Phragmites australis), a grass native in Europe and invading North America, to examine the relative importance of geographic, environmental (represented by climate here), and human effects on population genetic structure and its changes during invasion. We collected samples of P. australis from both the invaded North American and native European ranges and used molecular markers to investigate the population genetic structure within and between ranges. We used path analysis to identify the contributions of each of the three factors—geographic, environmental, and human‐related—to the formation of spatial genetic patterns. Genetic differentiation was observed between the introduced and native populations, and their genetic structure in the native and introduced ranges was different. There were strong effects of geography and environment on the genetic structure of populations in the native range, but the human‐related factors manifested through colonization of anthropogenic habitats in the introduced range counteracted the effects of environment. The between‐range genetic differences among populations were mainly explained by the heterogeneous environment between the ranges, with the coefficient 2.6 times higher for the environment than that explained by the geographic distance. Human activities were the primary contributor to the genetic structure of the introduced populations. The significant environmental divergence between ranges and the strong contribution of human activities to the genetic structure in the introduced range suggest that invasive populations of P. australis have evolved to adapt to a different climate and to human‐made habitats in North America.Identifying the factors that influence spatial genetic structure among populations can provide insights into the evolution of invasive plants. In this study, we used the common reed ( Steud., to identify the relative contributions of geographic, environmental, and anthropogenic factors to the formation of the patterns of spatial genetic variation. Phragmites australis is a wetland perennial grass with a worldwide distribution  and native European  populations using microsatellite (SSR) and AFLP markers. Specifically, we asked the following questions: (1) How are populations genetically structured in the native and introduced ranges and do their patterns differ? and (2) How are spatial genetic patterns influenced by geographical distance, climatic variation, and different intensities of human activities within and between ranges?22.1® SP Plant DNA Kit  according to the manufacturer's instructions for dried specimens.Plant samples used in this study were collected from wild populations in Europe and North America, thereby covering the current distribution range of the species on these continents Figure . All sam2.2trnT‐trnL  of which 92 are NA invasive and 74 are EU native, and 20 samples are native North American haplotypes  (NA native)  already used in previous studies of P. australis variation  and six microsatellites markers . The AFLP E‐primers and the microsatellites primers To estimate the error rates and test the reproducibility of the data, we randomly included three to six control samples in every plate which were amplified every time with the other samples in the plate.2.4RawGeno v. 2.0.1 , with the criteria of peak smooth, peak saturation, baseline subtraction, Local Southern sizing method, in the size range of 50–500 base pairs (bp). The fragments with peak height lower than 50 relative fluorescence units (rfu) were not scored due to the possibility of instrument error  , using GeneScan 500 size standard as a size reference. The obtained alignment was checked manually with Geneious R6 v. 6.0.6 . As The resulting two presence/absence matrices (one for AFLPs and one for microsatellites) consisted of 244 polymorphic AFLP loci from the two primer pairs and 50 binary loci from the six microsatellites markers. We analyzed the genetic structure resulting from the two datasets both separately and by pooling the data together. As the outputs of the three analyses were similar, we combined the two molecular matrices in one single matrix of 294 markers to increase the resolution of our dataset following Kettenring and Mock . The bin2.5The climate data for each genotype were extracted from the extended suite of the WorldClim data  infer population genetic structure based on AFLP and SSR markers and (2) correlate genetic data with geographic (IBD), climatic (IBE), and human impact (IBH) data and assess the contribution of IBD, IBE, and IBH to gene flow within and between ranges by path analysis and hierarchical partitioning.2.7.1adegenet , the package runs the K‐means clustering algorithm sequentially and identifies an optimal number of genetic clusters via the Bayesian information criterion (BIC) within the inferred K range. We ran DAPC twice, first with all three populations , and then without NA native, to detect the structure of the native and introduced populations of the invasive European lineage. The maximum K was set to 40 for both runs.The multivariate discriminant analysis of principal components , that is, when ∆K is highest. This test was run with STRUCTURE HARVESTER  clustering approach implemented in STRUCTURE v. 2.3.4  between NA invasive and EU native and quantified the number of shared and distinct alleles between the two ranges  among the individuals of each population with GenAIEx 6.5 , and between Euclidean genetic distances and pairwise Euclidean distances in human footprint index , respectively.Path analyses were used to quantify the relative contributions of geographical distance (IBD), bioclimatic environmental dissimilarity (IBE), and human influence distance (IBH) to the genetic structure within each range and between ranges (NA invasive vs. EU native) , standardized root mean square residual (SRMR), and the comparative fit index (CFI). Nonstatistical significance (p > .05) of the χ2 test indicates that the model significantly simulated the data. Values of RMSEA smaller than 0.05 indicate a very good model fit  for between and within the European and North American ranges. As a method of multiple regression, independent effect analysis estimates the average contribution of each explanatory variable to the variance of the response variable by testing all possible models that represent a subset of the explanatory variables . Contrary to STRUCTURE, the lowest BIC of DAPC analyses indicated four clusters in the whole dataset  corresponding to NA invasive and EU native and a substructure (K = 3) in agreement with DAPC  for the EU native but not for the NA invasive (p = .23)  contributed most  to the genetic distances between native and introduced ranges, while within EU native and NA invasive populations, geographic distances (DGeo) and human footprint distances (DHFP) were the primary contributors to the genetic distance within population, respectively  Figure b–d. Path) Figure b, and a ) Figure c. When w) Figure a. Hierary Figure d.44.1P. australis haplotype M lineage from its introduced and native ranges, we found that the invasive North American and native European populations genetically diverged, although they still share the majority of their alleles. This result was confirmed by all analyses performed, that is, DAPC and STRUCTURE, with both IBD and IBE (represented by climate in our study) playing a role. The loss of genetic diversity due to founder effect is comparable to that calculated by Dlugosch and Parker (P. australis in North America. The founder effect did not have a direct effect on quantitative trait diversity and the ability of evolving adaptive potential, at least for the modest genetic diversity losses as measured by Dlugosch and Parker (Novel environmental conditions in the introduced range may act as strong selection forces on some introduced species and lead to rapid evolution within the time frame of centuries or even decades (Colautti & Lau, d Parker  in 20 ind Parker .Sexton et al.  showed t4.2P. australis showed different genetic structure patterns within its native and introduced ranges. In the native European range, gene structure of P. australis populations reflected a positive IBD and IBE, a classic isolation pattern shaped by dispersal distance and environmental variations common to most wild species (Sexton et al., P. australis can reproduce both sexually (Haslam, The European lineage of  Haslam,  and vege Haslam,  and high Haslam, .P. australis haplotype M lineage was introduced into the East Coast of North America at least 150 years ago, but it started to become widely dispersed in middle and western North America only in the last half century (Kulmatiski, Beard, Meyerson, Gibson, & Mock, P. australis in North America could be due to Allee effect, that is, the time needed for the introduced genotypes to build up population density and fitness before spreading invasively across the continent. Several recent studies confirm that the introduced P. australis populations heavily rely on sexual reproduction (Belzile, Labbé, LeBlanc, & Lavoie, Theory predicts that under counter‐gradient gene flow, the strong directional gene transfer could prevent local adaptation (Sexton et al., 5P. australis in North America have evolved from their ancestral populations in Europe. A different climate in the two ranges contributes the most to the genetic differentiation of the two populations, followed by geographic isolation. Within the two ranges, geographic distances, climatic variation, and human impact have shaped the genetic patterns differently. In the native range in Europe, the population structure has been shaped since postglacial colonization (e.g., Ingrouille, The invasive populations of None declared.W.‐Y.G. and C.L. designed the experiments. W.‐Y.G. analyzed the data. W.‐Y.G. and C.L. wrote the manuscript with contributions from all authors.https://doi.org/10.5061/dryad.fj46f.Microsatellite matrix and AFLP genetic matrix have been archived in the Dryad Digital Repository:  Click here for additional data file.


Ovis aries) have been one of the most exploited domestic animals in Estonia since the Late Bronze Age, relatively little is known about their genetic history. Here, we explore temporal changes in Estonian sheep populations and their mitochondrial genetic diversity over the last 3000 years. We target a 558 base pair fragment of the mitochondrial hypervariable region in 115 ancient sheep from 71 sites in Estonia (c. 1200 BC–AD 1900s), 19 ancient samples from Latvia, Russia, Poland and Greece (6800 BC–AD 1700), as well as 44 samples of modern Kihnu native sheep breed. Our analyses revealed: (1) 49 mitochondrial haplotypes, associated with sheep haplogroups A and B; (2) high haplotype diversity in Estonian ancient sheep; (3) continuity in mtDNA haplotypes through time; (4) possible population expansion during the first centuries of the Middle Ages (associated with the establishment of the new power regime related to 13th century crusades); (5) significant difference in genetic diversity between ancient populations and modern native sheep, in agreement with the beginning of large-scale breeding in the 19th century and population decline in local sheep. Overall, our results suggest that in spite of the observed fluctuations in ancient sheep populations, and changes in the natural and historical conditions, the utilisation of local sheep has been constant in the territory of Estonia, displaying matrilineal continuity from the Middle Bronze Age through the Modern Period, and into modern native sheep.Although sheep ( Ovis aries) were domesticated around 11 000 years ago in the area of the Fertile Crescent [Sheep  and oCrescent . It has As the wild ancestors of domestic sheep are absent in northern Europe, the first zooarchaeological evidence for sheep is thought to be associated with the introduction of domestic animals. In Estonia, the first zooarchaeological evidence for domestic sheep is a bone awl of sheep metacarpal bone recovered from a Late Neolithic Corded Ware (2900–1800 BC) burial, dated by associated finds to the beginning of that period, 2900–2700 BC . This pee.g. [e.g. through the German Hanse), the development of towns, and growing human populations all influenced animal husbandry, supposedly increasing the amount of livestock. The local livestock populations may have been augmented by imported individuals, although this practice would have been rather exceptional and occurred only in wealthy manors as known from the later periods.The time of the crusades in Estonia (AD 1208–1227) which marks the transition from the prehistoric to the historic era and the beginning of the Middle Ages (AD 1225–1550), brought significant changes in power structures and settlement. These decades are illustrated by the Chronicle of Henry of Livonia, where he describes ‘countless’ numbers of sheep or ‘other livestock’ besides cattle, oxen and horses, that were taken from the local inhabitants during the campaigns . Medievath and 18th centuries is scanty, firstly, because of limited archaeological interest in the Early Modern Period (AD 1550–1800) the faunal remains are often not collected during the fieldwork, and secondly, it is difficult to archaeologically distinguish the layers of this period from more recent ones. The same issues apply to the following Modern Period  which also marks the beginning of large-scale breeding. The wide use of Merino sheep after the Middle Ages is believed to have been the most influential factor in the development of modern breeds [th century [th century (1794), where small sheep with coarse wool, long slender legs and relatively short tails have been described [th or in the beginning of the 20th century, and at a more limited scale [The Middle Ages came to an end with the Livonian war AD 1558–1583). Well dated zooarchaeological material from the 17–1583. Wen breeds , althoug century . In Esto century . The firescribed . At the escribed . In 1824escribed –15. Amoned scale .i.e., during the period of collective and state farming in 1950s–1991). These sheep are now being actively revived, based predominantly on a relict population from Kihnu Island in the Riga Bay , and ancient mitochondrial DNA (mtDNA) analyses have suggested a link between Kihnu and archaeological sheep populations in Estonia [e.g. [‘Native’ sheep survived in peripheral areas of Estonia in spite the introgression of improved and imported breeds, and the neglect of local aboriginal sheep under more recent Soviet influence  studies by increasing both the targeted sequence length, and the number of samples from different archaeological contexts to more fully investigate the temporal fluctuations of sheep populations in Estonia, and explore the degree of continuity in maternal lineages from the Middle Bronze Age to recent breeding in the Modern Period.i.e., from the Late Iron Age to the Middle Ages. This hypothesis is based in part on the preliminary ancient mtDNA results published by [Our first aim is to investigate the hypothesis of a shift in mitochondrial genetic diversity during the transition from the prehistoric to the historic period, ished by , but alsished by . Our secished by  for whicTo provide comparative ancient material from neighbouring northern, eastern and southern areas, and from the area closer to the domestication centre, we also extracted mtDNA from 19 ancient sheep from 11 archaeological sites in Latvia, Russia, Poland, and Greece, dated from around 6800 BC to AD 1700 . We compOur results provide additional basis for subsequent studies on maternal genetic variation in sheep and contribute to the understanding of the history of sheep husbandry not only in Estonia but also in broader areas in the north-eastern part of Europe.n = 134) of archaeological sheep bones were obtained from archaeological collections in Estonia , Latvia , Russia , Poland , and Greece   . Samplin; n = 2) .th century. The majority of the samples come from the mainland with fewer from the Saaremaa Island. Micro-regions yielding sheep remains over a long chronological span were preferentially targeted to identify temporal changes within a site or region, including Viljandi-Karksi, Tartu-Lohkva and Saaremaa Island. All samples were dated based on archaeological context or radiocarbon dating  morphological characteristics such as overall size and degree of epiphyseal fusion of long bones , and  n = 44) were kindly provided by veterinarian Anneli Ärmpalu-Idvand . DNA was extracted in the University of Tartu using the High Pure PCR Template Preparation Kit  according to the manufacturer’s instructions. A 1451 bp fragment of mtDNA control region was amplified with one primer pair (Previously collected blood samples of Kihnu sheep (mer pair . PCR washttp://www.geneious.com). Multiple alignments of the ancient and modern sequences were conducted using ClustalW  through Geneious. After the removal of primer sequences, the alignment was truncated to 559 bp (region 15978–16536 in GenBank accession NC001941).The obtained ancient sequences were edited using Geneious v.6.1.7. , haplotype (Hd) and nucleotide diversities (π), and Tajima’s D values (D) for each cohort were calculated with DnaSP v.5.10 [www.fluxus-engineering.com) with default values.The number of haplotypes  values [We assessed the continuity of haplotypes through time using a three-dimensional statistical parsimony network through TempNet  implemen) values  and anal) values .n = 86; alignment length 559 bp): (1) Middle/Late Bronze and Iron Age , (2) Middle Ages (n = 39), (3) Early Modern and Modern Period (n = 19), and (4) Kihnu sheep (n = 44). Note that two samples that could not be assigned to either the Iron Age or Middle Ages , Tartu-Lohkva (n = 12), and Saaremaa Island (n = 10) were analysed separately by a three-dimensional statistical parsimony network.The analysis was approached from two perspectives. Our primary focus was on Estonian material in order to assess the presence, continuity, and changes of unique and shared haplotypes through time. This was assessed by calculating the genetic diversity values and assessing the haplotype continuity for four temporal cohorts (n = 14), and available sequences from GenBank–Finnish ancient (n = 26) and modern (n = 32) sheep sequences by [e.g. [The secondary focus of the analysis was the comparison of Estonian ancient and modern data with other regions in Europe. A median-joining network was calculated with the Latvian, Russian, Polish and Greek ancient samples of this study . Amplification failed completely for 22 samples, including two specimens reported in [In total, PCR amplification of all five primer sets was successful for 102 of the 134 sampled archaeological sheep specimens dating from orted in ; and wasorted in ; KU67023Ovis aries; no other species  were identified. In general, the relatively high success rate for DNA amplification can be attributed to the recent antiquity of the samples, the correct osteological identification of species, as well as the taphonomic and climate conditions conducive to DNA preservation in Estonian ground.No significant differences in the preservation of DNA were detected in temporal or spatial cohorts  Tables. e.g. [e.g. [Surveys of ovine mtDNA variability have supported a broad genetic base during domestication . Variatie.g. –50. Haplg. [e.g. ,51–52. Ag. [e.g. –45,53–58c. 6400–6800 years ago, and lineage C at c. 4500 years ago.Previous studies on modern sheep genetics have shown that genetic diversity indices of the first domestic populations in the initial region of domestication have been very high and decreased with geographic distance from the centre –61. ThisOvis aries mtDNA haplotypes were defined by 40 variable sites  which is one of the most distant from the central haplotype in haplogroup B.The Sarakenos sheep from the Initial Neolithic Greece 123OaSara2), which age was determined on the basis of a series of radiocarbon dates of charcoal to 3OaSara2,t region –64, and t region . In our H4, sharing common ancestry with sheep from broad geographical and temporal distribution. Kihnu sheep have common ancestry also with medieval Russian and the Iron Age Estonian sheep (H30). However, there are also several haplotypes which are unique to Kihnu sheep (H45–H49). Interestingly, none of the Estonian samples share haplotypes with other modern Finnish breeds such as Kainuu Grey and Åland sheep. Likewise, none of the Finnish sheep (either ancient or modern), share haplotypes with the Estonian Kihnu sheep . In summary, Estonian ancient sheep show an affinity to eastern, southern, and northern European ancient sheep, as well as to native Finnsheep, while the Kihnu, Kainuu Grey and Åland native breeds appear to be genetically more distinct.Comparisons between Estonian and Finnish sheep reveal 10 shared haplotypes, while 20 haplotypes are specific to Finland and 33 to Estonia . In hapln = 119, 90%), with a lower frequency of haplogroup A . The three-dimensional network outlines the central structure of the haplotypes of Estonian sheep through time , again dominated by haplogroup B , but showed no significant changes between the last centuries of the Late Iron Age and the first half of the Middle Ages  or between the earlier and later part of the Middle Ages .Although the medieval period is not characterised by increasing genetic diversity indices, interestingly, Tajima’s D values seem to suggest a population expansion during this period .Overall, the mitochondrial genetic results obtained in this study support the historic and archaeological evidence for population expansion of local sheep, rather than significant introduction of imported animals during the medieval period.th and 20th century. Due to decrease in the use of local sheep and mixing them with modern imported breeds, local sheep were driven to almost extinction. This population decline was a gradual process which reached its low point during the last decades of the 20th century and is now reflected in the genetic diversity estimates of the Kihnu sheep analysed in this study. As revealed by the genetic differentiation estimates shown in D = 1.03872, p > 0.10, not significant), as one possible interpretation for this high value could be recent population contraction [π = 0.01140) compared to the ancient cohorts. The latter coincides with the high number of novel haplotypes among Kihnu sheep and might reflect the accumulation of mutations during population development [Our results suggest that following the Middle Ages, the indigenous sheep population in Estonia continued to expand until they became part of the large-scale breeding process in the 19traction . Secondlelopment .n = 22; period length 700 years). Using this shortened time span, the Late Iron Age still displayed no significant differences with any of the populations except for the Kihnu sheep .We recognize a potential bias in our grouping of temporal cohort, as Kihnu sheep represent a contemporaneous ‘living population’, while the ancient samples are drawn from a group of individuals living over several centuries, and thus may have artificially inflated diversity values. Also, the time periods used in the analyses are not of the same duration: 2225 years for Bronze/Iron Age, 325 years for medieval and 450 years for Early Modern / Modern Period. To test the possible bias for the longest, prehistoric cohort, five of the oldest samples from the Bronze and Early Iron Age were left out and statistical significances for genetic differences were re-calculated only with sequences from the Late Iron Age  observed in other parts of north-eastern Europe.th century onwards. Therefore, it seems that sheep husbandry in the Estonian area has been persistent and relatively autochthonous. Even if human-mediated animal movements did occur, these did not affect the majority of the sheep population, as demonstrated by a lack of significant genetic differentiation between the ancient populations. Nevertheless, some changes in maternal genetic diversity were observed in the first half of the Middle Ages when sheep populations seem to have expanded due to the growing human populations and their need for subsistence. Further investigation is needed to determine the extent to which changes in the animals’ physical size is related to this population expansion or to other environmental and climatic changes. We also explored three micro-regions to examine sheep husbandry in detail, supporting the changes seen in the 13th century, as predominantly new haplotypes were observed in medieval Viljandi-Karksi, Tartu-Lohkva and Saaremaa Island compared to the Late Iron Age. Due to relatively small sample sizes, questions of continuity at a micro-scale remain open.Based on previous ancient DNA and osteological data, we expected to observe temporal fluctuation in sheep genetic diversity, particularly within the medieval period. Instead, we identified relative stability in genetic diversity and continuity in the ancient matrilines from Middle Bronze Age to modern sheep, with no obvious interruptions, introgressions or imports from outside regions and in spite of the introduction of new breeds from the 18A significant change was recorded in the modern Kihnu native sheep, which had gone through a population decrease during the last centuries, resulting in lower haplotype diversity and high rate of novel haplotypes compared to the ancient populations. Nevertheless, our results suggest a clear connection between the earliest studied sheep and Kihnu native breed, and induce further research on their history and position among the northern European native sheep breeds.As studies of short mitochondrial DNA fragments have limitations, phenotypic and Y-chromosome DNA needs to be studied, especially by improved aDNA extraction and high-throughput sequencing. Further genetic, archaeological, and historical studies of modern and ancient sheep in Estonia and neighbouring regions would expand the understanding of both the early stages of the first domesticates in the region as well as the subsequent development of the populations.S1 Fig(PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 Table(PDF)Click here for additional data file.S5 Table(PDF)Click here for additional data file.S6 Table(PDF)Click here for additional data file.S1 Text(PDF)Click here for additional data file.S2 Text(PDF)Click here for additional data file.


Pentapharsodinium dalei were isolated and germinated from five layers in dated sediment cores from Koljö fjord, Sweden, spanning ca. 1910–2006. This fjord has, during the last century, experienced environmental fluctuations linked to hydrographic variability mainly driven by the North Atlantic Oscillation. Population genetic analyses based on six microsatellite markers revealed high genetic diversity and suggested that samples belonged to two clusters of subpopulations that have persisted for nearly a century. We observed subpopulation shifts coinciding with changes in hydrographic conditions. The large degree of genetic diversity and the potential for both fluctuation and recovery over longer time scales documented here, may help to explain the long‐term success of aquatic protists that form resting stages.Many marine protists form resting stages that can remain viable in coastal sediments for several decades. Their long‐term survival offers the possibility to explore the impact of changes in environmental conditions on population dynamics over multidecadal time scales. Resting stages of the phototrophic dinoflagellate  For details on sediment processing and dating, see Lundholm et al.  at 45 m water depth in April 2006 Figure . This sim et al.  and Ribem et al. . The sed2.23) of each of the five layers were placed in an ultrasonic bath for 2 min for separation of aggregated particles and wet sieved with filtered seawater through a 100‐μm sieve onto a 25‐μm metallic‐mesh calibrated sieve . Separation of resting stages from the remaining sediments was performed using a solution of sodium metatungstate with a density of 1.3 g/ml and centrifugation at 1,500 g for 10 min  were isolated by micropipetting and transferred into Corning 96‐microwell plates  each well containing 0.25 ml of TL growth medium  following the procedure described in Lundholm et al. . The PCR2.4P. dalei is haploid, null allele frequencies were estimated by direct observation. Strains for which more than half of the loci did not amplify after at least three re‐amplifications were removed from the dataset. The absence of amplification in the remaining strains was considered to indicate the presence of null alleles. If several clonal strains isolated from one germinated cyst were successfully genotyped, one randomly chosen representative was included in the final dataset.As Genetic diversity was assessed by calculating total number of alleles, and allelic richness and private allelic richness per sample using rarefaction with the software HP‐Rare which accounts for sample size effects by calculating unbiased estimates of allelic richness  was tested for each layer separately and globally using an exact test for LD for haplotypic data applied as implemented in Arlequin 3.5.1.2 with a Markov chain length of 10,000 and 1,000 dememorization steps for each of the age depths and Markov chain length of 1,000,000 and 100,000 dememorization steps for the global dataset. The sequential Bonferroni's procedure was used to test “table‐wide” significance levels Holm, . Pairwis2.5FST for the five layers were calculated based on allele differences among haplotypes within and among layers using Arlequin 3.5.1.2. Significances of the estimates were tested by permuting individuals between populations 10,000 times.To assess patterns of population structure, a principal component analysis was conducted in R  was assessed based on 20 iterations with K = 1–7, burn‐in length of 50,000 and additional 100,000 Markov chain Monte Carlo steps. As the five layers were temporally separated by at least 10 years, simulations were carried out with “no admixture” using sampling information a priori to assist the clustering (the LOCPRIOR model). This model is particularly informative when signals of genetic divergence are low, and when the number of loci and/or the samples size is restricted  was tested using a nonparametric permutation approach . Some cultures ceased to grow before a sufficient number of cells could be harvested for DNA extraction, resulting in 147 genotyped clonal strains. The layers were selected to span the known changes in NAO mode and shifts in P. dalei dominance from cyst records, as well as based on the number of viable cysts in different layers. The number of strains established from the lower layers represented the highest number we possibly could obtain as the entire samples were screened for viable cysts. The percentage of germinated cysts decreased downcore from a maximum of 65% .A total of 193 clonal cultures were established from more than 450 cysts of P. dalei being haploid. However, four strains yielded multiple alleles per locus indicating that a planozygote (swimming pre‐encystment cell) was genotyped or, alternatively, that two different cells from the germinating diploid cyst had been inadvertently isolated. These samples were not included in the analyses.All primer pairs amplified a single PCR product, consistent with 3.1G/N = 1 for all layers  indicating the presence of null alleles. The remaining five loci amplified in all clonal strains, except one single null allele, which was observed in one of the markers . The total number of alleles per locus ranged from 8 to 20 and the mean genetic diversity per locus ranged from 0.74 to 0.90. The total number of alleles observed in each sample ranged from 31 to 58 . Hence, 97.9% of the genotypes were unique. There was no significant difference among age depths in the frequency of nonamplifying genotypes (0.000–0.043). Tests of LD displayed no significant linkage between any loci after Bonferroni's correction.3.2FST showed weak or no genetic differentiation among samples  supported d et al.  and Joned et al.   and FCT = 0.021 . The AMOVA with no defined clusters partitioned 0.17% of the variation among sediment depths while the remaining 99.83% was partitioned among genotypes within depths (data not shown).The hierarchical AMOVA testing the potential substructure indicated by STRUCTURE  displayed that most of the variation was found within samples 99.03; Table ; Table 34The sediment record that served as basis for this study has a robust age control, clear indication of undisturbed sediment deposition, and yielded a large number of well‐preserved viable resting stages of several protist species  differing from the most recent (2006) and the oldest strains (1922). The mid‐core strains also had higher levels of genetic diversity especially in terms of allelic richness, and the number of private alleles suggesting that the mid‐core samples have more alleles at lower frequencies. The timing of these changes agrees with the well‐documented environmental changes in Koljö fjord, as the strains germinated from mid‐core layers correspond to a phase of predominantly negative winter NAO  or variable winter NAO indices with low average values (1985), as opposed to the positive NAO phases corresponding to the bottom (ca. 1922) and topmost (2006) sediment core layers Figure . StudiesP. dalei have been suggested to be an indicator of the importance of the phytoplankton spring bloom , but not by pairwise FST estimates. We therefore suggest that P. dalei in Koljö Fjord exhibit an overall stability in population structure, which has persisted for almost a century. The stable population maintains an intrinsic substructure with two alternating subpopulations, which shift concomitantly with changes in the predominant environmental conditions.The higher degree of genetic diversity in the middle section of the record and the changes in population substructure might therefore be explained by the environmental conditions driven by the NAO, resulting in one subpopulation performing better during negative NAO during which the bottom salinities are higher and the pycnocline stronger, and another doing better at positive NAO. During negative NAO, the high abundances of 4.1In aquatic protists, temporal genetic differentiation has mainly been studied over shorter time scales such as during a bloom, between blooms, or over days . Bell . Bell  argued tS. marinoi from another Scandinavian fjord  was found. This is in agreement with previous studies on populations of aquatic protists such as diatoms, dinoflagellates, coccolithophores, and raphidophytes in marine and freshwater environments, both in geographically restricted and in connected areas, with G/N ranging from 0.66 to 1.00  among the P. dalei strains both within age depths and maintained throughout a century  also contributes to maintenance of genetic diversity Bell, . IndicatAs genetic diversity did not decrease linearly with age, we do not expect any bias due to culturing and impact on the comparison among age depths. As in all other population genetic studies on phytoplankton (e.g., Rynearson & Armbrust, 4.3Emergence of resting stages may be thought of as migration from the past (Hairston & De Meester, P. dalei population will decrease in abundance and its genetic structure will resemble that of the 1920s and ca. 2006. However, since the beginning of 21st century, the winter NAO index has decreased in comparison with the 1990s (Figure It has been speculated that the mainly positive NAO phase seen since c. 1970 is related to increased greenhouse gas concentrations (Visbeck, Hurrel, Polvani, & Cullen, s Figure , and theThe authors declare no conflict of interests.NL, AG, and ME contributed to research design. NL, SR, and ME performed research. NL and LRN analyzed the data. NL, SR, AG, LRN, and ME discussed results and contributed to written manuscript.10.5061/dryad.195k3Data available from the Dryad Digital Repository: doi: Click here for additional data file.


Hemideina crassidens; Orthoptera), we found multiple mtDNA haplotypes for a protein-coding region (cytb/ND1) that differed by a maximum of 7.9%. From sequencing the whole mitochondrial genome of two representative individuals, we found evidence of constraining selection. Heterozygotes were as common as expected under random mating at five nuclear loci. Morphological traits and nuclear markers did not resolve the mtDNA groupings of individuals. We concluded that the large differences found among our sample of mtDNA sequences were simply owing to a large population size over an extended period of time allowing an equilibrium between mutation and drift to retain a great deal of genetic diversity within a single species.Mitochondrial DNA sequence is frequently used to infer species' boundaries, as divergence is relatively rapid when populations are reproductively isolated. However, the shared history of a non-recombining gene naturally leads to correlation of pairwise differences, resulting in mtDNA clusters that might be mistaken for evidence of multiple species. There are four distinct processes that can explain high levels of mtDNA sequence difference within a single sample. Here, we examine one case in detail as an exemplar to distinguish among competing hypotheses. Within our sample of tree wētā ( Although the acquisition of mitochondrial sequence data has been promoted as a tool for identification of specimens ,2, the aFour biological processes might lead to high genetic diversity at a locus within a single population sample. In a stable population, neutral genetic variants arise at a predictable rate and are lost stochastically (drift) at a rate determined by population size ,13. In lThe third and fourth processes leading to a putative single population sample containing relatively high genetic diversity are cryptic species  and receWolbachia could result in reproductive incompatibility between individuals infected and uninfected or between individuals infected with different bacteria stains [Wolbachia strains might resemble introgression. Maternally transmitted parasites are genetically linked to the mitochondria and if they cause cytoplasmic incompatibility will result in a loss of mtDNA diversity [Morphological variation derived from genotypic variation can provide the information needed to determine whether high genetic diversity results from sampling more than one species . Concorda stains . In the iversity . The preiversity ,33, prodiversity . In the et al. [In New Zealand, many endemic invertebrates have been subjects of phylogeographical investigations, with their mtDNA genetic diversity used to infer historical range changes ,35. Inteet al. , or anciet al. . The conet al. –41). Deset al. . By contet al. –44.Hemideina crassidens , was probably widespread in North Island New Zealand during the last glacial maximum [H. crassidens mtDNA haplogroup is restricted to wētā samples from the southern part of the species’ range, concordant with a distinct 19-chromosome race [H. crassidens  assigned to landscape coordinates at midpoints  of the connectivity edges, as described in Miller et al. [X and Y coordinates correspond to geographical locations (latitude and longitude) on a rectangular grid and surface plot heights (Z) reflecting genetic distances was generated [a) of 1. Additional analyses were performed using a variety of grid sizes , distance weighting parameters (a = 0.5–2) and raw and residual genetic distances to make sure that interpretations were not sensitive to these parameters, as recommended.The distribution of mitochondrial sequence variation within the natural range of a et al. ). We exare.net/, ). To do r et al. . Geneticenerated ,50, We s″ S; 176°0′10.80″ E) in central North Island New Zealand [H. crassidens using a combination of colour and spine traits that differentiates this species from other Hemideina species [High nucleotide diversity  was recorded in a tree wētā sample from Rangiwahia Forest Park  for each individual at Rangiwahia. Tubercles with rounded ends rather than points (as seen in spines) were recorded as half spines (0.5) if they occurred in the normal position for a dorsal tibia spine.Two morphological traits were recorded for each individual: total number of stridulatory ridges and total number of hind tibia dorsal spines. Neither of these traits correlates with wētā instar, and therefore, wētā at any age can be included in the analysis. Tree wētā use their abdominal stridulatory ridges to make rasping sounds when rubbed against pegs on the retrolateral surface of the hind femur, probably for intraspecific communication . Ridge n2.3.b (cytb), tRNA serine and part of NADH dehydrogenase 1 (ND1) sequences were checked, edited and aligned in Geneious v. 8.1.4  for the population sample using the method of Tajima [eneious. We estimated population size using the equation N = π/4u [Drosophila rate of 6.2 × 10−8 [Daphnia 1.5 × 10−7 [Nematode 7.6 × 10−8 [opArt [http://evolves.massey.ac.nz/Data/Rangiwahia%20weta.zip and http://dx.doi.org/10.5061/dryad.rg15p [Genomic DNA was extracted from each individual and an approximately 800 bp mtDNA fragment amplified, as previously described . The mtDous.com, ). We calf Tajima , with anN = π/4u . Three m2 × 10−8 ; Daphnia5 × 10−7  and Nema6 × 10−8 . The tRN6 × 10−8  was infe8 [opArt  using 292.4.One coding, putatively neutral nuclear locus  was ampHemideina [eneious microsat plugin and individuals manually genotyped (data available at http://evolves.massey.ac.nz/Data/Rangiwahia%20weta.zip and http://dx.doi.org/10.5061/dryad.rg15p) [Four microsatellite loci  were amplified using primers and PCR conditions developed for other species within the genus emideina ,64. One enepop v. 4.2 [n = 10; p < 0.005). Where two species are sympatric, but rarely or never interbreeding, alleles at independent loci are expected to show linkage disequilibrium as heterozygote individuals will be scarce or absent. With our wētā sample treated as a single population sample, detection of heterozygote deficiency would indicate non-random mating.Evidence of linkage disequilibrium and deviations from the Hardy–Weinberg equilibrium were examined using Gp v. 4.2 ,66, withn = 23) and wētā haplogroup 3 (n = 6), we looked for evidence for these being non-interbreeding sympatric populations (e.g. different species). If distinct, differentiation at nuclear loci would result in significant departure of FST from zero. Pairwise FST was estimated using genotypes at five nuclear loci with Genepop v. 4.2.With the data partitioned between wētā from mtDNA haplogroup 2  to synonymous substitutions (KS) identifies the selective pressures occurring in protein-coding genes; a ratio of 1 implies neutral selection, greater than 1 implies positive selection and less than 1 implies constraining (purifying) selection. Constraining selection is predicted for coding DNA in large populations. Total genomic DNA from two individuals (H. crassidens haplogroup 2 (North) and H. crassidens haplogroup 3 (South)) were separately processed through massive parallel, high-throughput sequencing (Illumina HiSeq 2000) to obtain whole mitochondrial sequences for a separate phylogenetic study [utadapt [olexaQA [de novo assembly with Velvet v. 1.1 [byss v. 2.3.3 [k-mer length of 49. Contig files were compared to annotated Orthoptera mitochondrial sequences available on GenBank (NCBI). We found evidence of nuclear copies of some regions of the mtDNA and were able to remove paralogs using relative copy number and protein translation. The resulting assembled mitochondrial genomes were 16 535 and 15 905 bp in length  with the same gene order previously identified in Ensifera [cox1. Fasta files of the two H. crassidens mitochondrial genome sequences can be downloaded from http://evolves.massey.ac.nz/Data/Rangiwahia%20weta.zip and http://dx.doi.org/10.5061/dryad.rg15p [Two individuals, representing two mtDNA lineages (haplogroups 2 and 3), were used to calculate the lculator . The ratic study . Genomic[utadapt . The qua[olexaQA  before dt v. 1.1  and Abysv. 2.3.3  using a Ensifera , but wit2.6.et al. [prcomp in R package ‘STATS’  . The first five principal components (cumulative proportion of variance was 0.664) were used in a model-based clustering approach using the Bayesian information criterion to select the number of genotypic clusters in the R package ‘Mclust’ [κ. Cohen's κ coefficient measures agreement of assignment to categories, where values between 0.75 and 1 indicate non-random agreement, but where there is no agreement among the methods other than what would be expected by chance κ ≤ 0 .Principal components analysis (PCA) was performed on the morphological and genetic data combined (excluding mtDNA). Stridulatory ridge counts and tibia spine counts were each treated as a single trait . For nuclear loci, we treated each allele as a character trait and each individual was given a character state per allele: 0 (if absent), 1 or 2 (if homozygous), as recommended by Odong et al. . Alleles‘Mclust’ ,76. Mode‘Mclust’ . The Mcl‘Mclust’ . The Mcl‘Mclust’ ,77. We c3.H. crassidens . Thus, we obtained no evidence of the spatial structure that would be expected from secondary contact between previously isolated populations, nor evidence of barriers or discontinuities within the range of mtDNA haplogroups 2 and 3.‘Peaks’ indicating greatest genetic distances among population samples were scattered through the species range. Qualitatively similar results were obtained using various grid sizes and a range of distance weighting parameters  for this population sample was high (0.1035). Using this nucleotide site diversity, estimates of population size ranged from 172 500 to 417 338 depending on mutation rate used. The only available insect example (Drosophila mtDNA) has the lowest mutation rate and thus gave the largest population size estimate.We increased sampling of H. crassidens diversity [a). When translated, nucleotide substitutions resulted in three amino acid replacements that could be subject to natural selection. However, when we calculated the Ka/Ks ratio for all coding regions of the whole mitochondrial genome . Hardy–Weinberg expectations were met with no evidence of homozygote or heterozygote excess . The two wētā haplogroups showed no evidence of genetic differentiation at nuclear loci. Population pairwise FST = −0.01777; genotypic differentiation was 0 .Each wētā from the Rangiwahia population sample was genotyped at five polymorphic nuclear loci. The number of alleles per locus ranged from 2 to 12 . None oft-test p > 0.05). Frequency distributions of these traits were unimodal (κ estimate was −0.050). Furthermore, the clusters inferred for just PC1 and PC2 did not coincide with the clusters resolved using PC1–5 (Cohen's κ −0.088).No differences between phenotypic traits were observed when compared for the two haplogroups. Stridulatory ridge counts averaged 11.2 and 11.3, and spine counts on hind tibia averaged 20.02 and 20.20  are contrary to a hypothesis of cryptic species. Mito-nuclear discordance owing to intercellular bacteria can be excluded as an explanation as parasitic Wolbachia is not found in this genus [FST) based on five loci was negative, which does not suggest a type II error, and our tests for significant deviations incorporated sample size. With the same sample size, the best model to explain the phenotypic and genotypic variation (using the Bayesian cluster-based approach) resolved more than one group, suggesting that our sample was sufficient to detect variation, had it existed.We confirmed that the population sample of tree wētā from Rangiwahia contains high mtDNA diversity, resulting from the presence of individuals at the same location with very different haplotypes. We focused on this site because sympatry of the mtDNA haplogroups allowed for detection of separate species (should they exist) using genotypic information . We consis genus . Using aH. crassidens. In a large population, a non-recombining gene without selection will tend to yield a distribution of pairwise differences that deviates substantially from the geometric distribution expected in a population with constant size. Pairwise differences of mtDNA sequence frequently show a bimodal or trimodal distribution simply because the history of coalescent events imposes a substantial correlation [H. crassidens mtDNA could be the result of a large population accentuated by recent decline resulting from destruction of forest habitat, or range expansion with leading edge effects.Although some mtDNA sequence differences were expressed, we detected constraining selection within the mitochondrial genome as a whole, rather than diversifying selection that would be more likely if balancing selection was maintaining the high mtDNA diversity ,79. We srelation . Simulatrelation  owing torelation . Thus, tHemideina crassidens was probably abundant throughout the Pleistocene (2.58 Myr). Evidence from niche modelling and patterns of genetic diversity suggest that H. crassidens retained large population sizes in New Zealand during the climate cycling of the last 40 K years [Our estimates of population size for this tree wētā of 170–420 thousand are plausible for a herbivorous ectotherm.  K years . These m K years .H. crassidens could be used to infer that the mtDNA lineages shared a recent common ancestor approximately 3–4 Ma into the Pliocene. Although the shape of New Zealand during the Pliocene was considerably different from that of the current islands [Sequence divergence observed between the three distinct mtDNA lineages within  islands , this ha islands .We have shown how hypotheses that arise from large mtDNA sequence differences can be tested using phenotypic and nuclear genetic data. As there is no evidence of balancing selection or that these mtDNA differences represent biological lineages with separate histories, we suggest that large population size explains the retention of high diversity. The origin of the divergent mtDNA haplogroups might result from complex biogeographical scenarios or they might simply represent normal, stochastic processes of mutation and extinction of a non-recombining locus within a large population as demonstrated by simulation studies ,18. We u


Alternaria alternata sampled from potato hosts at 16 locations across China. We found a single major cluster, no isolate-geography associations and no bottlenecks in the A. alternata isolates, suggesting a metapopulation genetic structure of the pathogen. We also found weak isolation-by-distance, lower among than within cropping region population differentiation, concordant moving directions of potato products and net gene flow and the highest gene diversity in the region with the most potato imports. These results indicate that in addition to natural dispersal, human-mediated gene flow also contributes to the generation and dynamics of the metapopulation genetic structure of A. alternata in China. Metapopulation structure increases the adaptive capacity of the plant pathogen as a result of enhanced genetic variation and reduced population fragmentation. Consequently, rigid quarantine regulations may be required to reduce population connectivity and the evolutionary potential of A. alternata and other pathogens with a similar population dynamics for a sustainable plant disease management.Metapopulation structure generated by recurrent extinctions and recolonizations plays an important role in the evolution of species but is rarely considered in agricultural systems. In this study, generation and mechanism of metapopulation structure were investigated by microsatellite assaying 725 isolates of  Species can be structured geographically into either a series of genetically unconnected subpopulations  of reproductive units of species from one or more nearby subpopulations either through an island Lande,  or steppIn recent decades, the study targeting to understand metapopulation genetic structures and dynamics of species has become a central theme and common framework in evolutionary ecology Hanski, . HoweverAlternaria alternata plant-pathogen system to test the hypotheses that many plant pathogens in agricultural ecosystems are spatially aggregated as metapopulation structure and anthropogenic activities play an important role on the population genetic structure of plant pathogens. A. alternata is an opportunistic haploid fungal pathogen causing leaf spots, blights, and other diseases on many plant species  to infer the contribution of human-mediated gene flow to the spatial population genetic structure of A. alternata. To achieve this goal, we assayed 725 A. alternata isolates sampled from 17 subpopulations across the main potato production areas in China with eight neutral SSR markers and evaluated “isolation-by-distance” to determine how geographic barriers may affect the gene flow in the pathogen populations. We then inferred the contribution of human-mediated dispersal on the population genetic structure of the pathogen by comparing the extent of gene flow (Nm) within and among potato cropping regions estimated from a classical island model  to investigate the spatial distribution of genetic variation in l Lande, , and thel Lande, .A. alternata isolates collected from 17 fields across four cropping regions in China  distinct clusters regardless of geographical origins while minimizing the gametic phase disequilibrium between loci within clusters  was used to determine the optimal value of K using an admixture model  with GenAlEx6 and genetic differentiation  with expected gene diversity (HEQ) based on the infinite alleles model, two-phase mutation models (TPM) and SMM (70% SMM and 30% IAM)  or deficit (a recent population expansion) in genetic diversity relative to the observed value with Bottleneck 1.2 . The overall gene diversity and number of alleles detected were 0.42 and 7.25, respectively, when all isolates from the study were pooled.Gene diversity ranged from 0.25 to 0.64 with an average of 0.38 and allele number ranged from 2.13 to 5.13 with an average of 3.58 in the 17 field subpopulations  = −0.284, p = 0.001] among field subpopulations  subpopulations  showed a normal L-shaped distribution with no significant excess or deficit in gene diversity  at different years and quick population turn-over of the pathogen  among geographically closer subpopulations. In China, potatoes in the same cropping regions are usually planted and harvested at similar times and exchange of potato products as planting material or food is much less common within than among cropping regions. As a result, gene flow within cropping regions is much lower than among cropping regions due to the reduced human-mediated dispersal of pathogen through infected tubers in the former.Higher genetic differentiation as a result of lower gene flow was observed within cropping regions than among cropping regions in the current study Tables , 4. SimiA. alternata is also supported by the directions of gene flow estimated by a coalescent approach (Figure Human-mediated dispersal of h Figure  and the h Figure . The estA. alternata as reported in other pathogen species (Linde et al., Our results clearly indicate that human-mediated dispersal plays an important role in the generation and dynamics of metapopulation genetic structure in Data for this study will be available at the Dryad Digital Repository after manuscript is accepted for publication.J-WM and D-CH: Collected pathogen isolates, generated and analyzed the data, and wrote the manuscript; WZ, L-NY, E-JW, J-HX, and L-PS: Collected pathogen isolates and generated the data; JZ: Conceived and designed the experiments, analyzed the data, and wrote the manuscript. All authors reviewed the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Tamias striatus and white‐footed mouse; Peromyscus leucopus) impact their genetic connectivity within the highly fragmented landscape of the Upper Wabash River Basin (UWB), Indiana, and evaluated whether landscape configuration and complexity influenced patterns of genetic structure similarly between these two species. The more specialized chipmunk exhibited dramatically more genetic structure across the UWB than white‐footed mice, with genetic differentiation being correlated with geographic distance, configuration of intervening habitats, and complexity of forested habitats within sampling sites. In contrast, the generalist white‐footed mouse resembled a panmictic population across the UWB, and no landscape factors were found to influence gene flow. Despite the extensive previous work in abundance and occupancy within the UWB, no landscape factor that influenced occupancy or abundance was correlated with genetic differentiation in either species. The difference in predictors of occupancy, abundance, and gene flow suggests that species‐specific responses to fragmentation are scale dependent.Conversion of formerly continuous native habitats into highly fragmented landscapes can lead to numerous negative demographic and genetic impacts on native taxa that ultimately reduce population viability. In response to concerns over biodiversity loss, numerous investigators have proposed that traits such as body size and ecological specialization influence the sensitivity of species to habitat fragmentation. In this study, we examined how differences in body size and ecological specialization of two rodents (eastern chipmunk;  The negative impacts of human‐induced fragmentation are numerous Andrén , and senIn the large body of literature on species‐specific responses to habitat loss and fragmentation, several ecological factors known to increase species sensitivity to the negative impacts of habitat loss and fragmentation have been identified , Indiana, USA. The UWB has been largely converted to agriculture and has been subject of extensive study to assess the ecological effects of land conversion on a variety of vertebrate species (Nupp and Swihart Tamias striatus) and white‐footed mouse (Peromyscus leucopus), are largely ubiquitous within the UWB, but are expected to exhibit contrasting patterns in gene flow based on their different life histories. Chipmunks are larger than white‐footed mice and are known to move farther distances  are highly fragmented and tend to be clustered around the major drainages within UWB because floodplains or topography was not suitable for agriculture. Currently, 96% of UWB is privately owned with 88% designated as agriculture.Our study area encompassed the Upper Wabash River Basin UWB; Fig  in north2 study sites  in the UWB during the summers of 2001–2003  varied by year and forest patch size. In 2001, we placed either 3 × 3  or 7 × 7 (large forest patches) grids of Fitch live traps spaced 15 m apart within study cells. Sherman live traps or a mix of Fitch and Sherman live traps, arranged as 3 × 3 or 5 × 5 trapping grids, was deployed in study cells in 2002 and 2003. Habitat corridors identified as treed and nontreed land cover features <30 m wide were fitted with 5 × 2 grids. Each trapping session consisted of a prebait period with traps locked open for 3 days, followed by a 5‐day trap‐check session, during which traps were checked twice daily. Traps were baited with black oil sunflower seeds, and upon capture, ear or toe clips were removed from animals using sterile scissors. Following sampling, animals were treated with ferric subsulfate if bleeding occurred and released. All animals were handled according to procedures approved by the Purdue Animal Care and Use Committee under protocol #01–024. We stored all tissues at −80°C until DNA extraction.μL of DNA out on a 2% agarose gel stained with ethidium 12 bromide, and then, DNA extracts were diluted to a final concentration of approximately 10 ng/μL. Chipmunk samples were amplified at 12 , and 2× Thermopol reaction buffer 2SO4, 10 mM KCl, 2 mM MgSO4, 0.1% Triton X‐100; NEB). The amount of primer for each locus (0.05–0.30 μM) was adjusted so that all loci in the multiplex reaction would result in approximately equal intensities of product. The amplification conditions were as follows: initial 94°C for 2 min, 35 cycles of 94°C for 30 s, primer‐specific annealing temperature for 30 s, 72°C for 30 s, then a final extension of 72°C for 10 min, and a soak at 60°C for 45 min. The PCR products were sized on an Applied Biosystems 3730 automated sequencer, and the genotypes were determined for all loci in all individuals using the software GeneMapper 3.7 .We used an ammonium acetate protocol and an ethanol wash to extract DNA from tissue samples . DNA quality was checked by running 3 We utilized multiple quality control methods to ensure accuracy of microsatellite genotypes. First, a negative control, two pre‐amplified positive controls, and a concurrently amplified positive control were run on every 96‐well plate. Any ambiguous samples were re‐amplified and genotyped again at all loci, and any missing genotypes were re‐amplified in the multiplex reaction up to two times. If there were still missing genotypes after re‐amplifying the multiplex, samples were amplified using single locus reactions to attempt to retrieve the missing genotypes. Finally, if missing genotypes remained, we re‐extracted the samples and genotyped the individuals at all loci. Only individuals with <30% missing genotypes were accepted into the final dataset. Following quality control, we removed 7 study cells that occurred within the eastern portion of the UWB from our dataset because they did not provide sufficient sample sizes of one of the species for genetic analysis. Our final dataset consisted of 1229 chipmunks  and 959 white‐footed mice (237 of 9630 or 2.461% missing genotypes) across 28 study cells.genepop . Two measures of genetic differentiation between study cells, FST and Jost's D  and linkage equilibrium (LE) were calculated in vegan  using Mantel tests. First, a simple Mantel test quantified the relationship between geographic distance and genetic differentiation; a positive relationship would indicate the presence of isolation‐by‐distance  that minimize deviations from HWE and LE. While these programs have been shown to be robust under a number of potential scenarios  at each K under the admixture, correlated alleles model. The optimal K was then determined via the Evanno et al.  at the optimal K were used to calculate q‐values, the proportion of each individual's genome that belongs to each cluster. Q‐values were averaged across the ten longer runs via the program clumpp  as a prior, and based on maximum likelihood and highest posterior probabilities, determines the optimal K. We tested K = 1 through 20 with 10 replicates per K using the “Spatial Clustering of Individuals” option and saved the output for the admixture analysis. Admixture between inferred clusters was calculated using 500 simulations based on observed allele frequencies.For comparative purposes, we also employed a spatially explicit approach in Gene flow in both species is likely a function of both landscape configuration between study cells and landscape complexity within study cells . Edges then function as resistors on an electrical circuit where the magnitude of each resistor can be defined by the resistance surface provided. Resistance distances between study sites for a given resistance surface are calculated by summing all resistors  across all possible pathways. Multiple pathways are more realistic than single path analyses , patch density (PD), and Clumpy . Our first analysis used multiple regression on distance matrices  and three average landscape complexity metrics. Explanatory variables were eliminated from each MRDM model based on Zuur et al.  surface. For the best overall model that combined both configuration and complexity, we selected the model with the highest adjusted R2 among the six reduced MRDM models for each genetic distance. Statistical significance of each explanatory variable and R2 were calculated via 10,000 permutations of the genetic differentiation matrices within the R package ecodist  using principal coordinates analysis (PCoA) and extracts all PCoA vectors that have positive eigenvalues. Then, a redundancy analysis is performed with the PCoA vectors as the response variable. Unlike MRDM, dbRDA requires site‐specific explanatory variables, so we transformed the resistance distance matrices into a connectivity index for each study cell using the following equation: Si is the connectivity index for study cell i, α is a scalar correlated with average dispersal distance of the species, and d is the resistance distance between sample sites i and j (Moilanen and Nieminen FST and DEST). We used the function “capscale” within vegan to perform dbRDA tests and function “ordistep” to perform forward selection to eliminate explanatory variables for each resistance surface. Finally, adjusted R2 for each reduced dbRDA model was calculated via the function “RsquareAdj” in vegan, and like the MRDM analysis, we chose the best reduced dbRDA among the six resistance surfaces based on the highest adjusted R2.Our second complementary analysis utilized distance‐based redundancy analysis (dbRDA), a multivariate analog to multiple linear regression (Legendre and Legendre R2), we sought to calculate measures of variance around each estimate from tested models. Therefore, we bootstrap resampled our dataset for each species 1000 times to calculate means and 95% confidence intervals around each F statistic, regression coefficient, and R2 value. All MRDM and dbRDA tests were performed on each bootstrap permutation as described above. This method of resampling allows for further comparison among models, particularly regarding how landscape resistance hypotheses compare to IBD and IBB. Specifically, if the 95% confidence intervals of model fit estimates  of landscape resistance models did not encompass those of IBD and IBB, we considered this strong evidence for landscape effects on gene flow.While both MRDM and dbRDA provide estimates of model fit , so we eliminated PO‐26 and Pml02 from all subsequent analyses. Although genetic diversities were similar between study cells for both species, many study cells exhibited heterozygote deficiencies  deviated from HWE in all study cells in white‐footed mice. P < 0.001) for both FST (range: 0.010–0.125) and DEST (range: 0.011–0.309). In contrast, only 77.31% of FST (range: −0.001 to 0.052) and 69.79% of DEST (range: 0.001–0.227) values were significantly different from zero in white‐footed mice. For both species, the smallest genetic differentiation values occurred between proximate study cells within the southwestern corner, the most heavily forested study cells, of our study area.All genetic differentiation metrics for chipmunks were significantly different from zero . Geographic distance had differing impacts on chipmunks and white‐footed mice across the UWB. Chipmunks exhibited strong IBD across the study area  and within 25 of 28 study cells . Simple Mantel tests were not significant for IBD across the study area for white‐footed mice , but 23 of 28 IBD tests within study cells were significant . Despite the differences in IBD test results, both species exhibited significant positive spatial autocorrelations for individuals captured 100 m or less apart  in the majority of study cells . Regardless of the size of distance classes within the spatial autocorrelations, results for the smallest distance class remained consistent. Although sample size prohibited distance classes smaller than 50 m, the consistent results across multiple distance classes provide strong evidence for restricted dispersal within study cells.We found that geographic distance impacted genetic differentiation for both species within the UWB, but not the Wabash River . The average likelihoods for K = 2 between the no priors (−56303.0) and location priors (−56304.2) runs also supported K = 2. The first major split generally corresponded to an east–west gradient where eastern individuals were highly assigned to the first cluster and western individuals to the second cluster , but assignments within these clusters were either weak (most q = 0.35–0.75) or clusters only occurred within a single study cell. Based on the strong IBD found within study cells and weak assignments within putative clusters, the further substructure likely reflects a combination of false clusters due to IBD . One cluster contained a few individuals (n = 4) scattered across the study area, a likely “ghost population”  and location priors , but were not distinct from any other K . Furthermore, the majority of q‐values ranged from 0.35 to 0.65, and when plotted, the spatial distribution of the clusters had little clarity , with the vast majority of individuals assigned to a single cluster . One of the clusters had only nine individuals and was not considered valid, whereas the second primarily occurred within a single study cell  regardless of the resistance surface included resistance distance as a significant explanatory variable (Table S3). Pairwise resistance distances  always were positively correlated with genetic differentiation regardless of genetic distance. No MRDM model included any landscape complexity variables.R2) among the resistance surfaces differed based on the genetic distance  explained the most variance (Radj2 = 0.071). The 95% confidence intervals around the adjusted R2 for the reduced IBD model indicated no models containing alternative resistance surfaces were competing models because the 95% confidence intervals did not overlap. For the six DEST reduced models, the reduced model that included the MoveL resistance distances and no complexity variables explained the most variance  incorporated connectivity indices calculated from the MortH resistance surface and Clumpy . Unlike the MRDM analysis, dbRDA found that resistance surfaces with highly resistant urban habitat explained genetic variation better than those with weak resistances for urban habitat This counterintuitive results indicate that MRDM, an extension of Mantel tests, may suffer from reduced power to detect landscape genetic patterns  was important in explaining genetic differentiation Table S4). Results from the dbRDA models also suggested that Clumpy affected genetic differentiation unlike the MRDM analyses. All reduced dbRDA models for . ResultsResults for white‐footed mice, in contrast, supported the evidence for limited genetic structure detected by the Bayesian clustering analysis. No configuration or complexity variables were significant for any resistance surface in the MRDM analyses. Similarly, forward selection in dbRDA always selected the null model across the 1000 bootstrap permutations for each resistance surface. Overall, it appears that landscape configuration and complexity had little impact on gene flow in white‐footed mice.2 UWB. Taken together, it appears that chipmunks’ greater dependence on forest cover explains the differences in gene flow observed between chipmunks and white‐footed mice. Thus, niche specialization on forests appears to be a more powerful influence on our focal species’ gene flow and corresponding sensitivities to fragmentation than body size within the UWB.Despite the ubiquity of chipmunks and white‐footed mice across the highly fragmented UWB, these species exhibited dramatically different patterns of genetic structure across the landscape. Chipmunks exhibited strong IBD, hierarchical genetic clustering, and patterns of genetic differentiation that were correlated with both resistance distances and patch aggregation (Clumpy). White‐footed mice, in contrast, had an overall lack of genetic structure with no signal of IBD, no discrete genetic clusters, and no evidence of landscape effects on genetic structure across the >20,000 kmGenetic differentiation in both chipmunks and white‐footed mice was strongly correlated with geographic distance within study cells. The simple Mantel tests and spatial autocorrelations suggested restricted dispersal within the majority of study cells, particularly with individuals separated by less than 100 m . For chipmunks, fine‐scale genetic differentiation in our study area is consistent with other studies that have documented short dispersal distances in both sexes  within the southwestern portion of the study area along the Wabash River. Forests were concentrated along rivers, so the presence of relatively continuous habitat within the southwestern area of the study area likely facilitated chipmunk gene flow between study cells. In contrast, many more clusters were found in the eastern portion of the study area than the southwestern area, but it is difficult to determine whether genetic differentiation was due to the greater proportion of agricultural lands between study cells or simply the large gaps between study cells that had suitable numbers of chipmunks. Sampling along a genetic gradient  in combination with low levels of genetic differentiation can create false clusters within Bayesian programs , MRDM and dbRDA suggested that landscape configuration metrics  explained more variance than IBD. Combined with the Bayesian clustering results and previous fine‐scale studies , whereas the most variance in dbRDA was explained with MoveH, the model with the highest resistance for urban. Several possibilities could explain this discrepancy. First, only dbRDA controls for the effects of IBD, so MDRM may have not had the power to differentiate landscape effects from IBD. Urban habitats, principally large roads, have been suggested to be barriers to chipmunks  fit the data best. Despite white‐footed mice being a generalist, the overall lack of genetic structure across the UWB was surprising because they possess several ecological and life‐history traits that would favor the formation of genetic structure. Decreased maximum movement by white‐footed mice within agricultural fields as compared to chipmunks  that quantified the relationship between landscape variables  and genetic distance (FST and DEST) in eastern chipmunks.Table S4. Parameter estimates for significant landscape variables (configuration and complexity) within the reduced dbRDA models for eastern chipmunks.Figure S1. Results of the structure analysis of K = 1 to 20 for all sampled chipmunks (n = 1229) across the UWB.Figure S2. Results of the Structure analysis for all white‐footed mice (n = 959) across the UWB.Click here for additional data file.


Angiogenesis is the development of new blood vessels, which is required for tumor growth and metastasis. Signal transducer and activator of transcription factor 3 (STAT3) is a transcription factor that regulates a variety of cellular events including proliferation, differentiation and apoptosis. Previous studies revealed that activation of STAT3 promotes tumor angiogenesis. In this review, we described the activities of STAT3 signaling in different cell types involved in angiogenesis. Particularly, we elucidated the molecular mechanisms of STAT3-mediated gene regulation in angiogenic endothelial cells in response to external stimulations such as hypoxia and inflammation. The potential for STAT3 as a therapeutic target was also discussed. Overall, this review provides mechanistic insights for the roles of STAT3 signaling in tumor angiogenesis. Angiogenesis is defined as the development of new capillaries from pre-existing vessels , 2. PhysGenerally, when the diameter of a tumor is larger than 2 millimeters, the tissue penetration can no longer sustain tumor growth, resulting in hypoxia in the microenvironment , 8. In hVEGF is the most potent pro-angiogenic factor . The expStat3 gene is located on chromosome 17q21.2 [The STAT family includes 7 members: STAT1, STAT2, STAT3, STAT4, STAT5A, STAT5B, and STAT6; all of which act as transcription factors –22. STAT 17q21.2 . STAT3 p 17q21.2 .aPTyr) binds to p65 (RELA) and p50, and the complex competitively inhibits the binding of NF-κB p65 homodimer to its targeted DNA elements, thus preventing NF-κB mediated gene expression during the acute-phase response [The activation of STAT3 is initiated by the binding of growth factors and cytokines with their receptors. As shown in Figure response . Other Sresponse . In mousresponse .Mek5 expression [Recent findings suggest that pyruvate kinase M2 (PKM2), a glycolysis enzyme, directly activates STAT3 , 34. PKMpression , 42. Ovepression .In addition to the canonical and non-canonical phosphorylation of STAT3, acetylation of STAT3 is a post-translational modification of STAT3 in response to cytokine stimulation . OncostaStat3αβ deletion leads to embryonic degeneration and lethality after egg cylinder stage between E6.5 and E7.5 [Stat3α does not cause embryonic lethality, but the newborn −/−Stat3α mice die afterbirth due to dyspnea. Mice lacking Stat3β survive to adulthood; however, they are hypersensitive to lipopolysaccharide (LPS) -induced inflammation. The expression of STAT3β rescues embryonic deaths caused by Stat3αβ-null mutation [STAT3 is required during embryonic development. Two STAT3 isoforms were generated by alternatively splicing, the full-length STAT3α and the C-terminal activation domain truncated STAT3β with a shift from TTCSNTI to FIDAVWK, corresponding to amino acids 716–722 , 47. Gerand E7.5 . Deletiomutation , 50. STAmutation .Stat3 gene in specific tissues or cells in vivo, the conditional knockout models of Stat3 have been generated and crossed with tissue specific Cre mice. Mice with conditional STAT3 deletions in ECs are healthy and fertile, but their mortality after LPS treatment is increased [in vivo [To delete ncreased . STAT3-dncreased , 53. At ncreased , perhapsncreased –58. Therncreased . STAT3 d[in vivo . TogetheVEGF gene promoter [STAT3 engages hypoxia-responsive gene transcription in tumor cells . As showpromoter . Inhibitpromoter . In addipromoter . Glycophpromoter . MMP2, Mpromoter , 69. In promoter . Recent promoter . The miRpromoter . These sThe intercellular communication between tumors and ECs is facilitated by STAT3 . ECs areSTAT3 also mediates pro-angiogenic signaling events in other cell types. For example, activation of STAT3 can be observed in VSMCs, macrophages, dendritic cells, bone marrow stem cells, and fibroblasts. STAT3 mediates the expression of a large number of genes upon different stimulations in multiple cell types Table . ExpressA genome-wide ChIP-assay in macrophages and T cells found that STAT3 binding sites are widely distributed , 101. STSTAT3 requires co-factors such as Sp1 to promote gene expression  Figure . In addip53, Src homology region2 domain-containing phosphatase-1 (SHP-1), and Nr4a3 [p53 at R175H, R273H, and R280K promote tumor angiogenesis [STAT3 also inhibits transcription of a variety of genes. This is associated with DNA methylation as shown in Figure nd Nr4a3 , 114. Innd Nr4a3 . Mutatioogenesis . SHP-1 iogenesis . Little http://stitch.embl.de/) to analyze proteins that interact with STAT3. Figure In addition to interacting with transcriptional co-factors, STAT3 has been reported to bind with many other proteins that may either promote or inhibit its activation. We utilized STITCH , and carbonic anhydrase 9 (CA9) in prostate cancer cells . As discpression . In addipression . Inhibitin vitro .in vitro [At hypoxia condition, ROS mediates STAT3 activation . Nicotinin vitro . In addiin vitro . Mammaliin vitro . TogetheActivation of STAT3 is also detected in ECs in response to abnormal oxygen supply. Hypoxia promotes human retinal microvascular endothelial cells (HRMVECs) migration, sprouting, and tube formation, mediated by increased expression of c-Jun via the proline-rich tyrosine kinase 2 (Pyk2)/STAT3 signaling pathway . HypoxiaChronic inflammation markedly increases the risk of cancer . It geneMuc4 promoter at −123 to −115 and up-regulates its transcription [IL-6, a pleiotropic inflammatory cytokine, is a marker of chronic inflammation . Many licription , 144. Incription . Competicription . IL-6 alcription . In chrocription . Moreovecription . These rin vivo [IL-17 increases IL-6 expression through the AKT pathway in hepatocellular carcinoma (HCC). The IL-17-induced IL-6 activates STAT3 and increases the expression of pro-angiogenic factors such as IL-8, MMP2, and VEGF to promote angiogenesis and tumor growth in vivo . In bothin vivo . The incin vivo . Hou, etin vivo .In vitro, IL-10 treatment increases EPC survival and augments EPC-mediated neo-vascularization via the activation of STAT3/VEGF signaling cascades [Under conditions such as myocardial infarction (MI) and hypoxia, anti-inflammatory cytokines also promote angiogenesis via the STAT3 signaling pathway. IL-10 is a potent anti-inflammatory cytokine. Its effect on angiogenesis has been well documented –158. IL-cascades . IL-10 acascades , and procascades . Other scascades . Other Icascades , 165. ILcascades . Leukocycascades . Taken tInhibition of the STAT3 signaling is a potential therapeutic strategy for tumor and other angiogenesis related diseases , 19. As genus pimedium), restricts renal tumor growth by inhibiting STAT3 activity and VEGF expression in vivo [Both natural materials and synthetic small molecules were developed to inhibit STAT3. Along with the increased understanding of microRNAs, a number of non-coding RNAs have been found to modulate the STAT3 signaling pathway. The most common inhibitors are designed to directly inhibit STAT3 expression and activation. For example, both tanshinone I and acacetin are natural materials that inhibit STAT3 activation by preventing STAT3 705 Tyr-phosphorylation in ECs and cancer cells. Treatment with these molecules down-regulates VEGF expression and attenuates vascular formation . Astaxan in vivo . As show in vivo . Other m in vivo , 192, mi in vivo , and mic in vivo  are also in vivo .2S) donor, suppresses STAT3 activation by effectively reducing p-STAT3 levels in cancer cells. GYY4137 treatment leads to apoptosis and reduced release of angiogenesis factors in HepG2 cells [Inhibitors might also target components of the signaling pathway upstream of STAT3. LCB03-0110, a thienopyridine derivative, inhibits VEGFR-2 and c-Src kinase activity via preferential binding to the ATP-binding site. Consequently, STAT3 activation in ECs is inhibited, and angiogenesis is reduced . IndirubG2 cells . MicroRNG2 cells . MicroRNG2 cells , and inhboswellia serrata, and butein has been isolated from numerous plants. Both AKBA and butein inhibit STAT3 via SHP-1 in human multiple myeloma [SHP-1, SOCSs and protein inhibitor of activated STAT (PIAS3) are negative regulators of STAT3 and become the targets for suppression of STAT3 activity . AKBA is myeloma , 200. Re myeloma . PIAS3 i myeloma . In addi myeloma , 204. In myeloma  and micr myeloma  are demoAngiogenesis is required for tumor development, and inhibition of angiogenesis is a promising strategy for tumor treatment. Angiogenesis is a complex process that is mediated by multiple factors, among which STAT3 is one of the most prominent transcription factors. Hypoxia and inflammatory factors activate STAT3 to promote tumor angiogenesis. Persistent activation of STAT3 contributes to HIF-1α and VEGF expression in cancer cells and other non-ECs. VEGF then activates STAT3 in ECs, and promotes cell proliferation, migration, and survival. In addition, STAT3 inhibits the expression of tumor suppressors, such as the anti-angiogenesis transcription factor p53.in vivo. The identification of more selective agents and combination use of the targeted inhibitors will be important challenges for development of therapeutic strategies.Although many studies have demonstrated the positive role of STAT3 in promoting tumor angiogenesis, further research is needed to illuminate the detailed mechanisms of STAT3 in this process. For instance, it is worth noting that members of the STAT family are highly conserved, and STAT1, STAT5 and STAT6 also participate in vascular formation. Current studies focused on the effects of STAT3 homodimers in angiogenesis, but STAT3 might also form heterodimers with STAT1 or STAT5. The interactions between these STATs in regulating angiogenesis need to be furtherly investigated. Treatment strategies involving the inhibition of STAT3 activity have been investigated and specific agents are demonstrated to be effective. Nevertheless, treatments with single agent have not achieved an optimal effect due to the complexity of STAT3 regulation