dataesr/scientific-paragraphs-categorization huggingface dataset:
After calibration of the transform function and derivation of the latent values for each day of the calibration dataset, days with similar rain statistics are pooled together by rain typing. The first three principal components of the latent field are preliminarily derived from the Karhunen-Loève transform of all latent values. Next, the parameters of the GMM are inferred using an expectation-minimization approach  (Fraley and Raftery, 2002). Finally, rain typing (i. e. , clustering) is performed by assigning to each day the type that corresponds to the most probable component of the GMM. After rain typing, the time series of observed rain types is analyzed in relation to observations of the meteorological covariates to calibrate the non-homogeneous Markov chain. The baseline transition matrix (γ ij ) is first estimated by counting the transitions between each pair of rain types occurring during the calibration period and normalizing the result by the total number of transitions.
Next, the parameters of the mean vector (µ ij ) and the covariance matrix ( ij ) used to make the Markov chain non-homogeneous are estimated by the method of moments applied to covariate observations. Conditional to each rain type, the joint distribution of the parameters of ψ is inferred by multivariate kernel density estimation using a trivariate Gaussian kernel. The bandwidth of the kernel is selected following Scott's rule  (Scott, 1979 (Scott, , 2010)) , i. e. , in the present case H ii = N -1 7 × σ i , (7) where H is the bandwidth matrix of the kernel, N the number of days in the calibration dataset, and σ i the standard deviation of the ith parameter (here i = 1. 3). Finally, because the latent field is simulated using an analog approach (see Sect. 2. 3. 3 for details), this part of the model does not require formal inference.
Stochastic rainfall generation After model calibration, stochastic rainfall generation is performed following the steps summarized in Fig. 2. Starting from a time series of meteorological covariates, rain types are first simulated using the non-homogeneous Markov chain described in Eq. (  6 ). Next, conditional to this simulated rain type time series, the parameters of the transform function are sampled from their joint distribution defined by Eq. (  7 ). Subsequently, one realization of the latent field is simulated using an analog approach  (Gangopadhyay and Clark, 2005; Mezghani and Hingray, 2009; Yiou, 2014) , i. e. , by randomly picking the latent field of a day belonging to the same rain type as the day to simulate from the calibration dataset. Finally, the simulated rain field is obtained by backtransformation of the simulated latent field (Eqs.
Model assessment The ability of the model to identify climatologically relevant rain types is assessed qualitatively by applying rain typing to the full study dataset of Sect. 2. 1 and scrutinizing the emergent spatial-temporal rainfall patterns for each type. The resulting classification is subsequently interpreted in terms of rain generation processes by confronting rain types with cooccurring meteorological covariates. However, in doing so, one should keep in mind that the rain-typing procedure is fully statistical and that the rain type description is based on emerging statistical patterns, not on physical modeling (e. g. , using a numerical weather model to reproduce the observed patterns). When discussing rain types and their link to raingeneration processes, special attention is paid to 1.
the emergence of spatial patterns in relation to orographic effects, 2. the seasonality of rain type occurrence in relation to the regional annual rain cycle, and 3. the relationship of rain types to the state of the atmosphere quantified by the set of climate covariates described in Sect. 2. 4 and used here at a daily resolution (i. e. , not aggregated at the monthly scale as is the case for the conditioning of the non-homogeneous Markov chain). After the assessment of the climatological realism of rain types, the ability of the model to stochastically generate rainfall is assessed using a leave-one-year-out cross-validation procedure. Data from one year are iteratively removed from the study dataset of Sect. 2. 1 and the stochastic model is calibrated using the remaining data (i. e. , 19 years of data are used for model calibration).
The model is fully recalibrated, which includes rain typing, inference of the transform function, and creation of a training dataset of latent fields. After model calibration, daily rainfall is simulated for each day of the target year, i. e. , the year excluded from the calibration dataset. Fifty simulations are generated to assess the uncertainty associated with stochastic rainfall generation. The same procedure is repeated for each year of the study dataset, which leads to a 20-year long validation set made of 50 simulations for each gauge of the O'ahu rain monitoring network. Simulated rainfall is compared to observations following a multi-criteria approach. First, simulation results are evaluated qualitatively by visual inspection of rainfall time series for the four target stations of Fig.
Model versatility To investigate the versatility of our stochastic daily rainfall model, the case study performed in Sects. 3. 1-3. 3 for the island of O'ahu (Hawai'i, USA) located in the North Pacific was repeated in Sect. S4 (in the Supplement) for the island of Tahiti (French Polynesia) located in the South Pacific. Figure  7  provides a brief overview of the results. This additional cross-validation shows that our model also performs very well for Tahiti, despite a wetter (annual rain reaches 10 000 mm in Tahiti) and more seasonal climate than the O'ahu case study. In addition, the model adapts automatically to different dataset sizes (86 rain gauges × 21 years for O'ahu, 26 gauges × 11 years for Tahiti) and rain climatologies due to the selection of different numbers of rain types. These results suggest that our model may be adapted to most high tropical islands across the globe.
Our model can also be used for stochastic downscaling of future precipitation projections and thereby contribute to the current efforts to better understand, manage, and secure tropical island water resources in a changing climate. An essential future investigation in this direction will be to assess how well GCMs simulate the set of meteorological covariates we selected to drive our stochastic rainfall generator, in particular the vertical temperature gradient used to inform shallow convection. Code and data availability. The implementation of the proposed stochastic rainfall model is open source (MATLAB implementation) and freely available in the following repository (https://doi. org/10. 5281/zenodo. 6462982; Benoit, 2022). The dataset of daily rainfall observations on O'ahu is open data and freely available on the Hawai'i Climate Data Portal (https://www.
hawaii. edu/climate-data-portal/; HCDP, 2022). An extract of this dataset is available in MATLAB format as a code demo in the same repository as the source code of the model. The dataset of daily rainfall observations on Tahiti is available upon request from Météo France (contact. polynesie-francaise@meteo. fr) and Groupement d'Etudes et de Gestion du Domaine Public de Polynésie Française (secretariat@equipement. gov. pf). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/hess-26-2113-2022-supplement. Author contributions. LB, LS, and TWG designed the experiment. MPL and LS compiled the daily rainfall datasets of O'ahu and Tahiti, respectively. LB and ADN selected the meteorological covariates and designed the non-homogeneous Markov chain.
Acknowledgements. The authors are grateful to the  Hawai'i Climate Data Portal  for providing the daily rainfall dataset of the island of O'ahu, and to the  French Weather Agency (Direction Interrégionale en Polynésie française -Météo France)  and the  Polynesian public service named Direction de l'Equipement  ( Groupement d'Etudes et de Gestion du Domaine Public de Polynésie Française -GEGDP ) for providing the daily rainfall dataset of the island of Tahiti. The authors are grateful to  May Izumi  from the  Publication services of the School of Ocean and Earth Science and Technology, University of Hawai 'i, for proofreading this manuscript. Financial support. This research has been supported by the  Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung  (grant no. P2LAP2_191395 ).
ACKNOWLEDGEMENTS The authors would like to thank the  French oceanographic  fleet for funding the ABRACOS1 survey (http://dx. doi. org/10. 17600/17004100 )and the officers and crew of the  R/V "Antea"  for their contribution to the success of the operations. This work is a contribution to the LMI TAPIOCA (www. tapioca. ird. fr). LFA is supported by  Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco (FACEPE) , process number  BFP-0106-1. 08/21. The authors would like to thank the anonymous reviewers for their precious comments, additionally,  Dr. Johanna Weston  for her important suggestions throughout the text. The first author would, especially, like to thank  Dr. J. C. von Vaupel Klein  for his support and friendship.
saLe of WiLD Yam in CunDinamarCa At the MPMPs that were visited, 80 people between the ages of 28 and 59 were interviewed. Of those, 66% were women and the remaining percentage were men, which suggests that the sale of medicinal plants is a task that, for reasons of custom and credibility, is done mostly by women. The men who are in charge of selling medicinal plants resort to developing a more elaborate argumentative discourse to persuade customers to purchase their products since, according to the sellers themselves, the women are the ones who traditionally know and use the plants medicinally to take care of the family  (Mosquera-Mena et al. 2015; Nascimento et al. 2013; Toscano 2006 ). However, the percentages are not sufficient to affirm that this is a trend. Despite the perception of the sellers and data from other regions of the country, in the Cundinamarca and Bogotá marketplaces, we observed that both men and women are representative figures of popular medicine and are sought out to alleviate health problems, regardless of gender, before consulting the conventional medical system  (Jaramillo et al.
Acknowledgments We would like to thank the sellers and collectors from  Cundinamarca and Bogotá  who generously provided us with the information required in this investigation and welcomed us to their stalls and collection sites. We also thank  Julieth Monroy  for her help with the thematic cartography. We kindly thank anonymous reviewers for their valuable comments and suggestions. We also thank  Álvaro Pérez-Quintero  for his support in editing the last modifications of the manuscript. This research was funded by the  Program for Strengthening Interdisciplinary Alliances in Research and Creative Arts ,  2018 , project no. 42117. During the writing phase of this paper, DP was supported by  MOPGA doctoral fellowship  and by  Ministerio de Ciencia, Tecnología e Innovación of Colombia  ( Convocatoria 885-2020 ).
In temperate coastal regions of Western Europe, the polychaete Sabellaria alveolata (Linné) builds large intertidal reefs of several hectares on soft-bottom substrates. These reefs are protected by the European Habitat Directive EEC/92/43 under the status of biogenic structures hosting a high biodiversity and providing ecological functions such as protection against coastal erosion. As an alternative to time-consuming field campaigns, a UAV-based Structure-from-Motion photogrammetric survey was carried out in October 2020 over Noirmoutier Island (France) where the second-largest known European reef is located in a tidal delta. A DJI Phantom 4 Multispectral UAV provided a topographic dataset at very high resolutions of 5 cm/pixel for the Digital Surface Model (DSM) and 2. 63 cm/pixel for the multispectral orthomosaic images.
Introduction The honeycomb worm Sabellaria alveolata (Linné) is a gregarious tubiculous reefbuilding polychaete, inhabiting mostly the intertidal zone of the European coast  [1]. It is distributed from Southwest Scotland to North Africa (see the recent updated database from  [2] ) but also occurs on the Mediterranean coast in closed subtidal environments  [3, 4]. It is protected under the EU Habitat Directive because it engineers biogenic structures (sheets, veneers, hummocks-see terminology in  Curd et al. , 2019) , which provide habitat for numerous other species throughout its geographic range  [5]. The largest intertidal reefs, covering hundreds of hectares from mid to low tide levels, are found in France in the bay of Mont-Saint-Michel (48 • 40N, 01 • 40W;  [6, 7] ) and Noirmoutier Island (47 • 00N, 02 • 10W;  [8, 9] ).
The camera system includes a sun incident light sensor on the top of the UAV that is combined with the UAV's internal GPS receivers (Figure  A1 ). The flight plans were designed using the DJI Ground Station Professional application and by taking account of the specificities of the multispectral camera (Figure  A1 ). As this study aimed to generate a very high-resolution multispectral orthorectified dataset, the Ground Size Dimension (GSD) image pixel was set at 2. 6 cm. This implied a flight height of 50 m above ground level over an area of 750 m long-shore × 1000 m cross-shore. The camera orientations were set to obtain nadir photographs with a frontal overlap of 80% and a lateral overlap of 60% with the multispectral sensor. In order to limit the sun glint contamination on pictures over wet surfaces, the flight paths were oriented westwardeastward to be perpendicular to the path of the sun.
The shooting interval was triggered at 2 s. Radiometric calibration of the camera was computed in post-processing from UAV factory settings written into image metadata and sunlight sensor records. (1), south of the Loire Estuary, France. This coast is surrounded by shallow waters (depth < 15 m) adjacent to the turbid Bourgneuf Bay (2) and to Yeu Island  (3). (B) The study site is located in the southern part of a large sediment cell (4) dominated by sandy deposits. The main longshore transport direction (5) is southward, and has resulted in the building of a large sand spit  (6)  and an ebb delta swash platform  (7)  adjacent to a ca. 1 km-wide tidal channel, the "Goulet de Fromentine"  (8). (C) The honeycomb worm reef is located on the ebb delta at the right bank of the tidal channel  (8).
Reef Contouring Using Topography from DSM The accurate elevation model represented a robust dataset for mapping the reef through landform analysis. Among many topographic attributes we selected indices that (1) are independent of cross-shore elevation changes, (2) highlight steep landforms such as reef flanks, (3) normalize elevation relative to the adjacent background sediment to identify the reef top, (4) are easy to compute and understandable for non-specialists in landform analysis. Two indices matched these conditions: the Topographic Openness (TO)  [42] , and the Topographic Position Index (TPI)  [41]. The TO has been related to how wide a landscape can be viewed from any position  [42]. It expresses the openness (positive openness) or enclosure (negative openness) of a landscape location in degrees or radian units.
The Geomorphon method was used to first describe reef landforms and then to map tabular reef shapes. This method is based on image texture similarity concepts  [60]  using the elevation of a point of interest relative to its neighbors, and secondly on a line of sight concept  [42]  which examines the degree of terrain openness along eight principal compass directions. Thus, Geomorphon provided a high degree of terrain autocorrelation  [61] , detecting transition between landforms. This method was used with its current implementation into SAGA GIS software  [57]. This tool was set with a 2 × 2 m window and pixels were considered as flat landforms with a <10 • slope (Figure  A3 ). We set this window size to match the metric-scale width of the reef and to highlight landform diversity by limiting the terrain autocorrelation.
The reef basement elevation was extracted from the DSM by applying a 10 cm buffer to the reef contour (Figure  A4 ). The interpolation of the reef basement level was simulated through a Triangulated Irregular Network (TIN) from the ESRI ArcGIS software. RH was obtained by subtracting reef basement level from reef DSM elevation (Figure  A4 ). RV was calculated by multiplying RH by the pixel area for positive reef height (RH ≥ 0) (Figure  A4 ). A theoretical tabular reef volume was built by modelling the reef top level from the topography of upper and tabular reef areas. These areas were selected by using a threshold of TPI > 1. 4. The simulated platform topography (SPT) was modelled using a global polynomial function of rank 3 from the "Trend" toolbox in the ArcGIS software. SPT represented the elevation of the reef assuming that it has a full tabular shape.
Discussion The UAV survey coupled with SfM photogrammetry has provided the first very highresolution image of the honeycomb reef of the Fromentine delta on the French Atlantic coast. The DSM was used to assess ground elevation and apply topographic indices combined with multispectral data. With a spatial resolution of 2. 63 cm for multispectral data and 5 cm for the DSM (Figure  2A, B ), the reef extent was identified from the smallest isolated reef clumps to the largest tabular structures. The new index VB, estimating the percent cover of the platform-like structure, is proposed here. In spite of the low spectral resolution of the multispectral camera, macroalgae and mussels were satisfactorily identified, but this was not possible for oysters colonizing the reef.
Conclusions S. alveolata intertidal reefs showed a high heterogeneity of shapes and spectral responses. They can be confounded with a sandy background, especially with coarse resolution sensors such as EO satellites. The present study highlighted the possibility to map the reef footprint, shape, and spatial distribution of epibionts from a combination of spectral and topographic analysis of UAV-based multispectral orthophotograph and DSM. For the topographic dataset, we recommend at least RTK corrections for the UAV, with, if possible, a Ground Control Points (GCP) coverage. The topographic analysis can be done with a standard RGB sensor but the multispectral data was useful to map the epibionts. High-resolution multispectral datasets processed with the SfM photogrammetry technique provided centimeter-resolution orthorectified multispectral images and DSM.
01 to 15 km 3. The probability of landslide occurrence has been analysed with a logistic regression describing the relationship between a binary response variable (existence or absence of landslide) and a set of predictor variables such as high seafloor gradients and presence of active faults. The analysis of the severity of a given landslide has been investigated based on the estimation of the probability that the landslide reaches a certain (high) level (e. g. tsunami run-up or submarine cable breaks) giving that it has occurred through the extreme value analysis. We have used the Shaltop code simulating landslide run-out on the basis of a depth-averaged model based on the hydrostatic Saint Venant equations and Coulomb-type basal friction considering a Bingham rheology. Our tsunami simulations include Shaltop output scenarios as a source of the generated tsunami through hydrodynamic simulations using the hydrostatic 3D Navier-Stokes code Freshkiss3d.
The in situ data set was strengthened by satellite and numerical modelling data. The area affected by the storm was characterised by waters mixed from depths down to 60 m up to the surface, with a clear dilution of the deep chlorophyll maximum, leading to abrupt changes in the phytoplankton abundances in surface waters. The study of phytoplankton at the singlecell level showed clear physiological changes with a drop in the carbon/Chl-a ratio associated with an increase in abundances and biomass. These physiological shifts can be regarded as a reaction to the sudden changes. The storm, although identified as a rare event in this area, should be considered an important feature to study with respect to finescale physical-biological coupling, especially under stratified surface oligotrophic conditions where nutrient increases can trigger pulse production and affect global biogeochemical budgets.
Acknowledgements. We thank the captain and the crew of the  RV Téthys II  for the cruise and their help with the deployment of the instruments. This research was supported by  CNES  ( BioSWOT  project) and by the  French National programme LEFE  ( Les Enveloppes Fluides et l'Environnement)   FUMSECK-vv  project ( PIs   Stéphanie Barrillon  and  Anne A. Petrenko ). The flow cytometer was funded by the  CHROME  ( PI Melilotus Thyssen , financially supported by the  Excellence Initiative of Aix-Marseille University  -A* MIDEX , a  French "Investissements d'Avenir" program ), and  FEDER  funding ( PRECYM flow cytometry platform ). The authors thank  Nicole Garcia  and  Patrick Raimbault  from the  MIO-PACEM  platform for the chlorophyll-a and nutrient analyses. SPASSO is operated with the support of the SIP ( Service Informatique de Pythéas) , in particular  Christophe Yohia ,  Julien Lecubin ,  Didier Zevaco , and  Cyrille Blanpain  ( Institut Pythéas, Marseille, France ).
, 2018). To analyze the authors co-occurrence, we first identified the numbers of articles published by each author in the corpus. For this analysis we kept only the authors that published at least three articles in the corpus, as we considered that a lower number of articles published meant low or no co-occurrence. The cooccurrence of the selected authors was identified using Veech's probabilistic model of species co-occurrence  (Veech, 2013). The analysis was performed using the cooccur function of the cooccur R-package  (Griffith et al. , 2016). All the analyses were performed using the R programming language (R Core Team, 2019).
| Molecular sampling and analyses In order to evaluate genetic variability and estimate phylogenetic relationships within the Tropiocolotes nattereri species complex, we constructed a dataset of 70 specimens of Tropiocolotes. The phylogenetic dataset included specimens of most Tropiocolotes species, apart from T. chirioi, T. hormozganensis, T. tassiliensis and T. wolfgangboehmei for which no complementary genetic sequences are available. We included 30 specimens of the T. nattereri species complex from across its distribution range, mainly from Israel with additional samples from Jordan and the Sinai Peninsula. We retrieved 40 sequences of other Tropiocolotes species from GenBank  (Machado et al. , 2019 (Machado et al. , , 2021;; Metallinou et al. , 2012). The monophyly of Tropiocolotes has recently been established  (Machado et al.
, 2019) , and we therefore included one specimen of the phylogenetically closely related species, Trachydactylus hajarensis, as outgroup. Sample codes, voucher codes, localities and GenBank accession numbers are given in Table  1. We extracted DNA from ethanol-preserved tissue samples using the Qiagen DNeasy Blood & Tissue Kit following the manufacturer's protocol. We amplified and bidirectionally sequenced two mitochondrial and two nuclear gene fragments for each individual respectively: the noncoding ribosomal 12S rRNA (12S; 423 bp), the protein-coding NADH dehydrogenase subunit 2 (ND2; 1039 bp), oocyte maturation factor MOS (c-mos; 393 bp) and melanocortin 1-receptor (MC1R; 666 bp). These markers were chosen because they were used in previous phylogenetic and taxonomic studies on Tropiocolotes  (Machado et al.