dataesr/scientific-paragraphs-categorization huggingface dataset:
Data The present study relies on the Climatic Research Unit (CRU) Time-series (TS). The CRU TS dataset was originally created and subsequently updated by the UK Nat- Ice Analysis (OSTIA) daily product from September 2007 to the present. These SST datasets closely align with the Reynolds observation product  (Yang et al. , 2021). The atmospheric parameters used in this study are SLP, zonal (u) and meridional wind (v) at 10 meters above the surface, and at the available pressure levels in ERA5. Additionally, geopotential height (Z) and specific humidity (q) are also used. Wind speed ( √ u 2 + v 2 ) is treated as an additional parameter: we calculate its monthly seasonal anomalies separately from the zonal and meridional components. Linear regressions of wind speed anomalies hence indicate whether the wind anomalies correspond to a weaker (negative anomalies) or stronger (positive) wind speed in comparison to the average.
JAS precipitation index for Senegal The highest rainfall in Senegal is observed during July, August and September (JAS), with the peak occurring in August. In the other quarters (not shown), it decreases to approximately 10-30% of this peak, consistent with prior research  (Rowell et al. , 1995; Sultan & Janicot, 2000; Grist & Nicholson, 2001; Lebel et al. , 2003; Fall et al. , 2006). JAS averages are presented for both the CRU observation-based data product (Figure  1a ) and ERA5 reanalyses (Figure  1b ). Both datasets exhibit a clear zonal symmetry, with values increasing from north to south (Figure  1a , b), consistent with previous studies  (Camberlin et al. , 2001; Moron et al. , 2006; Rust et al. , 2013). The maximum precipitation occurs in the southern region, ranging from 8 to 12 mm/day in the southwest, while it remains below 3 mm/day in the northern part.
EOF1 are very similar in ERA5 is highly significant, and they also demonstrate substantial covariability within the threemonth summer periods, with common extreme months (e. g. ,  August 1984 , September 1986 , July 1997 , July 2002 , September 2010, etc. ). The JAS mean and EOF1 precipitation for the entire Sahel region in both CRU and ERA5 datasets are not reproduced here but only in the annex (additional Figures  A2  and A3 ), as they align with the findings of numerous previous studies, such  as Quagraine et al. (2020). They exhibit similar zonal patterns, particularly covering the Senegal re-gion, with maximum anomalies located in the west in both datasets. Although the Sahel's first mode explains approximately 33% of the variance in ERA5 and slightly over 40% in CRU observations (see Table  A1 ), both Senegal's and Sahel's modes show a high degree of correlation in both datasets, indicating shared interannual / summer monthly intraseasonal variance of more than 50% in CRU and ERA5 (see Figure  A3c ).
75, larger than the σ interannual of 0. 66. This indicates that retaining three summer monthly values per year significantly enhances the representation of the intraseasonal (or intermonthly) variability in our analysis. The ERA5 EOF1 accounts for 48. 5% of the total variance (Figure  2b ). EOF1 are very similar in ERA5 and CRU: they both exhibit a monopolar structure (i. e. with values of the same sign all over Senegal) of the precipitation anomalies. With zonal symmetry, an increase in the anomaly amplitude is observed from north to south, logically reflecting the standard de-  The JAS mean and EOF1 precipitation for the entire Sahel region in both CRU and ERA5 datasets are not reproduced here but only in the annex (additional Figures  A2  and A3 ), as they align with the findings of numerous previous studies, such  as Quagraine et al.
(2020). They exhibit similar zonal patterns, particularly covering the Senegal re-gion, with maximum anomalies located in the west in both datasets. Although the Sahel's first mode explains approximately 33% of the variance in ERA5 and slightly over 40% in CRU observations (see Table  A1 ), both Senegal's and Sahel's modes show a high degree of correlation in both datasets, indicating shared interannual / summer monthly intraseasonal variance of more than 50% in CRU and ERA5 (see Figure  A3c ). In summary, we observe that ERA5 reanalyses effectively capture months of extreme precipitation in JAS, both at regional and local scales. These findings align with the conclusions of  Quagraine et al. (2020). They are also in agreement with the work of  Fall et al. (2006)  and  Wade et al. (2015) , who identified a moderate but significant correlation between seasonal rainfall in Senegal and the rest of West Africa.
Acknowledgments This work was supported by the  IRD (institut de Recherche pour le Développement  throught  PDI-MSC program  ( Programme Doctoral International-Modélisation des Systèmes Complexes). This work was also supported by the  Laboratoire Mixte International ECLAIRS2. manuscript submitted to Atmospheres Acknowledgments This work was supported by the  IRD (institut de Recherche pour le Développement  throught  PDI-MSC program  ( Programme Doctoral International-Modélisation des Systèmes Complexes). This work was also supported by the  Laboratoire Mixte International ECLAIRS2. manuscript submitted to Atmospheres Figure A3. JAS 1979-2018 precipitation, EOF1 over the Sahel (mm/day): a. in CRU observations, b. in ERA5 reanalyses and c. their corresponding time series (CRU in black and ERA5 in red).
ural Environment Research Council (NERC) and the US Department of Energy. In this paper, we utilized Version 4. 03 of the CRU TS dataset, which spans the period from 1901 to 2018 at a high resolution of 0. 5°× 0. 5°. Monthly averaged precipitation data for the mainland, covering the period from 1979 to 2018, were acquired from various weather services and other sources. CMWF Reanalysis v5 (ERA5) data are employed in this study to monitor the atmospheric dynamics associated with Senegal precipitation fluctuations. ERA5 is produced by the Copernicus Climate Change Service (C3S) and incorporates data assimilation, combining model data with observations from worldwide sources. It provides estimates for numerous atmospheric, terrestrial, and oceanic climate variables from 1979 to the present day, with a horizontal grid resolution of 0.
25 o ×0. 25 o and 37 vertical levels ranging from 1000 to 1 hPa and we also use monthly average data. Global SST data from ERA5 are used to identify global teleconnections with precipitation. These SST data are based on the Hadley Centre Sea Ice and Sea Surface Temperature dataset version 2 (HadISST2) from 1979 to August 2007 and the Office Operational Sea Surface Temperature and Sea.
Acknowledgements. The authors warmly thank  Marie-Claire Pierret ,  Hélène Jund ,  Marie-Anne Churka ,  Quentin Chaffaut ,  Flore Rembert ,  Sylvain Weill ,  Benjamin Belfort ,  Matthias Oursin ,  Jérôme Vergne , and  Sylvain Benarioumlil  for their help in the acquisition of the seismic data presented hereThe digital elevation model was obtained from aerial  LiDAR  images recorded in 2018 by the Helimap system. Computing time was provided by the  HPC-UdS.
Financial support. Meteorological and flow rate data collection was funded by the  Observatoire Hydro-Géochimique de l'Environnement (OHGE) , which is financially supported by  CNRS/INSU France  and the  University of Strasbourg. OHGE is part of the OZCAR research infrastructure (http://www. ozcar-ri. org). The  CRITEX ANR-11-EQPX-0011  project provided the instruments used for the seismic data acquisition, and the  ANR   HYDROCRIZSTO-15-CE01-0010-02  project funded the field campaigns.
Data availability The whole seismic data set is available on the H+ database (http://hplus. ore. fr/en/; Pasquet et al. , 2019), which stores the geophysical data collected on the CZ observatories of the OZCAR network. The fortran libraries developed to run the NIHM code are available upon request to the authors. The python library used to analyze the SRT data can be downloaded at 25 www. pygimli. org (Rücker et al. , 2017). The geostatistical software library, GSLIB, is distributed at www. gslib. com (Deutsch   and Journel, 1992).
Modeling clouds is a challenge we are currently facing in the development of Mars climate models. In particular, CO 2 clouds are exotic components of the Mars' atmosphere that may imply rethinking some microphysical theories. Moreover, available datasets that could allow a better understand of involved processes are rare and, thus, must be analysed the best we can to acquire more information. The Mars Orbiter Laser Altimeter  (MOLA, Smith and al. , 1999)  was an instrument aboard the Mars Global Surveyor spacecraft for a mission around  Mars between 1997 and 2006 (MOLA provided data until 2001 ). MOLA's first goal was to study Mars's topography using laser altimetry in order to draw a precise map. However, range measurements have been made with a better precision than expected, allowing detection of features that were not assignable to the planet surface.
Those features include clouds, especially polar winter CO 2 ice clouds that MOLA has been the first to detect. Previous studies conducted in the early 2000s  (Neumann and al. (2003), Ivanov & Muhlemann (2001) ) demonstrated that among all the laser returns, some were clearly clouds signatures coming from the atmosphere. However, the huge amount of data limited the manual analysis and forced the use of very strict distinction criteria, eventually leading to some misses. Our goal is to distinguish different categories of laser returns in MOLA data. A good modern option to computionally analyse MOLA data could be clustering methods, and K-means clustering methods in particular (Ackerman & Ben-David (2009)). For numerical reasons, we proceed to a first pruning to reduce the amount of data, assuming cloud returns could not be too close to the surface, by a typical range of less than ten meters from the MOLA vertical resolution (Abshire and al.
Data collection 2. 3. 1. Leaf water potential Leaf water potential (LWP) at predawn and at midday were measured using a Scholander-type pressure chamber. This was done for four central trees per subplot, per treatment, for two genotypes only, the hybrid Mercedes and the clone CI03 from somatic embryogenesis. Trees were relatively homogeneous, but the two genotypes differed in the way they react phenologically to drought conditions based on field observations. M maintains a green crown, whereas CI03 undergoes an intense crown defoliation. Per tree, six fully developed mature leaves from sunexposed branches were measured. For measuring midday leaf water potential (MLWP), leaves were excised between 13. 00 and 14. 00 h, and placed into plastic bags in a cooler, until measurements were done within 10minutes.
Probes were sealed with reflective bubble wrap and plastic bags to protect the sensors from environmental influences, such as rain and direct solar radiation. Differential voltages of the sensors were measured every 60 s and averaged every 30 min, using an AM16/32 multiplexer and CR1000X data logger (Campbell, Scientific Instruments, Logan, UT). Because of limited capacity of the data logger, measurements could not be done for all trees at the same time. The data logger was transferred from irrigated to non-irrigated plots and vice versa weekly. Sap flow density was calculated following  Granier (1987) : U = 0. 714 × K ∧ 1. 231 (1) where U is sap flux density (ml cm -2 min -1 ), and K was determined as: K = (ΔT M -ΔT)/ΔT (2) where ΔT is the temperature difference between two needles (mV) and ΔT M is the maximum value of nighttime ΔT (mV) when there is no sap flow (zero set value).
Other leaf traits Additional leaf traits were measured for each of the six trees for all six genotypes per treatment. Leaf trait measurements were done for the two dry and two wet seasons, in the middle of the season. Four leaves were randomly sampled in four directions within the crown of each tree. Leaf fresh weight and leaf thickness were determined. Leaf area was measured by scanning the leaves with a flatbed scanner, and by analyzing the images in Image J software  (Perez-Harguindeguy et al. , 2016). Leaf dry weight was determined after oven-drying the leaves at 65 • C to constant mass. Leaf water content was calculated, which is the amount of water per unit leaf dry mass (LWC; in %), and specific leaf area, the amount of leaf area per unit leaf dry mass (SLA; in cm 2 g -1 ).
Tree, and litter trap in the case of litterfall, were included as random effects. Similar to the first analysis, a model comparison was conducted, and we selected the best model based on AIC c. Effects were considered significant, if bootstrapped 95 % confidence intervals of the model coefficients did not overlap with zero. PostHoc tests were only performed in case of a significant interaction. All statistical analyses were performed using R Statistical Software 3. 5. 1 (R Core Team, 2018). LMMs were performed using the "glmmTMB" package  (Brooks et al. , 2017).
Acknowledgement This research was conducted within the framework of the  CocoaSoils program  (www. CocoaSoils. org), funded by the  Norwegian Agency for Development Cooperation (NORAD) , Grant number  RAF-17/0009-Cocoasoils. We thank  Nestlé R&D Center Zambakro  staff for the opportunity to conduct this experiment and their valuable help in the data collection process. We thank  World Agroforestry Center Abidjan  staff,  Houphouet Alain Kouadio  from  Nestlé ,  Leon Mossink ,  Eva Goudsmit ,  Ambra Tosto ,  Anicet Ebou  from  INPHB , soil laboratory INPHB staff, and many others who helped us. In addition, we thank  Bob Douma ,  Deo Gratias Hougni , and  Gildas Assogba  for their support in the statistical analysis.
Data acquisition To investigate temporal trends in non-native macroinvertebrate diversity, we used a recently collated European database of freshwater macroinvertebrate species abundances from lotic systems (i. e. , streams and rivers;  Haase et al. , 2023; Haubrock et al. , 2022; Soto et al. , 2023b). All macroinvertebrate species reported in this dataset were identified to the species level and sampled using different sampling methods and protocols across time series, but were kept consistent within each time series. Only time series with a minimum of eight yearsnot necessarily consecutivewere considered, obtaining a database containing 1826 European time series. We first sorted the time series data by country and then used visual analysis to pinpoint countries with clustered data by plotting all the corresponding sites.
We retained only those time series grouped by country that could be matched over the same time period, which allowed us to obtain more robust data on communities that exhibit similar temporal patterns and responses to environmental variables. This filtering resulted in three European countries which fulfilled the previous minimum requirements: Hungary, England, and the Netherlands. To additionally ensure the comparability among time seriesgiven the heterogeneity of the database (i. e. , different starting and ending years for each time series) -we selected the period with the maximum number of time series within each country (Fig. 1 ; Table  S1 ).
Fill missing values To account for missing years (i. e. , years that were not sampled), we used the mice function of the mice R package  (Buuren and Groothuis-Oudshoorn, 2011)  using multivariate imputation by chained equations for each country independently to fill the species' presence and their respective abundance in each time series. For this, we used the predictive mean matching (PMM) method, which calculates the predicted value of each target variable and creates multiple imputations (i. e. , replacement values) for multivariate missing data, and set the multiple imputations to five and established the maximum number of iterations to fifty. This approach enables the comparison of time series data, while avoiding the potential effect of missing years on long-term trends and permitting to obtain the same number of data points in each year making them directly comparable.
Identifying non-native species To identify non-native macroinvertebrate species in each country, we used three sources to verify the native range of species: the Global Alien Species First Record Database  (Seebens et al. , 2017) , the Global Biodiversity Information Facility (GBIF; https://www. gbif. org/) and the European Network on Invasive Alien Species (NOBANIS; https://www. nob anis. org/). When different sources presented conflicting results regarding the non-native status, we followed the information from the Alien Species First Record Database  (Seebens et al. , 2017) , as it is considered the most comprehensive and reliable database on non-native species worldwide. The list of species considered non-native can be found in the supplemental data (Table  S2 ).
Specific site predictors We compiled data on site-specific characteristics which may modulate the temporal changes in diversity and community metrics of both native and non-native species. First, we extracted the geographic coordinates of each site reflecting a time series  (World Geodetic System 1984, WGS). Then we obtained two climatic predictors, namely the average annual temperature and precipitation, which were extracted from the E-OBS gridded European-scale from Copernicus Climate Change Service observation-based dataset for each year and site (spatial resolution: 0. 1 • ; www. ecad. eu/E-OBS/;  Cornes et al. , 2018). The obtained daily measurements were then averaged across the 12 months preceding each sampling event. For each site, the distance to the nearest upstream barrier (such as dams or weirs) was obtained from the Global Reservoir and Dam Database v1.
3. Elevation data was calculated with the MERIT Hydro digital elevation model  (Yamazaki et al. , 2019)  using the Hydrography90m dataset that delineates stream channels at 90 m spatial resolution and includes a high density of headwater streams  (Amatulli et al. , 2022). Three spatial variables were calculated using GRASS GIS functions  (Jasiewicz and Metz, 2011; GRASS Development Team, 2017) : stream slope and Strahler order (i. e. , a hierarchical classification of stream networks based on the number of their tributaries and orders) were extracted using the r. stream. slope function  (Jasiewicz and Metz, 2011) , and distance from the outlet was calculated using the r. stream. distance function  (Jasiewicz and Metz, 2011). In addition, we calculated the percent coverage of urban areas (% urban area) and of water bodies that include: streams, rivers, ponds and lakes (%-water bodies) at each location and for every sampling year as indicators of upstream catchment land use, using the r.
Traits and trait and functional metrics To examine changes in trait occurrences over time, we used the www. freshwaterecology. info database to collect and summarize the biological and ecological traits of all macroinvertebrates in our time series  (Schmidt-Kloiber and Hering, 2015; Tachet et al. , 2010). We retained 11 biological and 8 ecological traits, encompassing 67 and 45 modalities, respectively (Tables  S4, S5 ). When no trait information was available for a species, we removed it from the dataset. In cases where a specific trait information was missing for a species, we supplemented the available information by referring to the respective genus level (except for Molanna angustata and Katamysis warpachowskyi, where information was completed at the order level). Then, all ecological and biological trait modalities were 'fuzzy coded' with the function prep.
fuzzy. var using the R package ade4  (Chevene et al. , 1994; Bougeard and Dray, 2018). The fuzzy coding procedure indicates to which extent a taxon exhibits each trait's modality by proportionally scaling them between 0 and 1  (Schmera et al. , 2015). This approach simplifies the synthesis of trait information and enables the integration of diverse biological data  (Baker et al. , 2021). Then, four trait metrics were computed separately for both biological and ecological traits using the alpha. fd. multidim function from the R package mFD (Magneville et al. , 2022): trait richness (T Ric ) and functional richness (F Ric ) as descriptors of the amount of niche space occupied by all species within a given community; trait divergence (T Div ) and functional divergence (F Div ) as measures of how widespread or clumped species are within the respective niche space, weighted by their relative abundance; trait dispersion (T Dis ) and functional dispersion (F Dis ) as the average distances of individual species to the group centroids of all species; and trait evenness (T Eve ) and finally functional evenness (F Eve ) describing the distribution of abundances across the niche space (i.
e. , in each trait;  Baker et al. , 2021; Renault et al. , 2022). Note that we used 'trait metrics' for metrics computed using biological traits and 'functional metrics' for metrics computed using ecological traits, to distinguish the nuanced differentiation between individual organism traits and traits reflecting the broader ecological processes and functions  (Baker et al. , 2021; Le Hen et al. , 2023). These metrics were calculated at the regional level by averaging data from all the sites sampled over time.
Trends of community diversity and trait metrics To identify the most important variables predicting changes in diversity metrics (α, β, and γ) and trait and functional metrics, we employed random forests (RF) to assess the importance of predictor variables in accounting for variance in calculated metrics. This was done using the function rfsrc from the R package randomForestSRC (Ishwaran and Kogalur, 2023). We set the number of trees to 2000 and the number of variables randomly selected as candidates for splitting a node to 7. In our final models, we retained only the variables that explained the most variation in variance (see Fig. S1 , S2, S3 and S4). Then, we checked the collinearity of the finally selected variables using the vifstep function in the usdm R package  (Naimi et al. , 2014)  for each model.
A conservative variance inflation factor (VIF) threshold of 5 was applied (Table  S6 ), and variables with elevated VIF values were evaluated according to their ecological relevance and by expert opinion  (Zuur et al. , 2009; Dorman et al. , 2013). Then, to investigate the temporal trends of diversity and trait and functional metrics within each country, we employed Generalized Additive Models (GAMs) using the gam function of the R package mgcv  (Wood, 2017). First, we visually inspected the residual distribution of each model using histograms and observed overdispersion in the data, leading us to select quasi-Poisson distributions for count data. Then, α or β diversity were analyzed as a response of the most-explanatory variables (see Fig. S1 ). For α and β diversity models, we specified each time series as a random effect to account for geographical variability (Table  S7  and S8 ).
Extrapolation over time To investigate the future trends of non-native species α, β, and γ diversity, as well as the trait metrics until 2040 for each country, we used quadratic robust regression models implemented in the lmrob function of the R package robustbase  (Maechler et al. , 2022). The predictions were only made as a function of time; i. e. , we did not select other predictors since such an approach would require it to extrapolate their values as well, thereby adding more uncertainty. The quadratic term was added to the model to account for potential non-linear trends in the data. We determined the maximum number of iterations using the find_scale function with a C level of 1000 and calculated the estimate of the covariance matrix using the vcov. avar1 argument. Then we used the predict function of the R package stats (R Core Team, 2022) to project future trends of the trait metrics until 2040.