Procambarus clarkii is one of the most threatening freshwater species in the world. Since 2016, it has been included in the Invasive Alien Species list of the European Union, and it is still actively colonizing new territories at the expense of native crayfish. The present study aimed to provide a genetic characterization for a better understanding of the invasion routes of this alien species in the Italian Peninsula. The analysis of the mitochondrial DNA of 153 P. clarkii samples collected from five Italian lakes and one river showed the presence of unique maternal lines in four of these basins, thus confirming the hypothesis of multiple introductions and strengthening the idea that knowledge of dispersion routes can be very useful to predict the invasiveness of this species and create control strategies to preserve biodiversity.The red swamp crayfish Procambarus clarkii is one of the most threatening freshwater species in the world. The aim of this study is to provide a better understanding of the phylogeography and the invasion routes of P. clarkii populations in the Italian Peninsula through the analysis of mitochondrial phylogeny. Mitochondrial control region and cytochrome c oxidase subunit I (COI) sequences of 153 samples collected from six Italian basins were analyzed and compared to worldwide data. Except for the lakes Bolsena and Posta Fibreno, a high genetic variability was found in the other basins. The mitochondrial DNA pattern of P. clarkii from the lakes Candia and Massaciuccoli confirmed the hypothesis of double introduction events. Another entry point could be represented by Lake Trasimeno, which shows haplotypes originating from Louisiana and not shared with other Italian basins. Moreover, unique lineages were also found in the Stella River, thus enhancing the hypothesis that multiple introductions of P. clarkii occurred in northern and Central Italy and strengthening the idea that knowledge about the dispersion routes of this alien species can be useful to predict its invasiveness and elaborate control strategies to preserve biodiversity.The red swamp crayfish  Biological invasions are believed to be one of the main drivers of global change, with negative environmental  and sociProcambarus clarkii  Cambaridae has spread from its native range  through both accidental and intentional human actions, becoming a worldwide invader, except for Australia and Antarctica [The Louisiana red swamp crayfish tarctica ,4. The htarctica , and cantarctica  and enviGirard, 152 Cambartarctica .Aphanomyces astaci) and an importer of pathogenic microfungal taxa such as Phoma glomerata (ubiquitous Coelomycetes), potentially dangerous to human health, flora and fauna [It is an asymptomatic potential carrier of the crayfish plague  in the Delivering Alien Invasive Species Inventory for Europe (DAISIE) in Regulation (EU) No. 1143/2014 of the European Parliament, and in the Council of 22 October 2014 on the prevention and management of the introduction and spread of invasive alien species. Invasive alien species management is closely related to the identification of the invasion routes and could be very helpful to establish possible approaches to prevent and control alien species invasions [Due to the abovementioned characteristics, nvasions .P. clarkii was reconstructed by analyzing the COI gene from 1416 samples, reporting two independent invasion routes in the USA, more introduction routes towards Europe and a single event into Asia [P. clarkii were carried out in Chinese populations and highlighted a high level of genetic diversity, with irregularly distributed populations in accordance with the genetic differentiation as a consequence of the geographic isolation [In this scenario, population genetics represents an informative tool to facilitate the effective ecological restoration of invaded ecosystems , and to nto Asia . Neverthsolation ,24,25,26P. clarkii in Ash Meadows (Nevada), demonstrating how measures of genetic distance and genetic diversity within populations could be very helpful to infer the frequency and the location of dispersal routes used by an invasive species [In 2014, Paulson and Martin analyzed the genetic landscape of  species . A surve species , who ide species , while d species . P. clarkii in Europe was firstly recorded in 1973, with the deliberate and legal introduction of this species into Spain as a commercial resource. The crayfish was firstly imported from New Orleans  into a farm in the Spanish province of Badajoz  and then into a pond near Guadalquivir  [The invasion of Seville) ; then, iProcambarus clarkii was introduced in Italy in 1977, even if its presence was recorded for the first time in 1989. The existence of P. clarkii in this area could be due to the presence of a small intensive crayfish farm from 1977 to 1985. It is not clear whether the release occurred accidentally or if it was human-mediated [P. clarkii mitochondrial control region, which have allowed a better understanding of its rapid invasion in China [P. clarkii populations of northern and Central Italy, in order to (i) outline their genetic relationships, (ii) investigate their origins and dispersal routes, (iii) identify likely source populations and (iv) determine whether multiple introductions have occurred in these areas, by focusing on the role of single versus multiple events.Several studies focused on its genetic diversity, and a low variability was observed both in France  and in tmediated . The spein China  and an iin China . In the P. clarkii were collected from six Italian basins with different biotic and abiotic characteristics: five lakes  and one river (Stella).A total of 153 individuals of P. clarkii [Lake Candia (ZSC-IT1110036) is in the north-west of the Italian Peninsula and is located near to Carmagnola (Turin province), which is considered the first entry point of this invasive species in Italy. Lake Massaciuccoli (ZSC-IT5120021) is a brackish lake located in the Migliarino, San Rossore, Massaciuccoli nature park  and is supposed to be the second biotope invaded by  clarkii ,38. The  clarkii . The vol clarkii  whose reAll samples were analyzed in order to determine DNA content and to sequence two mtDNA markers: the control region (CR) and the cytochrome c oxidase subunit I (COI).® Genomic DNA Purification Kit  and DNeasy® Tissue Kit di Qiagen  and a good quality of DNA was obtained from both.Genomic DNA was extracted both from muscle and hepatopancreas tissues using the WizardThe primers used for mtDNA control-region amplification were previously published , and the2O to a final volume of 25 µL. The PCR amplification was carried out as follows: 94 °C for 2 min, followed by 35 cycles of 95 °C for 30 s, 55 °C for 1 min, 72 °C for 1 min and then 72 °C for 10 min. The PCR fragments were purified using exonuclease I and alkaline phosphatase  and subsequently Sanger-sequenced with the following primers: LCO1490 and HCO2198 [www.genecodes.com (accessed on 12 August 2022)). In order to ensure the uniformity of all sequences, they were trimmed at the same range, from the nucleotide position (np) 4717 to np 5434 for the mtDNA CR marker, and from np 78 to np 651 for the COI region.The PCR reactions contained 1X Buffer GoTaq, 2.5 mM of each deoxyribonucleotide triphosphate (dNTP), 0.3 µM of each primer, 0.03 U/µL of GoTaq DNA polymerase , 30 ng of genomic DNA and H HCO2198  for COI  HCO2198 ) for theThe COI region was investigated in 53 samples, selecting at least 2 samples for each CR HT from each basin.www.ub.edu/dnasp/index_v5.html (accessed on 12 August 2022)). Haplotype number codes were assigned to each sample as previously classified in Dörr et al., 2021 [Mitochondrial DNA sequence variation parameters were estimated by using DnaSP 5.1 software  based on concatenated mtDNA (COI and CR) haplotypes was performed using the Excel software implemented by XLSTAT. Haplotype frequencies were used as input data.Finally, to construct a Bayesian phylogeny for the two combined mtDNA regions, we used BEAST.v2.7.3 and the analyses were run for 100,000,000 generations .P. clarkii have been widely described. It is a species native to Louisiana and arrived in Europe with a first translocation into Spain in 1973 [The migratory and diffusion routes of  in 1973 . Its ecoP. clarkii, sampled in six Italian basins  of n basins  and compThe overall alignment of 718 base pairs (from nucleotide position (np) 4717 to np 5434) of the 153 control-region sequences from this study showed an overall low haplotype diversity (Hd = 0.726), with a total of ten haplotypes (HTs) and 22 polymorphic sites (S) detected .We observed low values of haplotype diversity for individuals from lakes Bolsena and Trasimeno, and the Stella River , whereas the highest value was recorded for Lake Massaciuccoli (Hd = 0.806). These results seem to confirm the hypothesis that considers Lake Massaciuccoli as the area from which this invasive species spread across the Italian Peninsula through different man-made translocations ,43.Among ten mtDNA control-region haplotypes, the most represented was HT01, present in all Italian basins analyzed, followed by HT02 and HT03, with frequencies of 0.34, 0.29 and 0.27, respectively . The othP. clarkii individuals could have arrived in this basin through a direct translocation, probably from Spain, and not through a dispersal event from Massaciuccoli.The comparison with available sequences from USA, China and Japan , uniformNotwithstanding the general low genetic diversity, all the Italian populations showed haplotypes never observed before, with one mtDNA lineage (HT01) present in all basins here analyzed, but seven unique haplotypes (HT04-HT10) retrieved in three lakes , supporting the hypothesis of multiple introductions from different areas . In partThe analysis of 53 COI sequences showed a very low variability, with an overall haplotype diversity of 0.549 and a total of six haplotypes (HTCOI01-HTCOI06) identified .Despite the low number of analyzed samples from the Stella River, this basin appears to be the most heterogeneous, with four COI haplotypes identified  . The oppProcambarus clarkii shows a defined spatial genetic structure within the Iberian Peninsula, caused by two different invasion events that originated one genetic cluster in Portugal and another in Spain [By looking at the network analysis, there are three unique haplotypes  found in lakes Trasimeno and Massaciuccoli and the Stella River, respectively. The comparison with published sequences pinpointed a worldwide distribution of HTCOI01, found in American ,27,44, Ein Spain . After tin Spain , it was P. clarkii populations , each presenting different unique haplotypes among our samples, in some cases shared with published data from other countries , our analysis highlighted four heterogeneous ountries .Even if in the invaded areas a decline in diversity could be observed due to a founder effect, most studies reported high levels of genetic variability within and among introduced populations from non-native ranges . This isMoreover, through an accurate comparison between our CR and COI haplotypes, we observed that in some cases different CR HTs correspond to different COI HTs, while in other cases we found a direct connection, thus enhancing the importance of investigating different molecular markers in parallel. Control-region HT06 appears paired to HTCOI04, HT08 to HTCOI03 and HT09 to HTCOI01. While HTCOI01 is the most common COI haplotype among our samples and is connected to almost all CR haplotypes here identified, HTCOI03, typical of Lake Massaciuccoli, and HTCOI04, exclusively found in Lake Trasimeno, are each related to only one CR haplotype (HT08 and HT06) and have been stated as originating from western and eastern Louisiana, respectively ; this fiThe analysis of haplotypes obtained from combined mtDNA markers  showed aP. clarkii sampled in the Massaciuccoli and Trasimeno sites are clearly separated in the first and second quarter from the PC2, which pushes these two populations apart from each other due to the different distribution of concatenated haplotypes.The results derived from the merged dataset highlighted northern sites (Candia and Stella) clustering together, as for lakes Bolsena and Posta Fibreno, which showed a similar maternal clustering. Conversely, P. clarkii into Central Italy  markers showed 14 combined haplotypes and pointed out the presence of unique haplotypes above all in the Massaciuccoli and Trasimeno lakes, thus confirming our results and the possibility of two distinct introductions of al Italy .In a worldwide context, the analysis of mtDNA CR and COI haplotypes here identified highlighted the highest genetic variability in the USA with respect to the other geographic areas previously described .P. clarkii. Among the haplotypes identified in this study (HT01-HT10 and HTCOI01-HTCOI06), only seven (three from CR and four from COI) were retrieved in other countries. Except for those samples without a specified geographic origin , the other areas showed a decline in mtDNA diversity, as stated for the invaded regions. The control-region haplotypes HT02, HT09 and HT10 were previously found only in Louisiana [Even though there are only limited data we could consider for a comparison with our results, due to differences in type and length between our data and those previously published, this scenario is in agreement with the literature, as we could observe the maximum genetic variation within the native range of ouisiana  and wereouisiana ,23,25,26P. clarkii in the north-western part of the Italian Peninsula , and we here propose another entry point represented by Lake Trasimeno, which shows unique haplotypes originating from Louisiana and not shared with the other Italian basins here analyzed. Lastly, but no less interesting, the presence of unique haplotypes of P. clarkii also in the Stella River could enhance the hypothesis of multiple invasion routes into the Italian Peninsula, which deserves to be further investigated.The comparative analysis between our findings and those retrieved from GenBank could be improved by increasing the number of samples and sampling sites and expanding the molecular analysis at higher levels in order to complete the COI sequence or analyze the complete mitogenome. Nevertheless, the results here described demonstrate the importance of the mitochondrial genome  as a valuable molecular marker for exploring crayfish phylogeography. Additionally, our genetic data confirmed the hypothesis of the double introduction events of


Lasiurus semotus) locally known as ʻōpeʻapeʻa, is no exception. To effectively manage this bat species with an archipelago-wide distribution, it is important to determine the population size on each island and connectivity between islands. We used 18 nuclear microsatellite loci and one mitochondrial gene from 339 individuals collected from 1988–2020 to evaluate genetic diversity, population structure and estimate effective population size on the Islands of Hawaiʻi, Maui, Oʻahu, and Kauaʻi. Genetic differentiation occurred between Hawaiʻi and Maui, both of which were differentiated from Oʻahu and Kauaʻi. The population on Maui presents the greatest per-island genetic diversity, consistent with their hypothesized status as the original founding population. A signature of isolation by distance was detected between islands, with contemporary migration analyses indicating limited gene flow in recent generations, and male-biased sex dispersal within Maui. Historical and long-term estimates of genetic effective population sizes were generally larger than contemporary estimates, although estimates of contemporary genetic effective population size lacked upper bounds in confidence intervals for Hawaiʻi and Kauaʻi. Contemporary genetic effective population sizes were smaller on Oʻahu and Maui. We also detected evidence of past bottlenecks on all islands with the exception of Hawaiʻi. Our study provides population-level estimates for the genetic diversity and geographic structure of ‘ōpeʻapeʻa, that could be used by agencies tasked with wildlife conservation in Hawaiʻi.Island bat species are disproportionately at risk of extinction, and Hawaiʻi’s only native terrestrial land mammal, the Hawaiian hoary bat ( Lasiurus semotus), or ʻōpeʻapeʻa in the Hawaiian language   , as wellridanus) .No microsatellite marker studies of ʻōpeʻapeʻa have been published, despite this marker class remaining an important tool for landscape population genetic questions and suitability in cases where the use of SNPs are not a satisfactory tool . Our pren = 197) captured in mist-nets according to n = 142, 2007–2020) submitted from wind energy facilities, the Hawaiʻi State Division of Forestry and Wildlife, the U.S. Geological Survey’s National Wildlife Health Center Honolulu Field Station, the Hawaiʻi Wildlife Center (2007–2020), and from four museum skins  during 1988–1999, vouchered in the Bernice Pauahi Bishop Museum . Carcasses were refrigerated or frozen upon discovery. Muscle tissues from necropsies were stored at −20 °C. A sterile 3-mm biopsy punch was used to obtain tissue from each wing of carcasses and live bats. Wing tissues were stored in NaCl-saturated 20% DMSO or on silica gel desiccant beads in the field, then frozen at −20 °C until extraction. USGS procedures are consistent with the guidelines for capture, handling, and care of mammals according to the Institutional Animal Care and Use Committee (IACUC) through the University of Hawaiʻi (#04-039-17) and the American Society of Mammalogists  following the manufacturer’s protocol for purification of total DNA from animal tissues, or a QIAmp DNA Micro Kit  with protocol modifications for wing punches . Isolatevia ABI Prism 3500 Genetic Analyzer  at the University of Hawaiʻi at Hilo Core Genomics Facility (UHH CGF) or ABI 3730xl DNA Analyzer  by Sequetech DNA sequencing services . Sequence chromatograms were manually trimmed, edited, and contigs formed in Geneious Prime v.2020.2.4. All sequences were checked against GenBank using a BLAST search to confirm a match to ʻōpeʻapeʻa. Sequences produced in this study (n = 262) were combined with ʻōpeʻapeʻa COI sequences from (n = 59), available on GenBank (accession numbers KR350020 through KR350078). The combined dataset was aligned and trimmed by eye using MEGA-X v10.2.0 software (n = 166), Maui , Oʻahu , and Kauaʻi , spanning collection years 2005–2019  was amplified using the forward primer HCO2198 (5′-TAAACTTCAGGGTGACCAAAAAATCA-3′) and the reverse primer LCOI490 (5′-GGTCAACAAATCATAAAGATATTG-3′) . Polymerces from ; n = 59,software . The tot005–2019 .L. cinereus) were amplified in ʻōpeʻapeʻa samples (n = 24) of samples on both platforms and all microsatellite data were analyzed using Tandem  and expected (He) heterozygosity, based on Hardy-Weinberg Equilibrium (HWE), were performed in Arlequin v3.5.2.2 , and Watterson’s theta (Θ) (per site and per sequence) were calculated using DnaSP v6.12.03  and percent sequence divergence were calculated using MEGA-X v10.2.0 , whereasv6.12.03 . Those dv6.12.03  and visuv6.12.03 .n = 32 gene copies .Genetic diversity was assessed for nuclear microsatellites, with the number of polymorphic loci, number of alleles, expected and observed heterozygosity, gene diversity, and inbreeding coefficients calculated in Arlequin v3.5.2.2 . To estiFST and analysis of molecular variance (AMOVA) of mtDNA and microsatellite data, calculations were conducted in Arlequin v3.5.2.2 , and (2) with mostly even sample sizes . Structure parameters were set to assume admixture, correlated allele frequencies within populations . The interpretation of Structure output was aided by Structure Selector  with the program Structure v2.3.4 . Becauseulations , and theSelector , which cSelector ; and supSelector .FST/(1- FST)) and log transformed geographic distance in GenePop  for correlations between microsatellite-based matrices of Edward’s genetic distance and Euclidean geographic distances using package “adegenet” v2.1.3 in R ; and (2) GenePop . Isolativersus even migration rates between all pairs of islands; (2) archipelago-wide panmixia; (3) Maui as the source population of Oʻahu and Hawaiʻi populations (with no self-recruitment); (4) directional migration from Hawaiʻi to Maui and Oʻahu; (5) directional migration from Maui to Oʻahu and Hawaiʻi; and (6) directional migration from Oʻahu to Maui and Hawaiʻi. Migrate-n recommends limiting population modeling to two or three populations, thus we excluded Kauaʻi from this analysis. Microsatellite data were recoded using the amplicon length divided by repeat number (standardized in Tandem), and a Brownian motion model was used to estimate the stepwise mutation of microsatellites. Uniform prior distributions of theta were bound at 0 and 150, window size 15, and migration parameter (M) bound at 0 and 800, with window size set to 80 and the mutation rate option set relative to the data. Model runs consisted of 25 million generations, sampling every 500 generations, and using a burn-in of 6.25 million. Four variably heated chains were used following the methods of  that may have occurred recently between Hawaiʻi, Maui, Oʻahu, and Kauaʻi, microsatellite data were analyzed using BayesAss v3.0.3 . Each iser v 1.6  to ensurK = 4, based on model (K) selection. The migration rate priors were varied  to assess sensitivity  and we used a burn-in of 200,000 and run length of 500,000 MCMC chains.To complement the BayesAss analysis, recent migration was assessed with the gensback option (set at 2) in Structure v2.3.4 . SamplinFor carcasses that could not be reliably sexed from morphological features, genetic methods were employed to identify sex for tests of sex-biased gene flow. Of 113 samples that lacked morphological sex data, sex genotyping was performed by amplifying regions of introns on the Zfx and Zfy sex chromosomes using PCR and visualizing fragments by electrophoresis .n = 295, 4 groups) and for regional population samples within each island; H = 130, 4 groups; M = 99, 4 groups; O = 49, 2 groups; and K16, 2 groups. To compare dispersal probabilities between sexes of adults in our data, we evaluated the mean and variance assignment indices , FST and FIS, relatedness, observed heterozygosity (Ho) and gene diversity (Hs) measures as described in p one-sided hypothesis testing option using FSTAT v2.9.4  and long-term (Ne) effective population sizes were estimated using complementary mitochondrial and microsatellite DNA techniques. The mtDNA COI sequences represent the long-term historical female effective population size from tens to thousands of generations before present. These estimates were calculated with the equation θ=2Neu, with the Watterson’s estimator (θ) value determined using Arlequin v3.5.2.2  size was estimated from the average expected heterozygosity (He) of the microsatellite loci using two mutation models, the infinite allele model (IAM) and the stepwise mutation model (SMM). Here, Ne was calculated using the respective equations: SMM Ne = { (1/1-H)2-1)/8µ, and IAM Ne = H/(4µ(1-H) }  } , with muEf and Ne estimates do not differ from contemporary (recent generations) effective population size (NeC) was tested using the microsatellite linkage disequilibrium method in NeEstimator V2  simulated for each island using the software Critical M (ρg) to 0.2, the size of one-step changes (Δg) to 3.5, and varying θ from 0.01 to 10, which encompassed all estimates of θ produced by mitochondrial sequences  were genotyped at all 19 microsatellite loci, with a minority of samples  genotyped at only 9–18 loci (FIS) ranged from 0.013 (H) to −0.035 (O) and were not significant , consistent with absence of the Wahlund effect within individual island datasets  or within islands (1.76%), with 90.6% of variance due to the differences within individual bats . Consistent with population structure, global F-statistics across all microsatellite loci were significant  [−0.005–0.04], FST = 0.076 [95% CI [0.05–0.09]], and FIT = 0.093 95% CI [0.06–0.12]).An AMOVA test for mitochondrial haplotype differences showed significant differentiation among and within islands, with variance among islands at 67% compared to within islands at 33%, F  = 0.669, FST of nuclear microsatellites were lower than those calculated using mtDNA COI, a pattern that is typical of the two marker types. However, both datasets revealed significant genetic differentiation between all island pairs (p < 0.05), with the exception of Hawaiʻi and Kauaʻi  or O’ahu . Regression of genetic distance [(FST/(1- FST)] and log transformed geographic distance supported the alternative hypothesis that distance had an effect on genetic differentiation among collection sites across four islands , and within Maui . However, the null hypothesis could not be rejected for collection sites within Hawaiʻi, Oʻahu, or Kauaʻi.Genetic structure across all island bat populations was further corroborated by the Mantel test correlation between matrices of Edward’s genetic distance and Euclidean geographic distances across collection sites on four islands (FIS) measures had a pattern of weak male dispersal; however, this was not significant after p one-sided hypothesis testing. Only samples from Maui showed significance for male biased dispersal in mAIc, vAIc, and HS. Within Hawaiʻi, mAIc, FST, and FIS measures had a pattern of female-biased dispersal, but vAIc supported male dispersal. Within Oʻahu, mAIc supported female dispersal, while vAIc and FST indicated male dispersal; however, FIS supported no dispersal of either sex. Within Kauaʻi, mAIc, FST, and FIS supported female dispersal. The p one-sided hypothesis testing was not significant for any measures within Hawaiʻi, Oʻahu, or Kauaʻi. Within Maui, male biased dispersal was supported with mAIc, vAIc, and FIS measures, and for mAIc, vAIc, and HS was significant after hypothesis testing.Evidence for sex biased dispersal was inconsistent across test metrics and groups . Across Results from BayesAss produced contemporary migration proportions, with confidence intervals including zero, thus the proportion of migrants was not significantly different from zero . Rare dieµ)} was ∼3 times larger than O′ahu and ∼1.4 times larger than Hawaiʻi (−3) from Comparison of Bayes factors and model probabilities across six dispersal models from Migrate-n resulted in the probability of 1.00 for a model placing Maui as the “source” population of Oʻahu and Hawaiʻi migrants . In this scenario, Maui’s historical theta {Θ (4N Hawaiʻi . The mutEf) estimates from COI mitchondrial sequence data were similar across Hawaiʻi, Maui, and Oʻahu  with the Kauaʻi sample showing a smaller range . The long-term Ne trended higher for the SMM model compared to the IAM, with Ne bracketing a wide range of 248 to 62,162. Estimates from averaged results between the model types for each mutation rate had a pattern of larger Ne for Hawaiʻi and Maui , and smaller Ne for Oʻahu and Kauaʻi . The average estimates using the lower mutation rate produced values that fell within the range of historical NEf estimates from mitochondrial sequences.Long-term historical female effective population size (Nand Maui . Long teeC) of ʻōpeʻapeʻa across all islands and collection years were far smaller than historical long-term NEf and Ne, but confidence intervals included infinity for Hawaiʻi and Kauaʻi, which precludes making strong inferences and indicates potentially larger NeC populations , indicating either past population size reductions or possible bottleneck events across Maui ; Oʻahu (0.32 ± 0.20), Mc range [0.65–0.80]); and Kauaʻi (0.25 ± 0.15), Mc range [0.58–0.80]). For Hawaiʻi the M-ratio standard deviation encompassed critical M range  The Tajima’s D neutrality test indicated a past expansion for the Hawaiʻi population, showing significant negative values across most rarefied groups   . It is aMyotis bechsteinii  method, contemporary estimates of genetic effective population size (NeC) vary by island, with the Hawai′i estimate exceeding 1,000 individuals. On Hawaiʻi and Kauaʻi estimates were imprecise having infinity as an upper bound in confidence, and indicating NeC could be greater than 500, as the LD method is known to be more precise when effective population size is <500  and long-term Ne overlap when confidence intervals of the latter are taken into consideration. Historical genetic effective population sizes likely reflect the ancestral high genetic diversity of founding L. cinereus individuals from a large continental population.Long-term effective population estimates for all ʻōpeʻapeʻa sampled in this study produced a range of 248 to 62,162 bats. We reported both SMM and IAM models using high, average, and low mutation rates because evolutionary mutation rates can vary within and across loci. These averaged models produced similar estimates for each island and indicate that long term maintenance of genetically diverse populations may require several hundred to tens of thousands of ʻōpeʻapeʻa. Historical female genetic effective population estimates for ʻōpeʻapeʻa presented wider ranges of 1,384 to 286,658 bats; however, the precision of these estimates is unknown because confidence intervals cannot be calculated. Molecular population genetic estimates of genetic effective population size between historical female  rather than exclusive of reduction in population size or effective size , available on GenBank (Accession numbers KR350020.1 through KR350078.1).Female (F), male (M), unknown (U). MtDNA numbers include 262 sequences from the current work plus 59 sequences obtained from Click here for additional data file.10.7717/peerj.14365/supp-2Supplemental Information 2Lasiurus.cinereus) from Korstian et al. (2015) and Keller et al. (2014).Repeats, size range, and number of alleles represent those found with the loci in northern hoary bats , allelic richness (Ar), observed (H o) and expected heterozygosity (He), significance level of Hardy-Weinberg (HWE) tests, null alelle frequencies based on Van Oosterhout et al. (2004), and inbreeding coefficient (FIS). The term mono is used to describe loci that were monomorphic.Number of individuals amplified at locus (n), number of alleles , Watterson’s theta estimator (θ) per site and per sequence.Number of sequences obtained (n), number of haplotypes (H), number of polymorphic sites (S), haplotype diversity (Hd) with standard deviation (SD), nucleotide diversity , mean observed heterozygosity (Ho), average gene diversity, and population inbreeding coefficient (FIS). Means are reported with standard deviations.Sample size (n), number of polymorphic loci, allelic richness (Ar), mean number of alleles, mean expected heterozygosity  adjusted for null alleles using the ENA method in FreeNA  are located below the diagonal, while associated 95% confidence intervals are given in brackets above the diagonal.Pairwise FClick here for additional data file.10.7717/peerj.14365/supp-7Supplemental Information 7eC) by collection year period based on microsatellite linkage disequilibrium and 95% confidence intervals (CIs) from jackknife and parametric resampling for ʻōpeʻapeʻa (Hawaiian hoary bat: Lasiurus semotus). Values were computed with Ne Estimator V2  by island rarified to the number of cytochrome c oxidase I (COI) mitochondrial sequences available for each island.Fu’s Fs values for ʻōpeʻapeʻa  from cytochrome c oxidase I (COI) mitochondrial sequences by island and collection year period. ** P < 0.001, * P < 0.001Tajima’s D and Fu’s Fs values for ʻōpeʻapeʻa , Watterson’s theta per site (bold x), and Watterson’s theta per sequence in brackets above bars. Means are reported error bars that represent standard deviations.(A) number of haplotypes (h), number of polymorphic sites (S) appear above bars in brackets, mean haplotype diversity (Hd). (B) mean nucleotide diversity , including 5 individuals from Oʻahu with potential heteroplasmy. Unique haplotypes are represented by a colored circle, while islands are defined by separate colors. Lines with dashes between circles represent number of base pair changes between two haplotypes. Number of samples in each haplotype correspond to circle sizes in legend.Haplotype parsimony network constructed from 21 unique mitochondrial haplotypes of the CO1 region of 321 ʻōpeʻapeʻa  over ancestry membership in program Structure (Distribution of mitochondrial clade and potential identification of migrant ancestry for individual ʻōpeʻapeʻa (Hawaiian hoary bat: tructure  plots foClick here for additional data file.10.7717/peerj.14365/supp-14Supplemental Information 14Data from 298 individuals at 18 microsatellite lociClick here for additional data file.10.7717/peerj.14365/supp-15Supplemental Information 15Collection details for each individual, coordinates and groupings for IBD testing, groupings for sex biased dispersal testing.Click here for additional data file.


The Qinba region is the transition region between Indica and Japonica varieties in China. It has a long history of Indica rice planting of more than 7000 years and is also a planting area for fine-quality Indica rice. The aims of this study are to explore different genetic markers applied to the analysis population structure, genetic diversity, selection and optimization of molecular markers of Indica rice, thus providing more information for the protection and utilization on germplasm resources of Indica rice.NlaIII and MseI digestion, referred to as SNPs-NlaIII and SNPs-MseI, respectively) for this panel of 93 samples using the Illumina HiSeq2000 sequencing platform, were employed to explore the genetic diversity and population structure of 93 samples.Fifteen phenotypic traits, a core set of 48 SSR markers which originated protocol for identification of rice varieties-SSR marker method in agricultural industry standard of the People's Republic of China , and SNPs data obtained by genotyping-by-sequencing  and diversity index (He) were 29.72% and 1.83 ranging from 3.07% to 137.43%, and from 1.45 to 2.03, respectively. The correlation coefficient between 15 phenotypic traits ranged from 0.984 to -0.604. The first four PCs accounted for 70.693% phenotypic variation based on phenotypic analysis. A total of 379 alleles were obtained using SSR markers, encompassing an average of 8.0 alleles per primer. Polymorphic bands (PPB) and polymorphism information content (PIC) was 88.65% and 0.77, respectively. The Mantel test showed that the correlation between the genetic distance matrix based on SNPs-NlaIII and SNPs-MseI was the largest (R2=0.88), and that based on 15 phenotypic traits and SSR was the smallest (R2=0.09). The 93 samples could be clustered into two subgroups by 3 types of genetic markers. Molecular variance analysis revealed that the genetic variation was 2% among populations and 98% within populations (the Nm was 0.16), Tajima’s D value was 1.66, the FST between the two populations was 0.61 based on 72,824 SNPs.The average of coefficient of variation (The population genetic variation explained by SNPs was larger than that explained by SSRs. The gene flow of 93 samples used in this study was larger than that of naturally self-pollinated crops, which may be caused by long-term breeding selection of Indica rice in the Qinba region. The genetic structure of the 93 samples was simple and lacked rare alleles. Oryza sativa Xian group was simple and lacked rare alleles.It was found that there was a significant correlation between the genetic distance obtained by the two types of SNPs markers, while the lowest correlation occured between the genetic distance of phenotypic traits and SSR data. The population genetic variation explained by SNPs was larger than that explained by SSRs among DNA molecular markers. Bayesian clustering algorithm was superior to the other two clustering methods. The genetic structure of 93 samples representative of the diversity present in Qinba area in China of Oryza sativa L. are classified as O. sativa L. subsp. hsien Ting and O. sativa L. subsp. keng Ting proposed by Ding Y [O. sativa L. subsp. indica Kato  and O. sativa L. subsp. japonica Kato  for O. sativa L. proposed by Kato was used internationally at the present [NlaIII and MseI) were selected by simulated whole-genome enzyme digestion, which generated RAD tags for sequencing to obtain SNPs datasets, referred to as SNPs-NlaIII and SNPs-MseI, respectively. Simultaneously, a core set of SSR markers from NY/T1433-2014 [According to the origin and the history of rice cultivate in China, the two major types of y Ding Y , 2, and  present , in whic present –7. The I present , 9. In t present –12. Thes present –18. SSR  present . However present , 20. A s present . The fro present . In this433-2014  that oriCV) was 29.72% on average and ranged from 3.07% (brown rice rate) to 137.43% . The average diversity index (He) was 1.92 and ranged from 1.55 to 2.08, with brown rice rate and chalkiness having lower He, indicating that there were relatively few phenotypes in these two traits. Overall, the Indica rice materials tested had relatively more phenotypes on these 15 traits; the distribution in each phenotype was uneven Tables Data of the 15 phenotypic traits of the 93 samples are summarized in Table Average Euclidean distance was 5.19, ranging from 0.90 (between W723 and W742) to 13.73 (between W699 and W733). Clustering result based on the 15 phenotypic traits was shown in Fig. A total of 378 bands was detected using 48 core SSRs primer pairs Table . Among tPC, in which the first three PC  to select and their cumulative contribution of variance accounted for 15.76%, and the unweighted pair-group method with arithmetic means (UPGMA) were performed, which demonstrated that the 93 genotypes could be divided into 2 subgroups  lower limit of 0.05 using NlaIII and MseI digestion, respectively. Merged data of SNPs-NlaIII and SNPs-MseI, with a total of 72,824 SNPs including 67,621 SNPs that aligned to specific chromosomes and 5,023 SNPs unlocalized, were then obtained.A total of 39,872 SNPsR2 values, was calculated for the 84,255 SNP pairs. R2 value had a minimum of 0.2 and an average of 0.73. 46,322 SNP pairs (54.98%) had R2 values higher than 0.8, while 7,841 pairs (9.31%) were in complete LD (R2=1). The 12 chromosomes yielded a total of 6,568 predicted haplotypes , 326,873 (5.198%) were heterozygous. The 67,621 SNPs were unevenly distributed on the 12 chromosomes Fig. a; chromoFST) between the two populations was 0.61, and gene flow (Nm) was 0.16. Further investigation showed that the gene flow of selfing crops was the smallest, and that of annual herbaceous plants was the lowest. If Nm > 1, which indicates that the level of gene flow between populations is high, then genetic differentiation among populations is small; if Nm > 4, then gene communication between populations is more adequate and genetic differentiation is smaller; and Nm < 1 indicates that population differentiation may have occurred due to genetic drift. The gene flow was 0.16, which indicates that the gene flow among populations in the Qinba region is lower, but nearly 2.5-fold higher than that of conventional inbred plants, which may result in long-term artificial selection, leading to reduced genetic differentiation.The average MAF of the 93 samples was 0.21. Tajima’s D value was 1.66, which suggests low levels of both low- and high-frequency polymorphisms, indicating a decrease in population size and/or balancing selection that resulted in more haplotypes and lacked rare alleles in this population. Analysis of molecular variance (AMOVA) showed that the genetic variation was 98% within the population and 2% between populations, which indicated the existence of slight genetic variation among 93 samples. The genetic differentiation coefficient . Their cumulative contribution of variance accounted for 40.69%, 39.76% and 40.10% for SNPsPCs Fig. , with W3-NlaIII and SNPs-MseI data. The two most closely related materials were W710 and W711, and the two most furthest materials were W366 and W740.The unweighted pair-group method with arithmetic means (UPGMA) algorithm was performed and demonstrated that the 93 genotypes could be divided into 2 subgroups Fig. , which wSeventy-two thousand eight hundred twenty-four SNPs (MAF <5%) were used to assess the population structure of the entire pool of 93 samples. Delta K reached a maximum value at K=2, suggesting that the 93 samples were divided into two subgroups (consisting of 70 and 23 samples)  between the genetic distance matrices were 0.0914, 0.1726, 0.198, 0.876, 0.3478, 0.2713, respectively . Most of these markers are non-functional genetic markers and do not affect the application of SSR marker in clustering analysis. While the population genetic variation explained by SNPs was larger than that explained by SSRs , the accumulative contribution rate of the first three major factors analyzed by SSR was only 15.76%, far less than that using SNPs data (40.10%), indicating that the more DNA polymorphism, the more accurate the population variation can be explained. For association analysis between markers and traits, greater number of polymorphic sites is associated with higher mapping resolution. A natural population often contains multiple sub-populations, which could result in high degree of LD within the tested population and lead to pseudo-association between markers and traits. Therefore, LD and haplotype studies are necessary before carrying out association analysis. The core 48 pairs of SSR markers as well 72,824 SNPs had rich bands and high polymorphism in Indica rice genome; clustering result of SSR was concordant with that of SNPs, but different from phenotypic traits clustering.All analyses of the population genetic structure were based on the estimation of genetic distance or genetic similarity coefficient matrix between samples. Analyses were conducted using three methods: PC, UPGMA, and Bayesian clustering. Bayesian algorithm is more practical than UPGMA and PC analysis no matter which genetic marker is used given the prior pedigree knowledge of the 93 samples. At the same time, the size of gene flow of each sample can be seen from the population genetic structure graph based on Bayesian algorithm.Through the analysis of different types of materials , the results showed that the genetic basis of the restorer lines was richer than that of the maintainer lines, which was consistent with the conclusion of Ying Jiezheng et al. . The maiHigher number of genetic markers is related to higher explained population variation, especially functional DNA markers. The above showed that it is difficult to make certain the genetic nature of rice germ-resources using phenotype traits clustering. Clustering results based on different genetic markers showed that the genetic basis of 93 samples was single. Average genetic distance was 0.29 based on 72,824 SNPs of 93 samples, which may be due to many reasons, such as the wide exchange of variety resources among breeding units in the process of breeding, and similar breeding goals. Genetic effects in populations depend on the opportunity distribution of MAFs across the genome-wide, and different populations have different MAF values. Although the gene flow in the population composed of 93 samples was relatively large, the average MAF of the population was only 0.21, indicating the genetic structure of 93 samples is simple and lacked rare alleles. Though the amount of colored rice only take up a small proportion of rice resource in this study, it arose an extensive attention all over the world, due to its characteristics which include special nutrition, health care and artificial utilization. Measures to improve the genetic diversity of rice cultivars in the Qinba area are important in the future.Oryza sativa Xian group present in the Qinba area in China.A total of 93 samples were collected from the Shaanxi Rice Research Institute , comprising 57 restoring lines, 19 maintainer lines, and 17 special rice , which were representative of the diversity of Seeds were planted at the rice experimental farm  during three consecutive years , with planting dates of 2018 April 10, 2019 April 11, and 2020 April 8, and transplanted on May 24, May 24, May 20 according to a 16.7cm × 20cm split-split-plot design. Each sample was arrayed randomly at plots with three repeats, to no edge row between the plots.Six plants in the middle of each plot were selected to investigate the values of agronomic, economic and quality traits according to “Recording items, methods and standards of national rice variety test and observation” as well as “National Standard of GBT 17891-1999 high quality paddy”. The 15 selected phenotypic traits included sowing date, plant height, leaf length, leaf width, effective number of panicles per plant, panicle length, total number of grains per panicle, number of filled grains per panicle, 1000-grain weight, browning rate, milled rice rate, head milled rice rate, chalky grain rate, degree of chalkiness, and length/width ratio; the averages of the three-year data were used as the phenotypic data.CV) were computed. Shannon-Weiner index (H’) was calculated according to the following equation: H’=-∑PilnPi, where Pi is the proportion of samples ranked at ith grade for a given phenotypic trait among all samples . All of phenotypic trait data were the standardized using Z-scores, and hierarchical cluster analysis was performed using between-groups linkage method based on Euclidean distance. The above analysis was carried out with the IBM SPSS statistics 22.0 software; MEGA7.0 software was used for editing and visualizing cluster results.The mean value (x), standard deviation (δ), and coefficient of variation . PCR were carried out in a 10 μL volume containing 1 μL DNA template, 2 μL (10 μM) of forward and reverse primers (1μL each), 5μL 2×Taq Master Mix, and 2 μL RNase-free water. Reactions were programed as follows: initial denaturation at 94.0°C for 5 minutes, denaturation at 94.0°C for 1 minute, annealing at 50-60.0°C for 1 minute, and extension at 72.0°C for 1 minute, for a total of 35 cycles. Electrophoresis was performed using 8% non-denaturing polyacrylamide gel under 95V voltage; bands were visualized via silver staining. Following electrophoresis, each amplification band corresponded to a primer hybridization locus and was considered as an effective molecular marker. Each polymorphic band detected by a same given primer represented an allelic mutation. In order to generate molecular data matrices, clear bands for each fragment were scored in every accession for each primer pair and recorded as 1(presence of a fragment), 0(absence of a fragment), and 9(complete absence of band).http://www.bio-soft.net/dna/pic.htm). The level of polymorphism of each marker was assessed by the polymorphism information content, which measures the extent of genetic variation: PIC values smaller than 0.25 indicates low levels of polymorphism associated to a locus, PIC values between 0.25 and 0.5 imply moderate levels of polymorphism, while PIC values greater than 0.5 indicate high levels of polymorphism [The value of the polymorphism information content (PIC) was calculated using the PIC_Calc 0.6 program . The SNPs data obtained with NlaIII and MseI digestion were recorded as SNPsNlaIII-, SNPsMseI-, respectively. Polymorphism filtering of SNPs was done with dp., Miss and MAF of 2, 0.3 and 0.05, respectively, followed by annotation based on the reference genome (ftp://ftp.ensemblgenomes.org/pub/plants/release-37/fasta/oryza_indica/dna/).The genomic DNA of 93 samples was digested using the https://www.cog-genomics.org/plink2). The commands “--r2” and “--blocks” were used to calculate LD and assign SNPs to their respective haplotypes by calculating inter-maker LD within a 200kb window, respectively. Figures were constructed using the Origin8 platform (http://www.originlab.com/).Genotype data were then used to calculate LD between SNPs and to construct haplotypes using the EM algorithm implemented in PLINK1.07  and gene flow. The components of variance attributable to different varieties and breeding lines were estimated from the genetic distance matrix using the Tajima & Nei method, as specified in the AMOVA procedure in ARLEQUIN 3.1 . A nonpaPC analysis was performed under the Eigen module using NTSYS-pc2.10e .http://www.maizegenetics.net/tassel) was used to build an UPGMA tree. MEGA7.0 (http://http://www.megasoftware.net/) was used for editing and visualizing.Identity-by-state (IBS) distance matrix generated by TASSEL5.0 (http://taylor0.biology.ucla.edu/structureHarvesteroybase.org/tools.php), which applies a Bayesian clustering algorithm, was used to simulate population genetic structure based on SSR and SNPs data, respectively. Using a membership probability threshold of 0.60, population K values from 1 to 5 were simulated with 5 iterations for each K using 10,000 burn-in periods followed by 10,0000 Markov Chain Monte Carlo iterations in order to obtain an estimate of the most probable number of populations. Delta K was plotted against K values; the best number of clusters was determined following the method proposed by Evanno et al [http://taylor0.biology.ucla.edu/structureHarvester/) [STRUCTURE 2.3.4  .-NlaIII, 15 phenotypic traits and SNPs-MseI, SNPs-NlaIII and SNPs-MseI, SNPs-NlaIII and SSRs, SNPs-MseI and SSRs. It was carried out using the GenAlEx software with 9999 permutations [Mantel tests were used to measure the correlation between the genetic distance matrices generated using 15 phenotypic traits and SSR, 15 phenotypic traits and SNPsutations . r ≥ 0.9


Nations worldwide are mobilizing to harness the power of Artificial Intelligence (AI) given its massive potential to shape global competitiveness over the coming decades. Using a dataset of 2.2 million AI papers, we study inter-city citations, collaborations, and talent migrations to uncover dependencies between Eastern and Western cities worldwide. Beijing emerges as a clear outlier, as it has been the most impactful city since 2007, the most productive since 2002, and the one housing the largest number of AI scientists since 1995. Our analysis also reveals that Western cities cite each other far more frequently than expected by chance, East–East collaborations are far more common than East–West or West–West collaborations, and migration of AI scientists mostly takes place from one Eastern city to another. We then propose a measure that quantifies each city’s role in bridging East and West. Beijing’s role surpasses that of all other cities combined, making it the central gateway through which knowledge and talent flow from one side to the other. We also track the center of mass of AI research by weighing each city’s geographic location by its impact, productivity, and AI workforce. The center of mass has moved thousands of kilometers eastward over the past three decades, with Beijing’s pull increasing each year. These findings highlight the eastward shift in the tides of global AI research, and the growing role of the Chinese capital as a hub connecting researchers across the globe. But AI applications can also lead to adverse outcomes such as discrimination24, polarization and violence27, and the use of autonomous weapons30.The science of endowing machines with artificial intelligence (AI) has propelled the pace of innovation32. Pundits often speak about countries competing to exert control over the field of AI, but framing AI and its application as technologies that countries must compete for is too narrow a view when the goal is to understand the evolution of AI knowledge. More specifically, AI knowledge, as any other knowledge, builds upon existing knowledge. For example, new AI applications developed worldwide usually build on open source software frameworks created in the U.S., such as TensorFlow (Google), PyTorch (Facebook), or Microsoft CNTK. Thus, our approach is not one of competition but one of dependencies. In this paper, we document dependencies that take the form of networks of AI citations, research collaboration and scientists’ migration.The application of AI continues to grow in scope and sophistication, and as AI raises great opportunities, it also entails significant risks39. In our study of cities, we focus on AI research papers due to the role that science plays in stimulating innovation and diffusing knowledge across borders without frictions40. Knowledge from academic research is a public good—its consumption does not exclude others, nor reduces the amount available to them. Thus, unlike private goods such as AI-based products or services, knowledge stemming from AI papers should, at least in principle, flow unhindered by tariffs, transportation costs, or other political and economic constraints. Whether AI knowledge flows unhindered in practice is one of the questions we investigate in this paper. More broadly, we set out to address the following questions: How do cities compare in terms of their research output and AI workforce? Does AI research from a given city influence certain regions more than others? How do Eastern cities compare to Western ones? Are there cities that bridge East and West, effectively acting as a bridge through which AI citations, workforce, and collaborations flow between the two sides?Our focus is on networks of cities rather than countries, as aggregating knowledge production at the country-level blunts important variation across cities. Cities have always been the engines of innovation and wealth creation as well as the hot spots of production and consumption41, production of cultural goods42, communication patterns43, and epidemiology45. Research in this area revealed how geopolitical considerations shape the communities of cities46 and how network of cities create regional synergistic effects47. Papers that have studied the scientific activities of cities seek to understand how such activities are related to the city’s population size48 or economic complexity49. In contrast, we focus on the scientific activities of cities in the context of AI with a particular emphasis on East vs. West. AI bibliometrics have already attracted attention in the Science of Science literature. For example, Frank et al.50 examined the academic disciplines that frequently cite or get cited by AI papers, Tang et al.51 and Ahmed and Wahed52 analyzed patterns of citations between academic and industrial research, Tang et al.53 analyzed the rate of growth in AI papers and AI scientists using AI papers from arXiv.org (a popular repository of preprints), Bianchini et al.54 studied the effect of new techniques (such as Deep Learning) on practical outcomes , while Martínez-Plumed et al.55 studied the consolidation of AI research communities using papers from Papers With Code (a repository of Machine Learning papers and their associated code). We complement this body of work by examining world-wide dependencies at the city-level. Scientists’ mobility across institutions has also been studied extensively in the literature62. Most of the studies have focused on the incentives of scientists from all disciplines to relocate to different countries66. Some studies have also examined migration between cities, but their focus and goals are different from ours. In particular, Dyachenko67 studied the migration of physicists across Russian and American cities. Verginer and Riccaboni68 compared global cities to peripheral ones in their ability to attract highly prolific scientists. The same authors explored the mobility network of scientists across cities69 focusing on the role of country borders and research output. However, they do not distinguish between Eastern and Western cities, nor consider any particular discipline, AI or otherwise. Moreover, they ranked cities based on Eigenvector centrality, which is different from the novel centrality measure we propose to quantify a city’s role in bridging East and West. Cross-border collaborations have also been considered in the literature70, e.g., to study how they are influenced by geographic distance72 or how they affect national research efforts73. Our contribution to this last strand of literature is that we focus on AI citations while exploring the role that different cities play in bridging the East and West. Moreover, using a gravity model, we quantify the importance of size—in terms of papers and number of scientists—on the influence a city exerts on another.Networks of cities have already been studied in various contexts, including road transportation systems80 while several others use the Web of Science (WoS) dataset87, and some studies even use both datasets88. We chose MAG since it provides the strongest emphasis on comprehensiveness89. We downloaded the Microsoft Academic Graph dataset90 on September 12th, 2020. This dataset includes records of scientific papers specifying the publication date, the publication venue, the publication discipline, the authors’ names, the authors’ affiliations, and the network of citations. Additionally, the dataset specifies the publication type, which includes books, book chapters, conference papers, journal papers, theses, patents, datasets, and repositories. In our study, we only consider publications between 1990 and 2019 (inclusive) whose discipline is specified as AI according to MAG, and whose type is specified as either a conference or a journal paper. Following the convention that the last author is typically the principal investigator and “the head of the lab that hosted most of the research”91, throughout our study, the city in which the last author’s affiliation resides is considered to be the city from which the paper has originated. To this end, for any given affiliation, the corresponding city was identified using the Google Map Geocoding API92. This yielded a dataset of 2.2 million AI papers that are classified into cities. To quantify the scientific output of each city, we used three outcome measures. The first is impact—the number of citations that papers from the city have accumulated within the first two years post publication. Compared to other studies that consider five years74 or ten years93 post publication, our measure allows us to analyze the impact of more recent papers, which is particularly important when studying a rapidly growing field such as AI. Our second outcome measure is productivity—the number of AI papers that the city has produced each year. The third outcome measure is the number of AI scientists affiliated to an institution located in the city under consideration.In the Science of Science literature, several studies use the Microsoft Academic Graph (MAG) datasetFigure 94. Intuitively, this measure quantifies the degree to which a paper introduces something new that eclipses attention from the previous work upon which it was built, leading other scholars to cite it without citing its references. This measure ranges between 1 and -1; papers whose score is close to 1 are considered “disruptive,” whereas those with a score close to -1 are considered “developmental.” Supplementary Fig. 95, we counted the annual number of Eastern and Western papers published therein; see Supplementary Fig. Supplementary Fig. j impacts another city i when i cites j. Thus, in this heatmap, each cell reflects the impact of the column city over the row city, not the other way around. Figure 99. Analyzing the top 50 cities instead of the top 20 reaffirms the three patterns just described; see Supplementary Fig. Figure n denotes the number of papers produced globally, j, and i, then the expected impact of j on i under our baseline model is i, if more than 5% of the citations made by i are to Beijing, it suggests that i has a preference for Beijing, in which case we say that Beijing over-impacts i. In contrast, if fewer than 5% of i’s citations are to Beijing, we say that Beijing under-impacts i. More generally, deviations from this benchmark serve as a coarse yet intuitive measure of cities’ preferences. For more details on how the expected impact is calculated, see the Supplementary Note The impact network corresponding to Fig. mentclass2pt{minimi and column city j, the corresponding cell represents the difference between the number of times i cited j and the number of times i would be expected to cite j if citations were determined based on the baseline model. When interpreting this heatmap, recall that impact and citations flow in opposite directions, implying that a pink color indicates that the column city under-impacts the row city. The outcome of this analysis reinforces two of the aforementioned patterns observed in Fig. Figure Impact is an important metric to quantify global influence, but it is not the only one. Knowledge diffuses not only through papers but also through people, especially the part of knowledge that cannot be codified. To study this aspect, we identified AI scientists as those who published at least three papers, and at least half of their publications fall under the field of AI according to the Microsoft Academic Graph dataset. Figure i to the column city j in the five years between 2015 and 2019; this is calculated as the number of papers in which the last author is in i and at least one coauthor is in j. As with impact and migration, collaboration between Eastern cities is remarkable (top-left quadrant). Western cities also tend to collaborate frequently with one another (bottom-right quadrant). However, we observe relatively fewer collaborations between the East and the West than within the East or the West . Beijing emerges as a hub, not only for Eastern cities but also for a few Western ones, especially Redmond, Cambridge U.S., London, and Pittsburgh. Considering the top 50 cities instead of the top 20 reveals similar patterns; see Supplementary Fig. As an alternative to diffusion from one city to another, knowledge can be created by inter-city teams through collaborations. In Fig. 101. Roughly speaking, this measure quantifies the importance of any given node in a graph based on the number of shortest paths that go through it. Intuitively, a node with high betweenness centrality can be thought of as a bridge that connects different parts of the network. Since we are particularly interested in quantifying the role that Beijing—and every other city—-plays in connecting the East and the West, we use a modified version of betweenness centrality that only considers paths where the source node falls in the East and the destination node falls in the West, or vice versa; see Methods for a formal definition. Using this measure, we analyze the networks of impact, migration, and collaboration among the top 100 cities, i.e., the 100 cities that were most impactful in AI in 2017. These are identical to the networks corresponding to Fig. w in the West cites a paper b in Beijing, which in turn cites a paper e in the East, the paper b can be thought of as a bridge through which the knowledge in e is transitioned to w. Moving on to the migration network  degree centrality; (ii) closeness centrality; (iii) PageRank; (iv) influence measured based on the independent cascade model; (v) influence measured based on the linear threshold model; see Supplementary Note center of mass—a weighted average of each city’s geographic location (latitude and longitude) weighted by the metric’s value in that city. Intuitively, the center of mass concentrates all the influences that the world’s cities exert on one another into a single geographic point. Figure To assess the relative strength with which different cities influence the global AI landscape, we conclude our analysis by examining the geographical evolution of AI research over time. We take into consideration three different metrics: impact, productivity, and number of AI scientists. For each metric, and for each year starting from 1990, we compute the By analyzing more than two million AI papers published over decades, we were able to compare cities in terms of their AI research, focusing on the number of papers they produce, the number of citations they receive, and the number of AI scientists they house. Beijing has been the most productive city since 2002, the most impactful since 2007, and the one with the largest AI workforce for the past two decades. To understand the dependencies between Eastern and Western cities, we started off by studying the impact network of the most impactful cities worldwide. We found that (i) all cities cite themselves far more frequently than they cite others; (ii) top Eastern cities exert a relatively small impact on top Western cities; and (iii) Beijing cites other cities the most, regardless of whether their location lies in the East or the West, suggesting that its research builds on knowledge produced across the globe. We compared the observed citation patterns to those expected under a baseline model in which citations occur randomly. This analysis revealed a substantial dependency of Western cities on other Western cities, as they cite each other far more frequently than expected by chance. Next, we analyzed the migration of AI scientists between the top cities from 2013 to 2017. We found that migration mostly takes place from an Eastern city to another Eastern city. When studying the network of collaborations across cities, we found that East–East collaborations are far more frequent than East–West or West–West collaborations. Motivated by these observations, we set out to identify the cities bridging East and West. To this end, we proposed a version of betweenness centrality that only considers the shortest paths starting in the East and ending in the West, or vice versa. Using this measure, we analyzed the impact network, the migration network, and the collaboration network of the most impactful cities in AI worldwide. Beijing’s betweenness is far greater than that of any other city, regardless of the network under consideration, highlighting Beijing’s key role in bridging Eastern and Western cities. Finally, we tracked the center of mass of AI research, which weighs the locations of different cities by their output, be it AI impact, AI productivity, or AI workforce. The three centers of mass have all been drifting towards the East since 1990, with Beijing contributing significantly towards this phenomenon. Taken together, these findings underscore the growing role of the East in general, and Beijing in particular, in shaping the global AI landscape.103. Table 102 p. 32), we can fit a between-city migration model using Pseudo Poisson Maximum Likelihood. We present the full set of results in Supplementary Table Our results suggest that size matters. By comparing Fig. 1. Our study has shown that dependencies matter, so competition may not be the right approach to understand the evolution of AI. China’s capital already plays a critical role in AI research not only as a hub of knowledge creation but also as a bridge between East and West in terms of citations, collaborations, and scientists’ migrations. More often than any other city, Beijing seems to pick scholarly knowledge from one side of the globe, build on it, and then introduce it to the other side, acting as a gateway through which knowledge flows in both directions. Whereas researchers from prominent cities on the West Coast of the U.S. largely collaborate with scientists in other Western cities, Beijing-based researchers collaborate with scholars located in both East and West, possibly tapping into tacit knowledge and resources available to scientists across the globe. Also, more often than any other city, Beijing sees AI scientists passing through from one side of the world to the other; those scientists probably leave traces of their expertise and technical know-how along the way. Taken together, these findings highlight Beijing’s central role in global Artificial Intelligence research.Nations are racing to dominate the field of AI given its disruptive and transformative potentialA noteworthy limitation of our study is that we restrict our attention to AI papers while disregarding other forms of research. This choice was based on the fact that bibliometrics provide all the information required for our investigation, such as, e.g., the number of citations received  as well as the identities and affiliations of each author . Having said that, bibliometrics alone do not provide a complete picture of global AI research. Other aspects include the amount of funding spent in, and the number of patents produced by, different cities. Future work may consider the number of big-tech companies and start-up companies that reside in different cities, as well as the growth and revenue of these companies. Another avenue for future work is to analyze the subfields of AI, to determine which areas of research, if any, attract attention from the East but not the West, or vice versa. In terms of policy implications, there is room for initiatives that promote intra- and inter-national migration of and collaboration among researchers. We observe a divide between East and West that so far seems to be bridged by Beijing, but other cities may also provide such a bridge. If the goal is to advance humankind's AI knowledge, policy makers could also foster cities such as Hong Kong or Redmond U.S. as hubs by attracting knowledge and researchers from East and West, thereby diversifying their pool of talents and skills. Our findings may also inform AI scientists’ migration decisions, bearing in mind that the cities serving as hubs of AI knowledge and talent are likely to drive AI research in the coming years. Overall, these findings suggest that efforts from funders, research institutions, and scientists that aim at bridging the East–West divide could pay off. Building more bridges across the world could start by promoting incentives to share research more broadly across venues or to create more inclusive publication venues.V denote the set of vertices  in the network. Then, given two nodes, x to y is said to be a shortest path if it minimizes the sum of the edge weights along the path. Let us now define betweenness centrality.Given a directed weighted network, let x to y, and let x to y that go through v. Then, betweenness centrality is a function For any two nodes, v, we would have to count the number of shortest paths that go through v out of all shortest paths from Note that Definition mentclass2pt{minimE and W be the subsets of V consisting of the nodes that correspond to Eastern and Western cities, respectively, implying that Traditionally, betweenness centrality is applied in scenarios where edge weight represents distance, and where the flow of information between any two nodes follows a shortest path. As such, the greater the number of shortest paths that go through a given node, the greater the control that this node has over the network, since more information passes through it. However, the situation is different in the networks considered in our analysis, i.e., impact, migration, and collaboration. In each of these networks, a node is considered more central if it falls on paths that maximize, rather than minimize, the sum of edge weights. As such, to stay faithful to the intuitive interpretation of betweenness centrality, we replace each edge weight with its reciprocal before computing the centrality of each city. Importantly, since we are particularly interested in quantifying the role that different cities have in connecting the East and the West, we introduced a modified version of betweenness that only considers paths of which the source node falls in the East and the destination node falls in the West, or vice versa. Let us formally define this modified version, which we call East–West betweenness. In particular, the nodes in our setting correspond to cities that are classified into East and West, depending on their geographic location. Let x to y, and let x to y that go through v. Then, the East–West betweenness centrality is a function For any two nodes, 50. That is, the weight of the edge from city i to city j is:Finally, let us discuss the reason behind excluding the network of over-impact  will be assigned the maximum possible weight. Such extreme edges will greatly influence the East–West betweenness of cities that are adjacent to them, making the measure highly sensitive to outliers. To appreciate the difference between the normalized and non-normalized networks, consider a stylized example with three cities, With this normalization, if Supplementary Information.


Mitochondrial DNA (mtDNA) variations are often associated with bioenergetics, disease, and speciation and can be used to track the history of women. Although advances in massively parallel sequencing (MPS) technology have greatly promoted our understanding of the population’s history , the whole mtDNA sequence of many important groups has not been fully studied. In this study, we employed whole mitogenomes of 209 healthy and unrelated individuals from the Daur group, a Mongolic-speaking representative population of the indigenous groups in the Heilongjiang River basin .The dataset presented 127 distinct mtDNA haplotypes, resulting in a haplotype diversity of 0.9933. Most of haplotypes were assigned to eastern Eurasian-specific lineages, such as D4 (19.62%), B4 (9.09%), D5 (7.66%) and M7 (4.78%). Population comparisons showed that the Daurians do have certain connections with the ancient populations in the Heilongjiang River basin but the matrilineal genetic composition of the Daur group was also greatly influenced by other non-Mongolic groups from neighboring areas.Collectively, the whole mtDNA data generated in the present study will augment the existing mtDNA database. Our study provides genetic links between the Daur population and the aborigine peoples from Siberia and the Amur-Ussuri Region. But on the whole, compared with other Mongolic-speaking groups, the modern Daur population is closer to the East Asian ancestry group.The online version contains supplementary material available at 10.1186/s12862-022-02019-4. The Daur minority is one of the important members of the Mongolic-speaking population. They originally lived on the north beach of the Heilongjiang River (Amur River) . After tThe Daur ethnic group has appeared in many important genetic studies as one of the representatives of Mongolic-speaking populations and indigenous groups from the Heilongjiang River basin –6. Our pIn this study, the whole mitochondrial genomes of 209 healthy and unrelated Daur individuals from Northeast China were sequenced by massive parallel sequencing (MPS) on the HiSeq X Ten system . Based on the sequencing data, we analyzed the haplogroup distribution and genetic diversity of the maternal genetic structure of the Daur group. To shed more light on the genetic relationship of the Daur group with worldwide populations, especially other neighboring/linguistically close populations and some related ancient groups, we conducted comprehensive population genetic analyses via Principal Component Analysis (PCA) and otherA cohort of 209 unrelated Daur individuals  was collected after receiving informed consent. The individuals were considered autochthonous if their ancestors had lived in Hulunbuir, Inner Mongolia Autonomous Region of China, for at least three generations. Written informed consent was obtained from all participants, and the ethics committee of School of Life Sciences, Fudan University, Shanghai, People’s Republic of China approved this study.Genomic DNA was extracted from blood samples using a DP-318 Kit  according to the manufacturer’s protocol. The quantity of gDNA was measured with a NanoDrop ND-1000  according to the manufacturer’s protocol. In consideration of the requirements of downstream processing, the gDNA was normalized to 0.1 ng/µL and stored at − 20 °C until amplification.DNA libraries were constructed using an MtDNA Library Preparation Kit 2.0  and a WhoChrMT kit . PCR amplification was performed in a final volume of 30 µL containing 10 ng of template DNA, 5 µL RealCapChrMT Mix and 10 µL 3×EnzymeHF. Total reaction volumes were adjusted with nuclease-free water. The PCR was performed under the following conditions: enzyme activation for 3 min at 98 °C, 13 cycles of 20 s at 98 °C and 4 min at 58 °C, 7 cycles of 20 s at 98 °C and 1 min at 72 °C, 2 min at 72 °C followed by a 10 °C hold. The PCR products were purified with Agencourt AMPure XP beads (Beckman Coulter). Then, a second round of PCR amplification was carried out to introduce adapters and barcodes. The reaction volume (30 µL) was comprised of 10 µL 3×EnzymeHF, 18 µL nuclease-free water, 1 µL primer mix and 1 µL barcode mix. The PCR was performed under the following conditions: enzyme activation for 2 min at 98 °C, 7 cycles of 15 s at 98 °C, 15 s at 58 °C and 30 s min at 72 °C, extension for 2 min at 72 °C followed by a 10 °C hold. After purification, the libraries were pooled to a final concentration of 20 pM. Sequencing was performed on the Illumina HiSeq X Ten platform  with the corresponding Reagent Kit (PE150).The sequence data obtained from the Illumina HiSeq X Ten platform  were automatically analyzed by base recognition and converted into the original sequences in FASTQ format. First, redundant primers and indexes in the initial offline data were removed by cutadapt software . Second,Sequencing performance was evaluated by read depth. The mtDNA haplogroups were determined using HaploGrep 2  based onhttps://www.r-project.org/) based on haplogroup frequencies  per individual. The variants recommended by EMPOP as well as the haplogroup information and the mean sequencing depth of 209 Daur individuals are presented in Additional file Figure On the whole, the Daur population in this study embodies distinct regional and ethnic characteristics. Compared with earlier studies on Daur mtDNA –16, our Based on whole mtDNA sequence data, a total of 127 different haplotypes were identified from the 209 unrelated Daur samples, of which 81 (63.78%) were unique. Although close matrilineal relatives (first to three degrees) were excluded, 61.24% of the total samples still shared haplotypes with others. It is worth noting that the haplotypes belonging to M7b1a1+, G2a1 and Z3d were shared by 6 individuals. Moreover, one haplogroup was shared between five individuals, seven were shared between four individuals, seven were shared between three individuals and 28 were shared between two individuals. The overall haplogroup diversity was calculated as 0.9933 with a discrimination capacity of 60.77%. Additional file ® Plus loci in the Daur group, a total of 196 different haplotypes were observed in the sample of 203 Daur individuals, and the overall haplotype diversity was calculated as 0.9997 with a discrimination capacity of 0.9655 [Of course, the genetic diversity of maternal genetic markers was slightly lower than that of paternal genetic markers, which is more due to the limitations of mitochondrial genetic markers themselves. In our previous study of genetic polymorphisms of 27 Yfilerf 0.9655 –10.In our PCA results, 37.5% of the genetic variations were extracted by the first three components Fig. . The AfrIn the data visualization of the haplogroup sharing analysis Fig. , the DauAs mentioned above, haplogroup D4 not only has a high frequency 19.62%) but also contains abundant downstream clades in the Daur samples. According to previous studies based on partial sequences, D4 is also the high-frequency type of several ancient ethnic groups in Northeast China –44. In t9.62% butAfter comparing all raw whole sequences (both ancient and modern), we found two perfect matches between the Daur mitogenomes and published Buryat sequences Table , DMT040-The present study provided the first set of whole mitochondrial genome data of 209 Daur individuals residing in Northeast China. The investigation of the Daur maternal lineages revealed that the vast majority of haplogroups belong to the eastern Eurasian-specific component. Population analyses showed that the Daurians do have certain connections with the ancient populations in the Heilongjiang River basin but the matrilineal genetic composition of the Daur group was also greatly influenced by other non-Mongolic groups from neighboring areas. This study also shows that whole mitochondrial sequence data can improve the resolution and offers a high power of discrimination in maternal studies by comparison of whole and partial sequence data in genetic diversity and population comparative analyses. Overall, the mitogenomes generated in the present study will augment the existing Daur mtDNA database, which provides a deeper understanding of the genetic composition of the Daur group and could potentially be useful for regional-specific and prerequisite references for forensic, genealogical, and evolutionary purposes.Additional file 1: Table S1. Reference groups used in PCA and haplogroup sharing analysis. Table S2. Coarse haplogroup frequencies. Table S3. References used in networks. Table S4. The detail information for the full mtDNA sequences observed in 209 Daur individuals. Table S5. Diversity indices for the Daur population obtained with different mtDNA regions.