dataesr/scientific-paragraphs-categorization huggingface dataset:
MODCOU uses surface runoff and drainage as inputs for river routing and aquifer water head simulations, respectively, over all of France. SIM also needs physiographic parameters that describe the land cover, soil texture and orography of the studied zone. These parameters are provided by the ECOCLIMAP-II database. This physically based system has several applications in operational, research and climate services: it is used in flood risk forecasting, water resource management and climate projections  (Soubeyroux et al. , 2008). Further details about the model can be found in Le  Moigne et al. (2020). For the current study, SAFRAN and ISBA have been used to retrieve surface runoff and soil drainage estimations for each CTRIP pixel of the Rhône watershed over the period 1958-2016.
Lake Victoria is a buffer for watershed flows and its outflows follows the same pattern as the atmospheric forcings with a succession of low flows during the dry season and peak discharge during the wet season. Despite the Agreed Curve and the improvements resulting by including the lake model, CTRIP-MLake simulations do not capture well the peak discharge. However, the seasonality is well captured with the succession of increasing discharges during wet season and decreasing discharges during dry season. The effect of lakes is consistent with the Rhône results, but daily discharges are slightly under-estimated. Lake Victoria acts as a large retention area which sharply reduces and delays discharge peaks. Evaluation outflows data for the White Nile are available only on the period 1983-2006, owing these limited data, the metrics have computed on this period in this particular case.
This widening produces an overestimation of the outflows which can explain, in part, the better performance of smaller weir width simulations (such as the simulation labelled ctrip_mlake_w0. 5 in Section 5. 3). Global datasets with information on lake morphometry remain scarce and require extensive human intervention and financial resources. Furthermore, it might be impossible to gather reliable information on such on unsteady parameter which depends on either the downstream river morphology and the actual water head over the crest. A possible improvement would be the estimation of a generic width as a fraction of the lake circumference by clustering lakes based on both the observed outflows and the morphometry type. The characterization of the lake morphometry is one of the main sources of uncertainty in such models.
In order to satisfy global scale properties, the lake morphometry computation must remain computationally efficient and thus should be based on a relatively simple hypothesis that can capture the wide diversity. To do so, a specific study should be done to create a global scale dynamic data set in the SURFEX platform to account for semi-permanent areas surrounding lakes. Further developments will also require the integration of a strategy to correct the land cover type of these flooded areas during the dry season. The dynamic will allow the introduction of wetlands that would interact through sub-surface fluxes with the lake and permit an improved estimation of the evaporation. Last but not least, evaporation estimations will gain in accuracy with a fully coupled MLake-FLake system that will simulate the feedbacks of the lake within the global hydrological cycle.
8) 11 https://doi. org/10. 5194/gmd-2020-296 Preprint. Discussion started: 10 November 2020 c Author(s) 2020. CC BY 4. 0 License. where α and β are dimensionless parameters, respectively, equal to 5. 41 and 0. 59 (Vergnes et al. , 2014). Q mean is the mean annual discharge of the river calculated over the climate period. This empirical exponential function has been extended to the global scale by Decharme et al. (2019)  based on the comparison of two datasets: the Global Width Database for Large Rivers (GWD-LR: http://hydro. iis. u-tokyo. ac. jp/ yamadai/GWD-LR/) and the Global Lakes and Wetlands Database.
The study was performed through the energy optimization by the finite element method, using the Surface Evolver software (SE)  [28, 29] , by minimization of free interfacial energy of the liquid/solid and liquid/vapor interfaces at the bottom of the droplet. Since the numerical calculation for the whole system-the droplet and the solid surface-covered with hundreds or thousands of pyramids seems too complex and too much computation time-consuming, all calculations were performed for the periodic elementary cell of the surface containing only a single pyramid. Such a representation of the interface seems to be reasonable since the results can be extended to the wide range of droplet sizes (however, much larger then inhomogeneities) and the presence of gravity because both effects do not influence the apparent contact angle  [11].
In the environment, abiotic (climatic conditions, physico-chemical parameters), biotic (interactions between microorganisms, vegetation and fauna), and anthropogenic (stress, pollution) filters are driving the microbial diversity observed locally. A key question in microbial ecology is to understand the impact of these filters on bacterial diversity and ecosystem functioning. To highlight the responses of bacterial assemblages to these ecological filters, a new approach based on bacterial functional traits has been developed. This approach provides a functional picture of bacterial assemblages using morphological, physiological, and genomic traits as proxies of functions, and leads to a generalizable approach over a larger range of ecosystems with different bacterial diversities. We have created a user-friendly database of bacterial functional traits, thanks to the properties of 19,455 bacterial strains.
This database has been called BactoTraits. For example, oxygen preference, size and shape of bacteria, motility, optimum and range of pH and temperature, genome GC percent and trophic type are among the 19 traits included in BactoTraits. Based on the best-informed strains in the database, we identified five functional groups (i. e. mesophiles, competitors, colonizers, stress-sensitives and stress-tolerants) exhibiting a wide strain taxonomic diversity but with quite similar trait profile combinations. As an example of application, BactoTraits was used to characterize the traits and functional diversity of bacterial assemblages in soil samples from 10 sites with different physico-chemical properties and various levels of metal and polycyclic aromatic hydrocarbon (PAH) contaminations. Inference of functional traits was based on taxonomic diversity information obtained by high-throughput sequencing of 16S rDNA.
, 2008)  started to interest only recently the microbial ecologists  (McGill et al. , 2006; Kraft et al. , 2008; Barberán et al. , 2012; Barberán et al. , 2017; Bewick et al. , 2019; Madin et al. , 2020). Traits can (i) be analyzed across wide geographical ranges and species pools  (Bernhardt-Römermann et al. , 2011; Nelson et al. , 2016) , (ii) support the calculation of a variety of functional diversity indices  (Schleuter et al. , 2010) , and (iii) easily lead to estimation of functional redundancy  (Darr et al. , 2014)  and specialization  (Mondy and Usseglio-Polatera, 2014) , or inform on ecosystem functioning  (Bremner et al. , 2006). Studying microbial diversity from the perspective of functional traits is quite well developed for fungi due to well-established functional guild databases  (Nguyen et al.
, 2016; Zanne et al. , 2020)  and trait-based framework  (Crowther et al. , 2014). However, this approach for bacteria is far more complex  (Lajoie and Kembel, 2019). Current trends using bacterial community aggregated traits include: (i) shotgun sequencing metagenomic studies  (Tringe et al. , 2005; Selengut et al. , 2007; Kanehisa et al. , 2011)  reflecting bacterial functions  (DeLong et al. , 2006; Fierer et al. , 2014)  or deducing some bacterial traits  (Weimann et al. , 2016) , and (ii) 16S rRNA gene amplicon sequencing that infers bacterial functions based on reference genomes (e. g. , PICRUSt:  Langille et al. , 2013; or Tax4Fun: Aßhauer et al. , 2015). Despite on-going improvements in functional reference databases (e. g. , AnnoTree;  Mendler et al. , 2019) , and comprehensive web-server to assist in metagenomic analyses (e.
g. METAGENassist;  Arndt et al. , 2012) , the gold standard for defining bacterial phenotypic and ecological traits remains cultivation of bacteria  (Ernebjerg and Kishony, 2012)  to constitute databases easily usable for functional inference. On-line trait databases have been developed for various plants and animals in both aquatic and terrestrial ecosystems such as EFI + or freshwaterecology. info for European fishes, invertebrates, macrophytes, diatoms and phytoplankton  (Frimpong and Angermeier, 2009; Logez et al. , 2013; Schmidt-Kloiber and Hering, 2015) , or other databases for North-American lotic invertebrates  (Vieira et al. , 2006) , plants (TRY;  Kattge et al. , 2011) , carabids  (Homburg et al. , 2014) , soil invertebrates  (Pey et al. , 2014) , or ants  (Bertelsmeier et al. , 2013).
The best sources of information on bacterial traits are databases describing isolated bacterial strains such as BacDive (https://bacdive. dsmz. de) or bacterial known metabolisms such as the Kyoto Encyclopedia of Genes and Genomes (KEGG;  Kanehisa et al. , 2011). They provide comprehensive datasets on the taxonomy, morphology, physiology, geographic origin, genomic, and application of prokaryote strains. Recently, attempts have been made to link these phenotypic and environmental tolerance traits with taxonomy  (Barberán et al. , 2017) , to aggregate large phenotypic, genomic and environmental characteristics datasets  (Madin et al. , 2020)  and develop prediction tools (BugBase;  Ward et al. , 2017). These studies have laid the foundation for trait-based approaches using morphoecological or genomic traits of bacteria.
Nevertheless, they still did not explicitly test the potential of this trait-based approach to define bacterial ecological groups or to examine the impact of natural or maninduced changes on the selection of bacterial traits at the community level. Our first objective was to constitute a user-friendly database called "BactoTraits" aggregating information on 19 bacterial traits. Traits reflect bacterial behaviour and physiological requirements, as well as morphological and genome properties. In this study, we introduced the BactoTraits database workflow based on the inference of individual bacterial traits to the whole bacterial community thanks to taxonomic affiliation obtained from traditional high throughput 16S rRNA gene amplicon sequencing methods. We described how traits were coded, and defined bacterial functional groups sharing similar suites of traits that could be used as indicators of the functional organization of bacterial communities in various environments.
Source of bacterial strain trait information BactoTraits was built based on the global BacDive database for pure culture bacteria. Data were downloaded via the online portal in May 2019 (https://bacdive. dsmz. de/). From the 76,998 bacteria strains available on Bacdive, we kept in BactoTraits the 19,455 bacterial strains having at least one trait described (Fig. 1 ). The taxonomic affiliation of the bacterial strains was updated using Silva 132 taxonomy (https: //www. arb-silva. de/). These strains belonged to 34 phyla (including 19 phyla with at least 10 strains). At present BactoTraits (Table  1 ) covers 19 traits, each described by 2 to 10 attributes (i. e. , categories). It holds altogether 97,341 trait entries representing between 101 (for the trait "Pigment") and 15,516 entries (for the trait "Oxygen") (Fig.
Note that: (i) strains described as Gram variable were coded 0. 5 in Gram-negative and 0. 5 in Gram-positive, because this pattern of response represents only 84 strains, (ii) strains categorized as facultative anaerobe or facultative aerobe were gathered in one unique attribute category, (iii) mobile strains were gathered into the category "mobile" whatever their motility type (i. e. , amphitrichous, gliding, lophotrichous, monotrichous, peritrichous, and polar). The encoded BactoTraits database is available at the https://doi. org/10. 24396/ORDAR-53.
Trait inference from taxonomic data Interoperability ensures that information from different datasets can seamlessly be merged for subsequent integrated analyses. This interoperability has been performed from the taxonomic affiliation of the strains present in BactoTraits that was linked to the taxonomic affiliation of the Operational Taxonomic Units (OTUs) obtained from high throughput sequencing to infer traits to bacterial OTUs identified from environmental samples. This interoperability can be queried at any level of the taxonomy from genus to phylum. This process was automated thanks to a script programmed in R (v3. 6. 0; R Core Team, 2016). From a matrix with OTU numbers and their taxonomic affiliation (phylum, class, order, family, genus, species; referred as Taxonomy_table below and in Fig.
S1 ) the script successively looked if each OTU corresponded to a single strain or several strains of the same species in BactoTraits. If only one strain was found belonging to a given species in the database, its trait profile was directly allocated to the OTU. If this OTU corresponded to more than one strain of the same species in the database, the mean of the strain trait profiles was allocated to the OTU. For example, for the "Width" trait, if four strains, belonging to the same species, were recognized in BactoTraits with one belonging to the 0. 50-0. 65 µm category and three belonging to the 0. 65-0. 90 µm category, the trait profile allocated to the OTU was the mean of these four strain profiles with an affinity score of 0. 25 in the 0. 50-0. 65 µm category and an affinity score of 0.
75 in the 0. 65-0. 90 µm category. If no strain belonging to this species was found in BactoTraits, the script allowed to look for the strains belonging to the superior taxonomic rank (in order: genus, family, order, class, phylum), and the mean of the found trait profiles (for the genus, family, order, class or phylum) was allocated to the OTU as previously described. OTUs having the taxonomic affiliation "unclassified bacteria" were removed from analyses. As many OTUs get similar taxonomic affiliations giving similar trait profiles, these OTUs were grouped to simplify the datasets (OTUgroups_table in Fig. S1 ). Fig. 1. Description of the 19 traits and the number of strains described in BactoTraits (a total of 19,455 strains are informed). A) Number of strains with available trait profile (per trait) and their taxonomic affiliation at phylum level.
Environmental data used Bacterial diversity data from  Lemmel et al. (2019)  were reanalyzed to get OTU taxonomic affiliation through Silva 132 database, and then used to illustrate the ecological interest and evaluate the performance of BactoTraits. We worked on 30 top-soil samples (0 to 30 cm depth) from 10 sites (3 replicates per site, collected 1 m apart, in November 2015) from Northeastern France (Fig. S2 ) presenting both PAH and metal contamination gradients  (Lemmel et al. , 2019)  due to former steel activities during the 20th century. These soils were distributed into three types (controls, slag heaps, and settling ponds) based on their physicochemical properties and pollution level  (Lemmel et al. , 2019). Three uncontaminated soils, considered as the control soils (ctrl), were sampled at Hémilly (He), Montiers-sur-Saulx (Mo), and Dieulouard (Di).
Taxonomy affiliation of the OTUs was incremented using Silva 132 version. From an OTU_table with 14,149 OTUs (all samples combined), we generated an OTUgroups_table with 676 unique OTU groups by regrouping OTUs (addition of read numbers) with exactly similar taxonomic affiliation and eliminating OTU having unclassified bacteria affiliation. Based on these data, we used four tables (Fig. S1 ): (i) OTUgroups_table with the abundance (read number on a total of 29,689 to 33,839 reads per sample) of each OTU for the 30 samples (10 sites in triplicates), (ii) Taxonomy_table with phylum, class, order, family, genus and species affiliations of each OTU based on Silva 132 database, (iii) Traits_OTU-groups_table (generated from Taxonomy_table and BactoTraits, thanks to the procedure described in the section 2.
Statistical analyses of BactoTraits data and environmental data A Fuzzy Correspondence Analysis (FCA;  Chevene et al. , 1994)  was used to analyze the relationships among trait categories and to ordinate the bacterial strains of BactoTraits on the basis of their trait profile combinations. FCA also allowed identification of the traits that were the more contributive to the ordination of bacterial taxa  (Chevene et al. , 1994). Two traits ("Pigment" and "Trophic type") exhibiting a very low percentage of information in the database were discarded at this step of the analysis. Similarly, to avoid potential bias in analysis related to strains with low trait information in the database, the FCA was performed only on a subset of 5826 strains having at least five trait profiles available in the database.
, 1997; Hooper et al. , 2002). The 13,629 other strains -having one to four traits available in the database -were projected onto the first factorial plane of the FCA as "supplementary individuals" and assigned to one of the FGs depending on the distance of each strain to the nearest FGr centroid. Using the environmental trait dataset, we calculated the relative abundance of the OTU groups from each of the 30 top-soil samples, belonging to the FGr groups defined by FCA and clustering analysis. From log-transformed OTUgroups_table (i. e. pooled read data), we calculated the Community Weighted Mean profile of traits (CWM; [0;1]), the Community Specialization Index (CSI; [0;1]), the Overlap index (OL; [0;1]) and the Rao quadratic entropy index (RAO) for each trait and for each soil sample. CWM profiles give the relative proportion of each trait category in the community.
The Rao quadratic diversity index is a biodiversity metric widely applied to studies on functional and phylogenetic ecology  (Rao, 1982; Mouchet et al. , 2010). The package FD was used to compute different multidimensional Functional Diversity (FD) indices from the taxon-by-trait matrix  (Laliberté et al. , 2014). FD indices include the functional richness (FRic; here, standardized between 0 and 1), functional evenness (FEve), and functional divergence (FDiv)  (Villéger et al. , 2008) , the functional dispersion (FDis)  (Laliberté and Legendre, 2010) , and the Rao quadratic entropy (RaoQ)  (Botta-Dukát, 2005). Kruskal-Wallis tests (p < 0. 05) followed by Dunn's multiple comparison tests and p-value corrections (Bonferroni) were applied to compare functional indicator values among the ten studied sites and among the three soil types.
Variations in the bacterial community composition of sites, based on the trait community weighted mean profiles, wer visualized by nonmetric multidimensional scaling (NMDS) using the function 'met-aMDS' (from the package vegan for R;  Oksanen et al. , 2013)  applied to a square root-transformed Bray-Curtis dissimilarity matrix. To determine how soil types (control, slag heap and settling pond) could explain bacterial community composition, permutation-based multivariate analyses of variance (PERMANOVA) were done  (Anderson, 2001)  through the 'adonis2 ′ function of the vegan R-package, yielding 999-permutations based F-tests of significance. To validate PERMANOVA we examined the homogeneity of variances using permutational tests of multivariate group dispersions ('betadisper' function). Pearson's correlation tests were performed for each pair of trait categories (CWM) and a subset of soil parameters of interest (total Zn, Fe, Na and PAH concentrations, pH, DOC, total C and N, C/N ratio, and silt and sand percentage were selected as representatives when many soil parameters were correlated and gave similar correlations with traits).
P-values of the correlation tests were corrected to consider the false discovery rate  (Benjamini and Hochberg, 1995). The correlation matrix between both datasets was then used to build dendrograms, one for each dataset, to identify clusters of trait categories or soil parameters exhibiting similar "correlation profiles". The "correlation profiles" were hierarchically clustered using Euclidean distances and the UPGMA linkage algorithm (Unweighted Pair-Group Method using Arithmetic averages;  Borcard et al. , 2011)  proposed by the hclust function in R. A heatmap was used to illustrate the existing correlations between functional trait diversity descriptors and the soil characteristics (SampleMe-tadata_table). The final heatmap, including both dendrograms, was drawn using the function heatmap2 from the R package gplots  (Warnes et al.
Description of the strains of the BactoTraits database BactoTraits includes 19 bacterial traits defined at the strain level, and gathers information from 19,455 bacterial strains. These strains belong to 34 phyla, with 19 phyla having at least 10 representative strains (Fig. 1A ). BactoTraits consists of 33. 3% Proteobacteria (21. 7% gamma-and 8. 9% alpha-Proteobacteria), 27. 8% Firmicutes, 24. 0% Actinobacteria and 8. 4% Bacteroidetes. Members of these phyla are dominating diverse marine environments  (Aravindraja et al. , 2013)  and represent in average ca. 36%, 1%, 31% and 2%, respectively, of bacterial strains observed in soils  (Delgado-Baquerizo et al. , 2018)  and respectively 16-36%, 0. 4-37%, 10-26% and 5-14% of the bacterial strains in the soils from the 10 sites used as examples in the present study  (Lemmel et al.
, 2019)  and Firmicutes and Bacteroidetes are the dominant phyla in human gut microbiota  (Brooks et al. , 2018). Compared to the proportions of the different bacteria phyla in the environment, we are aware that some phyla were under-represented; e. g. , the Acidobacteria, Verrucomicrobia, Chloroflexi, Planctomycetes, and Gemmatimonadetes representing only 0. 27%, 0. 23%, 0. 21%, 0. 11% and 0. 01% of the strains in the database (53, 45, 41, 22 and 2 strains, respectively). These 5 phyla respectively represent in average ca. 14%, 4%, 5%, 7% and 2% in soil communities (Delgado-Baquerizo et al. , 2018) and 4-31%, 0. 6-22%, 0. 8-16%, 0%, and 0. 3-5% in the bacterial communities of the 10 sites illustrating the present study  (Lemmel et al. , 2019)  while the three first phyla could be abundant and widespread in marine environments  (Yilmaz et al.
Functional groups of strains based on similar suites of trait attributes A functional ordination of the strains included in BactoTraits was established through a fuzzy correspondence analysis (FCA; Fig. 2 ) based on the 5790 strains having at least 5 traits entered in the database (Fig. 1B ). The FCA of the strain-by-trait array allowed a description of both trait-based similarities among strains and the relationships among trait categories. FCA was already used for analysing trait-based functional diversity  (Ilg and Castella, 2006; Beauchard et al. , 2017). The distances between strains on the first factorial plane were used to cluster them into five functional groups (FGr; Fig. 2A , functional group labels were defined based on their location on the FCA first factorial plan, FGrC at the centre, FGrR on the right, FGrL on the left, FGrT on the top and FGrB on the bottom).