dataesr/scientific-paragraphs-categorization huggingface dataset:
Dispersal inferred from SGS and FSGS As our aim was to understand the local pattern of dispersal (by pollen or seeds) on genetic structure, we kept only one ramet per multilocus genotype in our dataset. No population structure was detected along the 1. 6 km section studied, however, individuals located no further than 55 m from one another (first distance class used in our global SGS analysis, Fig. 2 ) were significantly more related than by chance. This suggests that at short distances (50 m) dispersal is not homogeneous, but that at the 1. 6 km scale of the study reach, seeds from different parent trees are well mixed. In order to test the generality of our results, we reanalysed the data obtained by one of us (see the reanalysis of the  Barsoum et al. (2004)  data in supplementary material) in another river catchment (Garonne, France).
The first author was funded for his PhD by the  French Ministry of National Education, Higher Education and Research. This research was supported by the  French government IDEX-ISITE  initiative  16-IDEX-0001  ( CAP 20-25 ). Financial support was provided by the  LTSER Zone Atelier Loire -CNRS , the  Fédération des Recherches en Environnement  ( FR 3467 UCA/CNRS/INRA ),  Clermont-Ferrand and I-SITE Clermont CAP 20-25 projet Emergence 2018. have no conflict of interest.
These products are compared with thermosalinography (TSG) measurements, which provide continuous data from the open ocean to the coast along ship tracks. The comparison shows various skills from one product to the other, decreasing as the coast gets closer. The bias in reproducing coastal SSS gradients is unrelated to how the SSS biases evolve with the distance to the coast. Despite limited skill, satellite products generally agree better with collocated TSG data than a global reanalysis and show a large range of coastal SSS gradients with different signs. Moreover, satellites reveal a global dominance of coastal freshening, primarily related to river runoff over shelves. This work shows a great potential of SSS remote sensing to monitor coastal processes, which would, however, require a jump in the resolution of future SSS satellite missions to be fully exploited.
In addition, more recently, satellites missions were launched to monitor SSS remotely. Soil Moisture Ocean Salinity (SMOS) was first launched in 2009  [19] , followed by the Aquarius satellite in 2011  [20]. Soil Moisture Active Passive (SMAP) replaced Aquarius in 2015. The combination of these data with numerical models has greatly enhanced our understanding of the patterns of SSS variability and distribution at different spatial and temporal scales in the Pacific (e. g. ,  [5, 21, 22] ), Atlantic (e. g. ,  [23] [24] [25] ) and Indian  [26] [27] [28]  oceans. In situ SSS measurements are the foundation of this knowledge as they are also necessary to validate and improve the accuracy of numerical models  [29, 30]  and to calibrate/validate satellite SSS measurements  [31]. In spite of a high number of studies focusing on SSS variability and distribution, coastal SSS patterns and gradients remain poorly documented globally for at least three main reasons.
Data 2. 1. 1. In Situ Data In situ SSS data used in the present study come from underway TSG measurements available in the global Ocean from 1993 to the present (Figure  1a ). The main source is the French TSG database, including measurements from VOS, managed by the SSS Observation Service  [17] , from research vessels (RV)  [37]  and sailing ships  [38] , representing together about half of the global TSG network  [39]. Other sources include TSG data from the USmanaged Shipboard Automated Meteorological and Oceanographic System initiative  [40] , VOS managed by the US National Oceanic and Atmospheric Administration (NOAA), by the Japanese National Institute for Environmental Studies and VOS Nippon organization, RV operated by NOAA, the Australian Commonwealth Scientific and Industrial Research Organization and the Japan Agency for Marine-Earth Science and Technology.
In Situ Data In situ SSS data used in the present study come from underway TSG measurements available in the global Ocean from 1993 to the present (Figure  1a ). The main source is the French TSG database, including measurements from VOS, managed by the SSS Observation Service  [17] , from research vessels (RV)  [37]  and sailing ships  [38] , representing together about half of the global TSG network  [39]. Other sources include TSG data from the US-managed Shipboard Automated Meteorological and Oceanographic System initiative  [40] , VOS managed by the US National Oceanic and Atmospheric Administration (NOAA), by the Japanese National Institute for Environmental Studies and VOS Nippon organization, RV operated by NOAA, the Australian Commonwealth Scientific and Industrial Research Organization and the Japan Agency for Marine-Earth Science and Technology.
These measurements are mostly collected from the SBE-21 TSG manufactured by Seabird Electronics. The TSG is generally installed in the engine room of VOS. The depth at which the seawater is collected depends on the ship and its load but is around 5-10 m for VOS and a bit shallower for RV. The temporal resolution is 1 and 5 min for RV and VOS, respectively, which corresponds roughly to a 2-3 km along-track resolution for VOS, typically cruising at 15 knots, and even finer for RV. TSG data density is high in the north Atlantic and western Pacific, and relatively low in the southern hemisphere (Figure  1a ). The present study is based on TSG data available in the nearest 300 km from the coast, with a focus on a selection of five coastal regions: the Amazon River plume, northeast These measurements are mostly collected from the SBE-21 TSG manufactured by Seabird Electronics.
Reanalysis Product The Global 1/12 • Ocean Reanalysis version 1 (GLORYS12V1), developed by Mercator and distributed by the European Copernicus Marine Environment Monitoring Service (CMEMS), aims at producing state of the art eddy resolving ocean simulations constrained by oceanic observations by means of data assimilation  [43]. The ocean model component is NEMO, forced at the surface by ECMWF-ERA interim reanalysis including precipitation and radiative fluxes corrections. The assimilation scheme is 3D-VAR, providing a correction for the slowly evolving large-scale biases in temperature and salinity. The assimilated observations are Reynolds 0. 25 AVHRR-only SST, delayed time SLA from the satellites, in situ T/S profiles from the Coriolis Ocean dataset for Reanalysis version 4. 1 (CORAV4. 1) database  [44].
The ETOPO1 Earth topography at 1 arc-minute resolution was used for the deep ocean topography while the Global Earth Bathymetric Chart of the Oceans (GEBCO) 1-resolution dataset was used for the coast and continental shelf topography. The GLORYS12V1 product includes daily and monthly outputs of salinity, temperature, currents, sea level, mixed layer depth and ice parameters available on a regular grid at 1/12 • and 50 standard vertical levels. The product spans from January 1993 to June 2019. In this study, we used the daily salinity outputs estimated at the ocean surface (~0. 5 m depth). This product is referred to as GLORYS hereafter.
Methods The computation of coastal SSS gradients from TSG, satellite and reanalysis data is based on a linear least square fit method. Gradients were estimated in bands ranging from 50 to 150, 200 and 250 km from the coast, along cross-shore sections. Data in the first 50 km from the coast were excluded because of the poor skill of the satellite data in this band. Due to the spatial scales considered here, coastal data around islands smaller than 150 km in diameter were excluded from the present study.
SSS Gradient Calculation from Gridded Products Reanalysis and satellite products are available on a global regular grid. Therefore, the estimation of SSS gradients is different from the TSG. First, every 25 km along the global coastline, we compute cross-shore axes, which local direction is estimated relatively to a linear least-square fit of the local coastline (within a 200 km radius of the coastal point considered, Figure  3b ). Second, for each gridded product, coastal SSS data are selected (Figure  3a ) and those found in a 50-km wide swath following each cross-shore axis (blue rectangle in Figure  3b ) are interpolated at points every 25 km along this axis. Third, SSS cross-shore gradients are estimated at each time step by a linear least-square fit of these interpolated data. A minimum of three data points available on each cross-shore axis is required to estimate the gradient.
When considering only the cross-shore SSS gradients of the same sign, from 250, 200 or 150 km to 50 km from the coast (Figure  14b-d ), SMAP JPL performs better, followed by SMAP RSS, then (with STD as the main criteria) GLORYS, SMOS LOCEAN and SMOS BEC. This seems to reveal a trade-off between data availability and quality. Indeed, SMAP RSS has the lowest number of cross-shore sections collocated with TSG data, while SMOS BEC has the largest. In this respect, the SMAP JPL product shows the best compromise. It is interesting to note that most satellite products tend to show a positive bias corresponding to an underestimation of coastal freshening. The fact that two satellite products are better than GLORYS means that they globally provide more information in the near-coastal band than a state-of-the-art model fed with Argo profiles, satellite SST and SLA observations.
Acknowledgments: SSS data derived from thermosalinograph instruments installed onboard voluntary observing ships were collected, validated, archived and made freely available by the  French Sea Surface Salinity Observation Service  (http://www. legos. obs-mip. fr/observations/sss/),accessed on 25 June 2021. The authors sincerely thank the data producers who have made satellite SSS datasets available to the public MERCATOR-OCEAN (specifically  Fabrice Hernandez, Elena, Cedric and David ) and the producers of reanalysis data GLORYS12V1 (https://resources. marine. copernicus. eu/), accessed on  25 June 2021.
Further, there are several open-source repositories of CT data archiving OD-bearing lizards, including Digimorph (digimorph. org) and Morpho Source (morphosource. org). The potential for integrating the spatial morphological information with that provided by classical histology and functional modelling is currently being explored in several groups  (Iacoviello et al. , 2020)  Lizard ODs vary in size, shape, and body-wide distribution (Fig. 2 ). For example, whereas ODs are typically confined to the head of some lacertids (Fig. 2F ), their distribution is more pervasive in many scincids, anguids, and cordyliforms (Fig. 2A-C ). In these groups, ODs may form a comprehensive body-wide covering of imbricating or interlocking rectangular or cycloid plates, or may be confined to specific regions due to differing selective pressures  (Camp, 1923; Richter, 1994; Stanley, 2013 Stanley, , 2016;; Broeckhoven et al.
As an example, Figure  1C  shows a dot plot comparable to Figure  1A , but now comparing the genomes of Enterococcus faecium and Atopobium minulum. No diagonal line is seen because these genomes belong to different phyla and therefore have low sequence identity. Nevertheless, an exact match spanning 19,117 bp is found (diagonal green line highlighted by the red ellipse). The most parsimonious explanation for such a long match is a recent HGT event. In addition, the GC content of the match (55%) deviates strongly from that of both contigs (38. 3% and 48. 9%, respectively), another indication that this sequence originates from HGT  (Ravenhall et al. , 2015). Comparing the sequence of this exact match with all non-redundant GenBank CDS translations using blastx  (Altschul et al. , 1990) , we find very strong hits to VanB-type vancomycin resistance histidine, antirestriction protein (ArdA endonuclease), and an LtrC-family phage protein that is found in a large group of phages that infect Gram-positive bacteria  (Quiles-Puchalt et al.
Empirical length distributions of exact matches obey a power law To study HGT events found in pairs of genomes, we considered the statistical properties of r, the length of exact matches. Note that the number of long matches found in a single pair of genomes is usually very small. Hence, in this study we conduct all statistical analyses at the level of genera. To do so, we selected all bacterial genome fragments longer than 10 5 bp from the NCBI RefSeq database (1,343,042 in total) and identified all sequence matches in all pairs of sequences belonging to different genera ( » 10 9 pairs). We then analysed the distribution of the lengths of the matches, called the match-length distribution or MLD. The MLD for a pair of genera G A and G B is defined as the normalised length distribution of the matches found in all pairwise comparisons of a contig from G A and a contig from G B.
The normalisation ensures that the prefactor of the MLD does not scale with the number of genomes present in the database (see Empirical calculation of the MLD for pairs of genera and sets of genera in Materials and methods). A comparable approach has previously been applied successfully to analyse the evolution of eukaryotic genomes  (Gao and Miller, 2011; Massip and Arndt, 2013; Massip et al. , 2015). We first consider the MLD obtained by combining the MLDs for all pairs of genera. While the vast majority of matches is very short (<25 bp), matches with a length of at least 300 bp do occur and contribute a fat tail to the MLD (Figure  2 ). Strikingly, over many decades this tail is well described by a power law with exponent À3: mðrÞ ~rÀ3 : (1) The same À3 power law is found in the MLDs for individual pairs of genera (see Figure  2 ).
To verify that the observed power-law distributions were not the result of assembly artefacts or erroneous annotation, we constructed a smaller, manually curated dataset which included only long contigs (>10 6 bp, see Restricted dataset in Materials and methods). This restricted dataset still comprises 138,273 matches longer than 300 bp. MLDs computed with this dataset also consistently results in À3 power laws ( Figure  2 -figure supplement 1). Hence, the results are robust to assembly or annotation artefacts. Below, we will explain that the power law is a signature of HGT. Consistently, for matches shorter than 300 bp, the MLDs deviate from the power law (see Figure  2 -figure supplement 2), because in this regime vertical inheritance, convergent evolution and random matches contribute to the MLD.
By contrast, because of the normalisation of the MLD (see Empirical calculation of the MLD for pairs of genera and sets of genera in Materials and methods), A does not scale with the number of genomes in the genera being compared and is thus robust to sampling noise. Hence, the value of A can be used to study the variation in HGT rate among genera. In addition, the values of A estimated from the full and the restricted datasets (Figure  2  and Figure  2 -figure supplement 1) are very close, showing that the estimates of A are robust to assembly artefacts. Finally, our estimates are unlikely to be strongly affected by the presence of plasmids since only a small fraction of plasmids is longer than 10 5 or 10 6 bp  (Shintani et al. , 2015).
Long-distance gene exchange is widespread in the bacterial domain The analysis above has allowed us to identify a large number of HGT events. In addition, the derivations in the previous section provide a method to quantify the effective HGT rate between any two taxa by measuring the prefactor A. Supplementary file 1 and 2 contains the value of A for all pairs of genera and families. Using these results, we further studied the HGT rate between all pairs of bacterial families in detail. Figure  3  shows the prefactors A for all pair of families (see Figure  3 -figure supplement 1 for a similar plot for all pairs of phyla). Families for which the available sequence data totals less than 10 7 bp were filtered out since in such scarce datasets typically no HGT is detected (Figure  3 -figure supplement 2) and the prefactor cannot reliably be estimated (see Supplementary file 3 for the total length of all families).
We studied the HGT rate variations in more detail in the restricted dataset (see Restricted dataset in Materials and methods). The analysis of the restricted dataset reveals the extent of HGT, even between distant species (Figure  4 ). Indeed, we find that 32. 6% of RefSeq species have exchanged genetic material with a species from a different family. Moreover, we find that 8% of species in the database have exchanged genetic material with a species from a different phylum. Finally, the species involved in these distant exchanges are spread across the phylogenetic tree: the species involved in long-distance transfers belong to 19 different phyla (out of 34). Importantly, we repeat that the method is sensitive only to events that occurred in the last ~1000 years. Also, these estimates are lower bound estimates since the power of our detection method is limited in species for which only few genomes have been sequenced.
HGT rates of genes differ strongly between functional categories To better understand the factors that explain variations in observed HGT rates, we next conducted a functional analysis of transferred sequences. To determine whether particular functions are overrepresented in the transferred sequences, we first queried 12 databases, each specifically dedicated to genes associated with a particular function. Comparing to a randomised set of sequences (see Gene enrichment analyses in Materials and methods) reveals that the gene functions of the transferred sequences strongly impact the transfer rate, as we observe a 3. 5 orders of magnitude variation between the most and the least transferred categories (Figure  6  and Appendix 1-table  1 ). More specifically, antibiotic and metal resistance genes are among the most widely transferred classes of genes (resp.
37Â and 4Â enrichment compared to random expectation), in good agreement with previous evidence  (Huddleston, 2014; von Wintersdorff et al. , 2016; Evans et. Functional enrichment of the sequences involved in horizontal gene transfer (HGT) based on the analysis of 12 specialised databases. Enrichments for each gene category (vertical axis) are computed relative to a control set obtained by sampling random sequences from the contigs that contained the matches (see Materials and methods). Enrichment for genes offering resistance against various types of antibiotics and biocides can be found in Supplementary file 5. 2020). The enrichment of resistance genes is expected since their functions are strongly beneficial for bacterial populations under specific, transient conditions. Interestingly, genes providing resistance against tetracycline and sulfonamide antibiotics -the oldest groups of antibiotics in use -are the most enriched (see the full list in Supplementary file 5).
In addition, we also find a strong enrichment among the transferred genes of genes classified as integrative and conjugative elements, suggesting that these genes mediated the HGT events  (Paquola et al. , 2018; Nakamura et al. , 2004). In contrast, exotoxins and small regulatory RNAs are the least transferred genes ( » 100Â depletion). More generally, genes in the wider 'Transport proteins' and 'Enzymes' categories are strongly underrepresented in the detected HGT events. To obtain a better understanding of the function of the transferred sequences, we annotated the transferred sequences in two ways: using SEED subsystems  (Overbeek et al. , 2005)  and using gene ontology (GO) terms  (Ashburner et al. , 2000; Carbon et al. , 2021). While the 12 curated databases queried above are more complete and accurate on their specific domains, using the SEED subsystem and GO analysis allows to test for over-or underrepresentation in a systematic way and for a broader set of functions.
To avoid false positives, we retained only the functions for which the results showed good agreement between the SEED and GO approaches (see SEED subsystems and GO terms ontological classification in Materials and methods). The results of these functional profiling methods are in good agreement with the database queries as the broad categories linked to 'Phages, Prophages, Transposable elements, Plasmids', and to 'Virulence, Disease, and Defense' are found to be the most enriched, although with a smaller enrichment (4. 6-and 3. 4-fold enrichment, respectively, see Supplementary file 6). Those findings confirm the strong enrichment of expected functional categories among HGT events and validate the good resolution of our methods. In addition to previously known enriched functions, we also discovered a significant enrichment (1.
3Â, conditional test adjusted p-value <10 À16 , see SEED subsystems and GO terms ontological classification in Materials and methods) for genes in the 'iron metabolism' class. Indeed, a wide range of iron transporters, parts of siderophore, and enzymes of its biosynthesis appeared in our HGT database, in line with previous analysis focusing on cheese microbial communities  (Bonham et al. , 2017). Hence, the results show that the horizontal transfer of genes related to iron metabolism occurs in a wide set of species and is not restricted to species found in cheese microbial communities. Notably, the proteins in the 'iron metabolism' functional category can be identified in transferred sequences belonging to six different bacterial phyla. Two other interesting SEED categories are found to be enriched.
Discussion In this study, we developed a computationally efficient method to identify recent HGT events. Applying this method to the genomes of the 93,481 organisms of the RefSeq database, we identified an unprecedentedly large number of HGT events between bacterial genera. Our analysis reveals that HGT between distant species is extremely common in the bacterial world, with 32. 6% of organisms detected as having taken part in an event that crossed genus boundaries in the last ~1000 years. While a similar analysis has been conducted on a much smaller dataset (about 2300 organisms, see  Smillie et al. , 2011) , this study is, to our knowledge, the first to provide an extensive description of HGT in the microbial world at this scale. One striking result is the finding that HGT is also common between very distant organisms.
In contrast, some functional categories are strongly underrepresented in the pool of transferred genes. For instance, genes that are involved in transcription, translation, and related processes as well as those involved in metabolism are all depleted in our HGT database. One potential explanation is that these genes generally co-evolve with their binding partners  (Jain et al. , 1999). As such, their transfer would be beneficial to the host species only if both the effector and its binding partner were to be transferred together. As simultaneous HGT of several genes from different genome loci is very unlikely (unless they are co-localised), these genes are less prone to HGT. For additional discussion of the functional constraints on HGT, we refer to these reviews  (Thomas and Nielsen, 2005; Popa and Dagan, 2011; Pal et al.
, 2020). Finally, the RefSeq database is expected to contain misclassifications of contigs. This, as well as errors in genome assembly could bias the estimation of the effective HGT rate A. In addition, the representation of the various strains and taxa in the database is highly variable; this bias might affect the estimates, since our model assumes that there is a single parameter that represents the effective HGT rate between two taxa, whereas in reality the HGT rate can be different for different subtaxa/strains. In that case, the sampling bias of the database would bias the prefactor A towards the effective HGT rate of subtaxa/strains which are more represented in the database. Although it is widely accepted that bacteria often exchange their genes with closely related species via HGT, our large-scale analysis of HGT sheds new light on gene exchange in bacteria and reveals the true scale of long-distance gene transfer events.