dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The  IPSL-CM6  experiments were performed using the HPC resources of TGCC under the allocations  2019-A0060107732 ,  2020-A0080107732  and  2021-A0100107732  (project gencmip6) provided by  GENCI  ( Grand Equipement National de Calcul Intensif). The  IPSL -CM6 team of the  IPSL Climate Modelling Centre  (https://cmc. ipsl. fr) is acknowledged for having developed, tested, evaluated, tuned the IPSL climate model, as well as performed and published the CMIP6 experiments. This study benefited from the ESPRI computing and data centre (https://mesocentre. ipsl. fr) which is supported by  CNRS ,  Sorbonne Université ,  Ecole Polytechnique  and  CNES  as well as through national and international grants. BG acknowledges the  Climat AmSud program  grant  REPRISE 21-CLIMAT-13  for funding.
Supplementary information and data availability. The R script used to recode raw data into corrected coordinates, to calculate means and to draw 2D and 3D graphs is available on GitHub. F1 to F3 Files correspond to source data files (. txt) containing the raw data presented and analyzed in this paper. For each type, the files provide the ID numbers of the fish, condition (day1 to day4 or odor or control type), means of position, speed, X and Y round-trips and behavior for periods 10-25 minutes and 37-52 minutes, as well as the calculated individual olfactory score.
Acknowledgments We thank  Camille Lejeune  for her participation in the generation of the SF x CF F1 hybrid line,  Maxime Policarpo  for preliminary testing of tracking softwares and  Rose Tatarsky  for interesting discussions. Zootechnical care of our Astyanax fish facility was taken in charge by the  TEFOR Unit. This work was supported by  Equipe FRM (Fondation pour la Recherche Médicale)  grant  EQU202003010144  to SR.
Abiotic factors The calculation of the carbon budget is based on tide and irradiance data measured in 2008 near the study site. The emersion status of the site was defined every minute as either water height = 0 m (emersion) or water height > 0 m (not emersion). For this purpose, the water height was calculated by linearly interpolating (approx function, R Development Core Team 2017) the tidal information measured every 10 min by the Roscoff observatory with respect to C. D. and then adjusted to the mean height of the study site. The irradiance in the air is a dataset provided by the Service d'Observation du Milieu Littoral, INSU-CNRS, Station Biologique de Roscoff. Irradiance was measured as a 15-min average by a PAR (400-700 nm) sensor on the roof of the Station Biologique de Roscoff. To disentangle the general effect of irradiance from that of the cloud cover of 2008, a second light dataset assuming no cloud cover was created for every minute of the year.
Acknowledgments The authors thank all the people who helped carry out the benthic metabolism fieldwork, especially  R. Michel. We also thank  T. Cariou  for acquiring the PAR data. We acknowledge the SOMLIT network for performing the Sea Surface Temperature measurements and data access. We are grateful to the  CNRS-UPMC ABiMS bioinformatics  platform (http://abims. sb-roscoff. fr) for providing computational resources and support. This work also benefited from the  Montpellier Bioinformatics Biodiversity  platform supported by the  LabEx CeMEB , an  ANR "Investissements d'avenir" program  ( ANR-10-LABX-04-01 )". This work was part of the PhD thesis of C. G. , which was financially supported by the  Brittany region. This work also received funding from the  Science Foundation Ireland  and from the  MASTS pooling initiative  (The  Marine Alliance for Science and Technology for Scotland)  and their support is gratefully acknowledged.
Transcriptomic analyses RNA from L. havanensis root and leaf samples treated or not with nickel were extracted using Tri reagent (Sigma-Aldrich). RNA sequencing, de novo transcriptome assembly, transcriptome annotation, read mapping and differential gene expression analysis were performed essentially as described previously (García de la  Torre et al. , 2021). The de novo assembly of the transcriptome (Lhav_v2), was performed using RNA-seq reads from root and shoot samples of nickel-treated plantlets. The quality of the assembly was analyzed by the TransRate v1. 0. 3 package using the trimmed read sequences used to assemble Lhav_v2  (Smith-Unna et al. , 2016). The completeness of the assembled transcriptome was estimated using BUSCO (v 4. 0. 6) and viridiplantae_odb10 lineage dataset  (Seppey et al.
Acknowledgement / Funding We thank  Ludivine Soubigou-Taconnat  for the coordination of the Leucocroton havanensis RNA sequencing at the  POPS platform (Orsay, France) ,  Celeste Belloeil  for help with data presentation and  Sébastien Thomine  for comments on the manuscript. This work was supported by the  ANR  funding  EvoMetoNicks  ( ANR-13-ADAP-0004 ) and the  MITI-CNRS Defi Enviromics   GENE-4-CHEM  to S. M. D. A. G and R. R. F were recipient of  SCAC mobility fellowships  from the  French Embassy in Cuba.
Acknowledgements We thank  Carlos Melero  for supporting the compilation of experimental drift functions, as well as  Anja Gladbach  and  Andrew Chapple  for input on alternative drift deposition modelling approaches. This work was supported by the ' Crop Protection Environmental Impact Reduction ' project funded by the  Bayer AG, Germany ; and by the  SAGROPIA  project (grant agreement no. 101136677 ) funded under the  European Union 's  Horizon Europe Research and Innovation program.
Based on the image dataset combined with faunal sampling (by towed gears), and on environmental data collected at multiscale (~1400 km-60 m) from ocean databases (chlorophyll), current modelling, high resolution bathymetric data and image analyses (substrata), we aimed to: (1) compare community metrics (density, taxonomic richness and beta diversity) of several seamounts and island slopes and search for environmental driving forces explaining these differences, (2) describe spatial distribution patterns of megabenthic communities among and within sites, and assess the influence of multiscale environmental factors, and finally, (3) differentiate scales of spatial structures and their interrelationships with abiotic factors at local scale, along the slopes of Mayotte and Bassas da India Island.
Study sites and image data The data were obtained from the cruises PAMELA-MOZ01  (Olu, 2014)  onboard the research vessel (R/V) L'Atalante and PAMELA-MOZ04  (Jouet and Deville, 2015)  onboard the R/V Le Pourquoi Pas? These cruises were part of the PAMELA (PAssive Margin Exploration LAboratories) project  (Bourillet et al. , 2013)  and explored four seamounts along the Mozambique Channel, from North to South: a carbonate platform in the Glorieuses Archipelago, the Sakalaves mounds along the Davie Ridge, Jaguar, and Hall Bank, as well as a terrace in Bassas da India closely located to the latter (Fig. 1A, B, ). The BIO-MAGLO (BIOdiversité MAyotte-GLOrieuses) cruise  (Corbari et al. , 2017) , onboard the R/V L'Antea, completed the image dataset with an exploration of the outer slopes of Mayotte Island.
Environmental data We used bathymetry and acoustic reflectivity data acquired by the BATHYMAY (BATHYmetry MAYotte) cruise for the Mayotte island slopes  (Audru et al. , 2006). For all the other sites, multibeam bathymetric data were acquired during the PTOLEMEE (PasT glObaL changEs in the MozambiquE channEl)  (Jorry, 2014)  and PAMELA-MOZ01  (Olu, 2014)  cruises with a multibeam echosounder (Kongsberg EM122 for the deepest areas and EM710 for the shallowest areas)  (Courgeon et al. , 2016). CTD (temperature, salinity, pressure) and oxygen data were acquired during the SCAMPI dives from CTD SBE19 and optode (microcat) sensors mounted on the camera frame. The temperature sensor did not work for the northern slope of Mayotte, nor did the oxygen sensor for the dive on the Sakalaves platform.
Taxonomic data processing from images We annotated 9724 images using BIIGLE 2. 0  (Langenkämper et al. , 2017). Details of the method for identifying taxa in images are described in  Hanafi-Portier et al. (2021). The taxonomic identification is based on objective criteria (morphological characters of the taxon) and contextual criteria (knowledge of similar species collected in the region and their habitat). The characters that can be observed on images are not equally informative among the taxonomic groups. We selected a taxonomic rank for each taxon as follows:  (1)  to avoid taxonomic rank nesting (e. g. , in the family Nematocarcinidae most individuals were identified at the genus level Nematocarcinus, we thus selected the genus rank and removed the few individuals identified only at the family level except if we are certain that they do not belong to the genus Nematocarcinus), (2) to compromise between abundance (number of individuals identified per rank) and diversity (number of taxa included in the dataset).
Thus, different identification ranks were achieved in the dataset, with low taxonomic ranks (genus for echinoids and asteroids, morphospecies/species for decapods) and higher ranks (Phylum for brachiopods and annelids, Class for poriferans, molluscs and other echinoderms, Order for fish and cnidarians). Furthermore, we did not consider the individuals in the dataset that were identifiable only at high rank (e. g. , Actinopterygii spp. , Porifera spp. , Cnidaria spp. , Asteroidea spp. ), representing 16,7% of the total number of individuals. We exported the annotations from BIIGLE, then we compiled the faunal data at the taxonomic ranks selected for the analysis, to obtain a faunal abundance matrix (list of the taxonomic composition in Supplem. mat. 1). To obtain robust ecological signals accounting for the low faunal count per image in our study area, we aggregated the abundance per image into 200 m 2 (~60 m linear) sample units (i.
e. , polygons) defined spatially from the navigation of the camera and its speed, using the ADELIE software (internal dev. IFREMER; ArcGIS 10. 7 plugin). The 200 m 2 area was an optimum surface allowing to obtain at least three images per polygon for most of them, while having a compromise between fauna density and habitat heterogeneity. The slightly variable speed of the towed camera and the removal of poor-quality images (1294, ~13% of the total dataset) from the dataset meant that we observed a variable number of images per polygon (from four to eight in average). We therefore weighted the summed abundances in each polygon by the number of images in the polygon, and then standardized the densities by the area of the polygon. We deleted all polygons with less than two images and kept only polygons ranging from 170 to 200 m 2.
Environmental data processing To consider the influence of oceanic circulation, eddies, and related particulate export of the deep-water layers on the communities, average modelled current velocity, and variability (standard deviation) data were obtained for three depth layers (0-50 m, 50-200 m, 300-650 m). The current velocity simulation (MOZ36) was based on the CROCO ocean model (Coastal and Regional Ocean COmmunity model; http://www. croco-ocean. org/). CROCO, an evolution of the ROMS ocean model  (Shchepetkin and McWilliams, 2005) , solved here the primitive equations for the ocean circulation following the hydrostatic and Bousinesq approximations and using a topographic following vertical coordinate. We defined 60 vertical levels. To reach a horizontal resolution of 1/36 • (2. 9 km at 20 • S), we used three levels of nested grids communicating between each other, based on the AGRIF library  (Debreu et al.
, 2012). The bottom topography was derived from the Global Earth Bathymetric Chart of the Oceans (GEBCO, 2014) dataset  (Weatherall et al. , 2015). A smoothing was applied locally to prevent numerical imprecision. The surface boundary conditions were based on a bulk formulation  (Fairall et al. , 1996) , using the daily ERA-INTERIM atmospheric reanalysis  (Dee et al. , 2011). The lateral open boundary conditions were forced by monthly average outputs from the GLORYS 1/4 • oceanic reanalysis  (Ferry et al. , 2012). The model was run for the 1993-2014 period after a spin-up of 2 years. This simulation allowed us to show the influence of Mozambique channel rings on bottom currents and morphology  (Miramontes et al. , 2019b). We exported the monthly satellite data of daily mean surface Chla concentrations (4 km × 4 km resolution), from the AquaMODIS database.
We considered seven years of integration for the Hall, Bassas da India and Sakalaves sites  (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) , eight years for Jaguar  (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015)  and ten years for  Mayotte and Glorieuses (2007-2017) , depending on the dates of data acquisition for the different cruises and sites. We calculated the winter and summer climatologies, to consider the contrasted differences in primary productivity of the region (Tew-Kai and  Marsac, 2009) , as well as the minimum, maximum, average Chla (from seven to ten years of monitoring), and the average intra-annual and inter-annual variability, to test if extreme values and Chla variability influence communities' structure along the Mozambique Channel. CTD (temperature, salinity) and oxygen data from SCAMPI dives were georeferenced in ArcGIS to calibrate the time step of acquisition at each frame/image (CTD profile in Supplem.
mat. 2). High resolution bathymetry (Digital Terrain Model -DTM) and acoustic imagery were processed from multibeam echosounder data. DTM resolution was 10 m for the PAMELA project data (Charline Guerin, IFREMER Marine Geosciences lab), and 20 m on the explored areas of Mayotte  (Audru et al. , 2006; SHOM data). We quantified the terrain variability using bathymetric-derived indices with the Benthic Terrain Modeler (BTM) plugin in ArcGIS V10. 7  (Walbridge et al. , 2018). The scale of the measured indexes was therefore dependent on the DTM resolution. We considered the explanatory seafloor variables considered relevant in the literature for understanding the structure of megabenthic communities  (Wilson et al. , 2007). The Bottom Position Index (BPI) was calculated to measure the average differences in bottom elevation relative to a reference point successively within a moving window.
Statistical analyses 2. 5. 1. Community structure We performed statistical analyses using R software (V4. 1. 2) (R Core Team, 2021) and made graphical representations with the ggplot2 package  (Wickham et al. , 2016). For all analyses, faunal densities were quadratic-root-transformed to reduce the weight of highly abundant taxa compared to intermediate to poorly abundant taxa. This transformation affects large values more strongly than small values and thus reduces the skewness of the dataset  (Legendre and Legendre, 2012). A Hellinger transformation of the data was also applied to densities ('vegan' package  (Oksanen et al. , 2020) , decostand() function). This transformation is recommended for community-type datasets in ecology that usually contain many zeros, as it gives little weight to variables with low values and double absences  (Legendre and Gallagher, 2001).
We assessed taxonomic richness among sites, using sample-based (i. e. , polygons) rarefaction curves on faunal presence/absence data ('iNEXT' package  (Hsieh et al. , 2020) , iNEXT() function). The taxonomic richness rarefaction for the smallest sampling effort (n = 58 polygons) and extrapolation for a sampling effort close to the asymptote of each curve (n = 400 polygons) were obtained with the 'iNEXT' package, estimateD() function. We quantified a total beta diversity (BD) for each transect and two sub-indices as well: LCBD (Local Contribution to Beta Diversity) and SCBD (Species Contribution to Beta Diversity) with the 'adespatial' package  (Dray et al. , 2022) , beta. div() function. LCBD values reflect the level of uniqueness of each sampling unit, i. e. , polygons with high LCBD represent sites where the community composition is unique and tends to generate variability along the transect.
, 2016; Rojas et al. , 2017; Leroy et al. , 2019) , and it can detect the hierarchical structure of communities. Clusters detected by Map Equation with link weights have a high-intra group connectivity of links with high weights, and low inter-group connectivity of links with low weights. In other words, the algorithm groups together sites which share taxa in similarly high densities; different clusters have distinct compositions in terms of the most abundant taxa. We ran map equation with 100 trials to find the optimal partition in the dataset. We used the R 'biogeonetworks' package to perform the clustering procedure  (Leroy (2022) , step-by-step code available at https://github. com/Farewe/biogeonetworks). The main assets of the network-based approach over distance-based approaches are (1) the identity of species is preserved throughout the procedure, i.
Role of environmental factors We tested the environmental drivers of richness, density and BD differences among sites using simple and multiple regression models. For the density and beta diversity response variables (continuous quantitative), we used the lm() function of the 'stats' package (R Core Team, 2021). For the richness (discrete quantitative), we used generalized linear regression models, following a Poisson distribution, with the glm() function of the 'stats' package. We tested the environmental drivers of the assemblages composition using a redundancy analysis (RDA), with the 'vegan' package, rda() function. Since this analysis is sensitive to collinearities between variables, we first evaluated the collinearities between environmental variables using Pearson pairwise correlations and removed the variables with high collinearities (Pearson r > 0.
The co-variables tested were: spatial (latitude, longitude), geomorphology, depth, hydrology variables (current and chlorophyll) together and separately. On each island slope (Mayotte and Bassas da India), we quantified the overall spatial structure of the faunal dataset, which we decomposed into large, medium, and fine spatial scale sub-models, using distancebased Moran eigenvector map (dbMEM) analyses ('adespatial' package, dbmem() function). First, we applied RDAs between the response variables (taxon densities) after removing the trend from the dataset, and the explanatory spatial variables (called MEM for Moran Eigenvectors Map). Then, we used a permutation test of the RDA model to determine the significant axes and MEMs. The set of significant MEMs, which represent different scales of spatial structure, were then grouped together according to the spatial scales they represent.
We applied new RDAs separately on the community dataset according to each set of explanatory spatial variables (large, medium, and fine scales) and we assessed significance of the axes. We examined the correlation between significant axes, representing large, medium, and fine scale patterns, and the environmental (explanatory) variables using multiple regressions for each significant spatial axis with the environmental factors (lm() function). We assessed the conditions of applicationnormality and variance homogeneity of the residualsto validate the regression models. Finally, we performed variation partitioning analyses of the faunal dataset (Hellinger-transformed density) by integrating different matrices of explanatory environmental variables (geomorphology, substrate, terrain, hydrology, large, medium, and fine scale spatial models) to quantify the percentages of variance explained by each set of environmental variables or each spatial structure, and their interrelationships ('vegan' package, varpart() function).
Multiscale assemblage variability From the biogeographic network analysis applied on the whole dataset, we delineated 45 groups, half of which correspond to multispecific clusters indicating faunal assemblages generated by a limited number of highly abundant taxa. The remaining groups correspond to monospecific patches each composed of a single taxon, either locally highly abundant or with a very low density (rare taxon observed in at most 5 polygons). Out of the 45 total groups, 12 assemblages were the most represented in the area since they were present in at least 30 and at most 363 polygons, out of a total of 1608 over the nine camera transects. These 12 assemblages are multispecific clusters (except for the monospecific patch A9, ophiuroids) and are dominated by a few highly abundant taxa.
5%) of which 16. 1% represented medium-scale-structured environmental variables (Fig. 9B ). Conversely to the other sites, a fine-scale spatial structure contributed to the community variation along the eastern slope of Mayotte (6. 1%), half of which represented a significant pure spatial fraction that was not correlated with environmental variables. Along the western slope of Mayotte, the main part of the variance were residuals (92%). Along the Bassas da India slope, the environmental variables explained 25. 8% of the community dataset variance, of which 16. 3% represented broadly structured environmental variables (not shown). Parts of these spatial structures can be interpreted with the environmental variables considered in the study. The large-scale spatial structure along the northern slope of Mayotte (at ~3 km-scale) was significantly related to depth, slope, BPI 500 m, volcanic geomorphology, and substrate composition (gravels and biogenic bottoms).
Nevertheless, the high density on Sakalaves platform was mainly driven by benthic fish. Variability of megafaunal community characteristics among seamounts has been observed in some regions to be driven by currents  (Genin et al. , 1986; Kaufmann et al. , 1989) , surface productivity  (Clark and Bowden, 2015; Bridges et al. , 2022) , depth gradient  (Williams et al. , 2011; Schlacher et al. , 2014; Boschen et al. , 2015; Clark and Bowden, 2015; Lapointe et al. , 2020)  and substrate (e. g. ,  Sautya et al. , 2011; Boschen et al. , 2015; Morgan et al. , 2015; Lapointe et al. , 2020)  but such relationships need to be confirmed in other oceanic regions, notably those with low productivity, such as in the Indian Ocean or central Pacific  (Clark et al. , 2012). In the present dataset, densities varied among seamounts by a factor of ten with a decreasing density from north to south (~1400 km) along the Mozambique Channel.
, 2015; Victorero et al. , 2018; Auscavitch et al. , 2020a). For example, Mayotte island slopes encompass three different water masses (SCIW, AAIW, RSW) over a larger bathymetric gradient which can promote higher richness and beta-diversity than the Glorieuses platform which resides mainly inside the AAIW. Nevertheless, the absence of a significant role of depth and latitude over the whole dataset could reflect the relatively restricted depth gradient explored for most sites, and an insufficient level of taxonomic identification to establish a relationship with the richness component. Taxonomic composition varied among the studied seamounts and island slopes, even for taxa identified at a high taxonomic rank including habitat-forming taxa (anthozoan Orders, sponge Classes) or mobile ones. Sessile fauna usually dominated the assemblages, but some seamounts dominated by soft bottoms (Sakalaves terrace) were dominated by mobile fauna (fish and ophiuroids).