This is particularly true in China, a large and economically diverse country that has rapidly industrialized and urbanized and that has pledged under the Paris Agreement that its emissions will peak by 2030. We present new, city-level estimates of CO2 emissions for 182 Chinese cities, decomposed into 17 different fossil fuels, 46 socioeconomic sectors, and 7 industrial processes. We find that more affluent cities have systematically lower emissions per unit of gross domestic product (GDP), supported by imports from less affluent, industrial cities located nearby. In turn, clusters of industrial cities are supported by nearby centers of coal or oil extraction. Whereas policies directly targeting manufacturing and electric power infrastructure would drastically undermine the GDP of industrial cities, consumption-based policies might allow emission reductions to be subsidized by those with greater ability to pay. In particular, sector-based analysis of each city suggests that technological improvements could be a practical and effective means of reducing emissions while maintaining growth and the current economic structure and energy system. We explore city-level emission reductions under three scenarios of technological progress to show that substantial reductions (up to 31%) are possible by updating a disproportionately small fraction of existing infrastructure.As national efforts to reduce CO For example, Parshall et al.  administrative territorial approach, which is compatible and consistent with national and international emission inventories. The 46 sectors are classified according to China’s System of National Accounts . The sectors classification can also be mapped with other countries/cities around the world . According to the latest administrative planning report, there are currently 334 cities in China. We select 182 cities based on the data availability . The 182 case cities encompass 62% of China’s population as well as 77% of its GDP in 2010.We first classify cities into five groups according to their apparent development pathway and then quantify the potential for emission reductions among the city groups under a range of technological scenarios. Our results reveal the extent to which different policies may reduce emissions while in large part maintaining the current industrial structure and energy mix of cities and thereby minimize economic impacts.2 emissions of 182 cities, which are for 2010. Most of the 182 cities are located in the eastern half of the country  of the cities’ total emissions, with oil and natural gas combustion representing just 15 and 2%, respectively, and the remaining 9% from industrial processes such as cement production.2 emissions of the 182 cities range from <15 Mt (yellow) to >90 Mt . Higher levels of emissions prevail in the north and east, and the top-emitting cities represent a disproportionately large fraction of the total emissions from the 182 cities. The top five emitting cities  accounted for 11% of the total in 2010. In addition, our estimates of CO2 emission intensity, calculated as total emissions divided by GDP, range from <0.1 to >1.5 metric tons of CO2 per ¥1000, with a minimum of 0.04 and 0.05 metric tons of CO2 per ¥1000 in Shenzhen  and Huangshan , respectively; a maximum of 1.72 and 1.55 metric tons of CO2 per ¥1000 in Hegang  and Panzhihua , respectively; and an average of 0.22 metric tons of CO2 per ¥1,000 (see table S1).Population and socioeconomic development varies tremendously among these cities: from 0.2 million people living in Jiayuguan  to 28.7 in Chongqing (southwest) and from per-capita GDP of just ¥9068 in Fuyang  to ¥175,125 in Ordos . Similarly, colors in 2 per ¥1000, respectively, have small manufacturing and energy sectors  and larger service sectors . In contrast, cities such as Maanshan, Tangshan, and Panzhihua have iron and steel production as their “pillar” industries , with correspondingly high carbon intensities: 0.68, 0.43, and 1.55 metric tons of CO2 per ¥1000, respectively. Similarly, in cities where energy production and mining of natural resources are the dominant industries, such as Hegang, emission intensities are especially high (1.72 metric tons of CO2 per ¥1000) because the activities are emission-intensive, but such cities produce low value-added energy products .The detailed investigation of cities’ emissions by sectors and fuels helps in understanding the wide range in cities’ carbon intensity: cities such as Beijing and Shenzhen, whose emission intensities are just 0.07 and 0.04 metric tons of COn = 8), high-tech cities (n = 24), energy production cities (n = 32), heavy manufacturing cities (n = 51), and light manufacturing cities (n = 67) . Recognizing these characteristic differences, we use formal cluster analysis to classify the cities into five distinct groups based on their GDP and industrial output: service-based cities . The SDs of the energy production and heavy manufacturing cities’ emission intensity are the largest among the five city groups . The SDs of high-tech and service-based cities’ emission intensities are relatively small, which are 0.08 and 0.07 metric tons of CO2 per ¥1000, respectively.2 with low economic outputs. Conversely, high-tech and service-based cities rely more on the high-tech industries and service sectors. This led them to lower emission intensities. Such a difference in city groups’ energy intensity is determined by the cities’ economic structures. The energy production and heavy manufacturing cities have more energy-insensitive sectors, which emit high COz test of mean values  define the pillar industry of each city group. A city group’s pillar industry has the highest share in economic structure compared with other city groups. For example, the service sectors’ average share in GDP of the service-based cities (59%) is significantly higher than those of energy production (34%), heavy manufacturing (36%), light manufacturing (36%), and high-tech (43%) cities. Similarly, the energy production sectors’ average share in energy production cities (54%) is the highest among the five city groups.2 emissions, we next examine the potential for emission reductions in industrial sectors through scenarios of specific technological improvement. As each city has 39 industrial sectors , there are 7098 industrial city sectors in total. The term “city sector” refers to each industrial sector of each city; for example, thesector “coal mining and dressing” of Beijing is a city sector, while the sector “food processing” of Shanghai is another. We calculate the per–industrial output emissions for the 7098 industrial city sectors. We then identify three levels of superemitters of those city sectors based on the city sectors’ per–industrial output emissions  Above-average emitters have per–industrial output emissions greater than the sector mean.(2) One SD superemitters have per–industrial output emissions 1σ above the sector mean.(3) Two SD superemitters have per–industrial output emissions 2σ above the sector mean. The 2 SD superemitters represent the most carbon-intensive city sectors.2 emissions  Scenario #1: 2 SD superemitting city sectors reach the current national sector average emission intensity.(2) Scenario #2: 1 SD superemitters reach the current sector average.(3) Scenario #3: Above-average superemitters reach the current sector average.2 in energy production cities, 388 Mt of CO2 in heavy manufacturing cities, 36 Mt of CO2 in light manufacturing cities, and 1 Mt of CO2 in high-tech cities, or 52, 38, 40, and 11%, respectively, of those cities’ pillar industry emissions. Energy production and heavy manufacturing sectors are the primary emission sources of every city group due to their high emission intensities; therefore, we calculate the emission reduction capacities of the cities’ energy production (fig. S3B) and heavy manufacturing sectors (fig. S3C) as well.We then calculate the emission reduction capacities of the five city groups’ pillar industry, under the three scenarios (shown in fig. S3A). Under the strongest scenario, #3, reductions to cities’ pillar industries alone could avoid emissions of 544 Mt of CO2 emissions, particularly in heavy manufacturing and light manufacturing cities  unchanged. This is critical, given the rapidity of targeted reductions; the central government in China seeks to reduce national CO2 emissions decreased by 7.6 Mt during 2010 to 2015, but emissions in Hebei province increased by 87.1 Mt during the same period . Although a few affluent cities have reduced the proportion of coal in their energy mix [for example, Beijing has reduced its coal consumption by 61%, or 18.2 Mt from 2007 to 2015 , more affluent, service-based cities might be able to quickly outsource their more emission-intensive industries without economic hardship. However, such outsourcing by highly developed cities could increase overall emissions; by virtue of their level of development, these cities often have more advanced technologies in place, and outsourcing would thus tend to move carbon-intensive, heavy-polluting industries to less-developed regions with less-efficient technologies. For example, Shougang Corporation, one of the largest steelmaking companies in China, has moved progressively from Beijing to Hebei province (mainly to Tangshan) since 2010. Beijing’s COOn the basis of detailed analysis of cities and their industries, our findings suggest that China’s near-term goals of reducing its emission intensity may be feasibly accomplished by targeted technological improvements, buying time for the longer-term strategies of shifting to non-fossil energy and a more service-based economy. Moreover, improving and optimizing the energy and carbon efficiency of industrial production processes and operations could help lower the costs of advanced technologies and thus facilitate their deployment in less-developed cities and countries beyond China.2 emission intensity by 60 to 65% and running a nationwide Emission Trading Scheme over the next few years, policy-makers in the country need increasingly specific, subnational information about the sources of CO2 and the potential reductions and economic implications of different possible policies. By categorizing Chinese cities according to their development stage and industrial makeup, we offer policy-makers an opportunity to meaningfully differentiate across the wide range in city-level CO2 emissions (from 1.6 to 194 Mt) and emission intensity (from 0.04 to 1.72 metric tons of CO2 per ¥1000). Further, because the lower emission intensities of affluent cities  are supported by imports from less affluent, industrial, and energy-producing cities located nearby, consumption-based policies may allow more developed cities to subsidize emission reductions without undercutting the economic core of still-developing cities by directly regulating their manufacturing and the electric sectors.In order for China to cost-effectively reach its goal of reducing COHowever, where policies directly targeting manufacturing and electric power infrastructure are implemented, our sectoral analysis of each city indicates that targeted technological improvements may be a practical and effective means of reducing emissions while preserving cities’ current economic structure and energy systems. In particular, by focusing efforts on superemitting industry sectors in each city, roughly 30% of China’s carbon emissions might be eliminated.2 emissions in recent years is a tremendous watershed in the global effort to avoid dangerous climate change, the progress reflects sweeping policies to improve the country’s industrial technologies and energy systems. Further progress will increasingly depend on policies that differentiate among cities according to their economic structure, level of development, and infrastructure and are carefully crafted to target the largest and most cost-effective emission reductions.Although the leveling off of China’s COIn addition, rapidly growing cities with “new” investments in infrastructure and institutions tend to emulate already-established cities and thereby lock into the “old” development paths. Instead, promoting low-carbon transitions at early stages of industrialization implies careful consideration of “future fit”—how industries can be designed and run to optimize or decouple the relationship between their economic productivity and energy use . The socioeconomic and environmental characteristics of the 182 cities represent various stages of industrialization, from the preindustrialization stage  to the postindustrialization stage (such as Beijing and Shanghai). In turn, this cross section of Chinese cities we analyze may provide important insights for other developing countries seeking to target superemitting sectors/units in their cities, perhaps enabling them to bypass or abbreviate the most emission-intensive phases of industrialization.2 emissions of one country: the territorial-based, production-based, and consumption-based approaches. According to the IPCC  and the World Business Council for Sustainable Development  from the total consumption; this part did not emit CO2 either  and the corresponding emission factors. The method is widely used by researchers. On the basis of the IPCC method, the National Development and Reform Commission of China (NDRC) designed an emission account system for Chinese provinces  were calculated in process) were calculated in Here, we adopted the IPCC and NDRC methods. The COij refers to the CO2 emissions by fossil fuel type i and sector j; ADij, the activity data, means the corresponding fossil fuel consumption. NCVi, CCi, and Oij are known as emission factors. NCVi refers to net caloric value, which is the heat value produced per physical unit of fossil fuel combusted (in J/ton); CCi (carbon content) is the CO2 emissions per net caloric value produced from a given fossil fuel type i (in metric tons of CO2/J); Oij is the oxygenation efficiency, which refers to the oxidation ratio when fossil fuels are burned (in %).In i and CCi were collected on the basis of our previous investigations on China’s fossil fuel quality  were collected from IPCC  and 7 industrial processes . The fossil fuel–related emissions were calculated on the basis of 46-sectoral energy consumption; therefore, the cities’ emission inventories were constructed by 46 socioeconomic sectors , corresponding to the national sectors classification defined by the National Administration for Quality Supervision and Inspection and Quarantine (see table S2) .The 46 sectors include 1 farming, 1 construction, 3 service sectors, 2 household energy use , and 39 industrial sectors. The 39 industrial sectors can be grouped into four categories: energy production, heavy manufacturing, light manufacturing, and high-tech industries. The energy production category includes five sectors that produce either primary or secondary energy types, while the high-tech category refers to five sectors aiming at high and new technical industries. The remaining 29 sectors belong to manufacturing sectors, including both heavy and light manufacturing sectors. Here, we defined the heavy manufacturing category to include 16 sectors that input energy to produce intermediate products, such as “ferrous metals mining and dressing” and “nonmetal mineral products.” We classified 13 sectors as light manufacturing sectors, which mainly produce final products such as food processing and furniture manufacturing. We allocated the COij) were collected on the basis of the “energy balance table” and “industrial sectoral energy consumption table” from the city’s statistical yearbook. We collected the energy consumption data of the nonindustrial sectors  from the energy balance table. The energy balance table illustrates the energy data of one region, such as production, import, input and output transformation, final consumption, and loss (see table S5). Because of the poor data quality at the city level, only 10% (or 17) of the 182 cities have the energy balance table in their statistical yearbooks. The remaining 90% (or 165) of the cities do not have the table. Considering the completeness and consistency of cities’ emission inventories, we followed our previous study to scale down the corresponding provincial tables to obtain the city-level energy consumption data of these nonindustrial sectors . Here, we collected the energy consumption of the cities’ 39 industrial sectors from the industrial sectoral energy consumption table, which were investigated by the cities’ statistics office at the enterprise level.t) were collected from cities’ statistical yearbooks directly. Cities’ population, GDP, industry output, and other socioeconomic data were collected from cities’ statistical yearbooks and China City Statistical Yearbook 2011. Results and original data were freely available for download from China Emission Accounts and Datasets  after registration.The industrial productions of cities  were allocated to the power plants, rather than to their final consumption sectors. The scope and framework were consistent with the NDRC guidelines for provincial greenhouse gas inventories, which were widely used in China’s emission accounts and studies  of 182 cities fall in . The highest uncertainty appeared in Hegang , while the lowest appeared in Huizhou . Energy production and high emission cities usually have a relatively higher uncertainty of COIn our previous research, we compared our estimation of five cities’ emission with the calculation through a bottom-up approach  in the following steps:EP), heavy manufacturing (IOHM), light manufacturing (IOLM), and high-tech industry (IOHT). Taking Beijing as an example, IOEP−Beijing is equal to the energy productions’ industrial outputs divided by Beijing’s total industrial outputs (28%). IOHM−Beijing, IOLM−Beijing, and IOHT−Beijing are 37, 11, and 24, respectively, in 2010. This means 28% of Beijing’s industrial outputs are contributed by energy productions, while 24% are contributed by high-tech industries.(1) We calculated each city’s industrial output share (IO) of the four manufacturing categories: energy production  We then sorted the cities according to their sectors’ industrial output share from small to large and obtained the cities’ rankings of each manufacturing category. Taking Beijing as an example, IO(3) We changed the rankings into percentiles (IO′). Beijing’s high-tech industry share is ahead of 90.11% of the case cities .As the cluster analysis was operated on the basis of mathematical relationships, the results may be distinct with the practical situation. We adjusted the results slightly according to other information and the actual situations in this empirical analysis. For example, Xianyang was moved from group 4 to the energy group due to its high ranking in the energy sector category (88) and huge absolute value of energy production output .Following the cities’ industrialization process, the most developed high-tech cities will further develop into service-based cities. These service cities may go through every stage of the industrialization process in the past decades, and they have now successfully transferred their pillar industry to service sectors with low emission intensity. They presented sophisticated and practical roadmaps of economic structure optimization and low-carbon development. Therefore, this study extracted these service-based cities from the previous high-tech group to form a new group. Here, the service-based cities were defined as high-tech cities with a higher than 50.63% service sectors’ share in GDP. The 50.63% boundary was calculated as the high-tech cities’ average service sectors’ share in GDP . To validate the city group, we applied the z test of energy production sectors . Moreover, the sectors’ average share was also significantly different between the energy production and light manufacturing cities.In  sectors , light m sectors , and hig sectors . As for z test for the mean value between city groups verified the city clusters. The pillar industry of a city group was significantly higher than the others.Above all, the This study has some limitations, which mainly lie in the accuracy of the cities’ emission accounts. Further work should focus on the following three aspects to improve the accuracy of the cities’ emission accounts, as well as their emission reduction potentials.First of all, the downscale emission inventories developed in this study should be compared with the bottom-up emission inventories. As discussed earlier, the bottom-up emissions  and their emission-saving potentials. This was primarily restricted by the emission data for nonindustrial sectors. Further studies should try to improve estimation of emissions in urban service sectors. We would like to conduct a large survey to measure the differences between emission intensities of service-related sectors across 50+ selected cities in China.


Salvelinus alpinus) is often the only fish species inhabiting these ecosystems, it represents a valuable model for studying the impacts of climate change on species life‐history diversity and adaptability. Using a genotyping‐by‐sequencing approach, we identified 5,976 neutral single nucleotide polymorphisms and found evidence for reduced gene flow between allopatric morphs from two high Arctic lakes, Linne'vatn  and Ellasjøen . Within each lake, the degree of genetic differentiation ranged from low  to moderate . We identified 17 highly diagnostic, putatively adaptive SNPs that differentiated the allopatric morphs. Although we found no evidence for adaptive differences between morphs within Ellasjøen, we found evidence for moderate  to high genetic differentiation  among morphs within Linne'vatn based on two adaptive loci. As these freshwater ecosystems become more productive, the frequency of sympatric morphs in Ellasjøen will likely shift based on foraging opportunities, whereas the propensity to migrate may decrease in Linne'vatn, increasing the frequency of the Normal morph. The Dwarf charr was the most genetically distinct group. Identifying the biological basis for small body size should elucidate the potential for increased growth and subsequent interbreeding with sympatric morphs. Overall, neutral and adaptive genomic differentiation between allopatric and some sympatric morphs suggests that the response of Arctic charr to climate change will be variable across freshwater ecosystems.Arctic freshwater ecosystems have been profoundly affected by climate change. Given that the Arctic charr ( Notably, Arctic charr populations in the southern range of the species distribution do not exhibit anadromy  approach  mostly eat insect larvae in open water  is located on western Spitsbergen, the largest island in the Svalbard archipelago  were collected from Linne'vatn River as they migrated from the sea to the lake. Resident fish were classified as either a Dwarf or Normal morph based upon FL, morphology, and sexual maturity. Fish caught in the lake were dissected to determine sexual maturity. Fish that were sexually mature and <205 mm were classified as Dwarf charr. According to these criteria, twenty Normal  and 28 Dwarf  samples were collected from Lake Linne'vatn  measurements and sex (M/F) were recorded for each individual. A small piece of the adipose fin was removed from each fish and stored in a 1.5‐ml vial filled with 95% ethanol. In total, 24 Littoral  and 6 Pelagic samples  at 37°C for 2 hr. Following digestion, 91 barcoded adapters and a common adapter were ligated to individual samples using T4 Ligase (New England Biolabs) at 22°C for 2 hr, and heat inactivated at 65°C for 30 min. All samples were pooled and cleaned using QIAquick PCR Purification Kit (Qiagen) and 300–500‐bp fragments isolated on a Blue Pippin (Sage Science). Size‐selected fragments were then amplified with an Illumina primer under the following conditions: 98°C 30″; 98°C 10″, 68°C 30″, 72°C 30″ (15 cycles); 72°C 5′, 4°C hold. The PCR product was purified, eluted, and quantified using a Qubit fluorometer (Thermo Fisher Scientific). Quality was assessed on an Agilent 2100 Bioanalyzer and loading concentration determined by qPCR. The final library was run on one Illumina HiSeq 3000 lane, using paired‐end 150‐bp sequence chemistry. Only single‐end reads were used for the current study.DNA extraction, library construction, and sequencing were conducted at Oregon State University's Center for Genome Research and Biocomputing, largely following the methodology of Elshire et al. ; modific2.3fastqc . The entire read length was retained as a visual examination of terminal read quality showed no substantial decrease in base call accuracy . Raw reads were then demultiplexed and constructed into putative genetic loci using the stacks pipeline , truncated 142 bp single‐end reads were checked for the presence of the HindIII restriction cut‐site sequence, and those with base call errors were discarded. Using a sliding window (15% of read length), reads showing an average decrease in quality score  were also removed. Demultiplexed reads were then assembled into loci using stacksdenovo_map.pl wrapper program with default parameters, with the exception of the following: ustacks: ‐m 2 ‐M 2 ‐‐bound_high 0.05; cstacks: ‐n 2. The parameters are defined as: m = minimum depth of coverage to create a stack, M = maximum distance (in nucleotides) allowed between stacks, and n = number of mismatches allowed between sample loci when building a catalog. Following completion of the assembly, a population map was used to assign individuals to their respective phenotypic group , and stackspopulations module was implemented, requiring loci to be present in ≥60% of individuals in each group with a minimum allele read depth of 10× (‐r 0.6 ‐m 10). To maintain independence of loci with multiple polymorphisms, only the first SNP within each locus was retained for downstream analyses (‐‐write_single_snp).Raw Illumina reads were first assessed for quality using vcf file , overall and per‐locus observed heterozygosity (HOBS), expected heterozygosity (HEXP), and inbreeding coefficients (FIS). Tests of heterozygote excess/deficit within each morph were conducted with default parameters using the exact test implemented in genepop , the output and Seeb . Each lo2.4Delineation of both neutral genetic variation and adaptive genetic variation can be important for evaluating differentiation among populations  of 0.05. To reduce type I error, results were verified with a coalescent‐based approach by identifying putative outliers exhibiting high FST values relative to neutrality as implemented in lositan v. 1.6  in two or more morphs, the locus with the lowest genotyping frequency was discarded. The degree of pairwise genetic differentiation (FST) was then examined between morphs at neutral loci. To determine whether pairwise comparisons significantly differed from zero, 1,000 bootstraps were used across all loci and 95% confidence intervals (95% CI) were generated using the diversity package in R  of neutral SNPs was estimated for each morph using k‐means clustering algorithm implemented in the R package adegenet , identifying the optimal number of clusters using the Bayesian information criterion (BIC). Using the inferred group assignment (based upon k‐means clustering), a discriminant analysis of principal components (DAPC) was then implemented, replacing missing values with overall mean allele frequencies and using the optim.a.score function to prevent over fitting the model  and evaluated using the cross‐validation function (‐cv) to identify the value of k with the lowest associated error.To further evaluate the genetic relationship among Arctic charr morphs, we used the successive Jombart, . The fir2.6genodive  between morphs at adaptive SNPs was calculated using the same methods listed above (see differentiation among morphs section). Confidence intervals for neutral and adaptive pairwise FST values were then compared to infer evidence of divergent selection between morphs. Third, using the same methods listed above, bayescan was repeated using individuals from either Ellasjøen or Linne'vatn to test for repeated outliers at a local scale.In order to disentangle whether one or multiple morphs were driving any observed patterns of deviation from neutral expectation, three different approaches were used to attempt to resolve signatures of divergent selection. First, to provide a visual comparison of adaptive SNPs across all five morphs at each locus, allele frequencies were generated using stacks catalog, converted to fasta format, and blastn . The position of the top alignment for each query sequence was examined for annotated regions within a 50 kb window using NCBI's Salvelinus alpinus genome browser (http://www.ncbi.nlm.nih.gov/genome/gdv/), and UniProtKB (http://www.uniprot.org/) was used to infer potential biological function.The 142 bp consensus sequence of each outlier was downloaded from the 33.1stacksprocess_radtags script. Of these reads, 321,417,116  were retained, with demultiplexed samples averaging 3.84 million reads (±0.91 SD). A preliminary stacks run identified two individuals with low genotyping rate , which were excluded from the analysis. Assembling loci across the remaining individuals (N = 89) resulted in retention of 12,271 loci, containing a total of 18,095 SNPs (mean = 1.5 SNPs/locus). After restricting the analysis to one SNP per locus, and excluding 49 SNPs that were homozygous, 1,308 SNPs were identified as putative paralogs  <3 , resulting in a final matrix of 8,075 filtered SNPs for downstream analyses  was lower for Ellasjøen samples (AR range = 1.373–1.384) compared to Linne'vatn samples (AR range = 1.585–1.594), with little observable difference between morphs from the same geographic area (Table HE range = 0.153–0.159) compared to Linne'vatn samples , with all morphs exhibiting significant evidence of heterozygote deficit , with q‐values of candidate outliers ranging from 0.0002 to 0.049  ranging from 0.996 to 1.0. Overall, examination of bayescan and lositan output resulted in agreement of 17 putatively adaptive and 5,980 neutral SNPs , 49 Table . All of Ps Table , which w3.3r2 > 0.8) in two or more morphs, with the remainder  used to examine patterns of group differentiation and neutral population structure. Substantial variation in pairwise genetic differentiation was observed among morphs (FST range = 0.002–0.178). As expected, high differentiation was observed among allopatric morphs (FST = 0.152–0.178), with low to moderate differentiation among sympatric morphs , with the exception of the pairwise comparison between the sympatric Littoral and Pelagic morphs in Ellasjøen (FST = 0.002), which may be attributed to small sample sizes.Out of the 5,980 neutral SNPs, four SNPs were excluded based upon evidence of strong LD , from the Dwarf samples , yet a substantial range in genetic differentiation occurred among sympatric morphs  showed substantially different allele frequencies within the Linne'vatn samples  showed allele frequency disparities among allopatric morphs, largely driven by the presence of multiple monomorphic loci for Ellasjøen samples. Repeating n = 17), blastn had solid hits against the Salvelinus alpinus reference genome, with E‐scores ranging from 9.00e‐21 to 6.00e‐67 (Supporting Information Table www.genecards.org/cgi-bin/carddisp.pl?gene=IL1RAPL1).Using all of the consensus outlier sequences as queries  than those in Linne'vatn. Varying levels of genetic differentiation were detected among allopatric and sympatric morphs using 5,976 neutral and 17 adaptive loci. Fifteen of the identified adaptive loci were shared between Linne'vatn and Ellasjøen, and consensus sequences had solid hits against the 4.2FST estimates based on neutral and adaptive loci indicated high differentiation among allopatric morphs  was strongly characterized by chironomid Oliveridia tricornis  larvae, whereas larger Arctic charr  within the lake were mainly cannibals (Svenning et al., Anadromous and Normal morphs is potentially mortal risk to the reproductive success of Dwarf individuals. If cannibalism during spawning is common, we would expect selection to be strong against Dwarf individuals mating outside of their morph, unless such matings provide increased fitness, which could lead to the evolution of sexual cannibalism (Elgar & Schneider, Dwarf Arctic charr to determine whether this is a plausible factor to have affected gene flow.The high Dwarf morph represents a genetically distinct component of Arctic charr diversity in the Linne'vatn. Considering similar genetic evidence from other studies of Dwarf morphs (Alekseyev et al., Dwarf individuals are not restricted to small body sizes due paedomorphosis or other heritable genetic changes, it is possible that Dwarf individuals could reach large enough sizes to compete or reproduce more often with larger morphs (Budy & Luecke, Dwarf individuals are restricted to small body sizes, and whether increased freshwater temperature affects the growth rate of the morph.Overall, the 5Anadromous and Normal morphs in Linne'vatn indicate that migratory differences do not necessarily indicate substantial genetic differences within a single lake. In concordance with previous studies (Alekseyev et al., Dwarf Arctic charr in Linne'vatn were the most genetically distinct group observed, and the response of Dwarf morphs to climate change therefore warrants further investigation. Given that we observed substantial adaptive and neutral genetic diversity between just two lakes in the geographically remote Svalbard Archipelago, out results suggest that Arctic charr exhibit significant genetic diversity across the Arctic. The impact of continued climate change upon the diversity of other Arctic anadromous and freshwater fish species, and in turn, the wider ecosystem, fisheries, and affected indigenous communities, requires further investigation.The results of this study demonstrate the value of utilizing putatively adaptive loci to investigate intraspecific variation. Geographically isolated, allopatric populations of Arctic charr are likely to be genetically distinct, but genetic divergence among sympatric morphs varies, and response of populations and morphs to climate change probably needs to be considered on a case‐by‐case basis. That said, results from K.G.O. and A.N.B. designed the experiments; K.G.O. and A.N.B. performed the experiments; K.G.O. and A.N.B. analyzed the data; and K.G.O., A.N.B., and F.V. wrote the manuscript. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.


Hypoatherina harringtonensis, Anchoa choerostoma, Jenkinsia lamprotaenia, Harengula humeralis, Opisthonema oglinum and Sardinella aurita. Species identification based on molecular barcoding revealed some misidentification of individuals based solely on gross morphological characteristics, with an error rate of 11%, validating the usefulness of this approach. Interestingly, sequence results for the endemic Bermuda anchovy, A. choerostoma, were within 1% similarity to the more broadly distributed big-eye anchovy, A. lamprotaenia, and thus additional analyses are warranted to evaluate the genetic basis for endemism. Estimates of genetic diversity within and among baitfish assemblages in Bermuda were high, indicating high rates of local connectivity among sites for all species. As such, management should consider Bermuda’s baitfish species as single, highly mixed populations. However, with the exception of H. humeralis and the endemic A. choerostoma, significant genetic differentiation and population structure were found when comparing Bermuda’s baitfish populations with those from other regions, suggesting limited gene flow between other regions and Bermuda for these species. Limited regional connectivity has implications for management, as strong genetic divergence suggests that populations in Bermuda are predominantly self-seeding and thus not likely to be replenished from distant populations. These results therefore support precautionary management of baitfish species in Bermuda.Small-bodied marine fishes play an important role in the food web, feeding both larger fishes and seabirds. Often referred to as baitfishes, they concentrate seasonally in coastal areas in large, often heterospecific assemblages that are targeted by both commercial and recreational fishers. Given apparent declines in at least some of Bermuda’s baitfish species over the past 40 years, it is useful to determine the species composition of baitfish assemblages, and how it varies among sites, in order to inform management. Using genetic barcoding of the Cytochrome c oxidase 1 gene (COI), we confirm species identity, assess intraspecific genetic diversity locally, and determine rates of broader genetic connectivity for baitfish assemblages in Bermuda. Species analyzed included  Small-bodied, shoaling marine fishes are a critical part of the food chain, connecting plankton at low trophic levels to higher trophic level organisms such as seabirds and piscivorous fishes . These sHypoatherina harringtonensis  [Family Atherinidae], the endemic Bermuda anchovy, Anchoa choerostoma  [F. Engraulidae], and the Dwarf herring, Jenkinsia lamprotaenia  [F. Dussumieriidae], as well as the larger and morphologically similar Redear herring, Harengula humeralis , Round sardinella, Sardinella aurita , and Threadfin herring, Opisthonema oglinum , all [F. Clupeidae] . The Dwarf herring and Bermuda anchovy, which, along with the Reef silverside are collectively called ‘fry’, make up the bulk of the baitfish harvest . Prior to the banning of fish traps in 1990, these small, oily fishes were also placed in mesh bags that were added to traps in order to draw larger fishes to them from greater distances . The ann harvest . HoweverFor species targeted by fishing, overexploitation can lead to population declines . Other aAt present, regulation of baitfishing in Bermuda under the Fisheries Act, 1972, and the Fisheries Regulations, 2010, includes both gear and spatial restrictions. In particular, there are four inshore bays , within which the use of fishing nets and the removal of baitfishes is completely prohibited , but these bays are not evenly distributed around the island . FurtherLastly, if Bermuda’s baitfish populations are indeed experiencing declines, a greater understanding of regional genetic connectivity could indicate whether or not larval supply from other populations might assist with their recovery see . BermudaUsing genetic barcoding of the Cytochrome c oxidase 1 gene (COI), we aim to confirm species identity, assess intraspecific genetic diversity, and determine rates of local and regional genetic connectivity of Bermuda’s baitfish populations. Barcoding has proven useful for species verification in families with a high degree of morphological similarity, and has been able to identify new species by integrative taxonomic analysis . FurtherHypoatherina harringtonensis (Reef silverside), Anchoa choerostoma (Bermuda anchovy), Jenkinsia lamprotaenia (Dwarf herring), Harengula humeralis (Redear herring) and Sardinella aurita (Round sardinella). Representative samples of each species from each location were preserved in 95% ethanol for subsequent genetic analyses.Samples were collected from 10 locations around the islands of Bermuda: the Bermuda Aquarium Museum and Zoo dock in Flatts (BAMZ), Bailey’s Bay, Coney Island, Whalebone Bay, Turtle Bay, South Bay, East Whale Bay, West Whale Bay, Frank’s Bay, and Deep Bay, between July and August 2017 . Based o2O, 1.25 µl of 10x Buffer , 0.625 µL MgCl2 (50 mM), 0.125 µl of each primer cocktail, 0.0625 µl of DNTP (10 mM), 0.0625 µl of Taq Polymerase (Invitrogen), and 2 µl of DNA template. PCR was optimized at the following: 95 °C for 2 min, 35 cycles of 94 °C for 30 s, 52 °C for 30 s, 72 °C for 1 min, with a final extension at 72 °C for 10 min. PCR products were visualized using a 1.2% agarose gel, with concentration and purity measured using a spectrophotometer. Products were bi-directionally sequenced with universal M13 primers using Sanger Sequencing services provided by Gene Codes Corporation. Resulting sequences were manually edited and aligned using Sequencher® 5.4.6  and compared to known sequences in NCBI Blast and GenBank. Accession numbers are listed in http://www.ncbi.nlm.nih.gov/Genbank). Available sequences of the COI gene for conspecifics from locations outside Bermuda were downloaded from GenBank and used for regional comparisons of genetic structure and connectivity  from Florida  gene was amplified from extracted DNA using a primer cocktail developed for fish barcoding as described by ectivity . Sequenc Florida  were useSequences for all species, including those obtained from GenBank, were aligned using MUSCLE . Single Nh), number of polymorphic sites (Np), haplotypic diversity (h) (pn) (pd) between haplotypes  , nucleot(h) (pn) , and meaplotypes  calculatplotypes . Populatpes  to the widespread Atlantic species A. lamprotaenia. However, no COI sequences were available for A. choerostoma on public databases for comparison.All three samples from Deep Bay, initially identified as Harengula, Opisthonema, and Sardinella are more closely related to each other than to the other species examined, while species from the genera Jenkinsia and Anchoa are more closely related to each other than to the other species. The genus Hypoatherina, in the order Atheriniformes, was the most evolutionarily distant from the other genera.The single gene tree constructed based on maximum likelihood to infer phylogenetic relationships  indicateA. choerostoma and H. harringtonensis, and for H. humeralis, J. lamprotaenia, and O. oglinum, but was higher for the first two species than for the latter three. The mean number of pairwise differences was highest for A. choerostoma at 3.163, decreasing to 0.934 for J. lamprotaenia, 0.800 for H. humeralis, 0.780 for H. harringtonensis, and 0.429 for O. oglinum. Diversity of A. choerostoma within a given bay was higher at East Whale Bay than at West Whale Bay and BAMZ  and lack of replicate sites precludes their inclusion in diversity comparisons (Haplotype diversity was similar among species based on overlapping standard errors . Nucleotand BAMZ . For H. parisons .FST comparisons between locations within Bermuda were insignificant (α = 0.05) for all species examined, indicating no evidence of genetic structure and high levels of genetic connectivity within and among locations  between populations from Bermuda and those from other regions, indicating strong evidence of genetic structure and limited genetic connectivity across regions  were found to be more closely related to each other than to the other genera examined (Jenkinsia (Family Dussumieriidae) and Anchoa (Family Engraulidae) to be more closely related to each other than to the species in the Clupeidae, recent molecular phylogenies using multiple markers have inferred conflicting sister relationships between the Dussumieriidae, the Engraulidae and the rest of the Clupeoidei ; from A. lamprotaenia by having a greater number of lower gill rakers (23–30 as opposed to 17–21); and from the remaining similar congenerics by having a notably smaller axillary scale above the pectoral fin  is highly conserved among all respiring organisms and is therefore not subject to selective pressures that induce mutation . It is pJ. lamprotaenia was the most widely distributed, being found at five of the 10 locations, followed by H. harringtonensis at four of the 10 locations. Given the high estimates of connectivity among locations for all six species examined, however, distributions are likely wider than reflected by the somewhat limited sampling effort in this study.Of the locations examined around Bermuda, BAMZ had the highest species diversity, with all six species found at this location . ConversEstimates of genetic diversity within and among baitfish assemblages in Bermuda indicate high degrees of mixing between locations for all six species examined. For these small-bodied species, this mixing likely occurs predominantly during the larval phase , but mayS. aurita is found on both sides of the Atlantic Ocean and into the Mediterranean Sea , and significant divergence among regions (p < 0.05) for J. lamprotaenia, H. harringtonensis, O. oglinum, and S. aurita, as well as between congeneric populations of Anchoa sp. among regions. These results suggest that gene flow, and therefore exchange of individuals, is limited between populations of these species in Bermuda and those in other regions. Populations of H. humeralis, on the other hand, show no evidence of genetic structure, suggesting broad genetic connectivity exists across the Caribbean / Tropical West Atlantic for this species. Importantly, these results have major implications for management as strong genetic divergence in J. lamprotaenia, H. harringtonensis, O. oglinum, and S. aurita suggests that populations of these species in Bermuda are likely self-seeding and locally maintained. Thus, local declines in baitfish abundances are not likely to be replenished from distant populations.The present study documents significant genetic differentiation between conspecific populations of baitfishes in Bermuda and those in other regions .Detailed information regarding each specimen sequenced including sample ID, date of collection, Country, GPS coordinates of collection sites, forward and reverse primer names, and the assigned accession number for retrieving sequence data on the public genetic sequence database, Genbank (Click here for additional data file.


This research was conducted to study the genetic diversity in several Indonesian cattle breeds using microsatellite markers to classify the Indonesian cattle breeds.A total of 229 DNA samples from of 10 cattle breeds were used in this study. The polymerase chain reaction process was conducted using 12 labeled primers. The size of allele was generated using the multiplex DNA fragment analysis. The POPGEN and CERVUS programs were used to obtain the observed number of alleles, effective number of alleles, observed heterozygosity value, expected heterozygosity value, allele frequency, genetic differentiation, the global heterozygote deficit among breeds, and the heterozygote deficit within the breed, gene flow, Hardy-Weinberg equilibrium, and polymorphism information content values. The MEGA program was used to generate a dendrogram that illustrates the relationship among cattle population. Bayesian clustering assignments were analyzed using STRUCTURE program. The GENETIX program was used to perform the correspondence factorial analysis (CFA). The GENALEX program was used to perform the principal coordinates analysis (PCoA) and analysis of molecular variance. The principal component analysis (PCA) was performed using adegenet package of R program.Bos indicus cattle.A total of 862 alleles were detected in this study. The INRA23 allele 205 is a specific allele candidate for the Sumba Ongole cattle, while the allele 219 is a specific allele candidate for Ongole Grade. This study revealed a very close genetic relationship between the Ongole Grade and Sumba Ongole cattle and between the Madura and Pasundan cattle. The results from the CFA, PCoA, and PCA analysis in this study provide scientific evidence regarding the genetic relationship between Banteng and Bali cattle. According to the genetic relationship, the Pesisir cattle were classified as Bos taurus cluster ; Bos indicus cluster ; and Bos javanicus cluster .All identified alleles in this study were able to classify the cattle population into three clusters i.e.  The Pasundan cattle are an example of unrecorded local breed cattle in Indonesia ) were used in the polymerase chain reaction (PCR) process . The PCa), effective number of alleles (ne), observed heterozygosity value (Ho), expected heterozygosity value (He), genetic differentiation (FST), the global heterozygote deficit among breeds (FIT), and the heterozygote deficit within the breed (FIS), gene flow (Nm), Hardy-Weinberg equilibrium (HW), and allele frequency. The converted data was also processed using CERVUS version 3.0.7 program [Data of allele’s size (unit in base pairs) were generated using the multiplex DNA fragment analysis. The data was processed using CONVERT version 1.3.1  to conve program  to obtai program . Bayesia program . Ten ind program , which i program  was used program  was used program  was used program  of R ver program . The F-s program  and the  program .Twelve microsatellite loci revealed high polymorphism to evaluate the genetic diversity. The 12 microsatellite loci from the entire population were analyzed and 862 alleles consisting of 86 alleles detected in the Simmental Purebred, 103 alleles in the Simmental Crossbred, 118 alleles in the Ongole Grade, 60 alleles in the Bali, 70 alleles in the Pesisir, 78 alleles in the Holstein Friesian, 98 alleles in the Sumba Ongole, 100 alleles in the Madura, 56 alleles in the Banteng, and 93 alleles in the Pasundan cattle.ST), the global heterozygote deficit among ten cattle breeds (FIT), and the heterozygote deficit within the breed (FIS) among the twelve microsatellite markers  to 0.719±0.158  and the expected heterozygosity value ranged from 0.550±0.248 (Banteng) to 0.796±0.151 (Madura). The expected heterozygosity was higher than the observed heterozygosity in all populations studied . F-stati markers .ST, FIT, and FIS with values ranging from 0.140 to 0.538, 0.055 to 0.522, and −0.099 to 0.377, respectively. The estimated mean values of the total inbreeding (FIT), within line inbreeding (FIS) and genetic distance were 0.367, 0.160, and 0.243 respectively. The PIC values ranged from 0.715 to 0.935. Therefore, the microsatellite markers in this study were very informative.Among the 12 microsatellite markers, the estimation of the fixation index has been determined for FThe result of genetic distance analysis shows that the Simmental Purebred and the Simmental Crossbred subpopulation have the lowest genetic distance value. In contrast, the Simmental Purebred and the Banteng subpopulation have the highest genetic distance value . The locBos taurus cluster); Ongole Grade, Sumba Ongole, Madura, Pasundan, and Pesisir breeds in the second cluster (Bos indicus cluster); Bali and Banteng breeds in the third cluster (Bos javanicus cluster)  revealed three main clusters, with Simmental Purebred, Simmental Crossbred, and Holstein Friesian breeds in the first cluster (cluster) . The cluUsing STRUCTURE HARVESTER, K optimal was obtained at K = 5. At K = 5, Madura and Pasundan cattle have genetic similarities. Furthermore, Simmental Purebred and Simmental Crossbred cattle also have genetic similarities. In addition, the Holstein Friesian cattle were identified as a separated cluster. The STRUCTURE analysis generated quite similar interpretation  to the dBos taurus, Bos indicus, and Bos javanicus. Ongole Grade, Sumba Ongole, Madura, Pasundan, and Pesisir cattle were included in the Bos indicus cluster. Meanwhile, Simmental Purebred, Simmental Crossbred, and Holstein Friesian were clustered in Bos taurus cluster. Bali cattle and Banteng were separated in Bos javanicus cluster. The AMOVA result revealed that variation among individuals more varied (69%) than the variation in the inter-population (31%)  .e>0.6 [IS, FIT, and FST) among ten cattle breeds in this study were positive  and indicates that there has been a selection process in the population.Utilization of five microsatellite markers  to evaluate the genetic diversity of Indonesian cattle breeds  was reported and provides a phylogenetic relationships between the breeds . Howevere>0.6 . In thisBased on Agung et al , the morIn general, the Ongole Grade cattle are a crossbred of uncontrolled mating of Java cattle and Sumba Ongole cattle . This stGenetic distances based on PCA can illustrate the relative effect of intra and interspecies variation . In addiBos indicus cluster   and proposed as the most suitable cattle breed for sustainable small farming in Indonesia . The res cattle) . This cokacang (bean) cattle because of their relatively small body size [Bos javanicus and Bos indicus. The result of the genetic distance analysis shows that the Madura and the Pasundan cattle have low genetic distance value; thus, it can be interpreted that these cattle breeds have a very close genetic relationship, and this can serve as scientific evidence on the origin of the Pasundan cattle.Pasundan cattle have been classified as newest Indonesian local cattle based on the Decree of the Minister of Agriculture number 1051/Kpts/SR.120/10/2014. These cattle are often called ody size , and theBos indicus cluster. This result can also be scientific evidence that the Pesisir cattle in West Sumatera, Indonesia are a type of Bos indicus cattle breeds.Pesisir cattle have been classified as one of Indonesian cattle breeds based on the Decree of the Minister of Agriculture Number 2908/Kpts/OT.140/6/2011. The Pesisir cattle have a unique performance due to their small body , and their natural habitat was only in West Sumatera, Indonesia [The AMOVA is an essential element of the molecular eco logist’s toolkit. Coupled with hierarchical permutation tests, the approach facilitates rigorous statistical inference about the distribution of genetic variation in natural populations . The AMOBos taurus cluster ; Bos indicus cluster ; and Bos javanicus cluster .The 12 microsatellite markers in this study are highly polymorphic and highly informative in detecting the level of genetic diversity among the Indonesian cattle breeds. All identified alleles in this study were able to classify the cattle population into three clusters i.e.


Strigula is a pantropic foliicolous lichen living on the leaf surfaces of evergreen broadleaf plants. In South Korea, Strigula is the only genus of foliicolous lichen recorded from Jeju Island. Several Strigula species have been recorded, but the ecology of Strigula in South Korea has been largely unexplored. This study examined the distribution and genetic structure of Strigula on Jeju Island. The distribution was surveyed and the influence of environmental factors  on the distribution was analyzed using a species distribution modeling analysis. In addition, the genetic variations and differentiation of Strigulamultiformis populations were analyzed using two nuclear ribosomal regions. The distribution of Strigula was largely restricted to a small portion of forest on Jeju Island, and the forest availability was the most important factor in the prediction of potential habitats. The genetic diversity and differentiation of the S. multiformis population were found to be high and were divided according to geography. On the other hand, geographic and environmental distance did not explain the population differentiation. Distribution and population genetic analysis suggested that the available habitat and genetic exchange of Strigula on Jeju Island are limited by the lack of available forest in the lowlands. Foliicolous lichens are an ecological group of lichens living on the surface of plant leaves . More thStrigula, has been recorded from Jeju Island [Strigula exist [Strigula multiformis (Mycobank number: MB831530) is the most common species on Jeju Island. This species has been mistaken for Strigula smaragdula in South Korea based on their similar morphology, but both the molecular and morphological characteristics of S. multiformis can be used to distinguish it from S. smaragdula [Strigula has been largely unexplored. Given that Jeju Island is the optimal habitat of foliicolous lichens in South Korea, the distribution and the genetic structure of Strigula on Jeju Island need to be investigated to better understand the basic biology as well as the conservation practice of foliicolous lichens in South Korea. In South Korea, only one genus of foliicolous lichen, u Island . Jeju Isu Island . In addiu Island . Recentlla exist . Among taragdula . AlthougStrigula on Jeju Island. In addition, we hypothesized that environmental factors influence the distribution and genetic structures of Strigula populations. Specifically, this study had two objectives. First, the distribution of Strigula was surveyed on the whole island, and species distribution modeling (SDM) analysis was performed to identify potential habitats and the influence of environmental factors  on the distribution of Strigula. Second, the genetic structure of S. multiformis was analyzed using a nuclear ribosomal internal transcribed spacer (ITS) and partial large subunit (LSU) region, and the effects of environmental factors on the genetic structures were analyzed focusing on the geographic distance, climate, and vegetation.This study examined the distribution and genetic structure of Strigula habitat. Although Strigula is generally a pantropical species [Strigula species are found  [Strigula disperses to the mainland of the Korean peninsula. In addition, two Strigula species on Jeju Island have been newly described in scientific literature [Strigula, an island-wide survey for the forests covering the whole island where Strigula can live was performed to confirm the previous records and expand the records of Strigula habitats. The specimen records from the Korean Lichen Research Center (KoRLI) (2012–2014) and previous studies [Strigula occurrence. The leaves were thoroughly examined to detect Strigula  ,23. It id Japan) , which sterature . These s (KoRLI) 12–2014 aStrigula , and spepecimens . The leaStrigula species on Jeju Island. The climate, elevation, and forest availability were used as environmental variables. Nineteen sets of bioclimate data were acquired from the WorldClim database v. 2 [Strigula (evergreen broadleaves or mixed forest). Preprocessing of the geographical raster and vector data was conducted using gstudio [Strigula occurrence records and environmental variables using MaxEnt v 3.3.3 [SDM analysis is the method to predict the distribution and suitable habitat conditions based on species distribution records and environmental variables . Among vase v. 2  as a higase v. 2 . Elevatiase v. 2 . Vegetat gstudio , raster  gstudio , and rgd gstudio  packages gstudio , and QGI gstudio . SDM ana v 3.3.3 , impleme v 3.3.3  with a m v 3.3.3 ,38. The S. multiformis using a modified cetyltrimethylammonium bromide (CTAB) method [S. multiformis and its close Strigula species was developed on the intron site between the ITS5 and ITS1 primer positions [Strigula sequences from GenBank . The ITS region was amplified using ITS5/ITS4 or Strig1F/ITS4 [A total of 3–5 thalli from each tree stand were used in the analyses. From each leaf specimen, a single thallus was collected to avoid possible clonal strains living together on the same leaf surface. The genomic DNA was extracted from a thallus of ) method . Since gositions , using tg1F/ITS4 , and theg1F/ITS4 . The hosg1F/ITS4  and rbcLg1F/ITS4  primers,g1F/ITS4  and aligg1F/ITS4 . All seqS. multiformis populations were calculated for the number of variation sites (S), nucleotide diversity (π), number of haplotypes (h), and haplotype diversity (Hd) using DnaSP v. 6 [The genetic variations of aSP v. 6 . Prior taSP v. 6 . For theaSP v. 6  based onaSP v. 6 . The demaSP v. 6 . st and tested for geographic groups and host plant groups by Analysis of Molecular Variance (AMOVA) using Arlequin v. 3.5 with the option of “compute distance matrix” [st/(1 – ϕst)) [The genetic differentiation of the populations was examined using a pairwise ϕ matrix” . The seq matrix” . This me matrix” . The iso matrix”  based on – ϕst))  was calcStrigula  (Strigula occurrence decreased with the increasing amount of precipitation in the driest month (BIO14).A total of 12 forests were confirmed to have Strigula . The bioStrigula . In the = 0.902) b. The fo= 0.902) a. The toS. multiformis collected from five forests on Jeju Island were analyzed using the ITS and LSU regions. The length of alignment and number of segregating sites were 469 bp and 12 for the ITS region, and 906 bp and 15 for the LSU region, respectively. The nucleotide diversity was higher in the ITS regions than in the LSU region. In total, 19 ITS haplotypes and 17 LSU haplotypes were found . The high amount of variation was explained by the geographical groups , whereas the difference in the thalli within the subpopulation (tree stand) also explained the significant variations . The subpopulation difference within the geographical group had a low contribution to the genetic structure . For the host plant group, the fixation indices for the different groups were not significant for both the ITS (p = 0.548) and LSU (p = 0.126). The contribution of the host plant group on explaining the genetic variation was extremely low .The genetic structure of ng AMOVA . For thest for both the ITS and LSU regions  and significant (p < 0.05). For the ITS region, the lowest ϕst value was detected between the Andeok Valley (AD) and Cheonjiyeon (CJ) populations (0.171) and the AD and Jeoji Gotjawal (JJ) populations (0.148). For the LSU region, the Dongbaek hill (DB) and Nabeup forest (NE) populations had the lowest value (0.114). A high level of differentiation for geographical populations was detected from the high values of pairwise ϕ regions . The valS. multiformis . The IBE pattern was tested for the elevation and Bioclim variables , whereas no significant relationship was detected between any of the environmental variables and genetic distances. The IBD and IBE patterns were analyzed using a Mantel test. The geographic distance did not show a significant relationship with the genetic distances , forest covers 34.1%  and most of the forest is located on Mt. Halla, where the elevation is high. Considering the forest type and composition, the potential habitat of Strigula is only 8.0% of the total area of the island. The SDM analysis supports the importance of host plant presence for Strigula distribution. The contribution of the model prediction was highest for the forest availability , which agrees with the previous study that the community of foliicolous lichens is different depending on the elevation and tends to favor lowland areas [Strigula in that Strigula occurred in areas with low precipitation in the driest month , which showed a similar population structure among the host plant [S. multiformis populations on Jeju Island have great diversity within the population and are highly differentiated by geography. Based on DAPC analysis, the genetic structure was divided into the northern and southern areas  have been detected in Cetradonia linearis, Nephroma laevigatum, and Parmelina tiliacea [S. multiformis populations possibly because of a lack of relevant environmental variables. In this analysis, the macroclimatic variables from WorldClim were used, but the microclimate  can be important to lichens [The AMOVA result for the geographic group showed significant differentiation between populations that were also subdivided within individual tree stands . In contst plant ,66. Thesrn areas . This popatterns ,18,70. Tpatterns ,72,73,74 conidia . Therefotiliacea ,19,68. T lichens . In addi lichens ,77,78.S. multiformis populations may be influenced by the habitat availability. The low level of habitat availability due to forest fragmentation and deforestation can lead to a restriction of gene flow between populations [Strigula distribution (Strigula experiences a limitation of habitat, which means it is restricted geographically, resulting in a low level of gene flow. Under this condition, the genetic diversity can be reduced, and geography-specific genotypes can disappear easily if the habitat is destroyed.The high genetic differentiation of ulations ,80. In tulations . Given tribution b, the diStrigula was examined on Jeju Island. Strigula species were rare and distributed in restricted regions. The forest availability made the greatest contribution to the potential niche prediction, followed by elevation and precipitation. Strigula multiformis populations had high genetic diversity specific to each location and were highly differentiated by geography. Generally, populations were divided into the northern and southern areas, whereas the factors associated with population differentiation were unclear. Considering the restricted area of the predicted habitats and high differentiation level, Strigula on Jeju Island may suffer from habitat limitation and fragmentation. Therefore, the mechanism for the environmental effects on the distribution and genetic structure of Strigula needs to be investigated to better understand the ecology of foliicolous lichen and conserve the Strigula community in South Korea.The distribution and genetic structure of


The Han population of the province is approximately 59.15 million [D19S433, D5S818, D21S11, D18S51, D6S1043, D3S1358, D13S317, D7S820, D16S539, CSF1PO, Penta D, vWA, D8S1179, TPOX, Penta E, TH01, D12S391, D2S1338, and FGA). The Investigator HDplex kit  contains the amelogenin locus, one CODIS (D18S51), and 11 Non-CODIS loci . Both kits contain D18S51 and D12S391, which can be used for sample concordance.The Han population accounts for the majority of the Chinese population. Hunan is a large province located at 108°47′–114°15′ E and 30°08′–24°38′ N in Southern China, specifically in the area in the middle reaches of Yangtze River. The province has a total area of 218 400 kmGenomic DNA was extracted from the bloodstain samples by using the Chelex-100 protocol according to the description by Walsh et al. . DNA conhttp://genepop.curtin.edu.au). The allele frequency distributions among groups were compared using Arlequin version 3.5 software package [Genotype frequencies at each locus, power of discrimination (PD), power of exclusion (PE), probability of matching (PM), polymorphism information content (PIC), observed heterozygosity (Ho) and Hardy–Weinberg equilibrium (HWE) were evaluated with modified PowerStat (version 1.2) program . Linkage package . Pairwis package . Neighbo package .The currSE33 locus and the least number of 6 alleles at the TPOX locus. Genotyping results should be combined with the standard DNA allelic ladder to avoid genotyping errors because of the complex internal sequence structure of the SE33 locus [P-value for Hardy–Weinberg testing did not significantly deviate from HWE after Bonferroni correction (P = 0.05/29 = 0.0017), only D21S2055 (P = 0.000 2) and D7S1517 (P = 0.0012) had minor departures from HWE because of errors in random sampling. Among the 29 STR loci, the SE33 locus was the most informative marker, with a PD value of 0.9930 and a PIC value of 0.9500, whereas the TPOX locus presented the lowest forensic efficiency, with a PD value of 0.7590 and a PIC value of 0.5200. The Ho ranged from 0.5930 (TPOX) to 0.9240 (Penta E). The combined PM and combined PE for the 29 STR loci were 1.34 × 10−36 and 0.999 999 999 998, respectively. Pairwise LD tests demonstrated that only 18 pairs (Table S2) of the loci remained in LD for 406 pairwise comparisons after Bonferroni correction (P = 0.05/406 = 0.00012). These significant pairs in LD may be attributed to random sampling errors because the pairs are located on different autosomal chromosomes or chromosome arms.The allele frequencies and population genetics parameters of the 29 STR loci are summarized in Table S1. We detected 403 alleles across the 29 STR loci, with the largest number of 50 variants at the P-value) in the Investigator HDplex kit between the Hunan Han population and previously published data of other ethnic populations at the same loci. Evident differences (P < 0.05) were found between the Hunan Han and the Dutch [D3S1744, P = 0.0723) [D3S1744, P = 0.1267 and D8S1132, P = 0.2776) [D2S1360, D3S1744, D10S2325, SE33 and D12S391 with the Hebei Han [D4S2366, D8S1132, SE33 and D12S391 with the Sichuan Han [D3S1744, D10S2325, SE33 and D12S391 with the Shanghai Han [Table S3 shows the locus-by-locus population differences   and Swedhuan Han ; and at ghai Han . Table SD7S820, Penta E and TH01), Huai'an Han [Penta E), Yangzhou Han [D19S433, D3S1358 and TH01) and Taizhou Han [D16S539, Penta D and TH01) populations. Table S6 and Figure S2 present the genetic distances and phylogenetic tree between the Hunan Han and other populations, respectively. The minimum genetic distance and close genetic relationship were detected between the Hunan Han and Guangdong Han (0.0040) populations. The largest distance was found between the Hunan Han and Liaoning Manchu (0.0618) populations.Our results for 19 STR loci in the Goldeneye™ 20A kit compared with other populations are also summarized. Table S5 shows that the distribution of alleles for the Hunan Han versus Suzhou Han , Henan Hi'an Han  (except zhou Han  (except zhou Han  (except In summary, data suggested that the use of 29 STR loci could provide highly informative polymorphic data for forensic identification and establish a database for the Hunan Han group in China. The data also demonstrated that the distribution of allele frequencies varied in different areas or even in the same area for different subpopulations. Close genetic distance and genetic relationship were noted among regions separated by small distances because of their geographic proximity and high gene mobility.This paper strictly follows all the guidelines for publication of population data as required by this target journal.


Parkinson's disease (PD) is a neurodegenerative disease associated with progressive and inexorable loss of dopaminergic cells in Substantia Nigra pars compacta (SNc). Although many mechanisms have been suggested, a decisive root cause of this cell loss is unknown. A couple of the proposed mechanisms, however, show potential for the development of a novel line of PD therapeutics. One of these mechanisms is the peculiar metabolic vulnerability of SNc cells compared to other dopaminergic clusters; the other is the SubThalamic Nucleus (STN)-induced excitotoxicity in SNc. To investigate the latter hypothesis computationally, we developed a spiking neuron network-model of SNc-STN-GPe system. In the model, prolonged stimulation of SNc cells by an overactive STN leads to an increase in ‘stress' variable; when the stress in a SNc neuron exceeds a stress threshold, the neuron dies. The model shows that the interaction between SNc and STN involves a positive-feedback due to which, an initial loss of SNc cells that crosses a threshold causes a runaway-effect, leading to an inexorable loss of SNc cells, strongly resembling the process of neurodegeneration. The model further suggests a link between the two aforementioned mechanisms of SNc cell loss. Our simulation results show that the excitotoxic cause of SNc cell loss might initiate by weak-excitotoxicity mediated by energy deficit, followed by strong-excitotoxicity, mediated by a disinhibited STN. A variety of conventional therapies were simulated to test their efficacy in slowing down SNc cell loss. Among them, glutamate inhibition, dopamine restoration, subthalamotomy and deep brain stimulation showed superior neuroprotective-effects in the proposed model. There is a long tradition of investigation into the etiology and pathogenesis of Parkinson's Disease (PD) that seeks to link molecular  Hwang,  and subcThe first approach believes the primary factor that causes the degeneration of dopaminergic neurons of Substantia Nigra pars compacta (SNc) is its high metabolic requirements. SNc neurons are one of the most vulnerable and energy consuming neuronal clusters, due to their structural and functional properties. Here, we have listed down some of the plausible factors which make SNc cells to be most susceptible.Complex axonal arbors: Large axonal arborisation which requires large amounts of energy to drive currents along these axons  in PD causes excessive release of glutamate to the SNc, which in turns causes degeneration of SNc neurons by glutamate excitotoxicity  with a time step (dt) of 0.1 s. The average time for 50-s simulation was around 10 h, and it reduced to 5 h when ran on GPU card (Nvidia Quadro K620).All the nuclei were modeled as Izhikevich 2D neurons . All thevx) and the other one for membrane recovery variable (ux).Computational neuroscientists are often required to select the level at which a given model of interest must cast, i.e., biophysical-level, conductance-based modeling level, spiking neuron-level or rate-coded level. Biophysical models capture a more biologically detailed dynamics but are computationally expensive whereas rate-coded, point-neuron models are computationally inexpensive but possess less biologically detailed dynamics. To overcome this predicament, Izhikevich  developex at location , respectively, vpeak is the maximum membrane voltage set to neuron (+30 mV) with x being GPe or SNc or STN neuron.where, The presence of excitatory synaptic connectivity from STN to SNc was observed from anatomical and electrophysiology studies  and ERecep is the receptor associated synaptic potential (Recep = NMDA/AMPA/GABA). The time constants of NMDA, AMPA, and GABA in GPe, SNc, and STN were chosen from Götz et al. , p, q), Rm is the variance of Gaussian, Am is the strength of lateral synapse, m = GPe or SNc or STN.where, Several experimental studies demonstrate dopamine-dependent synaptic plasticity in STN . However, in the case of SNc and GPe it is contrary, that is, all the neurons will not tend to fire together as the lateral connection weights increases (due to inhibitory synapses). Lateral strength was modulated by DA as follows,cdstn, cdgpe, and cdsnc were the factors by which dopamine affects the lateral connections in STN, GPe, and SNc populations, respectively, DAs(t) is the instantaneous dopamine level which is the spatial average activity of all the neurons in SNc.where, cd2), which regulates the effect of DA on synaptic currents of GPe, SNc, and STN. As observed in Kreiss et al. , (cd2) is the parameter that affects the post-synaptic current, DAs(t) is the instantaneous dopamine level which is the spatial average activity of all the neurons in SNc.where, STN:i, j) is the summation of lateral glutamatergic input from other STN neurons considering both NMDA and AMPA currents and the GABAergic input from the GPe neurons.The total synaptic current received by a STN neuron at lattice position  is the summation of the lateral GABAergic current from other GPe neurons and the glutamatergic input from the STN neurons considering both NMDA and AMPA currents.The total synaptic current received by a GPe neuron at lattice position  is the summation of the lateral GABAergic current from other SNc neurons and the glutamatergic input from the STN neurons considering both NMDA and AMPA currents.The total synaptic current received by a SNc neuron at lattice position  at time t due to excess firing is calculated as,The stress of each SNc neuron at lattice position  at time t, calculated with a fixed sliding window Δt (1 s)  crosses certain threshold (Sthres) then that particular SNc neuron will be eliminated . The following equation was used to estimate the number of SNc cells (Nsc(t)) for a given course that survived after a given time t.For a given course of SNc cell loss, the half-life is the time taken for half of the SNc cells to be lost (sec−1), t = 0.where, λ is the rate of degeneration  was calculated by the following equation,The instantaneous rate of degeneration λ Inactivation of NMDA (N-methyl-D-aspartate), AMPA  propanoic acid) or excitatory metabotropic glutamate (Group-I - mGluR1/5) receptors (mGluR) by glutamate antagonists, and (2) Activation of metabotropic glutamate  receptors by glutamate agonists. NMDA antagonist MK-801 showed reduction of SNc cell loss in the neurotoxic rats  is the instantaneous change in synaptic weight of STN to SNc based on the number of surviving SNc neurons at time tNsc(t) is the instantaneous number of surviving SNc neurons, GI is the proportion of glutamate inhibition, Ni is the number representing SNc cell loss 〈i = 25% | 50% | 75%〉 at which therapeutic intervention was employed. In the present study, we have considered 25% (cells lost = 16), 50% (cells lost = 32) and 75% (cells lost = 48) SNc cell loss as early, intermediate and late stages of disease progression, respectively.where, The neuroprotective effects of DA agonists therapy are thought to be due to one or more of the following mechanisms: (1) L-DOPA sparing, (2) Autoreceptor effects, (3) Antioxidant effects, (4) Antiapoptotic effects, and (5) Amelioration of STN-mediated excitotoxicity  is the instantaneous change in dopamine level based on the number of surviving SNc neurons at time tNsc(t) is the instantaneous number of surviving SNc neurons, DAs(t) is the instantaneous dopamine signal from the SNc neurons, δDAA is the proportion of dopamine content restoration, Ni is the number representing SNc cell loss at which therapeutic intervention was employed.where, Subthalamotomy is still quite a common treatment amongst patients in advanced stages of PD where patients stop responding to L-DOPA (wearing-off) or chronic L-DOPA therapy results in motor complications such as L-DOPA Induced Dyskinesias (LID)  is the instantaneous number of surviving SNc neurons, Ni is the number representing SNc cell loss at which therapeutic intervention was employed.where, DBS therapy is preferred over ablation therapy of STN due to the potentially irreversible damage to the stimulated brain region in ablation therapy. It has been reported that long-term stimulation (DBS) of STN results in the slowdown of the progression of SNc cell loss in animal models , frequency DBS) were adjusted by using clinical settings as a constraint  was generated as the following,In the present study, the current pulses which given to neuronal network are in the form of monophasic and biphasic waveforms. The monophasic current pulse  was generated as the following,The biphasic current pulse  and neuronal position  increased which was modeled as a Gaussian neighborhood  and the spread of stimulus current was controlled by the width of the Gaussian (σ).The influence of stimulation on a particular neuron will depend on the position of the stimulation electrode in the neuronal network Cogan, . The effi, j) considering lattice position  as the electrode contact point at time t, x =  (Py(t) is the current pulse at time t for y = , σDBS−STN is used to control the spread of stimulus current in STN network.where, DBS was implemented in the proposed excitotoxicity model by the following criterion,i, j) based on the number of surviving SNc neurons at time t, Nsc(t) is the instantaneous number of surviving SNc neurons, Ni is the number representing SNc cell loss at which therapeutic intervention was employed.where, The mechanism of how DBS alleviates advanced PD symptoms is not precise. One of the theories behind the therapeutic effect of DBS is activation of afferent connections of STN which results in antidromic activation of cortical, GPi or GPe neurons  considering lattice position  as the electrode contact point, x =  (Py(t) is the current pulse at time t for y = , ADBS−GPe is the portion of DBS current pulse amplitude given as antidromic activation to GPe neurons, σDBS−GPe is used to control the spread of stimulus current in GPe ensemble.where, The DBS therapy with antidromic activation was implemented in the proposed excitotoxicity model by the following criterion,i, j) considering lattice position  as the electrode contact point with antidromic activation PerAA is the proportion of ADBS−STN applied as ADBS−GPe, Nsc(t) is the instantaneous number of surviving SNc neurons, Ni is the number representing SNc cell loss at which therapeutic intervention was employed.where, In-vitro recordings observed depression in the synapse of STN neurons with SNc and is believed to be due to the delivery of continuous high-frequency stimulation pulses .where, WASF is the weight matrix based on the percentage of axonal and synaptic failures (PerASF).where, We analyzed the dynamics of the network (STN-GPe-SNc) by firing frequency  at time t was calculated with a fixed sliding window Δt (0.1 s) which is similarly to Equations (17), (18). The mean firing rate of the population of neurons is simply the average of instantaneous mean firing rate across the number of neurons and the simulation time.The spike-count firing rate is the measure of the number of action potentials for a given duration of time  in the population of neurons within a network Golomb, . We had Rx(t) is the instantaenous synchronization measure (0 ≤ Rx(t) ≤ 1), x being GPe or SNc or STN neuron, N is the number of neurons in the network, θ(t) is the instantaneous average phase of neurons, ϕj(t) is the instantaneous phase of jth neuron, tj, k and tj, k+1 are the spike times of kth and (k+1)th spike of jth neuron, respectively, Tj, k∈.where, i, j) across time by following equation , three (triplet), four (quadruplet) or many spikes in it , Var is the variance of the spike times, E is the expected value (mean) of the spike times, ti, j, k, ti, j, k+1 and ti, j, k+2 are the spike times of kth, (k+1)th and (k+2)th spike of a neuron at lattice position , respectively.where, We have investigated the Izhikevich parameters of STN, SNc and GPe which were chosen from the literature  and high-frequency regular phasic or burst firing (~20 Hz) .From the simulated results, it is clear that as DA level increases the mean firing rate decreases in STN, increases in GPe and decreases in SNc . The netSTN-GPe dynamics is known to play an important role in PD pathological oscillations that are thought to be strongly related to the cardinal symptoms of PD  was fixed, and there was no SNc cell loss due to stress  which acts like an apoptotic threshold  which is analogous to the apoptotic threshold and is assumed to be dependent on the amount of available energy to the cell  which is analogous to synaptic modification and is assumed to be modulated by the excitatory drive from STN to SNc  increases as the stress threshold increases (Simulation results showed that the time taken for 50% SNc cell loss (ncreases . The ratncreases . These rncreases .t1/2) decreases as the STN-SNc synaptic weight increases (Simulation results showed that time taken for 50% SNc cell loss (ncreases . The ratncreases . These rncreases .We now extend the proposed excitotoxic model to study the effect of various therapeutic interventions on the progression of SNc cell loss. The following three types of interventions which were simulated: (1) drugs, (2) surgical interventions, and (3) Deep Brain Stimulation (DBS).The effect of glutamate agonists and antagonists on the progression of SNc cell loss was implemented in the manner specified in the methods section. The onset of glutamate therapy at different stages of SNc cell loss showed that cell loss was delayed or halted . For theThe effect of dopamine agonists on the progression of SNc cell loss was also implemented in the manner specified in the methods section. The onset of dopamine agonist therapy at different stages of SNc cell loss showed that the progression of cell loss was only delayed . For theThe effect of subthalamotomy on the progression of SNc cell loss was implemented in a way described in the methods section. The onset of STN ablation therapy at different stages of SNc cell loss showed that progression of cell loss was delayed or halted . The neuThe effect of deep brain stimulation on the progression of SNc cell loss was implemented in the way described in the methods section. Along with the stimulation of STN, the inhibitory drive to STN through the afferent connections as result of antidromic activation of the GPe population and the synaptic depression in STN as result of increased axonal and synaptic failures in STN were incorporated in the model.As specified earlier, different stimulation configurations and stimulus waveforms were implemented while exploring the optimal DBS parameters for therapeutic benefits. The STN population response for different types of DBS protocol was simulated. To study the neuroprotective effect, stimulation parameters which reduce the STN overactivity  of STN results in the slowdown of the SNc cell loss in animal models  ratio), is sensitive enough to alter the model results is a constant parameter. In order to achieve variability in this parameter, astrocytic and vascular network-level models can be introduced. With the astrocyte layer introduced, the effect of astrocytes on the therapeutic effect of DBS can be explored  learning in STN population for the long-term effect of DBS .The code of the proposed excitotoxicity model is available on ModelDB server (McDougal et al., VM and VC: conceived, developed the model and prepared the manuscript. AM: conceived and developed the model. SR: prepared the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Eupeodes corollae, is a worldwide natural enemy of aphids and a plant pollinator. To provide insights into the biology of this species, we examined its population genetic structure by obtaining 1.15-GB random genomic sequences using next-generation sequencing and developing genome-wide microsatellite markers. A total of 79,138 microsatellite loci were initially isolated from the genomic sequences; after strict selection and further testing of 40 primer pairs in eight individuals, 24 polymorphic microsatellites with high amplification rates were developed. These microsatellites were used to examine the population genetic structure of 96 individuals from four field populations collected across southern to northern China. The number of alleles per locus ranged from 5 to 13 with an average of 8.75; the observed and expected heterozygosity varied from 0.235 to 0.768 and from 0.333 to 0.785, respectively. Population genetic structure analysis showed weak genetic differentiation among the four geographical populations of E. corollae, suggesting a high rate of gene flow reflecting likely widespread migration of E. corollae in China.The hoverfly,  Eupeodes corollae is one of the most common hoverflies with a worldwide distribution [ribution , 2. The ribution –5 while ribution –8. Many ribution . UnderstCheilosia longula [Blera fallax [Sphaerophoria scripta and Episyrphus balteatus [E. balteatus and Scaeva selenitica, are only partially migratory [Hoverflies migrate seasonally as revealed by radar monitoring  and isot longula , Blera fa fallax , Sphaeroalteatus , populatalteatus , 19. Howigratory .E. corollae is a highly migratory species in Europe [E. corollae remains unclear in other areas. Eupeodes corollae is commonly found across China, but the ecology and biology of this species has rarely been studied [E. corollae in China. First, we obtained random genomic sequences of E. corollae using next-generation sequencing and developed an effective and informative set of microsatellite markers of E. corollae. We used this novel set of microsatellite markers to investigate the genetic structure of four E. corollae populations collected from four representative regions across China.Previous studies reported that n Europe –23, but  studied . In thisE. corollae was used for generating genome sequences. Four field populations of E. corollae were collected from China in March to July 2017 .A male adult from a laboratory (Sichuan Academy of Agriculture Sciences)-reared line of uly 2017 , Fig 1A.The extracted genomic DNA from a laboratory-reared individual was used in constructing a high-throughput sequencing library with 500-bp insert size using the Illumina TruSeq DNA PCR-Free HT Library Prep Kit . The prepared library was sequenced on an Illumina Hiseq4000 Sequencer using the Hiseq Reagent Kit v3  by Beijing BerryGenomics Co., Ltd. The paired-end 150 bp raw data were trimmed by removing the low quality reads using Trimmomatic 0.36  and thenE. corollae [Microsatellite markers were developed from genome sequences as in previous publications –30. MSDBcorollae . QDD wascorollae . The outcorollae , 34: (i)2O, 0.08 μl forward primer, 0.16 μl reverse primer and 0.32 μl universal primer labeled with fluorescence . The PCR protocol was set as: 5 min for 95°C, 35 cycles of amplification with 95°C for 30s, 56°C for 40s, and 72°C for 40s. Final extension was with 72°C for 15 min. PCR products were analyzed on an ABI 3730xl DNA Analyzer  using the GeneScan 500 LIZ size standard . Genotyping was conducted by GENEMAPPER 4.0 . Those primer pairs with amplification efficiency lower than 75%, showing monomorphism in eight individuals, or producing more than two peaks (non-specific amplification) were discarded.After screening primers from the QDD program, a universal primer (CAGGACCAGGCTACCGTG) was added to the 5′ end of each selected forward primer to allow efficient combining with the fluorescent label . AmplifiIS) and pairwise population differentiation (FST). Allele frequencies, expected heterozygosity (HE) and observed heterozygosity (HO) were calculated with the macros Microsatellite Tools [GENEPOP version 4.0.11  was usedte Tools .Population genetic structure was analyzed by STRUCTURE version 2.3.4 . The cluE. corollae was estimated to be 12,315 Mb. A total of 51.53 Gb paired-end (PE) sequences  was obtained. Trimmed reads were assembled into 2,563,327 scaffolds with a total length of 1.15 Gb ranging from 100 bp to 437.63 KB, with an N50 of 1510 bp. These contigs were used for microsatellite discovery.The genomic size of E. corollae with 5000 (6.32%) dinucleotide repeat (DNR) sites, 29221 (36.92%) trinucleotide repeat (TNR) sites, 30988 (39.16%) tetranucleotide repeat (TTNR) sites, 6635 (8.38%) pentanucleotide repeats (PNR) sites and 7294 (9.22%) hexanucleotide repeat (HNR) sites. The frequency of dinucleotide repeats in E. corollae is unusually low when compared with other insect species such as Grapholita molesta [Aphis glycines (Hemiptera) [Obolodiplosis robiniae (Diptera) [In total 79,138 microsatellite loci were isolated from the randomly sequenced genome sequences of  molesta  (Lepidopmiptera)  and ObolDiptera) , which sDiptera) , 45.E. corollae; six pairs with no polymorphism  and ten pairs  with low amplification efficiency (< 75%) were discarded. The remained 24 primer pairs that generated polymorphic genotypes were used for population-level examination.The QDD program initially generated 18114 primer pairs ; we seleGrapholita molesta [Frankliniella occidentalis [Carposina sasakii [E. corollae  and expected (HE) heterozygosity values ranged from 0.235 to 0.768 and from 0.333 to 0.785, respectively. Four loci  showed a significant gap between observed and expected values, while the inbreeding coefficient (FIS = (HE-HO)/HE) calculated by GENEPOP for these loci was relatively high  in Europe using 12 species-specific microsatellite markers also revealed a lack of genetic differentiation within species [E. corollae in China may be highly mobile. The geographically related pattern of population structure may indicate that migration is restricted by geographical barriers. Our study provides preliminary insight into the biology and ecology of E. corollae. Further denser sampling is required to assess the population genetic structure of this species as well as other approaches to investigate its migration pattern.A lack of population differentiation is common in hoverflies. For example, a previous study on the hoverfly c levels . Another species . High le species . Our stuE. corollae, and help to understand adaptive evolution in this species [Microsatellite markers are popular and powerful DNA markers because they are cost-effective and with a high diversity . With th species .E. corollae at a genome-wide scale which provides genetic markers for population genetic analyses of this species. Our preliminary examination of four geographical populations of E. corollae across China suggested weak but geographically lined population differentiation. The results provide insight into migration of E. corollae in China.We developed 24 microsatellite markers in S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.