dataesr/scientific-paragraphs-categorization huggingface dataset:
In this study, it is expected to obtain lower values than the ones measured over specific field. Therefore, we consider our results to be in good agreement with the obtained values in  Personne et al. (2015) , since ours reflect an average of a coarse patch of land of the size 55 × 70 km 2 approximately, with a 31-day mean. The mean emission potentials per ammonia source region in Europe (shown in rectangles in Figure  2  and Figure  3 ) and per case are shown in Figure  6 , and listed in Table  1. Table  1  shows the average lifetime from GEOS-Chem (hours), the average T skin from the three datasets that we used (°C), the average ammonia emission potential in all the cases examined (dimensionless), and the average ammonia columns from IASI and GEOS-Chem (molecules cm -2 ). The four cases show a similar pattern with the North European Plain exhibiting the highest emission potential.
Discussion and conclusions Agriculture worldwide has fed the human race for thousands of years, and will continue to do so, as mankind highly relies on it. Emissions from agricultural activities will inevitably increase, in order to meet the expected yield. In this study, we use a variety of state-of-the-art datasets (satellite, reanalysis and model simulation) to calculate the first regional map of ammonia emission potential during the start of the growing season in Europe. The emission potential can be used as a proxy to calculate ammonia columns in the atmosphere, and as such to assess its deposition, atmospheric transport, and contribution to PM formation. First, we show that the GEOS-Chem chemistry transport model is able to reproduce key spatio-temporal patterns of ammonia levels over Europe.
Funding information Rimal Abeed  is grateful to  CNES  for financial support. The research in Belgium was funded by the  Belgian State Federal Office for Scientific, Technical and Cultural Affairs (Prodex HIRS)  and the  Air Liquide Foundation  ( TAPIR  project). This work is also partly supported by the  FED-tWIN  project  ARENBERG  (" Assessing the Reactive Nitrogen Budget and Emissions at Regional and Global Scales ") funded via the  Belgian Science Policy Office (BELSPO). L. Clarisse  is Research Associate supported by the  Belgian F. R. S. -FNRS. C. Clerbaux is grateful to  CNES  for scientific collaboration and financial support. N. Evangeliou was funded by  Norges Forskningsråd  ( ROM-FORSK -Program  for romforskning of the  Research Council of Norway  (grant no. 275407 )).
Data accessibility statement The IASI-NH3 used in this study are retrieved from the Aeris data infrastructure (https://iasi. aeris-data. fr/nh3r-era5/). ERA5 skin temperature from 1979 to present are available for download in the following DOI: 10. 24381/cds. adbb2d47. The GEOS-Chem outputs used in this study are only available upon request. EC-Earth3 model output prepared for CMIP6 ScenarioMIP are retrieved here: https://doi. org/10. 22033/ESGF/CMIP6. 727. The MODIS land cover data are available for download in the following link: https://doi. org/10. 5067/MODIS/MCD12Q1. 006. https://doi. org/10. 5194/egusphere-2022-1046 Preprint. Discussion started: 3 November 2022 c Author(s) 2022. CC BY 4. 0 License.
| Species richness maps The richness of a total of 5557 mammal, 11,125 bird and 6666 amphibian species per 20 km × 20 km cell of the global terrestrial surface was determined using an area of habitat approach. The data used were provided by the IUCN (2020) and BirdLife International and Handbook of the Birds of the World (2018). Given that reptile species were represented incompletely in the IUCN Red List, with c. 30% of the species being unassessed at the time we started this analysis, this taxon was not included. The information on species geographical ranges, habitat affiliations and elevational ranges were combined with maps on potential pristine ecosystems, remotely sensed (current) land cover data, and a digital elevation model  (Brooks et al. , 2019). This approach provides a more accurate estimate of a species distribution than simply relying on unfiltered extent-of-occurrence range maps, because gaps attributable to a lack of suitable habitat or elevation are accounted for  (Brooks et al.
, 2019; Strassburg et al. , 2020). To assemble SER models (see section 2. 4) and to compare model projections to reconstructed realized and impending loss, we calculated three distinct SR maps, separately for each taxonomic group (Table  1 ). The first SR map included all observed, currently extant species per cell (SR obs ), the second included all species assumed to have gone extinct at a site (SR ext ), and the third included the subset of those species from SR obs currently categorized as threatened in areas covered by national or regional Red Lists (SR threat ). We describe the computation of these maps in detail in the Supporting Information (Supplementary Methods in Appendix S1). TA B L E 1 Description of different species richness maps derived from data sources or projected from models.
All environmental datasets were converted to an Eckert IV projection at a resolution of 20 km × 20 km. Variables available at a higher resolution were aggregated to 20 km × 20 km either by majority resampling, in the case of factorial variables such as zoogeographical realms or habitat types, or bilinear interpolation, in the case of continuous variables. (Potapov et al. , 2008; Potapov et al. , 2017).
Further development of biodiversity models should therefore focus on combining aspects of habitat loss with indicators of habitat degradation; for instance, by merging approaches that are based on the reduction of available area with those focusing on the reduction of available energy  (Semenchuk et al. , 2022). Despite uncertainties of mechanistic interpretation, projections of species loss based on our species-energy model are correlated with the realized and impending species loss reconstructed from independent data sources. The HANPP approach thus provides us with useful indicators of land-use effects on biodiversity. Of course, uncertainties relate to the construction of HANPP maps. Although the HANPP framework is built in a way that uses state-of-the-art datasets, a thorough ground-truthing remains impossible.
However, the HANPP approach integrates all datasets in a meaningful and consistent framework, in order to close data gaps and minimize the effect of these uncertainties  (Haberl et al. , 2007; Krausmann et al. , 2008 Krausmann et al. , , 2013)). Data inter-comparisons have shown that overall estimates and patterns of these types of HANPP assessments are relatively robust  (Kastner et al. , 2022). Our findings thus support the usage of HANPP measures in the spatial planning of biodiversity management. Spatially explicit information can, for example, contribute to the development of prioritization schemes for areabased conservation (e. g. ,  Jung et al. , 2021)  or ecosystem restoration  (Strassburg et al. , 2020)  by indicating the degree to which pristine diversity has probably already been depleted by human land use.
Reads were then dereplicated at the sample level and error rates were determined for each combination of libraries, orientations and runs separately using the DADA2 R package V1. 14. 1  (Callahan et al. , 2016). Amplicon sequence variants (ASV) were called using the DADA2 command dada, with the pool option set to true. The ASVs of run 1 and run 1 and 2 were used as prior knowledge to call ASVs with the dada command for run 2 and run 3 respectively. This ensures that abundant ASV in samples of the first runs was correctly detected in the following runs, even if they have low abundance. Pairs of reads were merged using the DADA2 command mergePairs with minimum overlap of 10 nt and a maximum of two mismatches. The paired reads originally with the reverse primer at the 5′-end of R1 library were reverse-complemented using the SeqinR R package command c2s.
Beta diversity analyses were based on Bray-Curtis dissimilarity indices of the Hellinger-transformed protist ASV matrix. A non-metric multidimensional scaling was used to visualise the change in community structure between filtered and bulk soil samples, while significant changes were tested with PERMANOVA. The protist ASV matrix was further split between filtered and bulk soil samples and constrained redundancy analyses were conducted separately on these two datasets in order to compare the best model explaining changes in community structures.
| Filtration effect on beta diversity Protist community compositions varied primarily between regions and habitats, while the filtration effect was more marginal (Figure  4a ). Permutational analyses of variances confirmed the NMDS patterns, with regions and habitats jointly explaining 41% of the variance, while filtration explained a lower (2%) but still significant proportion of soil protist community composition differences (Table  S2 ). In the constrained RDA analyses conducted on the two datasets separately using only quantitative factors as explaining variables, three variables were significant: elevation and pH in both models and bioavailable phosphorus for the bulk soil samples only (Figure  5 , Table  S3 ). A similar dominant significant predictive power of regions and habitats was observed for the other clades (animal, fungi, plants) and the total eukaryotic community (Table  S2 ).
Ellipses are 90% confidence intervals around samples from the same region and habitat. Yellow plain ellipses are for alpine habitat, red dotted ellipses are for forest habitat. The scatter plot presents the relationship between bulk soil and filtered Bray-Curtis dissimilarity values for the same pairs of samples (b). Density distributions of each dataset are presented in the margins. Filtered samples have a significant lower dissimilarity on average (paired Mann-Whitney test, pvalue <. 05). Blue line and surrounding grey area are the linear regression and its 95% confidence interval. Black plain and red dotted lines are mean and median values respectively. Effect of sample type on protist RDA. Constrained redundancy analyses were used to find the best model of soil parameters explaining the change in protist community compositions for bulk soil (a) or filtered samples (b), using all topographic and soil abiotic parameters to select best models based on AIC.
ACK N O WLE D G E M ENTS This work was supported by the  Swiss National Science Foundation (SNSF) , grant n°  182531  to EM. We thank  Amandine Pillonel ,  Coralie Belgrado  and  Emannuela Reo  for the laboratory and sequencing work. We are grateful to  Raphaël Solesse ,  Yoan Benoit ,  Juliette Prior  and  Pierre-André Wagner  for support in sampling in La Réunion, France,  Kenneth Dumack ,  Baptiste Bovay  and  Joris Weber  for support in sampling in the Swiss Alps. Open access funding provided by  Universite de Neuchatel.
DATA AVA I L A B I L I T Y S TAT E M E N T Raw sequences data and associated metadata are deposited on SRA (Bioproject PRJEB56206, Table  S1 ). Details of accessions for the samples used in this study are listed in Table  S1. All codes used to analyse the data are provided in the supplements (Data S1). The analysed sequences data (ASV sequences, community matrix and taxonomic assignments), the soil abiotic data and the samples metadata are provided as a R binary file in the supplements (Data S2).
We acquired data from 2015 to 2018 on large permanent plots located in the Central African Republic, the Congo Republic and the Democratic Republic of the Congo, on four contrasting geological substrates, in areas considered climatically homogeneous. We tested the following hypotheses: (i) competition and site have a significant explanatory power on individual tree growth; (ii) effects of competition on individual tree growth are consistent among sites. We also examined whether (iii) the ranking of sites according to growth rates is related to exchangeable Ca for medium and high wood density species and to exchangeable K and available P for low wood density species.
| Plots data We settled plots at each of the sites (Table  1 ). In the plots, trees larger than 10 cm in diameter at breast height (DBH) were located, botanically identified and their diameter measured almost every year since 1982 in M'Baïki  (Gourlet-Fleury et al. , 2013) , and since 2015 in Mokabi, Loundoungou  (Forni et al. , 2019)  and Yoko. To avoid the effect of annual climate variation on comparison between sites, we calculated tree diameter increments between 2015 and 2018, the 2 years in which all plots were inventoried simultaneously (Figure  1 ). Only trees alive in 2015 and 2018 were selected for growth analysis, allowing to reduce possible effects related to recruitment-trees appearing above 10 cm between 2015 and 2018 could be younger, faster-growing trees and to mortality-trees disappearing between 2015 and 2018 could be older and/or injured or diseased trees.
Each species present on the sites was assigned a regeneration guild, distinguishing between pioneer, non-pioneer lightdemanding and shade-bearing species (Appendix S1: Table  S2 ) using information for regional databases  (Bénédet et al. , 2019; Hawthorne, 1995). with data from other sources taken from Climate Engine, estimating CWD from estimated rainfall and estimated evapotranspiration did not modify the rank of the sites on the dry/seasonal vs moist/less seasonal gradient (M'Baïki < Loundoungou < Mokabi < Yoko).
| Interpretation of results We simplified the formulation of the results by interpreting the effects of the explanatory variables on tree diameter increment Δ D , instead of log( Δ D + 1). This simplification did not distort the interpretation because the transformation is increasing and bijective. We performed all statistical analyses with the R software (R Core Team, 2022) and with the package lme4  (Bates et al. , 2015)  for linear mixed modelling.
DATA AVA I L A B I L I T Y S TAT E M E N T Data are available on the CIRAD Dataverse: https://doi. org/10. 18167/ DVN1/T0DMFJ  (Sylvie et al. , 2023). The metadata are openly available, and the data are available upon request, due to the fact that data are co-owned by forestry companies and national research structures.
Spectra were recorded in the range from 500 to 5000 cm -1 and analyzed using OPUS 7 software (Bruker, Billerica, MA, USA). Thermal analysis. DSC measurements were carried out in a DSC 25 differential scanning calorimeter (TA Instruments, New Castle, DE, USA), calibrated using purified indium (99. 9%) as the standard reference material. Samples (3-4 mg) were cut off and placed in an aluminum pan. They were heated at a constant rate of 10 K min -1 using dry atmosphere of argon as carrier gas, in a temperature range of 30 to 200 • C. Thermograms were analyzed by TRIOS 5. 4 Software (Waters, TA Instruments, New Castle, DE, USA). Thermogravimetric analyses were performed at 10 K min -1 on a TGA/DT Perkin Pyris Diamond. (Perkin Elmer, Waltham, MA, USA). Small Angle X-ray scattering (SAXS). X-ray diffraction patterns were collected with Pilatus 300k detector (Dectris, Baden, Switzerland) mounted on home-made Guinier setup based on a Cu K α microsource (λ = 1.
| Systematic literature search We formulated our research question according to the PICO-structure (population, intervention, control, outcome): Do different seed addition techniques for the restoration or re-creation of species rich grasslands differ in their effectiveness to enhance the diversity of plants? (see Table  S2  for details on the question components). Based on the question components, we developed an initial search string, which went through a scoping process. This initial string was used in a search in the Web of Science database. We compared the search result with the reference lists of two reviews on the same topic  (Hedberg & Kotowski, 2010; Kiehl et al. , 2010). To achieve adequate sensitivity, we adapted the search string until no references of the reviews were missed. We used the final search string as a template for our database searches and adapted it accordingly to the requirements of the respec- Directory of open access journals (DOAJ) and eThOs.
In addition, we used the "Publish or perish" software  (Harzing, 2007)  to search articles in Google Scholar and retained the first 1000 hits. A detailed overview of the search string development and database searches can be found in the Supporting Information (Table  S3 , Appendix S1). To complement the database search, we looked for other publications and grey literature in Google, organizational websites and through direct requests to authors. The searches and requests were done in English, French, German and Polish. We removed duplicates automatically using the JabRef Reference Manager (JabRef Development Team, 2021).
| Data extraction and moderators The geographic location of each restoration site was recorded and, if necessary, changed into decimal degrees. If the site coordinates were not provided, we looked for a locality (such as a city, village or a protected area) in the site description of the respective article and determined the coordinates from Google Maps. As potential moderator variables (effect modifiers) we included the control type, type of study design, seed source, seed application method, seed bed preparation (seed bed preparation is requisite for efficient seed addition), former land-use, restoration duration, number of experimental replicates, as well as vegetation survey plot size (see Table  2  for a detailed definition of each moderator). These moderator variables are either linked to applied aspects of grassland restoration (e.
g. seed source), which are relevant for practitioners, or to experimental aspects (e. g. control type). As response variables we extracted the mean plant species richness and a measure of variance (which was converted to standard deviation if necessary) from the restored and control plots. We extracted these from summary tables, calculated it from raw data or extracted it from the figures using the WebPlotDigitizer  (Rohatgi, 2021). We contacted the authors by e-mail to request missing data if relevant data were unavailable in, or not extractable from the study. A list of data sources used in the study is provided in the Data sources section.
| Meta-analysis We selected 13 articles that met our rules for inclusion in the quantitative meta-analysis. This yielded 56 data points, that is, effect sizes, from 44 sites for our meta-analysis. Overall, 88% of the study sites were in the United Kingdom, Germany or the Czech Republic. Further sites were situated in Norway, Ireland, France and Italy (Figure  2 ). The overall effect of grassland restoration measures on plant species richness was positive (lnRR = 0. 34, 95% CI 0. 13-0. 55, p = 0. 002, Figure  3 ), with a mean increase in plant species richness of 17. 4% compared to control. The variance within certain articles was quite large, which was mainly explained by different restoration methods experimentally tested within a single article (Figure  3 ). The moderator "seed source" showed a significant moderating effect (F = 17.
TA B L E 3 Output summary for the moderator analysis. The first column gives the name of a given moderator variable (see Table  2  for details). The following three columns provide the residual heterogeneity (QE) together with its degrees of freedom (df) and p-value (p). The three columns afterwards provide the F-value (F) from the test of moderators with its degrees of freedom (df) and p-value (p). The moderator variables are ranked by their respective AIC value (last column). The model formula had the following structure: rma. mv(lnRR ~ moderator, RE = ~1 | study id/m 2 , REML), where "lnRR" is the log response ratio effect size, "RE" the weighted random effect, study "id" the unique identifier of each study and "m 2 " the species richness of a respective control site. These analyses were performed with the metafor package for R  (Viechtbauer, 2010).
, 2006) , select target species  (Johanidesová et al. , 2015)  or refer to the cover of different functional groups  (Conrad & Tischew, 2011). While these metrics are important to investigate the effect of a treatment on different aspects of the restoration outcomes, the diversity of metrics we encountered across studies, with little consistency between them, represented a major impediment to a proper meta-analysis. Researchers and practitioners active in the field of restoration ecology ought to render their data publicly available for future syntheses (FAIR prescriptions;  Wilkinson et al. , 2016). The Global Restore Project, a publicly accessible platform  (Ladouceur & Shackelford, 2021) , has for objective to standardize such datasets, checking notably for taxonomic consistency. Based on more solid foundations, future syntheses will be able to incorporate more fine-scaled information and help with interoperability of restoration monitoring schemes.
ACK N O WLE D G E M ENTS We thank the members of the  Biodiversity Synthesis group  led by  Prof. Jonathan Chase  from the  German Centre for Integrative Biodiversity Research (iDiv ) that hosted  Daniel Slodowicz  for a research stay and provided constructive feedbacks at an earlier stage of the systematic literature search. This project was funded by a  Swiss National Science Foundation  grant to RA ( 31003A_172953 ) and grants from the  Federal Offices for the Environment and Agriculture. Further funding stemmed from  ANRT (National Agency of Research and Technology  via the grant  CIFRE N°2017/0478 ). RE was supported by  CABI  with core financial support from its member countries. CABI  is an international intergovernmental organization, and we gratefully acknowledge the core financial support from its member countries (and lead agencies) including the  United Kingdom (Foreign, Commonwealth & Development Office) ,  China  ( Chinese Ministry of Agriculture and Rural Affairs) ,  Australia (Australian Centre for International Agricultural Research) ,  Canada (Agriculture and Agri-Food Canada) , the  Netherlands (Directorate-General for International Cooperation)  and  Switzerland  ( Swiss Agency for Development and Cooperation).
Daniel Slodowicz, Jean-Yves Humbert and Raphaël Arlettaz conceived the study and designed its methodology; Daniel Slodowicz, Aure Durbecq, Emma Ladouceur and René Eschen collected the data 26888319, 2023, 2, Downloaded from https://besjournals. onlinelibrary. wiley. com/doi/10. 1002/2688-8319. 12221 by Schweizerische Akademie Der, Wiley Online Library on [11/04/2023]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License and did the screening; Daniel Slodowicz, Emma Ladouceur and Jean-Yves Humbert analysed the data; Daniel Slodowicz led the writing of the manuscript with major contributions by Aure Durbecq. All authors contributed critically to the drafts, with thorough editing by Jean-Yves Humbert and Raphaël Arlettaz, and gave final approval for publication.
A thorough assessment of the reliability on applying this discrimination tool using detrital rutile traceelement compositions was made by  Triebold et al. (2007). Sand and rock samples were collected by these authors in the Erzgebirge area, carefully sampling sands where a clear association with the source rock/exposure could be made. Examining the data using a log(Cr/Nb)-Nb diagram (Fig. 3A ) highlights that i) with the exception of one mafic eclogite sample, the discrimination works well for metamorphic rutile grains collected from the exposed units in the Erzgebirge Eclogite-rich Gneiss Unit (EGU); ii) the majority of the sand samples show rutile grains with similar compositions to the source rocks, but a group of rutile grains, from 3 different sand samples, exhibits very low log(Cr/Nb) values combined with low Nb contents, which are unlike any available source rocks that were sampled; iii) there is a higher proportion of metamaficderived than metapelitic-derived rutile grains, reflecting the proportions found in the source area.