dataesr/scientific-paragraphs-categorization huggingface dataset:
Theory and Calculation: Evaluating the Water Balance Components The following sections describe the methodology developed and implemented to understand physical processes and to compute the various components of the landslide water balance. Numerical hydrological models are widely used for the simulation of the rainfall-runoff process  [70] [71] [72]. However, they often require numerous calibration parameters. Our model was designed to conform to realistic physical processes and was distributed in two main parts: surface (runoff) and subsurface processes (role of the soil as a reservoir, evapotranspiration, recharge of the landslide aquifer, outflows from the landslide, etc. ). Even if the available dataset on this case study is comprehensive, it was chosen to favor robust methods. For instance, the reservoir modeling described below to simulate the soil's water budget uses a global approach rather than a discretized one  [73].
The rainfall measured at the Grand Ilet rain gauge was used as input function (RG-GI, Figure  4 ). For surface runoff, the choice of the dataset was based on the characteristics of the watershed under consideration (Figure  8 ). For the rampart watershed, the runoff (S) per surface unit was calculated with the flow from the Ravine Blanche station (S1, Figure  4 ). When dealing with the landslide's watershed, the runoff per surface unit was calculated from the fast flows observed at the Clain station (S6, Figure  4 ). S appears to be significant only on the rampart watersheds. A soil reservoir, with a finite volume, was used for reproducing the phenomenon of temporary storage in superficial soil  Hydrologic cycles were based on the calendar year (1 January to 31 December) because the first heavy rains after the dry season occurred in January 2011 and 2012.
In addition, this choice allowed good initialization of the reservoirs for water balance calculations (initialization at the end of severe low-water level). The rainfall measured at the Grand Ilet rain gauge was used as input function (RG-GI, Figure  4 ). For surface runoff, the choice of the dataset was based on the characteristics of the watershed under consideration (Figure  8 ). For the rampart watershed, the runoff (S) per surface unit was calculated with the flow from the Ravine Blanche station (S1, Figure  4 ). When dealing with the landslide's watershed, the runoff per surface unit was calculated from the fast flows observed at the Clain station (S6, Figure  4 ). S appears to be significant only on the rampart watersheds. A soil reservoir, with a finite volume, was used for reproducing the phenomenon of temporary storage in superficial soil horizons (Figure  8 ).
Hydrodynamic Response of the Aquifer Even though some differences in hydrodynamic response were observed in the flow from springs at the scale of the landslide, their dynamics are homogeneous and very comparable to those measured in the breccia aquifer (observation wells P1, P2 and P3). Furthermore, the observed piezometric fluctuations in the breccia aquifer showed that only significant rainfall events (more than about 80 mm/event from our dataset) generate a piezometric response in the aquifer as identified on the piezometers (Figure  9 ) or at the springs. Rainfall outside the cyclone season does not generate recharge that is detectable in the piezometric records. In all, only six rainfall episodes (Table  3 ) generated detectable recharge between January 2011 and December 2012 (two events during the rainy season of 2011 and four during the rainy season of 2012).
It is worth noting that only these six rainfall episodes generated increases in the landslide's sliding velocity (Figure  3 ). Water 2018, 10, x FOR PEER REVIEW 16 of 26 measured in the breccia aquifer (observation wells P1, P2 and P3). Furthermore, the observed piezometric fluctuations in the breccia aquifer showed that only significant rainfall events (more than about 80 mm/event from our dataset) generate a piezometric response in the aquifer as identified on the piezometers (Figure  9 ) or at the springs. Rainfall outside the cyclone season does not generate recharge that is detectable in the piezometric records. In all, only six rainfall episodes (Table  3 ) generated detectable recharge between January 2011 and December 2012 (two events during the rainy season of 2011 and four during the rainy season of 2012).
It allows good reproduction (i) of the occurrence of piezometric variations and (ii) of their amplitude, as well as the landslide's dynamics (Figure  11 ). volume of required rainfall will be during the first rainy episode(s) of the rainy season before a recharge flux toward the aquifer will be observed. This is due to the fact that the soil reservoir presents a large water deficit at the end of the dry season. The deviation of the model is smallest when the HBr coefficient of the soil reservoir is about 250 mm (Table  4 ). It allows good reproduction (i) of the occurrence of piezometric variations and (ii) of their amplitude, as well as the landslide's dynamics (Figure  11 ). Furthermore, the piezometric fluctuations observed in the breccia aquifer showed that only significant rainfall events (more than about 80 mm/event from our dataset) generate a piezometric response in the aquifer (Figure  10 ).
Rainfall outside the cyclone season does not generate recharge that is detectable in piezometric records (Figure  10 ). This is consistent with the fact that the discharge  Furthermore, the piezometric fluctuations observed in the breccia aquifer showed that only significant rainfall events (more than about 80 mm/event from our dataset) generate a piezometric response in the aquifer (Figure  10 ). Rainfall outside the cyclone season does not generate recharge that is detectable in piezometric records (Figure  10 ). This is consistent with the fact that the discharge regime (both of the streams and of the aquifer-the piezometric decrease) is not sustained by slow water motion in the unsaturated zone of the landslide's aquifer during the dry season  [61]. This water precipitated during the dry season is, thus, exclusively used by the vegetation (evapotranspiration).
The LANDSAT 7/8 ETM+ raster (30 m resolution) and NDVI MODIS raster, all courtesy of the U. S. Geological Survey, were downloaded from https://earthexplorer. usgs. gov. The MODIS NDVI is a 16-day composite of MODIS data at a spatial resolution of 250 m. First, NDVI values were computed at the spatial resolution of the Landsat raster (30 m) using an updated version of the python script provided by Dr. J. Degener and available from https://www. uni-goettingen. de/en/524379. html. Second, the Landsat NDVI raster and the MODIS NDVI raster were clipped over the area of interest (projection UTM 31 / WGS84). Third, the MODIS NDVI raster was aligned on the Landsat NDVI raster using the nearest value. Both rasters were then loaded in the statistical software R (R Core Team 2015) using the packages "rgdal" and "raster" and, finally, the pixel values from both rasters were bound into a single dataframe.
Pixels with missing MODIS values (-3,000) were discarded from the dataset. MODIS NDVI values were divided by 10,000 to be within the range of the Landsat NDVI values. Landsat NDVI values were then normalized by performing a pixel-to-pixel regression of the MODIS NDVI values against the Landsat NDVI values (see Suppl. material 1: Figs S2, S3 for an illustration).
NDVI computation. -As suggested by Relationship between NDVI, habitat quality and grazing. -We analyzed the relationship between rescaled NDVI, vegetation productivity and grazing using data from a recent experiment. In 2015-2016, an enclosure of 8. 56 ha, located in the same area as our study site (Suppl. material 1: Fig. S4 ), was fenced between April and August to exclude sheep. One hundred plots (of 30 cm diameter each) were randomly selected to measure vegetation productivity indices within (50 plots) and outside (50 plots) this enclosure. Vegetation productivity indices included: vegetation height, coverage in forbs, coverage in dry vegetation and coverage in bare ground (i. e. no vegetation). We tested whether our four productivity indices reflected the impact of sheep grazing on vegetation using Wilcoxon tests to compare the measures between plots located inside and outside the enclosure.
Spatial association between grasshopper density, habitat quality and genetic variation. -We used the MAPI program  (Piry et al. 2016) , freely available at https://www1. montpellier. inra. fr/CBGP/software/MAPI/. We produced a geographical map of the spatial vari-ation of the mean genetic differentiation between individuals and then interpolated, within the cells of the map grid, grasshopper density and habitat quality (rescaled NDVI) to test for correlations between variables. The successive three steps of this analysis are detailed below. Map of genetic differentiation. -We attributed the values of the statistics â of  Rousset (2000)  to ellipses that spatially materialize the connections between the pairs of individuals. A grid of hexagonal cells with a half-width of 20 m was superimposed on the study area (cell's area = 1,040 m 2 ; see 'Grid of cells' section in  Piry et al.
Details on parameters can be found in Table  1  and Suppl. material 1 from  Piry et al. (2016)  and in pp. 29-31 from the MAPI manual. Finally, to ensure that MAPI results were not biased by the patchy distribution of individuals (higher number of individuals sampled in high density areas), we ran the analysis on 100 independent resampled datasets with a maximum of three individuals from each circle and checked for consistency of results. Density and NDVI spatial interpolation. -Grasshopper densities measured within circles and rescaled NDVI values from Landsat (captured the 18 of May 2001: LE07_L1TP_196030_20010518_2 0170205_01_T1. tar. gz) and MODIS (captured the first 16 days of May 2001: MOD13Q1. A2001129. h18v04. 006. 2015142065654. hdf) images were first interpolated into a raster (5 m resolution) using the function "using v.
surf. rst" of the GRASS software (GRASS Development Team 2012) with parameter values set as follows: maximum number of points in a segment (segmax) = 120, minimum number of points for approximation in a segment (npmin) = 60 and maximum distance between points on isoline (dmax) = 25. Values interpolated below 0 were reset to 0 using the GRASS function "r. mapcalc". Second, raster pixels were clipped to MAPI grid cells and interpolated density values from pixels belonging to the same MAPI cell were averaged using the ST_SummaryStats function of the PostgreSQL 9. 3 with the PostGIS 2. 1. 2 extension  (1996 -2016, The PostgreSQL Global Development Group:  http:// www. postgresql. org/ -http://postgis. net/). Spatial scales of association between variables. -Spatial cross-correlograms allow investigation of how two variables co-vary with geographic distance.
We used the non-parametric spline-correlogram approach implemented in the R package "ncf"  (Bjørnstad and Falck 2001)  to analyze the spatial scale of association (Sp) between interpolated values of: 1) grasshopper density and rescaled NDVI, 2) grasshopper density and mean genetic differentiation between individuals (i. e. MAPI cell values) and, 3) rescaled NDVI and mean genetic differentiation between individuals (i. e. MAPI cell values). Confidence envelopes at 95% (CE 95% ) for the estimated correlograms were calculated using bootstrapping (500 replicates). We also computed Spearman rank correlation coefficients (Rho) between all pairs of variables.
MAPI results from resampled datasets (of a maximum of 3 individuals within circles) were highly consistent with those obtained from the complete dataset (Suppl. material 1: Fig. S8 ). Finally, Spearman coefficients showed that grasshopper density and rescaled NDVI values were positively correlated (Rho = 0. 51; p-value < 2. 2e -16 ) but both negatively correlated to MAPI cell values (Rho = -0. 42 and -0. 34, respectively; p-value < 2. 2e - 16 for both). The three cross-correlograms showed that the spatial scales of association between variables were highly similar and quite small (Fig. 7    This hypothesis was supported by the results of the analysis of vegetation productivity indices and NDVI in relationship with grazing treatment. Indeed, we found that three out of the four measured vegetation indices were significantly different between plots located inside and outside the fenced enclosure (Suppl.
Acknowledgements This work would not have been possible without the good will, motivation and curiosity of 16 persons that shared the field experiment, in addition to the authors. We want to sincerely thank each of them:  Audiot P, Barrau E, Brouat C, Caizergues A. (and family) ,  Cosson JF ,  Estoup A, François A, Gauthier N, Genson G, Gillon Y, Leblois R, Loiseau A, Melis JP, Pellegrin F, Perrier J. and Silvy C (in alphabetic order). They spent time, energy and sweat, walking under the sun and in the middle of aggressive stones, and we greatly appreciate it. We also thank the ' Conservatoire -Etudes des Ecosystemes de Provence' (CEEP)  for help during sampling and other parts of the experiment. A special thanks to  A. Guichard , who participated in the preliminary analysis of this data set during her summer training with us.
X-Ray Powder Diffraction (XRPD) The bulk mineralogy of microbialites was determined by XRPD. About 1 g of each sample was ground in an agate mortar. The powder was deposited on a single crystal silicon sample holder. Measurements were performed with an Empyrean Panalytical diffractometer using Cu-Kα radiation. Data were recorded between 4 and 90 • (2 θ) with a step of 0. 013 • and a total counting time of half an hour. The PANalytical Highscore Plus software  (Degen et al. , 2014)  was used for background subtraction, peak identification and matching with XRD patterns of reference compounds from the International Crystal Structure Database (ICSD, FIZ Karlsruhe, Germany; US Institute of Standards and Technology, United States).
Physicochemical Parameters of the Lake Dziani Dzaha Water The following physicochemical parameters of the lake water have been measured during five different campaigns  (2012) (2013) (2014) (2015) : pH, temperature, concentrations of dissolved O 2 , major anionic and cationic species, conductivity and alkalinity. All the methods used are described in  Leboulanger et al. (2017). Vertical profiles were taken using either a MPP350 probe connected to a Multi 350i data logger (WTW GmbH) or an YSI 600XLM probe (YSI) to measure pH, dissolved O 2 , temperature and conductivity. The total alkalinity was determined by titration with 0. 01 M HCl after 100 times dilution. Major cations were analyzed to ±5% by inductively coupled plasma atomic emission spectroscopy (ICP-AES iCAP6200 Thermo Fisher). Sulfate was measured to ±3% using 20fold diluted samples by ionic chromatography (ICS 1100 Thermo Fisher).
Thermodynamic Analysis The saturation state of the surface waters with respect to selected mineral phases was evaluated from the relation: = log Q K where is the saturation index, Q is the reaction quotient or ion activity product, and K is the equilibrium constant for the reaction of dissolution of the mineral under consideration. The reaction quotient is defined by: Q = i a n i i where a i is the activity of the subscripted aqueous species and n i is the stoichiometric coefficient of that species in the reaction. Values of a i were computed with the PHREEQC computer program  (Parkhurst and Appelo, 1999)  using an extended Debye-Hückel model for the activity coefficients. Mineral stability diagrams were constructed using equilibrium constants calculated with the SUPCRT92 program  (Johnson et al.
Bioinformatic Analysis and Sequence Processing Bacterial and archaeal 16S rRNA paired-end reads were merged with a maximum of 10% mismatches in the overlap region using FLASH  (Magoc and Salzberg, 2011). Denoising procedures consisted in discarding reads with no expected length (i. e. , expected size between 450 and 580 bp, 370 and 580 bp, for bacterial and archaeal 16S rRNA genes, respectively) and the ones containing ambiguous bases (N). After dereplication, the clusterisation tool ran with SWARM  (Mahe et al. , 2014). In the present work, aggregation distance was equal to 3. Chimeras were then removed using VSEARCH  (Rognes et al. , 2016)  and low abundance sequences were filtered at 0. 005% [i. e. , to keep OTUs accounting for at least 0. 005% of all sequences  (Bokulich et al. , 2013) , in order to remove singletons from the datasets].
Taxonomic affiliation was performed with both RDP Classifier  (Wang et al. , 2007)  and Blastn+  (Camacho et al. , 2009)  against the 128 SILVA database  (Pruesse et al. , 2007). This procedure was automated in the FROGS pipeline  (Escudie et al. , 2017). To compare samples, a normalization procedure was applied to randomly resample down to 51,730 and 21,673 sequences for bacterial and archaeal 16S rRNA genes, respectively. Differences in bacterial community structure were visualized using non-metric multidimensional scaling (NMDS) ordinations on abundance-based (Bray-Curtis) dissimilarity matrices. Using non-parametric permutation-based multivariate analysis of variance (PERMANOVA, function adonis in R package vegan,  (Anderson, 2001)  on abundance-based (Bray-Curtis) dissimilarity matrices, we tested for significant differences in community structure.
Fluorescence image stacks were obtained with concomitant excitation at wavelengths of 405, 488, and 543 nm by collecting the emitted fluorescence between 425-475, 500-530, and 560-660 nm, respectively. For Raman analyses we used a 785 nm laser source and proceeded as described in  Gerard et al. (2013). Briefly, dynamic line-scanning Raman mapping (Renishaw Streamline) was performed in the range 387-1538 cm -1 by scanning the sample over selected areas (20 s pt -1 ) by using a motorized PRIORTM stage. Laser intensity was set at 300 mW. Light was 1200 grooves per millimeter and the signal was analyzed with a RECAM charge-coupled device detector. Compositional maps representing the intensity distributions of characteristic peaks were determined using the software Wire 3. 2. Laser Microdissection, Whole Genome Amplification (WGA) and Cloning-Sequencing of 16S RNA Genes Small individual Pleurocapsales colonies and associated coccoid and filamentous cells were isolated using a Zeiss PALM MicroBeam apparatus installed in a clean room.
Phylogenetic Analyses Taxonomic affiliations at the phylum level were first obtained by comparing several portions of partial 16S rRNA gene sequences with sequences of the GenBank database using BLAST  [Basic Local Alignment Search Tool (Altschul et al. , 1997) ]. Representative clones of the dominant phyla were then fully sequenced and analyzed with the ARB software  (Ludwig et al. , 2004 ) by using the 123 SILVA database  (Pruesse et al. , 2007; Quast et al. , 2013; Yilmaz et al. , 2014). The sequences were first aligned with the SINA online aligner  (Pruesse et al. , 2012)  and then added in the ARB guide tree using the ARB parsimony tool. The phylogenetic tree was constructed by adding to the aligned sequences, sequences of the closest cultivated bacteria and environmental clones in the RAxML (Randomized Accelerated Maximum Likelihood) program  (Stamatakis et al.
This phylogenetic variety of Archaea was not detected previously in the microbialites of Alchichica Lake which represent a similar environment  (Couradeau et al. , 2011; Saghai et al. , 2015). Regarding eukaryotes, the presence of diatoms and green algae belonging to the Picocystis genus (data not shown) sometimes abundantly entombed was reported in all types of stromatolite using CLSM and SEM. Furthermore, patches of fly pupae were present (probably belonging to the Ephydridae family, common in brine waters) and their entombment contributes significantly to the stromatolites construction (Supplementary Figures  S2, S3 ). For this study, only the surfaces of stromatolites devoid of fly pupae were analyzed.
Microdissection and Whole Genome Amplification In order to gain more information on the phylogeny and metabolic capabilities of the microorganisms associated with the cauliflower crusts, laser microdissection was used to isolate consortia of Pleurocapsales with closely associated coccoid microorganisms (Supplementary Figure  S13 ). The coccoid microorganisms were pink to purple pigmented (Figure  2 ). After whole genome amplification and 16S RNA gene analysis, one species of Pleurocapsales (in five different amplifications) and one species of Rhodobacteraceae (in two different amplifications) were identified. The Pleurocapsales species shares 98% 16S rRNA gene identities with one Pleurocapsales species associated with the microbialites of Alchichica Lake in Mexico (accession number JN825323).
However, this Pleurocapsales species was not the main calcifying Pleurocapsales (accession number JN825326) detected in the Alchichica microbialites (Supplementary Figure  S13 ). The Dziani Dzaha Pleurocapsales is also closely related to Pleurocapsales species belonging to the microbial community involved in marble dissolution (accession numbers JQ404416 and JQ404419) (Supplementary Figure  S13 ). The Pleurocapsales detected in Dziani Dzaha stromatolites could thus have the capacity of colonizing and dissolving calcium carbonate. However, we did not detect clear evidence of Pleurocapsales boring activity in the samples collected. Interestingly, we found that the microdissected Rhodobacteraceae is also closely related to bacteria of the same microbialites in Alchichica lake (JN825344) (Supplementary Figure  S14 ), suggesting that this type of Rhodobacteraceae may have an important role in the formation of microbialites not only in lake Dziani Dzaha.
The Rhodobacteraceae of Dziani Lake and Alchichica Lake microbialites share 98% identities at the level of their 16S rRNA gene  (Couradeau et al. , 2011). They are also closely related to Alphaproteobacteria detected in the extreme saline-alkaline soil of the former lake Texcoco (Mexico) (accession numbers JN178563 and JN177885). They are distantly related (94% identities) to known cultivated species such as Amaricoccus species isolated from activated sludge system  (Falvo et al. , 2001)  and phototrophic purple non-sulfur bacteria such as Rhodobacter blasticus  (Eckersley and Dow, 1980).
Population genetic methods are widely used to retrace the introduction routes of invasive species. The unsupervised Bayesian clustering algorithm implemented in STRUCTURE is amongst the most frequently used of these methods, but its ability to provide reliable information about introduction routes has never been assessed. We simulated microsatellite datasets to evaluate the extent to which the results provided by STRUCTURE were misleading for the inference of introduction routes. We focused on an invasion scenario involving one native and two independently introduced populations, because it is the sole scenario that can be rejected when obtaining a particular clustering with a STRUCTURE analysis at K = 2 (two clusters). Results were classified as "misleading" or "non-misleading". We investigated the influence of effective size, bottleneck severity and number of loci on the type and frequency of misleading results.
We showed that misleading STRUCTURE results were obtained for 10% of all simulated datasets. Our results highlighted two categories of misleading output. The first occurs when the native population has a low level of diversity. In this case, the two introduced populations may be very similar, despite their independent introduction histories. The second category results from convergence issues in STRUCTURE for K = 2, with strong bottleneck severity and/or large numbers of loci resulting in high levels of differentiation between the three populations. Overall, the risk of being misled by STRUCTURE in the context of introduction routes inferences is moderate, but it is important to remain cautious when low genetic diversity or genuine multimodality between runs are involved.