dataesr/scientific-paragraphs-categorization huggingface dataset:
These trends are evident for the AB platform for stations at > 500 m from the platform, which exhibits an increase in D and a decrease of the diversity index values for both the molecular and morphological datasets (Fig. S2 ). By contrast, the highest D and lowest diversity values for the molecular dataset are found at 0 m for the GA platform, with an evident increase in diversity for stations located 25 and 50 m away (Fig. S3 ). However, the morphological dataset of the GA platform shows clear trends that are similar to those of the AA platform data.
Beta diversity Mantel's test finds a significant positive correlation (r = 0. 3; pvalue < 0. 001) between the molecular and morphological beta diversity matrices (Fig. S4 ). The PERMANOVA results show that platform, axis, and distance have a significant effect on the observed compositional changes in both molecular and morphological communities (Table  S10 ). However, these factors only partly explain these changes; in fact, the unexplained variations (residuals) are 0. 51 and 0. 59 for the molecular and morphological communities, respectively (Table  S10 ). The PERMANOVA results reveal that the highest R 2 value corresponds to the platform for the molecular dataset and to the platform/axis/ distance for the morphological dataset (Table  S10 ). On the basis of the BIOENV, the most important environmental parameters are sand, As, Ba, Pb, PAHs, and hydrocarbons C10-C40 for the morphological dataset, and Ni, Zn, and TOC for the molecular dataset (Table  S11 ).
Detrended Correspondence analyses profiling The DCA is independently computed for both foraminiferal morphological and molecular datasets and reveals a clear break between 25 and 50 m from the platforms (Fig. 5 ). This trend is confirmed for all three platforms but the GA for the molecular dataset where the break is at 125 m (Fig. 5 ) and for each axis (break < 25-125 m) (Figs. S6  and S7 ) in both datasets.
Relationship between environmental parameters and benthic foraminiferal datasets The PCA shows that ~72. 4% of data variance can be explained by the first two principal components (Fig. 6 ). In particular, the eigenvalues of component 1 (~59. 9% of inertia) and component 2 (12. 5% of inertia) are 11. 9 and 2. 5, respectively. Sediment grain-size distributions, TOC, hydrocarbons and some trace elements (Al, V, Ni, Cr, Fe, Ba and Zn) are the predominant elements in the first component, while the contributions to the second component are mainly due to Pb, Cd, Ba, and PLI (Fig. 6 ). By projecting the diversity indexes (Fig. 6a, b ), the morphospecies (Fig. 6c ), and the MOTUs (Fig. 6d-f ) on the PCA plane, there is an increase in the morphological and molecular diversity and in the abundances of some morphospecies and MOTUs along positive values of the first component.
This component is related to the distance from the platform. In particular, the diversity indexes of the molecular and morphological datasets point in the same direction, that is, a decrease of diversity and an increase in dominance at stations closer to the platform (Fig. 6a, b ). Some morphospecies show a distance-dependent trend. Bolivina striatula, A. perlucida, and N. turgida are positively related to the positive values of component 1, while A. parkinsoniana exhibits the opposite trend, where it increases in abundance at closer stations (Fig. 6c ). Similar patterns are observed in the MOTUs assigned to Globothalamea, with A. aberdoveyensis showing affinity towards increasing levels of TOC and gravel, while all other globothalamid MOTUs are positively related to positive values of component 1 that imply an increase in abundance at stations more distant from the platform (Fig.
6d ). Micrometula sp. , B. flexilis, Vellaria sp. , Hippocrepinella sp. , C. alba and Cylindrogullmia sp. are positively related to the component 1,  whereas Clade Y, F. islandica, and SaccamATT1_ICE show an opposite trend (Fig. 6e ). An increase in abundance along either positive or negative values of component 1 is also observed for some unassigned MOTUs (Fig. 6e ). Variations in the relative abundance of MOTUs and morphospecies and in the diversity indexes for both datasets have been correlated with the distance from the platform and with the BIOENV extracted environmental parameters (Table  S12 ). The identified significant correlations confirm the findings of the PCA in which distance was not considered but rather used as an underling factor. Positive correlations are found between distance and H' and Fisher's α for both morphological and metabarcoding data (Table  S12 ).
Metabarcoding and morphology provide congruent diversity metrics A similar trend is documented for the diversity indexes of both the molecular and morphological datasets at all platforms. Specifically, lower values of diversity (Fisher's α and H') and higher values of dominance are associated with stations close to the Armida platform, in the area within 50 m. This is in agreement with the environmental characteristics of this platform. On the basis of changes in the macrozoobenthic communities,  Manoukian et al. (2010)  and  Spagnolo et al. (2014)  defined an impact area of 120 m from the platform during the installation and initial production stages. Three years later, the impact area changed to 30 m, which is comparable to our results. Manoukian et al. (2010)  also suggested that the effect on soft-bottom benthic communities might be influenced by the geographical position of the platforms (i.
e. , the distance from the coast might affect the sediment grain-size and the water circulation and characteristics). The lack of environmental parameters for the other two platforms (Garibaldi A and Agostino B) does not allow us to interpret the results obtained for them. Remarkably, the shift in diversity indexes in relation to the distance is more pronounced for the metabarcoding data than for the morphological data as also supported by the correlation values. This is particularly visible in the case of Fisher's α index, but other indexes show a similar trend. Large variations in the alpha-diversity scales are found between morphological and metabarcoding datasets. Indeed, the number of MOTUs are higher as naked and tiny foraminifera normally overlooked in the morphological analyses are included  (Pawlowski et al.
, 2014b). Although the beta diversities of the two communities (i. e. , morphological and molecular) are positively correlated, it seems that the signal contained in the metabarcoding data is stronger, which could be explained by higher number of taxa detected by molecular approach or their higher sensitivity to environmental changes (see below). Notably, the morphological analyses were performed on only one replicate and the interpretations and comparisons with the molecular dataset should be done with care. The PERMANOVA results show that both the platform and the distance might play significant roles in shaping both molecular and morphological communities. Differences in macrofaunal compositions between platforms in the northern Adriatic Sea were also noted by  Manoukian et al. (2010)  and  Spagnolo et al.
(2014). However, a primary control of distance on macrofaunal composition emerges when platforms are individually analyzed  (Manoukian et al. , 2010). BIOENV analyses independently performed on the two datasets reveal that the most important variables structuring the foraminiferal communities are Ni, Zn, and TOC for the molecular community and sand, As, Ba, Pb, PAHs and hydrocarbons C10-C40 for the morphological community. Nickel and Zn are the only two elements that consistently show values higher than the LCB, and frequently higher than LCL. Barium could not be compared to any SQG, but its highest concentrations are commonly associated with stations within 25-50 m from the platform. These three elements, along with Cr and Pb, might be present at relevant high concentrations in drilling fluids (e.
The results of our study perfectly illustrate the complementarity of both types of data. In the morphological dataset, A. parkinsoniana is found at increased abundance at stations close to the platform. Ammonia parkinsoniana is a common species all along the Adriatic Sea coast at water depths of 10-20 m  (Jorissen, 1988). It has been documented as the only morphospecies in the sandy bottoms in the northern Adriatic Sea  (Jorissen, 1988)  and as the dominant morphospecies in shallow water depths of the central Adriatic Sea  (Frontalini and Coccioni, 2008). This taxon, along with Elphidium species, was reported to prefer nutrient-poor and oxygen-rich environment  (Jorissen, 1988). This taxon has recently been assigned to Ecological Group I as a "Sensitive species"  (Jorissen et al. , 2018). In our study, the increasing abundance of this species might better reflect the sediment characteristics (i.
e. , coarsening of the grain-size) rather than the increase of TOC. In fact, TOC shows only minor variations, with a mean of 0. 96 ± 0. 21% around the AA platform. On the opposite side of the PCA plane, increasing abundances of N. turgida, B. striatula, H. pacifica, and A. perlucida are well documented. Such increases are associated with the increased content of finer sediments further away from the platform. Accordingly, N. turgida and Bulimina denudata have been found to increase along the clay belt in front of the Po River and in the zone most influenced by run-off products  (Jorissen, 1988). Nonionella turgida has recently been included in the Ecological Group V as a "Firstorder opportunist"  (Jorissen et al. , 2018). Another species found in the morphological datasets, Bolivina striatula, is regarded as particularly resistant to oxygen-depleted environments and has opportunistic and pollution-tolerant behavior.
6d ). The molecular systematics of foraminifera are not always in agreement with morphological classification, and it is possible that other species are also identified differently. This is probably for the case of species belonging to the genus Nonionella with N. stella in the metabarcoding data showing the same trend as the morphospecies N. turgida, both increasing in abundance far from the platform at stations characterized by fine sediment. Another species occurring in the metabarcoding dataset but not recognized in our morphological data is, L. scottii, which was positively related to the distance and the finer sediment fractions (i. e. , silt, clay and mud). Compared to these few hard-shelled morphospecies that are underrepresented in the metabarcoding data, the common single-chambered monothalamous foraminifera comprise a much larger number of candidate bioindicator species.
, 2015)  and benthic invertebrates (e. g. ,  Lejzerowicz et al. , 2015). Second, the metabarcoding datasets comprise a much broader range of taxa, including not only hard-shelled foraminifera but also soft-walled and naked species that are much more abundant and diversified. Therefore, metabarcoding provides a more holistic view of foraminiferal diversity. Many of the species revealed by metabarcoding seem to express specific adaptations to environmental variables considerably increasing the sensitivity of the approach compared to morphological analyses. Third, the taxonomic identification of species based on DNA sequence data overcomes the uncertainties of morphological identification based on personal expertise and increases the taxonomic resolution by taking into consideration genetic variations that might be related to specific ecological adaptations.
Fourth, metabarcoding offers the opportunity to process large amounts of samples, in a faster, easier, and relatively cheaper alternative to conventional biomonitoring  (Pawlowski et al. , 2018 (Pawlowski et al. , , 2016b;; Taberlet et al. , 2012). In addition to these advantages, the foraminiferal metabarcoding also has some limitations that should be taken into consideration when interpreting the data it produces. First, the presence of unassigned MOTUs might represent an important part of the molecular dataset; barcoding efforts are therefore required to close this gap. Indeed, the ecology of monothalamous species that predominate in metabarcoding datasets is poorly known, which impedes the direct assessment of ecological status based on a species list, as can be done for many morphospecies.
To overcome this limitation, it would be necessary to assign indicator values to particular species or MOTUs based on their occurrences in the metabarcoding data, as it has been done in the case of the taxonomy-free molecular diatoms index  (Apothéloz-Perret-Gentil et al. , 2017). Accordingly, this taxon-free approach allows inferring the diatom index based on much higher percentages of MOTUs (up to 95%) compared to the morphotaxonomy  (Apothéloz-Perret-Gentil et al. , 2017). Alternatively, it has been proposed to predict the ecological status using a machine learning approach  (Cordier et al. , 2018; 2017). However, this approach requires extensive training datasets that are not always available. Other limitations of foraminiferal metabarcoding as a biomonitoring tool are specifically related to the particularities of the method.
One of the major issues is the separation of the signal that originates from the extracellular "free" DNA molecules preserved in the sediments from the DNA present in the living cells. To improve the distinction of living cells, some authors have proposed to analyze environmental RNA that presumably has shorter life span than eDNA  (Laroche et al. , 2017). However, because RNA molecules are more unstable, working with RNA is far more difficult and costly, reducing its attractiveness for routine biomonitoring. Here, we overcome this issue by considering only MOTUs that are represented by more than 1000 reads. Other issues specific to metabarcoding are various biases related to DNA extraction and PCR amplification that can influence the produced data. The underrepresentation in our datasets of globothalamids compared to monothalamids can be related to the more difficult isolation of DNA from cells covered by hard shells  (Pawlowski et al.
, 2014a). The absence of some species or the overrepresentation of others can be related to the specificity of PCR primers, which might differently amplify some DNA molecules. In general, the relative abundance of species in metabarcoding data is only distantly related to their abundance in morphological analyses expressed by the number of specimens. In fact, large variations in the alpha-diversity scales are found between the morphological and metabarcoding datasets. Despite the technical factors, such as specificity of PCR primers, the relative abundance of foraminiferal metabarcodes can also be biased by the number of rRNA genes copies, the variable number of nuclei, or variations in ploidy related to alternation of generation in foraminiferal life cycle (Weber and  Pawlowski, 2013). These different technical and biological biases have to be taken in consideration when interpreting metabarcoding data.
Acknowledgements The authors are very grateful to two anonymous reviewers for their thoughtful and valuable comments that have greatly improved our contribution. The authors warmly thank the  SGS Italia  staff for the sample collection. This study was partly supported by the  Swiss National Science Foundation  grants  31003A_179125  and  316030_150817  (JP), and  ENI spa, Linea di Business UpStream Distretto Centro-Settentrionale.
Recently, several studies demonstrated the usefulness of diatom eDNA metabarcoding as an alternative to assess the ecological quality of rivers and streams. However, the choice of the taxonomic marker as well as the methodology for data analysis differ between these studies, hampering the comparison of their results and effectiveness. The aim of this study was to compare two taxonomic markers commonly used in diatom metabarcoding and three distinct analytical approaches to infer a molecular diatom index. We used the values of classical morphological diatom index as a benchmark for this comparison. We amplified and sequenced both a fragment of the rbcL gene and the V4 region of the 18S rRNA gene for 112 epilithic samples from Swiss and French rivers. We inferred index values using three analytical approaches: by computing it directly from taxonomically assigned sequences, by calibrating de novo the ecovalues of all metabarcodes, and by using a supervised machine learning algorithm to train predictive models.
In general, the values of index obtained using the two "taxonomy-free" approaches, encompassing molecular assignment and machine learning, were closer correlated to the values of the morphological index than the values based on taxonomically assigned sequences. The correlations of the three analytical approaches were higher in the case of rbcL compared to the 18S marker, highlighting the importance of the reference database which is more complete for the rbcL marker. Our study confirms the effectiveness of diatom metabarcoding as an operational tool for rivers ecological quality assessment and shows that the analytical approaches by-passing the taxonomic assignments are particularly efficient when reference databases are incomplete.
, 2019; Kelly et al. , 2018; Kermarrec et al. , 2013 Kermarrec et al. , , 2014;; Pérez-Burillo et al. , 2020; Rivera et al. , 2020; Vasselon et al. , 2017; Visco et al. , 2015; Zimmermann et al. , 2015). Some studies have been conducted to address methodological issues inherent to metabarcoding methods, focusing either on the generation of the data (DNA extraction  : Vasselon et al. , 2017; OTU clustering threshold: Tapolczai, Vasselon, et al. , 2019)  or on the interpretation of results based the current ecological knowledge of diatoms  (Keck et al. , 2018; Mortágua et al. , 2019; Vasselon et al. , 2018). Extensive efforts have also been made to build a comprehensive reference sequence database for freshwater diatoms  (Rimet et al. , 2019). More recently, analytical approaches aiming to expand the range of possible bioindicators beyond the sole fraction of assigned sequences, i.
e. the "taxonomy-free" approaches, have been proposed to optimize the use of diatom metabarcoding data for biomonitoring  (Apothéloz-Perret-Gentil et al. , 2017; Tapolczai, Keck, et al. , 2019). Finally, a recent study proposed using machine learning algorithms to predict diatom communities and infer water quality based on the divergence of the genetic community compared to those on newly defined reference sites  (Feio et al. , 2020). In this study, we compared the efficiency of two taxonomic markers (rbcL and 18S V4) and three analytical approaches for the inference of the MDI, in reference to the morphology-based Swiss Diatom Index (DI-CH). We used 112 biofilm river samples collected within the SYNAQUA project  (Lefrançois et al. , 2018). We employed (a) a taxonomy-based approach to compute the MDI values in a same manner as for morphological approach (Tax-Assign); (b) a taxonomy-free, indicator value approach, that calibrates de novo the ecological optimum and tolerance of all metabarcodes (Mol-Assign, see  Apothéloz-Perret-Gentil et al.
F I G U R E 1 Schematic representation of the workflow used in this study Raw FASTQ reads were demultiplexed to retrieve the R1 and R2 fastq files for each sample using the demultiplexer module implemented in SLIM  (Dufresne et al. , 2019). Quality filtering, removal of chimera and generation of the amplicon sequence variant (ASV) table were done using dada2 R package v. 1. 10. 1  (Callahan et al. , 2016). For taxonomic assignment, DECIPHER R package v. 2. 10. 2  (Wright, 2016)  was used with the v7 version of the Diat. barcode database  (Rimet et al. , 2019)  and a confidence threshold of 60 (i. e. , minimal proportion of bootstrap replicates that yielded a given taxonomic label, see  Murali et al. , 2018). To investigate the proportion of nondiatom sequences amplified with the two set of primers, we performed BLAST analyses against the GenBank database with 80% of identity on representative ASV and calculate the proportion of diatom, algae and other sequences for each site.
| Molecular index calculation Three analytical approaches to infer the MDI values were performed on the molecular data sets obtained from the two taxonomic markers. For all the approaches, the counts of reads of each ASV table were normalized using the cumulative-sum scaling method (CSS) implemented in the metagenomeSeq R package v. 1. 22. 0  (Paulson et al. , 2013). First, for the Tax-Assign approach, CSS normalized abundance was used with the ecovalues related to morphospecies registered in  Hürlimann and Niederhauser (2007)  to calculate the MDI, only species level assignment were used since the DI-CH gives ecovalues only to species and not genus level. The taxonomic assignment was obtained using curated Diat. barcode database  (Rimet et al. , 2019)  for rbcL and Genbank database for 18S.
e. , 80% in the extreme classes (1:3, corresponding to very good or 7-8, corresponding to bad status), the strongest weighting factor was given (value 8). On the opposite, if the 80% were distributed across more than three classes, the lowest weighting factor was given (value 0. 5), see graphical example in supporting information from Apothéloz-Perret-Gentil et al. (2017). Finally, for the ML approach, we trained predictive models using the Random Forest algorithm  (Breiman, 2001)  implemented in the ranger R package v. 0. 11. 2  (Wright & Ziegler, 2017)  as described in  Cordier et al. (2017). For both of the tested taxonomy-free approach (Mol-Assign and ML), a leave-one-sample-out cross validation procedure was performed to assess their accuracy.
| Sequence data The molecular data were obtained from two independent Illumina MiSeq runs, one for each taxonomic marker. Details of the filtration statistics during sequence data processing are given in Table  S4. For the rbcL marker, a total of 3,245,094 high-quality reads distributed across 112 samples were obtained, ranging from 10,127 to 54,559 reads per sample with a median value around 30,000 sequences. The proportion of diatoms, other algae and non-algal eukaryotes sequences were investigated using a BLAST analysis against the GenBank database (Figure  S1 , Table  S5 ). The median values are 82% of diatoms, 1% of other algae and 15% of other eukaryotes. For more accurate identification of diatoms, the assignment was also performed on the curated Diat. barcode database  (Rimet et al.
, 2019). The percentage of assigned sequences to species level (only diatom species) ranged from 22% to 99% per sample with a median value at 90% and the number of assigned taxa ranged from 12 to 64 per sample with a median value at 30 (Table  S6 ). For the 18S marker, a total of 6,568,535 high-quality reads distributed across 111 samples were obtained. The CH12 sample failed to amplify for the 18S marker and therefore no sequences were retrieved from it. The total number of sequences per sample ranged from 12,038 to 255,004 with a median value around 43,000 sequences. The proportion and taxonomic composition of diatoms, other algae and nonalgal eukaryotes were also investigated using the GenBank database (Figure  S1 , Table  S5 ). The median values are 94% of diatoms, 5% of other algae and 0.
4% of other eukaryotes. Assignment with the Diat. barcode database showed that the percentage of assigned sequences to species level (diatom and other algae) ranged from less than 1% to 99% per sample with a median value at 72% and the number of assigned taxa ranged from 2 to 48 per sample with a median value at 24 (Table  S7 ). Raw ASV table with representative sequences and taxonomic assignment with the confidence threshold are given in Table  S8  and S9 for rbcL and 18S markers respectively.