dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , the groupers sampled in Vietnam. The tree was rooted with Diploprion bifasciatum. The species name recovered using the morphology and the BIN provided by BOLD was mapped onto this tree, to flag polyphyletic species and to reveal intraspecific and interspecific diversity within the Epinephelidae. Heemstra and Randall (1993) ,  Craig et al. (2011) , and this study. In bold, species name that represent new records for Vietnam. For each species, the associated BINs in BOLD, in  Ma et al. (2016)  and in this study, are listed. In the  The second phylogenetic analysis was performed on a large dataset that comprised: -One sequence representative of each species and lineage (BIN) observed in this study. -All sequences published by  Ma et al. (2016) thatrepresent 87% of the known species diversity in the Epinephelidae and that may be considered as the reference.
-The COI sequences of Epinephelus moara resurrected by  Liu et al. (2013) , Epinephelus craigi described by  Frable et al. (2019) , and Epinephelus fuscomarginatus + E. magniscuttis described and redescribed respectively by  Johnson and Worthington Wilmer (2019). -One COI barcode of each BIN associated with a species name in BOLD, i. e. , those present in the updated species checklist for Vietnam. BINs have been identified through the public data portal of BOLD (consultation 29/11/ 2018). The aim of this second phylogenetic tree was to check concordance between species identification based on morphological criteria (present data) and DNA barcode (BOLD), to reveal misidentification or introgression in BINs associated with grouper species present in Vietnam and, finally, to highlight species that are polyphyletic due to the presence of several BINs.
A special attention was paid to polyphyletic species, those that present several BINs that form a clade in the phylogenetic tree, in order to determine the evolutionary significance of these BINs. All public COI barcodes belonging to BINs associated with a species name were uploaded and added to "Vietnamese" grouper data (Table  S4 ). Haplotypic networks were estimated using the TCS method of Clement et al. (  2002 BOLD:ACE6821 Epinephelus morrhua PY130516-01 HL060716-4 HL060716-5 HL060716-3 HL060716-1 Esp131015-4 Esp131015-1 Esp131015-2 Esp131015-3 BOLD:ACS0266 Epinephelus bruneus BOLD:AAD8873 Epinephelus polyphekadion 270518-KH1 CD161117-1 COI CD161117-6 COI BOLD:ACC0501 Epinephelus erythrurus 121117-A CD161117-9 TTVT-1 1 TTVT-3 2 BOLD:AAD8682.
fasciatus BINs (Fig. 3 ) and presented a strong allopatric distribution limited to the Red Sea and the West Indian Ocean (Fig. 4i ). All these results stress the presence of a species complex in E. fasciatus and, because the type locality of this species is the Red Sea, this suggests that the valid E. fasciatus should be limited to the Red Sea and the West Indian Ocean (= the BIN BOLD:AAB1334). All other BINs associated with the name E. fasciatus may correspond to another species for which phylogenetic relationships with E. irroratus is not clear. More taxonomic and phylogenetic work is necessary to solve 19| 23 this question, given that genetic data concerning E. retouti is limited to only 7 COI barcodes. Last, the two BINs (BOLD:AAF1066 and BOLD:ADB3169) associated with specimens identified as E.
Acknowledgments The authors are indebted to  Kayan Ma ,  Joey DiBattista  and  Alan Whitfield  for advice and data exchanges. We also thank  Đặng Thúy Bình  and  Trương Thị Oanh  who helped  J. -D. Durand  for the sampling in  Nha Trang. Thanks  to  D. McKenzie  for English language editing. We thank two anonymous reviewers for providing helpful comments on earlier drafts of the manuscript. Funding information This study was supported by  IRD  and " Fondation Total " (fondation. total. com)through the  "DECODIV" project (grant   BIO-2014-005 ).
Statistical Analysis Significant differences in the content of fatty acids and lipid quality indexes in the muscles of the fish examined were estimated using a one-way analysis of variance (ANOVA) after testing for the homogeneity of variance (Levene's test) at a significance level of p ≤ 0. 05. Similarly, differences in the mean content of mercury between species and organs of the same species were calculated using STATISTICA 12 software (StatSoft, Kraków, Poland). The correlation coefficients between the content of mercury in the organs of fish and their size (body weight and total length) were evaluated using STATISTICA 12 software.
Characterization of SiO 2 NPs and formation of allergen-NP conjugates Particle size analysis resulted in a distribution of the mesoporous SiO 2 NPs with an average diameter of 203 ± 4 nm in the CCM, as calculated from three individual measurements using NTA (Fig. 1A ). A primary particle size of 73 ± 11 nm was determined by analysing SEM pictures (Fig. 1B ) which was done by measuring 100 particles using Fiji software. 54 he suspension stability towards sedimentation of the SiO 2 NPs was similar in the different media, as the measured silica content of the sedimentation samples remained constant for up to 60 min in H 2 O, serumfree and serum-containing huAEC medium measured using the blue silicomolybdic assay. 44 At incubation times of 2 hours and longer, the measured silica content decreased rapidly (Fig.
1C ); coinciding with the ISDD modelling data showing a gradual increase of NPs at the surface of the adherent cells (Fig. 1D ). For the deposited dose under submerged conditions the ISDD calculations yielded NP concentrations of 0. 45 to 3. 55 μg cm -2 for the different exposure time points. The deposition efficiency of the Vitrocell Cloud system has been estimated to be between 50% and 80%, leading to a deposited dose of NPs onto the cells of between 2. 22 to 3. 55 μg cm -2. 51 For both the SiO 2 NPs and purified Bet v 1 the endotoxin contamination was determined using the HEK-Blue TLR4 assay (Invivogen, San Diego, USA). For the used concentrations of particles, the LPS amount was below the detection limit of the assay; which is 0. 02 ng mL -1. The LPS amount of the recombinant Bet v 1 was also below the detection limit of the assay for the protein concentration used for cell exposure.
Acknowledgements This work was funded by the  international PhD program "Immunity in Cancer and Allergy" of the Austrian Science Fund (FWF , grant no. W01213 ) and by the  Allergy Cancer Bio-Nano Research Centre of the University of Salzburg. The contributions of AC, ACG and TS were supported by the  Fonds National de la Recherche (FNR)  funded  VitralizeMe  project ( PoC18/12559604 ).
2014) , which were then further consolidated by  Burkhard et al. (Burkhard et al. 2018 ): 1. mapping of ecosystems; 2. assessment of ecosystem conditions; 3. assessment of ES; 4. integrated ecosystem assessment with link to national environmental accounting/ natural capital accounting systems. In accordance with the requirements of the EU Biodiversity Strategy 2020, a system of national initial assessment and evaluation of ES for Germany was first developed and coordinated. The assessment of ecosystem condition comprised mainly pressures, the natural condition of ecosystems and the state of biological diversity. Ecosystem services were measured and evaluated primarily based on land use, official data for agricultural and timber production, water use and population data  (Albert et al. 2015 , Grunewald et al.
The aim is to demonstrate the importance of ecosystems for society and to record their changes as a basis for political action. As in other statistically-orientated work (and in contrast to the field of planning), this is done here in the field of SEEA without the development of direct proposals for action. Currently, Germany's federal states do not follow a uniform mapping system to provide an exhaustive recording of ecosystems. To establish nationwide "ecosystem accounting", it is therefore essential to develop a common structure of ecosystem classifications, with the help of which diverse national datasets on the extent, condition and services of ecosystems (some of which use their own forms of classification) can be consistently integrated to create a standard ecosystem accounting system.
With this aim in mind, we present here a concrete proposal on how uniform and spatially accurate national datasets of GIS data on land uses and ecosystems, which are compatible with EU-wide approaches, can be integrated with other regularly updated data sources, including sample-based surveys, so as to generate as complete and up-to-date picture of Germany's ecosystems as possible, that can be filled with the available data on condition and linked with data and required models on ecosystem services. The challenge to be overcome is to find the degree of thematic detail, especially whether and which functional characteristics should be included in order to support the further accounting steps, e. g. analysis of spatial migration between ecosystems, integrating relevant condition parameters and applying very different models and valuation approaches to assess ecosystem services, including the contribution of the various ecosystems to biodiversity protection on the national scale.
Instead, the concept of "Partial Ecosystem Potentials" was developed, where for each type of use, such as agriculture and forestry, groundwater recharge and extraction, recreation or nature conservation, different types of quality and condition parameters are used  (Mannsfeld and Grunewald 2015). Thus, in the study carried out, where available, the specific parameters for the condition and suitability of ecosystems for each service were specifically integrated directly into the calculations for the service account. The data used, for example, for the calculation of the ecosystems' contribution to agricultural production, were based on a geo-dataset of the Müncheberger Soil Quality Rating, which in turn is an aggregation of various data on soil, climate, groundwater level, slope inclination etc.
(Mueller et al. 2007 ). The extent account itself can use quite highly aggregated ecosystem classes such as the CLC classes used here, if the detailed service-specific information on the ecosystems is specifically supplemented during the implementation of each service account. With regard to the project objectives, it was decided to wait for the results of further European and international discussions before presenting a separate approach for a general condition account. In the following, we first discuss the basis for recording data on ecosystems at EU and national level (Sections 2 and 3). Based on this, we present a concrete proposal for processing high-resolution data to classify ecosystems in Germany that is compatible with EU-wide approaches. With the ecosystem contribution to conserve biodiversity as an example, we show how this system can be complemented with detailed special information to assess certain services.
2014; Erhard et al. 2017). This regularly updated geo-dataset contains spatial information on land cover broken down into 44 classes. CORINE Land Cover data is provided for the years  1990, 2000, 2006, 2012 and 2018. The time series also includes a land-change layer, which highlights changes in land cover and land use. The spatial resolution is at least 25 ha. The European Nature Information System (EUNIS) offers a more detailed typology of ecosystems, taking into account the distribution of species and local spatial characteristics. The general MAES typology of ecosystems  (Maes et al. 2013 ) is organised into two levels. The first level is defined as "major ecosystem categories" and includes three main classes: 1) Terrestrial; 2) Fresh water; 3) Marine. At the second level, the major categories are subdivided into more detailed subclasses according to the character of their biophysical features.
The terrestrial ecosystems are subdivided into seven subclasses: 1) Urban; 2) Cropland; 3) Grassland; 4) Woodland and forest; 5) Heathland and shrub; 6) Sparsely vegetated land; and 7) Wetlands. The fresh-water ecosystem class contains only one subclass, namely Rivers and lakes. Marine ecosystems are subdivided into four subclasses: 1) Marine inlets and transitional waters; 2) Coastal; 3) Shelf; and 4) Open ocean. Annex 2 of the MAES report  (Maes et al. 2013)   In Slovakia, a generalised nationwide map of ecosystems has been created using agricultural, forestry and environmental data  (Černecký et al. 2020). The resulting polygons are classified as ecosystem/habitat types in accordance with the EUNIS classification system. The spatial precision of the data is determined by that of the field data, which was mostly created at scales of between 1:10,000 and 1:5,000.
The data are stored in the form of a geo-database containing more than 1,000,000 polygons. (Keil et al. 2015 ). An approach was developed to allow a redundancy-free description of all the land and marine areas under German jurisdiction by means of five main ETs, each of which encompasses three sub-types (or in the case of "forest and woodland", two sub-ETs). This system also takes account of dynamic or complex ETs such as "transitional woodland/shrub" (CLC 3. 2. 4) (see Table  1 ). These have been largely adopted to enable the correct assignments to be made and thereby ensure a precise and consistent Europe-wide ecosystem accounting (EEA 2014b;  Maes et al. 2014; Erhard et al. 2016). The relationship between the ETs and the EUNIS types can be found in Suppl. material 1 (Table  A : Proposal of a classification system for ecosystem types (ETs) in Germany, assignment to the European ecosystem types according to EUNIS and to the CLC types of the database LBM-DE).
Base data for a detailed spatial presentation and analysis The specification of EU-wide approaches and data can be realised for Germany by means of the "Digital Basic Landscape Model" (Digitales Basis-Landschaftsmodell or Basis-DLM), derived from the "Official Topographic-Cartographic Information System" (ATKIS) and the "Digital Land Cover Model for Germany" (LBM-DE)  (Schorcht et al. 2016) , which represent the official National Spatial Data Infrastructure. The Basis-DLM contains "areas of actual use" (Flächen zur tatsächlichen Nutzung or TN) that are regularly monitored by the surveying authorities of the federal states of Germany in accordance with standard rules and with no spatial overlaps  (Krüger et al. 2013; BKG 2016a). The LBM-DE for Germany is provided by the Federal Agency for Cartography and Geodesy (BKG), using classified satellite image data and the ATKIS-Basis-DLM, supplemented by other technical data.
It is compatible with CLC data (and obeys the same nomenclature), but has a much more detailed scale of 1:50,000 (minimum mapping unit 1 ha compared to 25 ha for CLC mapping)  (BKG 2016b , BKG 2018a , BKG 2019b). Since 2009, the LBM-DE has been used to derive the CLC dataset for Germany  (Hovenbitzer et al. 2014 ). However, it contains no detailed spatial information on issues that are required to assess ecosystem services, such as the natural contribution to agricultural production (natural soil fertility), flood protection, decomposition of pollutants in watercourses or the service of an ecosystem for nature conservation. Such information has to be integrated into the system from other sources (for agricultural production see the introduction, for nature conservation see 3. 4). Alongside the derivation of CLC, the LBM-DE is favoured as a basis for ET mapping due to the fact that data are regularly updated by the BKG, namely every three years.
Linear infrastructure and small structural elements (BKG 2018a) are missing in the LBM-DE, which are therefore not considered in the calculation of areal ratios. However, these missing elements can be taken from the ATKIS-Basis-DLM, as here linear objects narrower than 12 m are featured along with their object axes. These are modelled as buffered areas and introduced into the LBM. SEEA suggested to use the length instead of an estimated area for relatively long and narrow linear elements (e. g. rivers). They dissuade from creating areas by means of width specifications in the dataset, because overlaps with neighbouring areas might be produced (UN 2020). In order to guarantee a clear designation of areas, the resulting overlaps resulting from the linear infrastructure and small structural elements were simply removed.
Prioritisation rules were defined for areas of intersection between different ETs, in accordance with the procedures specified by the "Monitor of Settlement and Open Space Development" (IOER 2020a). The applied widths of spatial elements and the assignment to the CLC classes are shown in Table  2. The widths of railtrack, roads and airport runways, as well as unsurfaced roads and watercourses, are in most cases already supplied as an attribute in the ATKIS basic DLM. A type-specific average width was assumed for linear objects with no previouslyassigned values. We assigned a value of 6 m for the width of rock elements (determined by on-site observations in the Erzgebirge). An average width was also assigned to vegetation, determined from HNV farmland data for hedges and tree rows (in Baden-Württemberg, Saxony and Schleswig-Holstein).
Incorporation of nature conservation data to assess biodiversity and to account for the contribution of ecosystems to conserve biodiversity Biodiversity is an essential criterion for the condition of ecosystems (CBD 2010,  Maes et al. 2018 , Geschke et al. 2019). Moreover, the contribution of ecosystems to conserve biodiversity is also an ecosystem service (see CICES 5. 1, cultural services 3. 2. 2. 1 and 3. 2. 2. 2) Therefore, the accounting framework should be structured in a way that all regularly collected data, relevant for the assessment of biodiversity, can be evaluated within the accounting framework in a consistent way. Such data sources are: • the nationwide reporting on the extent and conservation status of FFH types according to Annex I of the Habitats Directive (Deutschlands Natur 2018), • the survey of HNV farmland (Hünig and Benzler 2017), which is regularly carried out on more than a thousand sample areas of 1 km² size within the nationwide monitoring of breeding birds, • the Federal Forest Inventory and • the classification of the ecological watercourse/body status according to the WFD.
In the near future, it may also be possible to integrate data from the planned sample-based ecosystem-monitoring. It is obvious that the above-mentioned sample-based data sources cannot deliver spatially explicit data. They can, however, be used to determine average values for the composition of the DLM-classes on the national level (i. e. what is the share of the different FFH-forest ecosystems in the overall forest coverage of Germany). Furthermore, they provide relevant information for biodiversity conservation on the condition of the DLM-classes as a whole (i. e. ecological status of water bodies) or on parts of them (i. e. conservation status of FFHhabitats). As a basis for the quantitative physical assessment of the contribution of ecosystems to biodiversity, so-called "biotope points" are used.
The scores range from "0" (pavement) to "24" (healthy mires, old (semi-) natural forests). All scores are considered as mean values that can be further increased or reduced by a maximum of three points due to the specific condition. A new list will be published soon which will be even more differentiated, particularly with regard to ocean and shores. At present, there is no database on the coverage or spatial distribution of these 500 biotopes in Germany. One prerequisite to integrate this list into the accounting system is, therefore, a reference system to assign the categories of the list to the ecosystem classification developed here for accounting purposes. Similar reference systems were developed between the biotope list and the categories used in the above-mentioned surveys and between the survey categories and the CLC-classes used for accounting.
The hierarchical overview of the EUNIS habitat types (EUNIS 2019a) was evaluated as a basis upon which to correlate, i. e. the Red List habitat types with the HNV farmland biotope types and the CLC classes (cf. Section 3. 2). As a result, the extended Even for the sample data of the Federal Forest Inventory, ways could be found to interlink between biotope points, inventory data and CLC. If the composition of the forests changes, for example, with regard the age of the tree stocks or the proportion of native tree species, the assignment of the frequencies of combinations of characteristics from the Federal Forest Inventory to biotopes present on the biotope value list enables us to estimate the change in the average biotope value of the forests. Corresponding statements can be made for grassland, for example, if the proportion of HNV grassland changes.
80 million ha for Destatis (Destatis 2019, Destatis 2020) compared to 1. 54 million ha for the LBM-DE (with added roads, unsurfaced roads and railway lines). This difference could be due, amongst other things, to the fact that the two sources assume different standard widths and take different account of marginal areas, such as railway embankments and roadway verges. In addition to the reference year 2018, the CLC classes for the years 2015 and 2012 were calculated using the indicated data (LBM-DE/ATKIS) and aggregated to sub-ET or main-ET. However, the relative brevity of the considered timeframe (three time periods) does not allow us to reliably determine any trends or shiftings, as these may be masked by methodological changes in the classification of land use and land cover in the LBM-DE (especially between the reference years 2012 and 2015) (BKG 2019b).
Any interpretation of the ETs is also complicated by the fact that, for areas such as grassland, the classification of semi-natural open land changes from one time period to the next, even though no actual change of use has probably taken place (see above). In addition, the ATKIS data serving as a base data for the LBM have been changed by a process of systematisation lasting several years (a task completed in 2014), undermining the temporal comparability (AdV 2019). Thus we find a better agreement of the ET area sizes when comparing the years 2015 and 2018 than when comparing 2012 and 2015.
Some regions, especially along Germany's north-western coast or in Bavaria in the country's south-east, experienced strong decreases. However, we also find sporadic regional increases in grassland ratios, especially in some regions of Bavaria, Baden-Württemberg and the Saarland (Fig. 6 ). Overall, however, a negative trend can be discerned. At the same time, it should be noted the IOER Monitor data underlying these evaluations are derived from the Basis-DLM (ATKIS), which, particularly in the case of grassland, does not place a high priority on regular updating. Furthermore, the fact that no statements can be made regarding the quality of grassland is a major drawback, especially with regard to ecosystem services, if we remember that grassland featuring diverse plant species and blossoms has declined significantly in extent (BfN 2009).
So far, no clear trend can be identified from the LBM data for the main-ET "semi-natural open area" (Suppl. material 3). The same applies when we consider hedges, tree rows (from the ATKIS base DLM) and areas of transitional woodland/shrub (CLC 324) (< 1 ha) (from the LBM-DE) in isolation. As groves have only been recorded in the official statistics separately from forests since 2016, it is not yet possible to derive any statements on this feature (Destatis 2019).