The development of informative polymorphic DNA markers for poorly studied genera is an importantstep in population analyses of living organisms, including those that play very important ecological roles in harshenvironments, such as desert and semi-desert area. Examples of those poorly studied desert species are Agriophyllumsquarrosum L. and Agriophyllum minus Fisch. & Mey. However, a recent RNA-sequencing project in A. squarrosumhas proposed a large set of hypothetical SSR (simple sequence repeat) markers. In this work, 11 novel polymorphicSSRs were found due to the screening of 24 randomly selected SSRs for three populations of A. squarrosumand one population of A. minus. The analysis of 11 SSRs revealed 16 polymorphic loci in two Agriophyllum species,8 polymorphic loci within three populations of A. squarrosum, and 6 polymorphic loci in the population of A. minus.Statistical analyses showed high interspecific, but relatively low intraspecific genetic diversity. The phylogeneticclusterization and population structure analysis have demonstrated a clear segregation of A. minus from A. squarrosum,as well as the separation of population 1 from populations 2 and 3 of A. squarrosum. Thus, we identified theset of novel and informative SSR markers suitable for the study of genetic diversity in Agriophyllum. Xerophytes and psammophytes (plants with adaptations tosurvive in water-deficiency environment) become the mainplants in the desert region. Despite the harsh environmentalconditions, many of these plants have adapted and became apart of complex and diverse desert ecosystems. Due to the widedistribution of desert territories, the study of its wild flora isvery important for the ecological prediction and conservationof biodiversity. The Central Asia region, including Kazakhstan,is having numerous large and small sand deserts, suchas Gobi, Taklamakan, Karakum, Kyzylkum and Moiynkym.Deserts and semi-deserts occupy more than half of Kazakhstanterritory and keep growing . The majorrepresentatives of wild desert flora here are herbaceous plants,subshrubs, shrubs and subtrees. The list of dominating speciesincludes genera Artemisia, Salsola, Ferula, Arthrophytum,Calligonum, Ammodendron, Haloxylon, and Agriophyllum. Many of them are endemic forthe Central Asian and Kazakhstan deserts.Agriophyllum squarrosum L. and Agriophyllum minusFisch. & Mey. are important desert species in Kazakhstan.They belong to the tribe Corispermeae within the subfamilyChenopodioideae of the family Chenopodiaceae . The genus Agriophyllum includes five species, andfour of them, including A. squarrosum, grow in Kazakhstan. A. squarrosum is also widely spread inall Central Asia territory, Caucasus, and China. Local peoplein the sandy desert regions of China consume the seed of thespecies during periods of food shortage, and refer to the plantas ‘shami’ in Chinese, which translates as ‘sand rice’ . Sand rice is an example of psammophyte perfectlyadapted for harsh desert environmental conditions. Morphologicallyit is a shrub-like plant with a height ranging from20 to 100 cm. The stem of sand rice at the young plant stageis firm, branched, green, and covered with short hairs. Leavesare small, sessile, green, and usually linear. Small flowersare organized in a spike-like inflorescence. A. squarrosumblossoms in the late summer and early fall, after that ovoidshape seeds are formed. Seeds of sand rice are very light andcovered by a thin husk. After ripening, the husk is crackedinto two parts, and seeds are easily dispersed by the wind.The root system is represented by a long taproot penetratingdeep into the soil to access stored moisture, and almostequally long lateral roots branching near the soil surface andhelping to fix plant in loose sand. Although, there is a lack ofinformation related to the structure of the sand rice’s genome,the transcriptomic analysis of A. squarrosum (2n = 18) hadshowed presence of 67 741 unigenes and approximately 43 %of them were annotated .Throughout history, sand rice was used for diverse purposes.Aboveground organs (stem and leaves) are eaten by both wildanimals and livestock of farmers in arid and semi-arid regions, especially in Western Kazakhstan on camel pasture. Historically,nutritious seeds of sand rice were an alternative to cerealsnot surviving in hot deserts. In China and Mongolia, the localvillagers consume sand rice seed in a variety of dishes. Thereare many reports about the rich nutrition value of A. squarrosumseeds close to its widely-used as food quinoa seeds . However, sand rice is not a domesticated plantspecies with several agriculturallyunfavorable traits, suchas fragile spikes and light seeds. Still, the works on possibledomestication of sand rice as a novel crop are reported . In addition to nutrition purposes, sand rice hadfound its application in medicine. Back in the days, it was usedas antipyretic and analgesic medicine . It was reported that the extract of A. squarrosumdecreases blood glucose levels in type 2 diabetic mice andhas the potential for further medical researches . Sand rice is useful in combat against shifting sands. Climate change and human activitiesled to the growth of sand desert areas and the migration of sanddunes to agricultural territories. The structure of the sand riceroot system and its ability to form seed banks in active sanddunes allow the fixation of the sand surrounding the plant.Thus, A. squarrosum has a tremendous potential together withother psammophytes to be used in a large-scale sand fixation. This species is an interestingmodel for different studies of morphology and physiology ofdesert plants. For example, earlier A. squarrosum was alreadyused for the study of growth under drought conditions  and for the study of fertilizereffect on psammophytes under different rainfall conditions.Endemic species with great economic potential like sandrice are an interesting subject for genetic and molecular researches.One of the most common methods utilized for thestudies on biodiversity conservation, population and phylogeneticstudies of wild plant species is the usage of molecularDNA markers . Examples of successfulapplication of the most common DNA markers in plantsinclude random amplification of polymorphic DNA (RAPD), amplified fragment-length polymorphisms(AFLP) , and other nuclear andchloroplast DNA markers . Nuclear ribosomalinternal transcribed spacer (nrITS) region and five chloroplastDNA (cpDNA) fragments have been used earlier for the studyof population dynamics of A. squarrosum in China . The maturase K (matK ) gene of the chloroplast genomeand nrITS were used for comparison of A. squarrosum andA. minus populations in two regions of Kazakhstan . The literature survey suggests that thereis a limited information on the study of Agriophyllum speciesby using SSR markers. However, a recent RNA-sequencing project of A. squarrosum populations in China has suggestedseveral thousands of potential SSR markers for this species.In this study, we selected 24 SSR markers from this Agriophyllumgenome resequencing project and used them for theassessment of genetic diversity within and among populationsof A. squarrosum, and between A. squarrosum and A. minus.Plant material. In total, leaf samples of four wild Agriophyllumpopulations were collected in South-East Kazakhstanand used for the analysis (Table 1). The list included threepopulations of A. squarrosum and one population of A. minussampled in Moyynkum desert of Almaty region in South-EastKazakhstan . Population 1 of A. squarrosum was collectedin 2016, while the populations 2 and 3 of A. squarrosumand population 1 of A. minus were collected in 2019. Thedistances between populations were at least four kilometers,and plants within the population were sampled in at least50 meters apart.DNA extraction, amplification and SSR marker assessment.Five young leaves from each sample were dried in silicagel. The total genomic DNA was extracted from dry leaftissues using CTAB method . The quality andconcentration of extracted DNA were assessed via spectrophotometrictest, and 1 % agarose gel electrophoresis.1 were selected from 6150 SSRs in thesequence of A. squarrosum genome reported by Zhang J. andco-authors (2018). SSR motives and expected sized of alleleswere obtained from the same source.(Supplementary Table)1 Supplementary Table is available in the online version of the paper: http://www.bionet.nsc.ru/vogis/download/pict-2020-24/appx10.pdfa ) for primer pairs and concentrationof reagents in a PCR reaction mix were determinedempirically. All successful PCR reactions were performed intotal 16 μl volumes, including 4 mM of each dNTP, 2 mMof MgCl2, 6.4 mM of primer mix (forward + reverse), 1.6 Uof Taq polymerase and 50 ng of DNA. The amplificationwas performed in Veriti Thermocycler  with an initial denaturation step at94 °C for 3 minutes, followed by 40 cycles of 94 °C for30 seconds, optimized Ta °C (see Suppl. Table) for 45 secondsand 72 °C for 1.5 minutes. The final extension step was at72 °C for 10 minutes. The PCR products were separated on6 % polyacrylamide gel (PAG). The SSR profile image was captured using the GelDoc gel documentation unit . Allele sizes were estimatedvisually based on the size of ladder bands and the controlsample on each gel.Annealing temperature (T2i ), where pi isthe frequency of the i-th band or percentage of individuals inwhich the fragment is present. Principal coordinates analysis(PCoA) and unweighted pair group method with arithmeticmean (UPGMA) hierarchical clustering were performed basedon genetic distances among populations and species. PCoAplot was made via GenAlEx ver. 6.5 software , UPGMA analysiswas performed in R statistical software environment . Bayesian clustering was based on the use of theSTRUCTURE software ver. 2.3.4 . Thevalue of K was set from 1 to 5 with five iterations for eachvalue of K. Both, length of burn-in period, and the numberof Markov Chain Monte Carlo (MCMC) repeat after burn-inwas set at 100 000. Web-tool STRUCTURE HARVESTER based on Evanno’s method  was used to determine the best fit value of K.The molecular variance (AMOVA) test was calculated usingGenAlEx software.Statistical analysis. Nei’s genetic diversity indices werecalculated via POPGENE software ver. 1.32 ;the polymorphism information content (PIC)  was calculated as follows: PIC = Σ, however, successful amplification was performedfor 18 markers only. Five of 18 SSRs were multilocusmarkers, while other 13 SSRs were single-locus. In total,18 markers allowed identification of 23 loci (Table 2), of which16 were polymorphic and suitable for the analysis (Table 3).The screening of sixteen SSR loci has allowed the identificationof 43 alleles in the analysis of three populations ofA. squarrosum and one population of A. minus. The molecularsizes of alleles in loci were ranged from 143 to 342 bp. Thenumber of alleles in the study of all four populations variedfrom 2 to 4.In total, eight polymorphic loci were observed in A. squarrosum,six polymorphic loci were found in A. minus, and16 loci had demonstrated polymorphism between two species(see Table 3). In total, 20 alleles were identified exclusivelyin A. squarrosum, 16 alleles were exclusive for A. minus, and7 alleles were found in both species. The average number ofalleles per locus in polymorphic loci was 2.69 ± 0.70 for bothspecies, 2.25 ± 0.16 for A. squarrosum, and 2.17 ± 0.17 forA. minus, respectively.Nei’s index and PIC value were calculated separatelyfor polymorphic SSR loci of A. squarrosum, A. minus, andjointly for two Agriophyllum species (see Table 3). The geneticdistance-based PCoA plot suggested a clear separationof A. minus from populations of A. squarrosum, as well asdistinguishing of the population 1 from populations 2 and 3in A. squarrosum . The UPGMA tree had also demonstratedtwo clusters corresponding to A. squarrosum andA. minus. The portion of samples in population 1 of A. squarrosum was clustered together with populations 2 and 3, whilethe remaining part formed a separate subcluster .Bayesian distance-based analysis of the structure among twoAgriophyllum species was congruent with the UPGMA clusterizationand also indicated that K = 3 is an optimal numberof groups in the study .AMOVA results for the set of data containing all Agriophyllumsamples revealed that the majority (88 %) of the geneticdiversity in Agriophyllum exists between two species ratherthan within them (Table 4).Genetic diversity in A. squarrosum populationsPart of this study was focused on the assessment of the geneticdiversity within and between populations of A. squarossum. Intotal, 26 alleles were identified in eight SSR loci. The largestnumber of unique alleles was found in population 1 ,while population 2 had only one unique allele (Table 5). Thelargest values of Nei’s index (0.27 ± 0.16) and PIC value(0.25 ± 0.09) were observed in population 1.The AMOVA for A. squarrosum had shown a higher molecularvariance within populations than the variance amongpopulations (Table 6).The availability of informative DNA markers is advantageousfor the study of rare species, their population structure, andfor the development of a proper conservation strategy toprevent their extinction . SSR markers are one of those types of DNAmarkers that reliably utilized in population structure analysisdue to their hyper-variability, co-dominance, and highreproducibility . Successful applicationsof SSR markers for the identification, classification, andtaxonomy of Chenopodiaceae species have been reportedpreviously . However, for the genusAgriophyllum, there were no previous studies with the use ofSSRs. The situation was changed recently due to the RNAsequencingproject of A. squarrosum accessions, and a largenumber of hypothetical SSR markers were suggested forpopulation studies . Hence, in this work24 SSRs were randomly selected for the assessment of threepopulations of A. squarrosum and one population of A. minuscollected in South-East Kazakhstan. The assessment ofselected SSR markers resulted in the identification of 11 novelSSR markers that allowed the identification of 16 polymorphicloci in two Agriophyllum species (see Table 3). The largepercentage of molecular variance (see Table 4) and a largeTable 5. Average values of diversity statistics for eight polymorphic SSR lociacross three A. squarrosum populationsA. squarrosum nea ne na Nei PICPopulation 1 8 1.43 ± 0.32 1.88 ± 0.35 0.27 ± 0.16 0.25 ± 0.09Population 2 1 1.02 ± 0.06 1.13 ± 0.35 0.02 ± 0.05 0.02 ± 0.05Population 3 0 1.03 ± 0.09 1.13 ± 0.35 0.03 ± 0.07 0.02 ± 0.06Note. nea – number of alleles exclusive for population; na – number of alleles per locus; ne – number of effective alleles; Nei – Nei’s geneticdiversity index; PIC – polymorphism information content.Table 4. AMOVA in two species of AgriophyllumSource df SS MS Est. Var. (%) pBetween species 1 435.289 435.289 14.840 (88) 0.001Within species 72 151.941 2.111 2.110 (12) 0.047Total 73 587.230 16.951 (100)Note. df – degree of freedom; SS – sum of squares; MS – mean squares; Est. Var. – estimated variance; p – significance level.genetic distance  observed between A. squarrosumand A. minus indicated good discriminative power of studiedSSR markers and confirmed a significant genetic differencereported between these species earlier . Fifteen SSR loci used for both species were in intermediatediversity group with PIC values ranged between 0.32and 0.50, and Nei’s diversity index ranged between 0.39 and0.56 (see Table 3).When species were analyzed separately, 8 out of 16 lociwere polymorphic for A. squarrosum, and 6 loci were polymorphicfor A. minus (see Table 3). The average diversity inA. minus was slightly higher than in A. squarrosum and lowerthan the average interspecies diversity (see Table 3). Thus,A. squarrosum and A. minus have maintained a low level ofgenetic diversity. It has been demonstrated that annuals likeAgriophyllum or short-lived perennials usually demonstratelow levels of genetic diversity compared with long-lived andoutcrossing species . However, thelimited amount of samples in studied species (particularly inA. minus), their close geographical locations, and a relativelysmall number of polymorphic SSR markers used for theanalysis could influence obtained results.The study of the genetic diversity within A. squarrosumsuggested that 66 % of the total variation was within and34 % between populations (see Table 6), indicating that thedifference of population 1 from populations 2 and 3 was rathersubstantial. The presence of relatively high diversity withinpopulations may be explained by the geographical proximityof A. squarrosum populations used in this study (see Table 1), which may cause a high rate of gene flow in a limited area. Populations 2 and 3 of A. squarrosumhad demonstrated an extremely low level of genetic diversityin comparison with population 1 (see Table 5), but the factthat samples from each population clustered together usingmultiple analyses   indicates that there is a high level of heterogeneity inthe species. The grouping of 5 samples from population 1on the UPGMA tree and on the STRUCTURE bar plot intoseparate subcluster in the entire species cluster may indicatethe presence of some barriers between groups of sampleswithin population 1 interfering gene flow. Therefore, even ifno physical obstructions were found during the sample collection,probably, two groups in population 1 may be consideredas subpopulations . This assumptionis also supported by the relatively higher genetic diversity inpopulation 1 comparing with the other two A. squarrosumpopulations (see Table 5).In this study, sequences of 24 pairs of oligonucleotides forSSR markers were randomly selected from A. squarrosumgenome resequencing project, and used for the study of geneticdiversity in the genus Agriophyllum. Hence, it is the first reportexploring the performance of novel SSR markers in the geneticanalysis of this genus. The study revealed 16 polymorphicloci in eleven SSR markers using two Agriophyllum species,8 polymorphic loci within three populations of A. squarrosum,and 6 polymorphic loci within the population of A. minus.Statistical analyses showed high interspecific, but relativelylow genetic diversity in populations 2 and 3 of A. squarrosum.The phylogenetic clusterization and analysis and populationstructure analysis demonstrated clear segregation of A. minusfrom A. squarrosum, as well as the separation of population 1of A. squarrosum form populations 2 and 3. As a result, weidentified the set of novel and informative SSR markerssuitable for the study of genetic diversity in Agriophyllumspecies. These results provide an important contribution tothe population study and approaches for the development ofconservation mechanisms for Agriophyllum species.The authors declare no conflict of interest.Abugalieva S., Volkova L., Genievskaya Y., Ivaschenko A., KotukhovY., Sakauova G., Turuspekov Y. Taxonomic assessment ofAlliumspecies from Kazakhstan based on ITS and matK markers.BMC Plant Biol. 2017;17(S2):258. DOI 10.1186/s12870-017-1194-0.Adams R.P., Turuspekov Y. Taxonomic reassessment of some CentralAsian and Himalayan scale-leaved taxa of Juniperus (Cupressaceae)supported by random amplification of polymorphic DNA.Taxon. 1998;47(1):75-83. DOI 10.2307/1224021.Ageeva N.T., Baitenov M.B., Goloskokov V.P., Kornilova V.S., PavlovN.V., Poljakov P.P. Agriophyllum. In: Pavlov N.V. (Ed.) Flora ofKazakhstan. Vol. III. Alma-Ata: Academy of Sciences of KazSSR,1960;241-243. (in Russian)Aitken K.S., Jackson P.A., McIntyre C.L. A combination of AFLP andSSR markers provides extensive map coverage and identification ofhomo(eo)logous linkage groups in a sugarcane cultivar. Theor. Appl.Genet. 2005;110:789-801. DOI 10.1007/s00122-004-1813-7.Almerekova S., Abugalieva S., Mukhitdinov N. Taxonomic assessmentof the Oxytropis species from South-East of Kazakhstan. VavilovskiiZhurnal Genetiki i Selektsii = Vavilov Journal of Genetics andBreeding. 2018;22(2):285-290. DOI 10.18699/VJ18.362.Austerlitz F., Mariette S., Machon N., Gouyon P.H., Godelle B. Effectsof colonization processes on genetic diversity: differences betweenannual plants and tree species. Genetics. 2000;154(3):1309-1321.Borger C.P.D., Yan G., Scott J.K., Walsh M.J., Powles S.B. Salsola tragusor S. australis (Chenopodiaceae) in Australia – untangling taxonomicconfusion through molecular and cytological analyses. Aust.J. Bot. 2008;56(7):600-608. DOI 10.1071/bt08043.Botstein D., White R.L., Skolnick M., Davis R.W. Construction of agenetic linkage map in man using restriction fragment length polymorphisms.Am. J. Hum. Genet. 1980;32:314-331.Chen G., Zhao J., Zhao X., Zhao P., Duan R., Nevo E., Ma X. A psammophyteAgriophyllum squarrosum (L.) Moq.: a potential foodcrop. Genet. Resour. Crop Evol. 2014;61(3):669-676. DOI 10.1007/s10722-014-0083-8.Conner J.K., Hartl D.L. A Primer of Ecological Genetics. Oxford Univ.Press, 2004.Doyle J. DNA protocols for plants. In: Molecular Techniques in Taxonomy.Berlin; Heidelberg: Springer, 1991;283-293.Earl D.A., von Holdt B.M. Structure Harvester: a website and programfor visualizing STRUCTURE output and implementing the Evannomethod. Conserv. Genet. Resour. 2011;3:429-431. DOI 10.1007/s12686-011-9548-7.Evanno G., Regnaut S., Goudet J. Detecting the number of clusters ofindividuals using the software STRUCTURE: a simulation study.Mol. Ecol. 2005;14(8):2611-2620. DOI 10.1111/j.1365-294X.2005.02553.x.Genievskaya Y., Abugalieva S., Zhubanysheva A., Turuspekov Y.Morphologicaldescription and DNA barcoding study of sand rice collected in Kazakhstan.BMC Plant Biol. 2017;17(S1):177. DOI 10.1186/s12870-017-1132-1.Gong B., Zhan K.X., Zhou Y.H., Zhang L., Hui Y.Q., Li Y.S. Separationand identification of chemical constituents from Agriophyllumsquarrosum (L.) Moq. Mod. Chinese Med. 2012;14(10):7-11.Huang Y.X., Zhao X.Y., Zhang H.X., Luo Y.Y., Mao W. Responses ofAgriophyllum squarrosum phenotypic plasticity to the changes ofsoil nutrient and moisture contents and population density. J. Appl.Ecol. 2008;19(12):2593-2598.Issanova G., Abuduwaili J., Kaldybayev A., Semenov O., Dedova T.Dust storms in Kazakhstan: frequency and division. J. Geol. Soc.India. 2015;85(3):348-358. DOI 10.1007/s12594-015-0224-5.Kramer A.T., Havens K. Plant conservation genetics in a changingworld. Trends Plant Sci. 2009;14(11):599-607. DOI 10.1016/j.tplants.2009.08.005.Kühn U. Chenopodiaceae. In: Kubitzki K., Rohwer J.G., Bittrich V.(Eds.). The Families and Genera of Vascular Plants. II. FloweringPlants: Dicotyledons, Magnoliid, Hamamelid and Caryophyllidfamilies. Berlin; Heidelberg; New York: Springer, 1993;253-280.Liu Z., Yan Q., Liu B., Ma J., Luo Y. Persistent soil seed bank in Agriophyllumsquarrosum (Chenopodiaceae) in a deep sand profile: variationalong a transect of an active sand dune. J. Arid. Environ. 2007;71(2):236-242. DOI 10.1016/j.jaridenv.2007.03.003.Ma J., Liu Z. Spatiotemporal pattern of seed bank in the annual psammophyteAgriophyllum squarrosum Moq. (Chenopodiaceae) on theactive sand dunes of northeastern Inner Mongolia, China. Plant Soil.2008;311(1-2):97-107. DOI 10.1007/s11104-008-9661-x.Mo W., Natori T., Jiang S., Nishimura N., Omasa K. Responses of photosynthesisand water use to drought in two desert annuals, Agriophyllumsquarrosum and Bassia dasyphylla. J. Arid Land Studies.1997;7:185-195.Nachtigall M., Bülow L., Schubert J., Frese L. Development of SSRmarkers for the genus Patellifolia (Chenopodiaceae). Appl. PlantSci. 2016;4(8):1600040. DOI 10.3732/apps.1600040.Nybom H. Comparison of different nuclear DNA markers for estimatingintraspecific genetic diversity in plants. Mol. Ecol. 2004;13(5):1143-1155. DOI 10.1111/j.1365-294x.2004.02141.x.Nybom H., Bartish I.V. Effects of life history traits and sampling strategieson genetic diversity estimates obtained with RAPD markers in plants. Perspect. Plant Ecol. Evol. Syst. 2000;3(2):93-114. DOI10.1078/1433-8319-00006.Peakall R., Smouse P.E. GenAlEx 6.5: genetic analysis in Excel. Populationgenetic software for teaching and research – an update. Bioinformatics.2012;28(19):2537-2539. DOI 10.1093/bioinformatics/bts460.Prinz K., Hensen I., Schie S., Debener T., Weising K. Microsatellitemarkers for the tetraploid halophyte Suaeda maritima (L.) Dumort.(Chenopodiaceae) and cross‐species amplification in related taxa.Mol. Ecol. Resour. 2009;9(4):1247-1249. DOI 10.1111/j.1755-0998.2009.02620.x.Pritchard J.K., Stephens M., Donnelly P. Inference of population structureusing multilocus genotype data. Genetics. 2000;155(2):945-959.Qian C., Yin H., Shi Y., Zhao J., Yin C., Luo W., Dong Z., Chen G.,Yan X., Wang X.-R., Ma X.-F. Population dynamics of Agriophyllumsquarrosum, a pioneer annual plant endemic to mobile sand dunes,in response to global climate change. Sci. Rep. 2016;6(1):26613.DOI 10.1038/srep26613.Rachkovskaya E.I., Safronova I.N., Volkova E.A. Botanical geographyof Kazakhstan and Middle Asia (desert region). St. Petersburg, 2003;192-222. (in Russian).R Core Team. R: a language and environment for statistical computing.R Foundation for Statistical Computing, Vienna, Austria, 2018. URLhttps://www.R-project.org/Sampson J.F., Byrne M. Genetic diversity and multiple origins of polyploidAtriplex nummularia Lindl. (Chenopodiaceae). Biol. J. Linn.Soc. 2012;105(1):218-230. DOI 10.1111/j.1095-8312.2011.01787.x.Saqier Bao S., Han S., Ao W. Effects of Agriophyllum squarrosumextractson glucose metabolism in KKAy mice and the associatedunderlying mechanisms. J. Ethnopharmacol. 2019;241:112009.DOI 10.1016/j.jep.2019.112009.Turuspekov Y., Genievskaya Y., Baibulatova A., Zatybekov A., KotuhovY., Ishmuratova M., Imanbayeva A., Abugalieva S. Phylogenetictaxonomy of Artemisia L. species from Kazakhstan based onMatK analyses. Proc. Latv. Acad. Sci. B Nat. Exact Appl. Sci. 2018;72(1):29-37.Waples R.S., Gaggiotti O. What is a population? An empirical evaluationof some genetic methods for identifying the number of genepools and their degree of connectivity. Mol. Ecol. 2006;15(6):1419-1439. DOI 10.1111/j.1365-294x.2006.02890.x.Wen-Ming B., Xue-Mei B., Ling-Hao L. Effects of Agriophyllumsquarrosum seed banks on its colonization in a moving sand dunein Hunshandake Sand Land of China. J. Arid. Environ. 2004;59(1):151-157. DOI 10.1016/j.jaridenv.2004.01.006.Yeh F., Yang R., Boyle T., Ye Z., Mao J. POPGEN Ver. 1.32. The userfriendlysoftware for population genetic analysis. Alberta, Canada:Molecular Biology and Biotechnology Center; Univ. of Alberta,1997.Yuan M., Xiao H., Wang R., Duan Y., Cao Q. Effects of changes inprecipitation pattern and of seaweed fertilizer addition on plant traitsand biological soil crusts. J. Appl. Phycol. 2019;31(6):3791-3802.DOI 10.1007/s10811-019-01838-1.Zhang C., Sun M., Zhang X., Chen S., Nie G., Peng Y., Huang L.,Ma X. AFLP-based genetic diversity of wild orchardgrass germplasmcollections from Central Asia and Western China, and therelation to environmental factors. PLoS One. 2018;13(4):e0195273.DOI 10.1371/journal.pone.0195273.Zhang J., Zhao P., Zhao J., Chen G. Synteny-based mapping of causalpoint mutations relevant to sand rice (Agriophyllum squarrosum)trichomeless1 mutant by RNA-sequencing. J. Plant Physiol. 2018;231:86-95. DOI 10.1016/j.jplph.2018.09.003.Zhao P., Capella-Gutiérrez S., Shi Y., Zhao X., Chen G., Gabaldón T.,Ma X.-F. Transcriptomic analysis of a psammophyte food crop,sand rice (Agriophyllum squarrosum) and identification of candidategenes essential for sand dune adaptation. BMC Genomics. 2014;15:872. DOI 10.1186/1471-2164-15-872.


Acyrthosiphon pisum in Chile along a climatic gradient using phenotypic assays and genetic‐based criteria to test the ecological short‐term advantage of sex in cold environments. Our results showed a latitudinal trend in the reproductive mode of Chilean pea aphid population from obligate parthenogenesis in the north to an intermediate life cycle producing both parthenogenetic and sexual progeny in the southernmost locality, where harsh winters are usual. These findings are congruent with the hypothesis of the ecological short‐term advantage of sex in aphids.The maintenance of sexuality is a puzzling phenomenon in evolutionary biology. Many universal hypotheses have been proposed to explain the prevalence of sex despite its costs, but it has been hypothesized that sex could be also retained by lineage‐specific mechanisms that would confer some short‐term advantage. Aphids are good models to study the maintenance of sex because they exhibit coexistence of both sexual and asexual populations within the same species and because they invade a large variety of ecosystems. Sex in aphids is thought to be maintained because only sexually produced eggs can persist in cold climates, but whether sex is obligate or facultative depending on climatic conditions remains to be elucidated. In this study, we have inferred the reproductive mode of introduced populations of the pea aphid  Acyrthosiphon pisum in Chile is structured along a latitudinal transect. The southernmost one, where winters are harsh, is the only one that exhibits genetic and phenotypic features of sexual reproduction.Invasive population of  Several studies about the clonal diversity and population structuring of introduced pest aphid populations have been conducted in Chile, including the grain aphid, A. pisum sampled from six localities in Chile along a nearly 4,000 km transect and covering distinct climates, to test the short‐term ecological advantage of sex as a type of independent‐lineage mechanism for the maintenance of sex. We hypothesized that in the introduced range (Chile), the predominant reproductive mode would be OP in warm and temperate areas, but if aphids are present in colder areas, we predict that A. pisum would exhibit sexual reproduction which should allow populations to cope with freezing temperatures.Here, we used individuals of 22.1A. pisum were collected in spring and summer during 2016–2018 on the same host plant  from three areas that feature different climates in Chile, hereafter named as North (apN), Central (apC), and South (apS)  and to maximize the chance to sample aphids with different genotypes, the sampling within each alfalfa field was made taking only one individual per plant, surveying at least 6 plants per field separated by 3 m, thus totalizing 181 individuals across Chile . Maintenance of separated laboratory parthenogenetic lineages was carried out on broad bean plants (Vicia faba L.) inside of 4 L transparent plastic buckets. Buckets were maintained in a climatic chamber at 20°C (±0.5°C) and long photoperiod (LD = 16:8) to ensure sustained parthenogenetic reproduction.Individuals of S) Table a. The sa2.22, 0.5 U of Platinum Taq DNA polymerase (Invitrogen™), 200 mM of dNTP's, 125 pM of forward primer and 500 pM of reverse primer for each primer loci, and 500 pM of marked‐M13 primer (6‐FAM or VIC). The amplification steps were the following: (a) initial denaturation step at 94°C for 2 min; (b) 32 cycles consisting of a denaturation step at 94°C for 30 s, annealing at 60°C for AlA12M and AlB07M primers, or 56°C for all the other primers for 30 s, and an elongation step at 72°C for 45 s; (c) 8 more cycles of denaturation at 94°C for 30 s, annealing at 53°C for 30 s for all the primers, and the elongation at 72°C for 30 s; (d) a final elongation step at 72°C for 2 min.Highly polymorphic genetic markers (microsatellites) were used to identify multilocus genotypes (MLGs). We genotyped 181 field‐collected individuals at nine hypervariable microsatellite loci, namely AlA09M, ALB04M, AlB07M, AlB08M, AlB12M, ApF08M, ApH08M, ApH10M . MLG characterization was made by checking visually the allele sizes of each sample using the software Geneious R8.0.5 (2.3We assessed the reproductive mode of each subpopulation following two strategies: (a) by inducing sexual reproduction in individuals from laboratory parthenogenetic lineages; and (b) by inference from population genetic data.2.3.1A. pisum, as for most aphid species, the shift from clonal to sexual reproduction is triggered by an autumn‐like photoperiodic regime (shortening of daylength) and is a transgenerational process. In order to characterize the reproductive phenotype, individuals of each laboratory lineage from different latitudes reared under long‐day conditions  were transferred to a climatic chamber at 15°C with a short‐day regime  was calculated, where G is the number of different MLGs per subpopulation, and N the number of sampled individuals in that subpopulation. The mean number of alleles (Na), expected heterozygosity (He), observed heterozygosity (Ho) and deviation from expected heterozygosity under Hardy–Weinberg expectations (FIS) were obtained using GenAlEx v.6.1  was constructed . Distance trees were calculated with the obtained values and then plotted as a neighbor‐joining tree using FigTree v1.4.3 (http://tree.bio.ed.ac.uk/software/figtree/).To estimate the relationship between MLGs, a matrix of shared allele distances  in the three tested lineages. The genotype APG3, which was mainly restricted to central Chile , consistently produced males along with parthenogenetic females, but no sexual females in all the seven tested lineages , whereas in the southernmost locality the G:N index was 0.78. The genetic diversity for all localities is summarized in Table p = .001, Table FIS = 0.001) for all six subpopulations. Nevertheless, although statistically different, the closest Ho to He  was found in the southernmost locality (apS). Finally, evidence of linkage disequilibrium was found in 26/28 (out of 36) comparisons between loci in the southernmost region  independent introductions of genotypes with distinct reproductive phenotypes, (b) a reproductive mode transition from CP to OP in previously introduced populations and, (c) a reverse transition from OP to CP. The first scenario is supported by the work of Peccoud et al. , which rA. gossypii, they found that in the cotton‐producing regions of west and central Africa only one of the two overrepresented genotypes was prevalent (>90%) in spite of the equal or even high performance of the second clone (accounting for <10%) on plants not sprayed with insecticides.The extremely low genetic diversity in the northern and central regions may also result from additional factors than few introduction events of asexually reproducing lineages. Indeed, selective agents as pesticides, extreme thermal events, or biotic interactions among others, can contribute to the paucity in clonal diversity  at an optimal temperature and with parthenogenetic reproduction system could be approximately 2,500  controlling the production or the function of sexual forms , not being present in warm and mild climates (mean winter minimum T° = 8.9 and −0.2°C for apN and apC respectively). In this regard, sexuality for this species could be maintained only as a strategy to thrive in areas with unfavorable environmental conditions.Along with mutations, sex is usually recognized as the main source of new additive genetic variance that drives eukaryotic evolution. Conversely, sex can act as a constraint on genomic and epigenetic variation, thereby limiting adaptive evolution , for which more studies are necessary to support the lineage‐specific hypothesis of maintenance of sex.Summarizing, the phenotypic assays and the genetic data analyses show that asexual reproduction dominates The authors declare no competing interest.Sebastián I. Martel: Conceptualization ; formal analysis (lead); investigation (lead); methodology (lead); visualization (lead); writing – original draft (lead); writing – review and editing . Carmen G. Ossa: Formal analysis (supporting); writing – original draft (supporting). Jean‐Christophe Simon: Conceptualization ; formal analysis ; resources (supporting); writing – review and editing . Christian C. Figueroa: Conceptualization ; resources (supporting); writing – review and editing . Francisco Bozinovic: Conceptualization ; funding acquisition (lead); resources (lead); supervision (lead); writing – review and editing .


Gene flow tends to impede the accumulation of genetic divergence. Here, we determine the limits for the evolution of postzygotic reproductive isolation in a model of two populations that are connected by gene flow. We consider two selective mechanisms for the creation and maintenance of a genetic barrier: local adaptation leads to divergence among incipient species due to selection against migrants, and Dobzhansky–Muller incompatibilities (DMIs) reinforce the genetic barrier through selection against hybrids. In particular, we are interested in the maximum strength of the barrier under a limited amount of local adaptation, a challenge that many incipient species may initially face. We first confirm that with classical two-locus DMIs, the maximum amount of local adaptation is indeed a limit to the strength of a genetic barrier. However, with three or more loci and cryptic epistasis, this limit holds no longer. In particular, we identify a minimal configuration of three epistatically interacting mutations that is sufficient to confer strong reproductive isolation.This article is part of the theme issue ‘Towards the completion of speciation: the evolution of reproductive isolation beyond the first barriers’. RecenIn this manuscript, we address when and how strong reproductive isolation can evolve between two parapatric populations with limited ecological differentiation. To this end, we first define measures that characterize the strength of a genetic barrier and compare this with the amount of ecological differentiation that is available between the two populations. We then focus on the role of epistasis and the pattern of incompatibilities among genes building the genetic barrier. Our results show that, for a broad range of conditions, the potential for ecological differentiation is indeed an upper limit for the strength of the genetic barrier that can be formed. However, we also show that this constraint can be broken and that particular patterns of strong epistasis enable the evolution of strong reproductive isolation in parapatry. A barrier of this type must involve at least three interacting loci: two interacting barrier loci and one locus that changes their genetic background. A strong genetic barrier can thus evolve parapatrically in  three steps from an undifferentiated initial state.2.(a)m per individual and generation. In the main part of this article, we consider a three-locus model, with diallelic loci A, B and C. Ancestral alleles are denoted by lowercase letters and derived alleles by uppercase letters. Allele A (resp. B and C) has a selection coefficient α (resp. β and γ) compared with the ancestral allele a (resp. b and c). We derive extended results for models with more than three loci in the electronic supplementary material. Below and in the electronic supplementary material, for multiple loci Ai and Bj, we use the following notation: allele Ai has a selective advantage αi over allele ai and its epistatic interaction with allele Bj is given by We consider a migration–selection model in a continent–island framework ,9. The mε, with the involved alleles indicated as subscript. For example, εABC denotes three-way epistasis between alleles A, B and C. The fitness of each haplotype (wi) is given in Epistasis can occur between any combination of derived alleles and is denoted by X on the island (e.g. X = abC) under the continuous-time weak-selection approximation is:mC = m if X is the continental haplotype and mC = 0 otherwise. Here, fR(X) describes the change in frequency of haplotype X due to recombination. The detailed ordinary differential equation system with an explicit expression of the complicated function fR(X) is given in the electronic supplementary material ). Our analytical results focus on two special cases that have been shown to capture most of the important behaviour [r → 0 for all recombination rates, fR(x) = 0) and loose linkage ). The second scenario corresponds to the assumption of linkage equilibrium between all loci, which approximately holds true when the recombination rates are much larger than the selection coefficients and migration rates [Mathematica [https://gitlab.com/evoldyn/strong.ri.We assume that the continent is always monomorphic. When evolution happens on the continent, each substitution is assumed to be instantaneous. That is because we are only interested in the  polymorphic equilibrium state on the island, where individuals from both populations meet and mix. The haplotype frequency dynamics of an arbitrary haplotype ehaviour ,9: tighton rates ,9 .With the continuous-time approach employed here, all selection and migration parameters are rates. For the study of equilibria, only relative rates matter and we can thus scale all parameters by the selective advantage of the (b)Our aim here is to assess scenarios in which a strong barrier to gene flow can evolve despite limited potential for  local adaptation. To this end, we need to define measures for both the barrier strength and the amount of local adaptation.et al. [A and B at the barrier loci, with allele A appearing on the island and B on the continent. To maintain both loci polymorphic, alleles A and b must persist on the island in migration–selection balance, because aB is the immigrating haplotype from the continent. A or b (or both) are lost. The barrier strength can also depend on the genetic background. We will include reference to this background in our notation whenever necessary by writing Ab) barrier in the background of the ancestral c or derived C allele, respectively, where either of these alleles at locus C is fixed on both the continent and the island. While other measures exist , we focΛ, depends on a subset of model parameters and the time of observation; the second, Λmax, depends on the whole set of model parameters.To measure local adaptation, we define two parameters that capture either the current state or the overall fitness landscape of the system. The first one, current amount of local adaptation on the island, Λ, as the fitness advantage of the fittest segregating genotype on the island over a continental migrant. Recall that the continent is always monomorphic in our model.  This measure is consistent with the verbal notion of local adaptation by Kawecki & Ebert [A appeared first and B appeared first. After the second mutational step, the current amount of local adaptation is given by For any state of the population, we measure the  & Ebert  and illumaximum amount of local adaptation that can occur in the model over the course of the differentiation process that results in a given genetic barrier as Λmax. Note that Λmax does not depend on the current state, but is a property of the full fitness landscape. It captures all states that could have occurred  during the adaptive process from a given ancestral state. We thus need to consider all possible evolutionary histories to determine Λmax. Using the two-locus barrier example mentioned above, the maximum amount of local adaptation, C).In addition, we define the Λmax for any smaller barrier that involves only a subset of these loci. For diploids, we consider the fitness differences between genotypes scaled by the ploidy of the individual. Using this definition allows us to maintain a consistent notation for haploid and diploid populations: for a single locus A, we always have Λmax is bounded by the ecology of the system. However, the fitness difference between the optimal island genotype and a hybrid  may be much larger, since these genotypes are not part of any evolutionary trajectories.From this definition, we see, in particular, that the maximum amount of local adaptation for a large barrier that includes many loci is always greater than or equal to the value of 3.(a)If the external environment sets a limit to the amount of local adaptation, does this also imply a limit on the strength of the genetic barrier that can evolve in the presence of gene flow? We address this question by asking whether the former restricts the latter, i.e. whether the maximum amount of local adaptation during the differentiation process mmax = Λmax since local adaptation (direct selection against migrants) is the only mechanism that can maintain a polymorphism. This result holds independently of whether a locally adaptive allele appears on the island or whether a maladaptive allele immigrates from the continent. This result readily generalizes to the case of n diallelic loci in tight linkage, which acts like a single-locus model with 2n alleles. Only two haplotypes can be maintained at equilibrium [m > Λ, the continental haplotype replaces the island haplotype. Since Λ ≤ Λmax, the maximum amount of local adaptation Λmax is always an upper bound for the strength of the genetic barrier, mmax ≤ Λmax. For weak, but non-zero recombination r, this result remains valid as long as r is small relative to the selection coefficients and migration rates .For a single-locus barrier in a haploid population, it is straightforward to see that ilibrium : the besΛ is simply the sum of the selection coefficients αi > 0 of segregating island alleles relative to the immigrating continental alleles at the same locus . During the differentiation process, this value is maximized in the final state when all mutations that contribute to the barrier under consideration have appeared, Ai polymorphic is given by the smallest selection coefficient: Λmax, this barrier is therefore maximized when all loci share the same selection strength: mmax < Λmax for more than a single locus, i.e. the maximum amount of local adaptation Λmax is again an upper bound for the strength of the genetic barrier. This result (mmax < Λmax) readily extends to intermediate recombination rates as recombination ends up breaking the best haplotype (once formed) without any additional benefits.In the absence of epistasis and for multiple loosely linked loci, the temporary amount of local adaptation Λmax, selection is not constrained by the ecology and can potentially result in a strong barrier. For two loci and negative epistasis (i.e. a DMI), the barrier strength under a combination of local adaptation and selection against hybrids has previously been analysed by Bank et al. [A appearing on the island and B appearing on the continent, with negative epistasis between the two derived alleles. From their result for Having shown that it is impossible to form a strong genetic barrier in the absence of epistasis or if all loci are in tight linkage, we now turn to the case with loose linkage and epistasis. This introduces the possibility of selection against recombinant hybrids. Since fitness differences between the optimal types and maladaptive hybrids can be much larger than the strength of local adaptation k et al. . The autr mmaxAb , eqn 11,From equation , it is cmmax ). With positive epistasis, a genetic barrier can exist only if allele B is deleterious on the island, and the maximum of this barrier is given by mmax ≤ Λmax = α − β. However, in contrast to the negative epistasis case, it is possible for a genetic barrier to reach A is neutral (α = 0) on the island, and B is extremely deleterious  any barrier that contains either an island allele that is not involved in positive interactions, or a continental allele that is not involved in negative interactions ; (2) any barrier where all derived alleles originate on the island or all on the continent ; (3) any barrier with only positive or only negative epistatic interactions between derived alleles (this directly follows from points 1 and 2) ; (4) any barrier where derived alleles on the continent and the island do not interact, or interact only through negative epistasis .This suggests that only more complex epistasis, with a combination of positive and negative interactions, can result in a strong genetic barrier. We thus consider a diallelic three-locus model in the rest of the manuscript, which is fully parameterized with three direct selection coefficients and four epistatic parameters, allowing for complex interactions.(b)(i)A and C, which appear on the island, and a continental adaptation B. The different possible cases illustrate the general rules above for the impossibility of a strong barrier.—A and B and B and C cannot result in a strong barrier. Indeed, in the absence of allele B, we have B is introduced on the continent, the marginal fitness of alleles A and C decreases, leading to A and C is a subset of the three-locus barrier, a.Negative pairwise epistasis between —A and B and B and C is not sufficient for a strong barrier. The genetic barrier formed by allele A and C, assuming B is fixed on the island, corresponds to a case described above (i.e. two non-interacting loci), therefore B is polymorphic on the island, then the marginal fitness of both allele A and allele C is reduced, leading to Similarly, pairwise positive interaction between —A and B interact negatively and alleles B and C interact positively. In the absence of allele C, this corresponds to the two-locus case mentioned above and therefore C appears on the island, it directly increases the marginal fitness of allele B on the island, facilitating its fixation on the island. In addition, through this effect on B (leading to a higher equilibrium frequency for B), it also indirectly and negatively affects the marginal fitness of allele A, facilitating its loss. As a consequence of its effect on the marginal fitness on alleles A and B, we obtain Ab|c’ barrier is a subcase of the ‘AbC’ barrier, leading to We now consider that one pairwise epistatic interaction is positive and the other negative: we assume that alleles —A and B interact negatively and A and C positively. In the absence of allele B, the genetic barrier obtained in loose linkage is smaller than its equivalent in tight linkage since recombination breaks the association between A and C. Or in tight linkage, the genetic barrier is equal to B mutation is introduced, the marginal fitness of allele A and C decreases owing to the direct (for A) and indirect (for C) interaction with allele B. We therefore obtain Finally, we consider that We first consider a case of with two pairwise epistatic interactions. First, we focus on a case with two island adaptations a) and electronic supplementary material, S4): the continental haplotype and the fittest island haplotype are connected by a fitness ridge (e.g. AbC and aBc in figure 2a), and all genotypes on this fitness ridge can be reconstructed from the (fittest) island and continental haplotypes by recombination (recombination of AbC and aBc in figure 2a).Similar arguments show that any barrier must be weak for two derived barrier alleles on the continent and one on the island . The fitness landscapes of all scenarios described so far share a crucial property a and eleA and a continental adaptation at locus B . In addition, we assume that a mutation can occur at a third locus (the C locus). We assume that the derived allele C is deleterious in the ancestral genetic background (γ < 0), but beneficial in the presence of either the A or the B allele . If C originates on the continent, it can fix on both the continent and the island –(S29); see figure 2c for the three potential evolutionary histories). We then obtain a two-locus barrier (loci A and B), but the derived alleles at this barrier interact with a fixed derived allele in its genetic background. We refer to this type of interaction as ‘cryptic epistasis’ since it will not be detected in a study that focuses on divergent alleles between both populations. Notably, the corresponding fitness (sub-)graph, illustrated in figure 2c , is characterized by the existence of two haplotypes (AbC and aBC) whose recombinants (abC and ABC) have very low fitness. Fixation of C thus deepens the observed fitness valley between Ab and aB.We now consider a case in which a genetic barrier with two barrier loci is combined with a change in the genetic background . We assume (as above) that there is an incompatibility between an adaptation on the island at locus γ′ as the effect of the mutation C in the background of at least one other derived allele: γ′ = γ + εAC. Notably, this system is equivalent to a C mutation that appears on the continent, which is advantageous on the island while generating strong negative epistasis with the ancestral background ab, εabC = −εAC. For the rest of the manuscript, we will use the alternative notation (εabC and γ′) as it is more convenient.To simplify the notation, we define For a haploid population and loose linkage, the dynamics simplify to the classical two-locus model  and are abC haplotype is deleterious and therefore no longer a potential step for evolution. When C is advantageous (γ′ > 0) on the island (and since α > 0), equation The maximum amount of local adaptation, which characterizes the ecological differentiation in the model, is unaffected by the new mutation; A and B are in tight linkage or in loose linkage. Our analysis is complemented with simulations for intermediate recombination rates (abC does not occur anyway). The barrier is therefore again limited by the maximum amount of local adaptation available, We investigate the impact of this change of the genetic background for two cases analytically: loci on rates . With ti(S5) and ). StrongεAB > α, i.e., when the incompatibility between A and B is stronger than the direct selective advantage of A; this is therefore a necessary condition for A and B .From the previous section, we know that a strong single-locus barrier can never form. However, the existence of a single-locus (unstable) equilibrium is a necessary condition for the two-locus genetic barrier to be globally stable . Therefore, if  A and B : as soonabC and ABC are inviable haplotypes and that A and B are located on different chromosomes. The results are qualitatively similar, i.e. for a range of parameters, a genetic barrier can be stronger than the maximum amount of local adaptation , figure S11). Finally, if we assume that the abC haplotype is inviable (εabC → −∞), the genetic barrier is given by Our assumptions of loose linkage and the continuous-time approximation both implicitly rely on weak selection. We therefore derive the equivalent of (ii)α, β, γ′) are additive and that epistatic interactions  can be either recessive or codominant . Both the recessive and codominant model simplify to their equivalent dynamics presented in [In the diploid model we assume that the direct effects of the mutations  or through the codominance of the interactions. When A and B are in tight linkage and epistasis is recessive, the genetic barrier is given by We have established above that the maximum amount of local adaptation, β = 0, is the easiest condition to form a barrier that exceeds B allele does not contribute to the maximum amount of local adaptation; therefore all local adaptation can be captured by the A adaptation , the amount of local adaptation between well-adapted types in both populations is a hard limit for the strength of a genetic barrier. We then describe a simple three-locus scenario in which a much stronger barrier can evolve. Crucially, the scenario relies on cryptic epistasis, i.e. epistasis between the divergent alleles and a derived background allele that fixes in both populations. In this case, a strong barrier is possible if a classical two-locus DMI is stabilized by positive epistasis of both interacting partners with such a background allele. Since the strength of the genetic barrier relies on strong selection against hybrids, this phenomenon requires sufficiently strong recombination between the interacting loci to be observable in haploid populations. In diploids, where hybrid genotypes also form without recombination, codominance of the incompatibilities and tight linkage between the loci involved in the initial DMI provide the best conditions for the evolution of strong reproductive isolation.(a)The accumulation of genetic incompatibilities due to selection or drift is a standard mechanism to explain the evolution of reproductive isolation between two allopatric populations . In the (b)Conceptually, speciation in the presence of gene flow requires a fitness landscape in which (at least) two peaks are connected via a high-fitness ridge of single-step mutations. Yet, to exceed the limit imposed by the maximum amount of local adaptation, any recombinants between the peak genotypes have to be strongly deleterious. This can be achieved by what we term ‘cryptic’ epistasis, i.e, when the interaction with (at least) a third derived allele turns the high-fitness ridge that allowed for the evolution of an initial DMI into a fitness valley. Importantly, this third allele must fix in the population, or otherwise the high-fitness ridge is not fully interrupted.C appears last. However, it is only required that C appears after the fixation of B on the continent and fixes after A appears on the island, leading to the three possible scenarios detailed in In a minimal model, three loci are necessary to form the required underlying fitness landscape. In this landscape, the first mutational step corresponds to the establishment of initial differentiation between the two populations, which requires some local adaptation (either on the continent or on the island) at the respective locus. The second mutation generates a derived–derived incompatibility with the first adaptation . At thiA and B correspond to adaptations leading to specialization for the prevalent food source on island and continent, respectively. Both come with a (large) cost of catching/exploiting the other one, such that AB individuals are not good at catching/exploiting either. Mutation C makes individuals stick to a single foraging pattern, which is bad for the ab generalists, but good for both specialists, and may thus fix in both populations.For a hypothetical example of cryptic epistasis, assume that mutations at loci 1 and F2 hybrid viability analysis [DMIs have been investigated mainly with respect to negative pairwise epistasis ,14,18–20analysis ,22; thuset al. [The importance of complex (non-negative pairwise) epistatic interactions in speciation has been stressed in several studies. Fraïsse et al.  compiledet al. , diverge(c)et al. [C mutation fixed on the island, but could not exclude the possibility that strong barriers can evolve even if the C allele remains polymorphic on the island.The results presented here were derived using an analytical framework, complemented with some numerical calculations. To do so, we used a continuous-time approximation, which has the disadvantage of having parameters that are meaningful only in relationship with each other. We confirmed that we observe a qualitatively similar pattern in a discrete time scenario, where parameters can be transposed to natural cases. Furthermore, we investigated this question under an infinite population size model. Adding genetic drift to the model is of great interest as temporal dynamics, as well as drift, may impact the final outcome. Adding drift may weaken the genetic barrier since the island population will be smaller. However, it may favour the introgression of background mutations from the continent to the island and therefore accelerate the formation of strong genetic barriers. Similarly, we focused mainly on cases of linkage equilibrium. Feder et al.  showed tet al. –28). It In our minimal model, a lot of deleterious hybrids will be generated, which comes at a cost for the island population. Co-existence of the ‘island species’ and the ‘continental species’ in this case thus relies on a sufficiently large population size on the island, such that the ‘island types’ are always in the majority relative to the continental migrants . In this case, the continental migrants suffer more from matings with the island types . The dynamics may change if subsequent evolution of prezygotic isolation strengthens the genetic barrier without requiring any further local adaptation. Indeed, our model should provide a favourable scenario for such reinforcement –31. Howe(d)Hybrid incompatibilities have been proposed as an engine of speciation in allopatry, where simple accumulation of individually neutral but negatively interacting mutations ‘almost necessarily’ leads to a ‘snowball effect’ and eventual reproductive isolation , a proceWe have previously shown that some local adaptation is indeed a necessary ingredient for the evolution of a genetic barrier in the presence of gene flow , and thaImportantly, we also discovered specific fitness landscapes that combine locally adapted alleles with specific epistatic interactions, which enable the evolution of much stronger genetic barriers and even complete isolation in the presence of gene flow. Whether strong reproductive isolation between parapatric populations might indeed evolve through the combination of local adaptation and epistasis described here is thus dependent on the existence of the necessary fitness landscapes in nature. If they exist, the route to strong reproductive isolation could require only a small number of mutational steps. If such fitness landscapes do not exist, strong postzygotic reproductive isolation in the presence of gene flow may never be reached even after a very long time. An important conclusion from our work is thus a strong dependence of the feasibility of parapatric speciation on the underlying genetics, which makes it difficult to infer and predict.


Pteropus dasymallus) is distributed throughout the island chain spanning across southern Japan, Taiwan, and possibly the Philippines. Although P. dasymallus is listed as VU (vulnerable) in the IUCN Red List, only few genetic works have been conducted to support its conservation. In this study we analyzed 19 markers (mtDNA haplotypes and 18 microsatellite markers) to evaluate genetic diversity and investigate the genetic structure of this species. mtDNA analysis was conducted with 142 DNA remote samples, mostly from faeces, and wing tissues collected on eight islands . 39 haplotypes were identified in 526bp of the control region, and haplotype network showed no clear genetic structure. Microsatellite analysis was also conducted with 155 samples collected on six islands . It showed that the Yonaguni population exhibits low genetic diversity, high inbreeding, and clear genetic differentiation from other populations. Gene flow between Ishigaki and Miyako through small stepstone islands might be preventing inbreeding of the Miyako population. We provide for the first time indirect proof of long-distance inter-island dispersal in the Ryukyu flying fox and revealed genetic diversity, gene flow and genetic differentiation among the archipelago’s populations. These results will be useful for delineating conservation units and designing specific conservation policies for each island based on metapopulation genetic structure.The Ryukyu flying fox ( The Chiroptera represent a speciose and highly threatened group of mammals . In JapaPteropus genus, serve important ecosystem functions as pollinators [Pteropus niger was reported to fly up to 92 km in a single night [Pteropus species have demonstrated an ability to migrate between islands [P. niger by the Mauritian government [Flying foxes, defined hereafter as paleotropical fruit bats belonging to the linators  and seedlinators , and plalinators . Their sle night , while s islands . This is islands , leaving islands . The int islands –14 and tvernment , 15–17.Pteropus dasymallus) is distributed across the Ryukyu archipelago in Japan, two small islands of Taiwan, and possibly in the Philippines [P. dasymallus mainly eats fruits, nectar, and sometimes leaves, and plays an important role in pollination and seed dispersal [P. dasymallus is listed as Vulnerable in the IUCN Red List [The Ryukyu flying fox .n = 95, tissue: n = 17, fruits: n = 23) were used for mtDNA sequencing while 155 samples collected in 6 islands  were used for microsatellite analysis. The number of analyzed individuals for each island is shown in In total, 135 samples from 8 islands (faeces: 5’-accagaaaaggggarcaacc-3’) and ppmtCR-RS2(5’-caagcatcccccaaaaatta-3’) [A part of control region of mtDNA was amplified through PCR. ppM01F (atta-3’)  were useh) and haplotype richness (hr) were calculated by Contrib [SNPs were detected through sequence alignment by MEGA7 . Haploty Contrib .P. dasymallus [5’-GTTGTAAAACGACGGCCAGT-3’) for fluorescent labeling. PCR was conducted in a final volume of 10 μl, containing 1 μl DNA, 5 μl Multiplex PCR Master Mix (QIAGEN), 0.2 μM of M13-tailed forward primer, reverse primer and a M13 fluorescent primer labeled with FAM, NED, or HEX, and 0.1 μg of T4 gene 32 Protein . The PCR conditions were: 94°C for 5 min; 45 cycles at 94°C for 30s, 60°C for 45s, 72°C for 45s; then 8 cycles for M13 at 94°C for 30s, 53°C for 45s, 72°C for 45s, and a 10 min final extension at 72°C. Amplicon size was measured using an ABI PRISM 3130xl Genetic Analyzer (Applied Biosystems), and genotypes were scored by eye with Peak Scanner Software (Applied Biosystems).18 microsatellite loci  which we developed specifically for symallus  were ampFst was calculated by GENEPOP on the Web [Genetic diversity analysis and Principal Coordinate Analysis (PCoA) were conducted by GenAlEx (version) , 36. Als the Web , 39 to cSamples were collected under permission granted to C.E. Vincenot by the Japanese Ministry of Environment . The experimental protocol for capture, handling and sample collection was also approved by Kyoto University’s Animal Experimentation Committee .45 SNPs were detected in 526 bp sequences (including 35 bp which is a part of neighboring translated gene) of 135 samples, and 39 haplotypes  were defined. The SNPs of each haplotype are shown in Haplotype network suggested that haplotypes from each island did not form any clusters and no clear genetic structure was detected . HoweverHaplotype distribution among islands revealed that 14 haplotypes out of 39 were shared between multiple islands . While sNh), the number of unique haplotypes (unique), haplotype diversity (h), and haplotype richness (hr) of 4 islands are shown in h of Ishigaki and Iriomote were 0.96 and 1.00, while that of Miyako and Yonaguni were 0.69 and 0.61, respectively. Also, the hr of Ishigaki and Iriomote were 7.51 and 9.00, while that of Miyako and Yonaguni were 3.77 and 2.79, respectively. Genetic diversity was higher in Ishigaki and Iriomote than Miyako, and the diversity of Yonaguni was the lowest.The number of haplotypes (n), observed heterozygosity (Ho), expected heterozygosity (He), and inbreeding coefficient (F) of each island are shown in Ho of Ishigaki, Kohama, Iriomote, Miyako were 0.666, 0.650, 0.631, 0.512, respectively. On the other hand, Ho of Yonaguni was 0.388 and F was 0.355. Yonaguni population had low genetic diversity and showed high level of inbreeding compared to the other islands.The number of analyzed individuals , and Miyako cluster .In STRUCTURE, LnP(D) suggested that K = 4 provides the best model , and theFst between Yonaguni and the other islands were generally high  [Ho = 0.388) had much lower value, while that of Miyako (Ho = 0.512) was slightly lower. The other islands’ were, however, higher (Ho = 0.631~0.666). Also, inbreeding coefficient was high in Yonaguni population. This indicates that genetic diversity has been getting low due to inbreeding depression in Yonaguni population.Genetic diversity was high in populations of the central area  and the lowest in Yonaguni population. Miyako population exhibited a medium level of genetic diversity. Comparing the h island  to that  0.5718) , YonagunIn mtDNA haplotype analysis, no clear genetic differentiations between islands was detected. However, in microsatellite analysis, populations of 6 islands were genetically divided into 3 groups: Miyako group (Miyako), the Central group , and Yonaguni group (Yonaguni). Populations in the central group had the same genetic composition, and Miyako group was partly differentiated from them. Yonaguni group was highly differentiated from the other 2 groups.The main habitats of the Yaeyama flying fox are Ishigaki island and Iriomote island  and populations in these two have higher genetic diversity than other islands. Other islands of the Central group might be keeping their diversity because of the gene flow linking them with the Ishigaki and Iriomote populations.The Miyako group is geographically far from the Central group, but there are some islands such as Tarama between Ishigaki and Miyako. There could be gene flow between the two groups through these islands, and that might be why Miyako group was not differentiated completely from the Central group , Table 3Pteropus pselaphon, the other Japanese Pteropus species, genetic diversity of an isolated island was similar to that of Yonaguni [As regards Yonaguni, there is no steppingstone between this island and the Central group. In a research on the endangered Bonin flying fox Yonaguni . Since YThe observation of gene flow between islands provides the first indirect evidence that long-distance inter-island dispersal is occurring in the Ryukyu flying fox. This indicates that this species is capable of long flights and thus that individual activity may not be assumed to be limited to a small range. This confirms results of ongoing VHF and GPS tracking studies  and casts doubt on the validity of general population trends deduced on the basis of counts conducted in only a small set of geographically localized foraging sites. Furthermore, our results show that island populations may be connected through flight capability and thus generate demographic and genetic interdependence, which need to be resolved to inform conservation policies. This interconnection into a metapopulation may also explain seasonal changes in perceived abundance, which so far have been considered signs of increased activity only. For instance, local inhabitants of Hateruma island, among others, have consistently reported spotting significantly more individuals in summer than the rest of the year, a phenomenon that they attributed to seasonal migrations from Iriomote island . Our findings, although inconclusive as regards Hateruma island, give some scientific credit to such local claims, especially on island featuring high gene flow.Considering the results of our genetic research, the Yaeyama subspecies of the Ryukyu flying fox could be divided into 3 conservation units: Miyako unit (populations in Miyako islands), the central unit , and Yonaguni unit (Yonaguni islands) . The cenIn conclusion, our analysis supports that island populations, especially the ones forming the backbone of the archipelago, should be considered as variably interconnected through exchanges of individuals. This observation may allow future population monitoring to better comprehend changes in local population sizes and, on the longer term, in genetic diversity by integrating the knowledge gained on gene flow among the different islands. We hope that the present results will be integrated into the design of conservation strategies, which are regrettably still lacking. We propose in particular that outer populations, such as the one on Yonaguni, be the object of focused attention and protection measures, as they seem most at risk of stochastic demographic collapse and inbreeding depression.S1 FigBase No shows positions in the 526bp sequence.(TIFF)Click here for additional data file.S2 FigEach haplotype is shown as a circle with ratio of the number of samples of each island. The sizes of circles represent the sample sizes.(TIFF)Click here for additional data file.S3 FigPopulation and sample number are shown in each place in the tree.(TIFF)Click here for additional data file.S4 FigLnP(D) shows the accuracy of the analysis for each K. K = 4 is chosen as the best model.(TIFF)Click here for additional data file.S5 FigEach ancestral population is shown with colors.(TIFF)Click here for additional data file.S1 Table(TIFF)Click here for additional data file.S2 Table(TIFF)Click here for additional data file.


Scaling laws are used to model how different quantifiable properties of cities, such as the number of road traffic accidents or average house prices, vary as a function of city population size, with parameters estimated from data. Arcaute et al. raised the issue of whether specific cities with extremely large population sizes, known as dragon-kings, should be considered separately from other smaller cities when estimating the scaling law parameters since the two types of cities tend to display different behaviour. Through the analysis of randomly generated samples, we find that the inclusion of dragon-kings in the scaling analysis does not affect the estimated values for the parameters but only provided that all the data points satisfy the same scaling law. We also analyse randomly generated samples where data corresponding to a particular city deviates from the scaling law followed by the rest of the cities. We then show that deviations corresponding to dragon-king cities have the most significant effect on the estimated values of the scaling parameters. The extent of this effect also depends on which estimation procedure is used. Our results have important implications on the suitability of scaling laws as a model for urban systems. This relationship is often mathematically modelled as a scaling law, whereby a quantifiable property Y is postulated to vary with city population size X according toY per capita. If Y per capita are constant across population sizes. If Y is said to increase superlinearly with city population size. When Y increases superlinearly, the values of Y per capita are higher for more populous cities. It has been found that urban properties such as the average water consumption2 grow linearly with city population size; economic productivity3 or the amount of coverage received by the media4 grow superlinearly and the number of road traffic accidents in cities grows sublinearly5.Due to the shift towards a mostly urbanised world9. For example, Arcaute et al.10 raise the issue that, on the one hand, cities with extremely large populations such as London in the UK, tend to behave differently from the rest of cities within the urban system due to their socioeconomic role; therefore, they should possibly be analysed relative to other cities with extremely large populations and separately to the rest of cities within an urban system. On the other hand, these extremely populous cities act as hubs, making the urban system highly integrated. From this point of view, all the cities in the urban system should be analysed together despite the fact that the value of the estimated scaling parameters might be dominated by the unusual behaviour of extremely populous cities.However, it is challenging to quantify reliably from data the value of the scaling exponents and their estimates usually come with caveats11. A ‘dragon-king’ is defined as an event that, due to its large size, is statistically and mechanistically considered to be an outlier of the underlying heavy-tailed probability distribution followed by the rest of events. Sornette shows that dragon-kings can be present in the distribution of city population sizes which, as is the case for many other systems in nature, follows a heavy-tailed probability distribution.Rather than considering extremely populous cities as a separate category of cities, in this paper we aim to understand how their inclusion might affect the estimated values of the scaling parameters. This approach is philosophically in line with the complex systems perspective by which systems formed by many interacting parts should be considered as a whole to properly understand their behaviour. In order to specify what we mean by extremely populous cities, we use the statistical concept of dragon-kingX as a random variable which follows a heavy-tailed probability distribution. Variable Y, which could for instance represent the number of road accidents in a city, is also modelled as a random variable. The scaling law in Eq.  probability distribution, also conditional on X, which can be parametrised in terms of the mean r that is related to the degree of overdispersion. Then, Y takes the value y with the following probability conditional on A variety of probabilistic models can be used to generate the value of We start with X from a power-law probability distribution with parameters n values by We draw a random sample of size We transform the sample by taking We check whether the original and the transformed random samples contain dragon-kings. If there is one dragon-king in the transformed sample and zero dragon-kings in its original form, we proceed to the next steps. Otherwise, we discard the original and transformed samples and go back to step 2.Y are kept the same for For each value X and Y corresponding to the original and transformed samples and increase m by one.We store the values of We return to step 2.If We use the package ‘powerlaw’21 to generate the power-law distributed values of X in step 2.(a). To check for the presence of dragon-kings in step 2.(c), we apply the DK-test and the U-test proposed by Pisarenko and Sornette13. For the U-test, instead of finding the parameter values through the maximum likelihood method as prescribed by the authors, we simply set them to the true values, i.e. those used in step 2.(a) to generate the data related to X. We consider that a sample does not contain any dragon-kings if (i) the p-values given by the U-test are above 0.1 for the top 100 cities, ranked by decreasing population size and (ii) the p-values given by the DK-test are all above 0.1 when we compare the first spacing to a number of spacings ranging from 1 to 25. We consider that a sample transformed according to step 2.(b) has one dragon-king if (i) the p-values given by the U-test corresponding to the top 100 cities, ranked by population size, are above 0.1 except for the largest city, which must be below 0.1 and (ii) the p-values given by the DK-test are all below 0.1 when we compare the first spacing to a number of spacings ranging from 5 to 25.Below, we give the specific algorithm that we use to generate samples where all the data points obey Eq. : We star22, dragon-kings are extreme events that arise from generating mechanism which are not necessarily active for the rest of the entities under consideration. As a consequence, dragon-kings tend to display unique behaviours and in the context of scaling laws, this may have an impact on the estimated values for the parameters. Indeed, Gomez-Liévano et al.23 show that the values of Y associated with extremely populous cities are prone to very large variance and this can result on biased estimators for the scaling exponents. To test these effects, here we also generate samples where the value of Y associated with Y corresponding to a different city of smaller population size is generated according to As established by Sornette and OuillonY associated to the cities in each sample following a known distribution conditional on X. When Y follows a Poisson distribution, the Poisson regression must be applied to estimate the scaling parameters. Similarly, when Y has a NB distribution, the negative binomial regression must be applied. However, it can be difficult to ascertain the distribution of Y when real data is considered and this can lead to the wrong choice of regression model. Hence, in order to show how this can affect the estimated values for the scaling parameters, in “Y is NB-distributed and the negative binomial regression to samples where Y is Poisson-distributed.We generate the values of Different sets of Y-data so that it satisfies Eq. . We can then conclude that appropriate knowledge of the distribution of Y can help us obtain more precise estimates for the parameters of the scaling law between X and Y.From the bottom two panels in Fig. X and Y satisfy the same scaling law for all the cities in each sample, the presence of dragon-kings does not have an impact in the estimated scaling exponent, regardless of the type of regression used for the estimation. In Fig. Y corresponding to a specific city in each sample satisfies a scaling law with exponent Y satisfy Eq. (Figure entclass1pt{minimaY is Poisson-distributed. The negative binomial regression is not as sensitive to the effect of the dragon-king, giving estimated values of the scaling exponent which are much closer to the true one. Generally, we observe that if a city with large population size deviates from the scaling law followed by the rest of cities, the effect on the estimated scaling exponents is more significant. The larger the city’s population size, the more this tendency becomes apparent.We observe the largest discrepancy with the true parameters for the case where the dragon-king does not satisfy Eq. . The PoiX variable does not affect the value of the estimated exponent Y satisfies the same scaling law for all the cities under consideration.From the results presented above, we conclude that the presence of dragon-kings in the Y might not necessarily follow the same scaling law as the rest of cities. If this happens, the estimated values of the scaling parameters given by the Poisson regression will be further from the true values than those given by the NB regression. This is due to the fact that the former regression method gives more importance to the largest data points. The Poisson or negative binomial regressions involve maximising a likelihood function via the IWLS method. This, in turn, involves successive iterations of a weighted least squares problem24. The weights assigned to different data points dictate their relative importance in determining the estimated parameters at each iteration of the IWLS method. Verhoef and Boveng24 show that the weight corresponding to the city with population jth iteration is X, such as the dragon-kings, have corresponding larger expected values of Y and will therefore receive relatively more weight in the Poisson regression.However, dragon-kings tend to display a different behaviour from other cities so, in real scenarios, The findings of this paper raise the following dilemma. Regression methods that place relatively less weight on the larger city population sizes, such as the negative binomial regression, will produce estimates of the scaling parameters which are closer to the true values. But, when it comes to cities, the interest is often in the really large fast-growing cities that host a significant percentage of a country’s population. If the tendency is for large cities to evolve towards the dragon-king status, then scaling laws are not a good model, as there is an inherent flaw in the parameter estimation procedure.In conclusion, if estimation techniques that give more weight to the larger entities are used, then the estimated exponents may be invalid unless the same scaling law is satisfied by all the cities in the urban system. But, if an estimation technique that does not give as much weight to these ‘outlier’ cities which are prone to deviate from the mainstream behaviour, then the most interesting part of the scaling law risks being neglected.


Rhinogobiusformosanus Oshima, 1919 has long been considered an amphidromous goby. However, a landlocked population recently found in the Jingualiao Creek upstream of the Feitsui Reservoir in Taipei suggests that R.formosanus may complete its life in the river. This study aims to verify the habitat use of the landlocked population of R.formosanus collected from the Feitsui Reservoir and an amphidromous population collected in Malian Creek using otolith Sr:Ca ratio analysis. The hypothesis that early life history varies between the landlocked and migratory gobies was also tested. Genetic analyses show that the Feitsui Reservoir and Malian Creek populations are not genetically different. Rhinogobiusformosanus from Malian Creek showed high-to-low otolith Sr:Ca ratios suggesting that these specimens spent a planktonic larval stage in the sea followed by a freshwater life at later stages. In contrast, R.formosanus from the Feitsui Reservoir showed constant lower otolith Sr:Ca ratios, implying a landlocked life history of fish in the creek upstream of the reservoir. In addition, the analysis of growth increments showed a longer pelagic larval duration for the fish in the Malian Creek (58.8 days) than those in the Feitsui Reservoir (38.8). Variation of pelagic larval duration in two genetically homogenous populations implies acclimatization to the reservoir by the landlocked gobies. This study shows that R.formosanus, like some other congeners, is capable of adapting to a freshwater landlocked environment in its early developmental stage and supports the hypothesis that landlocked populations may have a shorter pelagic larval duration. Sicyopterusjaponicus Tanaka, 1909 and S.lagocephalus Pallas, 1770, for example, are two small amphidromous gobies with a long pelagic larval duration (PLD) of 133 to 266 days and distributed across ranges of c. 2400 and 18000 km, respectively  ratios are extensively applied to study the migration between the sea and rivers for various fishes  and three undescribed species from Japan were considered facultatively amphidromous, and either migratory or landlocked depending on whether passage to the sea is possible  in Taipei. This population may be landlocked, since the dam of 122.5 m height completely blocks upstream migration of aquatic life. Even if the fish larvae survive the downstream passage from the reservoir, the juvenile and adult fish cannot return upstream of the dam  fragments were sequenced to provide molecular data of genetic differentiation between landlocked and amphidromous populations in order to infer the landlocked life history, if any, as a consequence of acclimation or adaptation.Based on the above facts, this study aims to test two hypotheses. First, R.formosanus were collected from two creeks in northern Taiwan:Jingualiao Creek, which flows into the upstream area of FR, representing a landlocked population with syntopic congeners R.candidianus and R.similis Gill, 1859; and Malian Creek (MLC), representing an amphidromous population with syntopic congener R.similis, directly connected to the sea , Kaohsiung. The voucher numbers of specimens are as follow: FR, DOS 03534–2, –11, –14, –15, –16, –19, –33, –35, –36, –37; MLC, DOS 02416–1, –2, –5, –6, –7, –12, –13, –15, –20, –22. All the procedures in this study were approved by the “Institutional Animal Care and Use Committee of National Taiwan University”.A total of 20 specimens of COI gene was amplified by polymerase chain reaction (PCR) with universal primers designated by -1), 3 μL of 10× buffer, 2 μL of dNTPs (2.5 mM), 1.2 μL of each primer (10 μM), 0.13 μL of ProTaq Plus polymerase  and 16.47 μL of deionized water. PCR cycling conditions included an initial denaturation at 94 °C for 5 min, followed by 36 cycles of denaturing at 94 °C for 30 sec, annealing at 50 °C for 30 sec, and extension at 70 °C for 1 min, and a final extension at 72 °C for 8 min. After checking qualities by electrophoresis, PCR products were purified using the SAP-Exo purification kit  according to the manufacturer’s protocols. Sequencing was conducted by an ABI 3730 automated sequencer. Newly generated sequences were edited manually using MEGA version X (MN187015–MN187034).DNA was extracted from fin clips using GeneMark DNA Purification Kit . The mitochondrial ersion X  and tranThe genetic diversity indexes of haplotype diversity (h) and nucleotide diversity (π) were calculated in DnaSP version 6 . A minimFR and MLC populations, respectively. The otoliths were cleaned and embedded in Epofix resin  before repeated grinding and polishing along the sagittal plane until the core was revealed on the surface. The otoliths were coated with a layer of carbon  to increase the electron conductance when the otoliths were analyzed by the electron probe microanalyzer . Quantitative analyses of Sr and Ca were conducted along a transect from the otolith core to the edge at 10 μm intervals. Electron beam conditions were 15 kV for the acceleration voltage and 3 nA for the current, with a 5 × 4 μm rectangular scanning beam size. The wavelength dispersive spectrum at the Sr Lα peak position was measured for 80 s and each of the upper and lower baselines for 20 s. The peak concentration of Ca Kα was measured for 20 s and each of the upper and lower baselines for 10 s. Synthesized strontianite  and aragonite (CaCO3) were used as standards to calibrate the concentration of Ca and Sr, respectively, in the otoliths. The Sr:Ca ratios were calculated after a correction using the PRZ (phi‐rho‐z) method  method . The dett% in Sr . The otoSr:Ca ratios, the otoliths were polished to remove the carbon coating and etched with 0.1 M HCl for 10–15 s to enhance the contrast of growth increment observed under a compound light microscope . Two experienced researchers counted the otolith growth increments from the core to a high‐contrast growth increment (an otolith check), or to a structural transition from clear concentric rings to ambiguous growth increments. This otolith check, appearing at the transition of high-to-low otolith Sr:Ca ratios, represented the ontogenetic change from pelagic larvae to demersal juvenile living in the river. If the two counts differed, the otolith was examined once more and final age was determined after discussion. The maximal distance from the core to the otolith check, or to a structural transition was also measured, which was further divided by the number of the growth increments to estimate the mean otolith growth rate during the pelagic larval stage of the gobies. One-way ANOVA was used to compare the otolith Sr:Ca ratios representing marine and freshwater life stages. The student’s t-test was used to compare the PLD and otolith growth increment width between the landlocked and amphidromous gobies. Statistical significance was set at α = 0.05.After the analysis of otolith COI (555 bp) from 20 specimens obtained from two localities  and nucleotide (h) diversities of FR were lower than those of MLC . Total haplotype and nucleotide diversity were 0.574 ± 0.122 and 0.00161 ± 0.00046, respectively  from the otolith core to around 200 to 300 μm, followed by low otolith Sr:Ca ratios (approximately 0–5 × 10-3) to the edge  beyond the otolith check . It is likely that the otolith check was formed when the gobies migrated from the sea into the river during the post-larval or early juvenile stages as found in other amphidromous goby species .For the gobies collected in dge Fig. . The higes e.g., . A high‐ios Fig. . The mea species . TherefoSr:Ca ratios from the otolith core to the edge was found in all the gobies collected in the Jingualiao Creek although some fish showed one or two relatively higher Sr:Ca ratios , which was significantly shorter than the PLD of the amphidromous gobies . However, the mean otolith growth rate before the settlement was similar between FR (5.5 ± 1.1 μm d-1) and MLC (5.3 ± 1.3 μm d-1) populations and not statistically significant .A different pattern of consistently low ios Fig. . In addiios Fig. . These res e.g., . The otoFR and MLC populations are mixed without reciprocal monophyly  is likely due to the complex amphidromous life history of R.formosanus. On the other hand, the stable environment in the creek or in the reservoir (Kolding and Zwieten 2012) may facilitate the larval development in a shorter and less variable time for the landlocked population. Furthermore, a shorter PLD can facilitate an earlier habitat change of larval fish from the upper water column to settlement in benthic habitats where shelter is more abundant. Therefore, a shorter PLD may enhance the survival rate of a pelagic larval goby living in a creek connecting to the reservoir.To the best of our knowledge, very little comparative data, if any, between the amphidromous and landlocked gobies has been reported. The present study found that the es e.g., . Our reses e.g., . Variati species . The tim species . In the c growth . The oto estuary . Althoug estuary , larval PLD may regulate speciation rate as an evolutionary mechanism (PLD is negatively correlated to isolation by distance, further supporting the concept of an evolutionary mechanism. Rhinogobiusformosanus may have a PLD as long as three months as observed in this study, which may allow the larvae to disperse over hundreds of kilometers, depending on the current speed and ocean hydrodynamics, and explain the wide distribution of this species in the southeastern coast of China and the northern coast of Taiwan. Due to lack of genetic differentiation between FR and MLC populations, the variation in the PLD of R.formosanus is probably a consequence of acclimatization rather than adaptation.Pelagic larval duration, usually considered a measure of dispersal potential, has been shown to be positively correlated to range size and negatively correlated to species richness, implying that echanism . Selkoe FR and MLC populations are mixed without reciprocal monophyly and the genetic diversity of the former is much lower than the latter (Table FR (FR population and range wide population genetic study on this species may be needed to provide more information.Molecular analyses show haplotypes of er Table . Lower gTable FR . On the


Meloidogyne enterolobii in mulberry in China. The COI mitochondrial gene (mtCOI) in M.enterolobii populations in Guangdong, Guangxi, and Hunan Provinces was PCR‐amplified, sequenced, and analyzed for genetic diversity. The total number of variations, haplotypes (Hap), the average number of nucleotide differences (k), haplotype diversity (H), and nucleotide diversity (π) of mtCOI were 25, 11, 4.248, 0.900, and 0.00596, respectively. Insignificant differences in Fst value (0.0169) and a high level of gene flow (7.02) were detected among the 19‐mulberry root‐knot nematode populations, and high genetic variation within each population and a small genetic distance among populations were observed. Both phylogenetic analyses and network mapping of the 11 haplotypes revealed a dispersed distribution pattern of 19 mulberry root‐knot nematode populations and an absence of branches strictly corresponding to the 19 range sampling sites. The neutrality test and mismatch analysis indicated that mulberry root‐knot nematode populations experienced a population expansion in the past. The analysis of molecular variance (AMOVA) revealed that the genetic differentiation of M. enterolobii was mainly contributed by the variation within each group. No significant correlation was found between the genetic distance and geographical distance of M. enterolobii populations. The findings of this study provide a profound understanding of the M. enterolobii population and will inform the development of strategies to combat and manage root‐knot nematodes in mulberry.This study explores the genetic diversity and structure of  This study explores the genetic diversity and structure of Meloidogdyne enterolobii (M. enterolobii) in mulberry in China. The mitochondrial COI gene (mtCOI) in mulberry root‐knot nematode populations in Guangdong, Guangxi, and Hunan Provinces was PCR‐amplified, sequenced, and analyzed for genetic diversity. For example, the rDNA‐PCR technology has been employed to identify 22.1The nematode samples used in this experiment were collected from the main sericulture areas in Guangdong, Guangxi, and Hunan Provinces, China Table .2.2M. enterolobii. Mature female  was identified  and placed into 5 μL of worm lysis buffer (WLB) containing proteinase K for DNA extraction , and the haplotypes were calculated using DNASP 5.0. The sequences obtained were submitted to GenBank and get its number.2.3https://chromas.updatestar.com/), and the DNA sequences were analyzed using the Seq Man program in DNA star 5.0 .The sequencing chromatography was analyzed by Chromas 2.3 , haplotype diversity (H), and nucleotide diversity(π) of mtCOI in ns Table  were 4.23.3Among the 10 haplotypes identified Table , Hap8 wa3.4Phylogenetic tree of 10 aforementioned mtCOI haplotypes was constructed using neighbor‐joining (NJ) method under Kimura 2‐parameter model in MEGA 7.0  and a Nm value of 7.02 (p > 4) . The variations within a population accounted for 95.5% of total variation, and the variations among populations accounted for 4.50% of total variation , YZ (the Yuezhong group), and YN (the Yuenan group). The intrapopulation differentiation parameter FST was 0.04498 , and nucleotide diversity (π)  of the rDNA, the cytochrome oxidase subunit II (COII), and 16S rRNA genes (mtDNA). The genetic diversity was analyzed by neutral molecular markers, namely AFLP, ISSR, and RAPD. The results showed a low level of diversity among the isolates tested, indicating that M. enterolobii is a genetically homogeneous root‐knot nematode species. However, the results of this study revealed sufficient gene exchange in the population, with a high genetic level. This may be caused by the fact that the host of M. enterolobii in this study was mulberry, which is different from previous studies. Additionally, all the 19 nematode populations were located in the south of China, and geographical factors may be another cause for the high risk of SNP. The used different genes and research methods are also possible causes for different results.In this study, the genetic diversity of M. enterolobii, M. incognita, and M. javanica using GBS and pool‐Seq. Compared with random amplified polymorphic DNA (RAPD), restriction fragment length polymorphism (RFLP) and PCR based on rDNA, mtDNA, ITS and IGS sequences, and satellite DNA probes, this method is time‐saving and low‐cost, and does not need multiple PCR analysis. As a novel and rapid molecular genotyping tool, GBS can obtain more detailed information about genetic diversity among nematode species. In the future, it is planned to analyze the genetic diversity of M. enterolobii in China using GBS, so as to obtain more detailed genetic information.Finally, Rashidifard M et al.  analyzedThe authors declare no conflict of interest.Hudie Shao: Data curation (lead); Formal analysis (lead); Investigation (lead); Methodology (lead); Resources ; Writing‐original draft (lead); Writing‐review & editing . pan zhang: Resources ; Software ; Writing‐review & editing . Chunping You: Conceptualization (lead); Funding acquisition ; Project administration ; Resources (lead); Writing‐review & editing (lead). Chuanren Li: Conceptualization ; Writing‐review & editing .Yan Feng: Resources ; Supervision ; Writing‐review & editing . Zhenwen Xie: Funding acquisition (lead); Project administration (lead).