations in South America  even when the nonsignificant comparison between Lago Fagnano and Alonso River was removed. The signal of strong genetic differentiation among Brook Charr collections was also apparent from DAPC results . Nearly all collections were distinct (K = 12), with the exception of Lago Fagnano and Alonso River from the same system that formed a single cluster that was most similar to the putative source stock from Paradise Hatchery . High values of Fy Figure .3.43.4.1k = 4) among the nine collections. Differentiation among populations was evident for specific latent factors  among the 12 collections. Differentiation among populations for each factor is shown in Figure −10 threshold  had a genetically similar native stock (Paradise Hat.) represented in our study. Lack of representative founder stocks for Parillar, Gallego Chico, and Los Lagos could have affected outlier results, but not consistently as no outliers were detected in the Los Lagos collection. False positives are known to occur in outlier tests due to genetic bottlenecks (Teshima, Coop, & Przeworski, The results from this study are consistent with the notion that dispersal potential of aquatic species can influence the extent of invasiveness as long as surrounding environments provide suitable habitat and that genetic diversity may be retained even in species with relatively low propagule pressure and distinct population structure. This information is critical as invasive species become established and cause a myriad of negative impacts that can lead to extirpation or extinction for native species (Crawford & Muir, Despite the negative consequences of introduced salmonids in South America, they continue to be promoted due to high economic value in fisheries and aquaculture production. Thus, there is a need to balance the social, economic, and ecological trade‐offs of these introduced species in South America (reviewed in Pascual et al., https://doi.org/10.5061/dryad.2f050Supplementary data of individual genotypes for all SNP loci are stored in a Dryad digital repository under accession ID:  Click here for additional data file. Click here for additional data file. Click here for additional data file.


Erinaceus roumanicus). It also discusses the role of the processes at edges of species distribution in its evolutionary history. The maternally inherited mitochondrial control region and the bi-parentally inherited nuclear microsatellites were used to examine samples within the Central Europe, Balkan Peninsula and adjacent islands. Bayesian coalescent inference and neutrality tests proposed a recent increase in the population size. The most pronounced pattern of population structure involved differentiation of the insular populations in the Mediterranean Sea and the population within the contact zone with E. europaeus in Central Europe. An interspecies hybrid was detected for the first time in Central Europe. A low genetic diversity was observed in Crete, while the highest genetic distances among individuals were found in Romania. The recent population in the post-refugial area related to the Balkan Peninsula shows a complex pattern with pronounced subpopulations located mainly in the Pannonian Basin and at the Adriatic and Pontic coasts. Detailed analyses indicate that parapatry and peripatry may not be the only factors that limit range expansion, but also strong microevolutionary forces that may change the genetic structure of the species. Here we present evidence showing that population differentiation may occur not only during the glacial restriction of the range into the refugia, but also during the interglacial range expansion. Population differentiation at the Balkan Peninsula and adjacent regions could be ascribed to diversification in steppe/forest biomes and complicated geomorphology, including pronounced geographic barriers as Carpathians.Although hedgehogs are well-known examples of postglacial recolonisation, the specific processes that shape their population structures have not been examined by detailed sampling and fast-evolving genetic markers in combination with model based clustering methods. This study aims to analyse the impacts of isolation within glacial refugia and of postglacial expansion on the population structure of the Northern White-breasted hedgehog ( The consequences of geographic isolation and gene flow limitation are highly debated topics in biogeography . Strong Erinaceus genus are a classical example of postglacial recolonisation of the Western Palearctic ) currently inhabits Central and Eastern Europe, the Baltic, and the Balkan Peninsula, and its eastern range stretches to western Siberia. The current data indicate that the Balkan Peninsula used to be the only refugium for E. roumanicus , which inhabits Asia Minor. It has been estimated that these two species diverged approximately 0.4–1.4 million years ago (E. europaeus Linne 1758) whose refugial area used to exist in the Iberian and Apennine Peninsulas. After the LGM, hedgehog species expanded from the southern refugial areas and created contact zones in the Baltics and in Central Europe E. roumanicus within the studied area using the spatial and non-spatial Bayesian clustering methods and discuss observed patterns within landscape genetic framework in the light of geomorphology and habitat quality of the region;assess the population structure of  (2)reveal the history of island populations and suggest possible way of colonisation of these islands. He hypothesize that insular effects like the founder effect, genetic drift and other factors which decrease genetic variation highly affected these populations; (3)analyse the populations in the secondary contact zone from Central Europe. We hypothesize that respective populations were affected by species interactions, including introgression and reinforcement; (4)analyse the populations from the post-refugial areas at the Balkan peninsula and assess the hypothesis of occurrence of the glacial subrefugia.Samples obtained from muscle tissue or digits or ears were collected from road-killed animals or from museum collections. No special permit is needed from the Institutional Review Board. The tissues were fixed in 96% ethanol and stored at −20 °C. The samples were obtained in cooperation with the Natural History Museum of Crete (Petros Lymberakis), the Slovenian Museum of Natural History (Boris Kryštufek), and the University of Agricultural Sciences and Veterinary Medicine Cluj Napoca (Attila Sandor). For the study, 314 samples were analysed from the following European countries: Czech Republic (49), Slovakia (26), Hungary (9), Romania (43), Slovenia (44), Italy (2), Croatia (20), Bosnia and Herzegovina (25), Montenegro (6), Serbia (10), Bulgaria (4), Macedonia (17) and Greece (59), which included several islands . Each sample’s origin, GPS coordinates, and date of collection along with the name of the person who collected it are listed in DNA was extracted using a commercial DNA Blood and Tissue Kit . A combination of nine microsatellite markers and mtDNA control region sequences was used. Laboratory procedures were performed according to the protocols by http://www.softgenetics.com). Data binning was performed with the AutoBin software (http://www4.bordeauxaquitaine.inra.fr/biogeco/layout/set/print/Ressources/Logiciels/Autobin). The presence of the null alleles , stutter bands and large allele dropouts was tested using Micro-Checker  based on analysis of likelihood. Structure is powerful to detect clinal variability and high geographic admixture in the dataset  for each population was tested by GenePop (HWE), number of alleles (Na) and allelic richness (AR) were estimated in FSTAT (N = 24 from Crete. The expected (HE) and observed (HO) heterozygosities were computed using Genetix . The relationships among the haplotypes were visualised by the median-joining network (http://www.fluxus-engineering.com). The demographic parameters were estimated by summary statistics of the genetic variability for each population . Tests for haplotype diversity , nucleotide diversity  and neutrality tests  were computed for each population in DnaSP v.5 (FS(for big sample sizes) and R2  are considered as the most powerful tests  and higher stability of algorithm. The first run of program was set to Kmax = 2–10 populations, then we plotted Kmax versus DIC values and selected the optimal Kmax  were obtained and represented 53 haplotypes among the analysed samples  was omitted from further analysis because it was suspected of having a hybrid origin. A follow-up analysis with the NewHybrids program  using thK = 1–14), division into the three populations (K = 3) obtained the highest support according to K value was observed for a division into the seven populations (K = 7). An increasing number of clusters resulted in further differentiation of the Balkan population by separation of the population from Pannonia (at K = 4), differentiation of the Slovenian population (K = 5) and separation of populations from the west and east of the Balkans (from K = 6). However, there is certain degree of admixture among particular clusters. Two different landscape genetic algorithms supported similar spatial pattern with three  for each population. Crete had the lowest AR and HE. The highest FIS was detected in Romanian population.The genetic variability parameters are given for the seven populations  and threThe median-joining network didn’t demonstrate reciprocally monophyletic lineages in the studied area . HaplotyThe genetic variability parameters for mitochondrial data were estimated for six populations according to the results of analysis in Geneland . All tesr = 0.303; p < 0.001) within our dataset. Geneland analysis of microsatellite markers determined that the division of our dataset into three populations  was the most likely to occur  and the population from the contact zone with E. r. nesiotes subspecies, which supports the findings by All analyses indicate strong differentiation of the individuals from Crete, Cyclades and from Peloponnese, Euboea and southern and central Greece. As the largest island of Greece and the fifth largest Mediterranean island, Crete is typified by impoverished native mammalian fauna and the high impact of recently introduced species. However, Crete is also known as a generator of novelty in the numerous lineages that were able to colonise the island. From a macroevolutionary viewpoint, Crete is the place where pygmy forms of deer, elephants and hippopotamuses occurred . From thb sequences, E.r. nesiotes, by Dorotha Bate in 1906 based on phenotypic data. These facts imply the occurrence of insular syndrome in the Cretan population. The markedly decreased genetic variation values in the insular population imply that factors such as the founder effect and genetic drift played substantial roles. The agreement between the genetic variation patterns and the phenotypic data indicates that insular syndrome, including dwarfism in hedgehogs from the Greek islands, has occurred , might alter the genetic architecture of the population’s expanding edge and change the genetic variation as well . DemograE. europaeus in Central Europe (Slovakia—Trenčianske Teplice) using traditional genetic markers. Interspecies interactions in the sympatry zone may have previously played roles in forming reproductive barriers. We hypothesised that hybridisation occurred when the two species came into contact for the first time, when the isolating mechanisms were not yet formed. Afterwards, during the reinforcement phase, species developed mechanisms for reproductive isolation and the hybrid event frequency decreased for follow-up generations. The history of possible hybridisation events in the Central European population should be tested using approaches that include whole genome data because the introgression modes may be complex. Future investigations should address the contact zone at the Slovenian/Italian border (located to the south of the Alps) and the effect of altitudinal heterogeneity on both species. This region involves the contact zone for both species and the transition zone between the Slovenian mountains and the Padan Plain.We are the first to report a single hybrid sample from the range margins of The lower genetic diversity associated with the range margins and the novel selective pressures applied by interactions with related species may be why the Central European population was highly divergent from the other populations in the dataset.E. roumanicus, this pattern might mirror geographical isolation, ecological differentiation, and also the quality of ancestral habitat and larger extent of steppe biome during glacial periods.The separation of the Pannonian population represents the most pronounced pattern after the split of parapatric and peripatric populations. The Pannonian basin is a confined area from the view of geographical isolation by the Carpathian arch and vegetation cover typified by grass steppe, i.e., it possess habitat similarity with the part of species range in Pontic-Caspian steppe. Consequently, it represents a pronounced biogeographical region of Europe and a presumed continental interglacial refugium of continental and steppe faunal elements . In E. rBombina bombina  have been located (Alnus glutinosa) in the regions around Bulgaria and Greece. Based on these examples, Further differentiation occurs between populations from the west of the peninsula at the Adriatic coast and the east at Black Sea coast. This differentiation might be ascribed to isolation by distance and barrier role of Carpathian Mountains. For example, based on the results provided by AIS, the largest genetic distances between the samples in our dataset are located in Romania. This pattern implies a restricted level of gene flow within the population, which might be ascribed to the high altitudinal heterogeneity of the Carpathian Mountains. However, this pattern may also mirror occurrence of forest refugia at Adriatic and Pontic regions. The Black Sea shore likely served as a glacial refugium for multiple animal species, such as  bombina  and Emysicularis . The preicularis . Palynolicularis , for exaicularis . In the  located . King & E. europaeus and, consequently, a local parapatic evolutionary process (see above).The population from Slovenia is separated from the Balkan genotypes in Structure analyses. This pattern suggests a gene flow limitation due to the geographical isolation, caused by the Alps, the Dinarids and/or specific climatic conditions. For example, a fully grown forest was present during the LGM in Slovenia . AnotherThe most pronounced pattern within our dataset was represented by the differentiation of populations at the edges of the recent range. This study indicates that peripatry and parapatry might be not only limiting factors to range expansion, but may also provide strong microevolutionary forces that shape the genetic structure of the species. Traditionally, population differentiation in temperate species has been ascribed to allopatric processes during glacial periods. Here, we provide an alternative example, showing that population differentiation may occur not only during the glacial restriction of the range into refugia, but also during the phase of interglacial range expansion. During the interglacial periods, peripatric modes of evolution for many taxa might be more frequent due to the rise in sea level, which cause increased shelf island formation. Parapatric processes are accelerated by the range expansions of lineages isolated in the refugia and by the formation of potential suture zones.E. roumanicus in post-refugial area at Balkan Peninsula and adjacent regions shows differentiation with particular subpopulation located in areas of interglacial steppic refugia and glacial forest refugia. A detailed habitat model and past niche modelling will be necessary to infer, which type of habitat was crucial for survival of E. roumanicus during glacial periods. However, certain degree of admixture among particular clusters is obvious, mirroring complex geomorphology and shape of geographical barriers, as well as altitudinal heterogenetity within Balkan Peninsula. The situation might be further complicated by the fact, that a mosaic of particular biomes often existed in refugia , allelic richness ( AR), expected heterozygosity ( HE), observed heterozygosity ( HO), inbreeding coefficient ( FIS) are given for each population.Number of alleles , their accession numbers (GenBank accession no.), annealing temperatures , fluorescent labels and repeat motifs. For control region, primers and annealing temperature are given.Click here for additional data file.10.7717/peerj.3163/supp-4Figure S1Genotypes of 260 individualsClick here for additional data file.


Tursiops sp.) are studied to address this broader question. They exhibit remarkable geographical variation for morphology, life history, and genetic diversity, and this high level of variation has made the taxonomy of the genus controversial. A significant population structure has been reported for the most widely distributed species, the common bottlenose dolphin (T. truncatus), in almost all ocean basins, though no data have been available for the western North Pacific Ocean (WNP). The genetic diversity of bottlenose dolphins in the WNP was investigated based on 20 microsatellite and one mitochondrial DNA markers for samples collected from Taiwanese, Japanese, and Philippine waters  during 1986–2012. The results indicated that there are at least four genetically differentiated populations of common bottlenose dolphins in the western and central North Pacific Ocean. The pattern of differentiation appears to correspond to habitat types, resembling results seen in other populations of the same species. Our analyses also showed that there was no evident gene flow between the two “sister species”, the common bottlenose dolphins, and the Indo-Pacific bottlenose dolphins (T. aduncus) occurring sympatrically in our study region.The evolutionary processes that shape patterns of diversity in highly mobile marine species are poorly understood, but important towards transferable inference on their effective conservation. In this study, bottlenose dolphins (The online version of this article (doi:10.1007/s00227-017-3232-8) contains supplementary material, which is available to authorized users. Tursiops sp.) has provided a number of classic examples regarding the parapatric or sympatric distribution of differentiated populations or species  and the common bottlenose dolphin . These two species are distributed parapatrically, or even sympatrically in particular areas. The distribution of IPBD is chiefly in the coastal waters of warm-temperate-to-tropical Indo-Pacific regions from southern Japan to western South Africa and southeastern Australia, where the water depth is always less than 200 m  control region sequence data for CBD and IPBD with samples from the western South Pacific Ocean and hypothesised that the coastal ecotype of CBD is lacking in the Indo-western Pacific Ocean and that this is because the coastal habitat has been occupied by IPBD  or multiplex polymerase , respectively, cycled at 95 °C for 120 s (15 m for multiplex polymerase), followed by 35 cycles of 40 s at 94 °C, 40 s at the best annealing temperature of the locus, and 70 s at 72 °C, and a post-extension at 72 °C for 10 min. Fragments were visualised on an Applied Biosystems 3730 DNA Analyser, and the allele size was determined by an internal standard marker  and visualised in Peak Scanner v.1 (Applied Biosystems). Every locus in each sample was examined at least twice and the scores blind confirmed by a second person.We examined 20 microsatellite loci and a 388 bp mtDNA control region sequence that have been conventionally used and validated in other bottlenose dolphin genetic studies  and then sequenced on an Applied Biosystems 3730 DNA Analyser. All sequencing results were visualised in FinchTV (PerkinElmer) and manually corrected using MEGA 5.05 , and expected heterozygosity (HE), and to assess the significance of any deviation from Hardy–Weinberg equilibrium (HWE) using Fisher’s exact test and Markov chain method . The inbreeding coefficient (FIS) for each locus in each putative population was estimated using FSTAT 2.9.3.2 . We used a non-parametric permutation approach with 10,000 permutations to assess the statistical significance (p < 0.05 after Bonferonni correction).We used Arlequin 3.5.1  reflecting the highest hierarchical level was determined by the Evanno method implemented in Structure Harvester  was set to vary from 1 to 6 clusters, and the maximum rate of Poisson process fixed to 41 (number of samples), uncertainty attached to spatial coordinates was fixed to 100 km, maximum number of nuclei in the Poisson–Voronoi tessellation was fixed to 123, and the posterior probabilities of population membership for each individual and each pixel of the spatial domain were calculated with a burn-in of 100 iterations and a spatial domain of 151 pixels along the X-axis and 250 along the Y-axis.We also used the R package Geneland  and long-term gene flow  were estimated using maximum-likelihood coalescent methods implemented in MIGRATE version 3.6.6  estimated for the “Japanese Coastal” population  with t tests using 1000 permutations.The effective population size  and haplotype diversity (h) for each putative population, as well as for the overall species. The neutrality tests Tajima’s D (Tajima r) of the observed distribution were calculated and tested to evaluate fit to models .We used MrBayes 3.2 ; while RST was 0.055, but not significant (p = 0.068 ± 0.002). As expected based on simulation studies  indicated K = 1, ΔK = 2 when the LOCPRIOR function is applied, and then differentiation between East Japan and Taiwan/West Japan could be detected .All samples were successfully genotyped, and the missing data rate was less than 5% for all loci and samples. No significant LD or deviation from HWE was detected. Summary statistics are shown in Table For comparisons among CBD populations using the “sur population” option, all population-specific clusters could be identified and FC1 and FC2 accounted for 82.95% of the variance Fig. . The WesK = 3. With the K fixed to K = 3 in the second step, the analysis suggested eight variations of population distribution patterns for CBD among the 10 runs with the highest LPP in 100 simulations. These eight variations all showed approximately the same clustering pattern, with a few samples showing inconsistent population membership  based on the result of Geneland analysis: a West Coast Cluster (the samples from Miyazaki and the west coasts of Taiwan and Japan), and an East Coast Cluster (the samples from the east coast of Taiwan and Taiji). The samples from Tainan, Aichi, and Shizuoka were excluded due to the uncertainty of their population identity. er Table . NeN−n = 344), we used a 388 bp consensus mtDNA sequence from a total of 393 sequences and reconstructed five putative CBD populations  and four IPBD populations   was 0.935 and nucleotide diversity (π) was 0.0197. The h and π for each population are shown in Table For CBD, we examined 353 sequences and identified 64 haplotypes defined by 82 variable sites, including two deletion gaps (Table S7). The overall haplotype diversity . The overall ll Table . TtHap_5ll Table .FST and ΦST comparisons were statistically significant  values supported K = 2 (Table S11). The ancestry assignment test showed three CBD individuals, from Japan, Taiwan, and Philippines, respectively, that might have had an IPBD grandparent, although the probability was only between 7.1 and 11.9% .FCA analyses showed a clear genetic difference between CBD and IPBD . Using STRUCTURE, both ΔGlobicephala macrorhynchus) , and Fraser’s dolphins (Lagenodelphis hosei) (Chen n = 4) to reveal such pattern, if it does exist. In contrast to some earlier studies  Chen . Howeverei) Chen , but ourBalaenoptera acutorostrata; Abe et al. Phocoenoides dalli; Hayano et al. On the other hand, Kasuya et al.  found a Our results further reveal that there may be another coastal CBD population in the vicinity of the Taiwan Strait (western Taiwan), although its relationship with the CBD population from West Japan (in the Sea of Japan) is ambiguous, and so we grouped the samples together as a West Coast Cluster. Earlier studies for bottlenose dolphins in the coastal region of WNP mainly focused on the ecological, morphometric, and genetic differences between CBD and IPBD . In general, there was no strong evidence for population expansion.The migrate analysis suggests that long-term gene flow between the East and West Coast Clusters is limited to less than one migrant per generation. On the other hand, the GeneClass analysis identified three contemporary first-generation migrants, suggesting the presence of ongoing gene flow between the two populations. Low levels of gene flow have been reported between the coastal and pelagic populations in the eastern North Atlantic Ocean , striped dolphin , spinner dolphin (Stenella longirostris), common dolphin (Delphinus sp.), or Fraser’s dolphin , Mikura Islands (southeastern Japan), and western Taiwan; and the FCA result showed distinct clustering for those samples . The microsatellite data are consistent with the population structure proposed based on mtDNA data Hayano , but to Our results indicate that there are at least two populations of CBD distributed parapatrically in the coastal waters around Taiwan and Japan, corresponding to the distribution of shallow continental shelf or deep continental slope habitats. Further sampling may well reveal further structure in the broader region. Although our analyses detected some recent immigration, the long-term estimates show limited gene flow between the two populations. This potentially agrees with earlier analyses that show that habitat specialisation plays an important role in differentiating inshore and offshore populations Supplementary material 2 (XLSX 77 kb)Below is the link to the electronic supplementary material.


Understanding human genetic diversity in Africa is important for interpreting the evolution of all humans, yet vast regions in Africa, such as Chad, remain genetically poorly investigated. Here, we use genotype data from 480 samples from Chad, the Near East, and southern Europe, as well as whole-genome sequencing from 19 of them, to show that many populations today derive their genomes from ancient African-Eurasian admixtures. We found evidence of early Eurasian backflow to Africa in people speaking the unclassified isolate Laal language in southern Chad and estimate from linkage-disequilibrium decay that this occurred 4,750–7,200 years ago. It brought to Africa a Y chromosome lineage (R1b-V88) whose closest relatives are widespread in present-day Eurasia; we estimate from sequence data that the Chad R1b-V88 Y chromosomes coalesced 5,700–7,300 years ago. This migration could thus have originated among Near Eastern farmers during the African Humid Period. We also found that the previously documented Eurasian backflow into Africa, which occurred ∼3,000 years ago and was thought to be mostly limited to East Africa, had a more westward impact affecting populations in northern Chad, such as the Toubou, who have 20%–30% Eurasian ancestry today. We observed a decline in heterozygosity in admixed Africans and found that the Eurasian admixture can bias inferences on their coalescent history and confound genetic signals from adaptation and archaic introgression. African genetic diversity is still incompletely understood, and vast regions in Africa remain genetically undocumented. Chad, for example, makes up ∼5% of Africa’s surface area, and its central location, connecting sub-Saharan Africa with North and East Africa, positions it to play an important role as a crossroad or barrier to human migrations. However, Chad has been little studied at a whole-genome level, and its position within African genetic diversity is not well known. With 200 ethnic groups and more than 120 indigenous languages and dialects, Chad has extensive ethnolinguistic diversity.3Important questions about Africa’s ethnic diversity are the relationships among the different groups and the relationships between cultural groups and existing genetic structures. In the present study, we analyzed four Chadian populations with different ethnicities, languages, and modes of subsistence. Our samples are likely to capture recent genetic signals of migration and mixing and also have the potential to show ancestral genomic relationships that are shared among Chadians and other populations. An additional major question relates to the prehistoric Eurasian migrations to Africa: what was the extent of these migrations, how have they affected African genetic diversity, and what present-day populations harbor genetic signals from the ancient migrating Eurasians? We have previously reported evidence of gene flow from the Near East to East Africa ∼3,000 ya, as well as subsequent selection in Ethiopians on non-African-derived alleles related to light skin pigmentation.In this work, we present a genetic dataset of 480 Chadian, Near Eastern, and European individuals genotyped at 2.5 million SNPs, in addition to high-coverage whole-genome sequences from 19 of these individuals. From Chad, we studied (1) the Toubou, who are nomads from northern Chad and speak a Nilo-Saharan language; (2) the Sara, who are a sedentary population from southern Chad and also speak a Nilo-Saharan language; (3) the Laal speakers, a population of just ∼750 individuals who speak an unclassified language isolate and live in southern Chad; and (4) an urban population from the capital city of N’Djamena. In addition to the Chadians, we included Greek, Lebanese, and Yemen samples whose location and history suggest that they might be informative about early African-Eurasian migrations. We used this dataset to advance our understanding of human genetic diversity in Africa and neighboring regions by focusing on population migration and mixing and how the admixture process has shaped present-day genetic variation.Samples were collected from Chad (238), Lebanon (126), Greece (96), and Yemen (20) A; detailThe genotyping data were merged with data from the African Genome Variation Project,Genotype data were processed with PLINK:−8 mutations per nucleotide per generation. Admixture masks to identify African and Eurasian segments within mixed high-coverage genomes were generated with PCAdmixPrincipal components were computed with EIGENSOFT v.4.2.−9  mutations per site per year.Phylogenetic analysis of whole Y chromosome sequences was carried out as described in Bergström et al.Population-mixture signals and proportions were tested with qp3Pop, qpDstat, and qpF4Ratio from the ADMIXTOOLS package.Heterozygosity on a per-individual basis was estimated with VCFtools v.0.1.13th percentile of the PBS distribution and looked for variants previously reported under putative selection in Europeans.Evidence for positive selection was tested with the population branch statistic (PBS)We performed an initial exploration of our dataset by using principal-component analysis (PCA).Ne) over time via the MSMC approach.Ne patterns during this period,Ne in ancient times was also observed in the Sara, Laal speakers, and other Africans, such as the Yoruba. We suggest that the deviation from the expected Ne pattern in the Toubou is related to extensive admixture history with Eurasians, like the Eurasian admixture seen in Ethiopians, and we explore this possibility directly with admixture tests below.We further investigated the genetic variation in Chad by estimating changes in the effective populations size , where a negative value with a Z score < −4 indicates that X is a mixture of Africans and Eurasians. We found, as expected, that most Ethiopians are a mixture of Africans and Eurasians. An exception is the Gumuz population, where f3 is always positive. The Gumuz language belongs to the Nilo-Saharan family, which could have isolated the Gumuz from the Afro-Asiatic-speaking Ethiopians. However, we found that the Toubou in Chad, who also speak a Nilo-Saharan language, are a mixture of Africans and Eurasians, making f3 always significantly negative. This suggests that the impact of Eurasian migrations today extends beyond East Africa and the Afro-Asiatic-speaking populations. We did not detect significant (Z score < −4) Eurasian admixture in the Sara (Nilo-Saharan language family) or the Laal speakers (unclassified language) with the use of f3 statistics . However, this statistic loses sensitivity with small mixture proportions and post-admixture drift,f3 statistics do not necessarily reflect a complete absence of admixture. We thus further tested for admixture by using ALDER and MALDER, which assess admixture-induced linkage disequilibrium (LD) and can detect small mixture proportions from a substantially diverged reference possibly missed by the f3 statistic. ALDER detected admixture in the Toubou, Sara, and Laal speakers (Z score = 11), a time close to the date of mixture in East Africans 2,500–2,700 ya (Z score = 26). The second mixture event occurred much more recently at 170–260 ya (Z score = 5). In southern Chad, we detected mixture events that were more ancient than those in the north. Mixture occurred 3,900–4,800 ya (Z score = 10) in the Sara and 4,750–7,200 ya (Z score = 5) in the Laal speakers  and f3, where X is a present-day non-sub-Saharan African population in our dataset and is related to one that contributed ancestry to the Toubou and Amhara (Z score < −4)  and that the most significant result was for present-day Sardinians. Exceptions to this correlation were the North African populations , who appeared to have contributed more ancestry to the Toubou than to the Amhara. We repeated the tests by using published ancient genomes ; early Neolithic farmers were the most significant contributors, as reported previouslyf3 and f3 and found a lower correlation of the f3 values , suggesting a more complicated history of gene flow from genetically different Africans to different populations in the Near East.Previous studies have suggested that the Eurasian backflow into East Africa came from a population related to early Neolithic farmers.re < −4) . We thenwo tests A. We fou genomes  and alsoK = 2 and the British and Yoruba as ancestral populations and (2) the f4 ratio α = f4/f4, where X is one of the populations in our dataset . Eurasian ancestry was estimated at 26%–30% in the Toubou, 0.3%–2% in the Sara, and 1.2%–4.5% in the Laal speakers. Eurasian ancestry in Ethiopians ranged from 11%–12% in the Gumuz to 53%–57% in the Amhara. African ancestry in the Near East ranged from 7%–14% (Yemen) to 0.7%–5% (Lebanese Christians).We next quantified the proportion of African-Eurasian mixture in the study populations by using two methods: (1) ADMIXTURE dataset . The resOur results from the PCA and MSMC analysis showed a deviation of the admixed populations from the patterns observed in unadmixed (or less admixed) populations in the same geographical region. The MSMC analysis, in particular, showed that admixed Africans had patterns indicative of a decline in heterozygosity , somewhat similarly to Eurasians. We tested whole-genome heterozygosity in these populations and found that it decreased in admixed Africans according to their Eurasian ancestry A. This dWe wanted to understand the consequence of admixture on the models that use the density of heterozygous sites to infer the demographic history of populations. We first tested whether the coalescent history estimated by MSMC was affected by a small proportion of mixture, such as the African mixture found in Greeks and Lebanese (ranging from 0% to 5%). We tested the Greek, Lebanese, CEU, and CHB  split times from the Yoruba and found that all populations split from the Yoruba ∼70,000–80,000 ya, implying that the low proportions of African admixture in the Greeks and Lebanese did not detectably affect the estimates of relative cross-coalescence rate A. We nexMCM6 (MIM: 601806) rs4988235, a variant associated with the lactase-persistence phenotype. This SNP was previously found to be under strong positive selection in Europeans, where it was probably advantageous to individuals living in pastoralist societies.HERC2 (MIM: 605837) rs1129038 a major contributor to blue eye color in EuropeansSLC24A5 (MIM: 609802) rs1834640, a major contributor to pigmentationWe found that, in addition to influencing the relative cross-coalescence rate, admixture can also inflate putative signals of positive selection. For example, using the PBSD = 0.0094. Neanderthals are also closer to the Toubou than to the Yoruba: D = 0.0041. On the other hand, we found that Neanderthals are closer to Europeans than to Near Easterners: D = 0.0056 and D = 0.0040.In addition to introducing to African populations genes that were positively selected in Europe, the recent African-Eurasian admixture carried Neanderthal alleles to Central and East Africa. Neanderthals are closer to the Amhara than to the Yoruba: r = 0.98; p value < 2.2 × 10−16) and inversely correlated with their African ancestry  also facilitated human migrations. The African Humid Period, for example, was a humid phase across North Africa that peaked 6,000–9,000 yaIn addition to the early Eurasian migration to Africa ∼6,000 ya, a second migration ∼3,000 ya affected the Toubou population in northern Chad but had no detectable genetic impact on other Chadian populations. This migration appears to be associated with the previously reported Eurasian backflow into East Africa, given that the source populations and dates of mixture are similar. Occurring at the start of the Iron Age, these migrations could have been facilitated by advances in warfare and transportation technology in the Near East. It is uncertain why the impact of this migration in Chad affected only the Toubou. The African ancestral component in the Toubou is best represented by the Laal-speaking population, suggesting that the African-Eurasian mixture probably occurred in Chad. However, ethnolinguistic barriers could have already been established at this time between the Chad groups, preventing a widespread dissemination of the Eurasian ancestry. The Toubou, despite their Islamic faith, do not show the genetic admixture detected in many Near Eastern and North African populations around 1,100 ya,Eurasian backflow into Africa thus appears to have been a recurrent event in the history of many Africans, given its considerable impact on their genomes. Although population mixture in general is a process that increases genetic diversity, we observed a decrease in heterozygosity in the admixed Africans. Our simulations showed that these results are expected after mixture at these proportions with the Eurasians who suffered a significant bottleneck at the time of their exodus from Africa ∼60,000 ya. Consequently, we found that mixture can complicate interpretation of the coalescent history inferred from models that use the density of heterozygous sites in their implementations. In addition, we detected in admixed Africans an inflation of positive-selection signals on alleles associated with adult lactose tolerance and pigmentation in Europeans, but we suggest that these alleles have drifted neutrally in Africans after admixture. Furthermore, we detected Neanderthal ancestry in admixed Africans and found it to be proportional to their Eurasian ancestry. Similarly, in admixed Near Easterners, we found a decrease in Neanderthal ancestry proportional to the gene flow they have received from Africans. Although a higher genetic affinity of Neanderthals to Europeans than to Near Easterners was previously interpreted as additional Neanderthal admixture in the history of Europeans,It is important to note that in this work we inevitably invoke Occam’s razor to support the simplest model consistent with our data; the history of the populations studied here, including the time and sources of the Eurasian admixture in Africa, could be more complex. aDNA from Chad and neighboring regions remains a challenge given the poor DNA preservation in hot climates, but future successful efforts in aDNA research could provide additional insights and reveal additional complexities not considered by the modern-DNA-based models favored here.Our study has shown that human genetic diversity in Africa is still incompletely understood and that ancient admixture adds to its complexity. This work highlights the importance of exploring underrepresented populations, such as those from Chad, in genetic studies to improve our understanding of the demographic processes that shaped genetic variation in Africa and globally.


The economic fluxes of the Indian Ocean trade network therefore directly shaped exchanges of genes, in addition to goods and concepts.The Indian Ocean has long been a hub of interacting human populations. Following land- and sea-based routes, trade drove cultural contacts between far-distant ethnic groups in Asia, India, the Middle East and Africa, creating one of the world’s first proto-globalized environments. However, the extent to which population mixing was mediated by trade is poorly understood. Reconstructing admixture times from genomic data in 3,006 individuals from 187 regional populations reveals a close association between bouts of human migration and trade volumes during the last 2,000 years across the Indian Ocean trading system. Temporal oscillations in trading activity match phases of contraction and expansion in migration, with high water marks following the expansion of the Silk Roads in the 5 The political unification of large territories in the third century BCE (Before Current Era) opened up new trading routes, most famously the Silk Roads and the maritime network along the coasts of the Indian Ocean4. These in turn triggered sustained interactions between major geopolitical poles, including states in China, India, Indonesia, Arabia and East Africa6. Trade was both diverse and intense, benefiting from specialized local production, such as cotton and beads from India, gold from East Africa, spices from the Malacca city-states, incense from Yemen and silk from China1. With population growth and technical advances, notably in agriculture, the first century CE saw a major intensification in the movements of goods and people3. The development of new sailing techniques, particularly during the 11th century CE, enabled movements over very long distance. Indonesian traders reached as far as East Africa and the Swahili city-states7; Arab sailors installed trading posts on Madagascar and the west coast of India1; and Chinese traded across Island Southeast Asia and East India. Far from competing, the various maritime and terrestrial routes created an intertwined and dense network that rapidly diffused goods, but also knowledge, beliefs and values, proving a unifying force across a diverse set of partners6. New urban spaces acted as hubs to the flow of trade and culture, often growing into cosmopolitan cities with large immigrant populations, such as in Baghdad and Zanzibar9. The intensity, stability and speed of these trading connections formed a large world-system, a precursor to the heavily globalized societies of today10. Yet whether Indian Ocean trade directly shaped the genetics of modern populations is less well understood.For more than 2,000 years, the Indian Ocean rim has been an area of intense interaction between African, Middle Eastern and Asian populations, driven by a strong tradition of wealthy maritime and land-based trading routes11 and EEMS12, is a structured space with distinct regional genetic ancestries allowing the fine-scale reconstruction of historical human migrations mediating gene flow  reflects the rise of the Silk Roads2, dominated by terrestrial and coastal migrations from China to Arabia . This occurred in parallel with the spread of Islam by Arab merchants, marked by an intensification of gene flows from the Middle East to East Africa and Central Asia  with the appearance of new maritime routes2 , with the Industrial Revolution driving another long period of strong international trade.However, the most drastic change occurred in the middle of the 14ury Fig. . OutbreaAll of these trade spikes were paired with physical population movements, showing that the flow of goods and ideas was linked to the movements of the people who brought them. At least in the Indian Ocean, at a global scale, trade and migration were coupled forces, and the Indian Ocean trade network therefore provides an early example of globalization, showing connections between human trade and mobility that are still apparent around the world today.25 to filter for i) close relatives, using an IBD estimation with upper threshold of 0.25 (second degree relatives); ii) SNPs that failed the Hardy-Weinberg exact (HWE) test (P < 10−6) were excluded; iii) samples with a call rate <0.99 and displayed missing rates >0.05 across all samples in each population were excluded. After filtering, our dataset included a total of 3,006 individuals, genotyped for 215,335 SNPs, from 187 different populations located in Southeast Asia, South Asia, East Asia, Middle East, East Africa, South Africa, and Europe  with Plink v1.925 resulting in an alternative dataset of 100,830 SNPs for specific analyses.Our dataset is based on previously published studies of populations around the Indian Ocean rim  and a genetic dissimilarity matrix between populations, we set a map of the Indian Ocean rim defining a grid of 1,000 demes. Depending on their location, several populations may be included in one deme. 3 × 106 MCMC iterations were run before checking for convergence of the MCMC chain. Plots were generated in R following the EEMS v112 manual  to perform a model-based Bayesian clustering of genotypes. Mutational rates (Mu) and effective population size (Ne) were estimated with an Estimation-Maximization (EM) algorithm running in CHROMOPAINTER v2.031, and was performed on all 22 autosomes for the entire dataset (10 iterations). The weighted average of these parameters, according to the SNP coverage of each chromosome and the number of individuals, was then used to compute the chromosome painting. Using fineSTRUCTURE v2.0731 with 2 × 106 Markov-Chain-Monte-Carlo (MCMC) iterations, discarding the first 1 × 106 iterations as “burn-in”, sampling from the posterior distribution every 10,000 iterations following the burn-in, a coancestry heat map and a dendrogram were inferred to visualize the number of clusters defined statistically that best describe the data.Before estimating admixture scenarios, we defined clusters of populations. We first performed a fineSTRUCTURE v2.0715. Population clusters were defined in two steps. From the fineSTRUCTURE v2.0731 results, each cluster was defined by: (i) high posterior probabilities given for the nodes of the population dendogram (>0.8); (ii) at least 100 individuals per cluster. This last criteria, although arbitrary, allowed us to define uniform clusters from statistically robust branches higher up in the tree, in a similar approach to that reported previously31. Subsequently an FST matrix was calculated with Eigensoft v5.0.232 between populations within each cluster to define outliers with FST values greater than one standard deviation from the mean  defining surrogates populations from all clusters but its own. Note that the numbers of populations within a given cluster is not correlated with the estimated dates of admixture (P = 0.95). The painted chromosomes obtained by CHROMOPAINTER v2.031 for each population were used in GLOBETROTTER v2.015 to estimate the ratios and dates of the potential admixture events that characterize them. Coancestry curves were estimated with and without standardization using a ‘NULL’ individual, and consistency for each estimated parameter was checked. 100 bootstrap resamplings were performed to estimate the p-value of the admixture events  and the 95% confidence interval for the obtained dates. The ‘best-guess’ scenario given by GLOBETROTTER v2.015 was considered for each target population. Admixture events whose estimated 95% confidence intervals of dates between both ‘NULL0’ and’NULL1’ models do not overlap were subsequently reclassified as ‘uncertain’, as described by Hellenthal et al.15. To obtain a second estimate of potential admixture scenarios, we ran MALDER v116, a modified version of the ALDER v1.316 software to observe any multiple admixture events, using the parental populations defined by GLOBETROTTER v2.015. Both analyses give highly correlated admixture dates . We performed an F-test to compare the models with time and volume of trade using SPSS v20.038. When required, the Bonferroni multiple testing correction was applied. Networks were generated with Cytoscape v3.2.139 and maps were generated using Global Mapper v.15. .In parallel with the admixture dates obtained from GLOBETROTTERSupplementary File


Lasallia pustulata along an altitudinal gradient in the Mediterranean region. We resequenced six populations as pools and identified highly differentiated genomic regions. We then detected gene-environment correlations while controlling for shared population history and pooled sequencing bias, and performed ecophysiological experiments to assess fitness differences of individuals from different environments.Many fungal species occur across a variety of habitats. Particularly lichens, fungi forming symbioses with photosynthetic partners, have evolved remarkable tolerances for environmental extremes. Despite their ecological importance and ubiquity, little is known about the genetic basis of adaption in lichen populations. Here we studied patterns of genome-wide differentiation in the lichen-forming fungus We detected two strongly differentiated genetic clusters linked to Mediterranean and temperate-oceanic climate, and an admixture zone, which coincided with the transition between the two bioclimates. High altitude individuals showed ecophysiological adaptations to wetter and more shaded conditions. Highly differentiated genome regions contained a number of genes associated with stress response, local environmental adaptation, and sexual reproduction.Taken together our results provide evidence for a complex interplay between demographic history and spatially varying selection acting on a number of key biological processes, suggesting a scenario of ecological speciation.The online version of this article (doi:10.1186/s12862-017-0929-8) contains supplementary material, which is available to authorized users. Fungi are diverse and ubiquitous, having evolved over time to occupy a wide range of ecological niches. Some fungal species are exceptionally proficient at surviving a broad range of environmental conditions. In nature, these species can inhabit latitudinal and altitudinal clines that span considerable temperature ranges . IndividNeurospora crassa, resulting in genomic islands of differentiation involved in cold-response and circadian rhythm [Saccharomyces cerevisiae [Neurospora crassa [Environmental factors have been shown to be drivers of local adaptation in diverse fungi. For example, temperature differences are responsible for the maintenance of differentially adapted populations of pathogens . Temperan rhythm . Differen rhythm . Furtherrevisiae . In somerevisiae , as is ta crassa . OverallLichen-forming fungi, which constitute about half of the described ascomycetes, are a nutritionally specialized group of fungi that form obligate symbiotic associations with green algae and bacteria . LichensAltitudinal gradients are suited to study local adaptation because ecological transitions are typically steep and occur at relatively short distances, thus limiting the confounding effect of distinct regional evolutionary histories . MoreoveLasallia pustulata (Umbilicariaceae), a species with a distribution from southern Europe to northern Scandinavia, which forms dense populations on exposed, siliceous rocks [Here we report on the population genomics of a lichen-forming ascomycete along an altitudinal gradient in the Mediterranean region. As model, we chose us rocks . Using gus rocks . TherefoLasallia pustulata is a foliose, rock-inhabiting, haploid lichen-forming ascomycete. Individuals are attached to the substrate with a central holdfast. L. pustulata has a mixed strategy of asexual and sexual cycles. Asexual reproduction via isidia – macroscopic dispersal units containing both symbionts that break off the mother thallus – is the typical (and in most populations only) way of reproduction. Isidia are considered to be detached from the thallus mainly by raindrops, and dispersed over short distances [2. For each population, we collected 100 thallus pieces of ~8 mm in diameter. Our sampling design aimed at capturing the maximal diversity present at the sites. The minimum distance between sampled individuals was 50 cm to maximize the inclusion of different genets. Samples were collected with sterile tools and transferred into sterile 2 ml tubes.istances . We collL. pustulata thalli (2 loggers per population) indicate that localities 1 to 4 have higher summer temperatures and are less prone to freezing during winter than populations 5 and 6. Logger data also showed that temperatures of the rock surfaces to which thalli are attached frequently exceeded 50 °C in summer. The populations therefore experienced seasonal temperature fluctuations on the order of >40 °C  Mediterranean pluviseasonal oceanic (populations 1–4), ii) temperate oceanic submediterranean (population 5), and iii) temperate oceanic (population 6) . The draft genome is composed of 3891 scaffolds (average length of 10Kbp) for a total length of 39.2 Mbp and an N50 scaffold of size 104.3Kbp. The genome is ~92% complete according to an assessment with the software BUSCO 2.0 [As reference genome, we used the draft assembly of USCO 2.0  and a liL. pustulata collected near Orscholz  in July 2013 using the method by [For gene model prediction we used both ab-initio based methods and RNA-Seq derived transcript mapping onto the assembled genome following the method described in . For thiethod by , and purethod by , with a nr database at an E-value cut-off of 1x10−3, and default weighting parameters. We also annotated each protein with InterPro domains using InterProScan [We used Blast2GO  to annotrProScan .L. pustulata genome using BWA-MEM [SortSam.jar and MarkDuplicates.jar. Single nucleotide polymorphisms were called with SAMtools  and filter-pileup-by-gtf.pl. The synchronized file was converted into a gene-based synchronized file using the script create-genewise-sync.pl in PoPoolation2 [subsample-synchronized.pl (−−without-replacement).We filtered out reads shorter than 80 bp, reads with N's, and reads with average base quality scores less than 26 along with their pairs using FastQFS . Trimmed BWA-MEM  and defa BWA-MEM . Reads wmpileup, ). Indelsmpileup,  using tholation2 . The covπ, a measure of polymorphism in a sample of sequences scaled to their length, ii) Watterson's θ (θW), a measure of the number of segregating sites, and iii) Tajima's D, a measure of the skew of allele frequency distribution. All estimates were calculated in non-overlapping 10-kb windows across the genome using PoPoolation [To characterize genome-wide patterns of variation, we estimated three population genetic parameters by accounting for pooling: i) oolation , assuminoolation , 47. Foroolation .ST) was calculated with fst-sliding.pl in PoPoolation2. We only considered SNPs with a minimum count of 4, a minimum quality of 20, and falling into the upper 0.5% tail of the FST distribution, corresponding to an FST threshold of 1.0. Highly differentiated SNPs were further inspected with Fisher’s exact test [fisher-test.pl in PoPoolation2 and a Bonferroni-corrected p-value of 0.003. In addition, we estimated average FST across all polymorphic SNPs for each gene and only considered those falling into the upper 5% tail of distribution to be truly differentiated. Based on this analysis, we calculated the percentage of overlap between SNP-based and gene-based lists. SNPs were classified as genic and non-genic loci. Genic SNPs were further classified as exonic, coding, and intronic. Non-genic SNPs located in the 600 bp 5'-flanking sequence of each gene were considered putative promoter SNPs.To identify strongly differentiated alleles, we adopted an empirical outlier approach. Genetic differentiation  of the full set of distances across all polymorphic SNPs. The resulting set of pairwise (quantile) distances was then jointly analyzed using a three-way generalization of classical multidimensional scaling . DISTATISTATIS, .f3 and f4 statistics. These statistics are formal tests for admixture as they detect correlations in allele frequencies that are not compatible with population evolution following a bifurcating tree. To calculate f3 and f4 statistics we used the threepop and fourpop functions in TreeMix on all possible triplets and tetraplets population groups. To further explore population relationships, we calculated neighbor joining trees for the correlation matrix obtained with Bayenv2.0 (see below) and for the matrix of pairwise FST values calculated across all polymorphic sites using the package ape in R [To reconstruct the historical relationships among populations using their current genome-wide allele frequencies, we used TreeMix v1.12 . We creaape in R .e) according to the relation θ = Neμ, assuming identical but unknown mutation rates (μ) in all populations. Haplotypes (20 per population) were obtained by parsing the output files of PoPoolation2 for genomic regions shorter than 90 bp, and containing three or more SNPs. To minimize the chance that the loci are linked, we retained only one such region per scaffold. This information was then used to extract the individual Illumina reads covering this region from the mapping file for each population. The reads were aligned and trimmed to the informative region. The alignments were filtered for a minimum coverage of 15x, maximum coverage of 100x and minimum length of 20 bp in all populations. This resulted in a data set of 5880 sequences from 49 loci, covering in total 1763 bp. All previously described steps were performed with custom Python scripts (see [6) was run for 1.8 × 106 generations, recording every 600th step after a burn-in period of 6 × 104 generations. Convergence was monitored with Tracer (http://beast.bio.ed.ac.uk/). All effective sample sizes of the MCMC chain were larger than 104.To corroborate the hypothesis of admixture, we estimated gene flow among the three major groups  across multiple intergenic polymorphic loci using the coalescent-based method MIGRATE-N 3.2.6 . We estipts (see ). Bayesipts  were considered as showing strong support for a non-zero correlation.To identify candidate loci for altitude specific adaptation, we correlated allele frequencies of populations with the environment using Bayenv2.0 . Bayenv2orr in R . Elevatihttp://www.geneontology.org/). In this system genes are assigned to predefined bins depending on their functional characteristics in a species-independent manner. An enrichment analysis will find which GO terms are over-represented in a given data set using the annotations for that gene set. We used the R package topGO [ST distribution, ii) the subset of these that are differentially fixed between the low altitude (population 1 to 4) and high altitude (population 6) genetic clusters, and iii) the set of genes containing the Baynev2.0 top 1% SNPs. All genes with a GO annotation were used as background. Significance for each GO-identifier was computed with Fisher’s exact test at α = 5%. We used the ‘elim’ method in topGO to iteratively remove genes mapped to significant GO terms from more general terms, thus reducing the rate of false positives. Only GOs with more than three associated genes were considered. We used the REVIGO tool [Gene Ontology (GO) term enrichment is a technique for interpreting the functions of a set of genes making use of the GO system of classification , we first determined the thallus size by photographing wetted thalli on scale paper using a binocular microscope and the AxioVision software . We determined the dry weights (DW) of these thalli by weighing after 3 days of oven drying at 60 °C. We also measured thallus chlorophyll content according to [We investigated the thalli for differences in biomass and chlorophyll content per surface area. To calculate the specific thallus area . We measured the response of net photosynthesis (NP) and dark respiration (DR) to thallus water content (WC) for a subsample of six thalli, representing the two main genetic groups present along the gradient. We measured complete desiccation cycles  at saturating light (750 μmol photons m−2s−1), ambient CO2, at 17 °C . We weighed the samples between each measurement and later extrapolated WC as a percentage of DW. We determined DW after 5 days in a desiccator over silica gel. We considered ninety percent of maximum NP to be a reasonable estimate for optimal water saturation. We measured the samples at 3–15 h intervals for NP and DR. Immediately after each measurement we removed the samples from the cuvettes and determined their weight to calculate the decrease in WC. This process was continued for 97 h with each sample until CO2 exchange ceased due to complete drying. Statistical significance of differences in TWC and maximum NP between groups was determined using a t-test.To characterize the physiological response to different light conditions, and different thallus water contents, we conducted COL. pustulata genome. In total 5747 genes were assigned a GO term.We identified a total of 8268 genes in the After adapter and quality trimming, we obtained 179,809,145 paired-end reads .π and θW. When averaged for all 10-kb windows across the genome, estimates of π were highest in population 5 (π = 0.006 ± 0.004), and lowest in population 4 (π = 0.004 ± 0.003). Estimates of θW were instead highest in population 2 (θW = 0.005 ± 0.003), and lowest in population 4 (θW = 0.003 ± 0.003). To examine deviation from neutrality, we calculated Tajima's D in 10-kb windows across the genome. Average D deviated from neutrality and differed significantly among populations. D was negative in populations 2 (D = −0.839 ± 0.713) and 6 (D = −0.260 ± 0.322), and positive in all other populations, being highest in population 5 (D = 1.112 ± 0.54)  to 0.236 (Pool2 vs. Pool6)  and high-altitude (6) population clusters. When calculating average FST quantile distance SNP matrix illustrated the close genetic affinities of populations 1 to 4, with population 5 occupying an intermediate position between these and population 6  from the other populations. The multidimensional scaling of the Fn 6 Fig. . The tren 6 Fig. , with ththreepop test we found clear evidence of admixture at the level of population 5. All population triplets having population 5 as the admixed group displayed significantly negative f3 values . We inferred higher significance for pairs grouping together population 5 and 6 with one of the lower altitude populations, respectively  to θ = ~0.065 in group B (pop. 5) . Of these, 42 SNPs were located in 39 genes , and higher chlorophyll a + b content per surface area unit (p = 0.002) (Table 2O) compared to G2 thalli . In relation to surface area unit, G1 thalli fixed almost three times as much CO2. In relation to thallus dry weight G1 and G2 specimens did not significantly differ in their CO2 fixation rates (p = 0.65) , and one composed of thalli from all remaining sites (G2). Differentiated SNPs in all six markers were monomorphic between the two groups  Table . Second,Temperature and precipitation drive large-scale distribution patterns of lichens. It is thus expected that much of the signal of adaptation among lichen populations should occur along these gradients . Here weL. pustulata populations. We found two genetic clusters along the gradient. One cluster is predominant at low elevations (up to ~800 m a.s.l.), while the other is predominant at high elevations (~1300 m a.s.l.). Our data suggest extensive admixture of these clusters at ~1100 m a.s.l. Given the high number of reciprocally fixed SNPs between the clusters and the high levels of clonal propagation in this fungal species, possible explanations for the observed pattern include ancient divergence, and a combination of limited gene flow, long generation times, and strong environmental filtering. Unfortunately there are no estimates for sexual or asexual generation times in L. pustulata from which we could calculate the age of the split between the clusters. Thus it is currently impossible to formally distinguish between the above scenarios. Ancient population splits and high genomic divergence have been reported in non-lichenized fungi. For example, strong genomic divergence, evidence for ancient population splits and introgression between subpopulations were inferred for the human pathogens Coccidioides immitis and C. posadasii [. Strong genetic structure was also found in locally adapted subpopulations of Neurospora crassa [L. pustulata correspond to the major bioclimatic zones covered by our transect, with their admixture zone coinciding with the transition between the Mediterranean and the temperate-oceanic climate. Therefore, environmental filters likely contribute to the observed genetic structure.Our study revealed significant differentiation and structure of osadasii . Strong a crassa . Numeroua crassa –64. InteOchroconis mirabilis in different habitats [NOP9), which has been reported among the 71 essential genes required for oxidative stress tolerance in Saccharomyces cerevisiae [Cch1, which has been reported to be involved in the Ca2+ release in response to exogenous oxidative stress in yeast [Hsp20, glutamate carboxypeptidase, and a putative thermotolerance protein. Other candidates for diversifying selection include genes involved in the regulation of gene transcription, in particular Isw1, a gene that functions in parallel with the NuA4 and Swr1 complexes to regulate stress-induced gene transcription via chromatin remodeling in yeast [Swr1 was detected among the candidates. Furthermore we found genes putatively involved in cellwall integrity and filamentous growth pathways , and in septin , 77)..OchroconWsc4, a MAPKKK-cascade protein kinase, a two-component osmosensing histidine kinase, and a calcium/calmodulin-dependent kinase (CAMK). CAMK proteins were reported to be involved in thermotolerance and oxidative stress survival in Neurospora crassa [L. pustulata, given a difference of ~6 °C in the mean annual temperature between top and bottom of the cline, and frequent winter frost above 1100 m a.s.l.Several of the top differentiated genes containing environmentally associated SNPs were also involved in the ESR . In parta crassa . The prea crassa . It is tST-based outliers was low in our study. As in other studies of local adaptation [post hoc. Future studies of L. pustulata will have to include more populations and replicate independent clines to fully disentangle demography from local selection.The overlap between environmentally associated SNPs and Faptation –83, diffaptation . Anotheraptation . Thus, cRamalina capitata [Cetraria aculeata [Catillaria corymbosa [Psora decipiens [Many candidate genes have known roles in stress response and growth regulation, and it is thus tempting to hypothesize that variation at these loci might affect fitness-related traits. Our ecophysiological experiments showed the presence of genetic lineages with differential fitness under different environmental conditions along the cline. The high altitude group seems to be better adapted to more humid conditions and to lower light intensities than the lowland group. In particular, samples from this population had thicker thalli and thus more biomass per area unit. In poikilohydric organisms such as lichens, an increased fungal biomass may be beneficial at wetter high-elevation sites and where winds speed up drying, especially on the exposed rocky outcrops where the species lives. This is because a higher fungal biomass may lead to i) higher mechanical stability against mechanical damage, ii) prolongation of the wet phase for an increase of the active period , and iiicapitata , betweenaculeata , betweenorymbosa , and betecipiens . Thallusecipiens . Future L. pustulata showed that several genes involved in sexual reproduction were significantly enriched, supporting the hypothesis of reproductive isolation. This is interesting, because we did not observe morphological evidence for sexual reproduction, such as presence of apothecia. Additional indication for reproductive isolation stems from the observation that genomic divergence between clusters is not limited to a few genomic areas, but rather widely dispersed across the genome. It involves many pathways centrally important for the response to environmental signals and stress, gene expression, growth, and metabolism. Overall, the high level of genomic divergence, and the presence of physiological differences between genetic clusters suggest the existence of L. pustulata ecotypes adapted to the Mediterranean and temperate-oceanic bioclimatic zone.Adaptation to divergent environments promotes environmental specialization and reproductive isolation among fungal populations , 9. In fPool-seq genome resequencing is a cost-effective and powerful approach to assess allelic diversity in populations, and to identify genes that are potentially under selection –92. HoweOur exploratory work into the genomics of adaptation of a lichen-forming fungus reveals numerous loci and pathways putatively involved in environmental adaptation, including many loci shown in other fungi to be linked to temperature and UV-radiation stress response. Such genes provide excellent targets for further investigations. Future studies based on individual genotyping, possibly including replicate populations from different regions, additional physiological analyses including more samples, and quantitative trait locus mapping experiments of the candidate genes in controlled and field settings will help to elucidate the drivers of local adaptation in this and other fungal species.Additional file 1:L. pustulata thalli (two loggers per population). (PDF 113 kb)Temperature data (in °C) collected between May 28th 2014 and June 2nd 2015 from loggers positioned at the level of Additional file 2:Plot of the first two axes of a PCA showing high correlation between 19 bioclimatic variables and altitude for the six populations along the cline. (PDF 524 kb)Additional file 3:L. pustulata samples for anatomical and physiological measurements. (PDF 113 kb)Genetic characteristics of the six loci used to genotype 18 Additional file 4:D, across 309 non-overlapping sliding windows of 10-kb. For the six scatterplots on the left, the data are plotted in ascending order of mean diversity across the six populations (solid line) together with the respective 95% confidence interval (grey envelope). Black crosses depict population-specific outlier SNPs. For the boxplot on the right, the data are summarized for each population across all windows. (PDF 517 kb)Distribution of nucleotide diversity, measured as Tajima's Additional file 5:π, Watterson's θ (θW), and Tajima's D in non-overlapping 10-kb windows across the genome of L. pustulata. b) Pairwise Tukey contrasts based on linear mixed effect models for each measure and population. (PDF 446 kb)a) The mean and standard deviation for three standard population genetic parameters, Additional file 6:ST values. (PDF 165 kb)NJ trees of the correlation matrix obtained with Bayenv2.0 and the pairwise FAdditional file 7:ST (lower triangle) and pairwise correlations of Bayenv2.0 correlation matrix among allele frequencies (upper triangle). (PDF 81 kb)Average pairwise FAdditional file 8:L. pustulata populations. The test was performed on 722,401 high confidence SNPs. Bold values indicate significant f3 (if z < 0) or f4 statistics (if |z| < 3). (PDF 236 kb)Three-(a) and four-(b) population test of the Additional file 9:L. pustulata found along the altitudinal gradient . (PDF 110 kb)Parameter estimates from MIGRATE-N based on 49 loci for pairs of three genetic groups of Additional file 10:Z Bayenv2.0 score), highly differentiated (top 0.5% FST distribution) SNPs. (PDF 126 kb)Allele frequency and annotation for the 42 environmentally associated  top 0.5% highly differentiated SNPs, b) top 0.5% highly differentiated SNPs differentially fixed between populations 1–4 and population 6. (PDF 228 kb)Additional file 12:Z Bayenv2.0 SNPs. (PDF 103 kb)List of significantly enriched GO terms for environmentally associated genes. Gene names in bold indicate genes containing top Additional file 13:L. pustulata used for the anatomical and ecophysiological measurements. Samples in bold and underlined (N = 6) were used for the gas exchange experiments. (PDF 165 kb)Neighbor-joining tree of the aligned concatenated gene fragments of the six loci at which we genotyped 18 thalli Additional file 14:Light (a), temperature (b) curves for 6 thalli representing two genetic groups . (PDF 556 kb)Additional file 15:Net photosynthesis at optimal thallus water content and optimal temperature for genetic group A  and B . (PDF 94 kb)