dataesr/scientific-paragraphs-categorization huggingface dataset:
This interpretation is supported by the fact that in both observations and simulations, at low discharge rates (corresponding to wintertime), DOC concentrations exhibit a strong positive correlation with river discharge, while this relationship becomes insignificant at higher levels of river discharge (Fig. 5b ). Thus wintertime DOC concentrations suffer from the same deficiencies in model representation as those for water discharge. In other words, the stand-alone representation of DOC leaching is satisfactory; when it is sensitive to river discharge, it suffers from the same shortfalls identified in Sects. S2 and S3. Modelled DOC concentrations in stream, river, and groundwater are evaluated against data and discussed in Sect. S5.
) throughput. Evaluation of modelled CO 2 evasion is beset by problems, not least that no data on this quantity to our knowledge have been recorded for the Lena (see Sect. S6). Figure  6  summarises some of the results from the simulated water body CO 2 outgassing flux. Year-on-year variation in basin-wide evasion from river, stream, and floodplain sources combined exhibits a marked increasing trend over the course of the 20th century, increasing from a minimum of ∼ 1. 6 Tg CO 2 -C yr -1 in 1901 to a maximum of ∼ 4. 4 Tg CO 2 -C yr -1 in 2007 (+300 %, Fig. 7a ). Smoothing the data over a 30-year running average yields a dampened net increase in basinwide evasion of ∼ 30 % (Fig. 7a ). Thus yearly evasion flux is some 105 % of yearly DOC discharge to the coast from the Lena basin and 51 % of C exported from soils to headwaters as CO 2 or DOC.
The high refractory proportion of drainage flow is expected, as drainage leaches older, relict soil and litter matter. Because of its longer residence time within the soil column, labile DOC carried downward via soil infiltration will tend to be metabolised in situ before it can be exported to the hydrological network, further increasing the proportion of refractory carbon. By contrast, floodplain   2009 ) (black crosses); and observations from a global non-peat temperate and high-latitude meta-analysis (black circles) reported in  Laudon et al. (2012). The blue region represents permafrost-affected areas, while the orange region represents permafrost-free areas. The green region bounds the area of overlap in MAAT between the observed and simulated datasets. The dark red shaded area corresponds to the MAAT "zone of optimality" for DOC production and transport proposed by  Laudon et al.
(2012). Regression curves of DOC against MAAT for each of the separate datasets are shown for each individual dataset. DOC export (Fig. 9 ) is composed of a more nuanced mix of both reactivity classes, reflecting its relatively greater dependence on the current year's "fresh" biomass as source material (62 % labile DOC versus 38 % refractory DOC, yearly averaged) for carbon leaching. For both the river and stream pool, mean DOC concentrations are dominated by refractory carbon sources. When averaged over the year, the dominance of the refractory DOC carbon pool over its labile counterpart is also evident for all DOC inputs to the hydrological routing except for floodplain inputs, as well as within the "flowing" stream and river pools themselves. This is shown in Table  2 , where the yearly averaged percentage of each carbon component of the total input or reservoir is subdivided between the "north" and "south" of the basin, and these splits are arbitrarily imposed as the latitudinal midpoint of the basin itself (63 • N).
Acknowledgements. Simon P. K. Bowring  acknowledges funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement no. 643052 , " C-CASCADES ". Simon P. K. Bowring  received a PhD grant. Matthieu Guimberteau  acknowledges funding from the  European Research Council Synergy  grant  ERC-2013-SyG-610028 IMBALANCE-P. Ronny Lauerwald  acknowledges funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 703813  for the  Marie Sklodowska-Curie European Individual Fellowship "C-Leak". Bertrand Guenet  acknowledges funding from the  European Union   Horizon 2020  Project  CRESCENDO  under contract  641816. Financial support. This research has been supported by the  Marie Sklodowska-Curie ESR  grant (grant no.
Code and data availability. The source code for OR-CHIDEE MICT-LEAK revision 5459 is available via https://doi. org/10. 14768/20181114002. 1 (last access: 1 August 209; Bowring, 2018). Primary data and scripts used in the analysis and other supplementary information that may be useful in reproducing the author's work can be obtained by contacting the corresponding author. This software is governed by the CeCILL licence under French law and abiding by the rules of distribution of free software. You can use, modify, and/or redistribute the software under the terms of the CeCILL licence as circulated by CEA, CNRS, and INRIA at the following URL: http://www. cecill. info (last access: 1 August 2018). Supplement. The supplement related to this article is available online at: https://doi. org/10.
Aim: The EUNIS Habitat Classification is a widely used reference framework for European habitat types (habitats), but it lacks formal definitions of individual habitats that would enable their unequivocal identification. Our goal was to develop a tool for assigning vegetation-plot records to the habitats of the EUNIS system, use it to classify a European vegetation-plot database, and compile statistically-derived characteristic species combinations and distribution maps for these habitats. Location: Europe.
Methods: We developed the classification expert system EUNIS-ESy, which contains definitions of individual EUNIS habitats based on their species composition and geographic location. Each habitat was formally defined as a formula in a computer language combining algebraic and set-theoretic concepts with formal logical operators. We applied this expert system to classify 1,261,373 vegetation plots from the European Vegetation Archive (EVA) and other databases. Then we determined diagnostic, constant and dominant species for each habitat by calculating species-to-habitat fidelity and constancy (occurrence frequency) in the classified data set. Finally, we mapped the plot locations for each habitat. Results: Formal definitions were developed for 199 habitats at Level 3 of the EUNIS hierarchy, including 25 coastal, 18 wetland, 55 grassland, 43 shrubland, 46 forest and 12 man-made habitats.
The expert system classified 1,125,121 vegetation plots to these habitat groups and 73,188 to other habitats, while 63,064 plots remained unclassified or were classified to more than one habitat. Data on each habitat were summarized in factsheets containing habitat description, distribution map, corresponding syntaxa and characteristic species combination. Conclusions: EUNIS habitats were characterized for the first time in terms of their species composition and distribution, based on a classification of a European database of vegetation plots using the newly developed electronic expert system EUNIS-ESy. The data provided and the expert system have considerable potential for future use in European nature conservation planning, monitoring and assessment.
Still, most of the terrestrial habitats of EUNIS can be successfully defined using methods of vegetation science. In recent years, the EEA recognized the EUNIS Habitat Classification as a key tool for assessing progress towards the European Union biodiversity targets and global Aichi targets. EUNIS became a European reference to which national and regional classifications and various data sets could be linked in the framework of the INSPIRE Directive. As such, EUNIS enables structured dialogue between different networks of experts, including those describing habitats through in-situ vegetation sampling, those working with satellite imagery, and those developing and evaluating various policies. To improve these uses of the EUNIS Habitat Classification, the EEA initiated a process of its revision at Level 3 (for the terrestrial realm) and 4 (for the marine realm) of the classification hierarchy.
eionet. europa. eu/). The public consultations resulted in further changes in the delimitation of individual habitats and their names. Based on the consultation proposals, a refinement of the classification for grassland, shrubland and forest habitats was made by  Schaminée et al. (2018) , for coastal and wetland habitats by  Schaminée et al. (2019)  and for vegetated man-made habitats by  Schaminée et al. (2020). The work on the remaining sections is under way. The recent compilation of the European Vegetation Archive (EVA;  Chytrý et al. , 2016) , a continent-wide integrated electronic database of vegetation-plot records, and the development of computer expert systems for classifying huge data sets of this kind  (Bruelheide, 1997 (Bruelheide, , 2000;; Kočí et al. , 2003; Chytrý, 2007 Chytrý, -2013;; Landucci et al.
, 2018). Such superficial, and in K E Y W O R D S coastal habitat, diagnostic species, distribution map, dune vegetation, European Nature Information System (EUNIS), European Vegetation Archive (EVA), expert system, forest, grassland, habitat classification, man-made habitat, shrubland, vegetation database, vegetation plot, wetland CHYTRÝ eT al. some cases inconsistent, characterization confused the meaning of the EUNIS habitats. Therefore, the current interpretation of the same habitat type can vary among European countries. Our aims here are to: (a) develop a classification expert system for automatic assignment of vegetation-plot records to coastal, wetland, grassland, shrubland, forest and man-made habitats of the revised EUNIS Habitat Classification at Level 3 of the classification hierarchy; (b) base this system on algebraic and set-theoretic concepts combined using formal logic; (c) assign all available European vegetation plots to EUNIS habitats; (d) define the characteristic species combination for each habitat based on a statistical analysis of the plots assigned to this habitat by the expert system; and (e) provide distribution maps of individual habitats based on the location of vegetation plots assigned to these habitats.
| Data sources The primary source for producing characteristic species combinations and maps for EUNIS habitats was a data set of European vegetation-plot records (henceforth "vegetation plots" or "plots"). Such plots typically contain a full list of vascular plant species, often also a list of bryophytes and lichens, estimates of cover abundance of each species and various additional sources of information on vegetation structure, location and environmental features in the plot  (Dengler et al. , 2011). These plots were extracted from the EVA database  (Chytrý et al. , 2016; accessed on 19 May 2020) , and several other databases not included in EVA (see the full list of databases used in this study in Appendix S2). The geographical scope was the whole of Europe (including the European part of Russia), Azores, Madeira, Canary Islands, Anatolia, Cyprus, Georgia, Armenia and Azerbaijan.
The data set was prepared using the Turboveg 3 program  (Hennekens, 2015)  and analysed using the Juice 7. 1 program  (Tichý, 2002). It is the most extensive data set of vegetation plots ever analysed (compare  Bruelheide et al. , 2019). The taxon names in this data set originated from various national and thematic international databases, most of them managed in Turboveg 2  (Hennekens and Schaminée, 2001) , which use different taxon lists with partly inconsistent taxon concepts and names. Taxonomy and nomenclature were unified using Turboveg 3 in two steps. Firstly, the names from the original databases were interpreted by regional botanists, considering the taxonomic concepts and nomenclature used in the focal region of each database. This step was important because it solved regional differences in the use and meaning of some taxon names.
In this step, taxon lists of most of the European vegetation-plot databases were matched to accepted names in the SynBioSys Taxon Database, an unpublished working database of taxon names and concepts used in the EVA project  (Chytrý et al. , 2016). Secondly, the names of vascular plants The names of bryophytes and lichens followed the SynBioSys Taxon Database. The cover of individual species was, in most vegetation plots, recorded using a cover-abundance scale (70% of plots were recorded using a variant of the Braun-Blanquet scale;  Westhoff and van der Maarel, 1973). We transformed all of these scales to the arithmetic mid-point percent cover values corresponding to the individual cover-abundance classes following the default conversion of the Turboveg 2 program  (Hennekens and Schaminée, 2001).
| EUNIS-ESy: An expert system for identifying EUNIS habitats in vegetation-plot databases The new classification expert system EUNIS-ESy (= EUNIS Expert System) was developed for identifying coastal, wetland, grassland, shrubland, forest and vegetated man-made habitats of the EUNIS Habitat Classification based on species composition and cover abundances of particular species or species groups. In the habitats that are difficult to distinguish based on purely floristic criteria, plot-location criteria were added. EUNIS-ESy is based on formal definitions of habitats written as logical formulas in an editable script stored as a TXT file (Appendix  S3 ). The computer program that runs the expert system evaluates all the plots of a vegetation database and checks for each of them whether it meets the conditions of one or more of the formal definitions of habitats included in this script.
Section 1 merges selected taxon names. In most cases, it merges subspecies, varieties or forms to the species level. We did so in order to improve the consistency of taxonomic concepts across the data set because some authors recorded only species while others also recorded infraspecific taxa. Further, we merged taxonomically difficult groups of species containing many misidentifications into species aggregates. Section 2 enumerates species belonging to individual species groups that characterize particular habitats or groups of habitats and are used in the formal definitions of habitats. A single species can be assigned to more than one group. The initial lists of species included in the groups were compiled from relevant phytosociological literature, national habitat handbooks, personal field experience, data from our previous EUNIS reports  (Schaminée et al.
s. l. ). A range of the quantitative criteria can be defined by a combination of two statements, e. g. , "<$$N Altitude (m) GE 500 > NOT <$$N Altitude (m) GR 1,000>" defines an altitude from 500 m to 1,000 m a. s. l. The location information must be provided for each vegetation plot in a specific database field ("header data" in the terminology implemented in Turboveg and Juice). The following variables were used in EUNIS-ESy to define the location-based assignment rules: • Country -a qualitative variable containing country names This means that a plot is assigned to the habitat type F12 if the total cover of the functional group of the Arctic and alpine bryophytes and lichens is greater than the cover of all the other species in the plot, at the same time at least two species of this group are present, the total cover of Sphagnum species or trees is not greater than 5%, and either the latitude is greater than 65°N or the plot is from Iceland.
| The hierarchical structure of the expert system The expert system was developed hierarchically. Each habitat definition was assigned a priority degree. When the expert system is running, the definitions with the highest priority are applied to the data set first, and the plots that meet the requirements of these definitions are assigned to the habitats, while other plots remain unclassified. Then, the definitions with lower priority are applied to the remaining unclassified plots. The current study deals only with the EUNIS habitat groups that were revised so far, i. e. N (Coastal), Q (Wetlands), R (Grasslands), S (Shrublands), T (Forests) and V (Man-made). However, the hierarchy of the expert system has been designed to include the vegetated habitats from the other groups (A -Marine, C -Inland surface waters, and H -Inland sparsely vegetated) once their revision is finished and published.
Dominant species are those that often reach high cover in a particular habitat, thus determining the habitat physiognomy. For the purposes of computing the characteristic species combination for each EUNIS habitat at Level 3 of the classification hierarchy, we used a data set of all the plots classified at this level, including the vegetated marine (A), inland surface water (C) and inland sparsely vegetated (H) habitats, which were defined provisionally in the expert system. We performed a stratified resampling of this data set  (Knollová et al. , 2005)  to balance the spatially uneven sampling effort across Europe, i. e. a high concentration of vegetation plots in relatively small areas contrasting with low density or absence of vegetation plots in other, often large areas. This procedure should reduce the bias in identification of diagnostic, constant and dominant species, especially for those species that are frequent in heavily sampled areas but rare or absent elsewhere.
The resampled data set contained 233,352 plots, i. e. 28% of all the plots classified to Level 3 habitats, but it was more balanced and more representative than the original data set. Diagnostic species were determined based on species fidelity, i. e. the degree of concentration of species occurrences in each group of plots representing a Level 3 EUNIS habitat. Fidelity was calculated using the phi coefficient of association  (Sokal and Rohlf, 1995; Chytrý et al. , 2002)  standardized as if each habitat was represented by the same number of plots  (Tichý and Chytrý, 2006). The species with a value of phi greater than 0. 15 for a particular habitat were considered as diagnostic for this habitat. This threshold was selected arbitrarily as F I G U R E 1 A scheme of the EUNIS-ESy classification expert system with eight hierarchical levels indicated by the numbers on the left side.
| Remarks on the practical application of EUNIS-ESy EUNIS-ESy can be used to assign vegetation plots to habitat types using the script provided in Appendix S3. • Plot location information. Because EUNIS-ESy classifies habitats (and not purely vegetation types), it requires that input data contain information on the location and some environmental features, in addition to species composition and covers. This information includes plots' geographical coordinates, altitude, and their location in specific countries, ecoregions  (Dinerstein et al. , 2017) , on the coast, or in coastal dunes. Location-based criteria are used in 90 (48%) habitat definitions. For some habitats, they are essential, whereas for others they are only used for removing geographical outliers. If the information on location was missing in the input data, the expert system would classify the plots, but the classification might be wrong, especially for the habitats for which the location criteria are essential to the definition.
If the location information (except coordinates) is not available in the input data, the Turboveg 3 export function can derive it from an overlay of plot coordinates with relevant GIS layers and store the values (e. g. , location on coastal dunes or in a certain ecoregion) to the header data of vegetation plots. However, the expert system itself does not extract this information from plot coordinates. • Tested vs not-tested habitats. The current version of the expert system was tested for the coastal, wetland, grassland, shrubland, forest and man-made habitats (EUNIS habitat groups N, Q, R, S, T and V). It also contains preliminary definitions of vegetated marine habitats, inland surface water habitats and inland sparsely vegetated habitats. However, these definitions have not been tested, and the proportion of plots misclassified by these definitions may be high.
| Characteristic species combinations and distribution maps of habitats Characteristic species combinations provided in this study are based on a statistical analysis of a large database of European vegetation plots, following the approach originally proposed by  Chytrý and Tichý (2003)  for an analysis of the Czech National Phytosociological Database and subsequently used for analyses of vegetation-plot databases in other countries  (Jarolímek and Šibík, 2008; Kącki et al. , 2013). The formal division of the characteristic species combination into diagnostic (concentrated in the habitat), constant (frequent, but not necessarily concentrated) and dominant (often attaining a high cover) provides a comprehensive characterization of each habitat through its plant species. Species lists for European vegetation/habitat types are also provided in the electronic factsheets of the European Red List of Habitats  (Janssen et al.
| CON CLUS IONS The expert system EUNIS-ESy introduced in this paper has been shown to effectively assign vegetation-plot records to EUNIS habitats with a high level of accuracy as evaluated by expert judgement. The novel possibility of combining floristic data with plot-specific geographic or environmental data as classification criteria allows enormous flexibility, which makes it possible to apply the expert system not only to floristically defined vegetation types but also to other habitat types that are defined through a combination of vegetation type and other criteria. The expert system approach to habitat identification has several advantages: (a) it enables identification of vegetation plots.
from the SynBioSys Taxon Database and the names from the original databases that did not match any name in the SynBioSys Taxon Database were translated to the nomenclature of the Euro+Med PlantBase (Euro+Med, 2006-2020; ww2. bgbm. org/EuroP lusMed), using a complete list of accepted names and synonyms of European and Mediterranean vascular plant taxa provided by the Berlin-Dahlem Botanical Garden and Botanical Museum in February 2020.
Taxon nomenclature and taxonomic concepts in the input data set of vegetation plots should correspond to those of the Euro+Med PlantBase. If an export from the EVA database is used for the analysis, nomenclature can be automatically converted to the Euro+Med standard in Turboveg 3 using the in-built SynBioSys Taxon Database. Standard data ex- ports from EVA include this conversion. If an export from another database (e. g. , a database managed in Turboveg 2) is used, taxon nomenclature used in this database has to be first converted to the Euro+Med PlantBase standard. For most European vege- tation-plot databases, we provide a tool for automatic nomen- clature conversion to Euro+Med in a series of files that contain conversion instructions for species lists used in individual data- bases (http://doi.
org/10. 5281/zenodo. 3841729). These files can be applied to the species-by-plot tables using the expert system functions in the Juice program. After they convert species names in the table to the desired standard of Euro+Med, another mod- ification of species names must be done using EUNIS-ESy, which guarantees that the taxon names and concepts in the analysed table are the same as used for classification by EUNIS-ESy. This second step follows the instructions in Section 1 of the EUNIS- ESy script. In particular, it merges subspecies and varieties to the species level and closely related species to broader taxonomic en- tities such as aggregates. This script can be run in Juice (https://www. sci. muni. cz/botany/juice/),Turboveg 3 or in R using the code developed byBruelheide et al. (https://git.