dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2018). This is why the temporal patterns extracted by PRESTOR are innovative and offer new opportunities to explore data on rivers. Here the life span of the different biological compartment studied were not taken into account. To go further, we will search the specific time length of sequences to consider with respect to biological indices to obtain the most relevant patterns. Moreover hydrological and hydromorphological pressures will be added in the dataset to produce richer patterns that will deepen the analysis.
PRESTOR (temporal PRESsure categories patterns extracTOR) was implemented specifically to extract patterns from a hydro-ecosystem database. The operator chooses several criteria such as the time-length of patterns or their minimum frequency. To check the efficiency of the method, the 809 patterns extracted from data collected all over France with a time-length of 24 months, were analyzed. Three metrics, complexity, scarcity and emergence, were used to select significant patterns according to their ecological status. Water managers need simple and transparent methods. Selected patterns extracted by PRESTOR are easily readable and match managers' needs. They could be used at large scale and as such, considered as a holistic approach, the kind of approach that is urgently needed  (Demars et al.
As they belonged mainly to the first three root orders, their mass was later only allocated to the absorptive root pool. Roots were scanned at a resolution of 800 dpi using a flat-bed scanner. Root length and volume were calculated using the software WinRhizo  (Regent Instruments, Quebec, Canada, 2009) , and root volume was recalculated using individual diameter classes  (Rose, 2017). The coarse root volume was used to obtain the fine-earth content by subtracting the coarse root and the stone volume from the total soil volume of each sample. The fine-root length was used for the calculation of the RLD (cm cm -3 fine-earth volume). All root fractions were dried for 72 h at 40°C and weighed with a highprecision scale. Mass of live and dead tree fine roots and of understory fine roots was calculated as g dry mass per m 2 soil surface area.
Statistical Analyses All statistical analyses were performed using RStudio version 1. 2. 1335 (R Core Team, 2018). Significance levels were set at P = 0. 05. To account for the nested design structure and the nonindependence of data points at the sub-plot level, linear mixed-effects models (LMMs) were used (package LME4,  Bates et al. , 2015). To assess broad effects across all four sites, models were fitted with random slopes and intercepts with plot nested within site as random effect structure. R 2 values were retrieved using the R.
Accepted Article package r2glmm  (Jaeger, 2017). The model structures are given in the corresponding result tables of the supplement. To quantify overall mixing effects on FRB and RLD, we modelled total values of FRB and RLD, respectively, as functions of tree species richness for each root functional type and depth layer separately. In addition, we tested whether tree species richness affected the distribution of FRB and RLD by fitting the inverse CV of FRB and RLD across depths, respectively, in response to tree species richness. To test species-specific responses to mixing, we computed models for each of the tree species with their (a) FRB, (b) RLD and inverse CV of (c) FRB, and (d) RLD, all of absorptive fine roots, as functions of tree species richness. To test the effects of conifer proportion, we modelled (a) overall values, (b) inverse CV across depths, (c) NE, (d), CE, and (e) SE, for both FRB and RLD, separately, as functions of triplet conifer proportion (% basal area) across sites.
Acknowledgements This study was funded by the  German Research Foundation  (Grant numbers:  BA 2821/16-1  and  SCHE 695/10-1 ). This research was part of the  SoilForEUROPE  project funded through the 2015-2016 BiodivERsA COFUND call for research proposals, with the national funders  Agence Nationale de la Recherche  ( ANR, France) ,  Belgian Science Policy Office (BELSPO, Belgium) ,  Deutsche Forschungsgemeinschaft (DFG, Germany) ,  Research Foundation Flanders (FWO, Belgium) , and  The Swedish Research Council (FORMAS, Sweden). FB was supported by the  Margarete-von-Wrangell Fellowship of the Ministry of Science, Research and the Arts Baden-Württemberg and European Social Fund. We thank  Lauren Gillespie  and her team for the measurement of forest floor characteristics at the experimental platform PACE at CEFE Montpellier, France.
Data Availability Statement The datasets used for this study are archived on a data portal associated with the FunDivEUROPE and SoilForEUROPE projects and available after a 1-year embargo (fine-root data: https://data. botanik. uni-halle. de/fundiveurope/datasets/518; triplet tree data: https://data. botanik. uni-halle. de/fundiveurope/datasets/517).
Acknowledgements. We thank  LATMOS  for providing infrastructures and the IPSL center for the access to the ClimServ server to download some of the data used in our study. Financial support. This work is supported by the  French National Research Agency  in the framework of the " Investissement d'avenir" program  ( ANR-15-IDEX-02 ) with the project  PREDISAHLIM  ( 2019-2021 ) and under grant  ANR-19-CE03-0012  with the project  STEWARd  ( 2020-2024 ). Review statement. This paper was edited by  Peter Knippertz  and reviewed by two anonymous referees.
DNA sampling and laboratory procedures Shark fishing sessions using rod and reel and barbless hooks were conducted to obtain tissue samples for genetic analysis. Once hooked, sharks were brought alongside the boat where they were inverted and placed in tonic immobility while biological data and tissue samples were collected. Each shark was identified by photo-identification of the dorsal fin, sexed and measured to the nearest centimeter  (Mourier et al. 2012; Mourier, Mills, et al. 2013). Fishing sessions were conducted directly after underwater surveys to avoid perturbations of the experimental setup  (Mourier et al. 2017)  and to increase the chance of getting DNA samples from sharks that were part of the social network. Fishing effort was maintained until sharks failed to respond to the bait (generally <30 min and after catching 2-3 individuals).
A fin clip was collected from the second dorsal fin or anal fin and samples were individually preserved in 95% ethanol and returned to the laboratory for genotyping  (Mourier and Planes 2013). DNA was extracted using the QIAGEN DX Universal Tissue Sample DNA Extraction protocol. PCR amplification and the microsatellite loci used are described in detail in previous studies  (Mourier and Planes 2013; Vignaud et al. 2013; Vignaud et al. 2014 ). The software MICROCHECKER  (Van Oosterhout Cock et al. 2004 ) was used to test for null alleles and other genotyping errors. We compared the suitability of seven pairwise relatedness estimators: five non-likelihood estimators  (Queller and Goodnight 1989; Li et al. 1993; Ritland 1996; Lynch and Ritland 1999; Wang 2002)  and two maximum-likelihood estimators  (Milligan 2003; Wang 2007)  in the R package related  (Pew et al.
Defining associations Using R package asnipe  (Farine 2013) , we calculated dyadic association strengths (i. e. associations among pairs of individuals) among photo-identified individual sharks seen in groups from the spatio-temporal co-occurrences, and the proportion of time two individuals were observed together at the same site given that at least one was observed, using the simple-ratio association index (SRI)  (Cairns and Schwager 1987). The SRI is the recommended association index when calibration data are unavailable  (Hoppitt and Farine 2018). To measure the diversity of associations, we calculated the social differentiation (S) in the network that is the estimated coefficient of variation (standard deviation divided by mean) of the true association indices. If the social differentiation of the network is 0, then relationships among members are completely homogeneous.
Influence of structural factors on social associations We quantified the contribution of all five structural factors in driving social patterns with a multiple regression quadratic assignment procedure (MRQAP) modified by  Farine (2013)  that enables null models built from pre-network data permutations to be used in conjunction with a MRQAP regression. This approach was shown to be more accurate than classic MRQAP procedures  (Farine 2017). We assessed possible linear relationships between the social associations and the structural factors using the SRI association matrix as the dependent variables and the matrices representing pairwise similarity of each of the five structural factors as independent variables. We used 20,000 permutations to build randomized distributions to compare with the empirical coefficient.
Social preferences and null models We used a null model to test both for social preferences and the significance of the observed network modularity. We generated 20,000 randomized association and affiliation networks based on 25,000 data-stream permutations of the raw observation data with a swapping algorithm  (Bejder et al. 1998). We permuted the empirical group-by-individual matrix constraining the number of groups, individuals and occurrences (matrix dimension and fill), group size (row totals) and individual frequency of observation (column totals). To minimize the effect of initial values potentially correlated to the empirical data, we removed the first 5,000 randomized matrices. From the randomised group-by-individual matrix, we calculated a simple-ratio index association matrix, with which we built a generalized affiliation index using the same predictors selected via MRQAP for the empirical data.
We then used these network metrics in order to determine whether males and females differed in their gregariousness. We constructed generalized linear models (GLMs) to test how sex affected the observed network degree (degree ~ sex) and strength (strength ~ sex). We ran these same models with randomized permutations of the network data to evaluate statistical significance  (Farine and Whitehead 2015; Farine 2017 ). To determine whether individuals within groups (size class and communities) were more or less closely related than expected, we compared the observed values for each group against a distribution of expected relatedness values generated by randomly shuffling individuals between groups for 1000 permutations, where size was kept constant, using the R package related  (Pew et al. 2015).
Data summary Of 241 catalogued sharks (150 males, 91 females;  Mourier et al. 2012) , 49 (36 males, 13 females) were observed on more than 15 occasions (mean resightings = 14. 92 ± 8. 04 SD, Supplementary Figure  S1 ). A total of 225 adult sharks were genotyped from the studied area. From the 49 sharks included in our social network analyse, 87% (43) were genotyped. Therefore, 43 individuals (30 males, 13 females) were included in the remaining analyses. This resulted in 156 observed groups (mean group size = 8. 60 ± 4. 92 SD). From the 17 microsatellite markers taken from our previous study  (Mourier and Planes 2013) , the presence of null alleles was detected at Cli12 which was then removed from our dataset for further genetic analyses. We conducted the genetic analyses with 16 loci (Supplementary Table  S1 ).
This algorithm draws polygons around each predicted tree crown; we then merge neighboring spectrally similar segments to reduce oversegmentation. To evaluate if segments are spectrally similar, we used hyperspectral data and RGB images. The resolution of the RGB images is high, allowing identifying crowns on their texture although their color is similar. We evaluate two approaches for identifying segments to merge. First, a logistic model using hyperspectral information and high-resolution RGB imagery as input, and second, a deep learning model using high-resolution RGB imagery  [23] ,  [36]. The workflows are presented in Fig. 1. Finally, we demonstrate how this approach can be used to track individual tree mortality over time.
A. Data 1) Study Site: The Paracou field station is situated in a lowland tropical rain forest with gently rolling terrain near Sinnamary (5˚18 N; 52˚55 W) in French Guiana. Its composition is typical of Northern Guianese rain forests; more than 500 woody species attaining 10 cm diameter at breast height (DBH) have been recorded (over 118 ha), dominated by the Lecythidaceae, Fabaceae, Chrysobalanaceae, Sapotaceae, and Annonaceae families (ordered in decreasing abundance). A total of 16 plots from the experimental site were included in this study (named plot  [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] , representing more than 62 000 trees with DBH ≥ 10 cm. Different types of silvicultural treatments of increasing intensity were applied between 1986 and 1988. In this study, unlogged control plots are accounted for the majority of the area (62.
5 ha). 2) Inventories: All trees of DBH ≥ 10 cm were located by Cartesian coordinates of their trunks to an estimated precision of +/-2 m, botanically identified and their DBH were measured in 2015 (all plots), 2016 (6 plots), and 2019 (15 plots). Plot corners and points along the plot border were georeferenced with centimetric accuracy using a total station when the experimental plots were set up. Trees were then positioned within subplots (20 × 20 m) using a measuring tape. Positions of newly recruited trees were later estimated from the position of older neighboring trees. Positions of the trees are taken at the bole center. 3) ALS: ALS data were acquired in October 2015, September 2016, and November 2019 by ALTOA, operating a RIEGL LMS-Q780 sensor. On all dates, the scan frequency was 400 kHz, the final point density was above 50 points•m -2 , the flying altitude was 800 m, and the scan angle was +/-25°.
4) RGB Imagery: RGB images were acquired during the same flights as the ALS scans with an IXA180 phase one camera and an 8 cm ground sampling distance. Orthorectification of the imagery was performed using the canopy digital surface model (DSM) produced from the ALS data on each date. A 5-m resolution DSM was created from the point cloud by selecting the point of maximum height on a 1-m resolution grid, resampling the DSM at 5 m, and interpolating between points using a cubic spline. 5) Hyperspectral Imagery: Imaging spectroscopy was acquired only in 2016, with a Hyspex VNIR-1600 (Hyspex NEO, Skedsmokorset, Norway) sensor-mounted alongside the Riegl scanner. Its 160 bands covered a spectral range of 414-994 nm (i. e. , visible to near infrared) with a spectral sampling distance of 3. 64 nm.
The flight took place on a cloudless day  (September 19th, 2016). Images were orthorectified and georeferenced to 1 m spatial resolution with the PARGE software  [37]  using the canopy DSM produced from the ALS point cloud. Atmospheric correction was applied using ATCOR  [38] , removing the atmospheric disturbance and obtaining apparent reflectance. Mean spatial filtering was applied with a 3 × 3 window.
C. Manual Correction Manual segmentation was conducted on a subset of trees in 2016 and the map of segmented trees was considered as our ground truth. We first drew polygons where we thought they should be crowns using the canopy height model (CHM) and the high-resolution RGB images from 2015 and 2016, and the hyperspectral information from 2016. These segments and their species label were then validated in the field. Hereafter, these segments are referred to as the "manually segmented crowns"; they account for 706 of the crowns in plots 3,8,9,10,12, and 16 from 155 species. Manual correction of the automatic AMS3D segmentation was conducted in three plots of the study site (plots 4, 7, and 15) with RS data from 2016. This correction was guided by the multispectral and hyperspectral data. The correction consisted of merging, splitting, or redrawing segments.
H. DeepForest The DeepForest package 1 was recently developed to segment trees from aerial RGB images  [52] -  [54]. The model uses a deep learning convolutional neural network (CNN) to predict individual crowns  [53]. The deep learning model was pretrained with annotations from sites of the U. S. National Ecological Observation Network. The model was tested with data from our study site, confirming that the method is robust and flexible enough to be applied in tropical forests  [53]. We fine-tuned 1 Online. [Available]: https://github. com/weecology/DeepForest the pretrained model with the bounding boxes drawn around the reference crowns that had been segmented by the AMS3D algorithm and corrected by hand (on 18. 75 ha, see Section II-C). Then, the model was applied to other plots for which manually segmented crowns were available (plots 3, 8, 9, 10, 12, and 16, summing to 706 manually segmented crowns; see Fig.
A. AMS3D In total, 132 678 clusters were segmented by AMS3D (2016 point cloud). Most of these clusters are too small to correspond to canopy trees, and once rasterized and drawn as polygons, 49 168 crowns remained. A total of 78. 2% of the manually segmented crowns had a congruent segment. However, 12. 7% of the crowns were oversegmented (see Table  II ). Tree crown segments were manually corrected in plots 4, 7, and 15 (see Fig. 3 ). Out of the 8097 crowns segmented by AMS3D in these plots, 1222 were fused with another and 234 were split, creating a reference dataset comprising 6875 segmented tree crowns.
Then, using an allometric relation between DBH, H, and crown diameter, we computed the RMSE between the observed DBH and the DBH predicted by the model. The better the segmentation method, the lower the RMSE should be. For large trees (DBH > 50 cm), the correction with DeepForest decreased the RMSE, which showed that large crowns were better segmented once DeepForest correction had been applied. However, for very small trees (DBH < 15 cm), the RMSE was higher after the DeepForest correction (see Fig. 5 ). This was due to the fusion of small segments with larger ones, removing these small segments from the dataset and leaving fewer small segments available for the pairing algorithm. The segmentation validation realized only on small trees (DBH < 30 cm) showed that the crowns of these trees are well segmented (82.
We showed here that adding ITC information to tree gaps' dynamics improves our ability to track individual tree mortality. Tracking individual tree fate (recruitment, growth, and mortality) from repeat ALS requires a high level of segmentation accuracy. The sources of discrepancy between the repeated ALS datasets are expected to occur over time, including geometric issues from the imperfect alignment of point clouds or from vegetation sway by the wind. Similarly, even the best quality imagery will suffer from its own limitations and a slight change in sun angle can reveal or mask, otherwise invisible or visible crown. Spectral data can suffer from illumination problems and parts, or even entire crowns can be hidden by shadows of neighboring trees. So, multiple images combined with ALS may indeed be required to achieve robust tracking of individual trees.
This work was supported in part by  British National Environment Research Council  under Project  NE/S010750/1 , in part by  French National Research Agency Investissement d'Avenir  under Grant  CEBA ,  ANR-10-LABX-0025 , in part by  French Centre National d'Etudes Spatiales , in part by  French Fonds Stratégique de la Forêt et du Bois , and in part by  European Fonds Européen Agricole Pour le Développement Rural. (Corresponding author: Mélaine.
They include satellite remote sensing observations done by different sensors such as SST (Sea Surface Temperature) and SSS as well as in situ observations (increasingly from autonomous sensors) at and below the surface and in the atmosphere. One key challenge is to extract interpretable information and knowledge from these new data sets and integrating between variables  (Reichstein et al. , 2019). Thus, we decided to apply machine learning analysis to different satellite-derived oceanic data sets (surface salinity and temperature, dynamic topography, surface wind stress) in the tropical western Atlantic region. The aim (and the challenge) of this study is to investigate how this promising technology is able to identify oceanic phenomena and concomitant variabilities between parameters in an area where so many space and time scales are implied.
SMOS SSS The SSS data were derived from the SMOS (Soil Moisture and Ocean Salinity) mission, which is part of the ESA (European Space Agency)'s Living Planet Programme. A new methodology for correcting systematic SSS biases  (Boutin et al. , 2018)  has been used in these SMOS SSS. The SMOS SSS are affected by biases coming from various nonphysical contaminations such as the socalled land contamination and latitudinal biases likely due to the thermal drift of the instrument. These biases are relatively weak and have almost no impact on soil moisture retrieval. On the contrary, for salinity estimation, the impact is non-negligible and can reach more than one salinity unit in some regions close to the coasts. These biases are not easy to characterize because they exhibit very strong spatial gradients and they depend on the coast orientation in the Field Of View (2008)) network.
DUACS ADT The altimeter products were processed by Ssalto/Duacs and Aviso+, with support from CNES (https://www. aviso. altimetry. fr/en/data/products/sea-surface-height-products/global/ssaltoduacsexperimental-products. html). They are presently distributed by CMEMS (Copernicus Marine Environment Monitoring Service). We used the multi-mission altimeter data set reprocessing DUACS DT2014  (Pujol et al. , 2016)  whose main priority was to improve the monitoring of the mesoscales in the global ocean. The data from all altimeter missions (Jason1 and 2, TOPEX/Poséïdon, ENVISAT, GEOSAT Follow On, ERS1 and 2, and GEOSAT) are processed by the Ssalto/Duacs system to provide a consistent catalogue of products for varied applications, both for near real time applications and offline studies. We used the "twosat" daily series to keep a homogeneous data set over the period.
The HAC clustering The large number of neurons (288 in the present case) provided by the SOM map permits to take into account the complexity of the input data set but could remain difficult to handle both from a computer time-consuming point of view and from a visualization and interpretation one  (Meza-Padilla et al. , 2019). To counteract this difficulty, this large number of neurons is aggregated into a smaller number of categories based on the similarities of the subsets. We extracted a few pertinent classes from the subsets by clustering subsets having similar statistical properties, expecting that the classes can be associated with common oceanic characteristics. For that, a HAC is applied to the SOM maps using the Ward distance in order to aggregate the neurons into a small number of classes facilitating their interpretation as physical processes.
The 'strong forms' An important step is to identify groups of ocean situation (or image) constituted of the five variables taken together. To avoid the curse of dimensionality due to the large number of variables with respect to the number of data, we trained 5 different SOM maps, one for each variable ( SST, ADT, SSS, τx and τy). For each variable, the SOM algorithm has been applied as a vector quantization in order to compress the information contained in the dataset. Thus for each SOM map, a reduced number of "similar" cases was estimated using the HAC onto each set of the SOM neurons: each variable is represented by a number "generic cases". Since each variable belongs to a class obtained at the end of the HAC classification, the inter-dependence of the five variables (the correlations) was taken into account by determining a common partition considering the class of each variable: the so-called "strong form" partition.