Squalidus multimaculatus, is an endemic and primary freshwater species restricted to the southeast coast of the Korean Peninsula. The distribution of this species is unique, given that other congeneric species, including its closely related S. gracilis majimae, as well as other cyprind species are observed throughout the peninsula except for the east coast. Phylogeographic analyses were conducted using three mitochondrial loci to identify the origin of S. multimaculatus and the historical pathways of dispersal. A strong phylogenetic affinity between S. multimaculatus and S. g. majimae and the genetic structure among populations indicated that S. multimaculatus originated from the eastward colonization of the common ancestor between S. g. majimae and S. multimaculatus via headwater capture through fault zones within successive mountain range. Following colonization, the ancestral S. multimaculatus likely migrated towards north via estuary coalescence along a well-developed continental shelf. Our study was the first empirical attempt providing insights into how freshwater organisms dispersed to the southernmost tip of East Asia, despite the potential loss of such historical imprints with anthropogenic interference.Separated river systems could create confluences via two geological processes, estuary coalescence in response to decreasing sea levels and headwater capture, allowing primary freshwater species to disperse across rivers.  Because of this fragmented nature, freshwater ecosystems can provide exceptional opportunities for analysis of the contribution of physical isolation to intraspecific genetic structure4. By combining the genetic data of intraspecific population structures with information regarding historical drainage structures and geological events, it is possible to infer how a freshwater species or its ancestor have dispersed across rivers shaping the present distribution6.In freshwater ecosystems, primary freshwater fish species cannot naturally move to other physically separated drainages, and landscape structures may even become barriers within a single drainage, limiting gene flow among populations8. First, the estuary regions of adjacent rivers were often connected in response to decreasing sea levels during the Quaternary period 11, though connections could rather rarely occur between rivers that did not share the same well-developed continental shelf13. This geological phenomenon seems to have occurred in regions with wide continental shelves, where rivers in geographical proximity are likely to share estuaries14. Second, the uppermost region of a river may be diverted from its own bed and merged with the headwater of other adjacent rivers (headwater capture)16. Although headwater capture is still observable with watershed erosion in a specific condition17, it is a geological phenomenon that frequently occurred via strong tectonic deformation along fault zones19.With time, separated river systems could create confluences via two representative geological processes, allowing freshwater species to disperse across rivers that are not currently connected21. The Yellow Sea probably created confluences with the present-day major river systems along the south and west coast on the Korean Peninsula23. Conversely, the east coast of this peninsula is geographically isolated by successive mountain ranges  restricted to the southern east coast of the Korean Peninsula27. The distribution of this species is somewhat unique, given that other congeneric species  are observed in most freshwater systems throughout the peninsula except for the east coast27. Among these congeneric species, S. g. majimae is morphologically and genetically close to S. multimaculatus28. Thus, it is conceivable that S. multimaculatus originated from the migration of the common ancestor of S. multimaculatus and S. g. majimae individuals inhabiting the drainage areas close to the east coast such as the Nakdong River, the longest river flowing to the south coast in South Korea28. Three hypothetical routes for this geodispersal process could be envisaged based on the geographical features on the east coast and previous studies  and Nakdong River to the Samcheok-Osip River (H1)16 or directly to rivers on the southern east coast  was established based on the existence of Yangsan Fault Zone between the Nakdong River and the southern east coast and its tectonic history. Considering the geographic proximity, the mouths of the Nakdong River  and southernmost east coastal rivers likely coalesced following the Quaternary climate changes  and 12S (949 bp), respectively. Among these, Squalidus gracilis majimae contained 5, 23 and 9 haplotypes in COI, cyt b and 12S, respectively. Except for two populations, JA and YO, no haplotypes were shared between these two species (Supplementary Tables b and 12S contained 41 (6.1%), 137 (12.1%) and 34 (3.5%) polymorphic sites, and 29 (4.3%), 104 (9.2%) and 21 (2.2%) parsimonious informative sites, respectively.The primers used in this study successfully amplified all individuals. A total of 16, 61 and 26 haplotypes were detected from the sequences of COI (670 bp), cyt b sequences  were resolved as the most likely sister group of S. multimaculatus, and Japanese S. gracilis was placed as the sister to the cluster of S. g. majimae and S. multimaculatus , the haplogroup SE was clearly isolated from others, but the distinction between ME and NE was somewhat difficult  MYA  MYA and 0.31 (95% HPD 0.19–0.45) MYA were allocated to the nodes leading to the separation of haplogroup SE and the divergence between NE and ME, respectively  generated highly concordant tree topologies in the phylogenetic analysis reconstructed based on cyt b and a few haplotypes of S. g. majimae were found in population YO  only contained NE haplotypes in the three loci, except that a single SE haplotype of cyt S. g. majimae than the other two haplogroups , it is unreasonable to regard this river as a starting point for colonization. Although S. multimaculatus individuals were observed in the Samcheok-Osip River as well as several other northern rivers (including population MP) in our field investigations and previous studies32, those individuals probably existed because of artificial transplanting given that the presence of this species has never been known until a few decades ago, despite intensive field investigations33. Furthermore, considering the results of our phylogenetic tree analyses that showed NE and ME were clustered together and SE was placed as a sister group, it is difficult to explain how this species could spread towards southern east coastal rivers.The first hypothetical route was established based on the possibility of migration from the major freshwater systems on the Korean Peninsula (south and west) to the Samcheok-Osip River on the east coast by headwater capture36, and two freshwater systems  were likely to have fused together during the geological changes  exhibited the highest affinity to the haplotypes observed in S. g. majimae in our haplotype network analyses, indicating that ancestral S. multimaculatus founders colonized the SE region for the first time in the east coast. As shown in the phylogenetic clustering pattern, it is conceivable that S. multimaculatus individuals naturally migrated north from this point. This second hypothesis can also be supported by the fact that species inhabiting the Nakdong River, such as Cobitis hankugensis (Cobitidae), Iksookimia longicorpa (Cobitidae) and Liobagrus sp. (Liobagridae), are also present in SE regional rivers39. Although it is known that a historical migration occurred between the Hyeongsan River (population HS) and the Nakdong River through the Yangsan Fault40, our data did not verify this possibility.The second hypothetical route was established based on the fact that the Yangsan Fault Zone between the Nakdong River and rivers along the southern east coast has undergone active crustal fluctuationsges Fig. . In this44. This inference is also supported by the ichthyofaunal similarity between the two rivers45. Our EBSP results indicated that the dispersal within the SE region was most active around the late Pleistocene (20 KYA), because sudden demographic growth was noticeable during this period, following the gradual decline since 200 KYA. How could S. multimaculatus that colonized the SE region disperse to the river in the north? Headwater capture has also likely contributed to the migration of S. multimaculatus founders to the Hyeongsan River (population HS). The Ulsan Fault Zone is located between the Hyeongsan River and the Taehwa River (population TH), and the watersheds of both rivers are in geographical proximity  first colonized the Taehwa River TH; Fig.  and thenIksookimia yongdokensis (Cobitidae) are observed in both the ME and NE region, but not the SE region25.Population HS exhibited an exceptionally high level of diversity in all three loci examined. In addition, haplogroup ME did not show any demographic decline or sudden fluctuations in our EBSP results. This was probably related to the physical habitat in the area inhabited by this population. Although most east coastal rivers are small and subject to weather-sensitive physical changes, even during a short-term period, the Hyeongsan River is large, conceivably providing stable and wide habitats that maintain a large-sized population. Since there is no fault zone from the Hyeongsan River towards the north, headwater capture might not have contributed to colonization in the NE region. Instead, there is a well-developed continental shelf, that is likely to have served as a place for the coalescence among river mouths. The historical northward migration from ME to NE is supported by the finding that natural populations of S. multimaculatus to the east coast. However, our results did not provide any evidence supporting such an assumption. The population JA did not exhibit the highest genetic affinity to population HY , located in the immediate north, but was closer to population HS . As discussed below, haplogroup ME did not show the highest genetic affinity to S. g. majimae. In addition to our results, we made an effort to find evidence of water connection around this area. No geological evidence has been known about the connection, for example between the Jangan River and any from SE region. Since the headwaters are not geographically adjacent around this area, the possible water connection might be the estuary coalescence responding to sea level fluctuation. However, the continental shelf on the coast is too narrow to imagine the effect of sea level change. Except for S. multimaculatus, there was no ichthyofaunal commonality between the Janan River and SE rivers.The third hypothetical route was made by the assumption that frequent estuary coalescence between south coastal rivers (including the Nakdong River) and southern east coastal rivers probably contributed to the migration of freshwater species. If this assumption is correct, the Jangan River , which is located where the south and east coast meet, might have served as a bridge for the historical colonization of Zacco platypus, Z. koreanus and Aphyocypris chinensis, indicating that some massive introduction might have occured32. However, the results of this study do not clearly indicate NE rivers from which this population originated. In particular, two cyt b gene haplotypes, NE04 and NE06, observed in population MP were not present in any of the NE populations analyzed in the present study, indicating the possibility of transplantation from the river(s) that was (were) not analyzed in this study.In our analyses, several populations showed quite unexpected genetic characteristics. First, population DJ was part of haplogroup NE in our analyses, despite its geographical proximity to SE or ME populations, which cannot be explained solely based on the biogeographical inference. This may be the results of artificial introduction, though this prediction should be examined by empirical studies in the future. Second, as already mentioned, MP was also considered to be created by artificial introduction. In the Myeongpa River (MP), there are several cyprinid species that were not observed in other adjacent rivers, including S. multimaculatus and S. g. majimae. Where did the individuals of this population come from? S. multimaculatus haplotypes in this population most likely originated from population HS. Only a single ME haplotype was observed, indicating that a small number of individuals were introduced to this population probably from the population HS. A single SE haplotype was also observed in this population, which may be the evidence of additional introductions from SE regional rivers, though the possibility of natural existence could also be regarded. However, the haplotype diversity of S. g. majimae was somewhat higher than that expected to be formed by artificial introduction of a small number of individuals. Therefore, it is conceivable that S. g. majimae individuals might have existed naturally prior to the introduction of S. multimaculatus in this river. In addition, some S. g. majimae haplotypes in this population were not observed in two populations from the Nakdong River, indicating the possibility of introduction from other river(s). However, caution is needed when drawing conclusions regarding the historical origins of this population, because sampling has not been performed throughout the Nakdong River. A low frequency of S. g. majimae haplotypes was also observed in the population JA, indicating that a small number of S. g. majimae individuals were introduced in this population. Examination with the nuclear marker (RAG1) showed clear evidence of active hybridization between S. g. majimae and S. multimaculatus in both populations.Population JA contained the haplotypes of both S. multimaculatus ancestral population likely occurred through confluence caused by headwater capture during geological changes in landscape structures. In the rivers around mid- and northern east coast regions where the continental shelf is wider than the southern part of the eastern coast, estuary coalescence could be presumed to be the main route of migration and colonization of this species. However, our study also showed the potential loss of such historical imprints that have been created over a million years because of anthropogenic interference.In our genetic analyses, we inferred the route of colonization and distribution origin of a freshwater fish species at the southeast tip of East Asia, though further comparative studies are required to confirm this result. The first colonization of Squalidus multimaculatus were collected from 11 east coastal rivers on the Korean Peninsula .All samples used were collected according to the Inland Water Fisheries Act and Wildlife Protection and Management Act of the Republic of Korea. The entire procedure of this study was approved by the Yeungnam University Institutional Animal Care and Use Committee (Protocol # 2015013). A total of 209 individuals of b), cytochrome oxidase subunit 1 (COI) and 12S rRNA (12S), were analyzed in this study (Supplementary Table S. multimaculatus and S. g. majimae specific nucleotides) known to occur in several S. multimaculatus populations28 . Thermal cycling was performed under a program consisting of initial denaturation at 94 °C for 2 m, 35 cycles of 94 °C for 30 s, 54 °C (or 56 °C for COI) for 30 s and 72 °C for 30 s and then final extension at 72 °C for 10 m. Amplified products were purified with the PrimePrep PCR Purification Kit  and sent to Genotech  for sequencing, which was conducted in an ABI3730xl DNA analyzer  with the same primer sets used in our PCR reactions. The residues of DNA samples were deposited at the Department of Life Sciences, Yeungnam University.Three mitochondrial loci, cytochrome b 51, number of haplotypes (h)51, haplotype diversity (hd)51 and number of segregating sites (S)52, were estimated using DnaSP 5.153.All of the haplotype sequences were deposited in GenBank (Accession number: KY486515–486635). DNA sequences analyzed for all four loci (including RAG1) were initially edited using Geneious 9.1b sequence is the most widely reported in fish species, however, the phylogenetic tree using key species as outgroup could only be reconstructed using this locus. The outgroup species were selected from Cyprinidae as follows: five Hemibarbus species including H. labeo (DQ267432), H. maculatus (JN003322), H. barbus (AB070241), H. mylodon (DQ225265), H. longirostris (DQ267422), eight Squalidus species including S. japonicas , S. chankaensis , S. chankaensis tsuchigae , S. japonicus coreanus , S. atromaculatus , S. nitens , S. argentatus  and S. gracilis gracilis , Rhodeus pseudosericeus (KF410783), Tinca tinca (NC008648) and Zacco seiboldii (NC008653). Jmodeltest255 was implemented in CIPRES portal version 3.156 to identify the most appropriate nucleotide substitution models for BI and ML based on the Akaike information criterion (AIC)57. The best fitting model was found to be TIM3 + I + G.Phylogenetic trees were generated under three different algorithms, Bayesian inference (BI), maximum likelihood (ML) and neighbor-joining (NJ). All three mitochondrial loci were used in the reconstruction of phylogenetic tree. Since the cyt 58 implemented in the CIPRES portal. Each BI analysis consisted of two parallel runs of 80 million Markov Chain Monte Carlo (MCMC) generations with sampling every 1,000 steps. The consensus tree for each data set was generated after omitting the first 25% of sampled trees as burn-in. The node confidence in the BI tree was presented with the Bayesian posterior probabilities. The convergence and stationarity of chains were determined based on whether the effective sampling size (ESS) reached more than 200 using TRACER 1.659. The ML tree was created using RAxML GUI 1.560 under the GTRGAMMA model with 1000 bootstrap replicates. NJ analysis was performed using MEGA6 under the Kimura 2-parameter model (K2P)61. Node confidence in the NJ tree was assessed based on 1,000 bootstrap replicates.BI analysis was conducted using MrBayes 3.262. The unrooted haplotype network was initially generated for four different datasets  based on the connection limit above 0.95 in probability using TCS 1.263. When the link was disconnected between haplogroups, PopART64 was applied to generate the connection.Hierarchical genetic structuring was analyzed by assessing the relative contributions of among-haplogroup, among-population and within-population components using an analysis of molecular variance (AMOVA), which was performed using Arlequin 3.565 using cyt b sequences in the CIPRES portal. The BEAST analysis was run using a strict and an uncorrelated lognormal relaxed molecular clock to examine the variation of rate across branches. Posterior distributions of parameters were estimated using 40 million MCMC generations, with samples drawn every 1,000th steps under the model chosen as the most-fit, TIM3 + I + G. The initial 10% of samples were discarded as burn-in. The analysis was conducted using a Yule speciation and a birth-death model for the tree prior to examine the sensitivity of the results to the choice of tree prior. The genus Hemibarbus was known to be a sister to Squalidus66 and its fossil record (5 MYA)67 was available as the calibration point to constrain the ages. This record was assigned in the form of uniform prior to provide a minimum age for the separation of Hemibarbus and Squalidus. TRACER 1.659 was used to confirm whether ESS exceeded 200 and the MCMC parameters of runs converged on the same stationary point with unimodal distribution. A consensus tree was generated after discarding the first 20% of trees as burn-in in TreeAnnotator 2.3.065, and was visualized in FigTree 1.4.268. The node credibilities of the consensus tree were evaluated by posterior probabilities. To examine dispersal scenario for S. multimaculatus, the distributional areas of common ancestor was inferred using the Bayesian binary MCMC method (BBM)69 implemented in RASP70. BEAST tree was used as an input for this analysis. Six areas are assigned based on the distributional area of the species used in the BEAST tree: (A) China; (B) Korean Peninsula excluding east coastal rivers; (C) the Taehwa and Hoeya River; (D) the Hyeongsan River; (E) the Myeongpa, Pyeonghae-Namdae, Baegrok, Chuksan, Youngdeok-Osip and Gokgang River; (F) Japan.Divergence time estimation among phylogenetic groups was conducted in BEAST 2.3.0D72, Fu’s Fs73, mismatch distribution74 and an extended Bayesian skyline plot (EBSP)75. Tajima’s D and Fu’s Fs were quantified using Arlequin 3.562 by coalescent simulations under the infinite-sites model. The mismatch distribution for each phylogenetic group was analyzed to examine the possibility of sudden population expansion76 in Arlequin. Finally, EBSP were generated independently for three mitochondrial loci using BEAST 1.8.277 to provide evidence of the historical expansion of geographic groups. BEAST was run for 60 million MCMC generations, with sampling every 5000th tree, based on TIM3 + I + G and a strict clock model (1% pairwise divergence per million years)78. TRACER 1.659 was used to confirm whether ESS reached above 200 and the MCMC parameters of runs converged on the same stationary point with unimodal distribution.Historical demographic changes of phylogenetic groups were examined based on Tajima’s Supplementary material


Thryorchilus browni) inhabits the highlands of the Talamanca mountain range, including western Panama, and the highest peak in the Central Volcanic mountain range of Costa Rica. Using microsatellites and song recordings we studied the effect of a geographic barrier and distance on song, genetic and morphological divergence among four populations in Costa Rica. A lowland mountain pass resulted in the largest genetic, vocal, and morphological (bill length) differences among populations, likely due to reduce the gene flow. Cultural drift and assortative mating by females selecting songs from their own population likely accentuates the effect of isolation and limited gene flow between populations. This pattern of population divergence has been found in other Neotropical highland birds, but over larger geographical scales. We conclude that mountain passes and distance both reduce gene flow between populations in recently-isolated highland species with restricted distributions.Populations may become isolated by distance, geographic barriers or both. Isolated populations often diverge in behavioral, morphological and genetic traits as a result of reduced inter-population gene flow. Highland species commonly present naturally fragmented distributions that confine populations to the highest mountain peaks, isolated by mountain passes and distance. The endemic Timberline Wren ( For most species, absent or limited gene flow will result in some degree of inter-population divergence . MultiplZonotrichia leucophrys oriantha, the spatial distribution of dialects limits gene flow across populations but does not act as an impermeable barrier [In birds, the spatial distribution of song dialects could also affect dispersal and consequently gene flow patterns. Oscine birds -and a few other groups- learn to sing early in life and have dialects which are restricted to small geographic areas . Oscine  barrier . In othe barrier –26.The highland avifauna of Costa Rica and western Panama includes a large number of endemic species with naturally fragmented distributions ,28. ClimThryorchilus browni; Troglodytidae) is a highland endemic with a fragmented distribution, confined to the highest mountain peaks above 2800m a.s.l. of the Talamanca Mountain Range in Costa Rica and western Panama, and the highest volcano of the Central Volcanic Mountain Range in Costa Rica. A deep mountain pass separates these two mountain ranges. T. browni is a territorial species that forages in dense undergrowth within montane forests and the páramo [The Timberline Wren (e páramo . Its sonQuercus costaricensis and Q. bumelioides) and the undergrowth by bamboo thickets (Chusquea spp.), Ericaceae and Hypericum shrubs.We selected four sites above 2800 m a.s.l. in two Costa Rican mountain ranges. Three sites were located within the Talamanca Mountain Range: Cerro Buena Vista , Cerro Vueltas  and Cerro Chirripó ; and the fourth site was in the Irazú Volcano , located in the Central Volcanic mountain range . Both moT. browni adult males with undetermined age between January and June 2012 and 2013 with eight to ten individuals per site; adultness was determined by checking skull ossification, only adults have complete skull ossification. We recorded a single focal male per day from 0500 to 0600h and did not use playbacks before or during recordings. This method controls for possible daily variations in songs [We recorded songs from 36 adult in songs ,33. We uAfter each recording session, we captured each individual with a 6 m long mist nets. We color banded each individual and extracted a 5–10 uL blood sample from the brachial vein and preserved it in 95% alcohol at -20°C until DNA extraction. For each male, we measured culmen length from the front of the skull to the bill tip, bill width at front of nares, tarsus from the tibiotarsus joint to the distal end of the tarsometatarsus, tail length, flattened wing length and body mass. We captured and color banded all recorded individuals. For genetic analyses, we obtained blood samples from additional individuals for which no recordings were obtained. We did not use anesthesia and birds were released unharmed after data collection. All collections were done according to the guidelines of national law of animal care (Law # 7451) and the collecting permit RESOLUCIÓN-001-2012 issued by Sistema Nacional de Áreas de Protección to G. Barrantes and A. Camacho.Fig 2). We defined a song element as the smallest unit that can be individualized from the spectrogram separated from other units by at least 50ms. In very few cases, some elements overlapped in time but not in frequency and were treated as different song elements. The elements followed the same order within a sequence in 99% of cases, but an individual can start its song at different points of the element sequence.We analyzed song recordings with Raven Pro 1.4 . Spectrograms were produced using a Hann function, 700 samples and a FFT size of 1024 samples and an overlap of 75%. The song of the Timberline Wren consists of a sequence of elements that is repeated one or more times during a variable period of time  visually identified each element based on their modulation pattern  without prior knowledge of which individual was recorded . For insNs/(R1+R2), where Ns is the number of shared elements, and R1 and R2 are the sizes of the respective repertoires for each pair of individuals. We subtracted this proportion of shared elements from 1 to obtain a dissimilarity index between individuals [We calculated an index of shared elements between individuals using the Sorensen index, 2ividuals . We calcWe tested for difference in shared song elements across sites using a Permutational Multivariate Analysis of Variance  as implek-medoids unsupervised machine-learning algorithm to estimate the optimal number of K clusters that group individuals based on song dissimilarity. This algorithm is less sensitive to outliers than the traditional k-means clustering [factoextra and cluster R packages.We also used the Partitioning Around Medoids (PAM), ustering . We calcustering  and implT. browni adult males to test the effects of a barrier and distance on six acoustical traits: sequence duration (s), peak frequency (Hz), high frequency (Hz), low frequency (Hz), frequency range (Hz), and the number of elements per sequence. These traits were measured for each element sequence in a song to obtain the mean value of each variable for each song, using Raven Pro 1.4, with spectrograms produced with a Hann function, 700 samples and a FFT size of 1024 samples and an overlap of 75%.We analyzed 187 songs of 39 We conducted a principal component analysis to visualize the distribution of populations in a multidimensional space defined by song features. We also used a discriminant function analysis to assess whether or not song traits are assigned to their own populations using the MASS package for R statistical language (Version 3. 2. 2) .Troglodytes aedon -2 [Thryophilus pleurostictus  and a FastDNA Spin Kit . We determined multilocus genotypes for all samples using six microsatellites isolated for other Troglodytidae: -C3(B)-2 ) and Thr(ThPl-14 ). We useWe used GenAlEx 6.0  to estimK) was determined by changing K between 1 and 6, and using 20 independent runs for each K value. All runs used the admixture model with correlated allele frequencies and consisted of 20 000 burn-in steps followed by 200 000 MCMC steps. To determine the most likely number of K clusters, we used Structure Harvester [We tested if individuals could be grouped into different clusters based on their allele frequencies using the Bayesian clustering algorithm implemented in Structure 2.3.4 . The mosarvester ,47.To test for isolation-by-distance, we estimated the correlation between Fst values and the shortest linear geographic distance between populations with a Mantel test; Fst estimates were transformed to Fs/(1-Fst) as suggested by Rousset . We perfadegenet package [jitter R function. The song dissimilarity index matrix was used to identify barriers between populations based on vocal divergence. Barriers to gene flow were identified using Euclidean distances between individual genotypes. Random noise was eliminated using Principal Coordinate Analysis with the dudi.pco function from the ade4 package. The Monmonier algorithm estimates boundaries based on maximum pairwise genetic distances or song dissimilarities.To identify possible barriers to gene flow, we used the Monmonier algorithm  implemen package . IndividT. browni males at all four sites. Timberline Wren male songs showed high inter-individual variation, and not a single individual shared the same elements or element order with another individual within or between populations. We identified 301 different elements from which 64 were present in at least two populations and 19 were present in all four populations. The population with the largest number of elements was IV with 151, while CBV with 98 had the smallest number. The mean number of different elements per individual (± sd) was 30.3 ± 7.63, and ranged between 11 and 62 elements .We recorded a total of 36 elements , and theThryorchilus browni males shared more song elements with individuals from their own populations than with other populations. Elements from IV differed the most when compared with other populations. Pairwise comparisons showed that IV and CCH shared fewer song elements (dissimilarity index score = 0.78) than any other pair of populations , and CBV and CV shared the largest number of elements (dissimilarity index score = 0.48). The optimal number of groups found by PAM analysis was K = 2. Most individuals from CBV, CV and CCH were grouped together, while all VI and 12% of CCH individuals were assigned to a second group  explained 39.2% of the variation in song traits, while the second component (PC2) accounted for 26.6%, with 65.8% of the total variation explained by the first two components. The highest loading variables for PC1 were sequence duration (0.47), frequency range (0.53) and number of elements per sequence (0.51), all loading positively . Individuals from VI had longer bills than those from the other populations . CBV and CCH had the highest mean number of alleles per locus, while IV had the lowest . The popWe found significant genetic structure between populations of the Timberline Wren . Pairwise comparisons of Fst estimates ranged between 0.01 (CBV-CV) and 0.178 CCH-VI; . STRUCTUr = 0.818, p = 0.04; r = 0.902, p = 0.04; r = 0.874, p = 0.044, We found significant evidence for isolation by distance on genetic divergence. Fst values increased significantly with increasing geographic distance  and Talamanca  mountain ranges. Populations within Talamanca had lower genetic divergence than those found between IV and the nearest population in Talamanca. Isolation and the relatively smaller population size of IV have likely contributed to the observed differences between this populations and those from Talamanca via genetic drift or local adaptation to particular environments ,5,12. Thg factor –26. Limig factor –14.T. browni could act as a prezygotic isolating mechanism, further increasing genetic and morphological divergence between populations [T. browni in the Central Volcanic mountain range is restricted to a reduced area of available habitat at the top of IV and limited dispersal and smaller population size could increase the effects of drift in this population, further reducing its genetic diversity. Climate change is expected to contract available habitat in mountainous regions, negatively impacting population sizes of highland residents. We found the lowest genetic diversity estimates at IV, and additional reductions in genetic diversity through genetic drift may increase the likelihood of local extinctions in this endemic highland species [Song diversity evolves faster than genetic differences , 56, 57,ulations . The dis species .T. browni varied little across these four populations. Bill length is the only trait that differed geographically, specifically between IV (longer bills) and populations in the Talamanca mountain range, a difference that was first described in 1910 [T. browni consist mostly of small insects like flies and beetles [T. browni may possibly contribute to significant differences in song acoustic traits between IV and other populations [The morphology of  in 1910 . Bill mo in 1910 , which f beetles . Bill le beetles ,29,31. Tulations , but ourT. browni in Costa Rica. In this species, distance was positively correlated with genetic and song structure, suggesting this species may have limited dispersal and highlights the effect of species-specific life history traits as important factors shaping the distribution of genetic diversity. Isolation by behavioral divergence through cultural drift may further increase the effects of genetic drift on smaller populations and impact their long-term survival. Distance and geographic barriers have been proposed as major factors driving the evolution and speciation of different taxa at large geographical scales [The climatic and geological events that took place during and after the Pleistocene glacial cycle are presumably responsible for the isolation and divergence across populations of birds in the highlands of Costa Rica and western Panama –30. Low l scales ,20–26. TS1 Dataset(XLSX)Click here for additional data file.


Acer miaotaiense (P. C. Tsoong) is a rare and highly endangered plant in China. Because of the lack of genomic information and the limited number of available molecular markers, there are insufficient tools to determine the genetic diversity of this species. Here, 93,305 unigenes were obtained by multiple assembled contigs with a transcriptome sequencing program. Furthermore, 12,819 expressed sequence tag-derived simple sequence repeat (EST-SSR) markers were generated, 300 were randomly selected and synthesized, 19 primer pairs were identified as highly polymorphic  = 8, expected heterozygosity (He) = 0.635, polymorphism information content (PIC) = 0.604) and were further used for population genetic analysis. All 261 samples were grouped into two genetic clusters by UPGMA, a principal component analyses and a STRUCTURE analyses. A moderate level of genetic differentiation (genetic differentiation index (Fst) = 0.059–0.116, gene flow = 1.904–3.993) among the populations and the major genetic variance (81.01%) within populations were revealed by the AMOVA. Based on the results, scientific conservation strategies should be established using in situ and ex situ conservation strategies. The study provides useful genetic information for the protection of precious wild resources and for further research on the origin and evolution of this endangered plant and its related species. Acer miaotaiense (P. C. Tsoong), a perennial woody plant belonging to Acer (family Aceraceae), is an endangered species mainly distributed in the Gansu and Shaanxi Provinces of China [A. miaotaiense was listed as a third-class nationally protected plant in the Chinese Red Data Book in 1992 [A. miaotaiense are seriously threatened, and the outlook is not good. As a distinctive species among the Aceraceae family, A. miaotaiense has nearly horizontal fruit wings and, therefore, is of great significance in studies of the morphological evolution and classification of Aceraceae. Additionally, it is a popular landscaping tree [A. miaotaiense having a large economic and ornamental value. However, a high seed abortion rate, weak natural regeneration, strict habitat conditions and frequent human exploitation have significantly reduced the number of natural populations and narrowed the geographic distribution range of A. miaotaiense, which resulted in it being classified as an endangered plant [of China ,2. A. mi in 1992  and was  in 1992 . Becauseed plant .A. miaotaiense were mainly concerned with raising seedlings, fruiting characteristics and reproductive biology [Origanum compactum [Phellodendron amurense [Centaurea borjae [Studies regarding  biology ,7,8, and biology ,10. To p biology ,12,13,14 biology ,16. Currompactum , Phellodamurense  and Centa borjae .Pinus dabeshanensis [Ziziphus jujube [Paeonia suffruticosa [Robinia pseudoacacia [A. miaotaiense in natural populations in China, developing SSR markers by transcriptome sequencing is a first step. This will aid in providing genetic protection for natural resources.Because of a lack of available genomic information, it was difficult to develop ideal SSR markers and to evaluate the genetic diversity and evolution of endangered species at the genome level ,21,22. Thanensis , Ziziphus jujube , Paeoniaruticosa  and Robidoacacia , to consdoacacia . Therefodoacacia ,32. To cA. miaotaiense. Here, 93,305 unigenes with an average of 668 base pairs (bp) and an N50 value of 1138 bp were identified. A total of 19 EST-SSRs with high polymorphism rates were selected to assess the genetic diversity among 261 A. miaotaiense individuals and the structures of their six natural populations in the Qinling Mountain areas of China. The diversity analyses and EST-SSR markers described in this study will contribute to the further studies on the protection and utilization of germplasm resources of A. miaotaiense.In this study, for the first time, we developed EST-SSRs based on transcriptome sequencing data. These were utilized in studying genetic diversity and population structure and in further implementing feasible production strategies for this rare and vulnerable species, A. miaotaiense individuals representing six wild populations were collected in this study, which contains its main distribution regions in China . Briefly, purified mRNA was obtained from total RNA (1 µg) per sample using poly-T oligo-attached magnetic beads; then, the mRNA was fragmented using divalent cations under elevated temperature in NEBNext First Strand Synthesis Reaction Buffer (5X). First, strand cDNA was produced based on the random hexamer primer and M-MuLV Reverse Transcriptase. Immediately after, a second strand cDNA was synthesized using DNA polymerase I and RNase H. After adenylation reaction of 3′ ends of DNA fragments, NEBNext Adaptor containing hairpin loop structure were ligated for hybridization. The fragments in library were purified with AMPure XP system  to firstly select for 240 bp cDNA fragments; 3 μL USER Enzyme (New England Biolabs) was incubated with size-selected, adaptor-ligated cDNA for 15 min at 37 °C and then 5 min at 95 °C. Then PCR reaction was performed using Phusion High-Fidelity DNA polymerase, Universal PCR primers and Index (X) Primer. Finally, PCR products were purified by AMPure XP system and the Agilent Bioanalyzer 2100 system was used to assess sequencing library quality and the clusters were sequenced on an Illumina Hiseq 2000 platform and paired-end reads were generated. Sequenced raw data described in this study were uploaded in the Sequence Read Archive (SRA) public database of the NCBI (No. SRR7195583).Three fresh tissues  were frozen and stored instantly in liquid nitrogen prior to RNA extraction. Total RNA was isolated from all the tissues collected above using the method described by Ghawana et al.  and was Before assembly, the raw paired-end reads were firstly used to obtain high-quality clean reads. In addition, the Q20, Q30, GC-content and sequence duplication level of the clean data were calculated. Afterwards, transcriptome assembly was implemented using Trinity  and the E-value < 1.00 × 10−5), including Nr (NCBI non-redundant protein sequences database); Pfam (Protein family database); KOG (eukaryotic orthologous groups); COG (Clusters of Orthologous Groups); Swiss-Prot (Swiss Institute of Bioinformatics databases); eggNOG ; KEGG (Kyoto Encyclopedia of Genes and Genomes databases); The representative GO (Gene Ontology) annotation was performed by the software Blast2GO [For functional annotation of the assembled unigenes, these sequences were aligned to the public databases by BLASTX program (Blast2GO .http://pgrc.ipk-gatersleben.de/misa/misa.html) for identifying SSRs with a minimum of ten repeats for di-nucleotide motifs, five repeats for tri- and tetra-, four repeats for penta- and hexa-nucleotide motifs. The primer for each SSR locus was designed using Primer3 version 0.4.0 (http://primer3.sourceforge.net/releases.php). The criteria for designing primers were as follows: primer length of 18–24 nucleotides; GC content of 40–60%; annealing temperature between 55 and 60 °C with 55 °C as optimum; PCR product size range of 100 to 350 bp.The transcriptomes were analyzed using the MIcroSAtellite identification tool (MISA)  and was used to check DNA integrity and purity using 1% agarose gel electrophoresis and NanoDrop 2000 (Thermo Fisher Scientific). PCR amplification of each locus was implemented as shown by . In addio), He, Na, effective number of alleles (Ne), Fst, inbreeding coefficient (F), Hardy–Weinberg equilibrium (HWE) and number of rare alleles (NRA). The gene flow (Nm) was calculated as Nm = (1 − Fst)/4 × Fst based on the Fst values. The PIC values of each SSR primer were obtained using the PIC calculation  progress [GeneMarker (version 1.5)  was usedprogress . To obtaA. miaotaiense populations using STRUCTURE version 2.3 [The Bayesian clustering analysis was implemented to evaluate the population genetic structure of sion 2.3 . Models sion 2.3 . In addision 2.3  was calcsion 2.3 . In ordesion 2.3  was furthttp://biodiversityinformatics.amnh.org/opensource/gdmg/index.php) . The relative migration networks among the six populations was performed using the function ‘divMigrates’ [Based on the longitude and latitude recorded by a global positioning system (GPS), the geographic distribution of the six populations was identified by the ArcGIS version 10.2 software . The geographic distance among the sampled locations was calculated by the Geographic Distance Matrix Generator (GDMG) version 1.2.3 software  functional classification, 24,267 unigenes (~45.32% of the assembled unigenes) were assigned, and these terms could be divided into three categories: molecular function ; biological process ; and cellular component   were annotated to known proteins in the Nr database, with Citrus sinensis having the top known hits , while unknown species represented 53.36%  (All of the  16.48%) . Moreove 16.48%) . Among t . The ana .A. miaotaiense transcriptome was one per 4888 bp. The mono-nucleotide was the most common type of repeat , followed by di-nucleotide , tri-nucleotide , tetra-nucleotide , penta-nucleotide  and hexa-nucleotide  repeats. In addition, the lowest number of repeat motifs per locus was five, with 10 tandem repeats of the EST-SSR  being the most common. This was followed by six , 5  and 11  repeats. The other numbers of the tandem repeats each accounted for <10% of the EST-SSRs , while the lowest diversity levels were in BMG  and TY . The fixation index (F) values of the six populations varied from −0.046 to 0.147, with a positive average, which indicated that the heterozygote is deficient, the homozygote is excessive and significant inbreeding occurred in the A. miaotaiense populations  among different populations, and then is regressed against the geographic distance. Mantel’s test indicated that the correlations established using the reduced major axis regression for genetic distance value and the geographic distance were very weak among the five populations . The greatest geographic distance was found between MJ and BMG, and the Rousset’s distance between them was also the largest. Of the Rousset’s distances, that between MJ and HZZ was the lowest. Thus, there was no significant correlation between genetic and geographic distances, which indicated that the geographic distances observed among populations were not key factors influencing genetic differentiation  was identified based on the LnP(D) and posterior probability (ΔK) values. K was tested from one to eight with 10 repetitions performed for each run. In this study, a clear peak in the value of ΔK was obtained at K = 2 b,c. The en K = 2 a. C1 conA. miaotaiense individuals were clearly separated into two broad groups across the first two axes  [Picrorhiza kurrooa (44.6%) [Gossypiumis (37%) [Acer palmatum (44.33%) [Chlorophytum borivilianum (44%) [A. palmatum  [Sepia esculenta  [A. miaotaiense appeared to be of high quality and thus was utilized for further studies on marker development, population structure and genetic diversity. To understand the biological functions of assembled transcripts in A. miaotaiense, the functional annotations of the assembled unigenes were obtained using diverse public databases. For the GO analyses, 24,267 (45.32%) unigenes were annotated into 41 GO terms and ‘the cell part and cell’, ‘binding and catalytic activity’ and ‘cellular process and metabolic process’ were the most abundant terms in the cellular component, molecular function and biological process categories, respectively, which was consistent with GO functional categories observed in Dipteronia oliver [A. miaotaiense but also mean that multifarious metabolites are synthesized in this species. For A. miaotaiense and its related species, the leaves are the most important metabolic organ and contain large amounts of terpenoids, esters and aldehydes, which aid in resisting environmental and biological stresses, such as freezing, drought and pests. Moreover, some of unigenes were observed in plant hormone signal transduction  that responds to environmental information, such as abscisic, jasmonic and salicylic acids, which are related to stress. These results provide valuable functional annotation information for future investigations of gene function, stress responses and biological pathways in A. miaotaiense.In total, 21,485,322 high-quality clean reads were generated with 88.82% Q30 and a 44.46% GC content using Illumina paired-end sequencing, which was consistent with the results from transcriptome sequencing in (44.58%)  and Picr (44.6%)  but was is (37%) , Acer pa(44.33%)  and Chloum (44%) , indicat 738 bp)  and Sepi 911 bp) —using tha oliver . In addiMichelia coriacea (Magnoliaceae) [Brassica oleracea [Z. jujube [A. miaotaiense, the major repeat type was di-nucleotide  after the removal of the mono-nucleotide repeats, which was consistent with previous findings, followed by tri-nucleotide repeats . The most dominant di-nucleotide repeat motif of A. miaotaiense was AG/CT , which was consistent with studies in peanut [D. oliver [A. miaotaiense, AAG/CTT for D. oliver and AAG/CTT for peanut). In particular, the CG/CG motif was not observed in A. miaotaiense, which further supports the conclusion that the CG/CG repeat is rare in many dicotyledonous plants [SSR markers have abundant types and are widely distributed in plant genomes . In the liaceae) , Brassicoleracea , garlic oleracea  and Z. j. jujube . In the n peanut  and D. o. oliver , but theepeats 23, 21.13%.eotide 27, 24.72% s plants .a = 8, Ho = 0.528, He = 0.635 and PIC = 0.604) in wild A. miaotaiense. These results are similar to those determined of seven wild populations of Acer davidii  as determined by using 11 primer pairs to evaluate the genetic diversity of SSR markers, He et al., 2017 [D. oliver  [A. miaotaiense is only naturally distributed in the Qinling Mountains, but its genetic diversity is higher than those of Oryza sativa  [Phaseolus vulgaris  [M. coriacea (Magnoliaceae) [a = 4.091, He = 0.505 and Ho = 0.412 for the SSR markers). Low levels of genetic diversity would not be beneficial in an expanded distribution region and could increase the possible susceptibility to diseases or insect pests. Thus, further studies on genetic protection should be used to construct a library of genetic diversity to preserve genetic variation within species. The distribution patterns of genetic structure are closely related to the breeding mechanisms and natural selection acting on species [A. miaotaiense individuals were divided into two major distinct groups for the highest ΔK value. However, each group was more or less mixed with the lineages of the other group, which was also reported in garlic [Genetic diversity plays a significant role in the genetic improvement of germplasm resources, which has been widely applied in many plants ,66,67. Tl., 2017 . However= 0.714)  at the s = 0.55)  and Phas= 0.308) . For othliaceae) , a restr species . In thisn garlic .Salix psammophila [Rhododendron jinggangshanicum [Ranunculus acris [A. miaotaiense, including shoulder-shaped leaves and flat fruit with three wings. Aditionally, it is perennial and wind-pollinated, which may be one cause of the high level of genetic diversity, the number of alleles, and the high PIC value among these samples. The genetic diversity varied in A. miaotaiense from different populations in China. XLS had the highest level of genetic diversity, while BMG had the lowest level of genetic diversity. The XLS is located in the western edge of the distribution area whereas BMG is located 1260 m at the southern edge of the Qinling Mountains. The marginal distribution may change allele frequency and generate a low level of genetic diversity. A. miaotaiense is mainly distributed in the Qinling Mountains  and is often used as an ornamental tree. A natural gene flow for A. miaotaiense over such a distance and altitude would be difficult, and one putative explanation may be frequent human actions. A. miaotaiense from different regions are often planted together, inducing gene flow and increasing diversity.Environmental conditions and species characteristics are also considered as critical factors and may affect genetic diversity. These have been shown in other plant species, such as mmophila , Rhododeshanicum  and Ranuus acris . In our st is an effective measure of genetic differentiation and gene flow between populations [st = 0.059–0.116) and weak population structure were found among the six natural populations. The greatest level of genetic differentiation was found between populations 2 and 5, and the distance between them was ~187 km, which indicated that long-term isolation may limit the level of gene flow between two populations. The same result was also shown in the relative migration networks of the six sampled populations. An AMOVA indicated that the greatest level of genetic differentiation in A. miaotaiense exists within populations (81.01%), while less genetic differentiation occurs among the populations (1.44%), and the same results have been reported in previous studies based on SSR markers [st value (0.19 > 0.15), indicating great differentiation among populations [Fulations . Moderat markers ,79. It pulations . This maulations .01%, whiA. miaotaiense, which inhabits relatively independent areas of Tianshui, Gansu Province, with relatively high number of Na and private alleles. Furthermore, the Fst value between XLS and other populations varied from 0.065–0.089, indicating moderate levels of genetic differentiation. This variation difference may be explained by the larger sample size, adult trees and specific mating systems of XLS compared with other populations. The lowest Fst value was detected in MJ and HZZ populations, suggesting a low degree of genetic differentiation between them, and this finding was consistent with the UPGMA analyses. It may be that the frequent human activity and geological history of the Qinling Mountains restricted the continuity of their distribution, but that the later development of a similar environment along ditch edges guided the evolution of the two populations in the same direction. The Nm value among A. miaotaiense populations ranged from 1.905 to 3.987, indicating the frequent flow of genes of populations. The clustering of A. miaotaiense populations showed a clear separation according to the dendrogram from Nei’s genetic distance matrix by UPGMA. In terms of population structure, the populations from G2 and G3 were nearly clustered into C2, while the populations from G1 were clustered into C1, except MJ, which is not consist with the known geographical location information . Similar results were also found using Mantel’s test for the correlation between genetic distance value and the geographic distance matrix . These results showed that the genetic distance was not significantly correlated with the geographic distance, suggesting that geographic distance is not the principal factor influencing genetic differentiation in A. miaotaiense.Genetic differentiation into different populations is strongly influenced by gene flow, long-term evolution, genetic drift, selection and mutations . In thisMaintenance of high genetic diversity and a reasonable population structure are the core goals for the conservation of the endangered species and are of great significance in the establishment of conservation policies and scientific measures. Only few adult fruiting trees were observed in the sampled regions, among which XLS populations had a relatively greater number of adult trees with only 46 individuals. Natural regeneration is difficult and there are very few young seedlings in the forest. Thus, an effective policy should first protect the habitat and natural populations, especially populations with higher levels of genetic diversity (XLS and HZZ), and take some measures for in situ conservation, such as the establishment of natural protective areas to shelter as many individuals as possible. The greatest level of genetic differentiation was found between the MJ and BMG populations. To protect independent population units, these populations are the most important for in situ conservation to maintain as much genetic diversity as possible. Furthermore, overexploitation may be the main cause for the reduction in population sizes according to the field survey. Therefore, necessary protective policies, laws and regulations must be instituted by the government to forbid the unlawful felling and exploitation of this endangered species’ environment. To avoid further losses in population size, all of these extant populations  should be protected.A. miaotaiense’ natural genetic resources. Because of the current habitat loss and low seed-setting rate, the TY population only contains seven individuals, including two adult trees, and its genetic diversity level was the lowest. The principal protection of the TY population should involve ex situ conservation to rescue the remaining individuals. XLS contains the greatest number of individuals and has the highest genetic diversity level; therefore, a seed bank and resource nursery should be established through breeding experiments and transplantation. Moreover, samples from the entire natural distribution range should also be collected in botanical gardens with suitable habitats to increase the chances of gene flow among populations. To summarize, a high genetic diversity level was observed in A. miaotaiense, but its distribution was narrow. Therefore, comprehensive protective measures should be taken along with in situ or ex situ conservation. In particular, we should attach great importance to the protection of extant A. miaotaiense populations and seriously implement protective strategies to maintain the high level of genetic diversity.In addition, impactful ex situ conservation approaches are of great importance for maintaining


DQA, DQB) in hares, Lepus capensis, along a steep ecological gradient in North Africa and tested the role of climatic parameters for the spatial distribution of DQA and DQB proteins. Climatic parameters were considered to reflect to some extent pathogenic landscape variation. We investigated historical and contemporary forces that have shaped the variability at both genes, and tested for differential selective pressure across the ecological gradient by comparing allelic variation at MHC and neutral loci. We found positive selection on both MHC loci and significantly decreasing diversity from North to South Tunisia. Our multinomial linear models revealed significant effects of geographical positions that were correlated with mean annual temperature and precipitation on the occurrence of protein variants, but no effects of co-occurring DQA or DQB proteins, respectively. Diversifying selection, recombination, adaptation to local pathogenic landscapes (supposedly reflected by climate parameters) and neutral demographic processes have shaped the observed MHC diversity and differentiation patterns.In natural populations, allelic diversity of the major histocompatibility complex (MHC) is commonly interpreted as resulting from positive selection in varying spatiotemporal pathogenic landscapes. Composite pathogenic landscape data are, however, rarely available. We studied the spatial distribution of allelic diversity at two MHC class II loci ( However, in some cases the time span between the separation of populations might be too short or gene flow might be too strong to leave a signal of differentiation at neutral loci3. In such cases, differences between populations can only be detectable at functional genes under selection3. Moreover, studying molecular polymorphism at loci under natural selection will help to understand the genetics of adaptive processes and to increase our knowledge on the importance of adaptive genetic variability in free ranging animal populations1. Local adaptation at functional markers has mainly been addressed in populations structured at neutral markers and rarely in the case of seemingly panmictic populations4. A study of Tunisian hare populations indicated a strong population structure at mitochondrial OXPHOS genes, which are under positive selection at several codons, despite a high gene flow at neutral markers5. These findings are consistent with local adaptation according to climate variation5.Genetic studies of natural populations based on presumably neutral markers such as mitochondrial d-loop DNA (mtDNA), microsatellites or SNPs are important to infer phylogeny, population history and gene flow+ and CD8+T-lymphocytes to initiate the adaptive immune response against pathogens. MHC class I and class II loci, the two main types of MHC loci involved in the adaptive immune system, have been shown to be highly polymorphic in a wide range of species9 and their adaptive significance in vertebrates has long been recognized11. Allelic diversity at these loci can be associated with variation across environmental and geographical scales as demonstrated from several studies13. The role of environmental characteristics in shaping selective pressures on MHC genes is suggested to be mediated by combined effects including recent and ancient demography as well as behavioural and ecological differences in pathogens exposure16. Moreover, host-pathogen interactions have been suggested to be affected by temperature, nutrient availability and environmental stress17. Whereas increases of ambient temperature can reinforce selection on immune genes, thereby accelerating host adaptation19, variation in nutrient levels can force physiological modification that could either impair or promote local adaptation of the host17.Adaptation to local or regional pathogenic landscapes is often addressed by analyses of major histocompatibility genes (MHC) which code for glycoproteins that recognize and bind antigens in order to present them to CD420 and host parasite co-evolution2. MHCII molecules are heterodimers formed by an α and a β chain encoded by A and B genes, respectively22. These chains are characterized by several typical structures, such as N-linked glycosylation sites, connecting peptides, transmembrane regions, and cytoplasmic domains, as well as the α1/α2 domain and the β1/β2 domain, respectively23. The α1 and β1 domains constitute the peptide-binding region (PBR), involved with the recognition and binding to the antigens25. These domains are highly polymorphic and are encoded by the second exon of A and B MHCII genes. Given their role in presenting antigenic determinant peptides to the cell surface, these loci are typically expected to be subject to strong positive selection, since their greater allelic diversity should be associated with a response to a wider range of pathogens16. Genetic diversity in the MHC is also generated through several mechanisms, including point mutation, recombination, gene conversion, sexual selection and maternal–foetal interactions28. The variation generated by these processes is thought to be maintained in individuals and populations by a combination of balancing and diversifying selection, driven primarily by variation in selection pressure from pathogens and parasites across space and time28. Balancing selection encompasses a number of different evolutionary mechanisms that result in a large number of alleles being maintained in populations for longer than would be expected relative to neutral genetic variation24. Such balancing selection is generally explained by three mechanisms a) over-dominant selection or heterozygote advantage where heterozygous individuals have higher fitness than both corresponding homozygotes; b) frequency dependent selection or rare allele advantage where individuals with rare genotypes have higher fitness30; c) fluctuation in selection pressure where MHC polymorphism is maintained by changing pathogen composition through time and space. Conversely, under diversifying selection at MHC loci, the spread of an advantageous allele (positive selection) would be expected to lead to a loss of genetic variation. Similarly, selection against disadvantageous alleles (negative or purifying selection) would also be expected to reduce diversity31. Among the different suggested mechanisms of balancing selection, fluctuation in selection pressure have been reported for class II MHC genes among populations of the Asian cygomolgus macaque (Macaca fascicularis)12, and the Atlantic salmon32, among other studies. Patterns of MHC selection can be inferred by comparing MHC genetic differentiation to differentiation at presumably neutral markers28. Neutral loci reflect demographic factors such as migration and drift, whereas loci under selection usually show higher differentiation under different local selection regimes or lower differentiation under balancing selection33 or under negative/purifying selection33. Higher differentiation at MHC genes could therefore reflect local adaptation to different parasite faunas prevailing in the respective populations.Among all MHC molecules, class II genes have received the most attention, due to their role in studying mechanisms and significance of molecular adaptation in vertebratesLepus capensis) are distributed throughout the whole country in diverse environments and over short geographic distances, reaching from the Mediterranean climate in the north to the pronounced arid desert climate (Sahara) in the south. This situation provides a good precondition to study natural selection and adaptation on functional genes5. The likely differences in differential parasite pressure in these habitats could lead to different selection pressure at MHC loci among the local populations. Such selective pressure would shape diversity and differentiation profiles in interaction with neutral demographic/stochastic factors. In this study, we examined the MHC class II polymorphism for DQA and DQB genes in hares from Tunisia along a steep ecological gradient. We first investigated the variation of genetic diversity in the different studied populations distributed in heterogeneous ecoregions from North to South. If diversifying selection is the main evolutionary force acting on MHC genes, we expected significant genetic differentiation despite observed high gene flow in neutral markers. Second, we used different approaches to evaluate the effect of selective pressure in each codon of the obtained sequences. Moreover, we compared the levels of allelic diversity of the considered hare populations with those obtained for a set of neutral loci. We expect that MHC diversity varies significantly between the regional populations under study given that spatial changes in parasite communities and dynamics with environmental conditions might alter the selection pressures they exert on their host populations17. However, neutral demographic processes  can also have significant effects on the genetic diversity of natural populations34.In Tunisia, hares  and 26 DQB alleles (accession numbers MH346143- MH346168) in 142 hares (L. capensis) from Tunisia with a total length of 228 and 222 bp, respectively. Among them, only three DQA alleles  were previously described in L. europaeus. No insertions, deletions, or stop codons were detected in either DQA or DQB sequences. The nucleotide sequence of each allele in both loci translated into a different amino-acid sequence  were specific to NT, one allele (Lecp-DQA*04) to CT and two  to ST. For DQB, only four alleles  were shared between the three regional populations. Nine alleles were specific to NT, two to CT, and two to ST. Frequencies of these specific alleles ranged between 0.0161 and 0.0968 for DQA and between 0.0137 and 0.0500 for DQB  deviation from Hardy-Weinberg Equilibrium in all three regional populations indicated that the MHC loci contained significantly fewer heterozygotes than expected. Finally, there was no significant linkage disequilibrium between genotypes at the DQA and DQB locus as indicated by the Linkage disequilibrium test .A comparison of the obtained genotypes revealed alleles specific to each regional population. For ST values (Table DQA locus ranged between 0.29 (NT vs CT) and 0.59 (CT vs ST), whereas those for the DQB locus ranged between 0.73 (NT vs CT) and 0.96 (NT vs ST). Similarly, pairwise FST . Both axes (F1 and F2) were structured by DQA alleles DQA*7, DQA*8, DQA*11, DQA*13 and DQB alleles DQA*1, DQA*3, DQA*9  and ST (p = 0.0017) populations when using the Two Phase Mutation model (TPM). No tests detected a bottleneck in the northern Tunisian (NT) population. Different Ne estimates were obtained for each population depending on the used method Table . The HetDQA locus, whereas recombination evidence was obtained by one test  for the DQB locus  and three  codons were reported under positive selection for the same locus by PAML  were under episodic diversifying selection in DATAMONKEY. Ten and eleven codons were detected with PAML  and OMEGAMAP , respectively. Four codons  were confirmed by all three methods to be under positive selection. Negative selection in the DQB locus was detected in five codons  as suggested by DATAMONKEY tests.For 35 indicated departure from expectation under a neutral evolution scenario for both the DQA and the DQB locus , in two other cases models with strong support (delta AICc > 8) and in two cases models with some support (delta AICc > 4). Two models had a deviance explained of more than 40%, which is notable, because in multinomial models nominal data are compared with quantitative predictions.In three cases we found models Table  with excDQA or DQB protein variants combinations in four models and eight times higher in the “dqa11gt”-model  had a high RVI-value (> = 0.89) indicating a high probability for these variables being in the best model. Furthermore, the delta deviance value of one of the geographic variables was more than ten times higher than the respective co-occurring DQA and DQB, in hare populations (L. capensis) along a steep ecological gradient in North Africa. We found 17 DQA and 26 DQB alleles among 142 L. capensis individuals. We have analysed the molecular mechanisms of sequence evolution in both loci, i.e., substitution, recombination, and positive selection. We have also described the genetic differentiation and population structure and compared this across both MHC loci and populations. Apart from significant positive selection and recombination events, we found a significant decrease of allelic richness at the two MHC loci from the northern population under Mediterranean humid climate to the southern population in the Sahara region. In addition, we have found a high population substructuring at the two MHC loci compared to supposedly neutral microsatellite markers.We assessed the MHC diversity and its distribution at two MHC class II genes, DQA and DQB loci. Such wide allelic diversity in both allele numbers and pairwise nucleotide distances is characteristic for MHC genes37. Moreover, this is in accordance with the results of our previous analyses of the same individuals using mtDNA control region sequences38, partial transferrin sequences39, allozyme40, and microsatellites38, which confirms the generally high genetic variability of Tunisian hare populations. Notably, the detected levels of polymorphism of DQA and DQB were higher than those reported for brown hares (L. europaeus) from Austria and Belgium43. Klein44 and Takahata et al.45 suggested that such differences in polymorphism could be due to either a difference in the speed of allelic diversification or in time of their persistence in the gene pool. Both loci in our study have exhibited very similar substitution rates, but a greater recombination rate was recorded for DQB. This effect may be due to a longer persistence of DQA diversity in the populations, whereas DQB allele diversification may be more likely due to higher intra-locus recombination. Indeed, we found evidence for multiple  recombination events creating novel DQA and DQB haplotypes. According to the different methods used (that vary in their conservative nature), breakpoints at position 120 and 221 were detected by only one test  of DQB was one and a half times more than the mean mutation rate (θ = 11.53) indicating that the generation of DQB diversity is driven by an accumulation of new recombinants relative to new mutations (ρ/θ = 1.455), which is similar to results obtained in other Lepus species43. For DQA, recombination seems to have a minor effect on its evolution compared to mutations (the ratio ρ/θ = 0.010). In contrast, Wegner46 showed that recombination rate and intensity of positive selection were positively correlated in MHC class I and class II sequences of nine different species of fish, suggesting that recombination and gene conversion played a significant role in the evolution of the studied MHC genes.Our results show a high level of polymorphism for the t Tables  and S2. ST and Dest values. But the same populations showed little genetic structure in the microsatellite loci, which is in accordance with mtDNA and nuclear sequences47, as well as microsatellite and allozyme genotypes40 studied earlier in these populations. Therefore, population genetic differentiation at MHC loci could result from either a divergence in qualitative and quantitative parasite pressure that would select for different types of alleles in the different ecoregions but may also be due to differences in modes of mutation and mutation rates between the two marker types. However, high gene flow was concordantly indicated by several markers40  characterized by different modes of mutation and mutation rates. Therefore, the observed pattern of differentiation only at MHC loci is likely the result of differential selection pressures across the studied populations which is consistent with theoretical expectations28.Genetic differentiation between the studied Tunisian hare populations was high in both MHC class II genes as suggested by high and significant FLecp-DQA*07 and Lecp-DQB*09 for NT, Lecp-DQA*08 & Lecp-DQB*01 for CT, Lecp-DQA*11 and Lecp-DQB*03 for ST) vary greatly in each population for each locus. Those predominant alleles were suggested by the PCA to be responsible for the genetic differentiation observed at MHC loci between the three ecoregions. Such differences in allele frequencies between populations have been demonstrated to be mediated by pathogens in affected and unaffected chamois populations48. In contrast, cohorts of brown hares characterized by similar quantitative and qualitative pathogens have exhibited the same frequent allele as a sign of stabilizing selection41. Moreover, estimations of effective population size and bottleneck signature indicate that the reduced allelic richness of the ST population may be the result of a smaller and/or drifting population. Therefore, the lower MHC polymorphism, in terms of number of alleles, in the ST population may be due to demographic processes, independently from specific parasite pressure. The results indicate that the population structuring at MHC loci is linked to qualitative MHC polymorphism  likely shaped by variation in parasite pressure, but the quantitative MHC polymorphism  may still be the results of demographic processes.Indeed, the few frequent alleles  in thirteen DQA alleles of L. europaeus populations from Austria and Belgium and four codons  among 19 Oryctolagus cuniculus alleles. For DQB, Smith et al.43 identified two codons (codons 26 and 60) under positive selection among eleven DQB alleles in L. europaeus, which are also under positive selection in the current study.Both loci showed excess of non-synonymous to synonymous substitutions, a signal of positive selection39 or be due to allelic dropout as different alleles might have similar conformation when using CE-SSCP techniques50. However, the distribution of MHC heterozygosity suggests that heterozygote advantage is unlikely given the observed positive selection in the studied populations. Indeed, the MHC FST values were clearly higher than those for microsatellite loci, suggesting an apparent effect of diversifying positive selection. This conclusion was also supported by the outliers test, where the DQA and DQB loci showed significantly higher mean FST than predicted from its mean heterozygosity  which can influence parasite species richness, as well as intensity of infestation of hosts. According to Klein and Horejsi51 an indirect assessment of parasite infestation can be seen via analysis of MHC class II molecules diversity in the host species, as these molecules primarily correspond to extracellular pathogens, such as bacteria, nematodes, cestodes. Indeed, associations between MHC variants and parasite load have been demonstrated in numerous cases28. Here we found significant effects of mean annual temperature and precipitation in the distribution of some MHC proteins/alleles which might indicate regional and climate effects. Concordantly, genetic differentiation and estimation of gene flow indicated regional distribution patterns for MHC loci compared to high genetic admixture in the neutral markers (i.e. microsatellites). Both results suggest that the MHC polymorphism across sampling populations might reflect an adaptation to variation in parasite pressure across the ecological gradient. We expect quantitative and qualitative changes of pathogens from the Mediterranean humid region in the north to the arid Sahara in the south. Indeed, Zvinorova et al.52 showed that the highest helminths and Eimeria infections in goats were observed in the wet season, whereas Pandey et al.53 suggested direct relationship between rainfall and intensity of infection with gastrointestinal nematodes in goats. Notably, diversity of the mammalian fauna that potentially constitutes a complex pathogen reservoir shows a reduction along the currently studied ecological gradient in Tunisia54, probably due to habitat changes and nutrient availability. Moreover, population density of hares from Tunisia seems to decrease from North to South Tunisia . This may point towards a combined effect of various aspects, mainly driven by environmental conditions, resulting in the reduction of pathogens in the Sahara region and hence resulting in lower MHC diversity.The observed heterozygosity values of MHC loci were rather low in all three regional populations showing a significant deficit. The low heterozygosity might fit to a pattern of recent expansion, as suggested by mitochondrial and nuclear sequencesity Fig. . Further55. We followed Wang56 who showed that linkage disequilibrium (LD) methods perform dramatically better than the other methods. Ne estimates using this method indicated a similar effective population sizes in all three populations. Moreover, our current Ne estimate for the ST population was less than 400 which was suggested to be the minimum effective population size necessary to maintain genetic variation in the long term in a population57. This later population was suggested also by the bottleneck test on neutral microsatellite loci to be under genetic drift. Furthermore, Olivier et Piertney58 have demonstrated that selection counters drift to maintain polymorphism at a major histocompatibility complex (MHC) locus in water vole bottlenecked population from Coiresa Island. Therefore, we consider that demographic factors (e.g. bottlenecks or drift) were strong enough to shape the allelic richness in ST population and/or selective pressures were weak to counter the effect of genetic drift.Estimation of effective population size showed that Ne estimates were not consistent across methods. This is expected as the accuracy and precision of the different methods to estimate Ne are still uncertainIn conclusion, our present findings from hares along a steep ecological gradient in North Africa indicated that MHC polymorphism and differentiation were shaped by a combination of neutral demographic processes and climate-driven positive selection of MHC genes across relatively short geographical distances. How changes in pathogenic landscape diversity on the one hand, and how immunologic resistance or tolerance of infected hosts on the other hand may interact with local or regional climatic parameters in the evolution of MHC diversity and in the course of adaptation to environmental characteristics needs, however, further studies with particular inclusion of infection patterns of the hosts.Lepus capensis, e.g.61) were collected during regulars hunts from various biogeographic zones within the major ecoregions in Tunisia. We never manipulated live animals; we got only hare samples during regular hunting season from hunters. All samples were collected with the agreement of the general forest department of Tunisia . Hares were grouped into three populations corresponding to the three main climatic zones in Tunisia . Acronyms of sampling localities and positions within northern (NT), central (CT), and southern (ST) Tunisia, as well as sample sizes are given in Fig. Quercus suber) Mediterranean forest and scrubland , across the semi-arid central part of Tunisia (CT) to the arid and hyper-arid Sahara in the south (ST), with typical plants that have adapted to dry conditions such as Acacia tortilis subsp. raddiana, Vachellia spp., Stipagrostis pungens vegetation63. Our study area represents a remarkably steep climatic and ecological gradient across a straight distance of less than 500 km, with means of annual precipitation and temperature ranging between 916 mm and 16.3 °C in the north and 77 mm and 21.26 °C in the south.A total of 142 samples of cape hares  from diverse tissues  of each specimenDQA and DQB) were performed according to Goüy de Bellocq et al.42 and Smith et al.43. The amplified 258 bp and 249 bp (including primers) correspond to the total exon 2 of DQA and DQB genes, respectively. We used capillary electrophoresis single-strand conformation polymorphism (CE-SSCP) to screen allelic diversity in the above mentioned genes. Amplification was carried out using the Multiplex PCR kit (Qiagen) following the manufacturer’s instructions in a final reaction volume of 10 μl. Forward primers for both gene segments were 6’FAM-labelled and reverse primers were NED-labelled.Amplification of exon 2 fragments for two MHC class II genes , observed (Ho) and expected (He) heterozygosity were calculated for each locus and for each regional population  with GENETIX66. Tests of deviation from Hardy–Weinberg equilibrium and linkage disequilibrium were calculated using GENEPOP 4.067 and GENETIX66, respectively. The allelic numbers for the cape hare sequences were assigned according to the guidelines of Klein et al.68 and begin at Lecp-DQA*04 and Lecp-DQB*01 for DQA and DQB, respectively. The FSTAT vers. 2.9.3.2. program69 was used to calculate locus-specific values of allelic richness (Rs) based on a rarefaction approach to account for different sample sizes. Allelic richness calculation was based on minimum sample size of 29 and 18 individuals for MHC and microsatellite loci, respectively.Sequences were edited and aligned by eye using the BioEdit v.7.2.5 program©, 1997-2013ST (10 000 permutations) in GENETIX66 and also Jost’s D (Dest)70 as a more robust measure than FST using GenAlEx 6.571. In addition, an analysis of molecular variance (AMOVA) from the MHC data was calculated for the three eco-regions  using the Arlequin 3.5 program72. The same genetic parameters were also estimated for genotypes at 14 microsatellite loci obtained earlier for the same individuals38 for NT, CT, ST.Differentiation between regional populations was determined by calculation of standardized pairwise F73, using the genotype data at both MHCII loci. Coordinates obtained for each allele were used for scatter plots using Past software74.Furthermore, in order to identify the contribution of the different MHC alleles to the population structure, a principal components analysis (PCA) was performed in ADE-475 implemented in the bottleneck software to test for bottleneck in the microsatellite data. As recommended by Piryet al.75, we used the Two Phase Mutation model (TPM) and the Stepwise Mutation Model (SMM).We then calculated effective population size using the neutral microsatellite data using Ne estimator76. Three different methods based on the information of linkage disequilibrium (LD), heterozygote excess (HE) and molecular coancestry (MC) were used.Finally, as demographic events might have affected the observed pattern of diversity and differentiation in MHC loci, we first use the “Wilcoxon signed-ranks test”77. To check for the presence of recombination in the alignment of our sequences, we first used PERMUTE (included in the software package OMEGAMAP78), which computes three different statistics  based on the correlation between physical distance of sites and their linkage disequilibrium. The RDP, GENECONV, MAXCHI, CHIMAERA and 3SEQ implemented in the RDP3 package79 were all employed to detect recombination breakpoint locations. In the latter analyses we used a significance level of 0.05. We also calculated the population scaled recombination rate ρ and mutation rate θ by using LDhat recombination rate scan implemented in theRDP3 package79. Finally, the GARD method80 was employed to search for possible recombination partitions.The assumptions of several methods of sequence analysis are violated if recombination is present81 as implemented in the DATAMONKEY webserver , OMEGAMAP78 and CODEML (PAML 4 package82). The first two software packages have the important advantage of taking recombination into account whereas CODEML does not account for that.In order to infer evidence of positive selection, three different methods that compare the rates of synonymous and non-synonymous substitutions separately for every single codon were used. We chose to use HYPHYet al.43. Two Markov chain Monte Carlo runs of 250 000 iterations (25 000 iteration burn-in) for DQA and 400 000 iterations (40 000 iteration burn-in) for DQB on population allele frequencies at each locus, were compared for convergence. Codons are considered as positively selected with posterior probabilities greater than 95%.The OmegaMap program is based on a Bayesian population genetics approximation to the coalescent theory and generates means and credible intervals for the selection parameter (dN/dS = ω) and recombination rate (ρ = 4Nr) for each codon . We used the same parameters as in Smith 80) that infers phylogenies for each putative non recombinant fragment. The output was used to run four different maximum likelihood methods for detection of selection: SLAC , FEL (fixed effects likelihood), REL (random effects likelihood), and Mixed Effects Model of Evolution (MEME)83. Significance levels of P < 0.25 in SLAC and FEL and P < 0.05 in MEME and Bayes factors >50 in REL were considered as indicating positively selected sites. We considered a codon to be positively selected only if it was identified by at least two of the methods implemented84.In DATAMONKEY, we first identified recombination break points with genetic algorithm recombination detection  was compared to M2a (adaptive model) and M7 (beta) was compared to M8 (beta plus omega). Pairwise comparisons on nested models were performed using the likelihood ratio test (LRT): twice the log-likelihood difference was compared with a χydistribution with degrees of freedom equal to the difference in the number of parameters between both models. When the LRT was significant, a Bayes Empirical Bayes (BEB) method was used to calculate the posterior probabilities of codon classes in models M2a and M8. Posterior probabilities of >0.95 were considered as supportive under the BEB method87. We considered a codon to be positively selected if it was identified by M2a or M8 or both.Several studies35, based on MHC genotypes, implemented in Lositan88 to compare the observed FST values estimated at each locus to a null distribution of FST conditional on heterozygosity. DQA, DQB and fourteen microsatellite loci38 were tested for neutrality under 50000 simulations, estimated neutral mean FST, infinite alleles mutation model, 99% confidence interval and false discovery rate of 0.1%.In addition to the applied tests to infer molecular signature of positive selection, we used the model-based approach of Beaumont and Nichols40. Thus, with a hypothesized higher pathogenic diversity in the more climatically diverse NT population, MHC diversity at the two studied loci was expected to exceed the neutral microsatellite diversity particularly in NT. In the model we used locus specific allelic richness as obtained by FSTAT as dependent variable, regional population  and locus type (MHC or microsatellite) as fixed factors and locus as random variable to account for potential locus-specific effects (particularly among the microsatellites) and specifically tested for a significant locus type by population interaction effect. The model syntax was as follows: lmer . We also tested if allelic richness varies across the ecological gradient independently from microsatellite diversity using a general linear model of the syntax: MHC allelic richness ~ region; where MHC allelic richness means locus-specific allelic richness (for DQA and DQB) and region means NT or (CT & ST). CT and ST populations were combined because allelic richness values  to check for significant variation of allelic richness from North to South Tunisia. Under a positive selection scenario for MHC loci we expected that allelic richness at both MHC loci varies between sampling regions in the case of different pathogen pressure. However, genetic diversity  for selectively neutral markers like microsatellites was not expected to vary between populations, as population genetic results indicated high gene flow across the whole study regionDQA  and the DQB  loci as response variables and geographical latitude, longitude, altitude as well as the protein combination at the respective co-occurring MHC locus as independent variables. However, mean annual temperature (r = −0.796) and annual precipitation (r = 0.831) correlated too closely with latitude to add them in the model, and latitude has the clearest effect of these three variables on the response variable (see Supplementary Table http://www.worldclim.org/bioclim.htm) and were automatically extracted using DIVA-GIS ver. 7.5. We also run a multinomial log-linear model with individual heterozygosity in DQA and DQB loci as response variable and the same geographic variables as independent variables, expecting a higher level in NT .Second, we used the statistical software package R 2.15.0  to run multinomial log-linear models separately for the most frequent protein variants at the DQA protein class ~ latitude + longitude + altitude + as.factor (co-occurring DQB protein class)),Multinom ),Multinom .The model syntaxes were as follows (using the package nnet):36  to obtain the best model explaining the data. Based on the global models (see syntaxes above), we run all possible models including the respective null-models and calculated the probability of each possible model being the best model using the Akaike weight based on AICc values . Adding up the weight of all models in which a given variable is present gives the “Relative Variable Importance”, RVI, Table 36. For each model we report delta AICc (difference in AICc between the best model and the intercept-only model) and the explained deviance as measure of goodness of fit (Table We used an information-theoretic approach and techniques of model selection and multi-model inferenceit Table .Sequence data from this article can be found in the GenBank under the accession numbers: MH346126-MH346168.Table S1 to S5


Alosa braschnikowi is a commercially valuable species native to the Caspian Sea. It is thought to have eight to nine subspecies, but the genetics of these populations remains to be investigated. The present study was performed to evaluate the genetic population structures of Caspian marine shad (Alosa braschnikowi) in the southern coast of the Caspian Sea using six pairs of SSR markers. A total of Alosa braschnikowi 140 specimens through five locations across the southern coast of the Caspian Sea were genotyped and 130 alleles were identified. The overall mean values of Ho and He were 0.58 and 0.87, respectively, with the highest and minimum value of Ho observed in Sari (0.67 ± 0.08) and Miankaleh (0.50 ± 0.04), respectively. The overall mean value of allelic richness was 12.6. The data suggest that there was a high rate of migration between populations of Alosa braschnikowi , with the highest value (19.07) between Gomishan and Mahmodabad locations. AMOVA results showed that 96% of variation was related to within populations and only 4% belonged to between populations. The mean Fst value of 0.019 indicates a low level of population differentiation. Our data suggest that there may be two genetically separate populations of Alosa braschnikowi across the southern coast of the Caspian Sea and a high rate of migration is likely to limit genetic diversity between them.Genetic diversity studies are essential in characterization of populations and species conservation.  Genetic diversity is one of the basic prerequisites for the preservation and survival of a species and its adaptation to habitats subjected to different environmental pressures. It is thought that high genetic diversity improves the competence of individuals and increases the probability of species survival . GeneticClupeonella and Alosa. The latter consists of four species , A. caspia caspia , A. kessleri , and A. saposchnikowii . Alosa braschnikowi and A. caspia are thought to be native to the Caspian Sea and are more distributed in its southern areas. A. braschnikowi has a herring-like body shape, typically reaching 30 to 50 cm, and it usually feeds on small fishes  and also crustaceans  were sampled in five locations across the southern Caspian Sea including Anzali , Gomishan , Mahmood-Abad , Miankaleh , and Sari   Figure 1Alosa sapidissima  with high allele count and polymorphism were selected from data on idissima  (Table perature  and the Observed heterozygosity (Ho), expected heterozygosity (He), and deviation from Hardy–Weinberg equilibrium (HWE) were determined using GenAlex 6.5 . Errors A. braschnikowi and to make an estimation of genetically detached populations (K) was estimated considering 10,000 as length of burn-in period and 100,000 of MCMC reps after burn-in, with an assumption of K = 1–5 and 100 iterations. The best K was decided based on the Delta K method suggested by A linkage disequilibrium test between pairs of genetic sites was done using the GENEPOP 4.0.10 software . Unbiaseulations . The numA. braschnikowi .The Genepop genotypic linkage disequilibrium analysis at each locus in each population revealed that there were no loci out of equilibrium in all populations  and lowest (0.21) values of Ho were found at loci AsaD392 (Sari location) and AsaC059 (Gomishan area), respectively Table 2.R2 = 0.25, P = 0.12). SSR cluster analysis revealed that the best K fitting to the data was 2, indicating genetically segregate populations   rather than variation among populations (4%). Average gene flow (Nm) was calculated as 13.57 and the highest rate of Nm (19.07) was observed between Gomishan and Mahmodabad Table 5.Alosa sapidissima)  in A. sapidissima   show a hpulation . The ovepulation . Also, apulation . Effectipulation . In the pulation . Unfortuidissima  are highectively . The levA. braschnikowi (P < 0.05). There are several reasons that can explain this significant reduction in Ho compared with He, namely, high rate of migration, errors in reading alleles, and inbreeding (P = 0.0016). Also, there was a high rate of deviation from HWE (26 out of 30), which can be because of mixing between populations  with different allele frequencies (Wahlund effect) (P = 0.01)  gene flow and/or ii) the lack of isolation for enough time to enable genetic drift and selection to cause a noticeable differentiation.The observed heterozygosity is significantly less than the expected value in breeding . The obt effect) . Based o = 0.01)  and a hiompressa . AnotherA. braschnikowi is known to exhibit migratory behavior through the Caspian Sea but there is a basic need to closely monitor them to avoid the loss of Alosa stocks in Iran. Also, it seems that there are at least two genetically different stocks of A. braschnikowi in the southern coast of the Caspian Sea: one clade including fish from Anzali and Sari locations (South to Southwest), and the second one comprising Gomishan, Mahmodabad and Miankaleh locations (South to Southeast). Based on A. braschnikowi in the present study was 0.76 to 0.93, indicating that all populations studied indeed belong to same species. The lower observed values of genetic identity may be due to a lower gene flow between some locations like Miankaleh and Anzali, suggesting that they may be different subspecies. A. brascnikowi subspecies have been recently classified based on phenotypic traits, such as the number of gill rakers and diet . The protocol was approved by the ethics committee of Gorgan university of Agricultural Sciences and Natural Resources.OJ designed and performed the experiments, analyzed the data, and wrote the manuscript. JF contributed to data analysis and reviewed the manuscript. MP assisted in sampling and laboratory experiments. A-AH contributed to writing the manuscript, and AS supervised the work and provided the required laboratory infrastructure.The authors are grateful to Gorgan University of Agricultural Sciences and Natural Resources (Iran) for providing the required financial, technical and administrative support, and to Nord University (Norway) for covering the open access publication fee.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.