Poecillastra laminaris, three corals - Goniocorella dumosa, Madrepora oculata, Solenosmilia variabilis) was investigated using seascape genetics. Genetic data  and environmental variables were employed to build individual-based and population-level models. The results indicated that environmental factors affected genetic variation differently amongst the species, as well as at different geographic scales. For individual-based analyses, different environmental variables explained genetic variation in P. laminaris (dissolved oxygen), G. dumosa (dynamic topography), M. oculata (sea surface temperature and surface water primary productivity), and S. variabilis . At the population level, factors related to current and food source explained the regional genetic structure in all four species, whilst at the geomorphic features level, factors related to food source and topography were most important. Environmental variation in these parameters may be acting as barriers to gene flow at different scales. This study highlights the utility of seascape genetic studies to better understand the processes shaping the genetic structure of organisms, and to identify environmental factors that can be used to locate sites for the protection of deep-sea Vulnerable Marine Ecosystems.Understanding the ecological processes that shape spatial genetic patterns of population structure is critical for understanding evolutionary dynamics and defining significant evolutionary and management units in the deep sea. Here, the role of environmental factors  in shaping the population genetic structure of four deep-sea habitat-forming species (one sponge -  The deep-sea environment experiences increasing pressure, decreasing pH, and generally decreasing temperature, with increasing depth4. Such single factor or multi-factorial gradients may strongly influence dispersal, settlement and recruitment patterns of deep-sea taxa7. Additionally, features such as bottom currents8, surface currents9 and bathymetry may play important roles in shaping the patterns of genetic connectivity amongst populations in many deep-sea species11, with the result that multiple environmental factors may act as barriers to or promoters of gene flow in the deep sea12 and thereby strongly influence population genetic structure at different spatial and even temporal scales. However, the complexity of the deep-sea physico-chemical environment and the logistical difficulties of sampling  such a large biome have limited our ability to understand multispecies patterns of population genetic structure and how these are influenced by environmental variation15.How spatially variable environmental and habitat features influence evolutionary processes and population genetic connectivity in the deep sea is poorly understood16. Coral reefs and sponge gardens are two VMEs in the deep sea16, indicator taxa of which include scleractinian coral species that build reef frameworks and large and/or abundant demo- or hexactinellid sponges that provide physical habitat for other organisms17. Although there are no studies that have quantified the relationships between environmental factors and the maintenance of genetic structure in the deep sea, there are several studies from other research areas that provide information on the importance of some environmental factors for coral reefs and sponge gardens. Generally, factors such as depth, temperature, salinity, particulate organic carbon (POC), and aragonite  and silica (for sponges) concentrations have been recognised as influencing the formation of deep-sea coral and sponge habitats25. In addition, topography and sediment supply are also regarded as important factors influencing the distribution of coral and sponge habitat-forming deep-sea species27. At small spatial scales, internal tidal mixing and pH variation may have significant effects on coral (and presumably sponge) larvae and their ability to connect different populations29. However, the relative influence (weak versus strong) and the mechanism of action by which these environmental variables moderate the interaction between habitat and population genetic structure and connectivity are largely unknown.Deep-sea vulnerable marine ecosystems (VMEs) are easily disturbed by anthropogenic activities, are very slow to recover, or may never recover, and are physically or functionally fragile31 and attempts to explicitly integrate spatial ecological information with population genetic data to characterise marine environmental factors that contribute to genetic connectivity and/or population genetic isolation33. Selkoe et al. summarised seascape genetics studies over the last 10 years and found that temperature, oceanography  and geography  showed equal prevalence of influence on spatial genetic patterns of shallow water species34. In addition, the review revealed that > 20 other factors also influence connectivity (gene flow) at distinct spatio-temporal scales. This latter point highlights the complexity of seascape genetics, in particular for the deep sea for which far less is known than in shallow/coastal waters, and there is high uncertainty about species-specific responses at different scales in time and in space. Thus, whilst seascape genetics holds much promise for delivering a new understanding of how environmental variation influences genetic variation  it is important to appreciate that seascape genetics is in its infancy and we are still learning about its limitations and possibilities. This is particularly the case in the deep sea, where there is an information vacuum for the largest single biome on the planet13.The discipline of seascape genetics is extended from landscape genetics38. Species-specific genetic variation in VME indicator taxa in the New Zealand region may be caused by a number of different factors, including historical processes (phylogeography), connectivity (high versus low gene flow within and amongst populations), and environmental variation (contemporary selection pressure)14. Whilst there is, at present, no indication that the patterns of species-specific genetic structuring are attributable to local or regional adaptation, the recognition of its existence as a pre-existing condition for seascape genetics analyses is important. Typically, genetic differentiation is described using neutral markers39, but with new marker types and new analytical procedures there is increasing focus on non-neutral markers (genes or non-coding regions closely linked to genes), at least some of which may be of particular value in determining the cause and functional mechanism of pronounced patterns of genetic structure that may arise as a consequence of selection41.Within the New Zealand Exclusive Economic Zone (EEZ), many species are endemic and most have distributions that encompass a range of environments, and are therefore amenable to quantification of the effect of environmental variation on population genetic variation and genetic connectivity42 indicates that the New Zealand region is composed of two provinces, a northern (BY6) and a southern (BY10) province with a boundary at latitude ~45°S. Secondly, superimposed on top of this biogeographic structure are three major currents and their associated fronts around New Zealand - the Tasman Front, the Subtropical Front and the Subantarctic Front43. These currents and frontal systems differentiate water masses in the north and the south of the region, and also influence the direction of northern and southern currents across the Chatham Rise  that may reduce or perhaps even block gene flow across this feature. Corresponding to these potential hydrographic boundaries, three distinct regions have been recognised3 - above 42°S is the north region, between 42–45°S is the central region that contains the Chatham Rise, and below 45°S is the south region. Thirdly, the seafloor of the New Zealand EEZ is distinguished by different geomorphic features  that are characterised by different topographies and hydrodynamics that may influence genetic differentiation45. As such, populations on major geomorphic features  and Goniocorella dumosa, Madrepora oculata and Solenosmilia variabilis  are all common VME indicator taxa that are widely distributed throughout the New Zealand EEZ  with relatively short planktonic dispersal phases of perhaps a few days or weeks50. There is an indication that larval dispersal potential may be positively correlated with oocyte size amongst Goniocorella dumosa, Madrepora oculata and Solenosmilia variabilis3.Because of its highly variable seafloor topography and bathymetry and its complex patterns of oceanic dynamics, New Zealand presents an ideal opportunity to test the effect of multi-factorial environmental influences on the genetic variation of deep-sea species. Such testing may be carried out at different spatial scales that are relevant to species’ distributions across the New Zealand region. Firstly, at the largest spatial extent, global assessment of the biogeography of the deep sea based on depth, temperature, salinity, dissolved oxygen, particulate organic carbon (POC), hydrographic and bathymetric parametersuthern BY0 provincuthern BY0 provincTo explore how environmental variation may affect patterns of genetic structure, in the present study, environmental variables and genetic data were employed to quantify the relationships between environmental and genetic variation of these four VME indicator taxa at three different spatial scales. Three main hypotheses were tested: (1) that species-specific genetic variation is independent of environmental variation at the level of individuals within the New Zealand region, and for populations at different spatial scales, (2) that factors such as variation in silicate (for the sponge) and calcite  explained significant variation in species-specific genetic variation, and (3) that multispecies genetic variation is associated with a common suite of environmental variables, that may include factors such as dissolved oxygen, some aspect of water movement (delivering suspended particle food and removing excretion products), and water temperature. The results provide information about which environmental variables contribute most to the genetic structure of populations of the four species, how connectivity amongst VMEs is influenced by environmental variation, if environmental variation plays a consistent role across multiple taxa in explaining population genetic structure, and how such information may be used to inform appropriate management measures, which in the present context, are likely to involve the establishment of a new offshore network of marine protected areas.r2 > 0.95), as were bottom temperature and temperature-pressure residual (r2 > 0.95). Therefore, aragonite was selected to represent nitrate, phosphate and calcite, and bottom temperature was selected to represent temperature-pressure residual in the modelling analyses. This selection procedure resulted in 22 variables for the sponge environmental data set  than AIC modelling, only AIC-based models were employed to identify environmental variables that explained genetic variation.For all 4 species using the different genetic markers a range of models was successfully built. However, because BIC modelling was less successful at building models  for Cytb variation contained 9 variables, and the best model fit for variation in the microsatellite data contained 6 and 11 variables for all (p = 0.031) and neutral (p = 0.232) loci, respectively  for individual-based on Table .r2 values of the models were low ( < 0.500), seven of 11 models were statistically significant (p < 0.05). For all 11 models, no one variable was significant in explaining genetic differentiation amongst individuals of all three corals. However, seven of the 21 variables were included in five models, and one variable, sea surface temperature, was included in six models.In total, eleven models were developed and tested for individual-based genetic variation of the three corals Table . AlthougG. dumosa, two of four models were statistically significant and both involved the microsatellite loci , but the whole model was not significant (p = 0.603). For the ITS sequence data the best model fit contained 8 variables , whilst for microsatellite variation the best model fit contained 3 and 7 variables for all and neutral loci, respectively. Amongst all 4 models, dynamic topography was always included and contributed significantly in three, whilst sea surface temperature gradient) was also a significant contributor to all models except for that based on the all loci microsatellite data (Table D-loop (p < 0.05)  Figure .M. oculata, two of three models were statistically significant  (Table For nt Table . For the5) Table .S. variabilis, three of four models were statistically significant , and a five-variable model (three were significant) also provided the best fit  for variation of the ITS sequence. The models for microsatellite variation included four variables  for both the all loci and neutral loci datasets. There were no variables that were shared amongst all four models, but all models except ITS included tidal current speed. A positive relationship was observed between tidal current speed and genetic distance, even in the non-significant ITS model  in both the all and neutral microsatellite loci data sets. For M. oculata, dynamic topography and bottom water temperature contributed to genetic differentiation in the all and neutral loci data sets, respectively, whilst salinity was the best interpreter for the genetic structure revealed from ITS sequences. For S. variabilis, the variable dynamic topography and dissolved oxygen explained variation in the all loci data set, whereas dynamic topography explained most variation in the neutral loci data set . However, such work has been exclusively focused on coastal and/or shallow water) species and systems, sometimes from the perspective of environmental or evolutionary biology, and sometimes with a management  focus . This study is amongst the first to apply the approach to deep-sea species and environments. It proved successful at uncovering relationships between genetic structure and environmental factors, although also revealed some issues that will need to be addressed in future applications of the method.Seascape genetics as a discipline has advanced dramatically over the last decade as environmental data sets have increased in size and coverage , and as new molecular markers and analytical tools have been developed  and a lack of comparative information about the relationships between environmental factors and genetic differentiation, the results generated here contribute to a more comprehensive understanding about environmental and genetic interactions at both small and large spatial scales.Contrasting patterns of population genetic structure have been reported for the sponge and the three coral taxa throughout their distributional ranges in the New Zealand regionr2 values in GLM. One possible reason for this result is that the mitochondrial genome is more sensitive than the nuclear genome to environmental variability, perhaps because almost all of the mitogenome is coding whereas most of the nuclear genome is non-coding. Microsatellites, which had a better performance in population genetic studies, were less sensitive to environmental factors, as might be expected for markers that are assumed to be selectively neutral. Whether there is a direct selection-based association between genetic and environmental variation, or whether the environmental variables simply act as barriers to gene flow  remains to be determined.The selected environmental variables varied amongst the different models for each species according to the different genetic markers. For DNA sequence markers, mitochondrial markers had a better performance than nuclear markers, as judged by higher Of note is the fact that a single environmental variable may be included in all models for a single species, but for one marker the variable’s contribution was positive and for another marker within the same species the variable’s contribution was negative , temperature (in various forms), bathymetry, and Chl-a concentration are often reported as being significant explanators of genetic variation in a range of marine taxa, including macroalgae, invertebrates and vertebrates .Topographic factors (such as bathymetric variation), physico-chemical factors , and biological factors (such as surface water Chl-a concentration) were examined directly in the present study. Such variables have previously been identified as environmental drivers for controlling the large-scale distribution of deep-sea corals and sponges61 and may therefore also reflect the dispersal of coral larvae. Significant contributions of dynamic topography to explain genetic variation in M. oculata and S. variabilis supported the hypothesis in a previous study that large-scale currents associated with the Tropical and Subtropical fronts shaped the regional genetic structure3. For G. dumosa, the regional structure was influenced by surface water primary productivity revealed in both all and neutral microsatellites, which is related to the amount of food available to suspension-feeding corals62. The importance of this variable is also probably related to the influence of currents and fronts on genetic connectivity between the Central region and the North and South regions. That is, G. dumasa was more common in relatively shallower water at 400–500 m (compared to M. oculata and S. variabilis) on the Chatham Rise of the Central region, which is an area of particularly high sea surface primary productivity coincident with the subtropical front (ref for coincidence of high prod and STF). Furthermore, the variables associated with bottom currents and fronts, such as bottom water temperature, dissolved oxygen and salinity, also support the contention that environmental factors associated with the Chatham Rise are responsible for patterns of genetic variation across the three regions3. In the sponge, P. laminaris, bottom current speed, bottom water temperature and silicate explained more than half of the regional variability in different models. The contribution of silicate to the regional structure was revealed by all microsatellites, whilst bottom current speed and bottom water temperature explained the regional structure detected by COI, Cytb and neutral microsatellites. This result might indicate that the genome of P. laminaris is under some sort of (unknown) selection pressure for silicate, and that the bottom current speed is acting as a genetic barrier by affecting larval settlement and/or the available food source. However, this suggestion will require further evidence to test it. Overall, bottom current was an important factor that contributed to the formation of genetic structures for deep-sea corals and a sponge around New Zealand, and is a variable that is likely to influence genetic variation on multiple spatial scales.At the North-Central-South regional scale, the main variables driving the genetic variation amongst coral populations were bottom water temperature, dissolved oxygen, dynamic topography, salinity and surface water primary productivity. Dynamic topography, which is a measure of relative sea surface current velocity, reflects large scale current movements63. Previous studies of corals have detected topographically associated genetic patterns: genetic differentiation was significantly different between slope and ridges in Desmophyllum dianthus5, and in the coral genus Narella11. However, neither of these studies discussed the relationship between genetic differentiation and topography: further studies are required to verify this relationship. Bottom current speed was another factor that shaped genetic structure in G. dumosa, and the terrain variables  were the most important factors that influenced the genetic variation of populations at the geomorphic features scale, in both the all and neutral microsatellite data sets of S. variabilis. For P. laminaris, water density  was the crucial explanator of population genetic variation at the geomorphic features scale. This variable reflects the temperature, salinity, and pressure of the water above the geomorphic features, and as such is probably operating as a single proxy variable for the influence of these water mass characteristics on genetic variation. A possible interpretation for all of the aforementioned variables acting as barriers to population connectivity relates to them reflecting the particular environment suitable for larval settlement and development amongst different geomorphic features. That is, the population genetic variation may be driven by different conditions amongst geomorphic features that challenged the larvae from other populations to survive, colonise and/or reproduce once established as an adult, such that little or no genetic contribution amongst local gene pools induced genetic differentiation between local populations. However, to our knowledge, there are no studies available for deep-sea species to provide evidence to support this suggestion.At the geomorphic feature level, the factors that affected genetic connectivity patterns varied across the different species, but seamount was included in the all and neutral microsatellite data sets of all examined species. This indicates that the topography, and associated hydrodynamics, of different geomorphic features may affect the genetic structure of these species - for example, providing an explanation for genetic isolation of populations from Kermadec Ridge, Louisville Seamount Chain and Macquarie Ridge, where topographically-forced oceanographic features such as Taylor columns on the seamounts or along ridge currents could potentially influence larval retention on these features65, but the importance of each variable in the present study was different from those identified by these previous studies. Sea surface temperature gradient was significant as an explanator of genetic variation in G. dumosa and M. oculata and sea surface temperature was also significant in most models for these species. Sea surface temperature and sea surface temperature gradient are both related to primary productivity, and the latter to the location of frontal systems66. Primary productivity and frontal systems are proxies for food availability68, and the genetic variation patterns of these two species might therefore be driven by food source availability even at small spatial scales. In S. variabilis, although no common variables were found in all four models, tidal current speed was included in three models but not in ITS. Tidal current speed is related to the distribution of sedentary suspension-feeding organisms, such as corals and sponges, that rely on currents to deliver food58. Overall, at small scales, the genetic variations were explained by food-related variables. Food is closely related to an organism’s growth, fecundity, larval dispersal and settlement74, and thus food availability indirectly effects the genetic contribution made by one population to another population (by fecundity) and/or the genetic connectivity between populations . However, the mechanism by which food resources manipulate genetic connectivity patterns is unclear, and still needs investigation. Interestingly, dynamic topography was an important contribution to explaining the individual-based genetic variation of G. dumosa in all 4 models but not at large scales  for this species . As noted above, dynamic topography is related to large-scale current dynamics and can influence large-scale patterns of genetic connectivity via larval dispersal. However, this variable has also been shown previously to be related to the distribution of suspension-feeding organisms through influencing food source availability and variability58, which provides more support for the explanation that food availability might explain the local genetic structure observed in G. dumosa.At the individual level, the variables that elucidated the patterns of genetic variation differed not only amongst species but also by genetic marker. The important variables identified at this level were similar to those identified by habitat suitability modellingP. laminaris, dissolved oxygen was the only variable that was included in all four models. Dissolved oxygen is directly related to metabolic rate and low concentrations of dissolved oxygen are expected to adversely affect sponge physiological performance75. In addition to dissolved oxygen, POC was also an important variable for P. laminaris in the GLM analysis. A significant relationship between POC and genetic structure was also detected in a shallow-sea reef sponge (Stylissa carteri)76. A previous study has found that POC is a very important food source for sponges77, further suggesting that food source availability might be the reason for the genetic differentiation observed in these deep-sea organisms even at small spatial scales. However, in contrast to the three corals, sea surface temperature and sea surface temperature gradient were not main contributors to the local genetic variations in P. laminaris. Similarly, sea surface temperature did not contribute to the habitat suitability model for predicting the distribution of the sponge Geodia78. This result possibly indicates that forces that influence the availability of the food sources and that shape the genetic structure may vary between deep-sea corals and sponges.For the sponge, 79, and this might help to explain the similar genetic differentiations that were observed across these species. However, the variables that influenced the patterns of genetic structure at the different scales were species-specific, a finding that has been reported previously in the limited number of shallow water multi-species studies (reviewed by36). Nevertheless, while our seascape genetics approach has revealed which environmental variables are potentially important, how these different variables modified the species-specific patterns of genetic connectivity is still unclear. Additionally, high correlation coefficients leave as yet unresolved the question of whether species-specific genetic variation is correlated with a single environmental variable or multiple variables. Investigations of genotypic, phenotypic and environmental variability, as well as of gene-environment interactions are required to resolve these questions.Generally, the results at the different spatial scales of this study suggest that bottom currents, food resource and terrain variability shape the genetic structure of both corals and sponges. A previous study has found that deep-sea corals and sponges have similar environmental demands82, and the results of seascape genetic modelling can help inform management options that conserve genetic structure and connectivity. To maintain population-level genetic diversity and effective population size, it is essential to understand the patterns of genetic connectivity that exist amongst deep-sea populations. However, open spatial population structures, logistical sampling difficulties and unknown barriers to gene flow present challenges to connectivity research. To overcome these challenges, seascape genetics research may open another window to improve understanding of linkages between genetics and the spatial ecology of populations83.Marine environmental managers are increasingly incorporating genetic information into management planning85) identified by this study as being correlated with genetic variation, which could then be used, along with other data inputs that represent other forms of biological variation in a region, to select the sites of the marine protected areas . Additionally, the identification of variables that help to explain coral and sponge genetic variation indicates that networks of protected areas should include representatives of different geomorphic features, and conservation priorities could be allocated to sites that contain particularly complex topography .The genetic separation of some deep-sea coral and sponge populations into regions or locations that are characterised by particular temperature or food availability regimes illustrates the importance of protecting these VME indicator taxa via spatial management options that include sites (e.g. marine protected areas) that represent these different environmental regimes. For example, these different environmental regimes could be represented by a spatial classification of variables .As seascape genetics moves beyond the search for patterns amongst populations of a single species to a more rigorous approach involving hypothesis testing, individual-based and population-level assessments, greater awareness of spatial and temporal coverage, and comparisons across multiple species, it is expected that new insights into the environmental factors influencing gene flow will be forthcoming, and that such information will be of value in evolutionary studies and management  invertebrate collection (NIC). Material had been collected from multiple sites throughout the South Pacific region over multiple years dating back to the 1960s14. Here, we recognise 12 distinct geomorphic features from which samples had been collected (Table 42), north-central-south regions  and geomorphic features   were sequenced for the sponge, and mitochondrial (D-loop) and nuclear (ITS) loci were utilised for the corals. In total, 10, 20, 24 and 27 microsatellite loci were genotyped for P. laminaris, G. dumosa, M. oculata and S. variabilis, respectively. Because neutral markers can provide unbiased estimates of time since reproductive isolation and the amount of genetic drift90, the neutral microsatellite loci identified by LOSITAN91 and Micro-checker92 were employed for all analyses, where appropriate  perform very similarly, but with some small and perhaps important differences14. The work described here uses the genetic data sets from the population genetics research described by Zeng et al.14, but analyses these in the novel context of environmental variation to test for associations between genetic and environmental variation  and genotyping , were employed to detect species-specific genetic variation. The methodological details of development and identification of appropriate markers and the sequencing and genotyping are described in previous studies61). The variable calcite was excluded from the analysis of sponge data, whilst silicate was excluded from the analysis of coral data due to their expected lack of biological relevance. Missing values in the environmental data set  were imputed by Primer-e (v7) using the expectation-maximization algorithm.Data for 26 environmental variables − 6 topographic, 17 physico-chemical and 3 biological variables - were obtained from various sources Table . Data fo94. Pearson correlation coefficients between all pairs of environmental variables were calculated for those data collected at the sponge and coral sample locations using the R project . To overcome colinearity, one of the predicator variables was eliminated from the data set when bivariate correlations were > 0.9537.Highly correlated environmental variables were identified and eliminated from subsequent analysis to reduce problems that arise for the interpretation of models that include a large number of collinear predictors(i)96. This approach is a modification  of the approach using mean pairwise genetic distance between populations employed by38 and37. GLM analyses were performed separately for each species using a stepwise model, and the best model fit was chosen based on the lowest Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) value models. All analyses were conducted using the package MASS in R. Scatterplots of the best-fit model were generated using the package ggplot2 in R.a generalized linear model (GLM), which is a multiple regression analysis between a number of environmental variables and a dependent variable . The individual pairwise distances from DNA sequences were generated using MEGA (v6), and a pairwise individual-by-individual genotypic distance matrix was calculated from the microsatellite data using the Codom-Genotypic distance option within GenAlEx(ii)97 that tests for associations between genetic and environmental variation37. DistLM was used to perform an ordination of fitted values from a given model and is constrained to find linear combinations of predictor variables  that explain the greatest variation in the data cloud . The best-fitting relationship was chosen for the final regression models by comparing the adjusted r2 selection value. Relationships between environmental parameters were initially examined by analysing each predictor separately , and then sequentially using the adjusted r2 selection procedure. Similarity matrices in DistLM analyses were built using Bray-Curtis resemblance matrices of haplotypic/allelic frequencies. For each species, haplotypic frequency matrices were calculated for each population in Excel, and allelic frequency matrices were calculated using GenAlEx. The p-values for individual predictor variables were obtained using 9,999 permutations. Distance-based redundancy analysis (dbRDA) plots were also generated using PERMANOVA+ to visualise the results once the best DistLM model of each species was obtained.a distance-based linear model (DistLM) routine in PERMANOVA+ (iii)38. The BVSTEP analysis in Primer-e was utilised to search for the best model fit between genetic and environmental variables. The best-fitting relationship was chosen for the final regression models by comparing adjusted r2 selection criterion. A Bray–Curtis resemblance matrix was employed for the haplotypic/allelic frequency variables and a Euclidean distance resemblance matrix was employed for the environmental variables. To test for species-specific correlations between the two matrices, the BIOENV subroutine of the BEST routine was implemented using the non-parametric Spearman correlation coefficient method (rS) to test all combinations of factors.a biological environmental stepwise analysis (BEST) that tests for the relationship between resemblance matrices of dependent  and predictor  variablesTo test the hypothesis that multi-factorial environmental variation does not significantly explain species-specific genetic variation, three statistical methodologies were employed:These three analytical approaches operate in slightly different ways by testing different associations between the species-specific and site-specific genetic and environmental data. GLM is based on genetic variation at the individual, not population level. Whilst DistLM and BEST are based on population-specific haplotypic/allelic frequencies per locus, the former is a permutational model building analysis whereas the latter is a non-parametric test of all possible models, from the 26 single variable models to the one 26-factor model. All three analyses are species-specific and employ the same population-specific environmental data sets.3 and sponge14 population genetic variation. However, the dbRDA and BEST tests do not allow for testing of difference between only two groups of data, which means that testing of differences at the Northern-Southern provinces level could not be carried out. Instead, we focus on analyses of regional and geomorphic population genetic variation. In total, we conducted 15 analyses of individual-based genetic diversity for all four species and their respective markers, plus we conducted a further 12 tests of population-level genetic diversity at the scale of regions and geomorphic features, across all four species and their respective markers  we only carried out seascape genetics analyses where significant spatial population genetic differentiation has already been identified. This is based on previous studies of deep-sea VME indicator coralDataset1.


A Constellation plot divided the studied germplasm into two populations on the basis of their 100-seed weight. Various mean genetic diversity parameters including observed number of alleles (1.99), effective number of alleles (1.54), Shannon’s information index (0.48), expected heterozygosity (0.32), and unbiased expected heterozygosity (0.32) for the entire population exhibited sufficient genetic diversity using 12232 silicoDArT markers. Analysis of molecular variance (AMOVA) revealed that most of the variations (91%) in world safflower panel are due to differences within country groups. A model-based structure grouped the 94 safflower accessions into populations A, B, C and an admixture population upon membership coefficient. Neighbor joining analysis grouped the safflower accessions into two populations (A and B). Principal coordinate analysis (PCoA) also clustered the safflower accessions on the basis of geographical origin. Three accessions; Egypt-5, Egypt-2, and India-2 revealed the highest genetic distance and hence might be recommended as candidate parental lines for safflower breeding programs. The mixed linear model i.e., the Q + K model, demonstrated that two DArTseq markers (DArT-45483051 and DArT-15672391) had significant association (p < 0.01) for 100-seed weight. We envisage that identified DArTseq markers associated with 100-seed weight will be helpful to develop high-yielding cultivars of safflower through marker-assisted breeding in the near future.Safflower is an important oilseed crop mainly grown in the arid and semi-arid regions of the world. The aim of this study was to explore phenotypic and genetic diversity, population structure, and marker-trait association for 100-seed weight in 94 safflower accessions originating from 26 countries using silicoDArT markers. Analysis of variance revealed statistically significant genotypic effects ( Carthamus tinctorius L.) belongs to the family Compositae and it is a self-pollinated crop with a genome size of about 1.4 GB [Carthamus species have been utilized since the pre-historic period as its archeological remains were found at sites in Syria since 7500 BC. Safflower was distributed from its center of domestication  to linked regions comprising Egypt, the Aegean region and southern Europe [Safflower  and multiplex sequencing, aid in the generation of massive genetic data for various applications . ApplicaLinkage analysis, also known as QTL mapping, helps in the identification of genomic regions controlling complex plant traits. QTL mapping is a time-consuming technique that needs mapping populations to be developed from bi-parents. QTL mapping captures less allelic variation utilizing bi-parental populations due to the very low rate of occurrence of recombination events and low mapping resolution . AssociaDuring this study, 100-seed weight at both locations (Pakistan and Turkey) was recorded with the help of an electronic seed counter and weighing balance by taking undamaged and fully matured seeds. Analysis of variance (ANOVA) for both locations revealed highly significant differences among the studied safflower accessions for 100-seed weight . MinimumThe implemented constellation plot clearly divided international safflower panel into two populations on the basis of their 100-seed weight. A total of 46 and 48 accessions clustered in population A and population B, respectively .DArTseq profiling of 94 safflower accessions resulted in a total of 29,048 silicoDArT markers. This dataset was filtered by accounting markers having less than 5% missing data, polymorphism information content (PIC) value of 0.10 to 0.50, call rate greater than 0.81, and 100% reproducibility, to retain 12,232 high quality markers for further analysis. Various diversity parameters such as observed number of alleles (1.99), effective number of alleles (1.54), Shannon’s information index (0.48), expected heterozygosity (0.32), and unbiased expected heterozygosity (0.32) reflected a good level of genetic variation in the studied germplasm . MaximumPairwise kinship coefficients ranged from −1.45 to 1.24 for the entire safflower panel. A total of 51.17% kinship values ranged from −0.40 to 0.4. 99% of the kinship coefficient values, which ranged from 0.60 to 1.00; however, 0.21% of the kinship coefficients ranged from 1.10 to 1.30, respectively . AnalysiThe Bayesian clustering model grouped the international safflower panel into three main populations implemented in STRUCTURE software on the basis of membership coefficient: 17 accessions in population A, 6 accessions in population B, and 21 accessions in population C. The remaining 50 accessions were clustered as admixture population . Clusterp-value; 1.17E-04 and 1.15E-04), respectively (The MLM (Q + K) model was performed to assess marker-trait associations for 100-seed weight in the international safflower panel. DArT-45483051 and DArT-15672391 showed marker-trait association for 100-seed weight by revealing statistically significant association  confirmed the statistically significant genotypic effects in both locations . These rDArTseq technology gained the attention of scientists globally due to low cost and high throughput nature. DArTseq technology has been used to explore the genetic diversity and population structure of different crops with a large number of entries and complex genomes ,39. HassPolymorphism information content (PIC) value is a measure of polymorphism which provides information regarding the genetic diversity or DNA segment in a studied population, and indicates the allele’s evolutionary pressure and mutations that occurred at a locus over a time period. The range of the PIC value (0.10 to 0.50) obtained in this study suggests the existence of a high level of genetic variation that might be derived utilizing a large number of good quality markers in a diversified safflower panel. An average PIC value of 0.31 across all the silicoDArT markers was obtained during this study. PIC values were distributed asymmetrically and were skewed towards the lower values. More than 50% of the implemented silicoDArT markers revealed a PIC value of more than 0.30, which indicates the informativeness and usefulness of these markers for genetic diversity, population structure, and marker-trait association in safflower.Diversity parameters including observed number of alleles (Na), effective number of alleles (Ne), Shannon’s information index (I), expected heterozygosity (He), and unbiased expected heterozygosity (uHe) for the entire population of 94 safflower accessions, which were 1.99, 1.54, 0.48, 0.32, and 0.32, respectively. Previous use of different gel-based marker systems obtained lower diversity metrics values than our current results from the silicoDArT marker system ,40,41. TThe Jaccard coefficients of genetic distance resulted in a mean value of 0.50 for the entire population of 94 safflower accessions. A maximum genetic distance was proposed between safflower accessions Egypt-2 and India-2, followed by Egypt-5 and India-2 with genetic distance values of 0.76 and 0.76, respectively. The highest genetic similarity was recorded between safflower accessions Spain-1 and Spain-2, with a genetic distance value of 0.14. The presence of higher genetic similarity between safflower accessions is possibly because of their origin from common parents . SafflowThe three clustering algorithms important to genetic diversity and population structure analysis  were implemented and revealed that the safflower accessions were successfully grouped by the silicoDArT markers based on geographical regions. Among the three clustering algorithms, more preference was given to the model-based structure algorithms. The reason for giving such a high preference to the structure is that this algorithm revealed more robustness in the previous works ,44. StruGrouping of the safflower accessions obtained from structure analysis was based on the geographical origins of germplasm. Population A clustered safflower accessions originating from Israel, Jordan, Syria, Egypt, Turkey, Hungary, Afghanistan, and Pakistan. Out of 17 of these accessions, 13 belong to the geographical locations situated in the Mediterranean region. Safflower accessions from the Mediterranean region clustered together and revealed their genetic similarity, and possibly shared a similar parentage. Clustering of the safflower accessions from Mediterranean countries proposes this region as a center of safflower domestication, with Syria having a predominant role . PopulatNeighbor joining analysis divided the studied germplasm into two populations based on their geographical origin . StructuSeed yield is a complex trait with many underlying factors contributing to it. Comprehensive understanding of the relationship between seed yield and other contributing traits is crucial to the process of selection and ultimately to crop improvement . In saffThe BLAST search against DArT-45483051 marker resulted in AT1G01040, a putative candidate gene. AT1G01040 is highly associated with seed embryo health and embryo shape at seed maturity and ovule development . TherefoA total of 94 safflower accessions originating from 26 countries were used as plant materials in this study. Seeds of the evaluated germplasm were provided by the United States Department of Agriculture (USDA) . The exp−1 DNA concentration was maintained. The prepared DNA samples were sent to Diversity Array Technology Pty, Ltd., Bruce, Australia, for DArTseq analyses of GBS [To extract the genomic DNA from each accession, fresh, healthy and young leaves were harvested and kept frozen in the laboratory at −80 °C. DNA isolation of each safflower accession was performed utilizing the bulk of leaves from 10 individuals. The individuals used for the purpose of DNA isolation were from plants from the original seeds from the gene bank. DNA isolation was performed according to CTAB protocol  and a sps of GBS .DArTseq technology is a complexity reduction method and next generation sequencing platform . DArTseqOnline software developed by Rathore et al.  for statAll images were analyzed from the DArTseq platform using DArTsoft v.7.4.7 . SilicoDArT are dominant markers that were detected through DArTseq and scored using the binary fashion, where 1 or 0 represent presence or absence of the restriction fragment in the genomic representation of each sample, respectively ,68. ScreThe proportion of shared alleles that were obtained from silicoDArT markers were used to compute the genetic distances among the safflower accessions using Jaccard's coefficients of genetic distance. Important diversity metrics such as observed number of alleles (Na), effective number of alleles (Ne), Shannon’s Information Index (I), expected heterozygosity (He), and unbiased expected heterozygosity (uHe) were estimated for the entire population using GenAlEx 6.5 . The kinPopulation structure of the studied safflower accessions was evaluated with STRUCTURE software . The moTo investigate the putative candidate genes, sequences of identified silicoDArT markers were used to perform BLAST searches against the National Center for Biotechnology Information (NCBI) , and thep value signifies the relatedness of a marker with the associated trait, and R2 reflects the proportion of phenotypic variation resulting from a significant marker [p = 0.01 were taken as significantly associated with the 100-seed weight. A Pseudo-Manhattan plot was developed using the qq-man R Package in the R 4.0.0 statistical software [A Mixed linear model  approach was applied to inspect marker-trait associations (MTAs) via TASSEL 5.0.5 . The popt marker . SilicoDsoftware . The current evaluation revealed a good level of phenotypic and genetic diversity in the studied safflower panel from the silicoDArT markers information. Analysis of variance (ANOVA) revealed the highly significant genotypic effect for 100-seed weight. Frequency distribution resulted in a normal distribution for 100-seed weight across the two locations (Pakistan and Turkey). The mean and range of 100-seed weight obtained from Turkey was higher than that from Pakistan. Analysis of molecular variance (AMOVA) revealed the division of total variations into two stratum i.e., among countries and within country. A total of 91% of the genetic variation was present within country and low variation (9%) was observed among the countries. Findings of AMOVA were also supported by the results of 100-seed weight and genetic distance. A good range of variations in 100-seed weight and genetic distance calculated at countries basis confirmed that mostly variations resulted in this study are because of diverse individuals within countries. Safflower accessions Egypt-5, Egypt-2, and India-2 showed the highest genetic distance among the studied panel and hence might be recommended as candidate parental lines for safflower breeding programs. Moreover, Egypt-5 is the only accession among the studied international safflower panel that reflected 100-seed weight of > 5.00 g at both locations and the highest genetic distance (0.76 with silicoDArT markers). Model-based structure analysis, Neighbor joining analysis and Principal coordinate analysis (PCoA) clustered the safflower accessions on the basis of their geographical origin. This is a pioneer study uncovering the marker-trait association for 100-seed weight in safflower. A total of two silicoDArT markers (DArT-45483051 and DArT-15672391) showed statistically significant association for 100-seed weight and these markers can be used in marker-assisted breeding to develop safflower cultivars with improved yield.


In situ adaptation to climate change will be critical for the persistence of many ectotherm species due to their relative lack of dispersal capacity. Climate change is causing increases in both the mean and the variance of environmental temperature, each of which may act as agents of selection on different traits. Importantly, these traits may not be heritable or have the capacity to evolve independently from one another. When genetic constraints prevent the “baseline” values of thermal performance traits from evolving rapidly, phenotypic plasticity driven by gene expression might become critical. We review the literature for evidence that thermal performance traits in ectotherms are heritable and have genetic architectures that permit their unconstrained evolution. Next, we examine the relationship between gene expression and both the magnitude and duration of thermal stress. Finally, we identify genes that are likely to be important for adaptation to a changing climate and determine whether they show patterns consistent with thermal adaptation. Although few studies have measured narrow-sense heritabilities of thermal performance traits, current evidence suggests that the end points of thermal reaction norms (tolerance limits) are moderately heritable and have the potential to evolve rapidly. By contrast, performance at intermediate temperatures has substantially lower evolutionary potential. Moreover, evolution in many species appears to be constrained by genetic correlations such that populations can adapt to either increases in mean temperature or temperature variability, but not both. Finally, many species have the capacity for plastic expression of the transcriptome in response to temperature shifts, with the number of differentially expressed genes increasing with the magnitude, but not the duration, of thermal stress. We use these observations to develop a conceptual model that describes the likely trajectory of genome evolution in response to changes in environmental temperature. Our results indicate that extreme weather events, rather than gradual increases in mean temperature, are more likely to drive genetic and phenotypic change in wild ectotherms. First, opt) and then sharply declines above that optimum , and these are closely related to the breadth of the TPC (Tbr). The performance breadth and critical thermal limits are thought to be under selection primarily as a result of changes in the variance of environmental temperature, although performance breadth is probably also affected by selection for changes in performance at intermediate temperatures (max) of the population. These five components of thermal performance curves can be thought of as “thermal performance traits” that combine to define the shape of the thermal niche and may or may not have the capacity to evolve independently of one another  and genetic correlations underlying the thermal performance traits that make up the thermal niches of animals. We searched the terms “quantitative genetics AND thermal physiology,” “genetic correlations AND thermal physiology,” “heritability AND thermal trait,” “genetics AND specialist-generalist AND temperature,” “genetics AND hotter-is-better,” “genetics AND thermodynamic effect,” “heritability of CTmax,” “heritability of CTmin,” “heritability of thermal optimum,” “heritability AND cold tolerance,” and “heritability AND chill-coma” in Google Scholar in October 2019. Due to the rapid decline of relevant studies after the first few pages of search results, we focused on the first 50 results for each set of search terms (ordered by relevance). To ensure that our sampling was robust, we subsequently (July 2020) included an additional 50 search results on Google Scholar  and conducted a separate set of searches with the same search terms in Thompson Web of Science, again ordered by relevance. In total, we examined more than 1400 results from these databases for possible heritability and genetic correlation estimates. Finally, we included additional studies that we were aware of but that did not come up in our literature searches. These various search avenues likely uncovered the majority of quantitative genetic parameter estimates for our target traits that were available in the literature. Our full database contained 98 independent heritability and genetic correlation estimates from 55 studies. Note that the temperature ramping rates used in these studies varied by several orders of magnitude, and ramping rate is known to affect heritability estimates . Namely,min and CTmax) were consistently and substantially heritable  that tested for genetic correlations corresponding to either a specialist-generalist tradeoff or a thermodynamic effect, the majority found evidence of one or the other. 87% of studies found evidence of a specialist-generalist tradeoff, while 67% of studies found evidence of a thermodynamic effect (opt) appear to have minimal capacity for rapid evolution.To our knowledge, there are only five estimates (from four studies) of the quantitative genetic parameters underlying the other major thermal performance traits: maximal performance, performance breadth, and the thermal optimum. Maximal performance was moderately heritable at an average narrow-sense heritability of 0.12. Every study that examined the performance breadth and the thermal optimum found zero additive genetic variation underlying these traits. Due to the low sample sizes for most of these traits, we did not conduct formal statistical comparisons. Of the studies (c effect . Additioc effect . All elsDrosophila melanogaster) compared to flies reared at intermediate temperatures  70 family seem to underlie phenotypic plasticity in thermal tolerance limits in the oyster Crassostrea gigas  in both Google Scholar and Thompson Web of Science during July 2020. These queries returned hundreds of journal articles, each of which we evaluated for relevance. Ultimately, this process yielded 36 articles containing 42 independent estimates of the effects of temperature on the transcriptomic response in ectotherms. These studies spanned early microarray work to recent experiments that leveraged high-throughput RNA sequencing, and they focused on acute, reversible gene expression responses rather than fixed changes that may occur over development .F = 13.0448, P = 0.0010; F = 2.1269, P = 0.1542: F = 3.3718, P = 0.0753] using linear regression models. These results indicate that brief, severe weather events could impact gene expression and phenotypic plasticity more profoundly than longer-term changes in thermal conditions.All species in these experiments, which range from arthropods to vertebrates and occur in diverse habitats across the globe, shift expression of their transcriptome in response to thermal changes . Howevermax and CTmin). Indeed, not all mRNAs that are transcribed will be translated into proteins  analyses have found that biological processes associated with protein synthesis, folding and degradation, oxygen transport, and biological and cellular responses to heat and other stress-stimuli are often significantly enriched . Heat shRapid environmental change can induce selection on the genome in two major ways. First, selection can target sequence variation in crucial protein-coding genes . This isopt) are not  appear to lack additive genetic variation  are exceedingly rare. To our knowledge, only three studies have estimated narrow-sense heritabilities of the thermal optimum and performance breadth. Two of these were on lizards  and the Most studies have examined genetic variation in either the baseline values of thermal traits or their plasticity, but rarely both. Future work should focus on the genetic basis of baseline values of thermal traits and their plasticity in the same populations to tease apart the independent contribution of both to local adaptation under environmental change. A rare example of such a study is Our review suggests that genes in the heat shock protein family are a likely target for selection when environments first shift, and the evolutionary potential of these genes may be a major determinant of populations’ resilience in the face of climate change. Past evolution of heat shock proteins is dominated by repeated duplications and insertion events, which might have been followed by neofunctionalization . At leasOur results suggest that the evolution of gene expression plasticity may be particularly important in maintaining fitness under climate change, not only because a number of thermal traits appear to lack genetic variation in their baseline values, but also because extreme weather events are rising in frequency. Moreover, past research has revealed that the capacity for gene expression plasticity can be heritable and evolve rapidly . AdditioOur review suggests that several general rules may be emerging from studies of the genetic and transcriptomic basis of thermal performance:1.In many species, there is more genetic variation in performance at extremely high or low temperatures than in performance at intermediate temperatures.2.Gene expression plasticity is rampant when organisms are exposed to acute thermal stress.3.Patterns (1) and (2) indicate that populations are more likely to evolve rapidly in response to extreme weather events than in response to gradual changes in mean temperature, and the rate at which different moments of the temperature distribution change will determine the dominant trajectory of phenotypic and genetic evolution.4.Gene regulatory networks linked to heat shock proteins are likely to be a major target of selection as environmental temperatures become warmer and more variable.Finally, our work highlights the need for further studies on the quantitative genetic basis of thermal performance curves and the interactions between baseline thermal tolerance and gene expression plasticity. Continued advances in this field should lead to substantial improvements in our ability to predict the viability of animal populations as our planet continues to change.Both authors conceived of the study, reviewed the literature, and wrote the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Anthropogenic activities are associated with its fragmentation, which alters ecological factors and directly threatens resident species. Vaccinium floribundum Kunth., commonly known as Andean blueberry or mortiño, is a wild shrub endemic to the Andean region and highly valued in Ecuador for its berries, which are widely used in food preparations and hold an important cultural value. Since it is a wild species, mortiño could be vulnerable to environmental changes, resulting in a reduction of the size and distribution of its populations. To evaluate the extent of these effects on the mortiño populations, we assessed the genetic diversity and population structure of the species along the Ecuadorian highlands. We designed and developed a set of 30 species-specific SSR (simple sequence repeats) markers and used 16 of these to characterize 100 mortiño individuals from 27 collection sites. Our results revealed a high degree of genetic diversity (HE = 0.73) for the Ecuadorian mortiño, and a population structure analyses suggested the existence of distinct genetic clusters present in the northern, central and southern highlands. A fourth, clearly differentiated cluster was also found and included individuals from locations at higher elevations. We suggest that the population structure of the species could be explained by an isolation-by-distance model and can be associated with the geological history of the Andean region. Our results suggest that elevation could also be a key factor in the differentiation of mortiño populations. This study provides an extensive overview of the species across its distribution range in Ecuador, contributing to a better understanding of its conservation status. These results can assist in the development of conservation programs for this valuable biological and cultural resource and for the páramo ecosystem as a whole.The Ecuadorian  Vaccinium floribundum Kunth., commonly known as mortiño or Andean blueberry, is a woody perennial shrub from the Ericaceae. It is endemic to the Andean region in South America ranging from Venezuela to Bolivia and can be found between 1600 to 4500 meters above sea level (masl) [V. floribundum grows in high-altitude ecosystems such as cool montane forests and páramos (tundra-like ecosystems) at temperatures ranging from 7 to 18°C and displays adaptations to withstand frost and freezing conditions [nditions .mortiño is valued primarily for its black-purple fruit widely used for the preparation of traditional drinks, ice creams, wines and preserves [V. floribundum efficiently reduces lipid accumulation in vitro through the inhibition of adipogenesis [In Ecuador, reserves ,4. The freserves . Phytochreserves –12. For ogenesis .Mortiño berries are hand-picked directly from the páramos and sold in local markets. To our knowledge, V. floribundum has not yet been domesticated and established as a crop. Despite various attempts, its propagation is problematic due to low rates of seed germination and a lack of knowledge regarding the factors required for its growth [s growth ,14.V. floribundum plays an important environmental and ecological role, being one of the first species that recovers after bouts of deforestation and man-made fires in the páramo ecosystems [páramo is considered a fragile ecosystem overall due to low levels of primary productivity and slow natural succession, which makes any recovery after human intervention relatively slow [páramo ecosystem during the past century, and its fragmentation in the Andean region has directly impacted the diversity of its species [V. floribundum in the Ecuadorian páramo are currently unknown.osystems . This isosystems . Howeverely slow ,18. Diff species –23. Stud species –29. The Vaccinium has been extensively studied, (e.g. V. corymbosum and V. macrocarpon) with the use of RAPD (random amplified polymorphic DNA), ISSR (inter simple sequence repeats), and SSR (simple sequence repeats) molecular markers, with a focus on plant breeding and horticultural crop programs [Vaccinium wild species, adapted to live in unique ecosystems like the páramo, are scarce. In a previous study, we used 11 heterologous (non-species-specific) SSR markers developed for V. corymbosum and found a moderate degree of V. floribundum genetic diversity in three provinces in northern Ecuador. However, this approach presented some limitations, mainly regarding the use of heterologous markers and the restricted sampling range [V. floribundum with the use of species-specific SSR markers across the complete distribution range of the species in Ecuador. For this purpose, we designed and developed 30 species-specific SSR markers and used a subset of these (n = 16) for the genetic characterization of 100 mortiño individuals from different localities across all ten provinces in the Ecuadorian Highlands. The data presented here contribute to our understanding of the genetic makeup of this species in Ecuador.The genetic diversity of a number of species of the genus programs –35. Howeng range . TherefoFrom June 2017 to August 2018, a total of 100 samples from individual plants were obtained from 27 collection sites (CS) distributed across ten provinces in the Ecuadorian Highlands: Carchi, Imbabura, Pichincha, Cotopaxi, Bolivar, Tungurahua, Chimborazo, Cañar, Azuay, and Loja. Three to seven samples were collected at each site . SamplesFive to seven young leaves were collected from each individual and transported at 4°C to the Plant Biotechnology Laboratory at Universidad San Francisco de Quito, where they were stored at -20°C. Total genomic DNA was extracted from frozen leaves using the CTAB method . DNA wasV. floribundum using the Galaxy-based bioinformatics pipeline reported by Griffiths et al (2016) [m) of 62°C  and a maximum difference among primer pairs of 3°C.Microsatellite markers were developed from a single DNA extraction of l (2016)  at the Ul (2016) , then fil (2016) . Microsal (2016) , configul (2016) ,43. Impel (2016) . Paired l (2016) . This piA total of 2 x 1,680,936 raw sequence reads was produced, with none flagged as poor quality. Sequence length ranged from 50–300 bp with a reported %GC content of 39. A total of 358 primer pairs were suggested, all amplifying SSRs with simple motifs. The majority of SSRs (333) were 2 bp motifs, but there were 22 with 3 bp motifs, one with 4 bp motif, one with 5 bp motif, and one with 6 bp motif.From the initial 358 microsatellites markers, the first 30 were selected as candidates for our study; 25 with three or more base pairs motifs (3–6 bp), and five with 2 bp motifs. The melting temperature (Tm) of the primer sets ranged from 58°C to 63°C, with lengths between 18 and 25 bp . The pri2, 0.15μM modified locus-specific forward primer, 0.5μM reverse primer, 0.5μM universal Tail A with a fluorophore , 0.2mM dNTPs, and 1U Platinum Taq Polymerase. The Tail A primers label the amplified products at the 5’ end through a three-primer system as reported by Blacket et al (2012) [Total genomic DNA (20 ng) was amplified in a 30μl reaction containing 1X PCR buffer, 2mM MgCll (2012) , as the Amplification consisted of 15 min at 94°C, followed by 40 cycles of 30 sec at 94°C, 90 sec at the standardized annealing temperature (58–63°C) , 60 sec polysat R-based statistical package [E), observed heterozygosity (HO) and the number of alleles (NA) per locus and per collection site were estimated using the adegenet [hierfstat [poppr R package [R), standardized to the minimum sample size (n = 3) through rarefaction, was estimated using the diveRsity R package [IS) were calculated using the adegenet R package [GeneMarker software (SoftGenetics LLC) was used to identify individual alleles and allele size. Polymorphic information content values for each SSR locus were obtained with the  package . Expecteadegenet  and hierierfstat  R packag package , while n package . Mean al package . Fixatio package  and descade4 package [ggplot package [DISTRUCT [A principal coordinate analysis (PCoA) was performed with the  package  and the  package . In addi package . The pot package  using th package . The ind package  and the DISTRUCT .poppr was used to perform an analysis of molecular variance (AMOVA) to evaluate genetic differentiation among and within the identified genetic clusters [ST genetic distances between these clusters were estimated with the hierfstat package [E), observed heterozygosity (HO), number of alleles (NA), number of private alleles (NPA) and the mean allelic richness (AR) standardized (n = 14) through rarefaction, were also calculated for each of the clusters. The differences in the HE between clusters were calculated through Monte-Carlo tests using adegenet R package [diveRsity R-based package [The R package clusters . Pairwis package  using th package . The sam package . Expecte package . Bonferr package , based oape R statistical package [V. floribundum, stats [ggpubr R [E) of individuals found at any given collection site and the mean elevation of that site.Given the geographic coordinates and the elevation (masl) of each sampled individual, a Mantel test was performed  using the  package , to evalm, stats  and ggpuggpubr R  statistiA), with an average number of 11.2 alleles per locus  (E) estimates ranged from 0.26 for Mo001 to 0.91 for Mo025  after genotyping, and were selected for further analysis. These markers proved to be polymorphic and highly informative with an average polymorphic information content (PIC) value of 0.69. Mo025 (PIC = 0.90) and Mo020 (PIC = 0.88) were the most polymorphic markers, while Mo011 was the least polymorphic (PIC = 0.26) . A totald Mo025) . Null ald Mo025) . Expecteor Mo025 .A = 65; HE = 0.6) and CS14 (Carihuairazo) , in the central region, presented the highest number of alleles and expected heterozygosity estimates. In contrast, CS24 (Cruces)  and CS23 (Cajas)  in the southern region, and CS12 (Quilotoa)  in the central region presented the lowest genetic diversity indicators (R) per collection site, an estimate that corrects for the sampling imbalance between collection sites (E = 0.73)  (Ndicators . Similaron sites . The glo = 0.73)  revealedA principal coordinate analysis (PCoA) was conducted to test genetic similarities between individuals and to define possible groupings. Two-dimensional plots were obtained  for the V. floribundum in the Ecuadorian Highlands. The STRUCTURE results showed four possible genetic lineages (K = 4), based on the highest ΔK calculated through the Evanno method [K = 3  (ST genetic distances show that greater differentiation occurs between cluster 4 and clusters 1 (FST = 0.171), 2 (FST = 0.249) and 3 (FST = 0.230). The lowest genetic distance was found between clusters 1 and 2 (FST = 0.078), suggesting higher genetic similarities between these clusters  shows most of the variation (78.64%) occurs within the genetic clusters, and only 21.36% between these clusters (= 0.001) . Correctclusters .ST were estimated according to Weir and Cockerham (1984) and corrected for null alleles through FreeNA software. Correction for null alleles decreases the possibility of bias in the estimation of genetic differentiation values. Higher values indicate greater genetic differentiation between the analyzed clusters.The FA) ranged from 57 alleles for cluster 4 to 120 alleles for cluster 2. The private alleles for each genetic cluster ranged between 8 (14.03%) for cluster 4 and 25 for cluster 2 (20.83%). The highest expected heterozygosity (HE) estimate was observed for clusters 1 (HE = 0.67), 2 (HE = 0.65) and 3 (HE = 0.68); with cluster 4 showing a significantly lower genetic diversity  (E were found between cluster 1 and 2 (p = 0.259), 1 and 3 (p = 0.777), or 2 and 3 (p = 0.331). The lowest diversity indices were found in the four locations assigned to cluster 4   . No statnd CS24) .A directional relative migration network was inferred for the four genetic clusters, given their spatially structured distribution. These results revealed different levels of gene flow and possible migration rates between the four genetic clusters . As obser = 0.29; p = 0.0001) between individuals, suggesting that a possible isolation by distance dynamic might explain the observed geographic structure of the studied populations. Furthermore, we observed a significant negative correlation between elevation and genetic diversity  among the different collection sites through the three regions  (E = 0.968–0.00015*Elevation) can be approximated as a mean genetic diversity decrease of 0.00015 per every 1-meter increment in elevation along our altitude sampling range.Mantel test results revealed a significant correlation between genetic and geographic distances ( 0.0045) . The regA = 11.2 alleles per locus) and the global expected heterozygosity (HE = 0.73) found in this study reveal a high degree of genetic diversity for V. floribundum in the Ecuadorian Highlands. In a previous study, where we assessed the genetic diversity of V. floribundum in the north of Ecuador, we reported on average 6.1 alleles per locus and an expected heterozygosity (HE) of 0.49, representing a moderate degree of genetic diversity [V. corymbosum) and the collection of individuals in a limited geographical area. The use of heterologous markers could generate the presence of null alleles that tend to bias the estimation of heterozygosity and inflate FST values [The mean number of alleles  in this study is similar to the He reported for V. sieboldii (HE = 0.73) and V. ciliatum (HE = 0.75), both wild endangered species [The total number of alleles obtained for m clones , and higultivars . Severalultivars ,71, whic species . A high  species .V. floribundum individuals were grouped into four genetic clusters (ST≤0.249). This cluster comprises individuals from CS12 (Quilotoa) in the central region, and CS22 (Toreadora), CS23 (Cajas) and CS24 (Cruces) in the southern region. Beyond their genetic similarities, these individuals share the uppermost elevations among the collection sites included in this study (3929–4160 masl) , with  cluster 4 presenting the lowest expected heterozygosity (HE = 0.27±0.06) and exclusive allele number (NPA = 8) (Castanopsis eyeri and Daphniphyllum oldhamii) suggest that factors like temperature, phenological characteristics, low population densities and small effective population sizes could play a role in the isolation and reduction of genetic diversity of populations at higher elevations [The Bayesian approach in STRUCTURE showed that clusters . From thclusters , and gre60 masl) . This hierpáramo ,74, and NPA = 8) . Observaevations .superpáramo) separated by lower elevation valleys can behave like islands where gene flow between different locations is decreased. This kind of “sky island” dynamics can further explain the larger divergence between the populations from cluster 4 and the rest of the mortiño genetic diversity in the Ecuadorian Andes.Furthermore, the evident differentiation of this cluster of high-elevation populations can be explained by an isolation by elevation model where gene flow from lower to higher elevations is reduced ,76. Diffr = 0.29; p = 0.0001), suggesting that an isolation-by-geographic distance model also explains part of the population structure of V. floribundum in Ecuador. Under this model, physical barriers could play an additional role in the reduced genetic flow between clusters [ST ≤ 0.120) (ST = 0.078) between clusters 1 and 2 could be explained by the emergence of mountainous barriers in the Andean Highlands during the Miocene, as we previously suggested [páramos during this period could have caused V. floribundum populations to be separated by ice masses, reducing or preventing their gene flow and partially isolating both clusters [Nonetheless, the questions of the similarities between geographically distant locations within cluster 4  and the genetic patterns observed in the remaining clusters persist. These patterns might be associated with the geological history and geography of the Andean region, important forces that drive the evolution and distribution of different species in the region ,80. In aclusters ,82, part≤ 0.120) . The moduggested  based onuggested ,84. The clusters . During clusters .V. floribundum to different ecosystems, prompting both diversifying selection and convergent evolution. For instance, the physical characteristics of different soil types, determined by their distinct geological origins, partially overlap with the distribution of specific genetic clusters. For instance, individuals from clusters 1 and 2 grow over Andisols developed from volcanic projections characteristic to the northern Ecuadorian highlands, encompassing the northern region and most of the central region in this study (CS1-18). The presence of multiple volcanoes and eolian deposits of volcanic material in these areas have generated a distinct pyroclastic layer over the soils [ST1 = 0.114; FST2 = 0.120), as the unique characteristics between the volcanic and metamorphic rock could act as a force for diversifying selection. Similarly, extreme climatic conditions at higher elevations  can lability –90. Thislability ,91.mortiño individuals living in specific habitats (such as the superpáramo) and explore the relationship between genetic diversity and the origin of the soils where the mortiño grows.Future studies should address the occurrence of different adaptations in V. floribundum displays a high degree of genetic diversity in the Ecuadorian Highlands. Population structure analyses revealed that individuals grouped into four genetic clusters, distributed according to their geographic location . While genetic distances are associated with geographic distances between individuals, it is noteworthy that elevation also has an effect on the genetic diversity of the populations. Interestingly, the group identified in the PCoA  was consistent with the cluster 4, found through the bayesian analysis. Individuals from this cluster were collected at higher altitudes, presented lower genetic diversity and a higher degree of genetic differentiation from the remaining populations, as well as low gene flow with the populations assigned to the other clusters. It is evident that the structure of V. floribundum populations in Ecuador was driven by an interplay between isolation-by-distance and isolation-by-elevation dynamics, and the geography of the Andean region possibly plays a key role in driving these dynamics through a combination of different geographic features, historical climatic fluctuations and the establishment of scenarios favoring the action of adaptive processes.The genetic data presented in this study demonstrates that V. floribundum in the ten provinces that encompass the Ecuadorian Highlands, the results presented here could serve as a basis for the development of conservation programs for this species and its habitat at the páramo, a relevant and unique ecosystem.Furthermore, the high values of genetic diversity that were found are encouraging for the conservation of this species. Since this study assessed the genetic diversity and population structure of wild S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 TableMean: the mean frequencies over the four genetic clusters.(PDF)Click here for additional data file.S4 TableA ; NPA ; AR ; HO (observed heterozygosity); HE (expected heterozygosity); and FIS (fixation index) are given for all 100 individuals genotyped at 16 SSR loci across 27 collection sites in three geographic regions.N(PDF)Click here for additional data file.S5 Table(PDF)Click here for additional data file.S6 Table(PDF)Click here for additional data file.S7 Table(PDF)Click here for additional data file.


Previous studies have investigated various factors that can determine the attitudes of the citizens considering genetic testing. However, none of them investigated how these attitudes may differ between the Visegrad countries.In this cross-sectional study a questionnaire developed by Dutch researchers was translated and used in Hungary, Slovakia, Czechia and Poland. In each country 1000 adult citizens were asked on the topics of personal benefits regarding genetic tests, genetic determinism, and finally, the availability and usage of genetic testing. Multivariate robust regression model was created including several possible influencing factors  to identify the possible differences between the four countries.The Hungarian citizens had the most positive opinion on the personal benefits of genetic testing followed by the Czech, Slovak and Polish. All differences were significant in this regard. Considering genetic determinism, the Slovak citizens had a significantly firmer belief in this issue compared to the Hungarians. No other significant differences were observed in this domain. On the topic of the availability and use of genetic testing the Hungarian citizens had the most accepting opinion among the four countries, followed by the Czech citizens. In this domain the Polish and Slovak answers did not differ significantly from each other.Significant differences were observed even when considering various confounding effects. As the underlying reasons for these discrepancies are unknown, future studies should investigate this enigma among the four countries. More than 15 years after the completion of Human Genome Project not only health professionals, but also the public have to face the more and more widespread implications of individualised medicine. Rapid development of genome-based diagnostics in the last decades resulted in the growing number of diseases with appropriate genetic testing . Many arThese findings are indicating that age, gender and educational level can significantly influence the attitude towards genetic testing. Younger and higher educated women show more accepting attitude , 10–12. As Condit suggests, attitudes towards genetic testing should not be considered as stable or univocal . AccordiTherefore, to get up-to-date information and to understand better the citizens’ attitude towards genetic testing, an international survey was conducted in the countries of the Visegrad group, i.e. in Hungary, Slovakia, Czechia and Poland (collectively known as the V4 countries) , 27. TheA questionnaire based international cross-sectional study was conducted in Hungary, Slovakia, Czechia and Poland. Private polling companies were contracted to collect data within the four countries, in Hungary the SZLEM Service L. P., while in Slovakia, Czechia and Poland the Český Národní Panel Ltd. In every country, the same study design and instrument were applied. The request was made to the companies to fill out the questionnaire with 1000 citizens per each country between the age of 18 and 65. The data collection was executed on an online market research panel, in which the panel members preliminary gave their consent for the data processor to handle personal data for the purpose of inviting them to market researches. In all countries the panels include citizens of the country from all demographic groups representing the whole population, 17,000–50,000 members . These wBefore the questionnaires were sent out ethical approval had been obtained in those countries in which non-intervention studies require one. In each country the online survey was conducted from May to June 2018. Answering every question was made mandatory for the participants.Parts of a questionnaire was used in this study which was developed by Henneman and her colleagues . The oriIdentical practice guide was used for translating and adapting the questionnaire for Hungarian, Slovak, Czech and Polish languages. First, two independent translators translated the English questionnaire into the target language. Then, based on the quality of the translations, the two were merged into a single one . In ordeWhile conducting the descriptive data analyses for the characteristics of the respondents and for the given answers to each question the answers of the four countries were not aggregated and were treated independently from one another. The internal reliability of each domain was tested with Cronbach’s alpha. The alpha value was deemed acceptable if it was equal or above 0.60 . The valThe validity of the four translated questionnaires were assessed with confirmatory factor analysis. For this aim Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Standardized Root Mean Square Residual (SRMR), and Root Mean Square Error of Approximation (RMSEA) were used and the thresholds were set based on the literature –37.P-value of ≤0.20 , alIn accordance with the literature, sex, age and education level significantly impacted the opinion on both the personal benefits and the usage of genetic testing –18. HoweThe finding that those who had previous genetic test within the nuclear family  have a more negative opinion on both the personal benefits and the usage of genetic testing could be explained with the hype around genetic testing . After tAs expected from one of the Dutch studies , being rPrevious surveys have already demonstrated that when comparing the citizens' attitude of various countries on the topic of genomic testing and genomic technology a noteworthy difference can be observed , 26. HowThe study had many limitations worth highlighting. First, as indicated previously, due to the nature of ecological studies we could only identify and describe the differences, but not the reasons why do these differences exist. Second, citizens above the age of 65 were not involved in the analyses because the companies conducting the surveys did not include them in their database. Since higher age is associated with a more rejective attitude towards genetic testing , 40, theThird, although the results of the confirmatory factor analysis regarding the four translations were close to the recommended thresholds, nearly none of these met the established criteria –37. BecaHowever, besides these limitations, there are several strengths that are also worth mentioning. First and foremost, the study had a large sample size from all four countries which was representative not only by age and sex but the number of residents living in the settlement and the regions were also considered. Thus, surveys in all the four countries could be deemed as comprehensive at a national level. Second, the questions adapted were proven to be reliable in the Dutch studies , 25, 32,Overall, this study has demonstrated that noteworthy differences exist between the Visegrad countries regarding the citizens’ attitude towards genetic testing. As this research cannot explain the underlying reasons behind the differences, future studies should investigate this question more thoroughly. In order to gain a more precise understanding we recommend that these studies specify the type and purpose of genetic testing, the circumstances these tests are performed, and the possible underlying health conditions of the respondents. Furthermore, an in-depth comparison between Hungary and Slovakia would be highly recommended as the differences were most noteworthy between these two countries.Although citizens become more open to genetic technologies as time passes , and des


The Iron and Classical Ages in the Near East were marked by population expansions carrying cultural transformations that shaped human history, but the genetic impact of these events on the people who lived through them is little-known. Here, we sequenced the whole genomes of 19 individuals who each lived during one of four time periods between 800 BCE and 200 CE in Beirut on the Eastern Mediterranean coast at the center of the ancient world’s great civilizations. We combined these data with published data to traverse eight archaeological periods and observed any genetic changes as they arose. During the Iron Age (∼1000 BCE), people with Anatolian and South-East European ancestry admixed with people in the Near East. The region was then conquered by the Persians (539 BCE), who facilitated movement exemplified in Beirut by an ancient family with Egyptian-Lebanese admixed members. But the genetic impact at a population level does not appear until the time of Alexander the Great (beginning 330 BCE), when a fusion of Asian and Near Easterner ancestry can be seen, paralleling the cultural fusion that appears in the archaeological records from this period. The Romans then conquered the region (31 BCE) but had little genetic impact over their 600 years of rule. Finally, during the Ottoman rule (beginning 1516 CE), Caucasus-related ancestry penetrated the Near East. Thus, in the past 4,000 years, three limited admixture events detectably impacted the population, complementing the historical records of this culturally complex region dominated by the elite with genetic insights from the general population. The ancient Near East has been at the center of interaction between the ancient world’s civilizations and was ruled at different times by the Egyptians, Hittites, Assyrians, Babylonians, Persians, Greeks, Romans, Arabs, Crusaders, Mamluks, and Ottomans, most of whom left a permanent cultural impact on the local population. However, their genetic contribution is not as evident: our previous ancient DNA (aDNA) work showed that people who live in the Near East today derive ∼90% of their ancestry from the local Bronze Age population that preceded all of the aforementioned historical conquests.To address these questions, we have now sequenced the genomes of ancient individuals who lived between 800 BCE and 200 CE at one of four different time periods: the Iron Age II (1000–539 BCE), the Iron Age III (539–330 BCE), the Hellenistic period (330–31 BCE), and the early Roman period (31 BCE–200 CE) . These d,We sampled the petrous portion of the temporal bones from 67 individuals buried in Beirut  and S2, ,,,,We combined the new data with published ancient and modern data, creating two datasets: set 1 included 2,012 modern humansqpWave,,qpAdm.We then estimated kinshipf4-statistics of the form f4, in which a result significantly different from zero could indicate that genetic changes related to “Ancient” (an ancient population in our dataset) have occurred between two successive periods in Lebanon. We found that significant genetic changes that were marked by an increase in Eurasian ancestry related to ancient Europeans and ancient Central Asians occurred after the Bronze Age and starting from the Iron Age II  is significantly negative (Z score = −4.13), but f4 has a value not significantly different from zero (Z score = 0.317), suggesting that either ancient Egypt did not receive the Eurasian gene flow that the Levant received during the Iron Age or that the Eurasian ancestry was replaced in Egypt as in Ashkelon, where in contrast to the Beirut_IAII, the European-related ancestry was no longer significant in the Ashkelon Iron Age II population.Having genetic representation from eight consecutive time periods A, we wern Age II A. We didn Age II B, and thn Age II C and usepAdm see  to explopAdm see  and S7. pAdm see B. We reppAdm see D. A potef4-statistics showed symmetry between the Beirut_Hellenistic and Beirut_ERoman populations  because several individuals had overlapping radiocarbon dates and the ulations . We founulations C. We theulations E and S9,lineages . Haplogrlineages  the coallineages . The pref4-statistics (We next tested the genetic changes between the Hellenistic/early Roman period and the late Roman period (Qed_LRoman) and found little genetic differences from the atistics , which iatistics . Howeveratistics .Z score = −2.4) and accounts for ∼2.9% of Lebanon_Medieval’s ancestry when ancient East Africans are used in the admixture model  under accession number ENA: ERP121575. Aligned sequences, genotypes, and imputed genotypes can be obtained from the corresponding author M.H.