Musical imagery is the human experience of imagining music without actually hearing it. The neural basis of this mental ability is unclear, especially for musicians capable of engaging in accurate and vivid musical imagery. Here, we created a visualization of an 8-minute symphony as a silent movie and used it as real-time cue for musicians to continuously imagine the music for repeated and synchronized sessions during functional magnetic resonance imaging (fMRI). The activations and networks evoked by musical imagery were compared with those elicited by the subjects directly listening to the same music. Musical imagery and musical perception resulted in overlapping activations at the anterolateral belt and Wernicke’s area, where the responses were correlated with the auditory features of the music. Whereas Wernicke’s area interacted within the intrinsic auditory network during musical perception, it was involved in much more complex networks during musical imagery, showing positive correlations with the dorsal attention network and the motor-control network and negative correlations with the default-mode network. Our results highlight the important role of Wernicke’s area in forming vivid musical imagery through bilateral and anti-correlated network interactions, challenging the conventional view of segregated and lateralized processing of music versus language. Previous studies suggest that imagery and perception tasks involve overlapping cortical areas4, which however may not code the same information5 or engage in the same network interactions6 during both tasks. To date, the neural coding and network bases of mental imagery remains largely unknown and has been a long-standing topic of active research8.Mental imagery, including the experiences such as “seeing in the mind’s eye”, or “hearing in the head”, is a hallmark example of human cognitive abilities. It refers to quasi-perceptual mentation that resembles vivid perception in the absence of any external stimuli11. A famous example illustrating human capabilities in this arena was Ludwig van Beethoven; despite deafness in his later years, he continued composing and conducting great symphonies guided by his “inner ear”12. Neurobiologically, functional neuroimaging studies suggest that musical imagery involves cortical areas within and beyond the auditory system13, including the auditory belt areas15, the association cortex16, the prefrontal cortex17, and the premotor and supplementary motor areas18. Furthermore, it has become well established that such areas do not function in isolation, but interact in networks. In fact, the coordinated network interactions among these areas have been thought to underlie various aspects of musical imagery, e.g. auditory processing15, sensorimotor coordination18, memory retrieval16, cognitive control18, and emotion18. Hence, musical imagery is a rich behavioral and cognitive context, for which characterizing the patterns and dynamics of cortical networks may help understand the interplay of seemingly segregated functional systems.Among various types of mental imagery, musical imagery may occur with surprisingly high accuracy19 while treating the musical-imagery experience as a series of discrete events divided by periods of rest rather than as a continuous process. Although such studies have been useful for revealing activations with simple musical imagery, findings obtained from them cannot be used to conclusively state how activated regions interact to form functional networks or what information their responses represent during complex musical imagery.To probe network interactions, it is necessary to gather sufficient samples of neural responses during musical imagery so that the functional relationships among different regions can be reliably measured. To meet this need, a musical-imagery task ought to be complex and sustained for several minutes or longer, especially if brain activity is observed with functional magnetic resonance imaging (fMRI). However, designing an experiment with musical imagery for such a prolonged period is challenging, because imagery is subjective, internally-driven, and thus difficult to control or access. For this reason, prior studies have focused on simple and short imagery taskset al. has used a karaoke-like visual display to cue subjects to imagine familiar songs for several seconds3. Although this methodology has not been used for a longer period of imagery, adopting a similar idea to control sustained musical imagery by using music visualization20 without lyrics may further dissociate music from language during imagery. Music visualization may enable musicians to vividly and repeatedly imagine a very complex music piece, such as a Beethoven symphony capable of arousing rich and dynamic emotion21. Moreover, repeated measures of sustained musical imagery allow separation of imagery-evoked responses from ongoing activity, making it possible to disentangle the task-evoked functional networks and spontaneously emerging (i.e. task-unrelated) networks23.Nonetheless, these limitations can be mitigated by using visual stimulation to guide musical imagery. Herholz 23, to map cortical activations and functional networks under the sustained musical imagery paradigm or during the perception of musical stimulus. Moreover, we explored the information coded in the responses evoked by musical imagery and perception by correlating the fMRI signal with the time series of the auditory features of the music. Lastly, we compared imagery and perception-evoked functional networks with those in the resting state to explore the likely different patterns of network interactions during internally versus externally-driven mental processes.In this study, we aimed to map and compare both cortical activations and functional networks during musical imagery versus perception. Unlike prior studies, we used a silent music visualization to guide experienced musicians to repeatedly imagine an 8-minute symphony in the absence of external auditory input. The visualization served to inform subjects of the music content and control the timing for the subjects’ imagery experience, to maximize subjects’ ability to imagine the music in a similar way as they listened to it. The fMRI data acquired with this paradigm were evaluated for intra-subject reproducibility and functional connectivityWhen a subject with musical training listened to an 8-min music piece eight times during the fMRI acquisition Fig. , left, aTo address this question, we visualized the music as a silent movie Fig. , right. We further compared the task-evoked responses between the imagery and musical perception conditions. This allowed us to localize the responses to musical imagery as opposed to the visual stimuli because no visual stimuli were given during the perception condition. Since the anterolateral belt and Wernicke’s area were activated by both tasks, they were likely the shared cortical substrates for both musical perception and imagery. To test this hypothesis, we assessed the fMRI signal correlation between each musical-imagery session and each music-perception session at each voxel. This analysis revealed the areas that showed consistent responses to both tasks Fig. . Such arIn four additional sessions, we also presented the music with the same visual display as used in the musical-imagery task, yielding a control condition for us to validate findings from the intra-subject reproducibility analysis. As expected, the correlation in the fMRI signal between the (auditory-only) perception condition and the  control condition revealed shared activations in the auditory cortex but not in the visual cortex . Although the smaller amount of repetitions reduced the statistical power and the signal to noise ratio in each subject, the intra-subject correlations within and between perception and imagery sessions based on a set of regions of interest (ROIs) were averaged across subjects to reveal the group-level effects. In general, the group-level results revealed a similar set of activated regions as those obtained at the single subject levels Figs  and 2: t24, and it reflects the music timbre25. As shown in Fig. 26  or may inversely depend on the cognitive load of the task.Moreover, our results demonstrate that task-evoked functional networks may differ from intrinsic functional networks in the resting state, despite a general correspondence between them17. Since the musical-imagery task did not involve any movement and all subjects reported no humming or other vocalizations, the activations of these motor-related areas were unlikely to be attributed to movements, but resulted from the mental imagery state. In line with this interpretation, the supplementary motor areas and the premotor cortex were activated with imagery, whereas the primary motor cortex was not. However, we did not explicitly monitor vocal or sub-vocal movement (e.g. with EMG recordings), and thus could not entirely exclude the possibility of all kinds of vocalization.In this study, the supplementary motor areas and the premotor cortex were activated during musical imagery, confirming findings in previous studies18. When playing an instrument, a musician reads music scores and controls fine motor movements to produce sound; the sound provides feedback for the musician to refine motor control49. Such sensorimotor interactions might also occur during musical imagery, involving a motor-control circuit in the prefrontal cortex, including supplementary motor and premotor areas. The motor-control network activated with musical imagery  may occur during musical imagery in musicians, or even during non-musical auditory imagery17.Sensory and motor experiences are often linked to one anotherery Fig.  appears 51. However, research on musical imagery has often tested musicians and non-musicians in separate studies, since musical-imagery tasks for musicians may be too difficult for non-musicians to complete52. For example, musicians are able to mentally imagine the temporal reversal of familiar melodies53, which is hardly possible for non-musicians. Likewise, the imagery task based on music visualization is also very difficult for non-musicians to perform, limiting the scope of this study to musicians with a long history of musical training.Musical imagery is not unique to musicians, and it is a common human experience that may even occur involuntarily to most people54. The motor imagery may come from the subvocal silent singing of notes, or the manual motor imagery of performing instruments. In this study, we used a similar paradigm as “notational audiation”, except that our visualization of music was more intuitive and better controlled in timing. All subjects in this study were musicians with a long history of musical training or practice. They all reported experiencing vivid musical imagery during experiments, and could imagine the music with accurate tempo and pitch in mind. Some even reported being able to imagine the instruments being played during mental imagery of the Beethoven symphony. The visually-cued imagery task in this study may also elicit the engagement of sensorimotor interactions and even motor imagery (as discussed in previous section).Musicians and non-musicians may process and understand musical information in distinctive ways. For musicians, “reading the visualized music” or “hearing the music” might involve similar cognitive processes as reading and processing the musical note – the “language” specialized to music as a result of their musical training and knowledge. Musicians may also generate vivid imagery during silent reading of music scores, namely “notational audiation”. Previous studies have shown that this specific type of musical imagery may trigger both auditory and motor imagery in musicians56 but also during non-musical auditory imagery tasks57. Previous functional neuroimaging study found enlarged auditory cortical representation in musicians, and the enlargement of tonotopic maps was correlated with their ages of starting musical training58. Our results also suggest that both musical imagery and perception evoke bilateral activations and networks in musicians. Recent evidence suggests that this breakdown of lateralization is more likely to be observed in musicians59. Since all participants in this study were trained musicians, we could not contrast our observations in musicians against non-musicians. As a result, we speculate, but do not assert, any causal relationship between musical training and imagery ability.Musicians often outperform non-musicians not only during musical imagery tasksNine healthy volunteers .As illustrated in Fig. 60. The subjects were instructed to report the vividness of their musical imagery based on a 1–5 scale . All subjects were able to vividly imagine the music, and self-reported 4.1 ± 0.6 for the accuracy of pitches, 4.7 ± 0.5 for the accuracy of tempo and 3.8 ± 0.9 for the accuracy of distinguishing different instruments. However, it should be noted that the above self-reporting by itself only provided subjective assessment of musical imagery, and could not be taken as fully objective evidence for the existence and vividness of musical imagery. Despite its limitation, the self-reported scores still offered reasonable support for the presence of musical imagery. Also note that the self-reported scores were not used in the fMRI analysis. So, the (subjective) behavioral assessment and the imaging-based evidence were entirely independent of each other.Prior to fMRI, each subject was trained for 1 to 2 hours, to become familiar with the music visualization and imagery task. For the purposes of this study, 1 to 2 hours of training was sufficient, as the visualization was intuitive for musiciansIn the musical perception sessions, each subject was instructed to listen to this music with his or her eyes closed while no movie was presented. During the musical imagery sessions, each subject was instructed to imagine the music piece while watching the silent music visualization. Three subjects performed the musical-perception task eight times and the musical-imagery task twelve times. The perception and imagery session order was counterbalanced but not randomized across subjects. The sessions were conducted over a period of five days; two perception sessions and two imagery sessions in each of the first four days; four imagery sessions in the fifth day. The purpose of the high number of repetitions was to reliably examine the cortical activations and networks evoked by each task separately and shared by both tasks at the individual-subject level. This was especially desirable for the imagery task, which was cognitively demanding and likely involved complex cortical processes that were more variable across subjects than the perception task. The other six subjects underwent two repetitions of the perception task intermixed with two repetitions of the imagery tasks in a single day. Among these six subjects, one subject was excluded due to excessive head motion during fMRI scans. Despite the limited repetitions of the latter subjects, data from these subjects, as well as from two sessions of data arbitrarily chosen from the first three subjects, served the purpose of mapping cortical activations and networks during musical perception and imagery at the group level.Each of the first three subjects also underwent six sessions of resting-state fMRI on a different day. In each session, the subjects were instructed to rest for 8 minutes with the eyes closed, without falling into sleep. Since the resting-state sessions were long separated (by days) from the tasks of musical perception or imagery, it was unlikely that the subjects were conditioned to “play the music in their heads” with a similar temporal precision without any instruction by the researchers.2 in-plane resolution, TR/TE = 2000/35ms, flip angle = 78°, field of view = 22 × 22 cm2). Each subject was scanned with fMRI for 26 sessions: 8 sessions during the musical perception task, 12 under the visually-cued musical imagery task, and 6 in the wakeful eyes-closed resting state. Each session was 8 minutes in length.MRI data were acquired in a 3 T MRI system  with a 16-channel receive-only phase-array surface coil . Structural MRI with T1 and T2-weighted contrast were both acquired with 1 mm isotropic resolution. Functional MRI data were acquired using a single-shot, gradient-recalled echo-planar imaging sequence with 3.5 mm isotropic spatial resolution and 2 second temporal resolution  space, transformed onto individual cortical surfaces, and then co-registered based on myelin density and cortical folding patterns. Following this step, slow trends in the fMRI time series were corrected by regressing out a fourth-order polynomial function. Then fMRI data were temporally standardized (with a zero mean and unitary variance) and spatially smoothed by applying a 2-D Gaussian kernel of 2 mm full width at half maximum.MRI/fMRI images were preprocessed using a similar pipeline as in the Human Connectome Project23. This reproducibility was measured separately for each voxel as the temporal correlation between different sessions of the same task: 28 inter-session pairs among 8 sessions for the perception task and 66 inter-session pairs among 12 sessions for the imagery task. To handle the dependences among the different inter-session pairs for assessing statistical significance test, we applied a parametric statistical test to the intra-subject reproducibility based on linear mixed-effects (LME) modeling62 implemented in AFNI (http://afni.nimh.nih.gov). Specifically, the Fisher-transformed inter-session correlation zij is expressed in a crossed random-effects model,b0 is the effect of interest, the average correlation across all inter-session pairs, θi and θj are the random effects associated with the ith and jth sessions, respectively, εij is the residual term, i, j = 1, 2, …, 8 (for perception), and i, j = 1, 2, …, 12 (for imagery). Using this approach, we localized the brain areas significantly activated during musical perception (p < 0.01) and musical imagery (p < 0.005). See more details about the LME in a recent paper by Chen et al.62.For the three subjects who underwent the greater number of repetitions, we mapped cortical activations during musical perception and imagery. For each task, intra-subject reproducibility was calculated at voxel-level, as previously used for measuring the reliability of cortical activity during naturalistic stimulation elsewhere63, namely primary auditory cortex (A1), auditory anterolateral belt (ALB), Wernicke’s area, supplementary motor area(SMA), premotor cortex (PMC), frontal eye field (FEF), intraparietal sulcus (IPS)  based on the multi-modal parcellation of human cerebral cortex62,κi and λj are the random effects are associated with the ith session of perception and the jth session of imagery, respectively, i = 1, 2, …, 8, and j = 1, 2, …, 12. From this approach, we located the brain areas in which activities were significantly shared between musical perception and musical imagery (p < 0.01).For each subject, we also mapped the cortical regions where the musical perception and imagery tasks evoked the same response. For this purpose, we calculated for each voxel’s the temporal correlation between each perception session and each imagery session. As such, the cross-task correlation was measured for a total of 96 pairs of sessions given 8 perception sessions and 12 imagery sessions. The significance of cross-task correlation was evaluated for each subject, based on a crossed random-effects LME modelWe also performed the ROI-based group-level analysis on the eight additional subjects; for each subject,  data were available from two sessions of musical imagery and two sessions of musical perception. Using the method described in the previous sub-section, we evaluated the intra-subject correlation between musical imagery and perception for each ROI, converted the correlation coefficients to z scores and then averaged them across subjects. The statistical significance for the group-averaged effect was tested using a one-sample t-test (p < 0.05).62 as Eq.  musical perception and  imagery tasks evoked the similar responses during the control condition . For this purpose, we calculated the temporal correlation in the voxel time series between 1) each perception and each control session at each voxel and 2) each imagery session and each control session at each voxel, in a similar manner as how the mapping of the common activations between musical perception and imagery was performed. The significance of the cross-task correlation was evaluated for each subject using the crossed random-effects LME model64. Both spectral flux and amplitude envelope are important sound features for music timbre. Spectral flux measures the change in the power spectrum of a signal, which is calculated as the 2-norm  between the normalized spectra from adjacent frames24. The envelope shows the global outer shape of the signal, which can detect musical events such as notes. The feature frame size was set to 1 second with a step size of 0.1 second. The auditory feature time series were convolved with the canonical hemodynamic response function (HRF)65. The resulting time series were correlated with the fMRI signal at every voxel. For both the musical imagery and perception tasks, the correlation was calculated after the voxel time series was averaged across all the repeated sessions of the same task. A one-sample t test was applied to localize the brain areas that were significantly correlated with the auditory features during perception  q < 0.05) and imagery  q < 0.05).We further explored the musical content coded in the fMRI response. For this purpose, the spectral flux and amplitude envelope of the music stimulus (in the auditory form) was extracted by the MIRtoolbox in Matlab27. Specifically, the time series at a particular seed location was taken from one session; then, the seed time series was correlated with every voxel time series during a different session of the same task. Four seed locations were chosen: 1) in the left Wernicke’s area, 2) in the right Wernicke’s area, 3) in the left Auditory anterolateral belt, and 4) in the right Auditory anterolateral belt. These choices were based on activated regions for both musical perception and imagery, as highlighted in our results  q < 0.01).https://engineering.purdue.edu/libi/lab/Resource.html.The datasets generated under the current study are available from the corresponding author upon reasonable request. The minimally preprocessed MRI/fMRI data are also available on Supplementary Figures


Elymus nutans and Elymus sibiricus are two important perennial forage grasses of the genus Elymus, widely distributed in high altitude regions of Western and Northern China, especially on the Qinghai-Tibetan Plateau. Information on phenotypic and genetic diversity is limited, but necessary for Elymus germplasm collection, conservation, and utilization. In the present study, the phenotypic and genetic differentiation of 73 accessions of the two species were evaluated using 15 phenotypic traits and 40 expressed sequence tag derived simple sequence repeat markers (EST-SSRs). The results showed that only 7.23% phenotypic differentiation (Pst) existed between the two Elymus species based on fifteen quantitative traits. Principal component analysis (PCA) revealed that leaf traits, spike traits, and some seed traits were dominant factors in phenotypic variation. Moreover, 396 (97.8%) and 331 (87.1%) polymorphic bands were generated from 40 EST-SSR primers, suggesting high levels of genetic diversity for the two species. The highest genetic diversity was found in the Northeastern Qinghai-Tibetan Plateau groups. Clustering analysis based on molecular data showed that most accessions of each Elymus species tended to group together. Similar results were described by principal coordinates analysis (PCoA) and structure analysis. The molecular variance analysis (AMOVA) revealed that 81.47% and 89.32% variation existed within the geographical groups for the two species, respectively. Pearson’s correlation analyses showed a strong positive correlation between Nei’s genetic diversity and annual mean temperature. These results could facilitate Elymus germplasm collection, conservation, and future breeding. Elymus is the largest genus of the tribe Triticeae with approximately 150 species widely distributed in temperate regions of the world [Elymus are hexaploid Elymus nutans Griseb with genomes StStHHYY, and tetraploid Elymus sibiricus with genomes StStHH. Both are self-pollinating, perennial grasses. Indigenous to North Asia, the two Elymus species occur naturally in Eastern Mongolia, Northern Russia, the Himalayas, and parts of Alaska and Canada [Elymus species are widely distributed in the high altitude regions of Western and Northern China, especially in the Qinghai-Tibetan Plateau [Elymus species have been widely used as forage crops in cultivated pastures and natural grassland due to their good forage quality, tolerance to diverse environmental conditions as well as important ecological functions in vegetation restoration, soil stabilization, and erosion control [Elymus species are closely related to several important cereal crops such as wheat, barley, and rye, and may serve as potential genetic resource for the improvement of these crops [d Canada ,3. In Ch Plateau ,3,4. The control ,7. Moreose crops ,9,10,11.Elymus species has seriously lagged behind crop plants and other forage grasses. In China, only eight E. sibiricus cultivars and four E. nutans cultivars have been developed from wild materials. The lack of available information about economically valuable traits in the germplasm might be a major reason for the slow progress in cultivar development. The evaluation of phenotypic traits such as plant height, leaf shape, and inflorescence architecture is an initial and essential step for screening germplasms with economically valuable traits, and is also the usual method for determining the relationships among the different accessions [Despite their ecological and economical importance as potential forage grasses, progress in cultivar development and improvement for the two cessions . Meanwhicessions ,14,15. Tcessions ,16,17,18E. sibiricus and E. nutans accessions or natural populations. The genetic and geographical divergence of E. nutans germplasm from West China were studied using gliadin [E. sibiricus accessions from the Qinghai-Tibetan Plateau was studied by sequence related amplified polymorphism (SRAP) markers [E. sibricus accessions, cultivars, and breeding lines from Northeastern Qinghai-Tibetan Plateau was analyzed by EST-SSR markers [Elymus species from a small geographical area in the Qinghai-Tibetan Plateau. The extent and patterns of phenotypic and genetic diversity of E. nutans and E. sibiricus at a large geographical scale are still largely unknown. The Qinghai-Tibetan Plateau is famous for its high elevation, low temperatures, and oxygen concentration where various Elymus species are widely distributed. In contrast, the Mongolian Plateau exhibits a dry and windy climate, and also contains a high diversity of Elymus species [Elymus germplasm, indigenous to the Qinghai-Tibetan Plateau and Mongolian Plateau, may be important not only for the identification and development of Elymus germplasm with economically valuable traits, but also for the conservation and utilization of Elymus species.Previously, several studies have examined the genetic diversity of  gliadin ,20 and i markers . The gen markers . However species . As a coE. nutans and E. sibiricus; (ii) estimate the genetic variability of E. nutans and E. sibiricus geographical groups; and (iii) analyze the genetic structure and relationship of the two Elymus species. These results could facilitate Elymus germplasm collection, conservation, and future breeding.The objectives of present study were to (i) differentiate taxonomically between E. nutans accessions and 36 E. sibiricus accessions were collected and evaluated based on phenotypic traits and EST-SSR markers. Materials were obtained from the U.S. Department of Agriculture Germplasm Resources Information Network (GRIN), Lanzhou University, Sichuan Agricultural University, and Sichuan Academy of Grassland Science , Northwestern Qinghai-Tibetan Plateau (NWQ1), Southeastern Qinghai-Tibetan Plateau (SEQ1) and Mongolian Plateau populations (MP1). E. sibiricus accessions were divided into three geographical groups: Northeastern Qinghai-Tibetan Plateau (NEQ2), Southeastern Qinghai-Tibetan Plateau (SEQ2), and Mongolian Plateau (MP2).A total of 37  Science . These m Science . These mApproximately 30 seeds of each accession were germinated and seedlings were maintained in a greenhouse under a 25/15 °C day/night temperature regimes until they were eight weeks old. Twenty individual plants of each accession were transplanted to the Yuzhong experimental field of Lanzhou University, Gansu, China . After transplanting, plants were watered immediately, and no fertilizer was applied after transplanting. In the second year, a total of 730 individual plants  of the two species were selected for evaluating 15 phenotypic traits. Flag leaf length (FL), flag leaf width (FW), length of second leaf from bottom (LB), width of second leaf from bottom (WB), plant height (PH), culm node number (CN), tiller number (TN), culm diameter (CD) spike length (SL), floret number per spike (FN), length of lemma (LL), width of lemma (WL), awn length of lemma (AL), 1000-seed weight (SW1) were measured using the methods described by Zhao et al. . Seed shE. lanceolatus (Elw hereafter), Pseudoroegneria (Ps hereafter), and Leymus (Lt hereafter), and 15 newly developed E. sibiricus (ES hereafter) EST-SSR primers reported by Zhou et al. [Ten individuals of each accession were sampled and pooled together for DNA extraction using the SDS (sodium dodecyl sulfate) method . The quau et al. . The PCRu et al. .pi Ln pi, where pi is the proportion of each phenotypic trait [2t/s)/(σ2t/s + σ2s), where σ2t/s is the variance portion among populations and σ2s is the variance portion within populations [The mean value, standard deviation, coefficient of variation (CV), Shannon’s diversity index (H’), and principal component analysis (PCA) of phenotypic traits were analyzed using Excel 2013  and SPSS 19.0 software . Shannon’s diversity index was calculated as follows: H’ = –∑ ic trait ,26. Accoic trait . All pheulations . p2 − q2, where p is the frequency of present band and q is the frequency absent band [Elymus accessions was analyzed using STRUCTURE v 2.3.4 software  with the ‘admixture mode’, burn-in period of 10,000 interactions and a run of 100,000 replications of Markov chain Monte Carlo (MCMC) after burn in [The amplified bands were considered as present (1) and absent (0), and the binary matrix data produced in an Excel file was used for further statistical analysis. Polymorphism information content (PIC) were calculated as follows: PIC = 1 − ent band . Geneticent band . The paient band . The binent band . A bootsent band . The gen burn in . For eac burn in . Maximum burn in . The Sha burn in . In addi burn in . Elymus accessions were studied using 15 traits. Based on the phenotypic differentiation coefficient (Pst) analysis, 12.67% and 17.10% phenotypic variation existed among E. nutans and E. sibiricus groups, respectively  ranged from 1.64 to 2.23, with an average of 2.03. Four traits: flag leaf length (FL), tiller number (TN), floret number per spike (FN), and seed shattering (SS) had a coefficient of variation (CV) greater than 15%. The greatest phenotypic variation was found for seed shattering (CV = 28.11%). For E. sibiricus, the H’ value varied from 1.51 to 2.26, with an average of 2.06. The greatest phenotypic variation was found for tiller number (CV = 19.97%), followed by floret number per spike (CV = 15.47%) and seed shattering (CV = 15.45%). Length of lemma (LL) showed the lowest phenotypic variation in E. nutans (CV = 4.99%) and E. sibiricus (CV = 7.12%), respectively. The phenotypic variations of 73 ectively . The gre species . In E. nE. nutans accessions, within each cluster there were no obvious correlations between the phenotypic traits and geographic origins. Some E. sibiricus accessions from eastern groups of the Qinghai-Tibetan Plateau (NEQ2 and SEQ2) had similar phenotypic performance (Cluster IV). In E. nutans, the principal component analysis (PCA) showed that the first three principal components could explain 60.77% of the total phenotypic variation. PC1 was mainly involved in leaf traits, PC2 was mainly involved in seed traits , flag leaf width (FW), length of second leaf from bottom (LB), width of second leaf from bottom (WB), while seed shattering showed a negative correlation with most traits detected ; stem and spike traits group ; stem traits group (CN and TN); and spike and seed traits group (FN and AL). The remaining four seed traits  were assigned to another group . Clusterd traits . In contdetected . E. nutans and E. sibiricus, respectively  than E. sibiricus at species level (. Genetic variation was found between E. nutans and E. sibiricus in the same geographic region. E. nutans had a higher level of genetic diversity in Northeastern Qinghai-Tibetan Plateau (NEQ) (PPL = 88.64%), Southeastern Qinghai-Tibetan Plateau (75.56%), and Mongolian Plateau (MP) (PPL = 69.88%) groups. The highest genetic diversity was found in Northeastern Qinghai-Tibetan Plateau groups (NEQ) for both the Elymus species . Correspondingly, the lowest genetic diversity was found in northwestern Qinghai-Tibetan Plateau (NWQ1) E. nutans groups (PPL = 38.52%) and Southeastern Qinghai-Tibetan Plateau (SEQ2) E. sibiricus groups (PPL = 53.68%).In this study, forty EST-SSR primers generated 405 and 380 bands for ectively . The polibiricus . In genees level . GeneticE. nutans accessions and 3 E. sibiricus accessions. Thirty-three E. sibiricus accessions and 6 E. nutans accessions were assigned to Cluster II. In general, the vast majority of accessions (83.8% E. nutans accessions and 91.7% E. sibiricus accessions) could easily be distinguished using selected EST-SSR markers. The results observed in the principal coordinate analysis (PCoA) were in agreement with the UPGMA analysis. The first three principle components explained 44.98% of the total variation. A moderate, but clear separation between E. nutans and E. sibiricus was revealed (E. nutans accessions from Northeastern Qinghai-Tibetan Plateau and four from Mongolian Plateau were grouped with E. sibiricus.The unweighted pair-group method using arithmetic averages (UPGMA) cluster analysis showed that all accessions were clustered into two major clusters with a bootstrapping value of 100% . Clusterrevealed . Two E. Elymus accessions was analyzed using STRUCTURE software. Based on maximum likelihood and delta K (ΔK) values, the optimal number of groups was two . Group 1 consisted of six accessions, mainly from the Mongolia Plateau; Group 2 consisted of 31 accessions predominantly from the Qinghai-Tibetan Plateau. Thirty E. nutans accessions from the Qinghai-Tibetan Plateau could be clearly divided into four subgroups when K was 4 (II). Eleven E. nutans accessions from Northeastern Qinghai-Tibetan Plateau (ENQ1) and two accessions from Southeastern Qinghai-Tibetan Plateau (SEQ1) were assigned to Cluster 1. Nine accessions (69.2%) in this group were categorized as having admixed ancestry. Ten SEQ1 accessions were assigned to Clusters 2 and 3, the remaining seven accessions were assigned to Cluster 4 based on the proportional membership (>0.5). There was no obvious relationship between geographic location and genetic structure in E. sibiricus accessions  and genetic distance (0.31) were found between Northwestern Qinghai-Tibetan Plateau (NWQ1) and the Mongolian Plateau (MP1). Among the E. sibiricus groups, the greatest genetic differentiation (Gst = 0.09) was found between Southeastern Qinghai-Tibetan Plateau (SEQ2) and the Mongolian Plateau (MP2). Genetic differentiation (Gst) and genetic distance among the four measures . Among tE. nutans, 89.32% for E. sibiricus) (E. nutans geographic group and 0.25 for E. sibiricus group with a higher gene flow (Nm = 1.96) across all E. sibiricus geographic groups.Analysis of molecular variance (AMOVA) was conducted to further evaluate the partitioning of genetic differentiation among and within the geographic groups. The results showed that less than 20% of the variation occurred among the geographic groups, whereas more than 80% of total variance existed within geographic groups (81.47% for biricus) . Moreover = 0.95 for E. nutans, p < 0.01; r = 0.78 for E. sibiricus, p < 0.05)  . While aElymus accessions. Similar results were also found in previous studies. Chen et al. evaluated the phenotypic diversity of 54 E. nutans accessions from Qinghai-Tibetan Plateau based on thirty phenotypic traits, and found 16.05% phenotypic variation [Elymus species [Elymus species. However, the process of phenotypic assessment is time-consuming, and the effectiveness and accuracy of assessment is limited due to the influence of environment factors. Compared with phenotypic assessment, molecular marker technology is an efficient supplement and alternative to phenotypic measurements. Our results showed relatively higher level of genetic diversity of E. sibiricus geographical groups (average PPL = 63.16%) when compared with a previous study. Zhang et al. employed sixteen start codon targeted (SCoT) markers for assessing the genetic diversity of E. sibiricus, and found 37.63% genetic variation among the five geographical groups [E. nutans accessions from the eastern Qinghai-Tibetan Plateau using simple sequence repeat markers (SSRs), and found 91.38% genetic diversity [E. nutans accessions from Southeastern Qinghai-Tibetan Plateau were analyzed, more than 75% genetic diversity was found. Therefore, the results of the present study showed that the two Elymus species had high level of genetic diversity, and also indicated that SSR markers were efficient in assessing the genetic diversity of E. nutans and E. sibiricus accessions. The measurement of phenotypic traits is considered as an important and fundamental method for diversity analysis ,39. Our ariation . Among t species ,40,41. Tl groups . Chen etiversity . In thisE. nutans had a relatively higher genetic diversity when compared with E. sibiricus, possibly due to the additional Y genome in the hexaploid E. nutans and a wider geographic range. Previous studies showed that genetic variability is usually higher in polyploidy species [Elymus species. Similar results were also found in other self-pollinating Elymus species such as E. trachycaulus and E. glaucus. Steven et al. analyzed four E. trachycaulus populations and found 85% within-population variation using SSR markers [E. glaucus [Elymus species played a more important role than pollen movement. Seed dispersal happens more commonly between populations with the same regions at similar altitudes [Elymus populations has contributed to their population genetic structure. In addition, the sample size used for the genetic diversity study may have also had a significant effect on the measurements of genetic variability among and within populations [In general,  species . Based o markers . Wilson  glaucus . The dis glaucus ,7. Previltitudes . There aElymus accessions. In the Qinghai-Tibet Plateau, towering mountains and deep valleys could also serve as genetic barriers of pollinator movement and seed dispersal, which could result in genetic difference among accessions especially in large geographical regions. Therefore, the diversity of geographical origin or ecological conditions of Elymus accessions in this study could explain the relatively high level of genetic diversity. Meanwhile, environment parameters are highly correlated with the magnitude and distribution of genetic diversity ,43. A spE. sibiricus genotypes with genetic and phenotypic differences to generate F1 hybrids. The molecular and phenotypic diversity analysis of F1 population revealed rich genetic and phenotypic variations. Seed shattering, flag leaf length and width showed the positive heterosis [E. sibiricus genotypes with seed shattering variation, and found high level of genetic diversity in hybrid populations using SSR markers, and the results also suggested more genetic variation could be captured in hybrid populations by crossing breeding [Elymus accessions from the Northwestern groups of the Qinghai-Tibetan Plateau had highest level of genetic diversity. The greatest phenotypic differentiation was found for seed shattering. Therefore, these wild accessions with affluent genetic diversity and desired traits could be used as important genetic resources for future Elymus breeding programs.Phenotypically and genetically diverse germplasm is a potentially valuable source for the improvement of desired agronomic trait such as seed yield, quality and stress tolerance. To broaden the genetic base, one of the important strategies is to screen and develop novel germplasm or breeding lines with desired traits. Wild germplasm could provide advantageous alleles like improved disease resistance, stress resistance, and higher yield for modifying crops by hybridization and introgression . Past steterosis . Zhao etbreeding . A previbreeding . Based oElymus accessions in Western and Northern China. E. nutans had relatively higher genetic diversity than E. sibiricus at the species level. The ecological factors such as annual temperature, precipitation, altitude played a role in the divergence. More genetic variation of the two Elymus species can be captured when sampling a larger number of germplasm from the Northeastern Qinghai-Tibetan Plateau. Meanwhile, these traits related to biomass yield and seed yield should be considered in future breeding programs.This study showed a high level of phenotypic and genetic diversity of 73


Perca fluviatilis L.) population in the Baltic Sea over a 24‐year period. We detected marked changes in size‐ and age‐distributions and increase in juvenile growth rate. We also observed reduction of age at sexual maturity in males that has frequently been considered to support the hypothesis of fisheries‐induced evolution. However, combined individual‐based life‐history and genetic analyses indicated increased immigration of foreign individuals with different life‐history patterns as an alternative mechanism behind the observed phenotypic change. This study demonstrates the value of combining genetic and phenotypic analyses and suggests that replacement or breakdown of locally adapted gene complexes may play important role in impeding the recovery of fish populations.Over the recent years, growing number of studies suggests that intensive size‐selective fishing can cause evolutionary changes in life‐history traits in the harvested population, which can have drastic negative effects on populations, ecosystems and fisheries. However, most studies to date have overlooked the potential role of immigration of fish with different phenotypes as an alternative plausible mechanism behind observed phenotypic trends. Here, we investigated the evolutionary consequences of intensive fishing simultaneously at phenotypic and molecular level in Eurasian perch ( Fishing causes substantial mortality in commercially exploited fish stocks, often exceeding the level of natural mortality (Stokes and Law Peromyscus leucopus) have been associated with immigration of non‐native individuals with a distinct phenotype that displaced the previously resident population due either to selective advantage in the changing habitat or to genetic swamping (Pergams and Lacy Gadus morhua) demonstrated that a heavily exploited population can be strongly affected by gene flow from neighbouring populations  population in Matsalu Bay, in the eastern Baltic Sea , and the Väinameri area and its salinity varies between 2 and 5 ‰. The shallow areas of Matsalu Bay are especially good spawning places for the Eurasian perch, providing suitable food for juveniles and adults  Archipelago is situated in the north‐eastern part of the Baltic Sea Fig.  and is vlts Järv . Prior tlts Järv . Based olts Järv  and seldlts Järv . In Matslts Järv .nd of May; range, from 15th of April to 28th of June) and neighbouring regions  using fyke nets (mesh size 16 and 18 mm). For phenotypic analyses, information was extracted from 1 570 individuals  , and ages and growth histories of these individuals were obtained through back‐calculations from operculum and scale samples  were selected . For subsequent population genetic analyses, all individuals were divided into cohorts according to their year of birth  using a three primer system  according to Boutin‐Ganache et al. . The 6 μ2O, denatured and electrophoresed on an ABI Prism 3130xl genetic analyzer (Applied Biosystems/Hitachi) along with GeneScan 600 LIZ size standard (Applied Biosystems). DNA fragments were genotyped using GeneMarker 1.60 (Soft Genetics). All genotypes were manually inspected.PCR products  were diluted in 100 μl of ddHn = 865; Table n = 12, 1988, n = 14, 1990, n = 7, 1993, n = 7, 1996, n = 8, 1999, n = 11, 2002, n = 7, 2009, n = 15 and 2010, n = 15). Estimated allelic error rates at each locus , where sex and a factor indicating whether the individual belonged to the ‘pre‐’ or ‘post‐’ collapse group, as well as their interactions, were set to fixed effects and the cohort was set to a random effect. These analyses were conducted separately for ages from 1 to 5 years, as no older individuals were present in the post‐collapse period, and thus comparison between pre‐ and post‐collapse groups was not possible.Maturity data were available for the pre‐collapse years 1987–1992 and post‐collapse years 2006–2008 and 2010. Sex‐specific maturity ogives (i.e. the age‐specific probability of being mature) in pre‐ and post‐collapse years were investigated through a generalized linear model (GLM) with a binomial error structure, having age, length, collapse indicator  as well as their two‐way interactions and the quadratic effects of age and length as effects. Model simplifications were made by stepwise model reduction based on either likelihood ratio tests (LME) or chi‐squared tests (GLM) as suggested by Crawley . Whole lHW) equilibria (5 000 iterations each). To assess levels of genetic diversity over time, allelic richness , mean number of alleles (A), observed (Ho) and expected heterozygosity (He) were calculated using FSTAT 2.9.3.2  that are in HW and gametic phase equilibrium. Twenty independent runs were performed for each value of K (K = 1–6), using prior population information, as suggested by Hubisz et al.  were also included in the analysis. To avoid spurious clustering of the individuals due to incomplete genotype data, individuals with more than two missing loci were excluded from this analysis .The program Structure 2.3  and immigration rate (m) of the Matsalu Bay perch population were estimated using a maximum‐likelihood method implemented in the program MLNE 1.0 . We subsequently created a putative donor population (source of immigrants) by pooling all the individual genotypes that clustered together with more than a 0.8 probability as ‘foreign’ genotypes, identified by Structure . We also carried out analyses using samples from neighbouring regions  as potential source populations (data not shown). The generation length for pre‐ and post‐collapse cohorts was set at 5.3 and 3.3 years, respectively, according to the mean ages of mature individuals. As the intervals between year classes were not integers, all estimates were adjusted according to the equations provided by Wang and Whitlock  and the subsequent year classes were analysed separately until 1990 (1.7 generations). Similarly, for post‐collapse analysis the 1989 and 1990 samples were pooled together (0 generation) and the subsequent year classes were analysed separately until 2008 (4.76 generations).The effective population size  were used in linear mixed models, having sex, cluster, and their interaction as fixed effects, and cohort as a random effect. Similar to the analysis described earlier, model simplification was made by stepwise model reduction based on likelihood ratio tests (LME) using R 2.11.1.P < 0.001) than fish of the same age in the pre‐collapse years (1987 and 1990), while 2‐year‐old perch were 10.9 mm longer in the post‐collapse years . As shown in Fig. P > 0.05).The mean age and length of perch in the Matsalu Bay population decreased considerably during the study period Fig. B. In then = 22), three (n = 54) and four (n = 33) were mature. In males, the probability of being mature (maturity ogive) was found to differ significantly between the pre‐ and post‐collapse groups  as well as to depend on the age of the individual ). In females, on the other hand, the probability of being mature did not differ between the pre‐ and post‐collapse groups , but was significantly affected by age ).In the pre‐collapse samples, 40% of 2‐year old, 37% of 3‐year old and 10% of 4‐year old males were immature, while in the post‐collapse group all the studied males at the ages of two (P < 0.05 before Bonferroni correction). However, after applying the Bonferroni correction, deviations in three recent cohorts (2006–2008) remained significant  equilibrium testing over all loci indicated that five out of 14 cohort samples from Matsalu Bay  deviated significantly from HW equilibrium proportions  from Matsalu Bay showed considerable fluctuations over the period of 24 years – the largest changes, however, were associated with the more recent post‐collapse cohorts born in 2006–2008  and allelic richness (Ar) were lowest in the 2006 cohort, whereas the mean number of alleles (A) and observed heterozygosity (Ho) had the smallest values in the 2008 year class  showed relatively stable allele frequencies over 16 years in Matsalu Bay  . Consistent with the FST analysis over all loci, individual allele frequency estimates reflected large genetic shifts in the post‐collapse samples from Matsalu Bay . The length of 1‐year‐old females carrying the local Matsalu Bay multilocus genotype was on an average 22 mm smaller than for fish carrying a foreign genotype in the post‐collapse sample (SL 70 mm and 92 mm respectively). Similarly, local Matsalu Bay males were smaller than the foreign individuals of the same age (SL 76 mm and 80 mm respectively). At the age of 2 years, the length did not differ between sexes, but perch carrying the local Matsalu genotype were still 16 mm smaller than the individuals of foreign origin . At the ages of 3, 4 and 5 years, however, no significant differences between the fishes of local and foreign origin were found . In contrast to the analysis of the whole data set . At the ages of 2–5 years, no differences in length between pre‐ and post‐collapse samples with local genotype were found .Analysis of the age‐specific length in post‐collapse cohorts (taking into account the Bayesian clustering information) showed that the individuals belonging to distinct genetic clusters also differed in their growth rates. The length of 1‐year‐old fish depended on sex, inferred cluster and the interaction of sex and inferred cluster (Ne) and migration rates (m) indicated that the estimated Ne of the Matsalu Bay perch population was small, while the immigration rates, especially in the post‐collapse samples, were high. Consistent with the drastic reduction in census size, the Ne estimate in the pre‐collapse sample  was seven times larger than in the post‐collapse sample . The estimated immigration rate was four times higher in the post‐collapse sample  compared to the pre‐collapse cohorts . More detailed cohort‐by‐cohort analysis, however, showed two distinct cases where m was much higher than in the other year classes: in 1986  and in 2006  supporting the results from the Bayesian clustering analysis  was seven times smaller than in the pre‐collapse period (cohorts 1981–1990). Contrary to expectations, this sharp decrease in Ne did not result in a rapid loss of genetic diversity. This can be explained by the increased influx of non‐native immigrants, which compensated for the expected loss of genetic diversity among the remnant native population. This increased influx of immigrants is not surprising, given that a reduced Ne can make the local populations more sensitive to immigration, potentially leading to genetic swamping and/or loss of local adaptations  most likely do not represent the source population(s) of immigrants that were found in Matsalu Bay. We therefore suggest that the immigrant genotypes observed in our study area might originate from near‐shore littoral perch from the River Kasari delta area that flows into Matsalu Bay, while the vast majority of genotypes from the pre‐collapse samples belong to the more open‐water pelagic form. Hence, the observed phenotypic and genotypic changes may be related to expansion or decrease of the spatial shift in the spawning areas of these populations. The occurrence of genetically and phenotypically distinct but geographically close populations in Eurasian perch is also supported by earlier research. For example, lake and river populations may exchange many migrants due to their proximity  colony  and significance levels from genic differentiation (exact G) test . Click here for additional data file.


Cimex adjunctus, whose host affinity is weak but the biology of the potential hosts have been well studied. This ectoparasite has been hypothesized to rely on its hosts for dispersal due to its low inherent dispersal potential. Here we describe genetic diversity and demographic history in C. adjunctus through most of its range in North America. We investigated variation at the cytochrome c oxidase 1 mitochondrial gene and nine microsatellite markers, and tested the prediction that genetic diversity in C. adjunctus is spatially structured. We also tested the prediction that demographic history in C. adjunctus is characterized by range and demographic expansion as a consequence of post-Pleistocene climate warming.Evolutionary histories of parasite and host populations are intimately linked such that their spatial genetic structures may be correlated. While these processes have been relatively well studied in specialist parasites and their hosts, less is known about the ecological and evolutionary consequences of relationships between generalist ectoparasites and their hosts. The aim of this study was to investigate the genetic structure and demographic history of a bat ectoparasite, C. adjunctus than has been quantified in two of its hosts, but contrast in amount of variation explained by host association with different genetic markers . Also, C. adjunctus’ history is not primarily characterized by demographic and range expansion, as is the case with two of its key hosts.We found stronger spatial structuring of genetic diversity in C. adjunctus than have been detected in two of its key hosts. Our results suggest an effect of a loose parasite-host relationship and anti-parasitism strategies on genetic structure and post-Pleistocene recovery of population size.Our study shows different patterns of genetic structure and demographic history in The online version of this article (doi:10.1186/s12862-016-0839-1) contains supplementary material, which is available to authorized users. Pagioporus shawi) permitted more detailed information on population assignments in its host, the steelhead trout (Oncorhynchus mykiss) than could be obtained by examining genetic variation in the host itself [Heligmosomoides polygyrus have revealed demographic and historic events affecting its host, the field mouse Apodemus sylvaticus [Apodemus species likely caused differentiation of both the Apodemus host and their Heligmosomoides parasite species [Parasites, through effects on host survival and reproduction, can modify the morphology, life history and behavior of their hosts. Parasites may also influence the dynamics of host populations thereby shaping communities . Hosts it itself . In addit itself , 7 whichlvaticus . Further species .However, it has recently been shown that a strong link between host dispersal and parasite genetic structure is not ubiquitous, and depends on factors that include the degree of association with the host and host mobility . Here, wCimex (Order: Hemiptera) are temporary ectoparasites of homeothermic animals. They do not remain on their host at all times but rather remain in nests or roosts between blood meals [Cimex species are associated exclusively with bats, while a few associate with a more diverse range of hosts [Cimex adjunctus is a widespread ectoparasite of bats in North America, occurring from the eastern seaboard to the Rocky Mountains, and from Labrador and the Northwest Territories south to Texas [Eptesicus fuscus) and the little brown myotis (Myotis lucifugus), two species that often roost in buildings [C. adjunctus is unknown, but is likely similar to that of the common bed bug C. lectularius, which can range from two to 12 generations a year depending on monthly temperatures [Insects in the genus od meals . Most Ciof hosts –16. Cimeto Texas . It parauildings –19. The eratures , and is Cimex species have a very low inherent capacity for dispersal over long distances, on the scale of kilometers. He thought it unlikely that adult Cimex species disperse on their own. He therefore hypothesized that Cimex species can disperse occasionally attached to a host’s body [E. fuscus, [M. lucifugus, [C. adjunctus, likely only a fraction of host dispersal events result in successful parasite dispersal so gene flow may be lower in C. adjunctus relative to these two host species. Furthermore, C. adjunctus may experience frequent extirpation and recolonization events. Bartonička and Růžičková [C. pipistrelli reaches a high. They also found the appearance of C. pipistrelli 21 to 56 days after the first bat visit in any given roost. Since C. adjunctus, like C. pipistrelli, does not stay on the host between blood meals, sudden host population decreases within roosts might drive local extirpation events.Usinger  proposedt’s body . Previou fuscus, , 21; M. cifugus, –25). Oveůžičková  identifiCimex pipistrelli is morphologically, but not genetically, differentiated among bat host species [C. adjunctus populations on different host species. Many different North American bat species temporarily roost together for short intervals during the night, such as many Myotis species, including M. lucifugus, and E. fuscus [C. adjunctus.Although different ectoparasite races are often associated with different host species –29, high species . This su. fuscus , potentiM. lucifugus and E. fuscus are hypothesized to have expanded their ranges from glacial refugia. Dixon [C. adjunctus on its host species for long-distance dispersal and colonization may have contributed to broadly congruent patterns of historical range expansion over large spatial scales.Much of North America was unsuitable for many bat species during the last Pleistocene glacial maximum, and both a. Dixon  suggestea. Dixon  suggestea. Dixon . We expeC. adjunctus across its range in North America. Because of its comparatively shorter generation time, the likelihood that only a fraction of bat dispersal events may result in ectoparasite gene flow, and the potential for local extirpations, we predicted stronger spatial genetic structure in C. adjunctus relative to its hosts. Because of the potential for movement among host species, we also examined differentiation among populations found on different host species. Finally, based on the hypothesis that post-Pleistocene climate warming had similar effects on the demographic history of C. adjunctus as that of its hosts, we predicted genetic signatures of demographic and range expansion in C. adjunctus.We investigated the spatial genetic structure and phylogeography of Cimex adjunctus samples from throughout its range in North America  gene for 154 C. adjunctus, and identified 41 haplotypes with 46 polymorphic sites . Finally, we used only sites with data for at least two individuals, and for which we obtained both mitochondrial and microsatellite data, for the analysis of molecular variance (AMOVA) analysis, which resulted in a dataset of 127 individuals from 26 sites (82% of the CO1 dataset and 85% of the microsatellite dataset).We collected 160 Among the nine microsatellite loci, we observed between two and 31 alleles. Across different sites and genetic clusters (identified by Geneland), average number of alleles ranged from 1.5 to 4, expected heterozygosity ranged from 0.18 to 0.62, observed heterozygosity ranged from 0.09 to 0.25, and the inbreeding coefficient varied between 0.00 and 0.77 , it hasrown bat  and littrown bat  than exarown bat , based oC. adjunctus than in its hosts. Analysis of microsatellite genotypes has revealed only two genetic clusters in both big brown bat [C. adjunctus. Likewise, very little genetic variation  occurs among spatially separate sites in big brown bat [C. adjunctus, about one third of the microsatellite variation and about one half of mitochondrial variation occur among sites (after taking out variation among host species). These observations suggest that C. adjunctus is more subdivided within its range than at least two of its hosts, and that its genetic structure does not entirely reflect the dispersal patterns of its hosts. Interestingly however, both genetic clustering and MSN results also offer some evidence of possible continent-scale long-distance movement in C. adjunctus, as reflected in the relationships among individuals from the Northwest Territories, Saskatchewan, Maritime Canada and the US Midwest. Relationships among C. adjunctus samples from these locations echo a pattern that was observed in M. lucifugus, where a set of sites in the central United States and central to north-western Canada are connected by high gene flow [However, the overall degree of genetic structuring appears to be higher in rown bat  and littrown bat , both atrown bat –25. In CC. adjunctus. For example, a finer genetic structure was found in an endoparasitic nematode H. polygyrus than in its host, the field mouse A. sylvaticus [C. adjunctus, which spends a considerable proportion of time living off of its hosts within cracks and crevices in roosting sites. First, only a small subset of dispersing bats are likely to be accompanied by C. adjunctus. Second, dispersal mortality in the parasite may be very high due to grooming behaviour of bats that can cause the parasites to fall off [C. adjunctus, and could explain the much stronger genetic structure we observed for relative to two of its key hosts.Spatial structuring of genetic diversity can arise when gene flow is not sufficiently high to homogenize allele frequencies throughout the study area, and across a broad range of animal species dispersal ability is correlated with both gene flow and population genetic structure . This halvaticus . One reafall off . Additiofall off . All of Cimex populations might be much smaller than populations of their hosts, although information on C. adjunctus population sizes is limited. In addition, it is possible that C. adjunctus experiences localized extirpations and recolonizations when roosts are abandoned by bats and subsequently re-occupied. The resulting founder events would further reduce effective population sizes and lead to higher genetic differentiation in C. adjunctus via genetic drift.In addition to gene flow and dispersal, genetic structure may also be influenced by genetic drift in small populations, which acts by increasing differentiation . Bat-assC. adjunctus associated with different host species. Interestingly, we found a sharp difference between mitochondrial DNA and microsatellite markers in this regard. Mitochondrial data suggested considerably less variation among populations associated with different host species compared to microsatellite data. At the same time, microsatellite data showed less variation among populations than did the mitochondrial data, indicating that the difference we observed with respect to host species does not reflect a generally poorer ability of the mitochondrial data to detect differentiation in C. adjunctus.We also examined the proportion of genetic variance among samples of C. pipistrelli that found no genetic differentiation among individuals associated with different host species, using mitochondrial CO1 and four nuclear loci [C. pipistrelli, although it is important to point that all nuclear loci in the study of Balvin et al. [C. adjunctus could be the reason for our results. Male-biased dispersal among roosts could lead to the higher proportion of genetic variation among sites in mitochondrial data than in microsatellite data. On the other hand, female-biased switching of hosts within roosts could be responsible for the lower proportion of genetic variation among host species observed in the mitochondrial versus microsatellite data. Autonomous  female-biased movements over short distances, such as between neighbouring apartment units, have been described in the common bed bug, C. lectularius [C. adjunctus also move more readily at short distances within roosts, that could explain both a higher rate of host-switching among females and a lower rate of transport among roosts by their hosts . However, there is currently no information available on sex-biased dispersal or host switching in C. adjunctus. Our results not only suggest sex-biased dispersal or host switching in C. adjunctus, but also highlight the need to use more than one type of marker when investigating genetic diversity in an understudied species.Our mitochondrial data are consistent with an earlier study on ear loci . Our micn et al.  showed atularius . If femaCimex is the human associated common bed bug, C. lectularius. Several studies have examined genetic structure in C. lectularius across a range of spatial scales [C. lectularius than we observed for C. adjunctus, which could be due in part to the smaller spatial scale of their sampling (eastern USA only). On the other hand, our genetic diversity estimates for C. adjunctus were strikingly similar to those found in one study on C. lectularius [C. lectularius populations associated with bats and humans found higher average numbers of alleles in the bat-associated populations than human-associated populations [C. lectularius in Europe [C. adjunctus associated with different bat species. One likely reason for this dissimilarity between C. adjunctus and C. lectularius is that the former is a weak generalist, associated with closely related species [C. lectularius genetic structure [The most well studied member of the genus l scales –42. Howetularius , althougulations . Anothern Europe  found hi species , while ttructure , 40, 41,tructure , 12, 43.C. adjunctus, based on the fact that there are widespread signatures of historic population expansion in many vertebrates, invertebrates and plant populations, including in the bat hosts of C. adjunctus. Such patterns are most probably attributable to postglacial climate warming [E. fuscus and M. lucifugus [C. adjunctus. This is unlikely to be a result of inadequate spatial sampling since our samples cover most of the known range of this species [We predicted signals of range and demographic expansion in  warming . Howeverucifugus –23, indi species .C. adjunctus using a variety of approaches. According to EBSP results, a gradual decline might have started at around 200,000 years ago, corresponding roughly to the Illinoian glaciation, a time of likely very harsh climate for most species in North America [M. lucifugus in eastern Canada [C. adjunctus effective population size indicated in the EBSP starting 30,000 years ago is in a similar timeframe as, but is of much smaller amplitude than, the demographic expansion found in both M. lucifugus studies. Relative to those studies, our analysis was able to span a larger amount of time, probably due to the larger spatial scale of our sampling.We found evidence of population decline in the demographic history of  America . A smalln Canada  and Minnn Canada . A smallC. adjunctus, an insect ectoparasite associated with a number of bat species in North America. This free-living parasite moves off the host between blood meals and could be actively removed by the host through anti-parasitism behaviour. Our results highlight that the genetic structure and demographic history of a weak generalist ectoparasite, particularly one that has a loose relationship with its hosts, can be very different from that of its hosts.Parasites that are mostly free-living, associate with multiple species of hosts, and have hosts that are highly mobile, such as some ectoparasites of bats, may be expected to show a genetic structure that contrasts with the dispersal patterns and genetic structure of their hosts . These sC. adjunctus across much of its North American range. Most samples are from mist-netted host individuals of E. fuscus, M. lucifugus or M. septentrionalis. Mist net capture locations were adjacent to a known summer roost  of either of the three bat species, or within forested national, provincial, state or territorial lands , 1.5 mM of MgCl2, 0.2 mM of each type of dNTP, 0.3 μM of each primer, 1 U of Taq polymerase (ABI), and 1 μL of DNA extraction product. We used the following PCR program: an initial denaturation step of 1 min at 94 °C, followed by 36 cycles of 30 s of denaturation at 94 °C, 45 s of annealing at 49 °C and 45 s of extension at 72 °C, finished by a final extension step of 5 min at 72 °C. We visualized PCR products by 1.5% agarose gel electrophoresis using SYBR Green (BIO-RAD) on a UV transluminator to check the quality and size of amplified fragments. Then, we sequenced the amplified gene fragment for every sample using Sanger sequencing with BigDye terminator chemistry (ABI) and analyzed the fragments on a 3730xl DNA Analyzer (ABI). We aligned all sequences using MEGA 6.06.We extracted DNA from the whole insect for all samples using the DNeasy Blood & Tissue Kit . We then amplified a 576-bp fragment of the n et al. ). We useCimex lectularius , 2.175 mM of MgCl2, 0.216 mM of each type of dNTP, 0.25 to 1.2 μM , we calculated average number of alleles, expected and observed heterozygosity, and inbreeding coefficient. For microsatellite loci, we tested for Hardy-Weinberg and linkage disequilibrium within each site with data for at least two sampled C. adjunctus individuals, using Genepop 4.2. For each type of test, we corrected for multiple tests using Bonferroni correction, with a threshold α of 0.05.For sites with data for at least five sampled C. adjunctus using genetic clustering, tests of isolation-by-distance (IBD), and an analysis of molecular variance (AMOVA). We conducted a Bayesian clustering analysis using Geneland 4.0.5, which takes into account geographic coordinates of individual samples. We used 100,000 iterations, thinned every 100th iteration, and a post-process burn-in of 200 (of the 1000 left after thinning), for K values between 1 and 20. We executed 10 runs, and kept the one with the highest posterior mean density, after burn-in. We attempted to identify the population to which each individual was assigned the most often, defined here as the population where the majority of Markov Chain Monte Carlo (MCMC) chains converged for any given individual. We also conducted a K-Means clustering analysis using GenoDive 2.0 on allele frequencies, for K values between 1 and 20, and using 50,000 simulation steps, to validate results obtained with the Geneland method. We used Bayesian Information Criterion (BIC) values to determine the most likely K value.We tested our prediction of range-wide genetic structure and an effect of geographic distance in W [W for each pairwise relationship, in order to obtain genetic distances. We calculated geographic distance (in km) between sample sites, corrected for sphericity of the earth, using the ‘rdist.earth’ function from the ‘fields’ package [W relatedness index, and most other relatedness indices, is that individuals are in a large random mating population without population structure [We conducted an individual-level analysis of IBD, using the estimate of genetic relatedness, rW , calcula package  in R v3. package , which utructure . In an aFor all sites with at least two sampled individuals, we used AMOVA to examine the proportion of genetic variation among sites, and among individuals associated with different host species. AMOVA was executed in GenoDive 2.0 for microsatellite data, and Arlequin 3.5 for mitochondrial data.C. adjunctus would show signals of demographic and range expansion, similar to some of its bat hosts, with a suite of methods for investigating demographic history using either mitochondrial data alone, or both mitochondrial and microsatellite data. First, we produced a minimum-spanning network of mitochondrial haplotypes (MSN) using TCS 1.21, with a 95% connection limit. MSNs can indicate past range expansions if they show starburst like patterns [C. adjunctus.We tested the prediction that patterns , 54. We DNASP 5.1. The purpose of this analysis is to compare the distribution of the frequency of each number of pairwise mitochondrial sequence mismatches in the dataset to the expected distributions under demographic expansion or constant population size through time. A unimodal peak at a non-zero number of pairwise mismatches is associated with demographic expansion, which we expected to observe, whereas more than one non-zero number of pairwise mismatches is usually associated with a constant population size through time [We executed a Mismatch Distribution (MD) analysis with ugh time .Triatoma infestans [Cimex. We used the gamma sites model to account for heterogeneity of substitution rate among individual loci. We used the default value of 10,000,000 Markov Chain Monte Carlo (MCMC) chains, logging every 1000 chains. We set the substitution rate to 0.575%/Ma, or half of 1.15%/Ma, which is the standard Arthropod mitochondrial pairwise substitution rate as reported by [E) multiplied by generation time (τ) since some time in the past. In the case of highly structured populations, Heller et al. [Then, we constructed an Extended Bayesian Skyline Plot (EBSP) using mitochondrial data in BEAST 1.8.4. We used a linear EBSP model, and random local clock, which reportedly performs better than strict and relaxed clocks for most situations using intraspecific data , 57. In nfestans , a speciorted by . All othr et al.  suggesteA > N1 > NB), mimicking an increase in effective population size from N1 to NA at time t, a decrease in population size from N1 to NB at time t, and finally constancy in population size at N1. Boundaries for NA, NB, N1 and t priors are available in Table A) to 10 times the initial upper boundary of effective population size (N1), to limit our analysis to population size increases of at least an order of magnitude. Similarly, we set the lower boundary of effective population size after a decrease (NB) to 1/10 of the initial lower boundary of effective population size (N1) to limit our analysis to population size decreases of at least an order of magnitude. If no change in population size of at least an order of magnitude occurred in the analysis timeframe, or if both a population size decrease and increase of similar magnitude occurred, then the scenario of constant population size would be most likely. We conducted a series of initial trial runs to determine the effective population size parameters for ABC analysis where we could achieve convergence between priors and observed values  for microsatellite markers. The estimated mutation rate for microsatellites is on the low end for such markers, but consistent with the relatively low variability observed for markers originally developed in a different species  on both mitochondrial and microsatellite data, using DIYABC 2.1.0. ABCs allow one to compare posterior probabilities of different demographic scenarios . As per es Table . As an eaxyridis . Trial rnt bias; ). We use


Mustelus mustelus) is the topmost bio‐economically and recreationally important shark species in southern Africa, western Africa, and Mediterranean Sea. Here, we used the Illumina HiSeq™ 2000 next‐generation sequencing (NGS) technology to develop novel microsatellite markers for Mustelus mustelus. Two microsatellite multiplex panels were constructed from 11 polymorphic loci and characterized in two populations of Mustelus mustelus representative of its South African distribution. The markers were then tested for cross‐species utility in Galeorhinus galeus, Mustelus palumbes, and Triakis megalopterus, three other demersal coastal sharks also subjected to recreational and/or commercial fishery pressures in South Africa. We assessed genetic diversity  and differentiation (FST and Dest) for each species and also examined the potential use of these markers in species assignment. In each of the four species, all 11 microsatellites were variable with up to a mean NA of 8, AR up to 7.5, HE and PIC as high as 0.842. We were able to reject genetic homogeneity for all species investigated here except for T. megalopterus. We found that the panel of the microsatellite markers developed in this study could discriminate between the study species, particularly for those that are morphologically very similar. Our study provides molecular tools to address ecological and evolutionary questions vital to the conservation and management of these locally and globally exploited shark species.The common smooth‐hound ( This presents an ideal opportunity to test whether the interplay of oceanographic features and life‐history traits are the drivers of population subdivision in these sharks. The tope shark is a highly mobile semipelagic demersal species that is widely distributed in temperate waters  of 118–150 cm and males at 107–135 cm LT. Reproduction is viviparous (no yolk‐sac placenta) with a triennial reproductive cycle  , regardless of the time at liberty  investigated here, the tope shark, common smooth‐hound, whitespotted smooth‐hound  and the blackspotted smooth‐hound (M. punctulatus) from the Mediterranean Sea, and two individuals of the hardnose smooth‐hound (M. mosis) from Oman in the northwestern Indian Ocean. Total genomic DNA was isolated using a standard cetyltrimethylammonium bromide (CTAB) extraction protocol of Sambrook and Russell (260) and 280 nm (A280) with a NanoDrop ND 2000 spectrophotometer . A small subset of samples was subjected to electrophoresis in 1× TAE buffer for 1 hr at 80 V. Five microliters of the isolated genomic DNA was loaded on 0.8% agarose gel stained with ethidium bromide to check DNA quality. The gels were photographed under a Gel Documentation system .A total of 144 finclip samples from four coastal shark species  were examined Table . The wes Russell . The con2.2Total genomic DNA from one individual of common smooth‐hound was isolated and sent to the Agricultural Research Council Biotechnology Platform in Pretoria, South Africa. One microgram of genomic DNA was used for 2 × 250 bp paired‐end library preparation with a mean insert size of 400 bp using the standard Nextera™ library preparation kit (Illumina). The library was sequenced on two lanes of an Illumina HiSeq™ 2000 sequencer. The generated sequence reads were submitted to a quality control (QC) step to remove artificial duplicates and/or reads that contained any “Ns” using PRINSEQ 0.20.4  one cycles of initial denaturation at 95°C for 2 min, (2) 35 cycles of denaturation at 94°C for 30 s, optimized annealing temperature (TA) for 30 s, elongation at 72°C for 2 min, (3) a final elongation of one cycle at 60°C for 5 min and thereafter stored at 4°C. Optimum annealing temperature was determined by experimental standardization for each of the primer pairs , and/or we observed clear size differences between different individuals. Polymorphic microsatellite loci were selected and primers fluorescently labeled with one of the following dyes: FAM, VIC, PET, or NED followed by multiplex optimization of two mutiplex assays (MPS1 and MPS2). A panel of 48 individual M. mustelus representatives of the two ocean basins (SEAO and SWIO) was genotyped for marker characterization. Multiplex PCR conditions were realized using the Qiagen Multiplex PCR kit and conducted according to the manufacturer's instructions except for varying primer concentrations . To determine the utility of these markers for future regional studies of intra‐ and interspecific genetic diversity in houndsharks (Triakidae), we also tested the 11 microsatellite loci on the blackspotted smooth‐hound, spotted gully shark, starry smooth‐hound, tope shark, and whitespotted smooth‐hound using the PCRs and microsatellite genotyping conditions described previously.Polymerase chain reaction (PCR) was carried out on a GeneAmp® PCR System 2700 in a 10 μL reaction volume that included 50 ng of template DNA, 1× PCR buffer, 200 μmol/L of each dNTP, 0.2 μmol/L of each primer, 1.5 mmol/L MgCle.g., Hardy–Weinberg and linkage equilibria). We only focused on the four coastal sharks that are commonly misidentified in South African fisheries, the common smooth‐hound, spotted gully shark, tope shark, and the whitespotted smooth‐hound. We performed the DAPC analysis on clusters defined by species and assessed the assignment of each individual to distinct genetic clusters using the membership coefficient, i.e., the percentage of the genotype's ancestry attributed to each genetic cluster. For successful species identification, membership coefficient values had to be ≥95%.To evaluate the reliability of using cross‐amplified microsatellites for species identification, we conducted multivariate clustering analysis using the discriminant analysis of principal components (DAPC) implemented in the R package ADEGENET Jombart, . Unlike 2.4p‐values for multiple comparisons  to minimize type I errors , allelic richness standardized for small sample size (AR), observed heterozygosity (HO), and heterozygosity expected under conditions of Hardy–Weinberg equilibrium (HE) using the DIVERSITY  was calculated in ARLEQUIN v. 3.5  and the α level  using a sampling scheme of two subpopulations with 20 individuals each. The analyses were conducted using 10,000 dememorizations, 100 batches, and 1,000 iterations per batch with the allele frequencies observed for the complete dataset of 11 microsatellite loci and our reported sample sizes for each species.Across sampling sites and species, we calculated the mean number of alleles per locus  on clusters defined by ocean basin. The number of clusters was assessed using the find.clusters function, which runs successive K‐means clustering with increasing number of clusters (k). For selecting the optimal k, we applied the Bayesian information criterion (BIC) for assessing the best supported model, and therefore the number and nature of clusters, as recommended by Jombart, Devillard, and Balloux  present in each species. We applied an admixture model with correlated allele frequencies for 10 replicates across K = 1 to K = 10 with each run consisting of 1,000,000 Markov chain Monte Carlo (MCMC) iterations and an initial burn‐in phase of 100,000 iterations assuming no prior population information. Given that only two groups of samples were compared for each species, the ad hoc statistic ∆K described in Evanno, Regnaut, and Goudet  for a given K, Pr(X|K), calculated by STRUCTURE was used to compute the mean likelihood L(K) over 10 runs for each K to identify the likely K for which L(K) was highest  was employed, where acceptance–rejection swaps were proposed at every step. The model comparison was made using log‐equivalent Bayes factors (LBF) that need the accurate calculation of marginal likelihoods. These likelihoods were calculated using thermodynamic integration in MIGRATE‐N. Models were ordered by LBF, and the model probability (PMi) was calculated in R. Additionally, we converted estimates of gene flow (M) to the number of effective migrants (Nem) from population i to population j using the formula:Finally, we used the coalescence‐based method in the program MIGRATE‐N 3.6.11 Beerli,  implemen33.1Mustelus mustelus generated 35 GB of raw reads. After trimming the raw sequences that included removal of adapters, N‐containing reads, and low‐quality reads, we retained a total of 17 GB clean reads. After the de novo assembly of the Illumina paired‐end reads, we recovered a total of 27,512,666 contigs. We identified a total of 82,879 contigs that were longer than 250 bp, of which 2,572 (3.1%) contained microsatellites. Dinucleotide repeats were the most frequent , followed by trinucleotide repeats (232 or 12.3%), and tetranucleotide repeats (31 or 1.6%). We selected 15 microsatellite containing contigs for primer design with an expected PCR product size ranging between 112 and 431 bp. Of the 15 loci tested, all were successfully amplified while only 11 were polymorphic based on initial screening via polyacrylamide gels . To assess the cross‐species utility of the two multiplexes, we tested these assays on six other triakid species, and cross‐species amplification rate of success ranged from 72% to 100%  putatively subjected to selection in the whitespotted smooth‐hound possibly due to issues surrounding small sample sizes. Assessment of the power of the multilocus dataset to detect population structure indicated that all loci used could accurately detect differentiation as low as FST = 0.003, for a population sample of n = 20, indicating that the dataset was suitable for population structure inference.Additionally, to validate the potential of these markers for within‐species population genetic analysis, we inferred genetic variation in samples collected from two different ocean basins for each respective species Table . In eachThe novel microsatellite loci demonstrated potential application in the identification of the study species. The results from the multivariate clustering analysis (DAPC) clearly depict four genetic clusters representative of each species with limited overlap Figure . Here, i3.23.2.1FST = 0.029, Dest = 0.021) and AMOVA . The DAPC analysis including location prior revealed two clear genetic clusters corresponding to ocean basins, whereas excluding location prior using the find.clusters function, the DAPC analysis identified the presence of five genetic clusters (k = 5) in the dataset based on the BIC score  revealed one admixed cluster (K = 1) as the most likely number of groups present in the dataset  Model 2  and showed that Θ was highest in the SWIO (Θ = 5.870) and lowest in the SEAO (Θ = 0.790)  Tables  and A6.3.2.2FST indicated significant population differentiation estimates, which were congruent with the results obtained with Jost's Dest between all putative populations. Pairwise comparison of the unknown samples (in terms of sampling region) with the samples collected from the SEAO revealed low differentiation , higher levels when compared with the SWIO samples . Notably, population differentiation estimates were significantly large for Atlantic versus Indian Ocean comparisons . Global AMOVA results indicated within individual variation explains a greater amount of the total genetic variation, with less variation among populations  (Table k = 3) in the dataset based on the BIC score . Estimates of Θ was highest in the SWIO (Θ = 19.660) and lowest in the SEAO (Θ = 0.540) (Tables Pairwise differentiation test using 1) Table . The DAP) Tables  and A6.3.2.3FST = 0.034, lower 95% CI > 0), while similar to M. mustelus, Jost's Dest indicated less pronounced levels of differentiation . The AMOVA results showed that there was no differentiation among populations , but a significant amount of variance was attributed to among individuals within populations  and within individuals  (Table k = 2) in the dataset based on the BIC score  identified K = 2 as the most likely number of groups present in the data  Model 2  and showed that Θ was highest in the SWIO (Θ = 98.100) and lowest in the SEAO (Θ = 0.100) (Tables Population differentiation between the SEAO and SWIO was significantly greater than zero (0) Table . The DAP) Tables  and A6.3.2.4FST = −0.012, Dest = −0.002, lower 95% CI < 0). The AMOVA results also showed no differentiation among populations , with most of the variation explained among individuals within populations  and within individuals  (Table K = 4) as the most likely number of groups present in the data  Model 2  and showed that Θ was highest in the SWIO (Θ = 6.820) and lowest in the SEAO (Θ = 1.380)  Table . The DAP0) Table . Bayesia) Tables  and A6.4™ 2000 for reduced genome sequencing was successful regarding speed, accuracy, and cost in generating microsatellites. It provided an efficient way to develop microsatellite markers, even though some factors such as library preparation, read length, and precision of the assembly can be improved in future studies. The relative richness of different types of microsatellite repeats is typical, and in sharks, dinucleotide repeats are generally overrepresented. Similar to the studies of the Australian gummy shark Mustelus antarticus  in sharks  based on the newly developed multiplex assays indicated that the microsatellite loci are informative for species identification as well as for population genetic analysis. Our preliminary population genetics estimates hinted at the combined effects of oceanographical barriers and life‐history differences  to be the major factors influencing the patterns of regional population structure in these sharks. We rejected the null hypothesis of panmixia in all the study species except for o et al.  and Maduo et al. , we dete Boomer, . In such Boomer, , and the Boomer, . Pereyra Boomer,  and Boom Boomer,  found no Boomer,  and Sand Boomer,  provided Boomer, , our stu Boomer, , particuResults from previous research indicated that levels of gene flow across the Atlantic/Indian Ocean boundary for the tope shark were relatively high  and showed that Θ was highest in the SWIO and lowest in the SEAO populations in all study species. Our finding of similar asymmetric migration patterns in these species might suggest that such patterns arose from the action of shared physical boundaries. Also, water temperature changes have been shown to influence movement of these triakid sharks and other closely related species (Chabot & Allen, None declared.