dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2019). From this database, we calculated the fire return interval (FRI) by dividing the number of days in one year (using 365 days per year) by the number of fires per year for each zone. The FRY database also contains the four main vegetation types according to the UNLCCS classification (United Nations Land Cover Classification System) called "burnt species" which burn each year. Based on the conversion carried out in the work of  Poulter et al. (2015) , the four main burnt species were grouped into four groups "Burnt Tree", "Burnt Shrub", "Burnt Grass" and "Burnt Nonvegetated", and into three categories "Burnt Open vegetation" dominated by grasses and shrubs, "Burnt Mixed vegetation" dominated by trees and shrubs, "Burnt Closed vegetation" dominated by trees (Table  S2 ). Climate, vegetation and fire data were averaged annually or seasonally (boreal winter season from DecembereFebruary, DJF; spring from MarcheMay, MAM; summer from JuneeAugust, JJA; autumn from SeptembereNovember, SON) according to microcharcoal preferential source areas (section 3.
4), using ArcGIS for Desktop software. We performed a principal component analysis (PCA) using the package FactoMineR  (Lê et al. , 2008)  on the seasonal land climate and vegetation data (Temperature, Precipitation, Net Primary Productivity) to explore environmental patterns on land which could affect fire regimes and charcoal production. Initial exploration of climate data indicated that winter and summer temperature and precipitation differences between the north and the south of the Iberian Peninsula are clearly seen when using mean annual temperature (MAT) and mean annual precipitation (MAP) (MAP and MAT are significantly and positively correlated with seasonal values, Fig. S2  and S3 ). However, differences in the net primary productivity between the north and the south of the Iberian Peninsula are only evident when using winter and summer seasons, and muted using annual average NPP (Fig.
S4 ). Annual climate data and seasonal NPP were then used to explore relationships with microcharcoal concentrations. The distribution pattern of microcharcoal concentrations and elongation ratio values between zones were explored using boxplots. We used the Student's T-test for the averages and the Kruskall-Wallis test for the medians in order to explore the differences and similarities of microcharcoal concentrations and elongation values between zones (Fig. S7 ). Cluster analysis (Fig. S9 ) was carried out on microcharcoal concentrations and water depths of samples (and elongation ratio values and water depths) using the single linkage (nearest neighbour) algorithm and stratigraphically constrained clustering (Past software,  Hammer et al. , 2000). Clusters are joined based on the smallest distance between the two groups and only adjacent rows, or groups of rows, are joined during the agglomerative clustering procedure.
33). Results show that the mean microcharcoal concentration increases with decreasing latitudes off the westernmost Iberian margin, and more elongated microcharcoal particles are observed in zones 3 and 4. In addition, a high variability of microcharcoal concentrations is also observed within each zone (Fig. 4a ). The comparison between microcharcoal concentration in marine samples and the different datasets (climate, fire regime and vegetation data) could be biased as the periods of data do not overlap. However,  Shafer et al. (2003)  showed that spatial variations of climate were larger than temporal variations over the period 1961e2008. Therefore, this temporal mismatch should not influence the results of our comparison, and the relative differences between hydrographic basins in terms of climate, vegetation and fire regimes are likely to have been preserved over the past decades.
Climate and net primary productivity (NPP) The principal component analysis yielded two dimensions with eigenvalues of 44. 3% and 25. 7%, respectively (Fig. 5 ), 70% of the    (Laurent et al. , 2018). Burnt Grass, Burnt Shrub and Burnt Tree derived from the FRY database  (Laurent et al. , 2018). Net primary productivity for all seasons: NPP_winter, NPP_spring, NPP_summer, NPP_autumn, Mean Annual Temperature (MAT) and Mean Annual Precipitation (MAP) derived from the GEFD4s database  (Giglio et al. , 2013). Preferential source areas of microcharcoal: zone 1 (blue circle), zone 2 (yellow circle), zone 3 (grey circle), zone 4 (red circle), zone 5 (blue circle). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article. ) variation therefore being explained by the first two eigenvalues.
Water depth, marine geomorphology and ocean currents The hierarchical grouping (clustering) considering microcharcoal concentrations and water depth of samples highlights seven groups (Fig. 8a  and Fig. S9a ). Four groups (group 1 with 10 samples, group 2 with 9 samples, group 3 with 12 samples and group 4 with 10 samples) are located on the shelf between 0 and 506 mbsl (Fig. 8b ). Group 5 (40 samples between 598 and -2280 m bsl) and group 6 (11 samples between 2323 and 3162 m bsl) are located on the upper and bottom of the slope, respectively. Group 7 (10 samples, between 3557 and 5357 m bsl) corresponds to the abyssal plain. Mean microcharcoal concentration values of groups 1, 2 and 3 are significantly similar, and different from group 5 (Student T-test Fig. 6. Microcharcoal concentrations versus fire regimes on the Iberian Peninsula over the period 2001e2016 (FRY database)  (Laurent et al.
Acknowledgements This work was supported by the  French INSU (Institut National des Sciences de l'Univers)  programme  LEFE (Les Enveloppes Fluides et l'Environnement )  CAMPFIRE  project; by the  ANR   BRAISE  project, grant  ANR-19-CE01-0001-01  of the  French Agence Nationale de la Recherche ; and by the  ESA   FIRECCI  project. M. Genet is supported by a  doctoral fellowship  from the  University of Bordeaux. We gratefully acknowledge  I. Billy ,  O. Ther  and  L. Devaux  for their technical help, and  M. Rasolonjatovonirina  for the initial burnt species data compilation. We thank EPOC and  LSCE laboratories  for giving us access to their repositories of marine sediments. We thank  IFREMER  for access to sediment cores curated at  CREAM  ( Centre de Resources en Echantillons et Archives Marins ) seabed sediment and rock samples repository.
7 GHz) vegetation optical depth (K-VOD) from AMSR-E satellite and traditional vegetation indices (NDVI, EVI, LAI) time series  (Jones et al. , 2011). Cband (6. 9 GHz) and X-band (10. 7 GHz) VOD (C-VOD and X-VOD) from AMSR-E satellite have been used to monitor the evolution of aboveground biomass carbon  (Liu et al. , 2015). Teubner et al. (2018)  showed that VOD at various frequencies was highly correlated to gross primary production (GPP) all over the world. The low frequency L-band (1. 4 GHz) VOD (L-VOD) measured with SMOS satellite was also proven to be highly sensitive to aboveground biomass (AGB) in Africa, with less saturation over dense forests than optical indices and than C-or X-VOD  (Rodríguez-Fernández et al. , 2018). A strong correlation was found between L-VOD and four AGB datasets (R = 0.
85-0. 94). These results were extended to the tropical range in  Vittucci et al. (2019)  and to the global scale in  Mialon et al. (2020). A strong correlation was found at the global scale between L-VOD and two AGB datasets (R = 0. 91-0. 94), but was shown to be highly dependent on the vegetation type. the passive microwave retrieval of SM and L-VOD over an inundated landscape, in order to observe whether the retrieved L-VOD could be impacted by dynamic surface water extent. The purpose is to explain the apparent cycle of VOD in seasonally inundated areas, in order to improve passive microwave biomass estimates.
, 2008). SM and L-VOD used here were derived from SMOS-INRA-CESBIO (SMOS-IC) retrieval algorithm  (Fernandez-Moran et al. , 2017)  version 105. SMOS-IC also uses the L-MEB as radiative transfer model, but is a simplified version of SMOS Level 2 and Level 3 products, as it considers the footprint homogeneous in terms of land cover. Footprints permanently covered with more than 20% of open water according to MODIS land cover  (Broxton et al. , 2014)  are excluded, but smaller water bodies and surface water dynamics are currently not taken into account by SMOS-IC algorithm. SMOS-IC dataset is computed on the global cylindrical Equal-Area Scalable Earth (EASE) Grid version 2  (Brodzik et al. , 2012)  with a grid resolution of 625 km 2 (25 km × 25 km at 30 deg. of latitude). Data from June 2010 to December 2019 were used.
AMSR-2 data In order to compare SMOS L-band data to passive microwave data at other frequencies, we also considered AMSR-2 VOD dataset  (Imaoka et al. , 2010). As part of JAXA Global Change Observation Mission (GCOM), AMSR-2 radiometer succeeded in 2012 to AMSR-E NASA's Aqua satellite  (Kawanishi et al. , 2003). Two frequencies were considered: 6. 9 GHz and 10. 7 GHz, corresponding to C1-and X-bands respectively. C2-band (7. 3 GHz) was not discussed in this paper as the data were mostly redundant with C1-band. We used the daily L3 V001 VOD products, processed with LPRM algorithm  (Owe et al. , 2008)  and distributed by NASA in a regular grid at 25 km × 25 km resolution. We computed monthly averages of merged ascending and descending overpasses (LPRM_AMSR2_A_SOILM3 and LPRM_AMSR2_D_SOILM3), from July 2012 to December 2019.
GIEMS-2 dataset Seasonally inundated areas were located with GIEMS-2 dataset  (Prigent et al. , 2019) , a new version of the Global Inundation Estimate from Multiple Satellites (GIEMS,  Prigent et al. , 2001). It provides a longterm global map of surface water extent by merging passive, active, visible and near-infrared data (SSM/I, ERS, AVHRR). The water fraction f w is delivered at a monthly time-scale from 1992 to 2015, on an equal area grid of 0. 25 deg. × 0. 25 deg. at the equator (~ 25 km). The maximum water fraction max(f w ) over a representative year for the period 1992-2015 is shown in Fig. 1. In this updated product, the estimation of microwave emissivity is less dependent on ancillary data, and the final water surface estimation was corrected of a known overestimation over low vegetation areas.
PROBA-V leaf area index Leaf area index (LAI) data were also considered in order to monitor the vegetation emergence cycle. We used ESA PROBA-V satellite dataset in version 2  (Verger et al. , 2014) , distributed at 1 km resolution each 10 days by Copernicus Global Land Service. LAI is defined as half the total area of green elements of the canopy per unit horizontal ground area, and is expressed in m 2 m -2. The satellite-derived value is obtained from the red and near infrared channels. Contrary to L-VOD, LAI is sensitive to the leave chlorophyll content but is independent of woody elements. Satellite LAI is widely used to monitor vegetation, but suffers from a fast saturation over dense vegetation, as only the canopy surface is observed; and cannot be obtained under cloudy conditions, which occurs frequently.
MODIS Land cover A land surface climatology map based on 10 years  (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010)  of the MODIS MCD12Q1 product at 500 m resolution  (Broxton et al. , 2014)  is used in SMOS-IC algorithm to exclude large water bodies (see Sect. SMOS data), and was also used in this study to filter the data (see Section 3). This land cover identifies 17 ecosystems based on IGBP (International Geosphere-Biosphere Programme) class labels (Table  4 ).
Satellite data processing The study was conducted from June 2010 to December 2019, even though AMSR-2 dataset starts in July 2012, and GIEMS-2 dataset ends in December 2015. Monthly averages were computed in order to remove daily variability and to focus on seasonal patterns. All datasets were resampled to SMOS EASE-Grid 2 with gdal (GDAL/OGR contributors, 2020). The high-resolution LAI product (1 km) was averaged to the SMOS grid. The 500 m resolution land cover map and the 4 km resolution IMS database were resampled by allocating the dominant class for each pixel. A nearest neighbour resampling method was applied to the coarse resolution datasets (AMSR-2C-and X-VOD, GIEMS-2 f w , GRACE TWS). The Pearson correlation coefficient map between SMOS-IC L-VOD and SM anomalies was computed in order to highlight areas where a significant increase (resp.
Actually, in Amazonian flooded forests, the water column can reach 10 to 15 m  (Parolin et al. , 2004). The water fraction f w can also be underestimated by GIEMS-2 dataset under flooded dense forest  (Parrens et al. , 2019). Finally, the vegetation itself may have a seasonal cycle, whereas we considered a static τ s value. This explanation also applies in the lower Mississippi River floodplain, mostly covered with croplands flourishing in spring and summer, where significant differences between SMOS-IC L-VOD and τ r were found (std = 1. 8 10 -2 ). As a conclusion, the modelling showed that floods impact more significantly τ r values over low vegetation than over high vegetation, mostly due to the different amount of submerged vegetation. This remark explains why the impact of water on SMOS-IC L-VOD is mainly visible over waterlogged grasslands and low vegetation areas more than over flooded forests (Fig.
The underestimation of L-VOD in areas affected by inundations can lead to a noticeable underestimation of the biomass amount (-15/-20 Mg ha -1 in the largest wetlands). It is thus important to better account for the open water extent and dynamics in SM/τ retrieval algorithm. Submerged grasslands become open water areas, and should be filtered out during floods. Over higher vegetation, the retrieval algorithm could be revised to take the dynamic water fraction into account. The most important and most challenging aspect is to have access to reliable estimates of water bodies temporal evolution. GIEMS-2 dataset could be used for that purpose, as it is produced at a monthly time scale  (Prigent et al. , 2019).
Acknowledgements A. M. , N. J. R. F. , E. B. , Y. H. K. acknowledge support by  CNES  ( Centre National d'Etudes Spatiales ) TOSCA program. SMOS-IC was obtained from the  "Centre Aval de Traitement des Données SMOS" (CATDS) , operated for the  CNES (France)  by  IFREMER (Brest, France). The authors would like to thank the  European Space Agency (ESA). AMSR-2 data were provided by  Vrije Universiteit Amsterdam (Richard de Jeu)  and  NASA GSFC (Manfred Owe ) (2014),  AMSR2/GCOM-W1  surface soil moisture (LPRM) L3 1 day 25 km × 25 km ascending V001, Edited by  Goddard Earth Sciences Data and Information Services Center (GES DISC)  ( Bill Teng ), Greenbelt, MD,  USA ,  Goddard Earth Sciences Data and Information Services Center (GES DISC ), Accessed:  29/07/2020 , doi:https://doi. org/10. 5067/M5DTR2QUYLS2.
In this context, the present study aims at (i) developing an objective protocol for the species' assignment to EGs, (ii) investigating if it is possible to assign benthic foraminiferal species from intertidal areas and TWs from the considered geographical zones to the five EGs described in  Alve et al. (2016)  according to species responses to sediment TOC content, (iii) if applicable, validating the established lists by testing them using a sensitivity-based index on separate and independent datasets from the same geographic regions, and (iv) testing the hypothesis that species ecological requirements may vary between habitats/ecosystems and along latitudinal gradients by comparing species assignment lists from the present study. Because of the particular characteristics of foraminiferal habitats and communities, we decided to present the foraminiferal species assignments split in two lists: one for the European Atlantic and one for the Mediterranean region.
Criteria to select datasets Only previous studies in areas that strictly follow the definition of intertidal and TWs were selected. In the European Water Framework Directive (WFD, 2000/60/EC), TWs are defined as « bodies of surface water in the vicinity of river mouths which are partially saline in character as a result of their proximity to coastal waters but which are substantially influenced by freshwater flows » (European Communities, 2000, p 6). In the Mediterranean Sea, TWs also include mesohaline, poly-euhaline and hyperhaline lagoons  (Reizopoulou et al. , 2018). We decided to follow the definition of  McLusky and Elliott (2007)  who specified the different types of water bodies belonging to intertidal and TWs in the context of the implementation of the WFD and further marine legislations (see Table  1 ).
Three further criteria had to be met to retain the datasets used for the assignments:  the study had to be based on living benthic foraminifera. Note that all datasets used in the present study are based on rose Bengal-stained fauna;  the foraminiferal and TOC data had to be from the same station (less than 1 meter from the sampling point); when the authors of the study only provide the OM content (studies 10; 12 and 18, Table  3 ), it was converted to TOC using the following formula: LOI = 2 TOC  (Barillé-Boyer et al. , 2003; Frangipane et al. , 2009) ;  only samples containing >50 stained specimens were considered following  Alve et al. (2016). All studies from the English Channel/European Atlantic except one (see Appendix A) are based on the >63 m fraction, whereas in the Mediterranean Sea studies are based on the fraction >63 or >125 m (Appendix B).
In total, 21 studies were selected from the English Channel/European Atlantic intertidal and TWs (Fig. 2 , Appendix A) and 21 ones from the Mediterranean Sea (Fig. 2 , Appendix B). The complete datasets are presented in  Bouchet et al. (submitted). Due to lack of standardization, different methods were used in the different studies to measure TOC; from the classical loss-on-ignition (LOI) to the more sophisticated use of an elemental analyzer (EA). While the latter directly quantifies TOC concentration, the LOI measures the OM content. It is still often used in large surveys because it allows for a quick and cheap measurement of sedimentary organic matter  (Luczak et al. , 1997). It is generally accepted that LOI may overestimate TOC content compared to EA  (Barillé-Boyer et al. , 2003). In the datasets from the English Channel/European Atlantic coast, the ranges of TOC obtained with LOI and EA are fairly similar.
Apart from one study in the Ebro delta, all the studies in the Mediterranean Sea used an EA to measure TOC content. Therefore, TOC data from the different studies should be compared with caution. Species names were homogenized among studies following the World Register of Marine Species (WoRMS,  Hayward et al. , 2020a). Only accepted scientific names from WoRMS are used in this study, and the unique AphiaID identifier is reported for each species. The Aphia platform is an infrastructure aiming at capturing taxonomic and related data and information  (Vandepitte et al. , 2015). Each taxon added to the Aphia database receives a unique and persistent identifier, the AphiaID. Once assigned to, the name and its corresponding ID cannot be physically deleted from the database, ensuring that the information is not lost.
, 2016)  and the Mediterranean Sea  (Jorissen et al. , 2018)  are both based on an objective review of available datasets, sometimes (in case of inconclusive data) with a small contribution of best expert judgment  (Jorissen et al. , 2018). These publications included a few plots exemplifying species-specific relative abundance patterns plotted against TOC, to better explain the followed methodology. In the present study's effort to produce lists of foraminiferal species for the European Atlantic and Mediterranean intertidal and TWs, the aim was to seek for an objective method for species assignments in order to avoid using « best expert judgment ». The weightedaveraging (WA) optimum and tolerance approach was used  (Birks et al. , 1990; Ter Braak, 1987). The idea behind WA is that in a coastal lagoon with a certain TOC range, benthic foraminifera with their TOC optimum close to the coastal lagoon's TOC concentrations will most likely be the most abundant species.
Testing the ecological assignment lists In order to test our species assignment to EGs according to their response to TOC, Foram-AMBI was applied on two independent datasets from the English Channel/European Atlantic coast area and the Mediterranean Sea, which were not included in the master tables for calculation of the species assignment (to avoid circular arguments). Great attention was paid to find studies where a clear organic carbon gradient enrichment was observed. In the English Channel/European Atlantic coast area, Foram-AMBI was applied on a study of the effects of oyster farming on living benthic foraminifera in SW France  (Bouchet, 2007). Oyster farming leads to organic matter enrichment in the surrounding environment  (Bouchet and Sauriau, 2008). The sampling stations were located in four different unvegetated intertidal mudflats harboring oyster parks actively used by shellfish operators: Rivedoux (R), Yves (Y), Charente (CH) and Daires (D) (Fig.
Statistical analysis The next two steps were followed to assign benthic foraminiferal species to the five EGs: 1. The WA optimum and tolerance were computed for each species to determine its preference with respect to TOC (%) using the optimos. prime R package  (Sathicq et al. , 2020). 2. Species assignment to EGs was done as follows: if a species had an optimum in the TOC range 0-2%, the species was assigned to EGI; in the range 2-2. 5%, the species was assigned to EGII; in the range 2. 5-3. 4%, 3. 4-4. 1%, and above 4. 1% to EGII, EGIII, EGIV and EGV. This is based on TOC-derived EcoQS (see Table  2 ;  Bakke et al. , 2010; Viaroli et al. , 2004). One species' list per region was produced. Typical example for each EG is shown for each region to visualize pattern of species response along the TOC gradient.
A locally weighted scatterplot smooth line (LOESS) was fitted to each scatter plot. Marginal plots were added to each scatter plot to show the frequency of distribution of occurrences along the TOC gradient. The median of the distribution of the occurrences was also computed. Two master tables (available in  Bouchet et al. , submitted)  were used in the abovedescribed procedure. These master tables included TOC percentages and species relative abundances data of the 21 studies from the English Channel/European Atlantic coast (587 samples) and the 21 studies from the Mediterranean Sea (301 samples). As the distribution of OM, AMBI and Foram-AMBI was normally distributed All these calculations were made using the statistical language R version 3. 6. 3 (R Core Team, 2020).
The English Channel/European Atlantic coast In the English Channel/European Atlantic intertidal and TWs datasets, TOC ranges between 0 and 16% (Appendix A). Figure  5  presents a typical example of species-response curves for each of the five EGs. Most of the five figured species have a characteristic unimodal distribution pattern. However, the relative abundance distribution of Cribroelphidium excavatum and C. williamsoni were bimodal, with peaks at 0-1% and 7-12%, and at 2-4% and 7-9%, respectively. In total, 77 species from this geographic region were assigned: 22 in EGI, 13 in EGII, 23 in EGIII, 6 in EGIV and 13 in EGV (Appendix C). Despite their high optimum values, typical salt marsh species (Balticammina pseudomacrescens, Entzia macrescens and Trochammina inflata), which are dominant in environments characterized by very high natural TOC values, were not assigned.