They also represented individual (n = 73) and team (n = 68) sports. The Imagination in Sport Questionnaire (ISQ), Trait Sports Confidence Inventory (TSCI-PL), and Temperament Questionnaire (EAS) were all completed by the participants. Results indicate that self-confidence is explained in about 13% by temperament and general imagery, but only general imagery  and negative axes of the temperament—emotionality . The test of the indirect effect of the negative axes of the temperament on self-confidence through general imagery, conducted by Preacher and Hayes bootstrapping procedure, revealed significant mediation  suggesting that about 46% of variance explained in self-confidence by emotionality is related to general imagery. The other 3% of variance explained in self-confidence by the positive temperament axes is related to general imagery, however, it was insignificant. The general imagery explains the relationship between emotionality and self-confidence only in individual sports athletes.The main purpose of our study was to investigate the relationship among temperamental traits, a general tendency to use imagery, and self-confidence in sport. The specific aim was to verify if general imagery mediates the relationship between temperament and self-confidence in sport, but also with respect to individual and team sport disciplines. The study involved 144 athletes aged 19–25 years (75 men and 69 women) with different lengths of training (from 1 month to 18 years) and presented different sports levels: international ( Temperamental traits are diversifying the level of stress, which showed in the study conducted on a group of cyclists . Some pePersonality factors can have a role as a limited indicator of confidence. Self-confidence can be significantly predicted by high scores in neuroticism, conscientiousness, and openness . NeurotiSelf-confidence correlates positively with narcissism in physical education students before the archery exam . MoreoveReflections in the context of imagery ability can be carried out with the inclusion of sport specifics. There are differences in the imagery abilities of athletes who train for individual sports and those who train for team sports. Athlete’s sport-oriented imagery skills vary depending on the type of sport, individual, or team, they practice. Most of the diversity of images was observed in representatives of individual sports. These athletes achieved higher results in the vividness of visual imagery ability and also in external visual imagery than athletes from team sports . AdditioBased on the above-mentioned literature, we decided to explore more in-depth the relationship among temperamental traits, general tendency to use imagery, and self-confidence in sport, but also specifically in individual and team disciplines. We expected that general imagery could mediate the relationship between personality traits and self-confidence. Therefore, we constructed the model presenting those linkages and formulated the following hypotheses.H1. General imagery measured by ISQ is the mediator between temperament and self-confidence in athletes’ population.H2. The sport discipline  is moderating the indirect effect of temperament on self-confidence through general imagery.The participants taking part in the study were students of the Academy of Physical Education and Sport in Gdansk. There were a total of 144 people aged 19–25 years, 75 men and 69 women. The athletes’ training experience ranged from 1 month to 18 years. Among the subjects, 12 athletes train at the international level, 46 at the national level, and 86 respondents train at a recreational level. The examples of disciplines that the examined athletes train on a daily basis are: team sports: volleyball, soccer, basketball, and handball; individual: swimming, combat sports, sailing, rowing, and tennis.All subjects gave their informed consent for inclusion before their participation in the study. The study was conducted in accordance with the Declaration of Helsinki, and the Ethics Committee of the University of Gdańsk approved the protocol (11/2015) before the commencement of the study.r = 0.55 to r = 0.74) over a 3-week period and sound internal consistency . A confirmatory factor analysis indicated acceptable model fit indices for the ISQ’s seven-factor structure normed chi-square (NC) = 2,416.63, degree of freedom (df) = 1,203, Goodness of fit index (GFI) = 0.944, AGFI (Adjusted Goodness of Fit index) = 0.944, root mean square error of approximation (RMSEA) = 0.056. In our study, we used only the last scale-general imagery as general tendency to use imagery. Situational imagery is connected to the specific competition situation, therefore we decided that it should not be the mediator between temperament and sport self-confidence. Below we present examples of items from the general imagery subscale: Do you generally create ideas easily?; Do you imagine the events waiting for you?; Do you use your imagination in everyday life?; and Do positive events dominate in your imagination?The ISQ  is a mulThe Trait Sports Confidence Inventory (TSCI-PL) . The questionnaires were distributed in random order. There was no time limit within which the athletes had to complete the above tests.The Mauchly Test of Sphericity and the Shapiro–Wilk Test of unidimensional normality have been chosen due to their practical use in the Box-Cox transformation . To testIn the athletes’ population, scores of tested psychological phenomena appeared to be not normally distributed, probably because of the special use of those measures, which are constructed to measure psychological phenomena in different populations, such as general young students (EAS) or high-level athletes (ISQ). This new use of those measures slightly decreases their validity and reliability. Scores transformed using Box-Cox logarithmic transformation appeared to be normally distributed, i.e., after Box-Cox transformation, results met assumptions of univariate normality and sphericity.r = 0.26 and p < 0.01), could mediate between temperament and self-confidence in a population of athletes.First, we conducted a Pearson correlation between general imagery from ISQ and EAS. Results are presented in p-values. To verify the main hypothesis that general imagery measured by ISQ is a mediator between temperament and self-confidence in the athletes’ population. An SEM procedure was conducted using generalized least squares (GLS) estimation. To obtain indirect effects, standard errors (SEs), and estimate exact their b = 0.22; p < 0.05), and negative axes of the athletes’ temperament . Test of the indirect effect of the negative axes of the temperament on self-confidence through general imagery conducted by Hayes and Preacher bootstrapping procedure revealed significant mediation  suggesting that about 46% of variance explained in self-confidence by negative temperament axes is related to general imagery. Analogous analysis conducted to test the indirect effect of the positive axes of temperament on self-confidence through general imagery did not reject the null hypothesis of zero indirect effect (non-significant mediation)  indicating that general imagery explains only 3% of the self-confidence variance explained by positive temperament axes. The results are shown in the  are moderating the revealed indirect effect of negative temperament axes on self-confidence through general imagery. To verify this hypothesis, the mediation moderated by discrete variable procedure described by Hayes and Preacher was applied.2(8) = 12.913; p = 0.115], which is presented in comparisons of constrained models presented in the Results of the analysis discovered that the latent variable of temperament axes formulation is similar in both athletes’ samples but regression weights in the model structure are different in both samples [Dcb1 = –0.45; p < 0.01; R2Med = 0.55) in individual sports sample, but an analogous effect was non-significant in team sports sample . It suggests that general imagery explains the relation between negative temperament axes and self-confidence but only in individual sports athletes. The results are shown in the , but analogous relation is rather trivial in team sports athletes sample . But the relation between self-confidence and positive axes of temperament is smaller in the sample of individual sport athletes  than team sport athletes . Self-confidence is either highly related to general imagery in individual sport athletes , but analogous relation in team sport athletes is trivial and non-significant .Detailed analysis of model obtained in both Individual and Teams Sports samples  revealedThese results suggest that general imagery explains the mechanism of being self-confident athlete in individual sport disciplines but is not a sufficient mediator in team sport disciplines.The main purpose of our study was to investigate the relationship among temperamental traits, a general tendency to use imagery, and self-confidence in sport. The specific aim was to verify if general imagery mediates the relationship between temperament and self-confidence in sport but also with respect to individual and team sport disciplines.The results of the analysis concerning the first hypothesis that general imagery measured by ISQ is the mediator between temperament and self-confidence in the athletes’ population were partially supported. However, we discovered that general imagery was a significant mediator between only the emotionality  and self-confidence and explained 46% of the variance. As far as positive temperamental axes were concerned, general imagery explained only 3% of self-confidence variance and it was insignificant.Emotionality has the direct effect of reducing self-confidence but also on general imagery ability. Moreover, emotionality negatively affects self-confidence because it probably diminishes the general usage of imagery, which could be a tool for building self-confidence . PositivThe results indicate that positive temperament traits do not directly promote self-confidence but directly promote general imagery, which in turn influences positively self-confidence. Additionally, general imagery does not mediate the relationship between the positive axes of temperament and self-confidence and in this sense does not explain why positive temperamental traits have no effect on confidence. This path is independent and other factors may influence this relationship more. If athletes have positive temperamental traits, then they use imagery positively and also present more often self-confidence. In the presented study, we analyzed only general imagery, which makes this study partially different from the previous research .As far as team vs. individual sports are concerned and the second hypothesis, it turned out that the pattern from the whole group is similar only for individual sports. However, for team sports, we discovered a marginally significant positive direct effect between positive temperamental axes and self-confidence. This result means that higher levels of sociability and activity support self-confidence in team sports. This can be explained by the fact that team sports require the ability to cooperate and only people with such abilities train for this kind of sport thus self-confidence increases. Self-confidence in athletes can be strengthened by interacting with peers, presenting higher levels of confidence . It is aNeither positive nor negative dimensions of temperament influence general imagery in the analyzed group of team sport. We also did not discover any relationship between general imagery and self-confidence. This is rather contradictory to previous studies, but they are mainly connected to the imagery intervention in team athletes  or imageOur study is not free from limitations. The main weakness is the relatively small number of respondents, and we are aware of the rather pilot character of the study. Other traits connected with individual differences, which we did not measure, can affect the imagery ability, but also can differentiate imagery ability, i.e., mental toughness or other temperamental features measured by other questionnaires. They should be investigated in future research. In our study, we used only general imagery, but in the future, other measures of imagery ability should be included either. In our study, we verify our models for individual and team sports but it would be advisable to validate them with respect to gender and experience. We included basic information in the The strength of our study and valuable insight is distinguishing the dependence of personal characteristics on imagery skills. Our study also extends the knowledge concerning aspects of “who” from the revised applied model of deliberate imagery use RAMDIU, , which sOur study also has practical implications, providing new concepts for imagery training. Working with general imagery ability as a feature (not only sport imagery) used in daily life may overcome temperamental difficulties associated with negative axes. However, it should be verified in the longitudinal study. Coaches and sport psychologists should be aware that athletes from individual disciplines who present higher emotionality would not be fluent in imagery ability, which in turn could be a tool for self-confidence. In conclusion, general imagery measured by ISQ is the mediator between emotionality and self-confidence in athletes. In terms of individual vs. team sports, we discovered that imagery ability plays a different role in relation to temperament and self-confidence. The spotlight on general imagery ability as the mediator between temperamental traits and self-confidence is a valuable insight into the research of imagery in sport.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Ethics Committee of University of Gdańsk (protocol 11/2015). The patients/participants provided their written informed consent to participate in this study.DB-P designed the study of the project, contributed to the acquisition, analysis, and interpretation of data as well as wrote the original draft of the manuscript. IH contributed to the interpretation of data and drafted the manuscript. KK contributed to the analysis, interpretation of data, drafted the manuscript, and concept and design of the project. All authors contributed to the revision of the manuscript and approved the final version for submission.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Genetic selection has been an effective strategy to improve calving traits including stillbirth in dairy cattle. The primary objectives of the present study were to characterize stillbirth data and determine the feasibility of implementing routine genetic evaluations of stillbirth in five non-Holstein dairy breeds, namely Ayrshire, Guernsey, Milking Shorthorn, Brown Swiss, and Jersey. An updated sire-maternal grandsire threshold model was used to estimate genetic parameters and genetic values for stillbirth. Stillbirth data with the birth years of dams from 1995 to 2018 were extracted from the United States national calving ease database maintained by the Council on Dairy Cattle Breeding. The extracted stillbirth records varied drastically among the five dairy breeds. There were approximately 486K stillbirth records for Jersey and more than 80K stillbirth records for Brown Swiss. The direct and maternal heritability estimates of stillbirth were 6.0% (4.5–7.6%) and 4.7% (3.3–6.1%) in Jersey and 6.8% (3.2–10.5%) and 1.1% (0.6–2.9%) in Brown Swiss. The estimated genetic correlations between direct and maternal genetic effects for stillbirth were −0.15 (−0.38 to −0.08) in Jersey and −0.35 (−0.47 to −0.12) in Brown Swiss. The estimated genetic parameters for stillbirth in these two breeds were within close ranges of previous studies. The reliabilities of predicted transmitting abilities in Jersey and Brown Swiss increased substantially, thanks to the substantial increase in available stillbirth data in the past 10 years. The stillbirth records for Ayrshire, Guernsey, and Milking Shorthorn, which ranged approximately between 3K and 12K, are insufficient to implement reliable routine genetic evaluations of stillbirth in these three dairy breeds. Estimated genetic (co)variances and genetic values deviated considerably from the reported ranges of previous studies, and the reliabilities of predicted transmitting abilities were low in these three breeds. In conclusion, routine genetic evaluations of stillbirth are feasible in Brown Swiss and Jersey. However, reliable genetic evaluations of stillbirth in Ayrshire, Guernsey, and Milking Shorthorn require further data collection on stillbirth. Stillbirth is a severe economic concern to dairy producers . In the Genetic selection has been an effective strategy to improve calving traits and reduce disease incidence over the past century, complementary to management, and clinic methods . The conThreshold models were introduced to genetic evaluations of calving traits such as calving ease  because The present study represented an effort to leverage the United States dairy data repositories available at the Council on Dairy Cattle Breeding (CDCB) toward the official implementation of genetic evaluations in five non-Holstein dairy breeds, namely Ayrshire (AY), Guernsey (GU), Milking Shorthorn (MS), Brown Swiss (BS), and Jersey (JE). There were four primary tasks: 1) determining the extent to which stillbirth data were recorded in these breeds; 2) characterizing stillbirth data in terms of stillbirth rates and their distributions; 3) performing preliminary single-breed genetic evaluations using an updated S-MGS model in these breeds, and 4) determining the feasibility of routine genetic evaluations of stillbirth for these breeds, given the currently available stillbirth data.https://www.uscdcb.com/), subject to a series of data quality edits  database maintained by CDCB .Variance-covariance components and genetic parameters were estimated using all the available data in each breed based on a Bayesian S-MGS threshold model implemented via Markov chain Monte Carlo simulation. The threshold model was computed using THRGIBBS1F90 . InitialSire variances The genetic correlation between direct and maternal genetic effects, denoted by The direct  was reported as PTA for %SB due to direct  additive effects in heifers in a herd with average management conditions, following T be the threshold between SB scores 1 and 2 on the observed scale, and Let Next, setting up the base for the animals represented in the appropriate group (indicated by *):c is added to achieve the desired base. From (11), we have:F. Finally, by substituting the above relationship, %SB is computed by:The above is equivalent to:i, Following GS model .Linear phenotypic trends were evaluated by regressing mean %SB in heifers on sire birth years in each of the five dairy breeds. Similarly, linear genetic trends were obtained by regressing mean %SSB and %DSB on the sire or MGS birth year, respectively, in each of the five breeds. Yearly genetic and phenotypic trends were plotted by smoothing splines. The latter are function estimates, which provide a means for smoothing noisy data by balancing a measure of goodness of fit to the noisy data with a derivative-based measure of the smoothness of the estimates .The extracted stillbirth records from historical data repositories at CDCB varied drastically among the five dairy breeds . There wp < 0.001; OR = 2.10) in Iranian Holstein cows. They also reported that for same-sex twin pairs, the odds of calf stillbirth were greater  for male (21.9%) than for female (16.0%) twin pairs.Overall, stillbirth rates were highest for primiparous heifers, regardless of calf sex, and lower in later parities. On average, the frequency of stillbirth records ranged between 5.31 and 9.64% in primiparous heifers and between 2.85 and 6.64% in multiparous cows across these five dairy breeds. The higher incidence rate of stillbirth in heifers was due to the increased calving difficulty attributable to their small, immature birth canals . The redGenerally speaking, the birth weight of a male calf tends to be heavier than a female calf, which possibly is one of the reasons for more difficult parturition and higher death risk associated with male calves . Yet, daGreater calving difficulty  was associated with a higher chance of stillbirths. Variance-covariance components of stillbirth were estimated in each of the five non-Holstein breeds before conducting the preliminary genetic evaluations. The estimated genetic correlations (%HPD) between direct and maternal genetic effects for stillbirth were −0.35 (−0.47 to −0.12) in BS cattle, and −0.15 (−0.38 to −0.08) in JE cattle. Negative, antagonistic genetic relationships between direct and maternal effects for stillbirth were previously documented. For example, The negative genetic correlation between direct and maternal genetic effects for stillbirth reflected a negative relationship between calf size and dam’s pelvic dimension. Selecting on direct effects of stillbirth can lead to small calf size, but it can also result in small heifers that face an increased risk of dystocia and stillbirth calving . Hence, Stillbirth heritabilities were over-estimated in the AY, GU, and MS cattle. Direct heritability estimates ranged from 6.8 to 12.9%, and maternal heritability estimates were close to 12% or higher in the AY, GU, and MS cattle. There could be several reasons for these over-estimated heritabilities. A primary reason was due to insufficient stillbirth data in these three dairy breeds. There were between 136 and 395 sires and between 515 and 1,278 MGS for each of these three breeds, but the total number of stillbirth records was between 3,022 and 12,541. Hence, the number of stillbirth records per sire or MGS tended to be very small, and genetic (co) variance components could not be estimated accurately. Secondly, the updated sire-MGS model included two additional interaction terms, which possibly offset the residual variance, thus leading to elevated heritability estimates. The third and probably critical one is that the computed phenotypic variances for the underlying scale, as shown in (8), ignored the interactions, assuming that the mating of a sire to its daughter was rare. The latter, however, was not rare in reality and its impact could be non-trivial, particularly when the data size was small and unbalanced. The estimated genetic correlation between direct and maternal effects of stillbirth was 0.35 in AY, 0.13 in GU, and 0.51 in MS cattle.The mean maternal stillbirth PTA is higher than the mean direct stillbirth PTA. For example, in the JE cattle, the average direct PTA was 5.45%, and the average maternal PTA was 6.40%. Smaller stillbirth PTA suggests a lower stillbirth rate due to favorable direct  genetic effects, whereas higher maternal PTA for stillbirths indicates a negative or unfavorable genetic contribution. Stillbirth PTA for AY, GU, and MS were also computed, though these (co) variance estimates were not obtained precisely. From a Bayesian perspective, the estimated (co) variance components are treated as priors. Varied prior variances lead to varying shrinkage of the estimated genetic effects, but their orders are mostly retained, given the same dataset. Alternatively, the estimated (co) variance components obtained previously in the same or different breeds e.g.,  can be uReliabilities of stillbirth PTA were estimated for each of the five breeds . The aveThe reliabilities of PTA for AY, GU, and MS were lower than those for JE and BS due to insufficient data size. Bulls with PTA of low reliabilities had very limited progeny information. The average reliability of direct  PTA was 44 (43) for AY, 47 (46) for GU, and 43 (41) for MS, respectively. The percentage of bulls with PTA less than 40% was approximately 48–49% for AY, 43–45% for GU, and 57–59% for MS, respectively. In contrast, approximately 40–41% of the BS sires and 36–37% of the JE sires had less than 40% reliabilities. Possibly, there were sires with limited progeny information. These results call for further stillbirth data collection in AY, GU, and MS before reliable routine genetic evaluations of stillbirth can be implemented in these three breeds. Previous results  have shoIn reality, the computed reliabilities can be inflated for two reasons. Firstly, calves with observations get credit for the underlying scale hertiability instead of actual heritability. This situation holds universally, yet its impact can be minimal because the heritability of stillbirth is low. Secondly, bulls get credit for sire, MGS, and any sons as if their BV were known instead of estimated. This phenomenon was apparent with Holsteins  but not p = 0.047). The reduced incidence of stillbirth in BS over the time period was due to correlated response to selection for updated calving ease in BS, thanks to the national genetic evaluation of United States BS (p = 0.873). There was no selection for either direct or maternal PTA for stillbirth or a correlated trait such as calving ease in JE cattle. The annual phenotypic trends of stillbirth were all positive and non-significant (p > 0.05) in AY, GU, and MS . The annual genetic trend of %DSB was also negative (-0.027) and significant (p = 0.004). A significant, negative genetic trend of stillbirth was favorable because it suggested a significant yearly drop in the stillbirth rate due to favorable genetic effects. The favorable genetic trends in the BS cattle resulted from correlated responses to selection for calving ease, implemented by the United States national genetic evaluation for the BS cattle since 2006 . The maternal genetic trend of stillbirth in JE was also negative and non-significant . These slight favorable genetic trends of stillbirth in JE cattle could result from genetic drifting over generations because there has been no selection officially implemented on stillborn or calving ease in the United States JE cattle. Linear direct and maternal genetic trends were all positive (0.012–0.032/year for SSB and 0.010 to 0.046/year for DSB) and non significant (p > 0.05) in AY, GU, and MS. p > 0.05) and could result from insufficient stillbirth data because year-to-year variations of SSB (DSB) were large for these breeds. These results otherwise suggested a need for a more accurate and complete recording of stillbirth data.Smoothing splines plots of genetic trends for %SB due to direct and maternal effects are shown in nce 2006 . FavorabStillbirth data with dam birth years ranging from 1995 to 2018 were extracted from the United States national CE database maintained by CDCB. The amount of stillbirth data varied drastically among the five non-Holstein dairy breeds. The majority (80.0%) of the extracted stillbirth records were represented by JE, followed by BS (13.6%). The AY, GU, and MS shared the remaining stillbirth records (4.4%). Based on an updated S-MGS mixed-effects model, the estimated genetic parameters (heritability and genetic correlation) for BS and JE were within close ranges with those from previous studies, asserting that stillbirth has a genetic component upon which selection can operate to achieve the expected genetic improvement. The genetic correlations between direct and maternal genetic effects for stillbirth were negative for BS and JE. Hence, both direct and maternal components of stillbirth need to be considered for genetic improvement, e.g., by including PTA due to direct and maternal effects in the selection index for sires. The average reliabilities of direct  PTA of stillbirth in BS and JE were higher than those reported by Unlike the cases with JE and BS, the stillbirth data sizes for AY, GU, and MS were insufficient for conducting reliable routine genetic evaluations using the S-MGS threshold models. The evidence of data inadequacy was seen in three main aspects. Firstly, genetic parameters in these three dairy breeds were considerably over-estimated and not consistent with previous studies. Secondly, around 43–59% of the evaluated sires had PTA with less than 40% reliabilities in AY, GU, and MS. In contrast, approximately 40–41% of the BS sire and 36–37% of the JE sires had PTA with less than 40% reliabilities. Hence, the sires with limited progeny information were significantly higher in AY, GU, and MS than JE and BS. Thirdly, we observed larger year-to-year variations in the phenotypic and genetic trends in AY, GU, and MS than AY and BS. Thus, our results call for further stillbirth data collection in AY, GU, and MS. Complete and accurate reporting of stillbirth is prerequisite to obtaining unbiased estimates of variance components and genetic parameters, increasing the reliability of PTA for stillbirth, and improving the accuracy of genetic evaluation of stillbirth.Finally, the present study represented a univariate analysis of stillbirth, following the United States national genetic evaluation of stillbirth in Holstein cattle. Yet, it is worth mentioning that bivariate analyses between stillbirth with a highly genetically correlated trait such as calving difficulty would be beneficial to account for the genetic correlation between the traits and help make the animal breeding strategists (and objectives) more effective (and accurate) with the consideration of a set of economic traits in the breeds under study. It is also worth mentioning that the study is not inclusive but a preliminary one, aiming at leveraging the current data repositories toward official implementing of genetic evaluation of stillbirth in non-Holstein dairy breeds. Hence, in the first place, we would like to know if there are sufficient data to conduct single-trait genetic evaluations of stillbirth in non-Holstein breeds, following the “norm” of the national genetic evaluation of calving ease and stillbirth in Holstein cattle. Still, multiple-breed evaluations of stillbirth will be considered in follow-up studies, which is likely to increase the reliabilities of SSB and DSB to some extent in these breeds.


O) of 0.32, expected heterozygosity (He) of 0.31, and nucleotide diversity (Pi) of 0.31. All these parameters indicated a remarkably high genetic diversity of TC pigs. Additionally, 184 runs of homozygosity (ROH) segments were detected from the whole genome of TC pigs with an average ROH length of 23.71Mb, ranging from 11.26Mb to 69.02 Mb. The highest ROH coverage was found on chromosome 1 (10.12%), while the lowest was on chromosome 18 (1.49%). The average inbreeding coefficients based on ROH (FROH) was 0.04%. Fourteen ROH islands containing 240 genes were detected on 9 different autosomes. Some of these 240 genes were overlapped with the genes related to biological processes such as immune function, reproduction, muscular development, and fat deposition, including FFAR2, FFAR4, MAPK8, NPY5R, KISS1, and these genes might be associated with such traits as meat quality and disease resistance in TC pigs. Taken together, population structure and genetic diversity results suggested that the TC pig represented a valuable genetic resource. However, TC pig breed conservation program remains to be further optimized to ensure adequate genetic diversity and avoid inbreeding depression. Our findings provide theoretical basis for formulating management and conservation strategies for TC pigs.Tongcheng (TC) pigs, distinguished by their superior meat quality, are a Chinese indigenous pig breed. Recently, the genetic resources of TC pigs are under tremendous threat due to the introduction of cosmopolitan pig breeds and African swine fever disease. To promote their management and conservation, the present study assessed genetic diversity and population structure of TC pigs using single nucleotide polymorphism (SNP) markers. A total of 26, 999 SNPs were screened from 51, 315 SNPs in 68 TC pigs. The multi-dimensional scaling (MDS) analysis and neighbor-joining tree revealed that all 68 pigs were from a purebred population. The effective population size decreased over time, and it was 96 prior to generation 20. Both linkage disequilibrium (LD) and neutrality test indicated a low selection of TC pigs with average LD value of 0.15 ± 0.23. Genetic diversity results exhibited a minor allele frequency (MAF) of 0.23, observed heterozygosity (H The Tongcheng (TC) pig, an important Chinese genetic resource, is mainly distributed in Tongcheng country . They are distinguished by superior meat quality, moderate intramuscular fat, and intense flavor . CurrentN), minor allele frequency (MAF), heterozygosity, and nucleotide diversity (Pi) in combination with linkage disequilibrium (LD) and effective population size (Ne) levels are good indicators reflecting the genetic diversity of the population, and that runs of homozygosity (ROH) and ROH island analyses can reveal the inbreeding of the genome  population structure, 2) linkage disequilibrium and effective population size, 3) genetic diversity, and 4) runs of homozygosity. An insight into population structure and genetic diversity of TC pigs is crucial for the formulation of rational management strategies and the conservation of the unique traits of TC pigs.From 15 lineages with pedigree records from the Genetic Resource Preservation Farm , we selected the representative 20 boars and 48 breeding sows as study object. Genomic DNA was extracted from ear tissues using a standard phenol/chloroform method . The extAll the individuals were genotyped with the “Zhongxin- I″ Porcine Chip , using Infinium XT 96-Sample BeadChips which could provide the highest throughput array format based on the Infinium data, and a total of 51,315 SNPs were identified. The reference genomes of “Zhongxin- I″ Porcine Chip contained 9 populations of Chinese local pigs such as two-end-black pigs, Sutai, Laiwu, Erhualian, and Bamaxiang. Thus, this “Zhongxin- I″ Porcine Chip was selected for this study.p ≤ 10–6) were excluded   to excluexcluded . FurtherThe Plink (v 1.90)  and TassTC pigs are currently a finite population, genetic drift and inbreeding are the two main reasons affecting genetic diversity. The degree of genetic drift and the increment in inbreeding is mainly determined by the smaller number of sexes, namely, the boar. So, the phylogenetic tree of 20 boars was constructed. The 20 boars cover all the pedigrees of TC pigs in the preservation farm.To estimate the genetic distances among TC pigs, all 26, 999 SNPs were used to calculate the average proportion of alleles shared, Dst, using PLINK. The def1 and IBS2 are the numbers of loci that share one or two alleles at one locus. The genetic distance (D) between all pair-wise combinations of individuals was calculated as follows: Where IBSNeighbor-joining (NJ) trees were conducted by using MEGA 11  based onThe classification criteria are based on the requirements of the document “Administrative Measures for Livestock and Poultry Genetic Resources Conservation Farms and Gene Banks” issued by the Agriculture Ministry of China, namely, a single breed that meets the breeding standard is supposed to have no relationship within three generations. Assuming that individual X and parents D and E, there are no relationships between parents D and E within three generations, A is the common ancestor of individuals D and E, the inbreeding coefficients were calculated as follows:And the relationships between parents is calculated as follows:D and FE is 0. According to the formula, FX is 0.03125 and Where D and E are non-inbred individuals, and thus the value of F2 was used to evaluate Linkage disequilibrium (LD) extent of whole genome of TC pigs, and r2 referred to the LD extent of each pair of SNPs per chromosome  .rromosome . The r2 (v 1.90) . The phyNe was estimated based on LD value , the for(t) is the effective population size prior to t generations; Ct is the recombination rate prior to t generations; radj2 indicates the estimated linkage disequilibrium after sampling bias was adjusted; and Where Ne2 values corresponding to various distances were used to estimate Ne at different time points using SNeP version 1.1 . The rsion 1.1 . The timN, MAF, Ho, and He were analyzed by software vcftools and Plink (v 1.90) . It is easy to understand that genetic diversity is the extent of heritable variation in a population, or Species. In this study, Seven indices were used to estimate TC-genetic diversity, of which P(v 1.90) . The seq(v 1.90) , and Pi (v 1.90)  were use(v 1.90) , Fu and (v 1.90) .To determine ROH, the Plink (v 1.90) parameters and thresholds  were setThe genomic ROH distribution was further investigated. Specifically, the mean number of ROHs per individual, the average length of ROH of population, the total number and average length of ROH per chromosome, and the percentage of chromosomes covered by ROH were calculated.ROH) according to the genome autozygotic proportion, as previously reported  online tool was used to search annotated genes within aligned ROH islands. DAVID (http://david.abcc.ncifcrf.gov/) was employed to determine the function of annotated genes.The R software was used to statistically analyze the occurrence frequency for which each SNP per individual fell inside a sliding window. Further, the top 1% of all SNPs across all ROH with the highest occurrences were defined as candidate SNPs under directional selection. All the adjacent SNPs within the top 1% were merged to form ROH islands. Our ROH islands were aligned against porcine Sscrofa11.1. The Ensemble Biomart . As shown in The mean ± SD of estimated reen SNPs . When phN, ranging from 0.668 to 0.905. The minor allele frequency (MAF) was 0.23 ± 0.15, varying from 0.01 to 0.50. MAF of more than 23.15% SNPs was higher than 0.40, and that of 22.09% SNPs was lower than 0.10  and 3.00 (p < 0.02), respectively.In TC pigs, He and Ho ranged from 0.01 to 0.5 and from 0.01 to 0.701, respectively, and Ho (0.32) was slightly higher than He (0.31) . The Pi ROH and calculated FROH for 41 individuals without full-sib families. Based on ROH data, individual FROH was evaluated as 0.04%, indicating a low genomic inbreeding level.A total of 184 ROHs was detected from the entire genome with an average ROH length of 23.71 Mb, ranging from 11.26 Mb to 69.02 Mb. The average number of ROH per individual ranged from 1 to 9, and the total length of ROH per individual was mainly distributed between 50 Mb and 75 Mb. The ROH number and the coverage ROH on each chromosome were illustrated in sus scrofa chromosome (SSC) 8. We identified a total of 14 ROH island regions (p < 0.05)  . Among tThe alleles of TC pigs were expected to be diverse due to the absence of intense directional selection of production traits. Maintaining genetic diversity within and across breeds is crucial for sustainable development of livestock production . To reveTo understand the population structure, we conducted MDS analysis and constructed Neighbor-joining (NJ) tree. N, MAF, heterozygosity, and Pi) and neutrality tests. All these parameters indicated a significant level of genetic diversity.Genetic diversity is affected by various factors. This study comprehensively assessed the genetic diversity of TC pigs by investigating LD, Ne, genetic diversity parameters  was 64, which was larger than the minimum threshold (50) set by the Food and Agricultural Organization (20) was estimated to be 96. The Ne20 of TC pigs was generally smaller than that of global pig breeds such as the Large White (214.4), Duroc (207.2) and Landrace (207.5) (Linkage disequilibrium (LD) can provide an insight into the evolution of one population. The degree of LD between SNPs can be used to infer ancestral Ne, and Ne is the number of individuals of an ideal population with the same inbreeding coefficients as the actual breeding population . Balancenization , and tha (207.5) , and it  (207.5) . This reIt has been reported that Chinese indigenous pigs are richer in genetic variability than commercial pigs . Hence, Short ROH segments are known as indicators of distant consanguinity, whereas long ROH segments are more likely to result from recent inbreeding . AverageROH was first defined by ROH is an accurate estimator of inbreeding coefficient, and thus it is used for studying inbreeding and selection in animal populations. FROH was the first reported in 2012 in pigs (ROH 0.17) , indicating a low inbreeding level and preservation of genetic diversity. in pigs , and recOH 0.17) . For exa in 2021 . In thisROH contributes to the investigation of inbred genomic regions within a population, which were first defined as ROH islands by FFAR2, FFAR4, MAPK8, NPY5R, and KISS1 located in ROH islands were found to be related to fat cell differentiation, fat deposition, and reproduction. Of them, FFAR2 and FFAR4 (free fatty acid receptors 2 and 4) were mammalian receptor of short-chain fatty acids (SCFAs). Free fatty acid receptors (FFAR) were found on the apical or basolateral membranes of enteroendocrine and immune cells at the gut mucosal lining  signaling pathway participates in multiple biological processes including innate immunity, cell growth, stress response, apoptosis, and differentiation  is expressed in hypothalamic areas that control feeding (NPY (including NPY1R and NPY5R) is mainly responsible for promoting food intake and reducing energy consumption as well as fat deposition in mice (NPY5R genes have also been detected in multiple Chinese local pig breeds, such as Tongcheng (KISS1 gene, is a neuropeptide responsible for regulating reproduction (KISS1 gene may be an essential gene affecting the reproduction performance of sows, which deserves more attention.For instance, some candidate genes such as l lining . Previouty acids . Recent ty acids . One stuimmunity . Howevery (FFAR2 . TC pigsarcasses . In addintiation . The MAPxication . Previouxication , indicat feeding . The NPY in mice . The selongcheng , Rongchaongcheng , Laiwu (ongcheng , and Anqongcheng . Kisspepoduction . Kisspepoduction . TC pigsN, MAF, He and Ho, and nucleotide diversity showed a remarkably high level of genetic diversity of TC pigs. The population structure and genetic diversity analyses indicated that TC pigs represented a valuable genetic resource, but their breed conservation program remains to be further optimized so as to ensure adequate genetic diversity and avoid inbreeding depression. Our findings lay a theoretical basis for conservation of TC pigs and provide genome data for subsequent studies of meat traits such as fatty acids and meat quality.Population structure and genetic diversity are of great importance for genetic improvement programs of indigenous pig and the conservation and effective use of genetic resources. The present study provides insights into the population structure and genetic diversity of TC pigs in China. The results showed that the studied TC pig populations were free from admixture of other breeds. Our investigated parameters such as P


Pisum sativum) are the fourth most cultivated pulses worldwide and a critical source of protein in animal feed and human food. Developing pea core collections improves our understanding of pea evolution and may ease the exploitation of their genetic diversity in breeding programs. We carefully selected a highly diverse pea core collection of 325 accessions and established their genetic diversity and population structure. DArTSeq genotyping provided 35,790 polymorphic DArTseq markers, of which 24,279 were SilicoDArT and 11,511 SNP markers. More than 90% of these markers mapped onto the pea reference genome, with an average of 2787 SilicoDArT and 1644 SNP markers per chromosome, and an average LD50 distance of 0.48 and 1.38 Mbp, respectively. The pea core collection clustered in three or six subpopulations depending on the pea subspecies. Many admixed accessions were also detected, confirming the frequent genetic exchange between populations. Our results support the classification of Pisum genus into two species, P. fulvum and P. sativum . In addition, the study showed that wild alleles were incorporated into the cultivated pea through the intermediate P. sativum subsp. jomardii and P. sativum subsp. arvense during pea domestication, which have important implications for breeding programs. The high genetic diversity found in the collection and the high marker coverage are also expected to improve trait discovery and the efficient implementation of advanced breeding approaches.Peas ( Pisum sativum L.) are a multipurpose and low-cost source of protein, that have been an essential source of animal feed and human food for centuries [https://www.fao.org/faostat/en/#home, accessed on 24 October 2022). Peas also improve the soil by fixing atmospheric nitrogen through symbiotic interactions with soilborne bacteria. These interactions have a beneficial impact on soil fertility and the nitrogen cycle [Peas  and genomic selection (GS) to facilitate and boost future QTL identification and breeding . The effPisum species and subspecies, to shed further light on the pea phylogenetic relationship, and serve as a first step toward implementing GWAS and GS in peas for agronomic traits and disease resistance.In this work, we established the population structure and genetic diversity of a pea core collection containing 325 accessions from a worldwide origin. The collection includes all The Instituto de Agricultura Sostenible (IAS) pea core collection contained 325 accessions. Pea accessions of this core collection were selected based on geographical and morphological diversity to preserve the underlying levels of genetic diversity. Disease resistance was at the base of the germplasm-gathering process. Therefore, previously described sources of disease resistance including powdery mildew, rust, ascochyta blight, fusarium wilt and broomrape, were included in the core collection ,27,28,29DArTSeq sequencing and assembling of the collection led to the identification of 66,643 SilicoDArT and 55,269 SNP markers. After the stringent data curation according to minor allele frequency (MAF), heterozygosity and missing values, 24,279 polymorphic SilicoDArT markers and 11,511 SNP markers were obtained. Mapping these DArTSeq markers onto the pea reference genome located 19,514 SilicoDArT and 10,125 SNP markers on the seven pea chromosomes (82.8% of the DArTSeq markers). In addition, 2703 SilicoDArT and 1155 SNP mapped to unanchored pea contigs and supercontigs (10.8% of the DArTSeq markers), while 2062 SilicoDArT and 231 SNP markers remained unaligned (6.4% of the DArTseq markers). Sequence read data from these accessions are available at the NCBI SRA archive as BioProject PRJNA890072. The DArTSeq marker datasets were deposited in the public Zenodo depository [e), observed heterozygosity (Ho) and inbreeding coefficient (FIS) for each DArTSeq marker , MAF, allele richness (Ar), expected heterozygosity , estimated as the ninetieth percentiles of r2, were also similar between marker types reaching values of 0.103 and 0.236 for SilicoDArT, and 0.118 and 0.28 for SNP markers, respectively  indicated that rectively . Plottinectively . Estimate lowest .P. fulvum, P. abyssinicum, P. sativum subsp. elatius var. pumilio, P. sativum subsp. transcaucasicum and most P. sativum subsp. elatius var. elatius accessions. The second group (Q2), representing 56.83% of the collection, contained the cultivars and landraces of P. sativum subsp. sativum var. sativum, P. sativum subsp. sativum var. arvense, P. sativum subsp. jomardii, P. sativum subsp. thebaicum and P. sativum subsp. cinereum. This group included six P. sativum subsp. elatius var. elatius accessions; two had a shared membership percentage (circa 50% Q2 and 45% Q1). The third group (Q3), containing 15.01% of the collection, was composed of a smaller set of P. sativum landraces from India  showed two peaks, suggesting the presence of three or six subpopulations a. Accordom India .P. fulvum and P. abyssinicum accessions and most of the P. sativum subsp. elatius var. elatius. By contrast, the second and smallest wild sub-group (Q6) contained the P. sativum subsp. elatius var. pumilio accessions [P. sativum, all accessions from the P. sativum subsp. jomardii and thebaicum, and two accessions initially assigned to P. sativum subsp. elatius. The second sub-group (Q3) contained the P. sativum subsp. sativum var. sativum cultivars, while the last sub-group (Q4) contained all the landraces from P. sativum subsp. sativum var. arvense. Finally, the Indian P. sativum landraces, that represented 12% of the collection, composed the Q5 sub-group  for each subpopulation pointed to the significant divergence between subpopulations  was performed to highlight the different variables that could explain the splitting of this population. The initial three principal components (PCs) explained 23.3% of the observed genetic variation in the pea collection. The first PC explained up to 13.62% of the variation, the second explained 5.58 and third PCs explained 4.07%. The first two PCs clustered the pea collection in three different groups a,b. The vs PC3 which revealed six clusters, corresponding to the six subpopulations previously estimated by STRUCTURE for K = 6 (Superposing the STRUCTURE clustering for K = 3 or K = 6 to the PCA clustering favours the estimation of six subpopulations b. In thior K = 6 c.The Neigbor-Joining (NJ) tree resolved into six distinct groups, as shown in Broadening crop genetic diversity is critical for efficient breeding. Therefore, the conservation and characterisation of crop genetic resources is a crucial breeding element. The development of core collections representative of the crop genetic diversity allows the successful exploitation of genetic diversity richness, and protects against genetic erosion. These core collections, therefore, represent unique genetic diversity donors to enhance genetic gain, boost production and reduce stress-induced losses ,32,33. TDespite pea agronomic importance and long cultivation history, its domestication events and population dynamics still need to be better understood ,24. The e mean values of 0.295 and 0.217, respectively. However, the genetic diversity harboured by each marker varied largely . Cultivated P. sativum, including P. sativum subsp. sativum var. sativum and P. sativum subsp. sativum var. arvense clustered in two additional groups (Q2 and Q3). The Q2 clustered most of the cultivated P. sativum accessions, and the Q3 were pea landraces from the northern regions of India. This clustering was supported by the phylogenetic analysis, while the PCA results separated the wild population into two distinct groups, suggesting additional genetics clusters . The second included all accessions assigned to P. sativum subsp. elatius var. pumilio (Q6). The division of these two varieties confirmed their genetic distinctiveness, as previously shown by examining several wild populations from Israel and other Mediterranean regions that differentiated between P. sativum subsp. elatius var. elatius and P. sativum subsp. elatius var. humile [Accordingly, the three main groups of the pea core panel can be further differentiated into six clusters separated by the NJ trees and the PC1 vs. PC3 representation of the PCA analysis. In this latest analysis, PC1 discriminated accessions based on their domestication degree (wild or domesticated), while PC3 discriminated among the subspecies. In this grouping, the wild group was separated into two clusters. The first cluster contained the . humile ,50.P. sativum accessions that formed the Q5 cluster. Central Asia  is a relevant secondary centre of pea diversity that is thought to be rich in primitive cultivated forms of field peas [On the other hand, the domesticated groups formed four distinct clusters. This result confirmed the genetic distinctiveness of the subset of Indian eld peas ,52. Preveld peas , providield peas ,52. A siP. sativum subsp. sativum var. sativum cultivars and landraces (Q3), and two large groups of highly admixed accessions containing the P. sativum subsp. jomardii (Q2) and P. sativum subsp. sativum var. arvense (Q4). Most accessions assigned to Q2 and Q4 by STRUCTURE showed a membership coefficient lower than 60%; 78.6% of the Q2 accessions and 68% of the Q4 accessions were admixed. The percentage of admixed accessions from other clusters only varied from 15 to 30%. Further examination of the individual membership coefficient from each cluster indicated many hybridization events between Q2 accessions and the wild subpopulations (Q1 and Q6). The results revealed the existence of a frequent genetic exchange between Q2 and Q4, and between Q4 and the other two domesticated clusters (Q3 and Q5)  . This su-workers ,53.P. fulvum and P. abysinicum from the wild P. sativum subsp. elatius var. elatius accessions in contrast with most previous studies [P. fulvum clustered together in a slightly separated group within Q1  from the “northern humile” (syn. P. sativum subsp. humile var. syriacum) and “elatius” accessions [P. sativum subsp. humile from the P. sativum subsp. elatius. Therefore, these observations support the classification of P. sativum subsp. humile as a subspecies within the P. sativum complex, as some authors considered [P. sativum subsp. jomardii were placed in the Q2 cluster together with many undefined P. sativum accessions. This result demonstrates that these accessions are genetically distinct from the other subpopulations. Despite P. sativum subsp. jomardii being described in 1818 [P. sativum subspecies based on three genetic loci [P. sativum subsp. jomardii as an intermediate population between wild and cultivated peas [P. sativum subspecies. Our phylogenetic and population genetic structure analyses support that this P. sativum subsp. jomardii forms an intermediate population during pea domestication  and the subdivision of P. sativum into at least five subspecies . Clarifying pea taxonomy is an important step towards efficiently exploiting pea germplasm and genetic diversity for breeding.The present study supports several taxonomic considerations. Firstly, many studies demonstrated that the domesticated on event ,54. Howeon event . Consequon event  postulate Weeden  proposedcessions . This refication ,54, althfication ,9. Secon Ambrose  taxonomiucasicum ,14. The  cluster . This clcessions ,50. Thesnsidered ,22. Thir in 1818 , its taxted peas . Our obstication . The Q2 cognised . This ressinicum . By contentities ,14,35. APisum in two species, and at least five subspecies of P. sativum. This work also highlights that, despite the extensive pea domestication history and its primary autogamic reproduction mode, cultivated peas have maintained a very high genetic diversity, valuable for breeding. In particular, population structure analysis showed that wild alleles had been incorporated into the domesticated pea through the intermediate P. sativum subsp. jomardii and P. sativum subsp. arvense, that form two highly-admixed subpopulations. The high genetic diversity in the IAS pea core collection and the high genome coverage with polymorphic markers also allow the efficient implementation of GWAS and GS approaches. These techniques will be very valuable in improving resistance to major pea diseases through breeding.Altogether, our study confirms the high genetic diversity of peas and their complex population structure. Our data also support the taxonomic subdivision of Pisum spp. collection of >3000 accessions initially provided by USDA , JIC , CRF , CGN , IPK  and ICARDA . The collection is representative of the different Pisum species and subspecies including accessions from P. sativum, P. fulvum, P. abyssinicum and the subspecies sativum, elatius, cinereum, jomardii, thebaicum and transcaucasicum of P. sativum. In addition, it comprises cultivated, wild and landrace pea types from worldwide origin, associated with large genetic and morphologic diversity  with Quantifluor® DsDNA system , following manufacturer recommendations. The DNA samples were then adjusted at 20 ng/µL before DArT marker analysis, using the high-density Pea DArTseq 1.0 array , adapted for wild Pisum spp. accessions, as previously described [PstI and MseI restriction enzymes, library construction, amplification and Illumina sequencing were performed by Diversity Arrays P/L, as described in [Pisum reference genome sequences [−4 and min % identify = 80%). The mapped markers were distributed to each chromosome with the LinkageMapView package in R [IS, were calculated using the radiator and diveRsity R packages with 1000 bootstraps in R [The pea core collection was genotyped with the DArTSeq approach by Diversity Arrays P/L . For this, the third composed leave from 20 seedlings (two weeks old), grown under controlled conditions, for each accession, was harvested, pooled together, flash-frozen in liquid nitrogen and freeze-dried. Then, DNA was extracted following the method stipulated by Diversity Arrays P/L, as previously described . DNA quaescribed . Complexribed in . DArTSeqribed in ,56. Accoequences   in the whole genome and per chromosome, significant intra-chromosomal r2 values were plotted against the physical distance (Mbp) between markers, with R using the function LDit developed by Ross-Ibarra group . Average LD50 was then estimated in R, as described in Marroni et al. [The disequilibrium matrix summarising pairwise measures of LD was estimated for SilicoDArT and SNP datasets, through a TASSEL 5 software  with a si et al. .


Passerina ciris) to investigate the genetic and ecological drivers of distinct molting phenotypes. Specifically, we use genome-wide genetic sequencing in combination with stable isotope analysis to determine population genetic structure and molting phenotype across thirteen breeding sites. We then use genome-wide association analysis (GWAS) to identify a suite of genes associated with molting and pair this with gene-environment association analysis (GEA) to investigate potential environmental drivers of genetic variation in this trait. Associations between genetic variation in molt-linked genes and the environment are further tested via targeted SNP genotyping in 25 additional breeding populations across the range. Together, our integrative analysis suggests that molting is in part regulated by genes linked to feather development and structure (GLI2 and CSPG4) and that genetic variation in these genes is associated with seasonal variation in precipitation and aridity. Overall, this work provides important insights into the genetic basis and potential selective forces behind phenotypic variation in what is arguably one of the most important fitness-linked traits in a migratory bird.The ability of animals to sync the timing and location of molting  with peaks in resource availability has important implications for their ecology and evolution. In migratory birds, the timing and location of pre-migratory feather molting, a period when feathers are shed and replaced with newer, more aerodynamic feathers, can vary within and between species. While hypotheses to explain the evolution of intraspecific variation in the timing and location of molt have been proposed, little is known about the genetic basis of this trait or the specific environmental drivers that may result in natural selection for distinct molting phenotypes. Here we take advantage of intraspecific variation in the timing and location of molt in the iconic songbird, the Painted Bunting ( In migratory birds, the potential costs associated with pre-migratory feather molting, a period when feathers are shed and replaced with newer, more aerodynamic feathers6, are thought to be outweighed by the benefits of increasing flight efficiency during migration7 and conferring optimal plumage coloration for sexual signaling8. However, feathers are also critical to providing birds with insulation from thermal extremes (either too hot or too cold) and, as a result, avoidance of extreme temperatures during feather molting is important to survival10. While previous research has demonstrated that intraspecific variation in the timing and location of molt in birds is at least in partly genetically determined15, very little is known about the genes involved in feather molting or the environmental factors which select for variation in this key fitness-linked trait.Seasonal migration is energetically costly, often requiring extensive morphological and physiological changes each year to prepare for long-distance movements. Molting, defined as the replacement of hair, feathers, skin, and/or exoskeletons to make way for new growth, is one such morphological change that can help prepare animals for long-distance migratory journeys  may contribute to the evolution of and the maintenance of migratory divides, regions of overlap between distinct populations with divergent migratory strategies29. For example, estimates of the geographic area and the environmental characteristics  of a molting individual can be determined from a single feather using stable isotope analysis (SIA). Once formed, keratinous tissues such as hair, feather or nail are metabolically inert and so their isotopic ratio of common elements such as hydrogen, carbon, and nitrogen, reflect the environmental conditions of where they were grown31. Thus, SIA has become one of the forefront techniques in avian ecology studies and offers an indirect approach for describing individual variation in the molt strategies33. Now that the research tool is in place for revealing the extent of intra-species variation in molting preferences and environmental conditions, we can begin to ask questions about the genetic and environmental drivers of this complex trait within species.While molting strategies have historically been defined on a species level, new research tools for assessing the molt locations of individuals have revealed previously unrecognized levels of intraspecific variation in molting strategies35. In particular, genome-wide association studies (GWAS) allow for the identification of genetic variants significantly associated with a phenotype of interest36, while gene-environment correlation analysis (GEA) analyses are used to identify putative environmental drivers of local adaptation. Given the similar goals of each approach and the fact that natural selection on complex life history traits is generally driven by environmental variation across space, a method that combines the two approaches has the potential to identify environmental drivers of a specific complex phenotype rather than environmental drivers of local adaptation more generally. Here we adopt a two-step approach that first uses GWAS to identify candidate loci underlying molt-migratory behavior in a migratory songbird, the Painted Bunting (Passerina ciris), and then uses GEA to identify environmental drivers of genetic variation linked to this phenotype.Next-generation sequencing has facilitated the ability to assess genetic and environmental drivers of complex traits using a variety of techniques that were formerly only available to researchers working on model systems37. It is an ideal system in which to investigate the genetic and ecological drivers of intraspecific variation of molt because field observations, tracking studies, and isotope analysis have documented clear differences in molting strategies across the range40. More specifically, previous work has shown that southwestern breeding birds are molt-migrants that stopover in western Mexico in the fall to complete their molt, while eastern populations follow the more common strategy of molting at the breeding grounds before migrating to southern latitudes for the winter42. However, within the southwestern region, the variation of the molt-migratory phenotype and its potential role in local adaptation and gene flow between populations exhibiting distinct molting phenotypes have not yet been described. Here we identify variation in hydrogen stable isotope values extracted from feathers as a proxy for environmental molting conditions experienced by individuals sampled across the breeding range  and then pair this approach with GWAS to reveal genetic variation associated with molting behavior. We then integrate GWAS and GEA analyses to test whether the environmental drivers of molt-linked genetic variation are in keeping with the push–pull hypothesis.The Painted Bunting is a songbird which breeds in two disjunct populations across southwestern United States (U.S.) and northwestern Mexico (larger western population) and along the Atlantic coast of the U.S.  from Florida to North Carolina43. Importantly, population sampling for genetic analysis focused on 13 populations spanning a range of hypothesized differences in molting behavior of the Painted Bunting . The fragmented DNA was then ligated with SBfI specific adapters prepared with biotinylated ends and samples were pooled and cleaned using 1× Agencourt® AMPure XP beads (Beckman Coulter). Pooled and clean libraries were sheared to an average length of 400 bp with 10 cycles on the Bioruptor NGS sonicator (Diagenode) to ensure appropriate length for sequencing and an Illumina NEBNext Ultra DNA Library Prep Kit (NEB) was used to repair blunt ends and ligate on NEBNext Adaptors to the resulting DNA fragments. Agencourt® AMPure XP beads (Beckman Coulter) were then used to select DNA fragments with an average length of 500 bp, libraries were enriched with PCR, and cleaned again with Agencourt® AMPure XP beads. The resulting libraries were sequenced on two lanes of an Illumina HiSeq 2500 at the UC Davis Genome Center using 250 bp paired-end sequencing.Genome scans were conducted using high-density RAD-Seq on all 192 individuals following a modified version of the bestRAD library preparation protocolstacks46 to demultiplex, filter and trim adapters from the data with the process_radtags function and remove duplicate read pairs using the clone_filter function. We mapped the processed sequences to the annotated genome of a closely related relative, the Medium Ground finch (MEGR), Geospiza fortis47 (NCBI Assembly ID: 402638 (GeoFor_1.0)). This genome is from a female individual sequenced at 115X coverage with HiSeq data. The genome is 1.07 Gb, and scaffold N50 is 5.2 Mb. We mapped the processed reads to the MEGR genome using bowtie248 and detected variants using the program HaplotypeCaller in the Genome Analysis Toolkit (gatk)50. For initial filtering, we used vcftools51 to remove indels, non-biallelic SNPs, and low quality and rare variants . This initial filtering resulted in 86,347 loci in 192 individuals. The final number of SNPs and individuals to be retained for the subsequent analyses was assessed by visualizing the tradeoff between discarding low-coverage SNPs and discarding individuals with missing genotypes using custom scripts within the R-package genoscapeRtools52. The final dataset included 41,786 variants in 124 individuals.We used the program plink53 and a genetic ancestry analyses using the program ADMIXTURE54 for the 124 individuals and 41,786 variants. The admixture analyses were run for K = 1 to K = 6 putative genetic clusters, with a model that accounted for admixture between populations and correlated allele frequencies. We ran 5 iterations for each value of K, with a burn-in period of 50,000, and a total run length of 150,000 generations. To determine the optimal number of genetic clusters we used the cross-validation method, a process of systematically withholding data points to identify the best K value55. We used this algorithm to detect the uppermost hierarchical level of structure across the Painted Bunting breeding range and visually inspected subsequent structure plots using pophelper56 in R to identify regions where geographic barriers to gene flow exist and/or where admixture homogenizes population structure.To determine whether population structure was associated with a transition between molting phenotypes, we assessed patterns of population structure using both a principal component analysis (PCA) using the program 2H) in feathers during molting, we collected wing feathers from 166 birds of which 114 individuals were also sequenced. We washed the ninth primary wing feathers in a 2:1 solution of Chloroform–Methanol following the protocol detailed in Chew et al.57. This step and additional washes in a 30:1 solution of deionized water and detergent ensured that debris and oil contaminants were removed from the samples. Feather washing was followed by a drying step of 48 h at room temperature and cutting to about 200 µg (± 10 µg) of the distal feather tip packaged into a silver capsule. Before mass spectrometry analysis, we stored our feather samples for a minimum of three weeks to allow for equilibration of exchangeable H to the laboratory environment (Wassenaar and Hobson58).To identify clusters of individuals based on their stable isotope values of hydrogen  from the standards (δ2Hsample = [(Rsample/Rstandard) − 1]) comparative to the Vienna Standard Mean Ocean Water [VSMOW]. We used Caribou Hoof Standard (δ2HCHS =  − 197.0‰) and Kudu Horn Standard (δ2HKHS =  − 54.1‰) as external calibration standards  and brown-headed cowbird feathers (δ2HBHCO =  − 40.9‰) as an internal blind standard. These standards were run after every 10 samples, and all samples were analyzed across three separate runs on the instrument. The absolute errors (‰) for all three standards across all runs (n = 12 combined) were: CHS, mean = 1.3, SD = 1.3; KHS, mean = 1.3, SD = 0.6; and BHCO, mean = 1.4, SD = 1.2.We ran all the samples through a Thermo Scientific™ TC/EA high temperature conversion elemental analyzer interfaced with a Thermo Scientific™ Delta V Advantage Isotope Ratio Mass Spectrometer via a Thermo Scientific™ ConFlo IV Continuous Flow Interface. We report δ2Hp) at each sampling site59 and then subtracted the stable isotope values of hydrogen in feathers (δ2Hf) collected from each individual at that site. Thus, we computed a simple differential index (δ2Hdiff) in R version 4.1.260 to estimate whether the keratinous tissue of each individual feather was grown at or nearby the sampling location:To explore variation in molting strategy across individuals, we extracted stable isotope values of hydrogen in precipitation  to identify single-gene effects. Given we do not know the genetic basis of molting phenotypes, the adaptive bslmm model allows us to infer it from the data. Bslmm combines both the linear mixed models (lmm36) and Bayesian variable regression models (bsvr63), giving the benefits of each when the underlying genetic basis of the trait  is unknown62. To statistically control for population structure in GEMMA64, we incorporated a genetic kinship matrix generated using 41,734 analyzed variants, where missing genotypes were imputed using beagle65. We then ran the bslmm model with the kinship matrix, sampling for 5 million generations and a burn-in period of 500,000 iterations. Candidate molt-migration associated SNPs were retained when they had a posterior inclusion probability (PIP) threshold > 0.166. Second, we employed a multi-locus GWAS method, FASTmrMLM in the program mrMLM67, which is designed to detect multiple loci while reducing the chance of false positives. We implemented FASTmrMLM in the program mrMLM using the kinship matrix to account for population structure, and distance between loci to account for interaction among loci given a certain distance . To calculate distance between loci, we placed the scaffold positions in the Zebra Finch (Taeniopygia guttata) chromosomal order using satsuma2 synteny68 prior to the analyses. Candidate molt-migration associated SNPs were those that were found to have a LOD score > 3. To identify genes located near outlier SNPs generated using both single-locus and multi-locus methods, we downloaded Ensembl gene predictions for medium ground finch . We used bedtools -closest to find the gene closest to the candidate variants detected69. We then retained only genes which occurred within a 50 kb of the candidate loci.We used two genome-wide association study (GWAS) analyses to identify loci associated with molting phenotypes defined by stable isotope groupings. We used a 13.7‰ cutoff to delimit six groups ranging from individuals with extremely negative δgradientForest70. Climate and environmental data consisted of 19 WorldClim71 variables, as well as vegetation indices , elevation data (http://www.landcover.org), and a measure of surface moisture characteristics (QuickSCAT from http://scp.byu.edu). The genetic data consisted of allele frequencies from the 412 candidate loci with non-zero effects identified via our single-locus GWAS analysis. To provide a ranked environmental variable list based on the relative predictive power of all environmental variables, we ran gradientForest over 100 trees without binning the data due to the small number of candidate loci. To visualize the predicted associations between genetic variation between genetic variation in molt-linked loci and environmental variation across space we used the resulting gradient forest model to predict the association between environmental variables at 100,000 random points across the Painted Bunting breeding range and plotted the relationships using principal components analysis (PCA). To visualize the different adaptive environments across the breeding range, we assigned colors to the breeding range based on the top three principal components axes70. To determine whether our results were significantly different from random, we compared our observed results with those obtained from gradient forests run by randomizing the match between allele frequencies and the environmental data (n = 1000).To identify the environmental variables that best explained genetic variation underlying the molting phenotype we used gradient forest analysis with the R package To further test the relationship between allele frequencies at the top ranked candidate genes identified in the GWAS above and the three highest ranking environmental variables identified by the gradient forest analysis, we used Pearson correlation .snps2assays72 to create designable primers  within 30 bp, and no additional variants within 20 bp of the targeted variable site). Additionally, we aligned 25 bp surrounding the targeted variable sites to the genome using bwa73 to confirm the primers mapped uniquely to the reference genome. Only the assay targeting GLI2, the top ranked candidate loci, passed all filters, and was subsequently genotyped on a Fluidigm™ 96.96 IFC controller. Allele frequencies were calculated for each location after removing individuals with greater than 20% missing data. Standard linear regression was used to test for associations between environmental variation at the top three climate variables and allele frequencies at GLI2 in both the original (RAD-seq) and validation (SNP-only) datasets .To validate the association between allele frequencies in molt-linked loci and environmental variation, we created custom assays to genotype 261 new genetic samples from 25 breeding populations  and were approved by the Institutional Animal Care and Use Committee of the University of Oklahoma (R18-013).75, the clear population clusters suggests that small but real variation within populations was detected. The admixture analyses support weak population structure between central, southwestern, central-southeastern, and eastern populations, but this may in part result from a lack of sampling at intermediate sites, particularly between sites in the Southwestern and Central regions. While overall genetic breaks within the breeding individuals were not concordant with hypothesized transitions in molting behavior, the population structure results can be incorporated into the subsequent GWAS analyses to reduce false positives related to population-specific genetic differences.Overall, analysis of 41,786 total variants identified via RAD-sequencing revealed significant levels of population structure across the Painted bunting breeding range  showed high variation ranging from − 92.9 to − 10.8‰. The differential index of hydrogen stable isotope values (δ2Hdiff) revealed limited variation at the extreme western  and eastern  sampling sites of the breeding range and a larger gradient of values moving from the interior western population  towards southeastern U.S.  and multi-locus (mrMLM) GWAS algorithms were used to find regions of the genome associated with molting, while accounting for population structure. In the bslmm analysis, a median of 91.1% of phenotypic variation was explained by the genotype (95% CI 45.2–99.9%), of which 46.7% was explained by SNPs with nonzero effects, but the credible intervals on this estimate were very high (95% CI 2.3–94.4%). Approximately 0.16% of the variants had non-zero effects (n = 412), however 67 were considered to have major effects. We defined the top candidate SNPs as those that were found with sparse effects in at least 10% of the MCMC runs (PIP > 0.1), after controlling for population structure , and on chromosome 2, located in the regulatory region approximately 15 kbp downstream from the HEPACAM2 gene. The multi-locus approach identified 5 additional genes associated with the molt-migration strategy .Both single gene . In particular, bioclimatic variables did best at explaining genomic variation, with nine of the 10 most important predictors in our model representing either temperature or precipitation measurements .Targeted genotyping using Fluidigm assays of the top-ranking gene, LI2 Fig. A, associ79. In addition, because feather molting leaves birds without a protective layer to buffer them from temperature extremes, there are likely strong selective pressures on choosing molting locations with minimal temperature fluctuations80. Here, we integrate isotopic and genome-wide genetic analyses to assess factors regulating the maintenance of variation in molting strategies in the iconic migratory songbird, the Painted Bunting. We found that the transition from molt-migrants in the west to migrants that molt on their breeding grounds further east is not coincident with a strong barrier to gene flow, running counter to hypotheses about the role of differences in the timing and location of molting in the early stages of divergence82. However, GWAS analysis identified several candidate genes associated with distinct molting phenotypes that are also known to be involved in feather morphogenesis and feather structure, providing the first window into the potential genetic basis of this key fitness-linked trait. Further, GEA analysis found that allele frequencies in loci linked to molt-migratory behavior are associated with environmental variables linked to precipitation, seasonality and aridity, in keeping with the push–pull hypothesis84. Overall, the results support the idea that locally adapted molting phenotypes have evolved in migratory birds as a means of facilitating life in seasonal environments.The timing and location of molting behavior has important fitness consequences for migratory animals. In particular, migratory birds must optimize access to food resources to survive high energetic costs associated with molting87, but empirical support for this idea has been limited27. Here, we investigated whether variation in hydrogen stable isotope values in Painted Bunting feathers is associated with reduced gene flow across a transect between populations with differences in the molting behavior. To address this question, we used an innovative approach that combined genomics and stable isotope analysis to reveal that the variation of the molt-migratory phenotype is not coincident with a barrier to gene flow in the Painted Bunting. Importantly, our genome-wide approach was generally in agreement with previous genetic studies, identifying four distinct genetic clusters across its breeding range, including an eastern, central, southwestern, and central southeastern population89 , suggesting that western birds experience a broader range of environmental conditions, possibly linked to moving to molting locations after breeding . By contrast, eastern birds and Texas birds at the very edge of the western breeding distribution exhibited smaller δ2Hdiff values, consistent with individuals molting in the same environments where they breed. While it is perhaps counterintuitive that molt-migrants of central part of the western range leave the breeding grounds to molt in more water-stressed regions, such as northwestern Mexico as previously demostrated39, it is in keeping with the idea that they are at these sites to take advantage of the monsoon period when productivity at its peak90. Thus, while overall we did not detect a barrier to gene flow coincident with a transition in molting strategies, the ability to identify molting phenotypes using stable isotope analysis opens opportunities for understanding the genetic and ecological factors underlying this important fitness-linked trait91.Specifically, individuals breeding in the central part of the western range exhibited larger variation in hydrogen stable isotope differential index  signaling pathway that specifies positional information required for the formation of adult flight feathers95. Moreover, GLI2 activates and is co-expressed with Follistatin96, a gene that is linked to the development of hair follicles in mammals and feathers in birds as demonstrated by gene knockout experiments in Mus musculus97 and ectopic induction of feather growth in Gallus gallus99. In addition to GLI2, our association analyses identified a second gene, CSPG4, which has a demonstrated role in feather structure100. Specifically, manipulative experiments designed to reduce expression of CSPGs resulted in significantly thinner feathers100. While GLI2 and CSPG4 are both good candidates for playing a role in molting, further work is needed to clarify the role of these genes in the molting process. In particular, to provide a functional connection between gene variants and environmental conditions, future studies will need to include a gene expression approach. Moreover, whole genome resequencing may help identify additional loci which contribute to the genetic basis of what is very likely a highly polygenic trait.Little is known about the genetic basis of molting behavior despite its potentially important consequences to fitness in migratory birds101, future work focusing comparisons between the results described herein and other molt-migrants may help clarify the generality of these findings.Here we take a landscape genomic approach to identify the putative environmental drivers of genetic differentiation at molt-associated loci and, in keeping with the push–pull hypothesis, we find a higher proportion of molt-migrant associated genotypes in regions characterized by higher seasonality and more extreme summer temperatures. Specifically, our GEA analysis supports the idea that the top environmental predictors of molt-associated loci include seasonality, productivity and aridity variables (BIO-15, BIO-13, and BIO-1; Fig. GLI2, our top-ranking candidate gene known to be involved in feather morphogenesis, is significantly associated with annual mean temperature. High frequency in this allele generally occurs in regions where late summer temperatures are more extreme, while low frequency occurs where late summer temperatures are less extreme (Fig. 104, here we are able to validate the association between allele frequency in GLI2 and late summer temperatures by genotyping an additional 261 individuals from 25 locations and find that the significant association holds true in both the original and validation datasets. While further work is needed to understand the specific role of GLI2 in feather production in Painted Buntings, our results suggest that molt-migratory individuals may be genetically distinct from traditional migrants at this gene and that such variation is driven by differences in summer temperatures.Looking more in depth at the top candidate genes identified within our GWAS analysis, we see significant correlations between allele frequency in genes linked to molting and putative ecological drivers related to precipitation and aridity Table . Specifieme Fig. B,C. WhilGLI2, allele frequency in our second ranking gene, CSPG4, a gene known to be involved in feather structure, was significantly associated with precipitation seasonality and to a lesser extent annual mean temperature. High frequency of this gene is found in regions characterized by greater extremes between wet and dry seasons, while low frequency of this gene is found in parts of the range characterized by more consistent precipitation patterns year-round (Fig. 100, it is possible that the environmental conditions which are pushing migrants to molt in Mexico simultaneously result in differential selection on feather structure relative to populations which molt on their breeding grounds. Unfortunately, we were unable to successfully design SNP-type assays that could be used to test if these associations, or any of the other associations with productivity and precipitation found in our top-ranking genes (Fig. 105. Until the GLI2 gene is proven to be differentially expressed in space and/or time in birds adopting distinct molting strategies across environmental conditions, our hypothesis remains possible but highly theoretical.In addition to und Fig. . Considenes Fig.  remainedHere we combine genome-wide genetic and isotopic analysis to identify putative genetic and environmental drivers of variation in molting strategy in the Painted Bunting. Counter to existing hypotheses on the potential role of differences in molting behavior in the fitness of hybrid offspring, we find no support for the idea that the transition in molt-migratory behavior is significantly correlated with a barrier to gene flow. Alternatively, the results of our GWAS support the hypothesis that differences between molt-migrants and traditional migrants in molting behavior is in part controlled by genes involved in feather morphogenesis and feather structure. Further, our GEA results suggest that genetic variation in these genes is driven at least in part by differences in aridity and precipitation patterns in keeping with the push–pull hypothesis. Overall, this work strongly supports the idea that molt-migratory behavior is a locally adapted trait that has evolved to help migratory birds cope with life in highly seasonal environments.Supplementary Legends.Supplementary Figure S1.Supplementary Figure S2.Supplementary Table S1.Supplementary Table S2.Supplementary Table S3.Supplementary Table S4.


Two fundamental questions for evolutionary studies are the speed at which evolution occurs, and the way that this evolution may present itself within an organism’s genome. Evolutionary studies on invasive populations are poised to tackle some of these pressing questions, including understanding the mechanisms behind rapid adaptation, and how it facilitates population persistence within a novel environment. Investigation of these questions are assisted through recent developments in experimental, sequencing, and analytical protocols; in particular, the growing accessibility of next generation sequencing has enabled a broader range of taxa to be characterised. In this perspective, we discuss recent genetic findings within the invasive European starlings in Australia, and outline some critical next steps within this research system. Further, we use discoveries within this study system to guide discussion of pressing future research directions more generally within the fields of population and evolutionary genetics, including the use of historic specimens, phenotypic data, non-SNP genetic variants , and pan-genomes. In particular, we emphasise the need for exploratory genomics studies across a range of invasive taxa so we can begin understanding broad mechanisms that underpin rapid adaptation in these systems. Understanding how genetic diversity arises and is maintained in a population, and how this contributes to adaptability, requires a deep understanding of how evolution functions at the molecular level, and is of fundamental importance for the future studies and preservation of biodiversity across the globe. Evolutionary theory states that the immense diversity existing on this planet does so through a complex combination of factors. These factors include genetics, epigenetics, and plasticity, and it is the interplay of these processes that allow species to evolve within our increasingly changing world . We are Population and evolutionary genomics play pivotal roles in answering these questions. Technical advances have given rise to cheap “reduced representation” data (subsampling genomic variation) and the growing feasibility of whole genome resequencing (WGS) for non-model organisms. Population genetics can now move beyond the characterisation of broad genetic patterns to uncover evolutionary important genetic variants at a resolution previously inaccessible to non-model organism studies , 2021. EStudies in invasive species genomics are fundamental to these efforts. By examining how invasive populations’ genetic diversity is shaped by novel selection regimes, we obtain insight into the molecular patterns underpinning evolution. Invasive species, by nature, are successful following genetic bottlenecks , and provide an avenue for understanding what aspects of genetic diversity    is a staulgaris) , the staulgaris) , has colThe Australian starling invasion has a well-documented, but complex, introduction history, with multiple geographically dispersed introduction points that are separated in some instances by thousands of kilometres . StarlinExploration of starling populations identified several broad trends that characterize patterns of genetic change in this species. First, comparing invasive populations on separate continents , or distSecond, selection  within starling populations often occurs at sites with moderate allele frequencies , and levThird, examining the location of putative sites under selection across the starling’s genome reveals a larger number of genomic sites than expected under selection in the major sex (Z) chromosome . These rLastly, assessing genetic alongside environmental and morphological data indicates that genetic variation is positively correlated with phenotypic variation, and this relationship becomes more pronounced under a temporally variable environment  contributions to adaptation. However, phenotypic plasticity itself can have different underlying causes and mechanisms. Deeper understanding the role of plasticity in shaping a populations’ adaptive potential requires expanding omics data collection beyond just the genome. Co-analysing genetic and phenotypic data will be greatly aided by the inclusion of, for example, transcriptome, proteome, or epigenome data . These cEvolutionary genomic studies often produce a list of genetic sites flagged as under putative selection or associated with phenotypic or environmental data. While generating a shortlist of biologically interesting variants is the first of many steps towards biologically validating these results, knowing whether a variant has an adaptive advantage (or disadvantage) may require additional data , or even “evolve and resequence” studies under one or more standard environmental conditions . It is vAcridotheres tristis) to yield insights into the repeatability of adaptive change in invasive taxa. While the lack of comparative studies is simply because the field is in its relative infancy, we are quickly moving into an age where enough studies have now been published for us to begin employing meta-analytical approaches to conduct formal inter -population or -specific comparisons. This will allow us to begin confirming whether results from a singular study are an isolated phenomenon within a particular system, or are broadly applicable over many. Comparing patterns of genomic change across a broad range of invasive species is of vital importance to invasion population genomics to answer questions about the importance of different genomic variants and the role they play in rapid adaptation and species persistence. Further, comparisons between invasion genomics and the fields of conservation and agricultural genomics also promise to yield interesting answers (As we acknowledge the utility in cross-study comparisons of variants of interest, so too must we acknowledge the lack of these within the field of evolutionary and population genomics. Such approaches can start more modestly, with comparisons across similar species (for example, comparison between the invasive sturnids, the starling and the common myna  answers .Technology continues to shape the nature of the questions that can be asked and reveals the importance of previously understudied types of variation. WGS captures genome-wide genetic information that improves on reduced representation sequencing approaches, as it does not rely on subsampling species-specific genomic loci. This provides a more uniform starting point for further analysis and more long-term utility, provided careful scientific reporting and best practices are followed . FurtherThe incorporation of published data into cross study comparisons is greatly aided by a high-quality species genome. While putative chromosomes may assembled using syntenic approaches e.g., , completThe study of invasive systems have and will continue to yield many important discoveries within the fields of population and evolutionary genomics. Characterising rapid adaptation from the molecular to the macro level within invasive populations enables scientists to appreciate how genetic variation interacts with a variety of selection processes to allow species to evolve. Within these studies, starlings present a valuable system for understanding evolution, providing opportunity to investigate everything from subtle morphological shifts within a region, to observing broad patterns of parallel change across the globe. The research conducted on this system demonstrates the complex nature of standing genetic diversity and the means through which it facilitates adaptation. From these results we also see some promising directions of future study within the field of evolutionary and population genomics. Collectively pursuing these directions using this system and that of other invaders will facilitate a deeper appreciation of how evolution functions at the molecular level. Ultimately, through deep studies across a broad range of taxa, we may learn to precisely explain and predict patterns of evolution, to protect precious biodiversity.