dataesr/scientific-paragraphs-categorization huggingface dataset:
In this model, a and b were quantified through a combination of aerial photograph interpretation and field surveys, whereas long-term topsoil (E Topsoil ) and gully (E Gullies ) erosion rates were assumed to be known and representative of any parts of the Kamech catchment. In this study, E Topsoil was set to the measured erosion rate between 2005 and 2012 at the experimental plot P1 (Es_P1, this study) and E Gullies was set to 80 t ha -1 yr -1 as estimated by El Khalili et al. (  2013 ). Erosion from dwellings Table  1. The reconstruction procedure for runoff (R, mm) or soil erosion (Es, t ha -1 ) used to evaluate missing data in the event database at the three monitoring stations: the experimental field (P1), the gully (P2+G), and the micro-catchment (MC); Q max is the maximum specific runoff discharge (m 3 s -1 ha -1 ) and R 2 is the linear determination coefficient.
Acknowledgments Nesrine Inoubli benefited from a Ph. D. grant from  IRD. This study was financially supported by  ARIMNet2  ( ERA-NET )  MASCC  project (Grant Agreement no. 618127 ) and  ANR TRANSMED   ALMIRA  project (Grant Agreement no. ANR-12-TMED-0003-01 ). The OMERE observatory (http://www. obs-omere. org),funded by the  French institutes INRA  and  IRD  and coordinated by  INAT Tunis ,  INRGREF Tunis , and  UMR LISAH Montpellier , is acknowledged for providing a portion of the data used in this study. We would like to acknowledge the field staff, and especially  Radhouane Hamdi , for the help in data collection.
Indeed, in order to adapt to such a constraining season, the vegetation reduces its photosynthetic activity, usually by dropping leaves and sometimes by merely putting the vegetative tissue into dormancy  (Nicholson, Tucker, & Ba 1998; Godron, 1984). Three main datasets are usually used in such studies: Global Inventory Modeling and Mapping Studies (GIMMS) NOAA AVHRR, SPOT VGT followed by PROBA-V  (Wolters et al. , 2015)  and MODIS  (Fensholt, Rasmussen, Nielsen, & Mbow, 2009). The main differences between these datasets are spatial resolution and the beginning of the time series. • The PROBA-V product offers the best spatial resolution with a 100-m pixel but temporal coverage begins in 2013. Here, changes have been studied in the longest possible period in a large enough region to obtain a medium to large resolution.
Therefore, GIMMS AVHRR is the most appropriate dataset. In addition,  Fensholt et al. (2009)  affirm about SPOT VGT that the replacement of VGT1 by VGT2 in 2003 involved a change in spectral response. Since these considerations, a great deal of efforts has been around since that time to reprocess the SPOT VGT dataset and the weakness stressed by Fensholt has been reduced  (Deronde et al. , 2014). About GIMMS AHHRR, the authors suggest that trends in the Guinean zone should be interpreted with great caution  (Fensholt et al. , 2009). To that effect, the analysis of a temporal series of NDVI images has been carried out in order to study the vegetation's seasonal vegetative cycles and these cycles' decennial variation  (Hmimina et al. , 2013). The aim is first to test the hypothesis that it is possible to map the limit between ecosystems with a vegetation following a seasonal rhythm representative of forests (evergreen to bimodal vegetative rhythm) on the one hand and ecosystems with a vegetation following a seasonal rhythm representative of savannas (deciduous with unimodal vegetative rhythm).
Data The NOAA GIMMS NDVI, downloaded from USGS Earth Explorer (http://earthexplorer. usgs. gov/), is a normalized difference vegetation index (NDVI) product available for a 25-year period spanning from 1981 to 2006. The data set is derived from imagery obtained from the Advanced Very High Resolution Radiometer (AVHRR) instrument onboard the NOAA satellite series 7, 9, 11, 14, 16 and 17. This is an NDVI data set that has been corrected for calibration, view geometry, volcanic aerosols, and other effects not related to vegetation change. (Tucker et al. , 2005)  The image resolution is 8 km, which strongly flattens all the local structures of land use and ecosystems to transmit only a phenological information on the overall vegetation cover. With this database, only large forest massifs can be distinguished from large farmland areas  (Andrieu 2008).
Beck et al. (2011)  present a comparison of the four NDVI products obtained from the AVHRR and affirm that among these results, "in the comparison of temporal-change values the GIMMS dataset performed best". It should be noted,  Fensholt et al. (2009)  warn that AVHRR GIMMS coarse resolution NDVI data set is well suited for long-term vegetation studies of the Sahel-Sudanian areas receiving less than 1000 mm rainfall, whereas interpretation of GIMMS NDVI trends in more humid areas of the Sudanian-Guinean zones should be done with certain reservations. Nevertheless, this paper, with all due caution, presents valid results. The set of data, from July 1981 to December 2006, covers a long period of major changes for West Africa. A period of drought, from 1968 to 1994  (Dai et al. , 2005; Nicholson, 2001) , was followed by a return of wetter conditions since 1994  (Nicholson, 2005).
Therefore, the temporal series begins at the heart of the period of drought, a few years before 1984, which is considered as the driest year by most stations  (Hulme, Doherty, Ngara, New, & Lister, 2001). It does not allow to study evolutions linked to the beginning of that period of drought, but conversely, it is suitable for phenological observations of the end of the dry period and the beginning of the wetter period  (Anyamba & Tucker, 2005). As regards the consequences of armed conflicts in Côte d'Ivoire  (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) , once again the dataset is not very well positioned in time, because it covers neither the second half of the conflict period nor the period from 2011 to date. That is why, this paper focuses on the context regional and decennial characterization that can be followed by studies of temporal series of NDVI MODIS with a higher resolution and a more recent time span.
Detailed data processing Data processing has been carried out directly on the whole NOAA GIMMS NDVI time series under © IDRISI SELVA software of © Clark Labs with a "stand alone license". Figure  2  depicts the data processing. First, in order to delimit these areas, after extracting the rectangle encompassing our zone of study on all images of the temporal series, an initial K-means unsupervised classification  (Diday, 1971 ) has been carried out on a sample of images so as to extract a class corresponding to the sea and a narrow coastal strip (1 pixel wide) which is blurred by the latter and is not concerned by the present analysis, thus producing a Boolean mask. Next, a PCA was carried out on the whole of the temporal series (applying the aforesaid mask). A forward T mode was applied over the variance/covariance matrix.
The main components (more than 1% of the eigenvalue) were numbered from 0 to 255 to create Principal component's coordinate as raster. The third and last part of this classification data processing is another K-means unsupervised classification (once more in 10 classes) still applying the mask. These 10 classes correspond to the 10 areas studied in this paper (Figure  3 ). The mean of each 15day unit (frequency of the dataset; e. g. , 1-15th   January, 16-31th January etc. ) has been calculated over the 1982-2006 period. It creates a dataset for the studied periods of 24 NDVI mean images. The NDVI mean of each class for each 24 NDVI mean images enabled us to draw the curves (Figures  4  and 5 ). The low-resolution and long-term mean is intended to filter small changes in land-cover and ecosystems dynamics to show only a macroregional climatic and biogeographical spatial structure.
In order to enlighten temporal decennial trends and particularly the difference between the drought and the wetter period between and after 1994  (Nicholson, 2005)  in addition to the massive deforestation observed in this area  (Barima et al. , 2016) , the mean of each 15-day unit (1-15th January, 16-31th January etc. ) has been calculated over the 1982-1994 period and over the 1995-2006 period. It creates 2 datasets for both periods of 24 NDVI mean images each. The NDVI mean of each class for each 48 NDVI mean images enabled us to draw the graphics (Figures  4  and 5 ). The second part of data processing is a pixelbased index of the kinematics in the same two periods  (1982-1994/1995-2006). It aims to create a map of the conversion from bimodal to unimodal or the opposite for each pixel.
(2010) ,  Gautier (1989) ,  Goetze, Horsch and Porembski (2006)  and  Spichiger and Lassailly (1981) ; in Togo,  Guelly, Roussel and Guyot (1993) ; in Nigeria,  Adejuwon and Adesina (1992) ; in Cameroon,  Dounias (1996 ), Happi Youta (1998)  and  Letouzey (1968) ; in the Central African Republic,  Boulvert (1990) ; and in Congo,  Foresta (1990)  and  Schwartz et al. (2000). However, opposite dynamics have been observed in certain regions of Côte d'Ivoire. A significant advance of savanna at the expense of forest has been reported in the contact area of Côte d'Ivoire's center-west  (N'guessan, Bellan, and Blasco, 2003)  and east  (Barima, 2009). These regions hardly appear at this scale, with this time span and this method, which is the reason why a study of the same subject using the MODIS database will be envisaged.
Acknowledgment This project was funded by " Partenariat rénové pour la Recherche au Service du Développement de la Côte d'Ivoire" (PRESED-CI ), from 2014 to 2016 under the direction of  Y. S. S. Barima. The author would also like to thank  Nicole Benazeth  (jpn. benazeth@wanadoo. fr) for the translation.
Datasets and WRKY protein identification The scheme of the in silico pipeline used for identification of the Theobroma cacao WRKY protein set is shown in the S1 Fig. The  Theobroma cacao protein sequences were downloaded from the CocoaGenDB database v1. 0 (Theobroma_cacao_v1. pep. faa. gz; http://cocoa-genomehub. southgreen. fr/gbrowse) [28]. The WRKY protein sequences of Arabidopsis thaliana were downloaded from the Phytozome database v12. 1 (www. phytozome. org) and plant transcription factor database v. 3. 0 (http://plntfdb. bio. uni-potsdam. de/v3. 0); only the non-redundant proteins between both databases were considered and used for the subsequent analysis (S1 Fig)   1 ). Two of the TcWRKY proteins presented a highly altered WRKY motif and/or zinc-finger motif (Tc00_g017240 and Tc02_g001170) and for this reason were excluded from the phylogenetic analysis (Table  1 ).
TcWRKY gene classification and chromosomal location After confirmation and identification of the final dataset of TcWRKY proteins (S1 Fig), all whole protein sequences, except the Tc02_g012180, were used for sequence alignment using the ClustalOMEGA software (http://www. ebi. ac. uk/Tools/msa/clustalo/). The cacao TcWRKY proteins were categorized based on Arabidopsis WRKY protein classification  [6]. The distribution of the TcWRKY sequences on cacao chromosomes was obtained from the CacaoGenDB database v1. 0 (http://cocoagendb. cirad. fr) [28]  using "WRKY" as input in the "Search by Keywords" tool (http://cocoa-genome-hub. southgreen. fr/content/search-keywords). The sequences downloaded from the CacaoGenDB database v1. 0, excluding the chromosome Tc00, were used as an input file for prediction of WRKY gene duplication and collinearity using the MCScanX toolkit, according to the manual  [29].
Identification of conserved motifs in TcWRKY proteins The detection of the motif composition in the 58 identified cacao WRKY proteins (all proteins except Tc00_g017240, Tc02_g001170 and Tc02_g012180) was performed with the MEME 4. 9. 1 program (http://meme. nbcr. net/meme/intro. html) [30]. MEME represents motifs as position-dependent letter-probability matrices which describe the probability of each possible letter at each position in the pattern  [30]. The maximum number of motifs was set at 20, the maximum motif length was set at 80 amino acids, the optimum motif width was constrained to be between 6 and 300 residues, and the other parameters were used with the default settings. The organization of the different motifs (e. g. WRKY DNA-binding domain, leucine rich repeat/LRR) present in the cacao WRKY proteins was performed with the SuperFamily database v.
Phylogenetic analysis A phylogenetic tree of amino acid sequences of WRKY domains from cacao (58 sequences, see paragraph above) and Arabidopsis (S1 Table ) was constructed. The amino acid sequences of WRKY domains were aligned using the MUSCLE program v3. 6  [32]  with default parameters. The MEGA 5. 1 software was used to construct a rooted phylogenetic tree  [33]. The tree based on WRKY domains of Arabidopsis and cacao was used to identify and classify putative orthologs. The statistical method used to construct the tree was neighbor-joining  [34] , the evolutionary distances were obtained using the p-distances method, and these distances were used to estimate the number of amino acid substitutions per site. The reliability of each tree was established by conducting 1000 bootstrap sampling steps.
To construct the tree with all species used in this study, the JTT evolutionary model plus gamma-distributed rate (JTT+G) was used as determined by the Modeltest program version 3. 7  [35]. The phylogenetic analysis was completed by an analysis of orthology using the plant transcription factor database v. 3. 0 (http:// plntfdb. bio. uni-potsdam. de/v3. 0).
Experiments also included a negative control (no template DNA). Real-time data acquisition was performed with the Stratagene MX3005P system containing the MxPro QPCR software (Agilent Technologies), which provided the values of cycle threshold (Ct) and of fluorescence. Amplification efficiency (E) was accessed using the Miner 2. 2 software  [50]. The gene expression level was analyzed with three experimental repetitions for both Catongo and TSH1188 genotypes with the comparative Ct method (2 -ΔΔCt ) using: i) MDH and GAPDH as reference genes (average of expression values from both genes); and ii) non-inoculated plants as a calibrator (at each harvesting time, a non-inoculated sample was collected and used as calibrator of the corresponding inoculated sample). Statistical analysis was done using the SASM-Agri software v.
Identification and classification of TcWRKY sequences In the cacao genome, 61 non-redundant sequences corresponding to putative WRKY proteins were found using BLASTP (Table  1 ). Among them, 47 had been previously annotated as WRKY proteins in the CocoaGenDB; the other 14 proteins identified by BLASTP had been previously annotated as uncharacterized, predicted or hypothetical proteins (data not shown). Among the 61 non redundant sequences, 60 contained at least one complete heptapeptide WRKY motif while one sequence (Tc02_g012180) did not present such a conserved stretch (Table  1 ). The TcWRKY proteins were classified into three groups according to the presence of WRKY motif and the zinc-finger motif type. Group I contained two WRKY motifs (one in the N-terminal region of the sequence, the other in the C-terminal region) and two C 2 H 2 zinc-finger motifs; this group contained 10 TcWRKY proteins (.
The expression of the TcWRKY genes was analyzed in two cacao genotypes, TSH1188 (resistant to witches' broom For both genotypes and for all the harvesting points, the PCR amplification occurred at the same melting temperature, showing that only the target gene was amplified (data not shown). RT-qPCR analysis showed differential expression between genotypes and/or between time intervals for all the analyzed genes. In Catongo, the TcWRKY genes Tc06_p004420, Tc09_p001530, Tc04_p016130 and Tc10_p016570 showed higher transcript abundance in the final stages of the infection (15 to 45 dai). The gene Tc01_p014750 showed higher transcript abundance 15 dai but also at the beginning of the infection (6 hai; about 6 times more expressed). The gene Tc06_p013130 showed higher transcript abundance (about 7 times more than the control) 24 hai while the gene Tc01_p018460 was more expressed 7 dai (Fig  6B ).
Besides the heptapeptide WRKYGQK and the C 2 H 2 /C 2 HC zinc-finger motifs-known to be involved in DNA-bindingsome TcWRKY proteins (Tc02_g001230, Tc02_g001200, Tc02_g001170 and Tc00_g017270) showed conserved motifs, such as NB-ARC and/or LRR (Fig  5C ), known to be involved in pathogen recognition, plant resistance and activation of plant immunity  [59, 60]. Expression and functional analysis of WRKY TFs could help in discriminating the role and function of these proteins at the tissue and organism levels. Here, we evaluated by RT-qPCR the expression of seven TcWRKY genes in resistant and susceptible cacao plants inoculated or not with M. perniciosa. The choice of the genes was based on previous indications in the genome databank (CocoaGenDB), phylogenetic analysis and putative function of the orthologues in Arabidopsis, showing that: i) Tc01_p014750 and Tc06_p013130 (indicated as TcWRKY28) were co-orthologous to AtWRKY8 and AtWRKY28; ii) Tc09_p001530 and Tc06_p004420 (both indicated as TcWRKY40) were both orthologous to AtWRKY40; iii) Tc04_p016130 and Tc10_p016570 were co-orthologous to AtWRKY54 and AtWRKY70; and iv) Tc01_p018460 was orthologous to AtWRKY1 (Fig  4 ).
The cacao proteins were screened to search for WRKY members using the local BLASTP program (blastall version 2. 2. 27; ftp://ftp. ncbi. nih. gov/blast/executables/blast+/LATEST) and the WRKY protein sequences of A. thaliana as input sequences (S1 Fig). The e-value for BLASTP was set at 1e-10 to obtain the final dataset of WRKY proteins. When more than one alternative splicing sequence was found for the same locus, only the longest non-redundant sequences were used for subsequent analyses. Subsequently, these candidate TcWRKY protein sequences were submitted to analysis using the InterPro (http://www. ebi. ac. uk/interpro/) and PFAM programs (http://pfam. xfam. org/) to confirm the presence of the WRKY domain, and analyzed using the SMART program (http://smart. embl-heidelberg. de/) to confirm the presence of the zinc-finger domain (S1 Fig).
This research was supported by  FAPESB  (project  DTE0038/2013 ) coordinated by FM. DSMA took a practical course at Unicamp funded by  Coordenacão de Aperfeicoamento Pessoal de Nı ´vel Superior (CAPES ,  PROCAD  project). DSMA and RJSS were funded by  FAPESB. DOJA was funded by  Conselho Nacional de Desenvolvimento Cientı ´fico e Tecnologico (CNPq). FM and KPG received a Productivity Grant from  CNPq  ( PQ1 ). LEDB was funded by  Fundacão de Amparo à Pesquisa do Estado de São Paulo (FAPESP). EBS was funded by  UESC. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Model Selection We use ESMs from the Coupled Model Intercomparison Project Phase 5 (CMIP5) to perform the BMA analysis on modeled runoff. Most of the models have realistic representations (e. g. , the representation of vegetation dynamics and subgrid scale hydrology in some models) of processes related to surface hydrology  [Moss et al. , 2010; Knutti and Sedláček, 2012; Flato et al. , 2013]. Only CMIP5 ESMs that provide monthly runoff of both historical simulations and scenario-dependent projections (Representative Concentration Pathway, RCP4. 5 and RCP8. 5) are selected in this study, resulting in a list of 31 ESMs (Table  S1  in the supporting information). Outputs of the selected CMIP5 models are downloaded from the British Atmospheric Data Centre. To facilitate comparison with observations, all model outputs for the period 1986-2100 are regridded to the resolution of 1°latitude × 1°longitude using the first-order conservative remapping technique  [Jones, 1999].
Data Sets The observation-based "diagnostic" annual runoff from University of New Hampshire/Global River Discharge Center global composite runoff fields (UNH/GRDC GCRF)  [Hall et al. , 2006; Fekete et al. , 2002; Fekete and Vorosmarty, 2011]  is used to constrain the multimodel ensemble of CMIP5 models. This data set provides annual runoff at a resolution of 1°× 1°for the period  1986 -1995 (Text S2) (Text S2). We also use mean annual discharge records from the Global River Discharge Center (GRDC) to further validate the accuracy of the two ensemble methods (BMA and AMA). From a previously defined set of top 200 large rivers  [Dai and Trenberth, 2002] , we select 28 rivers (Table  S2  and Figure  S4 ) that satisfy the following criteria: (i) discharge has been continuously measured for more than 10 years at the gauging station closest to the estuary, (ii) drainage area is greater than 100,000 km 2 , and (iii) geographic information system shapefile can be acquired from the GRDC.
, 2011] , there are three major challenges in process-based evaluations. First, such evaluation needs to pull together the multivariate and multiscale modeled and observed data from a wide range of hydroclimatic regimes. Although CMIP5 data set does provide a limited number of climatic variables, these are usually insufficient to fully understand what is happening within the governing equations for each land surface component. Moreover, even if a number of data streams are currently available, those data are also known to have issues like uncertainty in observations and the incommensurability between models and measurements. Second, it is difficult to devise meaningful ways (e. g. , diagnostic metrics) for evaluation. In particular, a key problem is how to evaluate the impacts of interlinked physical processes on system-scale responses at large scale  [Beven and Cloke, 2012; Wood et al.
Acknowledgments: This work is supported in part by the grant  ANR11EQPX0011  of the  Agence Nationale de la Recherche  allocated to the program "Critex"-Challenging equipment for the temporal and spacial exploration of the Critical Zone at the catchment scale- (20 March 2013). It won the calls to submit projects for the " 2012 Facilities of Excellence" competition named Equipex. The program Critex is a French national project, which groups together 21 laboratories from more than 20 universities and four research organizations. It will allow the study of surface and underground water relating to the characteristics of the natural environment and surrounding human activities. It will thus contribute to a better management of water resources in varied contexts. IPGP contribution number 3845.
This work describes the snow and meteorological data set available for the Izas Experimental Catchment in the Central Spanish Pyrenees, from the 2011 to 2017 snow seasons. The experimental site is located on the southern side of the Pyrenees between 2000 and 2300 m above sea level, covering an area of 55 ha. The site is a good example of a subalpine environment in which the evolution of snow accumulation and melt are of major importance in many mountain processes. The climatic data set consists of (i) continuous meteorological variables acquired from an automatic weather station (AWS), (ii) detailed information on snow depth distribution collected with a terrestrial laser scanner (TLS, lidar technology) for certain dates across the snow season (between three and six TLS surveys per snow season) and (iii) time-lapse images showing the evolution of the snow-covered area (SCA).