dataesr/scientific-paragraphs-categorization huggingface dataset:
75° resolution grid of the ERA-INTERIM database using nearest neighbors prior to any other transformation. Statistical corrections were applied on a monthly basis to account for seasonal variations in distributional differences. We used the quantile delta mapping method  54  for univariate bias correction and the MBCn algorithm  53  for the multivariate correction. MBCn is a multivariate generalization of quantile mapping that transfers all aspects of an observed continuous multivariate distribution to the corresponding multivariate distribution of variables from a climate model. As both methods yielded similar results (Supplementary Fig. S13 ), only the results for the univariate biais correction method were used. For each GCM-RCM (Supplementary Table  S1 ), the change in FWT frequencies between the current period (1985-2015) and two future periods (2031-2060 and 2071-2100) were calculated under the RCP4.
We therefore set the clustering algorithm to identify k = 5 FWTs. The stability of the clusters was verified by rerunning the algorithm with different initial random seeds. Furthermore, since the wildfires were not evenly distributed between the studied countries (Supplementary Fig. S2 ; Supplementary Table  S2 ), we also tested the stability of the clusters against variations in the proportion of wildfires from each country. The same k-means analysis was performed on 1,000 bootstrap-resampled wildfire datasets, in which the number of wildfires from each country was proportional to the corresponding number of wildfire gridcells. The FWTs in the original and resampled datasets had similar characteristics (Supplementary Fig. S14 ), confirming the robustness of this approach. For representation purposes, the weather and climate variables were compressed into orthogonal linear combinations using PCA.
To assess whether wildfires grow larger in size under particular FWTs, we calculated a fire size risk ratio (FSRR) for each FWT. The FSRR of an FWT and a fire size S is the ratio of the probability of a fire reaching size S in FWT T divided by the probability of a fire reaching the same size in the other FWTs. According to this definition, an FSRR higher (alternatively lower) than 1 indicates that a fire of size S is more (alternatively less) likely in FWT T than in the others. FSRRs were calculated for eight fire sizes (30, 80, 271, 550, 1,055, 1,295, 1,600 and 2,173 ha), corresponding respectively to the 1st, 50th, 80th, 90th, 95th, 96th, 97th and 98th percentiles of the data (Supplementary Table  S2 ). For each GCM-RCM (Supplementary Table  S1 ), FWT frequencies for the current period (1985-2015) and two future periods (2031-2060 and 2071-2100) were calculated under the RCP4.
Acknowledgements This work is a contribution from the  Labex OT-Med  ( ANR-11-LABEX-0061 ) funded by the  "Investissements d' Avenir" , a  French National Research Agency (ANR) program , through the A* Midex  project ( ANR-11-IDEX-0001-02 ). The fire data for Tunisia were provided by  Direction Générale des Forêts (Tunis)  and fully checked and corrected within the  FUME FP7   EU  project Contract Grant no. 243888. N. Koutsias received funding from the  European Union 's  Horizon 2020 Research and Innovation Program  under the  Marie Skłodowska-Curie Grant  Agreement no. 705067. A. Russo received funding from  FCT  ( Fundação para a Ciência e a Tecnologia, Portugal ) under project  IMPECAF  ( PTDC/CTA-CLI/28902/2017 ). R. Trigo received funding from  FCT  ( Fundação para a Ciência e a Tecnologia, Portugal ) under project projeCast ( PCIF/GRF/0204/2017 ).
Drainage network patterns influence the hydrological response of the watersheds and must be taken into account in the management of the water resource. In this context, it is important to identify the factors that control the configuration of drainage networks in and beyond specific climatic conditions. Here, we study 318 basins spread over three sectors (arid, semi-arid, and semi-humid) of Morocco where seven drainage network patterns have been identified. From each basin, 14 parameters were extracted, describing the relief, geology, morphometry, drainage network, land cover, precipitation, and time of concentration (Tc). Principal component analysis (PCA) and discriminant analysis (DA) processing were performed on the entire database and on each sector separately. The results show that the drainage network pattern is a feature of the landscape that contributes significantly to the variance of the basins.
Geological Parameters The geological characteristics for each basin were extracted from different data sources for the three study areas, i. e. , geological data of Africa provided by the United States Geological Survey (USGS, http://earthexplorer. usgs. gov/), Geological maps of Morocco at a scale of 1/1,000,000, 1/2,000,00, 1/100,000 and 1/50,000  [16, [26] [27] [28] [29] [30] [31] , as well as several previous geological studies carried out on the three study areas. The geological characteristics of the basins are represented by three parameters: the 'Rock-type' related to the dominant rock type in each basin. For the Central Anti- The Horton index "K h " of a watershed is the relationship between the surface area of the basin (km 2 ) and the length of the main river in kilometers  [21].
Geological Parameters The geological characteristics for each basin were extracted from different data sources for the three study areas, i. e. , geological data of Africa provided by the United States Geological Survey (USGS, http://earthexplorer. usgs. gov/), Geological maps of Morocco at a scale of 1/1,000,000, 1/200,000, 1/100,000 and 1/50,000  [16, [26] [27] [28] [29] [30] [31] , as well as several previous geological studies carried out on the three study areas. The geological characteristics of the basins are represented by three parameters: the 'Rock-type' related to the dominant rock type in each basin. For the Central Anti-Atlas, the rock types were grouped into four major groups, with the following coding: Quaternary sedimentary rock = 0, Palaeozoic sedimentary rock = 1, volcanic rock = 2 and granitic-meta-sedimentary rock = 3.
Multivariate Analysis A principal component analysis (PCA) was performed by diagonalization of the correlation matrix in order to identify and rank the different sources of variability within the extracted basins dataset. The principal components are linear combinations of the 14 parameters and thus behave as macro-parameters. They are orthogonal to each other and therefore represent independent sources of variability, i. e. , independent associated processes. Taking into account the main PCs makes it possible to concentrate the information in a reduced number of factorial axis while losing a minimum of the information contained in the dataset, which constitutes a dimensional reduction of the data hyper-space  [7, 9]. To determine the impact of climate differences on the factors that control the variability in drainage network patterns, PCA was first applied to the entire dataset (318 basins) and then individually to each sector.
Precipitation Parameter We used the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) to calculate the average annual precipitation over a 14-year period (2005-2019) for each sector (Figure  5c ). CHIRPS is a 35+ year quasi-global rainfall dataset from 1981 to present, with a spanning range from 50 • S to 50 • N (and all longitudes). CHIRPS incorporates a 0. 05 • climate CHPclim satellite imagery, together with in situ station data to create a gridded rainfall time series for trend analysis and seasonal drought monitoring  [37, 38]. CHIRPS have negative biases (-2. 01%) before 2000, while this underestimation was corrected after 2000. Temporal analysis and intensity distribution of the gauge-adjusted CHIRPS estimates are in line with the estimates of the Global Precipitation Climatology Centre (GPCC) in several regions of the world, such as the United States, Europe, and Africa  [39].
Analysis of the Database for Each Sector The first factorial plans (PC1-PC2) accounted for 51. 9%, 48. 7%, and 56. 5% for the External Rif, the Middle Atlas, and The Central Anti-Atlas, respectively (Table  2 ). For the External Rif, PC1 (37. 6% of the variance) was positively scored with the relief parameter "Slope, R1, R2 and H", "Line-Dens", "K h " and "Type", and negatively with "Mean-Ddrain", "Tc" and "K h " (Figure  7b ). PC2 (14. 3% of the variance) showed positive correlations with "K g ", "H", "Tc" and negative with "K h " (Figure  7b ). In the Middle Atlas, PC1 explained almost 30% of the observed variance (Table  2 ) and was correlated with the same parameters as PC1 for the External Rif (Figure  7c ). PC2 accounted for 19% of variances, and was positively scored with "K g " and negatively with "K h ", just as for the External Rif.
Analysis of the Total Database Six discriminant functions discriminated the drainage network patterns with a success rate of 63. 8% (Table  3 ). Five of the seven drainage network patterns had a classification rate higher than 75%, namely the dendritic, pinnate, parallel, herringbone, and barbed patterns. The trellis and rectangular patterns presented many confusions with classification rates of 19. 5% and 54%, respectively (Table  3 ). The first three discriminant functions accounted for 87. 28% of the discrimination (Table  4 ). The first function F1 (59. 4%), showed strong negative correlations with "Slope" and "Line-Dens" with values close to -0. 71. It was positively scored with "Mean-Ddrain" (0. 88) and secondarily with "Landcover" (0. 3) (Table  5 ). F2 (15. 93%) was moderately correlated with "K g " (0.
Multifactorial Influence in Determining the Type Drainage Network The first factorial axis (PC1) is correlated with the same parameters, whether we consider all the data or each sector separately. This shows the importance of PC1 as a macro parameter beyond regional heterogeneity. The associated eigenvalue is 4. 2 to 5. 3 (Table  2 ), depending on the processing, which means that it represents as much variability as four to five variables, which is considerable. We also note a strong contribution of the drainage network pattern to PC1 (Figure  7 ). The same is true for the results of the discriminant analysis. The first function F1 that displays a discrimination rate of 63. 8% for 318 basins and seven drainage network patterns is very satisfactory. F1 shows high correlations with the same parameters on each of the three sectors (Table  8 ) and on the entire database (Table  5 ).
Conclusions This work is based on a dataset of 318 basins and 14 parameters related to the drainage network pattern, relief, morphometry, geology, land cover, climate, and hydrological response. It aimed to understand, by conventional statistical methods, the factors that control the development and distribution of drainage network patters in three sectors of Morocco in a climatic range from arid to semi-humid. Seven drainage network patterns were identified, namely Herringbone, barbed, rectangular, trellis, parallel, pinnate, and dendritic. The results of the principal component analysis confirm that the configuration of the drainage network explains an important part of the variance of the basins, while being correlated with the relief (slope, roughness, and texture) the geological conditions (type of rock and geological structure), the drainage density, and the hydrological response, over the entire study area or over each sector analyzed individually.
Drainage Networks and Hydrological Response Taking into account the whole database, the time of concentration "Tc" is correlated with the first two principal components, suggesting that the drainage network pattern influences the hydrological response of the basins. The correlation is negative on the first factorial axis (PC1) (Figure  7a ), indicating that the drainage network patterns associated with high landforms and high structural control have lower Tc, i. e. , a faster hydrological response. However, this correlation being distributed on two factorial axes (PC1 and PC2) orthogonal in the hyperspace of the data, it must be attributed to distinct and independent processes. Both the External Rif and the Middle Atlas are part of the alpine domain characterized by the highest altitudes in Morocco and active uplift tectonics.
This suggests that it is the relationship between the different parameters that controls the distribution of each drainage network pattern and not the variability of each parameter taken individually. A contribution of climatic variables appears locally but indirectly through the interrelation with the relief and geology variables over the study area. The rate of discrimination among drainage network patterns is in the order of 64%, reaching over 80%, when each climatic sector is treated individually. Confusions between patterns can be explained by similar formation conditions or by transitions from one pattern to another. These confusions are similar whether we consider the entire database or each sector. Therefore, they are general and do not depend on specific regional conditions. The influence of.
This suggests that it is the relationship between the different parameters that controls the distribution of each drainage network pattern and not the variability of each parameter taken individually. A contribution of climatic variables appears locally but indirectly through the interrelation with the relief and geology variables over the study area. The rate of discrimination among drainage network patterns is in the order of 64%, reaching over 80%, when each climatic sector is treated individually. Confusions between patterns can be explained by similar formation conditions or by transitions from one pattern to another. These confusions are similar whether we consider the entire database or each sector. Therefore, they are general and do not depend on specific regional conditions. The influence of climate on the formation and distribution of drainage networks should be studied over a wider climatic range.
In order to test for a potential "tube effect" on mortality (control), similar observations were simultaneously conducted on the same number of individuals per species isolated in tubes filled to their maximum capacity with filtered bromeliad water. A constant water volume was maintained in the control tubes during the experiment. For each species, we stopped the observations when mortality reached 100% in the treatment tubes, resulting in right-censored data in the controls. Desiccation tolerance and resistance traits for each species consisted in an average value obtained from measurements of 30 last-instar individuals (Table  1 ). Body length (BL, the longest length from mouth to anus, mm) was measured from pictures of the individuals using Image J software. We used a microbalance (MX5; Mettler Toledo, Viroflay, France) to quantify fresh mass (FM, mg), and dry mass (DM, mg) after oven-drying individuals at 60°C for 48h.
2. 4. Data analysis Kaplan-Meier survival curves  (Kaplan and Meier, 1958)  were used to estimate the probability of survival of each species through time. Based on these curves, median lethal time (LT50, days) was calculated with the EcoTox package in R. Survival curves in the treatment vs. control tubes were compared using G-rho family tests of  Harrington and Fleming (1982)  implemented in the Survival package in R. Covariation among traits was examined using pairwise Pearson correlations. We then used generalized linear models (GLMs) to test the effect of desiccation tolerance-resistance traits on LT50. The models were fitted with Gamma error distribution and a log link function to deal with skewed distributions in the data. Model selection started from a model including all the traits listed in Table  1 , and backwards removal of less significant variable until all variables contributed with a p <0.
05 to the fitting of the model. RLQ analysis was used to analyse the relationships between environmental variables and resistance to desiccation as well as key ecological strategies of invertebrate species (hereafter "functional traits"). The RLQ analysis is a double inertia analysis of the environment and functional trait tables (R and Q, respectively) constrained by the abundance of invertebrate species (L table) (detailed in  Dray et al. , 2014). The original data is available in the online Appendix A, Tables  S1-S3. The R table included drought-related variables (drought treatment, dry bromeliad days, water depth at end of experiment) and Vmax. Vmax was included in the model to account for a potential effect of habitat size on functional community structure  (Petermann et al. , 2015). The functional trait table Q included LT50, and four other synthetic traits that represent ecological strategies to optimize resource use in bromeliad-dwelling invertebrates.
We used LT50 as the synthetic desiccation resistance trait, not measures of anatomical traits such as body mass or cuticle content, first because LT50 accounts for the combined role of multiple anatomical traits (see results), and second to prevent the issue of multicollinearity and variance inflation in subsequent ordinations. The other data in our Q table were obtained from  Céréghino et al. (2018) , who proposed a database of four synthetic traits for Neotropical bromeliad invertebrates, after an ordination of 64 traits states of all known taxa (852 species or morphospecies): trophic position (with increasing scores from predators to detritivores), habitat use (from pelagic to benthic), morphological defence (body armoured to the absence of defence), and life cycle complexity (from simple to complex).
Acknowledgements We acknowledge financial support for research provided by the  French Agence Nationale de la Recherche (ANR)  through the  Resilience  project (grant  ANR-18-CE02-0015 ) and an  Investissement d'Avenir grant  ( Labex CEBA, ref. ANR-10-LABX-25-01 ), by the  French national  grant  EC2CO-Biohefect Proseco , and by the  CESAB-FRB  as part of the activities of the FunctionalWebs Working Group. Two anonymous reviewers made useful comments on an earlier version of the manuscript. Competing interests: none.
Detailed analyses of benthic communities and development of databases are key for conservation strategies. Water management municipalities and/or enterprises should consider water quality and stream types for more sustainable management of these important ecosystems. family Chironomidae,  Robinson et al. (2016)  found highly diverse and spatially-structured assemblages with higher levels of rarity in mountain catchments. There is speculation that the insularity of high mountain catchments and the harsh environmental conditions associated with glacial influence may be the cause of this high degree of rarity often found in headwater streams  (Lencioni, 2018). In this study we examined local (α) and among site (β) diversity of benthic macroinvertebrate communities, as well as environmental instream conditions in 51 stream site types in a glacierised catchment of the tropical Andes.
We used the same macroinvertebrate database as  Cauvy-Fraunié et al. (2015)  to answer the following questions: (1) are macroinvertebrate diversity, rarity, commonness and spatial distribution patterns different among site types with different water sources? (2) which environmental variables influence the density and presence of macroinvertebrate taxa, and in particular of the subfamilies of the ubiquitous chironomids? We predicted that, compared to the other stream site types, glacier-fed sites would shelter a higher proportion of rare and specialized taxa adapted to the harsh local conditions. We also expected that environmental variables associated with glacial meltwater, such as turbidity, water temperature, conductivity, and physical stability would influence the distribution and density of freshwater macroinvertebrates and of chironomids in particular.
Shannon-Wiener, rarefied richness and true diversity indices were calculated in R software using the "vegan" package  (version 3. 6. 3, R Development Core Team, 2020)  We used a multivariate approach to assess the spatial variation in community composition (i. e. , Beta diversity). First, we tested similarity among two or more within-group dispersion matrices  (Borcard et al. , 2018)  with two indices: Sorensen (presence-absence data) and Bray-Curtis (raw abundance data). To further understand how community composition changed among site types, we partitioned our multiple-site overall beta diversity (calculated with the Sorensen index) into two components: taxa replacement (i. e. , taxa turnover) and nestedness (i. e. , species loss or gain), with the poorest assemblage being a strict subset of the richest one  (Baselga & Orme, 2012).
Finally, we evaluated differences in beta diversity components among site types, using analysis of variance, followed by Tukey's HSD post-hoc tests. These analyses were performed using the "betapart" package of R software  (version 3. 6. 3, R Development Core Team, 2020). Differences in macroinvertebrate taxa relative abundance among site types were further assessed with an analysis of similarities (ANO-SIM)  (Chapman & Underwood, 1999) , which tested the null hypothesis that within-group similarity was equal to between-group similarity. To determine which macroinvertebrate taxa contributed the most to differentiating site types, we performed a similarity percentage analysis (SIMPER). ANOSIM and SIMPER analyses were ran using the Bray-Curtis distance measure and 9 999 permutations, with the free software PAST (PAleontological STatistics, version 3.
Commonness was calculated as the number of taxa that were shared between all our site types. Before running RDAs we tested if our count data were suitable for linear ordination methods by testing their homogeneity with Detrended Correspondence Analysis (DCA)  (Lepš & Šmilauer, 2003). RDA computes axes that are linear combinations of the exploratory variables, in our case environmental variables, and preserves Euclidean distances among objects  (Legendre & Legendre, 1998). All correlated parameters (r ≥ 0. 7) were excluded from the RDA, to reduce collinearity (Supp. Table  S2 ). We ran the RDA on log10 (x+1) transformed environmental variables and on Hellinger transformed taxa density data, to reduce the effect of taxa with low counts and many zeroes. RDA analysis was performed with the "vegan" package of R (version 3.
6. 3, R Development Core Team, 2020). The effect of instream environmental conditions on community diversity patterns (richness and density) was further examined with a generalized linear modeling (GLM) approach. We included 12 environmental predictors as candidate variables, after the removal of highly correlated variables (r ≥ 0. 7). Depending on the response variables, we fitted models with a different structure. Poisson models were used for non-overdispersed count data, ''quasi-Poisson'' models were used for responses with an overdispersion lower than 15, negative binomial models were used in cases of overdispersion higher than 15, and binomial models were used for presence/absence response data  (Tagliaferro & Pascual, 2017). The best fitting models were chosen based on the lowest corrected Akaike's Information Criterion (AICc) value.
Quasi-likelihood methods for small sample sizes (QAICc) were used with overdispersed data  (Burnham & Anderson, 2002). Note that we also ran GLMs for Chironomidae density, and its subfamilies' density and presence-absence. Dispersion tests, negative binomial models, multiple models and multi-model inference were carried out using "AER", "MASS", "glmulti" and "MuMIn" packages, respectively, in R Software  (version 3. 6. 3, R Development Core Team, 2020).