dataesr/scientific-paragraphs-categorization huggingface dataset:
Phylogeny and Indicator Species Analysis of OTUs A phylogeny of sequences from the final list of OTUs was reconstructed. First, the OTU sequences were aligned against the rbcL sequences of the R-Syst::diatom database using the muscle algorithm  (Edgar, 2004). Second, OTUs were inserted in a reference phylogeny of 604 species with the evolutionary placement algorithm  (Berger et al. , 2011)  implemented in RAxML  (Stamatakis, 2014). Finally, the tree was dated in relative time using PATHd8  (Britton et al. , 2007). Indicator species analysis was performed to identify OTUs that discriminate diatom communities in U, H, and RU locations. Indicator values based on relative abundance of OTUs were calculated following the method proposed by  Dufrene and Legendre (1997)  implemented in the labdsv package  (Roberts, 2007).
Biological Diatom Index Biological diatom index is a biotic index calculated after morphological identification of diatom species present in natural biofilms. It is routinely applied for water quality assessment of running waters in France and is used in the context of the WFD  (Coste et al. , 2009). In this study, OTUs were affiliated on species (or genus when species not available) level and their normalized quantitative matrices, based on read numbers, were used to calculate molecular BDI for each sampling location and period and to evaluate thereby the effect of TEs on water quality in the recipient river. BDI calculation was done with OMNIDIA 5 software  (Lecointe et al. , 1993).
Vasselon et al. , 2017b)ampled to 11,306 reads (lowest read abundance obtained for a sample) to allow intersample comparison which resulted in total of 1,076 OTUs with minimum of 103 and maximum of 301 OTUs per sample. R-Syst::diatom v4 database enabled taxonomic affiliation of 103 different species from 48 different genera. 81% of the OTUs (90% of the reads) were affiliated at family level, 75% of the OTUs (85% of the reads) -at genus level and 57% of the OTUs (70% of the reads) -at species level. Those results are in consistence with other studies (e. g. ,Vasselon et al. , 2017b). Most of the unclassified reads were found in Ha location that represented the most extreme environment in terms of concentrations of micro-and macropollutants.
ACKNOWLEDGMENTS The article is based on collaborative work with  COST Action DNAqua-Net  ( CA15219 ), supported by the  COST (European Cooperation in Science and Technology) program. We thank  Frankreich-Schwerpunkt  ( University of Innsbruck ), and the  EU  co-funded project  Interreg Alpine Space "Eco-AlpsWater " for their support. We thank  L. Wiest  ( ISA, Lyon ) and  V. Lecomte  ( GRAIE ) for their contribution.
Particularly, it can monitor the actual phenological cycle of the crops  (Etchanchu et al. , 2017). It could thus offer precious information to assess more accurately the crop water needs and consequently, the irrigation amounts and its effects on hydrometeorological fluxes. Some of the studies mentioned above already make use of satellite earth observation for irrigation estimations. But it mainly consists in classifying irrigated pixels, or correcting the previously mentioned irrigated areas maps with MODIS data  (Ozdogan and Gutman, 2008; Evans and Zaitchik, 2008; Leng et al. , 2013). The resolution of MODIS (500m) is unsuitable for the determination of irrigation rules in Western Europe because a MODIS pixel usually contains several plots. This problem can be overcome by using high-resolution satellite data like LANDSAT, SPOT or the recent SENTINEL-2.
Forcing data The meteorological data used to force the model in this study are drawn from the Système d'Analyse Fournissant des Renseignements Atmosphériques à la Neige (SAFRAN,  Durand et al. 1993 ) reanalysis data  (Quintana et al. , 2007). It provides air temperature and specific humidity at 2 meters, air pressure, precipitations (in solid and liquid form), wind speed and direction, and solar radiation data. These data are provided at hourly time step on a 8 km resolution grid. These data were spatially linearly interpolated to match plot centroïds. For the soil parameters, we first used the Harmonized World Soil Database (FAO/IIASA/ISRIC/ISS-CAS/JRC, 2012), which gives the percentage of clay and sand at a 30 arc-second (~1 km) resolution. This database is used by default in SURFEX-ISBA.
Soil parameters are then calculated by the model with empirical pedotransfer functions  (Noilhan and Lacarrère, 1995). By default, SURFEX-ISBA uses the 1-km resolution ECOCLIMAP-II database  (Faroux et al. , 2013). This dataset describes land cover parameters as root depth, minimal stomatal resistance, albedo and LAI. The parameters evolving in time, like the LAI, are described with a climatology. As using a climatology is not sufficient to monitor the vegetation cycle and diagnose water needs, the LAI is derived from satellite imagery (cf. Sect. 2. 2. 1). But most of the other land surface parameters were taken into the ECOCLIMAP-II database. As the simulated plots are exclusively maize plots in this study, only the root depth was forced, first with an empirical value of 0. 9m, then with measured values.
The flux computation includes spectral frequency and Webb corrections  (Moore, 1986 , Webb et al. , 1980)  and various filters and statistical tests are performed on the data fluxes to remove out of range and non-stationary data  (Papale et al. , 2006; Reichstein et al. , 2005). The spatial representativeness (footprint) of the fluxes is also taken into account and if the calculated fetch including 90 % of the flux  (Kljun et al. , 2004 ) model for each half-hourly EC flux value (F-90) was higher than the distance between the eddycovariance tower and the edge of the plot in the main wind direction, fluxes were discarded. Eventually, the data were gapfilled to generate a full yearly database and depending on the duration of missing data, either the linear regression method or the mean diurnal variation was applied according to  Beziat & al.
CACG irrigation data The Compagnie d'Aménagement des Coteaux de Gascogne (CACG) is in charge of the management of the Neste channel which supplies 17 rivers in southwestern France. The targeted sector is mainly agricultural. Hence, the CACG is also in charge of advising farmers on irrigation practice in order to optimize the water resources availability. To forecast the availability of water resources at seasonal scales, the CACG monitors several irrigated plots every year. For each of these plots, the log containing the field operations has been collected. Thus, the irrigation dates and amounts are gathered. In this study, we used the data from the years 2013 and 2014. These data were collected as part of the MAISEO project.
Acknowledgments: We thank  Marjorie Battude  for her work on the irrigation estimation which constitutes the basis of this study. We also thank  Jean-François Dejoux  for his expertise on the maize crops in order to define the irrigation rules as realistic as possible. Thanks to  Valérie Démarrez  for her advices concerning the CACG plots and the irrigation processes. We thank  Ludovic Lhuissier ,  Mamadou Diarra  and  Jean-Jacques Webber , from the  CACG , for allowing us to use some of their data and for their expertise on the irrigation management of the maize crops. We thank all the people of the OSR who contributed to the collect and the process of data. OSR facilities and staff are funded and supported by the  Observatory Midi-Pyrenean , the  University Paul Sabatier, Toulouse, France ,  CNRS  ( Centre National de la Recherche Scientifique ),  CNES  ( Centre National d'Etude Spatial ) and  IRD (Institut de Recherche pour le Développement).
attention involve humans, for whom health often seems to be a microbiome matter. This marriage of medical interests and microbiome methods is still in its honeymoon phase, but has contributed to quantitative leaps in the amount of microbiome literature in publication databases (Fig. 1 ). Because of the potential medical and environmental applications of microbiome findings, a rising number of patent applications trail in the wake of the literature. An emerging sentiment in drug development is that "ignoring the microbiome is not an option"  (Olle 2013, p. 315). A fairly recent comparison of microbiome literature and international patents finds a fourfold increase in microbiome patents between 2004 and 2012  (Olle 2013). Our own examination of microbiome publications and patent data shows a very similar comparative growth dynamic (Fig.
ACKNOWLEDGMENTS This research was partially supported under the  Australian Research Council's Discovery Projects funding scheme FL170100160 "Philosophy of Medicine  for the 21st Century" and by the  French government via the "Investments for the Future" Programme ,  IdEx Bordeaux  ( ANR-10-IDEX-03-02 ). The funding agencies had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript. This paper was originally presented at the 15th International Congress of Protistology (2017, Prague) in a symposium funded by the  International Society of Protistologists.
org. uk/Home/Extra ctCom pare). The observer can confirm the lynx identification or not and browse through the highest-ranking proposed matches. The final decision is made by the observer based on an additional visual examination of the entire photograph set for this particular lynx. Pictures for which no match was found with the software were visually checked against our entire photograph library. Only when the match was undeniable was the individual recorded as a match, otherwise it was recorded as a new individual. All captures that did not fit automated or associated visual confirmation with no doubt, because of a poor picture quality (e. g. , blurry, overexposed), were classified as "unconfirmed" and excluded from the analyses. We recorded the date, time, sex whenever possible, and location of each photographic capture of a lynx.
We fitted standard models in the maximum likelihood framework using the R package Rcapture  (Baillargeon & Rivest, 2007). We estimated density as the ratio of estimated abundance over an effective trapping area (ETA). ETA was estimated by adding a buffer to the trapping area equal to the mean maximum distance moved (MMDM) or half of it (HMMDM). We calculated the MMDM by averaging the maximum distances between capture locations for all individuals detected at more than one site.
| RE SULTS We collected data from 413 camera trapping sites (two camera traps were set per site) resulting in 25,839 trap days (Table  1 ). In total, we identified 92 lynx over 532 detection events in the Jura mountains, including 16 females, 13 males, and 63 individuals of unknown sex. The number of detections per individual was 2. 6 on average and varied from 1 to 11. In contrast, we collected no lynx photograph in the Vosges mountains; therefore, we did not proceed with analyses for this area. For the Jura mountains, abundance estimates were similar whether we used spatial or nonspatial models, although always slightly higher for the former. Estimated abundance among study areas varied between 5 (SE = 0. 1) and 29 (0. 2) lynx in the spatial analyses, between 4 (0. 7) and 23 (0. 7) with model M0, and between 5 (1.
In this study, we applied both the traditional and molecular methods on 180 biofilms samples from Northern Europe (rivers and lakes of Fennoscandia and Iceland). The DNA metabarcoding data were obtained on two different DNA markers, the 18S-V4 and rbcL barcodes, with the NucleoSpin Soil kit for DNA extraction and sequenced on an Ion Torrent PGM platform. We assessed the ability of the molecular method to produce species inventories, IPS scores and ecological status class comparable to the ones generated by the traditional morphology-based approach. The two methods generated correlated but significantly different IPS scores and ecological status assessment. The observed deviations are explained by presence/absence and abundance discrepancies in the species inventories, mainly due to the incompleteness of the barcodes reference databases, primer bias and strictness of the bioinformatic pipeline.
Abundance discrepancies are less common than presence/absence discrepancies but have a greater effect on the ecological assessment. Missing species in the reference databases are mostly acidophilic benthic diatoms species, typical of the low pH waters of Northern Europe. The two different DNA markers also generated significantly different ecological status assessments. The use of the 18S-V4 marker generates more species inventories discrepancies, but achieves an ecological assessment more similar to the traditional morphology-based method. Further development of the metabarcoding method is needed for its use in environmental assessment. For its application in Northern Europe, completion and curation of reference databases are necessary, as well as evaluation of the currently available bioinformatics pipelines.
We aimed to compare the calculation of a diatom index and the subsequent assessment of ecological status and qualitative and quantitative species identification of benthic diatoms. Previous studies  (Rivera et al. 2018; Vasselon et al. 2017a; Vasselon et al. 2017b ) have shown that one of the main problems when using metabarcoding, along with the marker polymorphism, is the taxonomic coverage of the DNA reference library. The quality of the reference database is the most crucial factor for diatom identification at species level  (Kermarrec et al. 2013 ). To our knowledge, the most complete accessible database for diatoms barcodes is R-syst::diatom. This database has been set up as the micro-algal component of a database shared between several taxonomic groups of interest (R-Syst, available at https://www6.
inra. fr/r-syst_eng/)  (Chaumeil et al. 2018)  ). The database is regularly curated and updated  (Rimet et al. 2016; Rimet et al. 2018). However, the R-syst::diatom database was constructed with a focus on French monitoring needs and contains, until now, mainly barcodes from samples taken in temperate regions  (Kermarrec et al. 2014; Rimet et al. 2016). The water bodies of Fennoscandia (Finland, Sweden and Norway) are different from the ones of France and Central Europe. Fennoscandia freshwater have, on average, lower pH values, higher amount of humic substances and lower nutrient concentrations than freshwaters in Central and Southern Europe  (Erlandsson et al. 2008; Johansson and Persson 2001; Ramsay 1898). Considering that diatom assemblages are affected by the local water chemistry  (Smol and Stoermer 2010) , we would in turn expect different diatom communities in Fennoscandia  (Gottschalk and Kahlert 2012).
Both Fennoscandia water chemistry itself and the diatom flora are different from Central Europe, where the existing metabarcoding methods have been developed. Thus a lower efficiency when extracting and amplifying the DNA barcodes using these methods may be expected, due to untested water chemistry, possibly including inhibitors. A lower number of detected diatom taxa, mainly due to missing taxa in the established reference database and lower primer suitability are expected as well. Another challenge that could affect the results given by molecular analysis is the choice of the DNA marker. The two main markers, currently used for diatoms in Europe, are the 18S-V4 region (SSU rRNA) and rbcL (from the chloroplast genome). The rbcL-region has proved to be more efficient for diatom communities from temperate and tropical rivers of French territories  (Kermarrec et al.
2014; Vasselon et al. 2017a ) while the 18S-V4 was used in Germany and Switzerland  (Visco et al. 2015; Zimmermann et al. 2015)  because of the great amount of reference barcodes available. Contrary to the 18S marker which has potential to assess non-photosynthetic organisms, the rbcL marker allows focusing and putting all sequencing effort directly on photosynthetic organisms. Both of these markers are represented in the R-syst::diatom database. Since different markers possess different discriminatory power  (Kermarrec et al. 2013 ) and species-specific affinities  (Vasselon et al. 2017a ) we decided to work with both the 18S-V4 and the rbcL markers. Finally, the suitability of the molecular approach strongly depends on the quality of the generated taxonomic assignments  (Keck et al. 2017 ) and a great diversity of 'bioinformatics pipelines' have been developed in an attempt to produce the best taxonomic assignation while keeping up with the high amount of HTS data currently produced.
3. However, because of the dependence on reference databases that are potentially incomplete regarding the Fennoscandia diatom flora, the metabarcoding approach will not cover all taxonomic diversity. Some species will be missing and specific genera will be less covered than others. 4. The use of two different DNA markers, rbcL and 18S-V4, will lead to dissimilarities in the ecological assessment caused by differences in the taxonomic coverage of the associated reference databases and by primers specificity.
Sampling sites A total of 180 environmental samples were collected from benthic biofilms in 65 streams and 43 lakes in Fennoscandia (Sweden, Finland, Norway) and two sites in Iceland (Fig. 1 ). The sites cover a broad environmental gradient of agriculture, alpine and boreal catchment areas and water chemistry parameters (Table  1 ) were extracted from the Swedish National database (https://miljodata. slu. se/ mvm/) for the Swedish sites, from the Hertta system version 5. 7 of Finnish Environmental Administration (http:// www. syke. fi/en-US/Open_information) for the Finnish sites and from  (Friberg et al. 2009 ) and  (Ólafsson et al. 2010)  for the two Iceland sites. Water chemical analyses for the sites in Norway were performed by NIVAs accredited laboratory according to Norwegian standard methods.
Sequence data processing The sequencing platform provides fastq file for each sample after demultiplexing and removing the tags se- quences. The first filtering step of the sequences excluded too short and too long reads, so that only reads between 300-315 bp were left for the rbcL marker and between 320-340 bp for the 18S-V4 marker. Diatoms molecular inventories were obtained with the R-syst::diatom database  (Rimet et al. 2016 ) using the 18-02-2016: R-Syst::diatom (version 5) for the rbcL barcode (1625 reference sequences covering 180 genera and 605 species of diatoms) and 20-03-2017: R-Syst::diatom (version 6) for the 18S barcode (2652 reference sequences covering 222 genera and 844 species of diatoms). The taxonomic identification of DNA reads at species level (or at genus level when species level was not possible) was done following a two-step process.
First, a matrix of pair-wise distances between the unknown read and the reads from the reference database was computed, for each sample and each database, using the programme MPI-disseq. The MPI-disseq programme runs under Python as a parallel implementation of a Smith-Waterman algorithm with Message Passing Interface (MPI) and computations were done at the French National Computing Center (ID-RIS) on a Blue Gene Q hyper-parallel machine (1024 cores). Secondly, each matrix was processed with the Python programme Diagno-syst  (Frigerio et al. 2016 ) which selects, for each read, the references barcodes that are at a distance lower than a given gap and assigns the taxonomy to the unknown read only if all references at the same distance or lower have the same taxonomic name. We chose a 10 bp gap to make sure to encompass the similarity levels expected for species (strict 99% similarity with gaps 0-4 and relaxed 97% similarity with gaps 4-10).
A distance of more than 10bp difference translates as an identification above species level and has a higher possibility of being a mismatch and thus was not kept for further analysis. However, if a reference barcode for a genus (e. g. the reference barcodes KU179117 to KU179119 identify Caloneis sp. with the rbcL marker) was assigned to a read at a distance smaller than 10 bp, the taxonomical assignment was kept in the dataset. In this study, to compare valve counts and reads number, we used two comparable abundance thresholds following  Frigerio et al. (2016) : low abundance in the morphological assessments was defined as ≤ 1% relative abundance and high abundance was > 1%. Identification at ≤ 0. 0025% relative abundance was not taken into account. In the molecular assessments, low abundance was defined as number of amplicon reads ≤ 1000 and high abundance then was defined as > 1000 reads.
We then assessed if specific causes were linked to specific sites and ecology using Canonical Correspondence Analysis. For this analysis, the occurrences across the 180 samples for each mismatch code was extracted from the dataset as binary data (1= occurring problem, 0=no problem occurring). Additionally, we carried out a SIMPER analysis  (Clarke 1993)  to understand which species are likely to account for most of the observed IPS values deviations. A SIMPER analysis assesses which taxa are predominant in different groups of samples. Since a deviation of ± 1 in the IPS scores is considered method-bound uncertainty for a class (Swedish Environmental Protection Agency 2007) we used the following three clustering groups to define deviation from expected value: "positive deviation > 1", "negative deviation > 1" and "no deviation".
Then, we analysed, on species relative abundance data, which of the taxa were most abundant in each group, i. e. most likely responsible for the observed positive or negative deviations in IPS scores. Finally, each analysis mentioned previously was run on two datasets (one with the rbcL marker results and one for the 18S-v4 marker results) in order to compare the effect of the DNA marker choice. To evaluate the comparability with the established environmental assessment, we compared the ecological status class derived from the different markers directly and also analysed the extension and the impact of the lacks in the respective databases on IPS calculation and species assignment.
Shannon diversity and taxa missing from reference databases The Shannon scores of the morphological and molecular taxa inventories were significantly different (Student's paired-sample t-test, p < 0. 001 both for the use of 18S and rbcL). As expected, the two references databases are lacking a significant amount of species used in Fennoscandia water quality assessment, with only 15. 4% of all Fennoscandian taxa represented in the 18S database and 17. 8% in the rbcL database. Many genera had only few species represented in the reference databases, especially Achnanthidium, Eunotia, Gomphonema, Navicula and Nitzschia. However, regarding our own study, actually 70% of the species found by the morphological method were represented in the reference databases.
This mismatch was either associated with a lack of reference barcode in the DNA database (NR, in red in Fig. 3 ) or either by a lack of DNA detected by the molecular method, despite having a reference barcode for the species in the reference database (ND, in orange in Fig. 3 ). The probability of getting these MOR-NR or MOR-ND mismatches in the species list was high and  very similar for both the 18S and rbcL markers (MOR-NR: 50% and 53%, respectively and MOR-ND: 49% and 46%, respectively) (Fig. 3 ). Another, less common, mismatch was an abundance discrepancy (AB1 and AB2). This mismatch happens when a species is found by both techniques but in significantly different abundances. In the case of a higher abundance in morphological than in molecular species lists (AB1), 44% of the concerned species were found to be causing the error with both markers.