dataesr/scientific-paragraphs-categorization huggingface dataset:
For the spring experiment, calibration was performed with Dunaliella tertiolecta (Dunaliellaceae), with six chl-a concentration points ranging from 0 to 10 µg L -1. For the fall experiment, the fluorometers were calibrated with a culture of Tetraselmis chui (Chlorodendraceae) and with five chl-a concentration points, from 0 to 12. 7 µg L -1. After the data were corrected with calibration coefficients, they were additionally corrected using the daily chl-a concentration measured by HPLC from samples taken in every mesocosm once a day at 09:00. Finally, the chl-a sensor data were corrected for non-photochemical quenching (NPQ), as a strong decrease in chl-a occurred in almost all daily chl-a cycles during sunlight. To account for NPQ, chl-a data were linearly interpolated between sunrise and sunset  49, 50.
This interpolation assumed that spatial homogeneity in the phytoplankton community inside each mesocosm was achieved with the use of a pump to gently mix the water column  51. Each oxygen optode was calibrated before and after the experiments with three saturation points (0, 50 and 100%) at different temperature levels (17-22 °C). The 0 and 50% saturation points were obtained by adding potassium metabisulfite into distilled water, while the 100% saturation point was reached by bubbling air into the distilled water. The dissolved oxygen data obtained during the experiments were also corrected for salinity and temperature measured with the conductivity and the temperature sensors immersed in the mesocosms. The dissolved oxygen data were finally corrected with dissolved oxygen concentrations that were measured with the Winkler method.
The k value was taken as k = 0. 000156 m min -1 from the literature  58. Then, k was corrected for temperature and salinity using the high-frequency sensors data and following the procedure described in Soulié et al. 50. Then, instantaneous NCP was calculated from the following equation (Eq. 6): In this equation, O 2 (t) and O 2 (t -1) are the dissolved oxygen concentration at time t and t -1, respectively, and F(t) the exchange factor at time t. From this instantaneous NCP data, daily metabolic parameters were inferred. First, the respiration occurring during daylight, Rdaytime, was estimated with Eq. (  7 ): In this equation, Rdaytime is expressed in gO 2 m -3 d -1 , the mean instantaneous NCP in gO 2 m -3 min -1 , and the duration of the positive NCP period in h. The respiration occurring at night, Rnight, was estimated from Eq.
(  8 ): Similarly, Rnight is expressed in gO 2 m -3 d -1 , the mean instantaneous NCP in gO 2 m -3 min -1 , and the duration of the negative NCP period in h. Finally, daily R is the sum of Rdaytime and Rnight. Daily GPP is then calculated with the following equation Eq. (  9 ): Daily GPP and Rnight are expressed in gO 2 •m -3 •d -1 , the mean instantaneous NCP in gO 2 •m -3 •min -1 , and the duration of the positive NCP period in h. Daily NCP is then calculated as the difference between daily GPP and daily R. Cumulative GPP, R and chl-a data. As warming was fully achieved from day 2 onwards, cumulative data from d2 to d17 were calculated for GPP, R, and daily chl-a, to compare the overall consequences of warming on both experiments, using Eq. (  10 ): With x i the value of the investigated parameter on day i, and n the last experimental day for which the parameter was estimated (n = 17).
Acknowledgements The authors would like to thank the MEDIMEER crew for assistance during the mesocosm experiments,  David Parin  for setting up the sensor system and for retrieving sensor data, and  Remy Valdès ,  Solenn Soriano , and  Camille Suarez-Bazille  for their help with daily sampling of the mesocosms and for the analyses of the nutrient samples. Moreover, we wish to acknowledge  Julien Dupont ,  Thomas Trombetta , and  Jean-François Thévenot  for their assistance in the mesocosm experiments and HPLC data analysis, which was performed at the HPLC platform of MARBEC. The manuscript was edited for English language by  Editage. The spring experiment was part of the  PhotoPhyto  Project: funded by the  French National Research Agency  ( ANR-14-CE02-0018 ). The fall experiment was part of the  AQUACOSM  Project, which also funded a part of the Ph.
in culture collections and genomic reference databases. The exploration of this uncharted protist diversity in oceanic communities relies essentially on studying genetic markers from the environment as taxonomic barcodes. Here we report that across 6 large scale spatio-temporal planktonic surveys, half of the genetic barcodes remain taxonomically unassigned at the genus level, preventing a fine ecological understanding for numerous protist lineages. Among them, parasitic Syndiniales (Dinoflagellata) appear as the least described protist group. We have developed a computational workflow, integrating diverse 18S rDNA gene metabarcoding datasets, in order to infer large-scale ecological patterns at 100% similarity of the genetic marker, overcoming the limitation of taxonomic assignment. From a spatial perspective, we identified 2 171 unassigned clusters exclusively shared between the Tropical/Subtropical Ocean and the Mediterranean Sea among all Syndiniales orders and 25 ubiquitous clusters shared within all the studied marine regions.
Introduction The advances in high-throughput sequencing technologies have provided a new perspective to microbial diversity at a global scale. Studying the DNA of environmental microbial communities (i. e. microbiome) allowed to overcome the limit of non-cultivability and provided access to an unprecedented large quantity of high resolution genetic information  [1] [2] [3] [4]. In silico downstream analysis of genetic big-data shed light on a new challenge in microbial ecology: exploring the unassigned microbiome  [5, 6]. From the point of view of environmental genomics, the unassigned microbiome encompasses all genetic sequences that cannot be annotated with referenced biological information as they have no match in databases at a functional  [6] [7] [8] [9]  and/or taxonomic level  [5, 10, 11].
4 In this study, we explored marine planktonic protist communities at a wide spatio-temporal scale, in order to: (i) quantify the taxonomically unassigned sequences and reveal protist lineages for which there is a major scarcity of taxonomic references, (ii) highlight unassigned protist diversity shared between contrasted marine environments and (iii) identify unassigned taxa which are ecologically relevant and recurrent, that should be prioritized for further characterisation. We integrated 12 years of data and 155 different sampling locations from 6 environmental metabarcoding datasets, combining 3 coastal time-series (ASTAN, BBMO, SOLA), 1 European coastal Sea sampling project (BioMarKs) and 2 oceanographic campaigns (Malaspina, MOOSE). As a study case, we focused our analyses on the parasite group of Syndiniales and, by clustering the gathered metabarcodes in a Sequence Similarity Network (SSN), we revealed novel ecological patterns of Syndiniales at a taxonomic resolution of 100% similarity between V4 regions of the 18S rDNA marker.
Syndiniales Among the 343 165 metabarcodes we considered in our study (Table  S1 ), those that were taxonomically unassigned at a given taxonomic rank (i. e. , without any match with reference sequences under 80% of sequence similarity) according to the PR2 or SILVA reference databases were considered as unassigned at this taxonomic rank (Fig. S1A ). Unassigned metabarcodes occured in every sampled region and at every taxonomic rank, from kingdom to species (Fig. 1A , Fig. S2 ). Both the relative abundance and number of unassigned metabarcodes increased from high to low taxonomic ranks contributing respectively to an average of 0. 03% and 0. 28% of the whole protist community at the kingdom rank and to 69. 35% and 82. 67% at the species rank (Fig. 1A , Fig. S3B ). At kingdom level, 628 metabarcodes remained unassigned among which 87.
70% originated from bathypelagic samples (2 150 -4 000 m) of the Malaspina expedition (Fig. S4 ). The biggest increase in unassigned metabarcode proportion was observed from family to genus level for which 71. 14% and 58. 95% of metabarcodes were unassigned in relative number and relative abundance respectively (increase of 35% in unassigned metabarcodes). Overall, at the lowest taxonomic levels of our global dataset, i. e. genus and species, the proportion of unassigned metabarcodes was similar and represented more than half of the metabarcodes that could not be assigned to any referenced protist taxon (Fig. 1A ). The study of unassigned sequences was thus conducted from the viewpoint of the genus taxonomic level. Across protist divisions, a higher diversity index was obtained for unassigned metabarcodes belonging to Dinoflagellata for all datasets (Fig.
1B ). Overall, 54% of unassigned metabarcodes in relative number and 63% in relative abundance belonged to Dinoflagellata (Fig. S4A ). Among other protist divisions lacking taxonomic assignment at the genus level were Ochrophyta (all datasets), Ciliophora (BioMarKs, SOLA, Malaspina, MOOSE), Radiolaria (BioMarKs, Malaspina, MOOSE), Cercozoa (ASTAN, SOLA), Cryptophyta (BioMarKs, ASTAN, BBMO), Opalozoa (ASTAN, BBMO, SOLA, MOOSE) and Sagenista (BioMarKs, BBMO, Malaspina) (Fig. S5A ). A higher diversity index was obtained for unassigned sequences, compared to assigned sequences, for the divisions Opalozoa, Sagenista and Cercozoa (Fig. S5B ). Thus, when studying only assigned genera of the latter protist divisions, their diversity could be largely underestimated. Dinoflagellata metabarcodes represent 52% of our global dataset (179 615 metabarcodes).
Among unassigned Dinoflagellata, Dinophyceae and Syndiniales were the two dominant classes and Syndiniales represented 66% and 48% of metabarcodes in terms of number and abundance respectively (Fig. S6A ). Within these two classes, the proportion of unassigned metabarcodes at the genus level was 2-fold higher for Syndiniales, with 98% and 95% of metabarcodes unassigned in terms of relative number and abundance (Fig. 1C , Fig. S6B ). Only 4 species of Syndiniales had a taxonomic assignment (0. 01% of total metabarcodes and 0. 53% of Syndiniales metabarcodes). Syndiniales metabarcodes unassigned at genus level represented 21% of our global dataset (72 789 metabarcodes). Given the contribution and overwhelming majority of unassigned Syndiniales in our dataset, we decided to focus the rest of our study on this lineage.
Shared patterns of unassigned Syndiniales diversity between sunlit Mediterranean and Tropical waters To investigate the spatio-temporal distribution of Syndiniales at genus level we built connected components (CCs), i. e clusters of metabarcodes with 100% sequence identity and a minimum of 80% coverage. We consider the CCs as a proxy for clustering metabarcodes of the same Syndiniales genera or at least as pragmatic units to deal with Syndiniales molecular diversity across multiple datasets. After clustering, our global dataset contained 4 317 Syndiniales CCs (30% of all CCs) out of which 4 245 CCs were unassigned at the genus level (98% of Syndiniales CCs) (Fig. S7A ). These unassigned CCs belonged to 5 orders of Syndiniales, Dino-Group-I to III, Dino-Group-V and an "Unknown" order (rank not assigned).
Out of the unassigned Syndiniales CCs, 58% (2 478 CCs) were exclusively shared within 2 sea regions, being mainly the Tropical/Subtropical Ocean and the Mediterranean Sea (which both include samples at depth > 1 000 m), regrouping 51% of the unassigned Syndiniales CCs (2 171 CCs) (Fig. 2, N=2 ). Unassigned CCs endemic to one region represented 23% of Syndiniales CCs (961 CCs) and were mostly found at the surface of the Tropical/Subtropical ocean (Fig. 2 , N=1), while 12% of CCs (518 CCs) were shared between 3 regions (Fig. 2, N=3 ) and 7% CCs (288 CCs) were shared between more than 3 regions (Fig. 2, N>3 ). All studied sea regions shared 25 ubiquitous unassigned Syndiniales CCs, including 14 CCs belonging to the Dino-Group-II Syndiniales order (Fig. 2, N=6 ). Among Syndiniales orders, Dino-Group-II and Group-I were the most represented in our dataset (2 954 CCs, 70%; 1 056 CCs, 25% of unassigned Syndiniales CCs respectively (Fig.
8 To further investigate this hypothesis, we compared the community composition of protist divisions known to be major hosts for Syndiniales, between the marine regions of our global dataset. For Dinophyceae, Radiolaria and Ciliophora, the jaccard dissimilarity index was the lowest between Mediterranean sea and Tropical/Subtropical Ocean compared to community comparisons between the other sea regions (Table  S2 ). This was also the case for Syndiniales, further supporting the results illustrated above (Fig. 3A ). Neither of the remaining protist divisions found as having an important contribution to our dataset (Ochrophyta, Cercozoa, Cryptophyta, Opalozoa) showed the same tendency apart from Sagenista (Fig. S4 , Table  S2 ).
Rhythmic ecological indicators among unassigned Syndiniales community Temporal aspects of the Syndiniales community were studied across the three time-series (ASTAN, BBMO, SOLA) in our dataset. Unassigned Syndiniales clusters did not indicate any clear seasonal preference based on monthly abundance for any of the time-series (Fig. S8 ). The correlation of CCs to the overall Syndiniales community dynamics and their rhythmicity was computed with two methods. The Escouffier's equivalent vectors selected the CCs that are the best indicators of community abundance variation according to a PCA and the Lomb-Scargle periodogram algorithm detected if CCs follow rhythmic patterns of occurrence across time. In the studied time-series, 75% of the Syndiniales community response to environmental variation was described by 45 CCs at ASTAN, 36 CCs at BBMO and 17 CCs at SOLA (Table  S3 ).
What are we missing from eukaryotic diversity with metabarcoding ? In environmental genomics investigations, the 18S rDNA universal marker sequence constitutes the gold standard for the exploration of eukaryotic diversity in environmental communities, shedding light on uncultivable and rare taxa  [16, 30]. Yet, by integrating different metabarcoding datasets, we report that in the marine realm half of protist sequences cannot be taxonomically assigned at the genus level (57% of sequences in our dataset) and these unassigned protist taxa represent 36% to 82% of the protist community in terms of abundance across 6 diverse marine environments. Few metabarcoding studies have quantified unassigned protist diversity. In Tara Oceans, unassigned protist diversity revealed with the V9 region of the 18S rDNA marker at the supergroup level was found to be <3% of total reads  [14]  when referring to unassigned sequences as marker sequences with <80% identity with reference sequences.
Here, with the V4 region of the 18S rDNA marker we find that unassigned protist sequences represent in abundance <1% at the supergroup level. At the genus level unassigned sequences are not rare among the protist community as they represent in abundance >45% of metabarcodes in each studied dataset and up to 80% of metabarcodes for the Malaspina expedition dataset. This confirms the current biased view of eukaryotic diversity, mostly focusing on multicellular and cultivable taxa, neglecting >70% of eukaryote diversity, including key lineages for the evolution of 10 life and to understand ecosystems functioning  [30] [31] [32]. This missing picture can be addressed, for metabarcoding studies, in the context of sample acquisition but also data acquisition in reference databases. Some oceanic regions are more sampled than others, i.
e. coastal locations compared to deep / open-sea environments  [33]. Moreover, the maintenance and update of reference databases is a laborious but critical process whose pace is difficult to synchronize with the generation of an ever-increasing amount of environmental sequences  [34]. Metabarcoding assessments of the diversity also depends on the choice of ribosomal marker genes. In our study, the largest proportion of unassigned protist diversity was found at low taxonomic levels, a trend that has also been observed for prokaryotes  [6]. Universal ribosomal markers such as 16S rDNA and 18S rDNA can have a distinct taxonomic resolution depending on the lineage considered and within each lineage  [30, 35] , for instance in order to describe diatom diversity a threshold >95% similarity of the V9 regions of the 18S rDNA gene with reference sequences delimits some genera (e.
SSNs as integrative tools to prioritize unassigned protist taxa In this integrative study we have used a sequence similarity network to explore the ecology of the main components of the unassigned protist microbiome by combining 6 metabarcoding datasets. SSNs are relevant and efficient analytical tools for addressing the unassigned microbiome challenge as they allow studying simultaneously large datasets, in order to categorize and prioritize unassigned sequences. They have been recently employed among prokaryotes for surveying the coding part of genomes and metagenomes  [6]  and taxonomy across extreme aquatic environments  [12]. By exploring the biogeography of these sequences we can reveal core taxa shared across ecosystems  [12, 53]. Here we have explored both biogeographical and temporal patterns of protists at the species level without requiring a reference taxonomic match.
Our FAIR (Findable, Accessible, Interoperable and Reusable) computational workflow that allows to integrate data from heterogeneous ecosystem sampling protocols, such as coastal time-series and open sea campaigns and can be applied to any targeted protist group of any metabarcoding dataset, of the same marker gene, for example originating from the metaPR2 database  [54]  and Ocean Barcode Atlas  [55]. The taxa identified by the network could then be specifically targeted for in situ hybridisation  [43]  and isolation for singlecell omics  [31]. Other approaches to reduce the unassigned taxonomic load encompass long-read sequencing  [16] , sequencing multiple metabarcoding markers  [30]  and combining metabarcoding and microscopy  [34]. The unassigned microbiome holds an unexplored potential of novel taxa and functions that will surely challenge the current view of microbial ecology in the ocean and beyond  [5, 31, 56].
Gathering and homogenisation of metabarcoding datasets Metabarcoding datasets of 18S rDNA marker sequences containing the variable region V4 and originating from 6 distinct sampling projects were gathered. The datasets include three temporal series of bimensual samplings at a single station: ASTAN in Roscoff, English Channel, France (8 years of data), BBMO in Blanes Bay, Mediterranean Sea, Spain (10 years of data) and SOLA in Banyuls-sur-Mer, Mediterranean Sea, France (9 years of data) (Fig. S1D ); two oceanographic campaigns of punctual samplings across 148 locations: Malaspina Expedition (122 stations, circumglobal Tropical/Subtropical Ocean) and MOOSE (26 stations, Mediterranean Sea) (Fig. S1B, C ); and one European project of punctual samplings at 6 marine coastal stations: BioMarKs project (samples from: Oslo, Norway; Roscoff, France; Varna, Bulgaria; Gijon, Spain; Barcelona, Spain; Naples, Italy).
Sequencing was done with Illumina MiSeq technology, except for the BioMarKs project sequenced by 454 pyrosequencing. Each metabarcoding dataset contained the abundance tables of reads clean-processed and inferred into ASVs (OTUs for BioMarKs) and their taxonomic affiliation (details in Table  S1 ). The initial global dataset contained 539 546 metabarcodes. For homogenisation purposes, the same two filtering conditions were applied independently to each of the 6 datasets (Fig. S1A , Step 1): removal of sequences corresponding to metazoans, terrestrial plants (Streptophyta) and macroalgae (Florideophyceae, Bangiophyceae, Phaeophyceae, and Ulvophyceae); removal of sequences having less than 80% identity with reference databases. The latter threshold was chosen according to the original preprocessing of the datasets: the MOOSE dataset had beforehand implemented a minimum identity threshold of 80% and Malaspina and BBMO of 95%.
A 95% filter was considered too stringent, as too many unknown sequences of interest might be removed, a 80% threshold was applied to the global dataset for homogenisation. The global abundance table resulting from the homogenisation workflow involved at this stage 343 165 metabarcodes, and each sample was normalized by total read number and scaled from 0 to 1. To account for variations in the taxonomic assignment procedure (assignment tools, database versions) across datasets, a new taxonomic assignment (Fig. S1A,  Step 2) was performed on the global set of metabarcodes with the PR2 database (version 4. 12. 0, released on 08. 08. 2019, https://pr2-database. org ; blast parameters: -evalue 0. 01 -max_target_seqs 15,  [57] ). Only the best hit (best e-value) of each alignment was kept. These new assignments were filtered again for multicellular taxa and only sequences with a length greater to 200 bp were kept (Fig.
S1A , Step 3). The PR2 database includes 8 taxonomic ranks: kingdom, supergroup, division, class, order, family, genus, and species. To avoid prokaryotic contamination at the kingdom level, an assignment was performed using the SILVA database (https://www. arb-silva. de/, version 138) implemented in the DADA2 algorithm  [58]. 3 874 prokaryotic metabarcodes were removed out of the 4 519 unassigned sequences at the kingdom level. The taxonomic ranks that were left unassigned were marked as "Unknown" and the taxonomy of the sequence was considered unassigned at this given rank. Unassigned ranks located between attributed ranks were regarded as gaps in the taxonomic hierarchy and not as unassigned ranks. The diversity and abundance of unassigned sequences were explored on Rstudio (R version 4.
Homogenisation and analysis of environmental data Our global dataset included 1 531 samples (ASTAN: 374, BBMO: 327, SOLA: 154, Malaspina: 289, MOOSE: 272) (Table  S1 ). The metadata and environmental information associated with the studied samples were retrieved from the initial studies  [60] [61] [62] [63] [64] [65] [66]   ( mol/L), phosphate ( mol/L), silicate ( mol/L) and chlorophyll-a ( g/L). For ASTAN and BioMarKs datasets, when in situ environmental variables were missing, metadata were retrieved from public oceanographic databases (SOMLIT database (https://www. somlit. fr) ; World Ocean Database (https://www. ncei. noaa. gov/access/world-ocean-database-select/dbsearch. html), SeaDataNet (https://cdi. seadatanet. org/search)). No additional information could be retrieved for 2 locations (Varna and Gijon).
Sequence Similarity Network as a framework for heterogeneous datasets comparison The 343,165 metabarcodes were aligned against each other with the following options: e-value < 1e-4 ; >80% coverage for both subject and query (except for the alignments involving SOLA sequences (maximum sequence length = 230 bp compared to a mean of 430 bp for other datasets) in which case the coverage threshold was applied only to the SOLA sequence in order to avoid a misrepresentation of SOLA sequences in our analysis). Self-hits and reciprocal hits (same querysubject pair) were discarded. The filtered blast output (2 942 982 alignments) was used to cluster sequences by similarity in a Sequence Similarity Network (SSN), with 'igraph' R package (version 1. 2. 6, https://igraph. org/r/, [67] ). The sequences (i.
e. , the network nodes) were labeled according to metadata and taxonomic affiliation. The sequences were clustered into Connected Components (CCs) by setting an identity threshold of 100% sequence similarity, and CCs involving less than 6 sequences were removed (this number of 6 was chosen in order to enable the representativity of all 6 datasets in small CCs. The taxonomic homogeneity of CCs in the network was evaluated for known sequences at the genus level, and if only a single genus assignment was found this name was extrapolated to the other nodes of the CC even if these ones were of unknown genera. Thus, CCs were considered here as a proxy for studying taxonomic diversity at the genus level. The final network was composed of 12 619 CCs.