In Ecuador, the production of Ecuadorian Creole chicken is of crucial importance in the economy and for the nutrition of families. These chickens represent a focal point in scientific research for three main reasons: (1) they are an unknown genetic resource derived from 500 years of environmental and human selection and represent an important reservoir of genetic variability and adaptability; (2) Ecuadorian Creole chicken production is normally familiar, in a marginal dimension, and it is an important source of economic input for medium–low income communities; and (3) being a local genetic resource, it is available to local communities without intermediary international enterprises and represents the starting point for food sovereignty. We aimed to measure the level of genetic diversity and its phylogenetic position compared with other outgroup breeds using information from microsatellite and mitochondrial markers. Our results showed that these chicken populations represent a great reservoir of genetic variability; however, the genetic fragmentation owing to the high geographical diversity of the country could compromise the conservation status and, therefore, the establishment of an official breeding program is needed for the conservation and valuation of these avian populations, with this genetic characterization being a first step.Latin American Creole chickens are generally not characterized; this is the case in Ecuador, where the lack of scientific information is contributing to their extinction. Here, we developed a characterization of the genetic resources of Ecuadorian chickens located in three continental agroecosystems . Blood samples of 234 unrelated animals were collected in six provinces across Ecuador: Bolívar, Chimborazo, Cotopaxi, Guayas, Morona Santiago, and Tungurahua, in order to perform a genetic characterization and population structure assessment using the AVIANDIV project microsatellites panel (30 loci) and D-loop sequences of mitochondrial DNA and comparing with reference data from other breeds or genetic lines. The results indicate that Ecuadorian Creole chickens are the result of the admixture of different genetic groups that occurred during the last five centuries. While the influence of South Spanish breeds is demonstrated in the colonial age, genetic relationships with other breeds  cannot be discarded. The geographical configuration of the country and extreme climate variability have influenced the genetic isolation of groups constituting a homogeneous genetic status into the whole population. This is not only a source of genetic variation, but also a critical point because genetic drift produces a loss of genetic variants. In Ecuador, the Creole chicken is an important genetic resource utilized in backyard productions, and is part of the traditional diet and an important economic resource for families. The meat and eggs of these birds have a high nutritional value accessible to the entire population owing to its low purchasing power. According to the National Finance Corporation, in 2016, after conducting an economic analysis of poultry production in Ecuador, it was indicated that between 2013 and 2016, the raising of field poultry decreased by 27%, while an increase in the industrial rearing system was observed, drawing attention to the need for the study and characterization of the Creole chicken population [The origin of the Creole chicken on the American continent is still controversial; current Creole chickens derive from the introduction of European animals during the Spanish colonization in the 15th century . AccordiThe arrival of chickens from Polynesia is supported by different hypotheses. According to some authors, the relative geographic proximity between Chile and the archipelagos of the Pacific could justify some type of commerce ,5,6.It is probably that the current population is, therefore, the result of different admixture events, including the introduction of the industrial genetic types in recent times, as can be observed by the extreme phenotypic variability.These populations represent an important example of adaptation to different climates. In fact, Ecuador presents drastically changing climatic and orographic conditions  within a few kilometers, ranging from a few meters above sea level with very high levels of temperature and moisture to the central region at more than 4000 meters above sea level with a cold and dry environment.The adaptation of the local populations of chickens to these extreme environmental conditions on the one hand ensures a source of protein in the nutrition of the family and, on the other, offers a source of genetic richness to develop the new trends in sustainable free-range avian production systems, respecting the welfare of animals.Currently, these animals are at a permanent risk of extinction because of the lack of serious scientific studies regarding their characterization, which could support their general recognition. According to Alderson , the knoEthical approval was not needed for this study. Blood samples from Ecuadorian chickens were collected by qualified veterinarians during their routine practice within the framework of official programs aimed at the identification, monitoring of health, and parentage confirmation of the breeds and populations included in our study. In terms of collection, the fieldwork did not involve any endangered or protected species. Hair roots were manually collected without any injury in the back of the animals. No other kind of tissues  were used in this study. The other breeds are data proceeded from another study inside our research group.® FTA® Cards . The cards were dried at 25 °C for a minimum of 3 h and then stored in paper envelopes until use. DNA was extracted for both mitochondrial DNA and STRs (short tandem repeats) analysis, following a modification of the method described by Walsh et al. [A total of 244 unrelated animals of both sexes were randomly sampled from six provinces representing all the continental agroecosystems of Ecuador: Bolívar, Chimborazo, Cotopaxi, Guayas, Morona Santiago, and Tungurahua . Specifih et al. . BrieflyThe mitochondrial control region amplification was performed from nucleotide position 16,741 to 1280 of the complete mitochondrial DNA sequence (accession number NC007235) , includi2), 200 µM of each dNTP (deoxyribonucleotide triphosphate) , 0.2 µM of each oligonucleotide, 1 U of Taq polymerase , and about 30 ng of DNA. The reaction mixtures were placed in a thermocycler Bio-Rad C1000 touch  under the following amplification conditions: 95 °C for 5 min, followed by 34 cycles at 95 °C for 45 s, 59 °C for 1 min, and 72 °C for 2 min, with a final extension at 72 °C for 45 min.The PCR was performed in a total volume of 25 µL as follows: PCR buffer  1×  and 10 units of Exonuclease I enzyme  at 37 °C for 15 min and 80 °C for 15 min. Sequencing reactions were performed in both directions using PCR oligos at Macrogen Inc. services .Nucleotide sequences were checked and edited using MEGA 5.0 . A fragmhttps://aviandiv.fli.de/). Technical characteristics such as PCR and electrophoresis conditions were followed as reported in Ceccobelli et al. [We used a total of 30 microsatellite markers from the AVIANDIV project  [http://cmpg.unibe.ch/software/arlequin35/) [The number of haplotypes, polymorphic sites, and nucleotides and haplotype diversity estimates for the domesticated and wild populations were calculated using DnaSP v5 (/dnasp/) . Analysi/dnasp/)  was usedquin35/) .http://cmpg.unibe.ch/software/arlequin35/) [In addition, computed pairwise comparisons of FST  values with 1000 permutations were obtained using ARLEQUIN v3.1 (quin35/) .www.fluxus-engineering.com) and setting default parameters. The relationships between haplotypes were also analyzed using phylogenetic inference. The final dataset included all sequences analyzed in this study and those retrieved from the NCBI database, representing the main clades described in the literature: AF512092 (haplogroup A), AF512125 (haplogroup B), AF512263 (haplogroup C), AB007726 (haplogroup D), AB114061 (haplogroup E), GU448405 (haplogroup F), GU448894 (haplogroup G), and GU448603 (haplogroup K).To establish phylogenetic relationships between haplotypes and their frequencies, a haplotype network was constructed using the median-joining method  with NETFor microsatellites statistical analysis, a dataset was constructed considering 15 chicken breeds for population relationship and geneflow assessment, including, as outgroups, nine breeds from Spain , one from Africa, one Creole breed from Chile, and finally three commercial lines . ECU: Echttp://animalgenomics.ucd.ie/sdepark/ms-toolkit/) [https://sites.ualberta.ca/~fyeh/popgene.html) [http://www.montana.edu/kalinowski/software/hp-rare.html) [http://genepop.curtin.edu.au/) [p-values [https://kimura.univ-montp2.fr/genetix/) [The mean number of alleles per locus and population and observed and expected heterozygosity estimates were calculated using the EXCEL MICROSATELLITE TOOLKIT 3.1.1 (oolkit/) . Effectine.html) . In the re.html)  using thre.html) . Deviatiedu.au/) , applyinp-values  were calp-values . The FISp-values  and factenetix/) .http://bioinformatics.org/populations/) [Reynolds distances , showingations/) , and a nations/) .http://cmpg.unibe.ch/software/arlequin35/) [The pairwise FST matrix calculation and graphical representation and AMOVA were performed using Arlequin v.3.5 (quin35/) .locprior to the population model [http://clumpak.tau.ac.il/) to perform matrix averaging and obtain individual bar plot representation [http://taylor0.biology.ucla.edu/structureHarvester/) using both the method described by Evanno et al. and the log probability plot, and is reported in The genetic structure and geneflow were assessed using STRUCTURE v.2.3.4 . The sofon model . Each ruentation . The mosWe analyzed 244 DNA samples, and 234 mitochondrial DNA sequences were obtained and characterized, which comprised a length of 850 bp that included the central hypervariable region of the control region. The six populations were polymorphic, with several haplotypes ranging from 4 (Tungurahua) to 10 (Morona Santiago). A total of 24 haplotypes with 123 polymorphic sites were identified. The average haplotypic diversity was 0.359, ranging from 0.461 (Morona Santiago) to 0.292 (Cotopaxi). The average nucleotide diversity was 0.00547  and rangThe median-joining network  was consp < 0.05), whereas when Morona Santiago was compared with the rest of the provinces, a highly positive percentage of variation was obtained . In the AMOVA calculation based on the polymorphism of the D-loop sequence, tree comparisons were made , showingAll the microsatellites used were polymorphic, finding a minimum of three  and a maximum of 19 (LEI0234) alleles, with a mean 8.1 ± 4.21 number of alleles of . AE rangThe highest expected heterozygosity was found for LEI0234 with a value of 0.852 and the lowest for MCW014 with a value of 0.331; the mean value of the whole dataset was 0.637 ± 0.14 . The obsFIS index indicates the excess of homozygosity in a subpopulation and, with reference to molecular markers, informs if a particular pattern of reduction in diversity owing to several causes exists. In this study, FIS ranged from a minimum of −0.034 (MCW014) to a maximum of 0.727 (MCW014) with an average of 0.146 (0.1254–0.1638).p < 005), as were the results of all the populations with the exception of MLL, which registered a value of –0.016 .Genetic diversity parameters in the dataset including the 15 breeds used for the population study comparison and genetic relationship assessment are shown in Examining the diversity of the 30 microsatellite markers within the 244 animals in the 6 provinces of Ecuador , an averp < 0.05).The mean heterozygosity expected varied from 0.60 (Cotopaxi) to 0.65 (Guayas), while the heterozygosity observed varied from 0.52 (Cotopaxi) to 0.590 (Guayas), . The FISp < 0.05) in 56 cases (31.11%). Most specifically, an imbalance was found in 6, 5, 4, and 3 provinces in MCW014 and LEI0192; MCW330; MCW104; and MCW034, MCW078, MCW123, and MCW165, respectively. Chimborazo was the population with the most unbalanced loci (15), while Morona Santiago was the one with the least (6).In all the 180 HWE tests  of the Ecuadorian chicken not only against the IB, EAZ, CES, and CASN, a group of Spanish breeds, but also with NIG, BRAH, ARAU, and CORN. AAZ, LEGH, and MLL showed higher values of FST (about >0.3). Neighbor-net representing the Reynolds distance confirmed these findings; ECU clustered in an intermediate position between the Spanish breeds CASN, SUR, and UP and another branch formed by ARAU, BRAH, PPA, CORN, and MLL. We can observe the nearest position at the center of the admixture network, indicating a lower genetic distance of ARAU with the cluster comprising CES, AAZ, and CASN .The graph derived from STRUCTURE shows the analysis of the genetic structure of the Ecuadorian population and the other breeds used for comparison in this study. When K = 2, the individuals are grouped in two clusters; one includes AAZ, MLL, UP, and LEGH (yellow), while the second cluster (blue) grouped the rest of the populations . The aniExamining the result of the Bayesian analysis considering only the Ecuadorian samples and the geographical coordinate of the samples corresponding to the 26 municipalities , we can The map representing K3 shows a new group corresponding to the valley that hosts the city of Ambato, in the center of the mountain area .Ecuadorian chicken breeds are of great importance in the economy and for the nutrition of families within the country, and they are an important source of genetic richness. Even so, all these populations are in extreme danger of extinction owing to a severe reduction in population, uncontrolled genetic migrations from industrial breeds, and especially the lack of recognition by the administration and the society.In this study, we present the first results of genetic diversity in Ecuadorian chicken breeds following two different approaches. Mitochondrial DNA polymorphism was used to understand the evolution of the breeds, and the population structure and diversity were investigated based on genome composition and microsatellites. The aim was to augment the meager scientific knowledge existing about these populations with genetic characterization as the first step toward a conservation program .The D-loop sequence of the mitochondrial DNA control region analyzed in the ECU population showed high polymorphism .The Morona Santiago samples showed greater haplotype diversity than in the Cotopaxi samples, even though the values were low, similar to the value of 0.29 reported in the Zimbabwe chicken . Higher In contrast, the nucleotide diversity was higher in samples from Tungurahua than that in those from Bolívar; samples from Morona Santiago registered a higher value than those from Cotopaxi. Nucleotide diversity is a parameter that estimates the genetic diversity in the population, because this value addresses both the frequency of haplotypes and the differences in nucleotides between haplotypes .Phylogenetic analysis of the haplotypes showed that the Ecuadorian samples clustered in three common maternal lineages: E, F, and A, which are related to the Eurasian region ,45,46.Haplogroup E has been observed in the Chilean Araucana chicken , which rHaplogroup A is distributed all over Eastern Asia ,44,45. IThe results of the AMOVA did not show a clear divergence between groups; in fact, the percentages of negative variance would be a sign that there is no significant difference between the provinces. This, in relation to mitochondrial DNA, would indicate that all animals belong to the same genetic base. The province of Morona Santiago showed a small, but significant variation, perhaps indicating a strong genetic drift owing to geographical isolation.The results from the D-loop inference demonstrate that the Ecuadorian chicken originated from at least two different sources. Both linages were identifiable with the European strain and with the Asian type, demonstrating the presence of pre-Columbian influence on the actual population, as was also recently demonstrated in other South American populations, including the Ecuadorian chicken, by other authors . The comp < 0.05) and is similar to that obtained by Suh et al. [The mean FIS value of the population was 0.146  or ARAU as well as with CORN, were relatively low if we compare it with previous reports in chickens . This maThis may be because the CASN breed was one of the first chickens that Christopher Columbus took to America; the same one that is known as an autochthonous breed of Spain defined at the end of the 19th century. It was very important as a white-shell egg layer during the first half of the 20th century, and it is necessary to note the faneroptic resemblance it has with the animals of the present study. As for the Araucana chicken, its lower genetic distance could be because the geographic location is close to Ecuador, which increases the possibility of crossing, as it is a bird widely distributed in southern Chile. Chickens with some proportion of the Araucana type genome have the characteristics of producing the eggs with blue shells, which is highly valued in Ecuador because the consumer associates this characteristic with a traditional production system . In a stThe results of the Bayesian analysis further confirmed the findings from the previous analysis. From the analysis with the 15 breeds used in the comparative study, we determined that, as expected, there is a strong influence of the Iberian chicken strains in the genome proportions of the Ecuadorian chicken; however, this must be understood based on two aspects. First, there is a strong influence of SUR, because, as already described in our recent investigations in cattle and pigs , the breCavia porcellus), and other livestock, while they mainly buy fruit and vegetable products  for the local market in the Andean region. The relationship between these two areas is thus explained by the fact that it is probable that in Bucay, family farmers use genotypes from the mountain for their own production.In the analysis with STRUCTURE showing only the data for ECU, we see the formation of a cluster for K2 that includes the municipalities of the province of Chimborazo and, unexpectedly, the municipality of Bucay (Guayas). The municipality of Bucay is the gateway to the coastal region of Guayas for the population of the mountain region; a rich province of a commercial nature and home to numerous livestock markets. In fact, the vertical trade of mountain and coastal products is very common in Ecuador; the farmers of the Chimborazo province, in particular, trade potatoes, chickens, cuy . These two areas of the mountainous region of Ecuador have the greatest concentration of feed industries and, therefore, as in other parts of the world with industrial poultry farms , it is logical to assume that local producers have easy access to genetically improved animals of this industrial genotype and use it to improve the performance of the Ecuadorian Creole hens—in particular, to improve the persistence of egg laying. This is a common practice in all the Andean slopes because, in addition to exploiting the rustic qualities of the Ecuadorian Creole hens and the productivity of the Leghorn, the widespread nature of the blue egg is maintained (a genetic character derived from the pre-Columbian Araucana Type hen), and which is now absorbed in the Creole population. Some detail is needed regarding the results of most likely K according to the Evanno method. Several authors noted that misinterpretation or doubtful results in the value of K can be led by the convergence problem of the Gibbs sampler algorithm used in STRUCTURE ; the mulEcuadorian chickens reared in the three agroecological systems existing in the country showed a common pattern of haplogroups of mitochondrial DNA, indicating that these animals show the same maternal lineages. The patterns found seem to have the maternal origin in pre-Columbian Asiatic matrilines or in Iberian matrilines arriving from the Iberian Peninsula during colonization. Our mitochondrial findings demonstrate that the current Ecuadorian local chickens do not show maternal influences from commercial lines.The genetic parameters obtained from our microsatellite findings indicate high levels of genetic diversity; there are no evident effects of genetic drift and/or bottlenecks. This high diversity may be owing to internal heterogeneity, which permits a certain optimism in terms of conservation.Concerning the origin of the Ecuadorian chicken, microsatellite findings are consistent with that of the mitochondrial DNA. The high proximity to an old Spanish breed such as Castellana Negra, and to the Araucana breed under controversy for its potential origin in Asiatic pre-Columbian branches, demonstrates a double origin (Iberian/Asiatic) of the Ecuadorian chickens. Our study has found a certain internal substructure in the Ecuadorian population, but the absence of any breeding program, registers, or zootechnic management produces a high fragmentation of the potential Ecuadorian breeds. Urgent measures to define, conserve, and manage the genetic diversity of chickens found in Ecuador are necessary.This study is not only a first step to obtain the genetic characterization of the Ecuadorian chicken breeds, but it is also a model to be followed by other countries in the region, because ignorance regarding the chicken’s Latin American native resources is widespread.


Currently the most common imagery task used in Brain-Computer Interfaces (BCIs) is motor imagery, asking a user to imagine moving a part of the body. This study investigates the possibility to build BCIs based on another kind of mental imagery, namely “visual imagery”. We study to what extent can we distinguish alternative mental processes of observing visual stimuli and imagining it to obtain EEG-based BCIs. Per trial, we instructed each of 26 users who participated in the study to observe a visual cue of one of two predefined images (a flower or a hammer) and then imagine the same cue, followed by rest. We investigated if we can differentiate between the different subtrial types from the EEG alone, as well as detect which image was shown in the trial. We obtained the following classifier performances: (i) visual imagery vs. visual observation task (71% of classification accuracy), (ii) visual observation task towards different visual stimuli (classifying one observation cue versus another observation cue with an accuracy of 61%) and (iii) resting vs. observation/imagery . Our results show that the presence of visual imagery and specifically related alpha power changes are useful to broaden the range of BCI control strategies. BCI is not a perfectly accurate technology as it suffers from numerous issues, namely it is known to be a difficult task to reliably discriminate brain signal patterns from every subject. There are different approaches to improve the performance of BCIs. Most studies focus on signal processing and classification aspects. However, BCI performance can also be improved by optimizing the user’s control strategies and therefore by identifying new and efficient mental tasks to achieve reliable control4.A brain-computer interface (BCI) typically translates the electrophysiological brain signals into an output that reflects the user’s intent or mental activity. BCIs can be beneficial to people with severe motor disabilities5. It is used to control assistive technologies6, or even computer games7. However, motor imagery based BCIs are seldom used outside laboratories due to their lack of reliability. The following main findings can be highlighted. First, around 20% of the BCI users are not able to obtain effective control. This phenomenon is being sometimes referred to as “BCI illiteracy”8. Second, for even the remaining 80% of the people, motor imagery might not be the best mental task to control a BCI (not very intuitive for people with motor disabilities). One option is to explore other mental strategies for BCI control. A broad set of possibilities is proposed in the literature, such as imagining music9, phoneme imagery10, visual imagery of faces11, mental rotation and word association4.Nowadays, the most common imagery task used in BCI is motor imagery, asking a user to imagine moving a specific part of the body such as a hand or a foot12, and 90 percent of information transmitted to the brain is visual13. Since we are visual by nature, and given how important the visual modality is, we propose to use this skill to design control strategies for BCIs.Here we are interested in mental tasks that have not been much tested yet for controlling a BCI, namely visual imagery of objects. We focus on visualization in this work because we respond to and process visual data better than any other type of data: the human brain processes images 60,000 times faster than text14. Assessing mental imagery and the ability to distinguish between the different types of imagery thus requires a carefully designed experimental paradigm. For this end, we performed a study where imagery is guided by previously perceived stimuli. This also provides EEG data during visual perception focused on cued image, data that can be used to study differences in brain activity between perception and imagery processes.For our study we chose two visual stimuli: a flower and a hammer. However there is a challenging problem to interpret the performance of visual imagery : some participants may show good performance but which is due to resorting to other strategies that do not involve imagery in the target modality15. Visual imagery has been mostly studied with PET or fMRI. However, PET and fMRI require sitting/laying down in the scanner, which may not be realistic for some human-computer interaction settings. Another disadvantage for using PET lies in the fact that it involves injection of a radioactive material into the blood. The other problem is that although PET, similarly to fMRI has excellent spatial resolution, the delay between the shift in activity and the resulting change in blood flow is between 6 and 9 seconds . These acquisition tools are quite expensive, and not portable, which also limits their use cases. In our work we will focus on the EEG-based BCI systems as the currently most affordable ones for research labs and centers. We are particularly interested if EEG can provide evidence of visual imagery to achieve desirable end-user BCI applications, as EEG-based BCIs give a possibility of independent use, set-up simplicity and wearability.Visual imagery is defined as “the manipulation of visual information that comes not from perception but from memory”18.Visual observation (sometimes “attention” in the literature) has been used with EEG-based BCIs to test if different classes of stimuli, such as cars, mushrooms, chairs, shoes, animals, etc., evoke spatially and temporally different responses when observing the images19 explore whether the Event Related Potential (ERP) features that encode object category can be used to label images on a single-trial basis, without explicitly requiring users to consciously categorize the images but just to observe them. They perform an experiment where they show three different categories of images: faces, inanimate objects and animals, where the discrimination was achieved at more than 65 percent of accuracy. Simanova et al.20. build an offline BCI system based on conceptual category detection by studying event-related potential (ERP) activations. They show that it is possible to get a system that allows detecting the semantic categories of a few concrete concepts .In their work, Shenoy and Tanet al.21 builds on Simanova’s et al. study and explores further whether it is possible to build and asynchronous ERP-based BCI to distinguish between objects of similar and dissimilar “conceptual” categories. They obtained classification accuracies around 65% for similar conceptual categories and around 70% for conceptually distinct categories. Several studies reported that similar patterns of brain activity are observed when perceiving objects25.Another work by Kosmyna However, all the aforementioned studies focus on asking the participants to observe the objects on the images and there are almost no studies that investigate visual imagery of actual objects on the pictures using EEG. Moreover, we were interested in investigating visual imagery as a control paradigm to be used in offline and/ir online BCIs, thus for this study we chose two images which could be considered as “ecological” and “user friendly”  and they represent actual, real objects: a flower and a hammer. We include visual observation task in our experimental set-up to have a comparison with the state of the art on visual observation. We also include ERP analysis as well as one of the mostly well-studied brain activity that is used to analyse mental imagery - alpha rhythms.26. Over the last 20 years, this <<idling>> hypothesis has been revisited. A lot of studies report that “alpha activity can increase with cognitive load”28. Ray and Cole30 found increased alpha power in a mental imagery task, particularly at parietal sites. Klinger et al.31 observed similar findings of increased alpha during imagery process.The most commonly measured rhythm in the human EEG is the alpha rhythm, generally referring to the frequency band spanning from 8 to 12 Hz. For a long time the alpha activity was reported to be strongest when people keep their eyes closed and rest, and it was known as an <<idling rhythm>>32 introduced a theory about a wider role of alpha oscillatory networks in cognitive processes that involve attention and memory. The main principle is that “activation of this network leads to alpha desynchronization and the greater task demands are within the network, the more inhibition is needed, thus inducing alpha synchronization” meaning that alpha activity is higher with the cognitive load related to the task at hand difficulty increasing. Cooper et al.33 showed that alpha synchronization is related to internally versus externally directed attention. They presented sequences of stimuli in the visual, auditory and tactile domains and then asked their participants to imagine the sequences. They found that “alpha activity was higher during the imagery of stimulus sequences  than during their presentation ”. In these studies the observed synchronization of alpha activity has been interpreted to reflect “selective inhibition of task-irrelevant brain areas or inhibition of interfering external input”35, and to reflect “internal information processing involving top-down control of internally represented information” (e.g.37). The increase of alpha power over regions processing the distracting information was reported by Frey et al.38. In this study the participants were required to maintain the “representation of the face identities versus face orientation, and more alpha power was observed over the ventral visual stream”.Klimesch and colleagues40 use the resting state as an actual class for comparing it to the other mental imagery action. They evoke the ease of the designing of a BCI system, when considering resting state: the user needs to produce the desired brain pattern intentionally only during the periods in which he/she wants to activate the BCI, and he/she can remain in resting state at all other times.Based on the mentioned literature, we can conclude that up to now, very few studies proposed investigating the plausibility of using visual imagery as a paradigm for EEG-based BCIs. The objective of the current study is to examine if differences are present between the two visual imagery conditions in general. Moreover we investigate, if it is possible to distinguish between imagery and observation conditions. Finally, we test if we are able to differentiate the imagery condition from the “resting” state, e.g. when users are not controlling their brain activity in order to generate any particular brain pattern. Almost each BCI system nowadays uses this state, mostly to provide the users a break between the trials. However, several papersWe study the following null hypotheses that based on EEG,H1. visual observation and imagery tasks cannot be differentiated,H2. imagery of different visual stimuli cannot be differentiated,H3. observation task towards different visual stimuli cannot be differentiated,H4. observation or imagery tasks cannot be not differentiated from the rest state.We first present the obtained results on the observation tasks of two stimuli and then we discuss the results on imagery tasks. We refer to the observation task and the imagery task as “observation” and “imagery” when we talk about 2-class classification problem. We refer to observation task and imagery task as “observation-merged” and “imagery-merged” when we take a union of two tasks, e.g., we consider a classification result for observation task for the two stimuli that were present as one task and one result. This means that we collected all the trials regardless of stimuli (hammer or flower) where the task is to imagine as trials representing the ‘imagery-merged’ class. We do likewise for the ‘observation-merged’ class. Hence, the classification problem will still remain a 2 class problem, but there will be more trials per class.41. Further, to better understand the possible similarities in the behavior of the tested algorithms, we plotted Critical Difference (CD) images as introduced by Demšar, relying on post-hoc Nemenyi tests41 with a threshold alpha = 0.01. In the CD images, two algorithms are not significantly different if they are connected by a bold line. We relied on public R implementations to compute the tests42.The Spectrally Weighted Common Spatial Patterns (SpecCSP) classifier could distinguish between two observation stimuli with around 60% classification accuracy, which however does not exceed the confidence interval upper boundary for the design (65%). In the 2-class classification between the both observation stimuli merged together versus rest, the SpecCSP classifier could distinguish between observation and rest with a 72% classification accuracy . The results for classifying between the observation stimuli and between observation stimuli and rest are shown in Figs The behavior and meaning of the ERP1 and ERP2 classifiers will be discussed later. Please also refer to Table The SpecCSP classifier could distinguish between observation-merged task versus imagery-merged task with 71% of classification accuracy (confidence boundary at 60%). The results for classifying between the imagery categories and between observation stimuli are shown in Fig. The SpecCSP pipeline could distinguish between two imagery tasks with 52% classification accuracy, which is under the 65% confidence bound. In the 2-class classification between the imagery-merged task versus rest, the SpecCSP pipeline could distinguish between the two with the 77% classification accuracy (with confidence bound at 60%). The results for classifying between the imagery categories and between imagery-merged task and rest are shown in Figs These results indicate that although it is difficult to tell the two imagery categories apart by the pipelines we are using, as the result is close to random one, the imagery segments are nevertheless distinguishable from the rest condition. Thus, the null hypothesis H2 cannot be rejected, but H4 can be rejected.In addition to comparing the classification pipelines against two trivial random classifiers and the confidence bounds, we implemented a few other controls to investigate if we could find simple, alternative explanations to the obtained accuracies.3s, the latter like the other pipelines. The results indicate that switching from condition to condition in the experimental timeline does create event related potentials that can be discriminated from each other to some extent. I.e. if the condition is rest, the rest-onset can be discriminated from cue-display or imagery condition onsets. If the condition is observation, the cue type can be discriminated to some extent as well. The type of imagery could be predicted with much lower accuracy. However, after 1 s, this ERP-type activity has largely ceased and ERP2 working on a later time segment provides much lower accuracies. This suggests that the results obtained from the tested methods during the 3s segment may not be simply due to stimulus-locked ERP activity resulting from transitions from one condition to another.The ERP1 and ERP2 pipelines using the Window Means algorithm were run for different time segments, ERP1 for s and ERP2 for 2s and 3s after stimulus onset. Then, we considered these two segments as separate classes to discriminate; for example, first half of cue-display of hammer versus second half of cue-display of hammer. We tested the SpecCSP pipeline with various balanced, 2-class pairings, and obtained per-recording cross-validation accuracies from 40% to 64%. This suggests that tiny accuracy improvements over random can be obtained in our setting even if the underlying categories are not different. The implication is that the possible drift of the brain state that may segregate the first part of each trial from the second part is not well exploitable by the pipelines we test. The contrary would have given evidence that the classifiers are indeed able to use temporal drifts in the EEG.The chronological cross-validation in BCILAB mitigates accuracy biases from time-correlated EEG to some extent. To further test that the presented results are not trivially due to some slow temporal change of brain state during our experiment, we additionally split a trial of each type to two segments which were One possible criticism is that in our experimental design, the sequence of events is always repeated in the same order. In principle it could be possible that a specific trial is discriminable because of some pre-condition residual remains in the EEG until the actual condition. A proper way to control for this would be designing a new experiment where the condition order is perfectly randomized.Difference between the observation and imagery tasks was reflected in their ERD/S patterns.Alpha activity is significantly higher during the imagery than during the observation. This significance is strong. ERPs are significant on all the channels on the visual area and though all the time windows, beginning-end of the trial; Event Related Spectral Perturbations (ERSPs) are significant for all channels on the visual area Fig. .Figure 733 that showed that alpha activity was significantly higher during the imagery process than during the observation one. This finding contradicts the “idling theory” that alpha power desynchronizes when users are engaged when performing cognitively demanding tasks. Klimesch43 reported a “synchronization of alpha activity during the retention period in a short term memory task”. Synchronization of alpha activity was also reported by various studies45 during different cognitive tasks such as mental imagery, memorization tasks, so on.Significant main effects for observation versus imagery were observed in all channels on the visual area Fig. . This fiIn the current study we explored the potential of Visual Imagery for BCIs. We studied to what extent can we distinguish two different visual imagery tasks using EEG-based Brain-Computer Interfaces to provide a broader range of reliable control tasks.Firstly, for the observation-merged task versus imagery-merged task, we obtained the classification accuracy of 73%. This finding is further confirmed by a clear difference of alpha amplitude between imagery and observation stimuli.39).Our results confirm the state of the art studies reporting that “increasing alpha desynchronization for higher memory load or task complexity was found for tasks which require constant externally directed observation, while findings for increasing alpha synchronization for higher memory load (or task complexity) have been obtained in tasks which relate to retention and manipulation of internally represented information ”. Moreover, we could distinguish between two observation stimuli with around 60% classification accuracy. In summary, these findings suggest that observation stimuli could be further investigated for usage in a BCI paradigm. This suggestion is further supported by another finding of our experiment, as we obtained the classification accuracy of 77% between imagery task (for both stimuli) versus resting state, and the classification accuracy of 75% between observation task (for both stimuli) versus resting state. Collectively, these results are promising, as they demonstrate the potential of a BCI system with one “active” state corresponding either to imagery or observation task (as in46). Secondly, the problem might be due to our signal processing pipelines. We did not try to find alternative features to represent the data, but used well-known feature types from previous BCI studies. Another possible reason for our classification results are due to the naivety of our subjects. Almost none of them have performed a mental imagery task with BCIs before, and they only underwent a single recording session, which is not the case for most of studies that present mental imagery tasks, where users are asked to perform multiple sessions of training and recordings47. It should be also mentioned that we obtained the classification accuracy of around 60% for half of the subjects in our study , as in49. Moreover the two images we have chosen for our study could be considered as “ecologically” user-friendly, although this introduces one of the limitations: the images were not controlled for the visual attributes such as contrast, color distribution, total amount of lightness, covered volume of visual field, etc.25 showed that if two classes of visual stimuli have a low amount of within-class variation, it is possible to get around 60% of correct classification.As for the imagery versus imagery task, we obtained the classification accuracy of 52%. The difference to random accuracy is small, and this could be due to several reasons. Firstly, the selected images of hammer and daisy may be difficult to discriminate from the resulting EEG, regardless of technique. There is currently no study using these images that would prove otherwise. The reasons could be due to the nature how the neural assemblies work in the imagery case, due to volume conduction, etc. This limitation may even be user-specific taking into consideration our limited number of participants  observation task towards different visual stimuli and (iii) rest vs. observation/imagery. We thus conclude that presence of visual imagery and specifically related alpha power changes are useful to broaden the range of reliable BCI strategies. But how could we use these findings to improve BCIs?In this study we found that it is possible to discriminate: (i) presence of visual imagery Consider BCIs we have nowadays, which are applied to different domains and applications: communication ; environmental control ; recreation (games); creative expression ; attention monitoring; meditation training; computational user experience ; coma detection and rehabilitation.et al50. propose to use word association task to turn the wheelchair right; and there are more examples of this type, as the most well studied BCI paradigm for active control remains motor imagery. BCIs usually do not highlight the directness of the mapping between the user’s mental activity  and the semantics of the task carried by the system (telephone rings). Such lack of logical connection may impact the performance of the task drastically51. A broader range of imagery strategies could help developing a broader range of reliable BCI-based control applications. Consider a BCI that detects motor imagery poorly, but reliably detects visual imagery. This BCI might be only 50% accurate if the user tries to communicate via motor imagery but 65% accurate if the user imagines an object he/she wants to manipulate. In that case the BCI could be re-configured to rely on visual imagery. Having more options for BCI control is an opportunity for users for whom classical, state of the art imagery approach (e.g. currently - motor imagery) BCIs are not successful, as it could provide them with a possibility of having another try with another type of imagery. Nowadays, this option is not much studied in the research laboratories. To the best of our knowledge, there are only works that propose to replace one limb-type of motor imagery by another , so it will be possible to confirm if we are able to disentangle the imagery process from the observation process.Further investigation should be performed to study the use of observation task and/or alpha activity as a potential BCI control paradigm. We plan to conduct another experiment, where the stimuli will be present to the participants during the whole duration of the trial . These types of systems would go in the direction of user-centered design, essential to the design of real-life systems and yet little developed for BCIs. However, to confirm this statement, we would need to run additional online, real-time experiments.This study included 26 right-handed participants (7 women), between 18 and 45 years old, without any reported disabilities. All had normal or corrected-to normal vision. Each participant gave informed consent to the study. The experiment was approved and carried out in accordance with the relevant guidelines, regulations and authorizations of Inria Rennes  and with respect of the Declaration of Helsinki. 20/26 participants had never participated in BCI experiment before. 6 participants tried BCIs for the second time.Visual stimuli were two images: a hammer and a daisy Fig. . The rat48. Details on the experimental paradigm are summarized in Fig. th trial there was a pause with a duration between 120 and 240 seconds.Our experimental protocol is based on the standard Graz BCI training protocolWe make sure that the same observation cue is never presented two times in a row. Please refer to the video of the paper to see the experimental paradigm.Visual observation task: observe carefully the image you see in front of you;Visual imagery task: imagine the image that was previously displayed.Users were asked to perform the mental task following these instructions:Before users performed the task, they completed an overt exercise run: the users observed the two cue images and then they were asked to imagine them. The purpose of this exercise run was to familiarize the participants with the task and to ensure that they understood it and performed it according to the instructions.During the periods when the word REST appeared on the screen, the users were instructed not to perform any type of imagery/observation tasks.We used the electrode configuration of Iz, P7, P8, CP3, CPz, T7, T8, O1, Oz, O2, P3, Pz, P4, CP4, F7, C4, FC4, FC3, C3, FCz, F3, Fz, F8, POz, O9, O10, PO4, PO3, AFz, FPz, Cz, PO8, P8, PO10, PO7, PO9 using 2 g.tec USBAmp amplifiers. Each electrode was referenced to the left and grounded at the right mastoid. The data was filtered , amplified and digitized (512 Hz). The whole experimental set-up  lasted 1h20. The participants were instructed not to move. The stimuli were presented on a Dell notebook with a 15.4” TFT monitor that was positioned horizontally (flat on a table). The viewing distance was approximately 50 cm from the eyes to the center of the visual display.This was a within-subject experiment design where the total number of trials was 80 .54.To record the data, we used an open source software OpenViBE55 for the data analysis and classification, respectively. Note that the data was processed in two ways  and these two processing types do not have the same signal processing pipeline. In data analysis we wish the data to be as clean as possible for interpretability. However, as one of the motivations of our study was investigating the visual imagery for EEG-based BCIs, online BCI classification has to make predictions on the trials as they are received. Expert intervention or offline clean-up stages cannot be used. For this reason, we used BCILAB classification pipelines as they were defined in BCILAB, directly on the raw data.We used the freely available EEGLAB and BCILAB packageslassification. No trials or channels were removed for classification . For generating ERSPs/ERPs (in EEGLAB), e.g., for data analysis, most users have different amount of trials.The same amount of data was used for each user in c56. For each separate recording, we visually identified 0–2 ICA components corresponding to strong, frontally localized activity patterns and removed these components from the signal by setting them to zero, followed by reconstruction of the signal. Note that the present recordings did not seem to have many during-trial eye-artifacts, possibly due to the instructions to the users. Next, we epoched the data to 4.875 second segments starting one second before each stimulus (event) onset. The last 125 ms were removed from each epoch simply to avoid overlapping event markers in the epoched data. Next, we used an iterative algorithm to remove the outlying trials and channels from each recording. The algorithm worked as follows: a high initial threshold for acceptable signal range was set, and then all epochs were marked as outliers that exceeded the specified range. If more than 20% of the epochs per channel were marked as rejected, then the channel itself was rejected. The process was iterated with a little lower threshold each time while disregarding previously rejected channels and epochs until the total number of rejected epochs exceeded 10% of the total epoch count for the dataset. The rejected epochs were dropped from further consideration and the rejected channels were restored by spherical interpolation of nearby electrodes using routines available in EEGLAB. With our data, this procedure rejected around 0–3 electrodes per dataset and around 10% of the trials in each.We first bandpass filtered the signal to a frequency range of 0.1 Hz–40 Hz using a Hamming windowed FIR band-pass filter from EEGLAB and then removed the eye artifacts using Infomax Independent Component Analysis (ICA)For computing time/frequency power plots , we removed the Event Related Potentials (ERPs) from the epochs by mean subtraction in the time/voltage representation. Note that we have not done any other baseline removal or normalization for the displayed plots Figs  and 6, a55. Here we used the recorded data directly without discarding any trials or channels. We used only such preprocessing as the BCILAB classifier paradigms do by default (e.g. bandpass filtering). Out of the methods provided by the BCILAB toolbox, we chose the Spectrally Weighted CSP (SpecCSP)57 and the Window Means paradigms (ERP258). We selected two-second long segments of each trial to represent the trial, each segment starting 1 second after the event onset to avoid possible transition effects. As the Window Means classifier is usually used to classify ERP patterns, we also used it during a ms segment after stimulus onset (ERP1).In addition to statistical analysis of the EEG ERSP features, we also investigated classification of the trials of different types. For this, we applied classifiers from the BCILAB toolboxSpecCSP method run with a wide spectral band of 2.5–30 Hz of the signal. This was done to provide the methods with relatively loose spectral priors. The wide band allows the Spectrally Weighted CSP the possibility to select a tighter spectral weighting in that range. For the Window Means (ERP classifiers), we used 0.1–15 Hz band with six consecutive time windows, each 50 ms long and starting at 250 ms after the segment start. We used Logistic Regression as the classification technique for SpecCSP pipeline.We also created two trivial classification pipelines, one predicting the training set majority class label (‘majority’) and the other outputting random predictions using class weights from the training fold class label distribution (‘rand’). In several plots, we include the results of these control pipelines. The main intent is to visualize to what extent the real classification pipelines deviate from random baseline behavior.The accuracies we report were obtained by using 10-fold chronological cross-validation from BCILAB, with the margin width set to 5 trials. We used the chronological cross-validation to avoid nearby trials ending up in both train and test folds. We did this in order to reduce possible bias due to time-correlations between neighboring EEG trials.et al.59. They propose that the reported accuracies should be compared against the upper bound of a confidence interval around the expected accuracy of a random classifier. Accuracy averages inside the confidence interval cannot be rejected from being essentially random. The interval bounds depend on the amount of trials in the experimental design. In the accuracy plots we present, a horizontal line denotes the upper boundary for the confidence interval with a threshold α = 0.01 as obtained from Muller-Putz et al.59.To double-check if the classification results are significantly different from what could be achieved by chance, we follow Muller-Putz This study investigated the use of Visual Imagery for Brain-Computer Interfaces. We studied to what extent can we distinguish mental processes of observing and imagining two different stimuli in visual perception using EEG-based BCI system. 26 subjects were instructed to alternatively and selectively observe one of two visual stimuli (a flower or a hammer) and mentally imagine one of them following a visual cue. We investigated the EEG correlates of these tasks and analyzed the classification accuracies for visual imagery versus observation of visual stimuli but also versus rest. We found that it is possible to discriminate: (i) visual imagery vs observation task, (ii) observation directed towards different visual stimuli and (iii) rest vs. observation/imagery. We thus conclude that presence of visual imagery and specifically related alpha power changes are useful to broaden the range of reliable BCI control strategies.


Monopterus albus) is a typical sex reversal fish with high economic value. Several phylogeographic studies have been performed using various markers but comparative research between mitochondrial and nuclear markers is rare. Here, a fine-scale study was performed across six sites along the Yangtze River including three sites on the main stem and three sites from tributaries. A total of 180 swamp eel individuals were collected. Genetic structure and demographic history were explored using data from two mitochondrial genes and eight microsatellite loci. The results revealed the samples from tributary sites formed three separate clades which contained site-specific lineages. Geographic isolation and the habitat patchiness caused by seasonal cutoff were inferred to be the reasons for this differentiation. Strong gene flow was detected among the sites along the main stem. Rapid flow of the river main stem may provide the dynamic for the migration of swamp eel. Interestingly, the comparative analyses between the two marker types was discordant. Mitochondrial results suggested samples from three tributary sites were highly differentiated. However, microsatellite analyses indicated the tributary samples were moderately differentiated. We conclude this discordance is mainly caused by the unique life history of sex reversal fish. Our study provides novel insights regarding the population genetics of sex reversal fish.The swamp eel ( Monopterus albus) is a typical sex reversal fish, belonging to the family Synbranchidae, which usually inhabits swamps, ponds and rice fields  to explore swamp eel population genetics . PreviouMonopterus albus is a sex reversal fish with a unique life history .A total of 180 individuals were collected using net from six sites along the Anhui basin of the Yangtze River . SamplinTotal genomic DNA was extracted from muscle tissue using a standard phenol/chloroform procedure via proteinase K digestion , and theCOI) gene and cytochrome b (Cyt b) gene were chosen. Two pairs of primers were designed here for the amplification (2 (2.5 mol/L), 3 µL dNTP (2.5 m mol/L), 2 µL of each primer (5 µmol/L), and 1 U Taq DNA polymerase . PCR conditions were as follows: initial denaturation , then 35 cycles of denaturation , primer annealing , and elongation  and a final extension . All fragments were sequenced in both directions with an ABI3730 automated sequencer . Then these two gene sequences were combined for subsequent analyses.The mitochondrial cytochrome c oxidase subunit I (fication . PCR wer2 (2.5 mol/L), 2.5 µL dNTP (2.5 m mol/L), 2 µL of each primer (5 µmol/L), and 1 U Taq DNA polymerase . PCR conditions were as follows: initial denaturation , then 32 cycles of denaturation , primer annealing , and elongation  and a final extension . Genotypes were detected by ABIPRISM 3730.Eight unlinked polymorphic microsatellite loci were selected from previous studies  Lei et . PCR werhttps://mafft.cbrc.jp/alignment/software/)  .Fst was estimated in order to evaluate the levels of population differentiation  analyses. Tajima’s D  and Fu’sD  algorithm as implemented in Structure v.2.3.3 . The numv.0.6.94 .COI 665 bp, Accession number: MN097948–MN098127; Cyt b 1087 bp, Accession number: MN098128–MN098307) were obtained for analyses. The contents of the bases A, T, G and C were 24.6%, 29.3%, 14.6% and 31.5% respectively, which showed obvious anti-G bias  were much higher than the variation within the sampling sites   (Fs and Tajima’s D tests of main stem samples were significant (P < 0.01) but negative. No explicit expansion or decline were revealed for the tributary samples and the Fu’s Fs and Tajima’s D values except the Tajima’s D of FC were not significant (P > 0.05), indicating that sudden expansion could not be rejected  . Subsequ 0.9431) . Fu’s Fsnificant . Mismatcrejected . The BSPrejected .P > 0.05). High genetic diversity was also supported by microsatellite data. Expected and observed heterozygosity for the six sampling sites were 0.8052 –0.8749 and 0.5958 –0.7917, respectively (The eight microsatellite loci amplified unambiguous and repeatable products in the size range expected. All loci were in Hardy-Weinberg equilibrium (ectively .K = 4 (Structure results suggested the highest posterior probability for K = 4 . The ΔK K = 4 .P < 0.001), opposite to the results from mitochondrial data   .r = 0.8791, P = 0.004) (The correlation between genetic and geographic matrixes was assessed using Mantel test . The res= 0.004) .Hd = 0.708 and Pi = 0.002 based on mitochondrial D-loop sequences) . The significant differentiation of three tributary sampling sites was revealed by the population genetic analyses (Fst >0.25). The haplotype network and structure results suggested the tributary samples formed three separate clades which contained site-specific lineages. Significant correlation between genetic and geographic distance was detected. Interestingly, strong gene flow was detected among the main stem sampling sites and the expansion of main stem samples was detected.High levels of genetic diversity were found across sampling sites at both mitochondrial and microsatellite markers. The genetic diversity level of this study except FC sample site was higher than previous study in the same basin (quences) . The genIt is well known that the swamp eel is a burrowing fish whose fins are vestigial or absent . CompareFst > 0.25) and represented three monophyletic clades. However, microsatellite analyses suggest that the majority of genetic variation is within these sampling sites; samples between the tributary sites were moderately differentiated (0.05 < Fst < 0.15). Mean Fst values among six sampling sites based on mitochondrial and microsatellite data were 0.548 and 0.055, respectively. The mean mitochondrial Fst value (0.548) was almost ten times higher than the Fst (0.055) estimated with microsatellite data.Analyses of nuclear and mitochondrial markers revealed discordant population structure. Based on mitochondrial data, genetic variation was mainly found among sampling sites. Samples between the tributary sites were highly differentiated (Fst values also confirmed this hypothesis.Our study provided an interesting pattern of discordance between markers for population genetics. According to previous studies, sex-biased dispersal, genetic admixture and lineage sorting may be the potential reasons for the discordance caused by different molecular markers . ConsideOur study used two data sets, mitochondrial DNA and microsatellites, to explore the demography, genetic variation and population structure of swamp eels. Compared with previous studies, high levels of genetic diversity suggest that swamp eels are an abundant resource in the Anhui basin and have potential commercial value. Samples from each tributary site in this study should be treated as an independent genetic unit. The unique sex reversal life history of the swamp eel may be a significant factor affecting the population genetic structure and may generate the discordance we found between different molecular markers. Our study provides novel insights regarding the population genetics of sex reversal fish.10.7717/peerj.8415/supp-1Figure S1Click here for additional data file.10.7717/peerj.8415/supp-2Table S1Click here for additional data file.10.7717/peerj.8415/supp-3Table S2Click here for additional data file.10.7717/peerj.8415/supp-4Table S3Click here for additional data file.10.7717/peerj.8415/supp-5Supplemental Information 1Click here for additional data file.10.7717/peerj.8415/supp-6Supplemental Information 2Click here for additional data file.10.7717/peerj.8415/supp-7Supplemental Information 3Click here for additional data file.10.7717/peerj.8415/supp-8Supplemental Information 4Click here for additional data file.


Apodemus flavicollis population and to evaluate the roles of environmental and population ecology variables in shaping the spatial pattern of genetic variation using 768 samples genotyped at 13 microsatellite loci. Genetic variation was very high in all studied regions. The primal genetic subdivision was observed between the northern and the southern parts of the study area, which harbored two major clusters and the intermediate area of highly admixed individuals. The probability of assignment of individual mice to the northern cluster increased significantly with lower temperatures of January and July and declined in regions with higher proportion of deciduous and mixed forests. Despite the detected structure, genetic differentiation among regions was very low. Fine‐scale structure was shaped by the population density, whereas higher level structure was mainly shaped by geographic distance. Genetic similarity indices were highly influenced by mouse abundance (which positively correlated with the share of deciduous forests in the studied regions) and exhibited the greatest change between 0 and 1 km in the forests, 0 and 5 km in small woodlots. Isolation by distance pattern, calculated among regions, was highly significant but such relationship between genetic and geographic distance was much weaker, and held the linearity at very fine scale (~1.5 km), when analyses were conducted at individual level.The goal of this study, conducted in seven large woodlands and three areas with small woodlots in northeastern Poland in 2004–2008, was to infer genetic structure in yellow‐necked mouse  HoweverClethrionomys glareolus . The fraction of genetic variation lost during the bottleneck is a function of the population growth rate  occur at 6‐ to 9‐year intervals and trigger winter breeding in mice, their outbreak in the following year, and crash in the next year , Białowieża (BIAL), Borki (BOR), Knyszyn (KNYSZ), Mielnik (MIEL), Pisz (PISZ) and Rominta (ROM) Forests, as well as in more open terrain with small, fragmented woodlots along three transects (T) among four of the above listed woodlands: Augustów–Knyszyn (TAK), Knyszyn–Białowieża (TKB), Białowieża–Mielnik (TBM). The study area  spanned a maximum distance of 230 km in the S–N direction and 180 km in the E–W Figure . The cliPinus silvestris), Norway spruce (Picea abies), common oak, silver birch (Betula pendula), white birch (B. pubescens), and common alder (Alnus glutinosa). The woodlands differed in size , productivity, and mean age of tree stands. The Białowieża Forest is the oldest and the best preserved woodland in the whole study area  for rodents, large wooden boxes (11 × 11 × 24.5 cm) for dormice . Six to 10 small traps were placed on the ground, five large boxes were located on shelves stuck by sticky tape to tree trunks at a height of about 2 m. The trapping and sampling was conducted according to the protocol described by Niedziałkowska et al. . In tota2.2R < |0.5|) were selected for further steps of analysis: mean temperature of January (TJAN), mean temperature of July (TJUL), and share of deciduous‐mixed forest  in 1‐km buffer zone around the trapping sites.Precise location of each trapping site was identified using GPS GARMIN60CSx. Complete botanical description of forest floor vegetation, undergrowth and tree stand was collected for each trapping site. Based on this description, each plot was classified according to forest habitat type as coniferous, mixed or deciduous forest. In program ArcGIS (ESRI), a buffer was defined around each trapping site with a radius equal to the mean distance between all trapping sites (~1 km). Next, Corine Landcover 2006 (CLC2006) maps were used to measure the percentage cover of each land use category  within a defined buffer zone (Supporting Information Table 2.3Genomic DNA was extracted using DNeasy Blood & Tissue Kit (QIAGEN) according to the manufacturer's protocol. Quantity and quality control was performed using spectrophotometer NanoDrop ND‐1000. We selected 15 markers that were tested previously on yellow‐necked mouse by other researchers and therefore guaranteed amplification success: AF246520, AF246522, AF246523 . Detailed information on the number of analyzed samples from each woodland, transect, and year is presented in Supporting Information Table 2.4FIS) were calculated using FSTAT  were calculated in GenAlEx  was tested from 1 to 10 with 10 runs completed per each K. It is also known that sampling scheme might significantly affect the assessment of population subdivision  and separately on samples collected in each year to check for the consistency of results among years. Program STRUCTURE HARVESTER v. 06.94 ] and delta K  based on individual multilocus genotypes without any prior spatial information  to select the optimal interaction parameter using likelihood (w = 0.6). Next, 10 independent simulations were performed for each K value (2–12) using 50,000 iterations and a burn‐in period of 10,000 iterations to identify, which K values produced the highest likelihood runs (Kmax). Finally, we conducted 100 simulations for each Kmax and calculated membership probabilities from the 20 highest likelihood simulations for each Kmax   Rice, .2.5FST coefficient values calculated among 10 geographic regions were plotted against the mean geographic distances. Secondly, Rousset's genetic distances ar  for each distance class presented as a correlogram. The geographical distances were calculated as Euclidean distances between samples and the analyses were performed with an equal distance classes set at 1 km. Numbers of the calculated distance classes differed between regions due to differences in the spatial distribution of individuals in each region. For each analysis, we used 999 permutations to test the hypothesis of no spatial genetic structure, and 1,000 bootstraps to estimate 95% confidence intervals for r in a given geographical distance . Pairwise genetic distance and geographical distance matrices were used to calculate autocorrelation coefficient  was tested. Moreover, relationship between positive autocorrelation coefficient and mouse abundance was calculated.The number of individuals captured in a trapping point per 100 trapnights was used as an index of mouse abundance. Mean values per woodland and transect were calculated. Dependence of mouse abundance as well as calculated Nn = 3 transects).Mean values of genetic similarity/differentiation  were calculated in SPAGeDi for six geographical distance classes. In case of individuals captured in the woodlands, the classes were defined as follows (in kilometers): <0.001, 0.001–1, 1.001–2, 2.001–3, 3.001–4, 4.001–5, >5, and for individuals trapped in woodlots on transects: <0.001, 0.001–5, 5.001–10, 10.001–15, 15.001−20, 20.001–25, >25. Next, genetic parameters calculated for the first three distance classes in woodlands were plotted against the estimated abundance of rodents. Data from transects were not presented in that way due to small sample size (n = 752) to be assigned to Cluster 1   and genetic variation measured as probability of each individual (33.1Ho) calculated per geographical region ranged from 0.776 to 0.842 and was lower than expected heterozygosity (He) with values ranging from 0.841 to 0.869. All loci significantly deviated from Hardy–Weinberg equilibrium (HWE), when calculated for the entire population, but substantially lower number of loci showed lack of HWE when calculated per geographical region, with the exception of Knyszyn and Białowieża Forests, where consistently 10 and 11 loci significantly deviated from HWE. Proportion of detected private alleles was highest in Rominta and Białowieża Forests (0.69). In the whole sample of 10 regions, the proportion of private alleles was not correlated with the number of analyzed samples . The inbreeding coefficient values (FIS) were relatively small, with the highest values found in Knyszyn (0.108) and Białowieża Forests (0.101), and were correlated neither with samples size  nor with the mean pairwise Queller and Goodnight kinship coefficient .All 13 loci included in statistical analyses were highly polymorphic with number of alleles per locus ranging from 15 to 32 in the whole population of yellow‐necked mice in northeastern Poland (Supporting Information Table FST coefficient) showed low level of differentiation with the highest values found between Pisz Forest and the rest of the regions  and the lowest values among five southernmost areas: Knyszyn, Białowieża and Mielnik Forests and two transects: TKB, TBM   continued to increase until it reached a plateau at K = 5, ΔK  Piss Figure .Results of analyses in the Bayesian clustering programmes of mice captured only in seven woodlands confirmed the previously detected structure (data not shown) and indicated that analyses of samples collected along transects complemented the findings and did not change them significantly. The primal division between northern and southern areas, localized in the southern part of Augustów Forest, was highly concordant with the former results obtained for the whole data set. Analyses performed without Augustów–Knyszyn Transect indicated a sharper distinction between northern and southern parts of the study area, whereas inclusion of samples from the transect resulted in a more subtle genetic gradient. Importantly, spatial structuring observed in the yellow‐necked mouse population in NE Poland was also consistent between the consecutive years. High admixture of individuals from Knyszyn Forest was found also when analyses in STRUCTURE were performed solely on woodlands’ data with mice from transects excluded (data not shown).Principal Component Analysis (PCA) revealed a major division of mice into two genetic groups visible on the first PC axis with individuals from northern part of the study area  forming the most distinct cluster, and with further more subtle substructuring within the remaining samples Figure . Mice frFST) between the clusters (G1–G4) showed low values from 0.018 to 0.032, with the highest one between the most geographically distant subpopulations G1 and G4  of modal value at four subpopulations, whereas seven runs gave a mode at three for the posterior distribution of K. Obtained results supported the distinction of samples collected in the northern part of study area. Individuals from Pisz, Borki, Rominta and northern part of Augustów Forests were consistently clustered together  ranged from 33.2 to 281.6 . In our data, population abundance of mice was positively related to the share of deciduous forest in each region (Y = 2.23ln(x) − 0.23, R2 = 0.64, p = 0.005, Supporting Information Figure Y—number of mice per 100 trapnights, x—percent area covered by deciduous forests. The same trend, although not significant statistically, was found between effective population size and the share of deciduous forest in each region  between mouse populations in 10 regions was highly correlated with geographic distance . This correlation, calculated at the individual level, although still significant, was substantially weaker  and exhibited linear relationship only at very short geographic distances.Genetic differentiation , and (b) the maximum distance class, at which positive autocorrelation was found. These two measures were compared with the mean abundance indices of mice in 10 regions. The observed pattern of fine‐scale spatial structure was significantly related to mouse abundance  the number of 1‐km classes with continuous positive autocorrelation found  were calculated between all trapped individuals within the first 6 km for mice in woodlands and 25 km for mice in transects. In woodlands, the greatest change of values occurred between the first and the second distance class (0–1 km)  and the highest in Białowieża and Mielnik Forests  , the relationship got weaker at 1‐km distance, and disappeared at 2 km , individual genetic distances were significantly positively associated with an abundance index of mice at the first distance class . Winter severity (approximated by mean temperature of January) had the strongest impact on Cluster 1 spatial distribution  or individuals from adjacent areas that successfully reproduce are genetically similar. It was found in voles trapped in Białowieża meadow that recruitment of individuals from outside did not substantially affect the genetic results as they exhibited highly similar genetic profiles  and spatial  methods. Clear genetic distinction of northern subpopulation  was supported by all programs. The remaining samples with more continuous spatial distribution were less genetically structured, and formed a gradient but with several detected areas of disruption in gene flow, which resulted in a more subtle but still visible differentiation.FST  observed in this study are in a range of values found in similarly abundant and highly variable small mammal species. Nagylaki , when analyses were conducted at individual level. Such incoherent results are in high concordance with published data on small mammals  among the sampled areas had a profound impact on population density indices, what subsequently influenced kinship and spatial autocorrelation results. The decrease of relatedness in dense population was observed also in the root vole Canis lupus .None declared.BJ, MN, and SDC conceived the ideas and applied for the financial support, SDC and MN collected the samples, SDC, BJ, and TB analyzed the data, and SDC, MN, and BJ wrote the paper.https://doi.org/10.5061/dryad.f8q8qf5.Sampling locations, ecological and climatic data and microsatellite genotypes supporting the results are deposited in Dryad repository:  Click here for additional data file.


Rhinella marina) displays high phenotypic variation across its introduced Australian range. Here, we used three genome-wide datasets to characterize their population structure and genetic diversity. We found that toads form three genetic clusters: 1) native range toads, 2) toads from the source population in Hawaii and long-established areas near introduction sites in Australia, and 3) toads from more recently established northern Australian sites. Although we find an overall reduction in genetic diversity following introduction, we do not see this reduction in loci putatively under selection, suggesting that genetic diversity may have been maintained at ecologically relevant traits, or that mutation rates were high enough to maintain adaptive potential. Nonetheless, toads encounter novel environmental challenges in Australia, and the transition between genetic clusters occurs at a point along the invasion transect where temperature rises and rainfall decreases. We identify environmentally associated loci known to be involved in resistance to heat and dehydration. This study highlights that natural selection occurs rapidly and plays a vital role in shaping the structure of invasive populations.Invasive species often evolve rapidly following introduction despite genetic bottlenecks that may result from small numbers of founders; however, some invasions may not fit this “genetic paradox”. The invasive cane toad ( The genetic paradox of invasion  describeDespite the fact that invasive populations are thought to undergo genetic bottlenecks due to the translocation of a small number of founders from their native range to an introduced range , invasivAlthough low genetic diversity may limit the ability of an invasive population to respond to natural selection, rapid evolution can also occur through non-adaptive processes. 1) Genetic drift may occur on range edges, reducing genetic diversity across an introduced range ; 2) spatIt has recently been suggested that the genetic paradox of invasion may be rare; some invasive populations do not suffer a reduction in genetic diversity during introduction, and others do not face novel adaptive challenges in their introduced ranges . FurtherRhinella marina) is an invader that has exhibited rapid evolution despite apparently low genetic diversity  and high temperatures  . Howeverustralia .Here, we assessed genetic diversity and population structure using single nucleotide polymorphisms (SNPs) identified in RNA sequencing (RNA-Seq) data from samples originating from French Guiana (the “native range”), Hawaii (the “source”), QLD (the “range core”), NT (“intermediate” areas), and WA (the “invasion front”) to test whether the genetic paradox is evident in the cane toad invasion, and to investigate the evolutionary dynamics during introduction. We predicted high divergence between native and invasive populations due to a combination of genetic drift, selection, and spatial sorting, but little genetic differentiation within invasion phases due to a putative lack of standing genetic diversity. Because of the increase in aridity at intermediate areas and the invasion front, we predicted that loci involved in thermal tolerance would be under selection, which may underlie the success that toads have had dispersing through these areas.RNA-Seq data provide genome-wide information, but are limited to transcribed regions of the genome . RNA-SeqWe collected samples from French Guiana, the Hawaiian Islands, and across Australia Table S1We extracted RNA using the RNeasy Lipid Tissue Mini Kit  following the manufacturer's instructions, in conjunction with a genomic DNA removal step . We quantified total RNA using a Qubit RNA HS Assay . mRNA libraries were constructed using the TruSeq mRNA v2 sample kit , including a 300 bp selection step. In total, we sequenced 46 spleens and 72 brains 2 acrossFirst, we conducted quality control  using FastQC v0.11.5 . We thenR. marina transcriptome , we used the HaplotypeCaller tool in GATK  on our aTo improve accuracy of SNP-calling, we performed this in Genomic Variant Call Format (“GVCF”) mode. By using this mode, we avoided missing SNPs at loci that match the reference in some but not all individuals. We then used the GenotypeGVCFs tool to merge the GVCF files, re-calculate genotype likelihoods at each SNP locus across all individuals, and re-genotype and re-annotate all SNP loci. The results were written to one merged VCF file. Although we initially genotyped all spleens and brains together, we discovered during downstream analyses that there was an effect of tissue type on population assignment; even from the same individuals, and using only SNPs from transcripts expressed in both tissues, spleen, and brain samples were assigned to separate populations. Thus, we genotyped spleens and brains separately, resulting in two merged VCF files, and subsequently kept these separate for all downstream analyses. We retained both datasets because each provides a unique benefit: the spleen dataset includes native range samples, but the brain dataset has more extensive sampling of the invasive populations.We used the VariantFiltration tool to identify and filter “clusters” (sets of 3 SNPs that appear within a window of 35 bases) in each of our merged VCF files to avoid SNPs in linkage disequilibrium and also to filter variants with QualByDepth (QD) less than 2.0, depth of coverage (DP) less than 20.0, and allele frequency less than 0.05. This resulted in 803,489 SNPs from spleen data and 818,536 SNPs from brain data. We used bcftools  to incluWe used PLINK  to conveWe performed a redundancy analysis (RDA) using the vegan package  in R. RDST values, causing them to be outliers that can be detected by Bayescan v2.1  and our two genetic clusters in brain data .Loci that are under natural selection may have abnormally high or low Fcan v2.1  using thLoci under natural selection also may have allele frequencies that are associated with environmental variables. Using the same climatic data on rainfall during the driest quarter and maximum temperature in the warmest month mentioned above, we performed a latent factor mixed model (LFMM) in the lfmm v2.0 package  in R to ST outliers with each of our lists of environmentally associated loci. We further investigated only those loci with both an outlier FST value and an association with a putatively significant environmental variable (through LFMM).Because these scans reveal thousands of loci putatively under selection, and because outlier tests have high false positive rates , we tookST and pairwise FST , expected heterozygosity (He), and rarefied allelic richness (AR). We also computed Shannon's Information Index (SI) with the dartR package  and those associated with environmental variables . In spleen data, this allowed us to investigate the hypothesis that genetic diversity is maintained at ecologically relevant traits even if genome-wide diversity is lost. In brain data, this allowed us to examine the effect of natural selection on genetic diversity within Hawaii and Australia. We used Kruskal-Wallis tests to assess the significance of the differences in genetic diversity.To quantify levels of genetic differentiation and diversity, we computed basic statistics  in the h package . After cTo visualize the types of genomic regions in which our SNPs lie, we used SnpEff . We calcST, and then linearized these values using the formula: linearized FST = FST/(1 – FST). We performed four Mantel tests, using: 1) all loci, 2) loci with outlier FST values, 3) loci with outlier FST values and with an environmental association , 4) loci with outlier FST values but without an environmental association. A significant result across all loci may suggest that the prominent driver of genetic structure is genetic drift. However, clinal variation in allele frequencies may also result from spatial sorting , or from selection associated with environmental clines; distinguishing these evolutionary processes is difficult, and may not be possible with a Mantel test. However, we performed multiple Mantel tests on different subsets of loci to attempt to separate the effects of genetic drift and spatial sorting.To examine the effects of geographic distance on genetic distance across the Australian range, we performed a Mantel test using ade4 v1.7-5  using brUnlike RNA-Seq data, which consists of coding sequences only , RADSeq de novo assembly, alignment of matching DNA regions across samples, and SNP calling using a maximum likelihood framework. We allowed a maximum of five base mismatches between stacks within an individual and three base mismatches between stacks between individuals, resulting in 499,623 SNPs. We retained one random SNP per locus and investigated four combinations of parameter choices , ultimately choosing min MAF = 0.05 and MDT = 0.2. These filtering choices produced results that were most consistent with those reported by We used Stacks v2.0  for all We ran fastStructure using the same methodology as above. We also performed a discriminant analysis of principal components (DAPC) using adegenet . We compST outliers and environmentally associated loci) from the analysis produced identical results (data not shown).We found three genetic clusters using spleen data .Out of 65,195 SNPs from spleen data, 648 were identified as FHSP4, a gene encoding heat shock protein (HSP) 4 (70 kDa), which is involved in thermal tolerance (on exposure to heat stress) and protein folding and unfolding  axis and visual and renal function  based on the full SNP dataset, and even stronger divergences between native and intermediate/frontal populations (pairwise FST = 0.32). Differentiation between the source/core and intermediate/frontal populations was much lower (pairwise FST = 0.08). Overall divergence among natives and invaders was moderately high .Our spleen data show strong divergence between the native and source/core populations (pairwise FST = 0.07) based on the full SNP dataset. Differentiation between source and core toads was lower when calculated with brain data as compared to spleen (pairwise FST = 0.04). Generally, pairwise FST by invasion phase (ST = 0.04). In spleen and brain data, the distribution of FST values across the full dataset is wider than the distribution of FST values from the outlier dataset  but both are more diverse than the intermediate/frontal population . However, we also estimated these measures by region . At these same loci, diversity was lowest in the intermediate/front population . These trends are also shown in environmentally associated loci . When calculated by region  and in environmentally associated loci . When calculated by region  were significantly associated across all four of our Mantel tests using: 1) all loci  than the full set of SNPs. The outlier loci within HSP4 were both synonymous variants. However, there were no significant differences in proportions between the rainfall-associated outlier FST subset and the full set of SNPs.Annotation of SNPs revealed that most of our loci were either missense, synonymous, or found in the 3' untranslated region (UTR). A small percentage were found in the 5' UTR, and the remaining loci involved the loss or gain of stop or start codons S4. In tST subset and the full set. The locus within HSP4 was a 3' UTR variant, and the locus within ARNT2 was synonymous. The rainfall-associated outlier FST subset had a significantly higher proportion of 3' UTR variants (p = 5E−3) and lower proportion of missense variants (p = 0.04) than did the full set. Eighteen of the nineteen loci within MAGED2 were 3' UTR variants (the last was missense), and two of the four loci within STXBP1 were missense (the other two were synonymous).In the brain data, there were no significant differences in proportions of SNP variant types between the temperature-associated outlier FST = 0.09) or overall . We found no evidence of significant substructure within either of the two genetic clusters. The two groups were equal in allelic richness (AR = 2.00 for both groups), but expected heterozygosity was higher in core toads than in intermediate/frontal toads .Analysis of the RADSeq dataset through fastStructure and DAPC also showed two genetic clusters within Australia  native range toads, 2) Hawaiian source toads and eastern Australian range core toads, and 3) all toads from further west in more recently colonized areas (intermediate and invasion front). Differentiation is much higher between the native population and the invasive populations than between the invasive populations. Loci identified as FST values or associations with environmental variables through LFMM; de novo mutation rates may have been high enough to restore or even enhance adaptive potential in the source/core population . Previous studies on mitochondrial DNA  and micrpulation . From thOur two invasive genetic clusters (source/core and intermediate/front) diverge at the transition from coastal to inland areas within Australia, in an area where temperatures become higher and rainfall becomes scarcer, particularly during the dry season . The corDiversity estimates from our brain data suggest a reduction in diversity across the invasion in the full set of loci, but a much greater reduction in diversity in those identified as under selection Table 2.ST outliers associated with temperature are several within a gene encoding protein HSP 4 (70 kDa). HSPs protect protein folding during increased temperatures and provide cells with enhanced thermal tolerance (HSP4 included two synonymous variants (spleen data) and a 3' UTR variant (brain data); 3' UTR variants are known to alter levels of protein functioning through differential expression of genes , is a transcription factor that specifically acts on genes involved in the HPA axis and visual and renal function. The HPA axis is stimulated by high temperatures  were found in MAGED2 (encodes melanoma-associated antigen D2). This protein regulates the expression and localization of two co-transporters that facilitate renal sodium ion absorption, preventing excess loss of water and solutes via reabsorption through the kidneys  were found in STXBP1 . Dehydration is known to increase blood clotting rate  of the F kidneys . Similarpression . The higing rate , and excing rate . EvolvedInvasive species have been shown to rapidly adapt phenotypically to heterogeneous climatic conditions, enabling range expansion . Some invs. geographic distance in Australia, using loci putatively associated with environmental selection, we see a clear discontinuity in these data (We found evidence of isolation by distance (IBD) in Australia using the full SNP dataset, which is likely to be caused by genetic drift. However, loci under strong selection are less likely to show evidence of IBD. Unsurprisingly, when we plot genetic Our population structure analyses using the RADSeq dataset  yielded In conclusion, it appears that serial introductions have led to a reduction in genetic diversity of invasive cane toads. Nonetheless, cane toads do indeed face novel adaptive challenges in their introduced ranges, and the loci that we have identified as putatively under selection may suggest that they are able to respond genetically to natural selection, particularly in the harsh environmental conditions of northwestern Australia. The ability of toads to adapt to these conditions may reflect the maintenance of diversity at ecologically relevant loci, or it may reflect sufficiently high mutation rates to bolster adaptive potential; thus, it remains unclear whether the toad invasion represents a true genetic paradox. It seems likely that natural selection, genetic drift, and spatial sorting all shape the Australian invasive population, but current approaches do not have the power to distinguish between these processes due to their potentially similar effects on allele frequencies. Manipulative experiments investigating the candidates that we have identified may be especially informative. Additionally, genetic changes may not be the only mechanisms by which adaptation occurs; studies on plasticity and epigenetic changes may also be useful for uncovering the mysteries of rapid evolution.The datasets generated for this study can be found in Sequence Read Archive (SRA), BioProject Accession PRJNA510261 (spleen data from French Guiana and Hawai’i), PRJNA395127 , and PRJNA479937 .All procedures involving live animals were approved by the University of Sydney Animal Care and Ethics Committee (2014/562) and the Deakin University Animal Ethics Committee (AEX04-2014).DS designed the experiment, performed the lab work and bioinformatics analyses, and wrote the paper. MR collected samples, assisted with the experimental design and bioinformatics analyses, and edited the paper. RS assisted with the experimental design and edited the paper. JD and SD collected samples and edited the paper. LR collected samples, assisted with the experimental design and bioinformatics analyses, and edited the paper.This work was supported by the Australian Research Council  and the Equity Trustees Charitable Foundation (Holsworth Wildlife Research Endowment).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.