dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We acknowledge the developers of R-ape, R-phangorn, and  R-phylodyn , which facilitated our implementations, and thank the editor and two anonymous referees whose suggestions considerably improved the manuscript. This research was supported in part by  National Institutes of Health  grant  R01 GM-131404  and funding from the  Alfred P. Sloan Foundation  to J. A. P, and by  National Science Foundation   CAREER award   DBI-1452622  to S. R. A. V. was.
Summary Data are mainly presented through a table listing for the 4993 species-area associations, the main characteristics of the Food and Agriculture Organization (FAO) database (common name, scientific name, FAO area ID, current catch) and the assessed stock descriptors (the fishing mortality F, the fishing mortality at the maximum sustainability yield FMSY, the ratio FFMSY, the biomass B, the biomass at the maximum sustainability yield BMSY, and the ratio BBMSY, all with corresponding confidence intervals). The accuracy of the assessments is qualitatively determined through a classification which addresses the potential bias on the results and quantitatively by the confidence intervals of stock parameters. Classes are defined according to the available data (time series duration, missing data.
) and the necessary assumptions (for non-fish species and multi-species datasets). For 2917 stocks, the available data do not allow the use of the CMSY algorithm  [1]. These results of too many blank years in the time series, too short data time series, too many species aggregated in the dataset, or non-convergence of the algorithm. For these entries, F, FMSY, FFMSY and BBMSY are estimated from other areas. B and BMSY are calculated in accordance with the current catch. This table is supplemented for the 2076 directly assessed stocks by: (1) the graphical outputs of the CMSY algorithm, (2) the log files, and (3) graphs and corresponding data showing the temporal variation of the relative biomass versus the relative fishing mortality over the catch time series (with and without error bars).
Assessment class The assessments are sorted according to the available data defined in Hélias et al. [2]. Class I, II and III corresponds to stocks fully assessed by CMSY. Class I encompasses single marine fish species stocks. Stocks listed in class II are assessed with questionable hypotheses: (a) multi-stock dataset, but with five or fewer species, (b) non-fish species stocks, (c) stocks from an inland FAO area. Class III corresponds to (a) multi-stock dataset with more than five species, (b) non-fish species multi-stocks or (c) multi-stocks from an inland area.
Methods Data pretreatment and data processing are detailed in Hélias et al. [2]  (in the article and in the associated supplementary materials). The stocks descriptors are used in  [2]  for the computation of characterization factors for biotic resource depletion, but not provided. The recently proposed CMSY algorithm  [1]  (November 2016 version) allows the computation of fish population descriptors and parameters from only the resilience and catch time series. Resilience to fishing pressure values were obtained from FishBase  [3]  with the rfishbase v2. 1. 1 Library  [4]. Catch time series were used for species of FAO Global Fishery and Aquaculture Statistics, v2017. 1. 0, with time series spanning between 1950 and 2015  [5] , through the FishStatJ ® interface. The FAO Fisheries and Aquaculture Department provides global fish catch data from landings and by-catches reported by countries.
Reported catch has to follow the Coordinating Working Party on Fishery Statistics handbook  [6]   A fish stock (i. e. , a population in ecology) is defined by a given species in a specific habitat. With the FAO dataset, the stock is defined by an ASFIS-species in a FAO area. This could lead to additional inconsistencies between FAO-based stock parameters and real (but not available) values. The stock parameters have to be used accordingly and with caution, but a special attention in the qualitative classification has been devoted to multi-stock datasets, when several caught species or several associated habitats are merged into a unique available FAO dataset. See Hélias et al. [2]  for more details.
Environmental metabarcoding is an increasingly popular tool for studying biodiversity in marine and terrestrial biomes. With sequencing costs decreasing, multiple-marker metabarcoding, spanning several branches of the tree of life, is becoming more accessible. However, bioinformatic approaches need to adjust to the diversity of taxonomic compartments targeted as well as to each barcode gene specificities. We built and tested a pipeline based on Illumina read correction with DADA2 allowing analyzing metabarcoding data from prokaryotic (16S) and eukaryotic (18S, COI) life compartments. We implemented the option to cluster Amplicon Sequence Variants (ASVs) into Operational Taxonomic Units (OTUs) with swarm v2, a network-based clustering algorithm, and to further curate the ASVs/OTUs based on sequence similarity and co-occurrence rates using a recently developed algorithm, LULU.
Finally, flexible taxonomic assignment was implemented via Ribosomal Database Project (RDP) Bayesian classifier and BLAST. We validate this pipeline with ribosomal and mitochondrial markers using eukaryotic mock communities and 42 deep-sea sediment samples. The results show that ASVs, reflecting genetic diversity, may not be appropriate for alpha diversity estimation of organisms fitting the biological species concept. The results underline the advantages of clustering and LULU-curation for producing more reliable metazoan biodiversity inventories, and show that LULU is an effective tool for filtering metazoan molecular clusters, although the minimum identity threshold applied to co-occurring OTUs has to be increased for 18S. The comparison of BLAST and the RDP Classifier underlined the potential of the latter to deliver very good assignments, but highlighted the need for a concerted effort to build comprehensive, ecosystem-specific, databases adapted to the studied communities.
Recent bioinformatic algorithms alleviate the influence of errors and intraspecific variability in metabarcoding datasets. First, amplicon-specific error correction methods, commonly used to correct sequences produced by pyrosequencing  [32] , have now become available for Illumina-sequenced data. Introduced in 2016, DADA2 effectively corrects Illumina sequencing errors and has quickly become a widely used tool, particularly in the microbial world, producing more accurate biodiversity inventories and resolving fine-scale genetic variation by defining Amplicon Sequence Variants (ASVs)  [40] ,  [41]. Second, LULU is a recently developed curation algorithm designed to filter out spurious clusters, originating from PCR and sequencing errors, or intra-individual variability (pseudogenes, heteroplasmy), based on their similarity and co-occurrence rate with more abundant clusters, allowing obtaining curated datasets while avoiding arbitrary abundance filters  [42].
Indeed, after presenting their new algorithm on prokaryotic communities, the authors of DADA2 proposed that the reproducibility and comparability of ASVs across studies challenge the need for clustering sequences, as OTUs have the disadvantage of being study-specific and defined using arbitrary thresholds  [43]. However, clustering sequences may still be necessary in metazoan datasets, where very distinct levels of intraspecific polymorphism can exist in the same gene region among taxa due to both evolutionary and biological specificity  [44] ,  [45]. ASV-based inventories will thus be biased in favour of taxa with high levels of intraspecific diversity, even though the latter are not necessarily the most abundant ones  [46]. Such bias in biodiversity inventories based on ASVs is likely to be magnified in presence-absence metabarcoding datasets, commonly used for metazoan communities  [5].
Similarly, imposing a "universal" clustering threshold on metabarcoding datasets is also introducing bias, penalizing groups with lower interspecific divergence, and overestimating species diversity in groups with higher interspecific divergence. However, this can be alleviated with tools such as swarm v2, a single-linkage clustering algorithm  [47]. Based on network theory, swarm v2 aggregates sequences iteratively and locally around seed sequences and determines coherent groups of sequences, independent of amplicon input order, allowing highly scalable and fine-scale clustering. Finally, it is widely recognized that homogeneous entities sharing a set of evolutionary and ecological properties, i. e. species  [48] ,  [49] , sometimes referred to "ecotypes" for prokaryotes  [50] ,  [51] , represent a fundamental category of biological organization that is the cornerstone of most ecological and evolutionary theories and empirical studies.
Maintaining ASV information for feeding databases and cross-comparing studies is not incompatible with their clustering into OTUs, and this choice depends on the purpose of the study, i. e. providing a census of the extent and distribution of genetic polymorphism for a given gene, or a census of biodiversity to be used and manipulated in ecological or evolutionary studies. Here we evaluate DADA2 and LULU, using them alone and in combination with swarm v2, to assess the performance of these new tools for metabarcoding of metazoan communities. Using both mitochondrial COI  [52]  and the V1-V2 region of 18S ribosomal RNA (rRNA)  [16] , we evaluated the need for clustering and the effectiveness of LULU curation to select pipeline parameters delivering the most accurate resolution of two deep-sea mock communities.
We then test the different bioinformatic tools on a deep-sea sediment dataset in order to select an optimal trade-off between inflating biodiversity estimates and loosing rare biodiversity. As a baseline for comparison, and in the perspective of the joint study of metazoan and microbial taxa, we also analyzed the 16S V4-V5 rRNA barcode on these natural samples  [53]. Our objectives were to (1) discuss the use of ASV vs OTU-centered datasets depending on taxonomic compartment and study objectives, and (2) determine the most adequate swarm-clustering and LULU curation thresholds that avoid inflating biodiversity estimates while retaining rare biodiversity.
5 μM concentration of each primer, 1X Phusion Master Mix, and an additional 1 mM MgCl2 for COI. PCR amplifications (98 °C for 30 s; 40 cycles of 10 s at 98 °C, 45 s at 48 °C (COI) or 57 °C (18S), 30 s at 72 °C; and 72 °C for 5 min) were cleaned up with ExoSAP (Thermo Fisher Scientific, Waltham, MA, USA) and sent to Eurofins (Eurofins Scientific, Luxembourg) for Sanger sequencing. The barcode sequences obtained for all mock specimens were added to the databases used for taxonomic assignments of metabarcoding datasets, and were submitted on Genbank under accession numbers MN826120-MN826130 and MN844176-MN844185. Environmental DNA Sediment cores were collected from fourteen deep-sea sites ranging from the Arctic to the Mediterranean during various cruises (Table  S2 ). Sampling was carried out with a multicorer or with a remotely operated vehicle.
Bioinformatic analyses All bioinformatic analyses were performed using a Unix shell script on a home-based cluster (DATARMOR, Ifremer), available on Gitlab (https://gitlab. ifremer. fr/abyssproject/). The mock communities were analysed alongside the natural samples, and used to validate the metabarcoding pipeline in terms of detection of correct species and presence of false-positives. The details of the pipeline, along with specific parameters used for all three metabarcoding markers are listed in Table  S3.
Reads preprocessing Our multiplexing strategy relies on ligation of adapters to amplicon pools, meaning that contrary to libraries produced by double PCR, the reads in each paired sequencing run can be forward or reverse. DADA2 correction is based on error distribution differing between R1 and R2 reads. We thus developed a custom script (abysspreprocessing in abyss-pipeline) allowing separating forward and reverse reads in each paired run and reformatting the outputs to be compatible with DADA2. Briefly, the script uses cutadapt v1. 18 to detect and remove primers, while separating forward and reverse reads in each paired sequence file to produce two pairs of sequence files per sample named R1F/R2R and R2F/R1R. Cutadapt parameters (Table  S3 ) were set to require an overlap over the full length of the primer (default: 3 nt), with 2-4 nt mismatches allowed for ribosomal loci, and 7 nt mismatches allowed for COI (default: 10%).
Each identified forward and reverse read is then renamed which the correct extension (/1 and /2 respectively), which is a requirement for DADA2 to recognize the pairs of reads. Each pair of renamed sequence files is then re-paired with BBMAP Repair v38. 22 in order to remove singleton reads (non-paired reads). Optionally, sequence file names can also be renamed if necessary using a CSV correspondence file. Read correction, amplicon cluster generation and taxonomic assignment Pairs of Illumina reads were corrected with DADA2 v. 1. 10 [40]  following the online tutorial for paired-end HiSeq data (https://benjjneb. github. io/dada2/bigdata_paired. html). Reads were filtered and trimmed with the filterAndTrim function and all reads containing ambiguous bases removed. The parameters were set based on tutorial recommendations and trimming lengths were adjusted based on sequence quality profiles, so that Q-scores remained above 30 (truncLen at 220 for 18S and 16S, 200 for COI, maxEE at 2, truncQ at 11, maxN at 0).
6. 0) based on minimum similarity and minimum coverage (-perc_identity 70 and -qcov_hsp 80). An initial test implementing BLASTn+ to assign taxonomy only to the COI dataset using a 96% percent identity threshold led to the exclusion of the majority of the clusters. Given observed inter-specific mitochondrial DNA divergence levels of up to 30% within a same polychaete genus  [54]  or among some closely related deep-sea shrimp species  [55] , and considering our interest in the identities of multiple, largely unknown taxa in poorly characterized communities, more stringent BLAST thresholds were not implemented at this stage. The Silva132 reference database was used for the 16S and 18S SSU rRNA marker genes  [56] , and MIDORI-UNIQUE  [57]  was used for COI. The databases were downloaded from the DADA2 website (https://benjjneb.
github. io/dada2/training. html) and from the FROGS website (http://genoweb. toulouse. inra. fr/frogs_databanks/assignation/). Finally, to evaluate the effect of clustering, ASV tables produced by DADA2 were clustered with swarm v2  [47]  at d=1,3,4,5 and 11 for 18S and 16S, and d=1,5,6,7, and 13 for COI in FROGS (http://frogs. toulouse. inra. fr/) [58]. Resulting OTUs were taxonomically assigned via RDP and BLAST+ using the databases stated above. Molecular clusters were refined in R v. 3. 5. 1  [59]. A blank correction was made using the decontam package v. 1. 2. 1  [60] , removing all clusters that were prevalent (more frequent) in negative control samples. ASV/OTU tables were refined taxonomically based on their RDP or BLAST taxonomy. For both assignment methods, unassigned clusters were removed.
Non-target 18S and COI clusters (bacterial, non-metazoan) as well as all clusters with a terrestrial assignment (taxonomic groups known to be terrestrial-only, such as Insecta, Arachnida, Diplopoda, Amphibia, terrestrial mammals, Stylommatophora, Aves, Onychophora, Succineidae, Cyclophoridae, Diplommatinidae, Megalomastomatidae, Pupinidae, Veronicellidae) were removed. Samples were checked to ensure that a minimum of 10,000 metazoan reads were left after refining. Finally, as tag-switching is always to be expected in multiplexed metabarcoding analyses  [61] , an abundance renormalization was performed to remove spurious positive results due to reads assigned to the wrong sample  [62] , the original R script being available at https://github. com/metabarpark/R_scripts_metabarpark. To test LULU curation  [42] , refined 18S and COI ASVs/OTUs were curated with LULU v.
0. 1 following the online tutorial (https://github. com/tobiasgf/lulu). The LULU algorithm detects erroneous clusters by comparing their sequence similarities and cooccurrence rate with more abundant ("parent") clusters. LULU was tested with a minimum relative co-occurrence of 0. 90, using a minimum similarity threshold (minimum match) at 84% (default) and slightly higher at 90%, following recommendations of the authors for less variable loci than ITS. The vast majority of prokaryotes usually show low levels (< 1% divergence) of intra genomic variability for the 16S SSU rRNA gene  [63] ,  [64]. These low intragenomic divergence levels can be efficiently removed with swarm clustering at d=1. Although LULU curation may still be useful to merge redundant phylotypes in specific cases such as haplotype network analyses, this was not tested in this study.
Statistical analyses Sequence tables were analysed using R with the packages phyloseq v1. 22. 3  [65]  following guidelines on online tutorials (http://joey711. github. io/phyloseq/tutorialsindex. html), and vegan v2. 5. 2  [66]. The datasets were normalized by rarefaction to their common minimum sequencing depth, before analysis of mock communities and natural samples. To evaluate the functionality of the pipeline with the mock communities, taxonomically assigned metazoan clusters were considered as derived from one of the ten species used for the mock communities when the assignment delivered the corresponding species, genus, family, or class. Clusters not fitting the expected taxa were labelled as 'Others'. Apart from PCR errors, these non-target clusters may also originate from contamination by external DNA from associated microfauna, or gut content in the case of whole polyps used for cnidarians.
Alpha diversity detected using each pipeline in the natural samples was evaluated with the number of observed target-taxa in the rarefied datasets via analyses of variance (ANOVA) on generalized linear models based on quasipoisson distribution models. Homogeneity of multivariate dispersions were verified with the betapart package v. 1. 5. 1  [67]. Beta-diversity patterns were visualised via Principal Coordinates Analyses (PCoA), using Jaccard dissimilarities for metazoans and Bray-Curtis dissimilarities for prokaryotes. The effect of site and LULU curation on community composition was tested by means of PERMANOVA, using the function adonis2 (vegan), with the same dissimilarities as in PCoAs, and permuting 999 times. Finally, BLAST and RDP taxonomic assignments of the mock samples and the global dataset were compared at the most adequate pipeline settings for each locus.
BLAST-refined (minimum identity at 70%) and RDP-refined (minimum phylum bootstrap at 80%) datasets were compared on ASV-level for prokaryotes, and OTU-level for metazoans (swarm d=3, LULU at 84% for COI and 90% for 18S). As trials on MIDORI-UNIQUE resulted in very poor performance of RDP for COI (assignments belonging mostly to Insecta), the comparison was performed with MIDORI-UNIQUE subsampled to marine taxa only.
Results 1 Alpha diversity in mock communities A number of 2 million (18S) and 1. 5 million (COI) raw reads were obtained from the two mock communities (Table  S4 ). After refining, these numbers were decreased to 1. 3 million for 18S and 0. 7 million for COI. Seven out of ten mock species were recovered in the 18S dataset and all species were detected in the COI dataset (Table  1 ), even with minimum relative DNA abundance levels as low as 0. 7% (Mock 5). Taxonomically unresolved species were correctly assigned up to their common family or class level. Dominant species generally produced more reads in both the clustered and non-clustered datasets (Table  S6 ). When ASVs were clustered with swarm v2, this generally led to a slight loss of taxonomic resolution: Chorocaris sp. was not detected in Mock 5 for 18S at d > 1, and the two bivalves P.
kilmeri and C. regab were taxonomically misidentified for COI at d ≥ 1. Clustering sequences with swarm v2 reduced the number of clusters produced per species, but some species still produced multiple OTUs even at d values as high as d=11 for 18S (A. arbuscula, Munidopsis sp. , and E. norvegica) and d=13 for COI D. dianthus, A. muricola, Chorocaris sp. , and Paralepetopsis sp. ). Curating with LULU allowed reducing the number of clusters produced per species to nearly one for both loci, but the best results were obtained in datasets clustered at d > 1 for 18S and d ≥ 1 for COI. Moreover, LULU curation tended to decrease the number of non-target clusters ("Others") (Table  1 ). In the clustered COI dataset, curating with LULU at 84% minimum match resulted in the most accurate detection of community composition, and this for all d values tested.
These reads were mostly originating from the field controls for metazoans (48% for 18S, 55% for COI) and extractions controls for 16S (50%). After blank correction, data refining, and abundance renormalization, rarefaction curves showed that a plateau was achieved for all samples in both clustered and nonclustered datasets, suggesting an overall sequencing depth adequate to capture the diversity present (Fig. S1 ). The final 18S datasets (with and without clustering at selected d values) contained 8. 9-9. 6 million marine metazoan reads in 42 sediment samples (Table  S4 ), and comprised 57,661 ASVs and 19,504-44,948 OTUs (Table  S6 ). The final COI datasets contained 4. 5-6. 9 million marine metazoan reads in 40 sediment samples, and comprised 78,785 ASVs and 44,684-64,669 OTUs. The 16S datasets contained from 6.
Number of clusters among pipelines The number of metazoan clusters detected in the deep-sea sediment samples varied significantly between bioinformatic pipelines chosen (, and also varied significantly among sites (Table  2 ). However, the pipeline effect was consistent across sites although mean cluster numbers detected per sample spanned a wide range in all loci (100-800 for 18S, 150-1,500 for COI datasets, and 1,500-5,000 for 16S, Fig. 1 ). Expectedly, clustering significantly reduced the number of detected clusters per sample for all loci. Consistent to results observed in mock communities, clustering at d=1-13 resulted in comparable OTU numbers for COI, while significantly higher OTU numbers were obtained at d=1 than with d >1 for ribosomal loci (Fig. 1 , Table  2 ). DADA2 detected on average 863 (SE=61) metazoan COI ASVs per sample, and clustering reduced this number to around 500, regardless the d-value.
For ribosomal loci, clustering at d=3-5 reduced OTU numbers of around 25-30% compared to without clustering, while at d=11, cluster numbers were halved. LULU curation of metazoan ASVs significantly decreased the number of clusters detected at both tested minimum match values (Table  2 ). For OTU datasets, the decrease was significant only when the minimum match parameter was at 84%. The effect of LULU curation was stronger at a lower minimum match value for both loci, as LULU curation at 90% of ASVs or OTUs resulted in significantly more clusters than when the minimum match was at 84% (Table  2 ). The effect of LULU curation of was also more pronounced for the 18S locus: LULU decreased by 31-65% the number of 18S ASVs/OTUs, compared to 7-33% for COI. LULU curation of ASVs or OTUs resulted in comparable cluster numbers in the 18S datasets, regardless the d-value used for clustering.