Changes in climate extremes pose far-reaching consequences to ecological processes and hydrologic cycles in alpine ecosystems of the arid mountain regions. Therefore, regional assessments in various climates and mountain regions are needed for understanding the uncertainties of the change trends for extreme climate events. The objective of this study was to assess the spatial distribution and temporal trends of extreme precipitation and temperature events responses to global warming on the arid mountain regions of China. Results found that temperature extremes exhibited a significant warming trend, consistent with global warming. Warming trend in autumn and winter were greater than in spring and summer. Besides, precipitation extremes also exhibited statistically increase trend, such as number of days with heavy precipitation and rain day precipitation, etc. The distribution of the number of rainy days was showed a significant increasing trend in many sites, indicating that the increase of rain day precipitation mainly contributed by the increase of single precipitation event duration and moderate-rain days. The greater increasing trend of extreme climate events mainly existed in higher altitudes. This results lend an evidence to earlier predictions that the climate in northwestern China is changing from cold-dry to warm-wet. Thus, extreme climatic events have become of special interest worldwide due to global warming. Changes in, and consequences of temperature and precipitation extremes have been observed at higher rates during the last few decades than earlier, such as heatwave, high temperature, and multi-region heavy rainfall and flood10. These changes resulted not only in direct increases in the annual mean temperatures and precipitation, but also in the frequency and intensity of extreme climatic events, such as high temperatures, rainstorms, and floods. Consequences for society have risen, and included direct or indirect life and property losses due to extreme climatic events11. Consequences of extreme climate events for natural systems included extension of the growing season3, increased incidence of spring drought events12, and adverse effects on soil erosion processes13. Thus, a detailed analysis of the spatial and temporal characteristics of temperature and precipitation extremes is essential for establishing ecosystem management strategies for a changing climate. For example, the number of frost days, duration of warm spells, and changes to the growing season length are important to vegetation growth, water management, and public health14.Climate extremes play an important role in influencing society and natural systems. Given their importance and the uncertainty for prospect of changes in the future, it is very important to understand how and why climate extremes have changed in the past15. High relief and high gradients make mountain ecosystems vulnerable to slight changes in temperature and to extreme precipitation events16, because variations in temperature constitute the most stressful growth-limiting factors, and a challenge for stability and resilience of plant growth17. Spatial or temporal shifts in precipitation regimes would heavily impact the river systems originating in mountain areas, leading to disruptions of the socio-economic structures of people living in the mountains and downstream18. The Qilian Mountains are the source of several semi-arid inland rivers, including the Heihe, Shiyang, and Shule. The annual water yield volume entering the Heihe River is critical to people’s livelihood downstream15, because the Heihe flows through a region of irrigated agriculture and into two lakes in the desert.The Qilian Mountains in northwestern China are typical of the country’s arid mountain regions. Ecological processes and hydrologic cycles in the Qilian Mountains are closely coupled to atmospheric influences and may be highly sensitive to climate change19, while temperature and precipitation controlled the distribution and stocks of soil organic carbon21. In the past 50 years, the average temperature has risen by 0.26 °C / decade in this region, higher than the national rate of 0.14 °C / decade22. Moreover, the frequency of extreme climatic events (e.g. high temperatures and rainstorms) increased in recent years23. Previous studies on climate change in the Qilian Mountains have concentrated on average temperature and average precipitation series25. A detailed analysis of the spatial and temporal variability in temperature and precipitation extremes will further our understanding of climate-change effects on ecological processes and hydrologic cycles in this region.Recent research revealed that growing-season temperatures were an important limiting factor for tree-ring growth in the Qilian MountainsIn this paper, we focused on spatial and temporal variability in daily climate extremes in an arid mountain region to (1) provide a regional assessment of extreme climatic events from 1960 to 2014 for the Qilian Mountains by analyzing the annual spatial and temporal trends in temperature and precipitation extremes, and (2) examine linkages between the extremes and average annual temperature and precipitation.The summary of positive and negative trends for the calculated indicators of climate extremes is shown in Fig. th percentile (TX90p), and warm nights, defined as percent of days when daily minimum >90th percentile (TN90p)  Fig. .Figure 2Almost all of the stations in the Qilian Mountains experienced significantly positive trends in the indices of cold temperature extremes during the past 50 years Figs  and 3. AThe regional averaged time series of indices of temperature extremes in the Qilian Mountains are shown in Figs P < 0.01) for summer days (SU 25), and 0.64 (P < 0.01) for tropical nights (TR 20)  , maximum temperature (TXx), and percentage of days with TX > 90In contrast to warm extremes, indices that were related to cold extremes showed significant decreasing trends Fig. . In partMost of the indicators exhibited significant changes with seasons. Warmest day and warmest night (TXx and TNx) exhibited warming in every season, with significant trends in winter. Percentage of warm days and nights (TX90p and TN90P), and the lowest values of maximum and minimum temperatures (TXn and TNn) increased as well. However, summer and winter showed the greatest number of significant trends. For instance, significant warming trends were observed for daily mean maximum temperature (TXM) and daily mean minimum temperature (TNM) in winter, with rates of 0.99 and 1.55 °C/decade, respectively. It was noteworthy that the rate of TNM was greater than that of TXM. These results revealed that overall increases in mean temperatures may be attributed mostly to increases in mean minimum temperatures (TNM), particularly in winter.DTR, GSL, WSDI and CSDI are the duration indices reflected the time interval. Most stations showed significant decreasing trends in diurnal temperature range (DTR); however, 20% of stations exhibited an increase in DTR Figs  and 6. FAll stations showed positive trends for the growing-season length (GSL), which increased from 1.71 to 4.51 days/decade; this trend was significant for 93% of stations Fig. . Trends P < 0.01) over the past 50 years   decreased by 1.10 days/decade  Fig. , with 67Results revealed that extreme temperature indices correlated well with the daily mean temperature (P < 0.01) Table . The warCold extremes exhibited significant negative correlations with the daily mean temperature ranging from −0.49 to −0.93. Cold nights (TN10p) exhibited the highest correlation (R = −0.93). Furthermore, indices correlated well among themselves. For example, the highest correlation coefficients existed between the number of summer days (SU 25) and the maximum temperature  (R = 0.84), and between the number of tropical nights (TR 20) and warm nights (TN90p) (R = 0.85).Spatial distribution of trends in indices of precipitation extremes are shown in Fig. Almost all precipitation indices, except the number of consecutive dry days (CDD), exhibited increasing trends across the region; however, only a fraction was statistically significant.About 93% of the stations exhibited increases in annual total precipitation (PRCPTOT) over the past 50 years, with 40% being significant at the 5% level Fig. . Areas eThe number of days with heavy R10mm) and very heavy precipitation (R20mm) exhibited significant increasing trends at approximately 47 and 20% of stations, respectively, which were concentrated in the central part of the high altitude areas. Increases in very wet (R95p) and extremely wet precipitation (R99p) were noted at 73 and 80% of the stations, respectively; only 33% were significant at the 5% level for R95p and 7% for R99p, with distribution similar to that of annual total precipitation  were located mainly at the edge of the Qilian Mountains. In contrast, the consecutive wet days (CWD) exhibited no clear spatial trend and few significant trends. The distribution characteristics suggest that, over time, the dry spells became shorter, while the wet spells became longer for most stations.The annual regional averaged total wet-day precipitation (PRCPTOT) and simple daily intensity (SDII) exhibited increasing trends from 1960 to 2014, at rates of 7.60 mm/ decade and 0.24 mm/day/decade, respectively  and very heavy precipitation days (R20mm) paralleled those of other indicators, but the magnitudes and ranges differed Fig. . R10mm aThe duration indices, consecutive wet (CWD) and dry days (CDD), differed in trends across the region. There was a non-significant increase in CWD, and a significant decrease in CDD at rate of −4.35 days/decade Fig. .In general, consistency among indices suggested that average simple daily intensity and precipitation on wet days increased along with the amount of precipitation on extreme precipitation days and periods, but consecutive dry days decreased Fig. . This inThe extreme precipitation indices, except for consecutive dry days (R = −0.29), were highly correlated with total annual precipitation, with correlation coefficients >0.5, and statistical significance level of 0.01 Table .Table 3CExtreme precipitation indices were also correlated. For instance, heavy precipitation days (R10mm) and simple daily intensity (SDII) was highly positively correlated (R = 0.81).1. An arid mountain region is a sensitive area to global warming due to its unique physiographic characteristics and geographic location. The detailed analysis of the characteristics of climate extremes, represented by the variability of those climate extreme indices, substantiates the general understanding of the association of regional climate change to global warming, and thus should contribute to get a deeper understanding of the complex interactions between the regional climate variety and the global climate change28.Climate extremes is exacerbating and triggering due to global warming, including decreasing the frequency of cold days and cold nights and increasing the frequency of warm days and warm nights5.We used 55-years of daily records from 15 stations in the Qilian Mountains to calculate 16 extreme temperature and 10 extreme precipitation indices. Specifically, we summarized positive and negative trends, and the regional averaged trends, and analyzed the spatial changes in these indices. This information can be used to identify indicators that are most representative of extreme climate trends across the Qilian Mountains3 and the arid areas of northwestern China29. These results reveal that overall increases in mean temperatures may be attributed mostly to increases in the mean minimum temperatures, particularly in winter. Because the average minimum increased more than the maximum temperature, the diurnal temperature range (DTR) also decreased. Many studies indicated that DTR decreased under climate change, but the decrease differed among different areas. Thus, DTR decreased by 0.06 °C/decade in the Loess Plateau3, and 0.26 °C/decade in Xinjiang30 both in China; the greatest decrease was reported in summers for the southeastern United States7.Our results indicated increasing trends in temperature and precipitation extremes between 1960 and 2014. The highest increases in daily mean minimum temperature, daily mean maximum temperature, warm days, warm nights, coldest day, coldest night, warmest day and night were observed in winter months. This is consistent with reports from the Loess Plateau19. We found that all stations showed positive trends in GSL, some with substantial increases of 3.22 days/decade. Previous studies in the region revealed a 3.7 days/decade increase in GSL in Picea crassifolia forests during 2001–201122. Increases in growing season length may benefit ecological processes, for example, increasing in growing season length may affect forest productivity, and make the wood production and carbon sinks increased in the long term32, but have negative effects on the hydrologic cycle in the Qilian Mountains; for instance, transpiration in P. crassifolia forests increased by 3–4% as a result of the expansion of the growing season25.Growing season length (GSL) is the most important factor for tree-ring growth in the Qilian Mountains3 and Songhua River Basin12 of China, and the southeastern United States7. The decrease in consecutive dry days indicated that, rather than the length of dry spells in the rainy season, the length of the dry season decreased. Increasing amount of precipitation contributed to the changes in precipitation extreme events.All of the precipitation indices, except consecutive dry days, exhibited increasing trends for the whole region. The distribution characteristics suggested that dry spells became shorter, and wet spells became longer for most stations. The trends in precipitation indices generally agree with previous research in other regions; for example, increases in precipitation extremes were noted for the Loess Plateau7, Europe33, arid areas of northwestern China29, the Loess Plateau of China3, the Yangtze River region during 1962–201134, and globally6. However, the change magnitudes of extreme indices are not quite similar. The reasons for the above results may be an arid mountain region is a sensitive area to global warming due to its unique physiographic characteristics and geographic location. Climate extremes is exacerbating and triggering in the arid mountain region due to global warming.These results are consistent with reports for the southeastern United States26. The impacts of climatic change in an arid mountain region are likely to have significant repercussions not only on ecological and hydrologic processes of the mountains, but also on populated downstream regions that depend on mountain water resources for domestic, agricultural, energy, and industrial purposes.We conclude that the frequency or magnitudes of extreme climatic events exhibit increasing trends across the Qilian Mountain region and support previous predictions that the climate in northwestern China is changing from cold-dry to warm-wet35; these physical mechanisms 36, associated with potential changes in precipitation amount and temperature seasonality, will affect soil moisture, plant growth, groundwater reserves, and the frequency of flood or drought episodes in an arid mountain region. Thus how to gain an in-depth understanding of this complexity is an important item on our future research agenda.The climate trends imply higher rates of evaporation, and a greater proportion of solid to liquid precipitation 37. The area is composed of several parallel mountains and valleys, and stretches for about 850 km east to west, and 200–300 km north to south. The Qilian Mountains are in the transition zone between the influence of the East Asian Monsoon and the Westerlies. Climate is semi-arid to arid, continental mountain in the temperate zone, with cold and dry winters under the influence of the Mongolian anticyclone38. The annual mean temperature ranges from 6 °C at lower elevations to −5 °C at higher elevations. The annual precipitation ranges from 150 mm in the foothills to 800 mm in the high mountains and about 85% of the annual precipitation falls in the growing season from May to September39. Several inland rivers originate in the Qilian Mountains from precipitation and snowmelt water.The Qilian Mountains are located at in the northeastern margin of the Tibetan Plateau, adjoining the Badain Jaran Desert and the Tengger Desert to the north, the Qaidam Basin to the south, the Taklimakan Desert and the Tarim Basin to the west and the Loess Plateau to the easthttp://www.nmic.gov.cn).This study utilized data from 15 meteorological stations that recorded daily precipitation, and maximum and minimum temperatures in the Qilian Mountains [Details of the selection of meteorological stations are in Supplementary Figure http://etccdi.pacificclimate.org/software.shtml), which can automatically check for errors . Homogeneity assessments of the recorded daily temperature and precipitation data sets were conducted by software packages (software and documentation available for download from http://etccdi.pacificclimate.org/software.html): RHtests for temperature and RHtests-dlyprcp for precipitation series. The purpose of this assessment is to detect artificial shifts due to inevitable changes in the instruments, relocations, environment, and procedures during data collection40. The application consists of two steps: first, all missing values are replaced into an internal format that the software recognizes , but time series with more than 20% missing values within the analysis period 1960–2014 were excluded, and if precipitation values were below 0 mm, or minimum temperature exceeds maximum temperature, or observation were more than four standard deviations from the mean, they were replaced into NA. And then using the RHtest and RHtests-dlyprcp to perform the two phase regression to detect multiple step change points that could exist in a time series, which is a method to assess data homogeneity40. RHtests was applied based on the penalized maximal t test and the penalized maximal F test, which are embedded in a recursive testation algorithm, with the lag-1 autocorrelation of the time series being empirically accounted for. The RHtests-dlyPrcp was similar to the RHtests, except that it was specifically designed for the homogenization of daily precipitation data time series. It was based on the transPMFred algorithm, which integrates a data-adaptive Box–Cox transformation procedure into the PMFred algorithm40. After quality control and homogeneity assessments, RClimDex was used to calculate indices of extreme climate from the daily data.Data quality was checked with RClimDex package  is given byS is expected to have a mean of zero and a variance ofp if |Z| > Z1−p/2) is the value of the standard normal distribution with a probability of exceeding p/2. A positive value of Z indicates an upward trend, while a negative value represents a downward trend in the data.The Mann–Kendall test has two parameters that are important for trend detection. They are: the significance level, which indicates the trend’s strength, and the slope magnitude estimate, which indicates the direction as well as the rate of change. Under the null hypothesis, there is no trend in the data, and the distribution β, or the slope (change per unit time) is estimated using a non-parametric method proposed by Sen46 and extended by Hirsch et al.47:1 < k < j < n, or the slope estimator β is the median of all possible combinations of pairs for the whole data set.If a linear trend is present, the magnitude of the trend, xr,t is the regional averaged index for year t; xi,t is the index for station i in year t; xi is the index mean at station i over the period 1960–2014; nt is the number of stations with data in year t4.The regional averaged series for each index was calculated as follows:7. Therefore, we investigated seasonal trends in climatic extremes to reveal more detail about intra-annual dynamics (Table Changes in the occurrence of temperature extremes during certain times of the year can have important implications for some regionscs Table . SeasonsThe correlation analysis was used to examine the relationships between extreme temperature indices and the daily mean temperature, and extreme precipitation indices and total annual precipitation.Supplementary Figure S1 and Supplementary Table S1


Uncertainty remains about the true extent by which alcohol consumption causes a number of health outcomes. Genetic variants, or combinations of variants built into a polygenic risk score (PGRS), can be used in an instrumental variable framework to assess causality between a phenotype and disease outcome of interest, a method known as Mendelian randomisation (MR). We aimed to identify genetic variants involved in the aetiology of alcohol consumption, and develop a PGRS for alcohol.Repeated measures of alcohol consumption from mothers and their offspring were collected as part of the Avon Longitudinal Study of Parents and Children. We tested the association between 89 SNPs  and repeated measures of alcohol consumption, separately in mothers (from ages 28–48) and offspring (from ages 15–21) who had ever reported drinking. We modelled log units of alcohol using a linear mixed model and calculated beta coefficients for each SNP separately. Cross-validation was used to determine an allelic score for alcohol consumption, and the AVENGEME algorithm employed to estimate variance of the trait explained.p = <0.0001) in the mothers. No SNPs showed evidence for association in the offspring after correcting for multiple testing. The optimal allelic score was generated using p-value cut offs of 0.5 and 0.05 for the mothers and offspring respectively. These scores explained 0.3% and 0.7% of the variance.Following correction for multiple testing, one SNP (rs1229984) showed evidence for association with alcohol consumption (β = -0.177, SE = 0.042, Our PGRS explains a modest amount of the variance in alcohol consumption and larger sample sizes would be required to use our PGRS in an MR framework. Alcohol is a leading preventable cause of ill health in Europe , with Euet al (2014)  Identifying genetic variants that could plausibly be associated with alcohol consumption from genome wide association studies (GWAS) and the functional literature, [b] Estimating their association with alcohol consumption in mothers (heritability is estimated to be higher after college years) and offspring from the Avon Longitudinal Study of Parents and Children (ALSPAC), and [c] Creating PGRSs  and estimating the proportion of variance explained for both mothers and offspring’s phenotypes. We fitted both cross-sectional and longitudinal models, thus taking advantage of the repeated measures of alcohol consumption available at different time points in life to minimise noise in the definition/reporting of alcohol use.Data were taken from the Avon Longitudinal Study of Parents and Children (ALSPAC), a longitudinal study situated in South West England. ALSPAC recruited 14,541 pregnant women between 1991 and 1992, with over 14,062 live births resulting from these pregnancies. Comparison with the 1991 census shows the sample was broadly representative of the British population . Both moWe used data from 10 postal questionnaires completed by the mothers in the cohort over 18 years (ranging from a mean age of 28 at baseline to a mean age of 48 at 18 years post pregnancy) and questionnaires completed by ALSPAC offspring over 6 years (ranging from age 15 to 21 years). All individuals who had available genetic data (outlined below) and answered alcohol related questions at these time points were included in the analysis  glass of wine or a single (25ml) measure of spirit”, with each of these drinks containing approximately one UK unit of alcohol. Weekly alcohol consumption was treated as a continuous measure of UK units.How often have you drunk alcoholic drinks”. Participants selected one of the following responses: “Never”; “Less than 1 glass a week”; “At least 1 glass a week”; “1–2 glasses every day”; “At least 1–9 glasses every day”; “At least 10 glasses every day”. At zero years post pregnancy, this question related to the amount of alcohol consumed before the current pregnancy. At four years post pregnancy and between seven and 12 years post pregnancy, weekly alcohol consumption was calculated from self-reported beers/ciders, wines, spirits, other alcohol or low alcohol beverages consumed on each day of the previous week. Weekly alcohol consumption at 18 years post pregnancy was calculated by multiplying the number of days the mother generally drank by the number of drinks consumed on a typical drinking data ; highest level of education ; cannabis, antidepressant, amphetamine and opiate use in the past year (No/Yes) at one, two, three and six years post pregnancy.Offspring’s covariates included: Sex; ethnicity (white/other); DSM-IV classification of anxiety, depression, conduct disorder and ADHD at ages 7, 10, 13 and 15 years; Binge eating in the past year at ages 13, 14, 16 and 18 years; antisocial behaviour at ages 11, 13, 14, 15, 18, 19 and 21 years ; and mothers highest level of education .ALSPAC offspring were genotyped using the Illumina HumanHap550 quad chip genotyping platforms by 23andMe subcontracting the Wellcome Trust Sanger Institute, Cambridge, UK and the Laboratory Corporation of America, Burlington, NC, US. ALSPAC mothers were genotyped using the Illumina human660W-quad array at Centre National de Génotypage (CNG). Following quality control  9237 offspring and 8196 mothers were retained with 477482 SNP genotypes in common between them. SNPs were flipped to forward strand and haplotypes were estimated on the combined sample using ShapeIT (v2.r644). Imputation was performed using Impute V2.2.2 against all 2186 reference haplotypes (including non-Europeans) in the December 2013 release of the 1000 genomes reference haplotypes (Version 1 Phase 3). This gave 8237 eligible offspring and 8196 eligible mothers with available genotype data.a priori evidence of association with alcohol consumption/ potentially associated with alcohol consumption, we searched the NHGRI-EBI GWAS catalogue [−5. We supplemented this list with a search for candidate gene or functional studies of alcohol consumption, identifying a further 23 SNPs, bringing the total number of SNPs to 91 . We then extracted genotype data for the 91 SNPs from ALSPAC. 31 SNPs were directly genotyped. For the remaining 60 SNPs, imputed genotypes with imputation r2>0.8 were available (two SNPs with imputation r2<0.8 were excluded). 89 SNPs were therefore included in the analyses. A full summary of selected SNPs is provided in To identify SNPs with some atalogue  for publAll analyses were conducted using Stata13 . We testWe used the Bonferroni method to correct for multiple testing. Evidence for association was taken at p = 0.00056 (0.05/89) for repeated measures analyses and p = 0.000037 (0.05/number of time points*89) for the cross sectional analyses.To determine a PGRS for alcohol consumption, we randomly separated the individuals into 80% training and 20% discovery sets. Repeated measures of log units were modelled in the training set, separately for each of the 89 SNPs with beta coefficients, their standard error and corresponding p-values recorded. Using p-value thresholds of 0.01, 0.05, 0.1, 0.2, 0.4 and 0.5 for inclusion, we then created a weighted PGRS for each threshold. These scores were used to predict repeated measures of log-units in the 20% discovery set, with R-squared recorded for the score corresponding to each p-value threshold. The process was repeated five times, and the p-value threshold with the highest R-squared was taken as optimal. This was done independently for mothers and offspring data. Finally, the AVENGEME  algorithTo demonstrate a possible use of the PGRS, we tested the association between our PGRS on proxy measures for cardiovascular disease  which weThe above statistical methods were used to conduct the following sensitivity analyses using the mother’s data: (a) excluding individuals who were pregnant at completion of the questionnaire; and (b) excluding weekly alcohol consumption measures at four, seven, eight and 12 years post pregnancy in the mothers as these questions were phrased differently from the other time points .For the ALSPAC mothers, there were 67988 questionnaire responses to units consumed, between the ages of 28 to 48 years. Alcohol consumption increased over the course of the questionnaires and over age .p value < 0.05. Following correction for multiple testing, one SNP (rs1229984) showed evidence for association with alcohol consumption  . This SNP also showed evidence for association (multiple testing threshold) at baseline  and was consistently negatively associated with alcohol consumption across all time points  . In the e points .   .The best allelic score was generated using p value cut offs of 0.5 and 0.05 for the mothers and offspring, respectively, which resulted in including a total of 42 SNPs (out of 89) for mothers and 6 SNPs (out of 89) for offspring. For the ALSPAC mothers, the variance in units of alcohol per week explained was 0.3% (95% CI 0.13% to 0.76%). For the ALSPAC offspring the variance explained was 0.66% (95% CI 0.22% to 1.3%). Neither of the PGRS showed evidence for association with any of the confounders, i.e. there was no evidence to suggest that the PGRS could be violating the second assumption of instrumental variables  and therefore being invalid as an instrumental variable to proxy for alcohol intake .When modelling the effect of our PGRS on cardiovascular disease risk factors, there was evidence for association between the offspring PGRS and offspring diastolic blood pressure in the expected direction . However, our estimates of the association between the offspring PGRS and both HDL cholesterol and systolic blood pressure provided no strong evidence of association . Similarly, there was no statistical evidence for association between the mothers PGRS and any of the cardiovascular disease risk factors .The aim of this study was to develop a polygenic risk score for alcohol consumption, in view of using this to assess the causal impact of alcohol on health related outcomes such as cardiovascular disease. Literature searches of published GWAS and functional studies identified 89 candidate SNPs that had previously shown some evidence of association with alcohol-related phenotypes. Using repeated measures analysis of alcohol behaviour over the course of a 20 year period, we found strong evidence confirming that rs1229984 plays a role in alcohol consumption, confirming previous results [2 = 0.3% from the mothers PGRS result), or 54,200 [The score could in principle be used to conduct MR analyses for example in the field of cardiovascular disease (CVD), although large sample sizes would be required. The British Heart Foundation estimates that there are 7m people living with CVD in the UK (~10% of the population) , if the S result). SimilarS result), with 2.ALSPAC is a well characterised birth cohort with repeated measures of alcohol use, which have been used in several other studies of substance use –41. MoreThere are also some limitations which need to be considered when interpreting the results of this study. First, the set of SNPs identified through searches came from GWAS analyses of alcohol dependence –24 and sADH1B) was consistently identified as being associated with alcohol consumption in ALSPAC mothers. This SNP has previously been used in an MR framework, after Holmes and colleagues validated it as a genetic instrument by providing solid evidence for association with various alcohol phenotypes  in a sample of >200,000 participants [Burgess and colleagues suggested that variants with known biology are better for use in MR studies . The undicipants . In theiThe set of SNPs included in the mothers PGRS and offspring PGRS were different, possibly due to age and gender effects. Previous literature has suggested that the heritability of alcohol consumption changes across the life course. Estimates start to increase at the age of 15 years and peak in the mid-20’s –17. It iPGRS have previously been used in an MR framework to evaluate the causal effect of a number of traits/exposures, with proportions of the variance explained in the trait in the range or 1.5–3% Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S6 Table(DOCX)Click here for additional data file.S7 Table(DOCX)Click here for additional data file.


Arini, which has recently been subjected to a taxonomic revision. The previously broadly defined Aratinga genus of this tribe has been split into the ‘true’ Aratinga and three additional genera, Eupsittula, Psittacara and Thectocercus. Popular markers used in the reconstruction of the parrots’ phylogenies derive from mitochondrial DNA. However, current phylogenetic analyses seem to indicate conflicting relationships between Aratinga and other conures, and also among other Arini members. Therefore, it is not clear if the mtDNA phylogenies can reliably define the species tree. The inconsistencies may result from the variable evolution rate of the markers used or their weak phylogenetic signal. To resolve these controversies and to assess to what extent the phylogenetic relationships in the tribe Arini can be inferred from mitochondrial genomes, we compared representative Arini mitogenomes as well as examined the usefulness of the individual mitochondrial markers and the efficiency of various phylogenetic methods.Conures are a morphologically diverse group of Neotropical parrots classified as members of the tribe cytb, nd2 and nd6 genes, which are commonly used in parrot phylogenies. The strongest phylogenetic signal was found in the control region and RNA genes. However, these markers cannot be used alone in inferring Arini phylogenies because they do not provide fully resolved trees. The most reliable phylogeny of the parrots under study is obtained only on the concatenated set of all mitochondrial markers. The analyses established significantly resolved relationships within the former Aratinga representatives and the main genera of the tribe Arini. Such mtDNA phylogeny can be in agreement with the species tree, owing to its match with synapomorphic features in plumage colouration.Single molecular markers produced inconsistent tree topologies, while different methods offered various topologies even for the same marker. A significant disagreement in these tree topologies occurred for Arini and resolve the inconsistencies between the taxa that were previously classified as the broadly defined genus Aratinga. Although gene and species trees do not always have to be consistent, the mtDNA phylogenies for Arini can reflect the species tree.Phylogenetic relationships inferred from single mitochondrial markers can be incorrect and contradictory. Therefore, such phylogenies should be considered with caution. Reliable results can be produced by concatenated sets of all or at least the majority of mitochondrial genes and the control region. The results advance a new view on the relationships among the main genera of The online version of this article (doi:10.1186/s12862-017-1012-1) contains supplementary material, which is available to authorized users. Arinae [Conuropsis [Arinae still remains questionable and controversial. The current formal separation of some Arinae taxa is often based on quite intuitive and subjective morphological differences. Molecular analyses do not provide conclusive results and the phylogenetic position of many Arinae taxa varies depending on the published molecular datasets [Arinae is divided into the following tribes: [Amoropsittacini, Androglossini, Arini and Forpini. Among them, the Arini is represented by the largest number of genera. Of the 19 currently recognized genera, nine  form a morphologically diverse group called conures [The most species-rich group of the New World parrots is the subfamily Arinae , 2. In tnuropsis . A remarnuropsis . Understdatasets , 6. At p tribes:  Amoropsi conures .Aratinga is arguably the most controversial albeit noteworthy. Until now, the genus has been defined in different ways. One of the researchers identified 23 species within the genus [Aratinga acuticaudata and found that the species is more closely related to the representatives of three monotypic genera  than to any other member of the genus Aratinga. Aratinga acuticaudata was formerly included by Silveira et al. [Aratinga, drawing on only some morphological features. The delimitation of the clade consisting of Aratinga acuticaudata, two other conures  and the smallest macaw (Diopsittaca nobilis) resulted in reviving the monotypic genus Thectocercus for the former Aratinga acuticaudata. By virtue of the molecular revision of the broadly defined genus Aratinga, two additional genera, Eupsittula and Psittacara, were elevated for two of three previously recognized Aratinga lineages, whereas the ‘true’ Aratinga genus was established as the third lineage [From the taxonomical point of view, the genus he genus as well ahe genus  called ihe genus  providedhe genus , 11, 12.he genus  supplieda et al.  in one o lineage .Aratinga comprises six South American species: auricapillus, jandaya, maculata, nenday, solstitialis and weddellii. Aratinga solstitialis and Aratinga nenday are two species which are predominantly used as representatives of this genus in parrot phylogenies. Although the taxonomic status of the genus Aratinga within Arini tribe was clarified in molecular studies, the relationships of the former Aratinga members still remain debatable and controversial. The most comprehensive and species-rich phylogenetic analysis of the Arinae subfamily, which has been performed so far, classifies the species Aratinga as sister to Eupsittula clade [Aratinga are sister to the extinct Carolina parakeet [Eupsittula [Ara, Cyanopsitta, Orthopsittaca and Primolius [The currently recognized sensu stricto genus la clade . Howeverparakeet , the spepsittula  or macawrimolius , 12. Therimolius  and/or irimolius .Aratinga is still disputable and more molecular data are required to reconstruct their precise and well-resolved phylogeny. It was demonstrated that the complete mitochondrial genomes can provide useful information for the evolutionary studies of many taxa [Arini and solve the controversies about their relationships, we carried out comprehensive phylogenetic analyses based on the representative and complete mitochondrial genomes, applying various methodological approaches. We determined the usefulness and applicability of different phylogenetic methods and various molecular markers in the phylogeny of parrots at the tribe level. Even though various molecular markers  have been used so far in inferring the phylogenetic relationships in parrots, including Arini, a variety of tree topologies have been produced [Arini and checked the consistency between the markers as well as how, on the strength of these markers, the phylogenies correspond with morphology.Summarizing, the phylogenetic position of the present species any taxa . Hence, produced , 11, 12.produced , vertebrproduced –17, amniproduced , insectsproduced , teleostproduced  as well produced , 22 and produced –25. Yet,Arini   analyses in MrBayes  and Phylnd6 were considered as three additional separate partitions because the sequence of this gene showed a biased nucleotide composition in comparison to other PCGs  and pyrimidines (Y) –38. The In MrBayes analyses, two independent runs starting from random trees, each using four Markov chains, were applied. The trees were sampled every 100 generations for 10,000,000 generations. In the final analysis, we selected the last 22,360 to 78,090 trees, depending on the data set, that reached the stationary phase and convergence (i.e. when the standard deviation of split frequencies stabilized and was lower than the proposed threshold of 0.01). In PhyloBayes, the number of components, weights and profiles of the applied models were inferred from the data. Two independent Markov chains were run for 50,000 generations with one tree sampled for each generation. Depending on the data set, the last 5000 to 45,000 trees from each chain were collected to compute posterior consensus trees after reaching convergence, when the largest discrepancy observed across all bipartitions (maxdiff) was below the recommended threshold 0.1.All possible tree topologies, corresponding to 945 unrooted and 10,395 rooted trees, were evaluated for the 7-taxa set in TreeFinder and the following PAUP approaches: maximum likelihood (ML), minimum evolution (ME), weighted least squares (WLS) and maximum parsimony (MP). For the 11-taxa set, we set the search depth at 2 in TreeFinder, whereas in PAUP, the final tree was searched from ten starting trees constructed by stepwise and random sequence addition, followed by the tree-bisection-reconnection (TBR) branch-swapping algorithm for ML, ME, WLS and MP methods. To assess the significance of the individual branches, non-parametric bootstrap analyses were performed on 1000 replicates in TreeFinder and PAUP. In the bootstrap analysis, we applied the branch-and-bound algorithm for the 7-taxa set and the TBR algorithm for the 11-taxa set in PAUP.Topology tests were carried out in Consel  assumingArini markers were computed in MEGA package [Codon-based tests of positive selection (Z-test and Fischer’s Exact test) as well as the average evolutionary divergence for the individual  package , 46. The package  between Arini representatives, we computed the mean number of nucleotide substitutions per site  showed the lowest divergence of about 0.11. On the other hand, the most divergent were genes coding for NADH dehydrogenase subunits (nd4 and nd6) and ATP synthase F0 subunit 8 (atp8). These genes showed 0.14 and 0.16 substitutions per site, respectively. The control region (CR) was characterized by the largest divergence, i.e. 0.19.To compare evolutionary divergence among all mitochondrial markers of ten ite Fig. . Generalatp6 and nd2  and r = −0.364, p = 0.137 (for 11-taxa set). Similarly, the correlation between the mean tree distances and the mean number of substitution per site for alignments of individual markers was low and not statistically significant: r = 0.165, p = 0.512 for 7-taxa set and r = −0.150, p = 0.553 for 11-taxa set.One could assume that the consistency of tree topologies is related to the length and the evolutionary rate of molecular markers. However, the correlation between the mean tree distances for individual markers and their lengths was not statistically significant, although it was not unexpectedly negative, namely, the longer the sequence, the smaller the stochastic error: Thectocercus acuticaudatus + Guaruba guarouba clade was recovered by all the methods with the support of 17 or 18 markers. The second most frequent clade of Ara glaucogularis + Primolius couloni was retrieved for 10 to 12 markers by all the methods, with the exception of maximum parsimony. The clade containing the above-mentioned two taxa and Orthopsittaca manilata did not appear in the consensus tree of minimum evolution and neighbour joining trees, and in others was supported by 10 to 13 markers. The other clades were not resolved. It seems to indicate that the phylogenetic signal in individual markers is extremely variable and that the impact of selecting a given tree-building method rather than another is negligible.To check if the different phylogenetic approaches can generate similar topologies, we calculated 50% consensus trees constructed by the individual methods for all markers. However, the consensus trees with 7 taxa were fully polytomous for almost every method used. Only the maximum parsimony method resolved two clades, but with the marginal support of ten markers out of 18. The consensus trees based on the 11-taxa set were also poorly resolved. Only three clades of closely related taxa were produced. nd2 and nd5 genes. More significantly supported clades (with PP > 0.95 or BP > 70%) occurred only for eight markers. The CR trees again had three such clades on average. However, highly supported clades with the third level of significance were found only in CR and tRNA2 trees. Similarly, the trees with 11 taxa based on only the control region contained a substantial number of significant nodes with PP > 0.70 or BP > 50%, i.e. 7.3 on average, out of eight possible nodes  or bootstrap percentage (BP) for three levels of significance: PP > 0.70 or BP > 50%, PP > 0.95 or BP > 70% as well as PP > 0.99 or BP > 90%. Among all the 568 identified nodes for all the methods, all the markers and the 7-taxa set, there were 194 cases 34%) that satisfied the first level of significance. For the other two levels, the number of the supported nodes drastically decreased to 59 (10%) and 12 (2%), respectively. The inclusion of 11 taxa did not improve the support because for all 1152 possible nodes there were only 294 (26%), 128 (11%) and 69 (6%) cases for the three corresponding significance levels. The mean numbers of clades in trees based upon individual markers were presented in Table 4% that sr = 0.668, p = 0.002), PP > 0.95 or BP > 70%  and PP > 0.99 or BP > 90% . The correlation was also significant between the sequence divergence and the second  and the third level of significance . However, we did not observe such significant relationships for data with 7 taxa.For the trees with 11 taxa, we found a significant correlation between the length of the markers and the number of the clades with the nodes supported by three levels of significance: PP > 0.70 or BP > 50% . The clade grouping together Thectocercus acuticaudatus, Guaruba guarouba and Psittacara mitratus (TGP clade) was present in F and G trees, but H tree exhibited only Thectocercus acuticaudatus + Guaruba guarouba grouping. All the trees differed in the location of Eupsittula pertinax. In tree F, it was grouped together with APOA clade, in tree G it was at the base to APOA + TGP clades and in tree H it clustered with Psittacara mitratus. Tree F showed also common origin of Rhynchopsitta terrisi and Pyrrhura rupicola lineages, which branched off successively at the base of other trees. Comparing trees from 7- and 11-taxa sets, it can be safely concluded that only tree B and G show corresponding topologies.For the concatenated alignment of all markers with 11 taxa, we arrived at three topologies called F, G and H Figs.  and 7. Satp6 showed the topology B. It is noteworthy that two tRNA sets for the many methods were capable of yielding trees quite similar, with the distance 2, to the topologies derived from all markers. The ML trees of nd2 were also very close to the final topologies. Nonetheless, the most different topologies, with the distance 8, were very often generated because within the total of 18 markers 12 had at least two such trees. We did not notice any preference of methods in favour of such disparate topologies. Generally, the deviated trees were most often produced for nd1, nd4L, nd3 and cox2 sequences. Averaged for all the methods, their distance to the topologies based on the full-marker set was greater than 7.Having the topologies based on the most character-rich and presumably reliable set, it was interesting to check which individual markers and methods were capable of producing such topologies. Hence, we calculated the distances between these topologies and every tree constructed by the various methods for the individual markers. Unexpectedly, among all the 144 possible combinations of markers and methods, only nine trees with 7 taxa had the same topologies as the trees based on the concatenated set  branched off successively and did not create clusters like in the other topologies.Since the control region alone was instrumental in recovering the tree topologies based on the concatenated sequences of all markers and in providing the best supported and consistent trees, we removed it from the concatenated alignment to find which phylogenetic signal lasted in the remaining markers. Surprisingly, we also obtained the same tree topologies as for the whole set. In the case of the 7-taxa set, the topology A was again supported by MrBayes and two maximum likelihood methods, whereas the topology B by maximum parsimony Fig. . In addiRhynchopsitta terrisi and Pyrrhura rupicola. However, the other two topologies obtained by weighted least squares and maximum parsimony methods were markedly different. Not only did they separate Psittacara mitratus from Thectocercus acuticaudatus and Guaruba guarouba, but also placed Eupsittula pertinax between Rhynchopsitta terrisi and Pyrrhura rupicola. The trees for neighbour joining and minimum evolution methods were not fully resolved.Phylogenetic analyses of concatenated markers from 11 taxa, excluding the control region, produced four topologies Fig. . One of Aratinga solstitialis and Ara glaucogularis  occurring in topologies A, B and D with 7-taxa  in four cases than topologies B, D and E. Topology C fared poorly, compared with A in ten cases. Similarly, Bayes factor test also demonstrated substantial differences between the topology A and other topologies , were found in the genomes from Epinephelidae fishes [cox1 and cox3) that evolve more slowly than rRNAs. In our analyses of Arini members, all PCGs showed greater substitution rates than rRNAs. The studies of avian mitogenomes [Arini mitochondrial genomes was cox2, which, however, demonstrated much greater substitution rate in the global studies of bird and parrot mitogenomes [Arini mitochondrial genomes was atp8 and nd6 was next line. In the global analyses of Aves and Psittaciformes [nd2 and next atp6, which in Arini did not show the widest variation. The discrepancy is attributable to heterogeneous evolutionary rate of genes in time and various lineages, which should be included in global phylogenetic studies using members of different groups. Our study took into account short evolutionary distances (about 14 million years), whereas Pacheco et al. [Arini, but on a global scale we can observe an opposite effect. In agreement with that, atp8 showed the least clock-like behaviour and was not the fastest evolving gene in the avian study [Arini mitogenomes.Our study showed that tRNAs are on average the most slowly evolving genes, which may be attributed to the strong constraints on their structure and function. Sequences of rRNA genes changed on average 1.7 times faster, protein-coding genes 3.1 times and the control region showed the greatest rate, which was 4.4 times higher than tRNAs. Yet, there were two outstanding genes, tRNA-Glu and tRNA-Phe, that showed divergence 3.1 times greater than other tRNAs in e fishes . The orde fishes  and birde fishes . Howeverogenomes  seem to ogenomes . The mosciformes , the moso et al.  studied an study  but it dArini, we tried to infer their phylogenetic relationships using individual mitochondrial molecular markers as well as concatenated sets. The results for some markers were, however, not satisfactory. The trees based on the same marker and obtained by various methods most often showed disparate evolutionary histories. Similarly, the same method applied to the various markers also produced different phylogenetic relationships. It should be emphasized that it was possible to find conflicting groupings of the same taxa that were supported by all methods for various markers. It implies that inferring phylogenetic relationships based on individual mitochondrial markers can lead to wrong and contradictory conclusions. The individual trees were usually characterized by poor resolution. Generally, only one tenth of all identified nodes for all the methods and all the markers had posterior probability >0.95 or bootstrap percentage > 70%, both for 7- and 11-taxa sets.Using data for the complete mitochondrial genomes in the representatives of the main evolutionary lineages of cytb, nd2 and nd6 genes. The trees based on nd2, nd3, nd4L, nd5 and nd6 belonged also to the worst resolved. It is remarkable that cytb and nd2 markers are very often used in phylogenetic analyses, including parrots [The sharpest disagreement of methods was registered for  parrots , 11, 12. parrots , 51–58.Arini members obtained previously for various markers. For example, the tree based on cox1 and nd2 [trop and tgfb2) and coded gaps [Aratinga solstitialis and Ara glaucogularis together in one clade, whereas in the tree based on cox1, cytb, nd2 and rag-1, Aratinga solstitialis was clustered with Eupsittula pertinax [Arini members in the trees [The variable phylogenetic signal in various mitochondrial markers demonstrated in our study explains the inconsistent or unresolved phylogenies of  and nd2  and enhaded gaps  grouped pertinax . The weahe trees , 11, 12.Contradictory tree topologies obtained from various mitochondrial markers were reported also for various groups of animals –25. It ccox1, nd1, nd2 and the concatenated RNAs can provide molecular time estimates most similar to those based on the complete mitogenomes. The most informative markers or group of markers needed to reconstruct reliable phylogenies vary with groups and should be selected individually not only for big lineages [The usage of single markers was also criticized because their phylogenies did not match those based on many markers or entire mitochondrial genomes , 59. Forlineages  but evenlineages .On the other hand, Seixas et al.  showed tArini although the strongest phylogenetic signal was included in the control region and tRNA genes. The trees based on these markers were identical or very similar to the final topologies inferred from all markers for some methods. Interestingly, these two types of markers are characterized by opposite evolutionary rates in mitochondrial genomes. The former is rapidly evolving, whereas the latter is conserved on average. The usefulness of tRNA genes was usually underestimated. They were not commonly used in the reconstruction of phylogenetic relationships although their usefulness was also proved for bony fishes [Nevertheless, it is difficult to indicate an individual marker that could reliably recover the phylogenetic relationship within y fishes  and amphy fishes . NeverthArini may be obtained on the data sets consisting of concatenated alignments of at best all mitochondrial markers. The exclusion of the control region, which provided alone the strongest phylogenetic signal, did not remove vital information about the phylogenetic relationships. The most robust methods, Bayesian and maximum likelihood, were able to recover the same tree topology as for the complete data set. It is suggestive of sufficient information also present in other mitochondrial markers.Our results indicate that the most reliable phylogenetic relationship within Arini we applied eight methods on two data sets with 7 and 11 taxa to verify also the influence of the taxa number. The resulting topologies showed consistently the presence of two highly supported clades: (1) Aratinga solstitialis + Ara glaucogularis with the addition of Primolius couloni and Orthopsittaca manilata in the case of 11-taxa set, and (2) Psittacara mitratus + Thectocercus acuticaudatus with the addition of Guaruba guarouba in the 11-taxa set. The clades and the relationships within them were strongly supported and occurred also in many consensus topologies derived from individual trees constructed utilising various methods for individual markers.To infer phylogenetic relationships within Arini [nd5 gene, whereas all members of the second clade have the same starts and stops in all genes. Two members of the first clade have one-nucleotide spacer between tRNA-Val and 16S rRNA genes, whereas two members of the second clade share exclusively a two-nucleotide spacer between tRNA-Ala and tRNA-Asn genes. All species in the second clade have the longest 12S rRNA gene (more than 975 bp) of all studied Arini.Such groupings are supported by some common features present in mitochondrial genomes of the Arini  , whereas other Arini have a deletion  among the Arini under study , this taxon grouped together with the clade Psittacara mitratus + Thectocercus acuticaudatus. This tree was inferred on the set of all markers by powerful methods such as Bayesian and maximum likelihood, in contrast to maximum parsimony and distance methods, which favoured the alternative topology B. The topology A, which showed also greater support values for the conflicting clade, was significantly favoured by topology tests and was recovered on the alignment that was subjected to RY-recoding to reduce compositional bias. On the other hand, in the tree F, based on 11-taxa set and also supported by the powerful methods on the set of all markers and the RY-recoding alignment, Eupsittula was sister to the clade including Ara glaucogularis, Primolius couloni, Orthopsittaca manilata and Aratinga solstitialis. This clustering was also statistically more significant in support values and topological tests than alternative placements of Eupsittula. Tree A and F, including these two groupings, were much more probable than other competing topologies. These results illustrate that taxon sampling may influence tree topology. It is difficult to point to the most probable relationship on the basis of these data because support values for them are comparable. The topology F seems to be more probable because it is based on the set containing a larger number of taxa. The difficulties in determining phylogenetic relationships within Arini may result from a rapid divergence and differentiation of its members, as evidenced by short internal branches in phylogenetic trees.Among the inferred relationships, the position of p-value = 1. It suggests that a sufficient number of non-synonymous substitutions did not accumulate during the evolution of the tribe Arini to cause the selection-dependent introgression.As far as mitochondrial phylogenies are considered, we should be aware that mtDNA might not reflect the true species trees. The disagreement between the mtDNA and species tree may stem from introgression and incomplete lineage sorting following recent speciation . HoweverArini taxa studied here.Since mitochondrial markers are single-copy genes, they can be less susceptible to the incomplete lineage sorting than the nuclear ones. The nuclear genes more often duplicate and their different copies can disappear in various lineages, which can lead to hidden paralogy and disagreement between gene and species trees , 72–79. Arini representatives has only 3.6% of variable sites (98/2544 bp), whereas an alignment based on mitochondrial nd2 sequences from the same taxa has 49.9% (446/894 bp) of variable sites.The application of nuclear markers could provide additional information about phylogenetic relationships among parrots. However, the resolution of phylogenetic relationships based on available nuclear markers is much worse than mitochondrial ones , 81, 82.Arini relationships, we confronted the phylogenetic results with the morphological data. Curiously, drawing on the molecular markers the proposed most probable relationships do not contradict the classification based on the colouration of the wings and tail, which were believed by Remsen et al. [Even a well-resolved phylogenetic tree does not have to reflect true relationships between the taxa under study, i.e. the species tree. To check to what extent the mtDNA phylogenies may correspond to the real n et al.  to discrAratinga are violet blue and its tail is olive-green tipped violet [Aratinga is Ara, which shows greater differentiation. Apart from four species that are predominantly green, two others are mostly blue and yellow [Ara species are blue and the tail is also blue-tipped, regardless of interspecies differences in rectrices colouration [Primolius have also blue colouration of greater primary coverts and tail tip [Orthopsittaca is mostly green, its greater primary coverts are also bluish [Aratinga and three macaws, and could be present in its common ancestor. Eupsittula, which is sister to this clade in the trees with 11 taxa, also has blue greater wing coverts, like Aratinga. However, the blue is paler, reduced to a faint tinge in cactorum and does not extend to the outer primary coverts [Greater primary coverts of d violet . The sisd yellow . Two addd yellow . Howeverouration . Primolitail tip . Althougo bluish . It seem coverts .Aratinga, Eupsittula and three macaw genera, the greater primary coverts, as well as primaries and secondaries of Psittacara, Thectocercus and Guaruba are green [Guaruba Comparison of mitochondrial genomes from ten Additional file 2:Sequence data sets and nucleotide substitution models used in the present study. (PDF 53 kb)Additional file 3:Tree topologies obtained by eight methods for individual mitochondrial markers from 7 parrot taxa. When some methods yielded several equally viable topologies, the majority-rule consensus tree was presented. The following phylogenetic approaches were applied: Bayesian analyses in MrBayes (MB) and PhyloBayes (PB), maximum likelihood analyses with partitioned data in TreeFinder (TF) and not partitioned in PAUP (ML) as well as neighbour joining (NJ), minimum evolution (ME), weighted least squares (WLS) and maximum parsimony (MP) in PAUP. The same tree topologies have the same background colour. (PDF 32 kb)Additional file 4:Tree topologies built employing eight methods for individual mitochondrial markers from 11 parrot taxa. When some methods yielded several equally probable topologies, the majority-rule consensus tree was presented. The following phylogenetic approaches were applied: Bayesian analyses in MrBayes (MB) and PhyloBayes (PB), maximum likelihood analyses with partitioned data in TreeFinder (TF) and not partitioned in PAUP (ML) as well as neighbour joining (NJ), minimum evolution (ME), weighted least squares (WLS) and maximum parsimony (MP) in PAUP. The same tree topologies have the same background colour. (PDF 647 kb)Additional file 5:Data set with 7-taxa: distance of individual marker trees from trees A and B based on the concatenated alignment of all markers. The maximum distance = 8. When some methods yielded several equally probable topologies, we averaged the calculated distances for these trees. The following phylogenetic approaches were applied: Bayesian analyses in MrBayes (MB) and PhyloBayes (PB), maximum likelihood analyses with partitioned data in TreeFinder (TF) and not partitioned in PAUP (ML) as well as neighbour joining (NJ), minimum evolution (ME), weighted least squares (WLS) and maximum parsimony (MP) in PAUP. (PDF 49 kb)Additional file 6:Data set with 11 taxa: distance of individual marker trees from trees F, G and H based on the concatenated alignment of all markers. The maximum distance = 16. When some methods yielded several equally probable topologies, we averaged the calculated distances for these trees. The following phylogenetic approaches were applied: Bayesian analyses in MrBayes (MB) and PhyloBayes (PB), maximum likelihood analyses with partitioned data in TreeFinder (TF) and not partitioned in PAUP (ML) as well as neighbour joining (NJ), minimum evolution (ME), weighted least squares (WLS) and maximum parsimony (MP) in PAUP. (PDF 53 kb)


R2 = 0.9964) at maturation stage, and was also observed between fruit vertical diameter and fruit weight (R2 = 0.9605), which indicated that cell number in vertical direction contributed to the flat shape formation. Furthermore, in RNA-seq analysis, 4165 differentially expressed genes (DEGs) were detected by comparing RNA-seq data between flat and round peach cultivars at different fruit development stages. In contrast to previous studies, we discovered 28 candidate genes potentially responsible for the flat shape formation, including 19 located in the mapping site and 9 downstream genes. Our study indicates that flat and round fruit shape in peach is primarily determined by the regulation of cell production in the vertical direction during early fruit development.Fruit shape is an important external characteristic that consumers use to select preferred fruit cultivars. In peach, the flat fruit cultivars have become more and more popular worldwide. Genetic markers closely linking to the flat fruit trait have been identified and are useful for marker-assisted breeding. However, the cellular and genetic mechanisms underpinning flat fruit formation are still poorly understood. In this study, we have revealed the differences in fruit cell number, cell size, and in gene expression pattern between the traditional round fruit and modern flat fruit cultivars. Flat peach cultivars possessed significantly lower number of cells in the vertical axis because cell division in the vertical direction stopped early in the flat fruit cultivars at 15 DAFB (day after full bloom) than in round fruit cultivars at 35 DAFB. This resulted in the reduction in vertical development in the flat fruit. Significant linear relationship was observed between fruit vertical diameter and cell number in vertical axis for the four examined peach cultivars ( FASCIATED (FAS) (LOCULE NUMBER (LC) (SUN (OVATE (Slelf1) exhibits elongated fruit shape caused by increased cell layers in the proximal region of the ovary   and, LOCBER (LC)  conferriLC)  through regulating cell expansion , the typical fruit shape is round, but more and more peach cultivars producing saucer-shaped fruit have been bred in recent years (P. persica var. platycarpa) are called ‘Pantao’ in China and ‘Saturn peach’ or ‘donut peach’ in western countries. The flat fruit is a mutant of round fruit and is controlled by a single dominant gene as first described by Fruit shape is critical to the appearance of the fruit and affects the marketability of fruit crops. For peach . All trees used in this study were grown at Zhengzhou Fruit Research Institute, CAAS. Fruit diameters  of six fruit per cultivar were measured from full bloom to fruit maturity at 0, 10, 15, 20, 25, 30, 35, 40, 45, 55, 65, and 75 DAFB (day after full bloom), using a digital caliper. Fruit samples at 0 DAFB were collected from almost 200 full bloom flowers by removing the petals and styles. Vertical diameters were measured from the top to bottom of the fruit in the vertical axis and cheek diameters were between the cheeks of peach. In addition, six fruit per cultivar were used for microscopy analysis to determine histological differences. For microscopy, fruit samples were collected at 0, 15, 25, 35, 55, and 65 DAFB and fixed in FAA , then stored at room temperature until further analysis. To measure cell number and cell size of the two different fruit shape types, we selected regions in vertical sections from the middle of the fruit kernel to the top in the vertical direction, and the middle of the fruit kernel to fruit cheek side in transversal direction . Because of fruit stone hardening, samples prepared for microscopy analyses contained exocarp, mesocarp, and endocarp before fruit pit hardening (35 DAFB) and exocarp, mesocarp merely for the late two periods. For comparative transcriptome analysis, HY and ZH fruit samples were collected at 0, 15, and 65 DAFB for RNA sequencing with three biological replicates.Four peach cultivars were selected in this study, including two round ones ‘Zhong Nong Jin Hui’ (JH) and ‘Zhong Tao Hong Yu’ (HY), and two heterozygous flat ones ‘Xin Hong Pan Tao’ (XH) and ‘Zao Huang Pan Tao’ (ZH). They are widely cultivated in China with similar harvesting date but not directly related, thus can represent for flat and round peaches  . Images Figures 1b,c), we counted all cells using a light microscope. Six samples per cultivar were counted for each development stage. The cells were counted one by one along vertical or transversal axis through the eyepiece of a light microscope. Here, we provided a model for cell number calculating. The cells along the red dotted line in Figures 1b,c were counted in one straight single-cell line direction. The cell number that calculated in this way was regarded as cell number in vertical or transversal axis in this study. Cell area measurements were made using the ImageJ software1. Because of pit hardening, the target regions of early fruit contained parts of endocarp, whereas regions of late development fruit did not. Therefore, the cell number of sections of the late fruit developmental period was calculated by adding the cell number of endocarp for each cultivar.To determine the cell number in the target regions . The SAS correlation procedure was also used for other appropriate analysis.Supplementary Table S1). The 3730XLDNAanalyzer equipment  was used for genotyping.To validate the candidate gene for flat shape, the DNA marker PC4 was amplified using primer pairs FlatIn_F and IndelS_R as described in a previously published paper  from 72 Total RNA was extracted from 18 peach samples that represent for three biological replicates of two peach cultivars at three development stages , using the RNAprep Pure Plant Kit . RNA integrity was confirmed by 1% agarose gel electrophoresis. After digestion with DNase I at 37°C for 30 min to remove DNA residue, RNA quality and concentration were measured using an the NanoPhotometer spectrophotometer .TM 2000 platform in paired-end (PE) mode.Total RNA of 20 μg from each of the 18 RNA samples was sent to Novogene  for construction of RNA-seq libraries and sequencing. The mRNA was purified from the total RNA using the Oligotex mRNA Midi Kit , and assessed for quality using the Agilent Technologies 2100 Bioanalyzer . The mRNA was then broken into short fragment (approximately 300 bp). First and second strand complementary DNA (cDNA) was synthesized using a cDNA Synthesis System kit  using random hexamer-primer, following the manufacturer’s protocol. Then double-strand cDNA were purified and adapters were ligated to the short fragments. The constructed RNA libraries were sequenced on the Illumina HiSeq2 and annotation files were downloaded from GDR website3. The index of reference genome was generated by hisat2-build script. RNA-seq data was filtered to remove the low-quality reads using FASTX toolkit4 based on the Q20 value per base. Cleaned reads were mapped to the reference peach genome using Hisat2 software  were designed with primer-blast program from NCBI5. The qRT-PCR was performed by Roche LightCycler480 with the following procedure: 95°C for 5 min, followed by 45 cycles at 95°C for 10 s, 60°C for 10 s and 72°C for 20 s. The house-keeping gene RP-II was used as an internal control  analyses were carried out.Plant materials used for IAA measurement were the same as materials for RNA-seq. To determine the IAA content in each sample, the HPLC-MS/MS system . However, fruit diameter in the transversal direction was almost the same and increased at a similar speed between flat and round peaches from full bloom to fruit ripening . Collectively, these data indicated that there were no significant differences between flat and round shaped peaches in fruit cheek diameter, whereas there was a difference in fruit vertical diameter.After recording fruit vertical diameter both vertical and transverse directions, we found that vertical diameter of flat peach was much smaller than that of the round peach. This difference started from 15 DAFB and was present during entire subsequent development periods. The final vertical diameters of round shaped peaches were almost two times larger than flat ones, conferring obviously different shape phenotypes . The final cell number for the flat peach cultivars was approximately 2 to 3 times lower than that in the round ones. In the transversal axis, cell number stopped increasing at 35 DAFB in the round peach cultivars, while it continued to increase until 55 DAFB in the flat ones, which resulted in much more cells in the flat ones in this direction . Cell size of flat peach is smaller than that in round peach in transversal axis, which results in the similar fruit cheek diameter . Consequently, the reduction of cell number in the vertical diameter is one important factor which resulted in the limitation of fruit vertical development in flat peach cultivars.In addition, cell number were counted across the vertical and transversal axis. In the vertical axis, cell number continued to increase from 0 to 35 DAFB in the two round peach cultivars, whereas it ceased to increase at an early fruit development stage (15 DAFB) in two flat ones (R2 = 0.9627) which indicated that cell number was the main factor for fruit flat shape formation . However, there was no strong linear relationship between cell size and fruit vertical diameter (R2 = 0.5395) , which indicated that cell size contributed almost equally to fruit development, with cell area enlargement and vertical diameter increasing synchronously. Linear regression analyses were also performed for fruit weight vs. fruit vertical or cheek diameter. Strong linear relationship was found in the analysis of final fruit weight vs. vertical diameter (R2 = 0.9035), not in fruit weight vs. transversal diameter (R2 = 0.0241) . This observation indicated that cell number, not cell size, was the factor that controlled variation in flat fruit shape.Cell number and cell size are two vital factors that facilitate fruit development. Herein, we analyzed the relationship between fruit vertical diameter and cell number and also analyzed the relationship between vertical diameter and cell size to determine the main causes that may be responsible for variation in flat and round fruit shape at maturation stage. A relative strong linear relationship between fruit vertical diameter and cell number was observed , which indicated that both cell number and cell size played important roles in fruit transversal development. Collectively, these results suggested that cell size may not play a vital role in the variation in flat and round fruit shape development in the vertical direction, whereas the reduction in cell number in the flat peach may be the key factor.In addition, we also analyzed the relationship between final cheek diameter and cell number or cell area with the examined cultivars. There were no differences in fruit cheek diameter between flat and round peaches. In transversal axis, no strong linear relationships were observed between fruit cheek diameter and cell number, as well as fruit cheek diameter and cell area . The association rate was only 75%.Two candidate genes for flat peach have been identified in previous studies, including a003772m  and ppa0a025511m . The mar studies . However8 (Table 1).A total of 67.4 Gb cleaned data was generated from the 18 cDNA libraries. The minimum number of reads per library was 38.7 million for ZH2-3 and the maximum number was 50.1 million for ZH2-2. The read length was 150 bp. Approximate 90% of the reads were mapped to the pear reference genome V1.0Supplementary Table S3). There were 3231, 1049, 541genes identified as DEGs at the three developmental stages between flat and round peach with P-value < 0.05 . Comparing the three developmental stages, much more genes (3231) were differentially expressed at full bloom date when the difference in fruit shape started to emerge. There were less DEGs at 65 DAFB between flat and round peach when fruits were close to maturity. In addition, we also analyzed DEGs among different stages in the same cultivar and compared them with each other in order to find some common DEGs. There were 1458 DEGs in HY1_vs_HY2, 593 in HY2_vs_HY3, 1036 in HY1_vs_HY3, 1465 in ZH1_vs_ZH2, 442 in ZH2-vs_ZH3, and 1141 in ZH1_vs_ZH3 . And there were no common DEGs among them.Differential expression test was done between flat and round peach cultivars at three developmental stages  and 4165 DEGs were identified . Most of DEGs belonged to cellular component and biological process, with each category possessing 13 and 17 terms. The most common terms were cellular process and processes related to cells in the biological process category. Genes related to cell cycle, cell proliferation, cell death, and cell wall biogenesis were also enriched. In the cellular component category, the main terms were cell and cell part. Overall, lots of DEGs were enriched in cell-related processes.To identify genes important to fruit shape, we annotated the DEGs at 0 DAFB because full bloom is the earliest stage showing differences in cell number between flat and round fruits, thus is the critical time for identification of relevant genes. Based on GO (gene ontology) annotation, all the DEGs were clarified into three main categories, including cellular component, molecular function, and biological process , we found that some genes that were highly expressed in round or flat peach at 0 DAFB clustered together. The main factor leading to fruit shape variation in this study was the difference in cell number in vertical diameter between flat and round shaped fruit. Thus, genes that may be correlated with cell proliferation were further analyzed.Using the FPKM (fragments per kilo-base per million mapped reads) value of 4165 DEGs, the heatmap was made to show relative gene expression patterns. From the heatmap , 23 DEGs were selected as candidates for the flat shape gene, including 19 highly expressed in round peach and 4 highly expressed in flat one at 0 DAFB.First, we selected 346 genes that were located within 1 Mb up- or down-stream of the main GWAS (genome wide association study) locus for fruit shape . Of thes9. Of these 231 genes, 192 were detected in the RNA-seq data and 44 were differentially expressed. We further selected 10 DEGs, including 8 DEGs that clustered together with expression levels higher in the round peach and 2 in the flat one at 0 DAFB . In these 10 genes, five were associated with auxin, two with gibberellin, one with cell cycle, and two with cellulose. These 10 DEGs may act downstream of the flat shape gene to regulate the flat shape formation, because they are not located in the genome mapping site of flat shape gene.Then, we selected 231 genes that were associated with cellulose biosynthesis, auxin biosynthesis, cytokinin biosynthesis, gibberellin biosynthesis, cell cycle and cell proliferation, from the reference peach genomeFigure 7), we totally selected 33 DEGs that might participate in the process of flat shape formation, which were described in Supplementary Table S4.After these two aspects analysis .To confirm the accuracy of the RNA-seq results and validate the expression patterns of candidate genes, qRT-PCR was performed with 27 selected genes which highly expressed in round peach at 0 DAFB. The relative expression levels of all the 27 genes were consistent with the results of RNA-seq analysis, which have confirmed the reliability of transcriptome data . Nineteen were in the vicinity of major QTL of flat shape and nine were regarded as downstream genes, indicating that multiple genes may be involved in flat fruit formation in peach (Table 2). Two of the 19 genes were LRR-RLK (leucine-rich receptor like kinases) genes, including ppa025511m which was reported to be responsible for flat shape recently .However, we selected these 33 DEGs merely through the heatmaps, but not the definite FPKM values. Therefore, there might be some genes that were unexpected in these 33 genes. We preferred to select genes which were differentially expressed at 0 DAFB and highly expressed at 0 DAFB than the other two stages. According to the results of qRT-PCR and also FPKM value, the number of candidate genes was further narrowed down from 33 to 28. In this section, 28 genes were finally regarded as key candidate genes named recently  and ppa0Figure 9).There were differences in IAA content between flat and round peach at early fruit development stages (0 and 15 DAFB) when the fruit shape began to form. The IAA content was much higher in round peach than flat one at the first two stages, while there was no difference at 65 DAFB  for flat shape in peach, which is annotated with CONSTITUTIVELY ACTIVATED CELL DEATH GENES 1 , which indicated that PpCAD1 might play important functions at maturation stage in regulating fruit development. The gene PpCAD1 was identified through GWAS which was based on genotypes and phenotypes. However, in the present study, we focused on the gene expression level to identify genes related to fruit shape variation based on cellular proof. These two independent studies had different perspectives in identifying the flat shape gene. The high correlation between fruit shape and the SNP in PpCAD1 made PpCAD1 a more likely candidate, while, not like genes identified in the present study, the higher expression of PpCAD1 in round peach at fruit maturation stage could not explain the fruit shape variation very well at early fruit development when the flat shape began to form. So, in our opinion, there might be no conflict between these two studies. The candidates of these two studies might have functions in the process of fruit shape formation together.Recently, in our previous study, we discovered a candidate gene  with an approximate physical distance 700 Kb. The ppa025511m itself may not be the flat shape gene, while these two genes may involve in flat shape formation together or there may be other genes involved in it. Therefore, we still could not discard any of them. The limitation of transgenesis in peach made it quite difficult to validate which gene is the real flat shape gene.More recently, one gene named e region . The prie region . There we region . In our PpCAD1 may have function in fruit development at maturation stage, while ppa025511m at early stage. Therefore, in this study, 76 DEGs located within 1 Mb up- or down-stream of the main GWAS locus for fruit shape (Table 2). In this study, we totally provided 28 candidate genes. Further studies should be carried out to identify the flat shape gene and verify the mechanisms among genes, cells and fruit development.In our opinion, the flat shape gene resides in the genome region alongside these two previously reported candidate genes. it shape  were detit shape , celluloit shape , cell cyit shape  and cellit shape  were alsppa013047m) annotated as yellow leaf senescence which has function in leaf senescence through cell cycle regulation . ppa005951m and ppa005836m were annotated as Peptidase M20  under accession SRP116734. All other relevant data contained within the paper are available in Supplementary Files.LW and KC designed the experiments. JG and YL analyzed the data. J-LY suggested the histological experiment. JG, YL, and QW prepared DNA and RNA samples. JG performed gene expression and paraffin section analyses. GZ, WF, XW, CC, LG, and TD prepared the materials. JG prepared the manuscript. J-LY and CD revised 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.


Eurya japonica flowers. We examined whether artificial florivory decreased fruit and seed production, and also whether flower size affected florivory and the number of floral visitors. The petal removal treatment  showed approximately 50% reduction in both fruit and seed set in natural pollination but not in artificial pollination. Furthermore, flower size increased the number of floral visitors, although it did not affect the frequency of florivory. Our results demonstrate that petal removal indirectly decreased 75% of female reproductive output via decreased flower visits by pollinators and that flower size mediated indirect interactions between florivory and floral visitors.Species often interact indirectly with each other via their traits. There is increasing appreciation of trait‐mediated indirect effects linking multiple interactions. Flowers interact with both pollinators and floral herbivores, and the flower‐pollinator interaction may be modified by indirect effects of floral herbivores  on flower traits such as flower size attracting pollinators. To explore whether flower size affects the flower‐pollinator interaction, we used  There is increasing evidence that trait‐mediated indirect effects can link multiple interactions Ohgushi, . In geneEurya japonica plants and its associated pollinators and florivores. As E. japonica has a long bud period of several months, florivory will likely occur before pollination. Florivores attacking E. japonica often consume stigmas and all petals . Florivory on stigmas may directly decrease fruit and seed production and flower with a smaller size by florivory may indirectly decrease the reproductive output via decreasing pollinator attraction. Antiqueira and Romero , and flower traits such as flower size are critical to the strength of their interaction  is distributed across East Asia. This shrubby plant produces flower buds in summer, blooms in early spring, and bears mature fruits in autumn. Flowers are attacked by moth larvae including Ourapteryx nivea, Alicis angulifera, Somena pulverea, and Chloroclystis excise during bud and blooming stages : twigs were covered with nylon nets in the experimental period from 29 January to 19 May 2012.Artificial pollination (AP)Artificial florivory (AF) before APNatural pollination (NP): Net removal (NR) for pollination and bagged (B). Nets were removed for pollination on blooming days from 2 to 16 March. Then the twigs were bagged when all petals dropped from 20 to 24 March.To evaluate fruit set, the number of fruits and dropped flowers in the nets was counted. On the same day, we measured the length of fruit on the treated twigs and another untreated twig (N) using a caliper.2.2.2In 2013, we bagged treated twigs on 9 February, just before flowering. To examine whether petal damage affects fruit set via changes in pollinator attraction, we removed all petals leaving stigmas intact  using a pair of tweezers, as flowers lacking all petals were common following heavy florivory. Four treatments received artificial pollination (AP) from 3 to 24 March 2013. In addition to the same treatments in 2012, we conducted three additional treatments. To estimate the effect of the timing of florivory on female reproductive output, we removed stigmas after pollination. To examine the effect of petal damage on fruit and seed set, we removed petals of both artificially and naturally pollinated flowers. Seven treatments were as follows : twigs were covered from 9 February to 6 August 2013.Artificial pollination (AP)Artificial florivory (AF) before APAP and AF following a few days laterRemoval of all petals (PR) and AP on the same dayNatural pollination (NP): Net removal (NR) on the blooming day during the period from 3 to 12 March, exposure pollination for 6–16 days depending on respective twigs, and bagged (B) again on the day when petals of the twig dropped during the period from 15 to 24 March.Removal of all petals (PR) exposed to natural pollination (NP): the nets of twigs were removed, followed by removal of all petals of flowers on the same day. Thereafter, the uncovered twigs were exposed to natural pollination for the same period for each twig as treatment six did, after which they were covered with the net again.To examine fruit production, we counted the number of fruits and dropped flowers in the nets from 6 to 18 August 2013. During the same period, to examine seed production, we counted the number of developing seeds and dead ovules in fruits, and measured fruit length on the treated twigs and fruits on another untreated twig (N).2.3To confirm whether bud size determines flower size, we measured the size of buds and blooming flowers. We tagged 11 buds each of 15 male and 13 female plants using different color strings fastened on stalks on 4 January 2014 and then measured bud length using a caliper. Furthermore, we measured perianth length as flower size of the tagged flowers on the next day after flowers opened during the period from 13 February to 7 April 2015.2.4To examine whether bud size determines the proportion of buds with florivory, we checked bud damage and measured bud size of 13 male and 8 female plants on 2 January 2014. We counted the number of intact and damaged buds on ten 10 cm twigs of each plant. Finally, we measured the length of five buds of each plant using a caliper.2.5To estimate the number of floral visitors around plants, we set 1–4 sticky traps  on each of 17 male and 22 female plants from 12 to 14 March 2012. The traps were hung on twigs with twist ties 1–2 m above the ground. We directly counted the number of floral visitors that would have a function of pollination  on the traps on 14 March.Furthermore, we set 1–6 sticky traps on each of 18 male and 11 female plants on 19 and 23 March 2013. The traps were collected 24 hr later and soaked in canola oil overnight to detach insect samples. Collected samples were kept in 99% ethanol until 29 October 2013. The total number of floral visitors collected on 19 and 23 March was used for statistical analysis.To confirm whether larger flowers receive more floral visitors, we recorded flower size of the plants for floral visitor sampling using a caliper. We measured the perianth length of 10–17 flowers and 6–11 flowers of each plant on 11 March 2012, 13 and 18 March 2013, respectively.2.6To compare fruit and seed set among treatments with considering individual variation, we applied generalized linear mixed models (GLMMs) with a negative binomial distribution and a log link function, as dispersion was too large to use Poisson distribution. We used the number of fruit/developing seeds as response variables. Model predictors included treatment and log‐transformed number of flowers/ovules. Plant individual was used as a random effect. GLMMs were performed using the package lme4 . Plant individual was included as a random effect.To examine the association between bud size and blooming flower size, we applied a GLMM with a gamma distribution and a log link function, followed by likelihood ratio tests. We used perianth length as the response variable. Bud length, plant sex and their interaction were explanatory variables, as sex significantly affected flower size  with a negative binomial distribution due to overdispersion and a log link function, using the MASS package .321 = 1335.1, p < .0001). The experimental treatments significantly affected fruit set, fruit size, and seed set , but their interaction did not . Although the proportion of buds with florivory was significantly affected by plant sex, it was not affected by bud size . In contrast, Hymenoptera in 2013 was significantly more abundant on female plants . In 2012, the interaction between plant sex and flower size was significant for Diptera .The most abundant floral visitor was Diptera , followed by Hymenoptera (0.7% and 21.6%), and Coleoptera (0.1% and 8.6%). The number of Diptera, including flies, was positively affected by flower size in 2012  in 2012. These findings suggest that larger flowers are more preferred by insect pollinators, which is consistent with studies on other plant species Willson, .5E. japonica, the petal removal decreased flower size and clearly decreased natural pollination. Furthermore, the low‐level of flower visitation can be caused by reduced petal size. Thus, the flower size is a critical trait that determines the strength of trait‐mediated indirect interactions among florivores, flowers, and pollinators. Also, note that different taxa responded to flower size differently, and their responses differed between years. As abundance and species composition of flower visitors can differ among years (Price, Waser, Irwin, Campbell, & Brody, Our study demonstrated that flower damage decreased female reproductive output via weakened flower visitor attraction, and this is consistent with studies showing that florivory decreases fruit and seed set indirectly via decreasing pollinator attraction (Krupnick & Weis, The authors have declared that no competing interests exist.KT conceived and designed this study, conducted the experiment, and analyzed the data. KT and TO contributed to the writing of the manuscript.