Percilia irwini, knowledge that is critically needed to inform conservation strategies in light of current and anticipated further fragmentation initiatives in the system. We identified eight genetic units throughout the entire Biobío system with high effective sizes. A reduced effective size estimate was, however, observed in a single population located between two impassable barriers. Both natural waterfalls and human‐made dams were important drivers of population differentiation in this system; however, dams affect genetic diversity differentially depending on their mode of operation. Evidence of population extirpation was found in two river stretches limited by upstream and downstream dams. Significant gene flow in both directions was found among populations not separated by natural or anthropogenic barriers. Our results suggest a significant vulnerability of P. irwini populations to future dam development and demonstrate the importance of studying basin‐wide data sets with genetic metrics to understand the strength and direction of anthropogenic impacts on fish populations.An understanding of how genetic variability is distributed in space is fundamental for the conservation and maintenance of diversity in spatially fragmented and vulnerable populations. While fragmentation can occur from natural barriers, it can also be exacerbated by anthropogenic activities such as hydroelectric power plant development. Whatever the source, fragmentation can have significant ecological effects, including disruptions of migratory processes and gene flow among populations. In Chile, the Biobío River basin exhibits a high degree of habitat fragmentation due to the numerous hydroelectric power plants in operation, the number of which is expected to increase following new renewable energy use strategies. Here, we assessed the effects of different kinds of barriers on the genetic structure of the endemic freshwater fish  Theory predicts that, under migration–drift equilibrium, headwater populations in freshwater systems will exhibit lower genetic diversity than downstream populations. Extrapolation of such modeling predictions to the wild is, however, complicated by the fact that natural systems are in general not expected to be under equilibrium , a species endemic to the basin  relatively short‐lived (4 years) benthopelagic fish usually found in shallow areas (<1 m) with rocky substrate and slow (<0.5 m/s) moving waters  the presence of anthropogenic barriers (hydropower dams) resulted in higher levels of population genetic structure compared to natural barriers. (b) The level of population structure depends on the type of hydropower plant, whether reservoir or run of the river (see Section P. irwini appears to have been extirpated from. Our study provides insights on the vulnerability of a resident nonmigratory species in the face of increased habitat fragmentation. We discuss management implications of our findings providing guidance for stakeholders and government agencies.Our aim in the present study was to estimate the genetic diversity (28 microsatellite markers), differentiation, and gene flow among 22.1The Biobío River basin is currently home to 11 hydroelectric plants in operation in three main rivers from north to south geographically: Laja, Biobío, and Renaico–Malleco Table . One pla2.2Percilia irwini were collected by seine netting or electrofishing, over two consecutive years (2016 and 2017) with 13 out of 21 sites (distributed among the three major rivers comprising the Biobío system) visited only once, either in 2016 or in 2017, and eight sites visited on both years to increase sample sizes  were measured  and weighed. Fin clips were stored in 96% ethanol for subsequent DNA analysis. Fish were genotyped at a final total of 28 species‐specific microsatellites . DNA was then extracted using a MultiPROBE II Plus Liquid Handling System (PerkinElmer) using a glass milk protocol modified from Elphinstone, Hinten, Anderson, and Nock  were digested at 55°C for approximately 8 hr using proteinase and Nock .Identification and characterization of microsatellites were conducted as described in Yu et al. . Microsa2.4AR) per sample site using FSTAT 2.9.4  and expected heterozygosities (He) for each sampling site using GenAlEx 6.503 ) following the procedure by Rousset  of the F^STs conducted using GenAlEx 6.503  and visualized using the program DISTRUCT 1.1 ) , and age of the barriers, we conducted a series of Mantel tests  in GenAlEx 6.503 ) versus Wwd, BaN, BaA, Elevation, Slope, and AR. This procedure provides options for finding out significant explanatory landscape variables affecting the genetic distances between all sampling sites of the Biobío system, as suggested by Legendre and Anderson  of barriers and vice versa with partial Mantel tests. To support the Mantel correlations, we performed a distance‐based redundancy analysis (dbRDA) in R package, using the genetic differentiation matrix  with the linkage disequilibrium method as implemented in LDNe  and STRUCTURE results were, however, similar  exhibited signs of null alleles and departures from Hardy–Weinberg in 19 sampling sites; the analyses were performed both with and without this locus, and the results did not change. The remaining 27 loci were free of null alleles and exhibited no LD in any sample site; they also showed no evidence of large allele dropout or scoring errors. Two markers (Per13 and Per 39) were identified as putatively under selection. Diversity indices  with distance with neighborhood size = 120 km ; thus, waterway distance and number of barriers both appear to have an effect on genetic differentiation when data from all three rivers are considered. No other variables  were correlated with genetic differentiation  but not within the Laja  or within the Renaico–Malleco River   pattern when on Table  and were4) Table .R2 = .1907, p ≤ .0001), the correlation between the number of barriers and genetic distance was higher and significant  after controlling for distance (Table Given the collinearity between the number of barriers (BaN) and the pairwise waterway distances, two partial Mantel tests were conducted using the information from all locations, first controlling for distance and then testing for the number of barriers, and second, controlling for the number of barriers and then testing for the effect of distance. While waterway distance slightly correlated with genetic distance after controlling for the number of barriers  provided results consistent with the Mantel tests. The significant explanatory landscape variables affecting genetic differentiation were waterway distance and number of barriers , while the smallest effective size was detected in BC . This last population is bound by both upstream and downstream hydroelectric plants. Although data were insufficient for the estimation of effective sizes for some sites  or exhibiting waterfall and “run‐of‐the‐river” hydroelectric power plants (Laja). The influence of anthropogenic factors was most apparent in the upper reaches of the Laja River and in the river, most affected by fragmentation, the Biobío River. Below, we discuss the implications of these results considering the species' life history and the type and age of the various anthropogenic barriers to dispersal in existence in these rivers.In this study, we have uncovered the influence of both natural and anthropogenic factors on the genetic structure of 4.1P. irwini collected downstream of the natural barrier “Salto del Laja” were genetically distinguishable from the upstream aggregations   (but not BD) receives migrants from the Renaico–Malleco River (RM) may be a contributing factor.In the most fragmented river, the Biobío River, gene flow occurs in both directions when no barriers are present: For instance, upstream gene flow appears significant between populations BD and BD) Figure . Even heP. irwini exhibits low swimming capacity and is unlikely to swim for long periods of time . Hydroelectric power plants with run‐of‐the‐river operation may have lower barrier effects due to their low‐head or mobile dams  despite considerable sampling effort and historical records that confirm their former presence in those sites ; and to LU1 and LU2 . The absence of differentiation among collections from the upper Laja could in principle be due, at least in part, to this translocation experiment. However, the fact that 31% of all translocated individuals were released in EA1 . Five are located upstream of the “Salto del Laja,” and all are >12 years old while the sixth is located downstream of the natural barrier and was constructed recently 2015). The five upstream hydroelectric power plants, which altogether are responsible for three low‐head dams with lateral intake (as opposed to bottom intake), appear to have had no effect on the genetic structure of . The fivPercilia irwini is highly susceptible to changes in flow regime as a result of a decrease in suitable area with increased water flow  that create reservoirs, the effects of which have been described in numerous other systems , likely due to the presence of tandem barriers and a change in passive dispersal. Recent migration rates between BU‐BC and BC‐BD were high in the downstream direction, as also observed in other species . The effective size for the population in the central section of the Biobío, the section between dams 2 and 3 (BC), was the lowest of all 4.3P. irwini upstream, drastically reducing upstream contemporary gene flow. The size of the population plays an important role in retaining genetic diversity above barriers though, and large effective population sizes may explain why the effects of dams are not yet strongly manifested in the downstream population in Laja and Biobío rivers. The presence of tandem barriers, the consequent change in the characteristics of the physical habitat, and the hydropeaking regimes operating in some of the plants, however, are likely responsible for the two potential instances of population extirpation (EA1 and EA2) and for the drastic reductions in estimated effective sizes , allelic diversity, and richness (e.g., BC4, Table Natural barriers and hydroelectric plants with reservoir prevent the movement of P. irwini populations, using mitigation measures such as fish passes or elevators (Gouskov, Reyes, Wirthner‐Bitterlin, & Vorburger, Our study demonstrates the importance of studying basin‐wide data sets to understand the strength and direction of anthropogenic impacts on genetic diversity of fish populations. In the upper Laja River, where populations are influenced by run‐of‐the‐river power plants, the maintenance of habitat quality is likely a management priority. In contrast, in the central area of the Biobío River, where the BC population exhibits relatively low effective size, habitat restoration, and increasing connectivity between Current efforts to increase power generation while reducing greenhouse gas emissions are promoting the development of small hydropower dams as nonconventional renewable energy sources (Habit et al., None declared.The sampling was carried out based on the ethics and biosafety rules and procedures specified in Law 18.755 of the Agricultural and Livestock Service of Chile and the Ethics Committee of Universidad de Concepción. Click here for additional data file.


The Mediterranean swordfish stock is overfished and considered not correctly managed. Elucidating the patterns of the Mediterranean swordfish population structure constitutes an essential prerequisite for effective management of this fishery resource. To date, few studies have investigated intra-Mediterranean swordfish population structure, and their conclusions are controversial.A panel of 20 microsatellites DNA was used to investigate fine-scale population structuring of swordfish from six main fishing areas of the Mediterranean Sea.FST values while mixing was endorsed by the heterozygosity deficit observed in sampling localities indicative of a possible Wahlund effect, by sampling admixture individuals. Overall, our tests reject the hypothesis of a single swordfish population within the Mediterranean Sea. Homing towards the Mediterranean breeding areas may have generated a weak degree of genetic differentiation between populations even at the intra-basin scale.This study provides evidence to reject the hypothesis of a single swordfish population within the Mediterranean Sea. DAPC analysis revealed the presence of three genetic clusters and a high level of admixture within the Mediterranean Sea. Genetic structure was supported by significant  Sustainable harvesting of fish stocks is one of the primary objectives of fishery management . Fish spThis is particularly true for marine pelagic fish species where the identification of population structure is hampered by the low level of intraspecific heterogeneity  as a resSwordfish is a pelagic and highly migratory species, distributed worldwide from 45°N to 45°S in the open waters of the Atlantic, Indian, and Pacific oceans. Swordfish also occurs in the Mediterranean, Marmara, Black and Azov seas . The 201For management purposes, ICCAT considers populations in the Mediterranean Sea, North Atlantic, and South Atlantic populations as three separate stocks. Differentiation also occurs in biology with the Mediterranean swordfish having different characteristics compared to the Atlantic stocks such as a lower growth rate and younger age at sexual maturity than in the Atlantic populations . MoreoveDifferences among these basins were also observed in terms of discrete spawning areas, as well as different spawning periods . In the While there are several studies on the global genetic structure of swordfish, few have focused on the Mediterranean swordfish stock. Some of these studies suggest the occurrence of a homogeneous stock in the Mediterranean Sea  following the manufacturers’ protocols.A total of 298 swordfish were collected from six areas within the eastern, central and western Mediterranean regions, to obtain a representative coverage of the basin. Twenty-five swordfish from the eastern coast of Canada have been included in the analysis as a comparison , 0.3 µM of each primer, 0.2 U Taq DNA polymerase  and 40–80 ng of genomic DNA in a final volume of 10 µL for 2- and 3-plex and 15 µL for 4- and 7-plex, respectively. Each forward primer was labelled with a different fluorescent dye  and set up avoiding the overlap of similar allele sizes. PCR conditions were optimized for all loci using the following touchdown protocol: initial denaturation at 95 °C for 5 min, followed by 10 cycles of denaturation at 92 °C for 20 s, annealing at the specific temperature (Ta) (All specimens were genotyped for 20 microsatellite loci: three from ure (Ta)  for 30 sure (Ta) , to miniFST estimation. Therefore, global FST was calculated by both including null alleles (INA) and excluding null alleles (ENA). The bootstrap 95% confidence intervals (CI) for the global FST values were calculated using 50,000 replicates over all loci. Allelic richness (AR), which is a standardised index of the mean number of alleles per locus irrespective of sample size, was estimated using Fstat 2.9.3 (HO) and expected (HE) heterozygosity was computed with Arlequin v. 3.5.2.2  and the significance of HWE and LD values were adjusted with Bonferroni correction.Micro-Checker 2.2.3  was emplat 2.9.3 . Observe 3.5.2.2 . The Fis 3.5.2.2 . The samFST values . Furthermore, both the Bayesian clustering method implemented in STRUCTURE 2.3.4 (5 generations after a burn-in of 10 × 104 generations. The number of clusters that best fit the observed genotype data was determined comparing Delta K (ΔK) (q) to specific cluster were estimated. The DAPC analysis was executed using package adegenet (find.cluster which runs successive rounds of k-means clustering with an increasing number of clusters (K ranged from 1 to 5) and 500 runs at each value of K. Bayesian information criterion (BIC) was used to select the optimal number of clusters. Ideally, the lowest BIC value represents the optimal number of clusters, but, BIC values may keep decreasing after the true K value in the presence of genetic clines and hierarchical structure   and the a K (ΔK) . The indadegenet . The firtructure . To chooTwenty microsatellite loci were amplified for all 323 swordfish samples. The PCR failure per locus ranged between 0 and 5.9%, with an average of 1.1%.FST, including and excluding the ENA correction method gave comparable results; FST = 0.018 with the respective 95% CI [0.011–0.027]. No consistent evidence for linkage disequilibrium was detected between pairs of loci within populations.Significant deviations from Hardy-Weinberg equilibrium were detected in 18 out of 140 single locus exact tests at loci Xgl-94, Xgl-74, Xgl-14, Xg–66 and Xg–166 , consideAll the remaining 18 loci were polymorphic, with the number of alleles per locus ranging from two at locus Xg-402, to 21 at locus C8. Both, Xg-402  and Xg-394  loci were monomorphic in three samples GRE, SPA and CAN. The Mediterranean samples exhibited a significantly lower number of alleles per locus, allelic richness, and expected heterozygosity, compared to the Atlantic samples . InsteadFST showed a significant signal for genetic differentiation between Mediterranean and Atlantic populations . The FST values decreased when considering the Mediterranean samples separately . Pairwise FST across all samples ranged from 0 to 0.097. Low and statistically not significant values were detected between Mediterranean localities, whereas the higher and significant values (p < 0.001) were observed when comparing the Mediterranean and Atlantic samples, FST values ranging from 0.083 (CAN-GRE) to 0.097 (CAN-SAR). The Bayesian clustering analysis suggested the presence of two genetic clusters (K = 2) using ΔK method, while LnP(K) showed an increase to K = 2 before declining and subsequently increasing to K = 6 . Individuals were assigned to the specific cluster with a high score showing an average q = 0.98 for the Mediterranean group and an average q = 0.95 for the Atlantic group. Few individuals showed signs of mixing between groups .The global to K = 6 –2B. For n groups . ConsideThe exploratory multivariate analysis, considering a priori sampling groups, divided swordfish into two groups. The first discriminant component (81% of variance) distinguished between Atlantic and Mediterranean swordfish, clustering all Mediterranean sampling localities together. This result is consistent with inter-oceanic genetic differentiation .a posteriori assignment by DAPC analysis, despite the absence of a clear best value for the number of clusters, suggested the presence of more than two genetic clusters. The BIC graph showed an apparent decrease until K = 4, reaching a slightly lower value at K = 5  among Mediterranean clusters: 1 (FST = 0.13), 2 (FST = 0.09), and 3 (FST = 0.11). Among Mediterranean genetic groups, pairwise FST ranged from 0.05 (cluster 1–cluster 3) to 0.07 (cluster 2–cluster 3).Conversely, at K = 5 . For K = results , 4C, 4F.detected . All Atldetected . Increasnean Sea . All paiXiphias gladius, within the Mediterranean Sea and between NW-Atlantic and Mediterranean populations using a panel of 18 polymorphic microsatellite loci. These results indicate clear inter-oceanic genetic differentiation among NW-Atlantic and Mediterranean stocks with an improved ability to assign individuals to their population of origin compared to previous multi-locus works  and Bayesian genetic clustering using STRUCTURE. However, the estimates of differentiation (FST) calculated in this work between the two stocks (0.091), is higher than those reported by recent works also using a multi-locus approach  was reported between Atlantic and Mediterranean swordfish stocks using four highly polymorphic microsatellites   . On the FST estimation. Population structure within the Mediterranean Sea is not consistent with previous work based on microsatellite data , which shares the same spawning grounds as swordfish , a remarThe present study suggests genetic heterogeneity within the Mediterranean Sea swordfish stock supporting previous study . Despite10.7717/peerj.9518/supp-1Supplemental Information 1The first line indicates respectively: sample identification and Locus name for 20 loci. Loci highlighted in grey were excluded from further population genetic analysis.Click here for additional data file.10.7717/peerj.9518/supp-2Supplemental Information 2A number of alleles, AR allelic richness, H o observed and H e expected heterozygotes, F IS deviations from Hardy-Weinberg equilibrium (bold identified significand deviation after Bonferroni correction).N number of genotyped individuals, N Click here for additional data file.


Martes americana). Recent work has expanded evidence for the original description of two species in North America — M. americana and the Pacific Coast marten, M. caurina — but the geographic boundary between these groups has not been described in detail. From 2010 to 2016 we deployed 734 multi-taxa winter bait stations across a 53,474 km2 study area spanning seven mountain ranges within the anticipated contact zone along the border of Canada and the United States. We collected marten hair samples and developed genotypes for 15 polymorphic microsatellite loci for 235 individuals, and 493 base-pair sequences of the mtDNA gene COI for 175 of those individuals. Both nuclear and mitochondrial genetic structure identified a sharp break across the Clark Fork Valley, United States with M. americana and M. caurina occurring north and south of the break, respectively. We estimated global effective population size (Ne) for each mountain range, clinal genetic neighborhood sizes (NS), calculated observed (Ho) and expected (He) heterozygosity, fixation index (FST), and clinal measures of allelic richness (Ar), Ho, and inbreeding coefficient (FIS). Despite substantial genetic structure, we detected hybridization along the fracture zone with both contemporary (nuclear DNA) and historic (mtDNA) gene flow. Marten populations in our study area are highly structured and the break across the fracture zone being the largest documented in North America . With the exception of the Coeur d’Alene Mountains, marten were well distributed across higher elevation portions of our sampling area. Clinal NS values were variable suggesting substantial heterogeneity in marten density and movement. For both M. americana and M. caurina, elevationaly dependent gene flow and high genetic population structure suggest that connectivity corridors will be important to ensuring long-term population persistence. Our study is an example of how a combination of global and clinal molecular data analyses can provide important information for natural resource management.North American martens are forest dependent, influenced by human activity, and climate vulnerable. They have long been managed and harvested throughout their range as the American marten ( Martes spp.) are a group for which genetic data are changing the understanding of how populations and lineages are structured across large landscapes  and Pacific Coast (Martes caurina) marten  attached to a tree, remote camera , and 12.30 caliber bronze rifle bore gun brushes (US) or barbed wire (Canada) positioned concentrically around the tree that acted as hair snags  and 237 10 × 10 km survey cells in Canada. Grid size in Canada was larger due to remoteness and lack of accessibility of this portion of the study area. We deployed ≥1 winter multi-species bait station per cell with a total of 734 bait stations deployed in 680 cells. Bait stations consisted of a bait (ungulate portion or frozen beaver [cytochrome c oxidase I (COI) for 175 individual marten (GenBank accession numbers MT578095 – MT578269). Macrogen Inc., produced sequence reads using PCR product we provided and primers we designed .DNA extraction from hair samples and microsatellite amplification was performed using standard protocols . We genoMK320161 – MK320242). Sequence alignment was performed using Geneious 7.1.7 (Biomatters Ltd.) and the subsequent alignment and translation was checked by eye; no indels or stop codons were present. We then estimated the mtDNA gene tree under maximum parsimony (MP) and maximum likelihood (ML) optimization criteria using PAUP∗  and decision theory (DT) (To place our mtDNA data in geographic context we used published 493 base pair COI sequences  from 82 ory (DT) . We thenory (DT) , TBR braory (DT) .a priori constraints using the clustering software STRUCTURE v2.3  and expected (He) heterozygosity in each mountain range, estimate per locus FST values . Second, to contextualize these observations, we used 9 of 11 markers used in a Canada-wide dataset (Ne) using the linkage disequilibrium  method  method  as imple) method  and LDNe) method  at threeThe phylogeny estimation and STRUCTURE analyses identified a population break in our study and we used this break to inform our gene flow analyses. We used GeneClass2  to test M. americana) and southern (M. caurina) populations and evaluated 3 putative models of gene flow and subsequently compared those models to investigate possible restrictions in the directionality of gene flow. We used the coalescent-based program migrate-n (Neμ) and the mutation-scaled migration rate , which can be used to estimate gene flow by θ ∗M = 4Nem where Nem is the number of migrants per generation. In model M. americana ⇄ M. caurina, gene flow is permitted in both directions between northern and southern populations. In model M. americana ⟶ M. caurina, gene flow is possible only in the southward direction while in model M. americana ⟵ M. caurina, gene flow is only in the northward direction. For each analysis in migrate-n, we used Bayesian inference for both microsatellite and mtDNA data. Starting values of θ and M were estimated with an FST calculation and the following uniform priors were specified: for microsatellites, θ, minimum = 0.0, maximum = 50.0, delta = 1.0 and M, minimum = 0.0, maximum = 500.0, delta = 50.0; for mtDNA, θ, minimum = 0.0, maximum = 0.1, delta = 0.001 and M, minimum = 0.0, maximum = 1000.0, delta = 100.0. Analyses were conducted using four replicates, four static-heated chains  ran for 1,000,000 generations, sampling every 100, after 10,000 were discarded as burn-in. We inspected posterior distribution plots of estimated M and θ values (bin number = 1500) to assess convergence. We converted estimates of M and θ to the fraction of immigrants in population i that came from population j in the last generation via Nm = θi∗Mj →i . In order to compare the different gene flow models, we obtained marginal likelihoods for each model , observed heterozygosity (Ho), inbreeding coefficient (FIS), and genetic neighborhood size (NS) in a 15 km diameter surrounding each sampled individual.To capture potentially clinal genetic diversity patterns we used Spatial Genetic Diversity sGD, , 2014 toMarten were the most commonly detected mammal at bait stations and were well distributed across the study area with the exception of the Coeur d’Alene Mountains CDA, . We deteM. americana with 88% bootstrap support. South1 and South2 grouped with sequences M. caurina with 86% bootstrap support.Within the 493 COI nucleotides there are 10 concordant polymorphic sites that differentiate two haplotypes common to individuals from north of the Clark Fork Valley (North1 and North2) from two haplotypes common to marten south of Highway 200 (South1 and South2) , 2. AmonP < 0.0001) deficit of heterozygous genotypes. Of the 39 heterozygotes at Mvis020 we had identified 37 as female. This suggests that Mvis020 is on the X-chromosome , so we removed it from our population genetics analyses. None of the other 15 markers showed significant heterozygote deficits with a Bonferroni correction.We produced genotypes for 16 microsatellite loci for 241 samples that assigned to 235 marten individuals. We removed the six repeat individual samples leaving us with 235 marten individuals . Five individuals had incomplete genotypes, with a maximum of 2 loci missing per individual. Forty eight individuals were sampled south of the Clark Fork Valley and 187 to the north. Marker Mvis020 showed a large and highly significant (Genotypic differentiation (Genepop Option 3) was significant between all 21 pairs of mountain ranges. These results indicate that our population boundaries  generally corresponded as genetic barriers to movement . After cHo and He were similar across the study system with 15-locus averages ranging lowest in the Valhallas  to highest in the Central Selkirks . The Valhallas has the greatest heterozygote deficit (0.03) of any mountain range. The mean o-HeH deficit across all mountain ranges was 0.00, and averages of Ho and He across mountain ranges were and were similar for M. americana  and M. caurina  ( = 0.56) .K converged strongly at each K, with likelihoods peaking at K = 4. The largest lnP(D) increase  occurred at K = 2 identifying a major split at the Clark Fork Valley. Smaller, less discrete, genetic breaks were identified at K = 3 and K = 4, most notably identifying a sharp split between the South Selkirks and Valhallas. The new split introduced between K = 4 and K = 5 divided 1 of the 4 earlier ancestries in such a way that no individual’s ancestry could be traced predominantly to either new group, thus failing to identify evidence of five genetic populations within the study region , but slightly higher than the 9-locus analysis  and southern (0.03) mountain ranges.The 15-locus analysis produced Ne (NeEstimator and LDNe) and the three allele frequencies used produced similar results  and West Cabinets (Ne = 39.8–45.8) had the lowest and most consistent Ne values across allele frequencies. The remaining mountain ranges had higher and less consistent Ne values across allele frequencies.Both estimation methods for contemporary  results . Althougp < 0.0001)  .M. americana ⇄ M. caurina, M. americana ⟶ M. caurina, and M. americana ⟵ M. caurina) showed evidence of gene flow occurring under each model but gave most support (probability > 0.99) to M. americana ⟶ M. caurina for both mtDNA and microsatellite data. These results indicate gene flow occurring primarily, but not exclusively, from north to south. Estimates of the fraction of immigrants were relatively low for all models. The M. americana ⟶ M. caurina, model estimated the fraction of immigrants at 1.09  and 0.89 , M. americana ⇄ M. caurina at 1.18  and 1.99 (0.0–6.89 microsatellites), and M. americana ⟵ M. caurina at 1.05  and 1.82 .The evaluation of three models tested (Allelic richness was highest in the Joe (mean = 5.42) followed by South (mean = 4.66) and Central (mean = 4.62) Selkirks . The Joea priori population definitions. FIS values ranged from −0.02 to 0.04 with mountain ranges averaging lowest in the CDA (mean FIS = −0.02) and Joe (mean FIS = −0.01) and highest in the West Cabinets (mean FIS = 0.03) and Valhallas (mean FIS = 0.04). Genetic neighborhood sizes averaged lowest for the CDAs (mean NS = 31.20) and West Cabinets (mean NS = 48.81), was highest in the Joe (mean NS = 106.62), and there was a cluster of high NS values in the US portion of the South Selkirks  to those calculated in GenePop based on Selkirks .FST = 0.21–0.30) than was observed by FST = 0.21, M. caurina are among the 150 known species or species complexes that have disjunct mesic forest inland and coastal populations in Pacific Northwest  to M. caurina (south). This glacial history has lead us to today where, for practical management purposes, the Clark Fork Valley serves as a geographic boundary between the two lineages with the M. caurina lineage occurring to the south and M. americana lineage occurring to the north of the valley.The only individual we identified as likely to have a parent from either side of the break , or number of breeding individuals, are relatively modest considering the size of the mountain ranges. Estimates of clinal genetic neighborhood sizes (NS), also number of breeding individuals, range from 13.9 to 149.1 within a 15 km radius of individual marten. Estimation of clinal NS eliminates the assumption of panmixia from breeding individual estimates and has been shown to estimate larger numbers of breeding individuals than Ne  techniques to estimate number of breeding animals for each mountain range. Although our Ne confidence intervals were generally quite large, comparing them to the NS estimates show similar general patterns of abundance across the study area with moderate numbers (hundreds) estimated for most mountain ranges and depressed numbers in the CDAs  and West Cabinets .We used seven (M. americana) to south (M. caurina). However, all models predict less than one migrant per generation across the break. We suspect our sampling window was too short and sample sizes too small to detect the few first generation hybrids that may be on the landscape and that the longer term process is predominantly north to south, as the Migrate models suggest. This asymmetry may reflect a higher likelihood for M. americana individuals to disperse due to proximate local population conditions or stochastic events.The individual of recent mixed ancestry is direct evidence that contemporary gene is occurring. At first glance, this appears contrary to the Migrate results showing dominate gene flow from north  values are higher than South Selkirk values (FST = 0.03) showing an expected lowering in value with reduced distance. However, the adjacent South Selkirks (FST = 0.07) show a relatively high level of genetic disjunct with the Valhallas. Ne estimates varied widely for this range (82.6–872.7) but the relatively low mean allelic diversity (3.81) along the highest mean FIS value (0.04) in the study area suggest the lower Ne estimates may be more reflective of reality.The Valhallas stand out as the most genetically distinct FST = 0.06) from the South Selkirks given the MacArthur Wildlife Corridor purportedly connects the two ranges  is slightly lower despite no “official” wildlife corridor connecting them to the West Cabinets. Although modeling work has suggested this corridor is important for grizzly bear and wolverine (Gulo gulo) movement (Pekania pennanti) may not use the corridor  and West Cabinets . FIS values show disparity, however, with the lowest study area values in the CDAs (mean FIS = −0.02) and the second highest values in the West Cabinets (mean FIS = 0.03). Although suggestive of a depressed West Cabinet and genetically robust CDA population, the interpretation of positive FIS values can be less than straight forward. First, the signal created by null alleles, unrecognized population structure within mountain ranges, or a Wahlund effect , immigrants have genotypes that are consistent with the population into which they move, and thus their addition to the new population does not create a departure from Hardy-Weinberg expectation. By contrast, when immigrants move between populations with a history of strong isolation , they have genotypes that are inconsistent with origins in their destination population, and thus create a detectable Wahlund effect. The difference between these scenarios is not the current rate of movement, but the historical rate. Given the possibility of such an artifact, we cannot rule out that the identification of relatively high  FIS in the West Cabinets reflects greater historical separation, rather than contemporary differences in population processes relative to other mountain ranges that we sampled.The lowest population estimates in the study area occur along the fracture zone in the CDAs , the highest allelic richness (mean = 5.42), and some of the highest population estimates in the study area . These metrics suggest the Joe hosts a relatively large and genetically robust marten population. Our M. cqurina sample size (n = 48) is lower than M americana (n = 187) primarily due to the low population size in the CDAs and that the geographic area surveyed is smaller in the southern portion of our study area. Future work to determine how the Joe population is structured with other M. caurina populations to the east and south would be a valuable extension of this study.The Saint Joe Mountain Range had low M. caurina .The datasets presented in this study are publicly available. The raw microsatellite data are available in the Ethical review and approval was not required for the animal study because all samples were collected with non-invasive methods. Author institutions do not require approval. However, we do follow the ethics policy of the American Society of Mammalogists.ML conceived the overall study design, coordinated the data analysis, and wrote the manuscript. ML, LR, and SE conceived of the field survey design and oversaw the sample collection and database management in the United States. GM, DH, and AK conceived of field survey design and oversaw sample collection and database management in the Canada. DP oversaw and SG performed the laboratory analysis. SC, SG, JS, AR, and DP analyzed the data. LS contributed to the study design conception, preparation of spatial datasets, interpretation of the data, and the figure preparation. All authors read and approved the final manuscript.DH was employed by the Seepanee Ecological Consulting Company. AK was employeed by the Grylloblatta Ecological Consulting Company. DP and SG were employed by the company Wildlife Genetics International. LR was employed but they Idaho Department of Fish and Game during the course of this research. However, LR now is employeed by the Rainforest Ecological Consulting Company. The remaining 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.


Tanzania has been growing avocado for decades. A wide variability of the avocado germplasm has been found, and the crop is largely contributing to the earnings of the farmers, traders, and the government, but its genetic diversity is scantly investigated. With the purpose of comparing morphological and genetic characteristics of this germplasm and uncovering the correlation between them and the geographical location, 226 adult seedling avocado trees were sampled in southwestern Tanzania. Their morphological characters were recorded, and their genetic diversity was evaluated based on 10 microsatellite loci. Discriminant analysis of principal components showed that the germplasm studied consisted of four genetic clusters that had an overall average gene diversity of 0.59 and 15.9% molecular variation among them. Most of the phenotypes were common in at least two clusters. The genetic clusters were also portrayed by multivariate analysis and hierarchical clustering for the molecular data but not for the morphology data. Using the Mantel test, a weak significant correlation was found between the genetic, morphological, and geographical distances, which indicates that the genetic variation present in the material is weakly reflected by the observed phenotypic variation and that both measures of variation varied slightly with the geographical sampling locations. Persea americana Mill.) is an important fruit plant cultivated in tropical and subtropical climates. The fruit consumption is increasing worldwide, although much of the global production is in South and Mesoamerica [P. americana is a polymorphic species with three botanical groups or horticultural races  that are ecologically distinguishable [Avocado  to run the PCR reactions under the program that involved initial denaturation at 94 °C for 60 s, followed by 35 cycles of denaturation at 94 °C for 60 s, primer annealing at primer-specific temperature for 30 s, and primer extension at 72 °C for 60 s. Then, the 35 cycles were followed with a final extension at 72 °C for 60 s. The capillary electrophoresis of the amplified products was carried out on the Applied Biosystems 3500 Genetic Analyzer  using the GeneScan 500 LIZ size standard. The output, in the form of electropherograms, generated was imported to GeneMarker® software V2.7  for visualization and allele-calling. The allele dataset at the 10 microsatellite loci was then organized in an Excel spreadsheet for further analyses.DNA was extracted from the dry avocado leaf tissue of the 226 trees using a Thermo Scientific genomic DNA purification kit following the protocol included in the kit. The analysis of the DNA integrity was done by running 1.2% agarose gel electrophoresis, whereas the DNA quality and quantity were checked with the NanoDrop spectrophotometer. Ten microsatellite loci of the sampled trees were investigated, of which nine were genomic, and one was an EST (expressed sequence tag) based microsatellite . The tenWe employed a discriminant analysis of principal components (DAPC) to infer the genetic clusters (subpopulations) and explore the population structure of the sampled trees using the allele dataset. The allele dataset in the GenAlEx format was first converted into a genind object using the R program df2genind , and theA) and private allelic richness (RPA) were computed based on the rarefaction in HP-RARE. The estimation of the number of different alleles per locus, number of effective alleles, number of private, rare and common alleles per locus, Shannon’s information index, and average expected heterozygosity was done using GenAlEx 6.5 [ST and Nei’s genetic distance in Arlequin and GenAlEX, respectively.The total number of alleles scored, and the total number of different alleles observed were computed in HP-RARE . AllelicAlEx 6.5 . AverageAlEx 6.5 . The aveUsing GenAlEx, the Nei’s genetic distance was computed from the microsatellite data and then used for the principal coordinate analysis (PCoA) in the same software to study the relatedness of the trees with respect to their genetic clusters. The matrix used for PCoA consisted of 227 rows × 227 columns. The neighbor-joining dendrogram was computed in MEGAX  using NeMorphological characters of all the trees were organized in the Excel spreadsheet. Character variants that only occurred among some individuals of a particular cluster were identified. Principal components analysis of mixed data (PCAmix)  was perfThe geographic distance matrix was computed from the latitude and longitude of the collecting sites in GenAlEx. Correlation between genetic, morphological, and geographical distance matrices was computed with the Mantel test at 999 permutations in the same software.DAPC was employed to study the population structure of the sampled trees in detail. The ‘find.cluster’ function detected four clusters associated with the lowest BIC value a. These The DAPC plot  showed fThe total number of alleles scored among the four clusters ranged from 727 (Cluster 3) to 1414 (Cluster 1), while the total number of different alleles observed ranged from 66 (Cluster 3) to 118  revealed that the mean number of different alleles per locus was lowest in cluster 3 (6.60) and highest in cluster 4 (11.80). The lowest and the highest private allele richness was recorded in cluster 3 (1.00) and cluster 1 (2.09), respectively. The effective number of alleles was lowest in cluster 3 (3.62) and highest in cluster 4 (5.68). Gene diversity, the unbiased expected and observed heterozygosity were lowest in cluster 1, i.e., 0.55, 0.70, and 0.60, respectively, pointing to a lower diversity among individuals of this group compared to other groups. The gene diversity was highest in cluster 3, i.e., 0.63, while allelic richness, unbiased expected heterozygosity, and the Shannon information index were highest for cluster 4, i.e., 9.48, 0.79, and 1.93, respectively, pointing to a higher diversity in these two avocado groups.The number of alleles unique to a specific cluster, i.e., private alleles, per locus was lowest and highest in cluster 2 (0.5) and cluster 1 2.3; . The leaPCoA was used to study the genetic relationship among the investigated avocado trees. The grouping pattern of the trees in the PCoA  was moreThe Nei’s genetic distance matrix of the 226 avocado samples was used to study the genetic relationship among the four clusters identified by the DAPC. The dendrogram derived through the neighbor-joining cluster analysis method resolved clusters 1 and 3 into distinct groups except in a few cases  than among individuals within clusters (9.91%), with the within individuals variance being the highest, 74.18% .ST (ST = 0.174), whereas clusters 2 and 4 displayed the lowest differentiation (FST = 0.062). Likewise, the analysis of the Nei’s genetic distances between clusters revealed the largest genetic distance between clusters 1 and 2 (1.163) and the lowest distance between clusters 2 and 4 (0.310). Cluster 1 had the largest mean FST (0.077) and genetic distance (0.735) from the other three clusters. The lowest mean FST (0.055) and genetic distance (0.0486) from the other three clusters were recorded in cluster 4 and 3, respectively.The genetic differentiation among the four clusters identified by DAPC was investigated further by computing population pairwise FST , and theAnalysis of morphological characteristics among individuals of the four clusters revealed that the majority of the phenotypes appeared in at least two clusters .Morphology based-principal components analysis of mixed data (PCAmix) of the investigated trees showed intermingling of the individuals from the four genetic clusters with the first two axes showing a cumulative variation of 10.13% of the total variation . Similarr = 0.15, p = 0.001; r = 0.08, p = 0.001; r = 0.11, p = 0.001; The Mantel test indicated a low positive but statistically supported correlation between the genetic and geographical distances (The present study has demonstrated the effectiveness of the genetic markers (microsatellite markers) over traditional morphological markers in characterizing avocado, exploring the diversity and the relationships among the individuals. Likewise, the study has shown the utility of DAPC in establishing the population structure of avocado crops and providing in-depth information on the individuals of the identified genetic clusters, which is an important step for practical plant breeding and conservation.Ho: 0.53 and He: 0.64, for the three horticultural races included in their study. Higher values were estimated by Gross-German and Viruel [Ho: 0.66 and He: 0.71 (four populations), and Schnell et al. [Ho: 0.71 and He: 0.77 (six populations), indicating a comparatively higher diversity. While the overall average gene diversity in the present work was 0.59, Boza et al. [High diversity was noticed among the individuals of the four genetic clusters at the ten microsatellite loci. The mean number of different alleles per locus among the four clusters ranged from 6.60 (cluster 3) to 11.80 cluster 4), with an average of 9.40 across the four clusters and loci . Gross-G, with and Viruel , Ho: 0.6l et al. , Ho: 0.7ST. Boza et al. [The number of private alleles per locus ranged from 0.50 (cluster 2) to 2.30 (cluster 1) with a grand mean of 1.23 across all populations and loci . Privatea et al.  reporteda et al.  got a raa et al. . In our a et al.  got a raThe PCoA  and dendST, was 0.159 (p < 0.0001). This implies a substantial amount of diversity harbored by the trees investigated and that the four genetic clusters were significantly distinct. The level of population differentiation (FST) observed in this study was higher than the values reported by Juma et al. [ST = 0.061, p < 0.0001) and altitudinal groups . Gross-German and Viruel [The AMOVA indicated that the overall genetic differentiation among the four avocado genetic clusters, Fa et al.  for the d Viruel  and Bozad Viruel  found and Viruel  noted anST) and divergence (Nei’s genetic distance) revealed significant differentiation among all the clusters, with the lowest differentiation/genetic distance between clusters 2 and 4  and between the geographical distance and the morphological dissimilarity matrix . Prohens et al. [r = 0.11, p < 0.10) in their study of 28 Spanish eggplant accessions (Solanum melongena L). Contrary to our study, they observed a comparatively higher correlation between the geographical and morphological distances . Sreekumar et al. [r = 0.73, p = 0.009), whereas no correlation could be found between geographical distance and morphological trait-based distance  in their study of 60 breadfruit samples  in India. The weak correlation between geographical and genetic or morphological distances observed in the present study could be due to persistent movements and sharing of seeds between farmers of different areas [r = 0.11, p = 0.001). This suggests that there was no strong association between the studied morphological traits and the 10 SSR loci investigated. It also suggests that the morphological trait variation cannot fully display the pattern of genetic diversity in avocado. Working with 62 Ethiopian maize accessions, Beyene et al. [r = 0.39, p = 0.001), and also between SSR-based genetic and morphological distances . In a similar study on Vietnamese and Cambodian sesame accessions, Pham et al. [r = 0.88, p = 0.001) between agro-morphological and RAPD marker based distances between the accessions. Contrary to that, Roldan-Ruiz et al. [r = −0.06, p < 0.375) and a weak correlation between the sequence tag sites (STS)-based genetic and morphological distances  in 16 ryegrass varieties. Similarly, Sreekumar et al. [r = 0.01, p = 0.5) of breadfruit in India. Smith and Smith [A weak positive correlation was revealed between the geographical distance of the sampling locations and the genetic distance , were overlapping. The weak positive correlation observed between geographical and genetic or morphological distances indicates that the genetic and morphological characteristics of the studied trees varied slightly with the geographical locations. Similarly, the weak positive correlation observed between the genetic and morphological distances indicates a low level of agreement between the diversity patterns derived from the two distances.


Peroxiredoxins (Prxs) have the ability to transfer H2O2-derived oxidizing equivalents to redox-regulated target proteins, thus facilitating the transmission of H2O2 signals. It has remained unclear how Prxs and their target proteins are brought together to allow for target-specific protein thiol oxidation. Addressing the specific case of Prx2-dependent STAT3 oxidation, we here show that the association of the two proteins occurs prior to Prx oxidation and depends on a scaffolding protein, the membrane chaperone annexin A2. Deletion or depletion of annexin A2 interrupts the transfer of oxidizing equivalents from Prx2 to STAT3, which is observed to take place on membranes. These findings support the notion that the Prx2-STAT3 redox relay is part of a highly organized membrane signaling domain.Hydrogen peroxide  was previously shown to transfer H This led to the question of how H2O2 is able to oxidize individual protein thiols in a selective and efficient manner. We may not yet have the full picture, but we can now at least give partial answers to that question: Firstly, some redox-regulated proteins harbor thiols that can be directly oxidized by H2O2. These are proteins that have a primary function unrelated to H2O2 scavenging, but additionally evolved their own catalytic site to accelerate the reaction between H2O2 and one of their thiols. The two best-known examples are the bacterial transcription factor OxyR1 and the glycolytic enzyme GAPDH2. Secondly, it is conceivable that proteins lacking such built-in catalytic mechanisms are nevertheless directly oxidized by H2O2 when they are in immediate proximity to an H2O2 source, although it may turn out that H2O2 first needs to be converted into a different  kind of peroxide4. Thirdly, beyond direct oxidation, it has been recognized that some proteins can be oxidized indirectly, in a highly efficient and selective manner, through mediation by thiol peroxidases. The first known example was the Orp1-Yap1 redox relay discovered in yeast5. More recently, examples of peroxiredoxin-based redox relays were found in mammalian cells8, and there is emerging evidence for a role of cytosolic peroxiredoxins in transmitting oxidation to a multitude of target proteins9.Over the past few years, it has become clear that intracellular H2O2, barely larger than a water molecule, be specific in targeting particular thiols, when in fact the vast majority of protein thiols shows the same low H2O2 reactivity (k ≈ 1–10 M−1 s−1)10? It is hard to see how the tiny interaction surface between a H2O2 molecule and a protein surface can confer specificity towards specific sites for reaction. For a Prx-based redox relay the specificity question is much less of a problem: the Prx naturally acts as an ultra-efficient scavenger of H2O2 (k ≈ 105–108 M−1 s−1), and the specificity of target oxidation is then based on protein–protein interactions, namely those that bring together  the Prx and the target protein. Yet, although it is clear that protein–protein interaction can provide the necessary specificity, we still do not know how Prxs and their target proteins recognize each other and position themselves for productive oxidative transfer.In principle, the existence of thiol peroxidase-based redox relays can answer a fundamental question: How can H2O2? Secondly, which structural parts of the Prx and the target protein are required for the association to occur, and how specific is the interaction? Thirdly, do the two partners interact directly or is there a scaffold protein needed to template the interaction?In this work, we asked three questions: Firstly, do Prxs and their target proteins co-assemble already in the reduced state, that is, are they pre-positioned even before the Prx reacts with H2O2-derived oxidative equivalents to STAT3, generating disulfide-linked dimers and tetramers of STAT3 which in turn attenuate transcription from the serum inducible element promoter6. The redox relay complex appears to be associated with the plasma membrane, as stimulation of the cell surface IL-6 receptor, presumably associated with NOX activation, triggers the oxidative transfer from Prx2 to STAT36.To address these questions, we focused on one specific example, namely the redox relay formed between peroxiredoxin 2 (Prx2) and the transcription factor STAT3 in human cells. Prx2 was previously shown to transfer H2O2. (ii) Both proteins are highly selective towards each other: STAT3 strongly prefers Prx2 over the very similar isoform Prx1, and a single point mutation in STAT3 abolishes the interaction with Prx2. (iii) The interaction between Prx2 and STAT3 depends on a third protein, the membrane chaperone annexin A2 (AnxA2). (iv) The transfer of oxidative equivalents from Prx2 to STAT3 depends on the presence of AnxA2. (v) Prx2- and AnxA2-dependent STAT3 oxidation occurs in association with membranes. Combining these insights, we conclude that the Prx2-STAT3 redox relay is part of a highly organized membrane signaling microdomain.In this study, we show the following: (i) A non-covalent Prx2-STAT3 complex assembles independently of Prx2 oxidation, showing that the complex is already pre-formed in the reduced state and ready to respond to H2O2-induced disulfide exchange. STAT3 exhibits selectivity in its interaction with Prx2 over the closely related Prx1. Moreover, the interaction appears to be highly localized and restricted to small subpopulations of the overall Prx2 and STAT3 pools.We started by asking if STAT3 and Prx2 interact independently of and prior to pro-oxidative events that trigger disulfide exchange between the two proteins. To address this question, we first used a bimolecular fluorescence complementation (BiFC) approach. The N-terminal half of mLumin (LN) was fused to the C-terminus of STAT3, and the C-terminal half of mLumin (LC) to the C-terminus of either Prx2 or Prx1. HEK293 MSR cells stably expressing STAT3-LN were transiently transfected with either Prx2-LC or Prx1-LC  competed the interaction between STAT3 and Prx2, as measured by BiFC  known to interrupt the tetramerization interface13. The L78R mutation diminished the interaction between NTD-LN and Prx2-LC and the W37A mutation almost abolished it  has previously been shown to mediate both STAT3 oligomerization and interactions between STAT3 and other proteinsiFC Fig. . FurtheriFC Fig. . Likewis it Fig. . The sam it Fig. . Therefo14. In addition, our lab previously identified AnxA2 as an interaction partner of Prx29. Thus, we considered the possibility that AnxA2 acts as a scaffold for the Prx2-STAT3 interaction. First, we aimed to confirm that the three proteins interact with each other inside the cell. Prx2-SBP and SBP-STAT3 independently co-precipitated endogenous AnxA2 , dependent on distinct structural elements (NTD), to form part of the same complex . We then wondered if the interaction is direct or dependent on the presence of another protein to scaffold the interaction. Our attempts to demonstrate a direct interaction of the recombinant proteins in vitro were not successful. We therefore asked if there are proteins known to interact with both Prx2 and STAT3. Inspection of the BioGRID protein interaction repository revealed a single common interaction partner, annexin A2 (AnxA2): Interactions between Prx2 and AnxA2 and between STAT3 and AnxA2, respectively, have been reported independently of each otherxA2 Fig.  and endoxA2 Fig. . PLA furxA2 Fig. : As a poxA2 Fig. , nor by xA2 Fig. .Fig. 3AnWe then asked if AnxA2 is actually required for the formation of the Prx2-STAT3 complex. To this end, we depleted AnxA2 by siRNA and then performed co-precipitation experiments. Upon depletion of AnxA2, co-precipitation of endogenous Prx2 by SBP-STAT3 Fig. , the PLA6, STAT3 forms oxidation products  in response to H2O2. However, upon depletion of AnxA2, STAT3 oxidation was considerably diminished in two independent cell lines  in AnxA2 KO cells and found them to rescue the oxidation of STAT3, albeit somewhat less efficiently than wild type AnxA2  activity coupled to cytokine receptor stimulation. To address this question, we fractionated cells. We found oxidized STAT3 to reside almost exclusively in the membrane fraction  increases SIE transcription. These findings predicted that depletion of AnxA2  should show the same effect. Indeed, deletion of AnxA2 enhanced IL-6- or OSM-induced transcription from the SIE promoter -induced STAT3 oxidation dampens transcription from the serum inducible element (SIE)ter Fig. . In lineIn this study, we investigated how a peroxiredoxin and a redox-regulated target protein are brought into proximity to form a functional redox relay. To this end, we focused on the specific case of the Prx2-STAT3 redox relay.2O2 while being part of a pre-assembled complex, and therefore is poised to transfer its oxidative state to the proximal client. It makes sense that target thiols should be pre-positioned to “capture” the sulfenic acid (SOH) as soon as it becomes available on the Prx2 surface, considering that the SOH is prone to react with the resolving cysteine (or glutathione18) and therefore has a limited half-life.The first question we asked was whether the two relay partners come together prior to any oxidative event, while they are still in the reduced state. We show that Prx2 and STAT3 interact with each other non-covalently, in the absence of pro-oxidative stimuli, and before disulfide exchange intermediates or oxidation products can be detected. The interaction is restricted to very small subpopulations of both proteins, suggesting a specific microenvironment. Thus, it seems that Prx2 reacts with H2O2, presumably to allow for step-wise sensing of H2O219. Prx2 is most sensitively oxidized and will accumulate the SOH already at low nanomolar H2O2 concentrations. This suggests that Prx2 is the preferred sensor for the smallest changes in local H2O2 availability, as would be needed for (non-stress) signal transduction. Prx1 is a less sensitive sensor, accumulating the SOH only at higher H2O2 concentrations19, potentially reflecting stress conditions. Thus, it may not be a coincidence that Prx2 has been found in a relay with a transcription factor6 and Prx1 in a relay with a stress signaling kinase7.We then asked if there is evidence for the selectivity of the pairing. On the one hand, we found that STAT3 strongly prefers Prx2 over Prx1, although these two Prxs are very similar in structure and function. However, these two isoforms accumulate the SOH at different threshold concentrations of H11. Thus, Prx2 discriminates between structural variants of STAT3,  while STAT3 discriminates between the closely related Prx1 and Prx2 isoforms. Hence, the interaction appears to be selective in both directions. The pre-assembly prior to oxidative transfer, together with the mutual selectivity, strongly speak against the “null hypothesis”, i.e. that Prxs, once oxidized, indiscriminately transfer oxidizing equivalents to other proteins by random diffusion and chance encounters.On the other hand, a single point mutation in the NTD of full-length STAT3 completely abolished both non-covalent and covalent interactions with Prx2, and, consequently, the formation of STAT3 oxidation products. The mutation is known to alter the STAT3 oligomerization interface which is also the interface for interactions with other proteins8. These negative results can be taken to suggest that one (or more) additional proteins are needed to scaffold the interaction. This possibility is obvious because the first redox relay that was discovered, Orp1-Yap1, does not work without a dedicated scaffold protein, Ybp121. There are also physicochemical arguments, supported by mathematical models, suggesting that a Prx-based redox relay can only work when it is templated23. In this study, we show that the Prx2-STAT3 redox relay indeed requires (at least) one other protein, namely AnxA2. Like other annexins, AnxA2 has been described as a membrane scaffold protein, forming networks on membrane surfaces and providing a recruitment platform for other proteins, to organize membrane domains24. Our findings reveal that AnxA2 enables proximity between Prx2 and STAT3, the transfer of oxidizing equivalents from Prx2 to STAT3, and the formation of STAT3 oxidation products. Accordingly, the deletion or depletion of AnxA2 strongly diminished (by 85–95%) the non-covalent and covalent interactions between Prx2 and STAT3, and the formation of covalent STAT3 oligomers (as monitored by co-IP and gel mobility). In contrast, the effect of the AnxA2 deletion on the formation of STAT3-Prx2 PLA foci was less pronounced (≈60% reduction). However, given that the classical PLA method (using oligo-coupled secondary antibodies) can bridge proteins over a distance of up to 80 nm25, we suspect that Prx2 and STAT3 remain in relative proximity within membrane patches, even though their direct interaction is disrupted in the absence of AnxA2.The next question we asked is whether Prx2 is guided towards STAT3 by virtue of a direct protein–protein-interaction. We failed to reconstitute a Prx2-STAT3 relay from pure proteins in vitro, and the recently attempted in vitro reconstitution of another redox relay (Prx2-CRMP2) also failedInterestingly, the NTD point mutations that diminished or abolished the interaction of STAT3 with Prx2 did not affect its interaction with AnxA2. Thus, while the STAT3-Prx2 association requires both AnxA2 and the NTD, the STAT3-AnxA2 association does not require the NTD, nor the presence of Prx2. This finding can be interpreted in several ways. For example, it is conceivable that the binding of AnxA2 to STAT3 occurs outside the NTD and changes the conformation of STAT3 to allow interaction between the NTD and Prx2. It is also conceivable that AnxA2 binds STAT3 independently of its oligomeric state, while Prx2 can only bind dimeric and/or tetrameric STAT3 (the formation of which is prevented by the NTD mutation). Since all three interaction partners are oligomeric proteins  it seems possible that their relative oligomeric stoichiometry plays a role in the assembly of the complex.A more detailed understanding of the structural organization will require the in vitro reconstitution of a functional AnxA2-Prx2-STAT3 redox relay complex from recombinant proteins. Unfortunately, given the fact that AnxA2 assembles on membrane surfaces and is subject to various posttranslational modifications, reconstitution is likely to require conditions that are not easily mimicked in vitro. Moreover, reconstitution may also require additional protein components. We consider the in vitro reconstitution of a membrane-associated redox relay complex a long-term effort beyond the scope of the present study.26. S-glutathionylation of AnnexinA2 has been linked to its phospholipid and F-actin binding activity15. Moreover, we recently identified AnxA2 as a disulfide exchange partner of Prx29, suggesting that Prx2 can transfer oxidation to AnxA2, probably in a way similar to STAT3 oxidation. However, mutagenesis of AnxA2 cysteines affected STAT3 oxidation only weakly and in none of our experiments did we detect disulfide exchange intermediates involving AnxA2. Nevertheless, a role for the AnxA2 cysteines in the context of STAT3 redox regulation cannot be excluded and deserves further investigation.It is interesting to note that AnxA2 has been proposed to be a redox-regulated protein. It possesses four cysteine residues, two of which were reported to be redox-sensitive2O2 exposure, and the proportion is even lower when STAT3 oxidation is induced by cytokine treatment6. Considering protein copy number estimates determined for HeLa cells27, Prx2 and AnxA2 are highly expressed proteins (with similar copy numbers), both being more than 3 orders of magnitude more abundant than STAT3. From this, it would seem that STAT3 should be the limiting factor for complex assembly, yet only a small fraction of total STAT3 is in the complex and oxidizable. Formation of the complex may actually be limited by specific posttranslational modifications in any of the components. Also, we cannot exclude the possibility that complex formation is limited by another (currently unknown) protein.Only small sub-populations of STAT3 and Prx2 assemble into the redox relay complex. A very rough estimate is that at most 1-5% (but probably less) of total STAT3 becomes oxidized under conditions of H6. STAT3 oxidation does not seem to affect STAT3 tyrosine phosphorylation, yet it negatively influences downstream transcriptional activity. It may be speculated that STAT3 oxidation serves as a mechanism that limits the strength and/or duration of STAT3 signaling in response to changing metabolic conditions. We have reported earlier that the momentary STAT3 redox state is determined by the balance between Prx2-dependent oxidation and thioredoxin (Trx)-dependent reduction6. Thus, the balance between STAT3 oxidation and reduction within membrane domains likely makes STAT3 signaling responsive to oxygen availability (which determines H2O2 production) and the intracellular NADPH/NADP+ ratio (which determines Trx-mediated reduction). For example, EGF receptor signaling has been found to respond to oxygen gradients through the conversion of O2 to H2O2 and local protein redox regulation28. Most recently, Busker et al. described inhibitors of the thioredoxin system that inhibit STAT3 signaling in cancer cells with nanomolar potency29. They demonstrate that STAT3 signaling is blocked when Prx2-dependent STAT3 oxidation is not counterbalanced by the Trx/TrxR/NADPH system, leading to the death of tumor cells that depend on STAT3 signaling. We expect that further mechanistic understanding of the STAT3 redox relay complex will allow to dissect and manipulate the physiological roles of STAT3 oxidation in future studies.The functional role of STAT3 oxidation in the context of canonical STAT3 signaling and/or other STAT3 functions remains incompletely understood. We observed STAT3 oxidation to occur in association with membranes, in line with STAT3 oxidation being triggered by cytokine receptor stimulationl-glutamine (Life Technologies) and 50 units/ml penicillin and streptomycin (Life Technologies). The medium for HEK293 MSR cells additionally contained 50 units/ml Geneticin (Life Technologies). All cell lines were confirmed to be free of mycoplasma, viral infections and contaminations with other cell lines, based on multiplex PCR and SNP profiling. Antibodies used in this study were mouse anti-SBP , rabbit anti-tubulin , mouse anti-actin , rabbit anti-HA tag , rabbit anti-MYC tag , rabbit anti-STAT3 , mouse anti-STAT3 , rabbit anti-Prx2 , mouse anti-Prx2 , mouse anti-Annexin A2 , rabbit anti-Annexin A2 , mouse anti-S100A10 , goat anti-lamin B , mouse anti-Na+/K+ ATPse  and mouse anti-STAT3 phospho (Tyr705) . All antibodies were used at a dilution of 1:1,000. The secondary antibodies used for western blotting in this study were anti-goat , anti-mouse  and anti.rabbit . All secondary antibodies were used at a dilution of 1:10,000. All chemicals were purchased from Sigma-Aldrich, unless stated otherwise.HEK293 MSR , HeLa (ATCC), U2OS (ATCC) and Phoenix-AMPHO cells (ATCC) were maintained in Dulbecco’s Modified Eagle Medium , supplemented with 10% (v/v) bovine calf serum (Life technologies), 2 mM 30, were kindly provided by Stephan Pusch (German Cancer Research Center). The ORFs for peroxiredoxin 1 and 2 and STAT3 were provided by the DKFZ Genomics & Proteomics Core Facility. The Cerulean-5-Venus FRET control construct was obtained from Addgene (#31182). All expression plasmids were constructed by either using the Gateway™ recombination system (Life Technologies) or the Gibson Assembly Cloning Kit (New England Biolabs). Primers for Gibson Assembly were designed using the NEBuilder Assembly Tool. Point mutations were introduced with the QuikChange™ site-directed mutagenesis kit (Agilent). Primers used for mutagenesis are listed in Supplementary Table The gateway destination vectors pDest HA-LN151 and pDest MYC-LC151, described previously31. U2OS cells were transfected using Lipofectamine 3000 following the manufacturer’s instructions. For experiments performed in 96-well plates, HEK293 MSR cells were transfected using Lipofectamine 2000 following the manufacturer’s instructions. For generation of stable cell lines, pLPCX retroviral expression vectors were transfected into the packaging cell line Phoenix-AMPHO. After 24 h, viral supernatant was collected, filtered through a 0.45 μm cellulose acetate filter and then used to infect freshly thawed target cells. Transduced cells were selected with puromycin, expanded, and frozen for later use.HEK293 MSR cells were transfected using polyethylenimine (Polysciences) as described previously4 HEK293 MSR cells stably expressing either STAT3-HA-LN or Prx2-MYC-LC were seeded into wells of a 96-well plate. The next day, cells were transfected with a plasmid encoding LN- or LC-tagged proteins of interest  A plasmid encoding GFP was co-transfected for normalization purposes. For the Prx2/Prx1 competition experiment  was performed according to the manufacturer’s instructions. Briefly, HeLa cells were grown on imaging dishes (Ibidi), then fixed with ice-cold methanol for 7 min on ice. Cells were incubated with 3% BSA in DPBS for 1 h and then with primary antibodies overnight at 4 °C. Subsequently, cells were incubated with oligonucleotide-labeled secondary antibodies, followed by ligation, DNA amplification, and probe hybridization. Resulting signals were visualized by confocal microscopy.2 dishes. After 48 h, cells were either pulsed with different concentrations of H2O2 for 2 min or left untreated  or without 25 mM DTT (non-reducing). Samples were separated by SDS-PAGE and proteins transferred to polyvinyl difluoride (PVDF) membranes  using a transfer tank . Membranes were probed with appropriate antibodies and chemiluminescent substrate .Annexin A2 was depleted in HEK293 MSR or U2OS cells using ON-TARGET plus siRNA SMARTpool (Dharmacon) or a single pre-designed siRNA (Ambion) with the following sequence: GGAUAUUGCCUUCGCCUACTT (AM16708). The siRNAs were transfected using DharmaFECT 1 (Dharmacon) reagent as per manufacturer’s instructions. The ON-TARGET plus non-targeting pool (Dharmacon D-001810-10-05) or the Silencer™ negative control No. 1 siRNA (Ambion) were used as controls.5 HEK293 MSR or HeLa cells were suspended in 9 μl Neon electroporation buffer R. The crRNA:tracrRNA duplex , HiFi™ Cas9 nuclease  and Cas9 electroporation enhancer  were added to the cells. Electroporation settings were 1005 V, 35 ms, and 2 pulses for HeLa cells and 1600 V, 20 ms, and 1 pulse for HEK293 MSR cells. Post electroporation, cells were kept in a tissue culture incubator  for 72 h. Subsequently, single-cell clones were grown in a 96-well plate. Clones were expanded and Annexin A2 knockout clones were identified by immunoblotting.Guide RNAs (gRNAs) targeting the AnxA2 gene were designed using the Alt-R™ CRISPR-Cas9 guide RNA tool (Integrated DNA technologies). The gRNAs used in this study were: CTCAGCATCAAAGTTAGTAT (ANXA2.1.AA) and AACTGATTGACCAAGATGCT (ANXA2.1.AC). The Cas9 ribonucleoprotein complex, containing the crRNA:tracrRNA duplex and the Cas9 nuclease protein (Integrated DNA technologies), was electroporated into HEK293 MSR or HeLa cells. Briefly, 1 × 106 HEK293 MSR cells were seeded into 15 cm2 cell culture dishes and grown overnight. The next day, cells were treated with 100 µM H2O2 for 2 min or left untreated. Following treatment, the medium was aspirated, and cells were incubated for 7 min with ice-cold DPBS containing 100 mM NEM. Cells were harvested after washing them once with DPBS. Cytosolic, nuclear and plasma membrane fractions were prepared with the Minute™ Plasma Membrane Protein Isolation and Cell Fractionation Kit (Invent Biotechnologies) in accordance with the manufacturer’s instructions.In all, 7 × 10Renilla luciferase signal.HEK293 MSR cells were seeded into 96-well plates and transfected with Lipofectamine 2000 in accordance with the manufacturer’s instructions. In all, 24 h post-transfection, cells were treated for 8 h with either 50 ng/ml Oncostatin M  or 50 ng/mL IL-6/IL-6 receptor . The luciferase assay  was conducted according to the manufacturer’s protocol. Luminescence was measured with a microplate reader . The firefly luciferase signal was normalized to the Further information on research design is available in the Supplementary InformationReporting Summary


Our results revealed the Guizhou Hui people have a limited amount of West Eurasian related ancestry at a proportion of 6%, but show massive genetic assimilation with indigenous southern Han Chinese and Tibetan or Tungusic/Mongolic related northern East Asians. We also detected a high frequency of North Asia or Central Asia related paternal Y-chromosome but not maternal mtDNA lineages in Guizhou Hui. Our observation supports the cultural diffusion has played a vital role in the formation of Hui people and the migration of Hui people to southwest China was probably a sex-biased male-driven process.The origin and diversification of Muslim Hui people in China via demic or simple cultural diffusion is a long-going debate. We here generated genome-wide data at nearly 700,000 single nucleotide polymorphisms (SNPs) from 45 Hui and 14 Han Chinese individuals collected from Guizhou province in southwest China. We applied principal component analysis (PCA), ADMIXTURE,  The Hui people are an East Asian ethnoreligious group distributing throughout China with a population of approximately 20 million predominantly composed of Chinese speaking practitioners of Islam . AlthougPrevious studies from the genetic perspective revealed that the origin of Hui people in China had involved massive assimilation of indigenous East Asians inferred from paternal Y-chromosomal single nucleotide polymorphism (SNP) and short tandem repeat (STR) analysis . From thGuizhou province in southwest China is a multi-ethnic province with abundant of genetic and cultural diversities. The Han Chinese have the largest proportion of the population in Guizhou, accounting for 62.2% of the total population of the province. The Muslim Hui is about 90,000 people mainly distributing in Weining County, accounting for 0.5% of the total population in Guizhou province. Weining County was the earliest place for the Hui people moving to Guizhou Province . AccordiWe collected 59 saliva samples in Guizhou province with informed consent, including 45 Hui people from Weining County and 14 Han people from Guiyang city. We collected the samples following genetic population criteria. These samples enrolled in the present study were collected randomly from unrelated participants whose parents and grandparents are indigenous people and have the non-consanguineous marriage within the same ethnical group for at least three generations. We listed the detailed sample information in 1.Genomic DNA of 59 samples were extracted using DP-318 Kit . The DNA quality control was carried out at the experimental center of WeGene-Shenzhen. Genotyping was performed on the Illumina WeGene Arrays at the WeGene genotyping center, Shenzhen. We merged the data of Guizhou Hui and Han individuals with published populations from Human Origin Dataset , Simons P < 10–12). We carried out ADMIXTURE  using smartpca, part of the EIGENSOFT package . We usedDMIXTURE  analysisf-statistics and determined standard errors with a block jackknife and default parameters. We computed outgroup f3-statistics of the form f3 , which measured the shared genetic drift between the two populations X and Y since their separation from an African outgroup Mbuti. We computed admixture f3-statistics of the form f3  to explore possible sources for Hui_Guizhou and Han_Guizhou people. We also calculated the f4-statistics of the form f4  to show if population Test is symmetrically related to X and Y or shares an excess of alleles with either of the two. Moreover, we used qpAdm . The primary ancestry component assigned to the Hui_Guizhou population, shown in purple, also maximized in Neolithic DevilsCave hunter-gatherers  were consistent with the patterns observed in the above PCA and ADMIXTURE analysis, suggesting that Hui_Guizhou shared more genetic drift with Tu and Tibeto-Burman speaking populations  to explore possible sources for Hui_Guizhou and Han_Guizhou people in this study. The results in f3-statistics . f4 statistics in the form of f4  and f4 , respectively  but have a long history living in Guizhou province in southwest China. According to historical records, the Hui people came to Weining County in Guizhou province during the period of the early formation of Hui population in China . HoweverFrom the paternal perspective, we found the Y-chromosome lineages that are frequent in North Asia and Central Asia  reached qpAdm-based admixture models also demonstrated that Guizhou Hui shared more alleles with Guizhou Han than with other East Asian populations. The Guizhou Han related ancestry ranged from 70 to 85% in Guizhou Hui samples. These results indicate there have been massive assimilations of indigenous Han populations in the formation of Guizhou Hui people. The genetic affinity between Guizhou Hui and Guizhou Han is consistent with the previous evidence that the genetic profile of Muslim Hui people was closest to the indigenous Han Chinese populations .The data presented in the study are deposited in the zenodo repository, accession number  and were in accordance with the recommendations provided by the revised Helsinki Declaration of 2000. The patients/participants provided their written informed consent to participate in this study.JH and C-CW designed this study. JZ and C-CW wrote the manuscript. QW, ZR, HZ, JJ, YLiu, MY, and JH collected the samples. QW, ZR, HZ, JJ, YLiu, MY, YLi, GC, and JH conducted the experiment. JZ, JS, GH, JG, XY, JC, KZ, RW, and C-CW analyzed the data. All authors reviewed the manuscript.GC was employed by company WeGene. The remaining 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. The handling editor declared a past co-authorship with one of the authors C-CW.


Columba livia) living in cities in the eastern United States. We focused our sampling on the Northeastern megacity, which is a region covering six large cities . We performed ddRAD‐Seqon 473 samples, recovered 35,200 SNPs, and then used multiple evolutionary clustering analyses to investigate population structuring. These analyses revealed that pigeons formed two genetic clusters—a northern cluster containing samples from Boston and Providence and a southern cluster containing all other samples. This substructuring is possibly due to reduced urbanization across coastal Connecticut that separates Boston and Providence from New York and mid‐Atlantic cities. We found that pairs of pigeons within 25 km are highly related  and that beyond 50 km, pigeons are no more related than they would be at random. Our analysis detected higher‐than‐expected gene flow under an isolation by distance model within each city. We conclude that the extreme urbanization characteristic of the Northeastern megacity is likely facilitating gene flow in feral pigeons.Urbanization may restrict, facilitate, or have no effect on gene flow, depending on the organism and extent of urbanization. In human commensals, with high dispersal ability, urbanization can facilitate gene flow by providing continuous suitable habitat across a wide range. Additionally, suburban or rural areas with lower human population density may act as a barrier to gene flow for these human commensals. Spatial population genetic approaches provide a means to understand genetic connectivity across geographically expansive areas that encompass multiple metropolitan areas. Here, we examined the spatial genetic patterns of feral pigeons ( Spatial patterns of genetic variation often directly reflect the landscape composition, providing insight into the specific habitat features that are necessary for gene flow. Habitats that facilitate movement, and thus gene flow, are more likely to sustain genetic diversity and the ability of populations to adapt to evolutionary change Lande, . HoweverWhether an organism is more likely to experience urban fragmentation or urban facilitation is influenced by its life history strategy. Animals that are successful in cities are often dependent on resources provided by humans. These species are generally termed “anthrodependent”  are a common human commensal found in cities around the world. Pigeons were first brought to North America in the 17th century by French and English colonizers settling in Nova Scotia, Quebec, Massachusetts, and Virginia  habitat, whereas species with shorter dispersal ranges are more likely to be confined to city limits (isolation by barrier). Pigeons are capable of traversing the entire distance of the Northeastern megacity in a single day, though the probability of moving this distance within a day, or even within a lifetime, is low : Boston, MA; Providence, RI; New York City, NY; Philadelphia, PA; Baltimore, MD; and Washington, DC Figure . We deci2.2We extracted DNA from blood and tissue samples using the DNeasy Blood and Tissue Kit  according to the manufacturers protocol with the following modifications: (a) When using blood, we used 50 µl of blood, (b) when using tissue, we let tissue samples incubate with Proteinase K overnight, (c) we added 4 µl of RNase A to each extraction after incubation, (d) we heated the AE buffer to 75°C before placing it on the filter, and (e) we let the AE buffer sit on the filter for approximately 1 hr before eluting. We performed two elutions of 100 µl each that we then combined. We used an Infinite 200 Pro NanoQuant  to quantify the amount of DNA present in each sample. We then digested each sample with the restriction enzymes SphI and MluCI and prepared ddRAD libraries following a protocol adapted from Peterson, Weber, Kay, Fisher, and Hoekstra  and sele2.3process_radtags script using the individual barcodes that were ligated during the ddRAD library preparation. We aligned reads to the most recent pigeon reference genome, Cliv_2.1 . We built the initial catalog using all reference aligned samples with the ref_map.pl pipeline in STACKS . Next, we created our SNP dataset using the populations script with the following filters for retaining SNPs: (a) Only loci genotyped in at least 8 out of 9 metropolitan areas (to limit missing data), (b) only loci found in 75% of samples (to limit missing data), (c) only SNPs with minor allele frequency greater than or equal to 5% , and (d) only one SNP per locus (to avoid extreme linkage between SNPs) , expected heterozygosity (EH), observed heterozygosity (OH), nucleotide diversity (π), and inbreeding coefficient (FIS). Additionally, we used the linkage disequilibrium method implemented in NEESTIMATOR v2.1 . To reduce computing time, we randomly selected five subsets of 10,000 SNPs for our NEESTIMATOR analysis.We also used the 2.4.2adegenet  using the R package Jombart, . DAPC isJombart, . This apK = 1 to K = 15, running each K value for five iterations. The lowest values for cross‐validation error indicate the most likely values of K . The EEMS documentation recommends running the model multiple times, varying the number of demes; therefore, we examined deme sizes of 200, 300, 400, 500, 600, 700, 800, 900, and 1,000. For each deme size, we first optimized the proposal variances by tweaking parameters so that proposals were accepted about 20% to 30% of the time and the MCMC chain converged. Once optimized, we then repeated the analysis for each deme two more times, using a random seed each time for a total of 28 runs. We used an MCMC length of 12,000,000 iterations with a burn‐in of 4,000,000. We then visualized the convergence of runs and merged all 28 runs into a single plot using the R package reemsplots2 and produced maps of effective migration rate (m) and effective diversity (q).To visualize deviations from isolation by distance among pigeons across our study area, we used estimated effective migration surfaces (EEMS)  indicating weak population genetic differentiation (Table FIS) ranging from 0.010 to 0.041. We estimated values of observed heterozygosity (oH), expected heterozygosity (eH), and nucleotide diversity (π) for the nine cities that were sampled and found slight regional differences  contained all the Boston samples and 12 of the Providence samples; the second cluster (n = 413) contained all other samples. Visualization of the DAPC as a scatter plot  . We found that after 50 km, pigeons are no more related than they would be at random. When we repeated this analysis with related individuals removed, we found a similar pattern .A Mantel test showed a weak isolation by distance relationship and positive spatial autocorrelation (Mantel ) Figure . A Mante) Figure . From 0–We used EEMS to visualize deviations from isolation by distance. Unlike PCA, EEMS takes into account sampling locations when modeling regions of higher‐than‐average and lower‐than‐average historic gene flow. EEMS uses isolation by distance as a null model and represents deviations from isolation by distance in either blue (higher‐than‐average gene flow) or red (lower‐than‐average gene flow). Our EEMS analysis shows high‐than‐average gene flow and isolation by distance within most of the cities we sampled, but lower‐than‐average gene flow between cities Figure .4In this study, the use of ddRAD sequencing allowed us to examine the movement of pigeons and the evolutionary processes that shape feral pigeons at a much higher resolution than has previously been attempted for any urban region. We found a moderate clinical pattern in pigeons along a north–south axis. Feral pigeons in the Northeastern megacity form two genetic clusters, with pigeons from Boston, MA, showing genetic differentiation from pigeons in more southern cities. In Providence, RI we found evidence of admixture between Boston pigeons and pigeons from further south, indicating that there are unlikely to be any complete barriers to gene flow between Boston and cities further south. Overall, feral pigeons in the Northeastern megacity maintain high genetic connectivity over a large urbanized region, likely due to their ability to move through human‐dominated landscapes.Our results must be interpreted in relation to our sampling scheme. We were unable to sample pigeons in many of the smaller municipalities between major cities, which may have influenced our Mantel correlogram and EEMS results. However, many of these smaller cities and towns had few to no pigeons, thus making it difficult to conclude whether the patterns we found were due to inadequate sampling effort or simply few pigeons existing outside major cities. Even within the larger cities that we sampled, we found pigeons were concentrated in the downtown areas where human population density and activity was at its highest.4.1FST values, indicating little genetic differentiation between cities. Miles et al. (Latrodectushesperus) likely due to human transportation networks that facilitate dispersal within cities. Similarly, Combs, Byers, et al. (Rattus norvegicus) in four cities  exhibited high levels of genetic diversity within each city, likely attributed to connectivity and large effective population sizes within the urban habitat. Additionally, brown rats exhibited some evidence of occasional long‐distance dispersal, despite short (200 m) dispersal distances being more common  are expected to have low genetic diversity in areas where habitat fragmentation caused by urbanization prevents organisms from successfully dispersing and reproducing  is helpful for calculating the rate of evolutionary change in a population caused by genetic drift. eN is used to determine genetic variability within a population and the effectiveness of selection relative to genetic drift , bed bugs (Cimex lectularius), and brown rats, likely reflect human dispersal and settlements in their own patterns of dispersal and population genetic structuring. However, the connectivity between populations of these commensal organisms will depend on their ability to disperse alongside humans and/or though natural habitat. For example, German cockroaches are acutely adapted for indoor habitats, but are not known to exist as self‐sustaining populations in the natural environment anywhere within its considerable range , another common urban species, showed low genetic structure across 17 populations spanning the entire United States, suggesting that the patterns observed in starlings and pigeons may be common across non‐native birds.Due to their ability to easily transverse the anthropogenic environment, pigeons within the city of Paris and the city‐state of Singapore have been shown to comprise a single population within each city  and maximum likelihood estimation of individual ancestries (ADMIXTURE) both detected two genetic clusters—one cluster containing the samples from Boston and Providence, and a second cluster containing samples from more southern cities. Our study found a divide in pigeons that recapitulates the break in urbanization found between New Haven, Connecticut, and Providence, Rhode Island Figure . This arThe previous multicity study, conducted across Spain, France, and Switzerland, found that dispersal between cities was rare and that individuals living in geographically distant cities were unlikely to be related (Jacob et al., Previous research on the impacts of urbanization on avian populations has found urbanization often leads to population fragmentation (Delaney, Riley, & Fisher, Our EEMS analysis detected lower‐than‐expected gene flow between cities and higher‐than‐expected gene flow within cities, though we can only draw conclusions between sampled areas and not from areas that were unsampled. This pattern is consistent with our Mantel correlogram which showed that pigeons are often related at a distance up to 50 km. This finding is also consistent with previous research on feral pigeons in Europe which found that pigeons within cities are highly related and do not show genetic differentiation (Jacob et al., Similarly, Hofmeister et al.  found loTaken together, these studies and results from pigeons suggest that organisms dependent on humans exhibit fine‐scale spatial genetic patterns that reflect human patterns and distribution. Thus, the implications of urbanization on wildlife are dependent upon the life history traits of the organism, how humans are distributed throughout the landscape, and the extent of urbanization (Bonier, Martin, & Wingfield, Appendix S1Click here for additional data file.