It is estimated that around 10–15% of the population have problems achieving a pregnancy. Assisted reproduction techniques implemented and enforced by personalized genomic medicine have paved the way for millions of infertile patients to become parents. Nevertheless, having a baby is just the first challenge to overcome in the reproductive journey, the most important is to obtain a healthy baby free of any genetic condition that can be prevented. Prevention of congenital anomalies throughout the lifespan of the patient must be a global health priority. Congenital disorders can be defined as structural or functional anomalies that occur during intrauterine life and can be identified prenatally, at birth, or sometimes may only be detected later during childhood. It is considered a frequent group of disorders, affecting 3–6% of the population, and one of the leading causes of morbidity and mortality. Congenital anomalies can represent up to 30–50% of infant mortality in developed countries. Genetics plays a substantial role in the pathogenesis of congenital anomalies. This becomes especially important in some ethnic communities or populations where the incidence and levels of consanguinity are higher. The impact of genetic disorders during childhood is high, representing 20–30% of all infant deaths and 11.1% of pediatric hospital admissions. With these data, obtaining a precise genetic diagnosis is one of the main aspects of a preventive medicine approach in developed countries. The field of reproductive health has changed dramatically from traditional non-molecular visual microscope-based techniques  or G-banding karyotype), to the latest molecular high-throughput techniques such as next-generation sequencing (NGS). Genome-wide technologies are applied along the different stages of the reproductive health lifecycle from preconception carrier screening and pre-implantation genetic testing, to prenatal and postnatal testing. The aim of this paper is to assess the new horizon opened by technologies such as next-generation sequencing (NGS), in new strategies, as a genomic precision diagnostic tool to understand the mechanisms underlying genetic conditions during the “reproductive journey”. The odyssey of an infertile couple to become parents can be compared to the odyssey of those parents who realize that their child has some type of problem or illness but there are no answers or name for that “condition“. It can also be compared to adults that suffer from a genetic condition and want to know what it is, how to deal with it and their reproductive options, embarking on a long, emotionally draining and financially costly journey.There is an indisputable advancement that the emergence of molecular genetic techniques, such as next-generation sequencing (NGS), has allowed, which is to standardize pathways to achieve an accurate diagnosis. Genomic high-resolution and high-throughput technologies have opened new diagnostic pathways, allowing a more personalized clinical management paired with new challenges, such as the use, management and interpretation of generated databases needing great bioinformatic support.Increasingly often, more couples assess their reproductive potential without acknowledging the reproductive roulette of the risk for an associated genetic disease . The groAn evaluation of the overall well-beingMedical historySurgical historySocial and behavioral historyMedicationOccupational and education risksThe WHO  defines preconception care as the provision of biomedical, behavioral and social health interventions for women and couples before conception occurs. Its main aim is to improve maternal and child health, in both the short and long term . PreconcThere are many areas addressed by preconception care assessment including nutrition, environmental conditions, toxic habits , mental health and genetic conditions. We are going to focus on the last one, genetic conditions.Most genetic disorders that result in sterility or childhood death are caused by recessive mutations. Nonetheless, these variants can cause devastating diseases like cystic fibrosis when the patient carries both copies of the mutation. It is estimated that humans carry an average of one to two mutations per person that can cause severe genetic disorders or prenatal death when two copies of the same mutation are inherited . This meCurrently, there are many genetic tests that assess the “mutational state” of a patient or a couple to reduce the probability of having a baby with a genetic disorder. Genetic carriers screening based on NGS test the existence of mutations causing a vast number of recessive genetic conditions in an individual, that can be passed on to their offspring if the couple carries the mutation. Even though the standard of practice is to offer carrier testing only to those individuals who have a strong family history of a genetic disorder, or a history of genetic disorder in the partner and/or relatives of identified carriers, only a minority of carrier couples are identified. These reduced indications for testing can lead to children affected by recessive disorders with no known family or medical history .Autosomal/X-linked recessive disorders are more frequent than autosomal/X-linked dominant because the latter present a higher deleterious effect. The reproductive approach is different in these cases because patients are not only carriers, but they also suffer the illness, and so are aware that they can transmit the genetic condition to 50% of their offspring.When it is known that an autosomal and/or dominant disease is present in a couple, preconception counselling is crucial. During this process, we must evaluate the medical and family history to obtain an accurate clinical diagnosis. From here, the next step will be to carry out the most appropriate genetic analyses to identify the molecular cause of the disease, an essential requirement for any subsequent family study, including prenatal and pre-implantation analyses.At this stage of the reproductive journey, it is useful to group together couples with known reproductive problems  as well as those couples that have never tried but know there is a genetic condition running in the family. Therefore, all of them could benefit from assisted reproduction techniques such as pre-implantation genetic testing for monogenic diseases (PGT-M) and pre-implantation genetic testing for aneuploidies (PGT-A).Multiplex PCR (Polymerase Chain Reaction): Multiplex PCR uses targeted primers designed specifically for the mutation of interest combined with other markers for linked short tandem repeat (STR) markers.Whole-genome amplification (WGA).Karyomapping: High-density SNP (Single Nucleotide Polymorphism) array that allows evaluation of DNA haplotypes).Sanger sequencing.Multiplex Ligation-dependent Probe Amplification (MLPA) .PGT-M allows us to detect embryos affected by a known monogenic that has been previously detected in their parents. Molecular technologies used to perform the PGT-M test were several, for example:Besides genetic disorders caused by gene mutations/variations, other genetic conditions can have an impact on fertility, pregnancy, parents and newborns: the so-called chromosomal disorders.As women age, their fertility declines and there is an increased risk of numerical and structural chromosomal abnormalities, which can lead to implantation failure, early pregnancy loss, greater risk of congenital birth defects or severe chromosomal congenital diseases such as Down’s and Patau syndromes. Aneuploidy ranks as the most common genetic abnormality accounting for approximately 50% of miscarriages. More than half of the embryos produced by in vitro fertilization (IVF) are aneuploid [The process of detecting numeric or structural chromosomal abnormalities for the purpose of embryo selection is generally referred to as pre-implantation genetic testing for aneuploidies (PGT-A), introduced in the 2000s to increase implantation and pregnancy rates, decrease miscarriage rates and the risk of aneuploid offspring, as well as decrease the time to conceive ,6,7. EarCurrently, embryo biopsy is required for PGT-A testing. In the event of a poor blastocyst quality at biopsy, new effective approaches involving the sequencing of cfDNA (cell free DNA) secreted into the culture medium from the human blastocyst have been developed. In addition, PGT-A could mitigate the potential adverse effects associated with embryo biopsy ,13.Aside from the assessment of the embryo´s mutational/chromosomal status, additional genetic tests assessing fertility, based on high-throughput techniques such as NGS, are beyond the scope of an ordinary clinical practice to increase the reproductive chances of a couple, i.e., endometrial receptivity analysis and more recently, endometrial microbiome test ,15.Prenatal screening is the risk estimation of fetal aneuploidies based on high-resolution ultrasound scans, in order to assess ultrasonographic markers including nuchal translucency, combined with biochemical determinations in maternal blood samples of free beta-subunit of human chorionic gonadotropin (fβ-hCG) and pregnancy-associated plasma protein-A (PAPP-A) in the first trimester, and the alpha-fetoprotein (AFP) and beta-human chorionic gonadotropin (βhCG) in the second trimester . If thisCurrently, a rapid noninvasive prenatal test for the most common aneuploidies in the live newborn  can be performed by sequencing fetal DNA present in maternal blood. The genomics-based non-invasive prenatal test (NIPT) could be considered as a candidate to replace the conventional karyotype as a first-tier test in unselected populations of pregnant women undergoing aneuploidy screening or as a second-tier test in pregnant women considered to be high risk after first-tier screening for common fetal aneuploidies .Despite that cytogenetic conventional karyotype has been considered the gold standard for chromosomal assessment, new molecular microarray-based genomic copy-number techniques like chromosomal microarray (CMA) present some advantages.A variety of phenotypes.Multiple malformations.Congenital anomalies.Intellectual disabilities.Developmental delay.Epilepsy.Cerebral palsy.Neuropsychiatric disorders .The resolution of chromosomal analysis by karyotyping is limited to 5–10 Mb in size . Most chAlternatively, there are trade-offs, however, DNA extraction needed for prenatal purposes in order to perform a CMA still requires invasive techniques such as amniocentesis or chorionic villus sampling. Furthermore, a high resolution increases the probability of incidental findings of unknown clinical significance that, in turn, add a level of complexity to the genetic counselling as well as parent anxiety. We must take into consideration that CMA does not detect polyploidies or balanced rearrangements. In the vast majority of cases, the presence of a balanced rearrangement does not imply major clinical significance for the ongoing pregnancies, but there are still reproductive ramifications for future pregnancies if one of the parents is a carrier .Although aneuploidies are the most frequent genetic alteration during the prenatal stage, as well as one of the main genetic causes of congenital defects (10–15%), monogenic alterations are of considerable importance throughout this stage, reaching up to 10% of the congenital defects.Some of the clinical features of these monogenic disorders, especially those associated with syndromic forms, can be identified throughout pregnancy by ultrasonography analyses. In these cases, and depending on the clinical impression, specific tests can be used to analyze certain genes or variants, as well as more complex and nonspecific technologies, such as CMA, NGS gene panels or whole exome sequencing (WES), when a precise clinical guidance is not possible. In any case, when an ultrasound finding is detected during pregnancy, that pregnancy is labelled as high risk for a genetic disease, therefore an invasive test will be indicated to obtain a fetal sample to analyze using the most suitable technique depending on the type of ultrasound finding.Another significant and relatively frequent issue is when the couple first finds out about the presence of a genetic disease or knows that they are at high risk of being a carrier during an advanced stage of their pregnancy. As we have described previously, it is strongly recommended to face this situation in the pre-conceptional stage in order to approach the diagnostic process with sufficient guarantees and time. Once the pregnancy has started, the gestational age and prenatal diagnosis can be time-limiting, being critical in some complex cases. In any case, and whenever possible, it is recommended to identify the molecular cause of the familial disease prior to taking the fetal sample using an invasive approach.Unfortunately, miscarriages are the most common complication during early pregnancy. Clinically recognized pregnancy losses occur in approximately 15–25% of pregnancies, most of them occurring during the first trimester ,27,28,29Cell culture growth failure (the failure rate in POC samples cultured after curettage ranges between 5% and 42% ).Suboptimal chromosome preparations.Maternal cell contamination (MCC).Low-resolution limit that does not allow the detection of submicroscopic deletions and duplications that can cause miscarriages.Until now, products of conception (POC) studies have been carried out using cell culture followed by conventional karyotyping. However, when using these techniques, the incidence of chromosomal abnormalities in miscarriages in the general population ranges between 40% and 80%, depending on the culture methods adopted ,34. PropMolecular biology techniques such as NGS or CMA that are culture-independent can avoid such limitations, increasing the karyotype resolution ,37. GiveWhole exome sequencing (WES) is very useful for the detection of alterations in the sequence of any gene that may be related to the potential genetic condition that may have caused the spontaneous termination of the pregnancy in progress. This is especially important in the second trimester of gestation when monogenic disorders acquire a higher frequency. In these cases, identification of the molecular causes of the miscarriage can be very useful to prevent new similar situations in the couple.When miscarriage occurs in an advanced pregnancy, the clinical and anatomopathological evaluation of the fetus can be very useful to guide the genomic analysis. When clinical assessment is not possible, WES provides a high capability to identify sequence variants in genes associated to complex syndromes, but also, the optimization of bioinformatic analyses, making possible the identification of copy-number variations in these cases.Currently, 3% of live newborns will have a congenital alteration despite great efforts made in the different stages of the reproductive process, growing capacities of available technologies and the implementation of prevention programs. This is because, on one hand, technologies, although increasingly precise, are not infallible, and on the other, the use of prevention techniques and programs are not universal.In this stage, as in other previous stages, we will be able to apply screening measures in order to reduce the impact of congenital diseases. An example would be extended neonatal screening aimed for early identification of apparently healthy newborns that are at immediate risk of a congenital disease if an early and accurate diagnosis is not established and therapeutic measures are not taken as soon as possible.The neonatal screening allows us to detect a wide number of genetic disorders, causing health problems starting in infancy or early childhood, mainly metabolic disorders like phenylketonuria. Newborn screening programs are well-established as the standard of care in most developed countries, but the number of diseases and approaches differ between countries and health systems. Early detection and treatment can help prevent inborn errors of metabolism, intellectual and physical disabilities and life-threatening illnesses during the first hours of life. The advent of next-generation sequencing has resulted in attempts to expand the use of DNA sequencing in newborn screening to improve diagnostic and prognostic utility. Currently, in the market, we can find different commercial options for newborn screening panels that can detect, not only the most common metabolic disorders, but a great number of genetic disorders, or even gene susceptibility. Still, unexpected and medically irrelevant incidental findings must be carefully considered .On the other hand, we can also apply diagnostic methods in those neonates who have developed symptoms, especially for newborns admitted to the intensive care unit when disease progression is extremely rapid and a quick molecular diagnostic is relevant for clinical decision making, establishing a prognosis, defining specific therapeutic measures and providing genetic counselling and access to family studies aiming to reduce the risks of recurrence in the family. Monogenic diseases have a high impact in the neonatal morbimortality, accounting for ~20% of infant deaths and ~18% of pediatric hospitalizations. Genomic testing of these patients aims to provide a comprehensive molecular diagnosis that allows for early intervention of the patient and proper genetic counseling of the family in order to reduce the time spent in the diagnostic odyssey ,40. ThesBoth genetic assessment and diagnosis have a special impact during this stage of the reproductive journey, especially in young adults that may be developing a career, forming partnerships and potentially becoming parents. Pre-symptomatic testing may affect many facets of their future lives as well as the future of their upcoming families , but alsNGS technologies, especially introduction of the WES, has become a turning point, especially in the rare genetic diseases research. It has allowed development and implementation of strategies to uncover the mechanisms behind all rare diseases to sketch a “molecular atlas” showing links between molecular genetic profiles and states of health or disease. Rare genetic conditions affect around 2–3% of the worldwide population, usually causing diseases that drastically reduce life expectancy and quality of life as well as reproductive consequences in their offspring.Recent advent of genomic technologies and its clinical applications has allowed the scientific community, especially those involved in patient care and clinical management, to evolve from a diagnosis-based approach of fragmented or isolated data to a diagnostic-based approach of the “overall picture” point of view. This can be done as a result of huge datasets, using their unique genetic and physiologic characteristics to tailor the diagnosis and treatment of individual patients.To reach an accurate and reliable genetic diagnosis, or in other words, to interpret these data in a meaningful and useful manner for the patient’s clinical management, it is mandatory to develop bioinformatic tools, including novel data pipelines or computational tools, and also, acquire knowledge to manage and to interpret combined analysis of multi-dimensional data produced by these technological and scientific innovations. This is where clinical bioinformatics comes into play as an essential element to integrate laboratory and clinical data as well as the use of database-computational methods, algorithms, customized pipelines and other resources and methods, including machine learning or even artificial intelligence .Whole exome sequencing (WES) and whole genome sequencing (WGS) are the most recent technologies based on NGS that are developed for genomic diagnostic purposes when a genetic disorder for which single-gene or limited gene testing fails to provide a genetic explanation. WGS and WES—wherein the genome or the protein-coding parts of the genome are sequenced in its entirety—would be an option to replace panel tests in the near future. There is no doubt that genomic tools, especially WES and WGS, give us a global view of the disease, increasing the knowledge on the underlying mechanism causing this rare genetic condition, but also increases the probability to find variants associated with other health conditions which may or may not be medically actionable and are unexpectedly so named secondary or incidental findings. How to report and manage this secondary finding as well as how to assess patients are some of the main genomic diagnostic challenges .It is estimated that 350 million people globally suffer from a genetic disorder causing a rare condition. Genetic disorders can be assessed in several stages of our life, generating a different impact in our lifestyle and personal decision-making, in particular those involved with our reproductive options.Frequently, searching for an answer and an accurate diagnosis in those patients suffering from a genetic disorder can turn into an odyssey, especially in those cases where children or pregnant women are involved.Sometimes, patients and their families bounce back and forth from physician to specialist and back again only to receive multiple misdiagnoses. Genetic diagnosis serves to provide a linear or step-by-step flowchart with plenty of medical tests, reports and documentation of clinical manifestations. These lead to the concatenation of several visits, usually to different specialists, only to end up with the final step, which was a genetic analysis, usually by Sanger sequencing or NGS of a limited/selected gene panel when there was a suspicion of a genetic disorder. The emergence of a massive gene analysis tool like NGS has changed this diagnostic workflow in genetic diseases by providing a higher diagnostic yield, rapid, powerful and cost-effectiveness alternative for genetic analysis in the early stages of the process . This neCurrently, NGS-based tools can point to the implication of a single gene  and help establish a rapid diagnosis in just a few weeks or even less in a large percentage of cases.Lately, we have experienced a noticeable increase in the demand of NGS technologies in the clinical setting. Increasing the diagnostic precision of these technologies has been one of the main levers for change, alongside the optimization of processes, reduction of costs and development of new applications. These adjustments contribute to an improvement in the diagnostic capacity, improving the life of patients with monogenic and widely known diseases, patients with heterogeneous conditions, gene-associated phenotypes or even complex differential diagnoses.Genomics provides the tools and knowledge necessary to achieve an accurate diagnosis, making a difference in the lives of a growing number of patients suffering from genetic diseases. Relying on an accurate diagnosis enables a greater availability to different reproductive options, allowing the “reproductive journey” to be a comfortable and safe experience for the patient .


Linum catharticum in dry calcareous grasslands (Bromion) and wet litter meadows (Molinion), two habitats on opposing ends concerning, for example, moisture level, is reflected on the genetic and epigenetic level. Using AFLP (amplified fragment length polymorphisms) and MSAP (methylation sensitive amplification polymorphisms) analyses, we studied the genetic and epigenetic variation of L. catharticum from calcareous grasslands and litter meadows. From each habitat, we included five study sites with 16 individuals per sampling location. We observed lower genetic than epigenetic diversity, but considerable differentiation among habitats, which was stronger on the genetic than the epigenetic level. Additionally, we observed a strong correlation of genetic and epigenetic distance, irrespective of geographic distance. The dataset included a large portion of fragments exclusively found in individuals from one or the other habitat. Some epigenetic fragments even occurred in different methylation states depending on the habitat. We conclude that environmental effects act on both the genetic and epigenetic level, producing the clear differentiation among plant individuals from calcareous grasslands and litter meadows. These results may also point into the direction of ecotype formation in this species.Plant species differ in their ecological amplitude, with some species occurring in very different habitats under strongly differentiated environmental conditions. We were interested in to what extent the occurrence of  Linum catharticum. Using AFLP and MSAP analyses, we studied the differences among populations originating from different habitats: dry calcareous grasslands and wet litter meadows. We observed large genetic as well as epigenetic differences among populations from different habitats and conclude that Linum catharticum likely forms ecotypes depending on the environmental conditions.In this study, we investigated the genetic and epigenetic variation of  The origin of plant material for restoration of plant populations or habitats is important, as local adaptations may not be advantageous in new surroundings  moench  and Methylation Sensitive Amplification Polymorphisms (MSAP) to investigate the genetic and epigenetic structure of individuals of 22.1Linum catharticum L. occurs as an annual or biennial herb  , are paired with EcoRI. Due to their specific methylation sensitivity they allow for differentiation between different methylation states within their restriction site.  to create a binary matrix for each dataset, representing the presence and absence of a given fragment for each individual. The AFLP dataset included 158 fragments, while the MSAP dataset comprised 337 fragments. The 0/1 matrix of the MSAP data set was then scored for unmethylated (Epi_u) and methylated (Epi_m) and hemimethylated (Epi_h) epiloci using the scoring method “Mixed‐Scoring 2” as proposed by Schulz et al. , using ti) 2) was calculated using PopGene 32  genetic variation within study sites, expressed by Nei's Gene Diversity  , produced by the AMOVA, and the Euclidean geographic distance among sampling sites we performed simple Mantel tests using GenAlEx and partial Mantel tests using the “vegan” package  and did not differ significantly among habitats. However, genetic diversity within calcareous grassland study sites showed a higher heterogeneity, while for litter meadows the study sites showed very similar genetic diversity. Epigenetic diversity for unmethylated and methylated epiloci differed significantly among habitats, with individuals from litter meadows showing lower genetic and epigenetic diversity, than those from calcareous grasslands. . Additionally, we checked for epiloci, which were found as methylated in one habitat, while it occurred unmethylated in the other and vice versa. In total, we found 13 of these fragments, of which seven were primarily methylated (on average 93%) in litter meadow individuals and mainly unmethylated (on average 92%) in calcareous grassland individuals. The other six fragments showed methylation in calcareous grasslands (on average 95%), while they appeared primarily unmethylated in litter meadows (on average 97%).Differentiation among habitats was generally very high. Genetic differentiation among habitats was 80%. Epigenetic differentiation was lower among habitats than genetic differentiation  , which represented the two habitats  variation among study sites was estimated at 8% , even though the maximum distance between study sites was 150 km, spanning an even wider geographical range than our study. Lele et al., . Environmental disturbances are expected to be higher in calcareous grasslands, with multiple stress factors, for example, water limitation, grazing and trampling, nutrient limitations, contributing to a generally more heterogeneous habitat ; Investigation (lead); Project administration ; Writing‐original draft (lead). Peter Poschlod: Funding acquisition (lead); Supervision ; Writing‐review & editing . Christoph Reisch: Project administration ; Resources ; Supervision ; Writing‐review & editing .Supplementary MaterialClick here for additional data file.


Although taste and smell seem hard to imagine, some people nevertheless report vivid imagery in these sensory modalities. We investigate whether experts are better able to imagine smells and tastes because they have learned the ability, or whether they are better imaginers in the first place, and so become experts. To test this, we first compared a group of wine experts to yoked novices using a battery of questionnaires. We show for the first time that experts report greater vividness of wine imagery, with no difference in vividness across sensory modalities. In contrast, novices had more vivid color imagery than taste or odor imagery for wines. Experts and novices did not differ on other vividness of imagery measures, suggesting a domain‐specific effect of expertise. Critically, in a second study, we followed a group of students commencing a wine course and a group of matched control participants. Students and controls did not differ before the course, but after the wine course students reported more vivid wine imagery. We provide evidence that expertise improves imagery, exemplifying the extent of plasticity of cognition underlying the chemical senses. Experts—individuals with in‐depth knowledge and abundant practical experience in a domain . Our expert sample size, which was based on the availability of experts, was five times larger. There were 66 wine experts . They were either experienced professionals in the field of wine  or amateur connoisseurs with an attested interest in wine  following previous criteria of what constitutes a wine expert . The remaining 14 participants were excluded from analyses since they did not meet the expert criteria outlined above, but could not be considered wine novices either (n = 3). Others were excluded because experts and novices were recruited and tested in parallel, and novices could not be yoked to a wine expert based on age or gender (n = 11). A t test confirmed that the expert and novice group in the final sample did not differ in age, t(130) = 0.21, p = .836.Sixty‐six Dutch novices were matched to the experts in age and gender  had significantly higher wine knowledge than novices , t(130) = −18.49, p < .001, d = 3.2.To establish whether groups differed on wine expertise, the Wine Knowledge Test  as a within‐participants factor and Expertise (two levels: wine experts and novices) as a between‐participants factor. Corrections in the degrees of freedom for sphericity assumption violations were applied where appropriate.F = 28.93, p < .001,ηp2 = 0.18. In addition, there was a main effect of Modality, F = 15.02, p < .001,ηp2 = 0.10. This main effect must be interpreted in the context of a significant interaction between Modality and Expertise, F = 5.16, p = .006,ηp2 = 0.04. Pairwise comparisons showed that for novices, visual imagery  was more vivid than taste imagery , p = .002, d = 0.59 and odor imagery , p < .001, d = 0.46; and taste imagery, in turn, was more vivid than odor imagery, p = .04, d = 0.14. In contrast, wine experts showed no difference in vividness of imagery across modalities, ps > .05 .Wine experts reported more vivid imagery for wines overall, F, 130 = 2F = 16.09, p < .001,ηp2 = 0.110. There was a main effect of Odor type, F = 19.64, p < .001,ηp2 = 0.131, but there was also a significant interaction, F = 28.34, p < .001,ηp2 = 0.179. Pairwise comparisons showed wine experts reported more vivid imagery for wine odors  than novices , p < .001, d = 0.97, but there was no difference in vividness for imagery of everyday odors between experts  and novices , p = .245, d = 0.20.Wine experts did not, however, report enhanced imagery for everyday odors see Fig. . Using aWe show for the first time that wine experts have more vivid multisensory imagery for wines than novices. Novices' vividness of imagery followed the previously attested hierarchy of sensory imagery  with a statistical power of .80 for a mixed interaction, the targeted sample size was 82. All students (approximately 50) who enrolled in the course were invited to take part in the study. We were able to recruit 77 participants in total. Thirty‐two students  agreed to participate, 14 of whom reported having a background in the food and beverage industry, for example working in a restaurant (n = 5), working as a coffee roaster (n = 2), or working in the food department of a supermarket or deli (n = 7). All students had successfully followed entry‐level courses in wine. Twenty students  also completed the survey at time 2 (T2). A group of 45 control participants  with little experience of wine other than being occasional consumers were also recruited. Of this group, 37 (82.2%) also completed the survey after 6 months, at T2 . In the group of participants who completed the questionnaires both at T1 and T2, there was no significant difference in age, t(55) = 1.18, p = .211, or gender, χ2(1) = 2.75, p = .097.To be able to detect a medium effect . In addition, participants reported their wine expertise, using the Self‐rated Wine Expertise Questionnaire  to 9 (strongly agree). The SWEQ also had high internal consistency, McDonald's ω = 0.922. Finally, because the control participants needed to be smell and flavor novices, we asked all participants to report their background involvement in the food and beverage industry, using open questions . These questions were checked for aberrations , but otherwise they were not analyzed.At T1 the VWIQ, VOIQ, and WKT were administered. Internal consistency of these questionnaires was high  was also calculated. The SWEQ questions 2, 4, and 5 were reverse scored, and all five items then summed into a subjective wine expertise score.n = 57). One participant from the student group was excluded as they were an outlier on several of the questionnaires (following the outlier criterion M ± 2.5 SD).Only data from participants who completed the questionnaire at both times were included  had significantly higher scores than controls , F = 102.18, p < .001,ηp2 = 0.650. There was no effect of Time, F = 1.26, p = .267,ηp2 = 0.022, and no interaction between Group and Time on wine knowledge scores, F = 1.26, p = .267,ηp2 = 0.022. Students also rated their own wine expertise  significantly higher than controls , F = 91.79, p < .001,ηp2 = 0.625. There was no effect of Time, F = 0.01, p = .942,ηp2 = 0.000, and no significant interaction between Group and Time, F = 3.00, p = .089,ηp2 = 0.052. So, on these measures of factual wine knowledge and self‐reported knowledge, students were at ceiling.As expected, students and controls differed in their wine knowledge even before training according to both the WKT and SWEQ. Using ANOVA, with Group  as a between‐participant factor, and Time  as within‐participant factor, we found a main effect of Group on wine knowledge (WKT): Students  = 12.08, p = .001,ηp2 = 0.180. Critically there was a robust interaction between Group and Time, F = 8.35, p = .006,ηp2 = 0.132. Pairwise comparisons showed that before training, the difference between students  and controls  was not significant, p = .080, Cohen's d = 0.50; but after training, students (M = 3.86 SD = 0.60) reported significantly more vivid wine imagery than controls , p < .001, Cohen's d = 1.3  = 5.77, p = .004,ηp2 = 0.095, but there was no interaction between Group and Modality, F = 2.05, p = .134,ηp2 = 0.036, nor between Time and Modality, F = 0.385, p = .681,ηp2 = 0.007, nor was there a significant three‐way interaction between Group, Time, and Modality, F = 2.22, p = .114,ηp2 = 0.039. So training improved sensory imagery for wine across modalities.Nevertheless, during the 6‐month period that students received practical training in wine tasting as part of the professional wine course they attended, their vividness of imagery for wine increased. Using a mixed ANOVA with Group  as a between‐participants factor and Modality , and Time  as within‐participant factors on VWIQ, we found students had more vivid imagery than controls, F = 3.82, p = .056,ηp2 = 0.065; Time, F = 2.92, p = .093,ηp2 = 0.050; and no interaction between the two, F = 0.823, p = .368,ηp2 = 0.015. So there was no general change in olfactory imagery for everyday odors as a result of wine training. Finally, we compared groups on their general visual imagery (VVIQ) at T2. This showed no general difference between students  and controls , t(55) = 0.897, p = .373, d = 0.25, suggesting no difference in the ability to generate visual images of everyday scenes. Taken together, these results demonstrate that sensory imagery for an expert domain can become more vivid following training.We also compared students and control participants for their imagery of everyday odors and visual objects. We collected data for imagery of odors at two time points, just as with the VWIQ, so we analyzed the data for the VOIQ in the same manner. We found no effect of Group, 4While experts and novices reported similar vividness of imagery for everyday odors and visual scenes, they showed distinct response profiles for the imagery of wines. Specifically, wine experts had more vivid imagery for the color, odor, and taste of wines. Previous studies have found enhanced odor imagery with other types of expertise (Bensafi et al., Wine experts work with wines on a daily basis and odors and tastes are particularly important. For example, wine experts often create wine‐food pairings, imagining how the tastes and textures of food match those of a wine (Harrington, Training affected the ability to imagine the color, odor, and taste of wine, but not everyday objects. This is in line with previous proposals for how musical training affects sound imagery, where students became better at imagining the pitch and acoustic character of sounds, but not visual objects (Aleman et al., It has been suggested that odor imagery is critically embodied (Arshamian, Manko, & Majid, shiraz wine may simultaneously activate semantic concepts of “bold,” “Australia,” “red,” “vanilla,” and so on (Bensafi et al., As an alternative possibility, Bensafi and colleagues hypothesize that practice may enhance verbal and emotional associations, as well as changing sensorimotor components, with the effect that richer semantic associations are more easily activated during imagery, for example, the imagined The present study treated mental imagery for the color, smell, and taste of wine as distinct modalities, although imagery—similar to perception itself—likely works in a cross‐modal fashion (Spence & Deroy, We used self‐report measures to tap into mental imagery which risks the possibility that differences between groups or modalities are due to response biases rather than experienced imagery. However, it is difficult to explain how response bias would lead to the particular pattern of sensory imagery we find, for example, experts reporting enhanced imagery for wine odors but not everyday odors. Moreover, previous studies have shown that self‐reported vividness of mental imagery is related to fMRI BOLD activity in sensory‐specific areas of the brain (Huijbers, Pennartz, Rubin, & Daselaar, We found that students enrolled in a professional wine course had enhanced imagery for wine after 6 months, even though imagery is given no explicit role in the curricula of most wine courses. This stands in stark contrast to imagery in other types of expertise; for example, professional sports, where motor imagery is used to improve performance (Weinberg, Our results also suggest imagery training could be incorporated into wine courses to help students hone their skills further. Imagery training could be deployed as a tool for individuals learning to distinguish and describe wines, and to combine wine and food, and so further improve the efficacy of wine education. Additionally, other flavor domains, such as for coffee (Van Doorn, Wuillemin, & Spence, To conclude, we show that experience has a pronounced effect on mental imagery, underscoring the considerable plasticity in multisensory mental imagery—and cognition in general—which demonstrates the importance of considering human behavior in its diverse contexts. Furthermore, a focus on imagery lends itself to interesting possibilities for future research and application within the domain of food and drink.IC, LS, AA, and AM conceived of and designed the studies. Data collection and data analysis were performed by IC with analysis checked by LS. Selection of tests and interpretation were performed by all the authors. IC drafted the manuscript with critical edits by AA, LS, and AM. All the authors approved the final version of the manuscript for submission.https://github.com/ICroijmans/WineImagery.Neither of the studies reported in this article was formally pre‐registered. The SPSS data, including SPSS syntax, as well as questionnaires used in these experiments, have been made available via Github: https://github.com/ICroijmans/WineImagery.This article has earned Open Data and Open Materials badges. Data and materials are available at


Physalis peruviana L.) and related taxa germplasm collection. A total of 176 accessions representing, wild, weedy, and commercial cultivars as well as related taxa from the Colombian germplasm bank and other world repositories were screened using GBS. The pipeline parameters mnLCov of 0.5 and a mnScov of 0.7, tomato and potato genomes, and cape gooseberry transcriptome for read alignments, were selected to better assess diversity and population structure in cape gooseberry and related taxa. A total of 7,425 SNPs, derived from P. peruviana common tags (unique 64 bp sequences shared between selected species), were used. Within P. peruviana, five subpopulations with a high genetic diversity and allele fixation  were detected. Conversely, low genetic differentiation (FST: 0.01 to 0.05) was also observed, indicating a high gene flow among subpopulations. These results contribute to the establishment of adequate conservation and breeding strategies for Cape gooseberry and closely related Physalis species.A robust Genotyping-By-Sequencing (GBS) pipeline platform was examined to provide accurate discovery of Single Nucleotide Polymorphisms (SNPs) in a cape gooseberry ( Physalis peruviana L.) is an herbaceous Solanaceae species, native to the Andean region, with enormous potential for biomedical research and commercial purposes [Cape gooseberry , Oomycete (Phytium sp.), and fungal pathogens  [F. oxysporum sp. physali (Foph) [Cape gooseberry production in the Andean region has been diminished due to important phytosanitary problems caused mainly by bacterial ( others) , 15. Amoi (Foph) , represeThe available genetic diversity of cultivated and wild related taxa has helped to establish appropriate conservation, management, and sustainable utilization strategies of different crops . In partP. peruviana populations [Molecular markers have become valuable tools to assess genetic variation of worldwide plant repositories. Population studies, marker assisted selection, mapping and association studies, among other applications, have been used for this purpose . To dateulations –26. HoweFoph resistance [Recently, next generation sequencing (NGS) technologies have accelerated the screening of germplasm collections, identifying thousands of SNP markers in a cost effective and timely way , 28. Notsistance , 32.P. peruviana and 18 wild related species from the Physalis genus, including 95 technical replicates, to leverage the genetic diversity and population structure of this Andean crop towards conservation and sustainable utilization strategies.Although these previous advances in genetic diversity and association mapping have contributed to the exploration of cape gooseberry genetic resources, the species still lacks a more comprehensive GBS-SNP pipeline platform for SNP discovery. This study mainly seeks to provide a robust GBS-SNP calling pipeline for this species and related taxa by obtaining common genomic regions between tomato, potato, and cape gooseberry using the previously developed TasselPipelineGBS . MoreovePhysalis peruviana L.) and 18 related taxa accessions, containing one to seven individuals each, for a total of 644 individuals, were used in this study  following the manufacturer's procedure. DNA quantity was determined using NanoDrop (Thermo Scientific® ND 2000) and λ DNA/HindIII Ladder . DNA quality was assessed using EcoRI restriction enzyme digestions  and visualized on 1% (w/v) agarose gels stained with ethidium bromide (0.5 μg/mL).Solanum lycopersicum) and potato (Solanum tuberosum) [GBS library generation and Illumina sequencing were conducted at the Institute for Genomic Diversity (IGD) from Cornell University . An additional 95 individuals previously sequenced  were incberosum) , 36 as wberosum)  for readDifferent GBS parameters in the DiscoverySNPCaller and GBSHapMapFilters plugins were used for SNP calling and hapmap filtering, respectively , using aThe clustering patterns of the 739 individuals were compared through genetic distances generated by Tassel V4.3.5 in order to verify which parameters were correctly assessed. The parameter selection was based on genetic trees in which most of the technical replicates clustered together with their corresponding counterpart and those which better grouped the accessions according to their passport data . A totalP. peruviana accessions using the best GBS parameter combinations selected from the NJ analysis. The software ADMIXTURE V.1.23 [The population structure and genetic diversity analyses were carried out only for E V.1.23 , was useE), was calculated as ip is the frequency of the thi allele for k alleles. Observed heterozygosity (HO) and HE for each locus were estimated using Genepop version 4.7.5 [ST) and fixation (FIS) indices were calculated using the software mentioned above.Genetic diversity, referred to as expected heterozygosity , after the Illumina reads alignment . These cP. peruviana transcriptome) were selected to evaluate the clustering pattern in cape gooseberry and related taxa.A total of 54 genetic trees were generated, corresponding to each parameter and whether or not common tags were evaluated , using tP. philadelphica accessions and one accession from P. ixocarpa, and A2 harbors two accessions from both P. angulata, and P. floridana, and one accession from both P. pruinosa and P. viscosa. We also observed three P. peruviana accessions  in group A2. Furthermore, most cape gooseberry accessions , clustered in five different groups. Clusters B, C, D and E concentrated most accessions from the cape gooseberry producing regions, while cluster F harbors a high number of wild accessions from the Antioquia and Nariño departments of Colombia.The best NJ tree includes six clusters in which clear patterns emerged for the population under study . ClusterMoreover, the PCA revealed that the first three principal components explained 29% of the total variance, in which the first component contributed for almost all the variance observed, with 21.5%, followed by the second and third component with 4.7% and 2.8%, respectively .P. philadelphica accessions and one accession from P. ixocarpa, while Group A2 includes P. angulata, P. floridana, P. pruinosa and P. viscosa as well as three P. peruviana accessions  and P. peruviana to reduce the bias caused by the large amount of repetitive sequences, structural variations, and complex polyploid genomes present in plants [In NGS and GBS studies, one of the most important applications is the ability to accurately and comprehensively identify genetic variation. Obtaining unbiased results usually requires a complex multi-step processing pipeline that includes pre-processing, read alignment, and variant calling. Each of these steps uses its own set of modifiable parameters, creating a significant amount of possible distinct pipelines, which vary significantly in the resulting called variants . Therefoassembly , for rean plants . In partn plants , 51. In n plants , 53. As These parameters were selected based upon their ability to call SNPs in terms of read and taxon coverage, using additional values below and above to those reported in previous studies for cape gooseberry , 32. SimMoreover, the use of reference genomes to identify additional sources of diversity that went undetected when using a single reference, as has been previously used in similar species like tomato , was an P. peruviana and related taxa, as reported in previous studies [P. philadelphica and P. ixocarpa accessions from the others  and Tolima (09U291) departments of Colombia, and Poland (09U289). In another study based on orthologous genes Wei et al. [P. peruviana accessions cluster together with related taxa (such as P. philadelphica and P. angulata). One of these accessions corresponds to the 09U289 (PI28570597GI) from Poland, supporting the grouping pattern obtained in this study. Additionally, one possible explanation of finding P. peruviana accessions grouping together with related taxa could be associated to misclassifications within the cape gooseberry germplasm collection as previously reported [P. peruviana population structure and diversity analyses.The PCA revealed a grouping pattern in which most cape gooseberry cultivated accessions clustered together. However, the NJ tree uncovered additional subgroups (subpopulations B-F) within i et al. , found treported , 31. ForP. peruviana accessions neither by geographical origin nor by state of cultivation, as observed in other studies [E values indicates a high diversity within this species, possibly as the sum of multiple factors including mixed ploidy, heterozygosity, mating system, and marker informativeness. The HE values in this study agree with those found for the same species using SNP, SSR and COSII markers [P. peruviana germplasm collections. For instance, low HE values (0.22–0.25) have been uncovered in a small cape gooseberry population using random amplified polymorphic DNA and SSRs markers [ST values Click here for additional data file.S2 Table(XLS)Click here for additional data file.S3 Table(XLS)Click here for additional data file.


Ara rubrogenys), an endemic species to the inter-Andean valleys of Bolivia. We found a fine-scale genetic structuring in four genetic clusters. Genetic diversity was higher in wild compared to captive-bred macaws, but similar to that of captive wild-caught macaws. We found no clear evidence of severe genetic erosion in the population in recent decades, but it was patent in historic times, overlapping with drastic human habitat transformation and macaw persecution over millennia. We found no evidence of geographical and ecological barriers, owing to the high dispersal ability, nesting and foraging habits between genetic clusters. The lack of genetic intermixing despite long-distance foraging and seasonal movements suggests recruitment in natal colonies and other social factors reinforcing philopatry-related genetic structure. Conservation efforts should be specifically focussed on major threats in each genetic cluster as independent conservation units, and also considered in ex-situ management.Behavioural and socio-cultural traits are recognized in the restriction of gene flow in species with high cognitive capacity and complex societies. This isolation by social barriers has been generally overlooked in threatened species by assuming disrupted gene flow due to population fragmentation and decline. We examine the genetic structure and ecology of the global population of the Critically Endangered red-fronted macaw ( Limitations to gene flow by geographic barriers or limited dispersal ability are often highlighted as primary causes of differentiation and genetic structure owing to distance among population nuclei3. This process is often linked to geo-climatic features, which can also be a main source of ecological filters to gene flow leading to isolation by adaptation4. Distinguishing between these major mechanisms by their contribution to genetic structure of populations is often challenging for terrestrial birds with high dispersal capacity and generalised habitat use5. Recently, behaviour and socio-cultural traits have become increasingly recognized in the restriction of gene flow among populations of species with high cognitive capacity and complex societies, especially mammals like cetaceans and primates including humans6.Genetic structure of populations is the result of multiple ecological and evolutionary forces acting in concert8. Crows, jays and other corvids  and parrots and allies (Psittaciformes), are among the birds with a recognised high cognitive capacity, evolving in parallel to complex patterns of social behaviour driving population organisation10. Social barriers to gene flow can act through recognition among individuals from population nuclei showing particular social and behavioural identities11. For instance, socio-cultural factors may have played a role in driving genetic structure associated with tool use even at small spatial scales13. Complex hierarchical, social and ritualised interactions among group members, high mate and site fidelity, and complex vocalisations have been suggested to be involved in the extreme genetic structure in the Red-billed chough (Pyrrhocorax pyrrhocorax) despite high dispersal ability and large population size8. Evidence of these social traits and population organisation patterns also exists for parrots15, especially linked to complex vocalisations and dialects17. However, the contribution of geographical, ecological and social barriers to genetic structure of parrot populations has been poorly investigated at large population scales.In birds, evidence of isolation by social barriers is still scarce and restricted to species with complex societies18. In general, emigration and immigration between breeding nuclei have been highlighted as key factors driving the genetic structure of populations, since even a small number of these exchanges per generation can have a great influence on population homogenisation due to the mixture of different genetic pools19. Depending on the magnitude of connectivity between populations and on the reproductive outcomes of dispersers, the genetic structure and diversity can be more or less pronounced. This variability can be further modulated by historical trends due to past and ongoing threats eroding the viability of isolated nuclei and of highly fragmented and small populations of threatened species22. The influence of isolation by social barriers on the genetic structure of threatened species has been generally overlooked, often through assumptions that connectivity is generally disrupted due to population fragmentation associated with habitat loss and population declines. However, fine-scale genetic structure can be an intrinsic feature evolving in particular species24, as promoted by socio-cultural forces driving pairing, dispersal and group cohesion even in large and widely distributed populations of social species25. A comprehensive evaluation of these traits is paramount to understanding the structure and functioning of metapopulations and for adequate conservation management, including captive breeding and reintroduction of threatened species27.Evaluating the processes and mechanisms that determine the genetic structure of populations is essential to understand their demographic and ecological dynamicsAra rubrogenys). This species represents a suitable study model to investigate the potential drivers of genetic structuration of rare social species distributed in discrete population nuclei associated with breeding colonies interconnected by long-distance movements30. We inferred population structure from individual genotypes at nine microsatellite loci from a large part of the main breeding areas throughout its restricted global distribution range in the inter-Andean valleys of Bolivia30. We focussed on genetic diversity, inbreeding, differentiation and connectivity among population nuclei, and tested for patterns of contemporary demography through bottleneck analysis. Our main goal is to explore potential mechanisms promoting and limiting gene flow between subpopulations and genetic clusters, and whether isolating processes related to geographic barriers to dispersal, adaptation to particular habitats, and social organisation can explain the current genetic structure of the global population. We also evaluated the genetic characteristics of several captive populations to assess their most likely wild population nuclei of origin. We discuss ways to apply the information about genetic structure to the conservation of the whole population or of particular genetic clusters, and how this should drive ex-situ conservation management, including captive breeding and reintroduction.Here, we examine the global population genetic structure of the Critically Endangered red-fronted macaw (P(ID) < 0.0001 and P(ID)sib = 0.0013  were considered in the analysis after excluding alleles that did not amplify in at least one of the reactions. These unsuccessful amplifications are related to low DNA quantities obtained from feather samples. No evidence of false alleles were detected and only three allelic dropouts (one for locus AgGT90 and two for locus Peeμ11) were identified. The overall error rate was low (2.9%) considering that low quality DNA samples are more prone to genotype errors. The cumulative probabilities estimated for the nine microsatellite loci were 13 Table , Fig. S1The analysis of genetic relatedness and/or molecular sexing identified 94 different wild individuals  and highly correlated  , W2 (B), W3  and W4  , C2 (CCHA and LPQU) and C3 (LESL)  and 0.07 (AgGT90). Deviation from Hardy–Weinberg equilibrium (HWE) after Bonferroni correction was detected only for locus (AgGT90) in the W4 cluster  was the largest of all the wild groups  was detected in the wild population, except for W1, which showed a significant heterozygote deficit Table . This caW1 Table , probabler W1 0.1 was the er W1 0.1 was the NA = 4.50) in captive samples from Bolivia, while in captive-bred individuals from Spain the diversity was extremely low (NA = 2.7) (Table HO = 0.46)  and number of alleles (3.22 ± 1.39)  Table . Mean ob9) Table .Table 3SNe values  among genetic clusters were generally low, with a global mean value of 0.056 (ranging from 0.016 to 0.192)  Table . ModeratF-value = 0.104) under this model and the lowest estimates of immigrants per generation (M = 4.10), which can be inferred as the product of drift given its higher geographic isolation. Different F-values and immigrants per generation were estimated for the other three populations, namely W2 , W3  and W4 .Results from the 2MOD analysis in wild macaws supported a gene flow-drift equilibrium model over a drift-alone model [p (gene flow) = 0.969]. The cluster W1 showed the largest identity by descent value (r in captive groups were obtained for C1 (0.17 ± 0.25) and C3 (0.39 ± 0.22). The remaining wild and captive groups have mean r values ranging from -0.03 ± 0.24 (W4) to 0.10 ± 0.24 (W2)  for each population group were 57 (CI = 14-inf) for W1, 105 (CI = 17-inf) for W2, 81 (CI = 35–2698) for W3 and 38 (CI = 16-inf) for W4. The value of Ne considering all individuals as a single population was 87 (CI = 52–188).The cluster W1 showed the highest relatedness values (0.25 ± 0.24) among wild samples . The RMA regression line for Overall, we recorded the characteristics of the 42 breeding colonies located within the four genetic clusters , Soto  and cultivated maize , were dominantly exploited by flocks and individuals in all areas (Table Arachis hypogaea) crops were also exploited frequently in all clusters  except cluster W4. The exploitation of these four vegetal species accounts for about 52–92% of foraging flocks and 64–99% of foraging individuals in the areas corresponding to each genetic cluster. All the remaining vegetal species sum > 10.2% of flocks or > 6.6% of foraging individuals in the breeding areas. Foraging flocks outside the breeding areas were recorded feeding on the same four plant species, although the sample size was lower than in the breeding areas , was infrequently consumed in the non-breeding areas and not consumed in the breeding areas. The exploitation of crops was lower in the breeding areas  than in the non-breeding areas  . The size of flocks exploiting crops was similar between breeding and non-breeding areas . Pooling breeding and non-breeding areas and plant species, the size of flocks feeding on crops  was larger than that of flocks feeding on non-cultivated plants  .A total of 19 plant species were recorded being consumed by red-fronted macaws Table . Diet coas Table . Peanut 33 is unexpected given the short distances between population nuclei and the great dispersal potential of the red-fronted macaw30.In this study, we found evidence for a fine-scale genetic structuring across the whole population of the red-fronted macaw. Four genetic clusters were defined, corresponding to different breeding colonies spatially grouped along main rivers. The genetic structure over a much smaller spatial scale than that observed for other large parrot species with high dispersal ability30, when our genetic survey was also conducted. Despite the successful cross-species transfer of the nine microsatellites markers selected for this study, we cannot exclude a potential effect of ascertainment bias (i.e. microsatellite repeats tend to be longer and more polymorphic in the source species than in the new target species)34. Therefore, the comparison of our microsatellite data with results obtained for other species should be done cautiously. PIC values indicated a good informative level of these microsatellites, including five highly informative (PIC > 0.5), three reasonably informative (0.5 > PIC > 0.25) and only one slightly informative (PIC < 0.25). The cumulative probability of identity of our microsatellite data set was less than 0.01, as recommended for population studies using noninvasive sampling35. The collection of moulted feathers in the colonies and their close surroundings was relatively precise in assigning individuals to their actual population nuclei of breeding and natal origin, although this could only be ensured by sampling nestlings in their colonies. While the genetic heterogeneity of the whole population may reflect biologically relevant differences between population nuclei, low gene exchanges among sampling sites were also detected, as shown by clustering analysis, mean assignment values and estimates of migration rates. Therefore, the apparently low gene flow among genetic clusters may simply indicate the inclusion in the analysis of a few transient individuals moving from adjacent colonies, rather than actual genetic intermixing across generations that would have resulted in a lack of genetic structure36. The genetic clusters grouped nearby breeding colonies across geographically continuous cliffs along major water courses, although one of the clusters (W3) included discontinuous breeding areas separated by about 100 km. This suggests that macaws may not necessarily disperse to the nearest colonies over more distant ones. This may also be the consequence of population fragmentation, as several distinct genetic clusters were located within shorter distances of each other (e.g. W2–W4) compared to population nuclei corresponding to the same genetic cluster (e.g. W3). The effect of geographic isolation on genetic isolation appears to be more evident for the southernmost and geographically isolated genetic cluster (W1), which was supported by high assignment values. This was also supported by the lowest number of breeding pairs, a high proportion of non-breeding individuals30, and a low effective population size in this population nucleus. Furthermore, while coalescent-based simulations supported a model of drift-gene flow equilibrium for the whole population, the largest identity by descent value, the lowest estimates of contemporary gene flow, and high inbreeding and relatedness indices supported the effects of genetic erosion in the southernmost genetic cluster. These processes can exert a clear influence on a reduction in effective population size and contribute to unequal genetic erosion among population nuclei38. While a complete divergence among populations should translate into pronounced genetic differences, many private alleles and highly distinct gene clusters, a secondary contact of populations with a rather recent common ancestry should translate to weak differentiation and some level of gene flow40. This generally occurs because even a small number of individual exchanges may prompt genetic homogenisation19. Therefore, the low level of private allelic richness in each genetic cluster, together with some degree of admixture, suggests that the current genetic structure is most likely not the result of genetic fragmentation from a single homogeneous genetic unit in the past. This is supported by the inclusion in the same genetic cluster of breeding colonies separated by relatively large distance. The genetic differentiation observed is only supported by clustering analysis with sample location priors, which may suggest a weak population structure. However, the clustering algorithm with sample location priors assumes that the assignment probabilities to a population of origin vary among locations, not being biased to detect population structure where it does not exist41.Our genetic assay successfully identified a total of 94 wild individuals, which represent about 10% of the extant world population estimated in the last published survey42. On the contrary, the strong evidence of bottlenecks in M-ratio tests clearly support historical (distant past) population contractions eroding genetic diversity across the whole population44. The M-ratio can detect population size reductions up to approximately 125 generations if the population rebounded quickly after a severe bottleneck event, and up to 500 generations if the population size remains reduced45. This suggests that the genetic structure currently observed may likely not be a consequence of genetic erosion in the long-term, because even a reduced gene flow from individuals moving between clusters could have contributed to diluting any structure subsequently, at least during the historical time scale than can be inferred from the study of nuclear markers. Given the approximate generation length estimated for this species (12.7 years according to the IUCN46), the historic bottlenecks coincided with drastic habitat fragmentation and transformation in the inter-Andean dry forest of Bolivia by humans during the last Holocene , rather than with climatic conditions that were relatively stable during this period49. Habitat transformation by humans began 3000 years ago and was especially intense during the expansion of the Inca Empire in the fifteenth century, when the inter-Andean valleys were a main source of maize, pasture for llama (Lama glama) used for transport, and firewood51. During this period, the persecution of macaws because of their impact on maize crops, and their capture for trade as “prestige goods” and for colourful feathers used in decorative objects and as tributes56, could have contributed to the historic bottlenecks. Rather than due to human impact, older bottlenecks should be linked to geo-climatic events, which could be inferred by future analysis of mitochondrial DNA.Results support that genetic diversity has been probably more affected by historical events than contemporary ones. The lack of a clear significant influence of bottlenecks on heterozygosity-excess tests, except in particular clusters, further indicates a population structure that is not severely affected by genetic diversity loss due to recent demographic processes58. In contrast, diversity indices in the captive groups in Spain were particularly low, which suggest that the founder effect due to introduction of only a few individuals 30 years ago in the pet market resulted in a reduced diversity and higher inbreeding levels. In particular, the continued breeding between close relatives in the captive group in Malaga promoted the lowest genetic diversity and its classification in a single genetic cluster (C3), whose origin in wild populations cannot be identified among the sampled nuclei. This population shows the lowest allelic richness value but a high heterozygosity value, which is expected in the first generations after a founder event with a reduced number of individuals and absence of random mating60. Whether this cluster corresponds to an unsampled population in the wild or emerged as the result of artificial cross-pairing between relatives, it reveals that genetic identity can be maintained even under conditions of high inbreeding. On the contrary, the Bayesian clustering analysis classified captive individuals from a private owner in Cochabamba and those from Loro Parque as corresponding to the wild cluster W4. The captive group in the Zoological Park of Santa Cruz de la Sierra showed a mixed ancestry from two of the genetic clusters in the wild, which indicates that individuals from these clusters were simply maintained together. Our microsatellite panel allowing differentiation and assignment of both wild and captive groups could be a powerful tool for conservation and management actions. Further development of new nuclear markers and the analysis of mitochondrial markers are necessary to improve individual assignment tests and to deepen the knowledge on the diversity and structure of red-fronted macaw populations.Overall, the genetic diversity was higher in wild macaws than in captive-bred ones in Spain, but was similar to that observed in individuals maintained in captivity in Bolivia. This was expected, because wild-caught macaws maintained in captivity in Bolivia were captured over the whole range of the species, and founder events should have been limited. Since these individuals do not reproduce in captivity in Bolivia, they cannot be affected by additional inbreeding due to artificial pairing between close relatives, as often occurs in captive-breeding groups61, and population characteristics like density and demography62. Dispersal patterns have been highlighted to greatly contribute to the distribution of genotypes across geographical areas and distribution ranges1. However, even highly mobile terrestrial birds can show low levels of gene flow leading to strong genetic structure8. In general, parrots are generalist plant consumers able to make daily, seasonal, and altitudinal long-distance movements by tracking the abundance or preference of food resources63. Red-fronted macaws disperse over large areas through long-distance daily flights, including altitudinal movements during the breeding season, and longer seasonal movements to occupy distant foraging areas during the non-breeding season30. Distances between breeding nuclei forming the genetic clusters observed in the wild population were relatively short for precluding the genetic intermixing of individuals of this vagile species30. In fact, the analysis of isolation by distance showed a tendency of lower genetic differences at higher geographic proximity, although this was very influenced by the effect of the southernmost isolated genetic cluster. The extent of the altitudinal movements30 and the lack of high mountains or unsuitable habitat between genetic clusters indicate that isolation by distance or physical barriers are not important mechanisms limiting gene flow leading to the observed genetic structure. This is further supported by the use of non-breeding areas by flocks larger than those observed in each respectively nearest breeding area or genetic cluster. This suggests the intermixing of individuals from different population nuclei and genetic clusters during temporary associations in large flocks at non-breeding areas, mainly to exploit maize and peanut crops30. This intermixing should be confirmed by future genetic identification of individuals foraging and communally roosting in each non-breeding area, which would be useful to determine the importance of foraging and roosting sites for each genetic cluster.Genetic structure generally arises as a consequence of species-specific life history traits and their interactions with environmental features like habitat fragmentation and heterogeneity1. Among drivers of population genetic structure, landscape and ecological features can be major barriers to gene flow between populations65. This mechanism of isolation by adaptation to specific local conditions does not seem to influence the genetic heterogeneity of the red-fronted macaw population. This is based on the lack of important differences in the location and physical features of the cliffs with breeding colonies and their close surroundings in each genetic cluster. Only altitude of the colonies differed between genetic clusters, although the range of variation is lower than that of the daily and altitudinal movements of macaws30. This indicates an effect of the location of cliffs available for colonial nesting in the geographic location of genetic clusters, rather than actual ecological adaptation leading to genetic differentiation. However, among the nuclei not sampled for genetic analysis was a small population of red-fronted macaw nesting on palms66, which constitutes an exception that deserves future research for its implications on potential genetic identity associated with nesting habits. In addition, foraging habitat did not differ between population nuclei, and individuals from each particular genetic cluster can extensively move to exploit distant regional and seasonal resources67. In fact, diet composition showed no differences among genetic clusters, as four vegetal species were dominantly exploited across genetic clusters and non-breeding areas throughout the year, while a variety of food species are exploited seasonally at lower frequencies67. Differences in the frequency of different plants in the diet were even greater between breeding and non-breeding areas than among the breeding areas comprising the genetic clusters, mainly due to the primary exploitation of crops by large flocks in the non-breeding areas. Published models on foraging and habitat selection showed preference for humanized areas (degraded forest patches with crops), but not affected by densities of houses and livestock as proxies of human presence and disturbance30. In addition, this species is relatively tolerant in terms of human presence, since it frequently nests and forages in very accessible places and agricultural areas with constant human presence. Therefore, the ecology of macaws in each genetic cluster, as a filter to dispersal and effective interchange between population nuclei, does not support the hypothesis that adaptations to specific local conditions (affected or not by human activities) would reduce performance of dispersers elsewhere.Other studies have described a similarly unexpected fine-scale genetic structure not related to dispersal capabilities, but to a combination of ecological, behavioural and/or evolutionary mechanisms68. In this context, the spatial distribution of breeding populations is an important social factor in shaping the patterns of genetic diversity throughout a species’ range70, often associated with and maintained by coloniality, natal or breeding philopatry, and coordinated dispersal of relatives71. Several highly vagile and colonial species are faithful to their natal area, which may determine strong genetic structure over small spatial scales despite large-scale movements73. The red-fronted macaw nests in colonies that are repeatedly used across years74, which may favour a patchy distribution of suitable nesting habitat and potential philopatric behaviour of breeding pairs, as is common among parrots75. Social imprinting to natal colonies may also reinforce philopatry-related genetic structure32, but it may act to the detriment of genetic variability by promoting inbreeding76. As occurs in other long-lived and highly social species77, red-fronted macaws from different genetic nuclei can likely flock together in foraging areas and roosting sites used across daily, seasonal and annual long-distance movements30. The lack of actual genetic intermixing thus arises despite possible intermixing of flocks from different genetic clusters in non-breeding areas used temporarily, which indicates very limited effective dispersal leading to recruitment into non-natal genetic clusters. This can arise by individual recognition within social groups strongly attached to natal and breeding colonies in this species, which may be achieved through complex organisation and behaviour facilitated by informative vocalisations78 and dialects common among parrots79.In the absence of geographical and ecological barriers, isolation by socio-cultural and behavioural factors arises as a reliable mechanism driving fine-scale genetic structure in highly social species71. A combination of strong natal philopatry, close bonds among group members sharing social identity, within-group reproductive skew and kin-cooperation could provide the strong group stability behind pronounced genetic structure observed in these species71. A similar combination of factors may concur to explain the genetic structure in the red-fronted macaw irrespective of the potential loss of genetic diversity in particular genetic clusters due to population decline and inbreeding. Few studies have considered this mechanism to explain the unexpected fine-scale genetic structure despite the high mobility of parrots. For instance, social barriers could influence the unexpected fine-scale genetic structure of the cooperative-breeding El Oro parakeet (Pyrrhura orcesi)21, as complex social organisation in birds can drive decisions about dispersal from family flocks, and the skewed acceptance of dispersers into other groups71.Evidence of isolation by social behaviour has been suggested in bird species with complex societies, including cooperative breeding and lekking mating systems30, as is the case of other colonial cliff-nesting macaw species80, which may be indicative of restricted breeding opportunities linked to unfavourable environmental conditions and delayed breeding83. A non-mutually exclusive hypothesis states that, regardless of environmental limitations, a high proportion of non-breeding individuals can be a consequence of the within-group reproductive skew necessary to maintain the genetic identity of each cluster without losing genetic diversity. In fact, a high proportion of non-breeding individuals is typical of long-lived species with complex societies and pronounced genetic structure, where access to reproduction can depend on familiar and social factors84. Therefore, a high proportion of non-breeding individuals in long-lived species with no cooperative breeding could be a species-specific trait evolved to attain enough genetic variability allowing specific pairings among group members of extended family networks, which deserves further research. Inbreeding depression due to artificial pairing between close relatives in captivity represents the opposite process, as it can lead to a specific genetic signature associated with a patent loss of genetic diversity (e.g. cluster C3). More research on genetic diversity and structure of wild and captive populations is required to support this hypothesis, since high inbreeding levels can introduce biased inferences in clustering methods86.Remarkably, the breeding pairs (about 100 in the last census) represent a small proportion (c. 20%) of the whole red-fronted macaw population38 with geographically-restricted ranges87. Factors generally promoting these kinds of genetic and population patterns include geo-climatic events like glaciations isolating small populations in geographically-restricted refugia and habitats89, as well as the founder effect and extreme population isolation in oceanic islands90. The red-fronted macaw adjusts to this evolutionary pattern, as it evolved in isolation in the tropical inter-Andean dry-forest since the rapid uplift that occurred about 10 million years ago in this sector of the Andes range91. This event meant the isolation of the relatively small extension of the dry-valleys of Bolivia from the Gran Chaco region, and it was the origin of the high level of plant endemism present in the region93 with which the red-fronted macaw coevolved94. The genetic and population characteristics (including the high non-breeder proportion) currently observed in the global population of the red-fronted macaw represent intrinsic features that have evolved as adaptations increasing fitness and population performance under these particular eco-evolutionary conditions. If this hypothesis can be demonstrated as true, this would imply a specific attention on the singular genetic and population structure of this species as key targets for its conservation.Evolutionary history under isolation has been argued to be a major driver of genetic diversity and effective population size especially in rare and threatened species95, but similar or higher than those reported for endangered parrot species with restricted distribution ranges or recent population declines98. However, these results should be interpreted with caution when different markers were used to estimate genetic diversity indices99. In addition, low heterozygosity is not always derived from a reduction in effective population size60, as demonstrated by the relatively high heterozygosity of the captive cluster C3 despite high actual inbreeding and low allelic richness.Evidence for a fine-scale structuring in four relatively independent genetic clusters has important implications in the conservation of the whole population of the red-fronted macaw. This species currently shows variable levels of genetic diversity depending on the genetic cluster. However, there is no clear evidence of strong genetic erosion in the population as a whole in the last decades, but rather in the last centuries or millennia. Genetic diversity, reflected in heterozygosity values, is lower than that observed for widely distributed parrots30 have reduced the population size, and could have contributed to the reduction of genetic diversity in some breeding nuclei. The persecution outside breeding areas may further exert an important effect in the reduction of the total and non-breeding population buffering breeder mortality. Of concern is the loss of breeding pairs by direct persecution in the colonies and their surroundings, and by alterations in the nesting habitats, which may exert especially detrimental effects on the effective population size. This is based on the expected concerning effects on population viability and dynamics from adult mortality in long-lived species with deferred maturity and low reproductive rates, as in large macaws100. In addition, the impact on breeders may greatly reduce genetic diversity under the hypothesis that they harbour most of it as enhanced by highly selective pairings in each genetic cluster. Future investigation should be directed to confirm or reject this hypothesis.Recent declines due to habitat transformation and human-mediated mortality by persecution of adult birds in agricultural areas and nestling poaching for the local pet trade30, but especially on the particularly more detrimental threats in each genetic cluster as independent conservation units102. The restricted range composed of relatively homogeneous habitats may increase extinction risk for the whole population more rapidly through environmental correlation103 than due to genetic depression on each particular cluster104. Therefore, to implement a global conservation strategy preserving these evolutionary units as components of the overall genetic integrity, a comprehensive evaluation is required on the key habitats, home ranges and availability of food resources in each genetic cluster. Special attention should be directed to avoid nestling poaching for the local pet trade and persecution of adults, as key threats with a high impact shared by all population nuclei30. Finally, our genetic approach is sufficiently reliable to serve as a reference for future estimations on genetic diversity and potential current or future demographic bottlenecks in each genetic cluster. Future genetic analysis of the three wild breeding nuclei not sampled in this study will serve to determine the potential existence of additional clusters with a singular genetic identity. This will also help to better identify the origin of captive individuals, and those admitted in recovery centres after seizure from illegal trade. The assignment of captive individuals to each genetic cluster may be useful to form suitable captive-breeding nuclei for future potential reinforcement programs and reintroduction of individuals in their corresponding genetic nuclei in the wild.This study emphasises that conservation efforts for the Critically Endangered red-fronted macaw should be focussed on multiple fronts30. The habitat is dominated by tropical dry forest with a high proportion of endemism, transformed by long-term human activities to thorn and cactus scrub with scattered trees in hillsides and riverine forest and crops in valley bottoms92.The red-fronted macaw is a colonial, cliff-nesting species endemic to the inter-Andean valleys of Bolivia. This is a wide, rugged and dry area located on the eastern slopes of the central Bolivian Andes at altitudes from 900 to 3500 m, in the departments of Santa Cruz, Cochabamba, Chuquisaca, and Potosi46. Population estimates varied from 5000 individuals in the 1980s to 2000–4000 or as few as 700–800 individuals and less than 100 breeding pairs in recent years46. Forest loss and degradation due to agriculture expansion, overgrazing and firewood cutting, and poaching of nestlings for local pets, are considered the major threats to the species46.The small population size, reduced distribution range, and recent decline led to the listing of the species as “Critically Endangered” according to the IUCN Red List94. Briefly, all known breeding colonies and another found for first time, as well as communal roosting sites, were simultaneously monitored in 2011, allowing us to estimate the global population size , with c. 100 pairs breeding in 8 discrete areas , orientation , altitude (in m above sea level), and distance (in m) to the nearest river of the cliffs holding breeding colonies30. Breeding nuclei are located in river cliffs. The population size was much larger in the recent past30, and thus in the last decades some breeding cliffs have been abandoned. For instance, in the first more complete census of the species, it had already disappeared as breeder of six cliff areas, while it was found breeding in 12 new cliffs where its reproductive presence was not known before30. This species nests in a wide altitude range, where we found another 81 cliffs not occupied by the species, while in 41 of them nested up four other parrot species that usually nest coexisting with the red-fronted macaw in the same cliffs. Therefore, we can assume that at least a proportion of these cliffs could be suitable for macaws according to their features, similar to those of used cliffs. Therefore, the availability of cliffs is apparently not a limiting factor, although there is no more data on breeding areas than known after intensive searching by us, other research teams, and personnel of local governments, especially in protected areas.This genetic approach benefits from previous fieldwork conducted for assessing the global distribution and population size of the species, its seasonal movements, diet and foraging habits as detailed in our previous publications2, each range 61–459 km2, see Fig. 30. The foraging ecology and diet of the species was studied year-round during 2011–2013, through roadside surveys  conducted during 153 fieldwork days in eight expeditions, which allowed us to record 220 foraging flocks  and the plant species they were consuming94.Seasonal movements were studied in 2011–2012 (588 flocks recorded), showing changes in flock size and movements of macaws between 953 and 3094 m a.s.l. that increased after the breeding season to occupy six non-breeding foraging areas  and from a private owner in Cochamamba  in Bolivia. These macaws were captured within the last ten years from the wild population (pers. comm. from the keepers). Feathers (n = 36) and blood (n = 37) samples from captive individuals in Spain were obtained from different private owners  for each locus were estimated using the program GIMLET 1.3.3105.Genomic DNA was extracted from blood and feather samples. All individuals were genotyped using a microsatellite panel of 9 polymorphic loci  specifically selected for this study  using COLONY version 2.0.6.6Fst estimator of Weir and Cockerham108, as implemented in GENALEX v.6.5109. To further explore genetic evidence for subdivision among individuals (both wild and captive), the partition of the total genetic variation was explored based on a Principal Coordinate Analysis (PCoA) in GENALEX. Further, to be able to identify the actual subpopulations and assign individuals  to these populations, we used Bayesian analysis implemented in the program STRUCTURE v.2.1110. We used the model of correlated allele frequencies and admixture111. We ran 20 replicates for each K in the range of 1 to 8 in order to estimate the most likely number of clusters (K), using all individuals. Simulations were conducted with 1 000 000 steps of the Markov chain Monte Carlo (MCMC) after a burn-in period of 100 000 iterations without and with sampling localities as priors (LOCPRIOR option) to allow detection of weaker genetic population structure41. The outputs were processed in the CLUMPAK server112 to display the results in a graphical interface and to obtain assignment probabilities of individuals (q) to each cluster (using a MCL threshold for similarity scores of 0.90). The likelihood distributions were analysed with STRUCTURE HARVESTER113. The optimal K value was selected considering the following assumptions: (1) the likelihood distribution reached a maximum and began to plateau or decrease; (2) high stability of clustering patterns between runs (at least 60% of the 20 runs were similar in the primary mode); and (3) Kmax + 1 no longer identify new clusters 114. We also estimate the K value for each analysis using the method reported by Evanno et al. (2005)115. We tested a hierarchical STRUCTURE approach to search for population structure not detected at the first level of the STRUCTURE analysis115.We assessed pairwise genetic differentiation between red-fronted macaw populations with the 116, applying the Markov chain method with 10,000 dememorisation steps, 1000 batches and 5000 iterations/batch. Bonferroni corrections to significance values were applied to account for multiple tests. Genetic diversity (i.e. observed (HO) and expected heterozygosity (HE), and mean number of alleles per locus (NA)) was estimated for each locus and population using GENALEX v.6.5. The program FSTAT v.2.9.4 was used to calculate allelic richness (AR). Polymorphism information content (PIC), a measure of the informativeness degree of a marker used as a key parameter to evaluate the ability to detect genetic variation among individuals of a population117, was calculated for all loci per population using CERVUS v.3.0.7118.Diversity parameters were calculated for wild and captive populations in Bolivia, as well as for the two captive groups from Spain Table . Departu119. Two models were compared: the drift model relates to populations that are subject to drift alone, with no influence from gene flow, while the gene flow/drift equilibrium model (referred to as the gene flow model) relates to a balance between the two forces. The drift model assumes that mutation has not strongly influenced gene frequencies such that alleles are identical by descent, and the gene flow model assumes that the mutation rate is much smaller than the immigration rate119. We carried out five independent runs to ensure convergence of the MCMC algorithm, with 1000 000 iterations and the first 10% discarded as burn-in. The probability of a model was calculated as the proportion of times that the model was supported. The number of migrants per generation (M) for each population was calculated from F values according to Ciofi et al.119.We determined the relative contributions of drift versus gene flow across the distribution area, using the likelihood approach implemented in the program 2MOD120 and the M-ratio model implemented in the software M_P_VAL45 . Mean relatedness within each breeding site was estimated in the program GENALEX, using Queller and Goodnight’s R estimates121.FSTAT was used to explore the level of inbreeding in each population using the inbreeding coefficient  in each direction and rates of dispersal, we used a Bayesian approach (BAYESASS v.1.2122), and the program GENECLASS2 v.2.0123, a different approach to detect first generation migrants. See the Supplementary Methods To estimate recent migration rates between groups  of the whole population, as well as for each breeding colony, we applied the linkage disequilibrium method using the program LDNE v.1.31124. The LD method provides values of the Ne in the parental generation of the individuals analysed, but the precision of the estimates can be affected if occurred changes in population sizes in the recent past124. This program assumes random mating and excludes all alleles with frequencies lower than 0.02, which generally provides a good balance between precision and bias with highly polymorphic microsatellite loci125. The jack-knife procedure was selected to obtain the confidence intervals (CIs).To estimate the contemporary effective population size (Fst against distance in kms between breeding sites126. We used Mantel tests to test the correlation between matrices of genetic differentiation and geographic distances by 1000 permutations in IBD program v.1.52127.Patterns of isolation-by-distance (IBD) arise when population differentiation increases with increasing geographic distances, and are usually caused by local spatial dispersal. We analysed IBD by regressing pairwise estimates of All fieldwork and procedures, including moulted feather collection and blood sampling conducted in Bolivia were carried out in accordance with relevant guidelines and regulations to study endangered species by Viceministerio de Medio Ambiente, Biodiversidad y Cambios Climáticos of Bolivia (18052012) in collaboration between Museo de Historia Natural Noel Kempff Mercado (Bolivia) and Estación Biológica de Doñana, Consejo Superior de Investigaciones Científicas , and in accordance with the approved guidelines of the Consejo Superior de Investigaciones Científicas . Sampling protocols used in this study were approved by the Ethic Committee of the Consejo Superior de Investigaciones Científicas, CSIC (CEBA-EBD_11_27). The samples obtained in Bolivia were exported to Spain for molecular analyses under CITES permit no. 01998.Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.