dataesr/scientific-paragraphs-categorization huggingface dataset:
c. judaicus not included). More specifically, the minimum pairwise interspecific K2P-distance between J2 and the other unidentified taxa was 0. 064 opposed to 0. 087-0. 095 when taking H1 to H5 into account. J2 is one of three incomplete sequences and missing information from position 179 to 399 in the sequence of J2 where several simple nucleotide diagnostics are present (Appendix 2). Therefore, this sample was removed from the dataset for subsequent analysis. The congeneric interspecific K2P-distances between the western Palaearctic taxa and the eastern Asian species L. formosanus and L. hermani range from 0. 156 to 0. 198. Distances between taxa of Lucanus and Dorcus went from 0. 211 until 0. 259. K2Pdistances within and between the investigated western Palaearctic taxa of Lucanus are shown in Table  2.
Acknowledgements We want to thank the following people for generously providing tissue samples of stag beetles or information:  E. Atay ,  M. Avci ,  L. Barbiero ,  R. Bekchiev ,  G. Bonamie ,  S. Boucher ,  C. Bouget ,  H. Brustel ,  D. G. Carrilero ,  L. R. Castro ,  G. De Coninck ,  J. Ibero Caballero ,  A. M. Cimaz ,  I. de las Monjas ,  M. Fremlin ,  N. Gouix ,  C. Hawes ,  J-P. Huang ,  N. Jansson ,  A. Kairouz ,  S. Korneyev ,  V. A. Korneyev ,  D. Kovalchuk ,  I. López Pérez ,  Á. Martínez García ,  M. Méndez ,  M. Murat ,  L. Nádai ,  I. Nel ,  E. Orbach ,  H. Podskalská ,  S. Rastrero Sánchez ,  S. Reicher ,  O. Rittner ,  F. Roviralta Peña ,  P. Šípek ,  L. Valladares ,  J. T. Smit ,  Á. R. Quirós Menéndez. Also many thanks to  Leen Verschaeve ,  Nancy Van Liefferinge ,  An Van Breusegem ,  David Halfmaerten  and  Sabrina Neyrinck  ( INBO ) for laboratory assistance.
The overall objective of the present study is to introduce the new ECOCLIMAP-II database for Europe, which is an upgrade for this region of the former initiative, ECOCLIMAP-I, already implemented at global scale. The ECOCLIMAP programme is a dual database at 1 km resolution that includes an ecosystem classification and a coherent set of land surface parameters that are primarily mandatory in meteorological modelling (notably leaf area index and albedo). Hence, the aim of this innovative physiography is to enhance the quality of initialisation and impose some surface attributes within the scope of weather forecasting and climate related studies. The strategy for implementing ECOCLIMAP-II is to depart from prevalent land cover products such as CLC2000 (Corine Land Cover) and GLC2000 (Global Land Cover) by splitting existing classes into new classes that possess a better regional character by virtue of the climatic environment (latitude, proximity to the sea, topography).
Introduction Land cover regulates the surface energy budget and hydrological cycle, which are essential inputs for climate and weather prediction models. It is strictly defined as the observed physical layer that covers the surface of the Earth, including natural and planted vegetation, and man-made constructions. Actually, land cover is one of the most crucial properties of the Earth system for many areas of benefit to society  (GEOSS, 2005). Information on land cover is essential for the protection of environment quality and biotic diversity worldwide  (Sutherland et al. , 2009). It is also of primary importance for sustainable management of natural resources  (EEA, 2005)  and human needs  (Vitousek et al. , 1997). In climate modelling, originally 1-degree global land cover databases were derived that combined pre-existing land cover maps and other atlases  (Matthews, 1983; Olson et al.
At the scale of the European Union member states, there is the CORINE mapping initiative, which has produced two classifications at 30 m resolution: one for the year 2000, based on single-satellite images (Landsat 7 ETM), and the other one for the year 2006, based on images from two satellites (SPOT-4 or IRS LISS). The other most popular land cover maps use the International Geosphere-Biosphere Programme Data and Information System (IGBP DISCover)  (Loveland et al. , 2000) , University of Maryland (UMD)  (Hansen et al. , 2000) , Moderate Resolution Imaging Spectroradiometer (MODIS)  (Friedl et al. , 2002) , ECOCLIMAP-I database  (Masson et al. , 2003) , Global Land Cover (GLC2000)  (Bartholomé and Belward, 2005)  and GlobCover  (Bicheron et al. , 2006) , which were produced using data from global observation systems such as NOAA/AVHRR, MODIS, SPOT/Vegetation and Envisat/MERIS at a spatial resolution of few hundred metres to 1 km.
In addition to land cover classifications, the ECOCLIMAP product provides sets of surface parameters that are primarily useful in meteorology, notably surface albedo and leaf area index (LAI). However, stratification of land surface in Europe is permanently under investigation as knowledge of the geographic extent and dynamics of land cover is still incomplete, and broad levels of disagreement exist among current land cover maps due to the high level of landscape fragmentation at mid-latitudes  (Herold et al. , 2008; Fritz and See, 2008). The aim of the present study is to update ECOCLIMAP-I at the European continental scale. This database was specifically designed to answer the needs of the meteorological community in investigating natural and managed ecosystems in connection with weather forecasting and climate change modelling  (Masson et al.
, 2003). For example, the COSMO CLM initiative uses the ECOCLIMAP database (http://www. clm-community. eu). The rationale for building the new classification map (ECOCLIMAP-II/Europe hereafter) is to better discriminate the land cover classes over Europe than is done by the existing continental maps such as ECOCLIMAP-I, GLC2000, and MODIS products. The latter products refer to single annual cycles of satellite data and may capture some undesirable anomalies that could be avoided with a multi-annual time series analysis of consistent remote sensing observations, as has been clearly demonstrated over Africa  (Kaptué et al. , 2011a, b). Hence, ECOCLIMAP-II/Europe will take advantage of the improvements provided by SPOT/Vegetation (acquired during the 7yr period  [1999] [2000] [2001] [2002] [2003] [2004] [2005]  in regard to radiometry, calibration monitoring, atmospheric correction, and normalization of surface directional effects compared to the NOAA/AVHRR datasets (acquired between April 1992 and March 1993) that were used to produce ECOCLIMAP-I.
In this paper we describe the methods and datasets used to produce ECOCLIMAP-II/Europe. Section 2 first recalls the main characteristics of the ECOCLIMAP database and gives some technical information. In Sect. 3, we detail the satellite information used as input for the classification (SPOT/VGT NDVI) and the aggregation tool (MODIS LAI), the existing land cover products, and finally the elements of validation. The method of K-means employed for implementing ECOCLIMAP-II/Europe is thoroughly described in Sect. 4. 1, along with the strategy for maintaining a minimum number of clusters and the technique of aggregation, i. e. limiting the division of land cover classes into a reduced number of PFT. The description of the method is illustrated in Sect. 4. 2 by looking over the 273 ECOCLIMAP-II land covers and combining them with 103 upper categories for a more synthetic view.
Section 5 first draws a comparison between ECOCLIMAP-I and ECOCLIMAP-II in terms of functional types and LAI mean annual profiles, which should be relevant for the interpretation of further meteorological simulations. The validation procedure, which is the comparison with independent datasets at finer or coarser resolution, is detailed in Sect. 5. The last part, Sect. 6, summarizes the study and presents some perspectives for exploiting the results.
Principles of the ECOCLIMAP database The objectives of the ECOCLIMAP classification are first to perform a stratification of the landscape into land cover units. ECOCLIMAP-I consisted of a global land cover map of 215 ecosystems (or classes or covers) at 1 arc s resolution,  with a dataset of surface parameters associated for each grid mesh in tabular form: albedo, leaf area index (LAI), fraction of vegetation cover, fraction of photosynthetically active radiation (FPAR), roughness length, minimum stomatal resistance, and root zone. Figure  1  gives a schematic description of the organization of the ECOCLIMAP database, with the detailed terminology. This set of surface parameters is highly suitable for initializing Soil-Vegetation-Atmosphere Transfer (SVAT) models  (Boone et al. , 2009). As in other models investigating vegetation dynamics  (Bonan et al.
For each land cover and each PFT present in this land cover, ECOCLIMAP-I defined an annual profile of 10-day averaged LAI values, a root depth, a soil depth and a height for tree stands. Other surface parameters (notably fraction of vegetation, vegetation albedo, and roughness length) were only determined per PFT, regardless of the cover. They were assigned values that were constant or calculated with formulas relying on LAI, soil depth or tree height  (Masson et al. , 2003). Worth noting here is that the 10-day periods of ECOCLIMAP-I were defined from the 1st to the 10th; from the 11th to the 20th; and from the 21st to the end of each month. Known limitations of ECOCLIMAP-I are the obsolete character of the input datasets and the artificial delineation of the boundaries of the  Koeppe and De Long (1958)  climate zoning.
Because of the need for the highest possible resolution over land, some initiatives for updating the ECOCLIMAP-I database have already been implemented. For instance,  Han et al. (2005)  updated the land cover over France. An example is the improvement of the description of biomes for south-western France, with which winter and summer crops could be separated, thereby leading to relevant detailed simulations of the atmospheric carbon dioxide in the CarboEurope Regional Experiment Strategy (CERES)  (Sarrat et al. , 2007). More recently,  Kaptué et al. (2010)  developed a new ecosystem classification within the ECOCLIMAP-II programme, with 37 distinct types over West Africa. This database was developed over the AMMA (African Monsoon Multidisciplinary Analysis) zone to provide upgraded information on the land surface properties of the West Africa region.
Input and validation datasets The update of ECOCLIMAP database was limited to Europe -defined here as the region comprised between longitudes 11 • W and 62 • E and latitudes 25 • N and 75 • N (corresponding to the area represented in Fig. 2 ) -in order to serve as testbed prior to achieving the global extension. The implementation was performed according to three steps: gathering input datasets to produce a new land cover map, delineating the definition and characteristics of land cover types, and, finally, consolidating the database through validation exercises. The characteristics of information sources used either as input or for validation are indicated in Table  1.
CLC2000 consists of 44 classes with a fine breakdown into categories obtained by merging the consistent national products into one dataset. As CLC2000 can be deemed a fully dependable product, the European study area covered by this project appears trustworthy. The second land cover map used in this study was the Global Land Cover 2000 (GLC2000) database produced by the Joint Research Centre (JRC)  (Bartholomé and Belward, 2005). It yields a global product derived from the analysis of 14 months (November 1999 to December 2000) of daily global data acquired by the SPOT/Vegetation (VGT) sensor at a spatial resolution of 1/112 • (around 1 km). The product was developed on the basis of regional classifications [25 • N, 7 • 5 N] × [11 • W, 62 • E] [30 • N, 70 • N] × [10 • W, 60 • E] [30 • N, 70 • N] × [10 I I I I V I I I V V V made with the aid of regional expertise.
Climatic datasets A climate database is used in order to avoid grouping classes pertaining to different climates. Two climate maps were actually used. The first, proposed by  Koeppe and De Long (1958) , is global with 16 climate classes. The second, produced by the FIRS project (EC, 1995), covers Europe and suggests 23 classes. It is leveraged by geo-factors such as climate, soil and topography. A combined map covering all areas of interest was built by assigning values of the FIRS classes to  Koeppe and De Long's (1958)  classes, which extended the former out of their area of definition (Fig. 3 ). Slight evidence of the coupling at the boundaries between the two original maps was notable in southern and eastern parts of Europe, which reveals conspicuous agreement between the two climate maps.
Validation data Multi-scale land cover verification benefits from multi-scale ancillary information in order to consolidate the strategy of distribution of the fractions of PFTs within the land covers. In the procedure of validation, the comparative products must adopt same common projection, resolution and quantity although the downstream strategy may be more specific to a dataset. Statistics from the French Ministry of Agriculture and Forests can be found in the AGRESTE (http://agreste. agriculture. gouv. fr/) database. This database is composed of annual land use expressed in hectares for each French department, distinguishing different types of crops and nonagricultural areas. The available years are from 1999 to 2005, which are common with the available NDVI datasets. Another means of verification was the 1 • global map of percentage of C4 vegetation produced within the framework of the International Satellite Land Surface Climatology Project (ISLSCP-II) Initiative II Data Collection  (Still et al.
Description of the methods Prior to stratifying the domain of interest into land cover types, we used the C14 map (Fig. 2 ) to mask out pixels identified as water (either inland or oceanic) or urban areas. Then, the classification was performed according to 3 steps: (1) use of the K-means algorithm on the NDVI dataset to build a large number of clusters of homogeneous vegetation, (2) reduction of the number of clusters, and (3) integration of the information from the existing land cover and climate maps. After the classification process, the last step of our procedure was (4) to define, for each cover, the percentage of the 4 main surface types (land, sea, inland water and urban areas) according to the "tile" approach, and to determine the 12 fractions of PFTs inside the nature tile, and then the LAI profiles, the root and soil depths, and the heights of trees for each of the 12 functional types of natural land areas represented in the cover (i.
(Step 1) The K-means algorithm applied to the NDVI time series Clusters of pixels at 1 km resolution were formed using the K-means clustering algorithm of  Hartigan and Wong (1979). This is an unsupervised learning algorithm that is suitable for clustering multidimensional datasets. The K-means method seeks to partition all points into k clusters such that, in a multivariate attribute space, the total sum of squares (or squared deviations) from a set of individual points -represented here by the pixels -is minimized with respect to an optimum number of cluster centroids. The algorithm can be parsed as follows: (i) a number, k, of points is randomly placed in the space represented by the objects to be clustered (here, objects are NDVI time series); (ii) using the Euclidian distance measure each object is assigned to the closest centroid; and (iii) once all objects have been assigned, the positions of the k centroids are recalculated.
The process is repeated until the position of the centroids is stable. The final step is then to minimize the metric of the objects with respect to the k clusters. The K-means algorithm is sensitive to the initial configuration of cluster seeds and does not necessarily find the optimal configuration corresponding to the global objective function minimum  (Kanungo et al. , 2002). In practice, the K-means algorithm can be run several times to reduce this effect. We start with randomly chosen centres of clusters. Then, after a few iterations, rapid convergence is obtained for the centres of clusters based on the criterion of stability to decide that an optimal distribution of clusters has been reached. The K-means algorithm is well-known for its efficiency and robustness in handling the bulk of datasets.
However, some constraints and limitations seem to exist for this approach. Firstly, the number of clusters ascertains the precision for obtaining a sharp description of the whole dataset. Secondly,  Jain et al. (2003)  calculated the sensitivity of the algorithm to the initial positions of cluster centres because, in some situations, important populations may be misrepresented. Thirdly, the use of the Euclidian distance as a unique criterion may be too restrictive to describe the dynamics of NDVI time profiles. Nevertheless, in our approach, we did try to circumvent such difficulties by using first a large number of clusters k (around 2000), and then refining the selection using other criteria such as optimizing the combination between the correlation and the distance (Sect. 4. 1. 2, Eq. 1).
Comparison with ECOCLIMAP-I map With the elaboration of the new ECOCLIMAP-II database, a key objective is to update the former, rather obsolete ECOCLIMAP-I product. Another shortcoming with ECOCLIMAP-I was the use of AVHRR images having a pixel resolution between about 1 km (nadir) and 6 km (skewed scanning), which enhanced the existence of mixed pixels and overlap between classes. Also, the lack of highquality initial products like GLC2000 or CLC2000 at the time of implementing ECOCLIMAP-I may be perceived as a serious issue. Clearly, ECOCLIMAP-I was a first brick in the wall, and its reliability as well as interest were proven thanks to a wide utilization in weather forecast models. There is certainly a value here to quantify the consequences of the modifications and also input datasets used by performing a comparison between the LAI values of the two ECOCLIMAP versions in respect to the fractions of PFTs.
Comparison of LAI temporal profiles (Fig. 8) The evolution of LAI with time is analysed by pixel of 1 km, which already embraces the landscape aggregation of PFTs. The following three quantities are considered for further analysis as explained below: -The correlation between LAI temporal profiles of the two ECOCLIMAP versions, in order to assess the potential changes in the dynamics of LAI. This correlation is given only when two ECOCLIMAP PFTs other than just bare land, rock and snow are present (Fig. 8a ). High correlation indicates none or small differences between the two ECOCLIMAP databases, and low or even negative correlation means large differences. -The relative difference, diffrel (%), in maximum and minimum values of LAI (Fig. 8b  and c ), defined as diffrel = V ec2 -V ec1 (V ec2 + V ec1 )/2 × 100 (2) where V is the maximum or the minimum value of LAI on a given grid point and the subscripts ec1 and ec2 denote the ECOCLIMAP-I and ECOCLIMAP-II databases, respectively.
The value of LAI max (Fig. 8b ) in ECOCLIMAP-II is higher for the main forests and herbaceous areas, and lower for main crops and semi-desert regions. It has already been mentioned that MODIS LAI maximum values, on which the ECOCLIMAP-II LAI is built, are higher for forested areas than, for instance, the LAI values from CYCLOPES, which made use of SPOT/Vegetation observations  (Baret et al. , 2007). Otherwise, and perhaps incidentally, maximum heights were found to be generally equivalent between MODIS and CYCLOPES LAI values  (Weiss et al. , 2007) , and the reason why MODIS was selected was the availability of longer time series of datasets. The results of this comparison in terms of correlation are summarized in Table  2. Median correlation is always higher than 0. 9, showing the good correlation between the three datasets.
Different elements of an explanation can be found, like an increased number of burn scars for the Mediterranean region but also a better pixel resolution in the case of ECOCLIMAP-II. However, the greater area of bare soil in place of rock zones, notably at high altitudes in Norway and in the Alps, seems to be an artefact, rather than being inherent in the differences in method and data quality between the two versions of ECOCLIMAP. The fraction of bare land is lower along the coasts of the Mediterranean (-10 %), the explanation for which may be found in management policy. In the southern deserts of the domain, some bare soil has become rock and conversely so, which is thought to be due to the method. Concerning snow targets (Fig. 9c ), some snow plots in ECOCLIMAP-I are replaced by land cover with 10 % of snow and 85 % of bare land in ECOCLIMAP-II.
Comparison with AGRESTE for France Statistics from the French Ministry of Agriculture and Forests are brought together in the AGRESTE database (http://agreste. agriculture. gouv. fr/). AGRESTE is used here to compare with the ECOCLIMAP-II fractions of PFTs computed for each of the 95 administrative divisions (departments) of metropolitan France. The fractions of PFTs are first weighted by the representative fractions of the covers in each department and then summed. These fractions are also reduced to 6 common distinctive PFTs: forests, grassland, C3 crops, C4 crops, permanent crops, plus all the other PFTs grouped into one. Further, the information contained in AGRESTE hectares and ECOCLIMAP-II kilometre pixels is converted into percentages of land use in the administrative divisions, and compared at department level for these 6 types.
When implementing ECOCLIMAP-II over other continents, this classification process should probably be simplified and made more automatic. On the contrary, the strategy to assign fractions of PFTs and LAI time series to covers should be rather similar. It emerges that, at moderate resolution, typically of one kilometre, the majority of pixels are mixed at the scale of measurement, particularly near the Mediterranean. Since the genuine asset of the ECOCLIMAP-II database is to include coherent sets of biophysical variables primarily used in meteorology, the consistency of variables like LAI and fraction of vegetation was verified after broad-scale aggregation. The uncertainty given on ECOCLIMAP-II surface parameters according to their intra-class and inter-annual variability favours their use in data assimilation systems of carbon and water budget models.
b. Rank Product (RP) False Discovery Rate (FDR) values used to estimate differentially expressed genes in Puccinia monoica-induced pseudoflowers ('Pf') compared to uninfected Boechera stricta stems and leaves ('SL'). Genes with RP FDR value < 0. 05 are considered significant. Gene ontology terms describing biological processes (GOBP) from Arabidopsis thaliana TAIR database version 10. doi: 10. 1371/journal. pone. 0075293. t001.
This will also provide clues towards defining what makes a given strain more aggressive than others on a specific host. However such comparative genomic approaches are actually hampered by the fact that T3E inventories in multiple strains have not been accurately established: several T3E genes have been overlooked by automatic annotation programs and/or have been incorrectly predicted. Moreover, the lack of a unified nomenclature for RSSC T3E is confusing for a non-expert since many T3E genes from RSSC strains have different names in the published literature (Pop, Avr, Brg, Rip, Hpx or Lrp proteins). This doesn't help the already difficult task of identifying orthologous and paralogous genes in strains harboring between 46 to 71 T3E genes. This work presents an integrative and comprehensive database for the T3E of the RSSC.