Epomophorus gambianus) is an abundant species that roosts in both urban and rural settings. The possible role of E. gambianus as a reservoir host of zoonotic diseases underlines the need to better understand the species movement patterns. So far, neither observational nor phylogenetic studies have identified the dispersal range or behavior of this species. Comparative analyses of mitochondrial and nuclear markers from 20 localities across the known distribution of E. gambianus showed population panmixia, except for the populations in Ethiopia and southern Ghana (Accra and Ve‐Golokwati). The Ethiopian population may be ancestral and is highly divergent to the species across the rest of its range, possibly reflecting isolation of an ancient colonization along an east–west axis. Mitochondrial haplotypes in the Accra population display a strong signature of a past bottleneck event; evidence of either an ancient or recent bottleneck using microsatellite data, however, was not detected. Demographic analyses identified population expansion in most of the colonies, except in the female line of descent in the Accra population. The molecular analyses of the colonies from Ethiopia and southern Ghana show gender dispersal bias, with the mitochondrial DNA fixation values over ten times those of the nuclear markers. These findings indicate free mixing of the species across great distances, which should inform future epidemiological studies.The Gambian epauletted fruit bat ( In broad terms, migration is a seasonal two‐way movement usually associated with breeding sites, whereas dispersal is a one‐way movement often undertaken by sexually immature individuals  that are not typical features for long‐distance flight.s Figure , commonls Figure . Across s Figure , E. gambE. gambianusandMicropteropus pusillus (species within the Epomophorini tribe). Using only a region of the mtDNA, however, did not robustly identify introgression between these species  has historically been selected as a molecular marker for phylogeographic studies and has also been widely used for the study of speciation , and 20 s at 72°C; 30 cycles of 1 min at 94°C, 1 min at 54°C, and 1 min at 72°C; and a final extension of 7 min at 72°C; and for D‐loop, the conditions used were as follows: 5 min at 95°C; 40 cycles of 1 min at 93°C, 90 s at 55°C and 2 min at 72°C; and a final extension of 7 min at 72°C. PCR products were screened using 2% agarose gel electrophoresis and purified using the Exonuclease I/Shrimp Alkaline Phosphatase Method . PCR products were Sanger sequenced externally , and Geneious v8.1 software was used for quality‐trimming and alignment of the sequences. Both CYTB and D‐loop sequences were realigned using the software Gblocks 0.91b . Samples obtained from museum collections were extracted using the QIAamp DNA FFPE Tissue Kit . Tissues sampled from the pectoral muscle were digested overnight (24 hr) using 180 μl of ATL buffer and 40 μl of proteinase K. A paired‐end Illumina sequencing library was constructed, as previously described  imported to the “SSR_Pipeline” , and peaks were analyzed using the Geneious version 8.1 software.Microsatellite primers for nuclear DNA (ncDNA) were developed using 2.3FST values from 0.001 to 0.01. To test the CYTB sequences, the dataset was adjusted for the organelle data (mtDNA) halving sample size , 277 bats  were included in subsequent analyses. The statistical power of both mtDNA and microsatellite markers in the dataset to detect significant population differentiation was tested using the software POWSIM , observed heterozygosity (Ho), and corrected heterozygosity for unknown alleles (H'T) using both GenoDive software and the Adegenet package in R. The software packages BOTTLENECK version 1.2.02  and Δg (2.5 and 3.5). We also compared the empirical M value to the critical M (Mc) using θ =10 and Δg = 3.5 for 10,000 simulations of stable populations in the package Critical_M, which determines a past bottleneck when values lower than the Mc are detected. A graphical test of the distribution of alleles was analyzed as another approach to test a bottleneck event  was computed using the software DNAsp v5.10 , standardized to a minimum sample size (6 bats), was calculated using a rarefaction curve using the software Contrib . Calculation of genetic indices  and E. gambianus . The haplotype networks were constructed with the software NETWORK version 5.0 (www.fluxus-engineering.com) using a Median‐Joining algorithm  and the Bayesian information criterion in jModelTest. The model selected for the CYTB alignment was HKY + G, and for the concatenated fragment, the model selected was HKY + I + G. A gamma mixed model was also used for the latter. In order to quantify divergence times between the clades of 2.6F‐statistics, hierarchical differentiation of the populations, and spatial‐genetic distance correlations. Pairwise differentiation between populations was tested for both molecular analogues, using FST and ΦST statistics  were calculated using the GenoDive software. Hierarchical structure between groups of populations was also explored using molecular variance analyses (AMOVA) implemented in the software ARLEQUIN version 3.5  for each run, and each K analysis was replicated 15 times. Because STRUCTURE is sensitive to sample size differences, a second model was explored decreasing the number of samples from AC and VG to an averaged number of 16 individuals . The numbers of bats included from the other colonies were also reduced, but only to 20 individuals due to the previous model result showing low structure signal. The software STRUCTURE HARVESTER web v0.6.94  and the software STRUCTURE version 2.3.2  included in the analysis, 87 unique haplotypes (h) were found (Table Hd) of the entire sample set of E. gambianus (EG) was high (0.91). Within Ghanaian colonies, the Hd was high overall (Hd over 0.87), but there was reduced Hd in the colonies sampled in Ve‐Golokwati (VG) town from the Volta region (Hd = 0.78) and particularly in Greater Accra . The bats sampled in AC presented the lowest haplotype diversity found in any of the colonies sampled. Outside Ghana (GH), both Nigerian (NG) and Ethiopian (ET) colonies presented lower Hd than average ; however, these results are less robust due to having smaller sample sizes and collection from two separate colonies. In order to buffer the effect of uneven sample size between countries, the haplotype richness standardized to the minimum sample size (HR) was calculated. Although this standardized the results, AC maintained a particularly low diversity level (HR = 0.85) compared to the average (HR = 3.47).From the total number of CYTB sequences  and observed heterozygosity (Ho) of the sampled colonies are similar across the localities, having an overall AR value of 5.4 and Ho value of 0.81. Exceptions to this are NG (AR = 3.6) and ET (AR = 4.0), with ET having the lowest heterozygosity of the populations (Ho = 0.65). Even though the AC colony has the lowest RA between the Ghanaian colonies, this value, along with Ho, was above average across the range.The genetic diversity indices of 277 3.2Tajima's DT)(D was significantly negative in YE (p < 0.01) and TSG (p < 0.05). Fu and Li's D* (D*) and F* (F*) presented significantly negative values in seven colonies within the country. Significant low positive values of Ramos‐Onsins and Rozas’ 2R, and high expansion coefficients (S/d) correlate with previous population growth, signatures seen in the northern colonies within Ghana . Both AC and NG colonies have the lowest S/d values, which reflect a stable population size. However, no robust inferences can be made about the latter colony due to low sample size. The previous findings are corroborated by haplotype distribution statistics (Class II Fu's F), where large and significant negative values are shown for northern regions of the country, contrasting with the positive values of the AC and NG colonies . The pairwise distance statistics (Class III mismatch distributions) show that AC and NG have ragged (rg > 0.03) bimodal distributions, which reflects constant population size over a long period of time, in contrast to all other colonies that have a smooth (rg < 0.03) unimodal‐shaped distribution (reflecting rapid population growth). These results were also corroborated with coalescent extended Bayesian skyline analyses. Epomophorus gambianus show a rapid exponential population growth, displaying over an eightfold increase in the population in the past 30 years . The combination of Class I and Class II  in this colony is consistent with a female germ line bottleneck event. In contrast to the results from the mtDNA analysis, the graphical test developed by Luikart et al.  in the Wilcoxon test of the strict one‐step mutation model (S.M.M.), which is a signature of population growth, and there was no significance in the two‐phase model (T.P.M.). Both the S.M.M. and T.P.M. were selected due to their better analyzing microsatellite data compared to the infinite allele model (I.A.M.). The results of the M_P_Val analyses (Table SD = 0.19) was above the critical M value (0.688), reflecting mutation‐drift equilibrium. However, depending on the input values selected, there was evidence of a past bottleneck event , which would be consistent with the low haplotype diversity in this colony.Neutrality statistics that use the mutation frequency spectrum (Class I) show a strong signature of population expansion in Ghana Table , with th rg > 0.0 bimodal s Figure A, B whert et al.  shows a t et al. . The res rg > 0.0 bimodal  rg > 0.0 bimodal 3.3FST and ΦST values  and nonsignificant, indicating free mixing of E. gambianus between most of the population within Ghana and as far as, and including, the Central African Republic. There was, however, a high differentiation between the Ethiopian bats and the rest of the metapopulation. Large and highly significant ΦST values ranged from 0.78 to 0.92. The AC colony had significant ΦST values with all other colonies in the analysis, except for the relatively close VG colony. Significant values (p < 0.01) ranged from 0.25 to 0.9, showing the greatest differentiation with the ET colonies. The VG colony also presented significant differentiation from most of the other colonies. However, compared to AC, ΦST values were overall lower (0.11–0.82) and in most cases not as highly significant (p < 0.05).Genetic differentiation among populations was explored comparing pairwise es Table . With thFST values were much lower than ΦST values; nevertheless, pairwise FST values indicated the same structural pattern as did the ΦST estimates, except for the colony in Yendi. There is significant structure between the ET colonies and the others, as well as AC and all the other colonies (Table p < 0.05) differentiation with several other Ghanaian populations.es Table . AdditioST and FST at the population and group levels . When both AC and VG were removed from the Ghanaian group , it resulted in the lowest ΦST value (0.02) obtained for any combination tested, indicating panmixia among the remaining colonies. The same pattern was detected among rGHANA populations using microsatellite markers FST values, showing complete panmixia with almost 100% of the variability explained within bat colonies (Table 3). The high fixation values (ΦST = 0.8) among ET, CAR, and NG (Table 4) were mostly explained by variation among the grouped colonies (80%). Supporting this result, the analysis of grouped CAR, NG, and Ghanaian colonies against ET (Table 7) revealed that the highest variation was explained among groups (ΦCT = 0.79) with moderate ΦSC values (structure among populations within groups).A hierarchical AMOVA was used to test the results for both Φls Table , furtheres Table , 3. The NG Table , 4 were ET Table , 7 reveaFST values are not so decisive. Particularly, no great difference is detected when grouping Ghana by itself against NG, CAR, and ET combined, or when grouping Ghana and NG combined against CAR and ET combined . This might be due to the very small genotyping sample size for the Nigerian colony. These results were explored with additional more sensitive statistics, which were consistent with the FST findings  in the dataset ranged from 0.05 to 0.07, which suggests acceptable performance.To determine that the computed low Φ3.4ST for the CYTB . But after excluding the Ethiopian colony from the analyses, no correlations were detected. The same non‐significant result was found after removing CAR and/or NG. Within Ghanaian colonies, there was a significant positive correlation with both mtDNA: R2 = 0.2, p value <0.001 and ncDNA: R2 = 0.3, p value <0.01, but after excluding the colonies AC and VG from the analysis, the significance was lost. However, when only excluding AC from the Ghanaian colonies, significant values were still detected for both molecular markers.When analyzing the correlation between the logarithmic geographic distance and the genetic distance, either as linearized Φ3.5K = 2 in 90% of the replicated alignments with ET emerging in the subsequent division; the remaining 10% resulted in ET segregating first, following the previous model pattern. The highest number of clusters, selected by Delta K, that best explains the data for both models was K = 4 , there are five “private” haplotypes, found only within the Ethiopian samples. Even though there is a likely significant bias caused by these heterochronous samples (42 years apart to Ghanaian sequences), this geographical divergence was not present in other museum‐acquired specimens . Interestingly, all of the colonies except for VG contain the two central haplotypes and only Hap14 is represented in both AC and VG.The haplotype network of the CYTB fragment of s Figure a is charE. gambianus populations and out‐group species (Epomops franqueti and Rousettus aegyptiacus) were analyzed using an alignment of 94 CYTB haplotypes (with 139 variable sites) found in the 328 sequences aligned and of 200 haplotypes (267 variable sites) found in the concatenated fragment (from 301 sequences). These are shown using alternative substitution models, both for the CYTB  and Epomops franqueti are ancestral to E. gambianus. The intraspecific patterns shown in the phylogenetic tree corroborate the phylogeographic structure of the haplotype network: The Ethiopian bats (clade highlighted in black) are divergent from all other populations of E. gambianus sampled. A further subdivision of the non‐Ethiopian E. gambianus is shown in the three trees, separating haplotypes 14 and 2. However, there is no robust support for these branches. The estimated divergence time of the ET colonies and the rest of the E. gambianus population is approximately 1.6–1.8 Mya  E. gambianus population. Even though in some studies, mountains can be considered a weak obstacle for flying taxa , with consequent evolutionary radiation  were associated with processes of forest contraction and habitat fragmentation that shaped the vegetation and forest systems present in ancient Africa  and lakes with over 129 km2 of surface), are situated in the Volta basin, which lies to the north and east of Accra and Ve‐Golokwati. Even though E. gambianus is likely to be able to fly across the Volta river, genetic divergence due to rivers  has been reported for other bat species, including the insectivorous Eptesicus serotinus  had moderate and predominantly female‐biased genetic structuring compared to the rest of the populations. While the flying capabilities of bats usually secure free dispersal, water bodies have disrupted gene flow in both insectivorous  and other environmental developments  evaluated in this research are likely drivers of the regional genetic divergences.The results presented in this study confirm connectivity and free gene flow of None declared.Silke Riesle‐Sbarbaro designed and performed the research, analyzed the data, and wrote the first draft of the manuscript. Kofi Amponsah‐Mensah contributed in sampling methodology and acquired bat samples. Stefan de Vries supervised the development of nuclear microsatellite primers and contributed reagents (Illumina). Violaine Nicolas contributed in acquiring bat samples and extracted DNA samples. Aude Lalis contributed in acquiring bat samples and extracted DNA samples. Richard Suu‐Ire contributed in acquiring bat samples. Andrew A. Cunningham designed and supervised the research and reviewed and edited the manuscript. James L. N. Wood designed and supervised the research and reviewed and edited the manuscript. David Sargan designed and supervised the research, including the laboratory techniques and the experimental designs; reviewed and edited the manuscript; and contributed reagents. All authors improved the manuscript.https://doi.org/10.5061/dryad.m8m6142. Mitochondrial DNA alignments will be also included in GenBank. Primers for both mitochondrial DNA and nuclear microsatellite markers are included in the Supplemental Information.The mitochondrial DNA alignments used in this study, the nuclear microsatellite RAW data (.fsa) and the outputs of the STRUCTURE analysis performed using 277 bats, are included in Dryad Digital Repository:  Click here for additional data file.


Sargassum horneri) biomass, called ‘golden tides’, has frequently drifted and accumulated along the southern coastlines of Korea, causing devastating impacts on the local economy and coastal ecosystems. In this study, based on combined analyses of mitochondrial DNA cox3 gene and seven microsatellites, we investigated the genetic makeup of the floating S. horneri populations (N = 14) in comparison to Korean benthic populations (N = 5), and tracked their genetic sources. Given a shared mtDNA haplotype and oceanic circulation systems, the floating populations may have been originated from the southeastern coast of China . Population structure analyses with microsatellites revealed two distinct genetic clusters, each comprising floating and benthic populations. High levels of inter-population differentiation were detected within Korean benthic samples. The floating populations from the same periods during a 2015–2018 year were genetically more different from one another than those from different periods. These results suggest that the floating populations might be of multiple genetic sources within geographic origin(s). This study will inform management efforts including the development of “S. horneri blooming forecasting system”, which will assist in mitigating ecological and economic damages on the Korean coastal ecosystems in the future.In recent years, drifting and inundating brown seaweed ( The green seaweeds, Ulva including formerly known as genus Enteromorpha and the brown macroalgae, the genus Sargassum are especially notorious for the formation and inundation of drifting seaweed biomass along the coastlines globally2. Shortly after being accumulated along the coastlines forming like carpets, these seaweeds begin stinking by releasing toxic hydrogen sulphide (H2S), rendering devastating effects on the coastal ecosystems4. The physiologically atypical capability of sustaining their lives as an unattached (drifting) form of these algae enables them to easily build up a large amount of biomass, colonize new space, and form the massive seaweed tides1.Recurrent outbreaks of drifting seaweed masses, called ‘seaweed tides’, are a nuisance phenomenon worldwide, as they pose a serious threat to local biodiversity in coastal ecosystems and also cause severe damage to the local economy by disturbing tourism, aquaculture and fisheriesSargassum spp., called “golden tides”2 accumulates nearshore coastal waters usually during spring to summer, which causes detrimental effects on aquaculture and fisheries and also the whole coastal ecosystems in many parts of the world including the Gulf of Mexico, Caribbean, West Africa, eastern Pacific coast, China coast and the southwestern coast of Korea9. This phenomenon of the golden tides has been intensified dramatically in frequency, range, and magnitude over recent years perhaps due to growing anthropogenic pressure, such as climate change, nutrient loadings  and inadvertent algal species transport by ship movements11.Drifting biomass of Sargassum horneri is known to be the major golden-tide seaweed in the northwestern Pacific coasts including China, Japan and Korea12 as well as the northeastern Pacific coasts13. More recently, S. horneri that bloomed in the eastern coast of China has frequently drifted in a large biomass with oceanic currents and accumulated along the southern coast of Korea including Jeju Island, off the southernmost region of the mainland, and also the South and West Sea since 20155  form kelp forests . This species is dioecious with ellipsoidal pneumatocysts and has an annual life cycle with sexual reproduction using reproductive receptacles15. However, the reproductive mode of S. horneri in a floating state has never been examined, although an earlier study of floating Sargassum species in the Sargasso Sea suggested vegetative reproduction for their entire lives16.Despite its detrimental effects as the golden tide seaweed, S. sts Fig.  and playS. horneri populations in Korean waters are presumed to originate from certain areas in the east coast of China9. The Korean coast has a geological position that is primarily influenced by the Kuroshio Current as well as the Yellow Sea Warm Current and the Tsushima Warm Current that merge into the Kuroshio Current17. Because oceanic current systems are intricate and dynamic, it is, however, difficult to uncover the precise geographic origins of floating populations solely based on satellite images with the ocean currents.Given the direction of oceanic currents (e.g. the Kuroshio Current) according to satellite image data, floating 18. Several phylogeographic and population genetic studies have been performed to identify species/strains and source populations and pathways of colonization or to understand the genetic diversity and population structure dynamics of several nonindigenous seaweed species, such as Asparagopsis spp.19 and Sargassum natans20 and also our study species, S. horneri21. A recent genetic survey of floating and benthic populations of S. horneri primarily focused on the east coast of China (the Yellow Sea) and sample sizes of Korean samples used for their study are limited (N = 1 for each of the five samples)21. A more recent study investigated the genetic composition of only floating samples of S. horneri exclusively from China’s east coast22. Hence, the phylogeographic/population structure and genetic diversity of drifting populations of S. horneri in Korean waters remain largely unknown. Also, the genetic structure of benthic populations of S. horneri on the Korean coast has not yet been studied.Genetic analysis may provide a useful tool for identifying strains, and tracking geographic origins of nonindigenous populationscox3), we aimed to trace the geographic or genetic origins of 14 floating S. horneri populations to the Korean coasts including Jeju Island, the South Sea and the West Sea, and also in the East China Sea (ECS). We also analysed and compared the genetic structure between floating (N = 14) and benthic populations (N = 5) in Korean waters, based on seven nuclear microsatellite markers. Using floating populations collected at different time periods during a 2015–2018 year, we further tested whether there were changes in their genetic compositions over time. The results of our study will provide important insights into the golden-tide seaweeds in Korean waters and inform management efforts including the development of “S. horneri blooming forecasting system” that is now underway by the Korean government, which will help to prevent or mitigate ecological and economic damages on the Korean coastal ecosystems in the future.In this study, by performing a phylogeographic analysis of mitochondrial DNA (mtDNA) cytochrome c oxidase III  from 18 different localities along the Korean coasts including the South Sea, the West Sea and the East Sea as well as Jeju Island, which is off the southernmost region of the mainland, and also in the ECS from August 2015 to January 2018 (Table N = 14) included KF15, KF16-1, KF16-2, KF17, CF17, CF17a, KF17a-1, KF17a-2, KF17a-3, KF17a-4, KF17a-5, KF17a-6, KF17b-1 and KF17b-2. Of those, CF17 and CF17a were comprised of drifting populations from the ECS, approximately 250 km off the Korean Peninsula, and sampled twice from the same areas at different time periods in 2017 to test for the temporal genetic structure (Table N = 5) included K1 and K2 from the East Sea, K3 and K4 from Jeju Island, and K5 from the South Sea . Powdered samples were stored in a 1.5 ml microcentrifuge tube with silica gel and stored at −20 °C until genetic analysis.cox3 gene, previously shown to possess relatively higher polymorphism than chloroplast DNA markers23, was amplified for 230 samples using the published primers CAF4A and CAR4A24. Polymerase chain reaction (PCR) amplification was carried out in a reaction volume of 15 μl containing 1 × PCR buffer, 25 μM of each dNTP , 0.6 μM of each of the forward and reverse primers, 0.2 units of Taq DNA polymerase  and approximately 5−10 ng of genomic DNA. RCR thermal cycling conditions comprised an initial denaturation at 94 °C for 3 min, followed by 35 cycles of denaturation at 94 °C for 20 s, annealing at 50 °C for 50 s and extension at 68 °C for 60 s, and a final extension at 68 °C for 10 min. PCR products were checked on 2% agarose gels stained with Redsafe (iNtRON). The amplified mtDNA were purified enzymatically with Exonuclease I  and Shrimp Alkaline Phosphatase (New England BioLabs). The purified mtDNA fragments were subjected to direct sequencing only in the reverse direction using the same reverse primers as in the PCR and the BigDye Terminator 3.1 Cycle Sequencing Ready Reaction Kit in an ABI 3730xl automated DNA sequencer . The DNA sequences were edited using CHROMAS Lite v2.1.1 computer software and aligned with Clustal W25 implemented in BioEdit v7.2.526, and finally verified manually.Genomic DNA was extracted from pulverized leaf samples using i-genomic Plant DNA Extraction Mini Kit . A partial fragment (469 bp) of mtDNA 27. Each of the forward primers was labelled with a fluorescent dye . PCR reactions were conducted as described for the mtDNA cox3 gene. PCR cycling conditions were carried out as suggested in a previous study27. The PCR products were electrophoresed on an ABI 3730xl automated DNA sequencer (Applied Biosystems). Fragment sizes were determined with the ROX 500 bp size standard (ABI) using GENEMAPPER software v5.0 (Applied Biosystems).Seven polymorphic nuclear microsatellite loci were amplified with the published primers including SHORN18, SHORN26, SHORN27, SHORN30, SHORN31, SHORN34 and SHORN 41S. horneri, partial sequences of mtDNA cox3  representing the northwestern Pacific lineages including Chinese and Japanese clades were retrieved from GenBank17. Only 45 previous haplotype sequences were used for the final analysis, as the 51 haplotypes were merged into 45 haplotypes due to adjustments to a shorter length of 469 bp. The best nucleotide substitution model was tested using initial searches according to the AICc criterion with jModelTest v2.1.728, which selected the Jukes-Cantor (JC) model. The phylogenetic tree was then reconstructed with maximum likelihood (ML) method using as implemented in Mega v7.029. Sequences of S. muticum as outgroup were retrieved from GenBank (Accession Nos.: AB430582.1) and statistical support was estimated by 1000 bootstrap replicates.To trace original genetic sources of floating populations to the Korean coast, and also to determine phylogeographic relationships between mtDNA haplotypes of DA distances among the 19 samples were calculated using POPTREE230.Phylogenetic analysis was also conducted with a neighbour joining (NJ) approach, based on microsatellite allelic variation. S. horneri, the mean number of alleles per locus (NA), observed (HO) and expected (HE) heterozygosity, and allelic richness (AR) corrected for unbalanced sample sizes among the samples were calculated using GENEPOP v4.331 and FSTAT v2.9.3.232. The AR values were estimated after excluding the samples of KF17a-1, KF17a-5, KF17b-1 and K1 because of insufficient sample sizes (N < 10). A Mann-Whitney U test was performed to examine whether there was a significant difference in the level of AR between floating (N = 11) and benthic (N = 4) populations of S. horneri. The four samples with limited sample sizes were not included for downstream analyses, with the exception of AMOVA, STRUCTURE, FCA and phylogenetic analyses (see below). Multilocus tests for Hardy-Weinberg equilibrium (HWE) and linkage disequilibrium (LD) tests for genotypes among pairs of the seven loci were also performed in GENEPOP. The 95% significance levels for every exact test for both HWE and LD were corrected using a Bonferroni correction. We also tested for the presence of null alleles across the seven loci using MICROCHECKER v2.2.333 with 1000 randomization at the 95% confidence level.To investigate differences in levels of microsatellite diversity among the 19 samples of S. horneri, calculation of pair-wise FST estimates among the 15 populations as well as exact tests for population differentiation were performed using GENEPOP. The 95% significance levels for the pairwise comparisons were adjusted using a Bonferroni correction. Hierarchical analysis of molecular variance (AMOVA) was performed in ARLEQUIN v3.5.134 to assess temporal genetic structure among floating samples from the different sampling periods, based on microsatellites. The temporal AMOVA was performed by classifying the 14 floating populations into five different groups according to sampling periods .To assess the degree of genetic differentiation between and within floating and benthic populations of 35 under a model of admixed ancestry among populations and correlated allele frequencies with no a priori information on the geographic origins of the samples. STRUCTURE calculates a likelihood score when the data are forced into a given number of genetic clusters (K) = 1–19. We applied 10 iterations, with 10,000 burn-in steps followed by 100,000 Markov Chain Monte Carlo (MCMC) generations. STRUCTURE analyses were also carried out separately for floating populations and also for benthic populations at K = 1–14 and K = 1–5, respectively. The most likely number of genetic clusters  was determined by using the ΔK method35 implemented in the web-based tool STRUCTURE HARVERSTER (http://taylor0.biology.ucla.edu/structureHarvester/)36, based on the rate of change in the probability of data between successive K values. In addition, factorial correspondence analysis (FCA) based on genetic relationships between individuals with multi-locus genotypes was conducted using GENETIX v4.05.237.The genetic structure among and within floating and benthic populations was further analysed using an individual-based Bayesian population assignment approach implemented in STRUCTURE v2.3.4cox3 haplotypes from a total of 230 S. horneri individuals that we sequenced. While the 17 populations including 14 floating  and three benthic  populations shared an identical haplotype (Hap 1), the remaining two benthic populations (K1 and K2) from the East Sea shared another unique haplotype (Hap 2)  that was predominantly distributed in the northeastern coast of Japan  that was observed in populations exclusively from Zhoushan, Zhejiang province across nearly entire east coasts of China and also from the western coast of Japan .The estimated frequencies of null alleles at the seven loci ranged from −0.11 (SHORN41) to 0.09 SHORN34), indicating a low probability of null alleles. Microsatellite diversity indices 4, indicaFST estimates for the microsatellites between the 15 populations revealed significant genetic differentiation except eight comparisons, which were always from those within floating populations , but a highly significant genetic structure within temporal groups  .The temporal AMOVA based on microsatellites revealed absence of genetic structure among the five temporal groups (1) Table . These fK = 2) that were comprised of 13 floating (except KF17a-3) and five benthic populations for each separate cluster 17. The Hap 3 is only found in the floating samples of KF17a-3 and KF17a-4 from the South Sea across all the populations examined, which has never been detected in previous studies.Information on geographic or genetic origins (sources) of the floating S. horneri individuals share the Hap 1, which was found only in the Zhoushan, Zhejiang province across the east coasts of China (H7 in their study)17 might suggest that the populations from this area represent geographic or genetic sources of floating populations in Korean waters. This hypothesis is plausible, especially given influential ocean current systems (e.g. the Kuroshio Current) around this region39. Although the Hap 1 is also relatively common along the western coast of Japan, populations from this area are highly unlikely to serve as source populations to the Korean coast, considering the direction of major oceanic currents, such as the Kuroshio Current and the Tsushima Warm Current nearby. Therefore, given the shared distributions of mtDNA cox3 haplotypes between the Zhoushan, Zhejiang province and floating populations in Korea as well as the oceanic circulation systems in this region, the drifting S. horneri biomass in Korean waters may have been originated from this particular region on the southeastern coast of China. A previous study demonstrated that drifting S. horneri rafts of origins of Zhejiang province can disperse up to the eastern ECS under the effect of the Kuroshio Current12. A more recent study provides genetic evidence supporting the hypothesis that floating S. horneri rafts in the Yellow Sea might be of origins of Zhejiang region40. Given those earlier work and satellite data, our findings may not be unexpected, but the results of our study can provide direct (genetic) evidence supporting the hypothesis that floating golden tide seaweeds from the southeastern coast of China actually travel up to the Korean coast with ocean currents5.Our findings that although most Korean benthic samples also possess the same haplotype, the floating cox3 gene has frequently been used as a molecular marker to examine intraspecific polymorphism for S. horneri22, variation in the cox3 is not sufficiently enough to discriminate between benthic and floating populations in our study. Hence, further studies using more polymorphic markers with additional samples from “benthic” populations on the east coast of China, particularly from the Zhoushan region will be required to determine the origins of floating S. horneri biomass more precisely. Currently, we are developing molecular markers that can differentiate between floating and Korean benthic populations by exploring organelle genomes, but it may not be feasible because mt- and cp-DNA genomes of Sargassum species are known to be relatively conservative23. We are also applying recently developed microsatellite markers, which are assumed to be more polymorphic than ones used in this study, to testing whether they will provide a better resolution for elucidating the genetic structure of floating samples41. However, more extensive sampling, covering particularly from China’s east coast is the most critical for determining the origins, movement patterns and population structure of floating S. horneri biomass. For this, collaborative efforts, such as sharing S. horneri (DNA) samples for genetic analysis, with a counterpart of China through establishing a research consortium will help to advance our current knowledge of the golden tides in this region.Although mtDNA cox3 gene did not house enough variation to discriminate between benthic and floating populations, we applied more polymorphic microsatellites as an additional marker-set to assessing the genetic structure between these samples. Although a mtDNA molecule has been useful as a phylogeographic marker for inferring population or evolutionary histories of seaweed species, hyper-variable microsatellite regions may be more appropriate for understanding ‘contemporary’ processes of seaweed populations as they usually possess higher mutation rates17. Differences in the levels of polymorphism between mtDNA and microsatellite markers can also be linked to the pattern of maternal inheritance of mitochondria42. Mitochondrial inheritance is suggested to be maternal in brown algae43, although this has not been confirmed for S. horneri. The results of microsatellites show that floating S. horneri populations are clearly genetically different from benthic populations (except the KF17a-3 population), as suggested by our multifaceted-lines of analyses such as pairwise F-statistics, STRUCTURE, FCA and also NJ phylogeny. When we investigated all populations combined, strong genetic structure was found between floating and benthic samples, as indicated by two genetic clusters determined by STRUCTURE and FCA analyses. Unexpectedly, the floating KF17a-3 population, which was collected from stranded biomass on Porphyra cultivation rafts, shares a gene pool with Korean benthic populations, except for the three individuals that belong genetically to floating populations 44. Also, naturally detached S. horneri thalli due to wave action were sometimes observed in this area . Nevertheless, a further study with benthic samples from those particular sites will be needed to substantiate our hypothesis.Since the ons Fig. . We hypoS. horneri most likely results from limited contemporary gene flow occurring across these geographically disconnected populations42.When benthic and floating populations analysed separately, benthic populations show considerable genetic structure with four genetically unique groups, whereas floating populations had similar genetic structure with a rather, single genetically homogeneous group except for the KF17a-3 population. The Korean benthic populations are genetically more diverse and substantially divergent to one another with their own genetic integrities: one represents the East Sea (K1 and K2), one the south of Jeju Island (K3), one the north of Jeju Island (K4) and one the South Sea (K5). The observed spatial variation in the genetic structure among the benthic populations of S. horneri rafts represent indistinguishable genetic structure, at least in part due to the overall low levels of genetic diversity within floating samples, possibly caused by the vegetative growth during their floating life stage after detached from the benthic substrata16. Although drifting Sargassum species in the Sargasso Sea was shown to be solely vegetatively reproduced16, whether our study species, S. horneri uses the same reproductive strategy in a floating state is unknown. Therefore, the reproductive mode of floating S. horneri will need to be evaluated to test this hypothesis. Since 2011, the Chinese government has run a large-scale transplantation and farming project for the purpose of the construction of S. horneri forests around Zhoushan, Zhejiang province, which span an area of approximately 860,000 square kilometres45. Most seaweed farming exercises with a limited number of parental strains, which ensues the loss of genetic diversity in the entire Zhoushan population of S. horneri.On the other hand, the drifting S. horneri populations due to the temperature gradient of seawater along the latitude46, gene flow between floating and benthic populations might be hampered because of the differences in the timing of maturation (e.g. receptacle formation), given approximately one-to-three degree differences in the latitude between Zhoushan region of China and southern coasts of Korea. Another possible explanation would be that the establishment of southern floating populations on the Korean coast may be hindered because already established Korean benthic populations preoccupy space and resources, having negative impacts on their successful establishment (i.e. priority effect)47. Lastly, we could not simply sample admixed individuals between floating and benthic populations, although they are present. A more detailed analysis of benthic populations from areas where drifting assemblages are frequently observed will allow to validate the presence or absence of ‘introduced’ or ‘admixed’ individuals.While floating populations have continuously been introduced into Korean waters, particularly recently, the observed lack of gene flow between these and Korean benthic populations can be explained by several hypotheses. First, based on previous studies that found differences in the timing of reproduction in 21, we predicted that floating populations from the same periods will be genetically more uniform than from different periods, since they are more likely to be originated from the same origins. However, weak but some significant genetic variation was observed within floating populations particularly from the same periods, suggesting there may be multiple genetic sources being present. These findings imply that drifting populations in Korean waters at different locations from the same periods would consist of genetically dissimilar individual clusters, which may have been originated from populations with genetically different multiple patches within geographic origin(s). Therefore, the most plausible scenario would be that after S. horneri individuals were detached from genetically distinct natural beds in the southeastern coast of China , albeit low genetic diversity apparent, they may undergo a rapid vegetative growth in the ocean during their drifting lives16 and genetically heterogeneous massive seaweeds may then subsequently drift into the Korean coast49. Our temporal AMOVA seems to support this hypothesis that the genetic variation observed among the five temporal groups was considerably lower than within the groups. A recent study of mtDNA marker suggests that the floating golden tide populations in the Yellow Sea comprise at least two genetic lineages or sources22, supporting the hypothesis that multiple genetic sources exist for floating S. horneri rafts in Korean waters in this study.Based on a previous studyFST = 0.08−0.14, P < 0.05; Table FST = 0.05, P < 0.05), suggesting there may be of different genetic sources. Alternatively, assuming that drifting S. horneri individuals could undergo sexual reproduction, the considerable temporal variation can be interpreted as their low effective population sizes (Ne) that are small enough to be subject to genetic drift (‘sweepstakes’ hypothesis50). In other words, the reproductive success of the ECS population of S. horneri may be like a sweepstakes, in which only a few individuals contribute to producing offspring recruit to the whole population, while a majority do not yield offspring at all51.Yet, some genetic variation exists within temporally varying floating samples. For example, the KF17 floating population in Jeju Island in 2017 differs in the genetic structure from previous floating populations during a 2015–2016 year .In conclusion, although precise genetic sources of the floating S. horneri rafts can be removed directly from the ECS prior to their arrival to the Korea coast, if the source areas and the timing of golden tide blooming are ascertained. This idea may not be unrealistic, considering a government-funded project of ‘the development of S. horneri removal system off the ocean’ launched in 2018 in Korea. Second, establishing an international consortium with counterparts of China and possibly Japan for making cooperative efforts, such as sharing information on the origins/timing as well as the magnitude of S. horneri blooming in situ will be effective to forecast a particular golden tide in Korean waters. Third, applying floating S. horneri biomass as potentially valuable industrial resources, such as food, medicated or agricultural fertilizers is also important for developing the effective management2. Lastly, once floating golden tide seaweeds introduced to the Korean coast, developing a molecular-based fast and accurate detection method is important for their rapid removal. For this, constructing a genetic database for Korean benthic populations will be needed. This study, to our knowledge, first provides genetic information on floating and benthic populations of S. horneri in Korea and will inform management efforts including “S. horneri blooming forecasting system”, which helps to protect and mitigate ecological damages on the Korean coastal ecosystems.For management efforts, we suggest some possible controls to avoid or minimize damages on the Korean coast, based on our findings. First, drifting Supplementary Figure S1


Saxifraga sinomontana, which may have an evolutionary time scale <5 million years in the QTP and Himalayan regions. Using a total of 350 individuals from 29 populations, we studied the evolutionary history of S. sinomontana by analyzing cpDNA trnL-trnF, rpl16 and nrDNA ITS sequences. A total of 89 haplotypes and 158 genotypes were detected for cpDNA and ITS sequences, respectively. Only a few haplotypes/genotypes were widespread, while an extremely large number of haplotypes/genotypes were restricted to single populations, which were scattered throughout the current geographical range of S. sinomontana. This suggests the existence of microrefugia of this species during the Quaternary glaciations. In addition, the relationships of the haplotypes/genotypes were almost completely not resolved by phylogenetic reconstruction. Combining characteristics in terms of high haplotype richness, large proportion of private haplotypes, and shallow haplotype divergence, we speculate that recent intraspecific diversification has occurred in S. sinomontana. Molecular clock analysis estimated that the onset diversification within S. sinomontana to be 1.09 Ma (95% HPD = 0.80–1.45), coinciding with the extensive Quaternary glaciations on the QTP which started ca. 1.17 Ma. The Quaternary climatic oscillations may have triggered rapid intraspecific diversification in this QTP-Himalayan species. However, large niche breadth, as well as introgression/hybridization between the studied species and its closely related sympatric saxifrages, may also played a role to some extent on the current genetic structure of S. sinomontana, which need to be further studied.An increasing number of phylogeographic studies have been conducted for plant species in the Qinghai-Tibetan Plateau (QTP) and its flanking mountains. However, these studies have mainly focused on the determination of glacial refugia and routes of inter-/post-glacial expansions. Rapid intraspecific diversification of plants in this region have not been thoroughly discussed. Herein, we investigate the effects of the Quaternary climate changes on population genetic structure and diversifications of a herbaceous alpine species,  Evolutionary diversifications are usually considered as one of the main mechanisms that accumulate high levels of plant biodiversity in mountainous regions  and nuclear ribosomal DNA internal transcribed spacer (ITS) to reveal the population genetic structure and evolutionary history of S. sinomontana. In particular, we want to address: (1) whether intraspecific diversification has occurred in this QTP-HHM species; and (2) whether climatic oscillations during the Quaternary glaciations promoted the intraspecific diversification if it occurred.In the present study, we sequenced two chloroplast DNA (cpDNA) fragments  were sampled from each population, spaced at least 5 m apart. In total, leaf materials of 350 individuals from 29 populations were collected across the extant distribution range of na Table . Leaves c” and “f ” for trnL-trnF , 2.0 μl of 10 mM dNTPs, 1.0 μl of 10 pM of each primer and 2.0 μl (10–20 ng) total genomic DNA. The PCR profile included an initial pretreatment of 10 min at 94°C, followed by 30 cycles of 1 min denaturation at 94°C, 50 s at primer-specific annealing temperatures, 1 min elongation at 72°C, and a final extension at 72°C for 10 min. Annealing temperatures were 49°C for trnL-trnF, 58°C for rpl16, and 55°C for ITS. PCR products were checked on 1% agarose gels and then purified using a CASpure PCR Purification Kit . Purified PCR products were sequenced in both directions with the primers used for amplification on an ABI PRISM 3730xl genetic analyzer.Total genomic DNA was extracted from silica-dried leaves using the modified CTAB method . Primer regions were removed and DNA sequences were aligned in MEGA ver. 7.0.26  were then concatenated into a single matrix for subsequent analyses. For ITS sequences, a site was identified as heterozygous when a double peak occurred in the same position of both strands, with the weakest signal reaching at least a quarter of the strength of the strongest .The chromatograms of each sequence were contrasted for accuracy via visual inspection with Chromas ver. 2.6.4 (available at Phylogenetic relationships of the detected cpDNA/ITS haplotypes were reconstructed by means of Maximum Parsimony (MP), Maximum Likelihood (ML), and Bayesian Inference (BI). The MP analyses were conducted in PAUP 4.10b . Phylogenetic reconstructions of indels-coded cpDNA haplotypes were repeated as described above, however, phylogenetic resolution was not yet improved (Supplementary File http://www.fluxus-engineering.com), using median-joining method and MP calculation , total gene diversity (HT), as well as two coefficients of population genetic differentiation, GST and NST, were estimated as described by Pons and Petit (http://www.pierroton.inra.fr/genetics/labo/Software/PermutSSR). To assess whether the overall population differentiation exhibited a phylogeographic structure, a comparison was made between GST and NST using a permutation test with 1,000 permutations. Significantly larger NST value compared with GST value suggests the presence of phylogeographic structure  and nucleotide diversity (π) of each population were calculated using Arlequin ver. 3.5.2  and genotypes (Npg) within each population was also calculated to assess genetic diversity.Average gene diversity within populations  of ITS fragment as estimated by Ebersbach et al. , 89 haplotypes (H1-H89) were identified  were private, i.e., restricted to single populations. Most of these exhibited extremely low level of occurrence frequency  of cpDNA dataset within the 29 populations ranged from 0 to 1.0000, and nucleotide diversity (π) from 0 to 0.000425, with mean values of 0.8064 and 0.000144, respectively , as well as average within-population diversity , suggesting a disproportionate contribution of within-population diversity to the total in S. sinomontana. This was confirmed by analysis of molecular variance (AMOVA), which showed that 77.94 and 60.80% of total genetic variation was found within populations based on cpDNA and ITS datasets, respectively , indicating an absence of phylogeographic structure across the extant distribution range of S. sinomontana.Gene diversity  and Fu' FS  statistics produced significantly negative values for the overall gene pool, suggesting recent expansion across the distribution range of S. sinomontana. This was supported by mismatch distribution analysis, in which unimodal was drawn from the overall populations  and Harpending's raggedness index  were not significant, showing no deviation of observed mismatch distribution from simulation under a model of sudden demographic expansion.Both neutrality tests and mismatch distribution analysis based on cpDNA dataset suggested recent range or demographic expansion of S. sinomontana. Based on a mean substitution rate of 5.03 × 10−9 substitutions per site per year (s/s/y) of ITS fragments as estimated by Ebersbach et al. (S. sinomontana was estimated to be 1.09 Ma (95% HPD = 0.80–1.45), corresponding to the Quaternary climatic oscillations  at the species level of S. sinomontana are 0.25 and 0.45, respectively. These are higher than the values for other QTP herbaceous species, e.g., Aconitum gymnandrum , i.e., observed in single populations. Most of these haplotypes/genotypes exhibited extremely low frequencies, with some even represented as singletons. However, limited mutations have been detected among such a large number of haplotypes, and most neighboring haplotypes are separated by limited mutational steps. This is even more apparent in the ITS haplotype network, with most differing by only a single site mutation. Limited informative mutation sites among both cpDNA and ITS haplotypes resulted in an almost complete lack of resolution of haplotype relationships as reconstructed by means of MP, ML, and BI  across assayed populations of S. sinomontana. In addition, it should be notable that ITS dataset revealed a relatively high level of mean nucleotide diversity (0.003948), which could be attributed to high amounts of hybridization sites. Considering haplotype richness, proportion of private haplotypes, and haplotype divergence, it is reasonable to speculate that recent intraspecific diversification occurred in this QTP-Himalayan species, S. sinomontana.Recently, high geological heterogeneity and rapid climate oscillations have been highlighted as potential forces for species diversification in alpine plants , coinciding with the first of the four extensive Quaternary glaciations on the QTP which started ca. 1.17 Ma . One possible explanation should be efficient gene flow among populations  across its distribution range. The following developments and retreats of glaciers during the Quaternary may have fragmented distribution range of S. sinomontana into isolated patches, finally facilitating in situ allopatric divergence. The ancient genetic structure of S. sinomontana may have erased to some extent due to bottleneck effects and genetic drifts during the Quaternary glaciations, which was then replaced by current genetic pattern represented by large numbers of private haplotypes/genotypes.Phylogeographic patterns of plant species on the QTP during the Quaternary glaciations have been reviewed (Qiu et al., YL performed the experiments, analyzed the data, wrote the paper, and prepared figures and tables. Q-BG designed and conceived the study, analyzed the data and reviewed and wrote parts of the paper. Z-MG, L-KJ, and Z-HW performed parts of the experiments. S-LC conceived and designed the study.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Sideritis scardica Giseb. is a subalpine/alpine plant species endemic to the central part of the Balkan Peninsula. In this study, we combined Amplified Fragment Length Polymorphism (AFLP) and environmental data to examine the adaptive genetic variations in S. scardica natural populations sampled in contrasting environments. A total of 226 AFLP loci were genotyped in 166 individuals from nine populations. The results demonstrated low gene diversity, ranging from 0.095 to 0.133 and significant genetic differentiation ranging from 0.115 to 0.408. Seven genetic clusters were revealed by Bayesian clustering methods as well as by Discriminant Analysis of Principal Components and each population formed its respective cluster. The exception were populations P02 Mt. Shara and P07 Mt. Vermio, that were admixed between two clusters. Both landscape genetic methods Mcheza and BayeScan identified a total of seven (3.10%) markers exhibiting higher levels of genetic differentiation among populations. The spatial analysis method Samβada detected 50 individual markers (22.12%) associated with bioclimatic variables, among them seven were identified by both Mcheza and BayeScan as being under directional selection. Four bioclimatic variables associated with five out of seven outliers were related to precipitation, suggesting that this variable is the key factor affecting the adaptive variation of S. scardica. Elucidating the genetic basis of species adaptations to their environments, the nature of selection imposed by environmental changes and the capacity of a species to respond by evolutionary adaptation3 is one of the challenging tasks of evolutionary biology. Such information may direct conservation strategies for the long-term persistence of species, especially for those facing rapid climate changes and are threatened with extinction and population decline.Adaptive genetic variation evolves as a result of changing environmental conditions imposing selective pressure on individuals living in heterogeneous habitats4. Genotyping of numerous random loci across entire genomes enables distinguishing the effects of the evolutionary forces that act on the entire genome  from those influencing only individual loci 7. Loci that are characterized by higher than expected levels of genetic differentiation are referred to as outlier loci or candidate loci and are considered to be under strong natural selection7. Such loci that are exposed to divergent selection pressures across studied populations can be recognized by a higher pairwise index of genetic differentiation values (FST) if compared to the genome-wide pairwise-FST8. In non-model organisms with a large number of individuals included in a study, Amplified Fragment Length Polymorphism (AFLP) technique has been frequently used in search of such genome regions under selection, when no prior knowledge of their position or importance exists10. For AFLP genome scans, two analyses methods are most commonly applied: the frequentist method of Beaumont and Nichols11 as implemented in LOSITAN12 and Mcheza13 programs and the hierarchical Bayesian method as implemented in BayeScan program14. Between the two methods, it has been suggested that BayeScan is more efficient in recognition of outlier loci with low false positive rate15. In addition, by using both geo-referenced environmental data and individual molecular genetic data, a locus-based approach as implemented in Samβada16 is often used20. This approach correlates individual loci occurrence with environmental variables, thus aiming to determine whether each investigated molecular marker is selected by one or a set of specific environmental variables16. The described methods provide the opportunity to identify adaptive genetic variation in non-model organisms for which the genomic data are scarce and species are of conservation concern21.The potential of species adaptation to rapidly changing environments might be assessed by detecting genetic signatures of adaptation to prevailing or historic environmental conditionsArabis alpina L.23, Gentiana nivalis L.24, alpine plant species25, Eruca sativa Mill.26, Keteleeria davidiana var. formosana (Hayata) Hayata27, Cotinus coggygria Scop.28, Picea abies (L.) H. Karst.29, Liriodendron chinense (Hemsl.) Sarg.30, Geropogon hybridus (L.) Sch. Bip.21, Diplotaxis hara (Forssk.) Boiss.31, etc. Nosil et al.32 in the literature review state that the available studies usually report 5–10% of loci to be outliers, while Strasburg et al.33 reported that the proportion of loci identified as outliers ranges from 0.4 to 35.5% with an average value of 8.9%. However, both studies pointed out that the results from different investigations are difficult to compare, as they differ in methodologies used and significant thresholds chosen.Various investigations have been conducted in order to determine the genetic variation of different plant species in relation to the landscape, e. g. et al.25 suggested that genetic variation that appears to be caused by natural selection might be the results of isolation by distance, which limits gene flow among populations or the result of secondary contact of populations that survived isolated in glacial refugia. Past historic climatic oscillations have had a considerable impact on present-day biota37. Numerous phylogeographical investigations carried out over the recent years, have confirmed the latter, elucidating the impact of both ancient and recent history on current European flora and fauna distribution patterns and of genetic variations42.Manel Sideritis scardica Griseb is an outcrossing diploid (2n = 32)43 and perennial subalpine/alpine herbaceous plant, endemic to the central parts of the Balkan Peninsula. The species is distributed in southwest parts of Albania, North Macedonia, Bulgaria and Greece44. It is a plant of the alpine zone, occurring in dry stony meadows, mainly at altitudes 1600–2300 m a.s.l., and only occasionally down to 500 m a.s.l. on limestone46. In Greece, most abundant populations of S. scardica can be found in mountains of the North and Central regions such as Vermion, Voras, Tzena, Paikon, Pangeon, Menikio, Falakro, Mt. Olympus, Mt. Ossa, etc.46. In North Macedonia, it occurs in mountains of Central and Western parts of the country47, namely in Mt. Shara, Mt. Suva Gora, Mt. Bistra, Mt. Ilina, Mt. Kozuf and Mt. Kajmakchalan.Sideritis scardica has successfully established populations in contrasting environmental conditions and therefore represents an ideal system to examine the effect of specific environmental variables on genetic variation found between the genomes of individuals.S. scardica from southern parts of Balkan Peninsula that were exposed to contrasting environmental conditions. Altitudinal and climatic data were used to characterize the environmental conditions at each collecting site. To test our hypothesis that signals of natural selection (i.e. FST outlier loci) can be found across chosen S. scardica populations, we performed AFLP fingerprinting followed by comprehensive analyses of adaptive genetic variation and underlying drivers of adaptive divergence. In addition, possible influences of past climatic oscillations on population genetic structure and diversity of studied populations were addressed.In this study, we selected nine I) indicated that the total diversity (Ht) was 0.417 with average intrapopulation diversity (Hp) being 0.238. Similar levels of diversity within populations (Hp/Ht) (57.20%) and among populations (42.80%) were determined. Shannon’s index per population ranged from 0.175 (P05 Mariovo) to 0.305 (P07 Mt. Vermio)  were above average for five populations   ranged from 0.095 (P06 Mt Kozuf) to 0.133 (P07 Mt. Vermio)  Fig. . Estimat09) Fig. . ExpecteFST) ranged from 0.115 between P02 Mt. Shara and P03 Mt. Suva Gora to 0.408 between P01 Mt. Skopska Crna Gora and P06 Mt. Kozuf, with an average value of 0.263. The Analysis of Molecular Variance (AMOVA) further showed that most of the genetic diversity was attributable to differences among individuals within populations (63.25%). However, φST value for the among-populations component was significant  ], kept increasing with higher K as did the standard deviations among different runs for each K. The highest ΔK was observed for K = 2 (826.03), followed by that of K = 7 (9.89)  mixture analyses with or without spatially informative priors yielded nearly identical results to those obtained by STRUCTURE at K = 7  and BAPS, the DAPC analysis without prior information of individual population membership revealed that populations P02 Mt. Shara and P07 Mt. Vermio consisted of individuals grouped in more than one cluster, while the rest of the populations belonged to a single genetic cluster.In both Discriminant Analyses of Principal Components (DAPC) performed with and without prior information of individual population membership we retained 50 principal components representing 77.97% of total genetic variation. The scatterplot of the first analysis , and 15.7% of the genetic variation could be explained by geographical distances  was found in 61 cases, while in 58 cases a strong negative correlation (r < −0.70) was identified (Supplementary Table r > 0.70), while seven precipitation-related variables  exhibited a strong negative correlation (r < −0.70) with the first principal component (PC1) that explained as much as 76.52% of the total variation  = 1.697], none of them under stabilizing selection. All seven loci were also detected by Mcheza as outliers . With a confidence level set to 99%, Mcheza detected a total of 38 outlier loci (16.81%), possibly under selection, among which 12 (5.31%) are/were under directional and 26 (11.50%) under balancing selection Fig. . BayeScaers Fig. . After cers Fig. . Thus, oers Fig. . All 19 S. scardica populations. However, overall gene diversity levels were lower than the values reported by Nybom48 for the endemic plants using dominant markers (HE = 0.20). Private bands were detected in all analyzed populations, ranging from 3 to 15. Generally, south-eastern populations tend to have more private bands and higher DW values. In concordance with detected private bands, genetic differentiation among analyzed populations was significant (FST = 0.263), indicating moderate population differentiation. Sampled S. scardica populations are distributed among fragmented mountain ranges, separated by deep valleys and consequently, pollen and seed dispersal among them is limited. A positive and significant correlation between genetic and geographical distances revealed a pattern of isolation by distance across the distribution range of S. scardica and further confirmed limited gene flow between analyzed populations, which facilitates the establishment of local adaptations. However, more continuous distribution in the recent past and historical gene flow among analyzed populations is a possibility. Evidence for historical gene flow between neighboring populations of S. scardica is provided from both distance and model-based methods, i. e. populations with closer proximity to each other are generally also genetically closer. During the Quaternary, suitable habitats for mountain species became limited and less available40 and it can be assumed that the species survived by altitudinal shifts to adjacent lowlands, as it has been proposed for other mountain biota49, followed by postglacial remigration to higher altitudes. Such elevational shifts have been documented for other cold-adapted taxa, i. e. arctic and alpine species which escaped the postglacial warming to cooler climates by altitudinal and/or latitudinal range shifts50. Furthermore, it is possible that during these colder periods when the species presumably occupied significantly lower altitudes than today, inter-population hybridization involving different gene pools easily occurred, at least in some areas. As a consequence, in areas where genetically divergent groups of populations came into contact, admixed population-genetic patterns comprising of elements from different gene pools (07 Mt. Vermio) may be observed. During the post-glacial period, remigration to higher altitudes resulted in inter-population isolation. However, this recolonization happened relatively recently and there was not enough time for the development of more pronounced differentiation among populations. Some of the obtained results support this hypothesis, e. g. population P09 Mt. Paggaio, which is geographically isolated from populations P04 Mt. Ilinska, P05 Mariovo and P06 Mt. Kozuf in present days originates from a common ancestral population as revealed by STRUCTURE analysis at K = 2. For population P09 Mt. Paggaio, we presume that it survived isolated for a longer period. Our estimates of DW values (201.60) and detected number of private alleles (13) support this idea as their higher values are expected in long-term isolated populations where rare markers accumulate due to mutations41.The obtained results demonstrated relatively low within population genetic diversity and significant population differentiation. Within population diversity in terms of polymorphic markers (average 36.21%) and gene diversity (average 0.11) was somewhat higher for the south-eastern 52. When a population is founded by a limited number of individuals (i.e. through either bottleneck or founder effect), it is expected that its genetic composition may substantially differ from source population and other populations founded through the same processes, and from the identical source population, simply because of stochastic nature of genetic drift54. Consequently, it is possible to assume that at least some of the detected genetic differentiation originates from genetic drift, followed by the absence of inter-population gene flow, rather than solely natural selection.When discussing the detected levels of differentiation, it can also be assumed that because of already discussed climatic oscillations and resulting migrations along the altitudinal gradient, at least some of these populations have experienced severe fluctuations in size and consequently a strong genetic drift. Since the genetic drift, as well as its most extreme form (i.e. the bottleneck), is a stochastic fluctuation in allele frequencies, it could not only cause the loss of the genetic variation, but also increase the amount of genetic differentiation among populationsOverall, the results implicate that connectivity among populations might have been maximal during the glaciation periods and interrupted in the post-glacial period with remigration to higher altitudes, resulting in significant and moderate levels of genetic differentiation among populations.S. scardica populations sampled in environments reflecting variation in altitude, precipitation and temperature, by performing widely used FST-outlier methods Mcheza and BayeScan, and a genotype-environment association analysis with Samβada. Mcheza detects loci with unusually high or low FST values using the frequentist method, while BayeScan uses the Bayesian approach and directly estimates the posterior probability that a given locus is under selection by defining two alternative models (with and without the effect of selection) . In our study, Mcheza revealed 16.81% loci possibly under selection, among which 5.31% of loci exhibited higher FST values and 11.50% of loci exhibited lower FST values than the majority of loci, while BayeScan detected the lower proportion of loci under divergent selection (3.10%) than Mcheza. In the AFLP study on Ceracris kiangsu Tsai & P., the authors found the proportion of outliers detected by Dfdist to be 7.6% and by BayeScan 6.7%, indicating the more conservative nature of Bayesian method20. The comparison between the performance of the two software by different authors resulted in the identical conclusion that BayeScan performs more efficiently, as it detects a high percentage of outlier loci with a low proportion of false positives56 (discussed further in the chapter).A total of 226 AFLP markers were analyzed to find the signals of divergent selection among nine natural S. scardica populations, as they were identified by complementary and exhaustive methods. This proportion is in concordance with findings of Hoffmann and Willi3 which reported less than 5% of outlier loci, but bellow the values reported by Nosil et al.32, 5–10% respectively. Similar research on an invasive weed Mikania micrantha Kunth detected 14 outlier loci by both Dfdist and BayeScan corresponding to 2.9% of 483 investigated loci57. In Eruca sativa Mill. nine candidate loci were identified, however, only three of them (1.6%) were detected by more than one method26. A significantly higher proportion of outliers was detected in Geropogon hybridus (L.) Sch. Bip. Eleven outlier loci (8.9%) were detected by Mcheza and BayeScan as well as spatial analysis method (SAM) to be associated with precipitation21.The three implemented methods  identified seven outlier loci (3.1%) of high reliability in studied 59. Consequently, the results obtained in this study, which indicate that only 3.1% of the studied genome was influenced by intense natural selection, are not surprising. Since no prior knowledge of the genome structure of the studied species exists, we do not yet know the location or function of the detected outlier loci.It is expected that outlier loci occur only sporadically throughout the genome, thus involving only its minor part in a process of divergent selection61, it can be assumed that because of the ‘hitchhiking effect’ at least some of the detected loci are linked to the actual target60 and can thus be treated as the carriers of selection signature. Nevertheless, based on obtained results from PCA and Samβada analysis, we concluded that divergent selection detected among studied populations likely emerged because of their exposure to contrasting precipitation conditions. Although AFLP technique targets genome-wide anonymous loci that represent only a small fraction of the whole genome, it can be assumed that detected outlier loci are representatives of those genomic regions that evolved more rapidly than the remaining parts of the genome, thus enabling organisms’ adaptation to contrasting precipitation conditions and divergence of ecotypes. Temperature and precipitation are the parameters that strongly affect the survival of alpine species62 and probably act as the main triggers of their selective responses63. Numerous studies have revealed an important role of environmental factors in driving species adaptation. For example, temperature, precipitation and radiation were the three main factors affecting the local adaptation of Liriodendron chinense (Hemsl.) Sarg. whereas nine AFLP loci showed evidence of being outliers for population differentiation in both Dfdist and BayeScan detection methods30. Temperature and precipitation were the main drivers of adaptive genetic variation in 13 alpine plant species collected across the entire European range25. In Cotinus coggygria Scop. five ecologically relevant microsatellite alleles related to the precipitation were identified28. Investigations on Arabis alpina L. sampled on 208 locations across French and Swiss Alps identified 78 loci of ecological relevance that were mainly related to mean annual minimum temperature23. In Picea abies L. Karst sampled in the South-Eastern Alps 13 potentially adaptive loci mainly associated with precipitation were identified29.In addition, although it is expected that AFLP loci mostly originate from non-coding regions of genomeet al.64 reported that using 150 AFLP loci instead of 300 had a negligible effect on the estimates of genetic differentiation (FST). Nelson and Anderson65 used simulated dominant marker sets to determine the number of loci needed to obtain satisfactory results from the analysis of molecular variance (AMOVA) and Bayesian model-based cluster analysis (STRUCTURE). They concluded that the minimum number of loci needed depends on the level of genetic differentiation among populations as measured by AMOVA’s φST and that if φST is 0.3 or greater, adequate results can be achieved with only 45 to 90 loci. Leipold et al.66 investigated the minimum number of individuals and the required number of AFLP loci to describe a population’s genetic diversity using resampling procedure based on real data of 15 plant species and reported that approximately 120 loci were sufficient for a stable estimation of genetic diversity, while 14 individuals per population were needed to cover 90% of the total genetic diversity. Although simulation-based studies used to assess the power to detect markers under selection67 did not discuss the minimum number of markers, it is clear that FST-outlier analysis is univariate in nature but there must be a sufficient number of markers to obtain a reliable estimate of allele frequencies in populations and subsequently an unbiased estimate of FST (and other population genetic parameters). Thus, if the number of loci is sufficient to infer the population genetic parameters, the same would hold also for outlier detection. The problem that remains is that with the decreasing number of markers, the chance of not finding any being an outlier increases. However, the modest number of markers should not increase the probability of false-positives as compared to genome-wide association studies with millions of SNPs. Hoban et al.68 highlighted the importance of a sufficient number of sampled locations and individuals to maximize the power of local adaptation studies and stated that the methods based on allele frequencies require more than 10 samples from each sampling site. The authors also pointed out that the most empirical studies involve from 100 to 1,000 individuals from 5 to 40 locations.The number of marker loci required to infer the genetic structure and to detect local adaptation is especially important in case of dominant markers having a lower information content as compared to co-dominant markers. Bonin FST outlier detection methods69 and can be the result of weak genome coverage of AFLP markers21, statistical departures from the model assumptions, population structure70, and demographic processes. Demographic processes, such as bottlenecks, secondary contacts, allele surfing during population range expansion, and isolation by distance can create similar genomic patterns that imitate selection71, and complicate the process of distinguishing selection from demography72. The effective strategy to reduce the rate of false positives and increase confidence in identified outliers is simultaneous use of methods based on different assumptions and parameters57, as it has been done in this study. The comparison of the results obtained with these different approaches represents the cross-validation or a double-check and increases the reliability of the identified loci potentially under selection22. To further minimize the false discovery rate, we set the stringent threshold levels permitted by the outlier detection methods . Seven outlier loci (3.1%) that were concordantly detected by all three implemented methods  were regarded as of adaptive relevance. However, further exploration of functional mechanism operating at putatively adaptive loci is required.The occurrence of false positives is a typical problem encountered with the 73 spatial analyses methods used to measure associations between allele frequencies and environmental variables are prone to false positives, as they do account for population structure. As stated by Muller et al.21 the existence of isolation by distance pattern can result in false positive associations between environment and gene frequencies. To overcome this problem and increase the power to detect selected loci and minimize the occurrence of false positive associations, Lotterhos and Whitlock69 suggest the sampling design based on comparing geographically closer populations, that have the similar genetic constitution for neutral genes, but thrive in different environmental conditions and are differentiated by selection. In our study, the sampling scheme was as far as possible followed by that proposed by Lotterhos and Whitlock69, i.e. comparison between geographically nearby populations  sampled in different environmental conditions and altitudes was performed.According to De MitaSideritis scardica, as well as the identification of environmental factors that are responsible for the revealed genetic structuring. We believe that the obtained results could be applied in future conservation management, whose development is of utmost importance for S. scardica, for two general reasons. First, as an alpine species it is especially vulnerable to the ongoing climate changes, as highlighted for a number of cold-adapted species76, and second, as an important medicinal plant, it is endangered, mostly because of human-mediated overexploitation77.The present work was focused on the assessment of genetic and adaptive diversity, and population differentiation in S. scardica. AFLP markers are widely employed in the genomic investigations of non-model species10. However, due to their dominant nature and the fact that Hardy–Weinberg equilibrium cannot be tested, but it must be assumed to assess allele frequencies, sampling errors are likely to occur78. Additionally, inaccurate allele frequency estimates could also be generated due to a modest sampling size68. Therefore, the interpretation of the results should be treated with caution and the identified candidate loci could provide a priori hypotheses for further comprehensive analysis of the adaptive divergence.Two differentiation-based methods (Mcheza and BayeScan) and genotype-environment association analysis implemented in Samβada enabled us to identify loci that are potentially linked to the genes under selection, and precipitation as the key environmental driver of selection in S. scardica populations is of utmost importance. This could be done through genotype-phenotype association studies, where genotypic variation could be linked to phenotypic variation in drought and temperature tolerance related traits, and other mechanisms that affect the persistence of the species. The proposed approach could help conservationist to assess adaptive phenotypes and thereby improve the effectiveness of conservation practices. However, considering the fact that this research involved only a part of the natural geographical distribution of S. scardica the obtained results should be validated further for the entire distribution range of the species.Further work needs to be done to characterize the identified outliers, i. e. to identify chromosomal locations and functional mechanisms operating at loci of adaptive relevance. As climate changes are manifested through precipitation and temperature changes, validating genetic and phenotypic variation of different abiotic stresses tolerance in S. scardica in situ strategies should also be developed in order to protect its natural habitats, thereby conserving the overall genetic diversity of the species. Populations that are the most endangered and at imminent risk of extinction should be prioritized for conservation. Ex situ strategies should be carried out by storing germplasm in ex situ field collections and long-term germplasm storage facilities. It is crucial that principles and procedures for the sustainable collection of the species from the wild are adopted by collectors and all the stakeholders involved. Moreover, the genetic analysis should support further breeding research, contributing thus to the successful introduction of the species into cultivation and its sustainable exploitation as it is a promising candidate due to valuable medicinal properties and long tradition of use. Also, identified adaptive genotypes should be introduced to the breeding programs to conserve the adaptive potential of S. scardica.For Sideritis scardica natural populations were collected throughout species distribution range in North Macedonia and Greece . The sampling sites were chosen to represent different habitats. Three populations  were sampled in the north-western montane region of the studied area, at an altitude above 1,400 m a.s.l. Three populations  were sampled in the south-eastern montane region, at an altitude above 1,400 m a.s.l. Furthermore, three colline populations were sampled from lower altitudes ; thus, one population (P01 Mt. Skopska Crna Gora) was sampled in the north-western part and two populations  in the south-eastern part of the studied area (Table Leaves from nine http://www.worldclim.org/) for each collecting site. The sampling sites were described with 19 temperature and precipitation bioclimatic variables (BIO01-BIO19), representing the annual trends, seasonal variations, and extremes in temperature and precipitation . The DNA concentrations were measured with a Qubit Fluorometer .Total genomic DNA was isolated from individual young top fresh leaves collected et al.79, with minor modifications Carović-Stanko et al.80. The restriction, ligation and all amplification reactions were performed in GeneAmp PCR System 9600 . For selective amplification, four primers combinations were used: FAM-EcoRI-ACA + MseI-CAC, NED-EcoRI-AGA + MseI-CAC; VIC-EcoRI-ACG + MseI-CGA, PET-EcoRI-ACC + MseI-CGA.The AFLP analysis was carried out following the original protocol described by Vos The amplified fragments were separated by capillary electrophoresis in an ABI3130xl Genetic Analyzer .Npr) and Shannon’s information index (I). Shannon’s information index was computed by using the following formula I = −Σ (pilog2pi), where pi is the phenotypic frequency82. Shannon’s information index was used to determine the total diversity (Ht), average intra-population diversity (Hp) and the proportions of diversity within (Hp/Ht) and among populations [(Ht–Hp)/Ht]. AFLPdat83 was used for assessing the frequency down-weighted marker values (DW)41.To construct a binary matrix, the obtained AFLP fragments were scored as being present (1) or absent (0). Within-population molecular diversity was assessed in terms of the proportion of polymorphic markers, the number of private markers , the average gene diversity within populations (HW), the average gene diversity among populations in excess of that observed within populations (HB), and Wright’s FST statistics were assessed to describe the population genetic structure.To calculate the allelic frequencies at AFLP marker loci, a Bayesian approach suggested by Zhivotovsky87 and a Neighbour Joining88 tree was constructed. Statistical support was tested with bootstrap analysis using 1,000 replicates89. The calculations were made using PAST version 3.2290. Scores between 50 and 74 bootstrap percentages (BS) were defined as weak support, scores between 75 and 89% BS as moderate, and scores greater than 90% BS as strong support.Dice’s distance between individual plants was obtained as 1-Dice’s similarity index91 with the software Arlequin ver. 3.592. The variance components were tested using 10,000 permutations. Pairwise population comparisons resulted in values of φST that are equivalent to the proportion of the total variance that is partitioned between two populations and could be interpreted as the inter-population distance average between any two populations93.Total genetic variation among and within populations was analyzed by AMOVA94. Ten runs per each K were performed by setting the number of clusters (K) from 1 to 11. Each run consisted of a burn-in period of 200,000 steps followed by 106 MCMC (Monte Carlo Markov Chain) replicates assuming admixture model and correlated allele frequencies. The calculations were carried out on the Isabella computer cluster at the University of Zagreb, University Computing Centre (SRCE). The optimal number of clusters (K) was assessed by calculating an ad hoc statistic ΔK95 as implemented in STRUCTURE HARVESTER v0.6.9496.Bayesian model-based cluster analysis was performed on multilocus AFLP data by using the software STRUCTURE ver. 2.3.399 was applied for the population mixture analysis without the geographic origin of the samples used as an informative prior  and with this prior 100. BAPS software was run setting the maximum number of clusters (K) to 10 and each run was replicated 10 times. Results of the mixture analysis were used as input for population admixture analysis98, with the default settings.The software BAPS 6.0101 as implemented in the package adegenet v. 2.0.1102 for the software R v. 3.3.1103. The analyses were carried out both with and without prior information of individual population membership. The analysis without prior information was performed using the function ‘find clusters’ and the optimal number of clusters was selected based on Bayesian Information Criterion (BIC).As an alternative to Bayesian clustering, population structure was also assessed using Discriminant Analysis of Principal Components (DAPC)104 among S. scardica populations we carried out a Mantel test with 10,000 permutations as implemented in NTSYS-pc ver. 2.10s105.To test the significance of the isolation by distance (IBD)Sideritis scardica L. sampling sites. We constructed the biplot with two principal components (PC) displaying sampled populations and bioclimatic variables (as vectors).A Principal component analysis (PCA) was performed on 19 bioclimatic variables obtained from the Worldclim database for FST outlier loci and by correlating genetic variation with environmental variables. The identification of FST outlier loci was performed using the frequentist method11 implemented in Mcheza13 and the Bayesian method implemented in BayeScan ver. 2.0114. The associations between genetic variation and environmental variables were assessed using the spatial analysis method106 as implemented in Samβada107. AFLP markers with a low minor allele frequency (below 5%) were removed from the dataset since they systematically bias the FST estimates108. The frequentist method for the identification of FST outlier loci implemented in Mcheza detects loci with unusually high or low FST values. Loci under directional selection are expected to display significantly higher FST values than the majority of neutral loci in a sample, while loci with significantly lower FST values are considered to be under stabilizing selection. The neutral distribution of FST values was simulated using 106 iterations with ‘Neutral mean FST’ and ‘Force mean FST’ options. Results were corrected for multiple comparisons by setting the confidence interval (CI) to 99% and the false discovery rate (FDR) to 0.1.The detection of candidate loci under selection was carried out using two basic approaches: by identification of FST outlier loci implemented in BayeScan directly estimates the posterior probability that a given locus is under selection by defining two alternative models, one with and other without the effect of selection. Twenty pilot runs of 5,000 iterations were used to adjust the proposal distribution to acceptance rates between 0.25 and 0.45 for the runs. A burn-in of 50,000 iterations was used, followed by 500,000 iterations using a thinning interval of 50. We used prior odds of 10, corresponding to a prior belief that the model with selection is ten times less likely that the model without selection. The logarithm of Posterior Odds [log10(PO)] higher than 1.5 was taken as a ‘very strong’ evidence for selection109. The multitest correction on false discovery rates (FDR) was set at 0.01 to avoid overestimating the percentage of outliers.Bayesian approach for the identification of P < 0.01).A spatial analysis method implemented in Samβada uses the logistic regression approach to estimate the probability that an individual carries a specific genetic marker given the environmental conditions of its sampling site. The associations between genetic markers and environmental variables were assessed with both log-likelihood G ratio and Wald test using Bonferroni correction for multiple hypothesis testing (Supplementary information