Echium plantagineum L. (Boraginaceae)), is an herbaceous annual native to Western Europe and northwest Africa. It has been recorded in Australia since the 1800’s and is now a major weed in pastures and rangelands, but its introduction history is poorly understood. An understanding of its invasion pathway and subsequent genetic structure is critical to the successful introduction of biological control agents and for provision of informed decisions for plant biosecurity efforts. We sampled E. plantagineum in its native (Iberian Peninsula), non-native (UK) and invaded ranges  and analysed three chloroplast gene regions. Considerable genetic diversity was found among E. plantagineum in Australia, suggesting a complex introduction history. Fourteen haplotypes were identified globally, 10 of which were co-present in Australia and South Africa, indicating South Africa as an important source population, likely through contamination of traded goods or livestock. Haplotype 4 was most abundant in Australia (43%), and in historical and contemporary UK populations (80%), but scarce elsewhere (< 17%), suggesting that ornamental and/or other introductions from genetically impoverished UK sources were also important. Collectively, genetic evidence and historical records indicate E. plantagineum in southern Australia exists as an admixture that is likely derived from introduced source populations in both the UK and South Africa.Paterson’s curse ( Invasive species can be defined as those that are distributed beyond their native ranges and pose a significant negative impact on indigenous ecosystems, public health or agricultural production , 2. AnnuRecent investigations of invasive species have focused on anthropogenic activities that directly or indirectly drive global species dispersal –4, and oAccurate reconstruction of invasion histories is also fundamental to the understanding of biological invasions . SuccessSilene latifolia Poir. suggested that this species originated from Europe and was introduced on multiple occasions to the Great Lakes region (USA) via western North America , 40trnL h) diversity [FST) based on haplotype diversity [FST estimates was determined by permutation  as implemented in ARLEQUIN. Population genetic structure between historical (since 1800s) and recent (2016) samples collected from the UK was also estimated. Standardized haplotype richness was calculated for each population after rarefaction to the UK samples size of n = 25 using Contrib 1.40 [Nucleotide (π) and haplotype , but much less abundant in the Iberian Peninsula (17%) and in South Africa (4%). On the Iberian Peninsula, this haplotype was mainly found in southern Spain (7 out of 8). In contrast, Hap 3 was the dominant genotype observed in the Iberian Peninsula (45%), and the distribution of all 12 haplotypes found in South Africa was relatively uniform (h) in E. plantagineum was found among samples collected from the Iberian Peninsula (0.7567), Australia (0.7657) and particularly in South Africa (0.9037); UK samples demonstrated much lower haplotype diversity (0.3567) (π) were observed among samples in all regions .Region-specific haplotypes  were found in the Iberian Peninsula, Australia and South Africa, respectively Figs . Most Au uniform . High le(0.3567) . Standar(0.3567)  revealed regions . Echium FST) were significant , and also evident between the Iberian Peninsula and South Africa . In contrast, higher levels of genetic structure were evident in comparisons between reciprocal populations of these two groups .All pairwise estimates of population genetic structure .Vouchered specimens at the Natural History Museum, London dating back to the 1800’s provided an opportunity for comparison of historical (N = 15) and recent (N = 10) genetic structure of E. plantagineum to Australia, through incorporating considerable numbers of field and herbarium samples spanning nearly two centuries. Sampled populations from the Iberian native range were representative of a significant range of soil types and climatic variation across the peninsula. We were able to sample an important population in the UK where the species is currently rare and endangered [E. plantagineum was more closely aligned with populations collected in the UK, likely due to the prevalence of Hap 4 in both Australia and the UK, in contrast to those collected in the Iberian Peninsula and South Africa  and gorse (Ulex europaeus L.). The majority of vessels laying over in the Cape region of South Africa in the 16th to 17th century originated from areas in western Europe [E. plantagineum. Given the considerable time frame for active maritime transport from Europe to South Africa, even the rarest native haplotypes of E. plantagineum could have been successfully introduced to South Africa. Thus, the high level of genetic diversity and number of haplotypes found in South Africa, and the relative similarity in genetic structure between South African and the Iberian Peninsula, both support the hypothesis of transport of E. plantagineum from its native range to South Africa over a prolonged period of time. Despite widespread geographical sampling across the Iberian Peninsula, six haplotypes presented in the non-native ranges  were not detected. This could reflect low current frequencies of these six haplotypes in the native ranges and inadequate sampling intensity, or subsequent extinction of the haplotypes. Therefore, more intensive sampling could help to resolve this question. Additional studies focusing on the species phylogeography in the native range, encompassing a wider geographical extent in the native or anciently introduced areas in the Mediterranean basin, might also assist in detection of possible source locations. The presence of these six haplotypes in introduced ranges further suggests that multiple founder events associated with transport from South Africa may have allowed for the fortuitous entry of low frequency haplotypes from the native range and subsequently their greater representation in introduced localities.Since 1488, the Cape region of South Africa is noted as the most important seaport connecting Europe and Asia, and has long been a reservoir for numerous invasive European plants  that aren Europe  that werE. plantagineum, complex introduction pathways of invasive species have been found for many other plant invaders to Australia such as Lantana camara L. [R. fruticosus [U. europaeus [Rubus fruticosus and U. europaeus, which are native to western Europe, were both proactively introduced to Australia in 1800s [Lantana camara which is native to central America, was first introduced to western Europe and then to Australia via South Africa [E. plantagineum, it is clear that South Africa has played a critical role in Australian plant invasions.Similar to amara L. , R. frututicosus  and U. europaeus . Interesin 1800s , 59. Lanh Africa . TogethePrevious studies showed that some invasive plants developed more competitive traits including greater fertility and highE. plantagineum was reportedly cultivated as an ornamental plant in the UK since 1658 [th century through to the present. The low level of genetic diversity observed in UK populations , despite the fact that E. plantagineum has a minimal and restricted distribution in the UK. It is possible that Hap 4 was widely cultivated/naturalised as an ornamental species in the UK, followed by scattered introduction events of several additional haplotypes. What is clear from our results is that the UK is not a reservoir of genetic diversity for E. plantagineum. Low genetic diversity and absence of exclusive haplotypes do not support the presence of the species in the region over a long evolutionary time frame. In any case, it remains an open question as to whether some British populations originated from a relatively recent natural expansion from the mainland or are the result of human-mediated colonization. Additional research focused on the phylogeography and arrival of the species to the UK could properly address this question.Taxonomists and botanists in the UK have long speculated whether E. plantagineum, or Paterson’s curse, in Australia. Apart from proactive introduction as an ornamental plant in the 1840’s, accidental introduction associated with livestock movement and transport also appeared to play a critical role in the spread of this species across Australia. The high level of diversity detected in Australia is an indication that genetic bottleneck events (induced either by founder effects or selective sweeps) are unlikely to have affected the effective population size of the weed in Australia either during or after its introduction into the country. This suggests that environmental conditions in regions of establishment in Australia are broadly favourable to the sustained continuation of the species, creating the possibility of future spread and adaptation across Australia. Although our results evince multiple introduction pathways of the species to Australia, it would be still interesting to intensify the sampling both in the native and anciently introduced ranges across the Mediterranean basin and the Macaronesian archipelagos to unravel the distribution of haplotypes in the region and to assess the current occurrence of the six Australian/South African haplotypes undetected in our study in the Iberian native range. Our work offers a framework for further studies on the evolutionary processes involved in the invasiveness of the species, to address the question of whether pre-introduction adaptation/selection has occurred in populations from the UK or South Africa, which may have accelerated the invasion of Australia by E. plantagineum.Our findings demonstrate the important role of both UK and South African populations in shaping the genetic structure of the invasive weed species S1 Table23 were also included in this study.An additional 129 samples from Australia (accession numbers KX012236-KX012622) sequenced in our previous study(DOCX)Click here for additional data file.S2 TableM13-vector sequences (underlined) were added to the 5’ end of forward and reverse primers to simplify the sequencing process.(DOCX)Click here for additional data file.S3 Tablenh: number of haplotypes found in each region. * indicates the presence of particular haplotype.(DOCX)Click here for additional data file.S1 FigA large blank world map with oceans marked in blue.PNG” sourced from Wikimedia Commons, used under Public Domain license and modified using Adobe Illustrator CS5.This map is a derivative of “(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S1 Appendix(ZIP)Click here for additional data file.


Cyanistes caeruleus) collected across three breeding seasons. Additionally, we tested whether brood size manipulation affects the magnitude and direction of the relationship between genetic similarity of mates and offspring performance. Sixteen microsatellite markers were used to measure genetic similarity between biological parents. We found that the genetic similarity of parents negatively affects offspring immune response and this effect was independent of the experimental brood size manipulation.In birds, as in many other taxa, higher genetic similarity of mates has long been known to reduce offspring fitness. To date, the majority of avian studies have focused on examination whether the genetic similarity of social mates predicts hatching success. Yet, increased genetic similarity of mates may also reduce offspring fitness during later life stages, including the nestling period and beyond. Here, we investigated whether parental genetic similarity influences offspring performance using data from free‐living blue tits ( This is one of a few studies on natural populations of birds showing negative effects of parental genetic similarity on offspring performance. Specifically, we found that a higher genetic similarity of parents negatively affects offspring immune response to a novel antigen—phytohaemagglutinin. Specifically, we investigated whether genetic similarity within the pair influences offspring quality in terms of body weight, tarsus length, and immunocompetence. We expected to see negative effects of parental genetic similarity on these three measured traits. If the relationship between genetic similarity of mates and offspring quality and its magnitude depend on environmental conditions, one can expect a significant interaction between parental genetic similarity and environmental conditions. We therefore manipulated the brood size to alter conditions of nestling growth, and we expected to see a negative relationship between genetic similarity of mates and nestling quality that will be particularly pronounced among offspring from experimentally enlarged broods.Here, we investigate the relationships between genetic similarity of parents and offspring performance in the blue tit . The mean value of the three repeats was used in further analyses.Our study was carried out on Gotland, Sweden , during April–June, from 2009 to 2011. Adult birds were caught while feeding nestlings (May–June), using nest‐box traps or mist nets. Birds were bled by brachial venepuncture for genetic analyses. All nestlings were weighed on days 2 and 14 prior to the blood sampling and on days 11 through 12 as a part of the immune response assay. Additionally, on day 14 after hatching, nestlings were measured for tarsus length and ringed. To assess nestling T‐cell‐mediated immune response, on day 11 posthatching nestlings were injected with a nonpathogenic antigen, phytohemagglutinin (PHA) into their right wing web, and 24 hr later, the thickness of the wing web was measured with a pressure‐sensitive calliper  using a panel of sixteen autosomal microsatellite markers: Ase18; PmaTGA45; PmaGAn40; PmaGAn27; PCA7; PCA4; PCA9; Pocc6; PMAC25; Mcyl4; PK12; Cdi31; PCA3; Pma303; Pocc1; and Pca8. The PCRs were performed with the Qiagen multiplex PCR kit  as described in the study of Olano‐Marin et al. (r) and the 95% confidence intervals using the inbreed R package for R  .Sexing of nestlings was performed by using the P2–P8 primers  coeffic2.3p > 0.05) for all the measured traits and was thus deleted from the final model (Table We used a linear mixed model (LMM) in R  as a covariate. The data met the assumptions of a linear mixed model . We used the “MuMIn” package in R to estimate the coefficient  Bartoń, . Sample  Bartoń, . We conf3The interaction between parental genetic similarity and experimental treatment appeared nonsignificant for all measured traits Table  and was SD: control nests 11.25 ± 0.86 g; experimental nests 10.84 ± 1.14 g   (Edly‐Wright et al., Here, we showed that genetic similarity between pair members had a significant effect on offspring immunocompetence in blue tits. Specifically, in line with our prediction, we found a negative relationship between parental genetic similarity and offspring PHA immune response Figure . SimilarIn our study, the negative relationship between parental genetic similarity and offspring fitness‐related traits was visible only in the immune response. Similarly, in our previous study on blue tits we found that the immune response of extra‐pair young to phytohemagglutinin was stronger than that of within‐pair half‐sibs, but at the same time the superior quality of EPY was not confirmed in terms of body mass and tarsus length. This could theoretically be due to a causal relationship between the individual level of genetic diversity and the PHA immune response (Fossøy, Johnsen, & Lifjeld, In conclusion, this is one of a few studies on natural populations of birds showing negative effects of genetic similarity between pair members on offspring performance. More importantly, our data indicate that the effects of parental genetic similarity on offspring performance‐related traits are not limited to embryonic stage but are also important during later life stages. However, we failed to find any evidence that experimentally altered environmental conditions affect the relationship between parental genetic similarity and offspring fitness. Detailed studies are still needed to further test under which environmental conditions we may expect the relationship between the genetic similarity of mates and offspring fitness to vary.None declared.AA—contributed to the design of the work, genetic and statistical analysis, and interpretation of data, and wrote the manuscript; SMD—verified the analytical methods, designed the figures, and interpreted the data; SM—involved in genetic analysis; LG—supervised the long‐term study and worked on the manuscript; MC—contributed to the design and implementation of the research, to the analysis of the results and to the writing of the manuscript. Click here for additional data file.


SGDCs) is a challenging task that provides insight into the way that neutral and adaptive processes influence diversity at multiple levels. Genetic and species diversity are comprised by components that respond differently to the same ecological processes. Thus, it can be useful to partition species and genetic diversity into their different components to infer the mechanisms behind SGDCs. In this study, we applied such an approach using a high‐elevation Andean wetland system, where previous evidence identified neutral processes as major determinants of the strong and positive covariation between plant species richness and AFLP genetic diversity of the common sedge Carex gayana. To tease apart putative neutral and non‐neutral genetic variation of C. gayana, we identified loci putatively under selection from a dataset of 1,709 SNPs produced using restriction site‐associated DNA sequencing (RAD‐seq). Significant and positive relationships between local estimates of genetic and species diversities (α‐SGDCs) were only found with the putatively neutral loci datasets and with species richness, confirming that neutral processes were primarily driving the correlations and that the involved processes differentially influenced local species diversity components . In contrast, SGDCs based on genetic and community dissimilarities (β‐SGDCs) were only significant with the putative non‐neutral datasets. This suggests that selective processes influencing C. gayana genetic diversity were involved in the detected correlations. Together, our results demonstrate that analyzing distinct components of genetic and species diversity simultaneously is useful to determine the mechanisms behind species–genetic diversity relationships.Disentangling the origin of species–genetic diversity correlations ( Yet, historical separation between genetic and organismal ecology research has limited the development of a cohesive framework for multilevel analysis  Previous evidence suggests that neutral processes, dispersal in particular , cause genetic structure in C. gayana . (b) High Andean wetlands in this region are highly fragmented and experience highly variable environmental conditions due to large‐scale climatic variations and local abiotic fluctuations resulting from the sharp orography of the Andes. As a result, we expect the footprint of selection on adaptive genetic variation of high Andean wetland populations to be strong and to cause significant deviating patterns between neutral and adaptive genetic diversity.In this study, we focused on the species–genetic diversity relationship between a high Andean plant community and the herbaceous grass‐like plant 22.1Carex gayana is an herbaceous perennial sedge species of the Cyperaceae family inhabiting high‐elevation wetlands of the Andes Mountains. It is monoecious and displays both sexual reproduction and vegetative propagation through small rhizomes  at Cornell University for genotyping‐by‐sequencing . Samples from site 13 . PstI was not retained because the amplified library presented adapter dimer peaks and a smaller fragment size distribution. Although the four‐base cutter ApeKI generated the library with the largest fragment pool, we selected the six‐base cutter EcoT22I because C. gayana genome size and genetic diversity level were not known, hence ensuring a higher coverage per SNP locus due to the lower proportion of amplified fragments. Libraries were multiplexed with 95 samples assigned to each of two lanes and sequenced on an Illumina HiSeq 2000 platform as single‐end, 100 base pair reads. Lane 1 was composed mainly of samples from the Copiapo, Choapa, and Elqui river basins, and lane 2 mainly of samples from the Elqui, Huasco, and Limarí basins . The wetland surface estimates were calculated in Google Earth Engine Explorer based on Google Earth surface1 and surface2 from NDVI.Ten environmental variables were used to test for genome–environment–associations: mean annual precipitation (MAP), mean average wind speed (MAWS), number of days with snow cover (SnowNDays), mean annual temperature (MAT), soil moisture (TCI), slope, aspect, productivity (NDVI), and two independent estimates of wetland surface. MAP was estimated from average monthly precipitation (mm), calculated by interpolation of precipitation gauge network measurements over the 32‐year period from 1975 to 2006 . To identify loci deviating from neutral expectations, we combined two individual‐based approaches: one centered on the identification of outlier loci with respect to population structure and the second on genotype–environment associations. For the first approach, we used pcadapt , a genome–environment association (GEA) approach to distinguishing candidate loci under selection based on correlation between genotype and environmental factors expected to impose natural selection. RDA is a canonical ordination technique where, first, response variables (multiple loci) are modeled as a function of linear combinations of the predictors , then a PCA of the fitted values produces the RDA components that best explain, in sequential order, the variation among the fitted genetic values  over the loci using gstudio  evaluated after complete drying of the vegetal material in an oven at 70°C. Species α‐diversity was evaluated for each wetland as species richness (S) accumulated over the five quadrats and as species evenness using Pielou's evenness metric,The plant species assemblage was surveyed in each wetland based on five 30 × 30 cm quadrats. Details about the sampling strategy and method can be found in Bertin et al. . BrieflyH′ is the Shannon diversity of the community calculated from plant abundances measured as their dry biomass across the five quadrats, and Hmax=lnS, the maximum value of the Shannon diversity if every species was equally represented  of the plant abundances, based on the equation,where Cij is the sum of the lesser abundance values of only the species that occur at both sites, while Si and Sj are the total number of species counted at each site . To this end, we calculated SGDC with 999 randomized subsets of DS1, DS2 and DS3 containing the same number of loci as DS4 and DS5. β‐SGDCs were investigated with Mantel tests using the pairwise genetic distance matrices and Bray–Curtis dissimilarity matrix of the plant species assemblage based on 9,999 permutations.Before calculating SGDCs, we generated scatterplots to check whether the genetic and species diversity data were linearly related. These scatterplots are provided in the Figure 33.1Illumina sequencing produced around 2.5 million reads per individual. Following basic filtering steps conducted in the UNEAK pipeline, we retained 38,036 SNPs. With the additional, more stringent filtering protocol, we obtained 1,709 high‐quality SNPs for our overall dataset  on axis 2, moisture and wind speed (TCI and MAWS) on axis 3, and with temperature (MAT) and slope on axis 4.After selecting the predictor variables according to their correlations, none of those included in the RDA analysis showed a VIF greater than 5. The RDA model explained 20.9% of the genetic variance. Based on the elbow of the eigenvalues scree plot, we retained the first four components for outlier detection. All components were highly significant, with axes 1 through 4 explaining 35%, 22%, 16%, and 9% of the explained variance, respectively, all together accounting for 82% of the variance explained by the RDA model. In total, 95 loci had outlier RDA and pcadapt together identified a total of 229 outlier loci, of which 39 were identified in both methods (Table 3.3He) of DS1, DS2 and DS3 correlated almost perfectly . While the He estimates of DS4 correlated with those of DS1, DS2, and DS3 , no such relationships were observed between DS5 and the putatively neutral datasets . The genetic diversity estimates calculated from the nonoutlier datasets  and those obtained by Bertin et al. . Overall, the genetic diversity estimates tended to be higher with the SNPs than with the AFLPs. Two sites, however (sites 6 and 21), departed strongly from the main relationship, showing unexpectedly low SNP genetic diversity compared to their respective AFLP estimates  across all sites was 0.130. It reached 0.135 and 0.132 for DS2 and DS3, and 0.098 and 0.050 for DS4 and DS5  calculated with DS1, DS2, and DS3 ranged between 0.03 and 0.15. In all three cases, site 19 stood out from the rest of the sites for exhibiting many high genetic distance values . While the DCH varied between 0.02 and 0.19 for DS4, they reached much lower and higher extreme values for DS5, which ranged between 0.002 and 0.31. Yet, in both cases, site 21 was found to display noticeably high genetic distances compared to the rest of the sites . Plant composition distances measured as Bray–Curtis distances ranged between 0.27 and 0.87. No site showed noticeable higher differentiation levels than the others, but all of them were highly differentiated from some other sites in terms of plant composition, with all sites showing a BC value of at least 0.67.The genetic distances .The α‐SGDCs estimated from species richness were positive for DS1 and the two nonoutlier datasets DS2 and DS3). However, the correlations were only moderate in amplitude, ranging from 0.33 to 0.37, and marginally nonsignificant . The putatively non‐neutral datasets displayed lower α‐SGDCs than the putatively neutral datasets, reaching −0.24 for DS4 and ranging between 0.01 and −0.07 for DS1, DS2, and DS3.None of the α‐SGDCs estimated with species evenness were significant Table . In spit3.5p > 0.08, Table p < 0.05, Table p < 0.03 in all cases, Supporting Information Table The β‐SGDCs were all positive. For the full and putatively neutral datasets (DS1‐3), they ranged between 0.11 and 0.16 and were not significant or marginally nonsignificant . This finding is consistent with the expectation that migration more strongly influences species richness than species evenness  can be linked to drought, which may be a key factor structuring the composition of vegetal communities in high Andean wetlands  and AFLPs are of limited use in teasing apart the neutral and non‐neutral components of genetic diversity because these markers sample only a small proportion of the entire genome and usually produce a relatively limited number of markers, hampering the identification of numerous outlier loci. However, the combined information of AFLP and SNP data provided unexpected results. Overall, we found slightly higher genetic diversity estimates with the 1,709 SNPs than with the 85 AFLP data in all but two sites, which departed from this general trend and presented conspicuously low SNP genetic diversity estimates compared to their AFLP counterparts (sites 6 and 21, Supporting Information Figure The SNP genetic diversity of these two sites reduced the species–genetic diversity relationship with species richness, and once removed, the SGDCs with the full and nonoutlier datasets were much higher (and closer to the value reported in Bertin et al., NG and AB acquired and processed data. BF filtered the SNP dataset. BF, JH, AM, and VP conducted genetic structure and outlier analyses. JH, JLBP, and AB conducted regression analysis. VP, BF, JH, AM, NG, and AB drafted the manuscript. JLBP, AB, and VP produced figures. AB, NG, and SM guided the conceptual development of the manuscript. All authors contributed to revisions and approved the final version of the article.Raw files of the SNP data will be made available from the Dryad Digital Repository upon acceptance of the manuscript. Click here for additional data file.


The purpose of this study was to develop a new method for investigating sediment-inhabiting meiobenthos using the Flow Cytometer And Microscope (FlowCAM). Meiobenthos are widely recognized as a useful indicator for assessing the effects of anthropogenic and natural disturbances in both shallow and deep ocean ecosystems. These small benthic invertebrates are traditionally investigated by individually counting and identifying specimens under a microscope, which is labor intensive and time consuming. However, FlowCAM, which was originally developed to semiautomatically analyze microplankton, has the potential to resolve these challenges. Meiobenthic specimens were extracted from sediment using the centrifugal separation method and were then pipetted into the FlowCAM system and imaged. The images were then used to classify and count the specimens at high taxonomic levels to verify the effectiveness of this method compared with traditional methods. We found that FlowCAM system:•Enabled sufficient meiobenthic images to be obtained to allow the identification and classification of specimens at high taxonomic levels.•Obtained comparable numbers of individuals to traditional methods.•Has the potential to rapidly process large the volumes of meiobenthos samples that are required when monitoring seasonal and spatial variation in ocean ecosystems and conducting long-term environmental impact assessments. Specifications TableMeiobenthos are small benthic invertebrates that pass through a 500–1000 μm sieve and are retained on a 32–63 μm sieve. These animals are important components of deep-sea benthic ecosystems because they are more abundant than larger macro- and megabenthos , anthropTraditional methods of investigating meiobenthos include individually counting and identifying small specimens under a microscope. However, this approach is both labor intensive and time consuming. Therefore, alternative methods that allow high volumes of meiobenthic samples to be processed are required, particularly when monitoring spatial and temporal variations in ocean ecosystems or conducting long-term environmental impact assessments, to allow the rapid detection of drastic changes and subsequent environmental monitoring.The Flow Cytometer And Microscope (FlowCAM) system  was originally developed to semiautomatically analyze microplankton ,10 and wProblem: Sediment particles block the narrow flow cellSolution: Sieving and separationSediment samples typically contain more sediment particles than meiobenthic specimens resulting in the sediment particles becoming stacked in the flow cell when sediment samples are poured directly into the FlowCAM as well as many more images being required, which increases the volume of data and image-analysis time.Sieving is an essential process for separating meiobenthic organisms, which are in a specific size range, from bulk sediment samples. The bulk sediment was firstly sieved through 1 mm mesh and the sediments that passed through the sieve were retained on another mesh that was smaller than the size of the low cell to avoid stacking by particles. And then the sediment fraction that passed though the latter mesh was sieved another mesh that was equivalent to the lower size of meiobenthos.3) [Meiobenthic specimens could be separated from sediment and other debris using a centrifugal method with a high-density solution  ,12 to prWhen pouring the separated particles were poured into the FlowCAM with water, they moved through the flow line faster than the water due to the difference of the density, resulting in the flow cell becoming clogged with particles, which could stop the FlowCAM from running. This problem was resolved by using a high-density solution  in place of water, which enabled us to readily adjust the flow speed. Alternatively, high-density solutions that stabilize DNA/RNA  could be used for downstream genetic analysis.Problem: Syringe pump destroys meiobenthic specimensSolution: Use an external pumpFlowCAM usually transports fluids with an internal syringe pump. However, this type of pump can destroy meiobenthic specimens during retrieval. Therefore, an external peristaltic pump was used and connected to the flow line, as shown in Problem: Sorting the obtained pictures is time consumingSolution: Stain meiobenthic specimens with Rose BengalVisualSpreadSheet, which is the analytical software included with the FlowCAM. Once sorting was completed, the images of organisms could be selected and used to classify and count individuals by taxonomic group.The organisms were stained red with Rose Bengal to help discriminate them from inorganic particles. This allowed us to sort the organisms by color using a programmed function. The images of red-stained organisms were sorted either first or last according to the red/green ratio using the sort function in Shinsei Maru, KS-15-01, in March 2015. Each sample was fixed and preserved in 5% buffered seawater formalin and stained with Rose Bengal  until processing in the laboratory. The topmost 0–1 cm layers from a single core from each station was used to validate this novel method.To verify the effectiveness of this new method for identifying and quantifying meiobenthic organisms, we used it to examine sediment samples collected from four stations established off Otsuchi Bay, northeastern Japan  during a cruise of the R.V. VisualSpreadSheet and were then assessed and classified to higher taxonomic levels, such as Nematoda and Copepoda, by the study team.In the laboratory, the sediment samples were sieved through 1 mm, 250 μm and 63 μm mesh sieves. The fractions that were retained on the 63 μm mesh sieve were then resuspended and centrifuged three times in colloidal silica  at 800 × g for 10 min. Observations were made using a portable FlowCAM with a ×4 objective lens and a 300 μm-thick flow cell. The flow line was set up as described above and was filled with colloidal silica (Ludox HS-40) before examination of the samples. The separated samples were gently shaken and suspended in the colloidal silica and were then pipetted into the FlowCAM system. Images of the specimens were captured using FlowCAM’s Auto Image mode at an Auto Image Rate of 20 frames per second. The captured images were processed as previously described using After examination with FlowCAM, the samples were recollected and reexamined under a binocular stereoscopic microscope for classification and enumeration at higher taxonomic levels. The original conical tubes that contained the samples before they were examined with FlowCAM were also assessed for any remaining samples, and the inflow tube and flow cells of the FlowCAM were washed after each examination and checked for specimens using a stereoscopic microscope. It was found that < 3% of specimens remained in the original conical tubes, inflow tube, and flow cells, indicating that nearly all of the meiobenthic specimens were streamed into the flow cell and recollected after the FlowCAM examinations.A regression analysis was conducted to assess the relationship between the numbers of individuals observed with microscopy and those observed using the FlowCAM system. All analyses were performed using R, v 3.1.3 .r = 0.95, p < 0.05 for all meiobenthos; r = 0.95, p < 0.05 for Nematoda; r = 1.00, p < 0.01 for Copepoda; FlowCAM successfully captured the images of the meiobenthic specimens in the samples, including metazoan meiobenthos and protists  compare with the other stations (approximately 50%–70%) using both methods. The study area experienced a large earthquake in March 2011 that may have had different effects at the various stations, resulting in the observed differences in nematode ratios, as reported by studies in adjacent areas . Therefo


Echinococcus granulosus (sensu lato). In China where CE is endemic, a number of studies have shown that Echinococcus granulosus (sensu stricto) is majorly responsible for CE. However, E. canadensis (G6) which is the second leading cause of CE is now being detected in most parts of the country. In this study, the species diversity and genetic variation of Echinococcus granulosus (s.l.) in four counties in Tibet Autonomous Region of China were investigated.Cystic echinococcosis (CE) in humans and livestock is caused by Echinococcus granulosus (s.s.) in yaks and sheep was identified using NADH dehydrogenase subunit 1 and 5 (nad1 and nad5) mitochondrial genes while the genotype G6 of E. canadensis initially diagnosed with NADH dehydrogenase subunit 1 (nad1) was further confirmed by analysis of the complete mitochondrial genome and a phylogenetic network constructed based on the nad2 and nad5 genes.Infection with n = 54) and yaks (n = 31), 83 were identified as E. granulosus (s.s.) G1 (n = 77), G3 (n = 6) and 2 were identified as E. canadensis G6. Analysis of the nad1/nad5 genes revealed 16/17 mutations with 9/14 parsimony informative sites resulting in 15/14 haplotypes, respectively. Haplotype diversity (Hd) and nucleotide diversity (π) of E. granulosus (s.s.) population were 0.650 and 0.00127 for nad1 and 0.782 and 0.00306 for nad5, respectively, with an overall negative Tajima’s D and Fu’s Fs. A low FST indicated no genetic difference between isolates from sheep and yaks.Out of 85 hydatid cyst samples collected from slaughtered sheep  have been previously reported in sheep, goats, yaks and/or humans in different parts of China. While the G6 genotype has been previously reported in sheep in the Tibet Autonomous Region, the detection in a yak in the present study represents the first to the best of our knowledge. Therefore, we recommend future surveys and control efforts to comprehensively investigate other potential intermediate hosts for the prevalence and genetic diversity of the E. canadensis group  across the country and their inclusion into the existing CE control programme.Pockets of infection with  Echinococcus granulosus (s.l.), which infect humans and livestock upon ingestion of the infective egg stage from the definitive host resulting in the development of hydatid cyst of the metacestode larval stages in the liver, lungs and sometimes in other organs. Within the E. granulosus complex, high genetic variation which affects species/genotype infectivity and preference to intermediate hosts has been recognised based on studies of parasite mitochondrial DNA . Echinococcus canadensis G6/G7 reference sequences are from Laurimae et al. [e et al. . Note th


Rheum palmatum complex (rhubarb), an important herbal remedy in TCM, using a phylogeographic  and a chemical approach (anthraquinone content). Genetic and chemical data identified two distinct groups in the 38 analysed populations from the R. palmatum complex which geographically coincide with the traditional top-geoherb and non-top-geoherb areas of rhubarb. Molecular dating suggests that the two groups diverged in the Quaternary c. 2.0 million years ago, a time of repeated climate changes and uplift of the Qinghai-Tibetan Plateau. Our results show that the ancient TCM concept of top-geoherb and non-top-geoherb areas corresponds to genetically and chemically differentiated groups in rhubarb.Medicinal herbs of high quality and with significant clinical effects have been designated as top-geoherbs in traditional Chinese medicine (TCM). However, the validity of this concept using genetic markers has not been widely tested. In this study, we investigated the genetic variation within the  Many unique concepts have formed throughout this long historical process, one of which is the concept of geo-herbalism. The idea behind geo-herbalism is that the medicinal properties of herbs depend on the specific area of collection  and that plants collected from some areas have higher medicinal value than those collected from other areas. The ancient practitioners of Chinese medicine called medicinal plants obtained from regions with allegedly superior efficacy top-geo-herbs, while the ones from other regions were known as non-top-geoherbsShen Nong’s Herbal, which was written during the Eastern Han Dynasty (25–220 C.E.). Among the 500 commonly used TCMs, there are about 200 TCMs where the concept of geo-herbalism is applicable4. The consumption of these 200 TCMs accounts for 80% of the total consumption of TCMs6.The mentioning of top-geoherbs and the importance of provenance for medicinal plants in Chinese medicine was first discussed in 10 and species differentiation can be demonstrated by morphological, chemical, karyological, and genetic means. The first three traits are phenotypic in nature and might not necessarily reveal species differentiation because of phenotypic plasticity11. It is believed that plant secondary metabolites (PSMs) are the important constituents in medicinal plants, and differences in PSMs between top-geoherbs and non-top-geoherbs might not be the presence or absence of one or more compounds, but rather the content variation or the unique combination of certain compounds3. Over the last century, the differences in the composition of the main medicinal compounds between top- and non-top-geoherbs have been tested through various phytochemistry studies13. For example, the content of sarsasapogenin in zhi mu (Rhizoma Anemarrhenae) from top-geoherb regions was three times higher than that in non-top-geoherb regions13; the content of phenols in top-geoherb regions of hou po  was six times higher than that in non-top-geoherb regions; also see jin yin hua (Flos Lonicerae) and chuan xin lian (Herba Andrographitis Paniculatae)12. Although few studies have attempted to explain the genetic variation between top-geoherbs and non-top-geoherbs , the abovementioned studies were conducted either on very limited populations or medicinal plants derived from cultivars. In addition, whether chemical differentiation also corresponds to genetic differentiation between top-geoherbs and non-top-geoherbs has not yet been investigated. Phylogeography, the analysis of the spatio-temporal pattern of population genetic variation, has been used to elucidate the evolutionary history and differentiation of populations within species17 which is often initiated by large scale events such as the climatic fluctuations observed during the Quaternary20.It is well known that species are the fundamental units in all fields of biologyRheum officinale Baill., R. palmatum Linn., or R. tanguticum (Maxim. ex Regel) Maxim. ex Balf. (Polygonaceae). The major medicinally active ingredients in rhubarb are anthraquinones, which were used to assess the quality of rhubarb in the Pharmacopoeia of the People’s Republic of China 2015. All three species are endemic to China and form a clade based on molecular data21. According to Flora of China, the delimitation of these three rhubarb species is based primarily on the degree of leaf blade dissection and the shape of the lobes22. The blades of R. officinale and R. palmatum are lobed; the lobes of R. officinale are broadly triangular, and those of R. palmatum are narrowly triangular. The blades of R. tanguticum are parted, and its lobes are narrow and triangular-lanceolate22. However, the results of a morphological analysis indicated that the degree of leaf blade dissection is continuous among the three species17. Geographically, R. palmatum is the most widely distributed species from the eastern margin of the Qinghai-Tibetan Plateau (QTP) to Qinling extending to the Zhongtiao Mountains (Shanxi Province). The northwestern and southeastern distributions of R. tanguticum and R. officinale partly overlap with R. palmatum. The three species co-occur in northwest Sichuan Province, and form mixed populations23. Molecular evidence using inter-simple sequence repeats (ISSR) markers have shown that the populations from each species did not cluster according to species24. Genetic and morphological data therefore indicate that these three species can be regarded as one species . In TCM, rhubarb plants collected from Qinghai, Gansu, and Sichuan Provinces (in or near the QTP and the Hengduan Mountains) are classed as top-geoherbs compared to rhubarb collected further east, which are regarded to be non-topgeoherbs with inferior quality. Therefore, it is likely that chemical and possibly genetic differentiation exists in the R. palmatum complex.Rhubarb, known as the “lord or king of herbs” in China, is an important TCM that has been used for over 2,000 years as a purgative medicine. The herbal remedy “rhubarb” consists of the dried roots and rhizomes of any species of R. palmatum complex throughout its distribution in China. The specific objectives are to: (1) quantify the anthraquinone content from top-geoherb and non-top-geoherb areas; (2) determine whether genetic differentiation exists between top-geoherb and non-top-geoherb areas of rhubarb; (3) investigate the possible reasons that might have resulted in the genetic variation between top-geoherb and non-top-geoherb areas of rhubarb; and (4) test whether genetic differentiation is consistent with the concept of top-geoherb and non-top-geoherb in TCM.In the present study, we investigate whether chemical and genetic differences can be detected between top-geoherb and non-top-geoherb areas of the P < 0.05), while the physcion-8-O-β-D-glucopyranoside in the eastern group was significantly higher than that in western group (P < 0.05)  Fig. . We alsoR. palmatum complex resulted in a joint alignment length of 3,316 bp and 34 haplotypes defined by 57 substitutions and 14 indels. Populations were fixed for a single haplotype  and nucleotide diversities (π) were 0.960 and 0.0031, respectively. Non-hierarchical analysis of molecular variance (AMOVA) indicated that the genetic variation was mainly distributed among populations rather than within populations . Additionally, neutrality test statistics were all non-significant in combined sequences, which was also supported by the mismatch distribution analysis .Sequencing of the six cpDNA segments from 377 individuals from the CHS were 1,549 bp containing 68 variable sites and four indels yielding 95 haplotypes  among five nuclear loci ranged from Hd = 0.477 to 0.944, Watterson’s theta (θwt) from θwt = 0.0026 to 0.0071, and the silent nucleotide diversity (πsil) as well as the total nucleotide diversity (πt) ranged from πsil = 0.00058 to 0.00245 and πt = 0.00143 to 0.00505, respectively. All neutrality test statistics were nonsignificant . However, based on the STRUCTURE analysis, the hierarchical AMOVA indicated that 59.37% (four genes) and 57.35% (CHS) of the total variation was distributed among the two groups, and 22.93% (four genes) and 25.09% (CHS) of variation existed among populations within groups . We did not find any evidence for linkage disequilibrium (LD) in any pair of nSSR loci in any population , nor was there evidence for the existence of null alleles in any locus. Diversity estimates based on 14 microsatellite loci varied between all populations (Supplementary Tables FST = 0.214 (P < 0.05), and the standardized genetic differentiation G′ST was higher than FST across all loci , with 1.46% of the variation partitioned between the western and eastern clades, and 18.77% among populations within clades , and this effect also persisted when each sub-cluster was analyzed separately, except for sub-cluster pop4  in all the studied populations after Bonferroni corrections  increased progressively from K = 1 up to K = 38 in the STRUCTURE analyses. The highest ΔK was observed at K = 2 but was also significant at K = 5  and three sub-clusters  for cluster I and II, respectively , ranging from 0.23 (pop1 to pop4) to 2.88 (pop3 to pop5). The gene flow from the western clade to the eastern clade was 7.76 (95% CI: 7.25–8.32), and that of the opposite direction was 5.00 (95% CI: 4.65–5.39). Some migration rates were distinctly asymmetrical, for instance, being much greater from pop4 to pop1 (0.52) than the reverse (0.23)  from the west to east was 0.0028 (95% CI: 0–0.0071) and the opposite was 0.0021 (95% CI: 0–0.0050). The range of contemporary gene flow was 0.0016 (pop5 to pop4) to 0.0098 (pop4 to pop1). Some pairs also had asymmetrical values, including the pairs of pop2 and pop4, pop1 and pop4, and pairs within the eastern clade , much higher than for the other scenarios. This scenario depicted a situation that the three sub-clusters in the eastern clade diverged simultaneously. Then in step 2, scenario 1 also had the highest posterior probability . This scenario indicated that the divergences of the sub-clusters in the western and eastern clades were also simultaneous. In the final step, the simulations indicated the posterior probabilities for scenarios 1 and 2 were 0.4111 (95% CI: 0.4008–4212) and 0.4985 (95% CI: 0.4884–0.5086), respectively, much higher than for the other two scenarios , respectively. The estimated median mutation rates and the proportion of multiple step mutations were 2.84 × 10−5 and 0.568, respectively  were 3.88 × 105, 4.56 × 105, 6.56 × 105, 6.23 × 105, 6.09 × 105, and 3.33 × 105, respectively. The divergence times between sub-clusters within two clades (t1), between two clades (t2), and the time of changes of the ancient population size were 2.13 × 105, 3.68 × 105, and 1.31 × 106 generations ago, corresponding to 1.07, 1.84, and 6.05 Ma, respectively. The estimated median mean mutation rate and the proportion of multiple step mutations were 2.89 × 10−5 and 0.471, respectively , the corresponding expansion times for the R30 gene and other three genes were all at ca. 0.15 Ma, despite with different expansion degrees  analyses, the 25. Assuming this finding also applies to continental East Asia, we relied more on the MIROC simulation for the discussion of the projected LGM distribution. Overall, the whole distribution area of the R. palmatum complex had a northwestward shift from the past to the present.The ecological niche models (ENMs) had a high predictive power for the two gene pools, with AUC = 0.92 and 0.93 for western and eastern clades, respectively. This is fairly congruent with their current distribution, except for some eastern clade populations that are missing in current conditions compared with the genetic architecture of this species complex Figs . HoweverP < 0.05). The frequency distributions of each clade for each environmental variable are presented as kernel density plots . The first two PCA axes explained 82% of the variation for the present climate   Fig. . Additio05) Fig. , indicatR. palmatum complex (rhubarb), an important herbal remedy, as a case study.We investigated whether the concept of geo-herbalism in TCM is consistent with chemical and genetic differences between top-geoherbs and non-top-geoherbs using the 29 showed that the three species of the R. palmatum complex  do not show clear morphological separation, we used a number of molecular markers  to ascertain whether the three species can be told apart genetically. As none of the used markers retrieved three clearly separated groups 31. These divergences reflected a strong signature of highly restricted gene flow due to the geographical and/or climatic isolations . Indeed, our AMOVA results demonstrated moderate to high ΦST values between the two clades (ranging from ΦST = 0.2023 (nSSR) to ΦST = 0.8244 (CHS), Table CHS gene), assigned these populations to the western clade. The discrepancies between nuclear sequence and nSSR data may be caused in part by their difference in mutation rates. Generally, the mean mutation rate is 10−9 for nuclear genes and 10−4 for SSR loci. This high mutation rate makes nSSR data more suitable for determining the intraspecific genetic structure34.The consistent grouping of the c. 2.0 Ma ago with subsequent divergence within clades c. 1.0 Ma ago. It is possible that the major uplift of the eastern edge of the QTP during the Miocene and Late Pliocene35, associated with repeated climate changes in the Quaternary36, played a critical role in the differentiation of the clades. However, the possibility that the QTP uplift alone could have contributed to the intraspecific divergence of the R. palmatum complex cannot be completely ruled out40. For instance, the recent uplift of the QTP (c. 0.9–1.1 Ma)41 is proposed to have been one factor causing genetic divergence of other temperate species in this region42. However, there are some uncertainties about the time estimates such as the mutation model and homoplasy as well as the assumption of no gene flow when using DIYABC in our study, which might underestimate divergence times over large timescales43. Nevertheless, our time estimates in the present study are consistent with previous studies , indicating that our results reflect the rate of evolutionary divergence within the R. palmatum complex.ABC simulations suggested that the separation between the clades happened R. palmatum complex might have been due to increased human activities in the Himalaya–Hengduan Mountains during recent decades which caused a decrease in suitable habitats leading to higher levels of genetic drift and more bottlenecks  as a top-geoherb, which corresponds to the populations from our western clade  collected in Qinghai, Gansu, and Sichuan. This is clearly shown by the cluster analyses of nSSR and nuclear genes. Our results show that chemical variation and genetic differentiation of rhubarb populations between top-geoherb and non-top-geoherb regions are consistent with the concept of geo-herbalism. Our study revealed the contribution of the genetic factors to the formation of top-geoherbs, and also showed that the formation of top-geoherbs might be closely related to species differentiation. Future studies should focus on quantitative genetics to further understand the molecular mechanism of top-geoherbs.The present study used a chemical approach and genetic markers  to better understand the genetic differentiation at the population level of rhubarb plants throughout its distribution range. The analysis of both chemical composition and genetic markers showed that two distinct groups are present in the R. palmatum complex comprises two major clades (eastern and western). Quaternary environmental changes and/or the uplift of the QTP profoundly influenced the evolutionary and population demographic history of the R. palmatum complex as well as its current genetic structure. Chemical and genetic variation exists within the R. palmatum complex, and are consistent with the top-geoherb and non-top-geoherb areas of rhubarb. These findings reveal that genetic differentiation is at the core of the geo-herbalism concept.Overall, our results clarified that the current populations of the R. palmatum complex . The quality and quantity of DNA was determined on 1% TAE agarose gels and with a NanoDrop® ND-1000 spectrophotometer , respectively. The DNA was diluted to a final concentration of approximately 20 ng/μL.Dried root and rhizome samples were pulverized to extract free anthraquinones and anthraquinone glycosides. The powder was filtered through 180 μm sieves and accurately weighed when fine (800 mg). Then, 70% methanol (25 mL) was added and the mixture was weighed again. The mixture was extracted by ultrasonication for 1 h . After cooling, 70% methanol was added and transferred to a 25 mL volumetric flask. The supernatant fluid was filtered through a 0.22 μm membrane filter, and the filtrate was analyzed using high-performance liquid chromatography (HPLC) (see details in Note S1).trnL-trnF, ndhJ-trnF, psaI-accD, rpl20-rps12, rpl32-trnL, and psbA-trnH) and five nuclear genes  were sequenced. Primers and PCR amplification of the cpDNA regions were described in previous studies49  using an ABI 377 DNA sequencer . Clone sequencing was used for the CHS gene. More than ten colonies of each sample were randomly chosen and sequenced to survey sequence variations in multiple copies and then the program MEGA751 was employed to align and edit sequences. All newly acquired sequences have been submitted to GenBank with accession numbers ranged from MH457640 to MH457707 and MH465255 to MH465390.A total of six cpDNA regions . Allele sizes were determined in GENEMAPPER .All sampled individuals were genotyped at 14 nSSR loci (see Supplementary Table Hd) and nucleotide (π) diversity, the number of segregating sites (S), the Watterson’s parameter (θw)52, and the minimum number of recombinant events (Rm)53 were estimated, and tests were carried out for departure from the neutral model based on Tajima’s D54, and Fu and Li’s D* and F*55 using DNASP v5.156. The total gene diversity (HT), gene diversity within populations (HS), and coefficients of differentiation GST and NST were calculated using SPAGEDI57 based on 10,000 random permutations. Genealogical relationships of the haplotypes were identified using POPART based on a TCS network algorithm58. A mismatch analysis was conducted using ARLEQUIN v3.559 to test for spatial expansion of test populations. The goodness-of-fit was tested with the sum of squared deviations (SSD) between observed and expected mismatch distributions and Harpending’s raggedness index (HRag)60. We also employed jMODELTEST v1.061 to evaluate the best-fit model of nucleotide substitution for maximum likelyhood (ML) method to infer cpDNA haplotype relationships (GTR + I + G in the present case), and used PAUP∗ v4.0 beta 1062 to determine the relationships of cpDNA haplotypes and visualized results using FIGTREE v1.3.1 (http://tree.bio.ed.ac.uk/software/figtree/). The program STRUCTURE v 2.3.363 was used to infer the population genetic structure of nDNA sequences based on the admixture model with allele frequencies correlated. The number of clusters (K) was set to vary from one to 20. For each value of K, we performed 20 runs with a burn-in length of 200,000 and a run length of 500,000 Markov chain Monte Carlo (MCMC) replications. The best K values were determined using the methods described by Pritchard et al.63 and Evanno et al.64. The web-based program Structure Harvester65 was used for visualizing the STRUCTURE output. The estimated admixture coefficients (Q matrix) over the 20 replicates in each K were averaged using CLUMPP v1.166, and the graphics of optimal K were produced using DISTRUCT v1.167. In addition, in order to quantify variation in nSSRs and DNA sequences between and within the two main geographical regions, we performed analyses of molecular variance (AMOVA) in ARLEQUIN using 10,000 permutations. Additionally, we used BSP in BEAST v.1.7.568 based on the combined cpDNA data and four nuclear genes each to estimate effective population size changes within the species complex. Linear and stepwise models were explored using an uncorrelated lognormal relaxed clock. Runs consisted of 20,000,000 generations, with trees sampled every 5,000 generations. The BSP was visualized in the program Tracer v1.5, which summarizes the posterior distribution of population size over time. The cpDNA substitution rates for most angiosperm species have been estimated to vary between 1 and 3 × 10−9 substitutions per site per year (s/s/yr)69, while those for nDNA in shrubs and herbal plants vary between 3.46 and 8.69 × 10−9 (s/s/yr)33. Given the uncertainties in these rate values, we used normal distribution priors with a mean of 2 × 10−9 and a SD of 6.080 × 10−10 for cpDNA, and a mean of 6.075 × 10−9 and a SD of 1.590 × 10−9 for nuclear gene to cover these rate ranges within the 95% range of the distribution for our BSP analyses.The haplotype (70. Tests for departures from Hardy–Weinberg equilibrium (HWE) and linkage disequilibrium (LD) were performed in each population and a globally unified population using FSTAT v2.9.371 based on 1,000 permutations (α = 0.05), and corrected for multiple tests using the sequential Bonferroni method72. In addition, we conducted a modified version of FST outlier loci analysis73 under the assumption of the IAM using LOSITAN74 with 50,000 simulations to generate 95% confidence intervals (CIs), and the results indicated all 14 loci were neutral, so all loci were retained for use in subsequent analyses.For the nuclear microsatellite dataset, all 14 loci were checked for possible null alleles using MICRO-CHECKER v2.3.3AO), the observed heterozygosity (HO), the expected heterozygosity over all populations (HE), the genetic diversity within the population (HS), the total genetic diversity (HT), and the standardized genetic differentiation G′ST75 across loci with FSTAT and/or GENALEX v6.576 based on 1,000 bootstrap permutations. Compared with the traditional measures , G′ST is a more suitable measure of differentiation for highly polymorphic markers such as microsatellites77. For each population, we also used the program GENALEX to estimate the values of HO, HE, private allelic richness (PAR), fixation index (FIS), and allele richness (AS) across all loci.For each locus, genetic diversity was assessed by calculating the observed number of alleles  and that of the natural logarithm of the geographic distances at species and each gene pool revealed by STRUCTURE analysis, respectively, using the package Vegan78 in R 3.3.079.Using the nSSR dataset, genetic clusters were identified using a Bayesian analysis in STRUCTURE with the same parameter settings as above, except for the 80 was used to detect whether a population had experienced a size decrease over extended vs. more recent time scales .The Wilcoxon’s signed rank test and the mode-shift test implemented in BOTTLENECK v1.2.02Ne generations in the past82), between the STRUCTURE clusters and between sub-clusters within each STRUCTURE cluster (see results section), we used the program MIGRATE v3.683 to estimate the effective number of migrants . Also, we estimated the contemporary gene flow using BAYESASS v3.0384 (See details in Note S1).To estimate historical gene flow  to determine its migration route(s), and whether the genetic disconnection between the two clades was old or recent  in the present, the Last Glacial Maximum , and the last interglacial  using the maximum entropy method implemented in MAXENT v3.3.389, and used ENMTools v1.391 to calculate Schoener’s D92 and standardized Hellinger distance  to measure the niche similarity between clades (See details in Note S1).We used ENMs to infer suitable climate envelopes for the two clades of the 93) in R, and the distributions of all 20 environmental factors each in the two clades were displayed in kernel density plots using the R package ggplot294. Then also using R, we performed a permutational MANOVA analysis for all environmental variables simultaneously for the two clades to evaluate whether environmental conditions differed significantly between their sites of occurrence. Finally, we employed the R package ggfortify95 to perform a principal component analysis (PCA) to determine whether the genetic groups were ecologically differentiated.In order to evaluate the effect of present climatic conditions on the genetic differentiation between the main two geographical regions, three methods were used to evaluate the impacts of climate on population differentiation. First, the lineage-level divergence associated with each of the environmental variables was examined using one-way nonparametric ANOVA (i.e., Kruskal–Wallis testSupplementary information


Motor imagery has become a key issue in cognitive neuroscience and particularly in fMRI research. However, peripheral physiological effects of motor imagery were already being studied a century ago with some research hypotheses even tracing back to  Edmund Jacobson was one of the first researchers to study the physiological effects of mental motor imagery in detail. Indeed, in this context, he is often cited as one of the basic references on the peripheral physiological effects of motor imagery. However, despite being cited frequently within the framework of motor simulation phenomena, the full extent of his research is rarely recognized. For example, few people know that he published not just one basic article on this topic  but a bo1.Edmund Jacobson (1888–1983) gained his Ph.D. in 1910 at Harvard University where he came into contact with William James. However, this contact seems to have had little influence on his own work. After receiving his Ph.D. at Harvard, he moved to Cornell University for 1 year where he joined Edward Titchener as a research fellow. Afterward, he taught physiology in Chicago until 1937. During this time, he also started to run his own laboratory in which he focused mainly on relaxation methods and their clinical applicationsOne of Jacobson’s central interests was in understanding how introspection might work. In principle, introspection procedures can be viewed as an intrinsic part of imagery research. However, the different research paradigms in imagery research indicate that interest does not always focus merely on introspective reports on the quality of imagery. Indeed, in most experimental settings, imagery procedures start normally with an explicit instruction to imagine a certain motor act . This imAs a physiologist, Jacobson was especially interested in measuring bodily reactions in response to mental activities. In his first 1930 paper, he argued that “if the study of mental phenomena is ever to become a true science, it would appear to be the task of the physiologist to bridge the gap to physics” , p. 567.When appraising Jacobson’s experimental approach, one has to bear in mind that he was a true pioneer in the field of psychophysiology. Knowledge about psychophysiology in the early 1930s was far from the present understanding, and his attempt to bridge the gap between mental activities and peripheral physiological processes was ambitious, pioneering, and far ahead of its time. In his first 1930 paper, he followed a short theoretical introduction  by outlining the technical aspects of psychophysiological measurement on more than 11 pages. He elaborated the construction of his device extensively, referred to other measurement methods that were being introduced at that time, and discussed the meaning of the signals that were relevant in his experiments. Electrophysiology was a new and future-oriented approach, and Jacobson really pushed this approach in a new direction by, for example, discussing the properties of the string galvanometer . In 1924, the Dutch physician Willem Einthoven (1860–1927) had received the Nobel Prize for Physiology and Medicine for developing this electrocardiological device. Metaphorically speaking, psychophysiological methods were appearing on the radar at that time.The aim of the 1930 study was “to secure electrical records of neuromuscular states during specific acts of imagination” , p. 569.In the following paper , JacobsoAdditional data are reported in summary for very different imagery instructions such as writing one’s own name, boxing, or engaging in other actions using the right arm. The data demonstrated that the recordings from the biceps were stronger in imagining than in relaxation, but weaker than in bending the arm. There are also some references to recordings of alternating rhythmical movements such as climbing a rope, but the data are not reported in detail. These results underline once more the movement specificity of peripheral physiological effects relying on motor imagery.In a subsequent series of control experiments, Jacobson tried to investigate whether the electrical signal recorded during imagery is a result of muscle contraction . He presIn a further study, post hoc review revealed that this result could probably be ascribed to visualizing strategies especially in the first imagery trials  and control conditions (relaxation) by reducing muscular activity during relaxation. However, it has to be noted that studies investigating the influence of individual differences in imagery ability have found that participants with high imagery ability report significantly more relaxation than low imagers in relaxation tasks. Thus, imagery ability might be related to greater subjective responses to relaxation . It can A further interesting and experimentally farsighted manipulation was to implement different imagery modalities in his experiments. In contemporary literature, this is discussed as visual versus motor imagery see , he focuHowever, the principal merit of his studies is that they revealed task-specific electrophysiological activation for imagery in those muscles that were active during actual execution of the specific motor task. He was not the first to formulate hypotheses on this issue see . HoweverRegarding recent neuroscientific research within the field, task specificity has become a key topic in research on motor imagery and action observation. In this context, newer approaches like multivoxel pattern analysis enable to identify task-specific neural patterns within one area of interest in fMRI studies . For exaJacobson interpreted the task-specific EMG activity as the outcome of a specific mental activity that he then could contrast with peripheral effects during relaxation. He was not concerned with the nature of motor imagery as we are nowadays when we refer to Jacobson’s pioneering work. Nevertheless, his experimental approach had great predictive power, because his results fit in nicely with such concepts as mental simulation theory (MST) based on the work of JM defined the aims of the research question and wrote the manuscript. BK contributed to all parts of the research question and to all parts of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


At national level, the study revealed high level of genetic diversity and a significant phenomenon of admixture affecting all the Algerian breeds. At broader scale, clear global genetic homogeneity appeared considering both Algerian and Moroccan stocks. Indeed, genetic structuration was almost nonexistent among Arabia (from Algeria), Draa, Black and Nord (from Morocco), while the ancestral Kabyle and M’Zabite breeds, reared by Berber peoples, showed genetic distinctness. The study highlighted the threat to the Maghrebin stock, probably induced by unsupervised cross-breeding practices which have intensified in recent centuries. Moreover, it underlined the necessity to deepen our understanding of the genetic resources represented by the resilient North-African goat stock.North Africa represents a rich and early reservoir of goat genetic diversity, from which the main African breeds have been derived. In this study, the genetic diversity of four indigenous Algerian goat breeds ( Capra aegagrus) for their milk, meat, hair and skin  (IC95%). Calculation of pair-wise FST .Considering all the Algerian breeds, the mean Fwise FST  was usedMDS analysis of the pair-wise IBS distances between Algerian goats  showed aThis pattern was confirmed by STRUCTURE analysis, assuming two to four clusters . At K = i.e. same individuals as those distinguished in e.g. NK1) clustered with Arabia and Makatia.The co-ancestry heatmap  obtainedThe BAPS analysis of Algerian breeds, allowed visualization of gene flow . It highMDS analysis of Algerian and Moroccan goats  showed tIn the sPCA analysis, the consideration of eigenvalues suggested the possibility of a spatial pattern, as the three first positive scores were distinguished from other eigenvalues . The gloi.e. the genetic relatedness of Arabia, Draa, Black and Nord and distinct positions of M’Zabite, Kabyle, and to a lesser extent of Makatia. Considering the analysis conducted without prior breed information , show oST values of the goat dataset were significantly different from zero  of Algerian goats was of 0.40. Comparisons with other studies, using the GoatSNP50K BeadChip, lead to conclude that high level of genetic diversity existed in Algeria. Indeed, study of Nicholoso et al. ), ), et al.-African . This ma-African . Reports-African –55, the -African , 56]. I. Iet al.-African  already  in 1857 . In Moro country .i.e. a part of the North African reservoir of diversity shaped by long and complex history. The “livestock revolution” [In conclusion, this study highlighted the clear genetic dilution affecting the Algerian stock and a global homogeneity considering both Algeria and Morocco, olution”  has led S1 TableBreed details for the four Algerian goat breeds.(DOCX)Click here for additional data file.S2 TableGenetic diversity, considering four Algerian goat breeds.(DOCX)Click here for additional data file.


Phrynus marginemaculatus to understand the genetic structure and metapopulation dynamics of Keys populations that are otherwise isolated by human development and ocean.The biogeography of terrestrial organisms across the Florida Keys archipelago is poorly understood. We used population genetics and spatioecological modeling of the Amblypygi The Florida Keys archipelago and mainland Florida.DNA for 103 individuals of P. marginemaculatus from 13 sites in the Florida Keys and South Florida, binned into four regions. We used population genetic analyses to understand the population structure of the species throughout its US range. Furthermore, we used ecological modeling with climate, habitat, and human development data to develop habitat suitability estimates for the species.We sequenced a 1,238 bp fragment of mtWe found clear genetic structure between localities. The Lower Keys, in particular, support populations separate from those in other regions studied. Ecological modeling and genetic analyses showed the highest habitat suitability and genetic isolation in the Lower Keys, but urban development across the species range has resulted in the loss of most historical habitat.P. marginemaculatus gene flow patterns in the Florida Keys and mainland. Ocean currents likely play a role in metapopulation dynamics and gene flow for terrestrial Keys species like P. marginemaculatus, and genetic patterns also matched patterns consistent with geologic history. Suitable habitat, however, is limited and under threat of human destruction. The few remaining pockets of the most suitable habitat tend to occur in parks and protected areas. We argue that conservation efforts for this species and others in the terrestrial Florida Keys would benefit from a deeper understanding of the population genetic structure and ecology of the archipelago.A mainland‐metapopulation model best fits  That said most past biogeographic studies have been limited to nonnative species or excluded Florida mainland populations. No studies have investigated the biogeography of a native species occupying the entire archipelago and mainland, or have any investigated human impacts on structure and connectivity.Genetic research using native terrestrial species is scarce, but not absent. The mosquito a et al.  proposed2, has over 52,000 housing units. The natural areas that remain are mostly protected as state parks or national wildlife refuges, but are continually impacted by nearby human activity  have been employed in many conservation, evolutionary, and ecological applications  and raising above sea level during the Wisconsin glaciation  of large nocturnal predators  by performing 34 iterations of the following cycle on a thermal cycler: 30 s at 94°C, 35 s at 48°C, and 90 s at 65°C, beginning with an initial cycle of 2 min at 94°C and ending with 10 min at 72°C. Using illustra PuReTaq Ready‐To‐Go PCR beads and 400‐nM forward and reverse primers, the long or short read of COI was sequenced for each sample. LCOI1490 was used as the forward primer for both the long and short reads, while HCOI2198 was used as the reverse primer for the short reads and C1‐N‐2776 was used as the reverse primer for the long reads  and a 50‐mL final elution. We amplified a 1,238 nucleotide sequence of the mitochondrial gene cytochrome 2.5Amplified fragments were sent to the University of Arizona Genetic Core and Genewiz for sequencing. Subsequently sequences were assembled using the Chromaseq module , and Lower Keys. We produced genetic diversity indices for each region and used a hierarchical analysis of molecular variance  for inclusion in our models. We included elevation and a geologic map in our first set of models to test their contribution to informing model predictions. Both layers were obtained from the United States Geological Survey (http://www.usgs.gov). We ran a second set of models that included land use data based on imagery made publically available by the Florida Department of Environmental Protection (http://geodata.dep.state.fl.us/). This dataset included 195 categories of land use based primarily on human use  but also included subcategories of vegetation and other ecologically relevant habitat types . This included all land cover categories used in the Florida Land Cover Classification Systems  and model outputs of suitable habitat using the image‐processing software ImageJ . A hierarchical AMOVA revealed population structure among populations, but not regions  than those without land use but appear to have refined the habitat suitability of P. marginemaculatus  and common reef sponge  found much lower levels of divergence between regions than we found for P. marginemaculatus , which is somewhat restricted to shallow waters, showed genetic structure between Upper and Lower Keys that was explained by IBD .Few studies of Florida biogeography have been conducted, but generally align with our results. Ant gut microbiota show similar genetic structure between the Upper and Lower Keys, but also shoe divergence among the Lower Keys, which might be indicative of Caribbean migration (Hu et al., P. marginemaculatus. Indeed, this is where almost all of our observations occurred and is corroborated by published collection sites of the species for laboratory research (Hebets & Chapman, P. marginemaculatus suitable habitats are also areas where human disturbance has been, and continues to be, an imminent threat to the habitat and species.Our models identified areas of tropical hardwood hammocks and pine rocklands as the most suitable habitat types for P. marginemaculatus (Chapin & Hebets, P. marginemaculatus, all of which are dependent on remaining fragments of pine rockland habitat (Florida Natural Areas Inventory, P. longipes are often collected and used for construction and landscaping.Pine rockland forests, once common throughout southeastern Florida, are now one of the most threatened habitats globally, with at least 98% global loss (Noss et al., P. marginemaculatus and the endemic, endangered community in which they occur.Some areas of pine rockland and upland hardwood forest are protected today. These include patches within Everglades National Park, Big Cypress National Preserve, Key Deer National Wildlife Refuge, and state‐managed lands. Most habitat outside of these protected areas have already been destroyed, and many surviving fragments remain threatened and at risk of extirpation within the areas of Miami, surrounding suburban areas, and in the tourist‐dominated Florida Keys. This has dramatic implications for P. marginemaculatus populations. We can, however, glean insight from studies of other species. For example, Hurricane Andrew dramatically altered pine rockland communities when it struck portions of Everglades National Park and Big Cypress National Preserve, leading to ca. 90% mortality of mature pine trees (Maguire, P. marginemaculatus in southern Florida.Human development is not the only threat to pine rockland and upland hardwood forest habitats; sea level rise brought on by human‐induced climate changes is also threatening these habitats (Maschinski et al., Maguire,  which neMaguire, . Major sMaguire, . Our resP. marginemaculatus in southern Florida and the Florida Keys archipelago, but the species occurs as far southwest as Hispaniola, including populations in the Bahamas, Cuba, Jamaica, and the Turks and Caicos (Quintero, Both our genetic and ecological results are limited by our dataset, which is constrained in both time and space. Spatially, we only sampled 4.1P. marginemaculatus in the wild: habitat loss by human development. Approximately 77,000 people permanently reside in the Florida Keys (2 uninhabited by humans, much of which is seasonally or permanently flooded habitat unsuitable for many terrestrial species. Furthermore, this does not include the impact from commercial, transportation, and utilities developments. Fortunately, much of the remaining habitat for P. marginemaculatus is within protected areas managed by federal and state agencies. This, however, does not protect habitats from all threats, including the impacts of increased hurricanes, sea‐level rise, poaching, and microhabitat alterations.Our results point to a primary threat to the population health of ida Keys . Given tP. marginemaculatus is collected from the wild for sale in the pet trade. While we do not have data on the number of individuals collected for sale in the pet industry, personal observations lead us to believe that it must be in the hundreds to thousands. Wild populations of P. marginemaculatus would benefit from captive breeding that allows these fascinating animals to be kept as pets without reducing numbers in the wild. More research on wild populations is needed to assess population health, and we encourage researchers to conduct both field and laboratory studies on these fascinating organisms.Secondarily, None declared.K.J.C. conceived the study; K.J.C and D.E.W collected and extracted samples; I.A. and P.W. sequenced, cleaned, and aligned DNA; K.J.C. analyzed genetic data; D.E.W. analyzed ecological data; K.J.C. and D.E.W. drafted the manuscript; All authors contributed to writing and editing.DNA sequences have been under accession number MH478912‐MH479012.


Population genetic data underpin many studies of behavioral, ecological, and evolutionary processes in wild populations and contribute to effective conservation management. However, collecting genetic samples can be challenging when working with endangered, invasive, or cryptic species. Environmental DNA (eDNA) offers a way to sample genetic material non-invasively without requiring visual observation. While eDNA has been trialed extensively as a biodiversity and biosecurity monitoring tool with a strong taxonomic focus, it has yet to be fully explored as a means for obtaining population genetic information. Here, we review current research that employs eDNA approaches for the study of populations. We outline challenges facing eDNA-based population genetic methodologies, and suggest avenues of research for future developments. We advocate that with further optimizations, this emergent field holds great potential as part of the population genetics toolkit. The study of population genetics describes changes in the genetic diversity of populations through evolutionary mechanisms such as natural selection, genetic drift, mutation, and gene flow, the totality of which have evolutionary consequences and can lead to speciation e.g., ,3,4,5,6),4,5,63,4Hypophthalmichthys molitrix) [Population genetic research requires genetic sampling from organisms of interest. For example, blood and tissue samples have traditionally been sources of genetic material for large marine mammals, e.g., flipper clips from seal populations . Howeverolitrix) . Many stolitrix) ,24,25. Molitrix) . In mariolitrix) ,29,30,31olitrix) ,37,38,39complex mixture of genomic DNA from many different organisms found in an environmental sample.” However, we take a narrower view of environmental sample; defining eDNA as DNA taken from soil, water, or air, excluding samples from direct individual remains such as fecal matter, hair, or feeding traces [Here we adopt the definition of eDNA from Taberlet et al.  definingg traces ,37,41. Dg traces ,43. Fromg traces ,36,37, eg traces , densityg traces , diet [4g traces , and sexg traces ,47. Addig traces ,49, geneg traces ,51, evolg traces  and matig traces  have beeg traces . With eDg traces .Environmental DNA is already being used to improve biodiversity estimates , when a complete snapshot of biodiversity is difficult to obtain via traditional methods  ,55,56,57Triturus cristatus) in freshwater habitat [Pongo pygmaeus) and Sunda pangolin (Manis javanica), may leave eDNA traces in locations such as salt licks [Some of the species in eDNA population genetic studies are of conservation and management interest and can be difficult to sample ,69,71. S habitat ,77,78. Tlt licks . Given tlt licks .Lithobates catsebeianus) and Asian carp , among many others, have already been successfully detected with eDNA methods [Not only can eDNA be useful for managing imperiled species, but invasive species may also be suitable targets. For instance, freshwater invasive species such as bullfrogs , to assess shark population structure, and their trophic interactions [Euthynnus affinis) over two years, likely reflective of food web interactions [The use of eDNA in population genetics is in its infancy, but a handful of notable studies demonstrate the potential of eDNA to obtain within-species population genetic data. A recent landmark study used eDNA from seawater to examine the mtDNA haplotype variation of whale sharks (ractions . This woractions . In addiractions . This reOrcinus orca) explored whether eDNA-obtained haplotypes could be assigned to known haplotypic variation, and how long genetic material can be detected after known target animal presence [More recently, seawater eDNA work on Northeast Pacific killer whales  mtDNA control region haplotypes have been detected via seawater eDNA [Beyond assignment to known haplotypes, eDNA has been used to uncover previously unknown intraspecific genetic diversity. For example, previously unknown harbor porpoise  genus from seawater samples, also using strict sequence read filtering [Separately, 16S rDNA eDNA metabarcoding primers described multiple intraspecific haplotypes from the iltering . Both stiltering ,72. Thesiltering ,72. Impoiltering .Pandarus rhincodonicus) parasite [adegenet for R, or Bayesian Analysis of Population Structure (BAPS) [Despite recent success and future potential, eDNA population genetic methods require fine tuning before they can be widely applied. Perhaps one of the biggest obstacles in transitioning from species detection to population genetics using eDNA is assigning sequences to individuals. Previous non-invasive genetic techniques for terrestrial organisms have been able to assume different individuals from separate scats ,48 or feparasite . Howevere (BAPS) ,93) as pEven when sequences are obtained in sufficient quantities and can be assigned to individuals, many non-invasive genetic sampling techniques, including eDNA, suffer from amplification challenges. Allelic dropout , and false alleles , are notable examples ,49,94. PSome techniques for mitigating quantity challenges for eDNA samples, such as PCR, are not without biases which may impact allelic abundance ,100,101.Furthermore, the number of reads from eDNA-obtained genetic data for target species must be sufficient enough to be distinguishable from background noise to ensure confidence that true genetic variation has been obtained. If initial DNA copy numbers are insufficient , PCcytochrome oxidase I) in mtDNA are widely available in public databases for many metazoans [numts) occur in eukaryotes and can influence population genetic inferences [Numts are difficult to recognize and have been shown to inflate diversity estimates when used with barcoding techniques [Increasing the length of sequences and diversity of marker types that can be obtained from eDNA would also be advantageous for answering population genetic questions. Thus far, eDNA research has focused primarily on short (100–400 bp) mtDNA fragments. Additionally, commonly barcoded regions  for multiple individuals, not only for comparison of populations across space and year-to-year variation, but also to infer evolutionary histories with coalescence models . AdditioFor all eDNA population genetic studies, a robust sampling design will help minimize LCN DNA profiles. Choosing the correct substrate for sampling a target organism based on ecological knowledge, such as the position in the water column with the highest concentration of DNA of the target organism, will maximize potential copy number . On thisSeveral technologies that could help address the difficulty of assigning genetic information from eDNA samples to individuals, are already available or in development. One way forward might include single-cell sequencing . As eDNAGood sampling design, in tandem with technological developments, could also help mitigate issues around obtaining allelic ratios and abundance ,163. IncAnother challenge for population genetic eDNA methods will be to fully develop nuDNA markers for the species of interest using eDNA sampling techniques. Capture methodology, where bait molecules bind to target DNA of interest, is currently being explored for eDNA with some success ,167. ThiST, based on an analysis-of-variance framework, have been made using this technique to infer population structure using nuclear markers [Once environmental nuDNA has been captured, statistical programs can be developed specifically to analyze environmental genetic data. It may be possible to use rarefaction methods to estimate how well genetic diversity has been sampled in a location, as has been used in eDNA metabarcoding studies to estimate taxa obtained . Additio markers . However markers , and bec markers . This te markers ,170,172. markers ,174. Ori markers ,174. Thi markers . Potenti markers . This ha markers ,175,176. markers ,68,69. IThese exciting technological advances paired with environmental genetics techniques offer promising new ways to address broad evolutionary genetics questions without direct sampling. Amazing progress is occurring all the time in the use of environmentally derived genetic material. For example, despite the much more rapid degradation of RNA in the environment, even environmental RNA (eRNA) samples are now being used to provide a snapshot of the biological diversity and processes present at the community level  177,178178,179. The need for non-invasive sampling, especially for low-density and cryptic populations, will continue to drive technological development. Environmental DNA population genetic methods have the potential to fill this need if developed and tested rigorously. Limited conservation funding and a need to minimize stress to wild animals, coupled with increasing molecular expertise, declining taxonomic expertise, and declining sequencing cost make eDNA an attractive genetic sampling tool ,198,199.