Vriesea reitzii by genotyping seven nuclear microsatellite loci in 187 individuals from six populations. We characterized levels of genetic diversity and assessed the genetic structure among populations. Vriesea reitzii populations showed high levels of genetic variation  and moderate levels of genetic differentiation . The high levels of genetic diversity may be explained by species life-history traits, such as habit and mating system. The moderate structure may be a product of the combination of ancient and contemporary gene flow, resulting from the expansion of the forest in the Holocene, and/or due to facilitated dispersal mediated by the MOF’s mosaic landscape. The genetic results indicated no imminent threat to this bromeliad. However, the species is highly associated with the MOF, putting landscape conservation at the center of conservation efforts for the species’ maintenance.The Southern Brazilian Highlands are composed by a mosaic of Mixed Ombrophilous Forest (MOF) and grassland formations, an interesting landscape for the study of population structure. We analyzed the genetic diversity within and among populations of the MOF-endemic bromeliad  In preparation of the sample collection, we consulted herbarium records and contacted national parks and other locations, for which the species was described. In many of these places, the species was not found anymore, demonstrating the influence of human action on the distribution of V. reitzii. We believe that we sampled the main localities where V. reitzii currently occurs. The minimum distance between populations was approximately 38 km (CFRS to SFRS), with a maximum of about 360 km (CSRS to SMPR). Altitudes ranged from 778 m to 1031 m above sea level . Amplification reactions were carried out in a TC-412 Thermal Cycler , as described by All samples were genotyped for seven nuclear microsatellite loci described for different bromeliads species: e6B and e19, from P), allelic richness (RS), expected (HE) and observed (HO) heterozygosities, and the inbreeding coefficient , number of private alleles  to the range in allele size (r) using the software ARLEQUIN 3.5.2.2  obtained by simulating the distribution of M-ratios under specific demographic and mutational conditions using the software CRITICAL_M.EXE (http://swfsc.noaa.gov/ textblock.aspx?Division=FED&id=3298) according to FST , with a standardized Mantel test  and to estimate admixture proportions (Q) for each individual. The proportion of membership for each cluster was calculated without the consideration of sampling localities. The analyses were carried out under the admixture model assuming independent allele frequencies and using a burn-in period of 250,000, run lengths of 106 and 10 iterations per K, for K ranging from 1 to 8, to confirm stabilization of the summary statistics (K), we used the method proposed by ad hoc measure of ΔK that evaluates the second-order rate of change of the likelihood function with respect to K. The calculation of ΔK was done with STRUCTURE HARVESTER version 0.6.94 , using a heterozygosity excess test implemented in the software BOTTLENECK 1.2.02  between pairs of populations was estimated using a coalescent theory and maximum-likelihood-based approach using MIGRATE 3.0.3 (μ) for each locus were estimated from the data.The effective number of migrants  for any of the populations investigated.Relatively high levels of genetic variation were found in ulations . The numulations . Micro-CV. reitzii populations . The pairwise FST values also suggest low to moderate structure between pairs of populations, and geographical proximity does not seem to be the main factor determining the FST  and are 108 km apart, whereas SMPR and CSRS showed a non-significant FST = 0.030 and are 360 km apart (see r2 = 0.0001 (P = 0.364), suggesting the absence of isolation-by-distance.Moderate levels of genetic differentiation were detected among part see . AccordiP < 0.0001).AMOVA results indicated that the largest percentage of variation (87.42%) was attributed to the within populations component and only a small portion of the genetic variance (12.58%) was attributed to the between populations component . The admixture proportions (Q) for each individual are shown in V. reitzii populations varied from 0.145 to 2.671 (Nem) are presented in Table S2.Bayesian analysis confirmed that a model of to 2.671 , with apto 2.671 . ConfideV. reitzii, a species endemic to the MOF, by using nuclear microsatellite markers. Mountains, islands and even forest fragments are often rich in endemism, but these habitats are commonly at risk of depauperization, causing a reduction of genetic diversity of its populations  used in the present study  may be related to its life history, such as clonality and an outcrossing reproductive system. Outcrossing plants generally have high within-population genetic diversity , indicating an absence of isolation-by-distance among V. reitzii populations. Furthermore, Bayesian analysis revealed that V. reitzii comprises three genetic groups . These did not correlate to the geography of sampling localities, and we could find individuals with a predominance of the three genetic components in almost all populations (No correlation between genetic and geographic distances was detected by the Mantel test (ulations . An evalulations , points V. reitzii. However, if the species is pollinated by hummingbirds, an effective contemporary gene flow among the populations sampled may occur. Hummingbird behavior varies between species, but some species can migrate up to 2000 km, enough to cover the inter-population distances of V. reitzii, which range from 38 km to 360 km (V. reitzii has plumose seeds (LES personal observation; V. gigantea, an epiphyte inhabiting the understory of the Atlantic Forest, V. reitzii. On the other hand, V. minarum is more effective and not directly comparable to the results of Vriesea minarum is restricted to rocky systems in open environments, where the high incidence of winds can enhance seed dispersal. In addition, the usually low stature of the rupicolous vegetation poses fewer obstacles to dispersal than a forested area.Concerning contemporary gene flow, to date, there are no studies on the pollinator and seed dispersal mechanisms of Araucaria forest during the Holocene (Oreorchis, suggested a scenario of high levels of historical gene flow among neighboring populations along the main mountainous ranges in the Korean Peninsula. In a study on the altitudinal gradients of Silene ciliate, M-ratio found here indicate that V. reitzii populations did not suffer a bottleneck or reduction of genetic diversity (With regard to ancient patterns of gene flow, the current design of genetic structure may have been modeled by past events, such as the expansion of the Holocene  that mayiversity , emphasiV. reitzii populations may have been shaped by a range of factors, including the mosaic landscape of alternating grasslands and forests. The Araucaria Plateau, where we sampled V. reitzii populations, is formed by lightly to markedly undulating terrain at altitudes between 600 and < 1400 m. The MOF’s current mosaic landscape has been shaped by the phases of forest expansion and retraction resulting from climatic changes during the Holocene (V. reitzii. In summary, the moderate structure encountered in V. reitzii may be due to ancient gene flow, facilitated dispersal mediated by the mosaic landscape of the MOF, or a combination of these two factors.Therefore, the current genetic pattern of Vriesea reitzii populations showed high genetic diversity and moderate genetic structure, indicating no imminent threat to this bromeliad. However, as an epiphyte, this species is highly associated with the MOF, and the conservation of this landscape is therefore paramount for the species’ maintenance. Only 12.6% of the Araucaria Forest’s original cover remains, and those remnants are mostly distributed in small fragments amid various anthropogenic habitats, such as pasture, agriculture and exotic tree monocultures of Pinus and Eucalyptus (Araucaria Forest should therefore be considered as critically threatened.calyptus . The AraV. reitzii populations, almost all have private alleles (Despite the moderate levels of genetic differentiation between  alleles , emphasiV. reitzii is confirmed, with at least partial dependence on pollinators, strategies for the maintenance of gene flow among forest fragments should be put in place. Studies on reproductive biology and pollination will be essential to paint a complete picture of the biology of this species.If the mixed mating system in


Asellus aquaticus and Niphargus hrabei), and in doing so elucidate the possible roles of isolation and exaptation in the colonisation and successful adaptation to the cave environment.The underlying mechanisms and processes that prompt the colonisation of extreme environments, such as caves, constitute major research themes of evolutionary biology and biospeleology. The special adaptations required to survive in subterranean environments , and the geographical isolation of caves, nominate cave biodiversity as ideal subjects to answer long-standing questions concerning the interplay amongst adaptation, biogeography, and evolution. The present project aims to examine the phylogeographic patterns exhibited by two sympatric species of surface and cave-dwelling peracarid crustaceans  and epigean (surface) habitats in Hungary, and additional data from neighbouring countries were sourced from Genbank. Sequencing of mitochondrial and nuclear loci revealed, through haplotype network reconstruction (TCS) and phylogenetic inference, the genetic structure, phylogeographic patterns, and divergence-time estimates of A. aquaticus, a predominantly surface species with only moderate exaptations to subterranean life. For N. hrabei, in which populations exhibit a fully ‘cave-adapted’ (troglomorphic) phenotype, the lack of genetic structure suggests that subterranean environments do not pose a dispersal barrier for this surface-cave species.Isolation by cave environment, rather than distance, is likely to drive the genetic structuring observed between immediately adjacent cave and surface populations of The online version of this article (10.1186/s12862-017-1094-9) contains supplementary material, which is available to authorized users. One of the major recurring themes in evolutionary biology and ecology is discerning the drivers of genetic differentiation and diversity among populations, and their interplay with the environment. These patterns can often be associated to a wide array of geographic and environmental factors that influence population differentiation by means of both adaptive (i.e. selection) and non-adaptive (i.e. genetic drift) processes. As it is often observed in natural systems, reduced gene flow due to geographic distance can often result in distinct patterns of genetic differentiation across a spatial continuum . HoweverIn addition to how genetic diversity is distributed across distributional space, the underlying mechanisms and processes that prompt the colonisation of extreme environments, and more specifically caves, constitute one of the major research themes of evolutionary biology –10. TherAsellus aquaticus and the amphipod Niphargus hrabei are two aquatic crustacean species that serve as ideal models to explore questions regarding the colonisation, barriers to gene-flow, and evolution of cave fauna. Asellus aquaticus is a widespread species of freshwater isopod commonly found in surface waters throughout Europe [N. hrabei is an atypical representative of an almost exclusively cave-dwelling genus that has escaped the confines of the subterranean environment to colonise surface habitats. Its distribution spans an extensive area of central and eastern Europe with geographical ranges of up to 1300 km [A. aquaticus (e.g. in the Danube River and its floodplains). Observations suggests that N. hrabei populations are troglomorphic throughout its distribution in both caves and surface waters  provide a perfect natural experiment to address questions of cave colonisation, adaptation, and population differentiation. Despite marked environmental differences between hypogean and epigean habitats, both localities are inhabited by the species under study and are hydrologically interconnected with the Danube River Basin, one of the largest and most species-rich natural floodplains in Europe [The present study examines the phylogeographic patterns exhibited by sympatric surface and cave-dwelling populations of n Europe , 31. TheAsellus aquaticus and Niphargus hrabei) inhabit all the sites. Additional specimens of A. aquaticus were sourced from other locations in Hungary , a mitochondrial protein-coding gene , and a ‘Numt’  [PseudoND2). For N. hrabei the sequenced loci included: a mitochondrial ribosomal gene (16S), a mitochondrial protein-coding gene , a nuclear ribosomal gene , and a nuclear protein-coding gene (NaK). These loci have proved to be useful in inferring intra- and interspecific relationships across the subphylum Crustacea  [PseudoND2) [Genomic DNA was extracted from each specimen’s pereiopods and/or antennae using the commercially available QIAGEN DNeasy Blood and Tissue Kit (Cat. No. 69506). Several mitochondrial and nuclear loci were selected in order to maximise the resolution at the scale of interest (population level). Specifically, for segment) , 34 of NTS, NaK) –39 or weThe obtained sequences were visually inspected, quality trimmed, and cleaned manually with Geneious 8.0 . SequencHaplotype networks were subsequently built in PopArt 1.7  using TCA. aquaticus and N. hrabei populations were concurrently estimated using molecular clock rate calibrations for peracarid crustaceans’ COI  and Bay  and Bayon years ; and beton years ), which on years . For eac ‘strap’ . These w ‘strap’ .gsi) were calculated using the R package ‘genealogicalSorting’ to quantify intraspecific lineage divergence, which allowed for the evaluation of monophyly in each population [gsi (pairwise-gsi or pwgsi) was used to independently quantify the divergence of every population-pair [N. hrabei were excluded from this final analysis due to sample size requirements of the pwgsi approach. Nonetheless, this exclusion bears no impact on the comparisons amongst the target populations .Genealogical Sorting Indices  analyses [Additionally, we sought to better understand the demographic histories of analyses ) as implanalyses . Extendeanalyses . The parA. aquaticus  and N. hrabei  respectively. All sequence data from this project were curated, annotated with their respective metadata, and deposited in the NCBI’s Genbank database to allow for their dissemination and future use by other researchers  of nucleotide sequence data were recovered for P) of rejecting the null hypothesis of strict-neutrality in favor of the alternative hypothesis of positive selection was larger than 0.05 for all loci in both species, and therefore not considered significant at an alpha value (α) of 5%. The chosen loci were therefore deemed suitable for subsequent analyses.Fisher’s exact test of neutrality was employed to determine the suitability of the selected loci for phylogeographic inference by ensuring that they are not being subject to selective pressures. The probability  of N. hrabei are not only closely related, but also found widely distributed throughout its range . The final Maximum Likelihood and Bayesian trees for each species are nearly identical, with the exception of a few unsupported nodes, and are concordant with the *BEAST population trees estimated with all the sequenced loci  estimates follow patterns similar to those of the population tree topologies recovered , with the former having higher affinities to the south-western population of Balatonfenyves . The pwgsi between Molnár János Cave’s and Malom Lake’s N. hrabei populations on the other hand, provides no evidence of genealogical differentiation between these . Nevertheless, both populations display a statistically significant but modest degree of reciprocal monophyly when compared to the next geographically closest population, the Soroksár branch of the Danube River .The patterns observed in the haplotype network reconstructions are reflected in and confirmed by the concatenated phylogenetic trees . The 95% H.P.D. interval suggests that an evident population bottleneck followed by a rapid expansion occurred approximately 10 thousand years before present  analyses ), as impter Fig. . Contrasent Fig. , roughlyAsellus aquaticus, a pattern which is not reflected in Niphargus hrabei. Here, we will discuss the phylogeographic patterns in the light of alternate isolation mechanisms (geographic distance vs. environment). Second, we focus on the Molnár János Cave system, and discuss its potential role as a climatic refugium together with the role of exaptation in successful cave colonisation. Lastly, we conclude by illustrating future possibilities and directions for research in this emergent study system.The most intriguing finding of the present study is the population differentiation between cave and surface populations of A. aquaticus populations throughout Hungary are genetically diverse with each population being comprised mostly by distinct, but closely related, mitochondrial haplotypes exclusive to their respective localities. Phylogenetic and population tree analyses do provide strong statistical support for the genetic differentiation between Molnár János Cave’s subterranean population and its epigean counterparts. Our results further suggest that the observed genetic and phenotypic differentiation between cave and surface isopods result from the cave acting as an isolating environment. Nevertheless, despite this differentiation, the Molnár János Cave A. aquaticus population still falls well within the species range for A. aquaticus in a phylogenetic context  as a driver of genetic differentiation  and phenotype Figs.  & 4. Per and phene waters . This ree waters –4. It ise waters . InvestiA. aquaticus, haplotype network reconstruction and population tree analyses of N. hrabei show no evidence of genetic structuring between surface and cave populations  from next-generation sequencing methodologies would undoubtedly be of advantage to clarify whether this lack of genetic structure is truly due to unimpeded movement in and out of the cave or a by-product of N. hrabei’s recent colonisation of the habitats under investigation [Unlike that of ns Figs.  & 6. In ion Fig. . It is iCOI molecular clock rates [A. aquaticus populations from Molnár János Cave and Malom Lake at approximately 60,000 to 140,000 years ago , have been shown to thrive via chemoautotrophic processes [Caves have been known to act as refuges during rapid environmental changes on the surface , 69–71. rocesses .Niphargus hrabei is likely to have colonised Molnár János Cave thousands of years later, as suggested by the divergence-time estimates . However, an alternative explanation would be that older populations of N. hrabei once inhabited the Molnár János Cave, but were outcompeted by the obligate cave-dwellers in absence of migrants from surface waters. In fact, our EBS analyses show an abrupt population decline with a bottleneck and subsequent expansion at approximately 10 thousand years ago  has been initiated and will be of definitive advantage to address these outstanding questions . Advance


Phocoena phocoena), in the Atlantic shelf waters follows a pattern of significant isolation-by-distance. The population structure of harbor porpoises from the Baltic Sea, which is connected with the North Sea through a series of basins separated by shallow underwater ridges, however, is more complex. Here, we investigated the population differentiation of harbor porpoises in European Seas with a special focus on the Baltic Sea and adjacent waters, using a population genomics approach. We used 2872 single nucleotide polymorphisms (SNPs), derived from double digest restriction-site associated DNA sequencing (ddRAD-seq), as well as 13 microsatellite loci and mitochondrial haplotypes for the same set of individuals. Spatial principal components analysis (sPCA), and Bayesian clustering on a subset of SNPs suggest three main groupings at the level of all studied regions: the Black Sea, the North Atlantic, and the Baltic Sea. Furthermore, we observed a distinct separation of the North Sea harbor porpoises from the Baltic Sea populations, and identified splits between porpoise populations within the Baltic Sea. We observed a notable distinction between the Belt Sea and the Inner Baltic Sea sub-regions. Improved delineation of harbor porpoise population assignments for the Baltic based on genomic evidence is important for conservation management of this endangered cetacean in threatened habitats, particularly in the Baltic Sea proper. In addition, we show that SNPs outperform microsatellite markers and demonstrate the utility of RAD-tags from a relatively small, opportunistically sampled cetacean sample set for population diversity and divergence analysis.The population structure of the highly mobile marine mammal, the harbor porpoise ( Tursiops truncatus) populations [Stenella frontalis) [Orcinus orca) [Variation in genetic diversity across space is the result of past and present geographic, ecological, and behavioral barriers to gene flow, yielding locally disparate evolutionary trajectories of mutation, drift, and/or selection . In seemulations ,15, a hiontalis) , and preus orca) .Phocoena phocoena) populations in Western Palearctic waters offer another example of complex genetic differentiation in a highly mobile cetacean species [P. p. phocoena) from the northern Bay of Biscay to the coastal waters of Norway and Iceland is a continuous population with weak structure and significant isolation-by-distance (IBD). It is distinct from the Black Sea population (P.p. relicta), as well as from the harbor porpoises from the Iberian coast and Mauritania [Harbor porpoise  as a postglacial marine environment . The earThree harbor porpoise populations have been proposed to inhabit the waters between the North Sea and the Inner Baltic Sea; the North Sea/Skagerrak population, the southern Kattegat/BES population, and a population in the IBS, based on morphology and genetic markers  –36. The The present analysis aims to use genomics techniques to improve population structure resolution within the Baltic Sea and adjacent regions. We investigated population structure using a dataset of samples collected over the range of the harbor porpoise distribution from the Western Black Sea to the North Sea to the Inner Baltic, in order to place the population structure of the Baltic samples in a broader context. We combined double digest restriction site-associated DNA (ddRAD) libraries and high-throughput sequencing on an Illumina platform. Using an explicit spatial analysis and Bayesian clustering, we analyzed our genome-wide SNPs together with previously used molecular markers  for theTo provide a broad geographic framework for our population genomic study, and in order to place the findings in the context of existing knowledge on harbor porpoise population structure, we selected 44 samples from European seas. For clarity, we refer to three separate geographic regions throughout the manuscript: (1) the western Black Sea region (WBS), (2) the North Atlantic region with the Icelandic (ICE) and North Sea (NOS) sub-regions, and the (3) Baltic region . In the None of the sampling was performed on live specimens, nor has any live harbor porpoise specimen been targeted by any activity related to this study. All sampling was performed on carcasses by the respective national authorities allowed or even obliged to sample harbor porpoise carcasses. Specifically, Icelandic samples originate from the Marine Research Institute in Iceland, a governmental institute that does not require a special permit. German samples originate from the two institutes responsible for the collection of harbor porpoise carcasses at German coasts, i.e., the Institute for Terrestrial and Aquatic Wildlife Research, University of Veterinary Medicine Hannover, for Schleswig-Holstein and Deutsches Meeresmuseum in Stralsund for Mecklenburg-Vorpommern . Polish samples originate from Hel Marine Station, University of Gdansk, holding the permission to collect carcasses at the Polish Baltic coast. Swedish samples originate from the Swedish Museum of Natural History (SMNH) which is an official national institute holding a permission to collect carcasses for necropsy and sampling of various tissues, including harbor porpoises. Western Baltic Sea samples originate from Istanbul University, which does not require any specific permission to hold genetic samples of harbor porpoises from the Turkish coast of the Black Sea.We extracted total genomic DNA from approximately 25 mg of tissue (skin or muscle) from samples stored at -20°C (frozen or stored in ethanol) using the NucleoSpin Tissue Kit  following the manufacturer’s recommendations. We measured the DNA concentration using a NanoDrop 1000 . Using the Agilent 2200 TapeStation with the Genomic ScreenTape System , we additionally assessed sample quality and quantity.RAD-seq has become one of the most widely used genotyping methods in population genomics studies of non-model organisms including cetaceans ,41. It cPstI (a rare cutter) and MspI (a common cutter) by a commercial sequencing company . Briefly, the DNA samples were normalized and simultaneously digested with both enzymes. This step was followed by adapter ligation, where the PstI adapter contained a unique sample barcode. The reaction mix clean-up using polyethylene glycol (PEG) precipitation was followed by an amplification step to add the flow cell binding sites. This step included a concurrent reduction of the amount of fragments to be sequenced by elongating one of the two PCR primers by two bases [The ddRAD-tag libraries were prepared from total genomic DNA of 49 individuals  using the restriction enzymes wo bases . The indCasava v. 1.8.2 software . Samples were de-multiplexed with inline barcodes using LGC-developed software and clipped to remove Illumina TruSeq™ adapters and inline barcode remnants of all reads. Reads shorter than 20 bases were discarded; the remaining paired read was stored in a separate FASTQ file for single reads. FastQC reports (http://www.bioinformatics.bbsrc.ac.uk/projects/fastqc/) containing read quality metrics were generated for all FASTQ files.The libraries were sequenced on one lane of the Illumina HiSeq 2000 platform  with the 100 bp paired-end read module. Raw Illumina reads were processed using the All samples genotyped using ddRAD-seq were compared to thirty-three samples previously genotyped  at 13 poFor the same set of eleven additional samples described above, we amplified the 5’ end of the mitochondrial Control Region using primers ProL and DLH and following a previously published protocol ,46. UsinStacks v. 1.35 software package for analyzing RAD-seq data [Stacks program process_radtags. We concatenated the two paired-end read files into one common FASTQ file per individual. By running the three Stacks components , we identified the alleles in our populations set. The ustacks program aligns short sequence reads into matching stacks from which loci are formed and SNPs detected at each locus. We tested several combinations of parameter settings. For the final data analyses, the minimum depth of coverage required to create a stack and the maximum distance (in nucleotides) allowed between stacks were both set to 3; the removal of highly repetitive RAD-tags was enabled. To increase heterozygote calls, we enabled the bounded-error SNP calling model (upper bound: 0.01). A catalog of all loci across all individuals was created with the cstacks program with two mismatches allowed between loci when building the catalog. The sstacks program then matched loci from each individual back to the catalog. The rxstacks program, which makes corrections to haplotype calls in individual samples based on data from a population of samples, can also filter out catalog loci that have poor coverage or high sequencing error, and thus a low log likelihood of being correct. We filtered out catalog loci that had values below the minimum log likelihood of -20. We then used the rxstacks output to rebuild the catalog with cstacks and re-matched the reads to the catalog with sstacks. Loci were retained if they were present in six out of seven sub-regions, in 80% of all individuals per sub-region, if the depth of coverage at each locus was equal or higher than 10 reads per locus in every individual, and the minimum log likelihood for a locus was -10. To generate the subset of SNPs used in our Bayesian clustering analyses, a locus was retained if it was present in 97% of all 44 individuals  with a minimum coverage of 6 in every individual and a log likelihood value of -10. The populations program outputs basic population statistics and also enables output in several common file formats for downstream population genomics or phylogenetic analyses, such as the Structure format. The number of SNPs used in Bayesian clustering and spatial PCA analyses  falls into the ranges reported in other population genomics studies using the RAD-seq approach [We processed the sequenced data and analyzed reads from all individuals using several programs from the seq data ,51. Firsapproach –54. Deteapproach . Among tapproach ), detectapproach , discrimStacks outputs basic population genetics statistics for all positions and variant (polymorphic) positions, which include average frequency of the major allele, observed and expected heterozygosity. To correct for differences in sample size, we also subsampled all sub-regions to 3 randomly selected individuals per sub-region. For the microsatellite marker-set we estimated the mean observed and expected heterozygosities for all populations, as well as the 13 microsatellite loci across all populations, and tested for departures from Hardy-Weinberg equilibrium with Arlequin 3.5 [For the SNP marker-set, the populations program of quin 3.5 .ST comparisons between populations from the set of 13 microsatellite loci and SNP loci that passed filtering criteria in Arlequin 3.5 . For all regions and the NOS to IBS sub-regions, 672 and 870 SNP loci, respectively, were used for the analysis. Stacks reports nucleotide loci as ‘unknown’, if it cannot distinguish between a heterozygous and a homozygous state. We performed relevant significance tests using a Holm-Bonferroni correction [Arlequin 3.5. For the mitochondrial haplotype marker-set, we conducted AMOVA for the ΦST and FST indices. The hierarchy of the analysis, which was performed on the samples from all regions, and the North Sea to Baltic samples, was chosen based on geographic regions : (i) Western Black Sea (WBS), (ii) North Atlantic , and (iii) Baltic Sea . The same analysis was repeated for the NOS to IBS sub-region samples.We computed pairwise Frrection . We conda priori population assignment, we used spatial principal component analysis (sPCA) implemented in the R package adegenet, using our geo-referenced dataset of 2872 SNPs and the microsatellite marker-set [adegenet.To model the population genetic structure without rker-set . This aprker-set  using ArStructure software package v. 2.3.4 to explore the population genetic structure based on our SNP marker-set [Stacks pipeline, we used 1874 SNP loci typed in at least 97% (42 out of 44) of the individuals as input. From each RAD-tag locus, only one SNP was chosen to avoid using loci in tight linkage in Structure analyses [K (assumed number of genetic clusters), we used the freely available program StrAuto (www.crypticlineage.net/pages/software.html), modified for parallel processing (http://genome.smcm.edu/emersonLab/software.html). For analyses, we ran 100,000 burn-in iterations and 200,000 MCMC repetitions, with 15 replicates for each value of K, which ranged from 1 to 8, and the standard admixture model. Lambda, the Dirichlet parameter for estimating allele frequencies, was inferred to be 0.36. The StrAuto output builds a zip archive containing all result files which we uploaded to Structure Harvester (http://taylor0.biology.ucla.edu/structureHarvester/) [Structure output and implementing the Evanno method [K based on the Evanno ΔK values. To align multiple replicates of our data sets and facilitate the interpretation of clustering results, we used the computer program Clumpp (CLUster Matching and Permutation Program) [Structure results with distruct v. 1.1 (http://www.stanford.edu/group/rosenberglab/distruct.html).In addition, we used the Bayesian algorithms implemented in the rker-set –62. Afteanalyses . We condvester/) , a progro method . We chosProgram) . We visuStructure parameter settings, except for the allele frequencies parameter lambda, which was kept at the default value of 1. To improve the performance of Structure on the microsatellite marker-set with a weak signal for population structure, which may be the case in datasets with few loci or individuals, we used the LOCPRIOR model.For the microsatellite marker-set, we used the same The analysis of DNA quality revealed marked differences in DNA integrity between samples, as would be expected from opportunistically sampled tissue (from strandings and by-catches). Fragment lengths with highest intensities per sample ranged from 665 to over 19 thousand bp, with the mean length of fragments for all samples being 9,923 ± 4,782 bp .One lane of sequencing produced over 296 million de-multiplexed raw reads (or over 148 million raw read pairs) from 49 individuals . The average number of adapter-clipped read pairs per individual was 3,023,030 ± 949,813 (SD), with the lowest numbers just above 1.4 million and the highest 5.9 million read pairs per individual. Typically, samples of low DNA quality had a lower number of read pairs. The percentage of reads removed by quality and ambiguous RAD-tags filters in the process_radtags program, was 13.4 and 9.9, respectively, resulting in over 227 million, or 76.7%, of retained reads.Stacks catalog contained 847,321 loci, the average number of unique RAD-tag loci per individual was 370,725 ± 63,309 (SD). On average, 29,263 or 8% of those loci were polymorphic . After applying stringency filters in the populations program to ensure that the loci were present in 80% of individuals from each of the seven sub-regions with sufficient coverage, we retained a set of 2872 loci. For Bayesian clustering, we used a subset of 1874 loci, which were present in 42 out of 44 individuals.While the For the SNP loci that were polymorphic in at least one of the populations, the average major allele frequency was 0.94 in all sub-regions but WBS (0.97) , and theST comparisons between populations were calculated from the SNP and microsatellite loci that passed filtering criteria. Highest FST values were calculated for pairwise comparisons that included the WBS region . For the SNP data, FST values were 0.011 and -0.014 between adjacent sub-regions like NOS:SK1 and SK1:KB1, respectively , followed by variation among regions when considering nuclear markers . For theST indices from mitochondrial haplotype frequencies yielded sources of variation comparable to the AMOVA based on nuclear markers (ST index showed that the largest source of variation was among regions (over 67%) when considering samples from the Black Sea to the Baltic. The second largest source of variation was between individuals within sub-regions (over 28%). Both of these results were significant. For the NOS to the Baltic Sea sub-regions, the largest significant source of variation was at the lowest level, between individuals.AMOVA of conventional F markers . Here, oWe performed sPCA using both nuclear marker-sets on all sub-regions, and those ranging from the NOS to the IBS. Results indicated statistically significant global spatial structures for all combinations of markers and datasets, but no significant local structures . In ordeP-value = 0.63; microsatellite marker-set P-value = 0.73). Apparently, on this scale population divergence is not directly correlated to mere geographic distance. For example, Icelandic samples are assigned to the same cluster as North Sea samples, despite of the geographic distance among them. For the NOS to IBS sample subset, the test based on both marker-sets indicated significant IBD only for the microsatellites , while SNPs revealed a more complex genetic structure not directly correlated with distance (see below).We tested for IBD between all regions and the NOS to IBS subset using a Mantel correlation test between the genetic and geographic distance matrices for the SNP and microsatellite marker-sets (9999 permutations). IBD was not significant when combining individuals from all regions for either dataset , and with 2 clusters for the microsatellite sets (ΔK = 21.55)  and for the microsatellite marker-set (K = 2) are shown in the Supporting Information  , S2 Fig.nd K = 5 . For theStructure analyses for all samples (WBS to IBS) significantly differed in their mitochondrial haplotype composition , their main prey species [Structure clustering and pairwise FST comparisons, however, did not support differences between NOS and SK1. A higher sample number, improved sampling of Danish and Norwegian coastal regions, and consideration of seasonal migrations of porpoises from the Skagerrak would likely resolve these inconsistencies. Prey specialization for pelagic species like herring, and benthic species like cod (Gadus morhua) has also been linked to differences in skull morphology between the Skagerrak and the Belt Sea porpoises [The spatially explicit analysis in this study indicates a separation between the samples from the North Sea and the Baltic Sea individuals. The Skagerrak and northern Kattegat are high-density areas for the harbor porpoise, and correspond to the southern-most distribution of herring  subsequently detected a small, but consistent separation of the Baltic proper population from the Belt Sea, and urged for its precautionary acknowledgment as an independent MU [It has been argued  that thendent MU . In a rendent MU . Here, wThe Baltic population has been recognized as threatened in 2002 by the Parties of the Agreement on the Conservation of Small Cetaceans of the Baltic and North Seas (ASCOBANS) that prepared the ‘Recovery Plan for the Baltic Harbour Porpoises (Jastarnia plan)’ . In 2009While there is an urgent need to clearly define population borders for the purpose of monitoring and assessment of the conservation status of harbor porpoises, these borders are most likely dynamic. Seasonal migrations of porpoises have been reported for the Skagerrak populations, which migrate westward in the winter towards the southern tip of Norway, for the Kattegat populations, which may migrate southward through the Great Belt in the winter, as well as for the porpoises from the Belt Sea and inner Baltic Sea, which may migrate westward to the Pomeranian Bay and from the Kadet trench into Danish waters, respectively ,38,76,80a priori assignment of individuals to clusters [Structure analyses based on genome-wide SNPs. While microsatellite-based analyses were clearly informative, we obtained a better resolution in spatial analyses using genome-wide SNPs. The ddRAD-seq method provides a genome-wide sampling of loci that is much denser than with microsatellites and yields a much higher number of nuclear markers [Our study of population differentiation using the ddRAD-seq genotyping-by-sequencing method improved the harbor porpoise population differentiation inferred from microsatellite and mitochondrial control region sequence data  and reprclusters ,81. Over markers –85. ConsddRAD-seq has become the method of choice in population genomics studies, particularly for non-model organisms ,87. An iIn summary, this study demonstrates the feasibility of SNP analysis on opportunistically sampled cetacean samples for population diversity and divergence analysis. This approach should be applied to a larger sample set, such that specimens could be stratified by gender, and incorporate overlaps between populations associated with seasonal migrations observed in abundance estimates, morphological and genetic studies into future study design ,67. UsinS1 Fig(DOCX)Click here for additional data file.S2 Fig(DOCX)Click here for additional data file.S3 Fig(DOCX)Click here for additional data file.S1 File(STR)Click here for additional data file.S2 File(FASTA)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.


Mullus barbatus and Mullus surmuletus collected in the Mediterranean Sea, the biogeographic boundaries, genetic distribution among and within basins and the impact of prolonged exploitation in both species were investigated as a basis for understanding their population dynamics and for improving Mullus spp. stock management. Different level of diversity indices among these co-occurring species were obtained, with M. barbatus showing higher allele richness and higher mean observed and expected heterozygosity than M. surmuletus. Reduced contemporary effective population size (Ne) and M-ratio values found in both species likely reflects recent demographic changes, due to a combination of high fishing pressures, habitat fragmentation and naturally occurring fluctuations in population size. Different patterns of genetic connectivity among populations sampled within the Mediterranean were observed for both species. Higher genetic structure was found for M. barbatus as opposed to a more homogenous pattern observed in M. surmuletus samples. Adriatic populations, previously considered panmictic and isolated from other Mediterranean regions, showed geographical partitioning within the basin but also population connectivity with the northern Ionian and Tyrrhenian Seas. Our results highlight the need for temporal sampling in understanding the complex pattern of population connectivity in the Mediterranean, particularly for management purposes.Using thirteen microsatellite loci for  Mullus surmuletus L., 1758) and the red mullet  are distributed in the eastern Atlantic Ocean, from the North Sea to Senegal, and throughout the Mediterranean and Black Seas. Both species are demersal and share very similar biological characteristics, with the main distribution to depths of 200 m in the continental shelf. Goatfishes show bathymetric habitat partitioning and clear niche segregation in relation to the bottom type that constitutes their habitat1. However, recent studies have shown that depth alone is not as significant as previously thought, masking the influence of salinity and temperature. M. barbatus shows an affinity for low salinity waters and M. surmuletus for warmer waters, which may contribute to their spatial segregation2.The striped red mullet  and responses or rebuilding strategies to exploitation. For fish stocks, as subpopulations of a particular species, intrinsic parameters are the only significant factors in determining stock dynamics, while other factors, in particular immigration and emigration, are considered to have limited effect. Biological attributes and productivity of species may be affected if the stock structure and fisheries management are not well considered10. Thus, stock identification is of primary importance for population structure assessment of economically important species.Stock definition and identification and spatial structure information provide a basis for understanding fish population dynamics, and play a key role in fisheries assessment and management8. There are a variety of techniques for stock identification, including genetics, morphometry, biological descriptor , parasites and others11. However, most techniques give inconclusive results or are costly12. Therefore, fishery scientists delineate spatial entities, such as management units or geographic areas, for monitoring harvested stocks8 with biological, geographic, economic, social or even political factors used to outline these entities7. This is the case of the Mediterranean, where the General Fisheries Commission for the Mediterranean (GFCM) has established thirty management areas  based on political and statistical considerations, rather than on biological or economic factors13. Thus, the waters of Adriatic Sea were recognized as two independent sub-areas (GSA17 and GSA18). In some cases, stock assessments are performed joining two adjacent GSAs  based on previous stock assessments carried out at the national level, due to similar biological characteristics of the two populations of red mullet and similar changes in CPUE levels in those two GSAs14. However, a possible mismatch between the stock limits established by fishery managers and the biological stock boundaries can be expected. Moreover, it has been shown that stocks need not to be completely isolated to show demographic independence15.In practice, it is not easy to identify stocks, as the delimitation of adjacent populations involves many issues, especially in the sea where there are no clear geographical barriers16, revealing highly structured metapopulations. A sharp genetic division was detected when comparing striped red mullet originating from the Atlantic Ocean and from Mediterranean Sea. In the Mediterranean basin, the Siculo-Tunisian Strait seems to mark a transition zone between the Mediterranean’s eastern and western populations17. However, a straightforward conclusion on heterogeneity among Mediterranean populations could not be provided, due to the relatively low number of populations sampled, limited sample sizes and limited number of nuclear markers examined, especially in case of the Adriatic Sea, where no complete genetic structure analysis of Mullus sp. was previously conducted. Namely, the isolation of the Adriatic populations of M. barbatus (as a panmictic unit) in the Mediterranean Sea has been recorded18 basing only on samples from the western Adriatic Sea and a sample off the Albanian coast. A lack of complete sampling design, where eastern Adriatic populations were not considered, may have resulted in a failure to identify discrete populations. Moreover, in the case of M. surmuletus, there is no concrete information on stock identity or spatial structure for the Adriatic Sea. Thus, the foundation for understanding their population dynamics is lacking, which disables reliable stock assessment for fisheries management.Genetic studies have been carried out in the Mediterranean Sea on both speciesM. barbatus and M. surmuletus in the Adriatic and Mediterranean Seas by using 13 microsatellite loci. The study aimed to: (i) clarify spatial genetic sub-structuring as a good base for improving Mullus spp. stock management; (ii) understand population genetic connectivity among and within basins affected by oceanographic features, and (iii) compare current effective population size (Ne) and occurrence of recent bottlenecks between the species in order to assess the impacts of constant fisheries pressure. Moreover, we argue that the defined GSAs within the Mediterranean cannot be maintained a priori as unique areas for assessment and management purposes for specific fish species, in this case, red mullets.In the present study, we investigated the genetic variability of two sympatric species M. barbatus and 599 individuals of M. surmuletus , with tendencies towards heterozygote deficiency at the loci Mbar130 and Mbar133  in M. barbatus and from 0.60 to 0.76 in He in M. surmuletus (Tables vs 6.7), allelic richness (6.7 vs 9.9) and observed heterozygosity (0.67 vs 0.70)  in comparison to red mullet. No obvious geographical patterns were observed for the distribution of genetic diversity, except for the sampling site ITA_SAS , where the level of observed heterozygosity for both species was highest of all sites. The inbreeding coefficient, FIS, ranged from 0.035 to 0.131 in the red mullet dataset, and striped from −0.057 to 0.118 in striped red mullet, respectively, and was significantly higher than zero in 4 of the total 14 and 13 populations of both fish species  to 479 (AL_AS) where the upper confidence limit reached infinity in some cases (6 of 13 populations). The Ne of striped red mullet ranged from 67 (MN_AS) to 157 (PT_AO) with upper and lower 95% CI of 26 and 686  and Aegean Sea (Turkey) had undergone a recent reduction in population size. Wilcoxon signed-rank tests detected significant heterozygote excess (p < 0.05) under the infinite alleles model for each of these populations, while the observed M ratios were significantly lower than the simulated equilibrium distribution of M for all pre-bottleneck values  were significantly lower than the simulated equilibrium distribution of M for the ancestral θ value of 0.5  and with 57% (M. barbatus) and 35% (M. surmuletus) certainty for FST = 0.001.Assessment of the statistical power for both microsatellite data sets in POWSIM revealed that it was possible to detect genetic divergence as low as FST value was 0.027 (95% CI = 0.014 to 0.042) for red mullet and 0.011 (95% CI = 0.007 to 0.016) for striped red mullet, and both values were highly significant (p < 0.001), supporting the spatial heterogeneity. Pairwise FST across all samples of red mullet ranged from –0.030 to 0.084  showed a break in gene flow toward the north and middle Adriatic regions and further to the south, including the Ionian Sea (0.024 < FST < 0.049). No significant pair-wise differentiations were observed for populations within the eastern Mediterranean, while reduced gene flow was noted between the western Mediterranean region  and Adriatic Sea, but not between the western and eastern Mediterranean.At the broad Mediterranean scale, the overall 84 Table , with 53FST ranged from −0.001 to 0.064 ) and the Delta K method ) plateaued at K = 4, while the Delta K method indicated K = 6 . However, increasing the number of clusters beyond ons Fig. . Heterogons Fig. , while sons Fig. . The resons Fig. .p = 0.041), where the scatterplot of local densities of distances showed only one consistent cloud of points, indicating a continuous cline of genetic differentiation  was detected for the red mullet populations. However, the scatterplot of local densities of distances indicated a patched pattern of genetic differentiation among populations, due to the observed discontinuity of one single consistent cloud  analysis for the striped red mullet populations revealed a moderate isolation by distance pattern  developed for M. barbatus appear to exhibit evidence of a transfer bias, in which M. surmuletus had 50% reduced levels of He than those described in the focal species 29. Strong fluctuations in population size due to fisheries negatively affect the effective population size, thus increasing the effects of selection and genetic drift, which could lead to lower genetic diversity levels, as observed in this study.On the other hand, species that are heavily exploited and have overlapping generations, such as Mullus species showed reduced contemporary Ne in the order of several hundred . Such a pattern has previously been observed in several exploited marine fish species32. In addition to fishing pressures and habitat fragmentation, a strong bias in reproductive success, naturally variable recruitment patterns and size-dependent fecundity may additionally reduce the effective population size, in the sense that millions of individuals may be equivalent to an effective population size of only hundreds or thousands33. However, these interpreting Ne estimates for species with overlapping generations should be viewed cautiously, due to Wahlund effects caused by multiple cohorts in a sample34. Also, the heterozygosity deficit and significant departures from HWE observed in both Mullus species at some loci but not systematically for each location could be explained by population sub-structuring or selection, as previously reported by several authors22. Unnatural selection due to fisheries may induce evolution toward slow growth, early maturation at small size and higher reproductive investment35. Increased trend in the mean size related to lower fishing exploitation was observed for red mullet in the Castellamare Gulf, Sicily36, however long-term data series for other Mediterranean stocks lack further support for the above findings.Both 37. Still, evidence from M ratio tests, supposed to be more powerful at detecting bottlenecks compared to the heterozygosity excess test38, suggested that both Mullus species from the Aegean Sea may have experienced population bottlenecks. Furthermore, for two populations of M. surmuletus , M ratios were below the diagnostic value for genetic bottlenecks (0.70)39, while all sampled populations from both Mullus species had an M ratio less than 0.82, as the cutoff value for selecting demographically stable natural populations39. Even though interpretations of M values are not consistently supported across all the values of θ used40, these results might carry alarming signals that should not be overlooked.Neither bottleneck test used in the study identified coherent signals of genetic erosion, though there were some indications of demographic changes in certain populations. Significant heterozygote excess in the populations from Ionian and Aegean Seas was detected under the IAM mutation model, potentially corresponding to a bottleneck signal. However, the extent to which the IAM model truly describes the process of microsatellite mutation is questionableMullus sp. stock recovery through the increase of the size of the spawning fraction of population with a consequent increase on the recruitment success and spawner biomass42 or by promoting the admixture of individuals and increase of heterozygosity levels21. In the present study, we noted the greatest observed heterozygosity levels and the lowest values of inbreeding coefficients in both Mullus populations from the Italian side of the southern Adriatic Sea, which regionally coincides with nursery areas identified by means of indicator kriging44. Thus, nearby marine protection areas, such as Isole Tremiti and Torre Guaceto, play an important role in the protection of recruitments and preservation of the gene pool and genetic diversity of species. It is very interesting to note that according to last M. barbatus GFCM assessments45, GSA 18 diagnosis of stock status was sustainable exploited, supporting further our findings of greater observed heterozygosity level and the lower value of inbreeding coefficient within the region  when compared to its congener (FST = 0.011), both species showed significant population divergence within the Adriatic Sea localized in area of the South Adriatic Pit – the deepest part of the basin. In line with previous reports18, inconsistent genetic structure related with the major oceanographic barriers was observed for both species. While the main source of genetic differentiation of striped red mullet was the Atlantic-Mediterranean divide and Otranto Strait, red mullet populations were characterized with a puzzle pattern of gene flow discontinuity among the Sicily Channel, Otranto Strait and Aegean Front, with exclusion of the isolation-by-distance structure. Namely, red mullet from the Balearic Sea was significantly different from north and middle Adriatic populations, including the population from the Ionian Sea, whereas the opposite was noted for interactions among populations from the south Adriatic and eastern Mediterranean. Low values of FST and high gene flow in marine species does not necessary imply an absence of population structure46. Considering that few individuals per generation can maintain genetic homogeneity among populations47, more sampling is needed along the Mediterranean African coast, as well in the Sicily Channel and around Sardinia, to obtain representative patterns of genetic structure across a wide distribution range of the species. By using different markers, some authors confirmed genetic structuring at a relative small scale (RAPDs48) and also at a large scale, showing a structured population across the western and eastern Mediterranean Sea . Overall, the present study supports the findings of highly structured genetic distribution for red mullet17, where metapopulations are composed of independent, self-recruiting subpopulations with some connections between them.Different patterns of population structure were seen between the investigated species. While ST values has also been reported17 in a study that covered a large portion of the Atlantic-Mediterranean species distribution. The Almeria-Oran Front, the best-studied oceanographic discontinuity in the Mediterranean Sea, restricts gene flow in many other demersal and pelagic fish species51. Still, Bayesian analyses assigned the population from Portugal (Atlantic origin) to a separate cluster only partially (q > 65%), suggesting that adult dispersal and passive transport of eggs and larva occasionally occurs along the Strait of Gibraltar, likely due to interannual variability of the front that allows some degree of homogenization between localities52. As corroborated by the significant isolation by distance and continuous cline of genetic differentiation, the lack of strong genetic structure is largely the result of long distance larval dispersal (more than 400 km) by offshore currents, and the early life traits that differ slightly from those observed in red mullet21. Apart from spatial segregation of two sympatric species in relation to habitat use, where striped red mullet prefers shallower depths (10 to 50 m) and rough bottoms, while red mullet is most abundant on muddy bottoms in waters between 50 and 200 m deep1, the spawning and the recruitment of both species are temporally separated. Striped red mullet reproduction begins in March with a peak in April to May, and recruitment toward the bottom from August onwards, while red mullet reproduces from April to July with a peak in June and recruits toward the bottom in September and October54. Relatively small changes in water temperature have the potential to greatly influence patterns of fish growth and pelagic larval duration (PLD). It has been demonstrated experimentally that a 5 °C increase in water temperature decreased the time to metamorphosis by 2.8 d for the tropical species Upeneus tragula reared from midway through their pelagic life phase55. Similar observations were recorded for temperate flatfishes56. Thus, it could be argued that both Mullus species have a similar pelagic larval duration (28–35 days)57, with records based on very limited sample size , due to temperature differences between spring and summer seasons. In the case of striped red mullet, lower temperatures can prolong planktonic duration and dispersal ability, influencing genetic homogenization at a wider geographical scale6. Still, the impact of batch spawning and different spawning pulses during the reproductive season42, in contrast to the larval prolonged competency period through the active delay of metamorphosis53, may strongly contribute to fluctuations in levels of population connectivity at a finer spatial scale. However, it remains difficult to map directly how far and in what directions larvae disperse, thus complicating accurate stock definition.A Mediterranean-Atlantic division of striped red mullet detected by microsatellite-based pairwise FMullus species, where south-eastern Adriatic populations were strongly differentiated genetically from the north-middle and south-western localities, suggesting high larval retention and confined dispersal across the geographic range of the South Adriatic Pit  characterized by the narrow continental shelf and steep continental slope reaching a maximum depth of 1,270 m. Genetic differentiation of south-eastern populations were even more pronounced for red mullet. These findings agree with the hydrodynamic provinces in the Mediterranean from Lagrangian simulations and transport network reconstruction, where the eastern part of the Strait of Otranto was identified as a temporary stable gene flow barrier, even for larvae with long PLD (<30 days) and with an offshore larval distribution, such as Mullus species58. This also supported the north-south Adriatic subdivision already seen in some fish species59. In the present study, no genetic divergence was observed among populations from the western Adriatic coast and the northern Ionian Sea, in contrast to the outcomes of other works25 that revealed an isolation of Adriatic samples of red mullet from the neighbouring basins  and generally low structuring pattern within the Adriatic. The differences with our study could be due to the different resolution of molecular markers used or due to temporal fluctuation of population connectivity, highlighting the need to use temporal replicates when assessing population divergence patterns in marine fishes. Still, local oceanographic features support present gene flow findings, since the main sea current flows in the north–south direction along the western continental shelf60. It seems that the red mullet nurseries located in the south-western Adriatic Sea exploit the benefits of the continuous flux of the southern Adriatic water masses entering from the northern Ionian Sea43.Namely, both spatial analyses (Structure and DAPC) found relevant barriers to dispersal within the south Adriatic Sea area for both 61. Traditionally, assessment and management agents, such as the International Council for the Exploration of the Sea (ICES) and the GFCM, have delineated stock units by using geo-economic or political aspects related to the collection of fisheries data rather than based on population integrity7. Some flexibility in relation to new information can be seen nowadays. Namely, GFCM experts for Adriatic Sea pointed that management areas GSA17 and GSA 18 should not be maintained a priori as unique GFCM-GSA areas for joint stock assessment and management for M. barbatus, as conducted in previous years for M. barbatus62. They based their decision on the changing distribution of the red mullet population and the different selectivity of fleets exploiting this stock in both GSAs on the eastern and western Adriatic coast. Generally, M. barbatus seems to be in overexploitation but with relative high biomass although in some GSAs 61, which is particularly important if a shift from mono-specific to an ecosystem approach is to be adopted in Mediterranean.Our results showed that a precautionary approach is needed when declaring subpopulations of certain species in small areas, such as the Adriatic Sea, to be one unique stock. Mixing data from sub-areas with marked differences in the state of exploitation of their stocks would increase the risk of population declineThe methods involving animals in this study were conducted in accordance with the Laboratory Animal Management Principles of Croatia. All experimental protocols were approved by the Ethics Committee of the Institute of Oceanography and Fisheries.Mullus spp. were collected from 14 different locations within the eastern, central and western Mediterranean regions, including the eastern Atlantic Sea (only in the case of M. surmuletus) , amplification of the loci was run in 12.5 μL reactions, containing 10 ng DNA, on an Eppendorf Mastercycler Nexus Gx2 thermal cycler. Final concentrations of all primers were uniformly set at 0.2 µM. Thermal cycling consisted of an initial denaturation step at 95 °C for 5 min followed by 26 cycles of: denaturation at 95 °C, annealing at 57 °C, and extension at 72 °C for 30 s, 1.5 min, and 30 s, respectively. A final cycle at 60 °C for 30 min was performed. Fragments were separated on an ABI3130 automated sequencer (Applied Biosystems) while peak height values for each microsatellite allele were scored manually by two different persons using GeneMapper v.3.5 software (Applied Biosystems).Total genomic DNA from fins was extracted by proteinase K digestion, followed by standard phenol-chloroform extraction protocol. DNA quality and quantity were assessed by spectrophotometry , and each sample was diluted to 10 ng μL66 while the presence and frequency of null alleles were additionally examined by FreeNA19 following the Expectation Maximization (EM) algorithm. FreeNA was used to compute the FST statistic, both with exclusion and inclusion of the ENA (Excluding Null Alleles) correction method, considering that the presence of null alleles can result in an overestimation of population genetic differentiation. The bootstrap 95% confidence intervals (CI) for the global FST values were calculated using 50,000 replicates over loci.Scored alleles were checked for genotyping errors using MICROCHECKER 2.2.367 was used to perform an exact test for deviations from Hardy-Weinberg equilibrium by testing heterozygote deficiency and heterozygote excess in the microsatellite data. The linkage disequilibrium (LD) test for all pairs of loci was also performed in GENEPOP v.3.1b. Exact P-values for the individual population or locus tests were estimated using the Markov Chain algorithm  and the significance of HWE and LD values were adjusted by Bonferroni correction.GENEPOP v.3.1bHo) and expected (He) heterozygosity was calculated in GENEPOP v.3.1b. The mean number of alleles per locus (A) and mean effective number of alleles across loci (Ae) were calculated using POPGENE v.1.3268, while allelic richness (Ar) and inbreeding coefficient (FIS) were calculated using FSTAT v.2.9.369.Observed (70 and M ratio39 methods. Heterozygosity excess tests were performed with the program BOTTLENECK 1.2.0271 by the Infinite Allele model (IAM) and Two-Phased mutation model (TPM), incorporating 90% of single-step mutations and 10% of variance among multiple steps. Statistical significance was evaluated by Wilcoxon signed-rank test from 10,000 simulation replicates. Secondly, the software packages MPVal and CriticalM39 were used to trace for signatures of a bottleneck by the M-ratio method. The total number of alleles (k) divided by overall range in allele size (r) produces the ratio (M), which is expected to be smaller in recently reduced populations than in populations under mutation-drift equilibrium39. The simulated analysis using a constant mutation rate at μ of 5 × 10−4 was run assuming a TPM model with parameters ps (proportion of one-step mutations) = 0.88 and Δg (average size of multistep mutations) = 2.839. The empirical M values were compared to 95% critical values (Mc) below which a bottleneck is evident, derived from 10,000 simulations of stable populations using the program Critical_m. Ancestral theta (Θ) was set at three different values  corresponding to a pre-bottleneck effective population size of 250, 500 and 5000, respectively.Genetic evidence for a recent reduction in local population size were tested by Heterozygosity excessNe) using the single-sample linkage disequilibrium method72 for each of the 25 populations examined. Populations with a small sample size below 30 individuals (i.e. IS_LS and CP_LS) were not analysed. Low frequency alleles ≤0.02 were excluded from the analysis to minimize potential bias caused by rare alleles.The program NeEstimator 2 was used to estimate contemporary effective population size  was employed as a non model-based method recently developed and implemented in the Adegenet package75 for R software76. STRUCTURE analysis was conducted on the full sample dataset using the admixture ancestry model, correlated allele frequencies, a burn-in period of 503 iterations followed by 504 MCMC steps and K values from 1 to the maximal number of sampled groups with 20 replicates each. To assess the most likely number of clusters, ln P(D) and ∆K values were estimated in Structure Harvester 0.6.9377. CLUMPP v.1.1.278 was used to assess the average pairwise similarity of runs and plots of the clusters produced in Microsoft Excel. Finally, to obtain an easily interpretable representation of the estimated admixture proportions in geographic space for the Adriatic Sea, the membership coefficient matrix  was interpolated using the maps function from POPSutilities.R79 in R 3.3.3 software and by using the max option where only the cluster with the maximal local contribution to ancestry is represented at each geographic point of the map.Analysis of the genetic structure of sampled populations was performed using the Bayesian-clustering program STRUCTURE 2.375. The number of Principal Components (PCs) retained was optimized using the function xvalDapc.To explore how the genetic variation was partitioned among sampling localities, Discriminant Analysis of Principal Components was executed by the function dapc using the sampling locality as a priorAdegenet in R. Mantel’s test was used to check for correlation between Edwards’ distances and Euclidean geographic distances among populations with the mantel.randtest function (1000 permutations). To test whether the correlation between genetic and geographic distances is a result of a continuous or distant patchy cline of genetic differentiation, local densities of distances to disentangle both processes were plotted using a 2-dimensional kernel density estimation (function kde2d). The results were displayed with a customized colour palette using image in the MASS package81.To test the correlation between genetic and geographic distances, isolation by distance (IBD) analysis was performed with the package http://jadran.izor.hr/~tsegvic/aquapop/GenoBase.html) and is available from the corresponding authors on reasonable request.The full dataset of genotypes has been deposited into GenoBase of Institute of Oceanography and Fisheries (Supplementary Information


Theileria annulata, a tick-borne apicomplexan protozoan, causes a lymphoproliferative disease of cattle with high prevalence in tropical and sub-tropical regions. Understanding the genetic diversity and structure of local populations will provide more fundamental knowledge for the population genetics and epidemics of protozoa. In this study, 78 samples of T. annulata collected from cattle/yaks representing 6 different geographic populations in China were genotyped using eight micro- and mini-satellite markers. High genetic variation within population, moderate genetic differentiation, and high level of diversity co-occurring with significant linkage disequilibrium were observed, which indicates there is gene flow between these populations in spite of the existence of reproductive and geographical barriers among populations. Furthermore, some degree of genetic differentiation was also found between samples from China and Oman. These findings provide a first glimpse of the genetic diversity of the T. annulata populations in China, and might contribute to the knowledge of distribution, dynamics, and epidemiology of T. annulata populations and optimize the management strategies for control. Theileria annulata is transmitted by ticks of the genus Hyalomma and widespread in North Africa, southern Europe, India, the Middle East and Central Asia  in China employing polymorphic micro- and mini-satellite markers. Moreover, a comparative analysis was performed with the Oman populations to investigate the genetic difference over a large geographical scale. Overall, the information will help us to know the distribution of the different T. annulata population and to adopt different measures to prevention and control this parasite.Although genetic structure of d Turkey , the popSupplementary Figure S1). The sample distributions cover from arid and semi-arid to sub-humid climate regions. Approximately 10 ml blood sample was collected from the jugular veins of each animal, and injected into EDTA vacutainers tubes. Genomic DNA was extracted from 300 μl thawed blood using the QIAamp DNA Mini Kit  according to the manufacturer’s instructions and then stored at -20°C until used.Animal experiment was approved by the Science and Technology Department of Gansu province, China (permit SYXK2010-0003). The 330 blood samples were randomly collected from cattle and yaks with no apparent clinical signs of theileriosis in Gansu (24 from cattle and 16 from yaks), Hainan (20 from cattle), Inner Mongolia (44 from cattle), Liaoning (38 from cattle), Tianjin (24 from cattle), and Xinjiang (164 from cattle) provinces of China  was amplified using the specific primers AnCb-F (5′-CGGTTGGTTTGTTCGTCTTT-3′) and AnCb-R (5′-GCCAATGGA TTTGAACTTCC-3′)  . PCR wasThree micro-satellite markers  and five mini-satellite markers  were selected to detect the genotype of each sample based on the methods described by Na), number of effective alleles (Ne), and unbiased heterozygosity (uh) were calculated by the program GenAlex 6.5  was calculated to measure genetic variation for each population. Rarefied allelic richness and private alleles were calculated to correct for sample size using the program HP-rare  was explored from 1 to 10 with 6 times replications per K employing Bayesian Markov Chain Monte Carlo (MCMC) approach. To further confirm the genetic clusters, Discriminant Analysis of Principal Components (DAPC) was performed by the adegenet package  was applied to compare the different groups, and the lowest BIC value used to infer the ideal number of groups  which is conducted by the program LIAN version 3.7  for LE. Monte Carlo simulation or a parametric test is employed to test the null hypothesis that VD = Ve, and the results provide 95% confidence limits, denominated as LMC and LPara, respectively. When VD is observed greater than L, the null hypothesis is discarded and linkage disequilibrium (LD) is accepted.The null hypothesis of linkage equilibrium (LE) in each sion 3.7 . Varianccytb amplicons, 5 distinct haplotypes were obtained after sequence editing and alignment (GenBank accession numbers MG735205–MG735209).In total, 124  cattle  and 6  yak blood samples were positive employing AnCb-F/AnCb-R primers. Out of the 130 samples, 78 samples were successfully genotyped using the eight micro- and mini-satellite markers. A total of 52 samples were excluded due to the failed amplification by most of the markers. From the 78 Supplementary Table S1). The number of alleles per population ranged from 2.38 (± 0.32) in Tianjin to 8.50 (± 1.00) in Xinjiang, and the unbiased expected heterozygosity (He) per population from 0.46 (± 0.13) in Liaoning to 0.82 (± 0.04) in Xinjiang with mean He 0.64 (± 0.05) indicating high diversity across all the six populations in China (Table 1). For each population, allelic richness (Ar) ranged from 2.68 to 5.00, and the private alleles (Ap) ranged from 0.44 to 2.58 (Table 1). The population genetic characteristics analysis revealed that Xinjiang population showed more diversity than other populations of T. annulata.All the markers were highly polymorphic, polymorphic information content (PIC) varying from 0.625 to 0.902, and the number of alleles ranging from 8 (TS20) to 21 (TS12) at each locus (Table 2). All the populations in China reported average 1.61 alleles per locus and ranged from 1.33 to 1.85. The results from MOI showed that Xinjiang population was observed with more variation with a maximum of 2.75, whereas the population from Tianjin possessed a minimum value of 1.50 indicating less variation.Out of the 78 samples, 55 expressed multiple genotypes, representing a mix infection. MLG data of Gansu population were all from yaks, and Hainan, Inner Mongolia, Liaoning, Tianjin, and Xinjiang five populations were from cattle. The mean numbers of alleles across the eight loci for each isolate were calculated to estimate the MOI within each isolate (T. annulata populations across different areas occurred in panmixia with high level of gene flow using the standard index of association (IAS). No evidence for random mating was found when the six T. annulata populations from China were considered as one population. IAS value of 0.1224 was positive, and VD (2.6253) was tested greater than LMC (1.5727) and LPara (1.5635) indicating LD (p < 0.0001) (Table 3). When each region was treated separately in China, LE was observed in Gansu, Inner Mongolia, Tianjin, Hainan, and Xinjiang five populations, while LD was found in Liaoning (Table 3). When combining China and Oman populations, although the IAS value (0.0569) was lower, LD was detected (Table 3).Extent of LD was measured to estimate whether the Table 4. Most FST values demonstrated that moderate genetic differentiation was found between pairs of populations ranging from -0.020 to 0.264. However, high genetic differentiation was observed between Gansu and other populations with FST values ranging from 0.158 to 0.264 (> 0.150). Meanwhile, relatively high genetic differentiation was obtained between Xinjiang and Liaoning, and low differentiation was between Tianjin and Liaoning populations (Table 4).The levels of genetic differentiation among the six populations were shown in FCT (0.057) and FSC (0.162) (Table 5).Between the breed groups, AMOVA showed that most of the genetic variation (79.01%) was mainly partitioned within populations rather than among groups and among populations within groups, as determined by low values of Figure 1A). Bayesian clustering analysis showed that optimal number of clusters was observed at K = 3 , revealing that all the T. annulata populations could be divided into three subgroups . Obvious mixture haplotypes were found in all populations. Deep analysis revealed that the members from Gansu population were assigned to the Cluster 3 with the highest proportion (87.7%), and the members from Xinjiang population were assigned to the Cluster 1 and Cluster 2 with same rate (48.5%). DAPC analysis supported the division of T. annulata population into three groups . These findings indicate that a low genetic sub-structuring exists in T. annulata populations.The results of STRUCTURE analysis showed that the members from six populations were assigned to different clusters indicating weak population structure among populations .For the six Figure 3). Most of the samples from China and Oman were classified into different quadrants revealed that an obvious geographic sub-structuring was detected in association with independent of geographic origin.The PCoA analysis supported a high degree of genetic differentiation between populations in China and Oman , but a similar level with Portugal , which showed the samples from China and Oman formed completely discrete clusters. Geographical barriers along with the trade barriers and local agricultural policies greatly restricted host movement, which might be a reasonable explanation for this genetic subdivision, the reduction of gene flow and the formation of isolated populations between different countries . Moreover, despite the high MOI and abundant diversity, significant LD was observed in the overall Chinese population. Inbreeding might be a more realistic explanation for the inconsistent results. Such similar observations of strong LD and high diversity are in agreement with T. parva, P. falciparum, and P. vivax . High genetic differentiation was observed between the Gansu population and others , which could be related to the reproductive isolation. The obvious differences between samples from different host were confirmed by the high pairwise FST values and DAPC analysis. Furthermore, the yak is a special breed which was collected from the Tibetan Autonomous County in Gansu. The limited host movement and reproductive isolation might obscure the genetic exchange  in comparison with other populations. The low level of gene flow with other populations might be influenced by the nomadic lifestyle, local agricultural policy, and the limited trade with other regions of China. Xinjiang, the largest land area in China, is far away from other sampling sites (about from 2721 to 5073 km). A fact is that Xinjiang belongs to an arid and semi-arid region with temperate continental climate, where is suitable for Hyalomma species development. Therefore, the specific geographical environment and ecological characteristic might restrict the opportunities to media transmission, genetic recombination, and genetic exchange of T. annulata with other populations in China. In our case, the special geographical location, the life way of people and animals, and the climatic features would explain why the Xinjiang population appears to be high genetic differentiation.Moderate genetic differentiation was detected between the six populations in China  of the genetic variations within populations revealed that there were gene flow between these populations due to the movement of animals . This fiT. annulata in China for the first time. These results implied that moderate genetic differentiation, high degree of diversity, and significant LD were exhibited, and regional level of geographical sub-structuring was present in Chinese T. annulata populations. Furthermore, it indicated low levels of genetic exchange and obvious population subdivision between China and Oman at a large distance. This study not only improves the understanding of population structure of T. annulata, but also contributes to better management strategies for bovine theileriosis in China.We compared the genetic variations between sympatric HY and GG conceived the project. ZL, JuL, AL, YL, GL, and JiL collected the samples. FY performed the experiment and data analysis. FY and DS interpreted the data. FY prepared the manuscript with the support from DS and GG. All authors read and approved the final version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.