dataesr/scientific-paragraphs-categorization huggingface dataset:
ERA-Interim reanalyses and operational data from the European Centre for Medium-Range Weather Forecast (ECMWF) are used for the study, which includes an evaluation of the onset and breakup dates of the polar vortex during this period. At an interannual scale, the signature of the 11-year solar cycle, QBO and ENSO, is evaluated on the vortex edge evolution. This is the first study of the variability of the Antarctic stratospheric polar vortex edge and its persistence over a long period (42 years). The paper is organized as follows. Section 2 presents the ECMWF dataset and the data sources of the forcings (SF, QBO, and ENSO) used for the analysis of interannual variability of the polar vortex edge. Section 3 describes the methods used in the study, such as the MIMOSA (Modélisation Isentrope du transport Méso-échelle de l'Ozone Stratosphérique par Advection) model  (Hauchecorne et al.
Potential vorticity fields PV fields are calculated from ECMWF ERA-Interim reanalyses [1]  (Dee et al. , 2011). As these reanalyses end in August 2019, we used the operational data from ECMWF from September 2019 to December 2020. Recently, Millan et al. (2021)  compared the polar vortex evolution with different reanalyses, including ERA-Interim. Their results showed that all reanalyses where in agreement with the reanalysis ensemble mean (REM), which shows that we can be confident with the ERA-Interim reanalyses for our study. ERA-Interim temperature, geopotential, and wind data with a resolution of 1. 125 • latitude × 1. 125 • longitude are inputs for the MI-MOSA model, which is a three-dimensional high-resolution PV advection model  (Hauchecorne et al. , 2002). From MI-MOSA high-resolution PV fields it is possible to follow the evolution of polar air masses and filamentation processes of the polar vortex.
Conclusion and perspectives We analyzed the seasonal evolution of the stratospheric polar vortex edge intensity and position in equivalent latitude in the Southern Hemisphere at three isentropic levels, using ECMWF ERA-Interim data over the period 1979-2020. The interannual evolution of the vortex edge intensity and position, as well as the onset and breakup dates at these three isentropic levels, was evaluated. The parameters studied here display long-term and short-term variations over the period that were analyzed using well-known proxies of atmospheric variability in the stratosphere such as the solar cycle, the QBO, and ENSO. Among the main results of our study, the influence of the increasing ozone hole during the 1980s and 1990s on the studied parameters was clearly noticeable, confirming the results of  Bodeker et al.
Acknowledgements. The authors wish to thank  Cathy Boone  of  Institut Pierre Simone Laplace (IPSL)  for providing ERA-Interim data, and  ECMWF  for the availability of these data. Financial support. This research has been supported by a doctoral fellowship of the French ministry and by a contract with the  CNRS/INSU. The  LEFE BASICC  project funded the publication of this article. Review statement. This paper was edited by  Peter Haynes  and reviewed by two anonymous referees.
Code and data availability. The data that support the findings of this study are openly available in [1] ECMWF ERA-Interim https://www. ecmwf. int/en/forecasts/datasets/ reanalysis-datasets/era-interim (ECMWF, 2022) [2] Solar flux at 10. 7 cm ftp://ftp. seismo. nrcan. gc. ca/spaceweather/solar_flux/ monthly_averages/solflux_monthly_average. txt (National Research Council Canada, 2022) [3] Monthly mean zonal wind components https://www. geo. fu-berlin. de/met/ag/strat/produkte/qbo/qbo. dat (Institute of Meteorology, Freie Unversistät Berlin, 2022) [4] Multivariate ENSO Index Version 2 (MEI. v2) https://www. esrl. noaa. gov/psd/enso/mei (NOAA, 2022). The code for the determination of the vortex edge intensity and position is available upon request to Audrey Lecouffe (audrey. lecouffe@latmos.
, 2010). This approach is more flexible than the half-sib/full-sib method since it considers all known degrees of relatedness between measured individuals and it does not require a balanced breeding design. However, the main issue when using the animal model is the inversion of the additive genetic matrix (which is not diagonal) in haplodiploid species. Bienefield et al. (Bienefeld et al. , 2007 applied in Costa-Maia et al. , 2011; Faquinello et al. , 2011; Pernal et al. , 2012)  proposed a first solution using an approximation which was further improved by  Brascamp and Bijma (Brascamp & Bijma, 2014; Brascamp et al. , 2016). Even more recently, Bernstein et al. (Bernstein et al. , 2018)  published a new algorithm to facilitate the previous method on large datasets. Another approach to invert the additive genetic matrix is based on methods specifically developed for sex chromosome inheritance, which is functionally equivalent to haplodiploidy  (Bohidar, 1964; Crow & Roberts, 1950; Crozier, 1970; Fernando & Grossman, 1990).
In haplodiploid species, a mother contributes to the genome of both sons and daughters, whereas a father contributes only to the genome of daughters. Similarly, in sex chromosome, genes linked to the X-chromosome are transmitted to daughters and sons, whereas genes linked to the Y-chromosomes are only transmitted to sons. This method of matrix inversion is implemented in the R package nadiv  (Wolak, 2012) , which has been applied to wasps but never to honeybees  (Sheehan et al. , 2017). However, many traits under selection are genetically correlated and will not evolve independently from each other, due to linkage or pleiotropy (one loci influencing several traits). In such case, estimating genetic correlation between traits allows a better understanding of trait evolution. They are calculated following the same approaches as heritability estimates but require larger sample size to obtain reliable estimates (around 1,000 individuals when considering only two traits,  Brown, 1969).
g. ,  Postma, 2014) , could either be due to restricted statistical power or to biases inherent to meta-analysis: datasets diverge on several factors (analytical method, sample size, and traits) resulting in confounded effects. In addition, a few studies compared different methods in the same honeybee dataset and found that those methods yielded heritability estimates in the same range  (Brandes, 1988; Moritz, 1985; Moritz & Klepsch, 1985). Genetic correlation estimates are scarce (14 studies only in honeybee), and half of the estimates are not significantly different from 0 or were not provided with any significance tests or standard errors  (Koffler et al. , 2017). Overall, previous works do not allow to draw any firm conclusion on the performance of different methods, sample size, or experimental design to estimate genetic correlation and heritabilities.
One option is to use simulation studies to compare available methods (as well as sample size and design) based on their accuracy of estimates and on their statistical power to detect heritability and genetic correlation  (De Villemereuil et al. , 2013; Holand & Steinsland, 2016; Morrissey et al. , 2007). In this paper, we used both empirical and simulated datasets to compare the performance of three sibling analysis methods, average colony relatedness, half-sib/full-sib, and animal model, in the estimation of heritability and genetic correlations. Our empirical dataset is composed of three morphological traits measured on 853 workers from ten colonies of Apis mellifera unicolor sampled in two islands of the South-West Indian Ocean. The three traits (associated with mouthpart and forewing morphometry) are used to discriminate between species or populations, indicating relatively fast evolutionary rates  (Cornuet et al.
| Empirical dataset Honeybee workers were sampled in Mauritius and La Reunion, two islands of the Mascareignes archipelago situated in the South-West Indian Ocean. In this area, the local honeybee subspecies is A. m. unicolor belonging to the African lineage with recent import of the A. m. carnica, ligustica, and mellifera subspecies belonging to the European lineage  (Techer, Clémencet, Simiand, Turpin, et al. , 2017). La Reunion and Mauritius honeybee populations are genetically distinct  (Techer, Clémencet, Simiand, Turpin, et al. , 2017). We sampled 95 worker honey bees per colony, from the frames of the hives, in a total of ten colonies. Those workers are a mixture of full-sisters and half-sisters (sharing the same queen mother but different fathers). Five colonies were sampled on Mauritius in October 2014, whereas five colonies were sampled on La Reunion from 2011 to 2018 (Appendix S1).
The samples were run through a DNA sequencer ABI Prism 3130XL, and alleles were scored using GeneMapper 4. 0 (Applied Biosystems). Only fully genotyped workers have been considered in the reconstruction analyses. In addition, workers presenting incoherent genotype (potential drifting workers from other hives) have been excluded from the dataset. Thus, 424 individuals were kept from Mauritius (exclusion of 49 individuals) and 429 individuals from La Reunion (exclusion of 46 individuals) (Appendix S2). Patriline reconstruction was carried out on the basis of allelic frequencies previously estimated for those insular populations  (Techer, Clémencet, Simiand, Turpin, et al. , 2017)  using MATESOFT software  (Moilanen et al. , 2004). Patriline reconstruction is facilitated by the haplodiploid determination of sex  (Estoup et al.
| Morphometric measurements For each worker, the mouthparts and the right forewing were dissected and digitally photographed. The right forewing of each bee was cut at its base and mounted in water between micrometer blade and cover. The mouthparts were dissected and then mounted on a micrometer slide with a strip of adhesive tape. Pictures were acquired using a video camera, and measurements were made on the AxioVision SE4 software 4. 7. To study the variation in size of the mouthparts, we measured the length of the long segment of the right palpus (Appendix S3) which is not influenced by the extension of the proboscis and hence more repeatable than the proboscis  (Morimoto, 1968). For the wings, we measured the length of the cubital veins A and B and computed the cubital index (CuI) as the ratio A B , an index informing on the shape of the wing.
To create the inverse of the additive genetic matrix required by asreml-R, we used the makeS function of the nadiv package implemented in R  (Wolak, 2012). This function returns the inverse of the additive genetic relationship matrix for the sex chromosomes since haplodiploidy is equivalent to total sex-linkage of all genes. Here diploid females are coded as the heterogametic sex whereas haploid males are the homogametic sex. A "colony" (common environment) random effect is added to distinguish the effect of common environment from the genetic effect of the queen. Thus, heritability can be calculated as follows: H 2 = V A V A + V CE + V R where V A is the variance associated with the additive genetic matrix (remember that in our case, V A is confounded with V D ), V CE the variance associated with the common environment of the colony and V R is the residual variance.
| Quantitative genetics analyses of the empirical dataset We applied the three methods described above to calculate heritabilities and genetic correlations between our measured traits (A, B, CuI, and palpus). The ASReml-R package  (Gilmour et al. , 2009)  was used to perform variance decompositions for all three methods even if ASReml-R was only required for the animal model whereas the other two methods could be performed with lme4 or nlme R packages. Estimates were calculated using the whole dataset (853 individuals). Year and island were added as fixed effects in the statistical models. We assessed the significance of the fixed effects with a conditional Wald F test. Using the animal model, we also tested if the additive, common environment, and residual variances differed between islands using a bivariate model.
| Quantitative genetics analyses of simulated datasets We performed simulations to test the performances of the three methods in estimating heritability and genetic correlations. Our hypotheses were that (a) the average colony relatedness method would provide biased estimates in presence of common environment effect since this colony effect would be confounded with the genetic effect  (Kruuk & Hadfield, 2007)  pedigree with added relatedness between queens (a modified version of our experimental pedigree).
| Heritability Following the approach of Morrissey et al. (Morrissey et al. , 2007) , we first simulated a dataset of individual phenotypic values such that The genetic values A i were generated according to the pedigree (see details below) and V A using the grfx function of the nadiv package  (Wolak, 2012). We did not simulate any epistatic variance. Y i = + A i + CE k + i with μ, In each scenario studied, the heritability (h 2 ) and common environment (CE 2 ) values varied according to four fixed values: 0 (absence), 0. 1, 0. 3, and 0. 5 thus making it possible to compare 16 different phenotypic scenarios. For each scenario, 1,000 phenotypic datasets were simulated using one of the three tested pedigrees. The first one is our unbalanced experimental pedigree (with 853 workers in 10 colonies).
The second one is a simulated balanced pedigree. This simulated pedigree was built to be of similar size and design to our experimental pedigree: It is composed of 840 phenotyped individuals from 10 queens each crossed with 42 males (against an average of 38 males in our real pedigree), with a full-sib family size of two workers. The third pedigree was a modified pedigree from our experimental dataset (i. e. , with 853 individuals), where three queens of three different colonies (out of the 10 colonies in the dataset) were considered to be full-sisters, therefore adding relatedness relationship between those three colonies. Therefore we generated 48,000 datasets (16 phenotypic scenarios × 3 pedigrees × 1,000 datasets). We estimated the heritability, confidence intervals, and significance of the "genetic" effect (corresponding to the additive genetic matrix) of each simulated phenotypic dataset with the three previously described methods (average colony relatedness, half-sib/full-sib, and animal model), except for datasets generated with the third pedigree (with related queens) for which we compared only the half-sib/full-sib and animal model.
For each dataset, we computed the mean and 2. 5%, 25%, 75%, and 97. 5% quantiles to quantify the bias and the dispersion of heritability estimates. We also calculated the power to detect heritability as the percentage of simulated datasets with a significant genetic effect. However, when estimates are biased, power is not a suitable parameter to assess the performance of a method and should be replaced by the coverage of 95% of confidence intervals  (Bolker, 2008). Confidence intervals were calculated assuming a normal distribution of estimates (h 2 ± 1. 96 × SE). Coverages were computed as the proportion of datasets in which simulated value of heritability was within the 95% confidence interval.
| Empirical dataset All the studied traits showed significant heritability but estimates varied according to the method used (Table  1 ). The average colony relatedness method provided the highest heritability estimates whereas the half-sib/full-sib and the animal model gave strictly similar estimates from 0. 25 for palpus length to 0. 61 for the B length. Using the animal model, we estimated the effect of common environment CE 2 : low for the B vein and the cubital index (0. 01 and 0. 02, respectively), intermediate for A vein (0. 15), and high for palpus length (0. 65). The three methods gave very different genetic correlation estimates (Table  2 ). According to the animal model, the genetic correlations were significantly different from 0 between A and B veins (r G = -0. 49, 95% CI: [-0.
| Simulations Analyses carried out with the three methods with simulated datasets based on our experimental pedigree provided estimates close to the expected values in the absence of common environment effect TA B L E 2 Genetic correlation, standard error (SE), and 95% confidence interval (CI) between pairs of measured traits (the cubital veins A and B, the cubital index (CuI) and the length of the long segment of the right palpus) estimated using three methods: an average colony relatedness, a half-sib/full-sib model and an animal model (without island as random effect) Figure  1 ). Similar results were found using a balanced pedigree (Appendix S6). ( For the three methods (and the three tested pedigrees), the rate of false positive (probability to obtain a significant genetic effect whereas h 2 = 0) was low (<3%), except for the average colony relatedness method in presence of a common environment effect due to large bias in heritability estimates (as mentioned above, Figure  1.
, 2006; Pemberton, 2008; Wang, 2006). Here, the number of patrilines estimated for each colony in our experimental dataset fluctuates according to the number microsatellite markers used: expectedly, with seven markers instead of eight, the number of patrilines estimates per colony was lower. Similarly, heritability estimates were lower with seven microsatellite markers (Appendix S9). Indeed, when the number of patrilines estimated is less than the actual number of fathers, half-sisters will be considered as full-sisters while their resemblance will be weaker than expected, thus leading to a lower heritability  (Firth et al. , 2015). However, the magnitude of underestimation associated with a lower number of microsatellite markers was small (typically between 3% and 15% depending on the considered trait).
In addition, when colonies are sampled in distant areas, queens are less susceptible to be related. However, colonies sampled in the same apiary (as is the case for two colonies of our dataset sampled in Le Baril) may be related, if they originated from the division of one hive into daughter colonies. To explore this situation, we simulated a pedigree with related queens. We expected the half-sib/full-sib method, which ignores such additional relationship, to be more biased than the animal model, which takes into account all types of relationships between individuals. Surprisingly, this was not the case and both methods provided reliable estimates. This may be due to the fact that the added relatedness links were negligible, and it echoes the demonstration of  Liu and Smith (2000)  showing that moderate inbreeding may not notably bias the genetic parameters estimated by sib analysis.
Thus, in a population of infinite size, every possible genetic backgrounds are represented and the dominance effects average out to zero. In the case of La Reunion and Mauritius, the effective honeybee population size seems to be large  (Techer, Clémencet, Simiand, Turpin, et al. , 2017). Hence, the dominance variation, which are likely to contribute to our heritability estimates, will have a limited effect on the genetic variance of the population. One meta-analysis showed that the dominance variance is only important for domesticated species and is generally low (0. 15,  Wolak & Keller, 2014). More recently,  Class and Brommer (2020)  confirmed that the dominance variance represents a modest amount of genetic variance in a wild population of blue tits. As mentioned above, it is theoretically possible to determine the dominance variance in an animal model using the dominance matrix which can be obtained using the makeSd function of the nadiv R package (designed for sex chromosome inheritance and thus appropriate for haplodiploid organisms)  (Wolak, 2012).
, 2013). In honeybee, most colonies are easily accessible and colony sizes are very large. This allows sampling a large number of individuals, families, and colonies, and hence, the only limitation to obtain robust estimates is the technical and financial ability to phenotype and genotype all sampled individuals. According to the simulation results, we decided to discuss only the estimates provided by the animal model in our experimental dataset. This model yielded high heritability estimates for the four measured traits, which is consistent with the honeybee literature  (Moritz & Klepsch, 1985; Mostajeran et al. , 2006; Oldroyd et al. , 1991; Poklukar & Kezić, 1994; reviewed in Koffler et al. 2017). In general, morphological traits are known to display higher heritabilities than fitness-related traits  (Mousseau & Roff, 1987).
; (b) the animal model would allow to deal with unbalanced design and with various degree of relatedness between pairs of individuals. To test the first hypothesis, we simulated phenotypic datasets including a common environment effect resulting from the fact that workers from one colony share the same maternal environment. To test the second hypothesis, we used three different pedigrees: (a) our experimental pedigree which is an unbalanced pedigree, (b) a balanced pedigree with similar sample size as our experimental pedigree (840 workers in 10 colonies), and (c) a.
this study starts in 1961, whereas the Eurostat/OECD budgets start around 1985-1990; and (2) this study establishes soil surface N budgets for cropland (excluding permanent grassland), whereas Eurostat/OECD report land N budgets for the entire agricultural area  19, 20. Our aim is not to supplant either of the above-mentioned datasets, but to complement them. A key contribution of this study is the scope and detail of crop production, in particular fodder crops such as temporary grassland, green maize, and forage legumes. Fodder crops can play an important role in N budgets, as stressed by Zhang et al. 21 in an intercomparison of 13 global datasets, both because fodder crops sometimes contribute substantially to total crop N output and because forage legumes introduce non-negligible amounts of N through symbiotic fixation.
For these crops, we have combined the Eurostat crop production statistics database with a range of national databases and other sources. For other arable and permanent crops, we follow Lassaletta et al. 17  , using the FAOSTAT database which apart from fodder crops offers the longest and most complete time series of crop production. We report crop production data for 17 crop categories (see Fig. 2 ), providing a solid basis for understanding how crop mix and productivity have varied over time. A second key contribution of this study is the increased detail in the allocation of synthetic N fertilizer application between cropland and permanent grassland (see Fig. 3 ). We have assembled a comprehensive dataset and devised a rigorous method to process it. The result is the most comprehensive and consistent estimate of the allocation of synthetic N fertilizer between cropland and permanent grassland to date.
Geographically, the dataset presented here covers the 26 present-day countries Austria, Belgium, Bulgaria, Croatia, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, the Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, and the United Kingdom, i. e. , the countries in the former EU28 except Cyprus and Malta. Most of these countries are covered for the period 1961-2019 (see Method section for details). This paper will hopefully find reuse value in two ways. First, the results provide a consistent, complete, and detailed picture of N use in cropland, which can serve as a basis for future assessments of agricultural productivity, efficiency, and N losses. See Fig. 4  for an example of different trajectories that can be quantified using the results of this paper.