Large radiocarbon datasets have been analysed statistically to identify, on the one hand, the dynamics and tempo of dispersal processes and, on the other, demographic change. This is particularly true for the spread of farming practices in Neolithic Europe. Here we combine the two approaches and apply them to a new, extensive dataset of 14,535 radiocarbon dates for the Mesolithic and Neolithic periods across the Near East and Europe. The results indicate three distinct demographic regimes: one observed in or around the centre of farming innovation and involving a boost in carrying capacity; a second appearing in regions where Mesolithic populations were well established; and a third corresponding to large-scale migrations into previously essentially unoccupied territories, where the travelling front is readily identified. This spatio-temporal patterning linking demographic change with dispersal dynamics, as displayed in the amplitude of the travelling front, correlates and predicts levels of genetic admixture among European early farmers. In 1971, while anti-diffusionism was sweeping across archaeological circles, Albert Ammerman and Luigi Cavalli-Sforza showed that a continuous large-scale structured population movement analogous to a wave-of-advance could be inferred from a regression analysis of the then available radiocarbon record2. They later suggested that traces of this process of ‘demic diffusion’ could be identified in the spatial pattern of modern genetic variation, with corresponding East-West clines3.The suggested link between a large-scale population movement and the spread of early farming across Europe goes back over more than a century and has since been a constant feature of the corresponding scientific debate5, the association between demography and early farming still presents a strong explanatory power to explain the spread of the Neolithic across Europe. The relatively recent possibility of extracting and analysing DNA directly from ancient bone samples has since confirmed that, indeed, the inception of plant and animal domesticates in Europe was paralleled by the arrival of a new population introducing a genetic component so far absent from Mesolithic Europe and ultimately deriving from North-Western Anatolian sources7. If, at the continental scale, the pattern highlights the proportional contribution of this new genetic component8, it is noteworthy that regional studies point to more complicated local demographic scenarios, characterised by varying levels of admixture between this incoming population and local forager communities9. Similarly, recent work, coupling the vastly expanded radiocarbon record with new statistical and computational techniques, has equally confirmed the early work by Ammerman and Cavalli-Sforza and also shown higher level of complexity. Firstly, Ammerman and Cavalli-Sforza initially suggested that the spread of early farming was continuous, suggesting an average rate of spread of 1 km/year. Although their 1971 publication already hinted at more variation, the 1 km/year figure has reappeared in analyses using updated radiocarbon datasets13. Only a few analyses have explored, and observed, that the overall spread of the Neolithic was an uneven process both temporally and spatially, with cycles of expansion and stasis, and local rates of expansion ranging from 0.5 to 2.5 km/year15. Secondly, the inception of farming has been linked to population booms significant enough to be observed in the bio-anthropological records, leading to what has been called the Neolithic Demographic Transition17, further underlining the demographic causes of the spread of early farming.Despite having been met by extensive criticism20. Such Summed Probability Distributions (SPDs for short) have recently been used to infer demographic changes in prehistoric populations in Europe, North and South America24. Of relevance to the spread of early-farming in Europe was the work of Shennan et al. who observed that, shortly after the arrival of farming to several European regions, there seemed to be a population depletion, which they called the ‘boom-bust’ effect21.More recently, radiocarbon dates have been used in a novel way. By summing the probability density curves of all known radiocarbon dates for a given area, and controlling for the effects of the calibration curve, one gets an idea of population size changes over timeHowever, at present, the above results exist but have not been linked. In particular, the demic diffusion and SPD approaches have not been combined, despite the fact that they both rely on the same primary sources: radiocarbon dates. In the present paper, we work under the hypothesis that these analytical tools are sensitive to different facets of the same process of early farming dispersal: diffusion analysis highlights the dynamics and tempo of the travelling wave, whereas the SPDs highlight the connected demographic processes. As they are interrelated, it is important to understand the dispersal dynamics and its tempo so as to aggregate the radiocarbon dates in sensible spatio-temporal windows, from which SPDs are created, therefore ensuring that important patterns are not lost or downplayed by the improper aggregation of the data.14C determinations for the Mesolithic and Neolithic periods across the Near East and Europe  were removed during a phase of critical auditing.We assembled a database of 14,535 27 using the Bchron package28, and calibration curve Intcal1329, and their mode was identified and stored. Subsequently, the Neolithic convex hull, that is the smallest convex polygon that contains all Neolithic data-points within the time range being considered, was calculated for each 100-year interval in the range 12,000–4,000 cal BP. This created a series of stratified polygons corresponding to the increasing surface area covered by the spread of early farming in Europe. A heuristic analysis of the changes in convex hull area through time . The polygons corresponding to the last four pulse episodes were manually split into two regions based on the cultural attributions in the database. This was done to ensure that culturally distinct Neolithization processes and demographic signals were treated separately – e.g. for the LBK of Central Europe and the Cardial of the Western Mediterranean. This resulted in eleven polygons roughly corresponding to the following regions  method was then appliedWhereas Shennan and colleagues constructed their null model by fitting to the SPD for the whole of Europe in their time range of interest, we argue that, to identify demographic signals of Neolithization, the null model should be representative of the Mesolithic demographic trend, that is it should be constructed only from Mesolithic data up to the moment of Neolithization. Furthermore, since we are interested in exploring potential regional differences in the dynamics of Neolithization, this trend should be local/regional rather than continental. We have, therefore, fitted an exponential null model to a Mesolithic-only SPD, for each of the convex hull regions of interest.N values are number of site-phases identified by the MCSPD ‘binner’ function, whereas the p-values correspond to Timpson et al.’s ‘global p-value’. Figure 33  and Adriatic (IVb) regions, followed by Central Europe (Va) and United Kingdom and Ireland (VIIb). When these SPDs are staggered, a spatio-temporal sequential pattern of ‘booms’ emerges, where a boom in a region is preceded by a drop in the preceding region , Northern Europe (VIa) and Southern Scandinavia (VIIa), as well as, possibly, the Aegean (III) and Atlantic (VIb) regions. The last two do display short ‘booms’ above the null model’s confidence interval, however, due to their small sample sizes (100 and 139 respectively) and high p-values (0.5634 and 0.3588 respectively), these are likely to be false positives and, therefore, we will avoid over-interpreting them. Two of these regions yield p-values that could otherwise be considered significant (0.0006 and 0.0012 for regions Vb and VIIa respectively). However, these values are still much larger than those found in the regions belonging to the second regime and a cursive look at the corresponding SPDs shows that the largest deviations from the exponential growth model correspond to a sharp peak around 8,200 cal BP, a well-known climatic event that might have had demographic consequences in these regions of high density of hunter-gatherers, and therefore does not correspond to the introduction of farming23, we do not find evidence for any long-term major demographic upswing related to the introduction of farming across the research area during the period between 12,000 and 6,000 cal BP. The overall trend is rather one of continuous exponential growth since the early Holocene, with some short-to-medium term fluctuations, just as observed for North America23. This being said, the results also demonstrate a clear, punctuated link between demographic changes and farming, happening during the transition period to the new productive economy. This Neolithic Demographic Transition, already observed in palaeoanthropological data34, presents some regional patterning observed both in terms of the tempo of the expansion of farming and corresponding fluctuations of the SPDs.In line with previous work37. Recent aDNA research indicates the presence of several populations with distinct genetic signatures, centered upon Anatolia/Levant, and Iran/Caucasus8. Each of these groups presents continuity during the transition from foraging to farming, and admixture only happened to significant levels later during the Copper and Bronze Age39. Whilst the SPDs for western Anatolia and the Aegean basin are difficult to interpret given the small sample size, it is clear the diffusion of farming across this area happens relatively quickly during the first half of the 9th millenium cal BP42, and upon a background of limited preceding Mesolithic population43. This process exhibits regional variation, for instance in terms of stock-breeding strategies44, but from a population point of view, the picture looks different. Available aDNA sequencing from a cluster of western Anatolian sites indicates a coherent genetic identity, showing links with the Neolithic Levant populations8. It is this population that is responsible for the introduction of farming into Greece and, beyond, to the rest of Europe45.The SPDs for the Levant and eastern Anatolia suggest that the domestication process per se was not mirrored by any sudden increase of the population, but rather a constant rise stretched over a long period of time46. Our analysis shows that this divergence also concerns demography. The SPDs for the Balkans present a dramatic rise between 8,000 cal BP and 7,600 cal BP, indicating an incoming population – the travelling front – that replaces local foragers, only identified in specialised ecological niches such as the Danube Iron Gates. This interpretation is confirmed by aDNA studies which show very limited level of admixture with local foragers48. A single Eneolithic sample from Romania however suggests an increase in the proportion of Mesolithic genetic component, suggesting the existence of a complex population landscape in the area49. Around 7,600–7,500 cal BP, the SPDs for the Balkans present a marked dip, possibly affected by limited radiocarbon sampling50. Keeping this potential limit in mind, it is however noteworthy that this apparent ‘bust’ coincides with the start of the expansion of farming across central Europe. We consider that this correspondence points only to a limited depletion of the population in the southern Balkans, linked to the the movement of the travelling front of the wave-of-advance, rather than to a collapse related to changes in farming productivity. The amplitude of this expansion episode, associated with the LBK culture, is exceptional as it covers an area stretching from western Ukraine to the Paris basin and corresponds to a steep rise in the SPDs, which contrasts with the limited signal associated with the local Mesolithic. As for the Balkans, this demographic boom corresponds to the arrival of a large population, characterised by the same North-Western Anatolian genetic component with very limited level of admixture with the last foragers6.From the southern Balkans onwards, early farming spread along two main corridors across Europe: one along the Danube-Rhine axis, and another across the Mediterranean. Both streams are associated with distinct archaeological assemblages and agricultural practices51, but the SPDs indicate that the Neolithic population was larger than the Mesolithic one, sparsely identified in the Eastern Adriatic during the centuries preceding the introduction of farming52, and mostly confined to the Alpine region in the western Adriatic54. The demographic rise comes to an abrupt halt by 7600–7500 cal BP, at the same time as farming expands across the western Mediterranean. As for inland Europe, we suggest that this chronological coincidence points to the travelling front of the wave-of-advance. In this case however, the SPDs indicate that the front was limited in size and met a well-established foraging population, although regional variations in this process are likely55. This variation is reflected in the aDNA record, which unambigously shows the introduction of a new population, as in Sardinia for instance57, and which presents in Iberia a high level of admixture between the new North-Western Anatolian and the Western European Mesolithic genetic components58.The second stream of farming diffusion starts in the Adriatic basin. The SPDs point to a demographic peak by 8,000 cal BP associated with the onset of farming practice, although the extent of this expansion is relative given the small corresponding area. The current absence of aDNA makes any interpretation in population terms difficultth millennium cal BP and only resumes in southern Scandinavia, Britain and Ireland nearly a millennium later. This renewed expansion is played on two modes, each with distinct spatial, demographic and genetic signatures. Late foragers in both northern Europe and southern Scandinavia are well-recorded, and known to have been in regular contact with farming groups further south59. For both areas, the SPDs do not show any significant rise in population related to the shift to agriculture, but rather a continuous growth within the expected rate for the local Mesolithic population. This is in agreement with the suggestion that domesticated plants and animals were assimilated by local foraging groups, with only small-scale inward migration of pioneering farmers from central Europe60, as also reflected by the changing level of admixture between Mesolithic and Neolithic genetic components61. Noticeably, this gene flow was not unidirectional towards the North, but also in a reverse direction with the contemporaneous reintroduction in central Europe of haplotypes associated with Mesolithic groups64. Britain and Ireland exhibit a different situation where, upon a background of low, but not non-existent65, Mesolithic population, the introduction of farming during the early centuries of the 6th millenium cal BP coincides with a steep demographic ‘boom’ seen in the SPDs. This event has been identified before and interpreted as the sign of a large-scale migration19. Corresponding aDNA evidence is still rare for Britain and Ireland, but analysis of a single Middle Neolithic Irish individual seems to confirm the large size of this incoming population9, which introduced in Ireland the same genetic component of Anatolian origin as in continental Europe. However, this sample also presents a large admixture with Western European Mesolithic genetic components. In the absence of further data, it is impossible to discern whether this admixture corresponds to local interactions between the indigenous foragers and Neolithic migrants, or was already a feature of the incoming population, since comparable admixture has been recorded for the mid 7th millennium cal BP in the Paris basin66, a likely area of origin for this migrant population67. This major episode of growth is followed by a ‘bust’, sometimes interpreted as a depletion of Irish and British Neolithic populations21. This hypothesis, and especially the magnitude of the suggested demographic process, is however challenged, as many other factors are likely to have cumulatively affected the shape of the SPDs, including downfall of agricultural systems68, environmental factors69 and the changing nature of the archaeological record70.The spread of farming across Europe comes to a halt towards the beginning of the 7Our analysis of the radiocarbon record between 12,000 and 4,000 cal BP indicates that the spread of early farming was not a continuous process, but was rather structured by a series of punctuated expansions interspersed with periods of stasis of changing duration. In demographic terms, we identified three regimes corresponding, respectively, to: (i) the centre(s) of farming innovation, (ii) its expansion to essentially unoccupied territories, and (iii) its expansion to territories with high densities of hunter-gatherer populations.3, as indicated by the SPD across the entire research area.The previously identified ‘boom-bust’ demographic pattern is only observed for the second regime, roughly corresponding to the continental stream of Neolithisation, as well as the United Kingdom and Ireland, where the evidence is strongest. However, contrary to its interpretation as episodes of population growth and sudden collapse, their sequential staggering through time and space, identified here for the first time, suggests that they rather correspond to the demographic signature of a travelling wave-front. In this interpretation, the ‘boom’ is linked to the arrival of new people, whilst the ‘bust’ must be understood as due to outgoing migrants, resuming their spread into a new region. This interpretation is consistent with the expected properties of demic diffusion, as only the wave-front experiences a noticeable demographic pressure, whilst the meta-population follows a neutral growth curve71. Lastly, it must be stressed that the presence, or absence, of a demic wave-of-advance does not provide an explanation for the material variability associated with the spread of early farming across Europe. Likewise, more work is needed to characterise the factors affecting the changing tempo of this diffusion.These radiocarbon-based interpretations correlate well with the available aDNA evidence, which identifies an overwhelming genetic input from Near Eastern farmers in regions corresponding to our second regime. On the other hand, in the Western Mediterranean and Southern Scandinavia, which provide the strongest third regime signals, we observe a continuous growth that follows the trend set by the well-documented local Mesolithic populations. In these regions, a new population is still documented by aDNA but their relative size is not as significant. We therefore consider that our method offers a robust framework to test archaeological hypotheses regarding cases of demic diffusion for which aDNA evidence is limited or not yet availableSupplementary Information


Cochlearia bavarica is a strongly endangered plant species endemic to Bavaria in Germany, constantly declining since the late 1980s. Therefore, population reintroduction is intended. In this study, we analyzed genetic diversity within and genetic differentiation between all 32 remnant populations of the species in Swabia and Upper Bavaria using amplified fragment length polymorphisms. Our aim was to increase reintroduction success by providing data to avoid negative effects of inbreeding and outbreeding and to preserve the natural genetic pattern of the species. Genetic diversity within populations was low but similar to other rare and endemic species and varied strongly between populations but did not depend on population size. Our analysis revealed a strong geographic pattern of genetic variation. Genetic differentiation was strongest between Swabia and Upper Bavaria and at the population level, whereas differentiation between subpopulations was comparatively low. Isolation by distance and genetic differentiation was stronger among populations from Upper Bavaria than from Swabia. From the results of our study, we derived recommendations for a successful reintroduction of the species. We suggest using rather genetically variable than large populations as reintroduction sources. Moreover, the exchange of plant material between Swabia and Upper Bavaria should be completely avoided. Within these regions, plant material from genetically similar populations should preferably be used for reintroduction, whereas the exchange among subpopulations seems to be possible without a negative impact on genetic variation due to natural gene flow.Population reintroduction is a common practice in conservation, but often fails, also due to the effects of inbreeding or outbreeding depression.  Within these projects, it is intended to maintain and develop populations by protecting and restoring natural habitats of C. bavarica. Furthermore, it is purposed to augment small populations by population reinforcement and to reduce the loss of populations by population reintroduction.Cochlearia and its species already have been in the focus of many plant systematic and conservation studies  How large is genetic diversity within populations and genetic differentiation among populations of C. bavarica? (2) Which populations may serve as potential sources for population reinforcement of small populations facing extinction or population reintroduction? (3) Is it possible to draw general conclusions for the reintroduction of C. bavarica?The genus 22.1Cochlearia bavarica Vogt is endemic to Bavaria with a narrow distribution in Swabia and Upper Bavaria  in a 0.2‐ml reaction vessel, heating for 5 min at 95°C with a final 10‐min step at 25°C.Double‐strand DNA adapters were produced by adding equal volumes of both single strands of EcoRI (Thermo Scientific), 2.5 U MseI (Thermo Scientific), 0.1 μmol/L EcoRI and 1 μmol/L MseI adapter pair, 0.5 U T4 DNA ligase with its corresponding buffer (Thermo Scientific), 0.05 mol/L NaCl and 0.5 μg BSA (BioLabs/NBA), and a following incubation for 2 hr at 37°C and a subsequent enzyme denaturation step at 70°C for 15 min. The products were diluted 10‐fold with 1:10 TE buffer .Digestion of 6.4 μl of genomic DNA (7.8 ng/μl) and ligation of DNA adaptors were performed by adding 3.6 μl of a core mix consisting of 2.5 U EcoRI and MseI primers (Biomers) with a single selective nucleotide (MseI‐C and EcoRI‐A) and an AFLP core mix consisting of 1× Buffer S, 0.2 mmol/L dNTPs, and 1.25 U Taq‐Polymerase (PeqLab) were amplified under the chosen parameters: 2 min at 94°C; 30 cycles of 20 s denaturation at 94°C followed by 30 s annealing at 56°C and 2 min elongation at 72°C; finally 2 min at 72°C ended the elongation period; 30 min at 60°C and a cool down to 4°C completed the PCR run. After this, the products were diluted 20‐fold with 1:10 TE buffer for DNA.In the preselective amplification, a reaction volume of 5 μl containing the diluted DNA restriction–ligation product, preselective EcoRI primers were labeled with three different fluorescent dyes for fragment detection . After an extensive primer screening with eight randomly selected individuals, six primer combinations were chosen for further analysis: MseI‐CTC/EcoRI‐AGC and MseI‐CAC/EcoRI‐AAC (D2), MseI‐CAA/EcoRI‐AAG and MseI‐CAG/EcoRI‐AAG (D3), MseI‐CTG/EcoRI‐ACT and MseI‐CTA/EcoRI‐ACA (D4).For selective amplification, primers with three selective nucleotides were used. EcoRI (Biomers), 0.25 μmol/L MseI (Biomers) primers, and 0.75 μl diluted preselective amplification product. The PCR run started with 2 min at 94°C; then 10 cycles of 20 s denaturation at 94°C, 30 s annealing at 66°C (temperature was reduced every subsequent step by 1°C), and 2 min elongation at 72°C; then additional 25 cycles of 20 s denaturation at 94°C, 30 s annealing at 56°C, and 2 min elongation at 72°C, completed by a following 30 min step at 60°C and a cool down to 4°C.Selective amplifications were performed in a reaction volume of 5 μl containing an AFLP core mix , 1.25 U Taq‐Polymerase (PeqLab), 0.05 μmol/L selective Selective PCR products were diluted with 5 μl (D2) and with 20 μl (D4) 1xTE buffer for DNA.2EDTA , and 1 μl glycogen (Roche). Participation of DNA took place by adding 60 μl of ice‐cold ethanol , an immediate shaking and subsequent centrifugation for 20 min at 14,000 g at 4°C. The pelleted DNA was washed once by adding 200 μl of ice‐cold ethanol  and again centrifugation for 20 min at 14,000 g at 4°C. Afterward, the pelleted DNA was vacuum dried in a vacuum concentrator (Eppendorf) and dissolved in a mixture of 24.8 μl sample loading solution (Beckman Coulter) and 0.2 μl DNA Size Standard 400 (Beckman Coulter).Then, 5 μl amplified selective PCR product  was added to a stop solution, consisting of 2 μl sodium acetate , 2 μl NaAccording to fragment size, the fluorescence‐labeled selective PCR products were separated by capillary gel electrophoresis on an automated sequencer , and results were examined with DNA Size Standard 400 using the GeXP software (Beckman Coulter). For further investigations, results were exported as synthetic gel files (.crv), and the fragment pattern of every single individual was analyzed using the software Bionumerics 4.6 : Each strong and clearly defined fragment was taken into account as either present or absent.C. bavarica had to be excluded from the analyses, due to amplification problems.Samples with no clear banding pattern were repeated. Only three samples of For quality control of the AFLP procedure, 10% of all analyzed samples were replicated twice and a genotyping error rate was calculated, according to Bonin et al. , which w2.4i)ln(pi) and Nei's gene diversity (GD) H = 1 − Σ(pi)2, where pi represents the allele frequency, by using the software PopGene 32 . Spearman's rank correlation coefficient was calculated to test the impact of population size on genetic diversity.Employing the software Bionumerics 4.6, a binary (0/1) matrix was created for statistical analyses. If present, fragments of a given length were detected as 1 and in the case of absence as 0. Using the matrix, genetic diversity within each population and subpopulation was calculated as the percentage of polymorphic bands (PB), as Shannon's information index SI = Σ and geographic distances among populations was tested in a Mantel test with 999 permutations  following Lynch and Milligan  with nonGenetic relatedness of individuals was analyzed in the software MVSP version 3.12f Kovach,  using prK groups. Every single group is characterized by a set of allele frequencies at each locus, and samples from the data set are assigned randomly to groups. The number of groups was calculated using 10,000 Markov Chain Monte Carlo simulations with a burn‐in period of 100,000 iterations. Analyses for the predefined value of K were run 20 times per K = 1–40 , Shannon's information index (SI) from 0.11 to 0.19 (mean 0.16), and the percentage of PB from 20.71 to 33.34 (mean 28.18). The highest level of diversity was found in population Ollarzried‐Boschach and the lowest in population Katzbrui‐Mühle .Populations from Swabia and Upper Bavaria did not differ significantly in genetic diversity, and the estimated population size was not correlated with genetic diversity (Spearman's rank correlation coefficient: 3.2K = 743.8) reflecting the regions Swabia and Upper Bavaria. For K = 2, outputs of all 20 iterations were identical  than among populations from Upper Bavaria (ΦPT = 0.51). Further analyses revealed only a low level of genetic differentiation among subpopulations in Swabia (ΦPT = 0.13) and Upper Bavaria (ΦPT = 0.12).In the three‐level AMOVA, we also observed a very strong genetic differentiation between the two study regions Swabia and Upper Bavaria with a Φr = .80, p = .001). Additional Mantel tests have been implemented for each distribution area . In contrast, this correlation was very strong for populations in Upper Bavaria .A Mantel test including all populations revealed significant correlation between pairwise genetic distances and geographic distances  in a literature survey based on more than 150 plant species  and reflects the strong fragmentation and isolation of C. bavarica. Generally, genetic differentiation between populations depends on the interplay of gene flow and drift (Slatkin, C. bavarica, this process may be enhanced by potential self‐pollination further increasing genetic differentiation (Reisch & Bernhardt‐Römermann, With a ΦSlatkin, . Under hSlatkin, . In the However, the level of genetic differentiation varied in our study strongly between different spatial scales. Considering the whole distribution range, we found a strong differentiation between the two regions Swabia and Upper Bavaria. This observation is supported by previous studies revealing a number of alleles being characteristic for either Swabian or Upper Bavarian populations (Koch, Huthmann, & Hurka, 5C. bavarica from our study.The aim of this study was to increase the success of future population reintroduction and reinforcement, by providing data to avoid negative effects of inbreeding and outbreeding and to preserve the natural genetic pattern of the species. However, it should be kept in mind that every reintroduction project with its species is unique (Guerrant & Kaye, C. bavarica, large populations were not necessarily genetically most variable. Since bottlenecks, inbreeding and drift can be avoided best by taking plant material from populations with a high level of genetic diversity we suggest, therefore, to use rather highly variable than large source populations for the reintroduction or the reinforcement of C. bavarica such as the population Ollarzried‐Boschach in Swabia or the population Lungham in Upper Bavaria. Within these populations, plant material should be collected where possible from 50 up to 200 individuals of different age and size classes (Lauterbach, It has been demonstrated that reintroduction success can be enhanced by using plant material from large and stable source populations (Godefroid et al., Cochlearia bavarica exhibited a very distinct geographic pattern of genetic variation, and we would, therefore, strongly advise against using multiple source populations for reintroductions and population reinforcement. Instead, we suggest a graduated procedure for the reintroduction of the species, considering the observed pattern of genetic variation. Because our study revealed a very strong level of genetic differentiation between Swabia and Upper Bavaria, the exchange of plant material between these two study regions should be completely avoided. Within these regions, we detected different patterns of genetic variation. Although we found a clear pattern of isolation by distance in Upper Bavaria, the situation was more idiosyncratic in Swabia. Most likely due to historic gene flow, geographically adjacent populations were not necessarily genetically similar to each other. We suggest, therefore, different approaches for the two regions. In Upper Bavaria for reintroduction, plant material should preferably be used from closely located and, therefore, genetically most similar populations to avoid outbreeding. In Swabia, two different approaches are conceivable: if conservationists decide to preserve the current pattern of genetic variation, plant material for reintroduction should be taken from the genetically most similar population, and if they decide that the present pattern should not be kept since it resulted from former gene flow anyway, plant material should be used from the most variable source population. At the subpopulation level, we detected only a low level of differentiation with a high degree of admixture between subpopulations due to gene flow. The transfer of plant material between subpopulations should, therefore, be possible without changing the natural genetic pattern of the species and without the risk of outbreeding.Although mixing plant material from multiple source populations has been successfully used for reintroduction (Godefroid et al., C. bavarica individuals and a thorough management of the reintroduction sites.It has already been demonstrated that a specific management of the reintroduction sites increases the reintroduction success. Moreover, a reliable and continuous monitoring allows the evaluation of population reintroduction success (Godefroid et al., The authors declare that they have no conflict of interest.C.R. conceived and designed the study. F.K. collected the data and performed the analyses. Both authors contributed to manuscript writing.


Testudo graeca) of mixed origin in the Doñana National Park (SW Spain). The earliest records of tortoises in Doñana trace back to the 18th century, but several population reinforcements in the 20th century with animals from Morocco are well-documented. Consequently, different genetic lineages, which represent distinct subspecies, are thought to co-exist there. Our results confirmed the presence of distinct lineages by revealing that tortoises of the subspecies T. g. marokkensis were introduced into a local allochthonous T. g. graeca population. Unexpectedly, T. g. marokkensis haplotypes exclusively appeared in males, and admixture levels were statistically sex-biased toward males. The sex ratio of the population deviated from parity, with males being 2.36-fold more abundant than females. Our results indicated that population reinforcements had a strong effect on the genetic composition of this population and aggravated its sex ratio deviation. We predict that this sex-biased pattern of introgression is ephemeral and advocated to the near loss of T. g. marokkensis haplotypes.Human-mediated secondary contact of recently diverged taxa offers valuable opportunities for studying the evolutionary mechanisms involved in the establishment and maintenance of genetic boundaries between taxa. We used mitochondrial and microsatellite markers to examine a recently introduced population of the spur-thighed tortoise ( The resulting patterns, caused by hybridization and repeated back-crossing, largely depend on hybrid fitness and their ability to backcross with parental species5. It has been widely demonstrated that introgression does not occur homogeneously within populations [e.g. refs 9 or sex-biased levels of introgression1. Cytonuclear discordance, defined as a significant difference between mitochondrial and nuclear genomes, is expected since the mitochondrial genome is inherited maternally in most species and has a fourfold smaller effective population size than the nuclear genome11, which results in the quicker loss of ancestral polymorphisms over time12. Cytonuclear discordance is favored by mainly male-mediated gene flow, leading to sex-biased introgression. Sex-biased introgression is also often explained by Haldane’s rule, which states that “when in the offspring of two different animal races one sex is absent, rare, or sterile, that sex is the heterozygous (heterogametic) sex”13. Alternatively, differences in dispersal capabilities between males and females, or in selective pressures, can also promote such patterns1.Species boundaries originate from limited or lacking gene flow between species caused by a set of intrinsic natural barriers. Introgression can be defined as the exchange and stable integration of alleles between genetically distinct populations and across such boundarieset al.2 recently pointed out that introgression could either promote or retard divergence processes, and vastly varies among hybridizing taxa and their divergence stages. In recently diverged groups, genetic boundaries are often subtle and allow gene flow by incomplete reproductive isolation. This is often the case in introductions that cause secondary contacts of recently diverged groups [e.g. refs 15, or the transmission of genes from domestic to wild populations can even threaten natural processes in ecosystems (e.g. exchange of genes conferring herbivory resistance17).Abbott Testudo graeca Linnaeus, 1758, in southwest Spain, which putatively led to the admixture of relatively recently diverged genetic lineages from North Africa. The spur-thighed tortoise is one of the most widely distributed tortoise species in the world. Its western Palearctic distribution range embraces North Africa, the Middle East, Asia Minor and southeast Europe. Moreover, a few small isolated populations occur at two distant sites in southwest and southeast regions of the Iberian Peninsula, and also on Majorca, Sardinia and Sicily19. In North Africa, a monophyletic group of six mitochondrial lineages shows a west-east vicariant diversification pattern with allo- and parapatric ranges , the other population that inhabits the Iberian Peninsula remains genetically unstudied.Here we address introgression patterns by examining the origin and historic management of the spur-thighed tortoise, ges Fig. 20–22. Mea24 Fig. . Among texpressis verbis “icotecas” and “galapagos” to specifically differentiate between tortoises and turtles26. It has also been well documented that this population was repeatedly reinforced using tortoises from Morocco throughout the 20th century, as illustrated by some early photographs that document the history of this National Park  introduction or, alternatively, (ii) by it representing a relic from a formerly continuous range across southern Spainb) and genotyping them at eleven microsatellite loci. These molecular data were analyzed together with the available cyt b sequences for all the North African lineages  explore the genetic impact that the recent species management could have had; (ii) characterize the origin of the spur-thighed tortoises in Doñana. To do so, we analyzed 85 spur-thighed tortoises from Doñana by sequencing a mitochondrial DNA fragment . Another haplotype was present in nine males, which represented the lineage of T. g. marokkensis (B2.1). This haplotype is widely distributed in northern Morocco  and southeastern Spain (n = 102), as well as T. g. marokkensis from Morocco (n = 19). These markers not only corroborated the existence of two different lineages in Doñana, but also the admixture between them. One locus (GP81) proved monomorphic. For the remaining ten, 110 alleles were found, ranging from two  to 28 alleles (GmuD51). No significant linkage disequilibrium was revealed for any of the 55 pair-wise locus combinations, and none locus showed at sampling sites significant homozygote excess indicating the presence of possible null alleles. After these initial analyses, we used a cluster number (K) of four in Structure analyses32, with prior group information that corresponded to (1) Doñana, (2) T. g. graeca from southeastern Spain, (3) T. g. graeca from North Africa (Algeria and Morocco) and (4) T. g. marokkensis from North Africa (Morocco). The analysis supported the differentiation of these four groups. The Doñana samples were assigned mostly to a specific cluster. Of the 85 analyzed samples, 14 males showed consistent assignment values over 0.125 to the North African T. g. marokkensis group that were, therefore, identified as having mixed ancestries of T. g. graeca and T. g. marokkensis. These individuals corresponded to eight of the nine tortoises that harbored the mitochondrial haplotypes of T. g. marokkensis. Not one single female with mixed ancestries was found, and no sample showed consistent mixed ancestry with any other group .Subsequent analyses using microsatellites considered eles GmuD1. No sigT. g. graeca from Doñana, Structure analyses without prior population information revealed that K = 3 was the optimal cluster number, although K = 2 also yielded a high ΔK value  was found between the sites Guetourfa (GU) and Messad (MS) in Algeria, while the highest value (FST = 0.37) was observed between a site in the northern Molouya Valley (MO) in Morocco and another site in the north of the southeastern Spanish range . The lowest FST values (0.079 for both) for the Doñana population were found for two southeastern Spanish sites: south of the Bas Mountain (SB) and Chinas (CH).Pair-wise Structure. A more pronounced differentiation was revealed for K = 2, with 16.74% of global genetic variance between the Iberian and North African T. g. graeca, 7.73% among the sampling sites within clusters, and 11.61% that corresponded to the variance among individuals within sites . For K = 3, 12.99% of global variance occurred among the two Iberian and the North African populations, 7.4% among sampling sites, and 12.25% among individuals .Hierarchical AMOVAs supported the two-cluster solution of T. g. graeca tortoises from Doñana showed lower levels of genetic diversity for cyt b than the North African and southeastern Spanish representatives of the same genetic lineage, but higher levels compared to those from other recently introduced western European populations .The ns Table . The misT. g. graeca. The expected heterozygosity levels were similar across the three populations and all the FIS values came close to zero . Consequently, no recent demographic bottlenecks or expansions were inferred for the Doñana population.The microsatellite data also supported a stable population scenario for Doñana. The allelic diversities between the two Iberian populations were similar, but were slightly lower than for the North African Bananas41 matched the genetic differentiation revealed by Structure  and Chinas (CH) sites as representative for Doñana and southeastern Spain, which were characterized by the lowest pair-wise FST values. All the effective sample size (ESS) values for the estimated parameters exceeded 100 at the different replicates, which indicates sufficient Markov chain Monte Carlo mixing across the parameter space42. The marginal posterior probability densities of the demographic parameters did not differ among replicates. The split between Doñana and southeastern Spain was estimated to be 947.5 years, with 95% highest posterior density intervals (HPDI) of 200.8–2,599.5. The effective Doñana population size was estimated at 136.3 individuals (95% HPDI: 40.5–323.9) and at 2,816.8 individuals  for southeastern Spain. The plots of the marginal posterior probability densities of the demography parameters are shown in Fig. In order to set a date for the origin of Structure results for the microsatellite data indicated signatures of admixture between T. g. graeca and T. g. marokkensis in 16.6% of our samples. We did not find signatures of cytonuclear discordance as 57.14% of these samples harbored the mitochondrial haplotype of the T. g. marokkensis lineage.The T. g. marokkensis haplotype. These sex-biased distributions were significant according to the chi-square tests . Based on our sampling, the sex ratio of the population deviated from parity, and males were 2.36-fold more abundant than females . After removing the 15 males with mixed genotypes or T. g. marokkensis haplotype , the population’s sex ratio remained skewed (p = 0.03).However, and unexpectedly, not a single female from Doñana was identified as having a mixed ancestry according to the microsatellite data, and no female tortoise harbored the allochthonous T. g. graeca, which undoubtedly constitute the original genetic lineage in Doñana. However, approximately 15% of tortoises had a genetic impact from T. g. marokkensis, the subspecies that was introduced in the past century as part of population reinforcements.In the present study, we provide evidence for introgression signatures between two recently diverged lineages of spur-thighed tortoises that co-exist in Doñana as a result of introductions and historical management. In the Doñana population we found a major proportion of haplotypes and genotypes that represented T. g. graeca, a weak genetic differentiation appears, which suggests this population as the source of the tortoise population in Doñana. The southeastern Spanish T. g. graeca originated, in turn, from North African founders in prehistorical times, and the role that humans could have played as potential dispersers is still a matter of debate44.Our results reveal no population expansion or bottleneck signatures, but indicate demographic stability for the original population, but also corroborated a recent origin of the Doñana population. Compared to the southeastern Spanish 29. By considering the wider distribution range and the effective population size of the species in southeastern Spain, our results instead suggest that T. g. graeca was introduced into Doñana with founder individuals from southeastern Spain. In Doñana there are several examples of introduced species, including the recent introduction of fish (e.g. Gambusia holbrooki or Cyprinius carpio45) or game species (e.g. Dama dama46). Other examples include ancient introductions of chameleons (Chamaeleo chamaeleon) or genets (Genetta genetta), which may be related to Phoenicians or Arabs 48. Further historical and archaeological research is needed to gain a better understanding of these and other historical introductions across the Strait of Gibraltar because based on molecular data analyses alone, minor inaccuracies in calibration could dramatically change date estimates.According to our results, the populations from Doñana and southeastern Spain are very closely related. The low genetic differentiation between them and their young historical divergence definitively rule out the possibility that both represent vestiges of an ancient wider distribution in the Iberian Peninsula, as previously suggested26. In contrast, the majority of the T. graeca populations show balanced sex ratios [e.g. refs Testudo. In these cases, sex ratios skewed toward males correlate positively with high population densities and aggressive courtship behavior (T. hermanni in Greece53) or with an over-collection of larger females (T. graeca in Morocco49). In Doñana, highly admixed genotypes occur exclusively in males. However, one male with a weak admixed genotype harbors a T. g. marokkensis haplotype  or their poor adaptation to local environmental conditions (exogenous selection)58. In a similar vein, better fitness of hybrid males compared to pure males, known as the heterosis effect or hybrid vigor59, would increase the genetic signatures of the admixed males in the population. Long-term demographic studies in the Doñana have found that adult survival rates are high, constant and similar between males and females, while recruitment and juvenile survivals are variable. These studies have also observed a negative population trend over the 1980–1985 period, while a positive trend has been found for 1992–199560. Continuous population monitoring, combined with parallel genetic analyses, would help to elucidate whether exogamy impacts adult or early life stages, and if is related to the negative population dynamics reported after population reinforcements.The present study also provides firm evidence for the intrinsic mechanisms that cause a male excess in the Doñana population as a result of exogamy. This population’s sex ratio has been traditionally described as significantly skewed toward males, with ratios of up to 3.32:1 adult males:adult femalesT. g. graeca haplotypes will be fixed, but T. g. marokkensis alleles will persist across nuclear genomes. Similar patterns for the species have been reported in both eastern Algeria, in the secondary contact zone of T. g. graeca and T. g. nabeulensis25; and Transcaucasia, in the contact zone of T. g. armeniaca, T. g. buxtoni and T. g. ibera62. To date, these patterns have been explained by the low dispersal capabilities of females62. However, the present study suggests that exogamy could promote sex ratio deviations, which would play a role in maintaining the genetic integrity of genetic lineages across the species’ range1.Thus the observed male-biased sex ratio is a prelude of future and stable cytonuclear discordance in which Finally despite its anthropogenic origin, the Doñana population constitutes a valuable empirical system for studying the evolutionary mechanisms that underlie introgression. Our findings also underline the need to carefully manage wild tortoise populations. Unexpected consequences of inadequate introductions or translocations may threaten the viability of wild populations, especially if they have low densities.26. Sixteen additional samples with known sex were provided by the Doñana Biological Station, which were used in a previous study to address multiple paternity and sperm storage in T. graeca63. In summary, 85 samples from Doñana were analyzed.Two field surveys were conducted in autumn 2012 and spring 2013. Two sites were studied in the Doñana National Park, which were 12 km apart. Sixty-five tortoises were studied from “El Puntal” (DO) and four from “Marismillas” (DO2). Adult tortoises were sexed according to external secondary characters, while one subadult was treated as not sexableT. graeca in Doñana, our previously accumulated data set of 396 sequences from the western Mediterranean clade was used. The considered mitochondrial DNA fragment comprised the complete mitochondrial cytochrome b gene and 25 bp of the adjacent tRNA-Thr gene (1164 bp). This data set corresponded to 250 samples from North Africa, 97 from southeastern Spain, 36 from Sardinia, 12 from Majorca and one from Sicily, which were analyzed for previous studies  included samples from six southeastern Spanish sites  and from 26 sites from Morocco and Algeria . No samples from Majorca were considered because the origin of this population is very recent and is related directly with Algerian populations22.In order to represent the probable sources of tes Fig. . In addi64. Cyt b sequences were amplified by polymerase chain reaction (PCR) and subsequently sequenced. For this purpose, previously described primers  and PCR conditions66 were used. PCR products were purified by precipitation in 1 volume of PCR product (30 μL), 1 volume of 4 M NH4Ac (30 μL) and 12 volumes of EtOH . DNA was pelleted by centrifugation, washed with 70% ethanol and subsequently dissolved in 20 μL of H2O. PCR products were sequenced in an ABI 3130xl sequencer .Total genomic DNA was isolated from an ethanol-preserved blood samples by the salt-extraction protocol43: GmuB08HEX, GmuD16NED, GmuD51PET (designed for Glyptemys muhlenbergii67), GP55HEX, GP61HEX, GP816-FAM, GP966-FAM (designed for Gopherus polyphemus68), Test106-FAM, Test216-FAM, Test716-FAM and Test76HEX (designed for Testudo hermanni69). Microsatellite loci were individually PCR-amplified in a final volume of 20 μl using 1 unit of Taq polymerase  with the buffer recommended by the supplier and a final concentration of 1.6 mM MgCl2, 0.2 mM of each dNTP , 0.4 μM of each primer, and approximately 10–40 ng of total DNA. PCR products were analyzed by gel electrophoresis in two separate runs  in an ABI 3130xl sequencer and with the GeneMapper software (Applied Biosystems).Tortoises were genotyped using eleven fluorescent-labeled microsatellitesb sequences were aligned with ClustalW using the default parameters as implemented in Mega 4.070. The identified new haplotypes were deposited in GenBank and named following the nomenclature of previous works25. The mitochondrial DNA variation of the Doñana population was characterized by constructing a parsimony network because this approach is especially effective when representing phylogenetic relationships within closely related taxa71. Networks were built using Tcs72. To detect the genetic signatures of introgression in Doñana population, we calculated and plotted a mismatch distribution using cyt b sequences with Dnasp73. With an infinite sites model, stable populations would be depicted by stochasticity as ragged and multimodal distributions, whereas introgression processes would usually be reflected in bimodal distributions. The raggedness index rg was considered to test the fit of the observed data to the expected distribution74. The impact of the recent population reinforcements in the Doñana population was quantified by calculating the percentages of the individuals that pertained to different lineages. It was assumed that the lineage with a much smaller proportion was that introduced in the last century. To detect any biases in the percentage of introduced haplotypes between sexes, Chi-square tests were used.The mitochondrial cyt Micro-Checker 2.2.375. Genotypic linkage disequilibrium between pairs of microsatellite loci was tested by the Markov chain Monte Carlo (MCMC) method implemented in Genepop 4.076. Tortoise ancestry was examined by the Bayesian clustering approach of Structure 2.3.432. K = 4 was fixed by considering four groups: Doñana, southeast Spanish T. g. graeca, North African T. g. graeca and North African T. g. marokkensis. These groups were used as priors for the ‘locprior’ option77. Burn-in was set at 105 and the number of iterations was set at 106. The individuals from Doñana with cluster membership proportions of >0.125 to any of the other groups were treated as having mixed ancestries (corresponding to a single great-grandparent over three generations). To evaluate the convergence of cluster memberships across individuals, 10 replicates of the analysis were run. As mentioned before for the mitochondrial data, Chi-square tests for independence were used to detect any biases in the percentages of haplotypes between sexes.Microsatellite loci were examined for null alleles using Structure software also served to explore the genetic differences in the Doñana population and its potential source sites in southeastern Spain and North Africa (eastern Morocco and northern Algeria). For this purpose, Structure was run with and without prior population information from K = 1 to K = 25  using the admixture model and the ‘correlated allele frequencies’ option. Burn-in was set at 104 and the number of iterations was set at 105. To evaluate convergence and to estimate optimal genetic clustering, 10 replicates were run for each K value. The number of populations that best fitted the data set was defined by the ΔK method78, as implemented in Structure Harvester79. The assignment of genotypes to clusters was visualized using Distruct80.Microsatellite data and the h) and nucleotide (π) estimates of diversity from the mitochondrial cyt b sequences using Dnasp73. Genalex 6.5.0181 was used to calculate the descriptive statistics of genetic diversity from the microsatellite data in the genetic groups obtained from clustering: average number of alleles (NA), mean number of effective alleles (AE), average observed heterozygosity (HO), unbiased average expected heterozygosity (uHE) and inbreeding estimates (FIS), and to test for Hardy-Weinberg deviations. This software was also used to calculate the pair-wise FST values as measures of genetic differentiation among the considered sites, and hierarchical AMOVAs were run to determine how genetic variation was distributed among clusters, sites and individuals. In the calculation of the pair-wise FST values, only the sampling sites with at least five genotyped individuals were considered.We calculated the haplotype  and the two-phase mutation model (TPM) with 78% step-wise mutations (ps), 22% multistep mutations (pg), and a variance of 3.1 among multiple steps, as recommended for microsatellite data82, were used. Then the historical divergence of the sampling sites was inferred from the pair-wise analysis in the Bananas software41. This approach allowed us to identify the sister population of the Doñana population. Bananas uses Bayesian inference, and the Adaptive Metropolis Algorithm as an adaptive Markov chain Monte Carlo sampler, to infer Neighbor Joining trees in which branch lengths represent mean diversification times τ  among populations. The model is based on a diffusion approach to the transition density of a neutral infinite alleles Wright-Fisher model. The number of iterations was set at 105, burn-in at 104, and thinning at 10. Only those sampling sites with at least three genotyped samples were considered in this analysis. Finally, the split between the Doñana population and its sister population was estimated using the IMa2 software40. Four independent replicates were performed to simultaneously estimate divergence times (t) and effective population sizes (Ne). The Step-wise Mutation Model was assumed and a run length of 2 × 106 steps was computed after a burn-in of 105 steps. The upper bounds of the prior distributions for parameters were set at t = 0.1; qa = 10 ; q1 and q2 = 0.1  and m1 and m2 = 0 (the migration rates between both populations). For scaling the demographic parameters to individuals and years, a generation time of 17.72 years was used, which corresponded to the mean estimates for the Doñana population60 and the mutation rates of loci (μ) were between 3 × 10−9 and 2 × 10−7. The mutation rates were calculated from the number of alleles at each locus and the divergence time of the T. g. graeca lineage22. Parameter convergence and chain mixing were assessed by ESS above 100, and the consistency of results across replicated runs.The demographic and diversification history of the Doñana population was also inferred using microsatellite data. Sampling of spur-thighed tortoises in Doñana was conducted under the authorization and following the protocols approved by the Junta de Andalucía (permit nos.: 21/2012 and 2013/15). The sampling protocols were approved by the Ethic Committee of the Miguel Hernández University (DBA-AGC-001-12), in accordance with the approved guidelines.Supplementary material


Labeobarbus natalensis) is an abundant cyprinid, endemic to KwaZulu‐Natal Province, South Africa. In this study, we developed a single‐nucleotide polymorphism (SNP) dataset from double‐digest restriction site‐associated DNA (ddRAD) sequencing of samples across the distribution. We addressed several hidden challenges, primarily focusing on proper filtering of RAD data and selecting optimal parameters for data processing in polyploid lineages. We used the resulting high‐quality SNP dataset to investigate the population genetic structure of L. natalensis. A small number of mitochondrial markers present in these data had disproportionate influence on the recovered genetic structure. The presence of singleton SNPs also confounded genetic structure. We found a well‐supported division into northern and southern lineages, with further subdivision into five populations, one of which reflects north–south admixture. Approximate Bayesian Computation scenario testing supported a scenario where an ancestral population diverged into northern and southern lineages, which then diverged to yield the current five populations. All river systems showed similar levels of genetic diversity, which appears unrelated to drainage system size. Nucleotide diversity was highest in the smallest river system, the Mbokodweni, which, together with adjacent small coastal systems, should be considered as a key catchment for conservation.The KwaZulu‐Natal yellowfish ( SNP panel from double‐digest restriction site‐associated DNA (ddRAD) sequencing of a hexaploid freshwater fish, Labeobarbus natalensis. We use the SNP dataset to investigate the phylogeography of this species across the distribution and delimit it into five major populations grouped by drainage system. We encountered a number of unexpected challenges when working with this dataset, emphasizing the need for proper filtering of RAD datasets and optimization of parameter selection.We present the development of a high‐quality  This genus has recently been grouped into the tribe Torini  sometime prior to their colonization of Africa c. 13 mya  likely originated from a common ancestor invading the Orange River Basin c. 2–3 mya . This suggests historical isolation among these drainage systems. The most notable divide was between the northern and southern drainage systems. This disjunction does not correspond closely with any known biogeographic transition. In general, KwaZulu‐Natal has a rich and geographically varied freshwater fauna, but this diversity occurs as a complex regional mosaic, reflecting historical interchange among tropical and temperate faunal elements with substantial local endemism  data showed substantial differences between populations of Labeobarbus, in which ancestral hexaploidy has resulted in large and highly paralogous genomes. Consequently, we decided to use a reduced representation approach, restriction site‐associated DNA (RAD) sequencing  sequencing , Qubit® 2.0 Fluorometer , and agarose gel electrophoresis were used to assess DNA quality and concentration. High‐quality samples were sent to Beijing Genomics Institute Hong Kong Co., Limited  to undergo the ddRAD sequencing protocol as per Peterson et al.  and MluCI (/AATT) following the double‐digest paired‐end protocol of Peterson et al. , −c , and −q  were specified in process_radtags. Only reads that remained paired after processing were retained. Adapter pollution, remnants of adapter sequences that were not removed by earlier trimming, was filtered using custom bioinformatic expressions. The degree of overlapping reads and incomplete restriction digestion in the dataset was estimated using custom bioinformatic expressions and CLC genomics workbench 7.0.4 . The latter program was also used to evaluate read quality and to map filtered reads to the closest cyprinid reference genome, the common carp . Filtered reads were similarly mapped to the set of carp coding DNA sequences (CDS) from the same reference after removing mitochondrial genes. We used a mismatch cost of 2, insertion and deletion costs of 3, and length fractions and similarity fractions of 0.9 to retain only highly plausible mappings. Nonspecific matches were ignored.Data were cleaned both with custom bioinformatic expressions and using the program Andrews,  and S4. 2.4denovo_map.pl was used to identify diallelic SNP loci by executing ustacks, cstacks, and sstacks  for the assembly to avoid duplication of SNPs due to overlapping reads from fragment ends and false SNPs caused by adapter pollution. Optimal parameters were identified using the method outlined in Paris, Stevens, and Catchen  and distance allowed between stacks (−M) from defaults of −m 5 −M 3  was assessed by evaluating the change in number of polymorphic loci for n = M−1, n = M, and n = M + 1  against the available mitogenomes for five related genera—Labeobarbus , Varicorhinus (NC_031528.1), Tor , Neolissochilus , and Hypselobarbus (NC_031587.1). We removed potential paralogs that had been merged by identifying loci that possessed more than two haplotypes within a single sample. We compared this haplotype approach with the Hdplot method of McKinney et al.  filter of 0.04 to filter out singleton SNPs that may mask population structure  plots. Additionally, factorial correspondence analysis (FCA) plots were generated in GENETIX 4.05.2  against sequenced monomers available in cyprinids Acrossocheilus paradoxus (AJ241977.1) and C. carpio (M19418.1) and similarly assessed its contribution to population structure.Pairwise population 2.6DIYABC 2.1.0  using the equation from Tajima (Ne*μ. The mutation rate per site per generation (μ) is calculated using a rate of between 1 × 10−8 and 1 × 10−9 per site per year in SNPs  was used to determine the long‐term effective population size  that sequencing had extended into the adapters and barcode at the 5′ end. Reads contaminated by adapter pollution were filtered with custom scripts using regular expressions based on restriction enzyme recognition sequences. We observed a high degree of incomplete enzyme digestion, with 9% of reads containing more than one of the same restriction site.stacks pipeline , potentially paralogous loci , SNPs present in less than 60% of samples (536), SNPs with a MAF of less than 0.04 , and SNPs recorded as heterozygotes across all samples (125) resulted in a dataset of 826 high‐quality SNPs. This dataset was further reduced to a single SNP per locus based on the number of minor alleles present to accommodate some downstream analysis programs, resulting in the final dataset of 723 SNPs. SNPs found from the initial stacks pipeline and in the final dataset show transition/transversion ratios of 1.58 and 2.04, respectively. The SNP identification process is summarized in Table Selecting only the first read from each pair gave the current list of 50,740 loci and 17,256 SNPs identified through the ults see  below. Tults see . We opte loci 2,45, SNPs p3.3K = 2 between the northern and southern populations  of 0.05 and default parameters . We identified 24 loci as potential FST outliers. However, we did not find any difference in genetic signal when comparing these to other loci, and so all loci were combined for downstream analyses. Removing HindIII satellite loci was found to produce similar results, leading to their retention in the final dataset.Positive selection may potentially affect phylogeographic analyses. Consequently, loci in this dataset were filtered for The number of private alleles varied from 35 in Umfolozi to 57 in the Mkomaas population, except Umgeni which had only six private alleles Table . There wFIS values  were polymorphic. Despite exclusion of singleton SNP loci, the major allele frequency was relatively high across variable sites, suggesting that most loci comprise a common allele and a rare variant. Observed heterozygosity was similar across populations and higher than expected heterozygosity, resulting in negative lues see  below. NFST values between four of the five populations , followed by Mkomaas versus Tugela system (FST = 0.039) and Mbokodweni versus Tugela (FST = 0.039). The lowest positive pairwise FST values were recorded between the two northern populations  and the two southern populations .Pairwise ns Table  were sig3.4The best supported scenario from DIYABC analysis involved a split into northern and southern lineages followed by subdivision into three northern and two southern populations Figure . Other s44.1Danio rerio, genome (38.6%)  across the large number of sites identified, probably due to the high frequency of restriction sites for both enzymes, combined with incomplete digestion and size selection. These enzymes were selected to access many loci across the genome. Although efforts were made to restrict fragment size and to achieve complete restriction enzyme digestion, the retention of low levels of nontarget fragments is common to these methods and may have disproportionate influence on the sequence data. An unanticipated by‐product of these technical imperfections, combined with polyploidy of this lineage, is high genome coverage at low read depth. As is typical of many genomic studies, this resulted in a large loss of data through stringent filtering for quality control. In future ddRAD studies, this could be remedied using one rare‐cutting enzyme to reduce the number of fragments sequenced or to increase overall sequencing coverage to improve read depth per site. However, by specifying a minimum depth of five reads per stack, we were able to obtain loci of sufficient coverage (>25×) for population genomic analyses . This shows that paralogs are resilient to the numerous filtering approaches used here.A large number of paralogous sequences were expected due to the ancestral polyploidy of y et al. . The firs Figure , but ideCriteria for SNP representation across individuals have a strong effect on the data available for analysis. Recently, there has been a trend advocating the use of datasets with considerable missing data . This was surprising given that there were only 121 mtDNA markers of 50,740 loci prior to other filters. This highlights the need for effective mitochondrial‐marker filtering of RAD datasets, as these few SNPs influenced signal from all other SNPs. Additionally, we found that the presence of large numbers of singleton SNPs drowned out the signal of genetic differentiation, as observed previously by Rodríguez‐Ezpeleta et al.  . We similarly tested whether the 24 loci potentially under selection identified through BAYESCAN influenced our STRUCTURE results, but found that we obtained the same results whether we excluded these loci or not. As a result, both satellite loci and loci potentially under selection were retained in our final dataset.Cyprinid genomes include extensive repetitive regions  consistently grouped closely with those from the neighboring Tugela system Figures , 5, 6, 7We were not able to include samples from the southernmost limit of distribution, the Mtamvuna River. Similarly, the Mbokodweni River has two neighboring river systems of similar size, the Umlazi and Illovo Rivers which were unsampled, and may contribute to the unexpectedly high diversity found within this population. Further sampling throughout the Umgeni system should be a priority to determine the effect of the ongoing transfer scheme on the upper and lower systems. Other unsampled river systems, including the Umvoti, Amatigulu, and Mhlathuze, are lower priorities for analysis as these medium sized systems are flanked by the larger Tugela and Umfolozi.FST values also support the deeper split between the northern and southern populations, as pairwise comparisons between groups were consistently higher than those within these groups. Although all efforts were made to remove paralogs from analyses, negative FIS values within all populations suggest that some remained in the final dataset. Closely similar paralogs would be combined as allelic variants in stacks, resulting in excess heterozygosity. Where nucleotide substitutions result in consistent differences among paralogs, these combined loci would be excluded by our heterozygosity filter. Difficulties distinguishing interlocus from allelic variation are expected in polyploid species, resulting in excess heterozygosity  were not found in the southern lineages  and vice versa . Further investigation of private alleles after removing the potentially admixed Umgeni lineage revealed a split between the northern and southern lineages and the four populations. The Umfolozi lineage is clearly the most divergent according to the association of alleles between populations as it shares the least out of all populations. K = 2 was determined to be most likely. However, the most likely explanation for this incongruence is that there is gene flow occurring between these putatively isolated populations such that the finer‐scale nuclear structure within the northern and southern lineages is not substantial, but the mitochondrial locus is rapidly fixed within local populations and reflects a larger degree of difference between these populations. This may suggest a flaw with popular methods such as DNA barcoding, where the sequencing of mitochondrial genes is solely used to delimit populations or even species. Further investigation is necessary to determine whether this is the case here.The identification of five different populations in this study contrasts against the six haplogroups identified using mitochondrial data . This could be due to retention of ancestral polymorphisms at nuclear loci whereas the lower effective population size of the mitochondrial genome would allow population variation fixation at a more rapid rate. Alternatively, this level of fine‐scale differentiation may be beyond the current approach where major historical events could be masking signal from more recent or smaller‐scale events, such as in the case of the primary STRUCTURE analysis where 4.4Similar levels of support were received for three models in our scenario testing with DIYABC. The model with the most support matches the results previously observed in our other analyses, where an ancestral population diverged into the northern and southern lineages, which then underwent further subdivision into the current five populations. The ancestral lineage was likely located in Zululand (Umfolozi drainage system), although we cannot rule out the possibility of an Mkomaas ancestral group Figure .Ne produced here is an estimate assuming random mating and constant population size and is associated with the long‐term population size, not the contemporary census size. Parameters of population history were not estimated in DIYABC due to uncertainty of the priors. Because L. natalensis is distributed throughout most rivers of KwaZulu‐Natal, it was assumed that the effective population size and hence nucleotide diversity would be correlated with the geographic size of the drainage systems or with the number of associated rivers in a system. This was reflected in the nucleotide diversities for the Umfolozi, Tugela, and Mkomaas systems; however, the Mbokodweni population had nucleotide diversity even greater than the larger systems of Tugela and Mkomaas despite its restricted range of a single known river. Because this is a reflection of the long‐term effective population size, this may indicate that this population historically occupied a more widespread range or could suggest that we have not yet found the full extent of the distribution of this lineage across the coastal rivers in this area. This highlights the need for more comprehensive sampling across this area to define the current range of this population. The current restricted distribution of this population as shown in this study places it as a priority for conservation purposes.The 5Labeobarbus and other cyprinids.In this study, we used the ddRAD sequencing approach to reduce the genome complexity of a South African endemic hexaploid fish. Our SNP identification protocol was optimized using the new approach of Paris et al.  and exteP.B. and C.J.O. designed the research project. P.B., C.J.O., and M.J.C. supervised the research progress. C.S.S. performed research, developed custom scripts, analyzed and interpreted the data, and led the writing of the manuscript. M.J.C. assisted with many of the technical aspects of the project. All authors contributed to the final draft of the manuscript. Click here for additional data file.