Symphodus melops) escapees and admixed individuals in wild populations in western Norway. Applying this panel to ~2000 individuals, escapees and hybrids were found to constitute up to 20% of the local population at the northern edge of the species’ distribution. The introduction of southern genetic material at the northern edge of the species distribution range has altered the local genetic composition and could obstruct local adaptation and further range expansion. Surprisingly, in other parts of the species distribution where salmon farming is also common, few escapees and hybrids were found. Why hybridization seems to be common only in the far north is discussed in the context of demographic and transport history. However, the current lack of reporting of escapes makes it difficult to evaluate possible causes for why some aquaculture‐dense areas have more escapees and hybrids than others. The results obtained in this study, and the observed high genomic divergence between the main export and import regions, puts the sustainability of mass translocation of nonlocal wild wrasse into question and suggests that the current management regime needs re‐evaluation.Translocation and introduction of non‐native organisms can have major impacts on local populations and ecosystems. Nevertheless, translocations are common practices in agri‐ and aquaculture. Each year, millions of wild‐caught wrasses are transported large distances to be used as cleaner fish for parasite control in marine salmon farms. Recently, it was documented that translocated cleaner fish are able to escape and reproduce with local wild populations. This is especially a challenge in Norway, which is the world's largest salmon producer. Here, a panel of 84 informative SNPs was developed to identify the presence of nonlocal corkwing wrasse ( Lumpfish, whose potential use as a cleaner fish was discovered in 2014, has since become the most commonly used cleaner fish  are a large and diverse family of marine fish with over 600 described species worldwide. Many of these species show natural cleaning behaviour, that is they feed on ectoparasites from other fish species’ skin. The wrasse species utilized as cleaner fish on Norwegian fish farms inhabit shallow rocky areas along the coast from the Mediterranean Sea in the south, to the Norwegian coast in the north. In recent years, their abundance has shifted northwards and diminished in the south, which has been suggested to be due to increased seawater temperatures  and Skagerrak‐Kattegat (export region). Western sample sequences were taken from Faust et al.  and contFST values in all three pairwise comparisons. Reading and converting between file formats was done using VcfR radiator  and each of the three locations in Skagerrak‐Kattegat. A total of 387 SNPs, distributed over 270 contigs, were identified among the 500 highest SNPs displaying FST values >0.4  were used for SNP locus primer design and resulted in four assays with a total of 106 SNPs. Primer design, amplification and genotype calling were based on the Agena MassARRAY iPLEX Platform, as described by Gabriel et al. . Selecte2.22.2.1N = 40) were already used in Faust et al. (N = 365) and 2018 (N = 30) were collected for the current study. Smøla is an island municipality ~200 kilometres south from Flatanger with a high density of fish farms. In 2017–2018, 271 fish were collected there . Loci and individuals with more than 20% missing data were removed, leaving 1766 individuals and 85 SNPs. Genotyping robustness was evaluated by calculating concordance between 79 successfully genotyped technical replicates, removing any locus with more than 2 discordant genotypes. One locus showed several discrepancies between genotypes , “southern” (Norwegian south coast and Swedish west coast) or as “mid‐western” (>62°N), “south‐western”  and “south‐eastern”  Table . Unless 2.3.1He) were assessed by calculating FIS according to Weir and Cockerham  given by the STRUCTURE software and the ad hoc summary statistic ΔK by Evanno et al. , efficiency (>83%) and power (>81) to assign all six hybrid classes  at probability thresholds between 0.5 and 0.9. Furthermore, when pooling the F1, F2, western and southern backcrosses as a single hybrid class, these numbers increased to >97% accuracy, >95% efficiency and >95 power Figure .3.2p = 0.002). Similarly, allelic richness was significantly lower (p = 0.002) in south‐eastern samples (1.42–1.43) compared to the western samples (1.78–1.90). Differences in these diversity indices were statistically significant also when both western samples were compared separately with south‐eastern samples . Moreover, mean allelic richness was higher (p = 0.015) in south‐western samples (AR = 1.83) than in mid‐western samples (AR = 1.80), but no difference was observed for mean observed heterozygosity (p = 0.284). The majority of markers showed no deviation from HWE in any of the sampled locations and only two markers deviated significantly from HWE in more than six locations. Initial comparisons showed little to no difference in results when removing these two markers, and consequently, all markers were kept for further analysis. Overall, nine out of all sample populations deviated significantly from HWE. Eight of which showed heterozygosity deficiency (FIS 0.017–0.06) and one heterozygosity excess (FIS −0.012); all were observed in western Norway. Correlation statistics calculated by pairwise comparison of allele frequencies showed high positive correlation within each of the three geographic groups , followed by the mid‐western samples (FST = 0.0054 ± 0.0052) and highest in south‐western samples (FST = 0.0120 ± 0.0146). When comparing divergence within and between the three geographic areas, the genetic differentiation within the western samples were order of magnitude lower (mean FST = 0.0216 ± 0.0119) than between the western and the south‐eastern samples (FST (mid‐west_vs_south‐east) = 0.5155 ± 0.0699 and FST (south‐west_vs_south‐east) = 0.4757 ± 0.0549). Of the western samples, Flatanger17 showed clearly lower differentiation towards the south‐eastern samples (mean FST = 0.3704 ± 0.0089), with all other pairwise comparisons ranging between 0.4106 and 0.6070.~34% of the total genetic variation was distributed between the three geographic groups, and less than one percent between samples within these groups Table . Consistem Table . Within‐FST measurements, individual‐based clustering using STRUCTURE differentiated the south‐eastern cluster (pink) from the western samples (blue)  splits western samples into two distinct clusters between Sula and Måløy implying an additional genetic break  of population division, additional methods that were utilized favoured solutions for even higher levels of Ks , the vast majority of all fish (94.6%) had a q of ≥0.95 corresponding to their “own” geographic group (western or south‐eastern). However, six individuals from Flatanger and one from Årdalsfjorden in western Norway were assigned with a high proportion (q > 0.98) to the south‐eastern cluster. Moreover, nine individuals had roughly equally admixed genotypes (q = 0.4–0.6 to both clusters), 38 had moderate representation (q = 0.2–0.4) of the south‐eastern cluster in their genomes, and 40 rather low but still notable portions (q = 0.1–0.2), all suggesting varying degree of admixture between the clusters. When fish were assigned into three clusters (K = 3) instead of two, they were still highly concordant with their geographic origin  clearly separates south‐eastern samples (pink) from western samples (blue and green). The second axis  separates the mid‐western samples (green) from south‐western samples (blue), but with a degree of overlap between the clusters. The seven individuals previously identified in the STRUCTURE analysis clearly cluster together with individuals from the south‐eastern cluster also in the PCA. Individuals previously identified as possible admixed in STRUCTURE analysis are also in the PCA located between the western and south‐eastern clusters.The principal coordinate analysis (PCA) demonstrated a similar pattern as seen in the STRUCTURE cluster analysis Figure . The firp > 0.05) based on jack‐knife resampling. There were only minor differences between trees when not correcting for sample size, and thus, only the corrected models are shown. Inspection of the matrix of residuals  tree calculated from TreeMix, which infers the patterns of population splits and mixtures in the history of a set of populations, clearly displayed the known genetic break and captured 98.3% of the variance Figure a. South‐s Figure b indicatThe reference cline based on the STRUCTURE Q‐scores fitted an optN model, with the centre situated at 799 km (787–1087) Figure a. All th3.4W2016 = 314, p = 0.7; W2017 = 5642, p = 1). The two oldest individuals were classified as hybrids (aged 8 and 9 years), while the oldest individual with pure western genotype was 7 years old. Of the six individuals classified as of pure southern origin, all were larger than 175 mm (minimum size limit in the fishery for corkwing is 120 mm) and between 2 and 4 years old . Of the 1766 individuals analysed, all of them could be assigned with a probability >50% to be either pure western (blue) or pure southern (pink) or hybrid (green) Figure . When di4We developed and implemented a panel of informative SNPs to quantify the proportion of escaped and hybridized corkwing wrasse with a southern origin in middle and western Norway, where translocated cleaner fish are extensively used for parasite control. The panel of 84 SNPs, which can detect escapees and hybrids with a power >0.95, identified a total of 7 escapees and 79 potential first‐ and second‐generation hybrids among 1519 analysed fish on the Norwegian west coast. Most of these were identified in samples from the northernmost part of the species distribution in mid‐west Norway, which also represents the main area of import from southern latitudes. On the basis of these results, and, given the large documented genomic differences between corkwing wrasse between the export and import regions, current practices should be revised to take the natural population structure into account and avoid moving individuals between genetically differentiated populations.4.1The panel of informative SNPs was developed to identify genetic differences between source populations in southern Norway and Sweden versus local wild wrasse populations in receiving areas in western Norway. In concordance with previous studies  available from this region in the present study, all of them were clearly clustered within the south‐western group, indicating that there could be a stronger genetic discontinuity in this region than previously indicated. However, the markers used here are not ideal to resolve genetic population structure in this region as they were selected to distinguish south‐eastern from western samples. It is therefore not possible to disentangle the nature of this second break, that is whether selection or neutral processes are at play.In addition to the previously documented main genetic break located at south‐western Norway, a second weaker break in genetic structure was detected at ~62° N on the Norwegian west coast. Blanco González et al.  describe4.2q score of 6.5%; Figure q‐scoremean <0.1%). The only other pure south‐eastern individual was found in Årdalsfjorden, <60 km from the sandy beaches in Jæren, where the main genetic break has been previously identified , which is also the northern range of the species current natural distribution. It is noteworthy that besides fish identified as escapees or hybrids, majority of samples from Flatanger had some genetic resemblance to south‐eastern population  corkwing wrasse only expanded into Trøndelag recently and the population size is therefore small , almost twice the amount of corkwing wrasse were used in Møre og Romsdal compared to Trøndelag (Norwegian Directorate of Fisheries, During 2017 and 2018, an average of less than 0.4 million corkwing wrasse were imported from Sweden per year. During the same years, an average of 7 million wild corkwing wrasse were used in Norwegian aquaculture. Thus, Swedish imports constitute less than 6% of corkwing wrasse used as cleaner fish in Norwegian aquaculture. However, source and destination of corkwing wrasse caught in Norway are not reported. This makes it difficult to estimate how many of the corkwing wrasse used in commercial salmon farming originates from the southern coast of Norway, as opposed to local sources. Catch numbers suggest that on average 20% of wild‐caught cleaner fish are caught off the southern coast of Norway annually, but most years less than 1% of all cleaner fish is being deployed in the southern region (Norwegian Directorate of Fisheries, 4.3The effects of translocation between genetically distinct populations are difficult to predict and depend on many factors. Direct escapees can cause ecological effects and transmit novel diseases and pathogens. If hybridization occurs, genetic effects can also be anticipated as has been observed for a wide variety of traits in the case of domesticated salmon escapees in wild populations (Glover et al., The consequences of hybridization in the northern edge population are hard to predict but given the considerable difference in important abiotic factors between this region and southern Norway and Sweden, inadvertently translocated individuals are likely to be maladapted and have lower fitness in the recipient populations. For example, the onset of the reproduction is affected by photo‐period and temperature Stone, . If a geThe recently established Flatanger population is on the leading edge of the current species range and thus likely preselected to extremities of the species reaction norm, carrying favourable genetic material also for future range expansion northwards (Gibson et al., 5We developed a SNP panel with the ability to detect corkwing wrasse translocated from Skagerrak‐Kattegat to the Norwegian west coast as well as first‐ and second‐generation hybrids. Using this panel, we found that the geographical extent of escapees and potential hybrids is largely limited to areas at the northern edge of the species distribution where the number of escapees and potential hybrids may constitute up to 20% of the population. These results provide important knowledge, a baseline of the geographical extent and magnitude of hybridization, and a tool for management and monitoring of the future use of corkwing wrasse as a cleaner fish for parasite control. Moving genetic material between distant populations could alter the genetic composition, erode population structure and potentially result in loss of local adaptation, hampering the species expansion. The translocation and number of escaping cleaner fish are today poorly documented and regulated and the ecological consequences of escapees and hybrids remain unknown for this and other wrasse species. Recording the numbers, source and destination of wild‐caught cleaner fish on a per‐farm basis, as well as reporting escapees at the end of a season, would improve the ability to assess current and future risks associated with the use of cleaner fish for parasite control. Finally, based on the results of this and studies of other species of wrasse (Jansson et al., None declared.Supplementary MaterialClick here for additional data file.


First Person is a series of interviews with the first authors of a selection of papers published in Disease Models & Mechanisms, helping early-career researchers promote themselves alongside their papers. Li Wang is first author on ‘ Li WangHow would you explain the main findings of your paper to non-scientific family and friends?“Our research highlights the importance of STAT3 as a central mediator of vascular permeability.”Proper vascular function is essential for maintaining good health. Our body's vascular system distributes oxygenated blood to all tissues, returns deoxygenated blood to the lungs, and helps maintain balanced function among blood cells, lipids and immune cells. During tissue injury or disease, vascular barrier integrity is impaired. A protein called vascular endothelial growth factor (VEGF) is a central mediator of vascular permeability. Increased VEGF expression promotes hyperpermeability, oedema and tissue damage, which contribute to the pathogenesis of cardiovascular disease, cerebrovascular conditions, cancer, retinal disorders and acute lung injury. However, VEGF also plays a role in tissue repair following damage by stimulating the growth of new blood vessels. Our goal is to discover molecular signalling that can be targeted to reduce the tissue-damaging effects of vascular permeability, while not affecting the tissue-repair role of VEGF. In our study, we demonstrate that VEGF signals through a mediator called signal transducer and activator of transcription 3 (STAT3) to promote vascular permeability. In mice engineered for STAT3 deletion in their vascular  cells, we observe reduced VEGF-induced vascular permeability. Similarly, in Stat3 knockout zebrafish, vascular barrier integrity is maintained despite ‘turning on’ VEGF expression. Importantly, the process of blood vessel formation stimulated by VEGF is unaffected in these animals, which suggests that the function of VEGF in tissue repair is maintained upon STAT3 inhibition. STAT3 can be effectively inhibited using a drug called pyrimethamine, which has been US Food and Drug Administration (FDA) approved to treat parasite diseases. We show that the STAT3 inhibitor pyrimethamine substantially reduces VEGF-induced vascular permeability in zebrafish, mouse and human endothelium. STAT3 could be used as a target to reduce the pathological effects of vascular permeability in human diseases, including vascular conditions, cancer, eye disorders and COVID-19-associated acute lung injury.What are the potential implications of these results for your field of research?Our research highlights the importance of STAT3 as a central mediator of vascular permeability. We demonstrate that an FDA-approved STAT3 inhibitor called pyrimethamine inhibits VEGF-mediated vascular permeability, while not affecting the tissue restorative functions of VEGF. These findings suggest that targeting STAT3 may be an effective strategy to inhibit vascular permeability, oedema, and tissue damage associated with heart disease, stroke, retinal conditions, cancer and acute lung injury.What are the main advantages and drawbacks of the model system you have used as it relates to the disease you are investigating?in vivo models are quite useful for answering many important questions related to the molecular regulation of vascular permeability.In our study, we took advantage of a transgenic VEGF-inducible zebrafish model that is amenable to genetic manipulation and reproducible live imaging of vascular permeability in optically clear zebrafish embryos using fluorescently labelled tracers. This model can be used to identify novel regulators of VEGF-induced vascular permeability or to test the effects of candidate drugs on vascular barrier integrity. Additionally, the widespread recent emergence of CRISPR/Cas9 genome-editing techniques has enhanced the study of genetic regulators of endothelial barrier function. As such, we crossed CRISPR/Cas9-generated Stat3 loss-of-function mutant zebrafish to the VEGF-inducible fish to study the role of STAT3 in VEGF-mediated vascular permeability. We also used endothelium tissue-specific STAT3 knockout mice, which are healthy and fertile with no overt defects in vascular development or structure. These genetic STAT3 knockout animal models could be used in other studies related to STAT3 in other diseases. One drawback of using these model systems is the time and effort required to inject tracers into the zebrafish ventricle through the pericardium and deliver Evans Blue dye into the mice by tail vein injection. However, once an individual has mastered the injection techniques, these What has surprised you the most while conducting your research?Pyrimethamine is an FDA-approved drug used to treat microbial infections. Through a chemical-biology screen, our collaborator, Dr David Frank, found that pyrimethamine is an inhibitor of STAT3 function at concentrations known to be achieved safely in humans. Dr Frank and colleagues reported that the STAT3 inhibitor pyrimethamine displays anticancer and immune-stimulatory effects in murine models of breast cancer. As highlighted in our article, we discovered that the STAT3 inhibitor pyrimethamine reduces VEGF-induced vascular permeability in mice, zebrafish and several types of human endothelial cells. I was most surprised by the power of drug repurposing – using an ‘old’ drug to treat a different disease. I am excited that pyrimethamine is already FDA approved and can be used safely in humans, which will help translate our work into the clinic.Describe what you think is the most significant challenge impacting your research at this time and how will this be addressed over the next 10 years?“[…] the advent of the preprint era has benefited early-career scientists.”Discovering the molecular mechanisms underlying human disease is the first step. The next challenge is how to translate these basic science discoveries to clinical use to improve the lives of people afflicted with some of these devastating diseases. Over the next 10 years, perhaps it will become easier to synthesise drugs based on basic science findings. Pipelines to test candidate drugs in disease models will likely become more efficient and more effective. Hopefully these technical advancements will facilitate the development of new treatments and, ultimately, cures.What changes do you think could improve the professional lives of early-career scientists?At The Hormel Institute, University of Minnesota, many activities have been organised to improve the professional lives of early-career scientists, like external and internal seminars, career development opportunities, and the implementation of mentoring committees for postdoctoral fellows. My mentor, Dr Luke Hoeppner, also provided outstanding mentoring support throughout the whole project and has encouraged goal setting to facilitate my career growth. For most trainees, more time reading papers and more grant opportunities are desired. In regards to grant applications, I like that we can now cite preprints in proposals, and I think the advent of the preprint era has benefited early-career scientists.What's next for you?In our publication, we report that suppression of STAT3 activity inhibits vascular permeability mediated by VEGF in mice, zebrafish and human endothelium. Given that the STAT3 inhibitor pyrimethamine has already proven safe in humans, we hope to translate our findings to the clinic. To that end, we are currently testing pharmacological STAT3 inhibition in animal models of human diseases associated with vascular permeability. In terms of my own career progress, I am beginning to focus on postdoctoral grant-writing opportunities.


Estimating genetic parameters of quantitative traits is a prerequisite for animal breeding. In honeybees, the genetic variance separates into queen and worker effects. However, under data paucity, parameter estimations that account for this peculiarity often yield implausible results. Consequently, simplified models that attribute all genetic contributions to either the queen (queen model) or the workers (worker model) are often used to estimate variance components in honeybees. However, the causes for estimations with the complete model (colony model) to fail and the consequences of simplified models for variance estimates are little understood. We newly developed the necessary theory to compare parameter estimates that were achieved by the colony model with those of the queen and worker models. Furthermore, we performed computer simulations to quantify the influence of model choice, estimation algorithm, true genetic parameters, rates of controlled mating, apiary sizes, and phenotype data completeness on the success of genetic parameter estimations. We found that successful estimations with the colony model were only possible if at least some of the queens mated controlled on mating stations. In that case, estimates were largely unbiased if more than 20% of the colonies had phenotype records. The simplified queen and worker models proved more stable and yielded plausible parameter estimates for almost all settings. Results obtained from these models were unbiased when mating was uncontrolled, but with controlled mating, the simplified models consistently overestimated heritabilities. This study elucidates the requirements for variance component estimation in honeybees and provides the theoretical groundwork for simplified honeybee models. Breeding efforts in honeybees, as in other agricultural species, rely on the fact that selection traits are partly determined by genetic features and that superior animals can thus pass on their qualities to their offspring. In order to determine the extent to which this is the case, it is necessary to measure the magnitude of genetic variance as a contributor to the phenotypic variance in a population. Thus, estimates of genetic and residual variance components are important to judge the prospect of success in selective breeding schemes .In addition to their importance for evaluating the possibilities for breeding, genetic and residual variance estimates also belong to the input data of best linear unbiased prediction (BLUP) breeding value estimation  and are ijy of an individual j in an environment i is determined by a fixed environmental effect ib, a random genetic effect ju of the individual, and a random residual effect ije:Estimates of variance components in livestock populations typically rely on the animal model (AM), assuming that the phenotype ju  queen component Qu and a (direct) worker component Wu. The genetic effect of the worker group, iqwe, respectively, the variance of wwa is the average relationship between two workers in a colony  or to the worker group , while ignoring the other caste , 2021:. Furthermore, we use simulated data to investigate the influences of model choice, estimation algorithm, controlled mating, apiary size, and phenotype data completeness on the estimation of variance components in honeybee populations.In this section, we discuss what to expect if the simplified models QM and WM are used to estimate variance components from phenotypes that were created according to the CM. Our approach is analogous to the theory of transformed variances in other species, like they appear for example in sire-maternal grandsire models . Howeverw inherits its breeding value q and the drones d that q mated with: We start with the QM, which projects all genetic effects onto the queen. A worker group Note that (diploid) queens transmit only half of their genes to their offspring, while (haploid) drones pass their entire genetic information. Furthermore, no Mendelian sampling is modeled in the inheritance to a worker group because sampling effects for individual workers cancel out when average values are taken. For more details on modeling additive genetic inheritance in honeybees see e.g. . By inseq to the phenotype, as it is projected by the simplified model QM, is therefore not only The genetic contribution of Accordingly, the projected residual contribution isNote that, we assumed that a queen’s breeding value is independent from the breeding values of the drones she mated with, i.e. no assortative mating. The genetic variance projected to the queen by the QM is thus.The phenotypic variance for honeybee colonies equals [In consequence, since qu and We now turn to the WM, attributing all genetic effects to the worker group. The calculation of the projected genetic contributions when using this model is more involved, because the worker group’s true breeding value Thus, when using the WM to estimate genetic and residual variance components from phenotypes that were created according to the CM, the expected results are:wwa, and will thus differ for different mating strategies.The projected variances dn = 12 drones that were produced by a random selection of queens of ages between one and three years. In controlled matings, queens were paired with dn = 12 drones on one of ten isolated mating stations. Each mating station consisted of a sister group of eight drone producing queens, whose dam was randomly selected among the three-year old breeding queens. The respective implementations of controlled and uncontrolled mating were thus identical to earlier simulation studies . In populations with mixed controlled and uncontrolled mating of queens, we assumed wwa to be a weighted average between 0.29 and 0.37. Separate simulations were performed for eight traits, reflecting different ratios between genetic and residual variance, as well as different correlations p and traits, simulations were repeated 500 times.We used the program BeeSim  to simul studies . The resects see . The traas  and each combination of year and apiary was treated as a fixed effect. To investigate the effect of missing phenotype data, some phenotype records were randomly deleted prior to the parameter estimation. Seven rates q of phenotype data completeness were investigated , so that variance components were estimated based on between 1,000 and 10,000 performance recods. We used both the EMREML and AIREML algorithms for estimation with true genetic parameters as starting values. Aitken acceleration , In our simulations, phenotype records and pedigree information were partly incomplete, but they were always correct. In practice, misassignments of records can easily occur. In a study on cassava —(462225818 to Manuel Du) and by the German federal states of Brandenburg, Berlin, Sachsen, Sachsen-Anhalt, and Thüringen.The authors declare no conflict of interest.


Elaphurus davidianus), which originated from a limited founder population. However, the genetic structure of reintroduced Père David’s deer has not been investigated in terms of population genomics, and it is still not clear about the evolutionary history of Père David’s deer and to what extent the inbreeding level is. Conservation genomics methods were used to reconstruct the demographic history of Père David’s deer, evaluate genetic diversity, and characterize genetic structure among 18 individuals from the captive, free-ranging and wild populations. The results showed that 1,456,457 single nucleotide polymorphisms (SNPs) were obtained for Père David’s deer, and low levels of genome-wide genetic diversity were observed in Père David’s deer compared with Red deer (Cervus elaphus) and Sika deer (Cervus nippon). A moderate population genetic differentiation was detected among three populations of Père David’s deer, especially between the captive population in Beijing Père David’s deer park and the free-ranging population in Jiangsu Dafeng National Nature Reserve. The effective population size of Père David’s deer started to decline ~25.8ka, and the similar levels of three populations’ LD reflected the genetic impacts of long-term population bottlenecks in the Père David’s deer. The findings of this study could highlight the necessity of individual exchange between different facilities, and genetic management should generally be integrated into conservation planning with other management considerations.The reintroduction is an important conservation tool to restore a species in its historically distribution area, but the rate of reintroduction success varies across species or regions due to different reasons. Genetic evaluation is important to the conservation management of reintroduced species. Conservation concerns relate to genetic threats for species with a small population size or severely historically bottle-necked species, such as negative consequences associated with loss of genetic diversity and inbreeding. The last 40years have seen a rapid increasing of population size for Père David’s deer ( The reintroduction has been increasingly used as an important and effective conservation tool to recover locally extirpated species, and a common tool for restoring lost biodiversity . The ratEquus ferus; Bison bonasus; Oryx leucoryx; A key challenge of reintroduction efforts is to translocate individuals in a way that prevents loss of genetic variation, and avoids genetic differentiation relative to source populations . Such a The unbiased genome-wide estimates generally reflect more accurately levels of genetic diversity and inbreeding . The recElaphurus davidianus), called “Milu” in China, went extinct in the wild as a result of over-hunting and habitat degradation in the late 19th century, but a few remnants were introduced into Europe and preserved safely in captivity in several European zoos. The remaining 18 individuals distributed in Berlin, Paris, and Antwerp were collected by the 11th Duke of Bedford, forming a breeding herd at the Woburn Abbey, England, where only 11 individuals were reported to have the ability to reproduce. By 1945, the global population size of Père David’s deer reached 250. Three reintroduction attempts were made in China. The first 20 deer were reintroduced to Beijing, China from Woburn Abbey in 1985, and then 18 in 1987. The second reintroduction population was established in Dafeng, Jiangsu Province, through trans-locating 39 deer from seven British zoos in August 1986. The third trans-located program included 94 deer from Beijing to Shishou, Hubei Province by three batches in 1993, 1994, and 2002. As of 2020, the population size of worldwide Père David’s deer is around 10,000, and exceeds 8,000 in China, suggesting a dramatic recovery over the last 40years although after a severe bottleneck in the history  and two individuals of Sika deer (Cervus nippon) in Beijing Père David’s deer Park. Blood samples were stored in vacuum blood collection tube (Heparin anticoagulation). All samples were collected in 2018 and 2019 , classified as the captive population (MC), were collected from Beijing Père David’s deer Park, and four samples  as the free-ranging population (MF) in Dafeng National Nature Reserve, Jiangsu Province, and another four samples  as the wild population (MW) in Dafeng National Nature Reserve, Jiangsu Province. Here, the free-ranging population indicates the animals live in a quite large fence, but can feed and breed naturally there. In contrast, the wild population survives and reproduces in the wild absolutely. The free-ranging and wild population was separated physically despite being in Dafeng National Nature Reserve. Blood samples were collected from two individuals of Red deer . The libraries were prepared as described in stLRF kit, and whole-genome resequencing was performed based on the BGISEQ-500 platform (BGI). The adapter and low quality reads were removed by SOAPnuke . Clean rMultisample single nucleotide polymorphism (SNP) genotyping were carried out as follows. Variants calling was conducted using HaplotypeCaller and GenotypeGVCFs in GATK4 , and in π at the whole-genome scale for each population. The Watterson’s estimator (w) of each population was calculated using VariScan v2.0.3 , moderate (0.05<FST<0.15), high (0.15<FST<0.25), and extremely high , phylogenetic tree (NJ tree), and ancestral component analysis (fastSTRUCTURE). Based on the filtered set of 1,456,457 SNPs, we used Genome-wide Complex Trait Analysis GCTA;  to perfoptimal K . The webptimal K  was usedμ) of 1.5×10−8 mutations per site per generation for the three Cervidae species in this study was according to the reference (R2) between any two loci from each population was calculated using vcftools v0.1.14, with following parameters: “--ld –window -bp 500,000 –geno -r2,” and average R2 values were calculated for pairwise SNPs by keeping the same distance.Multiple sequential Markovian coalescence (MSMC) model  can be ueference . The geneference . Detectieference . To asseeference . To assep<0.01). Across all 18 samples from Père David’s deer, 367.266 indels were identified, with 54.61% insertions and 45.39% deletions, but neither the percentage of insertion nor deletion showed significant difference across Père David’s deer populations (p>0.05).A total of 12,744G clean reads were obtained among 22 samples from Père David’s deer, Red deer, and Sika deer . The resπ=4.03×10−5, w=3.58×10−5), whereas captive individuals (MC) had the lowest genetic diversity . Père David’s deer had lower genetic diversity than Red deer  and Sika deer  had the highest genomic diversity  can be found at: The animal study was reviewed and approved by China Wildlife Conservation Association.SZ and GL conceived the study. YL, QC, ZC, XW, and YS performed the experiments. GL and CL performed the bioinformatics analyses and data analysis. DH and JB revised the manuscript. All authors contributed to the article and approved the submitted version.This study was funded by Innovation Team Program of Beijing Academy of Science and Technology “Research on Genetic Resources Conservation and Fine Management of Milu” [IG201805N/C1 (newly added)].The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Understanding whether genomic selection has been effective in livestock and when the results of genomic selection became visible are essential questions which we have addressed in this paper. Three criteria were used to identify practices of breeding programs over time: (1) the point of divergence of estimated genetic trends based on pedigree-based best linear unbiased prediction (BLUP) versus single-step genomic BLUP (ssGBLUP), (2) the point of divergence of realized Mendelian sampling (RMS) trends based on BLUP and ssGBLUP, and (3) the partition of genetic trends into that contributed by genotyped and non-genotyped individuals and by males and females.We used data on 282,035 animals from a commercial maternal line of pigs, of which 32,856 were genotyped for 36,612 single nucleotide polymorphisms (SNPs) after quality control. Phenotypic data included 228,427, 101,225, and 11,444 records for birth weight, average daily gain in the nursery, and feed intake, respectively. Breeding values were predicted in a multiple-trait framework using BLUP and ssGBLUP.The points of divergence of the genetic and RMS trends estimated by BLUP and ssGBLUP indicated that genomic selection effectively started in 2019. Partitioning the overall genetic trends into that for genotyped and non-genotyped individuals revealed that the contribution of genotyped animals to the overall genetic trend increased rapidly from ~ 74% in 2016 to 90% in 2019. The contribution of the female pathway to the genetic trend also increased since genomic selection was implemented in this pig population, which reflects the changes in the genotyping strategy in recent years.Our results show that an assessment of breeding program practices can be done based on the point of divergence of genetic and RMS trends between BLUP and ssGBLUP and based on the partitioning of the genetic trend into contributions from different selection pathways. However, it should be noted that genetic trends can diverge before the onset of genomic selection if superior animals are genotyped retroactively. For the pig population example, the results showed that genomic selection was effective in this population. Genomic selection has revolutionized the livestock breeding industry and has doubled genetic gain in some species, such as dairy cattle , 2. OtheThe genetic progress of a population is a combination of the progress in different selection pathways , which aWhen a population is under genomic selection, genetic evaluation based on the conventional pedigree-based method i.e., pedigree-based best linear unbiased prediction (PBLUP), is biased because it neither considers genomic information nor takes genomic preselection into account –8. GenomThe average genetic merit of an individual can be decomposed as the sum of the parent average and a Mendelian sampling term that represents the deviation of an animal’s breeding value from the average of its parents. Thus, the superiority of the selected candidates over the mean of their parents represents the extra gain compared to the previous generation, by capturing the Mendelian sampling terms. Realized Mendelian sampling (RMS) terms have zero expectation when all animals are genotyped or when genotyping is at random. When animals  are selected based on estimated parent average, their RMS is also 0. However, the average RMS will not be 0 when the genotyped animals are selected based on own phenotype or progeny performance, i.e. with selective genotyping. Moreover, with selective genotyping, the average RMS will be greater with ssGBLUP than with BLUP because the latter does not account for genomic information and preselection. Consequently, a deviation of the average RMS from 0 would show selective genotyping, and a divergent trend in average estimates of RMS between ssGBLUP and BLUP would display the starting date of genomic selection.García-Cortés et al.  introducPartitioning the total genetic trend into contributions from genotyped and non-genotyped animals or into contributions from males and females can be used to determine the impact of different selection pathways over time. For instance, if genotyped animals have a greater contribution to genetic gain than non-genotyped individuals, it can indicate selective genotyping  or that genomic selection is effective and most of the parents are selected from the genotyped candidates. In species such as pigs, the number of progeny is smaller per male and larger per female than in dairy cattle. Therefore, the impact of female paths on genetic progress is potentially higher than that of male paths  and it iIn general, PBLUP and ssGBLUP are expected to estimate similar genetic trends before the starting date of genomic selection. However, when the elite animals are genotyped retroactively or genotyping is done after selection, PBLUP and ssGBLUP can estimate different trends. The pig population that was used here has experienced both situations for some of the traits. Thus, we used three approaches to identify and investigate changes in breeding practices over time, in particular the use of genomic selection, namely: (1) based on differences in genetic trends estimated using PBLUP versus ssGBLUP, (2) based on differences in trends in RMS estimated using PBLUP versus ssGBLUP, and (3) based on partitioning the estimated genetic trends into different selection pathways as in . These aThe phenotypes of the Landrace pigs used in this study were collected from 2012 to 2021 and included 228,427 records for birth weight (BW), 101,225 records for average daily gain from birth to the end of the nursery period (ADG) at 11 weeks of age, and 11,444 records for average daily feed intake during the finishing period (FEED) at 23 weeks of age. FEED was measured on males only. Descriptive statistics for each trait are in Table Analysis of the data was based on a multi-trait mixed linear model of the three traits considered, with the statistical model for each trait described in the following and with fixed effects denoted in uppercase and random effects in lowercase letters.For BW, the model was:For ADG, the model was:For FEED, the model was:In matrix notation, the general model for each trait can be written as:The assumed (co)variance structure of random effects for the multiple-trait analysis was as follows:r et al. :\documenVanRaden :\documenSolutions for the multi-trait PBLUP and ssGBLUP were obtained using the preconditioned conjugate gradient algorithm with iteration on data, as implemented in the BLUP90IOD2 program . The  is available, RMS is 0 on average. However, if the “best” animals based on progeny testing or own performance are genotyped (i.e. selective genotyping), then the RMS of the genotyped animals will be nonzero, . Since genomic preselection has taken place in most of the livestock populations, the divergence in RMS trends obtained based on EBV and GEBV of genotyped animals can also indicate the starting point of genomic selection. The same animals as used to estimate genetic trends were also used to estimate RMS trends.Predictions of breeding values can be partitioned to quantify the contribution of genotyped versus non-genotyped or males versus females as follows:where  parents . Followiion (Eq. ) can be This procedure was implemented using the R package AlphaPart 0.8.1. , using tEstimated genetic trends for genotyped individuals, in genetic standard deviation units, based on PBLUP and ssGBLUP are presented in Fig. The RMS trends for genotyped individuals, in genetic standard deviation units, estimated using PBLUP and ssGBLUP are shown in Fig. The decomposition of genetic trends into Mendelian sampling contributions from genotyped and non-genotyped individuals is shown in Fig. Figure We used the divergence of genetic and RMS trends obtained by PBLUP versus ssGBLUP to determine the effectiveness of genomic selection. We also partitioned genetic trends into contributions from genotyped versus non-genotyped animals and from males versus females. Quantifying the contribution of genotyped individuals to the genetic trends revealed the presence of selective genotyping  and the impact of genomic selection (faster genetic improvement) in this breeding program. Decomposition of genetic trends into contributions from males versus females allowed the monitoring of the contribution of males versus females to genetic trends after the implementation of genomic selection.The effective starting point of genomic selection was found to be in 2019 in the offspring of the first animals selected based on GEBV in 2018. This finding agrees with the history of genomic selection in this pig population. Genotyping started in late 2016, for pigs born between 2015 and 2017 that were active boars or culled boars with stored tissue samples. Consequently, the higher genetic trends estimated from 2015 to 2018 for ADG using ssGBLUP than using PBLUP can be the result of selective genotyping. The decline in genetic trend for ADG estimated by PBLUP after 2018 is due to genomic preselection bias. When only genotyped animals receive phenotype records, PBLUP do not account for the positive RMS of those young animals. Similar results are reported in the literature , 13, 22.In the pig population under study, genotyping was done retroactively, which means that for the genotyped animals born from 2015 to 2017 the selection decisions were practiced by another method such as PBLUP, thus even if ssGBLUP during this period results in higher accuracy than PBLUP, we do not expect a higher genetic trend for ssGBLUP. In fact, the accuracy of ssGBLUP will be greater than the accuracy of PBLUP at any point when a sufficient number of animals is genotyped. However, the prediction accuracy at the time of selection is what is reflected in the genetic trend. The reason is that if the company/breeder invested in genotyping but has not used the genomic information in selection decisions, the higher accuracy of evaluation by ssGBLUP compared to PBLUP using accumulated data does not necessarily translate into the genetic trend. Overall, the changes of prediction accuracies over time may not follow the genetic trends estimated by PBLUP or ssGBLUP.Although investigating the fluctuations in the genetic trend across time for each trait is beyond the scope of this study, the changes in genetic trends observed are consistent with the breeding practices and the periodic modifications of weights in the selection index and the genetic correlations between traits under selection. One example is the estimated increase of 0.6 and 0.7 SD in BW from 2015 to 2020 based on ssGBLUP and PBLUP, respectively. Although the direct genetic value of BW has not been selected for in this population, the maternal genetic value for BW has. The genetic correlation between direct and maternal genetic values is about 0.1. However, the genetic correlations of direct BW with growth rate in the nursery, finisher average daily gain, and finisher average daily feed intake are positive and high, e.g., 0.42, 0.31 and 0.25, respectively. Therefore, we believe that the observed genetic trend for BW is due to the correlated responses to selection.The RMS trends revealed signatures of selective genotyping for the three traits. All males born from 2017 to 2020 were genotyped, but only a subset of females selected based on phenotypes were genotyped during this period. Hence, the deviations of RMS from 0 for birth years after 2018 are due to the strong selective genotyping of females rather than of males. Moreover, inferior males, e.g., those with a small BW, may be removed from the tested population before genotyping, which can result in positive RMS. As genotyping becomes less expensive, genotyping more young animals becomes economically justified and we expect a convergence of the RMS trends estimated using PBLUP versus ssGBLUP if phenotypic records are available for all animals.The advantages of ssGBLUP in reducing prediction bias increase when animals have been preselected based on GEBV. Genetic evaluation using PBLUP assumes that RMS average 0, but when genotyped animals with positive or negative RMS receive phenotypes or progeny, the average RMS is no longer 0 , 9. In tTo quantify the contribution of genotyped individuals to genetic trends, we partitioned the genetic trends into the genetic gain derived by genotyped individuals and that achieved by non-genotyped individuals. Regardless of the trait, in recent years, genotyped individuals had a greater contribution to genetic gain than non-genotyped individuals. The greater contribution of genotyped individuals to genetic trends does not necessarily depict the effectiveness of genomic selection. For instance, if genotyped animals are preselected based on PBLUP EBV, we expect genetic trends to be higher for genotyped than for non-genotyped animals, which is the case for the period from 2015 to 2018 before genomic selection started.A greater impact of females on genetic trends would be because selection decisions in a maternal line are placed more on females than males, and in pigs, each selected female has a larger contribution because it produces more progeny. However, the pig breed analyzed here is a maternal line and 40% of the traits under selection are only measured in females, which results in the females being the main drivers of changes in these traits. Thus, it is expected that females contribute more to the next generation than males in a pig breeding program, although the selection intensity for males is higher than for females. We found that the contribution of females to the genetic trends for BW and ADG was greater than that of males after the implementation of genomic selection.For FEED, while a flat to slightly positive genetic trend was observed for males, the trend for females was positive and unfavorable. Feed intake has a negative economic value, while ADG has a positive value in the index. Therefore, the breeding objective is to achieve a positive response in growth rate and a flat or slightly positive response in FEED, which, in turn, improves feed efficiency. Few studies investigated the contribution of different selection paths to genetic trends. For example, García-Ruiz et al.  demonstrDivergence of genetic trends for genotyped animals estimated using PBLUP versus ssGBLUP indicates the presence of genomic selection. This divergence may occur before the onset of genomic selection if superior animals are genotyped retroactively. Presence of nonzero average RMS by ssGBLUP or PBLUP indicates selective genotyping. Selective genotyping can be deliberate, e.g., genotyping of animals with superior genotypes, or incidental due to removal of weak/sick/dead animals. Under genomic selection, trends for RMS are higher when estimated using ssGBLUP than using PBLUP, with the point of divergence indicating the effective onset of genomic selection. Partitioning of genetic trends into contributions by various classes of animals such as genotyped versus ungenotyped or males versus females allows the determination of the relative impact of genotyping for different groups of animals. In particular, the observation that nearly all the genetic progress is contributed by genotyped animals confirms the increasing interest in genotyping animals, and the observation that a large fraction of the genetic progress is contributed by females validates the importance of the females in the genetic progress of a pig population. In summary, post-processing of EBV and GEBV can help to investigate the effectiveness of genomic selection and assess breeding program practices.


Pinus radiata plantations. The economic losses in forest stands can be very severe because of its abundance in cones and seeds together with the low genetic diversity of the host. Pinus resinosa is not genetically diverse in North America either, and Diplodia shoot blight is a common disease. Disease control may require management designs to be adapted for each region. The genetic diversity of the pathogen could be an indicator of its virulence and spreading capacity. Our objective was to understand the diversity of Diplodia spp. in Spanish plantations and to compare it with the structure of American populations to collaborate in future management guidelines. Genotypic diversity was investigated using microsatellite markers. Eight loci (SS9–SS16) were polymorphic for the 322 isolates genotyped. The results indicate that Diplodia sapinea is the most frequent Diplodia species present in plantations of the north of Spain and has high genetic diversity. The higher genetic diversity recorded in Spain in comparison to previous studies could be influenced by the intensity of the sampling and the evidence about the remarkable influence of the sample type.Diplodia tip blight is the most ubiquitous and abundant disease in Spanish  Pinus radiata D. Don. is the most widespread exotic forest species susceptible to the fungal pathogen Diplodia sapinea (Fr.) Fuckel (syn. Diplodia pinea (Desm.) Kickx., Sphaeropsis sapinea (Fr.:Fr./Dyko and Sutton)) in Spain. The first record of the introduction of P. radiata D. Don in Spain is its presence in a garden in Lekeitio (Bizkaia) in the mid-19th century . Forest managers should be aware of forest ecosystem issues at global and local scales that will affect them directly or indirectly, such as the disease impact, epidemiology and genetic diversity of D. sapinea, which will lead to disease outbreaks when trees are physiologically stressed [D. sapinea) and new sources of entry of genetic diversity of the pathogen.Among the populations defined by sample type within the Basque Country, the cone population showed the highest genetic diversity based on the estimated parameters. These results are in accordance with the success and stability of hy trees . From thhy trees , this ststressed . DiseasePathogens impose strong selective pressures on their hosts. The way in which fungal populations respond to adverse conditions, such as those generated by control strategies, determines the risk of management failure due to pathogen adaptation conditioned by the type of genetic variability available.Changes in weather and climate may have implications for development of Diplodia tip blight. In northern Spain, spring temperatures have shown an increase over recent decades . Accordi


Based on the relatively homogeneous origin of the sheep breeds in Morocco that originate mainly from Iberia, it is highly relevant to address the question of how these very diverse sheep populations differentiated from each other. The Mountains of the High Atlas and Middle Atlas are expected to constitute North–South and West–East geographical barriers, respectively, which could have shaped the history of the differentiation of sheep breeds. The aim of this study was to test this hypothesis by considering the genetic structure and the spatial distribution of five major breeds  and one minor breed (Blanche de Montagne), by analysing the mtDNA control region, using 30 individuals per breed.ST values and Nei’s genetic distances revealed that most breeds showed a moderate genetic differentiation. The lowest and highest degrees of differentiation were retrieved in the Beni Guil and Boujaad breeds, respectively. Analysis of molecular variance (AMOVA) indicated that more than 95% of the genetic diversity occurs within individuals, while between- and within-population variabilities represent only 1.332% and 2.881%, respectively. Isolation-by-distance, spatial Principal Component Analysis (sPCA), and spatial AMOVA analyses evidenced clear examples of geographical structuration among the breeds, both between and within breeds. However, several enigmatic relationships remain, which suggest the occurrence of complex events leading to breed differentiation.Phylogenetic and network analyses did not indicate any clear separation among the studied breeds and discriminant component principal analysis showed some overlap between them, which indicates a common genetic background. The calculated pairwise FThe approaches used here resulted in a convergent view on the hypothetic events that could have led to the progressive differentiation between the Moroccan breeds. The major split seems to be linked to the West–East barrier of the Middle Atlas, whereas the influence of the High Atlas is less obvious and incompletely resolved. The study of additional breeds that have settled near the High Atlas should clarify the relationships between the breeds of the West part of the country, in spite of their small population size.The online version contains supplementary material available at 10.1186/s12711-021-00679-2. Unraveling how the sheep breeds of a region have progressively differentiated from each other is a difficult task. The main reason is that countries are often situated on a route of migration and thus have experienced several independent migration events in the past, resulting in further admixture of the populations. This has been verified for example in Italy , PortugaMorocco is a sheep breeding country and with more than 0.5 sheep per inhabitant, it has the highest production potential of the Maghreb countries . The numBased on historical information, the Moroccan sheep breeds are divided into three main populations. The first one is the Mountain population, previously known as Berber, i.e. the most ancient one in the country and constituted in part by the Blanche de Montagne breed. Many studies have shown that the Mountain sheep populations in Morocco are characterized by specific phenotypic traits and a good adaptation to their environment, which suggests a unique genetic diversity –17. The Besides these old historical considerations, it can be hypothesized that the mountains themselves have shaped the genetic structure of the sheep breeds. On the one hand, the High Atlas is a long chain that separates the Northern and Southern populations with few passes. On the other hand, the Middle Atlas constitutes another barrier that isolates the Western and Eastern populations. The aim of this study was to test these historical and geographical hypotheses in the differentiation of the Moroccan breeds. To reach this goal, our purpose was to provide new information about the genetic variability and structure of the main sheep breeds in Morocco, using mtDNA control region sequences, and to examine the pattern of their geographical distribution. The results are compared to other studies that investigated the diversity of sheep breeds in Morocco, in terms of morphological traits , blood pThe 191 sequences analyzed correspond to GenBank accession numbers MN229085 to MN229277 and represent the six main sheep breeds in Morocco: Sardi, Timahdite, Beni Guil, D’man, Boujaad and Blanche de Montagne. The mean number of individuals per breed was 32, ranging from 27 (D’man) to 37 (Timahdite). Between 13 and 17 flocks per breed were investigated. For each flock, the blood of one to three unrelated individuals based on their genealogy was collected. More details about the sampling are in Additional file The median-joining network was performed using the Network software 10.1.0.0 version . The “StDiscriminant analysis of principal components (DAPC), implemented in the adegenet package R  was applEvidence for population genetic structure was assessed using Analysis of MOlecular VAriance (AMOVA) that was conducted with the poppr.amova function as implemented in the poppr package . SpatialST values were calculated in order to analyze the degree of differentiation between the studied breeds. The FST values and their significances  were calculated between all combinations of breeds. Computation was performed with the hierstat package [ST values) and boot.ppfst  functions, while the overall FST value was calculated using the basic.stats function implemented in the same package. Nei’s genetic distances between the breeds were calculated using the dist.genpop function implemented in the adegenet package [http://tree.bio.ed.ac.uk/software/figtree/).The pairwise F package , using p package . These d package . The parSpatial analysis of principal components (sPCA) was computed with the R software  in the aIsolation-by-distance (IBD) analysis was performed using the R. Mantel’s test in the adegenet package to check the correlation between genetic and geographical distances among populations with the mantel.randtest function (1000 permutations). To test whether this correlation results from a continuous or distant patchy cline of genetic differentiation, local densities of distances to disentangle both processes were plotted using a 2-dimensional kernel density estimation (function kde2d). The results were displayed using the MASS package . The denAmong the 191 individuals that represent the six main sheep breeds in Morocco , 116 haplotypes based on the mtDNA control region were detected using DNAsp. The relationships between these haplotypes were examined by phylogenetic analysis. We constructed a consensus maximum likelihood tree of the 116 haplotypes  was performed in order to infer the genetic structure of the breeds and their level of admixture. The clusters in DAPC were defined by a priori assumptions of population membership (K = 6). The number of retained principal components was defined using the α-score optimization proposed by Jombart and Collins , resultiFigure ST and pairwise Nei’s genetic distances (D) to explore the genetic relationships among the breeds , except for the Beni Guil-D’man and Beni Guil-Sardi pairs (p > 0.1). Indeed, the Beni Guil breed is characterized by the smallest mean FST value at 2.71% and the Boujaad breed by the largest value at 8.08%. According to pairwise FST estimates, most breed pairs showed low to moderate genetic differentiation. The overall FST value was 0.042 . Most of the FST values were significant and the highest FST value was observed between the Boujaad and D’man breeds, while the lowest FST values (non-significant) were observed between the Beni Guil and Sardi and between the Beni Guil and D’man breeds. The genetic distance matrix results were analogous to those reported by the pairwise FST values , but they were significant for the third and fifth breed groupings   among breeds, (ii) between individuals within breeds, (iii) and within individuals. The AMOVA results indicated that the genetic variance could be attributed almost entirely to individuals (95.787%), with low between- and within-population variabilities   for the different subpopulations of Sardi, Timahdite and D’man breeds. In the case of the Sardi breed, we compared the subpopulation of the Beni Meskine area (North-West) with the populations of the El Brouj and Kelaa Sraghna regions (South-East). For the Timahdite breed, we tested its subpopulations from the Timahdite (Middle Atlas) and Ghoualem areas , and for the D’man breed, we tested its subpopulations from the Draa-Dades (West) and Tafilalet areas (East). The p-values associated to the Hd estimates were not significant for the Sardi subpopulations, but significant at 5% and 0.1% for the D’man and Timahdite subpopulations, respectively. We took the π estimates into account since their p-values were at the level of 0.01% for the three breeds concerned. The results showed that the Sardi subpopulations of El Brouj and Kelaa Sraghna had a higher diversity than the subpopulation from the Beni Meskine area. Furthermore, the Timahdite subpopulation of the Ghoualem area appeared to be significantly more diverse than that from the Timahdite area, and the D’man subpopulation of Tafilalet had a significant higher diversity than that from the Draa-Dades area  and haplotype diversity . sPCA eigenvalues for each global and local structures are represented in Additional file In order to substantiate the geographical structuration of the breeds, the first two autocorrelation values  of the sPCA were plotted onto a bidimensional plot Fig. . The resThe spatial pattern was also assessed using the isolation-by-distance (IBD) approach. The Mantel test over the whole dataset of the six breeds was not statistically significant  and rejected the hypothesis of the geographical effect on a global genetic differentiation. The Mantel regression plot  ranged from 2 to 5. At K = 2, the first breed to be separated from the others was Blanche de Montagne with a non-significant FTo better understand the history of the emergence of sheep breeds in Morocco, we investigated the genetic variability and structure of the six main Moroccan sheep breeds, by analyzing the mtDNA control region. Our results provide original information about the intra- and inter-genetic population structure and the geographical patterns of these breeds.ST value, was 0.042, which is significantly different from 0 and although it is low, it is consistent with the historical gene flow across the country [ST values reported by several authors on North African sheep breeds based on microsatellite data, except for Egyptian breeds [ST value of 0.036 for the five major sheep breeds of Morocco, and of 0.038 for six Algerian breeds [ST values of about 1.7% for a set of four breeds [The AMOVA results revealed that the greatest part of the genetic diversity occurred among individuals 95.79%), while the variation between breeds and among individuals within breeds contributed only 1.33% and 2.88%, respectively. Although the variation between breeds was small it was significant and it confirms previously reported results on Moroccan breeds  and Tuni country . Still, n breeds . Gaouar n breeds  reportedn breeds . Concernr breeds , 42, andr breeds . In contr breeds , 45, Eurr breeds –51, and r breeds , 53. The.79%, whir breeds . These eThe stacked bar plot of membership probability demonstrated that the Beni Guil breed had the lowest probability assignment, while the Sardi had the highest one. This large difference could result from differences in flock homogeneity. Several reasons may explain the low genetic differentiation of the Beni Guil breed. Until the 1960s, eastern Morocco was the center of the exportation of Beni Guil lambs to the European markets through Algeria under the “Petit oranais” label because they are highly appreciated for their organoleptic meat . As a reThe pattern of the geographical distribution and genetic structuration for these sheep breeds was investigated by several approaches, which provided some elements highlighting the history of the differentiation of Moroccan sheep breeds. First, the distribution of the sequences throughout the phylogenetic tree demonstrated that the haplotypes present in each breed did not show a specific clade of breeds. This ascertainment could be a sign of ancient exchanges of genetic material between the breeds in the past. Moreover, the haplotypes are positioned on the tree with generally medium and long branches, which reflect an ancient origin of the diversity and suggest an ancient signature of sheep population expansion . In addiSecond, the global test of isolation-by-distance, evaluated by the correlation between geographical and genetic matrices, was not significant, which refutes the idea of a progressive geographical structuration of the genetic diversity. This view is supported by the sPCA analysis which indicated no significant global and local spatial structures. However, the SAMOVA approach provided some interesting information, i.e. that the differences between breeds are significantly maximized when there are four groups, that there are two clusters each represented by two breeds, D’man and Beni Guil on the one hand, and Boujaad and Timahdite on the other hand, and that the other two breeds, Sardi and Blanche de Montagne, seem to be isolated.ST values, the results of the AMOVA and the minimum spanning tree of DACP, which all pointed out the proximity between the D’man and Beni Guil breeds. This view is strengthened by the results of Gaouar et al. [ST values, since it showed the highest degree of differentiation among the six tested Moroccan breeds, resulting in a position close to the Portuguese outgroup in the FST-based tree. In the same way, the Network analysis revealed that the Boujaad individuals are the most distinctly separated from the other breeds. In fact, the Boujaad breed represents only 1.42% of the total sheep population in the country [This structuration of breed affinities is somewhat supported by the data provided by the Fr et al.  based onr et al.  between  country , and is  country , could eIn summary, the strongest association concerns the Beni Guil and D’man breeds and to a lesser degree the Boujaad and Timahdite breeds. The geographical distribution is clearly related with these two identified pairs of breeds. Beni Guil and D’man are present in Eastern Morocco, while Timahdite and Boujaad are settled in the North-Center of the country. As a result, the main split revealed here could be the East–West axis of the mountains represented by the Middle Atlas. Within these two blocks, there are highly significant climatic boundaries between the members of these two pairs of breeds, as shown by Petit and Boujenane . With reAt a finer scale, i.e. at the scale of the breeds, it may be questioned whether there is a geographical structuration. In a first attempt, a significant relationship was found between the genetic and geographical distances for the Boujaad and Blanche de Montagne breeds, which suggest a low level of genetic exchanges within their breeding areas. In contrast, no significant isolation-by-distance was evidenced for the Sardi, Timahdite, Beni Guil and D’man breeds, which thus reflects a higher gene flow between flocks within these breeds. Using the more precise method of autocorrelation developed by Jombard and Collins , signifiThe weak geographical distribution of the genetic diversity found in the current study is consistent with previous studies on sheep breeds of Algeria using microsatellite data . In addiTo decipher the differentiation of sheep breeds within a country, it is necessary to combine several approaches, including the study of ancient DNA and the analysis of the genetic diversity and structure of the current populations. Due to a strong homogeneity of their genetic background, the sheep breeds of Morocco represent a favorable example, and the present work investigated the geographical structure of the breeds based on mtDNA sequences. Our findings show a good level of intra-breed genetic diversity and a low differentiation between breeds. Concerning the historical information about the origins of the different studied breeds, some elements are doubtful: there is no obvious opposition between the Mountain and Plateau breeds. Instead, we found a contrast between the Eastern breeds (D’man and Beni Guil) compared to the Western breeds, which may be in relation with the North–South barrier of the Rif and Middle Atlas. With regard to the Blanche de Montagne breed, it seems that the Anti-Atlas is a barrier that led to its isolation. Nevertheless, this breed does not seem to have any connection with the D’man breed, in spite of their geographical proximity. However, it would be useful to investigate other Mountain breeds to gain further insight into their genetic relationships with the Western Plateaus breeds. Moreover, a significant effort to enhance the number of samples of the Blanche de Montagne could increase our knowledge about the within-breed variation. Such improvements could help to better understand the situation of the Sardi breed. An interesting issue would be to consider the analysis of ancient DNA collected at a large scale to unravel the evolutionary events that have shaped the genetic structure of the current breeds. For example, there is a deep gap between the phenotypic traits of the D’man and Beni Guil breeds and gaining knowledge on the underlying process by which the D’man population originated is necessary.Additional file 1: Table S1. Details on the sampling of individuals . Table S2. General statistics on the diversity indices of the whole dataset.Additional file 2: Fig. S1. Sample map distribution, with the breeding areas of the breeds. Fig. S2. Phylogenetic tree of the haplotypes of Moroccan sheep breeds. The figure was drawn using the maximum likelihood method implemented in MEGA X [Fig. S3. Density plot of breed individuals based on the first discriminant function. Fig. S4. Unrooted neighbour-joining tree based on pairwise FST values. Churra Algarvia was used as outgroup. The figure was drawn using the FigTree version 1.4.3 software available at: http://tree.bio.ed.ac.uk/software/figtree/. Fig. S5. sPCA analysis eigenvalues and network connection. The figures were drawn using the R software [a) A variant of the plot of sPCA eigenvalues; (b) Decomposition of sPCA eigenvalues; the dotted segments indicate on the abscissa the maximum variance of a principal component, and in ordinate the minimum and maximum values of Moran's I; and (c) Connection network between individuals of the sheep breeds. Fig. S6. Scatter plot generated based on genetic distances  and geographical distances between Moroccan sheep breeds. The figure was drawn using the R software [Fig. S7. Scatter plot generated based on genetic distances  and geographical distances for each sheep breed. The figure was drawn using the R software [n MEGA X , with thn MEGA X , includisoftware  and the software : (a) A vsoftware  and the software . Fig. S7software  and the software .Additional file 3: Table S3. Analysis of molecular variance (AMOVA) in the six Moroccan sheep breeds. df: degree of freedom; SSD: sum of squares; MSD: mean squared deviations. Table S4. AMOVA results with different breed groupings. Table S5. Genetic diversity indices of subpopulations of the D’man, Sardi, and Timahdite breeds.


In this study, we used daily air quality monitoring data for 28 air pollution transmission channel cities in the Beijing–Tianjin–Hebei region during 2014–2019 to quantify the relative contributions of meteorological factors on spatiotemporal variation in PM2.5 concentration by combining time series and spatial perspectives. The results show that annual mean PM2.5 concentration significantly decreased in 24 of the channel cities from 2014 to 2019, but they all still exceeded the Grade II Chinese Ambient Air Quality Standards (35 μg m−3) in 2019. PM2.5 concentrations exhibited clear spatial agglomeration in the most polluted season, and their spatial pattern changed slightly over time. Meteorological variables accounted for 31.96% of the temporal variation in PM2.5 concentration among the 28 cities during the study period, with minimum temperature and average relative humidity as the most critical factors. Spatially, atmospheric pressure and maximum temperature played a key role in the distribution of PM2.5 concentration in spring and summer, whereas the effect of sunshine hours increased greatly in autumn and winter. These findings highlight the importance of future clean air policy making, but also provide a theoretical support for precise forecasting and prevention of PM2.5 pollution.Understanding the spatiotemporal characteristics of PM It was not until the air quality standard was published that real-time hourly PM2.5 and PM10 concentration data for the major cities were collected and released online, which is essential for providing detailed information about particulate matter pollution and helpful for understanding how local air quality has changed over time.Meteorological conditions and synoptic circulation patterns exert important influences on PMariation ,32. ReceH region . Gonzalehe 2050s . Howeverhe 2050s . Statistribution . Howeverion [2.5 ,38. On t complex ,39. PM2. heating  and temp heating  and chemeneities ,41. Ther2.5 concentration during 2014–2019 in the 28 cities of the air pollution transmission channel in the BTH region of China at seasonal and annual scales. Then we explored the spatial heterogeneity of the relationship between PM2.5 concentration and meteorological variables based on geographical weight regression (GWR) analysis. Finally, we performed partial correlation and multiple linear regression (MLR) analyses to quantify PM2.5 concentration response to meteorological variables and their contributions to the PM2.5 concentration variation from a time-series perspective. The findings of this study will contribute to a more comprehensive understanding of the influence of meteorological variables on PM2.5 concentration and will provide strong theoretical support for precise forecasting and prevention of PM2.5 pollution in the BTH region.In this study, we examined reductions in PM2 [The study area includes 28 air pollution transmission channel cities of the BTH region, covering an area of 275,000 km2 ,39. The 10 and PM2.5 concentration data (μgm−3) between 1 March 2014 and 28 February 2019 for the 28 cities in the air pollution transmission channel of the BTH region (Jincheng data started from 1 March 2015) were obtained from an online database . This dataset was constructed by averaging hourly concentration data for all national air quality monitoring sites within each city listed on the official website of the China National Environmental Monitoring Center [2.5 and PM10 concentration obtained by the continuous automated monitoring system based on the micro-oscillating balance method and the β absorption method. The dataset contained very few missing records  and very few zero values for PM10 concentration. We interpolated missing values from the mean values of the two closest days to maintain time series data continuity and comparability. In addition, although PM2.5 has been paid special concern, it was not systematically observed and reported for most cities in China until 2014. Meanwhile, it was unreasonable to effectively consider the relative contributions of different particles to local air quality and the composition characteristics of particulate matter based only on fine particles (and without consideration of coarse particles). Therefore, we removed zero values of PM10 concentration and the corresponding PM2.5 concentration values from the time series in order to calculate the PM2.5/PM10 ratio, which provided crucial information related to particle origin and allowed retrospective prediction of PM2.5 concentration without direct PM2.5 measurements.Daily PMg Center . In accomaxT, °C) and minimum temperature , average atmospheric pressure , sunshine hours , average relative humidity , maximum wind speed  and wind direction (WD) for the 28 meteorological stations were collected from a Chinese meteorological data sharing service system . The 28 meteorological stations were generally located at the center of each city  means that the city and surrounding cities have high PM2.5 concentrations; High–Low (H–L) represents that the city has a high PM2.5 concentration, but those of surrounding cities are low; Low–High (L–H) shows that the city has a low PM2.5 concentration, but those surrounding cities are high; Low–Low (L–L) means that both the city and surrounding cities have low PM2.5 concentrations; Not significant (NS) indicates no significant difference in PM2.5 concentration among cities. Global Moran’s I and Local Moran’s I statistical tests used a z-test at 95% confidence level. Finally, the coefficient of variation (CV) was calculated to investigate fluctuation in the temporal variability of PM2.5 concentration. We also explored the spatiotemporal variation in the PM2.5/PM10 ratios using the same methods.The variation in PMsoftware ,42. The 2.5 concentration and meteorological factors for each city at seasonal and annual scales; this method has been successfully applied in previous environmental studies to remove covariate effects among multiple influential factors. Next, we performed multiple linear regression analysis of daily PM2.5 concentration and meteorological factors at seasonal and annual scales for each city, and the explanation ability of meteorological factors on PM2.5 variability was evaluated using the coefficient of determination (R2) [p < 0.05.Partial correlation analysis was performed to determine the relationships among temporal variations in daily PMion (R2) ,43. All 2.5 concentration based on site data for the 28 cities. First, we normalized mean seasonal and annual PM2.5 concentration and meteorological variable data during 2014–2019 for each city; we then conducted the geographically weighted regression analysis using the ArcGIS 10.4.1 software. The geographically weighted regression was run for an observation point by using a spatial kernel that centered on the point and weighted other observation points by a distance decay function. In this study, the Gaussian model was selected as a weighting function, and the corrected Akaike’s information criteria (AICc) was used to obtain the optimal bandwidth.The geographically weighted regression was developed to deal with this non-stationarity, which allows relationships between independent and dependent variables to vary spatially by producing a set of local parameters to reveal spatial relationships by assuming heterogeneous influence of the same factor on dependent variables in different spatial units ,45. We p2.5 concentrations were observed in Xingtai (83.69 μg m−3), Handan (80.98 μg m−3), Shijiazhuang (84.16 μg m−3) and Baoding (85.35 μg m−3), and the lowest PM2.5 concentration was observed in Shanxi Province, with a minimum of 56.77 μg m−3 in Yangquan (2.5 concentration exceeded the CAAQS (GB3095-2012) Grade II standard of 35 μg m−3. Mean PM2.5 concentration showed wide seasonal variation that was similar among all cities. The highest pollution levels were observed in winter and the lowest values in summer. However, the seasonal fluctuation range seriously differed among cities. For instance, Beijing, Tianjin, Tangshan, Langfang, Binzhou, Zibo and Yangquan showed lower CV values, whereas Baoding, Anyang, Puyang and Kaifeng had larger CV values (>40%). CV values were higher in winter and lower in spring. From 2014 to 2019, significant reductions in annual mean PM2.5 concentration occurred across 24 of the 28 channel cities , with a mean rate of −7.14 μg m−3 year−1  Grade II standard in all cities, and PM2.5 concentration variation showed significant seasonal and spatial distribution differences among all 28 cities. Compared with other seasons, PM2.5 concentration in spring and summer exhibited the clearest obviously decreasing trend, with significant reductions in 22 and 26 cities, respectively, which were mainly located in the Hebei and Shandong Provinces. Significant downward trends were also observed in 20 cities in autumn and only 4 cities in winter.During 2014–2019, the highest annual mean PMYangquan . In all 3 year−1 . About h2.5 concentrations during 2014–2019 had a Global Moran’s I of −0.05 (p = 0.95) in spring, 0.09 (p = 0.35) in summer, 0.20 (p = 0.08) in autumn, and 0.30 (p = 0.01) in winter, indicating spatial patterns in winter across all channel cities. Local spatial patterns of average PM2.5 concentrations in winter displayed clear spatial agglomeration characteristics, with H–H spatial clusters from Dezhou in 2015, to Handan Anyang, Hebi and Puyang in 2019. The L–L spatial clusters appeared in Beijing and Tianjin and persisted thereafter , Tianjin (0.62) and Binzhou (0.61) and lower ratios observed in Taiyuan (0.49), Yangquan (0.51) and Jincheng (0.50), all of which are located in Shanxi Province. By contrast, despite similar spatial distributions of mean PM2.5/PM10 ratios in each season, clear seasonal differences were observed. Among the 28 cities, 27 cities displayed their highest mean PM2.5/PM10 ratios in winter (excluding Hengshui), whereas almost all cities showed their lowest mean ratios in spring. From 2014 to 2019, the annual mean PM2.5/PM10 ratios in most cities of Hebei and Shandong Provinces exhibited a clearly decreasing trend, with significant decreases in Langfang, Shijiazhuang, Dezhou and Heze, whereas most of the other cities fluctuated over time , Zhengzhou (p = 0.03) and Kaifeng (p = 0.01).Annual mean PM of 0.56 . PM2.5/Pver time . Compare2.5/PM10 ratios demonstrated that the Global Moran’s I ranged from 0.005 to 0.15, with no significant differences at the seasonal or annual scale, indicating that the PM2.5/PM10 ratio of each city did not influence those of the other cities in aggregate. However, local spatial autocorrelation analysis exhibited L–L spatial clusters of mean PM2.5/PM10 ratios in Taiyuan and Yangquan during spring and winter and H–H spatial clusters in Tianjin in summer and in both Tianjin and Beijing in autumn  and WS (except in Yangquan); it was positively correlated with maxT in 23 of the 28 cities and with H throughout the study period. By comparison, the correlation between WD and PM2.5 concentration was positive over the northeastern part of the study area , but negative over the southwestern region . Generally, minT and H  were critical factors affecting variation in PM2.5 concentration throughout the year.Partial correlation analysis results for PM2.5 concentration and meteorological variables were generally similar to those at the annual scale, with some exceptions  were much stronger than those at the annual scale in most cities, whereas those correlations between PM2.5 concentration and minT became slightly weaker. In spring, minT and H were the two most important factors affecting the variation in PM2.5 concentration in most cities, whereas the relationship between AP and PM2.5 concentration was significant only in seven cities. Among the four seasons, PM2.5 concentration showed the weakest correlation with AP and H in summer and was more strongly correlated with maxT and SH for more cities in summer than in spring and autumn. In autumn, PM2.5 concentration was significantly negatively correlated with minT in 27 cities, with AP in 25 cities and with H in 24 cities; minT had the greatest influence on PM2.5 concentration variation. Interestingly, partial correlation analysis also showed that PM2.5 concentration was positively related to WD in winter in eastern cities such as Tianjin, Cangzhou, Xingtai, Binzhou, Dezhou, Zibo, Jinan and Jining, but positively correlated with WD in summer in some northern cities .Seasonal correlations between PMceptions . In wint2) of the variation in PM2.5 concentration among the 28 cities during the study period; Shijiazhuang had the highest R2 value (44.46%), and Jinan had the lowest value (21.30%). At the seasonal scale, the mean R2 was highest in winter (0.43), with the highest R2 values in Beijing, Tianjin and the cities of Hebei and Shandong Provinces, whereas R2 was lowest in summer, with a mean value of 0.13 . WS and WD were negatively correlated with PM2.5 concentration in all cities except Tangshan, but had a smaller impact on PM2.5 concentration in Beijing, Tianjin, Langfang, Hengshui, Xingtai, Handan, Liaocheng, Jining, Heze, Anyang, Hebi and Puyang . Correlations between meteorological factors and PM2.5 concentration were generally similar in winter and autumn  in the BTH region during 2014–2019. Similar trends have been reported in previous studies. For example, average annual PM2.5 concentration was reported to have decreased throughout the BTH region during 2013–2016 [2.5 concentration can be attributed mainly to the Action Plan on Prevention and Control of Air Pollution in 2013 and the Air Pollution Prevention and Control Program in Beijing, Tianjin, Hebei and surrounding areas in 2017. However, it was noteworthy that the rate of decline in PM2.5 concentration slowed dramatically after 2017, even fluctuating upward in some cities (2.5 concentration of all channel cities in the BTH region remained much higher than the Grade II standard of CAAQS in 2019, which indicates that further measures are still strongly needed despite the improvements attributed to control strategies implemented within the past decade.In this study, we found that annual mean PM013–2016  and 2013013–2016  and to h013–2016 ,36. These cities . We also2.5 concentration exhibits a distinct seasonal cycle, with less severe PM2.5 pollution in summer and greater pollution in winter, probably due to the combined impact of unfavorable weather conditions and coal and biomass combustion for residential heating [2.5/PM10 ratio also showed a seasonal variation, decreasing in spring and peaking in winter, which can be attributed to increasing fuel consumption [2.5 in winter [2.5 concentration in only four cities in winter, the most polluted season, whereas PM2.5/PM10 ratios of most cites showed a fluctuating upward trend. The increase in PM2.5/PM10 ratios also have been reported at several monitoring sites in the UK [2.5 emissions and their formation mechanisms in winter.The results of this study demonstrate that PM heating ,47. The sumption  and the n winter . In addin the UK  and Indin the UK . These r2.5 pollution in Shijiazhuang, Xingtai and Handan, and found that regional contributions were larger in January than in July [2.5 concentration occurred in autumn and winter, indicating that PM2.5 pollution in adjacent cities may strongly interact among the 28 cities of the BTH region during the latter half of the year. However, the local spatial autocorrelation results of our study showed that regions with high agglomeration of PM2.5 concentration were mainly concentrated in the southern Hebei Province and northern Henan Province, but with some spatial variation, suggesting that joint variable control strategies should be emphasized for PM2.5 pollution over time.Air pollution has been suggested to be strongly affected by pollutants from adjacent cities . The int in July . In acco2.5 concentration. In this study, minT and H were the leading factors affecting PM2.5 concentration at the annual scale. minT was negatively correlated with PM2.5 concentration, which is consistent with the fact that air quality is better in summer and worse in winter. This negative correlation may be closely related to less dispersion and dilution of air pollutants at lower minT. At low minimum temperature, air convection is weak and temperature inversion can easily occur. These conditions are not conducive to the diffusion and dilution of air pollutants [H had the strongest positive influence on PM2.5 concentration. Increases in H increase the production of sulfate aerosols via aqueous reactions and also significantly affect the partitioning of HNO3 between the gas and particulate phases, leading to enhanced nitrate formation [Meteorological conditions played an important role in influencing PMllutants . By contormation . In addiormation . For exaormation . Howeverormation . This diormation ,57.2.5 concentration showed some interesting characteristics in different seasons. Generally, meteorological factors had a greater influence on PM2.5 concentration in all cities in winter, and the effects of these factors were much stronger in winter than in other seasons, which is consistent with the findings of a previous study [2.5 concentration in summer, when the variation in PM2.5 concentration depended mainly on changes in maxT and SH. Previous studies have reported that increases in temperature increased radical production rates and promoted aerosol formation, but the atmospheric photolysis occurred on organic carbon, reducing PM2.5 concentration [maxT and SH on PM2.5 concentration in this region.The correlations between meteorological factors and PMus study . In wintus study . Fewer mntration ,40 (Chenntration ,59. TherWS was negatively correlated with PM2.5 concentration in all cities, while maxT and minT exhibited a positive correlation with PM2.5 concentration in 82.14% and 60.71% of all cities, respectively, at the annual scale. The influence of temperature seems complex and differs regionally. We also found that SH strongly affected the PM2.5 concentration in areas east of the Taihang mountains, whereas AP strongly affected the PM2.5 concentration in cities west of this mountain range. These differences can be attributed partly to the topographical characteristics. Unique atmospheric circulation subject to the particular topography has been suggested to develop spatial distribution characteristics of air pollutants [The spatial distribution of air pollution has been associated with meteorological factors in previous studies . Liu et llutants .10 and PM2.5 concentration data for Jincheng was collected and analyzed from 1 March 2015 due to the limited monitoring record. In addition, there were about 44 missing air pollution records among 28 cities and few zero values for PM10 concentration, which were removed to calculate the PM2.5/PM10 ratios. Although we applied interpolation to maintain the continuity and comparability of these time series data, some uncertainty remains. Moreover, although the Local Moran’s I method used in this study could indeed shed light on the potential interdependence and specific spatial agglomeration patterns of PM2.5 concentration, the limited observations for 28 cities were slightly less than the restriction on the numbers required by this method. Additionally, the temporal and spatial variations of PM2.5 concentration were strongly correlated with meteorological factors, and their relationships varied significantly across seasons and geographical locations, probably relating to PM2.5 components. To better address this problem, GWR was applied in this study to capture the spatial nonstationary characteristics, but it could not simultaneously deal with temporal nonstationarity [This study had some limitations. First, PMionarity ,63, whic2.5 concentration in 28 cities within the atmospheric pollution transmission channel in the BTH region with associated meteorological influences during 2014–2019. Average PM2.5 concentration appeared to decrease significantly in most cities at the annual scale, but exhibited clear seasonal and spatial variation. The mean PM2.5 concentration demonstrated a significant decreasing trend in more than 20 cities in spring and summer, but only 4 cities in winter, indicating that strict measures should be continued, especially in the most severely polluted season, despite successful air pollution control plans implemented within the past decade. Temporal variation in daily PM2.5 concentration was strongly correlated with minimum temperature and average relative humidity. The higher the PM2.5 temporal concentration, the stronger the influence of the meteorological factors, with a mean explanatory power of 31.96% for the 28 cities. Spatially, wind speed was negatively correlated with PM2.5 concentration for all cities, while maximum temperature and minimum temperature exhibited a positive correlation with PM2.5 concentration in 82.14% and 60.71% cities, respectively. These results will provide valuable references for understanding the spatiotemporal variation of PM2.5 pollution, as well as for future clean air policy making.In this study, we investigated the spatial and temporal variation of PM