dataesr/scientific-paragraphs-categorization huggingface dataset:
Non-overlapping best hits were used. The above taxonomical protocol was applied and only eukaryotic scaffolds were conserved. An alternative taxonomical annotation at the single read level was computed by Kraken (v2) (Wood, Lu and Langmead 2019) using its standard database. This annotation was found to be much less comprehensive and was not used further. The diversity and sequencing effort of each sample was estimated by Nonpareil (v3. 304)  (Rodriguez-R et al. 2018)  on forward reads over 35 bp using the kmer algorithm. Contig taxonomy and coverage data was used for beta-diversity estimation together with the kmer frequency of reads. To do so, 15 bp kmer frequency was retrieved for forward and reverse reads by Gerbil (v1. 12)  (Erbert, Rechner and Müller-Hannemann 2017 ). An in-house script was used to merge the produced files and compute the cosine distance between samples.
O R I G I N A L U N E D I T E D M A N U S C R I P T The Pfam domains (Nov. 2021 database version,  Mistry et al. 2021 ) of all ORFs were searched with  Interproscan (v. 5. 39-77. 0, Jones et al. 2014). The non-overlapping matches with E-value < 10 -5 were retrieved with an in-house script.
β-lactamase gene detection and analyses ORFs with a best Diamond blastP match to a β-lactamase and β-lactamase-related proteins in the NCBI Refseq database were retrieved and analysed (Table  S2 ). Functional domains were inferred by Interproscan (v5) against Pfam, ProSitePatterns, and by Batch-CDsearch (Marchler-Bauer and Bryant 2004) against the COG database  (Galperin et al. 2021 ) (E-value < 10 -5 ) (Table  S3 ). ORFs at least 100 amino acids in length exhibiting a β-lactamase domain according to Pfam and COG (except for class D solely defined by COG domain) were kept for further analyses. We attempted to identify the β-lactamase-encoding contigs as potential plasmids using PlasClass  (Pellow, Mizrahi and Shamir 2020)  and PlasFlow  (Krawczyk, Lipinski and Dziembowski 2018). However, the results were found to be unreliable and not used further.
To normalize the β-lactamase count by a biologically relevant metric, we estimated the number of bacteria in our metagenomic dataset using 120 bacterial single copy genes from the Genome Taxonomy database using GTDB-Tk  (Chaumeil et al. 2020)  as reference (Table  S4 ). For comparison, the abundance of β-lactamases was also computed in all of the terrestrial metagenomics datasets available from the IMG/M database from June 2021  (Chen et al. 2021). 1802 datasets were initially downloaded, then checked for contig length, coverage and quality. 434 datasets from known biomes and of sufficient sizes (at least one contig over 10kb and at least 3 bacteria equivalent sequenced) were retained and analysed with the exact same protocol used to detect ORFs, β-lactamases and bacterial single-copy genes in our own dataset (Table  S5 ).
Sample diversity In contrast, the assembly of a similar number of sequenced reads from the different datasets, resulted in a highly variable number of contigs (≥5 kb). As these values are linked to the complexity of the sample (species richness-number of different species-and their relative abundance -species evenness-), this suggested that the samples exhibit globally different microbial population compositions and structures. As our samples are expected to be variable in richness and evenness while the number of reads is quite similar (except for sample B), the sequencing effort is expected to be different for each sample. This was confirmed by the Nonpareil tool that calculates the sequencing effort and an alternative to the Shannon diversity index computed from the kmer diversity of reads (Rodriguez-R et al.
With 30 other contigs most likely originating from green algae, this suggests that this 40,000-year-old layer originated from a lake bottom deposit (Table  1A ) (Fig. S5. B). Finally, according to the LCA assignment method, viruses were predicted to only represent 0. 4% of the global coverage of all samples (Fig. S3 ), with one sample (R) standing out by reaching 2. 3%. Such low representation prompted us to reanalyze our datasets using Virsorter, a leading software tool specifically dedicated to the identification of viruses in metagenomics data  (Roux et al. , 2015). This new protocol increased the average abundance of virus to 2. 68%, with only a handful of samples exhibiting values higher than 2%, including the R sample at 8. 6% (Fig. S6 ). families known to gather giant or large DNA viruses.
A high diversity that points out syngenetically trapped communities The global community analysis and an analysis of the kmer frequencies of reads in each dataset do not consistently put samples from the same borehole close to each other (L-N-R, P-K, Q-M-O) (Fig. S7 ). These analyses do not allow a meaningful clustering of samples originating from the different boreholes, or even distinguish surface samples from permafrost samples (Fig. S7 ).
3 ). Interestingly, the large distance separating sample Q from the same borehole samples M and O is consistent with its unfrozen state as part of an under lake talik (Table  1A ). Statistical analysis using ANOSIM confirms these observations (Fig. S7 ) showing that the distances between samples from the same borehole are not significantly lower than the distances between samples from different boreholes (R=0. 388, p<0. 069). As a complimentary attempt to interpret the microbiome differences between samples, we screened the Pfam database, identified a total of 9481 motifs associated to specific protein families and/or molecular functions, and computed their distribution across samples. We expected that this type of analysis could reveal ecological/environmental constraints shared by various samples in spite of their different microbial communities.
A list of the fifty most abundant Pfam motifs globally found in our dataset is given in Table  S6. This list includes seven of the twenty most abundant motifs found in the whole Pfam database, supporting the validity of our annotation protocol. Wright 2019) we specifically investigated the presence and diversity of these proteins/enzymes in our dataset. Although the ancient microorganisms trapped in the pristine permafrost layers studied here have little probability to come in direct contact with human-beings, the risk remains that they might contribute to new β-lactam resistances once in contact with modern bacterial species. A total of 1071 β-lactamase genes were detected by diamond blastP against Refseq and confirmed by a domain search (Fig. S9 ). Among them, 1066 were found in bacterial contigs, 4 in an archaeal contigs and 1 in an ambiguous contig.
7%), all of which are enriched in fungal-bacterial networks  (Bonito et al. 2019). A previous culture-based study of Siberian, Canadian and Antarctic permafrosts estimated the count of fungal cells around 10 2 per gram of dry soil, distributed among 49 species  (Ivanushkina, Galina and Ozerskaya 2005). As fungi are in form of spores in permafrost  (Vorobyova et al. 2001) , part of their diversity might remain unseen because of difficulties to extract their DNA. The proportion of eukaryotic cells might also be much lower than that of bacteria. In such case, their sequencing coverage might be insufficient to assemble contigs of a least 5kb in size. The larger size of eukaryotic genomes, the presence of introns, and their lower coding density due to large intergenic regions also strongly decrease the likelyhood of their identification by similarity searches in protein databases.
Variable bacterial community composition As expected, bacteria constituted most of the DNA sources (Fig. S3 ). Compared to bar-coding methods, metagenomics allows an exploration of the sample diversity that is, in principle, only limited by a threshold of minimal abundance for a given species. This threshold results from 3 main factors: 1-the proportion of cells (or viral particles) of a given species (mostly unicellular) in each sample, 2-the efficiency with which their DNA can be extracted (e. g. sporulating vs non-sporulating species), 3-our capacity to identify them by sequence similarities searches in reference databases. The last factor involves variations in proteincoding density (high in viruses and prokaryotes, much lower in most eukaryotes except for some fungi and protozoans), the fraction of the database corresponding to known members of the various phyla (high for Bacteria, much lower for viruses and Archaea), the level of sequence divergence among homologues within different domains (very high in viruses, lower in eukaryotes).
Finally, class D βlactamases, also known as oxacillinases (OXAs) evolved from degrading an extended spectrum of cephalosporins to hydrolyse carbapenems  (Sawa, Kooguchi and Moriyama 2020). However, from their sequences alone (except for near 100% identical residues) it is impossible to predict the substrate range and the potential clinical risk associated to the diverse β-lactamases identified in our dataset. The presence of antibiotic-resistant bacteria carrying various types of β-lactamases has been previously reported in pristine Arctic and Antarctic surface cryosols and ancient permafrost  (Allen et al. 2009; D'Costa et al. 2011; Petrova et al. 2014; Perron et al. 2015; Kashuba et al. 2017; Van Goethem et al. 2018; Haan and Drown 2021). However, these previous studies did not evaluate the proportion of bacteria carrying a β-lactamase gene or exhibiting a β-lactam resistance in the global population of soil bacteria, most of which are unculturable (or eventually dead).
Figure4. A presents a Bray-Curtis dissimilarity map computed from the Pfam counts in the samples. This map confirmed the functional similarity of the L-N-R samples, of the P-K samples (with the insertion of M from a different borehole, but from a similar depth and age as K), and to a lesser extent of two surface soil samples (D, E). The rest of the samples exhibited unrelated functional patterns, with the unfrozen talikderived sample Q as the most dissimilar. Table S6 also indicated the presence of 3 Pfam motifs, found in proteins involved in bacterial antibiotic resistance: TetR-like transcription regulators (PF00440, ranked 29 th /9481), Metallobeta-lactamases (PF00753, ranked 44 th ), and glyoxalases (PF00903, ranked 46 th ). This prompted us to focus our next analysis on Pfam motifs putatively associated with virulence factors, the result of which are presented as a heatmap in Figure4.
Acknowledgements We are deeply indebted to our volunteer collaborator,  Alexander Morawitz , for collecting the Kamchatka soil samples. We thank  Dr. Audrey Lartigue  for technical advices about DNA extraction, and acknowledge the computing support of the  PACA Bioinfo platform. We thank  M. Ulrich  ( DFG  project # UL426/1-1 ) and  P. Konstantinov  for helping with fieldwork at the Yukechi site as well as the  Alfred Wegener Institute  and  Melnikov Permafrost Institute logistics  for field support and sample acquisition.
Funding This work was supported by  France Génomique  (Grant # ANR-10-INBS-09-08 ) to  J-M C  and  CNRS PRC  research grant (# PRC1484-2018 ) to CA. E C-F was supported by a PhD grant (DGA/DS/MRIS # 2017 60 0004 ). GG and JS were funded by  ERC PETA-CARB  (# 338335 ) and the  HGF Impulse and Networking Fund  ( ERC-0013 ).
Data availability All raw reads are available through the EMBL-EBI under the study number: PRJEB47746 (secondary accession: ERP132049). Samples accession numbers: ERS7649018, ERS7649019, ERS7649020, ERS7649021, ERS7649022, ERS7649023, ERS7649024, ERS7649025, ERS7649026, ERS7649027, ERS7649028. Downloaded from https://academic. oup. com/microlife/advance-article/doi/10. 1093/femsml/uqac003/6564729 by SCDU Mediterranee user on 13 April 2022.
Climate change is imposing drier atmospheric and edaphic conditions on temperate forests. Here, we investigated how deep soil (down to 300 cm) water extraction contributed to the provision of water in the Fontainebleau-Barbeau temperate oak forest over two years, including the 2018 record drought. Deep water provision was key to sustain canopy transpiration during drought, with layers below 150 cm contributing up to 60% of the transpired water in August 2018, despite their very low density of fine roots. We further showed that soil databases used to parameterize ecosystem models largely underestimated the amount of water extractable from the soil by trees, due to a considerable underestimation of the tree rooting depth. The consensus database established for France gave an estimate of 207 mm for the soil water holding capacity (SWHC) at Fontainebleau-Barbeau, when our estimate based on the analysis of soil water content measurements was 1.
9 times as high, reaching 390±17 mm. Running the CASTANEA forest model with the database-derived SWHC yielded a 350 gC m -2 y -1 average underestimation of annual gross primary productivity under current climate, reaching up to 700 gC m -2 y -1 under climate change scenario RCP8. 5. It is likely that the strong underestimation of SWHC that we show at our site is not a special case, and concerns a large number of forest sites. Thus, we argue for a generalisation 1.
Introduction Evaporating water is vital for trees. The process of evapotranspiration plays a central role in the thermoregulation of leaves, the acquisition and transport of nutrients from the soil to plant organs and is an inescapable consequence of the stomatal opening for the acquisition of carbon from the atmosphere. In plant communities, orders of magnitude for the amount of water transpired per unit carbon gained through photosynthesis range from 190 grams of water per unit gram of carbon as a global estimate for terrestrial vegetation  (Cramer et al. , 2009)  to 330 g(H 2 O)/g(C) in the FLUXNET database  (Baldocchi and Peñuelas, 2019). This means that considerable amounts of water are necessary for sustaining the basic functioning of plant communities, and particularly of forests which can grow high leaf areas  (Asner et al.
, 2018) , it is urgent to improve our understanding of the provisioning of water to forest ecosystems, and to evaluate the sensitivity of modelled projections of ecosystem functioning to the parameterization of soil properties. Here we used soil water content data measured over a 150-cm soil profile, combined with deep soil water content estimates (i. e. sensors down to -300 cm) to evaluate the role of surface vs. deep water in provisioning tree transpiration needs and modulating canopy gas exchange in a temperate oak forest. We focused on the two contrasted, consecutive years of 2017 (a mild summer) and 2018 (characterized by a hot drought). Our objectives were:.
, Lymington, UK), respectively. Both EC systems runned concurrently at 37 m a. g. l. , i. e. 9 m above the dominant height. EC data were acquired at 20 Hz with two data loggers time-synchronized with a PTP server: a CR3000 (Campbell Scientific Inc. , Loughborough, UK) for the Li7500/R3-50 and the Smartflux 2 for the Li7200/HS-50 (LI-COR Inc. , Lincoln, NE, USA). Half-hourly fluxes of CO 2 and H 2 O were calculated using the EddyPro software (v7. 0. 6, LI-COR Inc. , Lincoln, NE, USA). Frequency response corrections were applied to raw fluxes, accounting for high-pass  (Moncrieff et al. , 2005)  and low-pass filtering  (Moncrieff et al. , 1997). The influence of air density fluctuations on the fluxes was accounted for  (Burba et al. , 2008). As usually done  (Baldocchi, 2008) , we did not correct eddy covariance fluxes despite a 18±6% average energy imbalance (calculated as the Energy Balance Ratio,  Wilson et al.
, 2002)  at the FR-Fon site. Half-hourly CO 2 and H 2 O fluxes have been acquired continuously from 2005 by the Li7500/R3-50 device and were considered the reference data series here. Fluxes computed from the Li7200/HS-50 device, running from 2012, were used to gap-fill the reference data through linear regressions established annually. Following this instrumental gap-filling, the calculated Net Ecosystem Productivity (NEP) and evapotranspiration (ETR) fluxes were quality-controlled, gapfilled, and NEP was partitioned into Gross Primary Productivity (GPP) and ecosystem respiration (Reco) according to standard procedures  (Reichstein et al. , 2005; Papale et al. , 2006). We estimated the uncertainty of annual GPP and NEP values as the combination of uncertainties originating from the choice of a u* threshold for filtering the data, and the propagation of random error inherent to EC measurements during the statistical inference by the gap-filling algorithms  (Delpierre et al.
In that context, different methods exist to extract RWU, from numerical model inversion  (Zuo and Zhang, 2002)  to the use of daily fluctuations in SWC  (Li et al. , 2002). Indeed, RWU manifests as fluctuations in soil moisture in the rhizosphere, influenced by evapotranspiration which occurs preferentially over daytime. As a consequence, RWU can be assessed using linear regression and differentiation of daytime vs. nighttime change in SWC. In a methods comparison  (Guderle and Hildebrandt, 2015) , this approach using daily SWC oscillations proved to be the best regarding a combination of criteria: the quality of estimations and lesser induced error due to moisture sensor accuracy (the calibration error). Consequently, we focused on this method, and calculated RWU at our site from SWC measurements with the python package rootwater  (Jackisch and Mälicke, 2020).
5 and 8. 5, see  Jourdan et al. , 2021  for details on the climate models). With those simulations, we quantified the sensitivity of simulated GPP to the SWHC parameter. SWHC is a forcing parameter to the model, and has been identified as very sensitive, with 10% variation of SWHC yielding 13-24% variations of annual NEP, 5-8% variations of annual GPP and 3-4% variations of annual ETR in simulated fluxes  (Dufrêne et al. , 2005). Soil database for parameterizing SWHC. We parameterized CASTANEA with two different SWHC estimates at the FR-Fon forest. We used our own estimate of SWHC, based on SWC measurements (see Results). Beside this, and similar to what is usually done for parameterizing ecosystem models (e. g. Cheaib et al. , 2012) , we used a SWHC estimate obtained from a soil database  (Badeau et al.
, 2010). Badeau et al. built a 1-km² resolution database of SWHC over France, from a soil database of soil texture and rooting depth  (Jamagne et al. , 1995). Each 1x1 km grid point encompasses different soil types, and the database gives for each grid point the average value of SWHC with respect to the area of each soil type over the 1x1 km grid.
2b ). The daily fluctuations were neat from May at 120-130 cm depth and their amplitude increased in July, when they also became visible in the 290-300 cm layer (Fig. 2b ). As for the upper layers (not shown), the SWC signal had a clear shape in the 290-300 cm layer, with a decrease during the day and a stall during the night, which was evocative of the day/night cycle of tree transpiration, and root water uptake (Fig. 2a inset ). We estimated the quantity of water extractable in each soil layer from SWC measurements (Suppl. Mat. S4). Field capacity (FC, the maximum moisture of unsaturated soil) was considered to be reached when SWC reached stable, high values (e. g. skyblue dashed line on Fig. 2a ), after checking the disappearance of a seasonal perched water table from piezometer data (not shown).
5c ). Sensitivity of an ecosystem model to SWHC parameterization. The SWHC value of 390 mm determined from SWC measurements was 1. 9 times higher than the value of 207 mm estimated from the 1-km French soil database for the corresponding grid point  (Badeau et al. , 2010) , and in the high end of the SWHC distribution of soils appearing in the database over France (Fig. 6a ). The CASTANEA model fairly simulated the seasonality of GPP observed at FR-Fon when parameterized with SWHC= 390 mm (Fig. 6b ), as well as the interannual variability of GPP (Suppl. Mat. SN1). On the contrary, parameterizing CASTANEA with SWHC= 207 mm, as obtained from the soil database  (Badeau et al. , 2010)  leaded to higher soil water stress and reduced GPP in summer, as compared to GPP derived from the flux tower measurements (Fig.
6a ). It was also very high when compared to estimates published at other sites, at which roots have been evidenced to go deeper than 1 meter (e. g. 165 mm for a 200 cm root exploration depth in an oak stand in  Bréda et al. , 1995, maximum   , 2020). The value of 390 mm for SWHC was 1. 9 times higher than the database estimate of 207 mm for the FR-Fon grid point  (Badeau et al. , 2010). It is noticeable that the latter estimate was obtained from the integration of pedo-transfer functions over a soil depth of 112 cm, far lower than the actual root exploration depth at FR-Fon (Suppl. Mat. S1). If considering a soil depth of 112 cm, we would estimate a SWHC of 191 mm based on our SWC measurements (integration of SWHC data per soil layers displayed in Suppl. Mat. S4). From this, we conclude (1) that our estimate of SWHC is conservative, meaning that deriving FC and PWP from SWC data (Suppl.
Mat. S4) yielded similar (actually, slightly lower) values of SWHC than obtained from pedo-transfer functions  (Badeau et al. , 2010)   et al. (2010) , our analysis of SWC data suggest that roots can actually extract water at least down to -300 cm at FR-Fon (Fig. 4 ), which is supported by the observation of roots down to -300 and even -400 cm on one soil core taken at the site (Suppl. Mat S1). Actually, we consider the 390-mm value as a lower-bound estimate for SWHC at FR-Fon. Indeed, we estimated this value on the basis of SWC measurements recorded during the dry 2018 summer. While we are confident that our estimates of water content at field capacity were reliable, we may have underestimated the values of the wilting point (Suppl. Mat. S4), particularly for the soil layers below 100-cm depth that may have remained above the wilting point during this dry episode (note the decreasing trend of SWHC with depth below 100-cm in (Suppl.
A variety of mechanistic models can be used to this aim, with some hypothesizing the forest vulnerability to rely foremost on its carbon balance (e. g. Dufrêne et al. , 2005) , some others simulating the integrity of the plant hydraulics  (Cochard et al. , 2021) , and some integrating both  (Davi and Cailleret, 2017; Naudts et al. , 2015). Whatever the model type, it requires an accurate estimate of the amount of soil water that can be used by trees. Several papers have pointed recently the role of "deep" water reserves as key to sustain ecosystem functioning  (McCormick et al. , 2021; Fan et al. , 2017; Carrière et al. , 2020). We showed that using a SWHC estimate of 390 mm, as opposed to 207 mm as predicted by a reference soil database, impacts strongly the simulation of GPP in the CASTANEA model (Fig.
Data acquisition. Soil water content (SWC, in m 3 water m -3 soil ) was measured from 2012 with five EnviroSCAN probes (Sentek Sensor Technologies, Stepney, Australia) located in the vicinity of the tower and connected to a CR1000 datalogger. Each probe had a 10-cm vertical distribution of 15 sensors from -5 cm to -145 cm, i. e. down to 150 cm depth. From 2016, a sixth probe reached the depth of -300 cm with the following vertical sensor distribution: -15, -55, -85, -135, -175, -215, -255, and -295 cm. The CR1000 datalogger achieved automatic data acquisition with half-hourly means calculation. In order to assess the spatial representativity of SWC measurement collected by the 6 probes, 24 access tubes of 160 cm long were installed in the vicinity of the tower in 2012 over a 3800-m 2 area.
Acknowledgements We thank  Augusto Zanella  and  Cécile Quantin  for their contributions in the description of soil cores,  Kamel Soudani  and  Eric Dufrêne  for insightful discussions and their critical readings of the manuscript,  Claude Doussan  for discussing with us questions relative to the role of capillarity, and students  Annaëlle Desgrippes  and  Jocelyn Ragu  for their contribution in the acquisition of root distribution data. IC acknowledges funding by the  ANR  under the " Investissements d'avenir " programme with the reference  ANR-16-CONV-0003  ( CLAND ).