dataesr/scientific-paragraphs-categorization huggingface dataset:
3 Patterns of beta-diversity between pipelines Community differences were visualized using PCoA ordinations (Jaccard and Bray-Curtis dissimilarities for metazoans and prokaryotes respectively) in clustered and nonclustered datasets (Fig. 2 , Fig. S3 ). Expectedly, PERMANOVAs confirmed that sites differed significantly in terms of community structure, accounting from 45% to 89% of variation in data. Evaluating the effect of LULU curation (at 84% and 90%) for metazoans showed that LULU-curated data resolved similar ecological patterns than non-curated data, accounting from 0. 5% (COI) to 1. 3% (18S) of variation in data (Fig. 2 ). Although ASV and OTU datasets detected similar levels of variation due to sites in PERMANOVAs, clustering levels affected the ecological patterns resolved by ordinations in rRNA loci (Fig  2 ).
At low d values (d=1-3), ecological patterns were consistent to patterns observed in the ASV datasets, with samples segregating by site and depth. Increasing d values produced stronger segregation among sites, thus resulting in differentiation among ocean basins rather than depth. This change in resolution occurred with d values as low as d=4 for 18S but was strongest at d=11 for both rRNA loci (Fig. S3 , Fig. 2 ). 4 Taxonomic assignment quality BLAST and RDP Bayesian Classifier assignments were compared in the mock communities and natural samples, on data clustered at d=3 and curated with LULU at 84% for COI and 90% for 18S. For prokaryotes, assignment methods were compared on the ASV-level. BLAST and RDP assigned similar amounts of OTUs in the prokaryote dataset, but BLAST assigned 20-70% less OTUs in the metazoan datasets (Table  S7 ).
Assigning with BLAST at a minimum of 70% hit identity resulted in comparable results as described above. Eight of the ten species were recovered with COI and six species were recovered with 18S, while the vesicomyid bivalves were taxonomically unresolved with both loci (Fig. S4 ). Although most species produced one single OTU, between one and three species still resulted in 2-3 OTUs in each mock sample. Assigning the 18S dataset with RDP resulted in comparable taxonomic resolutions, although more species produced more than one OTU. Assigning the COI dataset with RDP using the MIDORI-UNIQUE database resulted in assignments of the mock samples that did not match the expected taxa and were mostly belonging to arthropods, a problem not observed with BLAST (data not shown). When the database was reduced to marine-only taxa, all 10 species were detected, and this at expected OTU abundances, once data was filtered for phylum bootstrap levels ≥ 80% (Fig  S4 ).
However, applying a phylum bootstrap minimum of 80% resulted in a strong decrease in the number of final target OTUs, particularly for COI where only 226 OTUs remained after filtering (Table  S7 ). This reduced recovery with RDP after applying a minimum phylum bootstrap level was not observed in prokaryotes, where 51,000-55,000 ASVs were left after filtering with both assignment methods (Table  S7 ). BLAST hit identities of the overall datasets varied strongly depending on phyla and marker gene (Fig. 3 ). For 18S, most clusters had hit identities ≥ 90%. Poorly assigned clusters (hit identity < 90%) represented less than 20% of the dataset and were mostly assigned to Nematoda, Cnidaria, Tardigrada, Porifera, and Xenacoelomorpha. For COI, nearly all clusters had similarities to sequences in databases lower than 90%.
Overall, arthropods and echinoderms were detected at similar levels by both markers. The 18S barcode marker performed better in the detection of nematodes, annelids, platyhelminths, and xenacoelomorphs while COI mostly detected cnidarians, molluscs, and poriferans (Fig. 3 ), highlighting the complementarity of these two loci. BLAST hit identity was much higher for prokaryotes, with most clusters assigned with more than 90% similarity to sequences in databases. When datasets were filtered for RDP phylum bootstrap levels ≥ 80%, most assignments also had high genus bootstrap values for ribosomal loci. However, for COI, a considerable number of OTUs assigned to arthropods, cnidarians, molluscs, vertebrates, and poriferans still had genus bootstraps < 60%. Figure  3. Taxonomic assignment quality of BLAST and RDP methods on metazoan and prokaryote metabarcoding datasets of 14 deep-sea sites.
Such application transfers require adaptations to account for differences in both concepts and basic biological features. One example is the question of the relevance of using ASVs, advocated to replace OTUs "as the standard unit of marker-gene analysis and reporting"  [43] : an advice for microbiologists that may not apply when working on metazoans. First, metazoans are well known to exhibit variable and sometimes very high intraspecific polymorphism in 18S-V1 and above all in COI. Second, the results on the mock samples showed that single individuals produced very different numbers of ASVs, indicating that ASV-centred datasets do not reflect actual species composition in metazoans. As this "demultiplication" will be highly variable across taxa (as seen in Fig. S2 , and references such as  [73]  and  [74] ), the taxonomic compositions of samples based on ASVs will reflect genetic rather than species diversity.
When applying LULU to ASVs (DADA2) versus OTUs (DADA2+swarm) on 18S, similar cluster numbers were obtained (Fig. 1 ), suggesting a limited added effect of clustering for this marker once DADA2 and LULU are applied. This is in line with its slow evolutionary rate  [78]  leading to a limited number of haplotypes per species compared to COI. In contrast, for COI, LULU curation of the ASV dataset led to nearly twice the number of clusters (574 ± 38 at 84% and 742 ± 53 at 90%) compared to LULU curation of OTUs (at d=6: 334 ± 21 for 84% and 433 ± 31 for 90%). This confirms the higher intraspecific diversity of COI, and the need to combine clustering with LULU curation to account for intraspecific diversity in natural samples, especially with highly polymorphic markers such as COI. Finally, the reproductive mode and pace of selection in microbial populations may lead to locally lower levels of intraspecific variation than the one expected for metazoans.
Influence on beta diversity After focusing on alpha diversity estimates, i. e. on the numerical accuracy of inventories, the analysis of community structures showed that the LULU-curated datasets resolved similar ecological patterns as datasets not curated with LULU. However, clustering affected resolution of ecological patterns in ribosomal loci when d values were high, and this was not the case for COI, where similar patterns were resolved in all datasets (Fig. 2 ). This is in accordance with other studies reporting severe impacts of bioinformatic parameters on alpha diversity while comparable patterns of beta diversity are observed in ASV and OTU datasets, at least down to a minimum level of clustering stringency  [33] ,  [38]. Clustering and LULU curation mainly led to the decrease of the number of clusters assigned to particular taxa in both loci, such as annelids, arthropods, nematodes, or platyhelminths for 18S, and chordates, cnidarians, echinoderms, or poriferans for COI (Fig.
S2 ). The strong decrease in cluster numbers observed in these phyla suggests that the latter have greater intraspecific polymorphism, although the decrease could also be due to the merging of closely related species, as both markers have lower taxonomic resolution in particular taxa. This has been acknowledged for 18S in general, but in nematodes in particular  [81] , and reported in cnidarians with COI  [82]. Overall, based on alpha and beta diversity results observed in mock communities and natural samples, applying LULU at 84% seems to efficiently curate metazoan COI datasets without significant loss of species, but clustering is required, at least at d=1, in order to address high intraspecific polymorphism. For 18S, LULU curation seems to require values above 84% (e. g. 90%) in order to avoid the loss of species, as seen in the mock communities.
Taxonomic resolution and assignment quality The COI locus allowed the detection of all ten species present in the mock samples, compared to seven in the 18S dataset (Table  1 ). This locus also provided much more accurate assignments, most of them correct at the genus (and species) level, confirming that COI uncovers more metazoan species and offers a better taxonomic resolution than 18S  [83] -  [85]. Our results also support approaches combining nuclear and mitochondrial markers to achieve more comprehensive biodiversity inventories  [86] -  [88]. Indeed, strong differences exist in amplification success among taxa  [89] ,  [90] , exemplified by nematodes, which are well detected with 18S but not with COI  [44]. The high complementarity of COI and 18S in terms of targeted taxa (highlighted in Fig.
S2 ), also supports the approach taken by Stefanni et al. [91] , as subsampling each gene dataset for its "best targeted phyla" and subsequently combining both seems to be a very efficient way to produce comprehensive and non-redundant biodiversity inventories. Finally, compared to BLAST assignments, similar taxonomic resolution was observed using the RDP Bayesian Classifier on the mock samples for 18S (Fig. S4 ) and for COI when using the MIDORI-UNIQUE marine-only database. Poor performance of RDP using the full MIDORI database is likely due to the size of the database, and to its low coverage of deep-sea species. The problem of underrepresentation of deep-sea taxa is especially apparent with the BLAST assignments, which generally displayed low identities to sequences in databases, especially for COI (Fig.
3 ). Using minimum similarities of 80% for COI and 86% for 18S as cut-off values for metazoans has been shown to improve the taxonomic quality of metazoan metabarcoding datasets  [91]. However, phylogenies of marine invertebrates have found high levels of species divergence (up to ~30%), even within genera  [54]. Consequently, studies on deep-sea taxa have found that some invertebrate species had COI sequences diverging more than 20% from any other species present in molecular databases  [55] ,  [92]. At present, it thus seems difficult to work at taxonomic levels beyond phylum-level with deep-sea metabarcoding data when using large public databases. Small databases, taxonomically similar to the targeted communities, and with sequences of the same length as the amplified fragment of interest, are known to maximise accurate identification  [93].
When using the reduced marine-only COI database, RDP provided the most accurate assignments in the mock samples when the phylum bootstrap level was ≥ 80 (Fig. S 4) , although this filtering threshold drastically reduced the number of OTUs in the overall dataset (Table  S7 ). The development of custom-built marine RDP training sets, without overrepresentation of terrestrial species, is therefore needed for this Bayesian assignment method to be effective on deep-sea datasets. With reduced and more specific databases, removing clusters with phylum bootstrap-level < 80 should be an efficient way to increase taxonomic quality of deep-sea metabarcoding datasets. At present, if accurate taxonomic assignments are sought while using universal primers, we advocate assigning taxonomy in two steps: first, using BLAST and a large database including all phyla amplifiable by the primer set, extracting the clusters belonging to the groups of interest, then re-assigning taxonomy to these target taxa using RDP and a smaller, taxon-specific database.
Conclusions and perspectives Using mock communities and natural samples, we evaluate several recent algorithms and assess their capacity to improve the quality of molecular biodiversity inventories of metazoans and prokaryotes. Our results support the fact that ASV data should be produced and communicated for reusability and reproducibility following the recommendations of Callahan et al. [43]. This is especially useful in large projects spanning wide geographic zones and time scales, as different ASV datasets can be easily merged a posteriori, and clustered if necessary afterwards. Nevertheless, clustering ASVs into OTUs will be required to obtain accurate species-level inventories, at least for metazoan communities, with a more severe influence of clustering observed on alpha diversity estimates than beta-diversity patterns.
Considering 16S polymorphism observed in prokaryotic species  [63]  and the possible geographic segregation of their populations, clustering may also be required in prokaryotic datasets, for example in studies screening for species associations (i. e. symbiotic relationships) as symbionts may be prone to differential fixation through enhanced drift  [94]. Our results also demonstrated that LULU effectively curates metazoan biodiversity inventories obtained through metabarcoding. They also underline the need to adapt parameters for curation (e. g. LULU curation at 90% for 18S and 84% for COI) and clustering to each gene used and taxonomic compartment targeted, in order to identify an optimal balance between the correction for spurious clusters and the merging of closely related species. Finally, our findings also showed that accurate taxonomic assignments of deep-sea species can be obtained with the RDP Bayesian Classifier, but only with reduced databases containing ecosystem-specific sequences.
Acknowledgements This work is part of the " Pourquoi Pas les Abysses ?" project, funded by  Ifremer  and the eDNAbyss ( AP2016 -228 ) project, funded by  France Génomique  ( ANR-10-INBS-09 ) and Genoscope-CEA. We wish to thank  Caroline Dussart  for her contribution to the early developments of bioinformatic scripts and  Patrick Durand  for bioinformatic support,  Caroline Menguy  and  Stéphane Pesant  for their help to sort and post data, metadata and databases,  Cathy Liautard-Haag  for her help with lab management, and  Babett Guenther  for help in barcoding. We also wish to express our gratitude to the participants and mission chiefs of the  Essnaut (Marie-Anne Cambon Bonavita ),  MarMine  ( Eva Ramirez Llodra , project  247626/O30  ( NRC-BI) ,  PEACETIME  ( Cécile Guieu  and  Christian Tamburini) ,  CanHROV (Marie Claire Fabri)  and  MEDWAVES (Covadonga Orejas ) cruises, and  Joana Ruela Boavida ,  Peregrino Cambeiro  (Pirri),  Maria Rakka  and  Meri Bilan  for invaluable help during sampling.
The MEDWAVES cruise was organised in the framework of the  ATLAS  Project, supported by the  European Union   2020 Research and Innovation Programme  under grant agreement number:  678760. We thank  Stefaniya Kamenova ,  Tiago Pereira , and an anonymous referee for their comments on a previous version of this manuscript. Version 3 of this preprint has been peer-reviewed and recommended by  Peer Community In Ecology  (https://doi. org/10. 24072/pci. ecology. 100041).
Data accessibility The data for this work can be accessed in the European Nucleotide Archive (ENA) database (Illumina sequencing data are available in the European Nucleotide Archive under the following projects: PRJEB33873). The dataset, including raw sequences, databases, and ASV/OTU tables, has been deposited on https://doi. org/10. 12770/0b5d250b-8418-4dda-b39c-960c4481df93. Bioinformatic scripts, config files, and R scripts are available on Gitlab (https://gitlab. ifremer. fr/abyssproject/).
Given the accuracy of hazard assessment of volcanic eruptions is based on the accuracy of associated ESPs, we have re-examined the Plinian phase of the 1600 CE Huaynaputina eruption applying the most recent strategies in order to better assess erupted volume and plume height. This phase emplaced a massive tephra-fall deposit with 70-80 % pumice, 15-25 % free crystals and ~5 % lithic clasts  (Thouret et al. 2002a). Previous studies of the 1600 CE Huaynaputina eruption were carried out between 1999 and 2002  (Thouret et al. 1999 (Thouret et al. , 2002a;; Adams et al. 2001 ). In the past decade, both the characterization of tephra-fall deposits and the determination of ESPs have considerably evolved (see  Bonadonna et al. 2015  for a review). Our revision of the dataset in 2015-2017 was motivated by the application of recent models in order to better assess erupted volume, plume height, mass eruption rate (MER) and eruption duration.
In particular, the estimation of erupted volume was based on the models of  Bonadonna and Houghton (2005) ,  Bonadonna and Costa (2012)  and  Burden et al. (2013). Plume height was determined using the method of  Bonadonna and Costa (2013) ,  Biass et al. (2015)  implementation of  Carey and Sparks (1986), and Rossi et al. (2019). MER was determined based on the methods of  Wilson and Walker (1987) ,  Mastin et al. (2009) , and  Degruyter and Bonadonna (2012). Eruption duration was derived based on all combinations of erupted mass and MER. The TError package  (Biass et al. 2014 ) was also applied to investigate uncertainty propagation through the calculation of ESPs. Finally, the eruption was also classified based on the scheme of  Bonadonna and Costa (2013).
Thickness, grain-size and density measurements in the field Arid to semi-arid field conditions, and the quality of outcrops preserved around Huaynaputina and along the dispersal axis, make this tephra-fall deposit a unique reference for such a voluminous Plinian eruption, although part of the tephra fell into the ocean (~39 % of the 1 cm isopach area covers the Pacific Ocean, calculated with QGIS). The extent and thickness of Unit 1 has been investigated throughout extensive field work between 1995 and 2000. Peat bogs have been cored in marshes and small lakes to retrieve the deposit in distal areas  (Juvigné et al. 1997). Additional fieldwork has been carried out in 2015-2018 in the framework of the Huayruro Project (http://ovi. ingemmet. gob. pe/?tag=proyecto-huayruro), expanding the dataset with 42 new sections in proximal and medial areas.
Plume height We have used three methods to determine the maximum height H reached by the plume. As the dataset has been revised with additional data acquired with an improved strategy, we re-calculated H following  Carey and Sparks (1986). The method is based on a model of clast fallout from a convective column, in which clast dispersal is determined by two main parameters: column height and wind speed. This method presents two shortcomings: (1) the user needs to plot the data "by hand" on the diagram, which can lead to some inaccuracy; and (2) one diagram alone cannot encompass all clast sizes at a time. We have applied the MatLab implementation of the method proposed by  Biass et al. (2015). This implementation interpolates  Carey and Sparks (1986)  diagrams to a continuous range of clast sizes and provides well-defined plume height and wind speed estimates.
Mass eruption rate and eruption duration Three methods were used to determine the mass eruption rate (MER): (1)  Wilson and Walker (1987). This method relates MER to the maximum plume height H with the empirical relation: 𝑀𝐸𝑅 = ( 𝐻 𝑘 ) 4 (9) where k is a constant depending on the magma's chemistry. In this study we used 0. 236, the typical value for silicic magmas. (2)  Mastin et al. (2009). It is based on the best fit between the volumetric flow rate  ̇ (m 3 DRE per second) with maximum plume height H, calculated on a database of ~30 eruptions: 𝑉 ̇= ( 𝐻 𝑘 ) 4. 15 (10) where k is a constant set to 2. Knowing the DRE density of the magma, the volumetric flow rate can then be converted to MER. (3)  Degruyter and Bonadonna (2012)  developed a new expression considering the wind: 𝑀𝐸𝑅 = π ρ 𝑎0 𝑔 ′ ( α 2 𝑁 ̅ 10.
Uncertainty of eruption source parameters To assess the uncertainty of the revised ESPs' estimates, we used TError  (Biass et al. 2014) , a package of Matlab functions that handles various methods applied to several ESPs, namely: -Plume height and wind speed, after  Carey and Sparks (1986). -Mass eruption rate, after  Wilson and Walker (1987) ,  Mastin et al. (2009)  and  Degruyter and Bonadonna (2012). -Tephra volume, after  Fierstein and Nathenson (1992) ,  Bonadonna and Houghton (2005)  and  Bonadonna and Costa (2012). Mass is then determined using a constant bulk deposit density. -Duration, calculated as the ratio between mass and MER. For each input parameter, the user chooses a reference value and a relative input uncertainty (RIU), for instance ± 10 %. TError then runs Monte Carlo simulations to generate uncertainty distributions, symmetrical and centred around the reference value (considered as the best possible guess).
Thickness, grain-size and density of the deposit Including the 42 new sections, the dataset currently encompasses over 600 thickness measurements, which have been compiled with a GIS to build a revised isopach map (Figure  6 ). The dispersal axis is oriented WNW (the proximal WSW orientation is probably biased by the distribution of points). The ML isopleth map has also been revised (Figure  7 ). This distribution is characterized by a West elongation pattern. Finally, dry bulk density of each subunit of the Plinian tephra-fall deposit has been measured in 32 sites. It slightly increases between proximal, medial and distal tephra sections, but it does vary from base to top across tephra sections. The dry bulk density of Unit 1 rises with distance from the vent, with a mean of 0. 62 g/cm 3 for proximal deposits and 0.
Plume height estimates Table  2  shows the result of  Biass et al. (2015)  implementation of  Carey and Sparks (1986)   with what is observed nowadays above the tropopause in South Peru. We processed the NCEP/NCAR reanalysis 1 dataset  (Kalnay et al. 1996)  with TephraProb  (Biass et al. 2016). In February, the median wind speed reaches approximately 20 m s -1 at 20 km of altitude, and increases up to 35 m s -1 at 30 km. From 20 km upwards, the wind orientation is almost constant at 270°, i. e. blowing towards the West, which matches the dispersal axis of 1600 CE Huaynaputina Plinian tephra-fall deposit (Figure  6 ). Figure Figure  11  shows the Weibull fit of isopleth data. The corresponding parameter λML yields a plume height of 33. 5 km calculated with Eq. 8. ranges from 10. 8 to 11. 6.
Uncertainty propagation in eruption source parameters We have used the TError package to assess the uncertainty associated with our ESPs estimates. Table  S2  summarises a TError run after 10,000 iterations, with a 20 % relative input uncertainty normally distributed. This normal distribution can propagate in the output parameter when the input-output relationship is linear. This is the case of the plume height, for instance, which can thus be expressed with a single error range, i. e. 32. 2 ± 2. 5 km. Non-linear relationships yield asymmetric ESP distributions, like the case of volume estimated with the Weibull function. The volume should therefore be expressed as 14 -3 +1 km 3 , with both the lower and upper errors mentioned.
Conclusion The 1600 CE Huaynaputina eruption had first been studied in 1999-2002. Analysis of tephra-fall deposits has since evolved using both detailed field and laboratory studies. With this analytical progress in mind, we reconsidered the case study in 2015-2017, focusing on the first of the five phases of the eruption, i. e. the Plinian explosive event of 19 and 20 February 1600. This research consisted of two stages: (1) we revised the previous datasets, compiling all thickness and largest-clast data and adding bulk deposit density measurements; (2) we applied recent methods to estimate eruption source parameters (volume, plume height, MER) and the associated uncertainty. The key findings in terms of eruption dynamics are as follows: 1. The Plinian phase likely dispersed tephra WNW beyond 400 km from the vent over the Peruvian territory and the Pacific Ocean.