Real-time visual feedback delivered through the BCI did not significantly increase the ERD strength. Conclusion. The results imply that the potential benefits of MI could be yielded by well-habituated subjects with a simplified open-loop setup, e.g., through at-home self-practice.Background. Motor imagery engages much of the same neural circuits as an overt movement. Therefore, the mental rehearsal of movements is often used to supplement physical training and might aid motor neurorehabilitation after stroke. One attempt to capture the brain’s involvement in imagery involves the use, as a marker, of the depression or event-related desynchronization (ERD) of thalamocortical sensorimotor rhythms found in a human electroencephalogram (EEG). Using fast real-time processing, it is possible to make the subject aware of their own brain reactions or—even better—to turn them into actions through a technology called the brain–computer interface (BCI). However, it remains unclear whether BCI-enabled imagery facilitates a stronger or qualitatively different brain response compared to the open-loop training. Methods. Seven healthy volunteers who were experienced in both closed and open-loop motor imagery took part in six experimental sessions over a period of 4.5 months, in which they performed kinesthetic imagery of a previously known set of finger and arm movements with simultaneous 30-channel EEG acquisition. The first and the last session mostly consisted of feedback trials in which the subjects were presented with the classification results of the EEG patterns in real time; during the other sessions, no feedback was provided. Spatiotemporal and amplitude features of the ERD patterns concomitant with imagery were compared across experimental days and between feedback conditions using linear mixed-effects modeling. Results. The main spatial sources of ERD appeared to be highly stable across the six experimental days, remaining nearly identical in five of seven subjects (Pearson’s ρ > 0.94). Only in one subject did the spatial pattern of activation statistically significantly differ ( The voluntary mental rehearsal of body movements  is regarded as an effective tool for increasing sports performance  and is oAs a strictly mental exercise, motor imagery poses a challenge for practitioners who aim to measure motor imagery quality objectively. One strategy that is adopted for validating and enhancing mental practice is the use of real-time feedback enabled by a brain–computer interface (BCI)—a technology that uses real-time neural activity for the detection  of associated mental states . CommonlIt is important to note, however, that the amplitude of an imagery-related ERD depends on the resting-state SMR amplitude, which is an innate feature of one’s EEG and is unrelated to the imagery ability. This makes the BCI-based monitoring of motor imagery inaccessible for a significant portion of the population . FurtherIn the classical control-oriented BCI paradigm, the main goal is to achieve a fluent and error-free transfer of information between a brain and detector, which partly relies on the subject’s ability to inadvertently shift their cognitive strategy to whichever approach yields a better classification result ,13. ThisInstead of continuous real-time feedback, delayed discrete feedback is often adopted in training BCIs. Here, reinforcement is delivered on a sparse schedule (every 10 s or once per imagery block), which is far beyond the acceptable delay for brain rhythm modulation paradigms   phase. Additionally, a video-guided imagery protocol is proposed and was explored as an alternative open-loop approach for performing motor imagery.s03 and s05) were left-handed, as assessed with the Edinburgh handedness questionnaire [s03) to 64 (s05), with a median of 34 days. The experimental procedures were approved by the Lomonosov Moscow State University Committee for bioethics (protocol no. 25-ch). The study followed the Declaration of Helsinki Ethical Principles for Medical Research Involving Human Subjects. All the subjects were informed about the procedures of the study and gave their written consent to participate.Seven healthy volunteers participated in the study . Five participants were right-handed, and two . This rather large time window was selected to check if the subjects would unlearn the motor imagery without feedback as well as to assess the possible temporal deterioration/change of the EEG patterns. All the experimental sessions were conducted under the same conditions , predominantly during the early evening (3–7 p.m.). The subjects sat in an armchair in a comfortable, slightly reclined position with their hands either on the armrests (for shoulder movement imagery) or on the hard-surfaced laptop support (Ikea “Byllan”) with their fingertips contacting the surface (for finger movements). The stimuli (pictograms) for conditions requiring static cues were presented on a 24-inch LCD monitor positioned in front of the subject, 1.4 m away from their eyes. For the condition involving continuous video stimulus  short point of view video clips of the thumb movements were presented on a small, 10-inch portable screen, positioned immediately above the subject’s own hand to match the perspective and location of the hand on the video. Each experiment started with an EEG cap montage, which took 15 to 25 min.During the experiment, the subjects performed two classes of mental tasks: kinesthetic motor imagery (five variations) or a visual attention task. The tasks were organized in runs (“recordings”) that comprised twelve 6 s trials: six motor imagery trials (1 variation per recording) and six visual attention trials mixed in a pseudo-random order and separated by interstimulus intervals that lasted 2–3 s. During the visual attention task, a picture with complex geometrical shapes appeared on a screen. The subjects’ task was to silently count any elements of the picture  at their own comfortable pace. This condition was a reference non-motor state for all the types of imagery and served multiple purposes. Firstly, on a psychological level, this task helped the subjects to “switch” from mental imagery, requiring a comparable level of attention to perform it, and prevented the participants’ minds from wandering. Secondly, this task provided us with a stable and reproducible cognitive state, as it was familiar for subjects; note that this reference task was interlaced with the imagery trials that had to be compared with it. Thirdly, the visual attention caused the depression (ERD) of occipital alpha rhythms, while sensorimotor rhythms reacted in the opposite way, providing a good functional separation of sensorimotor and visual brain networks, thus serving as a functional localizer task.During the motor imagery trials, a pictogram with a left or right hand/arm appeared on the screen, serving as a cue for a subject to perform motor imagery of one of the five following movement types. Finger imagery (FR for right and FL for left hand finger imagery) corresponded to a random order of two to five individual finger presses . Shoulder imagery (SR for right and SL for left shoulder imagery) corresponded to a forward/backward circumduction from a seated arm-on-armrest position (referred to by the subjects as the “crawl stroke”). Right hand thumb swipe imagery (TR) corresponded to a movement mimicking sliding one’s thumb across a smartphone display (from the lower left to upper right corner). For this movement, we designed an ergonomic hand stand, which supported the palm in a neutral position and provided a smooth surface for thumb movement. The subject’s hand was inserted into the stand for the trials requiring thumb imagery. The thumb swipe imagery was performed in two variations: with an external pictogram-cue (similar to other movements and further denoted TR) and with a video stimulus, guiding the subject’s mental movements (TRv). In the latter mode, a subject had to kinesthetically imagine their thumb movements, mentally reproducing the phases and speed of movements on the video in real time. The goal of the TRv condition was to compare other TR variations (without and with feedback) in terms of the reduction in mental load while providing additional sensorimotor brain activation through visual congruency, yet without introducing a cumbersome feedback loop. Outside the TRv condition, all the imagined movements were performed in a self-paced mode at random intervals between movement phases . Both the TR and TRv movements were novel to the subjects and were performed only during Days 5 and 6 of the present study, while the other imagery types were thoroughly practiced in a similar environment during previously attended studies. The technique for performing the kinesthetic imagery of self-paced and self-randomized movements was also known by all the subjects; please refer to  for detaThe experimental runs were organized in blocks of two to three recordings with the same type of imagery . The FR condition was prioritized, and twice the number of runs of other types was performed . Based on the goals of the study, the experimental days (sessions) were divided into days with BCI assistance (with feedback) and those without feedback (cued imagery only). The first and the last session (No. 1 and 6) were BCI-assisted, while the sessions between these  were without feedback. This design was selected to examine whether the subjects would gradually “forget” how to perform imagery with no feedback. Continuous feedback was presented to the subjects during a mental task in the form of an animated bar displayed immediately below the cue pictogram . At the The EEG was recorded using an NVX52 DC amplifier  with 30 passive Ag/AgCl electrodes covering the sensorimotor cortex area according to the “10–10” international system in the following positions: F3, F4, FC5, FC3, FC1, FCz, FC2, FC4, FC6, C5, C3, C1, Cz, C2, C4, C6, CP5, CP3, CP1, CPZ, CP2, CP4, CP6, P5, P3, P1, PZ, P2, P4 and P6. The reference electrode was in position TP10. The electrode–skin impedance was kept below 20 kΩ. The signal was sampled at 500 Hz with a 50 Hz notch filter. For offline analysis, the signal was filtered using a zero-phase bandpass FIR filter with cutoff frequencies of 3 and 48 Hz.The data acquisition, stimulus presentation and feedback delivery were performed using the BCI2000  softwareWe focused our data analysis on three features of the motor-imagery-related EEG signal: the spatial patterns, strength of the sensorimotor rhythm ERD, and BCI-classification performance. In order to take advantage of the multichannel EEG signal while avoiding the electrode-wise mass univariate analysis, we aimed to extract a set of spatial components, or “sources”, related to the SMR-ERD reaction. It should be noted that the term “source” is used here in a strictly statistical sense—i.e., as directions or subspaces within a 30-dimensional sensor signal space, without considering the underlying neuroanatomy. To identify ERD sources, we considered the spatial directions in which the signal in the SMR frequency band  had the least variance in the motor imagery condition (sensorimotor active ~ desynchronization of SMR) while simultaneously having the most variance in the visual attention condition (sensorimotor passive ~ synchronization of SMR). This task could be solved by the generalized eigendecomposition  of signaT through the covariance of the active state: A ∝ W−T ∝ WTAfter calculating W, for ERD sources, we considered only columns  corresponding to the smallest After calculating and applying spatial filters, we performed time–frequency decomposition using a short-time Fourier transform . Then, for each frequency bin (1 Hz resolution) and each spatial component, we computed the normalized power distributions for the motor imagery condition and visual attention task  and calculated the distribution overlap (as a percentage)–Equation (3).The resulting score was called the ERDd  and was used to quantify the ERD strength. The ERDd score is negative when the power distribution of an active condition has a median lower than that of the reference condition, and positive otherwise. Thus, stronger desynchronization corresponds to more negative values, and synchronization, to positive values. In contrast to the commonly used average power amplitude difference, ERDd makes no assumptions about the shape of the power value distribution and is better suited to quantifying a steady-state process, such as the ERD, concomitant with continuous sensorimotor events. Readers may refer to  for a grThe ERDd scores calculated for each frequency bin and each spatial component were used to identify a set of user-specific spatio-spectral features through the following procedure. First, the components with the largest absolute ERDd scores within 7–26 Hz bins were selected. Then, neighboring frequency bins with the score above 30% of the largest were picked to form a frequency range. Average of the Fourier coefficients for these ranges was used later to recalculate the ERDd and to train the classifier. Components with the largest negative ERDd (closer to −100) later referred to as the “strongest ERD sources” typically corresponded to the first two to five columns of W in Equation (1), revealing the SMR ERD sources. Conversely, components with the largest positive ERDd (closer to 100) corresponded to the last columns in W and usually pointed at the occipital event-related synchronization (ERS), reflecting reduced visual information processing during motor imagery. While the rest of the paper mainly concerns the SMR ERD sources and features, an addition of ERS-based features could provide better classification accuracy and therefore was investigated using the ERS+ERD classifier (see below).To estimate the performance of the BCI for the trials with and without feedback, we performed two-fold cross-validation five times using the same classification algorithm as that used online. The data from runs of the same motor imagery condition during the particular experimental day were concatenated in one block for cross-validation; thus, the calculated accuracy estimates corresponded to day × condition, without differentiation into recordings. The classification accuracy was estimated using two sets of classifiers: one using only ERD features and another using the best of all the available features . In addition to the five iterations of two-fold cross-validation, we performed accuracy estimation using the time-series cross-validation, where the training set varied in size and was composed of all the previous trials related to the trial under consideration. The latter method served as a more realistic approach for estimating accuracy in the experimental session, since it uses only past data for the classifier training.p-value for the value observed with the true labels. A left-tailed (lower correlation in the feedback condition) p = 0.05 threshold was used to assess significance.The statistical inference of spatial patterns was performed using Pearson correlation coefficients. We calculated the correlation matrix of the mean forward models for six experimental days, then averaged the correlation between the patterns of all the feedback days and non-feedback days and subtracted this value from the mean correlation among the patterns on non-feedback days. Then, we repeated the procedure while permuting “feedback” labels 100,000 times, randomly assigning these labels to any two sessions. Using the post-permutation statistical distribution, we calculated the fitlme was used to fit the data using the restricted maximum likelihood (REML) method to estimate parameters. The statistical significance was evaluated for fixed effects using the anova function with Satterthwaite approximations for the degrees of freedom  [As the ERDd value was available for each experimental run (recording), we were able to use a linear mixed-effects model to capture the intraday and interday variance. We used the mean ERDd score of the two strongest sources  as a response variable and modeled it with fixed and random effects depending on the effects in question –(6), ). The MA for df) .Firstly, we tested if the number of days affected the ERD strength ), modeling the gradual deterioration of the brain response in the absence of feedback (Days 2–5). The statistical significance of the fixed effects of the “day” and the day–imagery type interaction was assessed using ANOVA. Then, models with and without a random “day” and fixed effects were fitted using the maximum likelihood (ML) method and compared using a simulated likelihood ratio with 1000 simulations. The latter was performed to asssess the significance of the “day” factor and thus reject/include it in further analysis.(“isFeedbacked”) factor and its interaction with the “subject” (“subj”), as well as the random effect imageryType and intercept for the grouping variable of the “subject” (Equation (5)). For this and the previous model, we used data from trials with familiar imageries .Secondly, we modeled the binary “feedback” videoORfeed . Furthermore, the TRv and TR + feedback levels were assessed to compare the two approaches directly.Thirdly, to estimate the effectiveness of the alternative imagery mode with the video stimulus, we used a model encompassing only data with newly introduced imagery (conditions TR and TRv) and fitted the model with the categorical factor p = 0.05.A two-way ANOVA was performed on the classifier’s output values using the factors “subject” and “feedback” to assess the differences in the accuracy of the BCI between subjects depending on the presence of feedback. For all the above-mentioned tests, alpha was set at Spatial filters for the ERD sources and corresponding forward model estimates were computed using GED using 10-fold cross-validation. Forward models of the two strongest ERD sources  and (2)) for all the subjects and experimental days were almost always symmetrical with strong ipsilateral or contralateral dominance . s04, showed a statistically significant difference (p = 0.009) in their pattern related to the feedback condition. Note that, on the feedback days (Days 1 and 6), the sources conformed to the tangential dipolar structure, while on days without feedback, radial and bilaterally smudged patterns prevailed. The spatial patterns of s02 were not significantly different in feedback conditions but showed, similarly to s04, rather high day-to-day variations in contralateral sources. The mean Pearson’s correlation coefficients for the contralateral sources among the days for s04 and s02 were 0.62 and 0.79, respectively, whereas for the rest of the subjects, the mean ρ stayed within the interval of 0.95–0.99.Using the permutation  of “feedback” labels for different days independently for ipsilateral and contralateral sources, we tested whether their forward projections differed according to the feedback condition. Only one subject, s02 showed the most variability  and had the lowest mean values across all the conditions.The event-related desynchronization strength was quantified using the ERDd score, representing the non-overlapping area between the probability distributions of the SMR power during the imagery and visual attention tasks . The ave = 1.9855, p = 0.15972—and the day–imagery type interaction—F = 2.0686, p = 0.10378. Following that, two models were fitted using the ML method: a more complex model (Equation (4a)) with df = 12 and a simplified model without the term “day”, both in fixed and random parts of the model (Equation (4a)) with df = 6. Those two models were then compared using a simulated likelihood ratio with 1000 simulations, which revealed no statistical advantage of a more complex model: the likelihood ratio test statistic was 9.202, and the simulated p was 0.13187 with a 95% confidence interval for a simulated p ranging from 0.11152 to 0.15441. For that reason, the “day” factor was excluded from further analysis.To estimate the possible gradual deterioration of the ERD response in the absence of feedback  up to four months (Day 5)), the effects of the day and the day–imagery type interaction were tested using the model from Equation (4a). No significant group effect was found for the day—FisFeedbacked revealed no statistically significant results—F = 2.9479, p = 0.086495—while the individual effects for isFeedbacked:subj were quite varied , corresponding to weaker desynchronization.Next, to assess the effects of feedback on the SMR-desynchronization strength, we fitted mean ERDd values for the two most prominent sources with a linear mixed-effects model and introduced and tested the group effects of factors of interest –(6)). Testing the group-level effect for e varied , with a  = 4.1636, p = 0.019278—with the ERD strength increasing from feedback to no feedback to video stimulus conditions. This result shows that the imagery condition does impact the ERD strength when learning to perform new imagery. When comparing the feedback and video stimulus conditions, the latter condition was found to induce a stronger ERD: F = 8.3263, p = 0.0051.The analysis of the ERD patterns of the novel movement type (TR or TRv) introduced in Sessions 5 and 6 yielded statistically significant differences among the approaches of imagery conditions—F = 0.31, p = 0.5837; ERD-only: F = 0.5244, p = 0.5244), while the “subject” factor was significant  = 5.88, p = 0.0001; ERD-only: F = 13, p = 1.374 × 10−7). According to both ANOVAs, Subject s02 exhibited lower classification accuracy, most noticeable in the ERD-only classifier, which was in line with the weaker ERD and highly variable spatial patterns reported earlier  = 19.01 and p = 4.076 × 10−5).The theoretical classification accuracy was estimated for all the days with the same classification algorithms used online, employing “5 × 2” and “time-series” cross-validation schemes see . The ave earlier . The samIn the present study, the term “motor imagery training” is understood in the context of a neurocognitive exercise and not in a sense of teaching or improving the imagery skill, even though practice might and often does lead to improvement. Imagery is an exercise that is usually performed to achieve some improvement of an existing motor skill or in an attempt to reactivate a damaged motor circuit—for example, when impacted by stroke ,5,30. PuIt is safe to assume that there would be no difference in the ERD patterns between those caused by the imagery of an index finger flexion and the imagery of an index finger extension, especially in the context of a single-trial detection in BCI. However, as shown by multiple studies , imageryOne motivation for the current study was to contest the premise of a group of SMR-BCI studies’ designs with a passive “no-feedback” group with a reported way to work with variable SMR patterns and low ERD values compared to those observed in our previous work. Let us consider the design and results of the multi-day study resembling our present work , which rThus, we speculate that the combination of three possible factors could explain the effects reported by that (or a similar) study: an improvement of the imagery technique accelerated by the ERD-based feedback, a more general instrumental conditioning promoted by the neurofeedback and a stimulation through movement observation (in conditions with hand animation). One way of estimating the proportion of those effects would be to conduct a no-feedback follow-up intervention after the last session for each group (which is similar to the approach taken in the current study). Even if most of the ERD gain in the feedback groups was due to the imagery technique improvement, the conclusion of Ono’s study might be that subjects with no instruction and no BCI feedback cannot learn imagery on their own simply by trying, while subjects with ERD-based BCI could improve the imagery technique by practicing with the feedback.Similarly designed studies with an explicit comparison of BCI and open-loop imagery interventions  conducteAlthough all our subjects in the current and previous studies received some BCI training, which might have contributed towards shaping their imagery technique, we still strongly suspect that teaching motor imagery through a thorough explanation and discussions with the trainee/patient is vastly preferable to neurofeedback; firstly, because the former approach shapes a correct and verifiable mental strategy, and secondly, because it allows trainees to focus their attention on imagery reflection, thus making imagery more controllable regardless of external stimuli, such as ERD-based feedback.The vividness of imagery plays an important role in its potential effectiveness in rehabilitation and sport . The purIn the study by Kober et al. , as withs01.Our own  and otheOne would expect that increased mental load while receiving external real-time feedback may cause the deterioration of both the form of internally controlled imagery and BCI performance. One approach to decreasing cognitive load is to make the feedback congruent with the imagined movement, e.g., by controlling an artificial phantom limb or orthosis. However, it would be impossible to achieve movement congruence, as the phases of imagined movement would not match those of the external limb. In our study, we tried to solve this problem with “video-guided” imagery. Our approach aimed to decrease the mental load of imagery by providing video instructions, thus relieving the subject from the duty of self-generating motor sequences. Congruent video stimuli also had an additional benefit of action observation to enhance sensorimotor circuit activation ,41. HoweMotivation is the one important yet difficult-to-quantify advantage of performing routine imagery in a closed loop, especially in a clinical setting. However, we would argue that short-lasting amusement from a BCI-enabled game or device would contribute little to the end goal of mental practice, whereas sustainable motivation born from a goal-orientated mindset may indeed improve imagery efficiency .Summing up the discussed points, we can conclude that SMR-ERD patterns concomitant to kinesthetic motor imagery performed by trained subjects remain stable over long periods of time. This shows that sensorimotor brain activation during motor imagery may be sustained during repeated interventions based only on internal sense image vividness and does not improve with the addition of a real-time feedback loop. With proper training and motivation, motor imagery practice could be fully accessible for patients requiring home-based training  and coulThe goal of the current study was to determine if the mental practice efficiency in experienced users could be enhanced by using a sensorimotor rhythm-based brain–computer interface compared to the open-loop training. To that end, we conducted six motor imagery training sessions that spanned four to five months, in which subjects performed the cued and continuous kinesthetic imagery of hand and arm movements. We found that both the SMR-ERD amplitude and the performance in the motor imagery BCI did not deteriorate with time, and the corresponding scores varied within user-specific ranges. The spatial patterns of ERD, extracted using the generalized eigendecomposition of covariance matrices, also remained stable across the experimental days. Continuous real-time visual feedback did not significantly affect the strength of the ERD at the group level, with a significant negative effect found only in one subject across all the imagined movement types. In summary, this evidence suggests that, in well-habituated subjects, motor-imagery-related ERD patterns remain stable regardless of the presence of real-time feedback, in turn implying that the potential benefits of MI could be yielded with a simplified setup, e.g., through at-home self-practice. However, state-of-the-art research suggests that the EEG-based control of the SMR-activation strength and consistency during an initial imagery habituation stage might still be a good idea. One alternative and potentially effective method of enhancing the learning and practice of motor imagery could be the addition of supplementary congruent sensory stimulation, exemplified in the present study by the “video-guided” imagery mode. Further research is required to identify the potential benefits of congruent stimulation for learning imagery in fully naïve subjects.


Trichinella spiralis ranks seventh in the risk posed by foodborne parasites. It causes most human cases of trichinellosis and is the most frequent cause of Trichinella outbreaks on pig farms and in wild boar, worldwide. Veterinary inspectors seek the source of outbreaks in hopes of limiting the spread. Established molecular tools are inadequate for distinguishing among potential T. spiralis infection sources because genetic variability in these zoonotic pathogens is limited in Europe. Microsatellite markers proved successful in tracing an outbreak of T. britovi, a related parasite harboring much more genetic variation. Here, we successfully employed microsatellite markers to determine the genetic structure of T. spiralis isolates from two pig outbreaks, discovering notable uniformity among parasites within each farm and discovering an epidemiological link between these two outbreaks.T. spiralis from two pig farms and from ten wild boars were genotyped using nine microsatellite markers to examine their genetic structure.The individual larvae from five isolates of Notably uniform parasite populations constituted each farm outbreak, and the parasites from the first and second outbreaks resembled each other to a notable degree, indicating an epidemiological link between them. Wild boar harbored more genetically variable larval cohorts, distinguishing them from parasites isolated from domestic pigs.T. spiralis, aiding efforts to track transmission. Each outbreak was composed of a homogenous group of parasites, suggesting a point source of contamination.Microsatellite markers succeeded in distinguishing isolates of the highly homogeneous The online version contains supplementary material available at 10.1186/s13071-021-04861-9. Trichinella, Trichinella spiralis most frequently causes human infections . The remaining larvae isolated from wild boar were more distant in the tree and variable among hosts.The UPGMA phylogenetic tree constructed from Nei et al.  genetic k = 2–8) affirmed that outbreak cohorts resembled each other more than they resembled isolates from wild boar  is marked in grey.Following the criteria established by Evanno et al.  to estimk = 4 simulations. This is consistent with STRUCTURE analyses using other values of k with the entire dataset  (suggesting these larvae were highly inbred). This occurs for the F1d pig of farm 1, the F2 pig of farm 2, and the WB5, Wb7, and WB10 wild boars. These samples had low allelic heterogeneity, resulting in minimal genetic structure. All individuals from pigs F1d and F2 were assigned to the same cluster, suggesting that these infections share the same recent ancestors, while individuals from WB5, WB7, and WB10, were assigned to different clusters, and separate ancestors are suggested.STRUCTURE often assigned each of a host’s larvae to the same cluster with > 70% certainty (Q membership < 0.7). A mixture of at least two lineages may have recently contributed to the ancestry of these larvae. Larva F1a/22 was unique among samples F1a-c in showing admixture with Q % of membership = 0.44 for cluster-1 and Q % of membership = 0.35 for cluster-2  hypothesized subdivisions (0.3 < Q ≥ 0.7) with little to no admixture between them. In cohorts F1a–c, mixed infection sources could be responsible for the patterns of cluster membership; they were analyzed independently with STRUCTURE, as in La Rosa et al. [The larval cohorts of pigs F1a and F1b and rat F1c from farm 1 and WB3 and WB6 wild boars each included individuals assigned to two different clusters .Attempts to distinguish among European isolates of iability , 41. Reciability , 26. HerNa of only 1.42 alleles/locus and an average Ho of 0.10. These low values are comparable to prior estimates from 41 wild boar larval cohorts (genotyped using 5 polymorphic loci) from Extremadura (Spain) (Na = 1.59 and Ho = 0.17) [Na = 1.27 and Ho 0 = 0.09) [T. spiralis cohorts harbor more genetic variability [T. spiralis isolates in Europe. These tools have adjudicated epidemiological hypotheses for T. britovi, which harbors more variation and therefore more discernable geographic structure [T. spiralis spread or may simply have maintained larger effective populations for longer [We reached this conclusion despite low overall genetic variability, consistent with prior regional estimates. Across all investigated loci, we observed globally an average  = 0.17)  and from = 0.09) . By contiability , 23. Rectructure . Asia mar longer , 43.Fst among larval cohorts from ten wild boars ranged from 0.045 to 0.934 (average 0.52). The seemingly large differences between cohorts reflects the biology of cohort formation: each host ingests a small number of infectious larvae that then have limited opportunities to outcross because the larvae from a particular meal reach adulthood at the same time. The result is frequent inbreeding, which minimizes the genetic variability of each the larval cohort in any host. Only rarely will a host eat sequential independent T. spiralis-infected meals, producing genetically distinct adults maturing at the same time in the same gut, enabling outcrossing. These rare occurrences can increase genetic diversity locally and limit allelic loss through genetic drift. From a molecular epidemiological perspective, these differences provide opportunities to distinguish infections, even when allelic diversity is limited.Previous studies have shown that individual hosts generally harbor larval cohorts that can be distinguished, to greater or lesser extents, from larval cohorts of other individual hosts . Here, pT. spiralis to the other. This study sustains this suggestion by identifying notably little genetic differentiation among the larvae derived from the five hosts on the two farms studied. The larvae sampled on farms account for 7% of the total genetic variation . When wild boars were considered, six times as much genetic variation was observed (mean Fst = 0.417), indicating that the larvae of outbreak cohorts share notably recent common ancestors.Prior epidemiological investigations of these outbreaks raised suspicion that pigs had been moved between the investigated farms; one farm likely introduced Fst matrix, confirms the genetic similarity of the larvae collected from the two outbreaks. The five larval cohorts from the four domestic pigs and from the rat trapped at the pig farm define a cluster distinct from all the larva cohorts from all sampled wild boar except for wild boar WB6; as shown in Fig. The multivariate analysis, based on the The UPGMA phylogenetic tree of MLGs of all individual larvae divided almost all individuals from wild boar from individuals isolated from outbreak hosts, creating a ‘domestic’ subclade within a ‘wild’ clade Fig. . The ‘wiT. spiralis in Spain and T. britovi on two Mediterranean islands [T. spiralis in Europe) may become distinct through successive passages, since inbred parasites lose alleles through genetic drift. Here, some larval cohorts are comprised of individuals assigned to a single cluster  but differing from one another . This suggests that these cohorts likely have two lineages that have undergone generations of inbreeding. Other cohorts harbor admixed individuals showing a plethora of different patterns of membership (0.3 < Q > 0.7), indicating that they may have undergone more recent admixture. This pattern occurred primarily in cohorts from wild boar, indicating significant gene flow among wildlife isolates. More hosts likely contribute to the sylvatic cycle, since wild hosts will eat from multiple infected carcasses over their lifetime. The admixed patterns in the wild samples described here likely reflect the history of sequential infections in the lifespan of wild animals.Analyzing multilocus microsatellite genotypes using the Bayesian algorithm in STRUCTURE confirmed epidemiological assessment despite the limited variability characterizing these isolates. This is similar to what has previously been reported in  islands , 26. IsoT. spiralis in Europe, which has a small effective population size, the admixture level is limited by the number of genotypes circulating in the environment [T. spiralis in Poland increases the likelihood that geographically disparate isolates share genetic similarities.However, the evolutionary forces driving genetic diversity depend on the amount of genetic variability in a population. In cases of species as homogeneous as ironment . Of the Trichinella spp., transmission of larvae from wild to domestic hosts typically occurs as a single, isolated event. The limited number and relatedness of larvae ingested in a single meal often results in further inbreeding in the domestic host. This inbreeding leads to clonal expansions that differ from subpopulations in other hosts. We have demonstrated that epidemiologically related infections can be recognized as especially similar even when genetic variability is low . This was only achieved through the use of microsatellite loci, which proved variable, despite the low genetic variability among European T. spiralis subpopulations. Although outbreak tracing would be further aided by greater polymorphism or the occurrence of private alleles, we conclude that these markers can help traceback efforts even under conditions of limited overall genetic variability.In T. spiralis population, microsatellite genotyping allows for the differentiation of the outbreak isolates from randomly selected wild boar. The clonal character of isolates from farm 1 confirms the preliminary diagnosis that these animals contracted infection from a shared source (most likely feed containing infecting Trichinella larvae). Moreover, we confirm the suspicion of a link between the two investigated farms, finding high genotypic similarity of larval cohorts within both farms. These conclusions were achieved using microsatellites markers capable of surmounting limited genetic variability. Other methods of sampling genetic or genomic variation may similarly contribute to defining transmission routes, identifying sources, and preventing the spread of infection. Methods capable of resolving such distinctions more quickly would have even greater application to real-time source tracking.This study shows that, despite the low genetic variability found in the Additional file 1. Allele frequencies and sample sizes. N = number of tested larvae per 15 larval cohorts of Trichinella spiralis from domesticated , synanthropic (F1c) and wild animals (WB1-10).Additional file 2. Student's t-test for equal means. Comparison of Fst values between outbreak larval cohorts vs Fst values between wild boar larval cohorts. Bootstrap N = 9999, permutations N = 9999.Additional file 3. Bayesian analysis of the multilocus genotypes of 281 individuals from 15 larval cohorts.Additional file 4. Evaluation of the optimal number of genetic clusters (k) present in Polish Trichinella spiralis subpopulations applying method proposed by Evanno et al. [o et al. .Additional file 5. Inferred Q percentage of membership of each individual for different values of k.Additional file 6. When no a priori degree of subdivision can be assumed for a population, STRUCTURE affords the means to consider how biological samples cohere, genetically, assuming greater or lesser degrees of subdivision.


Transportation infrastructure, which has always been regarded as an important element to promote regional innovation, accelerates factor flows and productivity spillovers. In February 2021, the State Council of China issued the outline of national integrated multidimensional transportation network planning (2021–2050), which proposed that during the 14th Five-Year Plan period, the Yangtze River Delta would speed up the construction of an integrated transport network to serve the dual circulation development pattern in China. However, few studies have systematically investigated the development of integrated transport in the Yangtze River Delta, especially the relationship between transport operating efficiency and regional innovation based on the theory of flow space. This study aims to calculate the integrated transport efficiency of 26 cities in the Yangtze River Delta and analyse the spillover effect of efficiency improvement on urban innovation. The results reveal that integrated transport efficiency is relatively stable at approximately 0.92. We find that the local innovation value would increase by 0.119% with every 1% increase in transport efficiency, and it would exceed 0.26% after introducing spatial factors. The spillover effect on the surrounding cities is significantly higher than that in the cities themselves, and the result is 0.292 under the economic spatial distance weight matrix. These findings will support the construction of the integrated transport network and provide useful references for government decision makers in the Yangtze River Delta. As a representative of the new economic geography, Castells opened a new era of studying urban innovation from the perspective of flow space by stating that material flows can realize space-time sharing without geographical proximity . The theWhen scholars attempt to adopt flow data to evaluate the structure of urban innovation networks, traffic data become an important data source because of their importance and convenience , 10. To The reform of information technology promotes the rapid expansion of transportation infrastructure. The rapid development of integrated transport in foreign countries in recent years has brought new opportunities for economic growth to many cities, such as Chicago, Kyushu, Berlin and others . Barnum Although the relevant research in China lags behind that in developed countries, with the continuous increase in the demand for convenient transportation, China is constructing an integrated transport system by constantly referring to the experiences of foreign countries. The 13th Five-Year Plan for the development of a modern integrated transport system issued by the State Council of China in 2017 clearly proposed that China would promote the further integration of different means of transport and build a safe, convenient, efficient, green, and economical modern transport system . In FebrThrough the analysis above, we believe that with the continuous enrichment of the integrated transport industry, urban agglomeration closely integrates various resources into a larger scope, and the spatial correlation effect of innovation elements is becoming increasingly significant. Although there are many studies on the network structure of regions, studies on the social and economic impacts of the network structure, especially regarding the spatial features of regional innovation, are relatively limited. Moreover, most studies focus on a specific transportation mode when studying the impact of transportation on urban innovation. The construction of an integrated transport system plays a multilevel and more critical role in promoting regional innovation. Therefore, based on the theoretical analysis of flow space, this paper studies the impact of integrated transport systems composed of multiple transportation modes on urban innovation and considers spatial factors in the model. Since there are few relevant studies on the Yangtze River Delta, this research benefits the Yangtze River Delta by providing policy suggestions to coordinate regional innovation and implement innovation-driven strategies.The remainder of the paper is organized as follows. The next section briefly discusses the conceptual framework. The third section introduces the study area, constructs the model, and discusses the selection of the variables and data sources. The fourth section estimates and analyses the integrated transport efficiency of 26 cities in the Yangtze River Delta and explores its spatial spillover effect on regional innovation using the spatial econometric model. The fifth part discusses the changes and influence of the scope in the Yangtze River Delta. Finally, this paper summarizes the main findings and provides some suggestions for decision-making departments.According to the theory of flow space, the essence of urban innovation is the process of innovation value flowing and transferring between different nodes. There is a typical technology gap within and between cities because of the influence of the economic base, cognitive distance and the diffusion channel . KnowledWithin a city, the construction of an integrated transportation network can improve transportation efficiency and reduce production costs so as to improve the accessibility of productive inputs and intermediate products. In addition, considering that the spillover of knowledge and technology has time and space limitations, the potential advantages of enterprises depend on the degree of asymmetry in the technological capability of the cluster . GeograpBetween cities, the spatial choice of innovation elements depends on the spatial structure and R&D environment, especially the relative local profits and rent costs. Specifically, some high-tech elements usually choose central cities with higher pay and higher rent costs . BecauseThe economic growth rate in the eastern coastal areas of China is higher than that in the inland areas. With the release of a series of policy documents, the eastern areas have taken the lead in developing an integrated transport network in the area. After more than ten years of rapid development, the total number of transportation facilities in the eastern region has increased significantly, and the efficiency of the integrated transport system has improved significantly, especially in the Yangtze River Delta.The Yangtze River Delta is one of the most active, open and innovative regions in China and plays a significant strategic role in the overall situation of national modernization and all-round opening-up. The Yangtze River Delta is located in the plain area downstream of the Yangtze River and has favourable conditions for opening up along the coast and the river. The Yangtze River Delta is composed of many cities within the scope of Shanghai, Jiangsu Province, Zhejiang Province and Anhui Province, with Shanghai as the centre. The integration of the Yangtze River Delta began in 1982, and the process has continued for more than 30 years. The spatial scope of Yangtze River Delta urban agglomeration has been changing and adjusting. According to the development plan of urban agglomeration in the Yangtze River Delta (2016) issued by the National Development and Reform Commission in China, there are a total of 26 cities in the Yangtze River Delta , includiY represents urban innovation and X represents the integrated transport efficiency calculated above. γ is the coefficient of the corresponding variable, and the formula also contains some controlled variables. i represents the city, t represents time, C is a constant term, and ε is the random error term.According to the model of neoclassical economic growth theory, this paper calculates the impact of integrated transport efficiency on urban innovation based on the improved production function under the assumption of constant returns to scale. The simple formula is as follows:n represents the number of cities under study and Xi and xj represent the specific attribute values of a certain city. Wij is the spatial weight matrix. Three types of spatial weight matrices are adopted in this paper. The adjacency weight matrix (Wr) is set according to whether the two cities are adjacent. When two cities are adjacent, the value is equal to 1; otherwise, the value is equal to 0. The time distance weight matrix (Wt) refers to the shortest time distance between two cities. It is often used to measure the correlation of cities in transportation. The economic spatial distance weight matrix (We) uses the geographical distance between the geometric centres of the two cities multiplied by the economic distance, which is the difference between the real GDP per person of the two cities. These matrices have all been standardized. The value of Moran’s I is between -1 and 1. If the value exceeds 0, it represents a positive spatial autocorrelation; conversely, it means the opposite.Geographical data are easily influenced by spatial factors, and there are certain correlations between research units. Before the spatial econometric model is established, the proposed model must pass the spatial autocorrelation tests, including global and local spatial autocorrelation tests. In order to judge whether the variables are correlated in space, Moran’s I index is used to conduct the global spatial autocorrelation analysis. The calculation formula is as follows:Getis−Ordi denotes the specific city. If the value of The local spatial autocorrelation test can describe the variation characteristics in a certain city. We use the Y is the explained variable, X is the input variable, WY and WX represent the spatial dependence, α is the constant term, and ε is the random error term.Considering the spatial characteristics of integrated transport efficiency, the main spatial econometric models are the spatial error model (SEM), the spatial autoregression model (SAR) and the spatial Durbin model (SDM). Their formulas are as follows, respectively:δ is the spillover effect of the integrated transport efficiency of neighbouring cities on local innovation, β is the coefficient of the elasticity of variables, θ is the coefficient of the spatial matrix, and ε is a random error term. If δ≠0, β and θ cannot represent the spatial effect of the explanatory variable.After incorporating the spillover effect of integrated transport efficiency, we take the logarithmic form of each variable and establish the spatial econometric model:Y) represents the annual number of authorized patents from each city in the Yangtze River Delta. There are two types of indicators to measure regional innovation: one is the innovation factor index, which is divided into an input factor and an output factor, and the other is the innovation efficiency index [Explained variable: Urban innovation (cy index . As the cy index . PatentsITE) represents the annual efficiency of each city in the Yangtze River Delta, which is calculated in the parts below.Explanatory variable: Integrated transport efficiency (Controlled variables:K) is estimated by the perpetual inventory method. We refer to the formula Kit = Ki,t−1(1−λt)+Iit/Eit, where Kit and Ki,t−1 represent the capital stock of city i in year t and year t−1, respectively. Eit is the fixed asset investment price index of city i in year t, and λt is the depreciation rate of fixed assets in year t. In this paper, a 10.96% depreciation rate is adopted for standardization.Capital stock (H) is based on the total number of employed people in each city multiplied by the average number of education years of residents. We refer to Wang to calculate the years of education of residents, and the formula is E = 6S1+10S2+16S3. S1, S2 and S3 are the numbers of students in primary school, middle school, colleges and universities per ten thousand people, respectively [Human capital (ectively .SI) is expressed as the proportion of secondary industry in the GDP of a city.The structure of the industry (U) is measured by the proportion of the municipal population with respect to the total permanent population of the city.The degree of urbanization (O) is the sum of the dependence on foreign capital and foreign trade. Among the components, the dependence on foreign capital is measured by the proportion of actual foreign investment with respect to actual GDP, and the dependence on foreign trade is measured by the proportion of the total foreign import and export trade value with respect to actual GDP. The total value of foreign import and export trade is unified in dollars and converted according to the average exchange rate of the corresponding year.The degree of opening to the outside , integrated development includes four aspects, namely, the transportation network, transportation service quality, business mode, and operating mechanism . Among tTherefore, we take fixed investment in the field of transportation infrastructure as the capital input variable, and the perpetual inventory method is adopted to adjust the value. Furthermore, transportation energy consumption is added as the energy input index. Since different cities adopt various measurement standards such as petroleum and natural gas in their energy consumption statistics, in order to achieve a unified comparison, all types of energy resources are converted according to the standard coal conversion coefficient and then standardized.Passenger and freight volumes, which reflect traffic scale, are mostly used as output variables in previous studies. For a better description of the efficiency of the integrated transport industry, we choose the turnover rate to represent both the quantity and quality performance of transportation. In addition, port container throughput is selected as the output index to measure the development situation of the port. Based on the index system of Li et al.  and AbadConsidering the availability and timeliness of the data, the paper takes cities as the research unit to measure the integrated transport efficiency of 26 cities in the Yangtze River Delta. Relevant traffic data mainly come from the Urban Traffic Yearbook, the White Paper on Traffic Development, the Statistical Bulletin of the Traffic Industry and the Traffic Operation Analysis Report. In addition, economic data come from the Urban Statistical Yearbook, and energy data come from the China Energy Statistical Yearbook.Although the integrated transport industry network is a comprehensive system composed of a variety of transportation modes, there are many unreasonable routes in the pipelines, such as many different oil routes, and most data on urban pipeline transportation are lacking. Therefore, we exclude pipelines from transportation modes. However, this does not mean that pipeline transportation does not play an important role in the integrated transportation network, and we expect to consider this important factor in future research.This paper estimates the integrated transport efficiency of 26 cities in the Yangtze River Delta from 2010 to 2019. In order to better compare the efficiency of different cities in the urban agglomeration, their average values in these 10 years are sorted. In addition, the overall efficiency of the urban agglomeration in different years is calculated. The evaluation results are shown in During the 2010–2019 period, there was an obvious growth trend of integrated transport efficiency, and the average value was approximately 0.92 in the Yangtze River Delta. As central cities in the urban agglomeration, Shanghai, Nanjing and Suzhou maintain a rising trend, and their efficiencies are all above 1.03. The efficiency of some cities surrounding Shanghai, such as Changzhou and Wuxi, shows a trend of first rising, then decreasing and finally rising again. Some cities with high efficiency, which are not close to Shanghai, also benefit greatly from neighbouring central cities. The efficiency of Yancheng, Nantong, Taizhou (in Zhejiang Province) and other cities in coastal areas is flat and slowly improves. With the gradual clarification of the positioning from relevant planning, these types of cities further integrate into the transportation network with Shanghai as the centre, and their integrated transport efficiency will be significantly increased. Yangzhou, Jinhua and other cities located in the geographical centre of the province should have location advantages, but in fact, due to the constraints of the interior economic conditions and the competition from the surrounding cities, their efficiency has been at approximately 0.80 for years.Generally, the top five cities are mainly the leading cities in the Yangtze River Delta, and they have better conditions for economic development. The average value of integrated transport efficiency in Shanghai and other leading cities exceeds 1.15. Leading cities attract increasingly more immigrants due to their strong economic foundations, which results in higher requirements for tourism and circulation. In addition, the increasing traffic demand forces the cities around the leading cities to improve their urban traffic networks as soon as possible. These cities need to expand their production and improve their production efficiency, and an efficient and low-cost integrated transport system will help them enter an era of better development.Cities with values that exceed 1 comprise nearly 1/3 of the cities studied, and most of them are close to the leading cities in the Yangtze River Delta. Because of the connections of various transportation modes in the Yangtze River Delta, these cities undertake the freight demand brought by the industrial transfer of leading cities and better meet the commuting needs of the reverse flow of the urban population. The connections to large cities not only improve the transport structure of leading cities on the periphery but also promote the efficient development of their own transport networks. Additionally, compared to the development conditions of other cities, such connections improve integrated transport efficiency, which makes the cities in the urban agglomeration more tightly connected.In addition, most effective cities have the necessary natural conditions. Regarding the natural conditions, coastal cities have better basic conditions than inland cities, such as ports and shipping, which play important roles in the urban integrated transport system. Taking Zhoushan as an example, a reasonable geographical location is conducive to the efficiency improvement and scale expansion of port transportation. In recent years, with the continuous improvement of port foundations, their transport efficiency has reached approximately 1.05 and still has great potential to increase in the future.In conclusion, it can be seen that most of the cities with a high efficiency keep a leading edge on the economic scale of the cities. The economic foundation is a vital force for promoting the development of urban transportation. The most critical reason for the improvement of the efficiency of the integrated transport industry in the Yangtze River Delta may be that it performs well due to its location advantage in foreign trade. Moreover, integration planning in the Yangtze River Delta brings policy advantages into play, which allow all cities to make good use of their advantages in the existing transport system and connect to the regional transport network in a better way. On this basis, the economic differences between the cities are narrowing, and the density of the transportation network is gradually being balanced.Through the global autocorrelation test of urban innovation and integrated transport efficiency in the Yangtze River Delta, the results show that the two indicators are both positively correlated under the three types of weight matrices, and the Moran’s I coefficient is the highest in the economic spatial distance weight matrix and the lowest in the time distance weight matrix, which illustrates that the influence of economic factors on the spatial dependence between cities cannot be ignored. Under the economic spatial distance weight matrix , the MorIn order to further test the local spatial autocorrelation of urban innovation and integrated transport efficiency, the paper takes 2019 as an example and explains their spatial distribution statuses using a Moran scatterplot diagram  and a Li2, LR and AIC of the SDM, SEM and SAR. The comparison of the R2s show that the degree of fit degree of the urban agglomerations estimated by the SDM is better than that of the ordinary regression, which is consistent with the conclusion of the spatial dependence test. With the introduction of a spatial econometric model, some useful conclusions are obtained in this paper.Without adding the spatial weight matrix, the data are first analysed using an ordinary panel regression . The resITE) show, in the general regression model, when the efficiency of the integrated transport industry increases by 1%, the urban innovation transformation will improve by approximately 0.119%. When the spatial factors are considered, the elasticity of the efficiency is almost higher than the result of the general regression, which means that the improvement of efficiency has a significantly positive impact on urban innovation transformation. Therefore, if the spatial correlation factors are ignored, we will underestimate their impact. After comparing the results of the three types of spatial weight matrices, we find that the results of the adjacency weight matrix and the economic spatial distance weight matrix are more significant than those of the time distance weight matrix. Under these weight matrices, when integrated transport efficiency increases by 1%, the urban innovation of the Yangtze River Delta will improve by more than 0.26%, which is mainly due to the significant marginal effect brought by the large-scale effect in the Yangtze River Delta urban agglomeration. The improvement of efficiency reduces the flow costs of elements and products, forms the agglomeration effect of the innovation element flow, and accelerates industrial progress, thus serving regional innovation.The existence of spatial correlation factors improves the positive impact of urban integrated transport efficiency on urban innovation. As the results of the main explanatory variable  and the degree of urbanization (U), and they both have positive effects on regional innovation in the research period. Each 1% increase in the industrial structure and urbanization rate in the Yangtze River Delta will cause 3.274% and 2.040% growth, respectively, in urban innovation under the economic spatial distance weight matrix. The cities in the Yangtze River Delta basically have manufacturing industry clusters in certain industries. In the urban economic growth process, with the gradual evolution of the industrial structure towards rationalization, the mode and scale of regional manufacturing industry clusters will continue to transform and improve. Through the flow and optimal allocation of resources between cities, production efficiency is improved and production costs are reduced, and thus structural adjustment and regional innovation transformation will be realized. According to urban economics, cities with higher urbanization rates have higher positive externalities, which can generate incentives for manufacturing enterprises to choose locations. An increase in the urbanization rate leads to population concentration; that is, population concentration not only provides sufficient labour for the development of the manufacturing industry but also provides considerable demand for manufacturing products. Therefore, an increase in the urbanization rate has a positive spillover effect in the Yangtze River Delta.Among the controlled variables, the main variables are the structure of the industry (H) in the Yangtze River Delta has a negative spillover effect on the surrounding cities under the three types of matrices. The increase in the number of labourers and more years of education for residents have alleviated the contradiction between labour resources and demand for efficient economic operations and has a positive effect on local innovation. However, the spillover effects on neighbouring cities are always negative under the three types of weight matrices, which shows that the polarization effect of the surrounding cities exceeds the trickle-down effect. The capital stock (K) and the degree of opening to the outside (O) both have a positive influence on the innovation development of urban agglomerations. The scale of capital stock determines the realistic foundation and economic strength of urban development and plays an important role in guiding the agglomeration of local innovation elements. In addition, the advantage of government policy provides the Yangtze River Delta favourable conditions for the introduction of foreign capital and the development of trade. The above conclusions show that there is a promising development trend in the Yangtze River Delta in science and technology innovation and cooperation.The increase in human capital Click here for additional data file.S2 File(ZIP)Click here for additional data file.


Analyses of the microsatellite data consistently suggested a differentiation between Hungary and Serbia, with the pheasant population in Hungary being much more genetically homogeneous, while that of Serbia has much more genetic mixture and admixture. This cryptic differentiation was not detected using a non-spatial Bayesian clustering model. The analyses also provided strong evidence for a recent population expansion. This fundamental information is essential for adequate and effective conservation management of populations of a game species of great economic and ecological importance in the studied geographical region.The common pheasant, a game species widely introduced throughout the world, can be considered as an ideal model to study the effects of introduction events on local adaptations, biogeographic patterns, and genetic divergence processes. We aimed to assess the origin, spatial patterns of genetic variation, and demographic history of the introduced populations in the contact zone of Central and Southeast Europe, using mitochondrial DNA control region sequences and microsatellite loci. Both types of molecular markers indicated relatively low to moderate levels of genetic variation. The mtDNA analyses revealed that common pheasants across the study area are divided into two distinct clades: B ( Characterizing the genetic diversity of populations, understanding the spatial patterns of this variation in the landscape, and investigating possible historical and current causes for these patterns, is fundamental knowledge for an informed conservation management of wildlife, which is especially critical for threatened populations5.Genetic variation plays a central role in the evolution and ability of species to adapt to continuous environmental changes, and in their long-term persistence in the face of an increasing and constant anthropogenic disturbances6, natural and anthropogenic barriers9, migration behaviour and sex-biased dispersal10, climate change11, and human introductions and hybridization14. The introduction of species to areas outside of their historic range is a widespread management action to support harvest in wildlife, fisheries, and forestry15. However, translocating species may lead to genetic admixture between taxa due to hybridization, and therefore change spatial patterns of genetic variation and population structure of native species, or cause gradual dilution of autochthonous genetic diversity and reduce individual fitness due to outbreeding16. Unrecorded releases of individuals from different subspecies for hunting purposes , besides possibly being detrimental to natural populations (e.g. outbreeding depression), further complicate our understanding of the genetic profile of introduced populations20.The spatial patterns of genetic variation and population structure of species may be influenced by geographic distancePhasianus colchicus L.), a non-migratory gallinaceous bird, can be considered as an ideal model for studying the effects of introduction events on local adaptations, biogeographic patterns, and possible speciation processes21. The common pheasant, with 31 described subspecies based on morphology, colour patterns, and range discontinuities22, is considered native from eastern Siberia to China, Iran, the Caucasus and the south-eastern Balkans23. The subspecies inhabit significantly different landscapes and climatic niches, from montane ecosystems to isolated oases in semi-desert regions, exhibiting distinct phenotypes and genotypes25. Although the common pheasant is widespread and relatively common over most of its range, local and regional populations are facing declines mainly due to over-hunting and habitat loss26. For example, P. c. talischensis, from Azerbaijan and northwest Iran bordering the southwest of the Caspian Sea, is now a very rare subspecies27 comprising a total population of 200–300 individuals28.The common pheasant , date back to 500–800 AD29. Much later, already in the eighteenth century, Mongolian (P. c. mongolicus) and Chinese ring-necked pheasants (P. c. torquatus) were also brought in to Europe29. The European populations are therefore descended from several subspecies and possibly from hybridization between them22. Therefore, the identity of these subspecies and the genetic composition of the introductions still need to be investigated because some common pheasant morphological subspecies are not supported by molecular data30, possibly as a result of recent range expansions and subspecies admixture31. Hybrids of different subspecies may also have been released in areas with native subspecies in Southeast Europe, which may lead to the extinction of the latter by genetic swamping32, as apparently in the case of the indigenous South Caucasus pheasant populations in Bulgaria22.The common pheasant has been introduced to Europe, North America, and Australia33. It is especially prized by hunters for its meat and for being suitable for group hunting with dogs34. Non-random removal of individuals from wildlife populations by recreational hunting may affect the genetic structure, morphology, life history, and, ultimately, the viability of the target population38. Moreover, the common pheasant in Europe has long had extensive re-stocking to maintain populations and for hunting purposes, in which released individuals are mostly of non-local origin and farm-reared39. These individuals may have low genetic diversity, maladaptive alleles, and lower fitness in the local natural environment than indigenous individuals. Hybridization and introgression of maladaptive alleles into local populations can degrade autochthonous genetic heritage and disrupt coadapted gene complexes, making these populations less adapted to their environment15. It is likely that a large percentage of captive-bred individuals released recently and today, due to their possibly lower ecological fitness in foreign natural conditions, have been quickly removed by hunters and predators and generally have low survival and reproduction. Therefore, it is probable that the reproductive contribution of captive-bred individuals to the next generations is less than could be expected, and their greatest impact may be the transmission of livestock diseases and parasites. The combined effects of the selective removal of individuals  and hybridization and introgression with individuals of lower fitness are likely to have a profound impact on the evolution, genetic health, and long-term persistence of native game populations40.In Europe, the common pheasant is historically considered as one of the most important game species for recreational hunting41. Thus, the genetic structure of introduced common pheasant populations should be strongly shaped by founder effects, increased genetic drift, small to modest effective population sizes (Ne), and inbreeding41.It has been suggested that processes of introduction, stocking, and restocking in the common pheasant may lead to genetic differentiation between locations, likely enhanced by founder effects and genetic drift associated with small propagules, and that is subsequently maintained by limited interpatch gene flow44, with the most recent data supporting eight major clades in this extant native range45. Evaluating genetic variation and structure in common pheasant populations, and identifying those with low and high levels of genetic diversity, is crucial for effective conservation and management of the species, for example to inform translocation efforts.Despite the long history of the common pheasant as one of the most important game birds in Europe, nothing is known about the genetic variation and structure of the European populations. From a genetic point of view, the species has been studied essentially in terms of phylogeography and subspecies taxonomy throughout its distribution across Asia46 and for assessing the geographic origin of introduced lineages48, and in a set of polymorphic nuclear microsatellites, which are known to be powerful markers for studying genetic structure and processes at small temporal and spatial scales49. It is recognized that the combined analysis of mtDNA and microsatellites can be very informative for phylogeographic and population genetic studies of natural populations51.In this study, we begin the investigation of the origin, genetic diversity and structure, and demographic history of the common pheasant in Europe, with the analysis of populations in the contact zone between Central and Southeast Europe. We also compared the genetic diversity in the study area with that which has been reported in Asian populations, and assessed the hypothesis that the studied populations are derived exclusively from introductions from Asia. To this end, we analysed variation in a fragment of mitochondrial DNA (mtDNA), a molecular marker of excellence for phylogeographyP. colchicus individuals, 29 samples from Hungary and 40 samples from Serbia  fragment of the mtDNA control region was sequenced for 69 bia Fig. . Additiobia Fig. . Among oP. colchicus haplotypes plus the three P. versicilor outgroup sequences yielded identical topologies, indicating the presence of six main lineages with significant support from bootstrap values and Bayesian posterior probabilities  and H51  and H79   fell into a distinct cluster Figure . The AMOgh Table .Table 2Ari) were not significant (P > 0.05) in any of the cases  since approximately the last 10,000 years Fig. . The misOf the 10 loci, we did not include the results for two of them in the data set for analysis because one (PC5) was monomorphic and the other (PC1) had a high proportion of missing data. The global test indicated significant deviations from HWE after Bonferroni correction  and heterozygote deficit in three loci . However, in separate tests for each of the two countries, these three loci showed significant deviation from HWE and heterozygote deficit only for Serbia, not for Hungary. This suggests that the data for these loci may not be affected by intrinsic problems, such as null alleles or allelic dropout, but that the significant test results in Serbia may be due to underlying genetic structure (Wahlund effect). Accordingly, estimates of the frequency of null alleles at these loci were generally small  and ΔK suggested the presence of two genetic clusters (K = 2) with either the model with prior information on the sampling location of individuals or the non-spatial model Fig. . With th2adj = 0.072), indicating that much of the spatial genetic pattern can be explained by IBD.The Bayesian spatial clustering of individuals performed in BAPS also supported the presence of two clusters (K = 2) as the most likely Fig. . Most ofThe spatial autocorrelation analysis showed significant coefficients (p < 0.01) for the smallest distance class , and the mean number of effective alleles, HO and HE were 3.82, 0.580 and 0.651, respectively , and under any of the three tested mutation models, based on the Wilcoxon’s signed rank test, except for the cluster 2   under the IAM (which was significant at α = 0.05). Based on the sign test, the Serbia data set showed evidence of a recent genetic bottleneck only under the SMM Table . Given t45, there is a great lack of this kind of information on the populations introduced into Europe. This study provides the first detailed genetic assessment of common pheasants in their introduced European range, particularly in the contact zone of Central and Southeast Europe (Hungary and Serbia), and the results are essential information for the development of effective conservation and management strategies.Although several studies have investigated the genetic diversity, population structure and demographic history of the common pheasant across its native range in Asia37); Hd = 0.500–1, π = 0.0009–0.0130). The phylogenetic analysis, including both the new control region sequences generated here and those previously published, inferred the same clades already identified in previous studies (e.g.31) , particularly when compared to that reported for native populations in Asia (e.g.29 proposed that the first common pheasants introduced into Europe were colchicus (as early as 500 AD), and only much later (eighteenth century) other subspecies (mongolicus and torquatus) would have been introduced. Our results, given the much greater frequency and distribution of clade F in the study area, are consistent with the hypothesis that colchicus was long introduced, so that this lineage had ample time and opportunity to spread throughout the region. Haplotype H1 of clade F and haplotype H2 of clade B were the most frequent and widespread in the study area  may have been erased as a result of a combination of factors such as the high mutation rate of microsatellites, rapid population growth, and the genetic enrichment resulting from the mixture of subspecies in the study area , and even less in the breeding season (0.02 km2)63. Our results, however, suggest that the scale of the genetic impact of releases of captive-bred individuals may be much larger than could be predicted by game managers based on the typical home range of the species. It has been observed and reported that the common pheasant populations of the two countries in the study area have been declining for the past 30 years. It is then apparent that the intended population reinforcements with captive-bred individuals have not helped to achieve the impact hoped for by wildlife farmers and could not prevent the decline in population size. However, in the absence of reference areas and populations, it is difficult to judge the exact impact of artificial releases. It may have even helped to strengthen natural populations, but at the same time it could only slowed the rate of population decline. But it may also have been negatively affected the population size trend by outbreeding depression, the introduction and faster spread of diseases and parasites due to birds introduced from foreign sources and the larger herd size, the increase in the number of predators and the neglected natural population due to supplementation.The results of this study provide essential information for conservation management, monitoring, and for comparison with future assessments of the genetic status of common pheasant populations in Central and Southeast Europe.An important conclusion from our data, both mitochondrial and nuclear, is the fact that they strongly indicate that releases over time to the present day of captive-bred individuals have apparently not led to the establishment of other lineages/subspecies distinct from clades B and F. Also, the detection of neighborhood size up to a distance of 50 km is of practical relevance for the species’ management. Thus, wildlife management practice has traditionally treated this distance value as the basis for isolating pheasant populations. However, the known home range of common pheasants is much smaller: on average 0.05–0.1 kmWe collected 74 fresh tissue samples in the contact zone of Central and Southeast Europe (Hungary and Serbia) between 2014 and 2015  were carried out in a total volume of 25 μL, with the reaction mixture containing 30 to 50 ng of genomic DNA, 10 pmol/L of each primer, 1X PCR buffer , 6.25 mmol/L MgCl2, 2.5 units of Taq polymerase , and 10 pmol/L dNTPs. The PCR conditions were as follows: an initial denaturation at 94 °C for 2 min, followed by 30 cycles of 15 s at 94 °C, 15 s at 55 °C and 1 min at 72 °C, and a final extension for 10 min at 72 °C. PCR products were run on 2% agarose gels, stained with ethidium bromide, and visualized under ultraviolet (UV) light. PCR product purification and sequencing were carried out by Macrogen Europe. Sequences were assembled using SeqScape v.2.6 (Applied Biosystems) and aligned with ClustalW in MEGA 6.065. The substitution saturation was examined in DAMBE 666.A 825-bp fragment of the mitochondrial control region in 69 common pheasants was amplified using the primers PHDL (5’-AGGACTACGGCTTGAAAAGC-3’) and PHDH (5’-CATCTTGGCATCTTCAGTGCC-3’)P. colchicus by Wang et al.55, were used to genotype 74 individuals from the study area. The PCRs consisted of 12.5 μL of Multiplex PCR Master Mix (QIAGEN), 0.3 μL of each primer (10 pmol/μL), 1 μL genomic DNA (15 ng/μL), and sterile water up to a final volume of 25 μL. Thermal cycling consisted of a 5 min initial denaturation at 95 °C, followed by 35 cycles consisting of denaturation at 94 °C for 30 s, annealing at 58 °C for 45 s, and extension at 72 °C for 1.5 min, and then a final extension at 72 °C for 10 min. The forward primers were labelled at the 5′ end with either 6-FAM, VIC, NED or PET fluorescent dyes  and the Bayesian Information Criterion (BIC). IQ-TREE 1.6.1269 was used to infer a maximum likelihood (ML) tree under a HKY + I + G model70, and clade support was assessed using 10,000 bootstrap replicates. BEAST 2.6.471 was used to reconstruct a Bayesian inference (BI) tree under a HKY + I + G model, based on 40 million generations of Markov Chain Monte Carlo (MCMC) chains and sampling every 100 generations. Three sequences of the green pheasant (Phasianus versicolor)   were included as outgroups. A median-joining network72, estimated in Network 10.0 (available at: http://www.fluxus-engineering.com), was used to infer and visualize possible genealogical relationships among haplotypes.New sequences in the present study were combined with 202 GenBank sequences from previous studies Table  to yield73, to estimate the presence of mitochondrial genetic clusters within the new sequences of P. colchicus. BAPS was run setting the method of ”clustering for linked loci” with two independent runs, and the maximum number of clusters (K) to 10. To examine genetic differentiation among clusters, analysis of molecular variance (AMOVA) and fixation indexes (FST) were calculated using Arlequin 3.5.2.274 with 10,000 permutations to test for significance.We used the Bayesian model-based clustering algorithm implemented in BAPS 6.0 75 in BEAST 2.6.471 to explore the demographic dynamics of detected population of the common pheasant in the study area (clusters B and F detected by BAPS). The latter analyses were conducted using a HKY substitution model, a strict clock model, and the coalescent extended Bayesian skyline as tree prior. The MCMC chain was set to a total 400 million generations, and the Markov chain was sampled every 10,000 steps. Tracer was used to assess the convergence and stationarity of the EBSP values, and whether the effective sample sizes (ESS) were > 200. We performed multiple replicate runs to ensure MCMC convergence. EBSPAnalyzer 2.5.271 was employed to construct the EBSPs. We used R 3.1.276 to generate the visualizations.We constructed extended Bayesian skyline plots (EBSPs)77 and Tajima’s D78, computed in Arlequin to test equilibrium of the population. A negative value of these statistics signify an excess of low frequency polymorphisms, and suggests a population size expansion and/or purifying selection of a population78. When we detect more than one distinct population cluster in the BAPS analysis, we carried out the EBSP analyses, mismatch distribution and neutrality tests for each cluster separately.Mismatch distribution analyses were used to examine demographic expansions in detected population clusters. To compare observed distributions with expected distributions under the expansion model, we estimated sums of squared deviations (SSD) and Harpending's raggedness index (RI) in Arlequin. Morever, we used the tests of Fu’s FS79 to test loci for Hardy–Weinberg equilibrium (HWE) deviations and linkage disequilibrium (LD) between pairs of loci, based on the whole dataset and separately for each region (Hungary and Serbia). Probability values were assessed for significance using sequential Bonferroni correction for multiple comparisons and a nominal significance value of 0.0580.We used exact tests in Genepop 4.281 to investigate the likely number of genetic clusters (K) in the study area. STRUCTURE was run using the admixture ancestry model, correlated allele frequencies, K from 1 to 5, 10 independent simulations for each K, 100,000 iterations as burn-in, and 1,000,000 iterations for sampling. We conducted Bayesian clustering with 82) and without prior information on sampling locations. We used STRUCTURE HARVESTER83 to process and visualize results from STRUCTURE, and we determined the most likely value of K based on the log probability of the data (Ln P(D)) and the ΔK statistic84. The membership probabilities  were used to assign individuals to clusters considering a membership threshold of either 50% or 70% (e.g.86). CLUMPP 1.1.287 and DISTRUCT 1.188 were used to average the membership probabilities over the 10 runs for each K and to plot the results, respectively. The number of genetic clusters present in the study area and the degree of admixture between them was also investigated using the spatial mixture model in BAPS 689. We tested K values from 1 to 5, and subsequently performed a separate analysis in the ‘fixed K’ mode with K = 2. Additionally, we used MEMGENE 1.0.190 to complement the analyses with STRUCTURE and BAPS and to visualize spatial genetic neighbourhoods. MEMGENE performs a spatially explicit ordination technique free of linkage and Hardy-¬Weinberg equilibria assumptions; in particular it uses Moran’s Eigenvector Maps (MEM) to detect weak genetic structure90. Spatial autocorrelation analyses were used to detect fine-scale patterns of genetic structure across the study area92. We divided the individuals into 22 geographic distance classes of equal sample size. A correlation coefficient r between geographic and genetic distance was calculated among individuals in each class. Significance tests were performed using 10,000 permutations to test the null hypothesis of no spatial autocorrelation. Confidence intervals around the estimates of r were determined using bootstrapping with 10,000 replicates. The results were plotted in a correlogram. Finally, we used the Mantel test within the R package Vegan version 2.5-793 to assess the hypothesis of isolation-by-distance (IBD)94. For this, we calculated genetic and log-transformed geographic distances among all pairs of individuals in GenAlEx 6.595. A total of 10,000 replicates were used to determine the significance of the test relative to the 95% quantile of the distribution of permutations.We used STRUCTURE 2.3.496, and POPGENE 1.3297. The inbreeding coefficient (FIS) was estimated using GENETIX4.0598 and the significance of values was evaluated based on 10,000 permutations. BOTTLENECK v1.2.026952 was used to test for evidence of recent bottlenecks in effective population size at two levels including cluster-level  and country-level (Hungary and Serbia). We ran BOTTLENECK using 1000 iterations and three different mutation models: the infinite alleles model (IAM), the stepwise mutation model (SMM), and the two-phase model . The statistical tests applied in BOTTLENECK were the Wilcoxon signed-rank test99, sign test, and the ‘mode-shift’ indicator100.We quantified genetic diversity using basic genetic variation statistics, including observed (HO) and expected (HE) heterozygosity, mean number of alleles (MNA), allelic richness (AR), effective number of alleles (NE), private allele frequency (AP), and polymorphic information content (PIC). The estimates and 95% confidence intervals were obtained for the total sample and separately for each country (Hungary and Serbia) using GenAlEx, FSTAT v.2.9.3.2Supplementary Figure S1.Supplementary Figure S2.Supplementary Figure S3.Supplementary Figure S4.Supplementary Legends.Supplementary Table S1.Supplementary Table S2.Supplementary Table S3.Supplementary Table S4.


Magnolia (Magnoliaceae) have high economic, scientific and ecological values. Although nearly half of all Magnolia species have been evaluated as threatened, to date there has been no population genetic study employing Next Generation Sequencing (NGS) technology in this genus. In the present study, we investigate the conservation genomics of Magnolia fistulosa, a threatened species endemic to the limestone area along the Sino-Vietnamese border, using a double digest restriction-site-associated DNA-sequencing (ddRAD-seq) approach. To increase the reliability of our statistical inferences, we employed two approaches, Stacks and ipyrad, for SNP calling. A total of 15,272 and 18,960, respectively, putatively neutral SNPs were generated by Stacks and ipyrad. Relatively high genetic diversity and large population divergence were detected in M. fistulosa. Although higher absolute values were calculated using the ipyrad data set, the two data sets showed the same trends in genetic diversity , population differentiation (FST) and inbreeding coefficients (FIS). A change in the effective population size of M. fistulosa within the last 1 Ma was detected, including a population decline about 0.5–0.8 Ma ago, a bottleneck event about 0.2–0.3 Ma ago, population fluctuations during the last glacial stage, and the recovery of effective population size after the last glacial maximum. Our findings not only lay the foundation for the future conservation of this species, but also provide new insights into the evolutionary history of the genus Magnolia in southeastern Yunnan, China.Elucidating the genetic background of threatened species is fundamental to their management and conservation, and investigating the demographic history of these species is helpful in the determination of the threats facing them. The woody species of the genus  Endangered species, especially those with extremely small populations, often face a high risk of extinction under climatic fluctuations and anthropogenic disturbance . In addiin situ and ex situ) of different evolutionarily significant units identified in threatened species will maximize their evolutionary potential to handle environmental change  is a conservation concept referring to plant species with small remaining populations , restricted habitat, exposure to serious human disturbance, and at high risk of extinction . Indeed,l change . Therefol change ,b.Advances in sequencing technology, especially reduced-representation genome sequencing, have allowed us to obtain thousands of genetic markers relatively easily , making Magnolia belongs to the family Magnoliaceae and is well known for its basal position in the flowering plants  Dandy is an evergreen shrub with broad oval leaves and extremely aromatic flowers  in 2010 by The Yunnan Provincial Government  and Cycas dolichophylla , a large (4 cm long) species of beetle. The main distribution of M. fistulosa was found to be in Hekou and Maguan Counties, while small numbers of disjunct individuals were also found in Gejiu and Jinping Counties. Throughout the range of M. fistulosa, there are varying degrees of human disturbance, such as crop planting and road construction  was adopted as reference genome to improve the quantity and quality of the SNPs makers. In this study, we reveal the genetic threats currently faced by M. fistulosa and lay the foundation for the future conservation and management of this species. We aim to (1) explore the population structure of M. fistulosa and infer the levels and direction of gene flow among populations; (2) estimate the genetic diversity and contemporary effective population size of each population; and (3) infer the demographic history of M. fistulosa and its potential association with climatic fluctuations.In this study, we use two different pipelines to process the sequence data generated from the double digested restriction site-associated DNA sequencing (ddRAD-seq) from Magnolia fistulosa individuals from seven sampling locations , M. officinalis , Principal Components Analysis (PCA) and Bayesian clustering. DAPC analysis was conducted using the R package “adegenet” . The fundmixture . To detephelper”  in R v3.phelper”  to visuaphelper”  as implephelper” , with 1. and 60% . AMOVA a and 60%  to quant and 60%  with 10,He), observed heterozygosity (Ho), fixation index (FST) and inbreeding coefficient (FIS) were calculated at each sampling location and genetic group using the populations module in the Stacks pipeline. Contemporary effective population size (Ne) for each sampling location and each genetic group (sample size = n) was estimated using the linkage disequilibrium method in NeEstimator V2 (The nucleotide diversity (π), expected heterozygosity  and a generation time of 10 years, which was estimated from the cultivation records in Kunming Botanic Garden and Gulinqing Nature Reserve. We generated Site Frequency Spectra (SFS) for all samples and for each genetic group inferred from the population structure analyses. Controlling overfitting is important in the inference of demographic history  and M. biondii (84.93%)  . M. siniUsing the reference-based analysis in the Stacks pipeline, we produced 15,274 SNPs (Stacks data set). The missing rates in the Stacks data set ranged from 0 to 30.8%, with a mean missing rate of 5.5% . Using tTajima’s D statistic showed that no selected sites were present in either of the two data sets, while BayeScan detected two SNPs in each data set under positive selection and a single SNP under balancing selection in the ipyrad data set. After filtering, 15,272 and 18,960 putatively neutral SNPs were retained in the Stacks and ipyrad data sets, respectively . After cde novo clusters within the samples only from Hekou and Maguan, and found LBI was separated from HM  showed that the genetic differences among samples in Hekou and Maguan were relatively small (FST < 0.15), while the genetic differences between QCP, QSH, and LBI were larger. QCP and LBI had the highest FST values, which were calculated to be 0.359 and 0.469 in the Stacks and ipyrad data sets, respectively (The fixation index (ectively . AMOVA aectively .Ho and He ranged from 0.034 (QCP) to 0.055 (LB) and from 0.027 (QCP) to 0.064 (MCP), respectively, in the Stacks data set, and from 0.048 (QCP) to 0.065 (MCP) and from 0.035 (QCP) to 0.081 (MCP), respectively, in the ipyrad data set. The values of the inbreeding coefficients (FIS) were negative in QCP and LBI  the Ne estimated by NeEstimator V2 in the other five sampling locations and two genetic groups in LB ranged from 2.0 (LBI) to 59.7 (MCP) in the Stacks data set and from 0.9 (LBI) to 48.9 (BSH) in the ipyrad data set , has the lowest genetic diversity. Although being geographically distant from the other populations, the genetic diversity of QSH was only slightly decreased compared to that of the HM genetic group.Small isolated populations often suffer from lower genetic diversity, due to inbreeding, genetic drift and reduced gene flow . The fouiversity . The HM Ne. MCP comprises more than 70 individuals of Magnolia fistulosa, which is the largest locality found to date. We only investigated 20 individuals in BSH, however, there is a further record of M. fistulosa in Bojia Village near BSH. Fewer than 20 mature individuals were found in QSH, which is close to the estimated Ne (30.7 and 24.5). Contemporary Ne is usually smaller than the real population size  compared to some endangered plants assessed using ddRAD-seq. For example, lower genetic diversity was found in Viola uliginosa , and Cyanea pilosa  , as dated by electron spin-resonance spectroscopy (The first population decline in –0.8 Ma) . The Nay–0.8 Ma) . A bottltroscopy . Temperatroscopy . During troscopy .Magnolia fistulosa population experienced multiple shrinkages in the past  Ex situ conservation process should be carried out to ensure the long-term survival of M. fistulosa. Based on the results of our population structure inference, QSH and QCP represent unique genetic resources and should both be collected and protected. Within the samples in Hekou and Maguan, MCP, BSH, and CEN can be considered as one genetic cluster, but the samples from JYZ and LB should be collected separately. (2) Most individuals in the Hekou-Maguan and QSH are already under protection in the Daweishan National Nature Reserve and Gulinqing Provincial Nature Reserve, however, land development may have damaged the connectivity in this area, which is reflected in the low recent gene flow within samples from Hekou and Maguan. To prevent further restriction of gene flow, we recommend restoring some areas of farm land to forest in the Daweishan National Nature Reserve and Gulinqing Provincial Nature Reserve, especially along steams, which are particularly suitable habitats for M. fistulosa. (3) Despite the fact that we conducted several investigations, only four individuals were found in the QCP, and these were close together. To prevent further genetic recession, we recommend adopting artificial outcrossing and propagation in this population. Furthermore, because the location of this population does not belong to any nature reserve, we recommend establishing mini-reserves to implement rescue protection of the QCP population  can be found below: WS developed the idea. FY and LC designed the experiment, interpreted the results, and wrote the manuscript. FY, LC, and ZD collected the leaf materials. FY performed the statistical analyses. WS and ZD acquired the funding. All authors read and approved the final 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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.