dataesr/scientific-paragraphs-categorization huggingface dataset:
factors are calculated for years when inventory data are available. Where inventory data are not available over the specified scaling time frame, remaining scaling factors are interpolated and extended to provide a continuous trend (Hoesly et al. , 2018). The biomass burning emissions for CMIP6 are based on merged satellite observation and fires models (van Marle et al. , 2017) , using GFED4 (Global Fire Emissions Database version 4), which include small-185 magnitude fires (available from 1997 to 2015). The emission of sea salt aerosols from the ocean follows the parameterization ofMärtensson et al. (2003), for aerosols with geometric diameter < 2. 8 µm. For aerosols with a geometric diameter ≥ 2. 8 µm, sea salt emissions follow the parameterization of Monahan et al. (1986). The emission of mineral dust particles is calculated based on the Dust Entrainment and Deposition Model(Zender et al.
, 2003). Volcanic 190 https://doi. org/10. 5194/acp-2020-677 Preprint. Discussion started: 11 August 2020 c Author(s) 2020. CC BY 4. 0 License. SO2 emissions were obtained through the Volcanic Emissions for Earth System Models (VolcanEESM) initiative, described by Mills et al. (2016). The VolcanEESM database contains estimates of total SO2 emissions by volcanic eruptions over the 1850-2016 period. The meteorology in the model has been nudged using MERRA2 (Modern-Era Retrospective analysis for Research and Applications, Version 2, https://rda. ucar. edu/datasets/ds313. 3) data 195 for the years 2000-2015.
180 factors are calculated for years when inventory data are available. Where inventory data are not available over the specified scaling time frame, remaining scaling factors are interpolated and extended to provide a continuous trend (Hoesly et al. , 2018). The biomass burning emissions for CMIP6 are based on merged satellite observation and fires models (van Marle et al. , 2017) , using GFED4 (Global Fire Emissions Database version 4), which include small-185 magnitude fires (available from 1997 to 2015). The emission of sea salt aerosols from the ocean follows the parameterization ofMärtensson et al. (2003), for aerosols with geometric diameter < 2. 8 µm. For aerosols with a geometric diameter ≥ 2. 8 µm, sea salt emissions follow the parameterization of Monahan et al. (1986). The emission of mineral dust particles is calculated based on the Dust Entrainment and Deposition Model(Zender et al.
| Species delimitation and genetic distances DNA sequence divergence was calculated using the Kimura 2-parameter (K2P) model. The midpoint rooted Neighbor-joining (NJ) tree of K2P distances was constructed to provide a graphic representation of the species divergence as implemented in the Sequence Analysis module of BOLD  (Ratnasingham & Hebert, 2007). Sequence divergence below and above species boundaries was determined by calculating the maximum intraspecific distance and the distance to the closest phylogenetic neighbor in the data set. Four sequence-based methods were used for species delimitation. From now on, species identified based on traditional morphological characters will be referred to as species while species delimited by DNA sequences will be referred to as Operational Taxonomic Unit (OTU), which represent diagnosable molecular lineages  (Avise, 1989; Moritz, 1994; Vogler & DeSalle, 1994).
The species delimitation methods used are based on different assumptions but they all focus on the detection of transition points between mutation/drift (within species) and speciation/extinction (between species) dynamics  (Hubert & Hanner, 2015). Each method is susceptible to pitfalls which is why our final delimitation scheme was based on a 50% consensus among methods in order to produce a robust delimitation Ambon  (Kekkonen & Hebert, 2014; Kekkonen, Mutanen, Kaila, Nieminen, & Hebert, 2015). OTUs were delimited using the following algorithms: in its multiple rates version as implemented in the R package Splits 1. 0-19  (Fujisawa & Barraclough, 2013). The mPTP algorithm uses a phylogenetic tree as an input file; thus, a maximum likelihood (ML) tree was first reconstructed using RAxML  (Stamatakis, Ludwig, & Meier, 2005)  based on a GTR + Γ substitution model.
Dirk Steinke  was supported by funding through the  Canada First Research Excellence Fund  as part of the  Food from Thought program. Sequence analysis was aided by funding from  the government of Canada through Genome Canada  and the  Ontario Genomics Institute  in support of the  International Barcode of Life  project. We thank  Alex Borisenko ,  Paul Hebert ,  Robert Hanner , and  Evgeny Zakharov  as well as both BOLD and CCDB staff at the  University of Guelph  for their valuable support. This project was partially funded by  DRPM-Indonesian Ministry of Research and Higher Education. This publication has ISEM number 2019-280-SUD. CO  N FLI C T O F I NTE R E S T None declared.
Genes encoding enzymes catalyzing the breakdown of plant polysaccharides showed a particularly high richness that is otherwise impossible to infer from available genomes or shallow metagenomics sequencing. The catalog expands the dataset of carbohydrate-degrading enzymes described in the rumen. Using an independent dataset from a group of 77 cattle fed 4 common dietary regimes, we found that only <0. 1% of genes were shared by all animals, which contrast with a large overlap for functions, i. e. , 63% for KEGG functions. Different diets induced differences in the relative abundance rather than the presence or absence of genes, which explains the great adaptability of cattle to rapidly adjust to dietary changes. Conclusions: These data bring new insights into functions, carbohydrate-degrading enzymes, and microbes of the rumen to complement the available information on microbial genomes.
Construction of a bovine rumen prokaryotic gene catalog To build a bovine rumen prokaryotic gene catalog, we collected samples of total rumen content from 5 Holstein cows and 5 Charolais bulls. To reduce the ecosystem complexity and to improve metagenome assemblies, rumen ciliated protozoa were depleted from the samples before microbial DNA extraction. A total of 1,206 Gb of raw metagenomic sequencing data were generated with a mean of 111 Gb clean data for each animal  [11]. This sequencing depth, much greater than that used for gut gene catalogs from humans and other monogastric animals  [12] [13] [14] , was necessary to enable the assembly of the more complex rumen microbiome. After de novo assembly, open reading frame (ORF) prediction, and removal of redundancy, 13,825,880 nonredundant genes were obtained with a mean length of 716 bp, and 39% of these genes were complete ORFs (Supplementary Table  1 ).
Compared to the rumen gene catalog published by Hess et al. [15] , the number of non-redundant genes discovered in this study is >5-fold larger; shared genes were in most cases also longer (Supplementary Fig. 1a  and b  and Supplementary Table  2 ). Thus, the mapping rate of reads from 77 additional rumen samples obtained in this study and 8 published rumen samples from UK cattle  [16]  increased from ∼10% using the previous Joint Genome Institute (JGI) catalog  [15]  to ∼40% (11-51%) (SOAP2, ≥95% identity, Supplementary Fig. 1c  and d ). This confirms that the representativeness of the rumen catalog was greatly improved, even though the mapping efficiency was still relatively low, as compared to 80% for the human gut microbiome  [12]. We also used protein sequences for reduced variability and compared this study to protein sequences  (10, 703, 199)  present in the large MAGs dataset of Stewart et al.
[7]. The proportion of nonredundant proteins in our catalog was 89%, 53%, and 29% when considered at a similarity cut-off of 100%, 90% or 50%, respec-tively, whereas for the MAGs dataset proportions were 84%, 39%, and 8% (Supplementary Fig. 1e ). Taken as a whole, this information indicates the large diversity present in this ecosystem. To compare our data with available genomes, MAGs were constructed on the basis of scaftig abundance profiles and an in-house co-abundance clustering pipeline. We identified 324 MAGs with a mean size of 1. 8 Mb (minimum threshold of 1 Mb; see Supplementary Table  3  for more information on these MAGs). More than half (173) were medium-quality (CheckM, ≥50% completion, <10% contamination) and 23 were highquality drafts (CheckM, >90% completion, <5% contamination)  [17].
In addition, we compared the proportion of mapping ratios of genomes and MAGs to an external dataset obtained from total rumen content samples of 77 cattle from 2 different genetic stocks that were fed diets characteristic of beef and milk production systems. Beef cattle, represented by the Charolais breed, were fed fattening diets high (FH, n = 16) or low (FL, n = 18) in starch and lipids; whereas Holstein dairy cows were fed a corn silage and concentrate diet (D, n = 23) or grazed a natural prairie (G, n = 20) (Supplementary Table  4 ). The 324 MAGs were present in all 4 diet groups from our validation cohort; ∼10% of reads from the 77 cattle datasets mapped to these MAGs (SOAP2, ≥95% identity). For genomes from the Hungate1000 project  [5] , which are representative of the diversity of cultured rumen bacteria and archaea, the mapping rate was 5.
The rumen catalog reported here encodes 545,334 CAZymes, of which ∼290,000 have degradative activity, that are affiliated to 114 distinct GH families (97. 4%) and 18 PL families (2. 6%). These 545,334 CAZymes were compared to GenBank and to the assembled genomes from rumen samples of Stewart et al. [6]  (Supplementary Fig. 5 ). Stewart and co-workers  [6]  described 69,678 CAZymes with 65-72% identity to other datasets, and 91% of novel CAZymes (defined as having <95% identity to other datasets). Our catalog displays similar features with mean 73% identity to the MAGs of Genbank and Stewart et al. [6]  and 89% novel CAZymes (482,759 sequences with <95% identity). It is acknowledged that the larger MAG dataset of Stewart et al. [7]  contains >400,000 predicted CAZymes, but these were not yet published when our comparison was made.
Common functions and influence of diet on the bovine rumen microbiome To investigate how different feeds affected the rumen microbiota in beef and dairy cattle we examined samples from 77 cattle described above. By using this 77-sample dataset, differences in α-diversity were observed between diets at the gene level. Animals fed fresh grass had the highest α-diversity and richness compared to other diets containing conserved feeds. Particularly, animals receiving fattening diets had a lower α-diversity. In contrast, the fattening diet rich in starch and polyunsaturated fatty acids (PUFAs) exhibited the highest β-diversity and/or had the highest disparity in interquartile range (box in the boxplot) for all indices (Fig. 3 ). The rumen microbiome of animals fed this diet also exhibited the highest dispersion on ordination analyses at the gene level (Supplementary Fig.
10 ). Such changes, akin to the described Anna Karenina principle  [28]  for microbiomes, probably reflected divergences in individual microbiome (and host) responses to PUFAs and may underlie a stress response to the diet. Genes were annotated to known functions (KEGG and Carbohydrate-Active Enzymes Database  [CAZy] ) and taxonomical information was derived. For functions, there were 43. 3% of the genes that could be classified into KEGG orthology and 2. 1% assigned to feed carbohydrate degradation. A total of 5,893 unique KEGG orthologs (KOs) and 45,683 unique CAZymes and binding modules were identified. Comparing the annotated genes for KEGG and CAZy functions showed a large overlap among groups, with 91% and 94% of shared genes, respectively (Supplementary Fig. 11 ). Contrasting with results on overall gene abundance, the highest α-diversity was observed for the corn silage diet group (Fig.
12b ). Concerning the diversity of genera found in the different groups, there was also a relatively large overlap. Of 242 genera identified by the taxonomic analysis described above, 182 (75%) were present in all 4 groups but only 67 (28%) were shared by all animals (Fig. 4  and Supplementary Fig. 11 ). This overlap was maximal for MAGs identified in this study, which were present in virtually all individuals (Fig. 4 ). The presence of common functions may explain the plasticity of the microbiota and adaptability of ruminants to digest various types of feeds even after sudden dietary changes. To get a better understanding of the functional changes induced by diet in these microbial communities, we analyzed the abundance of genes in the 77-sample dataset for functions, genera, and MAGs. To avoid possible confounding effect of breed and sex, the differential abundance analysis was performed within each breed.
Public data use The 5 public rumen microbial datasets used in this study include (i) a cow rumen microbiome sequenced at the U. S. Department of Energy's JGI in 2011 , which consists of 268 Gb of metagenomics sequences, 2,547,270 predicted genes, and 15 uncultured microbial genomes assembled from the cow rumen  [15]  (NCBI accession No. SRA023560); (ii) 8 rumen metagenomics samples from beef steers  [16]  (European Bioinformatics Institute [EBI], PRJEB10338); (iii) 501 rumen microbial genomes from the Hun-gate1000 Project (Integrated Microbial Genomes [IMG], JGI Proposal ID: 612/300816); (iv) 913 draft MAGs from Scottish cows' rumen (EBI, PRJEB21624); and (v) 4,907 draft MAGs from Scottish cattle rumen (Aberdeen Angus, Limousin, Charolais, and Luing) that were available at the time of the analysis (EBI, PRJEB31266).
[12]. Length discrepancies between shared genes in both catalogs that were <10% were considered as similar and those >10% were considered as longer or shorter. In a similar way, 10,703,199 unique genes were identified from the MAG database of Stewart et al. [7]  and compared to the 13. 83 M genes from this study. The comparison was made through BLASTP queries at 100%, 90%, and 50% identity levels using Dia-mond  [50]  (settings -sensitive -k 1 -max-hsps 1 -o matches. m8). High-quality reads of 77 rumen samples in the present study and 8 UK cattle rumen samples  [16]  were aligned against the present gene catalog (13. 83 M genes) and JGI-2011-gene-catalog (2. 46 M genes) using SOAP2 (≥95% identity)  [51]. The read mapping ratio was calculated as the number of mapped reads to the total reads in each sample.
Gene catalog annotation Taxonomic assignments of genes from rumen, mouse, pig, and human guts were performed using CARMA3  [18]  on the basis of BLASTP  [52]  (V2. 2. 24) against the NCBI-NR database (v20130906 for rumen, mouse, pig guts; v20160219 for human gut) (Supplementary Table  5 ). Microbiota from these 4 species were compared at different taxonomic levels. Functional annotation based on the KEGG database was performed using an in-house pipeline  [12]. Annotation of the CAZymes of each catalog was performed by comparing the predicted protein sequences to those in the CAZy database and to hidden Markov models built from each CAZy family  [53] , following a procedure previously described for other metagenomics analyses  [8]. Comparisons to the NCBI-NR and Stewart et al. [6]  datasets were made through BLASTP queries.
Because predicting CAZymes with automatic tools such as used by Stewart et al. [6]  might not be as exhaustive as CAZy curation, we compared our annotated CAZymes to the full protein datasets to allow an exact estimation of the repertoire novelty. For each annotated CAZyme, we recorded the best BLAST hit, based on bit-score, and computed the average identity percentages compared to each and combined datasets. Similar to the procedure used by Stewart et al. [6] , we considered as "novel" a protein without any hit above a threshold of 95% of identity. To allow a direct comparison of the results, annotation of ARGs was done as previously reported in the pig metagenome catalogue  [13]  by using the ARDB database  [54].
Quality assessment and taxonomic annotation of MAGs CheckM software  [59]  was used to calculate the completeness and contamination of these MAGs. The median percentage of completeness was high, at 62. 5% with a low, 2. 6% contamination. The combined MAGs showed higher completeness but also slightly higher levels of contamination and strain heterogeneity than singleton MAGs (Supplementary Figs  17  and 18 ). Taxonomic annotation for rumen MAGs was performed using CARMA3 on the basis of BLASTP against the NCBI-NR database (v20130906) and compared with MAGs from pig and mice (Supplementary Table  15 , Supplementary Fig. 19 ).
Acknowledgements The authors acknowledge technical support for animal care, sampling, and analytical measures on live animals provided by the personnel at  INRAE's experimental units of Herbip ôle  ( P. Faure  and  D. Roux ) and  Bourges , and the  Herbivores research unit  ( Y. Rochette ,  F. Anglard , and  B. Sepchat ). Particular thanks are due to  D. Graviou  for DNA extraction and biochemical analysis. We thank the direction of the  INRAE divisions Microbiology and Food Chain, Animal Genetics, and Science for Food and Bioproduct Engineering  for their support. We also thank  E. Forano  and  M. Naves  ( INRAE ) for helpful discussions during the set-up of the project.
Funding This work had the financial support of the  Animal Physiology and Livestock Systems Division of INRAE , the  INRAE metaprogramme Meta-omics of Microbial Ecosystems (MEM) , and  BGI-Shenzhen. This study was supported by the  National Science and Technology Major Project of the Ministry of Science and Technology of China  (grant No. 2017ZX10303406 ). The work on CAZymes was supported by a  European Union 's  Seventh Framework Program  ( FP/2007/2013 )/ European Research Council (ERC)  Grant  Agreement 322820  to B. H. Work by  Metagenopoliswas  supported from grant  ANR-11-DPBS-0001. Y. R. C. 's salary was funded by the  European Union , in the framework of the  Marie-Curie FP7 COFUND People Programme , through the award of an  Agreen-Skills' fellowship  (grant No. 267196 ) linked to the  MEM METALIT  project.
Hard substratum samples were collected prior to and one year after the disturbance, for eDNA metabarcoding using one marker of the mitochondrial Cytochrome Oxidase I (COI) gene and three markers of the nuclear 18S ribosomal RNA (18S) gene. We also generated a DNA barcoding inventory that consisted of taxa physically collected from Montségur and morphologically identified. This inventory contained amplified barcodes from COI, 18S and the nuclear large subunit ribosomal RNA (28S) gene. The resulting sequence information from the COI and 18S were used for eDNA taxonomic assignment. The eDNA datasets uncovered a high diversity of metazoan OTUs, which included macro-and meiofauna common to Lucky Strike. Baseline data collected at the start of the experiment identified higher OTU richness at sites peripheral to the active edifice, as well as at inactive sites.
1 Cycle Sequencing Kit (Applied Biosystems, Waltham, MA, United States) and read on an ABI 3730xl sequencer following the protocols supplied by the manufacturer. Resulting sequence chromatograms were visualized, assembled and edited using Geneious v. 10. 0. 5  (Kearse et al. , 2012). Finally, the NCBI blastn algorithm  (Johnson et al. , 2008)  was used to match sequences to publicly available databases and determine their nearest identities at <98 -100%; confirmed sequences were integrated into larger reference databases for the eventual matching to eDNA sequence data.
Aliquots of extract were used to determine the concentration of each sample via the Qubit R dsDNA HS Assay Kit (Thermo Fisher Scientific, Waltham, MA, United States) prior to submission to MR DNA (Shallowater, TX, United States) for amplicon generation, library preparation and subsequent metabarcode sequencing. Positive controls contained measurable quantities of DNA; none of the seven negative controls showed evidence of contamination and were sent, along with the true eDNA samples, to MR DNA for amplicon sequencing. Given the (1) extensiveness of the publicly available sequence databases for COI and 18S genes, (2) the ability of the COI and 18S primer sets to amplify across several taxa ("universality") and (3) the short length of amplicons necessary for working with fragmented eDNA, primers amplifying 18S-V1V2 (350-bp), 18S-V4 (270-bp), 18S-V9 (130-bp), and COI (313-bp) were used for eDNA metabarcoding.
Bioinformatic Workflow and Statistical Strategy for Analyzing eDNA Datasets Resulting raw sequence reads were processed with the FROGS v. 2. 0 pipeline  (Escudié et al. , 2017) , using both Galaxy web and command-line interfaces. Reads were first demultiplexed then merged at a mismatch rate of 0. 1 for each gene. Given the short length of the 18S-V9 fragment and the 2 × 300bp read length achieved by Illumina sequencing, Trimmomatic v. 0. 39  (Bolger et al. , 2014)  was used to trim the length of each direction for this dataset to 140-bp, prior to merging the reads in FROGS. After merging, the minimum and maximum amplicon lengths for each gene were as follows: 250 and 450bp for COI; 300 and 500-bp for 18S-V1V2; 280 and 500-bp for 18S-V4; 50 and 250-bp for 18S-V9. Next, datasets underwent swarm clustering  (Mahé et al.
, 2014)  at small local linking threshold of d = 2. Chimera and singleton filtering followed, with FROGS implementing VSEARCH  (Rognes et al. , 2016)  to detect chimeras de novo in each sample. Next, filtering was done to keep OTUs that had a minimum number of 2 sequences assigned. Afterward, blastn+ was implemented to assign taxonomy by finding alignments between the resulting OTUs and specifically compiled reference databases. FROGS recommended the use of taxonomic filters in situations of well-known communities, when poorly characterized taxa are of no interest, or if researchers are interested only in some chosen and known taxa. An initial test implementing Blastn+ to assign taxonomy only to the COI dataset using a 96% percent identity threshold  (Hebert et al. , 2003; Bellec et al. , 2018)  ejected the majority of the OTUs and reduced the power of statistical analyses.
Given this result, as well as our interest in the identities of multiple species in largely unknown and poorly characterized communities, taxonomic thresholds were not implemented at this stage. Additional filters, however, were performed during downstream bioinformatic processing describe below. Further, all resulting assignments produced e-values below 8e-7, while percentage identities were between 72. 5 and 100%, with metazoans matches from 85% and above. Reference databases for taxonomic assignment were compiled by retrieving publicly available sequences from the Silva release 132  (Quast et al. , 2012)  for the 18S fragments and the NCBI GenBank Core Nucleotide, October 2017 release for COI. DNA barcoded taxa from this study were concatenated onto the relevant public reference databases prior to taxonomic assignment.
Additional processing, including filtering, data normalization, total taxa and metazoan specific community composition visualization and statistical analyses of curated data was conducted using the Phyloseq R package  (McMurdie and Holmes, 2013)  in R Studio v. 3. 5. 1. Briefly, Phyloseq was implemented to examine the overall sequencing depth across samples before and after the removal of non-metazoan taxa to determine if sequencing was sufficient to characterize communities at the time of sampling, as well as normalization via rarefaction without replacement and the filtering of OTUs with fewer than 3 reads per sample. The generation of rarefaction curves and biodiversity analyses were performed within the R vegan package (v2. 5-2) (Oksanen, 2015) , including the estimation of metazoan species richness for incidence data  (Chao 2, Chao and Chiu, 2016).