dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2018)  as such approach has been suggested to be more suitable for compositional data than simple differential abundance analysis  (Quinn et al. , 2021). In our study, the algorithm identifies two groups of microbial taxa of which the ratio will statistically differ between samples belonging to different seafloors (For more explanation, see Supplementary Data 8). The script used to process the sequences is available from https:// github. com/MeinzBeur/LeMoineBauer-2022-Milos.
Seafloor types of Paleochori Bay The seafloor of Paleochori Bay exhibits a wide range of distinct visual diversity (Figure  1D ). These differences can easily be linked to the influence of the hydrothermal activity, and each seafloor type can be constrained by its temperature range  (Puzenat et al. , 2021). As temperature is known to be a major structuring variable of microbial communities in hydrothermal systems  (Lagostina et al. , 2021) , we decided to classify our samples according to the seafloor type they were taken from. This resulted in 8 different seafloor types, described here and shown in Figure  1D. (i) Background sediments are gray/beige and appear unimpacted by hydrothermal activity, with temperatures in cores at Saganaki and in the CarDHynAl dataset similar to that in local seasonal seawater  (19°C and 23-25°C, respectively).
(ii) Seagrass patches are covered by the sparse growth of Cymodocea nodossa. Despite being only 2 meters away from a venting place, the seagrass core at Saganaki shows temperatures close to the background seafloor, only rising from 19 to 21°C in the upper 20 cm of the sediments. Pore water chemistry from Saganaki's seagrass and background cores show strong similarities, except for slightly higher dissolved iron, As(III), dissolved organic carbon and total dissolved nitrogen in seagrass patches (data not shown). (iii) The bioturbation seafloor has the same color as the background seafloor but is easily identified by the burrowing activity of the mud shrimp Calianassa truncata. The zone is typically present as a band of up to a couple of meters width that surrounds the white hydrothermal patches but can also be found in bigger patches disconnected from visible hydrothermal activity  (Puzenat et al.
Microbial community structure of the different seafloor types A compositional data principal component analysis [CoDa-PCA;  (Aitchison, 1983; Pawlowsky-Glahn et al. , 2015) ] of all 81 seafloor samples provided a visual assessment of microbial community structures from OTUs to phylum level. The general clustering pattern remained consistent at each taxonomic level (Supplementary material 5), but here we present only the family level analyses, apart from the Shannon diversity index which was computed using OTUs. The family level was chosen for the following reasons: (1) We wanted to decrease the noise that would be present in OTU level analyses, (2) it separates the abundant families Arcobacteraceae, Sulfurimonadaceae and Sulfurovaceae which have a distinct distribution pattern and would be clustered together at a higher taxonomic level, (3) it allows to reduce the dissimilarity observed between the CarDHynAl and Saganaki datasets that could arise from differences in sample preparation and primer sets used, and (4) it decreases the occurrences of zero counts in the dataset that need to be accounted for in the compositional analysis.
The CoDa-PCA analysis also shows that the samples from the Saganaki dataset tend to separate from the samples of the CarDHynAl dataset along PC3 (Supplementary material 5), as well as PC2 for the Bioturbation and Mat patches (Figure  2A ). While the use of different primer sets (see Supplementary material 6 for the in silico primer analysis) are likely influencing the separation, real biological heterogeneity within each seafloor type is also possible. Despite these differences, the Saganaki dataset shows the same correlation between PC1 and temperature, strengthening our observation of temperature and hydrothermal activity being the main structuring factor (in agreement with  Dando et al. , 1995b; Sievert et al. , 1999; Sollich et al. , 2017). This hydrothermal impact on microbial communities is also observable on the diversity, as shown by the decrease of the Shannon diversity index along the temperature gradient (Figure  2C ;  Sievert et al.
Background and seagrass patches The background seafloor possesses the second highest absolute prokaryote abundance of our dataset after the bioturbation samples (Figure  2D ), with 16S rRNA copy numbers ranging from 1. 66e+7 to 1. 03e+8 copy. g -1 sediment. Deeper samples tend to have lower absolute abundances than the shallow ones, which is mainly linked to the decrease in Bacteria (Figure  3 ). Based on these qPCR measurements, Archaea copies account for 1. 0 to 22. 1% of the total prokaryote gene copies and are less influenced by depth than Bacteria (Figure  3 ). No quantitative data are available for the seagrass seafloor type but considering the similarity with the background seafloor type one might expect similarly high 16S rRNA copy numbers. The background and seagrass cores are the most diverse of our samples (Figure  2C ).
Their prokaryotic community composition at the phylum taxonomic level is shown in Figure  4  and at the family level in Supplementary Material 7. The composition of the seagrass core is very similar to the background core from the Saganaki dataset (Figure  4 ). Background and seagrass samples can be segregated from the others using the balance of Desulfatiglandaceae and Unclassified_Woesearchaeales against Desulfosarcinaceae, with balance values between -2. 54 and -1. 60 (median -1. 97) for the background and seagrass samples, and between -1. 58 and 4. 96 (median 0. 00) for the others (Figure  5A ; Supplementary Material 8).
Bioturbation and ochre patches The bioturbation zone has the highest prokaryote abundance of our dataset (Figure  2D ), with 16S rRNA copy numbers ranging from 1. 22e+6 to 1. 95e+8 copy. g -1 sediment. The deeper samples exhibit the lower counts, with a decrease of around 2 orders of magnitude in the top 15 cm (Figure  3 ). Based on these qPCR measurements, Archaea copies represent between 1. 8 and 20. 3% of the total prokaryote gene copies. The core from the ochre patch shows little variation in prokaryote copy numbers, with a total of 1. 6e+7 to 2. 48e+7 16S rRNA copy. g -1 sediment. Neither Bacteria nor Archaea copy numbers decrease in the top 15 cm, and therefore the relative abundance of Archaea remains between 6. 0 and 7. 7% (Figure  3 ). The low Archaea content in the bioturbation and ochre patches is similar to the background and seagrass seafloor type, suggesting a rather low hydrothermal impact on these zones.
The CoDa-PCA analysis shows that the bioturbation and ochre patches act as a transition zone, with the deepest samples clustering with the warmer seafloors, while the shallower samples are similar to the background and seagrass samples (Figure  2A ). This is also observed in the spatial distribution of these patches, which are often surrounding warmer white and mat patches. The bioturbation and ochre patches cores are less diverse than the background and seagrass samples (Figure  2C ). Their prokaryotic community composition at the phylum taxonomic level is shown Frontiers in Microbiology 08 frontiersin. org in Figure  4  and at the family level in Supplementary material 7. The high heterogeneity of the community within and between each bioturbation and ochre patch core (Figure  2A ), as well as its similarity with the center of the dataset (centers plotted close to the origin of PC1 and PC2 on Figure  2B ) makes it more difficult to characterizes this seafloor types using selbal.
The resulting balance opposes Unclassified_SPG12-343-353-B69 to Desulfobulbaceae, Unclassified_Anaerolineae and Marinifilaceae, with balance values between -3. 17 and -1. 60 (median -2. 06) for the bioturbation and ochre patch samples, while all other samples have values between -1. 75 and 2. 10 (median -0. 42; Figure  5B ; Supplementary material 8). Major compositional differences can be seen between the bioturbation cores from the Saganaki and the CarDHynAl datasets. We suggest that the Saganaki core exhibits   Microbial community compositions of the different seafloor types based on 16S rRNA analysis. The barplots show phyla that represent at least 10% of the community in at least one sample from the dataset. The color code to the right for the seafloor types is as in Figure  2. been shown through qPCR  (Callac et al.
The selbal analysis selected the balance Unclassified_ Dehalococcoidia against Unclassified_Acetothermiia to segregate this seafloor type from the others, with balance values between -6. 48 and -4. 95 (median -6. 00) for the mat patch samples and between -4. 74 and 3. 06 (median -0. 36) for the others (Figure  5D ; Supplementary material 8). We observe some differences between the Saganaki core and the CarDHynAl core, with for example a much stronger decrease in the logratio of Arcobacteraceae over Unclassified_Geothermarchaeota in the Saganaki core, along with a lower diversity (Figure  2C ). This could be linked to different core temperatures, as well as the different types of fluids at the sampling location: The Saganaki core shows the presence of low salinity fluids in the deep section while the CarDHynAl core had high salinity fluids (Supplementary Data 3).
Black and yellow patches These seafloor types have the lowest absolute 16S rRNA abundance of the dataset, with values ranging from 4. 0e+5 to 3. 4e+6 copy. g -1 sediment for the black patch and from 7. 2e+3 to 7. 5e+5 copy. g -1 sediment for the yellow patch (Figure  2D ). While both Archaea and Bacteria copies decrease with depth in the yellow patch, Archaea remains stable in the top 15 cm of the black patch (Figure  3 ). According to qPCR data, Archaea 16S rRNA copies account for 34. 1 to 99. 1% of the total in black patches, and 3. 7 to 42. 9% in yellow patches. Black and yellow patches are the seafloor types with the lowest diversity within our dataset. Their prokaryotic community composition at the phylum taxonomic level is shown in Figure  4  and at the family level in Supplementary material 7.
Seawater The seawater sample of the Saganaki dataset is completely different from any other sample taken in this study (Figure  4 ). The taxa with highest relative abundance belong to Clade I of SAR11 and the AEGEAN-169 marine group (both Alphaproteobacteria), the SAR86 clade and the Halieaceae (both Gammaproteobacteria) and the Puniceicoccaceae (Verrucomicrobiota).
Microbial metabolic capabilities in Paleochori sediments In addition to our extensive sequencing data of the prokaryotic communities, we have also quantified several functional genes using qPCR on the CarDHynAl dataset. In the following section, we discuss these results in light of previous studies on bioenergetic  10. 3389/fmicb. 2022. 1060168 Frontiers in Microbiology 13 frontiersin. org landscapes  (Lu et al. , 2020) , most probable numbers of various functional groups  (Sievert et al. , 1999) , and reaction rates  (Dando et al. , 1995b; Bayraktarov et al. , 2013; Gilhooly et al. , 2014; Houghton et al. , 2019)  to investigate some metabolic capabilities of the communities. It is however important to note that the detection of functional genes in the DNA extraction of a sample merely suggests the potential for the community to perform the metabolic pathway but does not imply its use.
Nitrate/nitrite reduction is likely still happening at higher temperatures, as supported by models showing that the energy released by sulfide oxidation coupled to nitrite reduction could support much of the chemolithotrophic primary production at the venting site  (Lu et al. , 2020). Such chemolithotrophic nitrate-reducing sulfur-oxidizing bacteria have been isolated from Milos in the past, but they are mesophilic and therefore unlikely to grow directly at the venting site  (Kuever et al. , 2002). The identification of potential denitrifier in our dataset using the 16S rRNA sequences is difficult, as the process is potentially performed by a wide phylogenetic range of microbes  (Helen et al. , 2016). Nevertheless, most Campylobacterota can use nitrate as a terminal elecron acceptor, and we also detect the presence in the bioturbation and ochre patch seafloors of the Calditrichaceae family which are moderately thermophilic and some members are known to grow by respiring nitrate (Bonch-Osmolovskaya and Kublanov, 2021).
We do not detect in our 16S rRNA dataset any Nitrosopumilaceae, a family known for the potential of its members to oxidize ammonia aerobically  (Qin et al. , 2017). Consistently, no bacterial or archaeal amoA gene was detected in the CarDHynAl dataset. However, we detect high relative abundances of Geothermarchaeaceae in the mat patch core from the Saganaki dataset. While very little is known about this family, they also belong to the Thaumarchaeota/Nitrososphaeraeota phylum. This, along with the observation that ammonia oxidation is exergonic under these conditions  (Lu et al. , 2020) , could suggest that Geothermarchaeaceae might be involved in this process. Frontiers in Microbiology 14 frontiersin. org.
, 2020) , throughout our dataset. While lower pH has been shown to inhibit sulfate reduction at Milos, some sulfate reducers can also adapt to life at low pH  (Bayraktarov et al. , 2013). In support, the thermophilic sulfatereducing bacterium Desulfacinum hydrothermale was isolated from pH 5 sediments  (Sievert and Kuever, 2000). However, in our study, the family Syntrophobacteraceae, to which D. hydrothermale belongs, is mainly detected in seafloors with colder temperatures and pH close to neutral. In our core from the black patch, we find the genus Staphylothermus, a sulfur reducing hyperthermophilic Archaeon belonging to the family Desulfuroccocaceae also isolated from Milos (Supplementary Data 7;  Arab et al. , 2000; Hao and Ma, 2003). In areas of hydrothermal activity, the advective influx of reduced sulfur species can also fuel the growth of sulfur oxidizing organisms  (Sievert et al.
, 2013). They report that Epsilonproteobacteria (now Campylobacterota) represent 60% of each prokaryotic community, which is much more than what we measured. However, superficial sediment communities are likely impacted by microbial mats and seawater communities, and are therefore highly dissimilar to deeper samples as seen in some of our cores (data not shown). The genus Thiomiscrospira, and the strain Thiomicrospira sp. Milos-T1 isolated at Milos, has been suggested to be a major actor of sulfur oxidation in Paleochori Bay  (Brinkhoff et al. , 1999) , but these results were not supported in a recent study  (Sievert et al. , 2022) , and in our dataset the family Thiomicrospiraceae is mainly present only in the yellow patch composition. Similarly, the family of the sulfur oxidizing bacteria Halothiobacillus kellyi isolated at Milos  (Sievert et al.
, 2000b)  represents at best 0. 9% of any sample. Finally, the family Desulfocapsaceae and the genus Desulfocapsa known for sulfur disproportionation  (Finster et al. , 1998)  are detected in the background cores (Supplementary material 7). This reaction had been shown to be exergonic in most niches around hydrothermal systems  (Alain et al. , 2022) , but we cannot confirm its use at Milos with our dataset.
Iron cycling We did not directly investigate the presence of genes involved in iron reduction in our study. Nevertheless, dissimilatory iron reducing bacteria have been found at Milos in the colder seafloor types, seemingly correlated to Fe(III) concentrations  (Sievert et al. , 1999). It is however difficult to identify which organisms are responsible for iron reduction. The iron reducer Deferrisoma palaeochoriense has been isolated from Paleochori Bay  (Pérez-Rodríguez et al. , 2016) , but we find Deferrisomataceae, its family, to represent a maximum of 0. 3% of the community of any sample in our dataset. Similarly, the Deferribacteraceae  (Huber and Stetter, 2015) , the sva1033 family of the Desulfobacterota (supposed to perform dissimilatory iron reduction  (Wunder et al. , 2021) ), and the Archaeoglobaceae (to which Geoglobus belongs  (Kashefi et al.
This observation matches the results from Callac et al. , who did not find arsenic to be a major selector for carbon fixation genes in the nearby Spathi Bay  (Callac et al. , 2017). Price et al. found some aioA (other name of aox) sequences in the mat patches, but the clones, belonging to Alphaproteobacteria and Betaproteobacteria, showed little overlap with their 16S rRNA data  (Price R. et al. , 2013) , suggesting that these are not major parts of the community composition. This is consistent with the models showing that reactions using As(V) as an electron acceptor can provide only very little energy at Milos  (Lu et al. , 2020). However, we found that As(III) is present in low concentrations in the shallows to middle depths in the bioturbation core from Saganaki (data not shown). In this core we also find the presence of the Sulfurospirillaceae family, to which the Fe(III) and As(V) respiring bacterium Sulfospirillium barnesii belongs  (Stolz et al.
(i) The physical, chemical, and microbiological heterogeneity of some seafloor types are not well constrained. Notably we present only one core taken from the ochre patches while they represent a massive surface area. As well, the difference between low-salinity and high-salinity communities in the mat patches needs further investigation. (ii) A major difference between deep and shallow hydrothermal systems is the possibility for phototrophic organisms to play a role in the food chain. During our sequence decontamination protocol, we removed in some samples up to 8% of reads assigned to chloroplasts. This mainly happened in the colder seafloor types, suggesting higher importance of phototrophy there. However, the importance of phototrophy in the ecosystem is yet to be investigated. (iii) While we have presented here a substantial dataset of 16S rRNA gene sequences, the general understanding of the microbial communities would highly benefit from transcriptomic studies.
Funding Funding for the Saganaki dataset and JPA and G-SL was provided by  NSF  grants  OIA-0939564  to the  Center for Dark Energy Biosphere Investigations (C-DEBI). The CarDHynAl project was partially funded by  INSU-CNRS Tellus  and Syster Projects to JEs (2016) andJ-EM (2018), and with partial support by RAMONES, funded by the  European Union 's  Horizon 2020 Research and Innovation Program , under grant agreement number  101017808  (to JEs and PN). The laboratory work was supported by the  Trond Mohn Foundation  and the  University of Bergen  through the  Centre for Deep Sea research  (grant  TMS2020TMT13 ). VP-G and JEg are supported by the  Spanish Ministry of Science and Innovation  and the  European Regional Development Fund  [grant numbers  PID2021-125380OB-I00  and  PID2021-123833OB-I00  ( MCIN/AEI/ FEDER )].
Data collection A total of 247 samples were collected and analyzed over 5 years for 16 lysimeters (topics 1, 2 and 3 together), i. e. ± 6,100 measurements of 27 parameters. The chromium measured was always total chromium analyzed with ICP. Total sulfur and total phosphorus analyzes were not performed for 92 samples. For total sulfur, often presenting high levels of several g/l, the averages of the data around 1000 or 100 mg/l were attributed to these missing data. For total phosphorus, systematically showing levels < 0. 01 mg/l except for lysimeters 9 and 12, no substitution of missing data was carried out. Each sample represents a volume (known by weighing) and therefore a calculated liquid/solid ratio (noted L/S in l/kg, i. e. the volume of the sample divided by the mass of MBR for topic 1, MBR or UMBR for topic 2 and lysimeter mining residue for topic 3).
All concentration data were expressed in mg/l and quantity data in mg/kg (concentration multiplied by the L/S ratio of the sample). The cumulative quantities were calculated by adding the quantities of each sample. The results by lysimeter, by year and by parameter are presented in the supplementary data (average concentration in mg/l of drain and annual cumulative quantity in mg/kg of MBR or mining residue). TOPIC 1 results: Emission of modi ed bauxite residue and revegetation As indicated in the Table  1 , the lysimeters number 1, 3, 5 and 16 were setup to adress this topic. The drainage/rainfall ratios showed that 99% of the rain was drained with a sand cover (which therefore protects e ciently against evaporation), 67% with a layer of non-vegetated MBR, and 52-20% in case of revegetation of the MBR.
Statistical analysis Using SPSS software version 28. 0. 1, the model fitting results were verified. The correlation between simulated data and experimental results was analyzed at 95% CI (confidence intervals) using a paired sample t-test. The mean difference between two sets of observations (simulated and experimental) is typically calculated using a paired sample t-test  [73]. According to Gupta and Kapoor  [74] , the two groups of data are regarded as "strongly correlated," "moderately correlated," and "least correlated" if their correlation coefficients lie between ± 0. 50 to ± 1. 00, ± 0. 30 to ± 0. 49 and less than ± 0. 29 respectively. All estimated operating parameters: MLSS, EPSc, EPSp, TMP and filtration resistance, derived from model-fitted results with experimental data, were statistically correlated (p ≤ 0.
Zooplankton sampling and imaging The Multinet, deployed vertically, was composed of five sequential plankton nets equipped with a 300 µm mesh and an aperture of 0. 25 m 2. It was equipped with a flow meter to estimate the sampled volume which ranged from 5 to 502 m 3 (median value of 113 m 3 ). All five nets sampled plankton at five different depth layers distributed between the surface and 1000 m based on an adaptive strategy depending on observed physical or biological features such as the deep chlorophyll maximum (see rationale in  (Soviadan et al. , 2022) ) across the 57 global stations. Collected samples were preserved in a solution of buffered formaldehyde (4% final concentration). In the laboratory, the samples were rinsed and fractionated with a Motoda box. The final fraction was scanned with the ZooScan system and processed with Zooprocess software which allowed a rapid and time-efficient analysis of the plankton samples in a digital format that can be easily stored before further processing  (Gorsky et al.
With this data aggregation strategy, we cumulated a sampled volume greater than 40 m 3 for each calculated NBSS which ensures a good representation of rare organisms in the water column. The datasets were grouped in three latitudinal bands (inter-tropical, temperate and polar), and three depth layers  (0-200, 200-500, 500-1000m)  to explore NBSS shapes and plankton community compositions across the globe. Here, we consider the tropical and temperate bands to be larger than their theoretical geographic limits, spanning from 30°N to 30°S latitude and up to 60° S and N. In total, the Multinet samples comprised nearly 400,000 images of organisms whereas the UVP5 image collection consisted of 769,297 images (including living and non-living particles), of which 5% (n=37,000) were of zooplankton.
Since these linear portions differed between the two approaches, we chose the lower threshold of 1 mm to effectively compare biomass in this study. This value corresponds to the size where both median NBSS values overlapped, and were similar to the value of 0. 934 mm used by  (Barth and Stone, 2022)  to compare these two methods during 5-day cruises of the Bermuda Atlantic Time-series Study. The upper threshold has been set at 8 mm. (Drago et al. , 2022 ) also estimated the total biomass of zooplankton within a size range of 1-50 mm from a global UVP5 dataset. Hereafter, we refer to NBSS estimates derived from zooplankton collected by the Multinet or imaged by the UVP5 as NBSS_Zmtn and NBSS_Zuvp, respectively.