dataesr/scientific-paragraphs-categorization huggingface dataset:
Meso-xeric grasslands show the highest means across grain sizes, both for vascular plants and for complete vegetation, while sandy dry grasslands are the poorest type. Alpine grasslands outperform meso-xeric grasslands for complete vegetation at smallest sizes due to the strong richness increase related to the inclusion of non-vascular plants. The relative importance of this fraction in the total richness is even stronger for sandy dry grasslands and Mediterranean grasslands, the latter reaching the second position in the richness ranking for the largest sizes. Figures S5. 13 to S5. 16 combine SARs generated with all GrassPlot data with SARs corresponding to the subset of plots belonging to nested series with seven standard grain sizes for all fine-level vegetation types and the four taxonomic groups.
GrassPlot Diversity Explorer The GrassPlot Diversity Explorer is an easy-to-use online interactive tool that provides users flexibility in exploring and visualizing richness data collected in the GrassPlot database. The GrassPlot Diversity Explorer can be accessed via the EDGG website (https://edgg. org/databases/GrasslandDiversityExplorer). The tool is organized into eight panels (Figure  6 ). The first panel shows species richness in different vegetation types, and two vegetation typologies are used, including the two-level vegetation typology used in the GrassPlot database (see Appendix S2, Table  S2. 2) and phytosociological classes (for details, see Appendix S2, Table  S2. 3). Users can explore species richness by generating boxplots for eight standard size grains and selected taxonomic groups.
The second panel presents species richness in geographical regions and biomes and the third one species-area relationships (SARs) for selected vegetation units. Details on the two-level vegetation typology can be found in the fourth panel, and the fifth panel presents descriptive statistics for the selected dataset. Users can generate these statistics for filtered datasets based on taxonomic group, vegetation types and region of interest. Besides graphs and descriptive statistics, users can also explore the distribution of plots based on selected criteria. Filtered plots are displayed on background maps in the "Map" panel. The seventh panel contains links to the main file of the GrassPlot Diversity Benchmarks. Finally, the last panel, "Information", explains the concepts of biomes, geographical regions and vegetation typologies used in the GrassPlot Diversity Explorer.
Data quality and methodological settings GrassPlot only includes phytodiversity data that were carefully sampled with the aim of recording complete species lists within precisely delimited plots. Large vegetation-plot databases at regional and national (see  Dengler et al. , 2011  for an overview), continental  (Chytrý et al. , 2016)  or global  (Bruelheide et al. , 2019)  scales are naturally less selective as their main aim is to get as many vegetation-plot records as possible. They include, to a large extent, traditional phytosociological relevés, in which the plot borders were often not precisely delimited in the field. Since the analysis of  Chytrý (2001)  of the plot data contained at that time in the Czech National Phytosociological Database, it is well-known that there are also other biases in the data.
This study found, in several phytosociological classes, that the mean richness decreased above a certain threshold area, a pattern explained by the tendency of phytosociologists to select largerthan-average plots in vegetation types that are inherently poorer in species. When comparing the mean richness data from  Chytrý (2001)  for the three classes that are also contained in GrassPlot (Festuco-Brometea, Molinio-Arrhenatheretea, Phragmito-Magnocaricetea) we found substantially lower mean richness in the phytosociological database than in GrassPlot (not shown). Similarly, comparing the mean richness data of Festuco-Brometea grasslands from the Nordic-Baltic Grassland Vegetation Database  (Dengler et al. , 2006)  with GrassPlot data from the same geographic region, we found a good match at 1 m², but increasing relative difference towards larger grain sizes (not shown).
Accepted Article This article is protected by copyright. All rights reserved unexpected as it is often assumed that phytosociologists preferentially sample plots with a species richness above average  (Holeksa & Woźniak, 2005; Diekmann et al. , 2007). By contrast, most GrassPlot data are based on systematic or random sampling or the approach of the EDGG Field Workshops  (Dengler et al. , 2016b) , which aims to maximize between-plot heterogeneity, i. e. , both presumably species-rich and species-poor stands are selected for making plots (which should not bias means, but possibly increase variance). A plausible explanation for the pattern found is that the average completeness of plots in phytosociological databases is lower than most researchers, including ourselves, would have guessed. This indicates that it might be risky to take the richness data from large phytosociological databases at face value.
A more comprehensive study comparing the GrassPlot benchmarks with the mean richness values derived from EVA or sPlot should explore how prevalent such a pattern is and whether its strength varies systematically between regions, vegetation types and grain sizes. While these findings underline the good suitability of typical data contained in GrassPlot for biodiversity analyses, we do not claim that the richness records are 100% complete. It has been shown repeatedly that this is nearly impossible, even when plots are sampled by more than one experienced author (see  Lepš & Hadincová, 1992; Klimeš et al. , 2001; Archaux et al. , 2006). However, the results support the view that the fraction of overlooked species must be minor compared to average phytosociological data and possibly even compensated by an equally minor fraction of erroneously recorded species.
Accepted Article This article is protected by copyright. All rights reserved smaller than between GrassPlot and conventional phytosociological databases. Finally, also the way of recording plants as present in a plot, shoot presence vs. rooted presence  (Dengler, 2008)  can influence richness records as highlighted by  Williamson (2003). In the habitats studied here, a visible effect occurs at grain sizes below 1 m² (Appendix S5, Table  S5. 2) which is consistent with findings of  Güler et al. (2016 ), Cancellieri et al. (2017)  and Zhang (2021). While we trust that our richness data for individual plots are more reliable than most other sources, the aggregated richness patterns reported in this paper in some cases might still be biased or misleading. First, data coverage in GrassPlot is sparser than in other big vegetation-plot databases.
Consequently there might be stronger biases concerning geography and vegetation types. Second, there are a few datasets in GrassPlot that have specifically been collected with the aim of studying sites of exceptional richness (e. g. Merunková et al. , 2012; Roleček et al. , 2014; Hájek et al. , 2020). However, GrassPlot also contains data that have been sampled in regions where a certain vegetation class is known to be poorer in species than in other parts of the respective country. In addition, a prevalence of vegetation plots from one subtype of a certain category might make this entire category appear relatively richer or poorer in species than it is in reality. Similarly, not all biomes contain the same subtypes of a vegetation type. However, the magnitude of such potential biases can be assessed using the GrassPlot Diversity Explorer/Benchmarks via filtering at different levels.
Potential uses and impact Vegetation ecology. In studies on certain vegetation types, it is useful for authors to compare not only the richness values within their sample, but also to know where these are positioned in relation to the average of this vegetation type in the country or biome. Modelling studies could also benefit if they had reliable benchmarks. For example, Bruelheide et al. (  2020 ) used a sophisticated approach to create richness maps of Festuco-Brometea communities in Germany at 1, 10, 100 and 1000 m² with three different approaches based on a traditional phytosociological database, but in the end, they could only "guess" which of their approaches performed best because they lacked benchmarks from more reliable data from exhaustively sampled plots. Macroecology. An increasing number of studies use the enormous amount of vegetation-plot data from national and regional (see  Dengler et al.
, 2011), continental (EVA; Chytrý et al. , 2016)  and global (sPlot;  Bruelheide et al. , 2019)  vegetation-plot databases. This approach has great potential for macroecology as it combines fine-grain sizes with large spatial extents, a combination that could contribute to a more mechanistic understanding of patterns, but for a long time was underrepresented in macroecology  (Beck et al. , 2012). Moreover, vegetation-plot data allow for a much wider range of macroecological analyses than species occurrence databases do  (Dengler et al. , 2011; Bruelheide et al. , 2019). Most of such plot-based macroecological papers take the information in the underlying databases as unquestioned facts. While such studies often address the unequal distribution of plots in space and time  (Lengyel et al. , 2011)  and the preferential sampling of more species-rich communities  (Divíšek & Chytrý, 2018) , and sometimes also their different plot sizes  (Večeřa et al.
Accepted Article This article is protected by copyright. All rights reserved and some studies on β-diversity, while studies on community-weighted means of traits or assembly rules are probably less affected, at least not when assuming that the overlooked species mostly were the rare ones with low cover. Depending on the sensitivity of the study topic towards biased species richness values, macroecological users of vegetation-plot databases have several options: (i) use a subset of regions, vegetation types and/or grain sizes that, according to the comparison with the GrassPlot Diversity Benchmarks, are least affected; (ii) apply contextdependent "correction factors"; or (iii) conduct the study instead with the GrassPlot database. Depending on the question at hand, researchers should also take into account the spatial coverage and representativeness of the respective vegetation-plot databases.
g. , the threshold for alpine grasslands will be different from that for wetlands. In any case, we would like to emphasize that species richness cannot be used as a single criterion, as several naturally species-poor habitats are more species-rich after degradation, such as lower levels of salinity in saline communities. Another typical question in this context is whether a particular management or restoration measure was successful or what is the restoration potential of a specific habitat type. Did the measure achieve the typical diversity of that habitat type? Referring to richness data from the literature is troublesome in such cases as they were often recorded on different grain sizes and usually only at a single grain size, making the "translation" to another grain size challenging. All this is much easier with the GrassPlot Diversity Benchmarks, acknowledging that they largely reflect the situation during the.
Accepted Article This article is protected by copyright. All rights reserved past two decades as there is only a small fraction of 20th-century plots included. We also acknowledge that species number should not always be used as a unique criterion for such assessments, as restoration projects often monitor richness of habitat-specific target species to avoid bias caused by sites with high richness of ruderal or alien species. Finally, we would like to advise again to carefully check plot number and spatial representativeness using the Explorer tool when using these benchmarks. Quality check of data. In all the above-mentioned applications, the GrassPlot Diversity Explorer can be helpful for researchers and students alike to get feedback on how complete their field records likely are. The GrassPlot Diversity Benchmarks provide vegetation-plot databases with the option of checking the reliability of datasets before including them.
For example, datasets with mean richness below the first quartile of the respective vegetation type × region × grain size should be considered carefully. They do not necessarily need to be excluded but could be labelled as doubtful unless the originators provide convincing reasons that the studied stands are actually so species-poor. This quality check may also be used when data from large vegetationplot databases are selected for specific projects. Although we only provide benchmarks for eight specific grain sizes, interpolation of richness data to any other grain sizes can be easily done, as explained in Appendix S6.
Conclusions and outlook The GrassPlot Diversity Benchmarks provide high-quality richness data from a wide range of open habitat types across the Palaearctic realm. The restriction to eight standard grain sizes, each separated by a factor of 10, is similar to some standardized sampling schemes on other continents, such as the Carolina Vegetation Survey in North America  (Peet et al. , 1998)  and the BIOTA Observatories in Africa  (Jürgens et al. , 2012). Seven of the eight grain sizes are already well populated with data, only high-quality observations for 1000 m² are still sparse (which is understandable, given the enormous time effort for a complete sampling of such an area; see  Dolnik, 2003). The amount of data in the underlying GrassPlot database and their spatial coverage are much lower than in the EVA  (Chytrý et al.
Accepted Article This article is protected by copyright. All rights reserved the diversity benchmarks. However, we have shown that species recordings are, on average, apparently much more complete in GrassPlot. Thus, depending on the research question, either EVA/sPlot, GrassPlot or a combination of both might be the best data source. Our study further emphasizes the advantages of standardized methodologies and a set of uniform standard grain sizes. We release this information open access in an easy-to-use format as the GrassPlot Diversity Benchmarks and in the conveniently queried online tool, the GrassPlot Diversity Explorer. Given the uneven representation of vegetation types across biomes, regions and countries, we advise users to check the number of plots available for the selected combination of region and vegetation type in order to obtain reliable benchmarks.
While the GrassPlot Diversity Explorer is already quite powerful, we are considering further extensions in the future, such as an online interface to interpolate richness data between two of our standard grain sizes based on the function presented in Appendix S6, provision of the slope parameter z of species-area relationships as a β-diversity measure (see  Dengler et al. , 2020b) , a selection option for different sampling designs and for phytosociological units below the class level, or maps of mean and maximum richness. While the provided open access data will suffice for many purposes, it is also possible to approach the GrassPlot Consortium with a project proposal (according to the GrassPlot Bylaws; available at https://edgg. org/databases/GrassPlot) for individual plot-based richness records together with environmental data.
Last but not least, GrassPlot is a "living database", which is constantly enriched and improved. Those who would like to join the GrassPlot Consortium with suitable data can approach the first author (IB), who is serving as a database manager. The GrassPlot Diversity Explorer will be dynamically updated with the newest versions of GrassPlot, while a new fixed version of the GrassPlot Diversity Benchmarks is planned to be released regularly.
https://edgg. org/databases/GrassPlot; Dengler et al. , 2018), to provide comprehensive benchmarks of fine-grain plant richness values of Palaearctic open habitats across biomes at eight plot sizes, each separated by order of magnitude: 0. 0001 m 2 , 0. 001 m 2 , 0. 01 m 2 , 0. 1 m 2 , 1.
GrassPlot incorporates standardized vegetation-plot data sampled in precisely delimited plots together with methodological, environmental and structural information from open habitats, e. g. grasslands and other plant communities dominated by herbs, dwarf-shrubs or cryptogams from the Palaearctic biogeographic realm (i. e. , Europe, North Africa, and West, Central, North and East Asia). Besides individual plots, GrassPlot specifically contains multi-scale datasets from nested-plot sampling schemes (e. g. ,Dengler et al. , 2016b).
Data availability statement The aggregated data (as used in this paper) for any combination of the taxonomic group, grain size, vegetation type, region, biome, and methodological settings (rooted vs. shoot; subsetting to only those plots belonging to nested series with at least seven standard grain sizes) are provided in Appendix S3. Future updates will be made available as GrassPlot Diversity Benchmarks (fixed versions) and dynamically in the GrassPlot Diversity Explorer (both at https://edgg. org/databases/GrasslandDiversityExplorer). The underlying plot-level data are available upon request from the GrassPlot database, following its Bylaws (https://edgg. org/databases/GrassPlot). Accepted Article This article is protected by copyright. All rights reserved Kier, G. , Mutke, J. , Dinerstein, E.
( https://edgg. org/databases/GrassPlot; Dengler et al. , 2018), to provide comprehensive benchmarks of fine-grain plant richness values of Palaearctic open habitats across biomes at eight plot sizes, each separated by order of magnitude: 0. 0001 m 2 , 0. 001 m 2 , 0. 01 m 2 , 0. 1 m 2 , 1.
GrassPlot incorporates standardized vegetation-plot data sampled in precisely delimited plots together with methodological, environmental and structural information from open habitats, e. g. grasslands and other plant communities dominated by herbs, dwarf-shrubs or cryptogams from the Palaearctic biogeographic realm (i. e. , Europe, North Africa, and West, Central, North and East Asia). Besides individual plots, GrassPlot specifically contains multi-scale datasets from nested-plot sampling schemes (e. g. ,Dengler et al. , 2016b) belonging to 225 datasets (Appendix S1). According to the typical species-area relationships in Palaearctic grasslands (Dengler et al. , 2020b), 10% difference in the area means only about 2% difference in richness or less, which is negligible compared to any other source of richness variation.
Thus, 2,372 plots deviating less than 10% from standard grain sizes (0. 0009 m 2 , 0. 09 m 2 , 9 m 2 , 10. 89 m 2 , 900 m 2 and 1024 m 2 ) were also selected and used for the benchmarks of the respective grain size. The final dataset contained 126,524 plots (Table Biurrun et al. , 2019)ng from 0. 0001 to 1,024 m². The last published version of the database (GrassPlot v. 2. 00;Biurrun et al. , 2019)contained more than 190,000 plots of different grain sizes across 22,422 individual plots and 5,749 nested-plot series with at least two grain sizes. For this benchmarking study, we retrieved all plots with grain sizes 0. 0001 m 2 , 0. 001 m 2 , 0. 01 m 2 , 0. 1 m 2 , 1 m 2 , 10 m 2 , 100 m 2 and 1000 m 2 contained in GrassPlot v. 2. 10 (version of 1 Oct 2020),.
A recent analysis of variation in six major traits conducted on a large worldwide sample of vascular plant species showed that three-quarters of trait variation was captured by a two-dimensional global spectrum of plant form and function ("global spectrum" hereafter). We developed the PhenoSpace application, whose aim is to visualize and export the position of any individual/population/species in the phenotypic space of the global spectrum. 2. PhenoSpace is a Shiny application that helps users to manipulate and visualize data pertaining to the global spectrum of plant form and function. It is freely accessible at the following URL: https://shiny. cefe. cnrs. fr/Pheno Space/. 3. PhenoSpace has three main functionalities. First, it allows users to visualize the phenotypic space of the global spectrum using different combinations of traits and growth forms.
Second, trait data from any new user-defined dataset can be projected onto the phenotypic space of the global spectrum, provided that at least two of the six traits are available. Finally, figures produced and loadings of the imported data on the PCA axes can be downloaded, allowing users to conduct further analyses. 4. PhenoSpace fulfills the practical goal of positioning plants in the phenotypic space of the global spectrum, making it possible to compare trait variation at any level of organization against the worldwide background. This serves a major aim of comparative plant ecology, which is to put specific sets of individuals, populations or species into a broader context, facilitating comparison and synthesis of results across different continents and environments using relevant indicators of plant design and function.
| INTRODUC TI ON Traits, which are measurable properties of individuals related to their functioning and modulating their fitness  (Calow, 1987; McGill et al. , 2006; Violle et al. , 2007) , are key to study biodiversity from a functional perspective  (Enquist et al. , 2015; Garnier et al. , 2016; Lavorel et al. , 2007 for reviews). In plants, an established body of research has been devoted to the identification of key traits allowing a synthetic description of phenotypes in a way that is relevant to their functioning and ecology  (Grime, 1977; Laughlin, 2014; Weiher et al. , 1999; Westoby, 1998). A recent study conducted on the largest dataset ever compiled of six major traits critical to growth, survival, and reproduction and measured on a sample of vascular plant species distributed worldwide, showed that three-quarters of trait variation could be captured in a two-dimensional "global spectrum of plant form and function" ("global spectrum" hereafter:  Díaz et al.
| THE FUN C TI ONALITIE S OF PH EN OS PACE Shiny is an R package that makes it easy to build interactive web applications from R  (Chang et al. , 2019). PhenoSpace, the Shiny application presented here, is designed to help users manipulate and visualize data pertaining to the global spectrum. It is available at the following URL: https://shiny. cefe. cnrs. fr/Pheno Space/. Its functionalities, accessible from three different tabs, are described below.
| Visualizing the phenotypic space of the global spectrum The first functionality of PhenoSpace ("Customize the PCA" tab) is to display the outcomes of multivariate analyses based on the original dataset explored by  Díaz et al. (2016). Six traits are involved in the definition of the phenotypic space of the global spectrum (Table  1  for definitions, units of expression, and functional significance): adult plant height (H), stem specific density (SSD), the area of a leaf (LA), leaf mass per area (LMA), leaf nitrogen content per unit mass (N mass ), and seed or spore dry mass (SM). The species geometric mean values of the six traits were log-transformed to fulfill normality assumptions, centered and scaled before the analysis. PhenoSpace allows dynamic visualization of the loadings of the 2,214 species available in the original dataset on any combination of components of the principal components analyses (PCA) run either on these six traits or on any user-defined subsets of these traits.