Clunio marinus , a species for which the correct timing of adult emergence is critical in natural populations.The intertidal zone of seacoasts, being affected by the superimposed tidal, diurnal and lunar cycles, is temporally the most complex environment on earth. Many marine organisms exhibit lunar rhythms in reproductive behaviour and some show experimental evidence of endogenous control by a circalunar clock, the molecular and genetic basis of which is unexplored. We examined the genetic control of lunar and diurnal rhythmicity in the marine midge Clunio marinus that differ in the timing of the diurnal and lunar rhythms of emergence. The phenotype distribution of the segregating backcross progeny indicates polygenic control of the lunar emergence rhythm. Diurnal timing of emergence is also under genetic control, and is influenced by two unlinked genes with major effects. Furthermore, the lunar and diurnal timing of emergence is correlated in the backcross generation. We show that both the lunar emergence time and its correlation to the diurnal emergence time are adaptive for the species in its natural environment.We crossed two strains of The correlation implies that the unlinked genes affecting lunar timing and the two unlinked genes affecting diurnal timing could be the same, providing an unexpectedly close interaction of the two clocks. Alternatively, the genes could be genetically linked in a two-by-two fashion, suggesting that evolution has shaped the genetic architecture to stabilize adaptive combinations of lunar and diurnal emergence times by tightening linkage. Our results, the first on genetic control of lunar rhythms, offer a new perspective to explore their molecular clockwork. Eunice viridis in Samoa [Lunar rhythms of reproduction are found in a number of species, especially from the intertidal zone -7, wherein Samoa ,10. Theyin Samoa ,12. In tin Samoa -15.free-run: After being entrained by a specific environmental cue, the so-called zeitgeber, the rhythm must continue under constant conditions, in the absence of that particular cue. This has only been shown for the lunar rhythms of a few species [Acropora millepora and the fish Siganus guttatus, so that their recently-documented transcriptional differences in relation to the lunar cycle [Despite the widespread occurrence of lunar rhythms and their impact on the development and reproduction of marine species, their physiological and molecular basis is largely unknown. The first important question following the discovery of a lunar rhythm is whether it is controlled by a biological clock and therefore endogenous, or whether it is merely a direct response to external cues in the lunar cycle such as the increased illumination during full moon nights. The critical evidence for an endogenous rhythm is that the rhythm must  species -4,16-21.ar cycle ,23, whicClunio marinus HALIDAY 1855  [Clunio is characterized by semilunar (every spring tide) or lunar (every second spring tide) rhythms as well as a diurnal rhythm. Upon emergence the midges immediately mate and oviposit and then die in the rising tide.One of the most striking and best studied endogenous lunar rhythms is found in the marine midge nomidae) . This spClunio populations are similar for all places along the coast [Clunio populations in the field have been found to be locally adapted to the respective time of low tide [The days of spring tide and the corresponding lunar emergence days of different he coast . Howeverlow tide ,25. Bothlow tide . The liglow tide , while flow tide ,25,26.Clunio marinus by crossing strains collected from two populations differing in diurnal and lunar time of emergence and in the period of the lunar rhythm, i.e. the time between consecutive emergence peaks. These differences have been maintained in the laboratory for more than three years under artificial LD and lunar entrainment. We found that the differences in both lunar and diurnal emergence time are under genetic control, and there is a genetic basis for correlation between the two timing phenotypes that is adaptive in the natural environment.We investigated the genetic basis of the diurnal and lunar rhythms of Previous results with mass crosses using laboratory strains collected from the French Atlantic coast at Port-en-Bessin  and St. Jean-de-Luz  suggested polygenic control of diurnal emergence time, as the continuously-distributed progeny emergence times of F1, F2 and backcross (BC) to Por occurred intermediately between their respective parents . To furtClunio differ greatly even within families, the individuals of one generation typically emerge in several cycles over 2 to 3 months. Jean strain individuals emerged in a lunar rhythm, within a single peak in a 30-day cycle at around day 12. The Por strain showed a semilunar rhythm, with emergences distributed equally into two peaks in a 30-day cycle centred at days 1 and 16. Thus the parental strains differed both in the period of the rhythm and the timing of the peaks, and these two properties behaved differently in the crosses.The same single pair crosses and additional mass reared crosses also revealed a complex genetic pattern of lunar emergence dates Figure , Table 1Most progeny emerged in a single peak, its phasing and breadth differing for F1 and BC generations. The major peak in the F1 (7.2) is intermediate between Jean (12.4) and the first peak in Por (1.2)  has a natural ls-tim variant that increases incidence of photoperiodic diapause independent of daylength [ls-tim also affects light sensitivity of the circadian clock [D. melanogaster glycogen synthase kinase 3  and casein kinase 2 (ck2), which besides their role in the circadian clock [ck2) [gsk-3) [Gene pleiotropy might be more likely as the basis of the correlation between lunar and diurnal emergence times, and there is an example from the field of photoperiodism that illustrates this possibility. The aylength . This praylength , althougan clock . Other can clock ,41 have ck [ck2) , microtu [gsk-3)  and wnt/ [gsk-3) ,45. HoweClunio, and gene pleiotropy is possible but suffers from a lack of candidate genes. Thus an alternative hypothesis should be considered: that different genes affect circadian vs. lunar timing, and that lunar and circadian genes occur in linked pairs. While increasing the number of hypothesized genes, it removes the onus of explaining how one gene affects two timing mechanisms that appear physiologically distinct. Moreover, it is consistent with the existence of large non-pairing regions in two of the three Clunio polytene chromosomes, which likely are not subject to recombination [Thus when trying to explain the genetic correlation in terms of the same genes affecting both timing processes, three difficulties arise: identity of the molecular clockworks is highly unlikely, there is currently no support for an effect of modular pleiotropy in bination .Clunio marinus to time the tides emerges from a physiological constraint due to the involvement of the same genes in both, or whether the high predictability of the combinations of physical factors imposing selection has shaped genome architecture resulting in genetic linkage and stable combinations of the timing traits.Irrespective of the details of the genetic mechanism underlying the correlation of diurnal and lunar emergence times, the non-independence of the timing traits stabilizes well-adapted combinations of lunar and diurnal timing. The two phenomena to which lunar and diurnal emergence times are adapted, i.e. the number of days between the occurrence of low tide at midnight and the spring tide, and the time of low tide on spring tide days respectively, both depend on the local tidal regime  and St. Jean-de-Luz  were established from copulae that were caught in the field during September 2007 (Por) and October 2007 (Jean) respectively. The laboratory strains were reared according to Neumann [Clunio marinus as well [®vibra_c from ESYS) for 545 hours in one minute intervals and analyzed with Chronos-Fit software [Since temperature and mechanical vibrations are known to act as zeitgeber for the lunar rhythm of  as well ,26, we rsoftware  for periThe newly established strains were kept under laboratory conditions for one generation before starting the crosses.In order to allow for genetic and molecular analysis, crosses were carried out as single pair matings. We first produced hybrids of the two strains in both directions  and then performed backcrosses to the Jean strain in both directions . This is in contrast to Neumann  who backF1 and BC were also raised under standard moonlight treatment. In the BC, two families with 57 or 20 individuals respectively were raised singly for future molecular analysis, these two families representing reciprocal crosses for both the F1 and the BC. A possible difference between these two backcross families in the lunar and diurnal emergence times was tested by comparing the phenotype distributions in a Kolmogorov-Smirnov test. We could not find significant differences in emergence times, neither lunar  nor diurnal (lunar peaks summed up: p = 0.66). The other BC clutches were reared in mass cultures.Lunar phenotypes were recorded for all, and diurnal phenotypes for most individuals. The lunar phenotypes (in units of days) are recorded as the day of the artificial moonlight cycle when the individual emerged, "day 1" being the first day with artificial moonlight in the laboratory cycle of 30 days. The diurnal phenotypes (in units of hours) were recorded in reference to the phase of the light-dark cycle in the respective climate chamber. Following Neumann [Both crossing and recording took place partly during the dark phase. During that time we worked under red light, using an custom-made hand-held LED lamp emitting a wavelength of 650 nm. While the midges are attracted to white light, they did not respond behaviourally to the red LED. As a control we also treated standard cultures with this lamp. We did not observe a shift in diurnal emergence times in these cultures, and so concluded that the red light presented in the first hours of the dark phase during our manipulations did not shift the circadian clock of the individuals in our experiment.Clunio marinus have shown that the diurnal time of emergence is not affected by temperature [Clunio [Clunio marinus have shown that reducing the temperature from 20°C to 14°C increases the pupal development interval by about 1 day [per se, but a certain stage in imaginal disk development in the early last larval instar. From this switching point larval develoment and thus timing of entry into the pupal stage are temperature compensated as well [Due to malfunctioning thermostats, the parental strains and most of the F1 families were unintentionally raised at 14°C instead of 20°C, while the single reared backcross families were reared at 22°C instead of 20°C. We made no adjustment to the observed diurnal emergence phenotypes due to this, because previous experiments on perature . This isperature ,50 and t [Clunio ,48. Howeut 1 day ,51 that All graphs and statistics were calculated using the R  statistiTo test for the number of genes involved in determining diurnal or lunar emergence time respectively, for the BC we calculated the expected distribution of phenotypes according to different numbers of genetic factors involved and tested the observed distribution of phenotypes against them. The expected distribution for one genetic factor was obtained by combining the observed distributions of the parents of the BC, i.e. the F1 and the Jean strain. For more genetic factors, we obtained the expected distribution by sampling 200,000 individuals from the observed distributions of the F1 and the Jean strain. For the process of sampling we divided the sample into the underlying genotypes according to Mendelian expectations, e.g. 1:2:1 for two genetic factors, 1:3:3:1 for three genetic factors and so on. The fraction of pure parental genotypes  was sampled directly from the observed parental distributions. For mixed genotypes we sampled one phenotype each from the observed F1 and Jean distributions and averaged the values according to the ratio of genetic factors coming from F1 vs. Jean. The combined distribution of phenotypes of all genotypes was assumed to be the expected distribution for the respective number of genetic factors. The expected distribution was then scaled to the size of the original sample  and compared to the observed distribution of the original sample in a Kolmogorov-Smirnov test, and in a G-test for independence referred to the Chi-square distribution with 18 degrees of freedom for the lunar data or 10 degrees of freedom for the circadian data respectively.resamp. If a model with a certain degree of linkage was already rejected, we did not test the genetic models that would allow for more independence of the traits, e.g. if a model with 3 circadian genes, 2 lunar genes and 2 linkages was rejected, we did not test the model with 4 circadian genes, 2 lunar genes and 2 linkages, as this model would necessarily imply a higher degree of independence of the two traits.The correlation of diurnal and lunar emergence times was assessed by Pearson's product moment correlation. To test which degree of genetic linkage would be required to achieve a correlation of the given magnitude, we compiled matrices of all possible genotypes in the BC for all genetic models from 2-4 lunar and 2-4 circadian genes. Linkage models considered were of the following form: 0 linkages: all lunar genes unlinked to all circadian genes; 1 linkage: one lunar gene linked to one circadian gene; all others mutually unlinked; 2 linkages: same as 1 linkage but in addition a second lunar gene linked to a second circadian gene; etc. Depending on the pattern of genetic linkage assumed, we sampled genotypes for 62 individuals  from the full matrix in case of freely recombining factors, or from the respective subsets of the matrix in the case of linkage. For these genotypes we then sampled phenotypes: For each gene coming from F1 or Jean strain we sampled a corresponding phenotype from the F1 or Jean distribution. Then these phenotypes were weighted according to the number of genes assumed to be involved in determining the respective trait, lunar or diurnal emergence time. The genes were assumed to have equal effects. The resulting lunar and diurnal phenotypes were grouped into classes according to those used in the observed BC and the correlation was assessed using Pearson's product moment correlation. We repeated this procedure 100.000 times for each genetic model. The fraction of p values smaller or equal to the p value of the observed BC distribution is the p value obtained, pThe timing traits of the laboratory strains relative to artificial moonlight were taken from ,30,53. IThe data on the local tidal regimes were taken from tide tables for the year 1979. As there is no notable difference between years, the choice of year does not matter. The tide tables, as well as all data and calculations based on them, are in Central European Time (CET). To obtain the interval between the light stimulus and the spring tides in the field we had to obtain a measure for these two events. To estimate the time of the moonlight stimulus, the date of all days with a low tide between 11 p.m. and 1 a.m. was noted. Usually this condition is fulfilled for 2 or 3 days in a row. There are two periods in a lunar cycle when low tides occur around midnight, but for every lunar cycle only the period during the moonlit quarters (i.e. the one closer to full moon) was considered, as only during this period will the moon be in the sky during the midnight low tide and can act as a zeitgeber. The spring tides occur around full moon and new moon, but the exact spring tide day varies by one or two days from place to place and across the year. Therefore we used the days of full moon and the days of new moon as a universal approximation of the spring tide days for all places. Finally, the time between the light stimuli and new moon or full moon respectively was averaged for all months of the year 1979. The time of low tide on full moon day or new moon day respectively was averaged for the whole year of 1979 as well.TSK conceived the study, designed the experiment, established the laboratory stocks, performed the crosses, analysed the data and wrote the manuscript. DN advised on laboratory culture, brought forward the interpretation of the data by continuous discussion and edited the manuscript. DGH advised on experiment design and data analysis and revised the manuscript. All authors read and approved the final manuscript.


Sus scrofa) from Northwest Europe to evaluate determinants of genetic population structure.Population genetic studies focus on natural dispersal and isolation by landscape barriers as the main drivers of genetic population structure. However, anthropogenic factors such as reintroductions, translocations and wild x domestic hybridization may also have strong effects on genetic population structure. In this study we genotyped 351 Single Nucleotide Polymorphism markers evenly spread across the genome in 645 wild boar . The relationship between pairwise kinship coefficients and geographic distance showed male-biased dispersal at the population genetic level.Our results demonstrate that wildlife and landscape management by humans are shaping the genetic diversity of an iconic wildlife species. Historical reintroductions, translocation and recent restocking activities with farmed wild boar have all influenced wild boar genetic population structure. The current trend of wild boar population growth and range expansion has recently led to a number of contact zones between clusters, and further admixture between the different wild boar clusters is to be expected. Most population genetic studies consider dispersal and isolation by landscape barriers to be the main drivers of genetic population structure . Howeverth century [th and early 20th century. One such event is the commonly known but undocumented reintroduction of wild boar to the Veluwe, the forested centre of The Netherlands, which occurred in 1904 at the orders of Hendrik, Prince-Consort of Queen Wilhelmina of The Netherlands, for the purpose of hunting [Wild boar became extinct in large parts of Western Europe in the 19 century . This ma hunting . These ath century due to hunting restrictions, reforestation, changes in agriculture and possibly climate change [Sus scrofa) are adaptive and opportunistic omnivores as well as good dispersers, being able to travel distances up to 250 km [Conditions for wild boar steadily improved during the 20e change ,13. Stare change ,15. Wildo 250 km  and fasto 250 km . Disperso 250 km ,17. Euroo 250 km . It is, th century to provide for a demand in luxury meat. Hybridization between wild boar and domestic breeds is common practise on these farms to achieve increased reproduction and growth rates [th generation backcrosses with wild boar) as well as advanced generation hybrids (resulting from reproduction among hybrids across multiple generations) were identified. However, the spatial extent of domestic introgression and its effects on the population genetic structure of European wild boar have not been studied in detail.Wild boar farming became popular in Europe in the second half of the 20th rates . Such hyth rates . This haFrom an evolutionary point of view, possible adverse effects of genetic introgression from a domestic or hybrid source include genetic adaptation to captivity and possibly outbreeding depression , while pIn this study we used 351 SNP markers, genotyped for 645 wild boar, including 88 samples from a previous study , to asseBlood or tissue samples were taken from a total of 645 wild boar in parts of The Netherlands, Western Germany and Luxembourg. This included 88 samples from a previous study , which wDNA was extracted using the Qiagen PureGene (Blood) kit protocol. Samples were genotyped for 384 SNPs selected from the Illumina porcine SNP60 genotyping beadchip  from locplink v1.06[2) and assuming a 0.2 threshold. Principal Components Analysis (PCA) was performed to visualise genetic variation and possible clustering patterns using the eigenvector method implemented in eigensoft 3.0 [n = 20 per breed, Additional files structure[et al.[FST values, SNPRelate [FST/(1- FST) between all sampled locations.Linkage Disequilibrium (LD) was analysed in ink v1.06 by calcusoft 3.0 ,33. For structure for popure[et al.. Phylogere[et al.. A numbere[et al. for hetere[et al. for calcNPRelate  for the NPRelate  based onNPRelate , and finNPRelate  for mantThe 351 genotyped SNPs had an overall call rate of 0.98 and 8 out of the 61075 possible pairwise SNP combinations (0.013%) interfered with linkage equilibrium. These pairwise LD SNP combinations were separated by 74-753 kb. As 50 kb is the maximum range of physical linkage in wild boar , the LD structure likelihood assignment minimum threshold of 0.25 (25%) to a sample of domestic pigs . Individual assignment proportions for K = 1-7 are indicated in Figure structure, in contrast to the four advanced generation hybrids . This percentage is similar to previous reports [We screened for wild boar-domestic pig hybrids by applying a  reports , but herstructure clustering and PCA show a clear wild - domestic separation  in the PCA  to high (0.15 < FST <0.25) genetic differentiation between the inferred clusters (Table p = 0.061), even though it was not significant within some of the clusters (Table p = 0.008) in the study area.Following the method of Evanno et al. , six genrs Table . In addirs Table , with onFST values after excluding all identified recent hybrids to avoid possible biases due to both increased genetic variation within clusters and decreased variation across clusters caused by the scattered presence of hybrids. This exclusion of hybrids resulted in on average 0.0093 (8%) higher pairwise FST values  between two individuals. Analysis of pairwise kinship coefficients in the wild boar dataset showed a decrease of pairwise kinship over geographic distance Figure . FemalesFST =0.050, Table The largest wild boar populations in this study are found in Germany  was included in a previous SNP60 study [The Hambach cluster has a small geographical distribution with two localised foci Figure . These t60 study . This inth century  to have existed in complete reproductive isolation initially. However, wild boar populations across Europe have increased their numbers dramatically since the 1960s [structure identified a relatively small number of admixed wild boar  reintroduced in the early 20he 1960s ,15. The structure algorithm identified 25 geographically scattered recent hybrids in 645 wild boar samples (3.9%). Hybrids are not more frequent in (putative) reintroduced populations, and seem to be recently introduced to various parts of the study area, possibly for the purpose of restocking local hunting grounds.The mechanism for genetic introgression from domestic pigs into wild boar populations is most likely deliberate or accidental introduction of hybrid farmed wild boar ,21. The structure algorithm relies solely on typical domestic pig allele frequencies for domestic-wild hybrid detection. Allele frequencies may change over time due to genetic drift and admixture with local wild boar gene pools. The figures based on hybrid detection by structure therefore only represent recent genetic introgression from domestic pigs and are likely to underestimate or disregard historical genetic introgression. Hybrid identification using a structure assignment threshold of 0.25 to the domestic pig cluster reliably identified all recent hybrids studied in a previous high-density SNP study [The NP study , but notNP study .Phylogenetic analysis indicated multiple separate lineages within the hybrid group Figure , suggeststructure, because allele frequencies are likely to have diverged over time from those of the source populations due to genetic drift and admixture with local wild boar gene pools. However, these hybrids should be detected when analysing Allele Frequency Spectrum Assessment, which is based on introgressed allelic states . Such aFST increase of 0.0093, corresponding to 8% of the average population FST (Table FST) or LD, as well as erroneous interpretations of population structure.Exclusion of recent hybrids from our total dataset resulted in an average population ST Table . This deThe presence of six well-defined genetic clusters in the study area can be attributed to two factors: the presence of a natural barrier: the River Rhine, and a history of marginalization, extinction and subsequent anthropogenic reintroductions in the Northwest of the study area. Widespread genetic signatures of recent accidental or deliberate restocking of local populations with hybrid farmed wild boar have been found, which confounded population differentiation statistics, but which do not seem to affect the existing population structure.In this study we demonstrate the effect of past landscape and population management on current population structure in an iconic wildlife species. Effects of historical deforestation and overhunting followed by reintroductions and restocking from farms are evident. Wild boar populations in the study area are currently expanding their range. Previously isolated populations are admixing in recently formed contact zones. The relative contribution of each of the current populations to future wild boar diversity may depend on a number of factors including the effective size of populations, habitat connectivity, founder effects, restocking activity, introductions and translocations.The authors declare no competing financial or non-financial interests.DJG. designed the research, performed data collection as well as laboratory and data analysis and wrote the paper. H-JM and PvH contributed to data analysis and writing of the manuscript. W.L. contributed to data collection and writing of the manuscript. RPMAC contributed to data collection and laboratory analysis. KL contributed to laboratory and data analysis. SEvW, RCY and HHTP supervised the research and contributed to the manuscript. All authors read and approved the final manuscript.The 351 SNP genotypes of the 645 wild boar in plink format.Click here for fileThe 351 SNP genotypes of the 645 wild boar in plink format.Click here for fileThe 351 SNP genotypes of the 120 domestic pigs in plink format.Click here for fileThe 351 SNP genotypes of the 120 domestic pigs in plink format.Click here for fileThe structure likelihood parameter L(K) ± s.d. and Evanno’s ΔK (grey line) plotted per number of clusters (K) for K = 1-10 for the wild boar dataset. Note that the domestic pig cluster was excluded.Click here for fileBoxplot indicating the variance of kinship coefficients over 10 km geographic distance classes. Sample sizes per distance class are given below the x-axis.Click here for file


Xylosandrus germanus, a species where the level of LMC is expected to be high.Local Mate Competition (LMC) theory predicts a female should produce a more female-biased sex ratio if her sons compete with each other for mates. Because it provides quantitative predictions that can be experimentally tested, LMC is a textbook example of the predictive power of evolutionary theory. A limitation of many earlier studies in the field is that the population structure and mating system of the studied species are often estimated only indirectly. Here we use microsatellites to characterize the levels of inbreeding of the bark beetle IS = 0.88). There was also strong linkage disequilibrium between microsatellite loci and a very strong genetic differentiation between populations. The data suggest that matings among non-siblings are very rare (3%), although sex ratios from X. germanus in both the field and the laboratory have suggested more matings between non-sibs, and so less intense LMC.For three populations studied, genetic variation for our genetic markers was very low, indicative of an extremely high level of inbreeding (FOur results confirm that caution is needed when inferring mating systems from sex ratio data, especially when a lack of biological detail means the use of overly simple forms of the model of interest. To avoid the cost associated with inbreeding, many organisms have evolved behavioural adaptations preventing matings between close relatives -7. HowevBecause it provides easily testable quantitative predictions, LMC theory is a textbook example of the predictive power of evolutionary theory ,9,13-20.Nasonia vitripennis, Burton-Chellew et al  and females disperse randomly from their natal patches, our estimates of the inbreeding coefficient should predict sex ratios of between 0.06 and 0.10 /2 /2 ,59 or (i2)0.5]/2 .The genetic structure of the three populations was characterized by Wright's fixation indices ,61. StanLK, KP, CB, MT conceived the study, and collected and analysed the data, and contributed to the writing of the paper. DS contributed to the writing of the paper. All authors read and approved the final manuscript.


Cardinalis cardinalis.Earth history events such as climate change are believed to have played a major role in shaping patterns of genetic structure and diversity in species. However, there is a lag between the time of historical events and the collection of present-day samples that are used to infer contemporary population structure. During this lag phase contemporary processes such as dispersal or non-random mating can erase or reinforce population differences generated by historical events. In this study we evaluate the role of both historical and contemporary processes on the phylogeography of a widespread North American songbird, the Northern Cardinal, Phylogenetic analysis revealed deep mtDNA structure with six lineages across the species' range. Ecological niche models supported the same geographic breaks revealed by the mtDNA. A paleoecological niche model for the Last Glacial Maximum indicated that cardinals underwent a dramatic range reduction in eastern North America, whereas their ranges were more stable in México. In eastern North America cardinals expanded out of glacial refugia, but we found no signature of decreased genetic diversity in areas colonized after the Last Glacial Maximum. Present-day demographic data suggested that population growth across the expansion cline is positively correlated with latitude. We propose that there was no loss of genetic diversity in areas colonized after the Last Glacial Maximum because recent high-levels of gene flow across the region have homogenized genetic diversity in eastern North America.C. cardinalis. The general implication of our results is that patterns of genetic diversity are best understood when information on species history, ecology, and demography are considered simultaneously.We show that both deep historical events as well as demographic processes that occurred following these events are critical in shaping genetic pattern and diversity in  A multitude of studies of various North American taxa have shown that historical events, such as Pleistocene climatic changes coupled with topographic, hydrologic and ecological barriers, were instrumental in generating phylogeographic structure within species -4. From A recent and critical historical event was the Last Glacial Maximum, a time when ice sheets covered much of present-day temperate North America. Entire biotas were fragmented and displaced to more southern latitudes . After tContemporary processes can affect the spatial distribution of genetic diversity in various ways. High levels of gene flow may homogenize haplotype diversity throughout a population that once showed genetic structure. Conversely, nonrandom mating with individuals in close geographic proximity can generate genetic structuring within a continuous population . AutecolCardinalis cardinalis). Cardinals are distributed from southeastern Canada, throughout the eastern United States, and in the southwestern states south through the Mexican lowlands to northern Central America . We unl2% rule"  to assesigneus and cardinalis have relatively large ranges and are comprised of several subspecies; therefore we examined the amount of genetic structure within and among subpopulations for each of these clades by performing an AMOVA  in the program Arlequin v. 3.11 [cardinalis and for igneus we used the regions Baja California Sur, Tiburón Island, Arizona/New Mexico and Sinaloa. We calculated genetic diversity (the average number of nucleotide differences per site between two sequences), haplotype diversity [Clades identified from our gene tree were used to estimate the genetic distance (internode to clade node distance) using an uncorrected p-distance for each clade in the program MEGA 4 . Two of  v. 3.11 . For theiversity , and theiversity  for eachcardinalis, coccineus, and igneus). We evaluated demographic history in the program BEAST v. 1. 5.4 [To perform hypothesis tests on cardinal historical demography, we re-estimated sequence models for each individual clade that had adequate sampling  and observation records from the Avian Knowledge Network http://www.avianknowledge.net/content and plotted all records to get an estimate of the C. cardinalis distribution. To build an ecological niche model for C. cardinalis, we used only records from observations and specimens collected for this study. We assumed that these locality records are the most precise because they have been collected in the last ten years. The number of observation records for C. cardinalis was heavily skewed towards records in eastern North America where it is a common "backyard" bird. Therefore, we randomly sampled a subset of the records in order to have a more even sampling scheme. In total we compiled 664 records for C. cardinalis.We downloaded locality records from two online databases, ORNIS  in eastern North America and 33° N in the southwest U. S. A. All records above these latitudes were not included in the analysis in the post-glacial ecological niche models.Beginning in the late 1800 s a well-documented northward range expansion has been recorded for er Basin ,62. We b records , we usedC. cardinalis during the Last Glacial Maximum. We visualized models in the program ArcGIS 9.3 . Because the distribution of C. cardinalis is well known we used a digital range map [We used the Bioclimatic variables from the WorldClim dataset (v. 1.4) with a resolution of 2.5 min . Eight oange map  to set tTo evaluate the impact of contemporary demography on genetic diversity, we used data from the North American Breeding Bird Survey, a long-term demographic study on breeding birds in the USA and Canada . BreedinAll authors have read and approved final manuscript. BTS: Designed study, collected samples, generated data, performed analysis, and wrote the paper. PE: Designed study and collected samples. BEHB: Designed study and collected samples. AGNS: Designed study and collected samples. SR: Designed study, collected samples, and wrote the paper. JK: Designed study, collected samples, and wrote the paper.Taxon List. List of taxa used for the study, their geographic localities, and GenBank accession numbers.Click here for file


Metapenaeopsis barbata, is a commercially important, widely distributed demersal species in the Indo-West Pacific Ocean. Overfishing has made its populations decline in the past decade. To study conservation genetics, eight polymorphic microsatellite loci were isolated. Genetic characteristics of the SSR (simple sequence repeat) fingerprints were estimated in 61 individuals from adjacent seas of Taiwan and China. The number of alleles, ranging from 2 to 4, as well as observed and expected heterozygosities in populations, ranging from 0.048 to 0.538, and 0.048 and 0.654, respectively, were detected. No deviation from Hardy–Weinberg expectations was detected at either locus. No significant linkage disequilibrium was detected in locus pairs. The polymorphic microsatellite loci will be useful for investigations of the genetic variation, population structure, and conservation genetics of this species.The red-spot prawn,  Metapenaeopsis barbata, is a widely distributed demersal species in Indo-West Pacific from the Gulf of Bengal to Japan and Indonesia [M. barbata, molecular markers are urgently needed to develop suitable strategies to maintain sustainable populationsThe red-spot prawn, ndonesia –3. It isndonesia ,4. It wandonesia . Furtherndonesia  and ecolndonesia ,5,7–10. ndonesia ,12 invesndonesia ,14, consndonesia ,16, and ndonesia , as wellM. barbata, including 21 from Taichung, Taiwan and 40 from Fujian, China, were collected. Eight di-nucleotide SSR loci were isolated. The characteristics of the innovative microsatellite loci and variability measures across two populations are described in HO) and expected heterozygosities (HE) ranged from 0.048 to 0.476 (averaged at 0.327) and ranged from 0.048 to 0.667 (averaged at 0.420), respectively, in Taichung; while the HO and HE ranged from 0.205 to 0.538 (average = 0.331) and from 0.283 to 0.654 (average = 0.392), respectively, in Fujian. No deviation from Hardy–Weinberg expectations was detected at either locus. There was no evidence of linkage disequilibrium between any pairs of loci. The locus-wise FIS for each population, shown in M. barbata due to its reliable, informative, co-dominant nature and ease of exchange of data among different studies. The results suggest that the microsatellite DNA loci identified in this study are highly polymorphic, and that these markers can be useful for investigating the genetic structure and management of M. barbata populations.In total, 61 individuals of M. barbata. Genomic DNA was extracted from muscle tissues preserved in 95% ethanol by following standard phenol-chloroform procedure [2, 0.5 U Taq polymerase (Promega), and 5 pmol of one RAPD primer. The PCR program were as follows: initial denaturing for 3 min at 95 °C for 1 cycle, 40 cycles of 1 min at 94 °C, 45 s at 42 °C, 2 min at 72 °C, followed by 10 min at 72 °C for an additional extension step. Approximately 100 ng of PCR product was ligated into a pGEM-T vector (Promega) according to the manufacturer’s instructions. The ligation mixture was then transformed into Escherichia coli competent cells to form the enriched microsatellite sequence library. Colonies of RAPD fragments were analyzed to verify existence of repetitive sequences. Clones were screened using repeat-specific and vector primers [M. barbata individuals collected from Fujian Province, China and PCR conditions were optimized for each pair of primers. Reactions were performed in a total volume of 15-μL containing 10 ng of genomic DNA, 0.2 mM dNTP, 2 mM MgCl2, and 0.12 μM of each primer. The PCR program consisted of 94 °C for 5 min followed by 35 cycles at 94 °C for 30 s, 30 s at primer-specific annealing temperature , and observed heterozygosity (HO) were quantified using the Arlequin version 3.5 [FIS) [Genotype data files were inter-converted for the various analytical software programs using CREATE  to minimsion 3.5 . Hardy–Wsion 3.5 . Resultssion 3.5 . Inbreed.5 [FIS)  and the .5 [FIS) ,28.M. barbata has declined in the last decade. Therefore, the development of microsatellites and their application, which can offer an effective tool for understanding genetic variation and population structure in M. barbata, is of vital importance. These eight polymorphic microsatellite loci reported here are the first microsatellite markers designed specifically for M. barbata, and the versatility of these new primer sets will provide for the further studies on the genetic structure, gene flow, sustainable management and molecular evolution of this susceptible species. The microsatellite loci are sufficient to perceive significant differences among populations of M. barbata in different fishing grounds of Asia in future studies.The wild resource of


Dalarna county, in north middle Sweden, which borders southern Norway, also appears to differ markedly from other counties, possibly due to this county having more individuals with remote Finnish or Norwegian ancestry than other counties. An analysis of genetic differentiation (based on pairwise Fst) indicated that the population of Sweden's southernmost counties are genetically closer to the HapMap CEU samples of Northern European ancestry than to the populations of Sweden's northernmost counties. In a comparison of extended homozygous segments, we detected a clear divide between southern and northern Sweden with small differences between the southern counties and considerably more segments in northern Sweden. Both the increased degree of homozygosity in the north and the large genetic differences between the south and the north may have arisen due to a small population in the north and the vast geographical distances between towns and villages in the north, in contrast to the more densely settled southern parts of Sweden. Our findings have implications for future genome-wide association studies (GWAS) with respect to the matching of cases and controls and the need for within-county matching. We have shown that genetic differences within a single country may be substantial, even when viewed on a European scale. Thus, population stratification needs to be accounted for, even within a country like Sweden, which is often perceived to be relatively homogenous and a favourable resource for genetic mapping, otherwise inferences based on genetic data may lead to false conclusions.Patterns of genetic diversity have previously been shown to mirror geography on a global scale and within continents and individual countries. Using genome-wide SNP data on 5174 Swedes with extensive geographical coverage, we analyzed the genetic structure of the Swedish population. We observed strong differences between the far northern counties and the remaining counties. The population of  It is important to understand the genetic gradients and stratifications within countries, from both population genetic and medical perspectives. Genetic gradients are represented by differences in allele frequencies, which have come about due to events such as migration, genetic drift or differential selection th century. Population densities, which impact on population structure, vary greatly across Sweden , e.g. the first Wellcome Trust Case-Control Study Swedes have been included in a wide variety of GWAS, e.g. type 2 diabetes, breast cancer, prostate cancer, schizophrenia, and bipolar disorder. Sweden has a population of only 9 million, a 40-year history of nation-wide medical registers, and high research study participation rates in an almost exclusively publicly funded healthcare system. This has made the collection of substantial numbers of cases possible, even for rare diseases.We have investigated the Swedish genetic structure and its effect on population-based genetic studies in Sweden using a comprehensive collection of more than 5000 control samples collected across the country. We have used information on each subject's village or city of birth, county of birth or county of current residence to link genetic structure to geography.Impute version 1 Our data consist of controls  collected in Sweden for GWAS on breast cancer , prostate cancer , diabetes type 2 , schizophrenia , rheumatoid arthritis  and an unselected twin-cohort study , that were typed on platforms from Illumina and Affymetrix . Some ofThe county of residence was known for controls from the CAHRES and EIRA studies, while the county of birth was known for controls in the TWINGENE-SW study. For controls from the CAPS study, the municipality of residence was known (as well as county). For DGI the city of sample collection was known. For SCZ-SW the birthplace (city or village) and birth county was known. The best estimate of the geographical origin of each sample was assigned according to the following order: city or village of birth, county of birth, municipality or city of residence and county of residence. Coordinates for the locations were retrieved from the geographical location database Geonames Plink version 1.07 R version 2.10 maptools was used for the plotting of maps Analyses and management of genotype data were carried out using We first restricted all data sets to the 760,053 SNPs available in the largest sample set (SCZ-SW) that passed QC and whose alleles matched between data sets. 775 SNPs for which alleles differed between data sets, mainly alleles from imputed data sets with only one allele present, were removed prior to merging.−6) in all studies combined. In addition, we removed SNPs that differed between studies in a 1-vs-rest comparison  with p<10−6 in any comparison, in order to remove undetected strand flips  and SNPs differing due to technical differences between the studies. After QC, there were 184,449 autosomal SNPs for analysis.We then conducted extensive quality control of the SNPs in order to remove genotyping platform artifacts and study sample collection differences. We removed SNPs that had a genotyping success rate of less than 95% in any of the included studies. We also removed SNPs that had a minor allele frequency <0.01 in all studies combined or that failed Hardy-Weinberg equilibrium  and transformed these distances to a standard-normal distribution. All samples that had a closest other sample more than 4 standard deviations from the mean were removed. This was repeated for the 2Dalarna county. We used the ten-fold cross-validation procedure, implemented within the program, to identify the number of “ancestral” populations for which the latent class model has best predictive accuracy.We performed an analysis of admixture using the program ADMIXTURE st values were calculated using smartpca from the Eigensoft package. Genomic inflation λ values  for a study consisting of 1000 samples with cases and controls coming from two different regions (i.e. a fully stratified data set) were estimated from the Fst values according to: E(λGC 1000) = 1+(1000 * Fst) The CEU founders from release 23 of HapMap were used in the comparison with the Swedish national areas and counties in the analysis of genetic differentiation. Pairwise FIn order to investigate the second principal component, two additional data sets were used, one consisting of 939 controls collected in Western Finland as part of the DGI diabetes type 2 study −8.In order to assess the effect of population stratification on the power of case-control studies to detect SNP association, we carried out a simulation study. We generated SNP data for repeated samples of 500 cases and 500 controls, under a variety of assumptions concerning minor allele frequencies (MAF), odds ratios (OR) and genomic inflation (λ). We calculated power to detect association based on using a genomewide significance threshold of 5×10We used Plink to call homozygous segments and used a 20 SNP sliding window that maximally could contain 1 heterozygous SNP if it was to be called homozygous. A SNP was determined to be in a homozygous segment if at least 10% of the overlapping windows were called as homozygous. We then filtered segments to be at least 1 Mb long, have a minimum of 50 SNPs and have no more than 5% heterozygous SNPs.The appropriate regional ethics committees  have given approval and all participants gave their written informed consent. The study was in accordance with the principles of the current version of the Declaration of Helsinki.We first removed SNPs that had a genotyping success rate of less than 95% in any of the included studies . This le2, respectively, and 63.9% when combined. Samples with suspected Finnish ancestry on the basis of their genetic data were then removed  . With re removed . In all, removed . After t removed .2 = 0.54, p<10−100). The first component was also associated with longitude , but little additional variance in the component was explained when adding longitude to a model already containing latitude (ΔR2 = 0.006) as latitude and longitude were correlated in our sample due to the country's elongation in the southwest-northeast direction.Principal components 1 and 2, from the principal components analysis with samples removed , were thNorrbotten and Västerbotten together accounted for 52 percent of the variance in principal component 1 .Novembre and Stephens parisons . Interesmean≠0p = 2.8×10−44)  . Full Finnish ancestry was defined as being either born in Finland or having had two parents born there. These 76 individuals had been previously removed from our data set. They had predominantly negative values (8×10−44) , i.e. va<10−100) .Dalarna county and the Finns (mean≠0p<10−100), In general the values were of smaller magnitude than the component values of the Finnish samples, although some subjects were negative outliers , in the expected direction; the extreme values found in the north-western parts of the county were associated with higher degrees of inbreeding.We also wanted to investigate other possible underlying causes of the observed distribution of the second principal component in the primary data set, broken down by geographical position, such as isolation and consecutive allelic drift. If isolated populations have yet to be broken up, we would expect to see more homozygosity among the samples from the isolated populations, due to a smaller effective population size. We tested for association between the second principal component and the inbreeding coefficient, as calculated by the –het command in Plink, in None of the other principal components showed any geographical clustering which was as distinct as for the first two components .Dalarna county samples, comparing models based on 1 to 5 ancestral populations. The single population solution had the lowest cross validation error.We also performed an admixture analysis using st was calculated by comparing both (21) counties and (8) national areas (groups of counties)   accordinst values ranging from 0.0037 for Stockholm to 0.0053 for the HapMap CEU founders .As expected, ethnic Finns showed the largest genetic distances to the other groups, both to the HapMap CEU samples and to the Swedish national areas with Ffounders . PairwisUpper Norrland. This was also true when the national areas were split into individual counties  and using a multidimensional scaling (MDS) plot. Because Finns have the largest difference from other groups, they were excluded from these plots - to avoid their overshadowing of differences between the other groups. The heat map  Västerbotten, (iii) Västernorrland and Jämtland, (iv) Dalarna, (v) Värmland and Gävleborg, (vi) HapMap CEU samples and (vii) all other Swedish counties. The MDS plot also clearly demonstrates that the northern counties are more clearly genetically differentiated from each other than the southern counties are from each other, and highlights also that Dalarna is differentiated from other Swedish counties  some differences are to be expected. Also, the studies may have collected samples in a more limited geographical region than the entire county, which may create differences when comparing samples from different studies.We also calculated between source study Fectively . The max−8 (if the sample was selected from a population without stratification). The power would be reduced to less than 20% if the genomic inflation λ was 2. If the minor allele frequency instead was 0.1 the power to detect association would be 40% in an unstratified data set but almost none if λ was 2. The values of λ which we observed for pairs of national areas within Sweden ranged from close to 1 to more than 3, for the most disadvantageous pair of national areas.In order to assess the effect of the observed stratification on power loss in case-control studies, we carried out simulations . For a SWe finally examined extended regions of homozygosity in the samples. We chose to look for extended regions instead of calculating an inbreeding coefficient based on all SNPs as imputation may bias the results. We did observe some differences in the mean number of extended homozygous segments between the studies (not shown). This does not necessarily need to have arisen due to technical artifacts but may be due to the geographical differences in sampling. However, as differences were observed, we chose to control for the sample set when comparing the number of segments between different counties.p<10−100) in a Poisson model where we controlled for sample study. In addition, we fitted a model with county instead of latitude as a covariate, with Stockholm county as baseline, controlling for sample study  also showed significant increases in the number of extended homozygous segments.The number of homozygous segments increased by 0.20 for each degree of latitude  are similar to what has been observed between the UK and Ireland st has been shown to be about an order of magnitude larger than what we observed among the southern Swedish national areas Upper Norrland, the northernmost national area, and the southern and middle national areas were much larger than the genetic distances we observed between the southern national areas and the HapMap CEU founders, highlighting the extent of the distances. The genetic differences observed between the southern national areas and the northernmost one were larger than what has previously been observed between Swedes and Danes, and Swedes and the Dutch The observed genetic differences between the southern national areas and counties are similar to what has been seen in the UK, when comparing southern England to Scotland, and the genetic differences between the southern national areas and In a genetic association study, the expected genomic inflation λ values that would arise if a stratified data set of 500 cases and 500 controls from different populations was used, indicate that less stratification would be present if the HapMap CEU founder population was compared against a sample from the southern national areas of Sweden than if the southern national areas were compared against the northernmost. Americans of northern European ancestry (in the CEU sample) are not generally considered to be suitable controls for Swedish cases, and have not been used in any genomewide asssociation scan of Swedish cases. Geographically poorly matched Swedish controls may be equally unsuitable.According to our estimates, a genomic inflation λ of 2 or more would be possible by sampling cases and controls in different counties in Sweden, illustrating the need to geographically match cases and controls in a study. Without having information on a genome-wide panel of SNPs, stratification would be hard to detect if only data on a small number of typed SNPs were available, making false-positives generated by genetic stratification a clear possibility. Having a genome-wide coverage of SNPs would rather result in a substantial power loss and not false positives, assuming the genomic inflation λ would be used to control for the stratification. Even if case and control sampling locations did overlap to some degree, genetic stratification would still exist, but to a lesser extent.The observed patterns of homozygosity further reinforce the picture of a genetic divide between the southern and northern parts of Sweden. While the differences in the number of extended homozygous segments were mostly small and non-significant for the southern national areas, the counties corresponding to the three northernmost national areas had a larger number of extended homozygous regions, suggestive of an increase in autozygosity. The northern national areas are much more sparsely populated than the southern with vast geographical distances between towns and villages, a fact that may have contributed to the observed loss of heterozygosity. The increased level of genetic homogeneity in the north may be an advantage if a study is set to search for recessive genetic variants with high penetrance, as by means of linkage studies.The main south-north pattern detected in the principal component analysis may be the amalgamation of many separate factors. Immigration of continental Europeans, and Britons to the southern parts of Sweden may be one such factor, together with isolation and consecutive allelic drift and admixture with the indigenous Sami people in northern Sweden.st. They do in general show less extended homozygosity than the three northernmost national areas. As indicated by both the first principal component and the table of Fst values, a gradient exists along the length of the country, with most of the variation driven by samples from the northernmost counties. The similarities between the southern counties and national areas make them suited for population-genetic sample collection efforts, as shown by the pairwise Fst.Our results show that even a study of modest size, but with poorly matched geographical distributions of cases and controls in Sweden, e.g. with cases from the north and controls from the south, would suffer a drastic increased type 1 or type 2 error rates (depending on whether stratification could be controlled for or not). The southern and middle parts of Sweden are more genetically homogenous, contributing only a small part to the variation in the first principal component and showing small population pairwise FThe picture of the Swedish genetic structure presented here can only be considered as a historical snapshot. Immigration and movements around Sweden can alter the picture we have described considerably within the span of only a few generations. Within country population movement creates genetic diversity and breaks up existing structures. Immigration from other countries leads to new stratification of a vastly complex nature. With increased movements, homozygosity, due to a high degree of kinship with geographically local possible partners, may swiftly decrease.When conducting a population-based case-control study, it is essential to obtain samples of cases and controls that are genetically similar. Geographical differences between case and control groups may obscure true genetic associations. We have shown that caution must be taken if Swedes from the northernmost counties are included in a population-based genetic study, since they differ genetically from southern Swedes and also amongst themselves. Genetic patterns do not necessarily follow national borders and substantial genetic differences may be detectable within a country.Figure S1Finnish ancestry principal component cutoff. Principal components stem from the first principal component analysis, which was performed prior to the removal of samples with Finnish ancestry.(PDF)Click here for additional data file.Figure S2Scree plot  of components 1 to 25 from the principal component analysis with individuals of suspected Finnish descent removed. The PCA was performed after the removal of samples with suspected Finnish ancestry that in turn was based on the first two components from the PCA with individuals of Finnish descent included.(PDF)Click here for additional data file.Figure S3Principal components by county. Principal components stem from the principal component analysis performed after the removal of samples with suspected Finnish ancestry.(PDF)Click here for additional data file.Figure S4Histogram of PC2 values in 76 Swedes of Finnish ancestry (collected as part of the SCZ-SW study). The samples were not included in the derivation of PC2 , rather the component was calculated by applying the allelic weights for each individual SNP and then summing over all alleles for each sample.(PDF)Click here for additional data file.Figure S5Histogram of PC2 values for 939 Finns from a separate study. The samples were not included in the derivation of PC2  rather the component was calculated by applying the allelic weights for each individual SNP and then summing over all alleles for each sample.(PDF)Click here for additional data file.Figure S6Histogram of PC2 values for 388 Norwegians from a separate study. The samples were not included in the derivation of PC2 , rather the component was calculated by applying the allelic weights for each individual SNP and then summing over all alleles for each sample.(PDF)Click here for additional data file.Figure S7Map plots of principal components 3 to 10. Principal components illustrated by colors on map with the most negative value as yellow and the most positive as black. Principal components stem from the principal component analysis, performed after the removal of samples with Finnish ancestry.(PNG)Click here for additional data file.Figure S8st values; counties in Sweden and HapMap CEU samples.Multidimensional Scaling plot of F(PDF)Click here for additional data file.Figure S9−8.Effect of stratification on power loss in a sample of 500 simulated cases and 500 simulated controls, using a genome-wide significance cutoff of 5×10 Case and control SNPs were simulated in R using rbinom, the chi-square statistic (1 df) was then calculated. This was repeated for 10 000 replicates to derive a distribution of chi-squares used to calculate power. The chi-squares were then corrected for the different levels of λ and power recalculated with respect to the adjusted chi-squares.(PDF)Click here for additional data file.Figure S10Small PC2 values close to Finnish cutoff. Samples in the lowest percentile of PC2  plotted in red with other samples plotted as either gray (removed due to suspected Finnish ancestry) or black (not removed). Axes are based on the principal component analysis with individuals of Finnish ancestry included.(PDF)Click here for additional data file.Table S1Swedish population density estimates 2010, per sq. km. by county. Source: Statistics Sweden (http://www.scb.se).(DOC)Click here for additional data file.Table S2Samples from each study, the number removed and the number remaining.(DOC)Click here for additional data file.Table S3sts and λGC 1000s between counties (excluding Gotland county).F(DOC)Click here for additional data file.Table S4sts and λGC 1000s between national areas, using data from the SCZ-SW study only.F Differences above 0.008 (Fst) or 1.8 (λGC 1000) have been made bold.(DOC)Click here for additional data file.Table S5st comparison. Stockholm county above and Skåne county below.Between-study F(DOC)Click here for additional data file.


Ctenophorus ornatus, a phenotypically variable species with a wide geographical distribution across the south-west of Western Australia. Phylogenetic analysis of mitochondrial DNA sequence data revealed two distinct evolutionary lineages that have been isolated for more than four million years within the C. ornatus complex. This evolutionary split is associated with a change in dorsal colouration of the lizards from deep brown or black to reddish-pink. In addition, analysis of microsatellite data revealed high levels of genetic structuring within each lineage, as well as strong isolation by distance at multiple spatial scales. Among the 50 outcrop populations’ analysed, non-hierarchical Bayesian clustering analysis revealed the presence of 23 distinct genetic groups, with outcrop populations less than 4 km apart usually forming a single genetic group. When a hierarchical analysis was carried out, almost every outcrop was assigned to a different genetic group. Our results show there are multiple levels of genetic structuring in C. ornatus, reflecting the influence of both historical and contemporary evolutionary processes. They also highlight the need to recognise the presence of two evolutionarily distinct lineages when making conservation management decisions on this species.Species inhabiting ancient, geologically stable landscapes that have been impacted by agriculture and urbanisation are expected to have complex patterns of genetic subdivision due to the influence of both historical and contemporary gene flow. Here, we investigate genetic differences among populations of the granite outcrop-dwelling lizard  The south-west of Western Australia is renowned for its extreme endemism, high species diversity and its threatened environments The levels of diversity, however they are delineated, have evolved in a region that is topographically subdued and in the relative absence of major vicariant forces typically used to explain speciation, such as, glaciation, tectonic or volcanic activity The ornate dragon is a small (20 g) agamid lizard endemic to south-west Western Australia. It has an allopatric distribution, where the majority of the species can be found south of Jibberding through to Esperance and its offshore islands, and a smaller pocket of populations persist north of Paynes Find to Meka and Sandstone C. ornatus exhibits significant genetic structuring at distances greater than 2 km C. ornatus populations across the species’ range. Our specific aims are 1) to determine whether the phenotypically northern populations are genetically differentiated to the southern populations and 2) examine the spatial scales over which populations become genetically distinct and whether these spatial sales vary among geographical regions.The ornate dragon lizard is a habitat specialist, restricted to granite outcrops, which act as a terrestrial island system, creating discrete habitat patches that are highly variable in size and distance to the nearest outcrop Ornate dragons were collected from outcrops throughout the species distribution . OutcropC. ornatus are known to lose toes as a result of predation and rock-associated injuries (LeBas pers. obs.).Lizards were captured by hand at first-light when they were cold and still relatively inactive by lifting exfoliated granite slabs. Each lizard was toe-clipped to provide a tissue sample for genetic analysis and then returned to its outcrop. Toe-clips were stored in 100% ethanol prior to DNA extraction. In lizards, toe and tail clipping is commonly used for identification and DNA analysis Australian Code of Practice for the Care and Use of Animals for Scientific Purposes. All field collection and handling practices were approved by the Animal Ethics Committee at the University of Western Australia (ref: RA/3/100/781).This study was performed in strict accordance with the et al. et al. DNA was extracted for microsatellite and mitochondrial analysis using the standard salting out method described in Sunnucks & Hales Ctenophorus caudicinctus and Ctenophorus reticulatusC. ornatus distribution in 2000. Doublestranded PCR amplifications were executed with 25 ul of 1 × buffer (Invitrogen), 2 mM MgCl2, 0.3 mM each dNTP, 0.4 uM of each primer, 1 U of Platinum Taq and approximately 20 ng of DNA. A segment of about 900 base pairs (bp) from the 12S rRNA gene was amplified with primers Co12SaF 5′GGCCCAGGACTCAAGATAACA -3′ and Co12SaR 5′TCGGCAAGTTCGTTAGGCT-3′ with the following sequencing conditions: initial denaturation at 95°C for 5 min followed by 35 cycles of 45 s at 94°C, 1 min at 56°C and 1 min at 72°C with a final elongation step at 72°C for 10 min. PCR product was run on 1.5% agarose gel for 30 min at 100 V. Samples were sequenced by AGRF  on the Applied Biosystems 3730 ×l DNA Analyzer platform.A subset of 30 outcrops, selected to represent each region, were used for the mitochondrial DNA analysis. For each outcrop, DNA from five adult females was sequenced. DNA from two closely related species, http://www.genecodes.com). Sequences were then aligned individually using MEGA v5 DNA sequence data were edited using SEQUENCHER v 4.10.1  between and within species and each major clade were calculated in MEGA 5 ST values across all loci and for each locus. As there was no difference  between these corrected FST values and the uncorrected values, the original data set was used for all remaining analyses.The program FreeNa The presence of linkage disequilibrium (LD) was tested for using FSTAT 2.9.3 FIS) and gene diversity (HE) were calculated. Genetic divergence between outcrops was estimated by calculating pairwise θ-values, an unbiased estimate of FSTFST/(1– FST) matrix was compared with a geographical distance matrix (log km) Microsatellite variation within each outcrop was measured using allele frequency data, from which the average allelic richness, inbreeding coefficient  of each outcrop were used to run the spatial model. The uncertainty of coordinates was set at 0.4 km as this is the mean diameter of sampled outcrops. Thus, on average, any individual could have been collected within 400 m of the given coordinates. The uncorrelated and null allele models were adopted for ten independent runs of K = 1 to 52. Each run consisted of 100 000 MCMC iterations with a thinning of 100 and a burnin of 1000. The most likely number of clusters was chosen as the modal K (from each independent run) with the highest posterior probability.K method as simulations have shown this method to be the most accurate estimator of the true number of populations (K) et al.et al.Analyses involving STRUCTURE are typically assessed using the ΔThe significance of hierarchical partitioning of genetic structure among geographic regions was examined using the Analysis of Molecular Variance (AMOVA) approach C. ornatus samples sequenced for the 12s rRNA gene, 18 unique haplotypes were identified with a mean nucleotide diversity (π) of 0.00036±0.00017 and mean haplotype diversity (h) of 0.14±0.05. All of the outcrops were found to follow a neutral model of evolution (Of the 145 volution .E from 0.43 to 0.85 (mean = 0.77). The average inbreeding coefficient was 0.08 (range −0.09 to 0.25). After correction for multiple comparisons, 22 of the 52 outcrops departed from HWE with a deficit of heterozygotes. Also, two loci (CoB111 and CoD108) deviated from HWE with deficits of heterozygotes in more than ten outcrops. These deviations from HWE may be due to null alleles. The presence of null alleles for each locus within each outcrop was tested with MICROCHECKER Estimates of microsatellite genetic variation were variable among outcrops , with ARTwo distinct haplotype networks were created, separating the outcrops from NP from all other regions . This inC. ornatus, C. caudicinctus and C. reticulatus) ranged from 0.092 to 0.129. For all intra species and clade pairwise comparisons, p-distances ranged from 0.001 to 0.028. Pairwise p-distances between haplotypes in the northern and southern clades were intermediate to these, ranging from 0.041 to 0.047. The finding that these p-distances were distinct and did not overlap with either the intra or inter species p-distance ranges further suggests that these groups are divergent evolutionary lineages, but are not sufficiently divergent to be classified as separate species. Based on divergence times between C. ornatus and C. caudicinctus from Melville et al.The pairwise genetic distances for all inter species comparisons  sampled from this clade, these outcrops were removed from the remaining analyses. Thus, genetic structure within the remaining 50 outcrops was analysed by STRUCTURE and GENELAND for values of K from 1–50.FST = 0.16, 95% CI = 0.14–0.17) as well as within each region (SEW: FST = 0.10 (95% CI = 0.09–0.12), NEW: FST = 0.13 (95% CI = 0.12–0.14), SW: FST = 0.14 (95% CI = 0.13–0.15), S: FST = 0.50 (95% CI = 0.39–0.62)). Similarly, IBD was identified across all outcrops  as well as within each region .Significant subdivision among outcrops was evident across all outcrops . The SW cluster had more hierarchical structure than the eastern cluster, where outcrops formed their own clusters after only two to three levels of partitioning. Details of the levels of partitioning are given in Hierarchical structuring was also found using AMOVA. A significant portion of the mtDNA and microsatellite variance was found among regions as well as among outcrops within each region, though the regional differences were much higher for mtDNA, especially when calculations were based on genetic distances among haplotypes .C. ornatus comprise separate species. Each group was monophyletic, which is a property of the species category under the phylogenetic species concept C. ornatus and its most closely related species, C. caudicinctus (9.5%). In addition, the ranges of pairwise sequence divergences between major clades did not overlap with those within species/clades or between species. This suggests that while the northern and southern clades are independent lineages, they may not be sufficiently diverged to be considered separate species. However, the strength of this result is limited by the low number of 12S sequences available for species in the genus Ctenophorus. In a phylogenetic study based on all Ctenophorus species using sequences from three protein coding genes , Melville et al.C. ornatus and C. caudicinctus. However, a few species exhibited divergences that were 50% lower than the sequence divergence between C. ornatus and C. caudicinctusC. ornatus may be sufficient to recognise the major clades as separate species. Therefore, whether there are two species currently classed as C. ornatus remains uncertain and more work is needed to clarify this situation. Nevertheless our study reveals that the two major clades do represent independent evolutionary lineages and, given that they also cluster by phenotype The phylogenetic analysis was unable to resolve clearly whether the two allopatric groups of populations of et al. Metacrinia nichollsi, restricting them to refugia, and eventually causing the formation of separate lineages. This may also be the case for C. ornatus, as granite outcrops are thought to act as refugia during times of aridity In keeping with other phylogeographic studies on vertebrates in south-west Western Australia, the two clades were estimated to have diverged in the late Miocene to early Pliocene, approximately 4.26 mya. This time period coincides with the onset of aridity in Australia Acacia shrubland, while in the south, eucalypt woodlands dominate C. ornatus is a granite outcrop specialist, they must travel through the intervening environment to enable gene flow between populations. It is therefore possible that different vegetation types differentially affected gene flow. Levy et al.C. ornatus, probably because it creates more open, exposed habitats with little shelter. Similarly, the sparser vegetation in the northern clade may have restricted gene flow. Pairwise FST for outcrops separated by similar distances (25–31 km) in the SW as those in NP (28.3 km) indicate that this may be the case. Outcrops in SW (where land clearing has been established for longer FST (mean FST = 0.18) than that in NP (0.17), whereas in the less cleared eastern regions, mean pairwise FST was lower (0.12) than in the NP. Therefore, if gene flow was affected by more sparse vegetation in the NP, dispersal between the clades could simply have become too difficult. Further, each clade could have become locally adapted to dispersing through different vegetation types, making dispersal between the northern and southern clades too difficult. A similar situation was identified in the heath dragon in south-western Australia, where allopatric populations had diverged substantially due to geographical isolation and subsequent adaptation to different habitats Another possible explanation for the divergence may be the different types of vegetation in the two areas. The southern clade extends only as far as the boundary between the South-West and Eremean botanical provinces Branchinella longirostris, an anostracan endemic to freshwater shallow, ephemeral rock pools on the tops of granite outcrops in Western Australia showed a similar pattern of restricted gene flow C. ornatus and B. longirostris inhabit fragmented habitats, historical gene flow, though restricted, does take place.Within the southern clade, regional separation between outcrops was seen, although the regions were not divergent enough to be classified as separate clades. Further, the SW was shown to be distinct from the eastern and southern regions . This geographic pattern of genetic structure reflects restricted gene flow throughout the southern clade with isolation by distance (IBD) creating regional sub-clades. C. ornatus gene flow is present between outcrops, but becomes restricted as the distance between outcrops increases. In other words, outcrops within regions were not isolated from one another by complete barriers to gene flow.Patterns of IBD were identified within regions by the microsatellite analyses. IBD occurs when the divergence between populations increases linearly with increasing spatial distance between populations C. ornatus support the isolation by distance analyses with distance between outcrops being important in determining levels of gene flow.The AMOVA for both mitochondrial and nuclear DNA showed there was significant genetic differentiation between regions and among outcrops within each region. These results are supported by the clustering analysis, which suggest there has been no recent gene flow between regions within the southern clade. On a finer scale, the three clustering analyses revealed largely similar patterns with neighbouring outcrops clustered together, for the GENELAND and non- hierarchical STRUCTURE approaches, or forming higher levels of structure within the hierarchical STRUCTURE analysis, and distant outcrops forming separate genetic groups. Thus, the genetic clustering patterns within C. ornatus becomes limited, is difficult to determine as there are several conflicting pieces of evidence to consider. The GENELAND and non- hierarchical STRUCTURE analyses generally indicate that outcrops more than 4 km apart are independent genetic groups. This is largely consistent with the findings of Levy et al.C. ornatus is restricted to granite outcrops, and therefore likely to show degree of genetic independence among populations from different outcrops.The distance at which gene flow in C. ornatus. However, over larger distances gene flow becomes restricted causing outcrops to become divergent.Within outcrops, gene diversity was similar to that observed in other reptiles . Genetic variation is maintained by large population sizes and/or gene flow among populations, but these also reduce genetic divergence between populations C. ornatus provide an insight into the patterns and potential processes that have driven the evolution of biodiversity in the south-west of Western Australia. The timing of divergence between evolutionary lineages is in line with those in other Western Australian vertebrates C. ornatus. Finally, the presence of two distinct evolutionary lineages also highlights the need to consider intraspecific variation in future research and when making conservation management decisions on this species.The phylogeography and population genetic structure of


Native pig breeds in the Iberian Peninsula are broadly classified as belonging to either the Celtic or the Mediterranean breed groups, but there are other local populations that do not fit into any of these groups. Most of the native pig breeds in Iberia are in danger of extinction, and the assessment of their genetic diversity and population structure, relationships and possible admixture between breeds, and the appraisal of conservation alternatives are crucial to adopt appropriate management strategies.A panel of 24 microsatellite markers was used to genotype 844 animals representing the 17 most important native swine breeds and wild populations existing in Portugal and Spain and various statistical tools were applied to analyze the results.Genetic diversity was high in the breeds studied, with an overall mean of 13.6 alleles per locus and an average expected heterozygosity of 0.80. Signs of genetic bottlenecks were observed in breeds with a small census size, and population substructure was present in some of the breeds with larger census sizes. Variability among breeds accounted for about 20% of the total genetic diversity, and was explained mostly by differences among the Celtic, Mediterranean and Basque breed groups, rather than by differences between domestic and wild pigs. Breeds clustered closely according to group, and proximity was detected between wild pigs and the Mediterranean cluster of breeds. Most breeds had their own structure and identity, with very little evidence of admixture, except for the Retinto and Entrepelado varieties of the Mediterranean group, which are very similar. Genetic influence of the identified breed clusters extends beyond the specific geographical areas across borders throughout the Iberian Peninsula, with a very sharp transition from one breed group to another. Analysis of conservation priorities confirms that the ranking of a breed for conservation depends on the emphasis placed on its contribution to the between- and within-breed components of genetic diversity.Native pig breeds in Iberia reveal high levels of genetic diversity, a solid breed structure and a clear organization in well-defined clusters. Until the 20th century, pig production throughout Europe was essentially based on local breeds, developed over centuries to fit specific production targets and environmental constraints. The situation changed dramatically with the intensification of agriculture in the mid-20th century, when pig production moved to more intensive systems based on a reduced number of transboundary breeds. This geNative pig breeds in the Iberian Peninsula are broadly classified as belonging to either the Celtic or the Mediterranean breed groups. The CelA detailed knowledge of the structure and relationships among breeds in a given species is a prerequisite to adopt appropriate conservation strategies and measures, which aIn this study, we used a set of microsatellite markers in a comprehensive sample of domestic and wild pig populations from the Iberian Peninsula to: (1) evaluate the existing levels of genetic diversity and corresponding population structure; (2) assess the relationships among breeds and the distinctiveness and homogeneity of the breed groups commonly considered; (3) investigate the possible admixture which may have occurred, both among domestic pig breeds and with their wild relatives; and (4) evaluate conservation alternatives, based on the current levels of between- and within-breed genetic diversity.Individual blood samples were collected from 731 animals representing the major native pig breeds that are recognized in Portugal and Spain, namely the Alentejano ALE, n = 66), Bísaro  and Malhado de Alcobaça  breeds from Portugal, and the Celta , Chato Murciano , Entrepelado , Euskal Txerria , Lampiño , Manchado de Jabugo , Negro Canario , Negro de Formentera , Negro de los Pedroches , Negro Mallorquín , Retinto  and Torbiscal  breeds from Spain. Samples were obtained from animals in different herds and preferably unrelated up to the third generation, in order to capture the largest possible representation of the existing genetic diversity. Blood samples were collected either in slaughter plants or by qualified veterinarians through their routine practice, in the framework of official programs aimed at the identification, health control and parentage confirmation of the breeds and populations included in our study. Therefore, no ethical approval was required for sampling of biological material. In addition to the native pig breeds, 113 samples from wild boars obtained in Spain  and Portugal  were also included in our study. With the exception of the NFO and NPE breeds, all the domestic breeds included in our study are officially recognized and registered in herdbooks. For most breeds, a separate herdbook is kept for each breed, but for the ENT, RET, LAM, MAJ and TOR breeds they are registered as varieties of the “Ibérico” breed, in separate sections of the herdbook. A panel of 24 microsatellite markers was established  is shown in Figure S2 [See Additional fileContributions of the assumed ancestral populations to each individual of the 17 breeds studied are presented in Figure The proportional contributions of the first three inferred ancestral populations to each continental Iberian domestic breed were used to investigate the geographical distribution of genetic differentiation across the Iberian Peninsula Figure . The resWTZ) above 10% . These correspond to breeds which tend to be more isolated in the neighbour-net of Figure The Weitzman approach prioritizes breeds for conservation based on their contribution to between-breed diversity, and generally more isolated breeds tend to have higher values of partial contributions to genetic diversity. The results of the Weitzman analysis Table  indicateHe), which takes into account the within-breed component of genetic variation  but is sConsistently, the most distinct feature regarding breed relationships was the separation between the ETX breed and all the other breeds, which could reflect its separate origin and geographical isolation, but is probably also a consequence of its recent re-establishment from a small number of animals. A similar differentiation relative to other breeds was found for the French Basque pig breed,36, whicThe NFO and NMA breeds from the Balearic Islands did not clearly separate from either the Celtic or the Mediterranean clusters, and the analysis with STRUCTURE confirmed that both could result from the admixture of these two groups. The NCA breed from the Canary Islands seems to share some genetic influences with the Celtic group, but it has become an isolated breed that is very distinct from all the others. Our results could not clearly establish whether this separation resulted from geographical isolation alone or from the possible existence of other influences, since large-scale exchanges are known to have occurred between the Canary Islands and Africa in the past, and perhaps the NCA breed reflects this influence. In any IS estimates, presumably as a consequence of their very small herd size, which would result in accumulated inbreeding and population fragmentation. Indeed, the BIS breed showed a distinct sub-structure, with two well-defined subpopulations, which may result from the fact that the population was restored in the mid 1980’s from a small number of animals from two distinct regions[The CEL, BIS and MAL breeds, which are spread in the northwestern part of Iberia, clustered in the neighbour-net with the CHM breed, which is raised mostly in the southeastern coast, forming a well-defined cluster of Celtic breeds. This Celtic group of breeds of the Iberian Peninsula is believed to derive from northern-central European pig breeds, with pe regions. The clo regions while th regions.As anticipated, no differences could be detected between Spanish and Portuguese wild pigs, but a well differentiated subpopulation was detected in Spain in the analysis with STRUCTURE. This subpopulation corresponds to a genetically isolated group of wild pigs of the National Park of Doñana, kept under highly protective conditions preventing any mixing of the animals with those outside. Furthermore, they probably have a different reproductive behavior, because they are not subject to the increased pressure due to hunting.The weak differentiation between wild and domestic pig populations found in our study was not completely unexpected, since signs of admixture between these populations have been reported in the past, based on both microsatellite markers and mitoThe different pig breeds of the Mediterranean group sampled in Portugal and Spain clustered together closely Figures  and3, sOverall, the distribution of the Mediterranean influence in pig breeds across the Iberian Peninsula Figure  almost eThe definition of conservation priorities for animal genetic resources is an unsolved issue. Different approaches have been proposed to quantify the importance of a given breed for the conservation of genetic diversity,29. HoweWhen conservation strategies focus on the contribution to genetic diversity, priorities will depend on the emphasis given to the between- and within-breed components of genetic diversity, which in turn depends essentially on the choice between a breed that is genetically unique or a breed that has a high genetic diversity, respectively. On the one hand, if only the between-breed contribution is considered (Weitzman approach), the conservation priority will concern the more distinct breeds ETX, MJA, NFO and NCA, which are generally the breeds with the lowest census size, but also have the lowest levels of expected heterozygosity. However, as suggested by Cañon et al., populatA more balanced solution would be to assess conservation priorities on the basis of the contribution of each breed to the overall diversity, based on its molecular contribution to between- and within-breed kinships. In our case, the priority would be given to the NCA, BIS, ETX and CEL breeds, which contribute most to the between- or within-breed components of genetic diversity. Thus, this solution represents a compromise between the two previous methods but no breed from the Mediterranean group would be selected for conservation, which would be in essence difficult to justify.Overall, our study of conservation priorities confirms that there is no single recommended approach, and that, depending on the emphasis placed on the between- or within-breed components of genetic diversity, the ranking of breeds for conservation may be very different, as discussed by Cañon et al.. FurtherThe analysis of genetic diversity and conservation priorities based on neutral genetic markers, as carried out in our study, does not take into account the genetic variability of markers associated with production and adaptation traits, which often differ considerably among breeds. In recent years, high-density panels were developed capable of simultaneously detecting variability in thousands of single nucleotide polymorphisms (SNP), and these could be instrumental in detecting genomic regions associated with both neutral and non-neutral sources of genetic variability. Hence, these SNP chips will provide a deeper insight into genetic diversity within- and between-breeds, population structure and selection signatures and their use in genetic diversity studies is very promising.Our results indicate that the genetic diversity in native pig breeds of the Iberian Peninsula is high and that the local isolated breeds, often with a small census size, are important reservoirs of genetic diversity. Several breeds had a significant deficit in heterozygosity, probably as a consequence of accumulated inbreeding, and the MJA and MAL breeds showed signs of genetic erosion. Nearly 20% of the observed diversity is due to differences among breeds, which is mostly due to variability among the Celtic, Mediterranean and Basque groups that are organized in very distinct breed clusters. The genetic distance between wild and domestic pig populations is small, and clear signs of admixture are observed between breeds from the Mediterranean group and their wild relatives but less so in the Celtic group. The Mediterranean populations included in our study represented varieties of the so-called “Iberico” pig group but, in spite of their close distribution, most of the varieties of this group were genetically distinct from each other and well-structured, while a few showed signs of admixture and/or fragmentation. The results presented here are useful to define conservation priorities and to adopt management strategies aimed at minimizing further losses of genetic diversity in the future.The authors declare that they have no competing interests.AMM, JVD, LTG conceived and designed the experiments; AAV, AMM, COS, IC, VL performed the experiments; AMM, JLV, LTG, OC, VL analyzed the data; AMM, JVD, LTG wrote the manuscript; AAV, COS, JLV, IC, OC, VL reviewed and edited the manuscript; Members of the BIOPIG Consortium provided biological samples and logistic support. All authors read and approved the final manuscript.Table S2. Genetic diversity parameters by locus across 17 pig populations. Total and effective number of alleles, allelic richness, heterozygosities and F-statistics per locus. Table S3. List of software used for statistical analyses. Programs used and corresponding parameters estimated. Table S4. Nei’s DA and Reynolds genetic distances among 17 Iberian pig populations. Pairwise genetic distances among the breeds studied.Breeds studied, acronyms, breed groups, approximate census size and sample size used in the present study. Baseline information for the breeds included in the study. Click here for fileFigure S2. Estimated posterior probabilities of the data for different number of inferred clusters in the analysis with Structure. Likelihood of different number of ancestral populations given the observed breed genetic diversity. Geographic distribution of the domestic pig breeds studied. Map showing spread of the 15 domestic breeds analysed. Click here for file