Sus scrofa coreanus), which is among the native porcine breeds in South Korea, in terms of its overall genetic diversity and ancestry. According to 60K single-nucleotide polymorphism (SNP) BeadChip analyses, the KNP pig showed similarity to Western breeds than to Chinese breeds. This conclusion runs contrary to popular belief as a Chinese origin of KNP pigs, as suggested by previous historical and genetic studies. We also describe the possibility of potential biases in the analysis results.Assessing and understanding the genetic resources of indigenous livestock populations is necessary to address issues associated with conservation and domestic supply, etc. This study examined the Korean native pig  is an indigenous porcine breed in South Korea considered as a valuable but dwindling genetic resource. Studies using diverse methodologies and genetic markers suggest that this population originated from the Manchu province of Northeastern China and migrated approximately 3000 years ago into the Korean peninsula. This study aimed to verify those findings by performing diversity and ancestral analyses using the 60K single-nucleotide polymorphism (SNP) BeadChip on 891 pigs of 47 breeds worldwide. We also performed principal component analysis (PCA), ancestry analyses, phylogenetic tree analysis using SNPhylo, and linkage disequilibrium analysis. Furthermore, we generated heatmap, obtained Nei’s genetic distance and STF values, and explored the heterozygosity of commercial and native Korean pigs. The results demonstrated that KNP pigs are more closely related to European breeds than to Chinese breeds. In addition, as previous studies have suggested, our admixture analyses indicated that KNP pigs showed distinguishable genetic structure.The Korean native pig (KNP;  Domestication of the pig took place approximately 9000 years ago . SubsequThe Nagoya Protocol on Access and Benefit Sharing  has estaSus scrofa coreanus) is, as the name suggests, one of Korea’s indigenous porcine breeds. This short-haired, black species lives in the Korean peninsula and is considered a local delicacy due to its distinctive “black coat color” pork meat. The origin of this species was first documented in the Manchu province of Northeastern China and migrated approximately 3000 years ago; it has been used for ceremonial purposes and as a source of meat throughout the Korean peninsula [The Korean native pig  for Chinese Kele (KL), Min (MZ), and Sutai (SUT) breeds was measured and visualized at the individual chromosome level. Loss of heterozygosity over generations in each breed was measured using as the following index:Principal component analysis (PCA) can be performed for genetic mapping of pig breeds using eigenvectors to facilitate understanding of similarities among breeds worldwide. In this study, PCA was performed using the R package SNPRelate  . Ancestr2r value, respectively. Heat mapping was used to determine Nei’s genetic distances and STF values using the R package StAMPP  [AU, USA) . Both meThe PCA results provided an overview of the genetic diversity of the breeds included in this study . Breeds In ancestry cluster analysis , 3–10 clPhylogenetic tree using maximum likelihood started with Argentina Feral (ARFE) . AfricanKL, MZ, and SUT were included by TreeMix as branches of the phylogenetic tree of KNP. In OH, expected heterozygosity (SH), and inbreeding coefficient (ISF) values for each breed are shown in ISF value, which ranges from −1 to 1, illustrates the degree to which a target population is inbred, where positive value indicates a high degree of inbreeding. The Asian population ranged from 0.153 to 0.363 for OH and 0.122 to 0.283 for SH; ISF ranged from −0.243 to −0.148. The mean ISF values were −0.192, −0.201, and −0.194 in the American, Asian, and European populations, respectively. For the KNP population, ISF, OH, and SH had values of −0.233, 0.214, and 0.167, respectively.The STF values for each population. Regions are distinguished by color. Most of the breeds were clustered into Eastern and Western clusters. The Asian breeds KNP, SUT, KL, and MZ showed similarity to the Western breeds and had average STF and Nei’s genetic distance values of 0.302 and 0.218, respectively. The STF and Nei’s distance values for KNP with respect to the American/European population were 0.323 and 0.253, respectively, while those with respect to the Chinese population were 0.378 and 0.409, respectively. The overall STF and Nei’s genetic distance values for KNP were 0.345 and 0.317, respectively.In the heat map , lower a2r values for each region and breed  showed high LD values . Regarding the European and African populations  . Finally, Mexican Cuino (CMX) and WD had 2r values of 0.37 and 0.33, respectively.LD analyzed based on average nd breed . The Y-aulations , GUN hadOH was depicted with boxplot for KNP, KL, MZ, and SUT. OH was less than 0.26 for KNP while, for KL, it exceeded 0.32 for all chromosomes. KL was the most heterogeneous breed with respect to chromosome 1, with a value of 0.42. The KNP population showed the lowest average OH value for chromosome 18 of 0.18. Based on TreeMix, the STF and Nei’s genetic distance values were 0.496 and 0.306, respectively, for KNP with respect to the Chinese populations; the values with respect to the Western populations were 0.359 and 0.219, respectively.Historical records show that KNP migrated from Manchu province in China to Korea . This reThe differences in results between this study and those of previous studies of KNP may be explained as follows. First, as also previously mentioned, northern Chinese breeds migrated to the Korea peninsula in the distant past, whereas European genes were introduced to the Korean pig populations in the 20th century. Second, BeadChip was designed for application to commercial pig breeds from Europe and America ,24,25, sWhole-genome sequencing (WGS) of KNP and commercial breeds have also been studied to characterize genetic variation therein; numerous novel SNPs were found in each population, which further underscores the need for up-to-date swine SNP arrays . Althoug2r value (see 2r = 1) LD combined markers were 189,751 (KNP), and 64,065 (SUT). Due to the ratio of complete LD markers to overall LD markers, some breeds, such as WB and KNP, had high levels of LD in entire distances, as compared to others, in addition to high LD levels in short distances, implying that KNP maintained its genetic integrity for many generations, similar to many aboriginal species. Though high-LD block pruning was applied, homologous diploid patterns, in many windows, through LD filtering, reflects that the KNP may occasionally be homozygotic . KNP is in the middle of the dendrogram. This pattern was also observed when KNP was added as ancestry see , implyinOH and SH) and ISF value are estimated to be lower than most of the breeds. These results are believed to have long maintained indigenous pig breeds of KNP since they entered Korea, but we can assume crossbreeding can also be seen. Therefore, it is necessary that more research to accurately identify the genetic structure will be required for the genetic improvement of KNP.In this study, the KNP population showed an authentic genetic profile among all of the porcine populations studied, especially in the admixed pattern. This population had an intermediate position in the PCA plot, more kin to Western breeds, and was located among Western and Asian breeds in the phylogenetic tree analysis. In addition, according to the phylogenetic tree and a genetic distance heat map, KNP was closer to the Western breeds than the Chinese breeds. This is estimated to be the result of the crossing of European pig breed before the 1930s. Since the KNP has maintained a small group with small breeding since it was introduced to Korea, the average heterozygosity value (


Thamnophis sirtalis tetrataenia), a species affected by alteration and isolation of wetland habitats throughout its distribution. The primary goals were to determine the population structure and degree of genetic isolation among T. s. tetrataenia populations and estimate effective size and population abundance within sites to better understand the present and future importance of genetic drift. We also used temporally sampled datasets to examine the magnitude of genetic change over time. We found moderate population genetic structure throughout the San Francisco Peninsula that partitions sites into northern and southern regional clusters. Point estimates of both effective size and population abundance were generally small (≤ 100) for a majority of the sites, and estimates were particularly low in the northern populations. Genetic analyses of temporal datasets indicated an increase in genetic differentiation, especially for the most geographically isolated sites, and decreased genetic diversity over time in at least one site (Pacifica). Our results suggest that drift-mediated processes as a function of small population size and reduced connectivity from neighboring populations may decrease diversity and increase differentiation. Improving genetic diversity and connectivity among T. s. tetrataenia populations could promote persistence of this endangered snake.Conversion and fragmentation of wildlife habitat often leads to smaller and isolated populations and can reduce a species’ ability to disperse across the landscape. As a consequence, genetic drift can quickly lower genetic variation and increase vulnerability to extirpation. For species of conservation concern, quantification of population size and connectivity can clarify the influence of genetic drift in local populations and provides important information for conservation management and recovery strategies. Here, we used genome-wide single nucleotide polymorphism (SNP) data and capture-mark-recapture methods to evaluate the genetic diversity and demography within seven focal sites of the endangered San Francisco gartersnake ( Is the manuscript technically sound, and do the data support the conclusions?The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: YesReviewer #2: Yes**********2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: YesReviewer #2: Yes**********3. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.The Reviewer #1: YesReviewer #2: Yes**********4. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: Yes**********5. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: This paper represents a rigorous application of advanced genetic and demographic analyses in the context of a protected species and will contribute greatly to conservation of the focal subspecies. Data are presented in a clear and concise manner and all methods and results are explained fully. While not a consideration for publication in PLoS ONE, the topic and results of the study are both broadly and specifically interesting, presenting a valuable example of the use of the methods herein that can be applied elsewhere while also having practical implications for the management of a specific species.I am recommending that the paper be accepted without revision. If revised, I provide the following recommendations and corrections.Materials and MethodsField methods and sample selectionLine 128 - describe tissue sampling. What samples were collected and how?Line 128 - explain focal sites. How/why were focal sites chosen? How do focal sites differ from "satellite" sites? Is it just sample size?Throughout - how do the sites compare to one another? In size? In habitat? In sampling coverage? It is mentioned in the discussion that some sites are much larger than the area that was sampled. Additional information to specify for which sites this was true and how it might affect the results or interpretation of the results would be helpful.A few grammatical errors:Line 401 ..."filters we applied..." should be filters were appliedLine 411 ..."where it was assigned as distinct genetic cluster..." should be assigned as a distinct genetic clusterLine 426 ..."each regional clusters that supported..." clusters should be clusterReviewer #2: Wood et al. survey numerous sites for Thamnophis sirtalis tetrataenia and generate a RAD-seq data set for this taxon to better understand how best to implement future conservation efforts. I think that this is an interesting and important data set that has been thoroughly analyzed and has real world conservation applications. I have only a few comments listed below.The sampling dates to look at temporal change in population genetic structure are 2004-2010 and 2015- 2018. How were these thresholds for change over time decided on? It seems arbitrary and not consistent across all sites.For the hierarchical structure analyses, was delta-K used in subsequent structure runs? If so, how was K=1 assessed (it is a known issue that with this particular method K=1 cannot be assessed)? Also I am assuming that the hierarchical structure analyses were run until there was no additional population structure? A bit more details would be good here.L451 – I’m not entirely sold that this method is highlighting urban development as influencing population structure. Perhaps more details on how this is the case would clarify this. Otherwise, I think it’s a bit overstated.That’s super interesting that T. s. tetrataenia is paraphyletic given how morphologically different it is!What’s the longevity of these snakes – are the authors certain that in some of these small populations that the same individuals weren’t sampled in both time periods used for temporal changes in genetic structure? E.g., in Pescadero there’s only 6 years between sampling periods, presumably these garters are living 6+ years and the small population sizes at these sites might make it more likely to sample the same individuals after six years.Were these tissue samples accessioned at a natural history collection? I really like the temporal sampling, and in the discussion the authors state that future studies could build on this study but if a different approach to generating sequence data was used it wouldn’t be possible. Perhaps I missed it, but I would really like to see these samples available.**********what does this mean?). If published, this will include your full peer review and any attached files.6. PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool,  30 Mar 2020The below responses to the reviewers was also attached with the manuscript.Response to PlosOne Journal ReviewsReviewer #1: This paper represents a rigorous application of advanced genetic and demographic analyses in the context of a protected species and will contribute greatly to conservation of the focal subspecies. Data are presented in a clear and concise manner and all methods and results are explained fully. While not a consideration for publication in PLoS ONE, the topic and results of the study are both broadly and specifically interesting, presenting a valuable example of the use of the methods herein that can be applied elsewhere while also having practical implications for the management of a specific species.I am recommending that the paper be accepted without revision. If revised, I provide the following recommendations and corrections.Materials and MethodsField methods and sample selectionLine 128 - describe tissue sampling. What samples were collected and how?Response to Reviewer 1: We added information regarding what type of tissue was taken and how this was conducted on page 6, lines 154-156.Line 128 - explain focal sites. How/why were focal sites chosen? How do focal sites differ from "satellite" sites? Is it just sample size?Response to Reviewer 1: We added text to help clarify the difference between focal & satellite sites with the section “Field methods and sample collection”. This can be found on page 6-7, lines 150-196. The focal sites were where we focused the demographic studies whereas satellite sites were not involved in demographic analyses and had much smaller sample sizes. Throughout - how do the sites compare to one another? In size? In habitat? In sampling coverage? It is mentioned in the discussion that some sites are much larger than the area that was sampled. Additional information to specify for which sites this was true and how it might affect the results or interpretation of the results would be helpful.Response to Reviewer 1: We have added estimates of the area effectively sampled by our traps for each site. Based on data we have on the distances moved by San Francisco gartersnakes between captures (97% of snake movements were < 200 m), we calculated the effective sample area for each site by buffering traps by 200 m and summing the area of habitat sampled by traps at each site. We have also added estimates of the total area of habitat available at each site , so readers can compare how much of the habitat at each site was effectively sampled for San Francisco gartersnakes. The following text has been added to the methods :“Sites differed in size of available habitat and in the area sampled by traps and cover objects. To define the total area of available habitat for each site, we created polygons in ArcGIS version 10.7.1 [33] that encompassed all suitable habitat, whether wetlands or non-forested uplands. The effective area sampled was then calculated by using a fixed buffer of 200 m around all trap and artificial cover object locations for each site. We chose a 200 m buffer based on the maximum distance moved between captures for greater than 95 percent of individual T. s. tetrataenia at our study sites . The total site area and effective area sampled for each site are given in Table 1. At Pacifica and San Bruno, nearly all of the available habitat for T. s. tetrataenia was within the area effectively sampled by traps, and the nearest known population of T. s. tetrataenia was more than 2.5 km away. In contrast, although the area sampled at Skyline and Crystal Springs was comparable to Pacifica, only 60-70 percent of the area was sampled because suitable wetland habitat was present nearby and additional habitat  is present along most of the 7 km corridor separating these two sites. Año Nuevo, Mindego, and Pescadero were much larger sites and the area sampled was less than 60 percent of the available habitat.”A few grammatical errors:Line 401 ..."filters we applied..." should be filters were appliedResponse to Reviewer 1: We fixed the grammatical error.Line 411 ..."where it was assigned as distinct genetic cluster..." should be assigned as a distinct genetic clusterResponse to Reviewer 1: We fixed the grammatical error.Line 426 ..."each regional clusters that supported..." clusters should be clusterResponse to Reviewer 1: We fixed the grammatical error.Reviewer #2: Wood et al. survey numerous sites for Thamnophis sirtalis tetrataenia and generate a RAD-seq data set for this taxon to better understand how best to implement future conservation efforts. I think that this is an interesting and important data set that has been thoroughly analyzed and has real world conservation applications. I have only a few comments listed below.The sampling dates to look at temporal change in population genetic structure are 2004-2010 and 2015- 2018. How were these thresholds for change over time decided on? It seems arbitrary and not consistent across all sites.Response to Reviewer 2: This was an opportunistic use of previously collected, older samples that were available. We decided there were two breaks in time between concerted tissue sampling (1) an earlier temporal sample with a wider range of dates from 2004-2010 depending on the site and (2) a later temporal sample across a three year span 2015-2018. For the hierarchical structure analyses, was delta-K used in subsequent structure runs? If so, how was K=1 assessed (it is a known issue that with this particular method K=1 cannot be assessed)? Also I am assuming that the hierarchical structure analyses were run until there was no additional population structure? A bit more details would be good here.Response to Reviewer 2: Yes, we used delta-K in subsequent runs for STRUCTURE analyses. As we mentioned in the methods, we evaluated the clustering results of our STRUCTURE analyses using an independent analysis of population structure, discriminant analysis of principal components (DAPC). This analysis differs from STRUCTURE analysis in that it does not require the assumptions of HWE. Given that we recovered the same clusters that were observed in the STRUCTURE analysis, cluster results above K=1 are much more likely. In addition, the results from the mean log likelihood  Geological Survey, or by any other parties for the Government of the United States, when no longer needed for investigations in progress shall be deposited in the National Museum [Smithsonian Institution National Museum of Natural History].AttachmentResponse to Reviews_R1_R2.docxSubmitted filename: Click here for additional data file. 7 Apr 2020Combining genetic and demographic monitoring better informs conservation of an endangered urban snakePONE-D-19-34941R1Dear Dr. Wood,We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication.https://www.editorialmanager.com/pone/, click the "Update My Information" link at the top of the page, and update your user information. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.Shortly after the formal acceptance letter is sent, an invoice for payment will follow. To ensure an efficient production and billing process, please log into Editorial Manager at onepress@plos.org.If your institution or institutions have a press office, please notify them about your upcoming paper to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, you must inform our press team as soon as possible and no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact With kind regards,Mark A. Davis, Ph.D.Academic EditorPLOS ONEAdditional Editor Comments :The original manuscript was well done, and this revision manages to improve upon the original's excellence. It is incredibly well-written, and of the highest statistical rigor. It will make a fantastic contribution to the Biodiversity Conservation Collection, and I commend the authors for their workReviewers' comments: 9 Apr 2020PONE-D-19-34941R1 Combining genetic and demographic monitoring better informs conservation of an endangered urban snake Dear Dr. Wood:I am pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. onepress@plos.org.If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact plosone@plos.org. For any other questions or concerns, please email Thank you for submitting your work to PLOS ONE.With kind regards,PLOS ONE Editorial Office Staffon behalf ofDr. Mark A. Davis Academic EditorPLOS ONE


This report provides the first estimates of the genetic basis of all key breast muscle myopathies (BMM) in broiler chickens  and their relationship with body weight and breast yield. Data from a pure bred high yielding commercial broiler line were analysed to estimate the genetic parameters using a multivariate animal model with the appropriate fixed effects and permanent environmental effect of the dam. Heritabilities of the BMM ranged from 0.04 to 0.25 and the genetic correlation of the BMM with body weight and breast yield ranged from -0.06 to 0.41. Here we highlight that the genetic variance of BMM accounts for a low proportion of the phenotypic variance and the BMM have a low genetic relationship with performance traits. The large contribution of residual variance to the phenotypic variance for the BBM was >71.5% which indicates the importance of the non-genetic effects on BMM. The data presented also show that the moderate to low genetic influence for the development of BMM can be used, through balanced selection, to reduce the myopathy incidence in the long term. The impact of genetic selection against BMM was tested empirically by comparing the incidence of WB and % breast yield of a commercial broiler with a high generation (HG) broiler. The HG broiler used represents 2 years of genetic improvement compared to the commercial broiler; the HG broiler had an 18.4% relative decrease in WB and a 1.02% relative increase in breast yield compared to the commercial broiler. This paper describes the relationship between the genetic and non-genetic factors influencing BMM highlighting the importance of understanding the non-genetic effects on myopathy incidence. It also shows that the genetic component of BMM can be reduced whilst at the same time improving breast yield as part of balanced breeding goals. The phenotypic data spans four generations collected over 3 years from 105 flocks, with the inclusion of an extra generation of pedigree relationships for the estimation of the genetic parameters. Within this study the key phenotypic traits of interest were broiler body weight (BW), breast meat yield (BY), deep pectoral myopathy (DPM), WB, SB and WS . The birAll birds were given a uniquely barcoded wingband after hatch and all traits measured were recorded to this number. The birds were individually weighed at 40 days of age and then taken to the processing plant. Following plucking and evisceration, the carcasses were air chilled with a target to reach 3°C within 8–10 h of kill. the carcases were deboned 24 h after kill and the presence of BMM was then assessed and recorded by a trained team of individuals as previously described in The traits BW, PW, BY, DPM, WB, SB and WS were analysed together in the following multivariate animal model to estimate genetic parameters:y denotes the vector of observations of the traits, b is the vector of the fixed effects accounting for the interaction between hatch-week, pen and contributing mating group of each flock. The vector of additive genetic effects is shown by a, the vector of permanent environmental effects of the dam (PEM) is represented by c, and e denotes the vector of residuals. X, Z and W represent incidence matrices relating the vectors b, a, and c to y. The assumed (co)variance structure was:Where: A represents the additive and genetic relationship matrix and I represents identity matrix. The variance and covariance matrices of additive genetic effects, PEM and residual effects are denoted by G, C and R, respectively. All variance component analyses were performed by REML using VCE , Great parents (GP) and Parent Stock (PS) as shown in ocessing . Hatchinocessing . PS for Birds were processed as outlined in the previous section above. Breast meat was weighed and recorded as a % of body weight to give breast yield. All breast fillets were scored for WB on the same 4 point scale described above.Due to hatch to hatch variation in performance, as shown by the mean BW in t-test.Any significance of differences between the two broiler groups in each hatch were determined with a student’s The summary of production traits and the mean scores of BMM for the pure line population can be seen in The phenotypic correlations between the BMM and the production traits BY and, BW were low to moderate ranging from 0.02 to 0.23; this indicates the BMM are not linked wholly to changes in BW or BY. Equally the phenotypic correlations between the individual myopathies were generally low to moderate ranging from -0.02 to 0.25; thus suggesting the individual BMM are independent of each other. The genetic correlation between BY with BW was low with an estimate of -0.06 indicating these two traits are independent of each other. The genetic correlations between the BMM and BW and BY were low to moderate with estimates ranging from -0.06 to 0.41. This indicates there is limited shared genetic background between BW and BY with the BMM. The genetic correlations between the myopathies were predominately low to moderate ranging from -0.04 to 0.52 with a higher genetic correlation between WB and WS of 0.74.Heritability is the proportion of phenotypic variance explained by genetic variance, and thus provides an indication of the genetic influence on variance on any given trait. The estimated heritabilities for BW and BY were 0.31 and 0.40, respectively, and the estimated heritabilities for the BMM ranged from low to moderate: 0.04 for SB, 0.06 for DPM, 0.07 for WB and 0.25 for WS. p < 0.001) and 2.4% more breast yield (p < 0.03) relative to the current commercial broiler. These results demonstrate that selection for increased breast meat in broilers can be achieved whilst selecting against the genetic propensity to develop BMM such as WB. The generational difference of the commercial and HG broilers used in this study represents 2 years. This means an annual realised relative reduction in WB of 9.2% and an annual realised relative increase in BY of 1.2%.The relative and mean BY and WB incidences for the HG and current commercial broilers across the four hatches are displayed in The importance of BMM in the poultry industry has been well documented in academic and popular poultry press over the last 10 years. Myopathies can lead to carcase downgrades in severe cases or, in the milder cases they can have a negative influence on the eating quality, ability of meat to take up marinates, cooking losses and consumer acceptance . A recenIn this study, BW and BY showed estimates of heritability of 0.31 and 0.40, respectively, which are similar in magnitude to those found in the literature . The herThe genetic correlations between the traits suggests that genetic selection for increased BW and BY has not had a concomitant increase in the genetic propensity for the expression of breast myopathies. The genetic correlations between the production traits and the BMM are low to moderate indicating that only a small proportion of the genetic component influencing BW and BY is shared with the genetic component of the BMM. Similarly, the phenotypic correlations between the myopathies and production traits are also low to moderate, ranging from 0.02 to 0.23. The lack of strong phenotypic correlations indicates that the myopathies can occur in birds of all sizes with a range of BW and BY. These results indicate that selection for increased BW and BY is not the sole cause of the myopathies. A comprehensive study comparing conventional and slower growing broilers by Within this analysis the contribution of the PEM to the phenotypic variance of each trait was estimated. For this dataset the PEM for BW, BY, WB and WS is negligible when compared to the heritabilities and standard errors for those traits. However, for DPM and SB the PEM was slightly larger and had it been ignored in the analysis the heritability for DPM would have increased from 0.06 to 0.09 and SB would have increased from 0.04 to 0.074. This highlights the importance of fitting the PEM when estimating genetic parameters in chicken datasets that are characterised by families with large sets of full sibs to ensure accurate discrimination of the additive genetic variance.Despite the modest influence of genetics on the myopathies, the genetic component has not been overlooked by primary breeders as it can be used as part of a holistic approach to reduce the incidence and severity of the myopathies. The results presented here show that, from the perspective of genetic selection, it is possible to continue to improve breast yield and body weight whilst reducing the genetic propensity for the expression of all the BMM as described by The ability to select for increased breast yield whilst reducing the genetic propensity for WB is testament to the low genetic correlation between the two traits. The effect of this selection over time can be seen in Whilst the genetic component of the myopathies can be targeted through selection, it must be acknowledged that genetic selection is not the sole solution for reducing the incidence of the myopathies. Again, non-genetic effects have a much greater influence on the variation in BMM and therefore offer a more successful short term opportunity to significantly reduce their incidence. Genetic selection against the myopathies is more of a long term strategy owing to the low heritabilities of the BMM and the time taken for genetic progress to pass from the elite population to the commercial broiler. Ultimately, the successful reduction of myopathies in broiler chickens will be the result of a holistic approach to understand and account for all the influencing factors.ad lib have activity associated with muscle fibre degeneration and adipogenesis (Flock growth and performance are influenced by a wide range of environmental and management factors such as brooding, nutrition, temperature and ventilation. Understanding how these factors influence bird growth in terms of muscle development may give more insight into the pathophysiology of the myopathies. Muscle growth, development and maintenance are reliant on the satellite cells; these are multipotent stem cells located on the periphery of muscle fibres and provide the required myogenic precursors for muscle fibre growth and repair . Incubatogenesis  leading ogenesis . Heat stogenesis . Thus itogenesis  and peerogenesis , b or inogenesis . ManagemThe aim of this paper was to characterise the genetic basis of BMM found in broiler chickens and their relationship with body weight and breast yield. Data from a pedigree broiler pure line showed that the residual variance accounted for a greater proportion of the phenotypic variance of the myopathies than the genetic variance. This indicates that the non-genetic factors have a greater impact on the variance of the myopathy traits than the genetic factors. It has been previously described how balanced breeding goals can lead to cumulative improvements in traits that have low genetic basis such as the myopathies . Using arbailey@aviagen.com.The datasets presented in this article are not readily available because it contains propriety information. Access to the data will be permitted by agreement. Requests to access the datasets should be directed to RB, The data used in this study came from the routine data collection and operation of the Aviagen breeding program where all animals are under the care of the Aviagen Veterinary Department, Huntsville, AL, United States. The data used in this trial is collected from birds going to slaughter as part of normal commercial poultry processing through an abbatoir. These birds were not experimental animals and there were no experimental procedures carried out on them.All authors were involved with the conception of the research. RB drafted the manuscript which was revised and reviewed by SA and ES.All authors are employed by Aviagen Ltd.


Carex nigra in a central alpine fen. For our analysis, we established 16 study plots randomly distributed over the fen. We determined the exact elevation of each plot as an indicator for the water regime and measured the content of phosphorous and potassium in the soil of each plot. Clonal diversity and genetic variation of C. nigra were assessed with nuclear microsatellites using leaf material collected in 20 subplots along a diagonal cross within each study plot. The influence of water regime and soil mineral nutrients on clonal diversity and genetic variation was estimated by Bayesian multiple regression. Our study revealed a clear impact of soil nutrient conditions on clonal diversity and genetic variation of C. nigra, which increased with the concentration of phosphorous and decreased with the concentration of potassium. Key background to these findings seems to be the relative offspring success from generative as compared to clonal propagation. Phosphorous acquisition is essential during seedling establishment. Clonal diversity and genetic variation increase, therefore, at sites with higher phosphorous contents due to more successful recruitment. High levels of clonal diversity and genetic variation at sites of low potassium availability may in contrast be mainly caused by increased plant susceptibility to abiotic stress under conditions of potassium deficiency, which brings about more gaps in C. nigra stands and favors the ingrowth from other clones or recruitment from seeds.In this study we analysed the impact of water regime and soil nutrients on the clonal diversity and genetic variation of the sedge  Clonal growth is one of the most remarkable characteristics of plants and is widely distributed among alpine  and wetlIn fens, environmental heterogeneity is mainly caused by water regime and nutrient conditions . DependiMoreover, soil mineral nutrients are heterogeneously distributed in fens. Generally, acidic fens on siliceous bedrock are mesotrophic to oligotrophic ecosystems  containiIt has been demonstrated in previous studies that clonal plants may react plastically on heterogeneous nutrient conditions. The modification of their growth pattern allows clonal plants to identify habitat patches containing high concentrations of nutrients and to concentrate most of their biomass in these patches . In nutrClonal diversity and genetic variation within plant populations depends on the balance of clonal and sexual reproduction . BesidesCarex nigra in a highly heterogeneous alpine fen. The study species is a very plastic, clonal species with creeping rhizomes producing a large number of ramets and internodes of variable length  Reichard is a wind-pollinated and perennial sedge native to European and Siberian wetlands  as well as the number of flowering shoots (Sf) per plot. Phosphorous and potassium are vitally important limiting nutrients in poor fens , 0.15 µl reverse Primer (10 pMol/µl), 1.0 µl Buffer S , 0.4 µl dNTPs (5 mM) 0.05 µl Taq-Polymerase  and 2 µl template DNA (7.8 ng/ µl). Thermal cycling conditions were 94 °C for 5 min; 34 cycles of 94 °C for 60 s, 50 °C for 60 s, and 72 °C for 60 s; and a final extension of 72 °C for 8 min. Amplified PCR fragments were analysed by capillary gel electrophoresis on an automated sequencer .In total we investigated 320 samples using six microsatellite loci , which hscoparia  and alsoa), effective number of alleles (Ne), observed and unbiased expected heterozygosity , and the fixation index (F) per plot in GenAlEx 6.5 (The length of each amplified microsatellite fragment was determined using the software Genome Lab (Beckmann Coulter). Based upon the length of the fragments the number of different multilocus genotypes was assessed. Samples with the same genotype were considered as originating from the same clone and the distribution of the multilocus genotypes within the plots was mapped . We deteAlEx 6.5 .2 of the resulting spatial model using Euclidean distances between plots using the R packages ade4 V. 1.7.13, adespatial V. 0.3.7, maptools V. 0.9.2 and spdep V. 0.7.7 , clonal diversity  and genetic variation  was estimated by Bayesian multiple regression using the rjags R package V. 4.6  were generated to account for spatial trends in our dataset. To this end, we chose a spatial weighting matrix by maximising the adjusted RV. 0.7.7 . Based oe V. 4.6  as well The elevation of the study plots ranged from 2299.4 to 2300.1 m above sea level indicating a maximum topographical difference of 70 cm among the plots across the whole fen . The cons) ranged 225 to 850, and the number of flowering shoots (Sf) from 1 to 90. In the microsatellite analysis, 15 alleles at six loci were revealed. Four alleles per locus were amplified at the loci S08, S245 and S175. In contrast, only one allele was found at the loci S180, S102 and S119. Locus S180 produced null alleles at 11 samples and was, therefore, deleted from the analysis. The 320 analysed samples resulted in 14 different multilocus genotypes (A-N). The number of clones (G) per plot ranged from 1 to 8 (a) per plot was minimum 1.40 and maximum 3.00, whereas the effective number of alleles (Ne) per plot ranged from 1.07 to 2.12. Observed (Ho) heterozygosity varied between 0.00 and 0.27 whereas expected (He) heterozygosity ranged from 0.05 to 0.44. The inbreeding coefficient (F) was minimum 0.25 and maximum 1.00.The total number of shoots per plot  as well as the potassium (K) content of the soil showed a credible impact or a trend for an impact on the number of clones (G) per plot, the number of alleles (Na), the effective number of alleles (Ne) and the expected heterozygosity (He) . In all C. nigra in our study (mean HE = 0.26 and mean HO = 0.11) was notably lower than reported previously for other widespread, long-lived and outcrossing plant species (mean HE = 0.56–0.65 and mean HO = 0.57–0.63)  , which c.07–0.71  and for .00–0.99 . Howeverand 0.23 , which iIn our study clonal diversity as well as the number of clones and the number of alleles present at a site were clearly related to phosphorus and potassium contents . We can Carex nigra is principally able to form arbuscular mycorrhizal symbiosis  are given together with the effective sample size (ESS) of all parameters. A 90% highest density interval (HDI) was computed for each model parameter . PDist is the percentage of the posterior distribution that is larger than zero. A credible impact of soil nutrients on clonal diversity and genetic variation is indicated by superscript a and a trend for the impact is indicated by superscript b.Click here for additional data file.10.7717/peerj.8887/supp-5Table S2Click here for additional data file.10.7717/peerj.8887/supp-6Supplemental Information 2Click here for additional data file.10.7717/peerj.8887/supp-7Supplemental Information 3Click here for additional data file.10.7717/peerj.8887/supp-8Supplemental Information 4Click here for additional data file.


The emergence of very high-resolution (VHR) satellite imagery  is creating new opportunities within the fields of ecology and conservation biology. The advancement of sub-meter resolution imagery has provided greater confidence in the detection and identification of features on the ground, broadening the realm of possible research questions. To date, VHR imagery studies have largely focused on terrestrial environments; however, there has been incremental progress in the last two decades for using this technology to detect cetaceans. With advances in computational power and sensor resolution, the feasibility of broad-scale VHR ocean surveys using VHR satellite imagery with automated detection and classification processes has increased. Initial attempts at automated surveys are showing promising results, but further development is necessary to ensure reliability. Here we discuss the future directions in which VHR satellite imagery might be used to address urgent questions in whale conservation. We highlight the current challenges to automated detection and to extending the use of this technology to all oceans and various whale species. To achieve basin-scale marine surveys, currently not feasible with any traditional surveying methods , future research requires a collaborative effort between biology, computation science, and engineering to overcome the present challenges to this platform’s use. Ursus maritimus) [Halichoerus grypus) [The study of wildlife using satellite imagery commenced with the Landsat-1 satellite , initially launched in 1972 to monitor land cover, and later extended for habitat surveys and to map the distribution range of some species . Since tEubalaena australis) in an image captured by the WorldView-2 satellite . The available spatial resolution at the time, did not allow for the identification of whale-defining features—such as the fluke. The launch of the WorldView-3 satellite in 2016, offering the highest commercially available spatial resolution of 0.31 m, is capable of capturing whale-defining features [Monitoring whales using VHR satellite imagery was first attempted using the IKONOS-2 satellite in 2002; however, the 0.82 m spatial resolution provided by this satellite was not sufficient to make confident identifications . A changfeatures , increasTraditional cetacean surveys are challenging and expensive, often requiring dedicated research vessels or aircraft and are thus limited in their temporal and spatial scope e.g., ,14. MuchBuilding on recent work using VHR satellite imagery, and with the development of accessible and inexpensive artificial intelligence (AI) tools, there is a potential to (1) fill data gaps on the distribution, abundance, density, and population trends of whales; (2) supplement field-collected data by helping better plan aerial and boat-based surveys; and (3) allow for continued monitoring of whales when person-dependent surveys are restricted—as we have experienced during a pandemic—building a mechanism for important conservation research to continue globally. However, this method is in its infancy, with further pilot studies and experimentation needed. Here we aim to provide an overview of the development of these methods so far, in order to provide advice on future directions to follow and the main challenges that remain.Because of the growing interest in using VHR satellite imagery as a new platform to study marine mammals, it is important to facilitate the discussion between stakeholders regarding the best approaches to data collection and analysis. Therefore, in December 2019, we led an international workshop entitled: “Marine mammal surveys from satellite imagery: Applications, automation, and challenges” during the World Marine Mammal Conference in Barcelona. A group of 33 participants from academia, consultancy, government, and industry came together to discuss the most recent advancements in satellite technologies and image recognition for marine surveys and the future of this field of study.VHR satellite imagery can either be accessed through satellite tasking , or archives of previously captured images . Archiva2. Ultimately image costs limit the potential to develop the application of VHR satellite imagery to study whales. Funding is limited in the field of marine conservation, and the current costs for acquiring imagery restrict the potential for investment by NGOs and research institutes, and for inclusivity and the opportunity of developing nations to utilise this platform. Currently all VHR satellites are commercially owned  with the exception of one governmentally-owned satellite (Cartosat-3 for India). As a result, acquiring VHR satellite imagery comes at a cost. When considering the cost of an aerial or ship-based survey in a remote region, the cost of imagery may be competitive , but cosWhen images are captured, environmental conditions can affect the quality of the image and the likelihood of detecting whales. Only cloudless images with few or no white caps will be useful for whale research, as clouds conceal the surface of the ocean, including whales, and strong winds create white caps, a confounding feature, that can create confusion and limit the confident detection of whales . It may 2 [Researchers have explored both manual ,11,12,272 . DevelopAutomated approaches are inherently more efficient than manual scanning, simply because humans are not required during the detection stages freeing up researchers to focus on other tasks. However, effective automation requires careful construction of a dataset of training images, from which the model can learn the characteristics of a whale in various ocean images. The training dataset must include a wide array of example images of different species of whales, in different environmental conditions, and displaying different postures/behaviors, as well as features that could be mistaken for a whale . Given tWith automated approaches it is expected that some whales will likely be missed  or that non-whale features will be counted as whales . Ideally with the appropriate training dataset, an automated method would yield no false negatives and only a few false positives. In this scenario, all positive detections can be manually checked and the process can improve over time by incorporating new observations into the existing training data . Anotherhttps://www.maxar.com/splash/it-takes-a-legion) and Airbus, by 2021 (https://www.airbus.com/space/earth-observation/portfolio.html), VHR satellite imagery is on its way to becoming a valuable tool to monitoring large whales, particularly in remote, understudied, and underfunded regions. Currently, we can reliably conduct presence/absence surveys through manual detection; however, continuing to develop automated detection systems will improve the efficiency. Throughout the development of the use of satellite imagery to monitor whales, we must ensure collaboration between various stakeholders , the creation of an open-source database of labelled whale images from satellites, and an appropriate analysis (coding) framework, as shown in With the continued development of high-performance computing infrastructure and the planned launch of 10 new 0.30 m resolution satellites by Maxar Technologies (Further experimentation and pilot studies will improve the reliability of this platform and help us understand how best to combine imagery methods with traditional field approaches to improve whale conservation. Future accessibility to VHR satellite imagery will dictate what can be achieved. To protect our world’s oceans, we need a global effort and we need to create opportunities for that to happen. Accessibility to VHR satellite imagery is critical to the development of expertise and to enhance conservation efforts.


As mitigating car traffic in cities has become paramount to abate climate change effects, fostering public transport in cities appears ever-more appealing. A key ingredient in that purpose is easy access to mass rapid transit (MRT) systems. So far, we have however few empirical estimates of the coverage of MRT in urban areas, computed as the share of people living in MRT catchment areas, say for instance within walking distance. In this work, we clarify a universal definition of such a metrics - People Near Transit (PNT) - and present measures of this quantity for 85 urban areas in OECD countries – the largest dataset of such a quantity so far. By suggesting a standardized protocol, we make our dataset sound and expandable to other countries and cities in the world, which grounds our work into solid basis for multiple reuses in transport, environmental or economic studies. Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12770162 As most humans live in urban areas and two-thirds of world population will live in cities by 20502, mitigating car traffic in cities has become crucial for limiting climate change effects6. Daily commuting is the main driver for passenger car use - about 75% of American commuters drive everyday ) - while alternative transport modes such as public transportation networks are unevenly developed among countries and cities .Motorized transport currently accounts for more than 15% of world greenhouse gas emissions11. A seminal result of transport theory, by Newman and Kenworthy10, correlated transport-related emissions with a determinant spatial criterion: urban density. Alternatively, Duranton and Turner11 claimed that public transport services were unsuccessful in reducing traffic, as transit riders lured off the roads are replaced by new drivers on the released roads. Such results, however, crucially lack both theoretical and empirical foundations15 and new research16 shows that the two main critical factors that control car traffic in cities are urban sprawl and access to mass rapid transit (MRT).Over the last decades, various attempts to assess the environmental impact of car use in cities have emerged from multiple fields, ranging from econometric studies to physics or urban studies18 for example. But what is a good measure of access to transit? Studies have mainly focused on the number of lines or stops21, length of the network or graph analysis24. Few works26, however, have considered investigating catchment areas of MRT stations, i.e. looking at the share of population living close to MRT stations, for instance within walking distance. Such conditions have however proved to be essential in explaining commuting behaviours and mobility patterns16.More generally, understanding mobility in urban areas is fundamental, not only for transport planning, but also for understanding many processes in cities, such as congestion problems, or epidemic spread25. It produces a rigorous dataset of the share of population living close to transit (less than 1 km) for 25 cities in the world (12 in OECD countries). However, definitions of urban areas and rapid transit systems in that dataset are multiple and need to be refined while the number of cities must be expanded.The most detailed definition of such catchment metrics is the People Near Transit (PNT), and originates from a 2016 publication from the Institute for Transportation and Development Policy (IDTP)Hence, in order to expand our global knowledge of urban mobility, we need a common, unified and universal definition of access to public transit as well as sound measures of such a quantity. In this paper, we clarify its definition and propose what is to our knowledge the largest global dataset of PNT.27. We restrict our measures to mass rapid transit, usually referring to high-capacity heavy rail public transport, to which we added light rails and trams. In our sense, mass rapid transit thus encompasses:Tram, streetcar or light rail services.Subway, Metro or any underground service.Suburban rail services.Our analysis uses functional urban areas (FUA) in OECD countries, a consistent definition of cities across several countries25, we do not exclude any form of commuting trains based on station spacing or schedule criteria. As we detail it in the Method section, we identify services and corresponding stops with the General Transit Feed Specification (GTFS), a common format for public transportation schedules and associated geographic information .Buses are not comprised in that definition. In contrast with28, we publish here a list of 85 OECD cities  projectPython 3.7.6  and package gdal 3.0.2  to convert the raster files into vectorized shapefiles. The resulting shapefiles are comprised of polygons with field value the population in each polygon. Since the polygonization process merges adjacent pixels with common value into single polygons, populations for each polygon must be recomputed from polygon area and density through the simple following rule:pixel = 250 × 250 = 62500 m2. This leaves us with a list of 224 shapefiles of population that cover land area on earth.We downloaded population tiles that cover land on the globe in the Mollweide projection (EPSG:54009) and in raster format (.tif files). These raster data are made of pixels of width 250 m with associated value the number of people living in the cell. We processed the downloaded tiles with 27 (reprojected into Mollweide projection), we can build a population-grided dataset of cities in OECD countries.By intersecting the resulting shapefiles with OECD shapefiles delineating Functional Urban Areas (FUA) in OECD countriesThese resulting files are the population substrates used for measuring population living close to MRT stations.de facto standard format for public transportation schedules and associated geographic information is the General Transit Feed Specification .A common and A GTFS feed is a collection of at least six CSV files (with extension.txt) contained within a.zip file. It encompasses general information about transit agencies and routes in the network, schedule information such as trips and stop times and geographic information for stops (geographic coordinates).https://developers.google.com/transit/gtfs):Routes: distinct routes in the network of a certain type. A route is a (one-direction) regular line, for instance a metro or bus line. The route types we use are .- Ferry. Used for short- and long-distance boat service.routes.txt, trips.txt, stop_times.txt and stops.txt allows us to bind stops with their associated route types. We can thus discriminate between bus stops and metro stops and thereby select objects according to our definition of MRT.Joining in this order the four tables In a nutshell each GTFS file can be processed to produce localized and route-typed stops.In order to measure PNT within urban areas, we must bind transit systems with their respective FUAs. We need to retrieve - and merge - all available GTFS files pertaining to a specific urban area and make sure that no rapid transit agency is excluded in the process.https://transitfeeds.com, 2020). For each city in our dataset, we cross-checked the OpenMobilityData with Wikipedia local network information  to ensure that we considered all agencies of rapid transit within the urban area.Most GTFS files for cities in the world are collected by the OpenMobilityData platform (GTFS für Deutschlandhttps://gtfs.de/ and Open platform for French public datahttps://www.data.gouv.fr). We also note that GTFS format is not common in South Korea, Japan and in the United Kingdom where we only found GTFS data for Manchester area on OpenMobilityData (https://transitfeeds.com) while we directly used station coordinates for London (Transport for London. TFL Station Locations available at https://data.london.gov.uk/dataset/tfl-station-locations).For some European countries , GTFS files were not availaible on OpenMobilityData and had to be retrieved from other sources  and buffer - still using gdal - catchment areas for several distance thresholds: 500 m, 1000 m and 1500 m. Intersecting the resulting buffers with the population-grided shapefiles gives us the total population living within catchment areas, that can be expressed as a share of the total urban area population resulting in the value of the PNT metric. Our results are shown in Online-only Table Figshare30.The Data Record of PNT in OECD urban areas is available online on mrt_access.csv)30.PNT levels at distance thresholds: 500 m, 1 000 m and 1 500 m for the 85 Functional Urban Areas are shown on the Online-only Table pops_close_to_MRT.zip)30.We also provide, for each city, grid-maps of population at different distances from MRT and Vancouver (19% vs 23%). The discrepancies observed for the other cases stem from different definitions of cities and from the different transit systems that were taken into account. While we work with Functional Urban Areas (FUA) only, the authors of 25 considered a definition of the LRT (Light Rail Transit) and Suburban Rail that depends on station spacing and schedule criteria. We didn’t choose this definition and for Boston and New York, we had therefore to exclude suburban trains - while keeping the definition of FUA - in order to retrieve results similar to those of Table 25 took into account the Bus Rapid Transit for Los Angeles, that we decided to exclude. Finally, in Paris the authors of 25 considered that the so-called RER trains were comprised in Suburban Rail, but not Transilien trains, while we included both systems in our analysis.Also, the authors of 25 and after checking on Table 25.The conclusion here is that for similar definitions for cities and transit systems, we obtain similar results, validating our method and calculations. In order to facilitate the comparison across future studies, we would recommend using the definition of cities given by Functional Urban Areas since it is very commonly used and already unified for OECD countries. Concerning transit systems, we think that it is more relevant and also verifiable to consider transit systems based on their types (Rail versus Road) rather that on spacing and schedule criteria that are specious and less universal. Hence, in comparing our results with results from the IDTP reportFor other cities in the dataset we have unfortunately found no existing data to compare with. Thus, we hope for future research to test and expand our estimations and results.Gitlab (https://gitlab.iscpif.fr/vverbavatz/mrt-access-project).Easy code and hints are given on Open Source Geospatial Foundationhttps://gdal.org, 2020) to handle geographic data with Python.We strongly recommand using GDAL (GDAL/OGR, Geospatial Data Abstraction software Library,


Xenospiza baileyi). This species offers an ideal model to investigate population and evolutionary genetic processes that may be occurring in a habitat restricted endangered species with disjunct populations (Mexico City and Durango), the census sizes of which differ by an order of magnitude. TLRs diversity in the Sierra Madre Sparrow was relatively high, which was not expected given its two small, geographically isolated populations. Genetic diversity was different (but not significantly so) between the two populations, with less diversity seen in the smaller Durango population. Population genetic structure between populations was due to isolation and different selective forces acting on different TLRs; population structure was also evident in COI. Reduction of genetic diversity in COI was observed over 20 years in the Durango population, a result likely caused by habitat loss, a factor which may be the main cause of diversity decline generally. Our results provide information related to the ways in which adaptive variation can be altered by demographic changes due to human-mediated habitat alterations. Furthermore, our findings may help to guide conservation schemes for both populations and their restricted habitat.The magnitude and distribution of genetic diversity through space and time can provide useful information relating to evolutionary potential and conservation status in threatened species. In assessing genetic diversity in species that are of conservation concern, several studies have focused on the use of Toll-like receptors (TLRs). TLRs are innate immune genes related to pathogen resistance, and polymorphisms may reflect not only levels of functional diversity, but may also be used to assess genetic diversity within and among populations. Here, we combined four potentially adaptive markers (TLRs) with one mitochondrial (COI) marker to evaluate genetic variation in the endangered Sierra Madre Sparrow ( Given the current biodiversity and environmental crisis, the evaluation of the genetic diversity of endangered species has become a necessary parameter in understanding their population status, resilience, and viability –4. Many Toll-like receptors (TLRs) are potentially adaptive genes involved in the innate immune system, acting as components of the first line of defense, by recognizing conserved structural patterns for specific microbial molecules . These rGenetic drift is the primary mechanism driving loss of evolutionary potential in small populations, and changes in allele frequencies may lead to maladaptive consequences, such as reductions of genetic diversity and fixation of deleterious alleles ; neverthXenospiza baileyi; Passerellidae) ,,www.dataF . Similar losses from other historic sites [Finally, genetic drift should not be completely discarded as a potential evolutionary force involved in Sierra Madre Sparrow genetic diversity. For the mitochondrial COI gene, Durango maintained lower genetic diversity than Mexico City as expected from comparative population size, and this lowered diversity may reflect the negative impacts caused by land-use change and habitat fragmentation through time ,46. In sic sites , caused ic sites  have almic sites  and 2016ic sites .contra Oliveras de Ita et al. 2012). “Management unit” (MU) status might be reasonable for each population, because while they are not completely reciprocally monophyletic, significant allele frequency divergence is found in both TLRs and COI [In conclusion, we have shown that while TLRs diversity is similar (and to a large extent shared) between disjunct Sierra Madre Sparrow populations, local variation may to some extent be driven by differing selective forces, as well as rapid anthropogenic-driven habitat fragmentation. We suggest the need to reappraise the status of Sierra Madre Sparrow and further suggest that due to observed genetic differences (that will certainly increase due to an order of magnitude difference in population size) each population should be considered as an independent unit for conservation  Durango, (B) Mexico City, and (C) both populations.(TIF)Click here for additional data file.S2 FigFirst run was performed with all TLRs except (A) TLR1A and a second run discarding (B) TLR4.(TIF)Click here for additional data file.S3 FigAcrocephalus brevipennis, A. sechellensis, A. taiti, A. scirpaceus, A. arundinaceus [Anthus berthelotii, A. campestris [Petroica australis rakiura [Melospiza melodia [Haemorhous mexicanus, Falco naumanni [Plot show data for nucleotide diversity of TLRs from several species Click here for additional data file.S1 File(DOCX)Click here for additional data file.S1 Text(TXT)Click here for additional data file.


The first breeding program in the world for durum wheat was conceived in Italy in the early 1900s. Over the decades, pressure exerted by natural and artificial selection could have progressively reduced the genetic diversity of the durum wheat germplasm. In the present study, a large panel of Italian durum wheat accessions that includes landraces, old and modern cultivars was subjected to genotyping using the Illumina iSelect 15K wheat SNP array. The aim was to assess the impact that selection has in shaping Italian durum wheat genetic diversity and to exploit the patterns of genetic diversity between populations to identify molecular signatures of divergence and selection. Relatively small differences in genetic diversity have been observed among accessions, which have been selected and cultivated in Italy over the past 150 years. Indeed, directional selection combined with that operated by farmers/breeders resulted in the increase of linkage disequilibrium (LD) and in changes of the allelic frequencies in DNA regions that control important agronomic traits. Results from this study also show that major well-known genes and/or QTLs affecting plant height (RHT), earliness  and grain quality  co-localized with outlier SNP loci. Interestingly, many of these SNPs fall in genomic regions where genes involved in nitrogen metabolism are. This finding highlights the key role these genes have played in the transition from landraces to modern cultivars. Finally, our study remarks on the need to fully exploit the genetic diversity of Italian landraces by intense pre-breeding activities aimed at introducing a new source of adaptability and resistance in the genetic background of modern cultivars, to contrast the effect of climate change. The list of divergent loci and loci under selection associated with useful agronomic traits represents an invaluable resource to detect new allelic variants for target genes and for guiding new genomic selection programs in durum wheat. Triticum turgidum subsp. durum (Desf.) Husn] is one of the most important staple crops and it is primarily cultivated in the Mediterranean regions, Southern Europe, and North Africa. Its domestication took place about 10,000 years ago in the Fertile Crescent, as a result of selection and domestication of wild and domesticated emmer  and natural selection that contributes to the adaptation of crops to particular climates and diverse environments .During the evolutionary history of wheat, the domestication process and the polyploid speciation affected genetic diversity, thus resulting in a reduction of genetic variability, especially in the transition between wild ancestors and landraces , which h1. The first breeding program ever was launched in Italy in the early 1900s and was based on the exploitation of the genetic variability in a few landraces cultivated almost exclusively in Southern Italy, North Africa, and West Asia (SENATORE CAPPELLI (herein after referred to as CAPPELLI), which is assumed to have been selected from the North African landrace JEAN RHETIFAH (sin. JENAH RHETIFAH) and released in 1915 by the renowned breeder Nazareno Strampelli, represents the most popular variety widely used in all breeding programs that were active in Italy until the end of the 1960s  1. The fiest Asia . In factest Asia . Within he 1960s . Its culhe 1960s  and it ahe 1960s .Early Italian durum wheat breeding programs, exclusively driven by public research institutes, were intended to endow the new varieties with increased resistance to lodging, earliness, improved grain yield, and adaptability to Italian environmental conditions. Afterward, following the introduction of the Law n. 580/67 for Pasta Pureness, which forced factories to produce pasta exclusively with durum wheat, and the Common Agricultural Policy reform, which financially supported the cereal farms in Southern Italy, the cultivation of durum wheat had a further boost coupled with the development of private seed companies. During this time, a new germplasm, mostly from CIMMYT (Mexico), France, and United States was introduced in Italy and the objectives of breeding programs changed. Consequently, modern breeding activities ultimately led to greater uniformity. This means that new varieties could have a lower inclination to adapt to climate change and to tolerate/resist new or re-emerging pests, diseases, and weeds .To date, numerous studies have been performed with different molecular markers to analyze how durum wheat genetic diversity varies over time . Most ofRecently, the availability of single nucleotide polymorphism (SNP) array platforms as well as of the wild emmer  and duruSeveral studies have investigated genetic variations underlying individual differences in qualitative and quantitative traits associated with the history of durum wheat breeding . HoweverAs artificial selection mainly acts on traits of agronomic interest, the identification of loci under selection is more effective if based on the comparison of patterns of genetic variation between populations, rather than on the genotype-phenotype associations . The latter are certainly suitable for identifying highly effective loci and easily measurable traits, but they disregard most of the genetic changes associated with crop improvement . Genome-FST  that might be associated with either natural or artificial selection  assess the genetic diversity in a large collection of durum wheat accessions (over 250) released since the early 1900s by mean of genome-wide high-density SNP array and highly informative gene-associated markers; (ii) to compare the patterns of genetic variation observed in landraces, old cultivars, and modern varieties, define the structure of the population under study, and better understand the history of Italian durum wheat breeding; (iii) to identify molecular signatures of divergence and selection. This work lays the foundation for expanding the genetic base  and increasing the genetic diversity of future durum wheat cultivars. In addition, it provides a list of genes/loci under selection, some known and some new, associated with useful agronomic traits to be used in marker-assisted breeding or as potential targets for the new plant breeding techniques.Triticum turgidum subsp. Durum (Desf.) Husn] were included in this study. All data on the samples such as geographical origin, ex situ collection site, and passport information are listed in Triticum turgidum spp. turanicum , and four durum wheat accessions from Russia (no. 2) and Tunisia (no. 2), widely cultivated in the past in Italy. RUSSELLO and TIMILIA accessions tagged as LR were representative of five and seven different “local populations” collected in 2010 from farmer fields in Sicily by the personnel of the University of Palermo. We defined OC as those accessions developed through a pure line within landraces (indigenous or exotic) or those that were derived from crosses or mutagenesis of cultivars with landraces in their pedigree. We included five accessions of DAUNO III and CAPPELLI collected from different seed banks. Among these, CAPPELLI-MP was considered a pure cultivar, as it is derived from the conservative selection carried out at CREA-CI, the institute in charge for the maintenance of pure seeds according to the DM07/10/2015 No. 20919. The set of modern cultivars included varieties carrying the semi-dwarf Rht-B1b allele, released in Italy between 1974 until 2016. In addition, varieties from other countries  were included in the MC group as founders of several Italian cultivars. For the purposes of this study, MC were further split up into three groups based on the year of release: MC1 (released before 1990), MC2 (released between 1990 until 2007) and MC3 (released after 2007). All the plant material was regenerated for 3 consecutive years before the present study and single plants for each accession were selected for genotyping.Two-hundred-and-fifty-nine durum wheat accessions [® iSelect 15K wheat SNP array, which contains 13,600 highly informative gene-associated SNP markers (DNA was extracted from leaves by applying the CTAB method . Genotyp markers  and is a markers  chromoso markers  and Svev markers .r2 = 0.80) using the SNP and Variation Suite (SVS) software package v.8.4.0 (Golden Helix Inc).SNP quality control was performed using Plink v1.07 . SNPs wiHigh quality SNPs were used to compute summary statistics for each of the five pre-defined populations and to infer the population structure. MEGA v.7  was usedGenalex v.6.5  was usedB/HA) when HA > HB and as = (HA/HB) – 1 when HA < HB. The statistics vary between −1 and 1 and is positive when group A has a higher diversity than group B. We calculated this parameter assuming that HLR > HOC > HMC.When comparing gene diversity among LR, OC, and MC the ΔGD parameter was used . This paThe analysis of the population structure was evaluated using different methods. Plink v1.07  was usedK = 1 to 20 and 1,000 bootstrap replicates. CV scores were used to determine the best K value. Clustering was performed by setting the number of hypothetical sub-populations (the K parameter) equal to 3 , 5 , and the best value of K as indicated by the CV score. We assigned each individual to a specific sub-population when the sub-population membership coefficient (qi) was higher than 0.60. Pairwise genetic distance between sub-populations was estimated using the Weir and Cockerham’s average FST, implemented in SVS v.8.4.0.Finally, ADMIXTURE version 1.23  was usedr2 statistic was estimated for each pair of SNPs using Plink v1.07 . The r2 values of one randomly extracted set of 41 individuals for each of the LR and MC sub-populations were then plotted against the physical distance (in Mb), and the Hill and Weir function was used to describe the decay of r2 (2 threshold below which pairwise loci are assumed to be un-linked (r2 = 0.2 (commonly used) was reported to allow a comparison of the results of the present study with previous ones  < 0.05. We performed an outlier test by comparing the LR, OC, and LR sub-populations. In addition, we input a SNP genotype matrix with individuals divided into K sub-populations . It is worth noting that admixed accessions were filtered out to avoid possible biases.Signatures of selection were identified using both BayeScan 1.2  and PCAdq-value v2.5.2 was chosen, using K = 20, the Mahalanobis distance method, an additional SNP filtering step (MAF > 0.05) and a p-value Bonferroni adjustment at p < 0.05, and a false discovery rate  threshold (<0.01). Physical and genetic positions of outlier SNPs were used to associate them with already known QTLs.Four distinct PCAdapt runs  were computed to identify outlier SNP loci related to artificial selection and cultivar diversification. In order to choose the cut-off for outlier detection, the R package Out of 13,006 SNPs spotted onto the 15K Infinium iSelect array, 8,491 were physically mapped on the 14 chromosomes of the wild emmer genome. A total of 7,817 high-quality SNPs  were retained after filtering  and used for downstream analyses. LD pruning was also applied to remove markers in strong LD, thus reducing the dataset to 3,541 SNPs. Indices of genetic diversity were computed to describe the variability within the 259 durum wheat accessions included in the whole collection and among sub-populations  .P = 3,059 and PIS = 2,819) to OC , while it increased again up to P = 3,084 and PIS = 2,689 in MC. Conversely, the trend observed for the number of singleton variable sites was downward moving from LR (174) through OC (272) to MC (268). A higher percentage of private alleles was detected in LR (40.7%) compared with other sub-populations  compared with OC  and MC  (OC < HMC)  and parsimony informative (PIS) sites were identified, while Nei’s gene diversity (H) and Shannon Index (I) were 0.24 and 0.38, respectively. The number of P and PIS sites slightly decreased moving from LR  . When thC < HMC) .The genetic structure of the 259 durum wheat accessions was investigated using parametric and non-parametric approaches to describe the genetic relationships among accessions.x-axis) clearly distinguished LR from MC and, in particular, from MC recently released (MC3), as few MC1 were similar to LR. Most OC were in an intermediate position, although some overlapped with LR and some with MC1. The y-axis further distinguished OC from the LR, with some exceptions  was investigated by computing pairwise FST values, confirming that the genetic differentiation was low between LR and OC (FST = 0.05), moderate between OC and MC (FST = 0.12), and higher between LR and MC (FST = 0.16) (The MDS plot in ceptions . Genetic = 0.16) .The neighbor-joining clustering  supporteK = 16 as the best number of genetic groups that fit the data   .K = 3  that are attributed to the P1b, together with some other OC . Similarly, group P2 includes two subgroups: P2a comprises all the LR genetically related to CAPPELLI, while P2b groups all the MC obtained through cross-breeding with CAPPELLI and those mostly released before 1990. The accessions in the cluster P3 are the same as in cluster P3 at K = 3, where most of the MC released after 1990 are placed. At K = 5, 45 accessions were admixed. Values of pairwise FST distance between the five clusters range from 0.19 (P3 vs. P2b) to 0.37 (P1b vs. P2b)  .K = 16 the genetic structure reflects a more composite stratification of the population  and all CAPPELLI accessions were included in G8 with the landraces BIDI and MARGHERITO. The groups G9 and G10 include OC and MC that have CAPPELLI as an ancestor; the accessions in G9 originated from the old cultivar CAPEITI-8, while the accessions in G10 descend from the cultivars VALFORTE and VALNOVA. Lastly, at K = 16 the P3 cluster previously identified at K = 3 was split into six groups (from G11 to G16), based on pedigree relationships. At this structure level, the number of admixed accessions raised to 96. High genetic differentiation was also observed between groups defined at K = 16  was estimated between G5  and G6 .At pulation . Specifit K = 16 . The greTIMILIA, LR), while it was the lowest in G5   were used to estimate the ancestry proportions for each of the five groups , considering qi as the contribution of each K ancestral source population . ResultsITO, OC) .LD analysis was performed on the three sub-populations following two different approaches. First, all accessions for each population was considered. Then, the number of accessions in LR and MC was reduced to 41 with the aim to numerically balance the three sub-populations.2 values  among the 10 subsets of 41 accessions randomly extracted from LR  and MC  sub-populations. By contrast, significant differences between the values of LD decay of the 10 subsets and the sub-populations that include 85 LR  and 133 MC  were observed (The Tukey–Kramer test resulted in no significant differences for the average robserved . This inobserved . As a coobserved .r2 = 0.078) compared with MC (r2 = 0.061) and LR (r2 = 0.056), while the lowest value of intra-chromosomal LD decay was observed within the whole population (r2 = 0.028)  . When lo= 0.028) , followe= 0.028) . When annless OC .FST) at individual SNP loci and fixed the significance threshold to >0.25 , we calculated, the pairwise fixation index , located, respectively, on chromosomes 4B and 5A, were in common after all three comparisons.A total of 56 and 83 loci with ectively . By contCAP8_REP_C7343_88 , WSNP_EX_C9343_15514531 , BS00081396_51 , and CAP12_C2701_221  are located in the coding sequence of the following genes: Glu-A1 (high molecular weight glutenin subunit), TaASN3 (asparagine synthetase 3), Glu-B1 (high molecular weight glutenin subunit), and NR2 (nitrate reductase 2)  genes involved in nitrogen metabolism (no. 2) and quality (no. 2) of wheat. In detail, ctase 2) .In order to support the identification of divergent loci, we aligned the chromosomal region around individual SNPs with the genomic position of well-known major genes and/or QTLs. Based on the average LD decay calculated on the whole collection, we considered an interval of ±6 Mb around each locus.One-hundred-and-thirty-four SNPs, which mainly mark the transition from LR/OC to MC, co-localized with genes involved in the nitrogen (N) metabolism , plant height (Rht1 and Rht12), resistance to the powdery mildew fungus (Pm3), vernalization , photoperiod response (Ppd), and kernel and semolina color  .FST > 0.50) with published QTLs . Then, the analysis was run using exactly 16 sub-populations . Twenty four outlier SNPs were detected with a FST threshold of 0.18 , spanning chromosomes 1B, 2A, 2B, 3A, 3B, 5A, 5B, 6B, 7A, and 7B  to detect putative signatures of selection , as an aInterestingly, two loci were outputted by both Bayescan and PCAdapt . They weFinally, we combined the results from all three approaches. Two loci were identified by all three methods; 13 outlier SNPs were shared by divergent loci analysis and BayeScan, while 21 SNP loci were outputted by both divergent loci analysis and PCAdapt .It is commonly accepted that the domestication process is associated with a genetic bottleneck that re-patterned genetic variability in wheat . ReductiDuring the last century, genetic improvement programs of durum wheat have had a significant impact on productivity gains and the improvement of grain quality as a result of the needs of the processing industry and of consumers’ demands . HoweverKnowing the molecular targets of artificial selection is essential in guiding the use of the germplasm to breed toward the development of new genetic materials, suitable to meet farmer and market needs while also being able to face climate change .In the present study, a large panel of durum wheat accessions, released for cultivation during the last two centuries in Italy, has allowed us to investigate the impact that selection has had on the genetic structure of the collection under investigation, and to identify and characterize putative molecular signatures of divergence and selection in the durum wheat genome.A deep knowledge on the extent of genetic diversity in durum wheat is expected to have remarkable importance on the maintenance and use of genetic resources, thus facilitating breeders to achieve profitable diversification in breeding programs.The number of Italian durum wheat accessions, which have been investigated in most previous studies , was limTo overcome some of these limitations, in this study a high-density SNP genotyping array and a wide representation of Italian durum wheat landraces was also included to counterbalance, from a numerical point of view, the old and modern cultivars developed since the early 1900s. On the one hand, this has allowed us to draw conclusions supported by more data on the effects produced by breeding on genetic diversity, while on the other hand, it has shown how important the exploration of genetic variability in the Italian durum wheat germplasm is, for broadening the genetic base of durum wheat varieties.Italian landraces were characterized by a moderate level of genetic diversity (He = 0.26), which is comparable with that of Mediterranean (He = 0.24) or Tunisian and Iranian (He = 0.25) landraces, as revealed by the analysis of patterns of genetic diversity using AFLP  and DArTCAPPELLI and its few closely related varieties for more than two decades was an example of how the genetic basis of mostly cultivated Italian durum wheat cultivars has been narrowed . They were the results of natural selection over a long period of time and/or of unconscious selection by farmers . The conRUSSELLO and TIMILIA, have been cultivated over time, especially in Sicily, because of their adaptability to extreme Mediterranean environments (K = 16), LR grouped into five clusters, two of which include the Sicilian landraces RUSSELLO and TIMILIA .In the decades following the introduction of CAPPELLI were collected from different seed banks in order to estimate the possible genetic distance among them and from the accession CAPPELLI-MP, the one present in the Italian National Variety Register. All the CAPPELLI accessions were in the same group (MARGHERITO and BIDI and two accessions (DURO DE NAPLES and TAGANROG) of unknown origin. Unexpectedly, the landrace JEANH RHETIFHA was genetically distant from the CAPPELLI group. This result agrees with what was assumed by BID\`I, MARGHERITO and CAPPELLI raises the need of developing new traceability tools to mitigate possible food fraud risks.In this work, five accessions of me group , thus sur2 = 0.2, the LD decay distance for LR and MC is comparable with that reported by r2 < 0.2) reported by r2 = 0.2) at 1.6 and 4.5 Mb for LR and MC, respectively. The increase of the LD decay distance in MC could be due to selective pressure on genomic loci. Altogether, these data suggest that selection by breeders has resulted in the conservation of haplotype blocks harboring beneficial gene combinations. The LD decay distance also varies across chromosomes. Even if there are few works in which LD decay have been reported in physical distance, it is possible to compare our results with previous LD patterns evaluated in cM. Among the MC chromosomes, 1B, 4B, and 6A showed the slowest decay both as physical and genetic distances. These results were partially confirmed in durum and bread wheat  in durum wheat using different classes of molecular markers . The avaad wheat .FST analysis (765 non-redundant SNPs with FST > 0.25), through PCAdapt (67 non-redundant SNPs), to Bayescan (24 non-redundant SNPs), of which only two were identified by all three methods  were in hot spot regions in which genes/QTLs for different traits have been previously reported in bread  and durum mating .FST > 0.25, most were in genomic regions well known to influence plant traits, such as height, photoperiod response, vernalization requirements, and frost tolerance -associated genes  and remarked the key role these genes have had in the transition from LR to MC . Increasvia genetic engineering. Our data indicate high-affinity nitrate transporter 2 (NRT2), nitrate reductase (NR), and Nitrite reductase (NiR) on chromosome 6A and 6B , the enzyme responsible for converting inorganic N into the organic amino acid glutamine. GS has been one of the most extensively studied enzymes and has been used to improve NUE in wheat . GlutamiFurther two genes, NADH-GOGAT and Fd-GOGAT, located, respectively, on chromosomes 3B and 2B were identified as loci under selection. NADH-GOGAT has long been considered one of the major candidate genes for cereal NUE, as described by Also, glutamate dehydrogenase (GDH) plays a key role in the maintenance of N-C balance by liberating ammonium during senescence . AdditioThe over-expression of the NADH-GOGAT gene causes an increase in grain yield in both wheat and rice due to the increase in kernel weight; its silencing, instead, is associated with weight reduction of the kernel and a consequent decrease in yield per plant . This poA comparison between old and modern cultivars in bread and durum wheat grown at multiple N rates, indicated a better response to N for gains in grain yields in the modern varieties, which was reflected in increased uptakes and NUE . This inFinally, since cooking properties of pasta are also related to high content of N in the grain, it could be very interesting to modulate the accumulation of proteins in durum wheat grains as recently suggested by The concentration of yellow pigments is mandatory to achieve the bright yellow color seen in pasta products, a consumer’s requisite, thus becoming an important target in modern breeding programs . Our datBayescan and PCAdapt were used to detect putative loci under selection. The smaller number of Bayescan outliers was expected as the two methods differ in type I  and type II  error rates . Only twFinally, although PCAdapt results were not confirmed by Bayescan, loci on chromosome 1A certainly deserve a focus . Indeed,Relatively small differences in overall genetic diversity have been observed in the panel of durum wheat accessions including landraces, and old and new varieties selected and cultivated in Italy in the last 150 years. Based on our results, the increase of LD decay and the shift in the allele frequency spectrum were the main effects produced by the demographic history along with the selection by farmers. Our study also remarked how under-exploited the genetic variability that characterizes the Italian durum wheat landraces is, and the need to recover the untapped variability present in the landraces. Finally, the list of loci under selection highlighted the key role NUE genes have had and could have in the near future to improve quality traits and agronomic performances of novel durum wheat varieties. Nowadays, it is necessary to develop new high yielding varieties that are able to grow with reduced agro-chemical inputs and under fluctuating climatic conditions . Sustain10.6084/m9.figshare.11835663).FT and PD designed the experiment. FT, PD, and RP established the durum wheat collection. ND’A carried out part of the bioinformatic analysis. FT, ND’A, and SP performed the genetic diversity analyses. MR performed the Linkage Disequilibrium analysis. FT, ND’A, and AP detected loci under selection. FT, ND’A, MR, SP, NP, RP, and PD were involved in the data interpretation. FT, ND’A, and PD wrote the manuscript. All authors critically revised the manuscript. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.