dataesr/scientific-paragraphs-categorization huggingface dataset:
The  Warton et al. (2006)  test showed that the acoustic response between the EK60 and EK80 echo-sounders was similar for data from the surface layer. Whereas the test showed a significant difference for data from the deep layer, with higher values for the EK80 echo-sounder, but ultimately had a very small impact on the overall dataset results. This difference, which was not highlighted by the Wilcoxon test on averages, is small and thus not noticeable for fish management or research perspectives. To complement the previous comparison works, we studied the difference in TS obtained from the two generations of echo-sounder. The fish size distribution is important to calculate fish density by unit area  (Rudstam et al. , 2012; Simmonds and MacLennan, 20)  and to monitor fish size spectra  (Wheeland and Rose, 2016) , a reliable metric of fish population evolution  (McKenzie et al.
, 2021). Working in situ on fish populations is a reliable approach to compare systems  (Blanluet et al. , 2020; Draštík et al. , 2017; Guillard et al. , 2004; )  and will serve as references for lake fishery studies. Statistical differences were observed in the deep layer when comparing the TS values obtained between EK60 and EK80. This was true on the mean test and using  Warton et al. (2006)  test, which showed a deviation to the line 1:1 when comparing the TS by pairs. The difference in TS between the two generations of echo-sounder was weakly significant in the upper layer and was not observed when the dataset was considered as a whole. If we referred to the mean TS per layer, the difference in dB between the two generations of transducers could be considered as small, < 0. 5 dB. The mode of digitization differs between the EK80 and the EK60 could be at the origin of this variability observed between the two generations of echo-sounders.
The number of SEDs was not the same  (Demer et al. , 2015). For this kind of approach, to minimize potential biases related to the acquisition chain, the EK80 software was used to record the data and perform the calibrations. A strict comparison of the echo-sounders should have been made with the same transducer, as in De Robertis et al. (  2019 ), but it requires a specific electronic installation not accessible during our surveys. In this case, we overcome the differences specific to the use of two independent transducers, which do not have the same characteristics, the equivalent beam angle is not characterized by the standard calibration method, and then can be a major source of uncertainty in echo-integration measurements  (De Robertis et al. , 2019). In our configuration, the difference can be due to the own characteristics of the transducers and obviously from calibrations, which despite the care taken, imply variability.
Annex S A values according to the echo-sounders. No significant difference in the S A means between the two generations of echo-sounder, regardless of the layer, were found (Table  1  -Annex). The box plots of the S A values (Fig. 1  -Annex) also showed the similarities between the datasets acquired by the EK80 and the EK60, regardless of the layer. Using the procedure of  Warton et al. (2006) , the data were compared by EDSU (Fig. 1 -Annex ). For the surface layer here was no difference between the line of the major axis and the 1:1 relationship (p-value = 0. 7). For the lower layer, there was a significant difference with the line 1:1 (p < 0. 05), as for the overall dataset (p-value p < 0. 05). This resulted in slight deviance between the two echo-sounder for a few high values. Following De Robertis et al.
Acknowledgements This work was supported by  Pole ECLA (ECosystèmes LAcustres ) (OFB,  INRAE, USMB)  and had support from  AnaEE-France and Observatory of LAkes (OLA)  (boat and technical facility). The authors want to thank  Jean-Christophe Hustache  for his help in the sampling preparation and the fieldwork, and  Arthur Blanluet  for our valuable discussions and his relevant proofreading.
Fire data We used the Canadian National Fire Database (CNFD; Natural Resources Canada 2021) to extract information on fires having occurred from 1965 to 2019 on the Tłıchǫ First Nation territory. We removed very small fires (<1 ha) that are usually human-caused and located near settlements  (Stocks et al. 2002). We then calculated the number of fires per year or annual fire occurrence (AFO), annual area burned (AAB), and the fire size distribution (Table  1 ). We log-transformed the AAB data set (LogAAB) to meet the normality assumption, and we used Pearson's Rho test to measure the relationship between AFO and LogAAB. We measured the direction, strength and significance in time series of AFO and AAB from 1965 to 2019 by conducting a Mann-Kendall test using the 'Kendall' package in R  (McLeod 2015).
Climate data and fire season length To estimate average climate and fire-weather conditions during the fire season from 1965 to 2019, we extracted daily weather data from the four weather stations located nearest the study area (Lac La Martre, Lower Carp Lake, Rae Lakes, Yellowknife Hydro; between 62° 42' N, 113°54' W, and 64°05' N, 117°17' W; Figure  1a ) using the BioSIM software  (Régnière and Saint-Amant 2014). For each weather station location, we extracted daily weather data including temperature (°C), wind speed (km h -1 ), relative humidity (%) and total daily precipitation (mm, as water equivalent) measured at noon. We then used the BioSIM software to produce time series of daily weather data for each centroid of 10km grid cells covering the study area (n = 475 grid cells). BioSIM interpolated data from the four closest weather stations using inverse distance weighting output, while adjusting for differences in elevation and location differentials with regional gradients  (Régnière and Saint-Amant 2014).
Next, we used interpolated daily weather data as input to the Canadian Fire Weather Index (FWI) daily model implemented within the BioSIM software for calculation of the daily fuel moisture content and fireweather conditions for each centroid. The FWI is an internationally used fire danger prediction system based on daily weather observations affecting the potential of fires to ignite and spread (  Van Wagner 1987). Fuel moisture content is described by the Fine Fuel Moisture Code (FFMC), the Duff Moisture Code (DMC) and the Drought Code (DC) (Table  1 ). The FFMC, DMC, and DC indicate the moisture content in surface fine fuels, organic and deep organic layers, respectively, with higher values of these indices representing lower moisture content (  Van Wagner 1987; Wotton 2009). The other three components are indices of fire behavior, and their values are proportional to fire danger (Table  1 ): rate of fire spread (Initial Spread Index; ISI), fuel availability (Buildup Index; BUI), and fire intensity at the fire front (FWI) (  Van Wagner 1987; Wotton 2009).
Similarly, we assumed the fire season ended with the accumulation of 2 cm of snow on the ground for seven consecutive days or after three consecutive days with daily minimum temperature below 0°C, whichever occurred first  (Lawson and Armitage 2008). To assess the accuracy of our fire season length estimation with regard to snowmelt, we compared fire season length estimates with snowmelt timing extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) images for 2001-2015, after having resampled grid cell sizes from 250-m to 10-km resolution. We found a mean difference of 22 days between data sets, with MODIS images showing a later snowmelt than fire season length estimates. However, we also found that data sets were significantly and positively correlated (Pearson's r = 0. 72, p = 0.
Associations between elevation, land cover types and fires We examined the spatial associations between elevation, land cover types, and fire regime components during the study period. First, we summed the number of fire ignitions and area burned [log(total area burned by cell) +1] between 1965 and 2019 in each of the 475 cells of 10-km resolution covering the study area. For each grid cell, we extracted the mean elevation from a 10-m Canadian Digital Elevation Model (Figure  1b ; Natural Resources Canada 2016) and we resampled the data, calculating the mean at 10-km resolution. We then measured the spatial correlations between elevation and fire regime components in each cell covering the study area for the total study period. We extracted the proportion of land cover types in 2010 at a 30-m resolution and resampled it for each 10-km grid cell within the study area using Landsat data from the Canada Centre for Remote Sensing (NALCMS 2017) (Figure  1f ).
Seven land cover types were distinguished: sub-polar needleleaf forest, taiga needleleaf forest, sub-polar shrubland, sub-polar grassland, grassland-lichen-moss, barren lands, and water (Supplemental Material S4). We restricted the period of analysis for associations with land cover types (2010-2019) because we did not have information before 2010, and land cover types likely changed since 1965, particularly as a result of fire. To assess spatial associations, we used the 'SpatialPackv0. 3-8' package in R  (Osorio et al. 2019 ) and conducted a modified version of the t-test, which accounts for the spatial structure (latitude and longitude) of predictor variables  (Dutilleul et al. 1993 ) and prevents type I error inflation.
Detection of extreme wildfire years As suggested by the definition of extreme events (IPCC 2012), we defined extreme wildfire years (EWY) as years at the upper end of the AAB data set from 1965 to 2019. We applied Bartlett's goodness-of-fit test using the 'Renext' package in R  (Deville et al. 2016)  to confirm the exponential distribution of AAB (p < 0. 001). We estimated the parameters of the AAB data set, retaining only fire years (i. e. , 48 out of 55 years), by fitting a model including the 20 th -80 th percentile range corresponding to the bulk of observations. We used the 'getOutliers' function in the 'extremevalues' package in R (Van der Loo 2020) to identify the residuals of the estimated AAB data set higher than the 80 th percentile, i. e. , years within the highest 20% of the distribution and with a low probability of occurrence (p = 0.
Identification and characterization of extreme wildfire years We differentiated EWY from other years based on AAB. Nine years were in the top 20% (>80 th percentile) of the AAB data set  (1966, 1973 1976, 1979, 1998, 2008, 2013, 2014 and 2016)  and were thus characterized as EWY (Figure  2a  and Supplemental Material S5). The nine EWY had AAB > 365 km 2 (Figure  2a ). The remaining 39 years having recorded fires had small to moderate AAB, each between 0. 14 and 365 km 2 depending on the year. Eight of the nine EWY (all but 1973) recorded fires >20,000 ha (Figure  2a ), sometimes exceeding hundreds of thousands of hectares such as in 2014 (Supplemental Material S10). A total of 17 fires >20,000 ha were recorded from 1965 to 2019. These very large fires essentially occurred from the end of June to the middle of July (Figure  2b  and Supplemental Material S11).
Acknowledgments The authors would like to thank  Xiao Jing Guo  ( Laurentian Forestry Centre, QC, Canada ) for helping with modelling, and  Raphaël Chavardès  ( UQAT, QC, Canada ) for providing a friendly review of the manuscript. We also thank the two reviewers who made comments that helped us improve the manuscript. Permission to work in the area was granted by the  Tłıchǫ government.
Déclaration de disponibilité des données Le jeu de données détaillé incluant l'ensemble des relevés de sections et laisses de crue est disponible sur demande raisonnable auprès de l'auteur contact de cet article, Olivier Payrastre, ainsi que dans la base de données HyMeX (https://mistrals. sedoo. fr/?editDat sId=1621). Les estimations de débits de pointe obtenues sont détaillées dans  Payrastre and Nicolle (2021) , ainsi que dans  Sevrez (2020).
Remerciements Les enquêtes post-événement HyMeX sont financées dans le cadre du programme  MISTRALS/HyMeX du CNRS, et recoivent également un soutien financier du Ministère de la Transition Écologique (DGPR/ SCHAPI ). Nous souhaitons remercier les équipes et organismes qui ont contribué à la collecte des données de terrain :  Cerema Mediterranée ,  CNES ,  EDF-DTG, Hydrosciences Montpellier (HSM) ,  INRAE  ( UR HYCAR et RiverLy ),  ONERA, Opsia ,  Université Côte d'Azur (Géoazur et Espace) ,  Université Gustave Eiffel  ( GERS-LEE ), SMIAGE.
Par ailleurs, trois facteurs ont joué un rôle clé : la zone atelier Armorique, le temps relativement long du projet initial et les réseaux de gestionnaires. La zone atelier fournit une base de données issues d'observations reposant sur un protocole fixe, réalisé par le même technicien (disparition des biais liés aux observateurs) sur plusieurs années. C'est un socle scientifique pouvant servir de référence. Les fluctuations interannuelles des populations de coléoptères carabiques sont intégrées. Par ailleurs, les travaux sur les bocages ont aussi abordé d'autres groupes biologiques (plantes, oiseaux, papillons, etc. ) qui ont nourri la construction des critères intégrés au PGDH. Le projet initial « Bocage et biodiversité » s'est étalé sur quatre années. Ceci a permis de nombreuses réunions et discussions pour acquérir une culture commune et s'accorder sur les concepts.
A. GERMAINE et R. BARRAUD, 2013 ; S. DOURNEL, 2020 ; S. PIOCH et J. C SOUCHE, 2021). C'est dans cette démarche que s'inscrit l'ANR DIGUES (Digues, Interactions, Gestion, Usages, Environnement et Scénarios), qui a pour objectif de proposer différents scénarios relatifs au devenir des digues et d'en évaluer la faisabilité et l'acceptabilité (https://anr. fr/Projet-ANR-18-CE03-0008). Ce programme, qui se réfère au milieu fluvial (Loire, Marais Poitevin, Seine-et-Marne) et au milieu côtier (Dives, Veys, Authie), doit permettre, entre autres, de caractériser les évolutions possibles des systèmes d'endiguement et de déterminer des trajectoires envisageables dans un territoire. Celles-ci pourront prendre diverses orientations, souvent combinables, dans les domaines de la défense dure ou douce contre les eaux, de la gestion de la végétation, de la fréquentation et de la patrimonialisation.
a. Relevés aéroportés : acquisition de données à l'aide d'un drone Deux missions de survol aéroporté ont été réalisées entre juin et juillet 2020. Elles ont permis de couvrir une surface d'environ 47 ha (Fig. 3 ). 20 km ont ainsi été parcourus en 1h52' pour la réalisation de 2032 clichés (images géolocalisées haute définition, formats JPG et DNG) ainsi que 3 vidéos. Après traitement, le volume de données couvre environ 170 Go. Le modèle de drone utilisé est un DJI Mavic Pro (Capteur : 1/2. 3" (CMOS), nombre de pixels effectifs : 12,35 M, nombre total de pixels : 12,71 M) avec un objectif FOV 78,8° 26 mm (35 mm format équivalent f/2. 2). Les logiciels de programmation et de contrôle des vols utilisés sont Map Pilot V4. 1. 12 et DJI GO 4. Traitement et analyse des données ont été effectués avec les logiciels Agisoft Metashape (photogrammétrie) et QGis 3.
Study site: CLIMED long-term drought experiment All field data were collected in February-June 2018. We used a subset of established plots that were part of the CLIMED (CLImate change effects on MEDiterranean biodiversity) long-term drought experiment situated at Massif de l'Étoile in Marseille, France (43° 22' N, 5° 25' E). This site is under the management of Aix-Marseille University for research purposes only and therefore no fieldwork permit was required. This site has a typical woody shrub community dominated by three species: Quercus coccifera Linnaeus, 1753 (Fagaceae; anemophilous and a resource of very limited use to pollinators in the region;  Ropars et al. , 2020a) , Salvia rosmarinus Spenn. , 1835 (Lamiaceae; previously Rosmarinus officinalis;  Drew et al. , 2017) , and Cistus albidus Linnaeus, 1753 (Cistaceae;  Montès et al.
Floral scent We randomly selected up to 14 plant individuals per species in each treatment (control vs. drought) with a maximum of two (four for T. vulgaris) plants in the same plot. A few samples were lost during laboratory analysis, hence final sample sizes were 23 (control: 11; drought: 12) for S. rosmarinus, 22 (control: 11; drought: 11)  for C. albidus, and 19 (control: 6 female, 6 hermaphroditic; drought: 5 female, 2 hermaphroditic) for T. vulgaris. Floral scent sampling and data processing is described in detail in Supporting Information (1. 1. ). Briefly, the collection of floral scent volatiles was performed via dynamic headspace sampling in situ. Inflorescences from plant canopy bearing around 30-50 (S. rosmarinus), 2-3 (C. albidus) or 100-400 (T. vulgaris) flowers (1 st -3 rd quantiles) were enclosed in 2 L bags and placed under a constant flow of purified air (inlet 1000 mL.
Floral scent First, we analysed how drought altered floral scent chemical profiles of the three species independently. We performed a multivariate analysis on 4 √-transformed emission rates of all quantified volatile organic compounds (VOCs). Such transformation reduces variance heterogeneity across VOCs spanning several orders of magnitude in natural plant chemical profiles  (Hervé et al. , 2018). Typical metabolomic datasets often comprise many more molecules than samples, and these molecules or explanatory variables are often strongly inter-correlated, notably due to shared metabolic pathways  (Junker et al. , 2017). The canonical powered partial least squares discriminant analysis overcomes these biases by combining classification and regression and is commonly used for the analysis of chemical datasets (function 'cppls' with parameters 'centre' and 'scale' to true, R library 'pls';  Mevik et al.
, 2008) , using the treatment as factor and three components (function 'MVA. test', R library 'RVAideMemoire'; Hervé, 2019; model: "PLS-DA", cmv=TRUE, and otherwise defaults parameters). In short and for each species separately, all samples were randomly divided into three sets of equal length and respecting the proportions of control and drought samples in the full dataset. The first was set aside as the test group (outer loop). The second and third sets were used for model training and validation (inner loop). The first set was then used to test the consensus model. Inner and outer loops were repeated in 999 permutations. The function outputs the classification error rate and a P-value computed using the  Benjamini and Hochberg (1995)  correction for multiple testing. For T. vulgaris flower dimorphism is also likely to be reflected in floral scent, so we added the sex (female or hermaphroditic) as an additional response in both the multivariate analysis and the cross-validation test (parameter 'Y.
rosmarinus versus C. albidus) and the functional group (A. mellifera, bumble bee, large wild bees, small wild bees, Coleoptera and Diptera) as fixed effects, along with the scaled observation duration and the scaled counted number of flowers as co-variables to control for differences in the number of flowers per plot. The second test worked on the number of visits, assessing the interaction strength between each plant species and each visitor functional group (how intensely are each plant species expected to be visited?). We selected data with visits only (removing all zeros), and we used the same fixed and random effects as in the first test, but with a negative binomial distribution (function 'glmer. nb'). Since the interaction term was significant in this second test, we performed a post hoc comparison of means within functional groups and plant species and between treatments (function 'emmeans': 'specs = pairwise ~ treatment | pollinator functional group * plant species'; R library 'emmeans';  Lenth, 2019).
Finally, we analysed the drought impact on the pollinator community composition at a species level. We calculated the abundance and species richness of the total number of insect visitors caught in each plot through the season. Species identified to genus only were not counted in species richness (except one single specimen of Glyphipterix sp. , Table  S12 ), and such data was also used to estimate sampling completeness (methods in Table  S13 ). Drought impact on the abundance and species richness were analysed using t-tests. We also performed a constrained correspondence analysis to test whether drought affected community composition, using a permutation test with 999 permutations and a Fisher test (functions 'cca' and 'anova. cca', R library 'vegan';  Oksanen et al. , 2019)  on the same species used for species richness, but removing singletons.
Acknowledgments This study was funded by an AXA Postdoctoral Fellowship to CCJ  (AXA Research Fund, www. axa-research. org) , and by internal funding from the host institution IMBE. VOC analysis was performed by the Chemical Ecology and Metabolomic joint service from IMBE. Access to greenhouse facility was provided by the University of Oxford. We thank the GDR MediatEC (GDR 3658 , French national research group in chemical ecology -CNRS INEE) for their support and their contribution to Open access publication fees. We would like to thank Diane R. Campbell, Robert R. Junker, Robert A. Raguso, Lise Ropars, P. Camilo Zalamea, Mathieu Santonja, Nicolas Barthes, Maxime Hervé and Eric Meineri for useful comments on the methodology and experimental design, Julien P. Renoult who gave us access to a spectrophotometer, and David Genoud, Matthieu Aubert, Eric Dufrêne, Christophe Lauriaut and Jean-Pierre Hebrard for expert identification of insect specimens.
Remerciements Cette étude a été réalisée dans le cadre d'un Master 2 (FG;  2017-2018 ) à l'ISEM (encadrement LB),  Université de Montpellier  en partenariat avec l'UMR 7269 LAMPEA Aix en Provence (co-encadrement GG). Ce travail s 'insère dans un projet financé par l'Institut Danone France/Fondation pour la Recherche Médicale 2015 (Women and diet  at the beginning of farming, 5th -3rd millennium BC, France: a bioanthropological approach; Dir. G. Goude 2016-2017; http://institutdanone. org/nos-prix/femmesalimentation-les-premieres-societes-agropastorales-ve-iiie-millenaires-av-j-c-france-approche-bioanthropologique/). Nous exprimons toute notre reconnaissance à Jean Courtin pour nous avoir permis d'analyser le matériel des sites de Fontbrégoua et de la Font aux Pigeons dont il a dirigé les fouilles.
Précipitation Les lames d'eau de la base de données "ANTILOPE J + 1" de Météo-France ont été utilisées pour la modélisation pluie-débit et la modélisation hydraulique. Cette réanalyse est disponible 24 heures après chaque événement et est construite à partir des pluies estimées par analyse des réfléctivités radars, corrigées avec les données des pluviomètres de Météo-France  (Laurantin, 2008). Deux pluviomètres de Météo-France sont situés sur le bassin de la Brague (à Antibes et à Valbonne), ils sont représentés en bleu sur la Figure  1i. En plus de ces deux pluviomètres, d'autres pluviomètres ont été récemment installés à Sophia-Antipolis sur les toits de l'école d'ingénieurs Polytech Nice Sophia (losange rouge Figure  1i ) et du laboratoire Géoazur (losange orange Figure  1i ). Brigode et al.
Cette alternative permet d'améliorer à la fois l'efficacité et la précision sur les estimations de vitesse (voir les annexes C & F de Rousseau (2019) pour plus de détails sur les procédures). L'algorithme ainsi qu'une application sur les vidéos de la Brague@Biot est à disposition sur un dépôt GitHub (https://github. com/groussea/opyflow). Le paramètre CN (sans unité) reflète la capacité d'infiltration du bassin versant étudié (fonction du type de sol, de l'occupation des sols, des pluies antécédentes, etc. ). Le CN varie entre 30 et 100  [-]  : un bassin versant caractérisé par un CN de 100 est un bassin versant n'ayant aucune capacité d'infiltration (pluie nette = pluie brute), alors qu'un bassin versant caractérisé par un CN de 30 est un bassin avec une très grande capacité d'infiltration (pluie nette ≪ pluie brute).