dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistics Shifts in isoprene emission, gas exchange and chlorophyll fluorescence under the three watering treatments applied over 8 months were tested using a two-way repeated measures ANOVA with treatment (C, MS and SS) and month as factors and the studied traits as dependent variables. Changes in soil and plant water status, LMA as well as concentration of phenolic compounds were analysed using two-way ANOVA, since these parameters were measured on the destructive set of plants. In either case, when these two factors interacted significantly (P,0. 05), one-way ANOVA was applied for the significant factor. Over the text, simple or repeated ANOVA measurements are named as ANOVA (F). Tukey post hoc tests were used to test differences between months and treatment. Data were log transformed when necessary to achieve normal distribution and homoscedasticity requirements.
Acknowledgments We thank the staff of the administrative plant nursery of  Les Milles (Departmental Directorate of Agriculture and Forestry of BDR, France) , especially  P. Brahic  for his useful comments and all the staff for technical assistance. We are also grateful for chemical analysis support provided by  S. Greff  and  C. Lecareux  as well as support for physiological and growth measurements provided by  S. Dupouyet. Y. Monnier ,  L. Hardion ,  M. Santonja  and  A. Cellier  also participated to field work.
This research was supported by  ADEME (Agence de l'environnement et de la maitrise de l'energie) , the  region Provence-Alpes Cote d'Azur (PACA)  and the  European Union  and was funded by the  French National Research Agency (ANR)  through the  SecPriMe2  project ( ANR-12-BSV7-0016-01 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Fluorescence microscopy Cells were observed in Minimal Media to have only a single copy of each chromosome after division. Protocols for Microscopy are detailed in Text S1. The snapshot images were analysed using the Matlab-based software MicrobeTracker  [45, 46] , see details for the analysis in Supplementary Text S1. WT (left) and DparS1 (right). (B) Reconstitution of the segregation choreographies, as in Figure  1C. For the WT choreography, strain cell sizes were realigned as in Figure  2. For the DparS1 choreography, the cell sizes of ADV128 were aligned to the cell size of ADV27 using the first cell size interval where $50% of cells contained 2 foci of ori II and R1 II , respectively. (C) Relative distance between R1 II and ter II loci measured in the cells containing only one focus of each, as in Figure  1D.
Acknowledgements. This study was partially supported by the  NASA   Atmospheric Composition Program  ( NNX07AM45G ), Climate  Data Record Uncertainty Analysis  ( NNX11AL22A ), and Modeling,  Analysis and Prediction Program. We have also been partially supported by the  RTRA/STAE foundation from Toulouse, France. We acknowledge the  AIRS, TES , and MLS science teams for the satellite products used in this study. We also thank the  NASA INTEX-B  and  NSF  and  NOAA HIPPO science teams  for providing high-quality in situ measurements. Edited by:  Q. Errera.
Study area and extraction of extreme events Data used in this study are taken from tide gauges located on the French Atlantic coast. Water sea level database was provided by the French Oceanographic Service SHOM (Service Hydrographique et Océanographique de la Marine). It was shown that the mean observed tide levels in the French Atlantic coast increase with time and mean predicted ones are constant. To compare the observed and predicted sea level data, observations at each site have been corrected so that sea level rise did not affect the average annual observed levels skew. Storm surges time series were extracted from these predicted and observed tide levels. The reader is referred here to  Bardet et al. (2012). The selected peaks for a particular year have to be drawn from distinct and statistically independent storm surges events.
calcipolaris-like amplicons from different samples and 30 libraries for the Gloeomargarita clade. Clone inserts were PCR-amplified using flanking vector primers, and inserts of expected size were partially sequenced (Beckman Coulter Genomics, Takeley, UK) with reverse primer 1227R or 1231R, yielding sequences of ca. 800 bp. A total of 201 cyanobacterial clones were sequenced (33 for Themosynechococcus/S. calcipolaris and 168 for Gloeomargarita clades). All the sequences obtained were highly similar (>97% identity) but several clones that appeared slightly different were fully sequenced by using forward primers. Complete sequences were assembled using Code Aligner (CodonCode Corporation; www. codoncode. com) prior to phylogenetic analyses. Sequences were deposited in GenBank with accession numbers KJ636536-KJ636761.
PHYLOGENETIC ANALYSES Environmental 16S rRNA gene sequences retrieved from our samples were compared with sequences in the GenBank database (http://www. ncbi. nlm. nih. gov/) by BLAST  (Altschul et al. , 1997). We retrieved the closest sequences found in the database and included them in an alignment containing also sequences from the closest cultivated members and some representative sequences of major cyanobacterial taxa. Sequences were aligned using MUSCLE  (Edgar, 2004). Ambiguously aligned positions and gaps were eliminated using Gblocks  (Castresana, 2000). The resulting sequence alignments were used as input to build phylogenetic trees by approximate maximum likelihood using Fasttree  (Price et al. , 2010)  with a General Time Reversible (GTR) model of sequence evolution, and taking among-site rate variation into account by using a four-category discrete approximation of a distribution.
, 2011)  and a sequence from a filamentous thermophilic cyanobacterium (tBTRCCn 301) retrieved from a thermal spring in Jordan (Figure  1 ). In the case of Ca. S. calcipolaris G9, the clade seemed less diverse, with relatively few related sequences in GenBank. Indeed, the closest sequences to Ca. S. calcipolaris G9 corresponded to those of cultured species, including four highly related Synechococcus species (S. lividus and Synechococcus sp. PCC6716, PCC6717, and OH5) and Thermosynechococcus elongatus, all of which are thermophilic  (Dyer and Gafford, 1961; Miller and Castenholz, 2000; Nakamura et al. , 2002)  (Figure  1 ). Therefore, in the following, we will refer to this clade indistinctly as the Thermosynechococcus clade or the Synechococcus calcipolaris-like clade. Because both G. lithophora and S.
calcipolaris G9 were enriched from carbonate microbialites of the alkaline Lake Alchichica and because their closest relatives in GenBank had been most often identified in thermophilic microbial mats, we searched to identify these clades in a broad collection of samples collected worldwide that included thermophilic settings compatible with photosynthesis (40-70 • C) and carbonate-rich environments, such as microbialites and karstic areas (Table  1 ). In particular, we explored the presence of the two clades in different microbialite samples from Lake Alchichica collected at different locations along the shore of the lake, time, and depths (from 0 to 15 m depth). We also analyzed microbialite samples from neighboring lakes, including Quechulac, La Preciosa, and Atexcac. We tried to amplify the 16S rRNA genes from the two clades in a total of 62 samples (Table  1 ).
ACKNOWLEDGMENTS We are grateful to  Ana Isabel López-Archilla ,  Ludwig Jardillier  and  Emmanuelle Gérard  for providing some of the samples tested in this study ( Chiprana, Les Causses, Mayotte ), and to  Samia Amarouche-Yala  for the thermophilic mat sample on which SEM analysis was performed. The research leading to these results has received funding from the  European Research Council  under the European Community's  Seventh Framework Program  (FP7/2007-2013 Grant Agreement no. 307110 -ERC CALCYAN ). The Scanning Electron Microscope (SEM) facility of the  Institut de Minéralogie et de Physique des Milieux Condensés  has been purchased owing to the  Région Ile de France  grant  SESAME 2006 N • I-07-593/R ,  INSU-CNRS, INP-CNRS, University Pierre et Marie Curie -Paris 6.
Data were originally provided at 3. 75 • × 2. 5 • (longitude by latitude) and monthly resolution. For the anthropogenic emissions (excluding direct agricultural emissions, which are accounted for in O-CN), we used EDGAR-4. 1 (Emission Database for Global Atmospheric Research) inventory data. These data were provided at 1. 0 • × 1. 0 • and annual resolution. Since N 2 O emissions from these sources are small relative to the terrestrial biosphere and ocean sources, and because they have relatively little seasonality, annual resolution is considered sufficient. The biomass burning emissions were provided by the Global Fire Emissions Database (GFED-2. 1) at 1. 0  variations may not be in phase with observations  (Dutreuil et al. , 2009). Data were provided at 1. 0 • × 1. 0 • and monthly resolution.
Observations Atmospheric observations were pooled from a number of global networks, independent sites and ship-based measurements (see Figs. 1  and 2  and Table  1 ). Long-term records of N 2 O mole fraction are available from the Advanced Global Atmospheric Gases Experiment (AGAGE, http://cdiac. ornl. gov/ftp/ale_gage_Agage/AGAGE/) and the NOAA Halocarbons and other Atmospheric Trace Species (HATS, http: //www. esrl. noaa. gov/gmd/hats/), including both the OTTO and the Chromatograph for Atmospheric Trace Species (CATS, http://www. esrl. noaa. gov/gmd/hats/insitu/cats/) programmes, which were established in the 1990s. The early data, however, cannot be used to constrain regional fluxes of N 2 O owing to the less precise instrumentation available at that time. During the 1990s significant improvements to the measurement technique were made and, thus, sufficiently precise data from these networks are available from the midto late 1990s.
Both the AGAGE and CATS networks consist of stations equipped with in situ gas chromatographs with electron capture detectors (GC-ECD) to measure the dry air mole fraction of N 2 O (ppb) and provide measurements at approximately 40 min intervals. The AGAGE data are reported on the SIO1998 scale and have an uncertainty on individual measurements of about 0. 1 ppb  (Prinn et al. , 2000) , while the NOAA CATS data are reported on the NOAA2006 scale and have an uncertainty of about 0. 3 ppb  (Hall et al. , 2007). In the late 1990s and early 2000s, NOAA also established a flask network under the Carbon Cycle and Greenhouse Gases (CCGG, http://www. esrl. noaa. gov/gmd/ccgg/) programme. Flask samples from this network are also analysed by GC-ECD in a central laboratory and are reported on the NOAA2006A scale.
These data have an uncertainty of 0. 4 ppb based on the mean value of differences from paired flasks. There is some concern that there could be a calibration shift between NOAA CCGG data collected before and after 2001, owing to a poor calibration routine used prior to 2001. To test for the influence of this shift on the retrieved fluxes, we include an inversion test in which minimal NOAA CCGG data are included (test IAVR; see section 2. 7 for details). The Commonwealth Scientific and Industrial Research Organisation (CSIRO) in Australia have also operated a flask network since the early 1990s (data available from the World Data Centre for Greenhouse Gases, http://ds. data. jma. go. jp/gmd/wdcgg/). These measurements are reported on the NOAA2006 scale with an uncertainty of approximately 0.
3 ppb for each flask measurement  (Francey et al. , 2003). In addition, we included data from two stations operated by the National Institute for Environmental Science (NIES) in Japan, which operate in situ GC-ECDs  (Tohjima et al. , 2000)  (data available from the World Data Centre for Greenhouse Gases, http://ds. data. jma. go. jp/gmd/ wdcgg/). These data are reported on NIES's own scale, which is approximately 0. 6 ppb lower than NOAA2006A for ambient concentrations as determined from an intercomparison of NOAA standards (see http://www. esrl. noaa. gov/gmd/ccgg/ wmorr/results. php?rr=rr5&param=n2o). Lastly, we include data from ship-based flask measurements in a programme operated by Tohoku University, Japan (data available from the World Data Centre for Greenhouse Gases, http://ds. data.
(2011) , but, in addition, we corrected the observations based on our prior estimated calibration offsets before running the inversion. Using both approaches means that the inversion will only "fine-tune" the offsets, thereby limiting the degrees of freedom that can be used for this adjustment. Prior calibration offsets were estimated relative to NOAA CCGG (NOAA2006A scale) based on the comparison of observations from two or more networks at the same location, e. g. in American Samoa, where NOAA CATS, NOAA CCGG and AGAGE all have measurements. Overlaps between NOAA CCGG and AGAGE also exist at MHD, CGO, RPB and THD, as well as between NOAA CCGG and CSIRO at ALT, MLO, CGO and SPO, where the data were corrected to the NOAA2006A scale using the linear trend and offset calculated at each site (see Table  2 ).
At sites where there was no overlap, the mean of the corrections applied to other sites within the same network was used. For NIES, which has no overlap with other networks, a temporally fixed offset was used and was based on shared cylinder intercomparisons with NOAA CCGG (Y. Tohjima, personal communication, 2012). The comparisons of AGAGE and NOAA CCGG data show a significant trend in the offset (0. 02-0. 04 ppb a -1 ), which is consistent at all compared sites and points to a possible calibration drift in the scales relative to each other. However, without further detailed investigation it is not possible to tell how much each scale may have drifted and in which direction, but this drift could be significant when considering N 2 O emission trends. All data were filtered for suspicious values using flags set by the data providers.
Observation error covariance matrix Another key component of the Bayesian inversion is the observation uncertainty, which is described by the error covariance matrix, R. A thorough description of the observation error variances is needed to avoid giving too strong weighting to very uncertain observations, which becomes particularly important if these observations are far from the expected value. The observation error variance takes into account the measurement and transport model errors. Measurement errors consist of random and systematic components. Random errors are assessed in determining the measurement reproducibility, while systematic errors, such as errors in the calibration, instrumentation, air sampling, etc. , are more difficult to determine. For the measurement error, we have used the estimates given by the data providers, which includes random and, as far as is known, systematic errors and is approximately 0.
Thus ran a set of inversions with as consistent as possible data coverage (the reference data set) and a set of inversions using all available observation sites. For the reference data set inversions, 15 sites were included, these having data throughout the inversion period and no gaps of longer than 6 months, while for the other inversions, 59 sites were included and no gap criterion was applied (see Table  1  for the list of sites). The reference data set inversions also serve as a test for the influence of a potential NOAA CCGG scale shift (see Sect. 2. 4) as only one NOAA CCGG site was included in this data set. The reference data set inversions consisted of one run using the interannually varying prior fluxes (IAVR) and one run with climatological prior fluxes (CLMR) to test the influence of the assumed flux interannual variability.
Acknowledgements. We are very grateful for the input and feedback from  S. Zaehle ,  L. Bopp  and  W. Lahoz. We thank  G. van der Werf  for use of the GFED data and the  EDGAR team  for the use of their inventory data. We also acknowledge the support of the LSCE computing services. This work was jointly financed by the  European Commission  under the  EU Seventh Research Framework Programme  (grant agreement no. 283576. MACC-II) and by the  Norwegian Research Council  (contract no. 193774, SOGG-EA). Edited by:  J. Kaiser.
Sequence data analyses All reads that had mismatches to the 16S rRNA primers, contained ambiguous nucleotides (N) or were shorter than 270 nucleotides (excl. the forward primer) were removed. The remaining sequences were subjected to stringent quality trimming to remove reads containing $3% bases with Phred values ,27 (0. 2% per-base error probability). This minimizes the influence of erroneous reads when clustering at 97% for OTU definition  [28, 29]. Sequences were then de-replicated and clustered at a 97% threshold using Uclust  [30]. Sequences from each OTU were classified by comparison to the Greengenes database  [31]. Read quality filtering and length trimming, dereplication, clustering at 97% sequence identity, taxonomic classification and dataset partitioning based on barcodes were conducted with Pyrotagger  [32].
The taxonomic affiliations of the most abundant OTUs (. 1% of the sequences) were further verified against sequences from the NCBI databases using BLAST  [33]. To compare bacterial communities for diversity analysis, all samples were randomly resampled to the size of the sample containing the fewest sequences (n = 798) using Daisy Chopper  [34]. Calculation of the Shannon diversity index (H9) and cluster analysis were performed using the software PAST  [35]. A similarity percentage analysis, SIMPER  [36] , was conducted to identify the phylotypes contributing the most to the dissimilarity between different samples. Sequences have been submitted to MG-RAST (http:// metagenomics. anl. gov/linkin. cgi?project = 5773) for public availability. Because the 16S rRNA genes of free-living bacteria and gill bacteria from Xylophaga were amplified with different primer pairs, the obtained sequences did not cover identical gene fragments.
However, the overlap included the entire hypervariable V3 region (E. coli positions 433-497) that is widely used in phylogenetic studies using next generation sequencing methods, and this region yields sufficient information for a direct comparison between wood, pellet and gill communities of Xylophaga. All sequences were therefore realigned and an OTU table based upon 100% sequence identity built from the common overlapping region of 80 bp. The analysis was done in mothur  [37]  using the Silva SEED database provided as a reference alignment.
The work of  PE Galand and SK Fagervold  was supported by the  Agence Nationale de la Recherche (ANR)  project  MICADO  ( ANR-11JSV7-003-01 ). The present work was developed within the framework of the projects PROMETEO ( CTM2007-66316-C02-01/MAR ) and DOSMARES ( CTM2010-21810-C03-03 ). This paper is also a contribution of  D Martin  and  C Romano  to the  CRG   2009SRG665  funded by the  Generalitat de Catalunya. ANJ was funded within the DIWOOD research project by the  Max Planck Gesellschaft ; CB was funded within the Cluster of Excellence 0The Ocean in the  Earth System0 at Marum, Bremen , by the  German Research Foundation (DFG). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
We first give a brief description of the ORCHIDEE model in Sect. 2, including its carbon cycle module (Sect. 2. 2). The two soil hydrology parameterizations and their coupling with the river-routing scheme are detailed in Sects. 2. 3 and 2. 4, respectively. The atmospheric forcing data and the different observations used to evaluate each version of ORCHIDEE, are described in Sect. 3. In Sect. 4, we evaluate the water budgets from the observations (Sect. 4. 1. 1) and the two soil hydrology schemes (Sect. 4. 1. 2) in five Amazonian subbasins (Solimões, Madeira, Tapajós, Xingu and Negro). In each sub-basin, simulated TWS is compared to GRACE observations (Sect. 4. 2). ET and Q differences between the two simulations are given in Sect. 4. 3. We then focus on the Xingu sub-basin in the drier southeastern part of the Amazon Basin (Sect.
Soil hydrology modeling with SECHIBA SECHIBA is the physical module of ORCHIDEE and simulates water and energy fluxes from the soil, through the vegetation, into the atmosphere, at a 30 min time step. Two soil hydrology schemes (the 2-layer scheme (hereafter called "2LAY") and the 11-layer scheme (hereafter called "11LAY")) are available to simulate the soil water fluxes and storages that control runoff and ET fluxes. In both schemes, ET is the sum of evaporation of water intercepted by the canopy, transpiration (controlled by a stomatal conductance calculated by STOMATE as a function of water availability in the soil column and a fixed root density profile (De Rosnay and Polcher, 1998)), bare soil evaporation (which decreases with soil moisture in the top layer), snow sublimation and open water evaporation from floodplains.
2LAY The 2LAY scheme  (Ducoudré et al. , 1993; Guimberteau et al. , 2012b ) is frequently used with the STOMATE module, most recently for the Coupled Model Intercomparison Project Phase 5 (CMIP5) IPCC (Intergovernmental Panel on Climate Change) climate scenarios. It is a conceptual model, in which maximum water storage is regarded as the available water-holding capacity, between the wilting point and the field capacity, and globally set to 300 kg m -2 over a 2 m soil depth. The hydrological scheme relies on two layers linked by a downward water redistribution flux, involving three empirical parameters  (Ducharne et al. , 1998). The top layer is subject to root extraction and bare soil evaporation, which are both limited by a resistance depending on the layer's moisture  (Ducoudré et al. , 1993).
River-routing module The routing module  (Polcher, 2003; Guimberteau et al. , 2012a)  calculates the daily continental runoff to the ocean. This scheme is based on a parameterization of water flows on a global scale  (Miller et al. , 1994; Hagemann and Dumenil, 1998). A global map of the major watersheds  (Vörösmarty et al. , 2000)  delineates the basin boundaries and allocates one of eight possible directions to the water flow within each grid cell. The 0. 5 • × 0. 5 • resolution of the basin map is higher than the resolution of the atmospheric forcing data commonly used and it is therefore possible to have more than one basin in an ORCHIDEE grid cell (sub-basins). Water is transferred between each sub-grid basin through three linear water reservoirs, with no direct interaction with the atmosphere (except over floodplain areas).
Comparison of the two soil hydrology schemes To facilitate their comparison, the 2LAY and the 11LAY are used here with the same 2 m soil depth and the same PFTdependent root density profiles. They are both coupled to the same soil thermal scheme (using a 7-layer discretization over 5. 5 m), to the same routing module, and to the STOMATE vegetation module. The differences between the hydrological simulations performed with these two schemes (described in Sect. 3. 1, and analyzed in Sect. 4), are together due to the different description of soil water flow and storage, and to the related parameters (Table  1 ), because these two components are intimately linked. This is not particular to the present study, but is true of any comparison between a soil hydrology scheme that relies on available water content (between wilting point and field capacity), with conceptual parameterization of soil water flow, and a scheme that is physically based, and relies on volumetric water content (between residual moisture and saturation) and the Richards equation.