dataesr/scientific-paragraphs-categorization huggingface dataset:
Geographical origin of commercialized frogs For the species Fejervarya cancrivora, 16S sequences of specimens from different geographical regions are available in GenBank (Table  2 ). We conducted a phylogeographic analysis to see if there is any phylogeographic signal within this species and whether the geographical origin of the commercialized frogs could thus be determined based on 16S gene sequences. To this aim, 16S gene sequences of F. cancrivora for which the geographical origin was known were retrieved from GenBank. To minimize the number of missing data, 257 sequences of 494 bp were retained in final analyses (206 sequences of commercialized frogs + 51 specimens from GenBank). Relationships among haplotypes were inferred by constructing a network using the median-joining method available in Network v.
Species identification After alignment, our dataset contained 585 sites. Eighteen haplotypes were recovered (Appendix), and they clustered in three groups: (1) haplotypes h01 to h16 (206 individuals) which differ from one another by less than 6 mutations (i. e. , 1% sequence divergence), (2) haplotype h17 (one individual) and (3) haplotype h18 (2 individuals). Haplotype h17 differs from haplotypes h01 to h16 by 50 to 56 mutations (8. 5 to 9. 5% of absolute sequence divergence) and from haplotype h18 by 104 mutations (17. 8% of absolute sequence divergence). Haplotype h18 differs from haplotypes h01 to h16 by 108 to 111 mutations (18. 7 to 19. 0% of absolute sequence divergence). Our nucleotide BLAST analysis showed that haplotypes h01 to h16 correspond to the species Fejervarya cancrivora (best close match: 99 to 100% of sequence identity; Table  1 ).
Acknowledgements This project was supported by the  network 'Bibliothèque du Vivant ' funded by the  CNRS , the  Muséum national d'Histoire naturelle (MNHN) , the  Institut national de Recherche agronomique (INRA)  and the  Commissariat à l'Energie atomique (CEA, Genoscope). This work was also supported by the ' Action Transversale du Muséum: Taxonomie moléculaire ,  DNA Barcode & gestion durable des collections' and the 'Service de Systématique moléculaire' of the MNHN (UMS 2700, Paris, France). We are grateful to  Tesceline Tremaud  and  Dario Zuccon  for their help in molecular lab work. The  Action thématique du Muséum (ATM)  ' Biodiversité actuelle et fossile. Crises, stress, restaurations et panchronisme: le message systématique' funded the launching of this project, in particular travel costs.
The mean time to complete the questionnaire was 10 min. Reliability and factor analysis with SPSS 24 was carried out for a descriptive and psychometric study of the scale, which is the most usual procedure for establishing dimensionality of scales  (Fabrigar et al. , 1999; Embretson and Reise, 2000). Descriptive analyses (means, standard deviation, kurtosis, and asymmetry index) and reliability analyses (Cronbach's alpha) were also performed.
Climate change is considered as one of the main factors controlling sediment fluxes in mountain belts. However, the effect of El Niño, which represents the primary cause of inter-annual climate variability in the South Pacific, on river erosion and sediment transport in the Western Andes remains unclear. Using an unpublished dataset of Suspended Sediment Yield (SSY) in Peru (1968-2012), we show that the annual SSY increases by 3-60 times during Extreme El Niño Events (EENE) compared to normal years. During EENE, 82% to 97% of the annual SSY occurs from January to April. We explain this effect by a sharp increase in river water discharge due to high precipitation rates and transport capacity during EENE. Indeed, sediments accumulate in the mountain and piedmont areas during dry normal years, and are then rapidly mobilized during EENE years.
Nevertheless, up to now, no complete or robust dataset existed on sediment transport for western Peru  28. Indeed, no single institution monitors, processes, and supplies a national hydro-sedimentology database using consistent methods. Therefore, records are often incomplete and not widely available, in turn preventing a clear and robust assessment of sediment transfer in Peru from the central Andes to the coastal plane and to the Pacific Ocean. Thus, it is still unknown how EENE impacts the hydrosedimentary processes and hazards involved during sediment transfer from the continent to the ocean. In response, this study compiles an unpublished and extensive database of SSY in Peru and investigates whether EENE is the main agent responsible for the spatial and temporal variability in SSY along the western Andes.
Sediment flux data analysis on the western Peruvian Andes. This study compiled an unpublished hydro-sedimentological dataset from 20 watersheds (Fig. 1 ). Hydrologic stations have basic equipment, such as limnigraphs/limnimeters, to record hourly water levels. Daily to monthly stream gauging has occurred since 1968 to monitor changes in the calibration curves and to calculate hourly water discharges. Daily average SSC samples are available from 1972 to 2012. Long-term data series include the EENE of 1982-1983 and 1997-1998  (Table  2 ), yet some hydro-sedimentological stations have no information during EENE (Supplementary Table  1 ). Nevertheless, SSC samples at each station include extreme water discharge peaks, those considered above 95 th percentile of the historic Q time series  48  (see shaded areas in the Supplementary Fig.
2 ). Estimate of SSY uncertainty due to the infrequent sampling of SSC was based on Morera-Julca, et al. 49. They indicate that a daily SSC sampling identifies annual and seasonal sediment flux cycles in the study area. Because the SSC dataset includes some temporal gaps, we rely our analysis on the use of sediment rating curve (SRC) to complete the time series (see Dataset and Data Analysis section). Distribution of monthly averaged SSC and Q for each station shows two marked patterns, one with a power-law univocal relationship and one with a clockwise hysteresis loop (Supplementary Fig. 1 ). Univocal SSC-Q relationships were found for mountain stations located downstream of low-slope alluvial plains with extensive sediment deposits. We suggest that these extended sediment deposit zones act as buffer zones accumulating sediments during dry phases and releasing them by re-incision  19  during wet phases, especially during EENE (Supplementary Fig.
3a  and d ). Indeed, cumulated seismic moment is mainly focused along the subduction interface, below the coast line, while SSY tends to be greater in the east, close the main water divide. This suggests that large-magnitude earthquakes were not primary factor influencing sediment production and transport efficiency at the regional scale in Peru during the monitored period. However, the potential long-term influence of large-magnitude earthquakes older than the timeframe of the seismic catalogue is completely unknown. On the other hand, annual precipitation is estimated using the Tropical Rainfall Measuring Mission (TRMM) satellite data (see section Dataset and Data Analysis). During normal years, the TRMM data reveals a strong E-W precipitation gradient in the western Peruvian Andes (Fig.
Conclusions The impact of extreme El Niño events on sediment transport in the western Peruvian Andes (3-18°S) was investigated by compiling an extensive daily suspended sediment dataset (1968-2012) for 20 mountainous watersheds. During normal years, the orographic effect of the Andes causes a strong gradient in precipitation, runoff (1. 2-23. 8 l/km 2 /yr) and SSY (123-2000 t/km 2 /yr). We observe that the spatial distribution of mean annual SSY is correlated to the spatial distribution of precipitation and water runoff. Although the Peruvian coast is in one of the most tectonically active regions in the world, the spatial distribution of historical seismic moment is not correlated to the spatial distribution of SSY, suggesting that earthquakes are not one of the primary factors influencing SSY at the regional scale.
Dataset and Data Analysis Topographic data and hypsometric curves. Accurate topographic data are difficult to obtain for remote mountainous regions. Global digital elevation models (DEMs) provide a valuable source of information. The freely available 90 m DEM from the Shuttle Radar Topography Mission (SRTM-V4. 1) was used to determine geomorphologic characteristics. Landscape morphology and the degree of fluvial dissection were characterized using hypsometric curve  63. Normalized hypsometric curves were plotted as the relative area (a/A) vs. the relative elevation (h/H), where A is the total area, a is the area above h, h is the minimum elevation and H is the maximum elevation minus h (Fig. 1 ).
Regional precipitation data. Sparse networks of meteorological stations and gaps in time-series data are common issues in the Peruvian Andes. This lack of information was addressed by using remote sensing precipitation products such as the Tropical Rainfall Measuring Mission (TRMM). Products at high spatial resolution and high temporal frequency for periods longer than 15 years can be obtained freely from the Goddard Earth Sciences Data and Information Services Center (http://disc. sci. gsfc. nasa. gov). We estimated average annual watershed precipitation from daily TRMM (3B42-V7) at 0. 25° × 0. 25° spatial resolution for the period 1 January 1998 to 31 August 2012.
Earthquake database. Several high-magnitude earthquakes have occurred along the subduction zone of Peru over the past century, such as the Mw 8. 2-8. 4 great Peru earthquake in 2001 or the Mw 8. 0 Pisco earthquake in 2007  10. Large earthquakes can trigger many landslides, which in turn can have pronounced and prolonged impacts on landscape dynamics e. g. 8. In this study, following Dadson, et al. 55  , we use the spatial distribution of the regional cumulative seismic moment as a proxy to infer the potential impact of earthquakes on the spatial distribution of erosion rates. The calculation uses the Advanced National Seismic System composite earthquake catalogue hosted by the Northern California Data Center  64  , which records earthquakes from 1962-2012 for the study area. A circular moving window with a 100 km search-radius is used to estimate the cumulative seismic moment of earthquakes.
To decrease the influence of deep earthquakes, which are less prone to trigger landslides, we consider only earthquakes shallower than 50 km. Since the magnitude of completeness is approximately 4. 5 in the study area, we integrate only earthquakes with magnitudes higher than that to decrease statistical bias. This is not a major limitation, since earthquakes with lower magnitudes do not generally trigger landslides. Water discharge, suspended sediment concentration and sediment yield. This study compiled an inedited hydro-sedimentological dataset from 20 watersheds on the western Peruvian coast (Fig. 1 ), which was processed and homogenized (Table  1 ) using the freely available hydro-sedimentology software Hydraccess 4. 6 (http://www. ore-hybam. org). Five multipurpose hydraulic projects and the SO-HYBAM monitor sediment yields along the Andes.
1982 to Aug. 1983 and Sep. 1997 to Aug. 1998), if observed SSC were not available. We did not expect to complete the entire SSC time-series during the study period (e. g. 50 years), because in most cases SSC records were concentrated in continuous several years (e. g. 6 years). Gap percentages of daily values were quantified for each year on the period that extends from 01 September year1 to 31 August year2. In order to increase the accuracy of the infer SSC, three variants of the SRC were tested and then the best fit was kept: Variant I. -SRC (see Equation 1 below) If there are less than 40% of gaps over one year, we use an annual SRC model computed only from the daily data obtained during the same year; otherwise, we use a global SRC model computed from the pluri-annual daily dataset. Variant II.
-Piecewise SRC (see Equation  2 below) The use of segmentation and truncation to better fit the trends of data highly can improve SSC estimations  66. Here two continuous functions were fitted (lower and upper segments), consequently, two pre-factors, two exponents and two coefficients of determination (r 2 ) were calculated. The breakpoint was defined in two steps: (i) We divide our datasets in lower and upper discharge classes by locating a temporal breakpoint at the intersection of the median of the SSC and the median of the Q original values  66  , however this procedure rarely leads to the largest r 2. The lower piecewise SRC segment comprises low water discharges and they influence up to 3 ± 2% of the annual SSY estimation. The rest 97 ± 2% of the annual SSY depends of the upper piecewise SRC segment.
Therefore, the second segment will determine the prediction accuracy of the annual SSY. (ii) Hence, in order to find the largest r 2 on the upper SRC segment, we explore in systematic way changing up and down several times the upper segment range of the independent variable (Q), until finding the largest r 2. Then the rest of the dataset will defined the lower segment. Lastly, the breakpoint was defined for the intersection of the lower and upper segment projection. Variant III. -Direct runoff piecewise SRC (see Equation  3 bellow) To remove the potential contribution of groundwater, the baseflow (Qb), was computed using the generic filtering method  67. The direct runoff (Qd) was excluded from total Discharge (Q), Q = Qd + Qb. Then, the whole procedure in variant II was followed, however this time we use Qd instead of Q.
We assume that this may be attributed to reservoir sedimentation and narrow river floodplain areas, respectively. Unfortunately, the limited SSC dataset at both stations (Table  1 ) prevented us from exploring in more detail the causes of the clockwise hysteresis loops for those catchments. Along the study area the, annual piecewise SRC, coefficient a and b are highly variable without unequivocal trends (increase or decrease), and there is no evolution of the slope SRC after and before the EENE (1982-83 and 1997-98). In general, piecewise regression model showed better fit than linear least-square fitting algorithm, see Table  2.
Quantitative analysis of the observed climatology, hydrology and fluvial sedimentology of the monitored watersheds. "Historic" indicates that the entire database was considered. "Without EENE" excludes the period 1 September 1982 to 31 August 1983 and 1 September 1997 to 31 August 1998. a, a1, a2, c1, c2 and b, b1, b2, d1, d2 are the constant and exponent in equations (1), (2) and (3) (see Dataset and Data Analysis). Bold number indicate the chosen SRC variant.
Acknowledgements This study was performed at the  Geophysical Institute of Peru , and it was supported by the  Peruvian PPR-068 program  " Reducción de Vulnerabilidad y Atención de Emergencias por Desastres", INNÓVATE PERÚ  (www. innovateperu. gob. pe)through the project " Monitoreo, caracterización e identificación de las principales fuentes de erosión y sedimentos durante fuertes crecidas o eventos extremos El Niño en las cuencas binacionales Puyango-Tumbes y Zarumilla ",  CONCYTEC  (https://portal. concytec. gob. pe), the HYBAM observatory and the  GREAT ICE Laboratory. Also, we would like to thanks the following institutions: Proyecto especial binacional Puyango-Tumbes, Proyecto especial  Chira-Piura , Proyecto especial  Jequetepeque-Zaña ,  Proyecto especial Chavimochic, Proyecto especial Tacna and Duke Energy Company ; all of them provide valuable information to our study.
Genetic analyses were performed with a generalized linear mixed model using the Glimmix procedure of SAS software  [43]  and ASReml  [44]  using the following equation: where y is the vector of n observations (the binary trait dead = 0 and alive = 1 in our case), and g -1 (•) is the inverse of the logit function e η 1+e η. X is a (n × p) design matrix of rank k that links the observations to the vector β of the p fixed effects. Here, we considered three fixed effects: intercept, blocks (tanks or bags) and family average individual weight. Depending on the analysis, other fixed effects were added: life-stages (five life stages: Spat 1, Spat 2, Juvenile 1, Juvenile 2, and Adult) or pathogen treatments (OsHV-1 or V. aestuarinaus) (see below). Z is a (n × r) design matrix that links the observations to vector γ of the r random effects.
y = g -1 (Xβ + Zγ), σ 2 s ⊗ j 0 0 σ 2 d ⊗ j , Both programs gave similar estimations (see Additional file 1: Figure  S1 ), but since reaching convergence is more difficult with SAS when several random effects are considered with the pseudo-likelihood techniques, we only reported ASReml estimates. Heritability of survival in each experiment was computed for the narrow sense h 2 n , as follows: where V a is the additive genetic variance, estimated as four times the variance among sires (σ 2 m ) and V t is the total phenotypic variance, with σ 2 e being π 2 3 for a logit link  [46]. σ 2 f is the variance among dams. In addition, we also calculated genetic correlations of survival between the field experiment and the experiments under laboratory conditions, as well as between the raceway experiment and the experiments under laboratory conditions using bivariate models.
Sampling, Generation and Selection of Single Amplified Genomes Surface (SUR) seawater samples (5 m deep, not pre-filtered, Sample ID: TARA_G000000266) were collected for single cell analysis from the North Indian Ocean during the circumnavigation expedition Tara Oceans, station TARA_039 1  (Figure  1 ). The environmental setting of the station was inferred from its physical report and can be found at Pangaea website in the following link: http://store. pangaea. de/Projects/TARA-OCEANS/Station_Reports/TARA_039_oceanographic_context_ report. pdf. Replicated 1 mL aliquots of seawater were cryopreserved with 6% glycine betaine (Sigma) and stored at -80 • C. Samples used in this study were collected from TARA_039 (SUR; 5 m deep) located in the North Indian Ocean (18. 59-66. 62) on March 18th, 2010 (Supplementary Table  1 ).
Sequencing and Phylogenetic Analysis Polymerase Chain Reaction (PCR) products were purified and sequenced by Genoscreen (Lille, France) with OneShot Sanger sequencing and aligned against NCBI's nBLAST and xBLAST databases for identification. Reference sequences used in this study were retrieved from Integrated Microbial Genomes and Metagenomes (IMG) 4 Data Management or NCBI database. Using Geneious Pro 4. 8. 5 software all sequences were aligned (using default Geneious progressive pairwise aligner) trimmed and manually checked. The software was also used to obtain the complete 16S rRNA gene assembling the amplicon sequences from MDA screening and the phylogenetic markers amplification. Alignments were processed into phylogenetic trees with Mega 5. 2. 2 Software choosing Maximum Likelihood statistical methods along with 1000 bootstrap replications.
Fragment Recruitment Analysis (FRA) Nucleotide-Nucleotide BLAST 2. 2. 28+ was used to recruit metagenomic reads from several metagenomic samples similar to all the available sequenced Kordia genomes. Considering the aim of this part of the study, which was to determine the presence and distribution of the novel Kordia sp. CFSAG39SUR in different oceanic regions, depths and size fractions, a database containing all 4 Kordia genomes was generated. This ensured a competitive recruitment between the genomes for each metagenomic sample analyzed. The quality and genome completeness of the four reference genomes in the database was measured with software checkM  (Parks et al. , 2015)  and fetchMG  (Sunagawa et al. , 2013)  (Supplementary Table  3 ). The available genomic sequences of Kordia AAA285-F05 did not code for any of the marker genes or COGs used by the software, resulting in a genome completeness estimation of 0% despite being 283.
58 Kb long. The other three reference genomes (K. algicida, K. jejudonensis, and K. zhangzhouensis) were estimated to be complete and free of contamination. Recruitment regarded only one read per target gene (-max_target_seqs 1), an identity percentage higher than 70% (-perc_identity 70), as well as an e-value lower than 0. 000001 (-e-value 0. 000001). All other nBLAST settings were set on default. In order to avoid random alignments, a filtering process was applied using R software (R Core Team, 2013) (i) excluding alignments with coverage lower than 90% of the sequence length, (ii) removing duplicated reads within each genome and (iii) masking reads belonging to the ribosomal operon. This operon does not follow the species delimitating recruitment patterns as the rest of the genome does and its presence would overestimate read recruitment (Caro-Quintero and  Konstantinidis, 2012).
All contigs from each genome were concatenated to a single sequence and the file containing the four sequences (one for each genome) was used to generate the database. This step was done using NCBI's makeblastdb application. As query we selected those metagenomic samples which contained the most metagenomic reads 16S rRNA and metagenomic Illumina tags (miTags)  (Logares et al. , 2013)  annotated as Kordia (data not shown) using SILVA database Release 115  (Quast et al. , 2013). These were stations TARA_039 (0. 2-20 µm) and TARA_085 (0. 2-3 µm) from Tara Oceans (Supplementary Table  1  and Figure 1A )  (Sunagawa et al. , 2015). In addition, we used 4 metagenomes from the deep waters of Malaspina 2010 circumnavegation expedition  (Acinas et al. , in preparation) , two of them from the Brazil Basin in the Atlantic Ocean and two of them from Circumpolar Deep Waters (Supplementary Table  1  and Figure 1A ).
Due to the flexibility of Bacteroidetes' lifestyle, the metagenomic selection was done from a limited pool of deep ocean metagenomes covering the free-living fraction of prokaryotes (0. 2-0. 8 µm) and the fraction of prokaryotes attached to particles or aggregates (0. 8-20 µm) of a same seawater sample. The coverage of different deep ocean biomes and the highest abundance of Kordia related metagenomic read recruitments was also taken into account for the metagenomes' selection for the study. All queries consisted on merged, paired-end reads with different sequencing depths. Merging was done using Mothur v. 1. 33. 3  (Schloss et al. , 2009). Recruited data normalization was done: (i) by reference genome size (resulting in recruited reads/genomic bp), then (ii) upscaling this ratio to 1 Mb (as done in  Swan et al.
Accession Numbers Accession numbers for PCR products of Kordia CFSAG39SUR: 16S rRNA gene (MF187452), ITS (MF187454), 23S rRNA gene (MF187453), rpoB (MF187456), and proteorhodopsin (MF187455). Accession numbers for partial 16S rRNA gene sequences are: MF187458 for AAA285-F05 and MF187457 for AAA242-P21. The genomes used as reference for the FRA are the following: Bacteroidetes bacterium SCGC AAA285-F05 (JGI GoldStamp Id Go0060979), K. algicida strain OT-1 (NCBI RefSeq NZ_ABIB00000000. 1), K. jejudonensis strain SSK3-3 (NCBI RefSeq NZ_LBMG00000000. 1), K. zhangzouensis strain MCCC 1A00726 (NCBI RefSeq NZ_LBMH00000000. 1). The Tara Oceans metagenomes used as query are the following (INSDC run accession numbers):.
Phylogenetic Reconstruction of a Natural Kordia Population Amplification of the three phylogenetic markers of the ribosomal operon was successful in 78 out of 98 Kordia SAGs, and their alignment indicated a 100% identity among them. The resulting sequences contained full 16S rRNA gene (1,495 bp), ITS-1 (536 bp) and partial 23S rRNA gene (415 bp). The alignment of the resulting full 16S rRNA gene sequence against NCBI database assigned the SAGs to the genus Kordia, family Flavobacteriaceae. Its best hit with a nucleotide identity of 99% and sequence coverage of 97% was an uncultured Bacteroidetes sampled in the Puerto Rico Trench (clone PRTBB8540, acc. HM798967) at 6,000 m depth. The closest sequenced cultured relatives were K. algicida strain OT-1 with a 96. 8% of nucleotide identity and 99% coverage (complete sequence, acc.