Pantholops hodgsonii, chiru) and Tibetan gazelle (Procapra picticaudata), both are endemic on the Qinghai-Tibetan Plateau, where their habitats overlap, but have different migratory behaviors: the chiru is inclined to have female-biased dispersal with a breeding migration during the calving season; in contrast, Tibetan gazelles are year-round residents and never migrate distantly. By using coalescence methods we compared mitochondrial control region DNA sequences and variation at nine microsatellite loci in these two species. Coalescent simulations indicate that the chiru and Tibetan gazelle do not share concordant patterns in their genealogies. The non-migratory Tibetan gazelle, that is more vulnerable to the impact of drastic geographic changes such as the elevation of the plateau, glaciations and so on, appears to have a strong population genetic structure with complicated demographic history. Specifically, the Tibetan gazelle population appears to have experienced isolation and divergence with population fluctuations since the Middle Pleistocene (0.781 Ma). However, it showed continued decline since the Upper Pleistocene (0.126 Ma), which may be attributed to the irreversible impact of increased human activities on the plateau. In contrast, the migratory chiru appears to have simply experienced population expansion. With substantial gene flow among regional populations, this species shows no historical population isolation and divergence. Thus, this study adds to many empirical studies that show historical and contemporary extrinsic and intrinsic processes shape the recent evolutionary history and population genetic structure of species.The extent to which a species responds to environmental changes is mediated not only by extrinsic processes such as time and space, but also by species-specific ecology. The Qinghai-Tibetan Plateau uplifted approximately 3000 m and experienced at least four major glaciations during the Pleistocene epoch in the Quaternary Period. Consequently, the area experienced concurrent changes in geomorphological structure and climate. Two species, the Tibetan antelope ( Comparisons of genetic structure among sympatric species provide insights into the extent to which extrinsic and intrinsic factors interact to influence the geographic scale of population differentiation In phylogeographic studies of animal taxa on the Qinghai-Tibetan Plateau, the relationship between environmental history and ecology is particularly pertinent as the plateau uplifted several times by approximately 3000 m during the Quaternary Period The chiru and Tibetan gazelle primarily inhabit the Qinghai-Tibetan Plateau as sympatric bovines, but these species have distinct migrating behaviors. The chiru has wintering habitats and calving habitats. The female individuals migrate distantly (about 300 km) to the calving habitats in May and June each year to give birth to their calves, then they migrate back with their calves in early August to reunite with males in the wintering habitats Previous studies on the mtDNA haplotype data for the chiru and Tibetan gazelle suggested discordant demographic histories The chiru and Tibetan gazelle are listed in the Category I and Category II in the National Key Protected Wild Animal Species under the Wild Animal Protection Law of China respectively. Sample collection of these protected animals and field studies in Kekexili and other regions on the Qinghai-Tibetan Plateau adhered to the Wild Animals Protection Law of the People's Republic of China. All necessary permits were obtained for the described field studies.Before the field expeditions, we obtained permission to conduct research and collect samples on the chiru and Tibetan gazelle from the Kekexili Nature Reserve, Hargai Nature Reserve, Changtang Nature Reserve and Arjinshan Nature Reserve. A total of 61 chiru muscle/skin samples were collected from the calving habitat of Zhuolaihu Lake, Kekexili, Qinghai Province  in June Total genomic DNA was extracted from the muscle, hair and skin samples using the standard proteinase K digestion and phenol/chloroform extraction procedures, after washing with excess NTE  to remove possible protease or PCR inhibitors. Approximately 580 bp of the mitochondrial DNA (mtDNA) control region was amplified from 51 Tibetan gazelle and 61 chiru samples. Forty-six samples of the Tibetan gazelle were used in a previous study; others are new to this study . PrimersETH225, ILSTS5, Aam7, CSSM43, TGLA122, BM1824, BM4107, BM1225, and BM1818) 2, 200 mM dNTPs, 0.4 mM of each primer, and 0.5 U of Taq DNA polymerase (Fergment). Amplified fragments were separated by capillary electrophoresis on an ABI PRISM 377-Avant Genetic Analyzer (Applied Biosystems). Allele sizes were determined relative to standard marker GeneScan-500 ROX (Applied Biosystems) using GENESCAN 3.7.Nine microsatellite loci, originally isolated in cattle and pronghorn (h) and nucleotide diversity (π) as implemented in DnaSP version 4.0 MtDNA control region sequences for all individuals were aligned using CLUSTAL X −8 substitutions per site per year (S/S/year) Genealogy of haplotypes within species were estimated using maximum-likelihood (ML), neighbor-joining (NJ) and maximum-parsimony (MP) by PAUP*4.0b8 Due to the low variation at the intraspecific level, traditional phylogenetic analyses often result in poorly resolved haplotype trees. In addition, coexistence of a persistent ancestral haplotype and its multiple descendants results in a haplotype tree with multifurcations FST and ΦST values for mtDNA were calculated using the program ARLEQUIN (version 2.0) ST was calculated using genetic distances estimated under the TVM model with specified gamma shape parameters for the chiru (α = 0.65), and under the HKY model with gamma distribution (α = 0.41) for the Tibetan gazelles. Because of the high proportion of unique haplotypes in the control region, estimates of population differentiation based on pairwise distances among haplotypes (ΦST) FST). For this reason, we reported ΦST values only. The same program was used for analysis of molecular variance (AMOVA) ST estimates the proportion of genetic variation within populations relative to the genetic variation for the whole sample, ΦCT estimates the proportion of genetic variation among groups of populations relative to the whole species, and ΦSC estimates the variation among populations relative to a regional grouping of populations. The significance of Φ-statistics was tested by random permutations of sequences among populations. The groupings that maximize values of ΦCT and are statistically significant indicate the most parsimonious geographical subdivisions.Population pairwise FST values and were set as initial values. We performed 10 short chains (500 trees used out of 10 000 sampled) and three long chains (5000 trees used out of 100 000 sampled). These runs were repeated using the same condition until consistent results were obtained.Furthermore, we used the program MIGRATE 3.1.6 D* is designed to detect an excess of old mutations, characteristics of a population that has experienced a historical reduction in effective population size Fs is sensitive to an excess of recent mutations, a pattern typical to both demographic expansion and selective sweep D* was calculated in DNASP (version 4.0), Fu's Fs differences were tested for significance with a coalescent simulation program (1000 simulations), as implemented in ARLEQUIN 2.000.Historical population dynamics of the species were estimated using coalescent-based Bayesian skyline plots (BSP) ST values and geographic distance using the Isolation-by-Distance Web Service 3.16 ST values converted to zeros.Mantel tests were employed to determine whether significant isolation-by-distance exists among populations by testing for correlation between pairwise ΦIntraspecific allelic richness and heterozygosity were calculated in FSTAT (version 2.9.3.2) K). Population numbers K = 1–7 were tested for 20 times at the population level based on 100,000 generations (MCMC) after a burn-in period of 10,000. For all STRUCTURE simulation runs we used the admixture model and the independent allele frequencies model, either with or without the location prior model, and set all other run parameters to their default values. Because it is reported that in some cases the number of K estimated by structure does not correspond to the real number of subpopulations K rates of change of Ln P (D) (estimated log probability of data) for K inferred clusters were analyzed here. To display STRUCTURE Q plots, DISTRUCT The software STRUCTURE version 2.3.3 The program BOTTLENECK (version 1.2.02) AY744081–AY744130; other 36 haplotypes were obtained in present study, GenBank accession Nos. JQ292928–JQ292963). For the Tibetan gazelle, 193 variable nucleotide sites were observed, and 130 were parsimony-informative, which defined 25 haplotypes (GenBank accession Nos. DQ017352–DQ017355 and DQ423488–DQ423508). For both species, the haplotype diversity was about the same (0.975–0.991), but the nucleotide diversity of the Tibetan gazelle was 4.2 times that of the chiru  showed significant heterozygosity deficit in both species. Observed heterozygosity was 0.792 and 0.752  in the cThe gene tree topologies from maximum likelihood and Bayesian Inference (using BEAST) analyses are identical. The BF strongly favored the constant population model for the chiru (log (BF)>6), but positively supported Bayesian Skyline model for the Tibetan gazelle (log (BF)>3). Thus, Bayesian Inference analyses were performed under these models respectively. For the chiru, no strong geographic structure was inferred by the phylogenetic analysis of mtDNA , and theST statistics of the mtDNA sequences showed no apparent subdivision in the chiru . Consistently, no significant historical gene flow was detected from most of the possible source-recipient relationships between pairs of the regional Tibetan gazelle populations , Arjinshan (ARJ) and Kekexili (KKXL) corresponded to the cluster 3, which was consistent with the mtDNA results.Likewise, in microsatellite data set of the chiru, no evidence of population structure was detected in the Bayesian clustering analysis (with no prior population assignments) with the highest likelihood score at K = 1. Consist K = 2–7 . Howevernd K = 4 . Furtherat K = 3 . Based o3  was observed either among the chiru  or Tibetan gazelle populations  in mtDNA sequence data. The results indicate that the different population structure between these two species was not explained by geographic distance.Fs test detected highly significant departures from the neutral/equilibrium expectations (p<0.001). Fu and Li's D* test showed result of no significance (Fs (p = 0.712) and Fu and Li's D* indicated that the sequence evolution of the Tibetan gazelle is consistent with the expectation of selective neutrality and stable demographic history. Results of the test for demographic fluctuation based on microsatellite heterozygosities of the Tibetan gazelle were nonsignificant under the IAM, SMM and TPM, and the microsatellite allele frequency distribution was L-shaped (data not shown), suggesting no obvious population expansion or bottleneck While the mismatch distribution of mtDNA was not unimodal for the chiru, the accumulation of low-frequency mutations was characteristic of nonequilibrium population dynamics . In addiificance . Consistificance , althougificance . ConsistST = 0.363 with 6/10 significantly differentiated population comparisons), comparing to that of the chiru (average ΦST = 0.019 with no significantly differentiated population comparison). In addition, the Tibetan gazelle exhibits a stronger pattern of isolation between geographic populations (a significant ΦCT was found in AMOVA) than the chiru, but it seems this isolation is not related to the distance.Overall agreement between genetic data and ecological associations indicates that genetic differentiation corresponds to biologically meaningful differences in environment and ecology (behavior). The deep level of population genetic structure in the Tibetan gazelle contrasts with the shallow divergence in the chiru, indicating the discordance of the population structure between these two sympatric species on the Tibetan Plateau. Although high haplotype diversity was detected both in the chiru and the Tibetan gazelle populations , more poBos grunnience), and domestic cattle were estimated to have diverged approximately 4.9 million years ago Whether the historical environmental changes have a differential or overriding impact on the population depends on the complicated interaction between various factors. Large sequence divergences have been reported, for example, among divergent mtDNA genotypes separated by geographical barriers or distance In contrast, phylogeographic analyses for the chiru, even with haplotypes from all regions including the wintering habitats and calving habitat, indicate spatial homogeneity in mtDNA sequence variation, which is comparable to the findings in other migratory species Despite the high overlap in current habitat associations and geographic distribution, demographic analyses for these two species demonstrate different population histories. BSP results suggested that the population size of the Tibetan gazelle was relatively constant over a long period of time, even during the periods with extensive geographic changes (the elevation of the plateau) and glaciations . This maHowever, the chiru showed a simpler demographic history. The shape of the mismatch distribution is generally in good agreement with a model of population growth. Although Bayesian Factor showed no support for the hypothesis, the Bayesian skyline plot demonstrated that the population size of the chiru has been in a pattern of growth. But it was slightly suppressed during the Upper Pleistocene Period , which may lend support for the constant population hypothesis suggested by BF. This slight population decrease could also be the result of both environmental changes (geographic and climate changes) as well as increased human activities. Migration, the significant characteristics of the chiru, is clearly required for successful breeding. But during the glacial periods, the ice sheets could make the migration difficult or even impossible, which could limit the breeding behavior. Also, increased human activities and constructions Our study underscores the intrinsic and extrinsic roles played by the elevation of the Tibetan Plateau, glaciations as well as migration in shaping the patterns of the genetic structure and demography among closely related species. For the Tibetan gazelle, historical separation and the absence of gene flow between localities would be expected, in time, to lead to heterogeneity of mtDNA haplotypes


Mogurnda adspersa, Eleotridae), a small-bodied freshwater fish that is widely distributed in eastern Australia. The species is threatened in parts of its range but is common in coastal streams of central Queensland where this study took place. Microsatellite (msat) and mitochondrial DNA (mtDNA) variation was assessed for nine sites from four stream sections in two drainage basins. Very high levels of among population structure were observed  and evidence for contemporary migration among populations was rare and limited to sites within the same section of stream. Hierarchical structuring of variation was best explained by stream section rather than by drainage basin. Estimates of contemporary effective population size for each site was low , but compared favorably with similar estimates for other freshwater fish species, and there was no genetic evidence for inbreeding or recent population bottlenecks. In conclusion, within a stable part of its range, M adspersa exists as a series of small, demographically stable populations that are highly isolated from one another. Complimentary patterns in microsatellites and mtDNA indicate this structuring is the result of long-term processes that have developed over a remarkably small spatial scale. High population structure and limited dispersal mean that recolonisation of locally extinct populations is only likely to occur from closely situated populations within stream sections. Limited potential for recolonisation should be considered as an important factor in conservation and management of this species.Freshwater fish are a group that is especially susceptible to biodiversity loss as they often exist naturally in small, fragmented populations that are vulnerable to habitat degradation, pollution and introduction of exotic species. Relatively little is known about spatial dynamics of unperturbed populations of small-bodied freshwater fish species. This study examined population genetic structure of the purple spotted gudgeon ( Biodiversity is being lost in freshwaters at a faster rate than in any other environment For those species that inhabit freshwater systems in the drier parts of the Australian continent  in catchments where the species occurs. This is because the spawning success of M. adspersa depends upon stable, low-flow velocity habitat during the summer breeding season M. adspersa population viability by alternative water management options and this assessment informs decisions about water management in the relevant Water Resource Plan. Effective use of this species as an ecological indicator in water resource planning and effective conservation of the species in southern areas where population declines and extinctions have occurred both require information on rates of dispersal among populations which can be obtained using genetic data , the inbreeding coefficient (FIS) and evidence for recent population bottlenecks. Each of these measures is commonly used to evaluate the effect of small population size on patterns of neutral genetic variation in conservation genetic studies The Pioneer River flows into the Pacific Ocean in central Queensland. The climate is tropical, with high rainfall, which occurs mostly in the summer months. Unlike in sub-catchments of the Murray-Darling system, M. adspersa populations would be consistent with limited dispersal within and between streams. We also predicted that subdivided populations in freshwater stream habitats would correspond with low effective population sizes and exhibit genetic evidence for inbreeding and population bottlenecks.We used sequence data from mtDNA ATPase 6 and 8 genes combined with genotype data from 11 microsatellite loci to test the prediction that genetic structuring of 2) tissue sample was excised from the caudal fin and stored in 100% ethanol before release at the point of capture.Sampling was undertaken during April, 2008 in the upper reaches of several adjacent streams in two coastal drainage basins of central Queensland. Between 16 and 81 fish were sampled from each of five sites in two sub-catchments of the Pioneer River basin and two sites in each of two coastal streams of the Plane basin . Fish weAll field and experimental protocols carried out in this study were approved by the Griffith University Animal Ethics Committee. All procedures were carried out according to Australian Ethics Committee protocol number ENV/12/08/AEC.http://striweb.si.edu/bermingham/research/primers) and the protocol described by Woods et al.Genomic DNA was extracted from fin tissue using a standard phenol-chloroform extraction et al. A total of 357 individuals were genotyped with eleven polymorphic microsatellite loci on an ABI 3130 sequencer (Applied Biosystems). Genotyped loci included 2mog1, 2mog2, 2mog3, 2mog4, 2mog6, 2mog9, 2mog10, 3mog3, 3mog6, 4mog2 and 4mog3, which were amplified using multi-tailed multiplexing methods described in Real Tests for linkage disequilibrium (LD) and departure of genotypic proportions from those expected under Hardy-Weinberg Equilibrium (HWE) for the microsatellite dataset were calculated with exact tests for each locus/population combination using default settings in GENEPOP v4.0 K) ranging from one to nine using STRUCTURE 2.3.1 6 iterations after a burn-in of 5×105 iterations. The most likely number of homogeneous clusters was assessed using the second-order rate of change L̋ (K), or ΔK following Evanno et al. We tested for the existence of distinct genetic groups in the set of individual multi-locus genotypes using a model-based Bayesian clustering method. The probability of an admixture model was tested for clusters  framework. Estimates of FST were obtained for each locus separately and as a weighted average over the eleven microsatellite loci. For the mtDNA locus, the fixation index was calculated using haplotype frequency weighted by genetic distance among haplotypes (ΦST). Statistical significance of FST and ΦST estimates were determined by 1000 permutations of individuals among populations. Hierarchical structuring of variation was tested using AMOVA in ARLEQUIN v3.5.1.2 FCT), among sites within groups (FSC) and among all sites (FST). For the mtDNA dataset, the analysis was conducted using both haplotype frequencies (FST) and haplotype frequencies weighted by genetic distance (ΦST). Genealogical relationships of mtDNA haplotypes sampled in the present study were estimated using statistical parsimony in TCS v1.21 Geographic structuring of genetic variation was assessed in ARLEQUIN v3.5.1.2 jim, the proportion of immigrants in population i that arrive from population j) over the past few generations were estimated using a Bayesian assignment method implemented in the software BAYESASS v1.3 FST >0.05) and that loci are in linkage equilibrium 7 iterations, sampling every 2000 iterations with a discarded burn-in of 107 iterations. Delta values were adjusted to 0.12 to ensure that chain swapping occurred in approximately 50% of the total iterations as recommended by Wilson and Rannala jim were made for all pairs of sites and rates were reported that fell outside the 95% confidence interval simulated for uninformative data Contemporary migration rates  were obtained using the software MIGRATE-N v3.1.6 i.e. Pioneer north  =  FH, CU, CL; Pioneer south  =  BL, TC; Sandy Ck  =  SC, PC; Bakers Ck  =  BA, RC) so that we could address the question of migration at this slightly larger scale and over a longer timeframe. This approach jointly estimates migration rate and effective population size using a coalescent model where the probability distribution of genealogies for the migration matrix is explored using a Markov Chain Monte Carlo (MCMC) search. Microsatellite mutation was modelled as a continuous Brownian process. The Bayesian implementation of MIGRATE-N involved an MCMC search of 50,000 burn-in steps followed by 2,000,000 steps with parameters recorded every 50 steps; a static heating scheme of four chains with temperatures ; uniform prior on theta ; uniform prior on migration . MIGRATE was run six times with parameter values starting from FST-based estimates and the distribution of parameter values was compared across runs to ensure overlap of 95% C.I. Effective sample size was >7000 for all parameters. For comparison with BAYESASS, jiM was converted to jim using the equation: jim  =  jiM × μ under the assumption that microsatellite mutation rate (μ) equals 5.0×10−4 per site per generation jim can be interpreted as the proportion of population i that are immigrants arriving from population j each generation.Long-term (equilibrium) estimates of migration rate (Ne) based on patterns of individual relatedness was obtained for each sampling site using the software COLONY v2.0 Ne of the generation preceding the sample as a function of the frequencies of half and full siblings identified from multi-locus genotype data Ne estimate produced by this method approximates the effective number of breeders (bN) in the preceding generation. Sibship assignment was implemented using the full-likelihood score option in COLONY v2.0. Replicate runs starting from different random number seeds indicated that Ne estimates were highly consistent across runs so a single analysis was performed for each sampling site.A contemporary estimate of effective population size (Ne) was obtained for each stream from the MIGRATE-N analysis as described above. The population-scaled mutation rate (Θ) was converted to Ne using the standard assumption that microsatellite mutation rate (μ) equals 5.0×10−4 per site per generation and substituting these values into the equation Ne  =  Θ/4 μ.A long-term (equilibrium) estimate of effective population size  was calculated as an average over all microsatellite loci for each sample. Randomisation tests were used to determine whether populations suffered from a heterozygote deficit that would be expected to result from mating between close relatives within small populations. Observed FIS values were compared to a null distribution for each population created by randomising alleles within individuals 2000 times using the software FSTAT v2.9.3 Genetic evidence for a recent reduction in local population size was tested using the software BOTTLENECK v1.2.02 A total of 96 mtDNA sequences were obtained for the ATP region, the alignment was 670 bp in length, including 138 bp of the ATP subunit 8 gene and 532 bp of the ATP subunit 6 gene. There were 14 variable positions contributing to 13 haplotypes, which formed a shallow network spanning a maximum of seven parsimony steps . HaplotyThe genealogical network of mitochondrial haplotypes showed evidence of very strong spatial structure. One clade, consisting of haplotypes 2, 10, 11 and 12, and three parsimony steps from haplotype 4, occurred only in the north branch of the Pioneer River. Haplotypes in the southern branch of the Pioneer River also formed a closely related group, with the exception of haplotype 5 which was more closely related to the common haplotype in Sandy Creek. Bakers Creek had only a single haplotype, haplotype 1.Tests for linkage disequilibrium (LD) revealed 26 cases of non-random association between genotypes (α = 0.05) from a total of 476 tests that could be performed. Correction for Type-I error using the FDR B-Y method (corrected α = 0.0074) reduced the number of significant LD comparisons to six, each involving a different pair of loci and indicating that the microsatellite dataset is not biased by linkage. The mean expected heterozygosity per population across eleven microsatellite loci ranged from 0.467 to 0.654 . All locBoth microsatellites and mtDNA suggested very high levels of genetic structure from pairwise comparisons . Based oWhen sites were organised into a hierarchy with drainage basin as the highest level, in an AMOVA, both microsatellites and mtDNA suggested more differentiation among sites within basins than between the two basins . Howeveri.e. no immigration could be detected. The two exceptions were at PC, where a significant immigration rate of 0.29  was detected from nearby SC, and at RC where a significant immigration rate of 0.28  was detected from nearby BA. In both cases, this dispersal appeared to be in only one direction, i.e. from SC to PC and from BA to RC.Estimates of contemporary migration from the BAYESASS analysis supported findings from the population structure analysis, indicating that for seven of the nine sites, all individuals appeared to have come from that site, K) was seven (q>0.82) to the CU cluster, and one individual from BL that was assigned to the TC cluster . Most individuals from the northern and southern branches of the Pioneer River were assigned to two distinct clusters . The lowest level of historical migration has been from Bakers Ck to the other streams, with Sandy Ck and the south arm of the Pioneer falling somewhere in between  were quite variable and ranged from 140 for Bakers Ck to 1710 for the northern branch of the Pioneer (Table S4). The other two streams (southern Pioneer and Sandy Ck) had identical median estimates of Ne of 390 although different credibility intervals (Table S4). Evidently, the north branch of the Pioneer had a much larger Ne than the other streams and also historically has contributed more migrants to the other streams. No site showed evidence for a recent bottleneck as no significant excess in heterozygosity was found in any of the nine samples using Wilcoxon sign-rank tests (FIS) across loci for each of the nine sites ranged from −0.121 to 0.070 and none of these values corresponded with a significant heterozygote deficit expected under a scenario of inbreeding within small populations.Because of the extremely high levels of population structure and indications of very little migration  between sites, we estimated contemporary nk tests . AdditioM. adspersa had limited dispersal abilities. We hypothesised that one explanation for the fact that the species has become threatened in parts of its range Based on other studies, albeit only using mtDNA and on a larger geographic scale, we had predicted that we would find evidence that Our results clearly support the notion that this species is a very poor disperser. All analyses suggested that dispersal among sites, even between those that are quite close and in the same segment of stream, was rare. The sites that we sampled were all within 20 – 30 km of another sampled site, yet, based on most analyses, there was no contemporary dispersal among the majority of them. The STRUCTURE analyses in particular, showed that each of the sites sampled in the Pioneer River appeared to be a separate population, with only a couple of examples of individual movement between proximate sites. The BAYESASS analysis was only able to detect contemporary movement of individuals between proximate sites within Sandy Ck and Bakers Ck. This evidence for highly restricted dispersal was expected given the results from mtDNA studies in other regions and at larger scales M. adspersa fit the Stream Hierarchy Model of Meffe and Vrijenhoek M. mogurnda in tropical northern Australia FST values, because less of the variation occurs within populations. Furthermore, in that study, both the geographic distances between sites and their elevation were substantially greater than in the present study. This further emphasizes the poor dispersal abilities of M. adspersa.The results of this study show that patterns of genetic differentiation in both mtDNA and microsatellites in Craterocephalus stercusmuscarum in coastal drainages in north Queensland uncovered very high levels of divergence within drainages, but analysis of mtDNA suggested that the high and low altitude populations probably represented different historical colonisation events Pseudomugil signifer, showed that populations tended to fit an isolation by distance model, rather than the stream hierarchy model, although there were significant differences between populations within sub-catchments M. adspersa. In terms of magnitude, there are several cases of freshwater fish that show higher levels of population genetic differentiation than M. adspersa over similar spatial scales. For example, a recent study of marble trout found a global FST of 0.716 for microsatellites FST's up to 0.927 have been recorded for guppies e.g. 53,54,55]. Population genetic structure recorded here for M. adspersa is remarkable in that microsatellites and mtDNA both indicate strong complimentary patterns of differentiation that result from long-term processes, and have manifested over a relatively small spatial scale.Very few studies have examined genetic structure of small-bodied freshwater fish in eastern Australian coastal streams and rivers, particularly at this relatively small scale. A study of the fly-specked hardyhead eN less than 50 which appears low relative to suggested values of eN  = 50 to minimize inbreeding depression and eN  = 500 to maintain sufficient evolutionary potential eN values obtained using this method for other highly structured freshwater fish populations indicate that the eN values recovered for M. adspersa are not unusually low. For example, eN point estimates for three-spined stickleback ranged from 15 to 39 eN estimation method M. adspersa samples measured in this study are therefore on par with those of other freshwater fish species. From a conservation genetics point of view, it seems unlikely that extinction risk for populations of M. adspersa is exacerbated by genetic stochasticity associated with small population size Estimates of effective population size based on the sibship method M. adspersa will be necessary to evaluate whether population declines are associated with the effects of genetic stochasticity. However it is unclear whether a general relationship between low effective population size, reduced fitness and elevated extinction risk should be assumed given the potential influence of factors including chance, selection and history Future studies of populations within threatened parts of the range of eN obtained using the two different methods applied here have been observed in other studies. As demonstrated by our results, coalescent-based equilibrium estimates of eN are generally higher than contemporary eN values obtained from short-term estimators. Fraser et al. eN estimates were 2 – 10 times higher than short-term eN estimates in populations of trout and salmon. Potential explanations for this pattern are that populations have experienced declines at a recent point in their history, or that assumptions made by the equilibrium-based methods are violated such as overlapping generations or application of an incorrect mutation rate eN values are generally low, they have probably been low for a significant time in the recent past. For values of eN around 50, the test should detect bottlenecks for 25–250 generations after the initial reduction in size M. adspersa given weak demographic links that exist among stream populations. The results support our idea that the specific life-history of this species has contributed to its significant reductions in distribution in the Murray-Darling system, where human disturbance and drought have significantly reduced hydrological connectivity among sites.The different estimates of M. adspersa has experienced declines in the southern part of its distribution, it is still reasonably common in coastal Queensland rivers and streams. The reasons that the species has remained common in the northern part of its distribution could partly be due to the fact that extraction of water has less impact in these sub-tropical and tropical rivers, or alternatively, the impacts of human disturbance are yet to be realised. From our genetic analysis, we suggest that, although the species is currently apparently unaffected in many northern parts, further human disturbance may have serious impacts.While et al. M. adspersa in that system.For example, Pusey Furthermore, the fact that the species is highly structured and appears to fit the Stream Hierarchy model allows us to predict the impacts of local extinctions at different spatial scales. If all populations in a single tributary are extirpated due to some spatially limited disturbance, such as pollution, then recolonisation of local sites is very unlikely. Alternatively, if a disturbance is not as severe, but more widespread across a catchment, such as a drought which causes some pools to dry out and others to remain as refugia, then recolonisation, although it may be slow, is more likely, as it can come from source populations in refugial pools within the same tributary.M. adspersa in this region house significant genetic diversity. Effective population sizes could be calculated for the whole region, either by adding all the individual eN values or by calculating a pooled eN, as suggested by Palstra and Ruzzante eN value is similar to the sum of all the individual eN's. However, when there are only moderate levels of structure, the pooled eN value is much reduced relative to the sum. In the case of these gudgeons, this presumably means that each time a local population is no longer viable, the extremely limited connectivity between populations will result in extinction with limited chance of recovery.While the effective population sizes estimated here are apparently low, it could be argued that, as a whole the Table S1mtDNA haplotype list. Haplotype and nucleotide diversity given for each site and Genbank accession number associated with each haplotype. Haplotype relationships are depicted in (DOC)Click here for additional data file.Table S2Immigration rate among four stream sections based on Bayesian analysis using MIGRATE-N. Sites pooled together into stream sections include: Section 1 ; Section 2 ; Section 3 ; Section 4 .(DOC)Click here for additional data file.Table S3Estimates of effective population size for each sample site. Values correspond with data presented in (DOC)Click here for additional data file.Table S4Estimates of effective population size for four stream sections. Sites pooled together into stream sections include: Section 1 ; Section 2 ; Section 3 ; Section 4 . Bayesian analysis using MIGRATE-N.(DOC)Click here for additional data file.


Passer domesticus). We studied sparrows outside the breeding season in a confined small valley dominated by dispersed farms and small-scale agriculture in southern France. Population surveys at 36 locations revealed that sparrows were more abundant in locations with high food availability. We then captured and genotyped 891 house sparrows at 10 microsatellite loci from a subset of these locations (N = 12). Population genetic analyses revealed weak genetic structure, where each locality represented a distinct substructure within the study area. We found that food availability was the main factor among others tested to influence the genetic structure between locations. These results suggest that ecological factors can have strong impacts on both population size per se and intrapopulation genetic variation even at a small scale. On a more general level, our data indicate that a patchy environment and low dispersal rate can result in fine-scale patterns of genetic diversity. Given the importance of genetic diversity for population viability, combining ecological and genetic data can help to identify factors limiting population size and determine the conservation potential of populations.Genetic diversity is one of the key evolutionary variables that correlate with population size, being of critical importance for population viability and the persistence of species. Genetic diversity can also have important ecological consequences within populations, and in turn, ecological factors may drive patterns of genetic diversity. However, the relationship between the genetic diversity of a population and how this interacts with ecological processes has so far only been investigated in a few studies. Here, we investigate the link between ecological factors, local population size, and allelic diversity, using a field study of a common bird species, the house sparrow ( Understanding factors that drive population size is central to ecology, population genetics, and conservation biology  as our study system. The house sparrow is one of the most numerous and widespread bird species in the world and is closely associated with human settlements  compared population size and genetic diversity between locations; (ii) analyzed the population genetic structure; and (iii) assessed whether patterns of genetic diversity were correlated with environmental variables.The data for this study were collected in a population of house sparrows in Lantabat , about 40 km to the east of Biarritz, southern France, between May 2007 and March 2009. Lantabat is located in a confined, narrow valley that is surrounded by a continuous mountain ridge on three sides. The settlements in the valley are more or less evenly distributed along the valley's length and range in size from single houses (about 50) to three larger hamlets with up to 30 houses . The size of the surveyed locations varied between one and 30 buildings (mean = 4.6). We visited all these locations 10 times and counted the number of sparrows seen during 15-min intervals. We used a scan-sampling protocol where we scanned the location for sparrows once per minute. At each location, we selected the spot that gave the best view over the location, allowing us to assess the maximum number of sparrows. In the three larger locations with more than five buildings, three observers scanned simultaneously with a nonoverlapping observation range. While our sampling protocol did not allow for the counting of the maximum number of individual residents in a location, it gave a rough proxy for the number of sparrows in a location. In particular at locations with many individuals, this method will underestimate the number of sparrows, whereas it gives good abundance estimates for locations with no sparrows or only a few individuals present , or whether there was no active farm present. In addition, we assessed the numbers of livestock and food availability, which was measured as the degree of animal food spillage and serves sparrows as a main source of food  and observed (HO) and expected heterozygosities (HE) in each population location. In addition, allelic richness (AR) was estimated using FSTAT, version 2.9.3.2  and genetic homogeneous between populations within a group . In SAMOVAs, all possible groupings were assessed, and statistical significance was tested by 1024 permutations. We further identified the number of genetic clusters (K) using the Bayesian admixture model with LOCIPRIOR option and correlated allele frequencies implemented in STRUCTURE, version 2.3.4  based on microsatellite genotypes using with GenoDive, version 2.0b23  , we regressed linearized genetic differentiation between locations, measured as  Rousset , and geor) among pairs of individuals at overlapping distance classes to be calculated. The autocorrelation coefficient, varying between −1 and 1, is a measure of pairwise genetic similarity between any pair of individuals within each distance class, relative to the overall genetic similarity. We calculated r among 891 samples at ten overlapping distance classes. The first distance class size was 0–500 m and increased by 500 m until 0–6000 m. We tested for the significance of observed r-vales by comparing it with a null distribution (r = 0). The 95% confidence intervals of each distance class of this null distribution were obtained using 999 permutation among individual genotypes within the given distance class. One thousand bootstrapping procedures over 10 loci were used to generate 95% standard errors around observed r-values.Secondly, we tested for spatial genetic structure in house sparrows at a fine scale using spatial autocorrelation analyses with several distance class sizes based on microsatellite genotypes  showing significant correlations. The remaining five factors  led to alternative 32 models  that were considered in the simulations. The probabilities of each model were generated using a reversible jump MCMC approach by estimating the number of times that a linearized algorithm of the relationship between population-specific FST and environmental factors was visited by each model. GESTE eventually detects the model with the highest posterior probability that best explains the genetic structure. We performed 10 pilot runs of 1000 iterations to obtain the parameters of the proposal distributions used by the MCMC implemented in GESTE. We further applied an additional burn-in of 50000 iterations and a thinning interval of 20. All estimates were derived from a sample size of 10000. Each analysis was executed for three independent replicates to ensure consistency of results.To analyze the effects that different environmental factors may have on the genetic structure of house sparrows, we used a hierarchical Bayesian approach implemented in GESTE. This approach computes population-specific N = 14 locations), and in others, up to 51 individuals were observed. Both the mean and maximum number of sparrows observed in a location depended on farming practices and food availability , but locus Pdo10 and Pdo22 exhibited a high level of null alleles . Excluding these two loci slightly decreased the FIS values (0.08–0.15), but all these values were still significantly larger than zero (data not shown). Thus, it seems these two loci were not the only cause of heterozygosity deficits, and therefore, all 10 loci were retained to calculate genetic diversity indices and estimated genetic structure  and 0.05 (Puchulia).Genetic diversity varied across the 12 locations (mean observed heterozygosity: 0.73–0.80) and was lower than expected (mean expected heterozygosity 0.85–0.90). Significant heterozygote deficits were observed in 51 of 187 locus-specific tests. Twenty-four of 540 tests  showed significant deviations from linkage disequilibrium after Bonferroni corrections, but no systematic pattern occurred either between specific pairs of loci or population. In all 12 locations, the inbreeding coefficient FST value: 0.017; P < 0.001). For pairwise genetic comparisons among locations, 60 of 66 comparisons among locations exhibited low but significant genetic differentiation  .The exploratory PCA method based on individual microsatellite genotypes revealed no distinct geographical substructure among individuals and large overlap between individuals from the different locations by plotting of the first two axes Fig. . OverallFCT values kept increasing with rising numbers of genetic clusters, and reached the maximum value when 11 separate groups were assumed . However, analyses of spatial autocorrelation indicated local genetic substructuring at a very fine scale  between individuals were significantly positive in the first distance class (500–1000 m) and the signals of genetic similarity diminished after this distance interval and fluctuated randomly.Based on the findings from the population genetic analyses, we tested how geographic distance and ecological factors affected the genetic differentiation between locations. The results of the Mantel tests did not support a significant correlation between populations and geographical distance , which deviated significantly from zero. The deficits in observed heterozygosities were retained, even after we excluded the two loci with high null allele frequency , but this signal is missing over larger distances (1–6 km; Fig. 00–1000 mvice versa. This in turn shapes genetic composition and thus intrapopulation genetic variation (Jensen et al. The most interesting finding of this study is that food availability was found to influence the observed pattern of genetic variation found between locations, as well as population size. Although fine-scale population structure driven by ecology and habitat structure has been reported in birds (Edelaar et al. Although the house sparrow is categorized as a species of least concern according to the IUCN Red List (BirdLife International Secondly, this study successfully links ecological factors to species abundance and genetic diversity. This implies that local environmental variables may substantially influence the population viability of sedentary species. Our results suggest that food abundance had the predominant influence on genetic diversity, while increasing distance to the next sparrow sampling location reduced genetic diversity. These findings are in line with studies in rural England which found that reduced food availability increased mortality, which in turn restricted dispersal between populations (Hole et al. Finally, our study provides an example of how population size and neutral genetic diversity vary across a contiguous agricultural landscape. As population size alone cannot always be a proxy to assess the viability of subpopulations (Frankham et al.


Inference of population and species histories and population stratification using genetic data is important for discriminating between different speciation scenarios and for correct interpretation of genome scans for signs of adaptive evolution and trait association. Here we use data from 24 intronic loci re-sequenced in population samples of two closely related species, the pied flycatcher and the collared flycatcher.We applied Isolation-Migration models, assignment analyses and estimated the genetic differentiation and diversity between species and between populations within species. The data indicate a divergence time between the species of <1 million years, significantly shorter than previous estimates using mtDNA, point to a scenario with unidirectional gene-flow from the pied flycatcher into the collared flycatcher and imply that barriers to hybridisation are still permeable in a recently established hybrid zone. Furthermore, we detect significant population stratification, predominantly between the Spanish population and other pied flycatcher populations.Our results provide further evidence for a divergence process where different genomic regions may be at different stages of speciation. We also conclude that forthcoming analyses of genotype-phenotype relations in these ecological model species should be designed to take population stratification into account. Using genetic data to infer the demographic history of a species or a population is of importance for several reasons. For instance, a central goal in evolutionary genetics is to understand which forces have contributed to the observed patterns of genetic variation in natural populations. Of particular interest is the identification of genomic regions that evolve under pressure of natural selection and characterization of functional elements underlying fitness traits . Since dFicedula hypoleuca) and the collared flycatcher  are small, migratory, passerine birds (family Muscicapidae) which occur over large parts of the western Palearctic  assess the level of genetic diversity in different parts of the distribution range of each species, ii) identify potential population structure within species, and iii) use allele frequency data to look for signs of demographic events in the history of the species.Blood samples were available from previous studies ,28,29 anFicedula flycatchers and to represent genes widely distributed over the genome, including both macro- and microchromosomes . All PCRs were run on a Tetrad PTC-100 thermal cycler (MJ Research) with the general temperature profile; initial denaturation 5 min at 95°C, 20 cycles with 30 s denaturation at 95°C, 30 s annealing starting off at 65°C and decreasing the annealing temperature with 0.5°C per cycle and 1 min elongation at 72°C, 20 cycles with the same temperature profile but with fixed annealing temperature at 55°C, and finally finishing off with an extra elongation step for 5 minutes at 72°C. PCR products were purified with Exo-SAP IT (USB Corp.) according to recommendations from the manufacturer. Purified PCR products were prepared for sequencing by running a 30 cycle sequencing reaction using ≈100 ng product together with 3 pmol of either forward or reverse primer, 0.875 μl BDX64, 0.125 μl BigDye3.1, 1.5 μl 5X dilution buffer and 10 μl ddH2O (Applied Biosystems). The temperature profile was initiated by a denaturation step for 3 min at 96°C followed by 30 cycles including 10 s at 96°C, 5 s at 50°C and 2 min at 60°C as suggested by the manufacturer. Sequencing reactions were purified using the XTerminator system according to manufacturer’s protocol and sequences were run on an ABI3730xl DNA Analyzer (Applied Biosystems). Likely as a consequence of length polymorphisms in some loci in some populations, all loci could not be sequenced to full length in all individuals. The number of chromosomes sequenced for a specific population and a specific locus is given in Table We selected 24 autosomal, gene-based (intronic) markers from a set previously developed for amplification in a wide range of avian taxa . The mares Table . PCR ample et al., the genSequences were edited using Sequencher 4.7 (Gene Codes Corp.) and trimmed to include only non-coding parts (introns). Locus specific alignments were created using Clustal W  as imple5 genealogies after a burn-in of 1 million steps with prior ranges as follows: q1 = 0–25 q2 = 0–25, qA = 0–25, τ = 0–25, m1 = 0–15, m2 = 0–15. After inspecting the posterior distributions for the parameters a second, equally long, M-mode run was performed with narrower prior intervals and a different random seed number. The prior ranges for the second run were generally q1 = 0–1 q2 = 0–1, qA = 0–1, τ = 0–5, m1 = 0–10, m2 = 0–10. ESS values varied substantially between runs, the second analysis of allopatric populations (Spanish pied flycatchers and Hungarian collared flycatchers) had lower ESS values than the other analyses, but there was good agreement in HiPt values and posterior distribution ranges between independent runs. Subsequent analysis and the interpretations were therefore restricted to the runs with narrower priors only. All parameter estimates were scaled by a mutation rate of 1.4*10-9 and to another dataset including both autosomal (D = −0.31 and 0.32) and Z-chromosome data D = 1.12 and −0.04; [All pied flycatcher populations showed slightly positive Tajima’s d −0.04; . Admitted −0.04; . Since bd −0.04;  suggest FST estimates varied only slightly among population comparisons between species and averaged at 0.31. This is lower than a previous estimate using a large set of Z-chromosome linked loci [0.36; [The i [0.36; , as expei [0.36; ,59, and i [0.36; . The lati [0.36; . Within FST, was insignificant in this species. Extensive genomic tools are currently under development in both the pied flycatcher and the collared flycatcher. Our data indicates that forthcoming analytical efforts using these tools should be designed so that within species stratification is taken into account using e.g. Genomic Control [Recent data from large-scale genotyping assays have revealed a potential to identify genetic structure on a very detailed scale . For exa Control , or perfWe sequenced 24 autosomal intronic loci in population samples of the pied flycatcher and the collared flycatcher, two model species for ecology and behavior. The data indicate substantial differences in effective and census population size for the pied flycatcher, population stratification within species and shorter divergence time for autosomes than previously reported for mtDNA. We also observed unidirectional post-divergence gene-flow between the species. Our findings support a scenario where different portions of the genome can be at different stages of speciation and they provide important information about population stratification that will be useful for forthcoming analyses of the link between genotypes and phenotypes in these ecological model species.http://dx.doi.org10.5061/dryad.dh53m.All sequences have been deposited in the Dryad database under doi:The authors declare no financial or non-financial competing interests.NB and HE conceived of the study. NB designed the study, did the molecular work and analysed the data. GPS contributed materials. NB and HE wrote the manuscript with input from GPS. All authors approved the final version of the manuscript.Supplementary information: Nested model analysis test values (2LLR) and corresponding p-values. df = degrees of freedom. * = 2LLR test statistic follows a mixed chi-squared distribution (Hey & Nielsen 2007). Allopatry 1 = Spanish pied flycatchers and Italian collared flycatchers, Allopatry 2 = Spanish pied flycatchers and Hungarian collared flycatchers, Sympatry = pied flycatchers and collared flycatchers from the Baltic Sea islands, Species = samples from all four populations combined for each species, respectively. (PDF 65 kb)Click here for file


Terrapene c. carolina, a species that is in steep decline across its range. To inform conservation planning for this species, we address the hypothesis that disruptions to demographic and movement parameters associated with the decline of the eastern box turtle has resulted in distinctive genetic signatures in the form of low genetic diversity, high population structuring, and decreased gene flow. We used microsatellite genotype data from (n = 799) individuals from across the species range to perform two Bayesian population assignment approaches, two methods for comparing historical and contemporary migration among populations, an evaluation of isolation by distance, and a method for detecting barriers to gene flow. Both Bayesian methods of population assignment indicated that there are two populations rangewide, both of which have maintained high levels of genetic diversity (OH = 0.756). Evidence of isolation by distance was detected in this species at a spatial scale of 300 – 500 km, and the Appalachian Mountains were identified as the primary barrier to gene flow across the species range. We also found evidence for historical but not contemporary migration between populations. Our prediction of many, highly structured populations across the range was not supported. This may point to cryptic contemporary gene flow, which might in turn be explained by the presence of rare transients in populations. However these data may be influenced by historical signatures of genetic connectivity because individuals of this species can be long-lived.Rangewide studies of genetic parameters can elucidate patterns and processes that operate only over large geographic scales. Herein, we present a rangewide population genetic assessment of the eastern box turtle  The quantification of genetic parameters of species is basic to understanding their natural history. This is especially important in declining species in need of appropriate conservation approaches Wide-ranging species of conservation concern, whose interpopulation migration patterns are influenced by habitat fragmentation, also could be expected to exhibit genetic evidence of disruption to large- and small-scale movement behaviors A genetic population is commonly defined in the field as a group of conspecifics that are genetically similar and are to varying degrees separated from other populations of conspecifics and are likely to be more locally adapted Terrapene c. carolina is a declining species whose conservation plans are difficult to develop because of the complexities of studying a species that is cryptic, long-lived . We automatically scored genotypes with Genemapper  and checked each call manually at least twice. We reamplified ∼10% of all genotypes to ensure repeatability and reamplified any that disagreed a third time. We used Cervus  from 1–25 over 1,000,000 MCMC iterations with thinning every 100th iteration and a burn-in of 50,000. We assumed uncorrelated allele frequencies to avoid artifacts of uneven geographic sampling k associated with the use of correlated frequency allele models k and used the highest mean probability density value as the inferred k value. We also estimated the probability of population assignment for each individual.To improve analytical robustness against violations of software-specific assumptions, a common approach in population genetics tructure . To visualize and infer the most likely value of k we used Structure Harvester . We set the number of barriers to k - 1, with k being the number of populations identified by Geneland and Structure.To estimate the location of specific natural or anthropogenic barriers to gene flow which might have contributed to population structure identified across the species range, we used the maximum difference algorithm of Monmonier Terrapene species suggest that populations may operate at geographic scales greater than most management jurisdictions, such as state or national parks, and perhaps even larger than a single state Previous studies in the population genetics of ayesass . Microchecker estimated the potential presence of numerous null alleles at low frequencies . Additionally, the spatial autocorrelation analysis estimated that the pairwise genetic distance in the 300 – 500 km distance class (Ay = 0.795) exceeded the mean pairwise genetic distance for all pairwise distances (Av = 0.793). This indicates that significant isolation begins to operate at this geographic distance and that panmixia operates at geographic scales less than this threshold Isolation by distance was significant across the species range  where adult males traveled roughly linear paths that were many times longer than the width of the standard home range  high gene flow across vast areas. Though box turtles are reported throughout much of Appalachia Our data also indicate that isolation by distance is operating over relatively large spatial scales across the range of this species. The spatial autocorrelation analysis demonstrated that at approximately 300 – 500 km, mean pairwise genetic distances begin to exceed the average for the entire data set, suggesting a geographic extent at which populations operate. The same analysis by Hagood Terrapene, with longevity in the wild at least 100 years Click here for additional data file.Table S2Locus-specific heterozygosities for populations of the eastern box turtle Terrapene c. carolina.(PDF)Click here for additional data file.


We critically examine the current biological models of individual organizational behavior, with particular emphasis on the roles of genetics and the brain. We demonstrate how approaches to biology in the organizational sciences assume that biological systems are simultaneously causal and essentially static; that genotypes exert constant effects. In contrast, we present a sociogenomic approach to organizational research, which could provide a meta-theoretical framework for understanding organizational behavior. Sociogenomics is an interactionist approach that derives power from its ability to explain how genes and environment operate. The key insight is that both genes and the environment operate by modifying gene expression. This leads to a conception of genetic and environmental effects that is fundamentally dynamic, rather than the static view of classical biometric approaches. We review biometric research within organizational behavior, and contrast these interpretations with a sociogenomic view. We provide a review of gene expression mechanisms that help explain the dynamism observed in individual organizational behavior, particularly factors associated with gene expression in the brain. Finally, we discuss the ethics of genomic and neuroscientific findings for practicing managers and discuss whether it is possible to practically apply these findings in management. The psychopath inside, neuroscientist James Fallon describes his discovery that scans of his own brain showed patterns of activation indicating potential psychopathy . In spite of such legislative barriers to direct use of biological research in employment settings, interest among organizational researchers remains high, as evidenced by the forthcoming book edited by Collarelli and Arvey, The biological foundations of organizational Behavior.Additionally, there are many reasons why, even if we could make such identifications, we would not want to use this in the day-to-day practice of management. For instance, genetic screening or brain imaging could be expected to lead to a form of “genetic discrimination.” Such discrimination may be problematic for ethical reasons, and practically, as long as the biological indicators measured are weakly predictive of behavior. For instance, in the United States, the Genetic Information Nondiscrimination Act of 2008 (GINA) Title II prevents employers (and some other non-employment agencies) from requiring or requesting genetic information as a condition of employment ,The effect of being raised in the same family (shared environment) is smaller than the effect of genes, andA large percentage of the variation in human behavioral traits is not accounted for by either genes or by shared environment (unique environment),The “are leaders born or made” question is an example of a very common question about genetics in the social sciences, which is: what wins, nature or nurture? Unfortunately, this question is effectively a straw man, because human action is the result of  Ridley, —one may  Ridley, , p. 2. T Ridley, , p. 160:unique experience, both play important roles in the development of complex behavioral characteristics. By this logic, leaders are both born and made. An additional important point is that it is meaningless to take the slightly more nuanced position of “if both are important, which is more important?” In the current essay, we review the etiology of leadership through the lens of sociogenomics. For these purposes, we consider leadership largely at the level of the individual leader—the individual characteristics and behaviors that allow that individual to emerge, be accepted, and be effective as a leader. However, each stage of this process involves social interactions with other people. We therefore do consider the influence that individuals have on one another. So, while our perspective speaks most directly to behavioral and trait-based approaches to leadership, the overarching perspective has some bearing on interpersonal and dyadic perspectives, as well.which together show that genes and experience, especially an individual's The sociogenomic framework articulates the mechanisms through which genes and environments interact to help shape observed behavioral characteristics. The promise of sociogenomics lays in using the genome–the entirety of an organisms' hereditary characteristics–as the basis for understanding behavior . Another researcher may be concerned with showing that some early life experience influences these same leadership characteristics. In contrast, the sociogenomic approach embraces both of these explanations simultaneously.We contrast the sociogenomic view with three contemporary perspectives on the biological substrate of leadership: 1. The existence of genetic effects indicates that leaders are “born,” not made  may also be transgenerationally heritable  systems, that there is some psychobiological association is unsurprising. Where there is interest is in the position Turkheimer  and fraternal (dizygotic) twins. In the equation, P is the phenotypic trait. Phenotype means that the trait is observed or measured. Examples of phenotypic traits are height, eye color, measured intelligence, or the occupation of a leadership role. The A-term refers to the additive genetic component. The C-term refers to common environment, factors that are not genetics that make twins more alike. Typically, these factors are considered related to family upbringing and common schooling or early life experiences shared between twins. The E-term represents unique environment (confounded with error), or the percentage of variance attributable to experiences that are wholly unique to each individual, and other purely idiosyncratic variance. This model is typically estimated using samples of identical and fraternal twins, though adoption studies are sometimes used. Identical twins share roughly 100% of their genetic material—copy number variations can differ across identical twins, and random mutations can occur during development, but for practical purposes, identical twins share 100% of their genetic material, while fraternal twins are no more similar genetically than any other siblings—sharing on average 50% of their genetic material. Therefore identical twins have perfect genetic correlations, whereas fraternal twins have genetic correlations half as strong. Both types of twins have equally strong common environmental effects, and the unique environmental effect is specific to each individual. This model allows behavioral genetics researchers to estimate the heritability coefficient, h2, which is the population-level variance in the phenotypic trait, P, that is associated with the variance in genetic material /Var(P). It is extremely important to note that the h2 coefficient is a population statistic—it does not apply at the individual level, so it should never be interpreted that an h2 of 0.50 means that half of an individual's trait level is genetic. The statistic only indexes the population's proportion of phenotypic variance attributable to genotypic variance.In order to understand the literature that genetic research in leadership is built on, it is necessary to understand absolutely do not suggest that leadership cannot be taught, as heritability does not imply immutability. Instead, these findings only imply that identical twins are more similar on the transformational and transactional leadership scales than fraternal twins due to inherited genetic factors.Several studies have investigated the heritability of leadership styles and occupancy of leadership roles (such as supervisor or manager). For instance, Johnson et al.  examinedExtending these findings within the same sample of twins, Johnson et al.  examinedAgain, these correlations are at the genetic level, so it is likely that transactional leadership shares some of its underlying genetic substrate with Conscientiousness, Extraversion, and Agreeableness, while transformational leadership shares genetic substrates with Conscientiousness, Extraversion, and Openness. Specifically, based on this study, transformational leadership appears to have genetic causes in common with Conscientiousness, Extraversion, and Openness. One possible avenue for future research that these findings suggest is that any physiological system that is implicated in one of these personality traits may be a candidate for study with leadership style. For instance, the serotonin system has been implicated in self-control or impulsivity  of the gene being expressed or the degree of expression are the key featuresThese two mechanisms are so tightly intertwined that it is absolutely untenable to frame the question whether leaders are born vs. made, or even to simply assert that they are born and made. A dichotomous viewpoint is demonstrably false. Both the genetic mechanism and the environmental mechanism operate on the same substrate: the genome itself. We cannot emphasize this point strongly enough. Environments wield their influence by affecting the production of proteins—gene expression. Both are capable of influencing gene expression and both can affect brain functioning similarly. We believe that nature and nurture should be viewed not as two distinct processes but merely two sides of a coin . Such findings are often built around an improperly specified model: one that does not explicitly account for the environment  and by traits . From the standpoint of developing leaders, these episodes are key. Like stress remodeling the brain as described above, how can leader development interventions be constructed to redesign the neural architecture of the leader? We see the point of leader development interventions as using the environment to induce states that ultimately alter trait levels.We believe that the key difference between the current models of biology employed by leadership researchers and the sociogenomic perspective is one of generativity. The sociogenomic perspective, as outlined above and summarized in Figure The current approaches to the biology underlying psychological characteristics seem to view genetics as an unchanging causal force on behavior. In such conceptualizations, consequential social phenomena, such as leader effectiveness, lie at the end of a causal chain begun with the biological substrates underlying personality traits . If the genetic polymorphisms one is born with are invariant over the life course and they guide the development of the neural architecture we think with, how can it be otherwise? Researchers operating within this framework have a tendency to demonstrate that a genetic component exists for leadership  across time and situationsBeyond the previous considerations, the individual's physiology has implications for any conceptualization of leadership, and the measurement systems used should incorporate those considerations. As an example, consider the serotonergic system. It is implicated in dominance, and dominance appears important to attain and maintain status. Now, we are left with a host of research questions regarding the role the serotonin system plays in dominance and status attainment. For example, how does variability in serotonergic functioning affect leader emergence? Does attaining leadership status, in turn, affect the serotonergic system  in the right frontal cortex may be associated with visionary communication  that will allow a person to develop into a leader and are there interventions that can lead to better leadership?The key insights from a cross-species, sociogenomic view of leadership demonstrate how critical particular environmental experiences can be for profound behavioral—and sometimes physical—change. Avolio  discusseA sociogenomic leadership approach would help to develop a science of leader development in two key ways: to help understand what contexts are developmentally important and when they can be expected to occur ? For example, consider the US Military Academy at West Point. West Point has a strict organizational hierarchy, with cadets attaining various ranks that mirror the active duty Army. Additionally, West Point has the explicit goal of As a further example, can traumatic experiences catalyze the development of leadership within a person, such that a person experiencing traumatic events becomes more resilient and more capable of exerting leadership . The genes a person possesses may express themselves differently  conditional on the environment. Further, environmental changes may impact the individual's psychological states, which could then affect gene expression and remodel the person's neuroanatomy (cf., Roberts, One of the major purposes of this review has been to demonstrate Let us return to the example from the beginning, of the “psychopath” neuroscientist, James Fallon. If we lived in a world with rigid genetic or neuropsychological screening, he would perhaps never have been admitted to graduate school to earn a Ph.D. We would then not have his example to illuminate the possibility that our genes are not our destiny, that an individual whose genes appear to code for psychopathy, and whose neurological functioning bears this out, can be a successful scientist with a close family. Under the sociogenomic framework, there is a complex path from the particular variant of a gene that an individual possesses and the behaviors they are likely to exhibit; as a result, it seems to us that organizational interventions based on genetic or neurological information are a long way from being tools in the practicing manager's kit.This paper is meant to incorporate theoretical insights from molecular biology within leadership research, using a framework that has been profitable to understand social behavior across species, time and outcomes (Robinson, Further, sociogenomics urges leadership researchers to attend to the evolutionary context in which leadership emerged: this may provide key insights into how these functional mechanisms operate within modern organizational contexts. For instance, how is social status attained within an organization, and which mechanisms facilitate its attainment? A sociogenomic leadership theory would provide a modern biological framework for interpreting genetic research in leadership by encouraging detailed research questions regarding the mechanisms underlying genetic and environmental effects found in contemporary behavioral genetic studies.Recent interest in and efforts to incorporate biological reasoning into management and leadership seem to point to a bright future. To this end, we have borrowed and elaborated on a theoretical model from biology. This is a model that has some traction in disciplines that have close ties to leadership theory, most notably personality psychology. We advocate a move to a sociogenomic leadership theory. The perspective offered by this model shows us that DNA is not always the causal driver of behavior. Environmental conditions interact with genes to build the biological architecture upon which behavior plays itself out.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Gonyostomum semen (Raphidophyceae) is a freshwater phytoplankton species that has increased in abundance in Northern Europe since the 1980's and has expanded its habitat range. In this study, we aimed to determine the genetic population structure of G. semen in Northern Europe and to what extent it reflects the species' recent expansion. We sampled lakes from 12 locations (11 lakes) in Norway, Sweden and Finland. Multiple strains from each location were genotyped using Amplified Fragment Length Polymorphism (AFLP). We found low differentiation between locations, and low gene diversity within each location. Moreover, there was an absence of genetic isolation with distance . According to a Bayesian clustering method all the isolates belonged to the same genetic population. Together our data suggest the presence of one metapopulation and an overall low diversity, which is coherent with a recent expansion of G. semen.Biological invasions often cause major perturbations in the environment and are well studied among macroorganisms. Less is known about invasion by free-living microbes.  Invasive species can pose a significant threat to ecosystems with a wide range of effects on the newly colonized environment such as loss of biodiversity, disappearance of native species, shift in species dominance in the community, and alteration of ecosystem function Alexandrium tamarenseAphanizomenon ovalisporum and Cylindrospermopsis raciborskiiGonyostomum semenIn aquatic systems, microbial planktonic algae (phytoplankton) are key players as primary producers that form the base of the food web in aquatic systems. Studies concerning invasive phytoplankton are scarce, and only a handful of microalgal species are currently described as invasive. These include the marine dinoflagellate G. semen, which is considered a nuisance species and invasive. This species can form dense blooms over extended periods of time reaching cell abundances of 1.5 millions cells per liter, despite very low growth rates G. semen expels slimy threads upon mechanical stress which can cause skin irritation on bathers et al.G. semen have appeared in new lakes during the past twenty years. These findings are in line with the results of a previous study in Finland G. semen into new lakes. Thus, the species is now considered invasive in Northern Europe. It has been proposed that an invasive species colonizes new habitats, spread quickly, and forms dominant populations The present study focuses on the raphidophyte species G. semen in Northern European lakes, studies exploring the environmental conditions of G. semen lakes and describing the species ecological characteristics have been undertaken G. semen growth has been shown to be favored by humic substances G. semen has been suggested to be favored by a reduced grazing pressure by zooplankton G. semen populations, and no study yet attempted to explain the invasion pattern of the species. Presumably, a recent (decades rather than thousands of years) invasion should be reflected in the species population genetic structure. Several studies on phytoplankton population genetics have shown that populations are typically highly differentiated with low gene flow even between neighboring populations et al. With the expansion of G. semen in Northern Europe to get a better understanding of the species expansion. To this end, we used the DNA fingerprinting method Amplified Fragment Length Polymorphism (AFLP) to determine the population structure and genetic diversity of G. semen in 11 lakes spread across Sweden, Finland and Norway.In this study, we aimed at characterizing the population genetic structure and diversity of G. semen blooms. At each sampling station G. semen cells were collected from the shore in the surface water using a 20-µm mesh-size plankton net. Large zooplankton which might feed on G. semen, were removed from the sample using a 150-µm net. Water from each lake was collected for preparation of culture medium for the G. semen isolates.During summer 2010 (July-August), 12 stations from 11 lakes were sampled across Sweden (7 lakes), Norway (3 lakes), and Finland (1 lake) . The samNo specific ethical permits were required for the specific study according to the Swedish, Finnish, and Norwegian laws. The locations are not privately-owned or protected in any way.et al.−1 and 50% sterile-filtered water from the respective lakes to increase the isolation success. Survival rates of the strains for each population (in%) were calculated after two months of culture, by dividing the number of strains alive by the number of cells isolated. When the cell concentration of the cultures had reached approximately 2000 cells mL−1, the cells were harvested by centrifugation according to Lebret et al.Single cell isolations were performed to obtain a final number of approximately 20 clonal cultures per sampling location . The isoet al.DNA was extracted using a CTAB-based protocol described by Lebret et al.TCT x MCGA, ETCT x MCCG, ETAG x MCGG, ETCG x MCAG, ETCG x MCGG and ETCG x MCGA. PCR products from three primer combinations labeled with the different dyes  were combined in single wells of a 96-well plate (Applied Biosystems). All samples were analyzed by ABI37730XL capillary electrophoresis using a MapMarker 1000 bp size standard at the Uppsala Genome Center, Sweden.Amplified fragment length polymorphism (AFLP) analyses were performed on the samples as described by Lebret et al.ST values to estimate genetic differentiation between sampling locations, the p-values were determined using 1000 permutations, and the FST were considered significant for p≤0.05. Genetic differentiation was also estimated with Jost's D distance et al. et al.The raw data was analyzed with Genemapper  and AFLPscore version 1.4 A total of 194 strains from 12 sampling stations (11 lakes) across Sweden, Norway and Finland , were suet al.To analyze the STRUCTURE results, the number of populations (K) was determined using two approaches. Based on the calculated lnP(K) value , the smaST between sampling locations were statistically highly significant, although low. The Mantel test showed that the differentiation between the lakes could not be explained by genetic isolation due to geographic distances (P = 0.50). The highest FST was observed between the samples collected in Helgasjön 1 and Lillsjön  based on genetic distances, showed that the first axis explained 30% of the observed variation, the second 23% and the third 15% . Althougch other . Along tWe performed an outlier analysis in Arlequin to determine if our dataset contained loci that might be under selection. The outlier analysis showed that 8 loci were potentially under selection pressure. Four of these loci were only present in the strains collected from Helgasjön station 1. Single unique loci were only observed in the Mjöträsket, Lillsjön, Isesjøen, and Gjølsjøen strains respectively.G. semen shows signs of range expansion in Northern Europe, and the species has been described as invasive G. semen, to gain a better understanding of patterns of invasion. Our results showed the presence of a single metapopulation  in Northern Europe, with low differentiation between sampling locations. The results are discussed below in light of the recent expansion (during the last decades) of the species in Northern Europe.Previous studies have revealed that G. semen are consistent with these patterns.According to population genetic theory, a recently established population is expected to have low diversity because most likely only a fraction of the source population would have dispersed, generating a diversity bottleneck in the new population ST values between two phytoplankton populations identified by STRUCTURE typically range ≥0.22–0.4 in other studies ST ranged between 0.048 and 0.306). Similarly, Logares et al.Peridinium aciculiferum in two Swedish lakes.The presence of one single metapopulation  over large distances, most likely reflect the recent invasion, as more differentiated populations could be expected from non-invasive phytoplankton populations. For instance, FST) among the lakes. This discrepancy can be explained by the different approaches and assumptions of FST and STRUCTURE to analyze population structure. The STRUCTURE analysis assigns individuals to potential populations assuming that the populations are in Hardy-Weinberg equilibrium, whereas the FST analyzes the heterozygosity between pre-defined populations. Thus, the STRUCTURE analysis is often considered more conservative, and might detect higher level of population structure. However, the statistical significance observed for our FST values between locations might result from low within location variation, rather than reflecting strong differentiation. The statistical differentiation observed for the FST analysis can also result from the presence of a few loci only observed in specific sampling locations . In fact, our data does show the presence of several loci that may be under selection, which are only present in specific lakes. These results need to be interpreted with caution, as they can result from false-positive or hitchhiking phenomenon, where alleles appear to be under selection due to linkage to loci under true selection ST and STRUCTURE analyses, we also determined Jost's D distance as a measure of population differentiation ST analyses, thus being more relevant to study population differentiation G. semen between the different locations. The combination of low FST values and the absence of differentiation according to Jost's D distance suggest that there is little or no differentiation between the sampling locations, thereby supporting the results of the STRUCTURE analysis.Although the STRUCTURE analysis suggests the presence of a single population in Northern Europe, there was still a significant but low genetic differentiation , we would have expected to find high differentiation of populations with a pattern of isolation by distance G. semen would have colonized the lakes gradually from South to North following the receding of the ice cap. Our data does not support the latter hypothesis, thus we conclude that our results reflect the recent (past decades) invasion of lakes.The lack of genetic differentiation among lakes and low genetic diversity within lakes are best explained by a recent colonization of the lakes, which has likely occurred during the last decades or century as suggested by the monitoring data G. semen even if the lakes were colonized thousands of years ago. However, that explanation seems less likely as monitoring data suggest that G. semen has colonized new lakes G. semen's presence was previously undetected due to very low abundance in cell numbers remains. For example, a recent study showed that the freshwater diatom Stephanodiscus binderanus, which was described as an exotic species in the Great lakes since the mid-20th century, was already present in these lakes since a least three centuries There are, however, alternative explanations for our results without invoking a recent invasion. A single metapopulation could also have resulted by high gene flow among lakes in G. semen showed low gene diversity compared to other phytoplankton population studies. For example, freshwater dinoflagellates had a Nei's gene diversity between 0.07 and 0.37 G. semen alternates between asexual and sexual reproduction, with a mainly asexual phase during the growing season followed by sexual reproduction at the termination of the bloom G. semen, the low gene diversity observed in the different populations might be explained by a bottleneck effect resulting from colonization by few individuals, which was maintained in the populations due to the life cycle characteristics involving vegetative growth. However, the gene diversity data alone cannot rule out that the observed diversity is not due to an increase in population sizes of populations that were already present. Nor do we have data from a native range to compare with that of the current range.Invasive species are expected to present lower diversity in the invasive range than in the native range G. semen blooms  are the lakes with the oldest known A further complication is that the measured diversities are most likely underestimates of the real diversity as it was calculated using the strains that were able to grow in culture. Isolation and culture techniques are known to be a form of selection of strains G. semen populations from the different locations, and relatively low gene diversity. We suggest that the genetic pattern observed in this study might reflect the recent expansion of G. semen (during the last decades), and the colonization of new lakes.In conclusion, our results showed the presence of a single metapopulation in Northern Europe with low differentiation of