dataesr/scientific-paragraphs-categorization huggingface dataset:
DNA Barcode Database Morphological inventories identified several taxonomic groups collected from the Montségur edifice; from these, 22 were available in sufficient numbers for DNA barcoding for the present project, which encompassed a total of 144 individuals (Table  2 ). The barcoding effort produced a total of 580 sequences; 318 (55%) of these sequences were amplified from the 18S regions V1V2 and V9, as well as the short COI fragment (313-bp), and were added to their respective taxonomic reference databases, against which the eDNA metabarcoding datasets were assigned. The remaining 262 sequences (45%) were amplified from 28S regions D2D3 and D9D10, 18S region V7V8 and the long COI fragment (500 -660-bp, Supplementary Material S1) and were not added to any reference databases for the present study, as we did not survey eDNA samples using these amplicons.
eDNA Metabarcoding Datasets A total of 18 eDNA samples and seven negative extraction controls were sent for metabarcoding using four gene fragments: COI; 18S-V1V2, 18S-V4 and 18S-V9, which will be referred to as V1V2, V4 and V9. None of the negative controls produced amplicons for library preparation, and thus were considered clear of contamination. After bioinformatic filtering, the metabarcoding effort yielded a total of 701,880 sequences for V1V2, 618,384 sequences for V4, 920,241 sequences for V9 and 1,098,025 sequences for the COI datasets. Additional information on sequencing depths for each dataset, mean number of reads per sample and OTU, as well as mean number of OTUs per sample can be found in Supplementary Figures  S1, S2  and Supplementary Table  S1. Non-metazoan taxa included archaea, bacteria, fungi, plants (photosynthetic eukaryotes), protists or organisms unknown/unassigned at the phylum level; these non-metazoans comprised between 65.
86% (COI) to 93. 43% (V9) of all OTUs. Figure  2  illustrates the top 10 most common taxonomic groups, including non-metazoans, in each dataset, described by percentage of OTUs versus percentage of sequences for each phylum. While taxonomic patterns of OTU percentages were variable among markers, Annelida and Mollusca were clearly represented in each eDNA datasets in terms of high percentage of sequences assigned to few OTUs. Regarding the total number of sequences, Annelida represented 29. 8% (V9), 33. 1% (V4), 32. 7% (COI), and 40. 7% (V1V2), while Taxon code is followed by known taxonomy and the number of individuals barcoded. A list of fragments ("barcodes") successfully amplified for each taxon is provided by gene. Finally, the gene fragment is listed for a taxon if it was recovered within the respective eDNA dataset.
Italicized gene fragments indicate that for these eDNA datasets, the taxon uncovered was either only the genus, or a different species. "Unidentified" indicates that our morphological expertise was only able to identify the collected individuals at the time of this study to the following groups: Zoanthid, Copepod, Ostracod, and Nematode. NCBI BLAST results for these unidentified taxa were as follows: colonial anemone (Order: Zoantharia), Ostracod (Family: Pontocyprididae), Copepod (Family: Dirivultidae), Nematode (Genus: Oncholaimus). Mollusca represented 17. 3% (V4), 35% (V9), 37. 1% (V1V2) and 37. 6% (COI). For V1V2 and V4, the majority of OTUs originated from Protista, although the percentage of sequences comprising these OTUs was much smaller, suggesting the presence of many distinct species (Figure  2 ).
Within V4, however, many OTUs were unknown/unidentified, though they were only a small proportion of total sequences. Further, the V9 fragment dataset uncovered a high percentage of OTUs and sequences assigned to Bacteria and other non-metazoans (Figure  2 ). COI identified the most OTUs assigned to Arthropoda, compared to few OTUs originating from Annelida and Mollusca, comprising nearly two-thirds of all sequences in this dataset. As with the V4 dataset, unknown/undefined assignments dominated a large percentage of OTUs within COI. The 18S fragments uncovered Nematoda and Platyhelminthes within the top 10 most common taxonomic groups, while COI identified Chordata, Cnidaria and Porifera. After non-metazoan taxa were filtered from each dataset, metazoan OTUs numbered 414 for V1V2, 304 for V4, 291 for V9 and 560 for COI.
Common Metazoans Uncovered in eDNA From Montségur Every metazoan taxon that underwent DNA barcoding was recovered in the global environmental DNA dataset of four markers (Table  2 ). This included taxa not yet identified to the species level, such as zoanthids (colonial anemones), copepods, ostracods and nematodes (Table  2 ). In addition, nearly all taxa identified to the genus or species level were found within the eDNA dataset for which they had been successfully barcoded. Exceptions were Paralepetopsis ferrugivora and Prionospio sp. , which despite being barcoded for V1V2, were not identified within that eDNA dataset, as well as the unidentified zoanthid taxon, found only in V1V2, and not in V9 eDNA (Table  2 ). Bathymodiolus azoricus was the only barcoded species present in every eDNA dataset.
Furthermore, Protolira valvatoides gastropods and Glycera sp. polychaetes either matched to the genus level (Protolira sp. ) or to a particular species (Glycera capitata or tesselata), despite being DNA barcoded. The top 10 most common species, in terms of sequence abundance, for the V1V2 and COI datasets are shown in Figure  3. Each graph identifies expected species (those collected from Montségur and DNA barcoded, see Table  2 ) and additional species. "Additional" species are those not barcoded by us, yet present within the Silva or NCBI public databases, some of which are not known from hydrothermal vents (Figure  3 , non-bold). The top 10 most common species uncovered within the V1V2 dataset are all found at vents; however, the "nonvent species" for V4, V9 and COI datasets represented only 4.
5, 7. 3, and <2%, of these sequences, respectively (Figure  3  and Supplementary Figure  S3 ). The V1V2 dataset found that expected species Amphisamytha lutzi, Bathymodiolus azoricus, and Branchinotogluma sp. comprise the majority of metazoan sequences. Oncholaimidae sp. , a family of nematodes whose members are found in aquatic sediments world-wide including at LS, is the next most abundant group in this dataset (Figure  3 ), supporting the presence of local vent nematode species matching to this family. The COI dataset identified expected species A. lutzi, Branchinotogluma sp. , Protolira valvatoides, Mirocaris fortunata, and Lepetodrilus atlanticus dominating metazoan abundance, while sequences assigned to Hesiolyra cf. bergi, an hesionid polychaete known from hydrothermal vents in the eastern Pacific  (Blake, 1985) , represents another common species in the dataset.
While expected species such as B. azoricus, Branchinotogluma sp. , Branchipolynoe seepensis and P. valvatoides are some of the most abundant within the V4 dataset, there are few species not expected from Montségur (including non-vent taxa, Supplementary Figure  S3 ). Finally, for the V9 dataset, the majority of metazoan sequences were assigned to expected species B. azoricus, A. lutzi and B. seepensis (Supplementary Figure  S3 ).
The exception is COI, in which higher metazoan diversity is maintained for inactive, compared to peripheric and active sites (Figure  5 , bottom left). Noting the differing graphic scales for baseline data between all taxa and metazoans-only, comparisons between the two clarify that much of the diversity recovered from the inactive habitat is non-metazoan taxa, though V1V2 and COI also identify similar or even higher levels of metazoan diversity at inactive sites, comparative to the other habitats (Figures  4, 5 ). These patterns are supported by estimated OTU FIGURE 3 | The top 10 most common species, in terms of sequence abundance, for the 18S-V1V2 and COI eDNA datasets. "DNA barcode" are those species that were physically collected from Montségur and DNA barcoded in the current project (Table  1 ).
"Additional" are species that were not DNA barcoded by the present project yet were within the NCBI GenBank database prior to the taxonomic assignment of eDNA. Species in bold are those found at hydrothermal vents, while non-bolded species are not found at hydrothermal vents. The presence of these non-vent assignments suggests that these sequences were assigned to the closest taxon within the database, and not that these non-vent species are present in the deep-sea. 3 ), which varied depending on the habitat and location. Notably, the COI dataset identifies the inactive site as having the highest number of OTUs at 321 and an estimated OTU richness of 484. 8 + 30. 5, compared to the other habitats (Table  3 ). The 18S datasets, however, show contrasting values, with active sites, particularly the base of Montségur, as having higher estimated OTU richness than the structure itself, the periphery or inactive sites.
richness (Table ANOSIM testing revealed that the habitats host significantly different metazoan communities, a pattern seen across all four eDNA datasets (Table  4 ). Habitat differentiation based on community is spatially illustrated by the CCA ordination for V1V2 and COI in Figure  6 , as well for V4 and V9 in Supplementary Figure  S6. With 26. 3-35. 5% of taxonomic variation explained within the two axes, habitat is supported as the most important factor influencing taxonomic composition and distance away from or toward active sites determines composition (Figure  6 ). PERMANOVA testing was also significant for the habitat factor, supporting a lack of homogeneity of communities among the habitat sample groups (Table  4 ). Pairwise comparisons between the differing habitats identified the strongest distinction between active and inactive sites (p < 0.
05). Following the location factor that compares Montségur structure and base active communities, statistical analyses reveal dissimilarities among these communities from V4 and V9 datasets, though PERMANOVA testing suggest weak effects of this factor, as does the unclear separation of communities shown in Supplementary Figure  S6. The dissimilarities between structure and base communities, however, are most strongly supported within the V9 dataset (Table  4 ). Examining taxonomic composition identified specific differences in metazoan composition across habitats, samples and eDNA datasets. Higher numbers of OTUs assigned to Annelida were found at active locations, compared to inactive sites, which was true for all datasets (Figure  7  and Supplementary Figure  S7 ). However, fewer OTUs originating from Arthropoda were observed at active sites, compared to the non-active sites.
Higher numbers of OTUs assigned to Cnidaria, Echinodermata and Porifera were observed within the COI dataset, specifically within periphery and inactive samples, compared to 18S (Figure  7 ). Conversely, 18S markers identified higher numbers of OTUs from Nematoda, particularly V1V2 (Figure  7 , left) and V4 (Supplementary Figure  S7 ). Furthermore, the 18S datasets uncovered less well studied meiofaunal taxa, such as Xenacoelomorpha at peripheric and inactive sites within V1V2 and V9, as well as Gastrotricha and Kinorhyncha from V4 and V9 (Supplementary Figure  S7 ).
Recolonization Processes: Effect of Time and Cages Accumulation curves comparing 2017 "baseline" samples with 2018 caged and uncaged samples identified similar trajectories for these communities, across all datasets (Figures  4, 5  and Supplementary Figures  S4, S5 ). Considering all taxa, the curves for each dataset begin to level into a plateau shape, but still indicating that additional diversity remains undescribed (Figures  4, 5 , top right). When focusing on only metazoan taxa, we observed no specific pattern of differentiation between years or cage presence (Figures  4, 5 , bottom right). Estimated OTU richness is consistently higher in 2018 active communities compared to 2017 active communities, while noncaged communities have higher richness compared to those that were caged, a finding seen across all eDNA datasets (Table  3 ).
The lack of community differentiation depending on time and predation factors seen in Figures  4, 5  was also supported by statistical testing (Table  4 ). In terms of taxonomic composition, no strikingly different patterns based on sample or treatment are observed, though the COI dataset uncovered higher numbers of OTUs originating from Cnidaria and Mollusca across Baseline data focuses on sample communities from differing habitats/vent activity, as well as those from differing locations on the active edifice (base and structure). Recolonization data focuses on sample communities from differing years, as well as those that were caged against those non-caged at active sites. Note the difference in scales for number of OTUs and number of sequences between "All taxa" and "Metazoan taxa only.
DISCUSSION The present study offers a broad taxonomic characterization of LS Bathymodiolus assemblages and their associated macroand meiofaunal communities, present along an environmental gradient from an active sulfide edifice, to a more distant, inactive zone. We also provide original findings from an in-situ experiment and monitoring effort for tracking faunal recolonization at an active hydrothermal vent edifice located within the LS vent field (MAR). For this, we implemented the normally less intrusive environmental DNA (eDNA) metabarcoding method for comparing community temporal changes following the induced disturbance. Our protocol for processing hydrothermal vent material produced a comprehensive eDNA dataset composed of information from four genetic markers and encourages replication along a longer temporal scheme, as well as for future, unrelated survey efforts.
Deep-sea protists, specifically foraminifera, have been found to be important for structuring metazoan communities The experimental variables listed under baseline and recolonization are followed by the collection year of the samples, in parentheses. N refers to the number of samples analyzed within the specific variable, and OTU refers to the number of operational taxonomic units recovered within those samples. The value following + indicates the standard error of Chao2 metric. and are associated with high eukaryotic richness  (Levin, 1991; Lecroq et al. , 2009). Within our eDNA survey, protists represent a substantial number of OTUs across all four gene marker datasets (Figure  2 ) but lack investigation in active and inactive vent environments. Integrating protist taxonomic information with that of metazoans would likely reveal previously unknown information about biodiversity patterns in the vicinity of vents.
, 2017). Additional strategies have extracted eRNA of microorganisms, as eRNA is less stable and degrades more rapidly than eDNA and can thus improve biodiversity assessment of contemporary communities  (Barnes and Turner, 2016; Pochon et al. , 2017; Laroche et al. , 2018; Brandt et al. , 2019). Next, the incompleteness of public databases used for taxonomic assignment during high-throughput sequencing projects, as well as the selection of genetic markers, heavily influence eDNA studies. Gaps in databases will continue to be a limitation for habitats where communities are not well characterized to the species level  (Thomsen and Willerslev, 2015) , though repositories are rapidly expanding  (Porter and Hajibabaei, 2018). In our case, some taxa denoted as "common" correspond to species not found in the deep sea or at hydrothermal vents.
When taxa are poorly represented or absent in the database, sequences may have no match or may match to the nearest relative that has more abundant and available data  (Dayrat, 2005; Kvist, 2013). The presence of non-vent assignments in our datasets suggests that sequences were assigned to the closest taxon within the database and not that these "non-vent species" are present in the deep-sea. Alternatively, our bioinformatic assignment protocol may not have been optimal for finding the best matches present. To address these shortcomings, the best strategy includes the continued inclusion of well curated data from hydrothermal vent and deep-sea fauna into public inventories, to which we have contributed in the present study via our barcoding efforts. Furthermore, the use of a standardized bioinformatic pipeline targeting poorly surveyed marine habitats that outlines specific parameters and filters would allow more precise and uniform results and this effort is currently underway  (Brandt et al.
, 2008; Barnes et al. , 2014)  but in some cases, may be too short to be phylogenetically informative. The marker 18S-V9 was chosen due to its short amplicon length and availability of primers, yet the 18S-V9 dataset primarily uncovered taxa of non-interest such as Bacteria (Figure  2 ), possibly as a result of both its conservation and size. Thus, for environments where community composition remains largely unknown, such as deep-sea vents, future studies could instead employ less conserved mitochondrial mini barcodes, such as the COI-mini barcode proposed by  Günther et al. (2018). In comparison, the Cytochrome Oxidase I (COI) gene has often been designated the DNA barcode standard for animals, as it is better at resolving taxa at the species level given its faster mutational rate, as well as the presence of robust "universal" primers, all aided by an extensive reference databank for taxonomic matching  (Hebert et al.
Comparing taxonomic results between COI and 18S, some taxa were found within certain datasets, yet not others (Figure  2 ). Previous authors also had similar findings while employing COI and 18S markers on environmental data  (Cowart et al. , 2015; Günther et al. , 2018) , linked to the factors described above. As an example, COI is known to have poor species resolution in some taxa and is unreliable for barcoding nematodes  (De Ley et al. , 2005; Pochon et al. , 2013)  and as such, 18S is preferred in these instances  (Bhadury et al. , 2006 (Bhadury et al. , , 2008)). This further supports that several factors, including marker amplicon length, the presence of pseudogenes or introns and differing evolution rates influence taxonomic "preferences" of markers. Despite these differences, the comparison of our eDNA datasets identified congruent statistical findings (Table  4 ), which included differences in community composition across habitats, as well as high diversity in inactive sites, similar to what has been seen using more traditional sampling strategies  (Vanreusel et al.
ACKNOWLEDGMENTS We would like to thank the captains and crews of the oceanographic cruise campaigns of Momarsat 2017 and 2018 aboard the vessels N/O Pourquoi pas? and  L'Atalante , as well as the ROV Victor6000 team. We are also sincerely thankful to the following people for their assistance in various aspects of this project:  Sophie Arnaud-Haond ,  Laure Quintric ,  Patrick Durand ,  Johanne Aube ,  Olivier Soubigou ,  Colomban de Vargas ,  Cathy Liautard-Haag , and the technicians and engineers of the deep sea Ifremer lab.
DATA AVAILABILITY STATEMENT DNA barcoding and metabarcoding datasets are available online at NCBI under GenBank accessions MK591147-MK591726 and SRA accession PRJNA540908. DOI of the cruises: SARRADIN Pierre-Marie, CANNAT Mathilde (2017) MOMARSAT2017 cruise, RV Pourquoi pas?, https://doi. org/10. 17600/17000500; CANNAT Mathilde (2018) MOMARSAT2018 cruise, RV L'Atalante, https://doi. org/10. 17600/18000514.
De novo identification of microsatellite sequences in the B. aethiopum genome and assessment of potential SSR markers In order to enable a more precise evaluation of genetic diversity in B. aethiopum, we developed specific B. aethiopum markers from de novo sequencing data. A total of 23,281,354 raw reads with an average length of 250 bp have been generated from one MiSeq run. Raw sequence reads have been trimmed resulting in 21,636, From the contigs, the QDD software identifies a total of 1618 microsatellite loci (Additional file 1), of which 1327 (82. 01%) are perfect (i. e. repeat size 4 bp or smaller and repeat number 10-20). Among the perfect microsatellite loci, 83. 86% are composed of di-nucleotidic repeat units, 13. 06% of tri-nucleotidic units, 2. 39% of tetranucleotidic repeats and 0.
Microsatellite-based characterization of genetic variation of B. aethiopum in Benin The newly identified set of 11 B. aethiopum-specific SSR markers has been used for the characterization of genetic diversity in our full panel of 180 individual samples from nine locations distributed across Benin (Fig. 1 ). Map generated from publicly available resources of the Institut Géographique National du Bénin (IGN; https:// geobenin. bj/) and the "Major Rivers of the World" dataset from the World Bank Data Catalog (https://datacatalog. worldbank. org/dataset/major-rivers-world; Creative Commons Attribution 4. 0 International license), using the ArcGIS software by ESRI (www. esri. com). As shown in Table  3 , among our sample set the number of alleles per microsatellite locus ranges from 2 for marker Mbo41 to 6 for markers Mbo34, Mbo35, and Mbo50, with an average value of 4.
There might be further genetic separation between Southern B. aethiopum samples and those from the Central sites of Agoua and Biguina, resulting in the splitting of one group into two subgroups. However, with our current dataset it is not possible to achieve this level of discrimination in our analyses. Additional sampling campaigns from intermediate locations in the Central and Northern regions will be necessary in order to make progress on the subject. Among the nine locations studied in Benin, samples from Savè appear to be the most diversified (He = 0. 451) and constitute the exception to the general distribution according to geographical distances. This site located in the Sudano-Guinean transition zone of Benin is currently the most active for the production of B. aethiopum hypocotyls, and it acts as a supplier for the whole national territory (  [76] ; V.
Amplification products visualization was performed using the GeneMapper software version 3. 7 (Applied Biosystems). De novo identification of microsatellite loci in the B. aethiopum genome, marker selection and diversity analysis One B. aethiopum leaf sample (originating from the Togbin site) was randomly selected and used for genomic DNA purification according to the protocol of Mariac et al. [84]. The DNA was then used for the construction of an Illumina paired-end library, as described in Mariac et al. [85] , before high-throughput sequencing on a MiSeq v3 platform (Illumina; average read size 250 bp). Demultiplexing of the raw data output was performed using the Maillol script (https://github. com/maillol/demultadapt), with a 0-mistmatch threshold. Adapters were eliminated using Cutadapt version 1.
10  [86]. (http://code. google. com/ p/cutadapt/) with the following parameters: overlap length = 7, minimum length = 35 and quality = 20. Highquality reads (Q > 30) were filtered using the following script: https://github. com/SouthGreenPlatform/arcad-hts/ blob/master/scripts/arcad_hts_2_Filter_Fastq_On_Mean_ Quality. pl and the resulting filtered reads were deposited into GenBank under BioProject ID PRJNA576413. Pairedend reads were then merged using FLASH version 1. 2. 11 (https://github. com/SouthGreenPlatform/arcad-hts/blob/ Finally, microsatellite motif detection and specific primer design were carried out after elimination of redundant sequences using the QDD software version 3. 1. 2 [87]  with default settings (detailed in Additional file 3). Using selected primer pairs, test amplifications were performed with two randomly selected B.