dataesr/scientific-paragraphs-categorization huggingface dataset:
Data collection and pre-processing Sentinel-1 and Sentinel-2 images were selected for this study  (Table  1 ). These data have been proven to be effective for mangrove mapping  (Wang et al. , 2018; Mondal et al. , 2019; Wang et al. , 2019; Jia et al. , 2021; Lu and Wang, 2021; Liu et al. , 2021; Xiao et al. , 2021). The Sentinel-2 Level-2A data is available through Google Earth Engine. This product offers orthorectified Bottom-Of-Atmosphere (BOA) reflectances for all 12 bands in a 16-bit unsigned integer format that has been scaled by a factor of 10,000. We selected all available images (n = 79) from both sensors between 01/05/2020 and 01/06/2021 on the GEE platform. For Sentinel-2, the images were filtered with a value of 20% cloud cover. All processing of the S2 time series was performed on the unmasked pixels using 'QA60' quality layers.
, 2018). The normalized difference moisture index (NDMI,  Gao, 1996) , in particular the DBSI (Dry and Wet Soil Index) was designed to specifically identify bare areas in dry climates, according to  Rasul et al. (2018). They were selected to identify the wet and dry areas constituting the sometimes semi-arid landscapes of the study area. The previously mentioned indices (NDVI, NDMI, NDWI, and DBSI) were used to calculate the GLCM textural indices introduced by  Haralick et al. , 1973. Finally, timeseries variables were used to complement reflectance and spectral indexes dataset. The phase and amplitude of the harmonic model  (Clinton, 2016)  were extracted from the annual time series of four spectral indices in the GEE. The Sentinel-1 satellite imagery in the Earth Engine 'COPERNICUS/ S1_GRD' image collection consists of Level-1 Ground Range Detected (GRD) scenes that have been processed to calculate the backscatter coefficient (σ • ) in decibels (dB).
In the absence of top-down data for this area, only bottom-up data were selected, and the two polarizations (VH and VV) were combined. A total of 33 images for the period from May 1st, 2020 to June 1st, 2021 have been processed. For the SAR data, the ratio of VH to VV polarization (VH/VV) was calculated. Four variables were extracted from the VH, VV, and VH/VV bands over an annual time series: the median, minimum, maximum, and coefficient of variation (standard deviation divided by the mean). The dataset consisted of 102 predictor variables before being optimized through selection. (Table  3 ).
Mangrove and land cover mapping (Level-1) First, the mangrove is mapped using a combination of OBIA and machine learning approaches, specifically Random Forest. Although the pixel approach has been extensively used for mangrove classification  (Bunting et al. , 2018; Mondal et al. , 2019; Jia et al. , 2021; Lu and Wang, 2021; Liu et al. , 2021; Xiao et al. , 2021) , we chose an object approach  (Myint et al. , 2008; Wang et al. , 2018; Wang et al. , 2021)  to reduce spectral confusion by smoothing the dataset features within objects and reducing the statistical noise of the images  (Blaschke, 2010; Blaschke et al. , 2008; Dronova, 2015; Hossain and Chen, 2019). This reduces the complexity of the images and the size and dimensionality of the dataset. In addition, the optimal features for classification were evaluated.
Training and validation samples The training and validation of machine learning classifications require reference sample data for land cover. Four classes were referenced: Water, Mangrove, Salt flats, Other land covers (all continental surfaces, including bare soil, other vegetation, urban, and cropland). Reference data collection followed a labeling procedure using ESRI (2017) and Google Earth (2018, 2021) high-resolution imagery, with a Sentinel-2 color composition check from 2021 to ensure the class stability spotted on slightly older THRS imagery. Stratified random sampling was applied to avoid bias during the validation  (Olofsson et al. , 2014). Therefore, a random draw was performed in the nine classes of the ESA WorldCover 10 m 2020 v100 map  (Zanaga et al. , 2021)  with 400 points per class.
Classification processing and accuracy assessment The classification into water, mangrove, salt flats, other land covers was performed using the random forest algorithm  (Breiman, 2001)  on the GEE platform. Although the random forest model has the ability to handle many variables  (Belgiu and Drȃgut ¸, 2016) , we chose to reduce the dimension of the dataset by retaining only the so-called optimal variables. To develop simple and parsimonious classification models, the use of a variable selection algorithm allows the maintenance of a small number of variables. This will allow for fewer data collection procedures in the future and a more efficient evaluation of the variable importance. Speiser et al. (2019)    statistical software was used to select variables. The 'Out of Bag' (OOB) error was used as a minimization criterion and the least important variables were eliminated.
For the calculation, the objects were in a vector format. We considered the adjacency of the segments as spatial proximity; if a single vertex of an entity is adjacent to another entity, these two segments are neighbors. Therefore, we constructed a matrix of spatial weights. Several studies  (Johnson and Xie, 2011; Wang et al. , 2021)  have set the values of w ij to zero (non-neighbor) or one (neighbor). Binary matrices can induce statistical bias, generating different weights for individuals depending on their neighbor numbers. To overcome this problem, inline standardization of the matrix was performed. Thus, the sum of the weights in each row is equal to one. This operation was performed with the package 'spdep' in R  (Bivand et al. , 2015). The values of wVar and MI were normalized for comparison according to the following normalization formula (Eq.
The objective here is to discriminate these zonations on an annual basis rather than revealing the variation itself. The annual average is indeed a feature that mostly influences classification. The small sample size from the field made the training and validation process of a supervised classification difficult; therefore, unsupervised classification with the K-means algorithm was preferred. The objective was to obtain a typology aimed at maximizing the contrasts between classes while reflecting the zonation and physiognomy of the vegetation. Field data were used to interpret the clusters. To ensure the overall significance of the classes, the Kruskal-Wallis test was performed. In addition, pairwise multiple comparisons between groups were used to evaluate the significance between classes using the Wilcoxon test.
Stability assessment of the typology The detection of mangrove zonation typology must be reproducible over time to ensure its monitoring and reliably interpret its changes. Indeed, a typology in which the classes do not have enough contrast at certain times of the year could lead to errors in detecting changes. To evaluate typology and temporal stability, we used a three-step process. First, the methodological chaining presented so far was repeated on the data from 01/11/2019 and 01/11/2020. An intersection between the two classifications allowed us to isolate the objects assigned to the same class on both dates, which did not undergo any major changes. Finally, these were used as a basis for training (70% of the objects) and validation (30% of the objects) of the Bayesian model. For this stage, the probabilities of membership were calculated using Bayes' theorem.
Mangrove mapping At the end of the RF classification of the objects, the Saloum mangrove map (Fig. 5a ) presented relatively high accuracy scores (Table  4 ) using a reduced dataset of 11 discriminating variables. After ten cross-validations, the optical bands in the NIR and SWIR wavelengths proved to be the most effective for mapping the mangrove in this environment, followed by the SAR data (Fig. 5b ). From then on, the SAR data constituted >50% of the selected variables. Texture variables and time-series harmonic model coefficients were negligible for the desired land cover discrimination. Finally, 11 variables were selected: VHmin, VHmed, VHmax, VVmin, VVmed, VVmax for SAR data and B8, B8A, B11, B12, NDWI for optical data. The consistent appearance of SAR data in the group of optimal variables suggests that it is a valuable source of information for understanding and predicting the distribution and behavior of the studied system.
Extraction of significant objects for mangrove monitoring The databases on mangroves in West Africa cover only the surface area. Zonation maps are desirable for guiding conservation strategies  (Jia et al. , 2018; Wang et al. , 2019). However, global or continental databases contain inaccuracies that bias the mapping of plant formations. Here, the differentiation of the mangroves approaches an accuracy of 98. 91%, in line with those obtained by  Andrieu et al. , 2020 and Liu et al. , 2021. We aimed to establish a map of mangroves because the quantifications of surfaces in the literature have significant   2020 ) mentioned a surface area of 63,189 ha at 30 m, in 2015, for the mangrove of the Saloum and Joal hydrosystems, but without the mangrove of the Gambia River tributary, which is within the administrative boundaries of Senegal.
In this study, we obtained a surface area of 60,988 ha in 2021 at a spatial resolution of 10 m. A cartographic comparison showed that the areas mapped by  Liu et al. (2021)  were overestimated (Supplementary material). The mapping had inconsistencies; for example, some water channels were mapped as mangroves. However, this mapping was carried out over the entire West Africa, and currently, it is the most accurate database for all West African mangroves at this scale. Furthermore, the areas were likely overestimated by  Fent et al. (2019)  because the values were widely higher than those estimated by other studies  (Andrieu et al. , 2020; Liu et al. , 2021). Plant formations were mapped on a mangrove mask using the OBIA approach. The vegetation fraction was segmented by linear spectral unmixing.
Data availability Publicly available datasets were analyzed in this study. This data can be found here: The original satellite data from Sentinel-1 and Sentinel-2 are publically accessible from the open cloud platform Google Earth Engine. Sentinel-1 Data Link: https://developers. google. com/ earth-engine/datasets/catalog/COPERNICUS_S1_GRD. Sentinel-2 Data Link: https://developers. google. com/earth-engine/datasets/catalog/ COPERNICUS_S2_SR_HARMONIZED. The import of Sentinel-1 and Sentinel-2 data, the calculation of the number of images, the number of cloud-free pixels in the time series, and the harmonic model on the vegetation fraction derived from linear spectral unmixing are available in the following Google Earth Engine code: https://code. earthengine. google. com/29061ee4b48e419baf491180809e7530.
Finally, we counted how often our sites of interest lie on the shortest path between each pair of nodes in the network in order to obtain centrality values, which were then scaled to the mean (Table  1 ). All these measures (i. e. , distance from the confluence and centrality) were computed using Geographic Information System software (QGIS Development Team, 2008, V 3. 12) with the "Shortest path" option from the network analysis tools.
Genetic Analyses Descriptive statistics. Hardy-Weinberg expectations were calculated using Arlequin V. 3. 5. 2. 2  [44]. Only the loci that showed deviations from Hardy-Weinberg equilibrium in all populations were excluded from the analysis. The loci that showed HWE deviations for at least one population were retained. F IS , observed (Ho), and expected (He) heterozygosity per population were calculated using GENETIX V. 4. 05. 2  [45]  after 10,000 permutations of alleles within each population. The potential occurrence of null alleles and scoring errors due to stuttering or large allele dropout in the dataset was assessed using the software Micro-Checker V. 2. 2. 3  ([46] , Supporting Information S1). Genetic diversity. FSTAT V. 2. 9. 4  [47]  was used to compute the allelic richness (Ar) as a standardized measure of population genetic diversity, adjusted for the smallest sample size (n = 13 individuals in our case)  [12].
The local diversity estimate based on expected heterozygosity (He) was also tested along the environmental gradient. Genetic differentiation. As a measure of differentiation, we computed the Pairwise F ST. The pairwise F ST was calculated using GENEPOP V. 4. 7. 5  [48]  for each marker independently. We then computed the mean values of the pairwise F ST for each population (hereafter mean F ST ) as a measure of population genetic uniqueness (Table  S4 ; Refs. [12, 49] ). Population genetic structure. In order to determine the number of genetically homogeneous groups, a Bayesian genetic clustering algorithm implemented in the package 'rmaverick'  [50]  of the R statistical software v. 3. 6. 1 (R Development Core Team 2015) was performed on each type of genetic marker. Log-likelihood plots were obtained using the thermodynamic integration procedure implemented in 'rmaverick' to determine the optimal genetic clusters K (ranking from 1 to 10).
The relationship between the distance from the confluence and the centrality was not significant (Spearman's rho = -0. 30, p = 0. 255). We used the corrected Akaike information criterion (AICc) to identify the best model. In our case, a set of top models had a comparable AICc (∆AICc < 2) and/or no model had a weight > 0. 95 (i. e. , the probability that a given submodel is the best; [51]). We thus performed a model averaging method on this set of best submodels (∆AICc < 2) using the zero method with the model. avg function implemented in the R package MuMIn V 1. 47. 1  [52]. This procedure involved calculating a weighted average of parameter estimates, so that parameter estimates that provide the least information about the variance in the response variable are given little weight. In that way, the parameter's relative importance (that is, the sum of weights over all the best submodels in which the variable appears) reflects the 'importance of the effect' that an explanatory variable may have on the response variable  [53].
Additionally, we performed a correlation analysis between genetic markers using the cor. test function implemented in the R package Stats V 3. 6. 3 [54]  to examine whether populations with higher neutral genetic diversity/differentiation were also those with higher functional genetic diversity/differentiation. The relationship between population pairwise genetic (F ST, computed as F ST /1 -F ST values according to  [55] ) and hydrological distances for each type of marker was evaluated using a simple Mantel test via the mantel. rtest function implemented in the R package Ade4 V 1. 7. 13  [56]. To control for the potential impact of hydrological barriers such as dams or weirs on the population structure, we performed a partial Mantel test based on Spearman's rank correlation using the mantel.
Acknowledgments: The authors wish to thank  Laurent Garmendia ,  Allan Yotte , the  Fédération de Pêche de l'Ariège and Armand Lautraite  for their help. Some of the data used in this work were produced through the GenSeq technical facilities of the  Institut des Sciences de l'Evolution de Montpellier  with the support of  LabEx CeMEB , an  ANR  " Investissements d'avenir" program  ( ANR-10-LABX-04-01 ).
, 2005; Price and Giovannelli, 2017). As of 2020, around 70 shallow systems are reported in the InterRidge 3. 4 database  (Beaulieu and Szafranski, 2020). While they occur in a variety of tectonically active settings, they are mostly associated with submarine volcanism, island and intraoceanic arcs, ridge environments, intraplate oceanic volcanism, continental margins, and rift basins  (Tarasov et al. , 2005)  and can be found all around the world (see for a review  Price and Giovannelli, 2017). Despite similarities with their deep-sea counterparts, shallow systems present also significant particularities, such as the presence of light allowing photosynthesis, and the common presence of a gas phase due to a lower hydrostatic pressure than in the deep sea. They are furthermore exposed to stronger time-dependent external forcing such as tidal influence, wind-and wave-driven circulation, and coastal processes such as land-derived nutrient loading  (Chen et al.
Materials and methods In this study, data collected during two different field campaigns are presented, called thereafter Saganaki and CarDHynAl datasets. The two datasets were produced using protocols with slight differences which are highlighted below. Both datasets describe microbial community composition using deep sequencing of the 16S rRNA marker gene, while qPCR analysis was performed only on the CarDHynAl dataset. A graphical summary of the Material and methods can be found in Supplementary material 1.
Sampling Saganaki dataset Saganaki is a single vent site located ∼300 m offshore in Paleochori Bay (36. 671490053°N, 24. 516882251°E) at a water depth of 12 m (Figure  1B ). It is characterized by temperatures up to 76. 2°C in the shallow subsurface (~10 cmbsf), weak to moderate gas venting, and the colonization of the sediment surface by a white ∼1 cm thick microbial mat. Areas of flourishing seagrass surround the area of active venting, separated from the white mats by a transition zone (~1 m wide) of sediment where the burrowing activity of the mud shrimp Calianassa truncata is visible. In May 2014, scuba divers measured in situ temperatures and collected sediment cores and water samples. Sediments were cored with polycarbonate tubes and sealed underwater with rubber caps. Four sediment cores were collected along a 2 m transect, starting from the center of a white mat, through a transition zone, and ending in a seagrass-covered region (Figure  1D ).
CarDHynAl dataset In September 2019, a field campaign was organized for the CarDHynAl project to map and characterize temperature outflows  (Puzenat et al. , 2021)  and microbial communities in Paleochori Bay. The sediments were sampled throughout Palaeochori Bay, from 3 to 10 m depth, and from a variety of visually different seafloor types such as background sand, bioturbation sand, ochre sand, white precipitates, zones covered with microbial mats, yellow sand, and black sand (Figure  1D ). A hydrothermal hexagonal seafloor pattern in the south-east of the bay (36. 672334236°N, 24. 519504665°E; Figures  1B, C ) was used to sample a high-resolution sediment transect across a zone of diffuse hydrothermal outflow. Sediment cores were taken using plexiglass tubes of 3. 6 cm in diameter. Upon recovery of the cores, the sediment was immediately pushed out of the tubes and subsampled by using tip-sectioned syringes.
Fluid analyses Within an hour after sampling, the fluids from the CarDHynAl dataset were split into two aliquots of 10 ml. Few grains of zinc acetate were added to the aliquot for anion analysis, and 0. 3 ml of concentrated nitric acid was added to the aliquot for cation analysis. Samples were then kept at room temperature until analysis. Major anions were measured using Ion Chromatography (IC, Metrohm CompactIC), and major cations were measured using Inductively coupled plasma optical emission spectrometry (ICP-OES, Thermo Scientific iCAP 7,600).
DNA extraction and sequencing Saganaki dataset Around 0. 5 g of sand/sediment samples were homogenized using a sterile mortar and pestle, and bulk environmental DNA was extracted from both sediments and fluids according to the method of  Mills et al. (2012). Negative controls were routinely used to confirm the absence of contamination during the process. Extracted DNA samples were then sent to Molecular Research DNA (Shallowater, TX, United States) for sequencing of the 16S rRNA gene using an Illumina Miseq platform with universal 515f and 806r primers  (Caporaso et al. , 2011). The list of all primers and PCR programs used in this study can be found in Supplementary material 4.
CarDHynAl dataset DNA was extracted from ∼0. 5 g of sediment/mat using the Dneasy ® PowerLyzer ® Power Soil Kit (Qiagen) following the manufacturer's instructions and a FastPrep 24 Tissue Homogenizer (MP Biomedical). Negative controls were routinely run to assess contamination throughout the protocol. In order to produce amplicon libraries for sequencing, a 2-step amplification approach was used as described in  Berry et al. (2011). First, triplicate PCRs were run on each sample using the HotStarTaq kit (Qiagen) and the 519F and 805R primers (see Supplementary material 4). After pooling of the triplicates and cleaning of the amplicons using AMPure XP beads (Beckman Coulter, Inc. ), a second PCR was run to attach tags and sequencing adaptors to the PCR products. The final products were cleaned again using AMPure XP beads, and pooled equimolarly prior to sequencing on an Ion Torrent 7467 PGM machine at the University of Bergen.
Sequence processing After retrieval of the sequences, they were processed using an adaptation of the «alternative VSEARCH pipeline»  (Rognes, 2021). In short, the two datasets were first separately trimmed of their primers using Cutadapt 3. 2  (Martin, 2011) , truncated at 220 bp and quality filtered at a max expected error of 1 for the Saganaki dataset and 2 for the CarDHynAl dataset using VSEARCH v. 2. 19. 0  (Rognes et al. , 2016). The reason for the difference is that Illumina produces higher quality sequences, allowing for a stricter cleaning process. Both datasets where then concatenated and processed in VSEARCH as follow: Dereplication and removal of singletons, clustering of OTUs at 97% similarity, denovo and reference-based chimera removal (using SILVA138. 1  (Quast et al. , 2013) ), and finally mapping of the pre-dereplication sequences to the OTUs.
The OTUs were further curated using LULU v. 0. 1. 0 (Frøslev et al. , 2017) , prior to be given taxonomic assignments using the CREST4 LCA classifier  (Lanzén et al. , 2012)  and the SILVA138. 1 database. Finally, the remaining OTUs were run through a thorough decontamination process: Removal of OTUs with no domain assignments, contamination removal using the frequency approach in the decontam package  (Davis et al. , 2018) , contamination removal using a list of known contaminants  (Eisenhofer et al. , 2019) , and removal of low-abundance OTUs  (Bokulich et al. , 2013). The script used to process the sequences is available from https://github. com/ MeinzBeur/LeMoineBauer-2022-Milos.
qPCR Quantitative PCR was used on the DNA extracted from the samples of the CarDHynAl dataset in order to quantify a selection of genes involved in various metabolic pathways. Several genes were successfully quantified: aprA, dsrA and soxB (sulfur cycle), arrA and aoxB (arsenic cycle), nirK and nirS (nitrogen cycle), and mcrA (CO2/methane cycle). For these genes, the standard curve allows to quantify down to ~10 3 copies per gram of sediments. As well, two primer sets targeting separately the archaeal and bacterial 16S rRNA genes were used to quantify prokaryotic abundances. However, primer sets targeting the archaeal amoA, bacterial amoA, and hzo (nitrogen cycle) did not show any product during preliminary PCR screening on a selection of samples. They were therefore not used for qPCR and the genes were considered to be not present in the dataset.
Statistical analysis The different cores were positioned and assigned to seafloor types by carefully comparing in situ observations and published photomosaics  (Martelat et al. , 2019; Puzenat et al. , 2019a,b). All statistical analyses were made in R v. 4. 1. 2 (R Core Team, 2021), with the recurrent use of the phyloseq package  (McMurdie and Holmes, 2013)  and the ggplot2 package  (Wickham et al. , 2022). The alpha diversity was assessed using the Shannon diversity index, as it is robust to differences in sequencing depth. The index was computed at the OTU level. Barplots of the compositions are shown at the family, phylum, and domain level. For the rest of the study, the analyses were made at the family taxonomic level, as this showed to be a good balance between keeping relevant taxonomic information and decreasing the noise created by having more and rarer taxa in the dataset.