Juniperus thurifera is a key element of the forest communities in arid and semi-arid areas of the western Mediterranean. Previous genetic and morphological investigations suggested that Algerian populations are genetically more similar to European than to Moroccan populations and advocated their recognition at the variety rank. We aimed to investigate the spatial genetic structure in J. thurifera to verify the distinct character of the Algerian population in terms of the genetic breaks reported among several North African taxa. We also modelled species distributions since the Eemian to recognise the impact of past climatic changes on the current pattern of diversity and predict possible changes in species distribution in the future. Species-specific microsatellites were used in the analysis of 11 populations from Algeria, Morocco and Europe. We revealed the significant genetic distinctiveness of the Algerian populations from the Moroccan and European stands that may have important taxonomic and conservation implications. The diversity pattern revealed for J. thurifera reflects the east-west genetic splits reported among some North African plant and animal taxa and suggests an impact of shared historical processes. Additionally, modelling of the distribution allowed us to identify possible glacial refugia and their impact on the modern pattern of differentiation in J. thurifera. Reduction of species occurrence, especially in the European domain, is likely according to the future projections of the species distribution. However, in some studies that were focused on the evolutionary history of the North African tree species, the distinctiveness between Moroccan and Algerian/Tunisian populations emerges recurrently. In investigations on the cpDNA variability of Quercus canariensis3 and Q. ilex4 and mtDNA diversity of Pinus pinaster5, the Algerian populations were shown to have different haplotypes from those occurring in Morocco; a similar conclusion was drawn for Olea europea6. Furthermore, Moroccan stands of Cedrus atlantica and Alnus glutinosa are also genetically distinct from the Algerian populations8. The divergence revealed in A. glutinosa also included differences in ploidy level, such as Moroccan species displaying tetraploidy in comparison to the diploidy displayed by Algerian and European populations. Phylogeographic parallels among plants are accompanied by numerous examples among animal species12, and both advocate for a more general hypothesis of the phylogeographic convergence among North African biota. Such a pattern would assume an east-west split among North African biota, implying a common historical process in action.Compared to the northern Mediterranean, the genetic diversity and differentiation patterns of woody species in the southern part of the region that comprises North Africa is rather poorly exploredJuniperus thurifera L. (thuriferous juniper) occurs in Europe and North Africa but the species range is greatly fragmented17. The major distribution area covers the mountains of the Iberian Peninsula, followed by the areas of the High and Middle Atlas Mountains, with a separate subpopulation in the Anti-Atlas in Morocco20. The species distribution has even been observed to extend into Spain through colonization of abandoned fields and abandonment of livestock operations21. Beyond the Iberian Peninsula, the species is considered rare in Europe, and it occurs in the Southern Alps, the Pyrenees and Corsica. In reference to the African range, the Moroccan populations are extremely degraded22, but the most isolated and confined populations of this species are in Algeria22.J. thurifera is exclusively found in six locations in the Aurès Mountains, which are the eastern continuation of the Saharan Atlas Mountain range and are considered to be one of the 39 key biodiversity areas in northern Algeria23. The areas with the most inventories indicated the existence of relatively dense stands consisting of 3,500 to 8,000 mature individuals24. In addition to the climate-driven contraction of juniper’s range in Africa, its decline is strictly related to human pressure. First, it has declined as a result of overexploitation due to logging and wood harvesting, because juniper wood has been used in North Africa as a fuel for the last several millennia16. Second, maintaining a high level of livestock and traditional pasture culture in Mediterranean countries, which results in overgrazing, is also an important factor affecting the species decline in Algeria and Morocco25. Overgrazing and branch cutting negatively affect the regeneration ability of this species, since the organic matter that accumulates beneath the trees’ crowns is the most suitable site for recruitment16. A lack of such conditions hampers regeneration and leads to demographic collapse. Currently, all Algerian stands suffer from a lack of natural regeneration, and old individuals prevail in most of the populations, which probably also decreases their reproductive abilities. Generally, thuriferous juniper in Algeria produces seeds sporadically, and most of them are empty , which suggests the occurrence of some serious environmental or genetic factors (or likely both) that interfere with the reproduction process, causing its failure. Additionally, in all stands, an imbalanced sex ratio is noted, and female trees represent a maximum of 20% of the population size. Since recruitment success is highest under female trees, an insufficient number of such trees in a population is another factor that negatively affects the regeneration process26.In Algeria, J. thurifera corresponds to its morphological and genetic differentiation, which led to subdivision into intra-specific taxa. The major taxonomic split follows the Ibero-African species distribution and resembles the isolating effect of the Strait of Gibraltar29. The morphological and genetic distinctiveness of populations from Europe and North Africa led to the recognition of the two subspecies: J. thurifera subsp. thurifera and J. thurifera subsp. africana (Maire) Romo et Boratyński34. Within the European species range, the geographic varieties are described, namely, J. thurifera var. gallica in the Alps and the Pyrenees, J. thurifera var. hispanica18 in Spain and J. thurifera var (chimovar.) corsicana Gauquelin, Idr. Hass. & P. Lebreton in Corse35. A suggestion to separate the Algerian relict populations under the name J. thurifera var. aurasiaca Véla & P. Schäf., var. nov. was made by Vela and Schäfer35 based on the geographic and morphological criteria. The distinctiveness of the Algerian stands was confirmed by Zeraib et al.36 in chemotaxonomic studies. However, the intermediate genetic character of a single Algerian population analysed by Terrab et al.37 suggested that it was more closely genetically related to the European stands.The highly fragmented distribution of J. thurifera has been classified as being of least concern, which may be reasonable in the case of Spanish populations that occupy large areas38. However, in North Africa, species exhibits population fragmentation, reduced regeneration and strong decline in Algeria39. Particularly, investigations of Algerian populations are important because they may deliver information about the species’ genetic resources and reveal whether those populations form a distinct genetic lineage that should be recognized as a separate conservation unit. The very marginal location of the Algerian populations in the Aurès Mountains that practically precludes gene flow from other parts of the species’ geographic range may pose a threat to its long-term survival, especially in terms of ongoing climate change40. Moreover, genetic responses to climate-driven environmental changes will likely be based on standing genetic diversity, which requires the maintenance of high within-population variability41.According to the International Union for Conservation of Nature (IUCN), J. thurifera in reference to the Moroccan and European stands, as previous studies were unconvincing37. We then asked two questions: (i) Does the spatial pattern of the genetic diversity of J. thurifera subsp. africana match the east-west genetic breaks reported for some North African organisms? and (ii) Does the intraspecific genetic differentiation have taxonomic and hence conservation implications for the Algerian populations? To predict the impact of climate change and evaluate the potential risks involved, species distribution modelling (SDM) was performed. Recently, this method has been widely used in simulations of future changes in species distribution patterns induced by climate change and has been recognized as a useful tool that may efficiently support conservation genetic studies and further planning of conservation activities44.In this paper, genetic analysis was undertaken to examine the differences between the Algerian populations of J. thurifera from Spain, France (Corsica) and Morocco (Middle and High Atlas Mountains) that were previously biometrically analysed by Boratyński et al.33 and two populations from Algeria (Aurès Mountains) were used in this study for the screening of polymorphisms at nuclear microsatellite loci (nSSR)  Table , Fig. 1A45. A set of six nSSR markers originally developed for J. thurifera that were highly polymorphic according to Teixeira et al.34 were used: JT_01, JT_04, JT_30, JT_33, JT_40 and JT_46  with the following conditions: an initial denaturation step of 15 min at 95 °C, followed by 40 cycles of denaturation at 94 °C for 30 sec, annealing at a temperature specific to each multiplex for 90 sec (57 °C for multiplex I and 55 °C for multiplex II), and extension at 72 °C for 60 sec, with a final extension step at 72 °C for 10 min. PCR products were analysed using an AB 3130 Genetic Analyzer  capillary electrophoresis system with an internal size standard, GeneScan™ 500 LIZ®. Genotypes were scored using GeneMapper 4.0 software .Genomic DNA was extracted from dried leaves using the CTAB methodJ. thurifera is a tetraploid species46, which has important practical consequences for the scoring of genotypes and subsequent statistical analysis of nSSR data48. There is substantial ambiguity in genotype assignment due to the difficulty of resolving the number of allele copies in polyploids. This methodological issue leads to situations in which genotypes cannot be truly inferred based on phenotypes; therefore, neither allele frequencies nor genotype frequencies can be estimated, which are necessary to implement population genetic-based inferences49. Additionally, fixation of heterozygosity is a common feature, because genetically different isoloci may share the same alleles, which precludes statistical evaluation of deviation from the Hardy-Weinberg equilibrium or gene diversity level50.51, which enables population genetic analysis of polyploids by correcting for allele copy number ambiguity, was used for computations of basic diversity parameters, such as the number of alleles (N), average number of alleles (Na), effective number of alleles (Ne), number of private alleles (Pa) and heterozygosity within populations (Hs). The missing alleles in genotypes were filled with the option overall allele frequencies. The overall genetic differentiation among populations was assessed using the fixation index, Gst52, and tested with 9,999 permutations. Finally, pairwise population differentiation (Fst) was calculated from an analysis of molecular variance (AMOVA), which is exactly equivalent to Weir and Cockerham’s53 statistic, and the significance of Fst was tested with 9,999 permutations.GenoDive 2.0b23 software48. First, AMOVA-based k-means clustering analysis54 implemented in GenoDive was applied. This method uses the AMOVA framework to calculate the among-cluster sum of squares and divides individuals into an a priori assigned number (k) of groups in such a way that the among-cluster sum of squares is maximized based on a simulated annealing algorithm. Analysis was performed on all generated datasets for k values from 2 to 9, using 20 repeats of the simulated annealing algorithm, with 50,000 MCMC steps. To estimate the most likely number of clusters , the Bayesian information criterion (BIC) and Caliński and Harabasz pseudo-F statistic55 methods were used. The clustering results that were biologically justified were discussed, according to the recommendation of Meirmans54.We applied different multivariate analysis methods for detecting population structure that can be applied to polyploids56 implemented in package ‘adegenet’ in R 3.4.358 was used. This multivariate analysis provides an alternative to model-based analyses, because it is not limited by assumptions about the population genetic models; thus, it may serve as a more efficient tool for unravelling complex population structure56. Analysis was performed on a genetic dataset that was transformed to a binary matrix of the presence and absence of each allele without defining groups a priori. DAPC is a multivariate two-step procedure in which data are first transformed by principal component analysis (PCA) as a prior step to a discriminant analysis (DA). The function ‘find cluster’ was used to identify the optimal number of clusters based on the BIC. The number of principal components (PCs) to be retained for the DAPC was chosen according to the α score using the ‘optim.a.score’ function; the DAPC was conducted based on 12 PCs.Second, to infer the geographical pattern of genetic differentiation and detect potential genetic admixture, a discriminant analysis of principal components (DAPC)59 was used to identify genetic discontinuities among populations. This approach was confirmed to be efficient in identification of intraspecific lineages60. The analysis was run using Nei’s genetic distance matrix and the geographical coordinates of each population. The significance of each inferred barrier was assessed by bootstrapping with 100 replications of the genetic distance matrix. SEQBOOT and GENDIST programmes implemented in the PHYLIP package 3.696 were used to obtain multiple matrices data for bootstrapping.Monmonier’s maximum-difference algorithm implemented in BARRIER 2.261 was used to determine the spatial genetic structure. A non-spatial admixture model with correlated allele frequencies was used. To deal with the 4n ploidy level that generates genotype ambiguity, we used a recessive alleles option as recommended by the software’s manual and recent paper of Meirmans48. Ten independent repetitions for each number of groups (K) ranging from 1 to 12 were performed with a burn-in set of 100,000 steps, followed by 200,000 MCMC iterations. The CLUMPAK platform62 was used to align replicated runs and average individual assignment probabilities for the most likely K-value and to estimate the optimal number of clusters according to Evanno’s ΔK method63.Finally, a model-based Bayesian clustering approach implemented in STRUCTURE 2.3.3(k = 4). The significance was tested with 9,999 permutations, and distances were calculated using the ploidy independent infinite alleles model (Rho). To test for isolation by distance (IBD), the geographic distance matrix was correlated with the genetic distances (Fst) using the standard Mantel test implemented in GenoDive and significance was assessed with 9,999 permutations.To determine the hierarchical distribution of genetic variation, AMOVA implemented in GenoDive was performed for two defined configurations: among geographic regions  and groups revealed by clustering analysis conducted by DAPC 64.Graphical presentation of genetic data was made with using the QGIS Geographic Information SystemJ. thurifera was built using the maximum entropy approach implemented in MaxEnt 3.4.166. The data on occurrences of J. thurifera were obtained from the literature and personal observations  and that at the last glacial maximum . The CHELSE database has been shown to deliver more precise estimations in terms of precipitation than WorldClim, because the precipitation estimation algorithm is based on statistical downscaling of atmospheric temperatures, which delivers improved climatologies, especially in mountainous landscapes69. The spatial resolution of 19 bioclimatic variables68  will be 0.3 °C to 1.7 °C.The species distribution model for –5, and the maximum number of iterations was 10,000. The ‘random seed’ option was applied, 20% of the data were set aside as test points, and the output was set to logistic. To reveal areas of long-term species persistence, such as Pleistocene refugia, we also modelled the Eemian (c. 120–140 ka BP) theoretical range of J. thurifera in Europe and Africa. However, the Eemian projection was available only with WorldClim. We performed modelling of the present and LGM distributions using occurrence data (1) jointly for African and European populations, (2) for African populations only and (3) for European populations only using both climatic datasets. As a criterion of model accuracy, the receiver operating characteristic (ROC) curve and value of the area under the curve (AUC) were used72. The results of the analyses were visualized in QGIS 2.18.2064.The bootstrapping analysis was conducted with 100 replicates, the convergence threshold was set to 10J. thurifera was verified by PCA analysis in order to characterize ecological differences between the African and European populations. Analysis was performed on dataset generated for 583 occurrence records of species . Ten climatic variables, which had the highest VIF values (> 300), were removed from the analysis to avoid autocorrelations. PCA analysis was run using the ‘prcomp’ function implemented in R.In addition, the relative influence of each bioclimatic variable on the current distribution of Graphical presentation of modelling results was made using the QGIS Geographic Information System.a = 12.500; Ne = 5.985) than the European  and Moroccan populations  (Table s) ranged from 0.687 (FR_2) to 0.809 (AL_2), with an average value of 0.772. A comparison between the European part of the J. thurifera range and the African populations indicated that the population from Algeria had the highest significant average value of within-population diversity , but this value was not significantly higher than the other values  to 44 (JT_33), for a total of 144 alleles detected across the 255 individuals analysed . Accordingly, the populations’ pairwise Fst values ranged from 0.002 (between populations MO_1 and MO_3) to 0.059 (between populations FR_2 and AL_1) and were significant (P ≤ 0.001) in most populations  with no clear geographic pattern .AMOVA showed that most of the genetic variation 94%) occurred within populations, while the variation attributable to geographic regions and genetic clusters defined by the clustering analysis accounted for 3.6% and 4.5% of the total variation, respectively , minimum temperature of the coldest month (BIO6 – relative contribution: 22.8%), mean temperature of the driest quarter (BIO9 – relative contribution: 11.4%) and precipitation of the warmest quarter (BIO18 – relative contribution: 10.2%) . Analysis of the LGM based on WorldClim using all occurrence records . The LGM distribution outlined based on the European-only data retrieved from WorldClim was vestigial to the current one  jointly for the European and African datasets (WorldClim) suggested a profound reduction of occurrence for era Fig. . Only curds Fig.  and thatone Fig. . Suitablone Fig. . Howeverone Fig. . Except one Fig. . It predJ. thurifera. Accordingly, all species occurrence records were divided into two main groups according to the first two principal components (PC1 and PC2), which explained 70.9% of the total variation . This difference probably reflects the different types of markers used in the latter study, particularly the dominant nature of AFLP markers. However, we observed higher differentiation (4.5%) the four genetic clusters detected . This matches the prominent geological rearrangements in the region that left detectable signals in the genetic structure of some species . The most recent estimates date to the Miocene/Pliocene (5.9–3.7 Ma), which is also acknowledged as a period of high diversification in the Mediterranean triggered by climatic changes76. According to geological reconstructions77, in both of these periods, especially in the Miocene, independent colonization of the African continent from the western and eastern directions was feasible. The largest number of private alleles that we recorded in Spanish populations . The two Algerian populations studied here clearly separated from the populations from the other regions, although they showed very limited admixture of the Spanish and Moroccan gene pools  have been demonstrated previously for P. pinaster5, Laurus nobilis78, Carex panormitana79 and J. phoenicea80, which suggests the complex evolutionary history of plant species in this part of the Mediterranean. Even more puzzling is the genetic affinity of Algerian and Tunisian populations of A. glutinosa to a single Scottish stand and not to geographically much closer Moroccan populations8. In terms of such unexpected genetic proximity, the authors concluded independent  colonization of the western and eastern parts of North Africa by ancestral populations of A. glutinosa. Accordingly, Algerian and Tunisian populations are more recent colonizers, while Moroccan populations are defined as relicts. The diversification scenario proposed for the North African firs also involves two temporally and spatially independent colonization routes from Europe to North Africa81. The first one, depicted for the ancestor of Abies pinsapo and A. maroccana, would have run from the Iberian Peninsula and might have been related to the evolution of the Betic-Rif orogenic belt since the Miocene82. The second route dated to the Pliocene-Pleistocene would have involved colonization from the Apennine Peninsula and subsequent vicariance, resulting in the evolution of A. numidica81. Ultimately, the problem of colonization routes and their temporal aspects in the evolutionary history of J. thurifera subsp. africana remains unsolved in this study since solving it would require molecular dating and better sampling of the Algerian range. However, there is still ample evidence to consider the bidirectional colonization of North Africa, which is a convincing argument explaining the west-east genetic differentiation of J. thurifera subsp. africana and other plant and animal species in the region.In contrast to our results, in the study of Terrab 83. Our species ecological niche modelling revealed interesting but very puzzling results. Surprisingly, projections made for the periods spanning the time from the Eemian (c. 120 ka BP) to the current time clearly suggest that the widest theoretical species occurrence is the one under current climatic conditions  a significant decrease in the annual temperatures (by c. 7–8 °C) and the current level of precipitation or (2) more precipitation than currently observed and a smaller decrease in the mean annual temperatures86. J. thurifera subsp. africana prefers warm and dry habitats and exhibits strong continentalism; it is limited by the influence of the Atlantic climate19. Hence, irrespective of the climatic reconstruction supporting glacier formations in the High Atlas, the conditions for occurrence of J. thurifera subsp. africana were inadequate in North Africa. Consequently, MaxEnt failed to identify any climatically suitable conditions for the persistence of this taxon during the LGM in Africa based on current African-only locations using both climatic datasets   and BIO11 . Current populations of African subspecies adapted to dry and hot climates could not be present during the LGM likely because of too low temperatures. The most important climatic factor affecting the distribution of J. thurifera subsp. africana (in fact a lack of its occurrence at that time) was BIO11 (31.2%). However, on the second position BIO18 (precipitation of the warmest quarter) was ranked (25.0%), which may suggest that it was also too arid during the LGM for current African taxa. According to Cheddadi et al.83, the mean annual temperatures in North Africa during the glacial period could have even been 15 °C lower than today, and the mean annual precipitation did not exceed 300 mm. In this way, our findings contradict previous assumptions made by some authors that the last cold period favoured a wider occurrence of this species37. Additionally, there is a compelling similarity between the Eemian  Fig. . When us5%) Fig. . Additiogia Fig. . Currentian Fig.  that sugJ. thurifera, including in this work. According to the proposed scenario, spatial isolation in refugia defined with MaxEnt in Central System in Iberian Peninsula and Atlas Mountains in Africa  in the early Holocene and its expansion in the mid-Holocene92, which was cooler than the early Holocene. However, the general aridification trend in North Africa throughout the Holocene prevailed, and Juniperus withdrew at the expense of Cedrus only in more humid periods92. Our hypothesis of climate-driven differentiation between the European and African lineages is also somewhat supported at the morphological level. The biometric analysis revealed generally smaller dimensions of structures  and a smaller number of seeds per cone in the Moroccan populations as compared to in the European ones, which might be ascribed to adaptation to the severe arid climate of the Atlas Mountains33. However, this issue requires deeper investigations involving searching for genomic signals of adaptation91.Given the mutually exclusive ranges projected with only African or only European records Fig. , we hypoica Fig.  and the ica Fig. . The mos 19 Fig. . Second,J. thurifera are a genetically distinct lineage (Figs. J. thurifera var. aurasiaca, as previously suggested35. This species is one more excellent example of the extremely high intra-species genetic diversity noted in the North African region, representing another hotspot of evolution within the Mediterranean region. Unfortunately, this high biodiversity is presumed to face great environmental challenges that may lead to the irreversible loss of many species and thus plant community depletion. Ultimately, such a loss will affect ecosystem functioning and human well-being. In the African range of J. thurifera, the species is currently subjected to strong human pressure, fragmentation and reproductive failure16. All of these effects are negatively reinforced by ongoing aridification of the region, which makes populations vulnerable to additional disturbances and thereby exposes them to extinction risk. There are well-known examples of species in North Africa that are already trapped in the extinction vortex, such as endemic Cupressus or Abies species76. The most pessimistic projections of the future species distribution confirmed the serious reduction and almost extinction of J. thurifera (Supplementary Fig. J. thurifera is a key component of the high mountain forest communities of arid and semi-arid areas in North Africa and the Iberian Peninsula. A definitely more optimistic scenario was obtained using the CHELSA dataset (Fig. 69. Particularly, the precipitation algorithm that works in CHELSA includes orographic predictors such as wind fields or valley exposition with a subsequent bias correction that makes it work better and more reliable in case of the mountainous landscapes. Consequently, our CHELSA-based computations also indicated a reduction of the species’ range but not in such a drastic scale as WorldClim-based computations. In this case, the European part of the range was predicted to be more vulnerable for future climate changes than the African part of the range (Fig. In this work, we demonstrated that Algerian populations of ge Figs.  and 2 annge Fig. . The cornge Fig. . For Afrnge Fig. . This paJ. thurifera since at least the Eemian (Fig. 93. Refugia are areas that have facilitated species survival during past climatic and environmental crises. However, as shown by our modelling, the buffering capacity of these future refugial areas in Central System in Iberian Peninsula (Fig. J. thurifera under expected environmental transformation. If the current trend in climate change continues40, these areas may no longer be efficient in supporting the species’ survival. Therefore, the evolutionary value of the Algerian populations should be immediately acknowledged by including them in a protected areas network, because they are more isolated and spatially confined than Moroccan stands.Using a combined approach in which genetic analysis was supplemented with ecological niche modelling, we denoted the areas of the current species distribution that have constituted long-term refugia for ian Fig. . Therefoula Fig.  can be l94. However, genetic adaptation that may help to mitigate the climate-induced species and population demographic crisis leading to local extinctions require high levels of genetic diversity stored in populations and maintaining genetic connectivity among them. The more crucial is to focus conservation activity on species such as the fragmented and isolated J. thurifera subsp. africana in Algeria.As a final remark, we need to stress that predictions of future changes in species distribution done in this work are solely based on the climatic variables. Although they set the environmental boundaries of the species’ current or possible performance, they ignore local adaptations and so species evolvability. Meanwhile including evolutionary potential may change the predictions and fates of speciesSupplementary information.


Pinus yunnanensis common garden experiments established at high and low elevation sites to assess the differences in growth and survival among populations and between the two common garden sites. The studied traits showed significant variation between the two test sites and among populations, suggesting adaptive divergence. To detect genetic variation related to environment, we captured 103,608 high quality SNPs based on RNA sequencing, and used them to assess the genetic diversity and population structure. We identified 321 outlier SNPs from 131 genes showing significant divergence in allelic frequency between survival populations of two sites. Functional categories associated with adaptation to high elevation were found to be related to flavonoid biosynthesis, response to UV, DNA repair, response to reactive oxygen species, and membrane lipid metabolic process. Further investigation of the outlier genes showed overrepresentation of the flavonoid biosynthesis pathway, suggesting that this pathway may play a key role in P. yunnanensis adaptation to high elevation environments. The outlier genes identified, and their variants, provide a basic reference for advanced investigations.Local adaptation, adaptation to specialized niches and environmental clines have been extensively reported for forest trees. Investigation of the adaptive genetic variation is crucial for forest resource management and breeding, especially in the context of global climate change. Here, we utilized a  In harsh environment such as in high elevations, natural selection may result in changes in allele frequency to maximize fitness . UnderstPinus, with >100 species, is the largest conifer genus with widespread natural distribution ranging from arctic and subarctic to subtropical and tropical regions in the Northern Hemisphere   . P. yunnnditions . Recent nditions .Pinus and other conifers are known for their exceedingly large and complex genomes, varying from 16 to 35 Gbp  from expressed regions including 8,595 genes were captured using RNA-Seq. Genetic diversity and population structure were resolved. SNPs, genes and pathways related to adaptation to high elevation environments were identified based on FST outlier analyses.In this study, we aim to discover the genetic variation related to adaptation to high elevation environments in P. yunnanensis common gardens were established at a high altitude site in Linzhi , Tibet and a low elevation site in Kunming   is located in the central distribution of P. yunnanensis and characterized by mild, moist, and low seasonality climate, with mean coldest-month temperature of 8.1°C, mean warmest temperature of 20.2°C, mean annual temperature of 15°C, average annual precipitation of 1,035 mm and 334 frost-free days. Bulked seeds from 7 natural populations  were collected to represent P. yunnanensis’ wide range of natural genetic variation (th year. In the high elevation site (LZ), all surviving trees  originated from the KM population were sampled (KM(LZ)). Additionally, we randomly selected 49 individuals representing 5 BS(KM), 4 GS(KM), 10 LJ(KM), 10 YL(KM), 5 YX(KM), 5 ZD(KM), and 10 KM(KM) as representatives of the low elevation site (KM)  Figure 1Fresh needles from current year branches of all 54 sampled individual trees were collected for RNA-seq in June 2017, and immediately placed in liquid nitrogen and stored at –80°C until further use. Total RNAs were extracted using RNAprep pure Plant Kit  according to the manufacturer’s protocols. NanoDrop 2000 Spectrophotometers and Agilent 2100 Bioanalyzer were used to evaluate RNA concentration and integrity. NEBNext Ultra RNA Library Prep Kit (New England BioLabs) was used for cDNA library construction. Briefly, the mRNA purification was performed with magnetic oligo (dT) beads using a Dynabeads mRNA Purification Kit (Invitrogen). The mRNA fragmentation was implemented using RNA fragmentation Kit . Random hexamer primers and reverse transcriptase (Invitrogen) were used to synthesize the first-strand cDNA. Subsequently, DNA polymerase I (New England BioLabs) and RNaseH (Invitrogen) were used to synthesize the second-strand cDNA. Adapter was ligated to the double strand cDNA fragments with a single ‘A’ addition after end repair. Approximately 450 bp cDNA fragments were selected using Ampure XP beads (Beckman). The selected cDNA fragments were PCR amplified to complete library preparation. The concentration and fragment size of the cDNA library were assayed using Real-Time PCR system and Agilent 2100 Bioanalyzer, respectively. The library was sequenced on an Illumina HiSeq X Ten sequencing platform to generate 150 bp paired end raw reads.http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) was used to assess the quality of the raw and clean sequence data. Clean reads from each sample were mapped to the P. taeda reference genome v1.01 (http://broadinstitute.github.io/picard/). A GATK command, “SplitNCigarReads”, was used to split reads spanning splice junctions, and reassign mapping qualities to all good alignments. To minimize the mis-alignment of bases around insertions and/or deletions (indels), local realignment around indels was performed using RealignerTargetCreator and IndelRealigner from GATK. The final BAM files were produced from the local realignment for further analysis.Raw reads were filtered and trimmed using Trimmomatic  to removme v1.01  using STme v1.01 . Single me v1.01  and BCFtme v1.01  with defme v1.01 . The comSNP calling was performed with “HaplotypeCaller” from GATK to produce a genomic variant call format (gVCF) file for each sample, and “GenotypeGVCFs” from GATK was then used to perform the multi-sample genotyping, which produced a raw set of joint SNPs and indels. Several filtering steps were used to minimize the number of false positive SNPs and to retain high-quality SNPs: (1) we kept only biallelic SNPs with at least 5 bp away from any indels; (2) GATK hard filtering was applied to remove SNPs with a criteria of RMSMappingQuality (MQ) < 40.0, QualByDepth (QD) < 2.0, FisherStrand (FS) > 30.0, StrandOddsRatio (SOR) > 4.0, MappingQualityRankSumTest (MQRankSum) < –12.5 and ReadPosRankSumTest (ReadPosRankSum) < –8.0; (3) SNPs with genotype quality (GQ) < 20 and depth (DP) < 10 in a single individual were treated as missing data, and we removed those SNPs with missing rate > 20%; and (4) SNPs with minor allele frequency (MAF) < 5% were removed. The remaining 103,608 SNPs were used in downstream analysis.P. taeda genome v1.01 using SnpEff (version 4.3T) (http://planttfdb.cbi.pku.edu.cn/), were used to retrieve TFs annotations.We built a local database based on released gene annotation for on 4.3T) . SubsequK) ranging from 2 to 5, and 10 replicates were run for each K. The most likely K value was identified by minimizing the cross-validation error evaluated in the 10-fold cross-validation procedure. To eliminate the effect of linkage disequilibrium (LD), we thinned the SNP set to select one SNP from each interval of 5 kb and run ADMIXTURE under both all SNP  and thinned SNP  sets.Principal component analysis (PCA) was performed using the R package SNPRelate (version 1.16.0) . ADMIXTUN), synonymous substitutions per synonymous site (dS), and their ratio (dN/dS) for individual genes. Nucleotide diversity (π) was calculated for each gene using VCFtools vs. KM(KM) and YL(KM)) between the high-elevation selected population (KM(LZ)) in high elevation site (LZ) and the survival populations from Kunming (KM) and Yiliang (YL) in the low elevation site in KM (KM(KM) and YL(KM)). Here, populations from KM and YL were in an identical genetic cluster, hereafter named KM-YL(KM). In addition, a second comparison, KM(LZ)vs. ALL(KM) which included 6 survival populations in Kunming site excluding population BS, was performed with the aim of providing more information for reference, regardless of the impact of population structure which may potentially cause false positives. Population differentiation (FST) values of each SNP were calculated using VCFtools . First, SNPs with the top 1% FST values were selected as putative outlier SNPs for each comparison. Next, we removed the putative outlier SNPs that are likely to be false positives found by chance when defining population comparisons with randomization procedure. For each population comparison, 100 permutations were performed to produce 100 randomized population comparisons by randomly re-sampling the same number of individuals from the original sample set. Then, putative outlier SNPs were also detected respectively for each randomized population comparison. Finally, for each putative outlier SNP from the real population comparison, we retained only those with a recurrence rate less than 0.01 to generate the final set of outlier SNPs.Outlier SNPs were determined by combining http://kobas.cbi.pku.edu.cn). GO and KEGG enrichment analyses were performed using ClusterProfiler . GO and KO terms with a corrected P value < 0.05 were treated to be significantly enriched.Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) annotation were implemented using an online annotation server, KOBAS 3.0 .At the high elevation site (Linzhi: LZ), the seedlings of all populations suffered heavy mortality. In contrast, survival rates were comparatively high and constant at the low elevation site (Kunming: KM). By the third year, survival rates produced exceedingly contrasting results with 7.6 vs. 78.2% for the Tibet (LZ) and Kunming (KM) sites, respectively . In the P. yunnanensis natural populations. In the low elevation site (KM), the local population  and two exotic populations from farthest Southwest  generally displayed the fastest growth. The most south population (YX) showed the slowest growth (183.2 ± 26.2), and the populations from the Northwest  showed intermediate seedling heights. YL, a population close to the local population (KM), showed slower but medium growth performance (190.1 ± 15.5) in comparison to the local population.In addition, significant differences of growth between populations were observed between common garden sites, suggesting adaptive divergence among P. taeda reference genome v1.01 ) was split from the other populations. For K = 3, three northwestern populations (GS(KM), ZD(KM), and LJ(KM)) further formed a northwest cluster and the remaining 3 central populations (KM including KM(KM) and KM(LZ), YL(KM), YX(KM)) formed another central cluster. Under the K value of 4, population YX(KM) from the most south was further separated from the central cluster. The high-elevation selected populations KM(LZ) and KM(KM) and YL(KM) were in the same genetic cluster under K = 2, 3 and 4.The population structure inferred using the full SNP set  was identical to that from the pruned SNPs , suggesting little impacts of LD in the ADMIXTURE analysis S1. Cros(KM) was separated from the other populations, supporting the ADMIXTURE finding at K = 2. PC2 was accounting for 4.18% of the total variance and showing 3 population clusters, YX(KM), northwest (GS(KM), ZD(KM), and LJ(KM)) and central (KM(KM), YL(KM), and KM(LZ)), which were distinguished from each other. PCA revealed a continuous genetic differentiation across the P. yunnanensis range, consistent with the ADMIXTURE results at K = 4.PCA provided further support to the patterns detected by ADMIXTURE (FST procedure detected 321 outlier SNPs overlapping 131 genes for the comparison of KM(LZ)vs. KM-YL(KM), including 79 (24.61%) synonymous, 87 (27.10%) nonsynonymous, 3 (0.93%) 5′ UTR, 21 (6.54%) intronic, 22 (6.85%) 3′ UTR, 1 (0.31%) splice site, and 108 (33.64%) intergenic mutations ((LZ)vs. ALL(KM) were also identified regardless of the impact of population structure which may cause false positives ((LZ)vs. KM-YL(KM) ((LZ) and KM-YL(KM) showed greater π across coding regions , and slightly higher but not significant dN/dS values  with a mean of 0.4751 and maximum of 2.7400, in the high-elevation selected population KM(LZ) (.Our (LZ)vs. KM-YL(KM) suggested that five “flavonoid biosynthesis pathway” genes were significantly overrepresented  vs. KM-YL(KM) reoccured during our permutations. Thus, we find that the outlier genes differ from the genomic background and that the significant terms are unlikely to be observed by chance.P. yunnanensis SNPs, most of which are from expressed genes. Based on all 103,608 high quality SNPs, a clear population structure emerged with two distinct population clusters comprising one southwestern corner population (BS) and all other populations, in which continuous genetic differentiation was found. These results are in agreement with previous findings that were based on combining mitochondrial and chloroplast DNA markers  are considered to be under divergent selection vs. KM-YL(KM). After introducing additional FDR 0.01 by random sampling, we retained 321 outlier SNPs, suggesting that the FDR randomization procedure is potentially effective in reducing the rate of false positives.Scan 2.1 , is baseelection . Howeverterature . Thus, inditions . Howevernditions , and itsnditions . Conside signals . The oth signals . Additio signals , that in(LZ)vs. ALL(KM). Considering that SNPs differentiated between KM(LZ) and ALL(KM) but not between KM(LZ) and KM-YL(KM) suggests they may be involved in population differentiation rather than adaptation to high elevation. Thus, we focused only on the results of the comparison of KM(LZ)vs. KM-YL(KM). An limitation of our study was the restricted number of samples surviving in the highland. However, we consider this exceedingly high mortality as evidence for maladaptation and thus we treat the results as exploratory in nature and are indicative of genetic variation to high elevation adaptation. Further functional evaluation may provide clearer insights into the genetic responses of P. yunnanensis to high elevation environments.Genetic differentiation between populations with different geographic origins have been found in ADMIXTURE and PCA analyses, and the observed differentiation between populations is an important confounding factor for detecting outliers in the comparison of KM(LZ) and KM-YL(KM). Genes with outlier SNPs showed significantly greater π than the genome average across coding regions and slightly higher dN/dS values in the high-elevation selected population KM(LZ). These outlier SNPs consist of 79 (24.61%) synonymous, 87 (27.10%) nonsynonymous, 3 (0.93%) 5′ UTR, 21 (6.54%) intronic, 22 (6.85%) 3′ UTR, 1 (0.31%) splice site, and 108 (33.64%) intergenic mutations. Previous studies have shown nonsynonymous, synonymous, and noncoding SNPs can show signatures of selection. Of these outlier SNPs, nonsynonymous substitutions causing amino acid substitutions and protein sequence changes have been usually considered to be the main target of natural selection. However, synonymous SNPs may affect mRNA alternative splicing, mRNA stability, translation kinetics, and protein expression and function, as previously documented vs. KM-YL(KM) were significantly enriched for flavonoid biosynthesis pathway with 5 outlier genes coding 3 key enzymes. And this pathway has been shown to help withstand harsh environments in high elevations  and Fundamental Research Funds for the Central Universities (2018BLCB08).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.


Gadus morhua) is one of the most important fish species in northern Europe for several reasons including its predator status in marine ecosystems, its historical role in fisheries, its potential in aquaculture and its strong public profile. However, due to over-exploitation in the North Atlantic and changes in the ecosystem, many cod populations have been reduced in size and genetic diversity. Cod populations in the Baltic Proper, Kattegat and North Sea have been analyzed using a species specific single nucleotide polymorphism (SNP) array. Using a subset of 8,706 SNPs, moderate genetic differences were found between subdivisions in three traditionally delineated cod management stocks: Kattegat, western and eastern Baltic. However, an FST measure of population differentiation based on allele frequencies from 588 outlier loci for 2 population groups, one including 5 western and the other 4 eastern Baltic populations, indicated high genetic differentiation. In this paper, differentiation has been demonstrated not only between, but also within western and eastern Baltic cod stocks for the first time, with salinity appearing to be the most important environmental factor influencing the maintenance of cod population divergence between the western and eastern Baltic Sea.Atlantic cod ( The management units are defined for reporting on stock assessment and catches by different countries. The issue of inconsistency between existing management units and population biology and genetic differentiation has been reported for some marine fish species6. Presently, population genetic analysis using classical genetic markers is being replaced by more detailed genomic analysis which provides qualitatively new information on stock differentiation and identification14. Specific methods include genome-wide genotyping using a large number of single nucleotide polymorphisms (SNPs) and next-generation sequencing (NGS). NGS for population analyses is a powerful tool that arguably provides the greatest insight into population genomics but can be expensive and demands significant data analysis. In contrast, genotype data for relatively many SNPs can be generated quickly for large numbers of individuals using genotyping arrays and raw data requires little pre-processing before it can be analyzed15. A large number of SNP loci in comparison with few genetic markers are better able to report subtle differences in genomic variation and their robustness is an advantage in evolutionary and population biology studies including exploited species with high dispersal potential in the oceans18. Additionally SNPs may affect protein function and expression levels directly and hence are subject to evolutionary selective forces19. Genotyping a significant number of SNP loci also provides an opportunity to identify ‘outliers’ (i.e. loci under selection21), which can be more informative markers in defining conservation units in comparison with neutral SNP markers26.Sustainable exploitation of living marine resources by fishery, aquaculture and biotechnology, and monitoring and predicting the effects of climate changes require an understanding of taxonomy and population biology. Populations are sustainably exploited if the removal of individuals does not reduce the ability of a population to reproduce and maintain its phenotypic and genetic diversity. Such populations have been defined for conservation purposes as “evolutionary significant units”28, that have provided insight into the links between genomics, biology and life-history. For example, studies using neutral molecular markers, such as microsatellites, have detected very weak differentiation between populations of cod29, whereas loci under selection from environmental conditions as temperature, salinity and depth, often display much stronger differentiation32. SNP analysis revealed diversity between eastern and western Atlantic and Baltic cod populations36, and using the same technology divergence within a genomic region between migrating and stationary ecotypes has been found despite high connectivity41. Genomic rearrangements (e.g. inversions of chromosome fragments) in cod populations differing in ecological behaviour, such as migration routes have been found42, and a subset of the SNP loci analyzed in this study has been reported as significantly correlated with temperature in North Atlantic cod populations44. It has been shown that despite mixing and migrations between the northeast Arctic cod and the Norwegian coastal cod populations, genomic islands undergo selection and reduced recombination, which promote divergence of these populations despite habitats overlap45. Finally, frequencies of some SNPs have been shown to be correlated with salinity in Baltic herring populations46.Compared to many other harvested and aquacultured fish species, Atlantic cod has been subject to extensive population genetics analysis48, which could tolerate salinity as low as 5–7 ppt, including Atlantic cod. This species adapted to living in low salinity waters of the Baltic Sea despite requiring salinity above 12–14 ppt53 for successful spawning55. For the fisheries purposes, the Baltic cod is assessed and managed as western and eastern stocks, located in ICES Subdivisions 22–24 and 24–32 respectively56.The Baltic was transformed from a freshwater lake receiving melting ice waters into today’s brackish water sea body over 7000 years ago. It was colonized by a variety of marine species populations58 and mixing of western and eastern stocks of Baltic cod in the Arkona Basin (ICES subdivision 24) have been reported62. The dynamic changes of salinity and oxygenation in the Danish Straits and the open waters of Baltic Sea undoubtedly affect the condition and genetic divergence of the cod stocks in this area. Western populations of the Baltic cod may hybridize with the North Sea cod, as was suggested by Nielsen et al.29. Besides the important physiological differences between western and eastern Baltic cod such as haemoglobin polymorphisms64, genetic structure differences were reported at the level of population markers, e.g. microsatellites, Pan I locus, mtDNA29,67 and using SNP analysis69. Implications of population genetics structure in Baltic populations for management, have been summarized by Östman et al.6 and Wennerström et al.71.Significant transport of cod larvae from the North Sea to Skagerrak and Kattegat72. Eastern Baltic cod spawning regions were Bornholm Basin, Słupsk Furrow, Gdańsk Deep and Gotland Basin in Southern Baltic. The main area of spawning is the Bornholm Basin where the eastern Baltic cod migrated every season75. Due to reduced inflow of North Sea water into the Baltic Proper and other changes in environmental and ecological conditions , eastern Baltic cod underwent changes in the biology; slower growth rate and maturation at a smaller size77. The anoxic or hypoxic conditions in the Baltic Sea, exceptionally pronounced in recent years inflicted contraction of southern populations77, and limited the size of the reproductive volume of eastern Baltic cod54. In recognition of a serious threat to the eastern Baltic cod stock, fishing for cod in ICES subdivisions 24, 25 and 26 has been banned by the European Commission in 2019, and restricted beginning on 1st January 2020.Two main cod spawning areas in the Kattegat are situated along the Swedish coast. Western Baltic cod spawning areas include Sound, Kiel and Mecklenburg Bays and Arkona Basin14. In the Baltic, differences in adaptation of western and eastern cod stocks to low salinity have been described69. SNP array has been used for the first time to characterise different stocks in the Baltic. Earlier population genetic studies conducted with this technique included only limited number of individuals or few sampling locations inside Baltic. However, to date no genetic differentiation within sub-stocks of western and eastern Baltic cod populations have been reported. The aim of this research was to characterize for the first time differences in structure of sub-stocks within eastern and western Baltic cod populations and the transition zone with the North Sea using a large number of SNP loci. The possible existence of sub-structuring of the eastern stock within a management unit is indicated.Genetic differentiation at functionally important genes between cod stocks in the north-western Atlantic have been related to local adaptations caused by differential selection pressure among spawning aggregationsFIS obtained by AMOVA was very low (−0.0032) and statistically insignificant (p = 0.62). The global FST across all nine populations was 0.0396 (p < 0.01), which indicates a moderate78 level of differentiation. Low, but statistically significant levels of differentiation were found between pairs of samples from North Sea (EGR), Kattegat (KAT) and west Baltic (SCH). There was no statistically significant differentiation for the pair from the Belt Sea (SCH) and Øresund (ORE) in western Baltic. While the greatest divergence, above 0.08, was observed between Scotland (MRF) and four populations from the eastern Baltic   and samples from the eastern Baltic were at a similar level: from 0.0510 for the pair LIT-SCH to 0.0721 for the pair GDN-KAT  remained non-significant, while FST for pair SCH-ORE became statistically significant. Pairwise FST values between remaining populations increased significantly and generally reproduced mapping of FST relations between samples.Genetic relationships among cod populations and possible genetic admixture was calculated using the Bayesian algorithm in STRUCTURE. When a full set of 8076 polymorphic SNPs was used for all nine cod samples, the most probable number of populations was 2 ΔK = 1329.3), with four samples from the east Baltic  distinguishing themselves from the remaining populations. The 29.3, witloci (Supplementary information Table FST was observed among LAT (Latvia) and LIT (Lithuania) samples . The highest difference was noted for the pair BOR (Bornholm) – LAT  and intermediate values for other pairs of samples. Pairwise differences (FST) values suggest some difference between the BOR sample and the remaining samples from the Baltic Sea . Pairwise FST distances were generally close and reached 0.06912 for LAT – GDN pair, 0.07044 for LIT – GDN and 0.08387 between LAT – LIT.The five samples from western Baltic, Kattegat and the North Sea were analyzed with a set of 175 outlier loci  performed for the full marker set 8076 SNPs) showed low values of percentage of variation between axes, however the potential clades are well separated  samples. This divergence was represented by a clear split between the analyzed 9 populations and clustering of samples from the west Baltic together with samples from North Sea, FST values reduced 10-fold and showed a lack of haplotypes shared with samples from the East Baltic Sea. In this study, the isolation-by-distance (IBD) between samples tested by Mantel test for all 8076 SNP loci was significant and correlation between genetic diversity and geographic distance and bottom salinity were detected. The PCoA results suggested that the main differentiating factor could be explained by variable salinity represented by the 1st axis what was further supported by results from outlier loci distribution and presence of different LDs associated with environmental factors. For outlier loci calculated for all 9 populations rapid change of maximum salinity level is the best explanation for the clear separation of groups from the North Sea and the Baltic Sea. Differences in salinity tolerance and subsequent low fitness of transplanted cod from the Baltic Sea and the Skagerrak/Kattegat83 and eastern (Gdańsk) and western (Kiel Bight) Baltic may be the result of genetically based adaptive differences between populations52, which potentially explain transcriptomic differences of G. morhua from the Baltic Sea that have also been observed84. Johannesson and André80 assumed that the cause of lost diversity of Atlantic cod was an efficient barrier to gene flow, which has evolved as a consequence of divergent selection on reproductive traits, such as egg buoyancy, sperm motility85 and different time of spawning season. Local adaptation of these traits can be manifested by selection evidence related with presence of outlier loci and their relations can be detected across genome by analysis of linkage disequilibrium86. The observed patterns of detected LD distribution in western dataset are congruent with earlier studies which indicated the presence of large LD region located on LG2 in cod from North Atlantic87. It was suggested that they are associated with salinity and oxygen level at spawning depth68. It is important that we did not observe significant outlier loci from LG2 in Baltic dataset. Significant LDs for Baltic samples were located on LG12 and LG13 for analysis with Bornholm cod and only on LG16 for easternmost cod. In first case, observed LDs concerned loci ss1712298167 described as associated with surface temperature and loci ss1712298916 located on important scaffold 08672 associated with many environmental correlation including surface and bottom salinity, oxygenation and temperature68. LDs detected in easternmost dataset also concerned outliers associated with bottom salinity 68 but they were located on different LG and this may be related to the existence of adaptation to lowering salinity in the Baltic Sea from west to east. Furthermore, these loci were not indicated as outliers in western dataset and Bornholm cod.The status of Atlantic cod in the Baltic Sea has been reported as an example of a geographically and genetically separated marginal subpopulation89. In present study with a large number of SNP loci, the Kattegat sample was closely related to the Scottish cod suggesting a high share of the North Sea cod. A low pairwise difference between North Sea and Kattegat samples has also been reported with microsatellites29, and may be explained by the significant transportation of cod larvae from the North Sea stocks into Kattegat90. High connectivity with offshore populations in Scandinavian fjords has been characterized recently using a large number of SNPs38. In the current study a sample of cod collected from the North Sea  was found to be slightly statistically different from the Kattegat and West Baltic, which coincides with presumably different local spawning areas in the western Baltic . However, analysis of the outlier loci for the group of samples from North Sea, Kattegat and western Baltic showed inconsistency between geographic origin and genetic distance of samples. The samples from the Egersund fjord (EGR) differed both from the Moray Firth and Kattegat samples. Genetic characteristics of the EGR samples could be potentially occurred due to a relatively closed coastal population breeding locally.The cod stocks in the North Sea and Kattegat were described as an indicator of the condition of Atlantic cod populations92. Samples from EGR and SCH shared the same haplotypes , and Schlei fjord (SCH) suggests closer relationships with cod living under similar environmental conditions characterized by periodically reduced salinityes Table , which rFST, distinguishing this population from other Baltic samples . The genetic structure analysis showed that eastern Baltic stock samples formed four close clusters. This supports the assumption that the analyzed samples included individuals representing local populations, not the migrants. Genetic variability in the Baltic Proper samples was much lower than among western samples including also the west part of the Baltic Sea. The values of FST were lower suggesting that some specimens from each sample share the most functional spawning area in the Bornholm Deep. This is reflected in the results of the assignment tests where no clean baselines were observed. This is also indicator of high gene flow and significant level of mixing within the stocks. Additionally, low self-assignment of samples from Bornholm area with high share of easternmost stock is another argument that eastern Baltic cod occur in SD 24 on west cost of Bornholm what was also clearly demonstrated in recently published study by Hemmer-Hansen et al. 201961.The most divergent sample in the Baltic Proper is Bornholm (BOR), which was collected in July, after the spawning season, in order to avoid the migrants from other sub-locations. This sample, when tested with outliers, showed a relatively high value of 94. The data here suggest that, thanks to the salt water inflows, spawning areas like Gdańsk Deep might have retained limited functionality in supporting divergence between local subpopulations96. The salinity factor seems to support more the divergence between west and east Baltic Sea than local divergence57. The potential spawning area is also determined by oxygen availability54. Genetically divergent but geographically close subpopulations have been identified, for instance, in Icelandic waters97, in the North Sea88, and along the Skagerrak coast99. For such differentiation to be preserved - even for small genetic differences - reproductive isolation is implied. In the Baltic Sea, local niches settled by cod are characterized by a unique set of environmental features like the diurnal and seasonal exchange of water masses100, vertical distribution of salinity and temperature101. These features and homing behaviour affect the genetic profile of the local subpopulation and maintain the distribution of genes/alleles responsible for local adaptations89.Since the mid-1980s, successful spawning of eastern Baltic cod stock has been generally restricted to the Bornholm Basin28. Studies of the relationship between population units and ICES subareas for North Sea Atlantic cod (Gadus morhua L.) have revealed that the genetically derived population units did not map accurately enough onto the existing cod management units35. One strategy for compensating for this situation is extending the spawning closure areas72. In Norway, along the coastal area, cod stocks were divided into two: north and south of 62o latitude. Finding genetic structures along the Norwegian coast line by sampling 55 locations and analysing microsatellites28 and the pantophysin (Pan I) locus102 provided strong evidence to support possible revision of cod management strategy. The resilience of cod populations in the Kattegat may also be different when considered on smaller spatial scales than those delineated by traditional stock management boundaries99. Cod populations in the Baltic and Danish Straits have been managed for fishery purposes as 3 stocks differing in morphometric and genetic structures: Kattegat (subdivision 21), western (22–24) and eastern (25–32) Baltic105. Similarly to other studies, here we observed variation at the SNP loci between both Baltic stocks. Genetic differentiation between samples from the western and eastern Baltic stocks has been indicated as a tool for separation of western and eastern Baltic cod in mixed stock occupied SD2461. In addition, using outlier SNPs, this study was able to demonstrate genetic differences among populations from subdivisions. Genetic differences revealed between GDN and LIT (subdivision 3d 26) samples of cod collected from the eastern Baltic stock were supported by statistical analyses. Despite strong mixing, possible hindrances in connectivity between the Gdańsk Deep and Gotland Basin can be considered as explanation for the observed spatial differentiation of the eastern Baltic cod stock.Population genetic analyses facilitate detection of mixed stocks within the management units, and catch quotas are estimated on the assumption that a management unit includes only one stock. If more than one stock occurs in a management unit, the less abundant component becomes overfished and may collapseThis study demonstrated that genotyping with Norwegian cod SNP array constructed in CIGENE enables detection of genetic differentiation at a fine and local geographic scale in marine pelagic cod populations in the Baltic and adjacent waters. The sensitivity of the array towards identification of cod stocks can be enhanced by putting larger number of SNPs on the chip, including those polymorphic in the Baltic cod. Outlier SNPs are more informative markers in finding differences between Baltic cod populations in comparison with neutral SNPs. Here, with outlier SNPs, differentiation was identified between cod populations from subdivisions of existing management units in the Baltic. A tentative discrepancy between Lithuanian (LIT) and Polish (GDN) cod samples within one subdivision was also observed and can be related to possible isolation by environmental barriers between spawning areas in the Gdansk Deep and Gotland Basin. It is recommended to carry out further survey of Baltic cod populations using advanced genetic techniques on a larger number of specimens including larvae in order to further document the observed genetic pattern in the eastern Baltic cod population. Changes in time in genetic composition of Baltic cod stocks may be anticipated after periodic restrictions on fishing activities.International Council for the Exploration of the Sea) subdivisions along a transect across the Baltic Sea, Kattegat and North Sea  containing 10,923 SNP assays, and developed by a Norwegian consortium composed of four research organisations: Norwegian University of Life Sciences (NMBU), University of Oslo (UiO), NOFIMA AS, and the Institute for Marine Research (IMR)69. Samples were processed according manufacturers instructions and genotypes obtained from Genome Studio (V2011.1). After filtering to remove poorly clustering SNPs , a total of 8221 diploid SNPs remained. This data set was further trimmed to remove: SNPs with relatively a high missing data level , monomorphic SNPs (n = 32), and SNPs with minor allele frequencies (MAF) < 0.01 (n = 98). The final data set included genotypes from 8076 loci.A total of 240 cod individuals from 9 locations at 7 ICES .106. Genetic structure was analyzed using the program STRUCTURE v2.3.4107 which assigns individual genotypes to a specified number of groups, K, based on membership coefficients estimated from the genotype data. The analysis for 9 cod population samples was conducted from K = 1 to 12 using a burn-in period of 100,000 steps followed by 200,000 MCMC (Monte Carlo Markov Chain) replicates with 5 iterations, assuming an admixture model. The most probable number of clusters was defined by calculating the ΔK value108 determined by Structure Harvester109. Clumpp v.1.1.1110 was applied to average cluster membership using the Large K Greedy algorithm. Output from Clumpp was visualized in Distruct v.1.1111.Allele frequencies and MAFs in each sample were calculated from spreadsheet data using Arlequin v. 3.5.1.3FST estimates and inbreeding coefficient FIS estimates. The number of polymorphic loci and genetic diversity was calculated by measuring observed and expected heterozygosity (Ho and He) with p < 0.05 and with exact test using a Markov chain with chain length =1,000,000 and dememorization steps =200,000. To adjust P value for each pair in multiple tests, Bonferroni corrections were included. GenAlex 6.502 was applied to perform a principal coordinates analysis (PCoA)113. Assignment tests were conducted using GeneClass114 with the allele frequency-based method. This enabled the identification of potential migrants or their descendants115. Relationships among 9 cod populations were examined using Poptree2116 with neighbor-joining (NJ) method based on FST distance with sample size correction117 and the number of bootstrap replications at 1000. The Mantel test based on dissimilarity matrices118 was applied to investigate the significance of relationships between genetic distance, geographic distance and bottom salinity with 999 permutations used to test the statistical significance of the values in GenAlex 6.502. Results were cross validated in Arlequin 3.5.1.3. Bottom salinity values were obtained from models GETM119, BALANCE120 and INSPIRE121. The hierarchical island model, implemented in Arlequin, was used to detect outlier loci. Loci as candidates under selection exhibited FST values out of the 99% quantile, based on coalescent simulations . Outlier loci were calculated with 50,000 simulations and number of demes at 100. Outlier loci were segregated and those with FST ≤ 0 or with FST > 0.01 were excluded. Separate structure investigation of outlier loci for west and east Baltic populations were carried out with increased burning  and MCMC . GenAlex was applied to perform a principal coordinates analysis (PCoA). Linkage disequilibrium (LD) was estimated for outlier loci by calculating the square value of correlation coefficient (r2) between pairs of markers122 using the TASSEL 5.2.58 software123. A threshold of r2 > 0.8 was considered to indicate LD. The level of LD was estimated for the entire panel and for the specific subgroups identified with STRUCTURE v2.3.4. Within these subgroups, LD was calculated considering only the detected panel of candidate outlier loci. The p-values for each r2 estimate were obtained with a two-tailed Fisher’s exact probability test and a threshold of p < 0.0001 was considered as significant. LD visualization was done by heat maps based on P values for pairwise r2 estimates to assess the overall view of LD patterns and evaluate LD blocks in various chromosomes at specific map locations. Additionally the distribution and clustering of detected outlier loci on linkage groups (LG) were indicated by Manhattan plots constructed for same subsets as for LD analysis using the R package “qqman”124. Homology searching was done through BLAST search of the available flanking sequences125 for each detected outlier loci on the NCBI and Ensembl public databases126. Functions of annotated outlier loci were determined using UniProt database. These analyses were done for three selected ouliers datasets specific for all Baltic, west Baltic and east Baltic  with number of permutations = 90,000. Variance among the cod populations was detected by STRUCTURE, among samples, among individuals within populations and within individuals. The differentiation was tested amongst pairwise fixation index Supplementary information.


Heritiera littoralis. This study aimed to assess geographic distribution of genetic variation of this widespread mangrove in the Indo‐West Pacific region and identify the phylogeographic factors influencing its present‐day distribution. Analysis of five chloroplast DNA fragments’ sequences from 37 populations revealed low genetic diversity at the population level and strong genetic structure of H. littoralis in this region. The estimated divergence times between the major genetic lineages indicated that glacial level changes during the Pleistocene epoch induced strong genetic differentiation across the Indian and Pacific Oceans. In comparison to the strong genetic break imposed by the Sunda Shelf toward splitting the lineages of the Indian and Pacific Oceans, the genetic differentiation between Indo‐Malesia and Australasia was not so prominent. Long‐distance dispersal ability of H. littoralis propagules helped the species to attain transoceanic distribution not only across South East Asia and Australia, but also across the Indian Ocean to East Africa. However, oceanic circulation pattern in the South China Sea was found to act as a barrier creating further intraoceanic genetic differentiation. Overall, phylogeographic analysis in this study revealed that glacial vicariance had profound influence on population differentiation in H. littoralis and caused low genetic diversity except for the refugia populations near the equator which might have persisted through glacial maxima. With increasing loss of suitable habitats due to anthropogenic activities, these findings therefore emphasize the urgent need for conservation actions for all populations throughout the distribution range of H. littoralis.Phylogeographic forces driving evolution of sea‐dispersed plants are often influenced by regional and species characteristics, although not yet deciphered at a large spatial scale for many taxa like the mangrove species  Heritiera littoralis) in the Indo‐West Pacific region. Being conducted at a large spatial scale, our study identified that natural geographic barriers, contemporary oceanic currents, and species ecology act interactively to shape present‐day distribution of the species in this region. This is the first report of the geographic distribution of genetic variation of a widespread mangrove ( We initially screened published primers to select five cpDNA loci having single nucleotide polymorphism (SNP) which were sequenced and analyzed further to infer the population genetic structure of H. littoralis.This study was therefore conducted to examine the phylogeographic pattern of 22.1H. littoralis were collected from 37 locations across its distribution range  that showed polymorphism within or among samples were finally chosen for the study . The optimized thermal profile consisted of 5‐min initial denaturation step at 94°C, followed by amplification for 30 cycles of denaturing for 45 s at 94°C, annealing for 45 s at 53°C, extension for 1.5 min at 72°C, and a final cycle at 72°C for 10 min. Amplification products were inspected in 1% agarose gel with ethidium bromide and photographed under UV light. Purified DNA fragments were sequenced for both strands on an ABL 3730XL DNA Analyzer (Applied Biosystems Inc.).Total genomic DNA was extracted from dried leaf samples using the HiPure Plant DNA Mini Kit (Magen Technology Inc.) following the manufacturer's protocol. We initially screened 24 published primers for different cpDNA regions on a subset of three individuals per population randomly collected from six populations  that were the farthest apart. Five fragments . To view intraspecific relationships among the cpDNA haplotypes, an un‐rooted network was constructed using the median‐joining approach implemented in NETWORK ver. 5.0.1.1  to identify single nucleotide polymorphisms (SNPs) across all sequences. The representative cpDNA haplotype sequences of K) ranging from 1 to 10 and for each K, we used 1,000 simulations of annealing process for each of the 100 repeated runs. The samples were grouped under four populations (K = 4) for which largest genetic differentiation (FCT) was obtained  using GENALEX ver. 6.5 ] and geographic distances  was examined using Mantel tests  following , total gene diversity (HT), coefficient of genetic variation over all populations (GST), and coefficient of genetic variation influenced by both haplotype frequencies and genetic distances between haplotypes (NST) were estimated for the populations using the program PERMUT  and nucleotide diversity  approach implemented in the software DIYABC ver.2.0  using the “model checking” option in the software.In all model scenarios, t# represented time scale measured in the number of generations and N# represented the effective size of the corresponding population groups during the relevant time period . The standard Hudson's algorithm was applied as minimum allele frequency criterion to simulate the SNP data sets. We used default prior values for all parameters, except for maximum population size and the maximum values of time scale  based on the findings of the preliminary test runs. Genic diversities, 33.1H. littoralis is highly conserved. The alignment lengths of the five fragments were 760 bp for atpB‐rbcL, 975 bp for accD‐psaI, 946 bp for rpl16, 894 bp for trnS‐G, and 876 bp for trnV‐M. In the total concatenated length of 4,451 bp, we found 19 polymorphic sites (6 singletons and 13 parsimony‐informative mutations) when including 5 long‐fragment indels, which corresponded to 13 haplotypes  suggesting that chloroplast genome of The NETWORK analysis grouped the 13 haplotypes into at least three haplotype groups Figure  in line 3.2FCT value (0.894) when the 37 samples were divided into four populations  was found to be weakly related to geographic distance  as revealed from the Mantel test  along which four separate and distinct population clusters were observed Figure , being ct Figure . We founπ) were found to be 0.786 and 0.343 respectively across the distribution range of H. littoralis. High genetic diversity was observed in POP III  and POP II  (Table HT = 0.79 ± 0.04) was much higher than the average intrapopulation diversity (HS = 0.098 ± 0.03)  and POP II . Overall population differentiation was high  across its distribution range (Table NST and GST was not significant (p = .29). Hierarchical AMOVA analysis revealed that 89.46% variation can be attributed to the differentiation among these four population groups  except POP III in which 67.85% variation was attributed to the differentiation among samples (Table Haplotype diversity (Hd) and nucleotide diversity 3) Table , suggest Table , 3.3HRAG values for POP II and POP IV  and POP III  , N2 (EPO), N3 (SPO), and NA  were 74,000, 10,800, 38,200, and 76,300 respectively , N2 (WPO), N3 (EIO), and NA  were 79,000, 5,250, 23,300, and 88,600, respectively , scenario 3 in ABC2  and in ABC3 , and did not overlap with the 95% CI of other scenarios Appendix . Absence4H. littoralis, and identified how glacial vicariance, contemporary ocean currents, and long‐distance dispersal events might have influenced the current distribution of the species in the Indo‐West Pacific region.Using chloroplast DNA sequences, our study estimated genetic diversity and population structure of 4.1HT = 0.79) was observed for H. littoralis in accordance with previous studies  which showed, more or less unanimously, that the distributional range of the species can be divided into at least four population clusters. The haplotype composition was different in these population clusters and phylogenetic differentiation between the frequently observed haplotypes was revealed by the median‐joining NETWORK analysis. Furthermore, high pairwise FCT values (0.68–0.97) along with maximum proportion of variation due to differentiation among population clusters were found through AMOVA analysis. Although we observed high levels of cytoplasmic structure (GST/NST = 0.898/0.911), the non‐significance (p > .05) of this phylogeographic structure suggested that historical disruptions of gene flow might have influenced the lineage composition of contemporary populations of H. littoralis in the IWP region. Overall, these findings indicated impediment of genetic exchange between populations of H. littoralis in the IWP, as observed for many mangroves  pattern found in other mangroves like Rhizophora mangle  and from H9 and H4 haplotypes in around 1.26 MYA (95% CI = 0.31–1.95 MYA). Furthermore, we observed close relationship between the haplotypes of NPO (H1) and SPO (H3) which diverged in around 0.52 MYA . This weak genetic differentiation between Indo‐Malesia and Australasia is in contrast with other studies which identified plate tectonic movements of the Indo‐Australian Archipelago (IAA) as one of the factors causing high genetic differentiation between these regions for many mangrove species [e.g., Urashi, Teshima, Minobe, Koizumi, & Inomata, H. littoralis might have been established in the IWP recently by the rapid expansion of small number of founder populations from the EPO region. Besides having the H6 haplotype, the sampling locations of this region were found to have high haplotype and nucleotide diversities, thereby suggesting possible presence of refugia populations of H. littoralis in these regions. The glacial phases of Pleistocene caused loss of suitable habitats and environments for the mangroves in the IWP, and confined them near the equator  was found to be genetically similar to the east of the peninsula. In this region, corridors like the Strait of Malacca opened during the short interglacial period with the sea levels rising  in the Indian Ocean. Our findings indicated that the minor seasonal flow via the South China Sea (SCS) exiting either to the Timor Sea or the Lombok Strait might carry the propagules of the WPO through the South Equatorial Current across the Indian Ocean to the EA  is one of the longest floating periods of the mangroves  when the surface current of the tropical Indian Ocean flows northward into the South China Sea and then toward the Pacific Ocean through the Bashi Strait ; Formal analysis ; Investigation (lead); Methodology ; Software ; Validation ; Visualization (lead); Writing‐original draft (lead); Writing‐review & editing (lead). Wuxia Guo: Conceptualization ; Formal analysis (supporting); Investigation ; Methodology ; Project administration ; Resources ; Validation ; Writing‐original draft (supporting). Sitan Qiao: Data curation ; Formal analysis ; Investigation ; Methodology ; Software . Weixi Li: Data curation ; Investigation ; Methodology ; Resources ; Software ; Validation . Fen Xing: Data curation ; Formal analysis ; Investigation ; Methodology ; Resources . Yuting Lin: Data curation ; Formal analysis ; Investigation ; Methodology ; Software ; Validation . Zhuangwei Hou: Data curation ; Formal analysis ; Investigation ; Methodology ; Software ; Validation ; Visualization . Sen Li: Conceptualization ; Formal analysis ; Investigation ; Methodology ; Validation ; Visualization . Ying Liu: Conceptualization ; Project administration ; Resources ; Supervision ; Visualization ; Writing‐review & editing (supporting). Yelin Huang: Conceptualization (lead); Funding acquisition ; Investigation ; Project administration (lead); Resources ; Supervision (lead); Validation ; Visualization ; Writing‐original draft ; Writing‐review & editing .Appendix S1Click here for additional data file.Appendix S2Click here for additional data file.Appendix S3Click here for additional data file.Appendix S4Click here for additional data file.Appendix S5Click here for additional data file.Appendix S6Click here for additional data file.Appendix S7Click here for additional data file.Appendix S8Click here for additional data file.


Amyelois transitella), the principal pest of almond orchards in California, differing in bifenthrin resistance status to examine insecticide-induced changes in the population genomic landscape of this species. We detected an exceptionally large region with virtually no polymorphisms, extending to up to 1.3 Mb in the resistant population. This selective sweep includes genes associated with pyrethroid and DDT resistance, including a cytochrome P450 gene cluster and the gene encoding the voltage-gated sodium channel para. Moreover, the sequence along the sweep is nearly identical in the genome assembled from a population founded in 1966, suggesting that the foundation for insecticide resistance may date back a half-century, when California’s Central Valley experienced massive area-wide applications of DDT for pest control.Among the most familiar forms of human-driven evolution on ecological time scales is the rapid acquisition of resistance to pesticides by insects. Since the widespread adoption of synthetic organic insecticides in the mid-twentieth century, over 500 arthropod species have evolved resistance to at least one insecticide. Efforts to determine the genetic bases of insecticide resistance have historically focused on individual loci, but the availability of genomic tools has facilitated the screening of genome-wide characteristics. We resequenced three contemporary populations of the navel orangeworm ( Although it is widely known that insecticide resistance is rapidly acquired by target insects, comprehensive population genomic scans that assess the evolutionary trajectory of the development of resistance are scant. This is particularly true for insect pests that are nonmodel organisms, some of which are especially important due to the substantial damage they can cause to crops and as consequence to food supply. This study looks at the genome footprints and consequences of intensive insecticide usage in a pest of almonds in California. We detected a large selective sweep, an unequivocal signature of positive selection, in a region of the insect genome comprising several genes with potential roles in target insensitivity and insecticide detoxification.Among the most familiar forms of human-driven evolution on ecological time scales is the rapid acquisition of resistance to pesticides in agriculture . CompareBy querying the full genome and its variation among individuals and among populations, population genomic approaches can illuminate multiple specific targets of positive selection, as well as whole affected regions. A genome-wide approach can also help to distinguish this positive selection from the effect of the demographic history of the populations under examination . A classPrunus dulcis), a multibillion-dollar industry centered in the California Central Valley, can be severely affected by the navel orangeworm , the most important pest of this commodity. Insecticides are routinely and intensively used in almond orchards to manage this pest. Although insecticides approved for use include representatives from five structural classes, pyrethroids have been widely used in recent years due to their low cost and high efficacy. Prior to 2013, insecticide resistance was not known to occur in navel orangeworm populations; in 2013, however, 10-fold resistance to bifenthrin was documented in populations in Kern County . This reference was based on the “SPIRL-1966” line established in the USDA laboratory facility in the San Joaquin Valley in 1966, during a period preceding the use of pyrethroids and during which the Central Valley was subjected via crop-dusting to “thousands of tons of DDT alone” , and Kern County (R347) in the California Central Valley were sampled and sequenced . compared with ALM and FIG  . The top 1% and bottom 1% values of the Tajima’s D, which fall in the extreme positive and negative part of the range, respectively, were used to evaluate regions departing from neutrality  . Negativutrality  online. ve sweep  online.protein kish-A gene (XM_013328236.1), with no SNPs detected along this coding sequence, followed by the voltage-gated sodium channel para (XM_013328250.1), with a single SNP. In the FIG population, the lowest π value (0.0001) was also found in para, followed by the gene encoding the cytochrome P450 CYP6B56 (XM_013328369.1). In the R347 population, π was zero at the gene encoding CYP6B56 , two uncharacterized proteins, and an extensin-like protein-coding gene  at the  CYP6B56 . The coving gene .para gene , with the mutation L934F. All three populations carried the mutation in 100% of the sequenced reads covering the position, in contrast with the reference genome  mutation in the para gene that confers target-site resistance to DDT and pyrethroids  had nonsilent mutations segregating in 82% and 91% of the sequenced reads from ALM and FIG, respectively, and in only 9% of the reads in the resistant R347. KL-9 overlaps the region where all three populations have low diversity and the region where only the R347 population has reduced π values. The read alignment to these two genes and all of the polymorphisms segregating on each of the three populations across the genomic sequences are shown in supplementary figure S2B, We manually screened the few polymorphisms segregating along the region where all three populations have near-zero π values. Only two genes had nonsynonymous and nonconservative mutations in all or in a portion of the reads covering the position across the three populations  online. ethroids . Althougboratory  online. FST between populations showed that ALM and FIG are probably indistinguishable as separate populations . The FST between ALM and R347 was 0.0289 and between FIG and R347 was 0.0290. These values are highly reflective of the demographic origins of each population and agree with the genome-wide average Tajima’s D calculated earlier. Genes controlling traits that differ between populations might present large differences in allele frequencies, thereby generating higher FST values comparable with average FST across the genome . The SNPs with top 1% FST values across the whole genome were concentrated in the regions flanking the identified selective sweep . FST were recalculated after eliminating the sweep region from the genome, and the resulting FST values were almost identical to the ones obtained with the full genome, indicating that, other than the region surrounding the selective sweep, these populations are also differentiated due to geographically restricted selection that is not related to insecticide selection and could include population bottlenecks, genetic drift, or migration.Genetic differentiation measured as e genome . We scanve sweep . These hFST method may not account for unexpected population structure and requires that each sequenced individual is assigned to a single population, an assumption that might not be valid. To verify our findings by overcoming the limitations of FST, we utilized an approach based on principal component analysis (PCA) on the calculated major allele frequencies. With this test, we explored the 20 most-differentiated SNPs according to their FDR-corrected P values. Of those, only 2 SNPs were differentiated between the resistant and susceptible populations: one in scaffold NW_013535509.1, in an intergenic region near an ecdysteroid kinase gene cluster, and a second in the NW_013535362.1 scaffold, just downstream of the selective sweep in position ∼4.1 Mb    42 genes that fall within the defined selective sweep. Of these, the CYP6B tandem showed the highest expression values across all the treatments . In addition, a pattern consistent with elevated expression of CYP6B56 upon bifenthrin treatment in the R347 was supported with ANOVA tests ( ≤ 0.05) . A gene  ≤ 0.05)  online.  < 0.05)  online; X2 = 45.52, Pcorr ≤ 0.000) , further supporting a role of these P450s in adaptation. We also found significant differences in selective pressure after the duplication that led to CYP6B54 and CYP6B55 (Branch No. 23)  but not to CYP6B56 (Branch No. 25)  in CYP6B-coding sequences from seven species of Lepidoptera showed that a model allowing for an increase in the rate of positive selection along the branches originating after the duplication of the most recent common ancestor of the CYP6B tandem (Branch No. 24  No. 25)  online.kdr mutation. The CPQ population was the most susceptible to bifenthrin, with an LC50 value of 0.38 ppm; in contrast, the R347 population displayed the highest level of resistance, with a LC50 value of 24.27 ppm, confirming field observations of insecticide failure. The LC50 of ALM was 7.45, comparable to that of FIG reported by 50 for DDT between the ALM and the R347 populations, both displaying relatively high resistance to DDT  compared with CPQ (LC50 = 25.32 ppm)  . These va County  online.D in this same region are below −2, a strong indication of a departure from neutrality. In addition, the regions flanking both sides of the selective sweep show very high FST values between the resistant R347 and either of the susceptible populations. This difference could be due to differences in the point in time when the frequency of the sweep increased or when mutations accumulated after the sweep reached fixation, but they could also be due to the presence of an allele or alleles possibly linked to the difference in resistance in either or both of these regions.Among the identifiable footprints of positive selection are selective sweeps. Footprints associated with evolved insecticide resistance could help understanding processes and events that ultimately shape the genomes of insects in response to human-driven selection. In this study, we utilized pooled DNA sequencing (Pool-seq) as an efficient and cost-effective method  to screeZea mays, with similar evidence of multiple selection targets; this sweep likely resulted artificial selection for domestication exerted over thousands of years (LCT) gene in lactose-tolerant northern European populations . Several of these studies identified genes encoding cytochrome P450 associated with the sweeps.  cuprina . A regios tandem . Song etkdr locus is a single recessive point mutation resulting in an amino acid substitution from L to F in the S6 transmembrane segment of domain II of the para gene, a voltage-gated sodium channel, and the main target of both pyrethroids and DDT . According to our bioassays, resistance to DDT coincides with the presence of the kdr mutation, but resistance to bifenthrin does not, indicating that other genes or loci are also responsible for the R347 resistance. It is of special interest that the kdr mutation has been linked to preadaptative resistance to pyrethroids in multiple mosquito species , where CYP6B56 has a π value of zero in R347. Although the precise substrate specificity of these A. transitella P450s has not been defined, they belong to a CYP subfamily for which there is both direct  is high between resistant and susceptible populations, include a cytochrome c oxidase, an enzyme that has been directly associated with pyrethroid resistance in at least one insect species  after decades of discontinuation of the insecticides that selected for them has been documented in mosquito species  in 2016. Larvae from the bifenthrin-resistant population “R347” were collected from almond orchards in Kern County and sent to us by Brad Higbee (Trécé) in 2016. All three populations were maintained in an incubator at the University of Illinois at Urbana-Champaign at temperatures of 28 ± 4 °C and photoperiod of 16:8 (L:D) h cycle until individuals eclosed, whereupon they were frozen at −80 °C. Genomic DNA was extracted and prepared into sequencing libraries following standard procedures as described in One hundred individuals from each of three navel orangeworm populations displaying different levels of bifenthrin resistance in the Central Valley were sampled and sequenced in pools. The “ALM” and “FIG” populations of A. transitella reference genome (NCBI accession ASM118610v1) using bwa mem v. 0.7.12-r1039 with paired-end mode . Low-quality alignments , improper pairs, and unmapped mate reads were removed using SAMtools v.1.7  were calculated on Tajima’s D values. On a separate pipeline, a multiple-pileup file was created that incorporated the three trimmed and quality-filtered mapped libraries, indels were removed as described, and libraries were synchronized using “mpileup2sync.jar” from Pooplation2 software (v.1.201) , and Tajima’s v.1.201) . The 3-pFST values might present bias if the sample is too small or if nonequimolar amounts of DNA for each of the individuals from each population were used. Bias can also be introduced during the PCR amplification step before sequencing . Both methods yielded similar results, but Popoolation2 does not report overall FST between populations; accordingly, we report results from both packages. “Pcadapt” v.1.1  for bifenthrin and DDT in the sequenced populations, we conducted feeding assays with semisynthetic artificial diet (50). Differences between populations were considered significant if their respective 95% confidence intervals in the Probit analysis did not overlap. We were unable to perform these assays with the FIG population because we did not establish a colony from the population used for sequencing; however, for the purposes of comparison, we incorporated the bifenthrin LC50 established in our laboratory for this population by To establish the median-lethal concentrations . Briefly, fifth instar caterpillars from the ALM and the R347 populations were placed on artificial diet containing either 0.5 ppm bifenthrin or methanol as the control solvent. After 48 h, the larval midguts were dissected and flash-frozen in liquid nitrogen. There were three replicates per treatment and population combinations for a total of 12 samples, each consisting of a single midgut. Total RNA from each of the 12 samples was extracted using a Nucleospin RNA kit  according to the manufacturer’s protocol. The RNA was sequenced on an Illumina HiSeq 4000 . After sequencing, reads were preprocessed, mapped to the reference genome, and quantified for differential expression following previously described methods . The GADPH  was used as housekeeping gene. Primer sequences are in About 1 µg of each of the RNA samples that were used for RNA-seq were reverse transcribed to cDNA with a Protoscript II kit . The resulting cDNA was diluted 10-fold with nuclease-free water. qPCR reactions were assembled with SYBR green fast-qPCR mix  and primers specific for either Nonsynonymous to synonymous substitution rate ratios (ω) were calculated across the phylogeny of a representative set of 7 species of Lepidoptera with fully curated sets of cytochrome P450s, for a total of 22 CYP6B sequences. Nucleotide coding sequences were codon aligned using MUSCLE , and thepara gene containing the kdr mutation (Forward 5′-ACCAAGGTGGAACTTCACAGAT-3′ Reverse 5′-AGCAATTTCAAGAAGTCAGCAACA-3′). Amplicons were Sanger sequenced and trace analysis and alignments were performed in Geneious.DNA was extracted from ten frozen whole-body fifth instar larvae from the reference genome population SPIRL-1966, and ten midguts of fifth instar larvae of the CPQ population that fed on semisynthetic artificial diet . ExtractGenome Biology and Evolution online.evaa234_Supplementary_DataClick here for additional data file.