dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2016 (Ashouri et al. , , 2015;; Funk et al. , 2015). Many studies focus on the evaluation and improvement of these various products, mostly for Asia and Latin America, and increasingly for Africa. This is done through direct comparisons between the precipitation series (P) observed on the ground and the P series of the various estimation products. In 2012, seven P datasets were tested on the Volta basin: CMORPH, GPROF-v6, GSMaP-MVK v5, RFE-2. 0, TMPA-v6, PERSIANN and ERA-Interim  (Thiemig et al. , 2013). Seven P datasets for Burkina Faso  (ARC2, CHIRPS v. 2, RFE v. 2, TAMSAT v. 2, TMPA v. 7 and TMPA RT v. 7)  were also evaluated on a daily, decadal, and monthly scale  (Dembélé and Zwart, 2016). In 2017, TAMSAT v. 3 was compared to the previous version (TAMSAT v. 2) and to six P datasets  (ARC 2, CHIRP v.
, 2013; Ramarohetra et al. , 2013). In 2017, the HBV light model was used to evaluate 10 precipitation datasets  (CFSR, CHIRPS, CMORPH v. 1 RAW and CRT, TAMSAT v. 2, TMPA v. 7,  for six basins located in Burkina Faso, Nigeria and Ghana  (Poméon et al. , 2017). Likewise, TRMM, CMORPH, CPC-FEWS, PERSIANN and RFE2 were assessed on Senegal and Niger basins  (Bodian et al. , 2016; Casse et al. , 2015; Stisen and Sandholt, 2010). More recently, in 2020, a study focused on the evaluation of 17 precipitation estimation datasets over the Volta basin using the fully distributed mesoscale Hydrologic Model (mHM)  (Dembélé et al. , 2020). Similarly, research was conducted on the use of gridded data as forcing data for the lumped hydrological model HMETS with a dataset consisting of GPCC, CPC Unified, CHIRPS, PERSIANN-CDR, MSWEP V1.
Objectives This article aims to assess different types of gridded precipitation estimates products (ground-based, satellite-based, re-analysis and combined products) through rainfall-runoff modeling at a monthly time step and on a regional scale including basins from both West and Central Africa. Specifically, this involves an evaluation of the performance of a set of 15 precipitation estimation products at a monthly time step using a hydrological calibration-validation procedure. Our study contribution to the former ones relies on the combination of a large dataset with a wider regional scale. In fact, the previous studies on Africa mentioned above, apart from  Tarek et al. (2020) , focused on short number of isolated or transboundary basins either on West Africa or Central Africa but not both areas at once.
Study area Our study area is made up of nineteen main basins some of which cross national boundaries. The largest is the basin of the Niger River, which is 4200 km long and runs through Guinea, Mali, Niger, Burkina Faso, Benin, and finally Nigeria, where it flows into the Atlantic Ocean. In this region straddling tropical and equatorial area, the seasons and the division into climatic categories are mainly governed by rain (Fig. 1 ). The rainfall regimes vary from a desert regime in the far north, in the Sahara zone, to more humid regimes towards the Fig. 1. Map of the study area: Annual rainfall  and main basins (Sources:  (Fick and Hijmans, 2017) ; SIEREM database, HydroSciences Montpellier). NB: The letters G, R and S respectively represent ground measurements, re-analyses and satellite estimates.
Observed streamflow dataset The flow data comes from the SIEREM database (http://www. hydrosciences. fr/SIEREM/), developed by researchers and technicians at HydroSciences Montpellier  (Boyer et al. , 2006). It consists of monthly flows observed at hydrometric stations (348 stations) distributed over the study area. These timeseries contain different rate of gaps with majority of end dates in the 1990 s and early 2000 s  (Ouermi et al. , 2019; Paturel et al. , 2010).
Meteorological datasets The rainfall dataset consists of GPEs based on satellite estimates (ARC-2, CHIRP v. 2, CHIRPS v. 2, PERSIANN-CDR, MSWEP v2. 2, TAMSAT v3), re-analysis products (ERA5, JRA-55 Adj, MERRA-2 PRECTOT, MERRA-2 PRECTOTCORR, WFDEI-CRU, WFDEI-GPCC), and GPEs based on ground observations  (CPC v. 1, CRU TS v. 4. 00 and GPCC v. 7 ). The choice of the GPEs was led by the temporal coverage of SIEREM flow dataset. Table  1  summarizes the characteristics of these products by specifying the origins of the data used to create each precipitation grid and its spatial and temporal resolution. There is an order of importance in the "Data Source" column: the first item listed is the preponderant source of rainfall information. "R, S, G" means that the estimation product comes from re-analysis (R) reinforced by satellite estimates (S) and corrected with ground-based data (G).
Likewise, while 21 years may seem short in a climate perspective (with a climate normal computed on 30 years), several studies have shown that as little as 8 years could be sufficient to calibrate a hydrological model  (Arsenault et al. , 2018; Singh and Bárdossy, 2012). Other authors in the same region only used 10 years for calibration of hydrological models (for instance,  Dembélé et al. , 2020 , Poméon et al. , 2017). Then, to limit the impact of gaps in hydrometric series on the modelling, the selection of hydrometric stations was based on a maximum gap rate of 50% over the periods  1986-1995 and 1996-2005   (Table 2 ). Based on this gap threshold, 68 hydrometric stations were retained for the calibration-validation procedure. Using the GrandDam database  (Tramblay et al. , 2021) , we made sure that the basins corresponding to the selected hydrometric stations were not or only slightly impacted by dams or human activities.
Overall performance of precipitation datasets The calibration and validation of the GR2M model was carried out for each of the pairs of periods listed in Table  2. Fig. 5  presents the "violins" of the values of the KGE2 score of the 15 rainfall products for the pairs of calibration/validation periods SST1 and SST2 (hereinafter respectively denoted pair SST1 and pair SST2) and the 68 stations selected. The rainfall products are arranged from left to right according to decreasing median value of KGE2 in calibration ("Cal" for calibration and "Val" for validation). The "violins" are read like a boxplot (min, max, median, and 1st and 3rd quartiles) but with additional information: the width of the "violins" is proportional to the number of values and thus transcribes the distribution of values.
Only 4 other products are less effective. • Products based only on re-analyses or only on satellite outputs (or composites of the two) are not very effective (CHIRP, ERA5, MERRA). Overall, the best products are either composites of ground/re-analysis/satellite data or based on ground data only, which shows the importance of ground data. The poor performance of the CPC product may be linked to the low density of the stations that were used to generate it for our study area. Since the robustness ratio reflects a deviation from KGE2 in calibration, it is essential that the value of KGE2 in calibration be good.
Spatial performance of precipitation datasets Spatial analysis shows whether satellite products systematically lead to poor/good model performance in a given geographical area. It also makes it possible to determine the influence of the size of the basins on the performance of the GPEs associated with the GR2M model. Our analysis is based on a subdivision of our study area into two subsets, West Africa (WA) and northern Central Africa (CA). Fig. 6  presents the mean values of KGE2 in validation for the two pairs SST1 and SST2. We have chosen the threshold of 0. 6 as the minimum acceptable value  (Kling et al. , 2012). This figure exhibits a balanced performance of the GR2M model for a given rainfall product, among the 7 best gridded precipitation estimates (Table  5 ), between WA and CA areas.
In fact, colors observed on WA are quite the same over CA. This balanced result is not observed with poorly ranked rainfall products such as MERRA2 (PRECTOT and PRECTOTCORR), ERA5, and CPC. These latter products performances seem worst over CA as colors are fully red and dark red apart from CPC (KGE2 < 0. 7) while on WA yellow and blue colors can be observed on few basins (KGE2 ≥ 0. 7). However, singularities appear for certain basins for which almost all the rainfall products lead to poor model performance (KGE2 <0. 6). There can be many reasons for this, and a more detailed analysis of the data and results provides some explanations: the data is too incomplete, or the Mouelhi GR2M model cannot be adapted to the basins because the hydrological regime of these basins is of a complex nature (often the case of large basins that cover multiple rainfall regimes) or because the basin is heavily anthropized.
Importance of bias correction and the quality of data source in GPEs performances The mixed product CHIRPS (satellite, re-analysis and ground data) appears to be the best product overall in our study area when using the GR2M model, Mouelhi version. It is followed by products based on ground observations and products based on re-analysis and satellite coupled with ground observations. It is therefore noted that the integration of ground observation data strongly influences the ability of a rainfall product to reproduce good validation with the GR2M model. The comparison of the CHIRPS product to its raw CHIRP version, free of ground data, is explicit from this point of view. The differences between the "raw" version and the version "adjusted using ground observations" are more nuanced with the re-analysis products (MERRA-2 PRECTOTCORR and MERRA-2 PRECTOT).
This result highlights the ineffectiveness of the bias correction method of the corrected version MERRA-2 PRE-CTOTCORR on our study area. It is also important to note that no raw re-analysis or satellite product could obtain an overall score greater than or equal to 12 in our ranking (Table  5 ). "Mixed" products seem more appropriate for good modeling of the rainfall-runoff relationship. However, the density of the climate stations and the quality of the raw satellite data are sources of sometimes significant differences between satellite-based GPEs even in the top five P datasets. Furthermore, specificities of algorithms and observed rainfall datasets for algorithms calibration or bias correction of output timeseries amplify these differences among the GPEs. Despite, both TAMSAT and CHIRPS approaches are mainly based on Cold Cloud Duration (CCD) rainfall estimates from satellite thermal infrared recording, CHIRPS has a better performance.
Especially, TAMSAT relies on Meteosat TIR imagery provided by The European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) while CHIRPS relies on two geosynchronous TIR archives, the 1981-2008 Globally Gridded Satellite (GriSat) archive produced by NOAA's National Climate Data Center and the 2000-present NOAA Climate Prediction Center dataset (CPC TIR). However the asset of CHIRPS is that it also includes Tropical Rainfall Measuring Mission 2B31 microwave precipitation estimates, CMORPH microwave-plus-infrared based precipitation estimates, monthly mean geostationary infrared brightness temperatures, and land surface temperature estimates in addition to a wider meteorological database  (Funk et al. , 2015; Maidment et al. , 2017). Likewise, WFDEI-CRU and WFDEI-GPCC rely on WATCH Forcing Data methodology applied to the reanalysis ERA-Interim data.
Nevertheless, CRU data have a higher spatial resolution (0. 5 • ) compared to GPCC (1 • ) linked with a better density of ground stations providing observed rainfall  (Weedon et al. , 2014). As a result, WFDEI-CRU (resp. CRU) shows better performances for rainfall-runoff modelling than WFDEI-GPCC (resp. GPCC) on the basins considered in this study. The ARC2 product, developed especially for the African continent, does not perform well in hydrological modeling. The TAMSAT product, also developed for the African continent, did better: 4th place in our overall ranking. If both products are based on EUMETSAT IR database, TAMSAT includes 30 min (prior to July 2006) and 15 min (till July 2006) TIR imagery unlike ARC2 which includes 3hourly IR data. Algorithms employed are also different  (Novella and Thiaw, 2013).
Nevertheless, slight variations are observed depending on the quartile and the GPE considered. Fig. 7  shows an increase in KGE2 min and max values in calibration and validation on the SST2 period pair for the WA zone compared to SST1 period pair for the top seven P datasets. That is more mitigated for CA zone. In order to better understand the impact of the choice of the study period on the performance of the GR2M model, a study of the statistical characteristics of the precipitation data was undertaken but not presented here. The CRU rainfall product was used as reference data for the study of the stationary or non-stationary nature of the time series, as well as for the detection of possible breaks in the mean. This study shows that 82% of the series are non-stationary in WA versus 43% in CA.
Regionalization efficiency The application of a regionalization method leads to a different ranking of the P datasets than that observed previously. The performance of satellite products like ARC2, PERSIANN-CDR, TAMSAT and MSWEP improves. This observation testifies to the fact that the regionalization reduces the impact of the biases in these precipitation series on flow simulation. These biases are often linked to tendencies of GPEs to overestimate or underestimate rainfall amount in certain localities  (Satgé et al. , 2020).
Conclusion Our work made it possible to evaluate a set of 15 P datasets: three based only on ground observation data  (CPC v. 1, CRU TS v. 4. 00 and GPCC v. 7) ; six based on reanalysis (ERA5, JRA-55 Adj, MERRA-2 PRECTOT, MERRA-2 PRECTOTCORR, WFDEI-CRU, WFDEI-GPCC); and six based on satellite data  (ARC v. 2, CHIRP v. 2, CHIRPS v. 2, MSWEP v2. 2, TAMSAT v3. 0). The approach used is based on the outputs of the GR2M hydrological model, fed with GPEs as forcing data, as well as on a regionalization according to the near-neighbor spatial method. The results prove the usefulness of ground observation data insofar as the best P datasets are those corrected with ground data, such as GPCC, GPCP and CRU. Of course, the GPCC and CRU observation based GPEs performed well on the majority of the basins.
As for the poor performance of the CPC observation product, it highlights the need for a good mesh of rainfall stations for the constitution of gridded precipitation estimates. The blended product CHIRPS combines ground observations from great international and national meteorological databases with re-analysis timeseries and satellite estimates. Furthermore, from a spatial point of view, the CHIRPS results are balanced over CA and WA. Regionalization by the near-neighbor method  (Defrance et al. , 2014; Oudin et al. , 2008)  shows that some previously poorly ranked products such as TAMSAT, PERSIANN-CDR and ARC2 become usable. More complex regionalization methods could further improve model outputs for a larger number of GPEs. The period of availability of both hydrometric data and P datasets led us to limit ourselves to 15 P datasets over the period 1985-2005.
CRediT authorship contribution statement Christopher Kouakou: Developed the methodology, Set up the data sets and models, Performed the numerical processing, Wrote the bulk of the paper. Jean-Emmanuel Paturel: Contributed to the setup of the methodology, datasets and models through discussions with the lead author, Contributed to the discussion of the results and the writing of the paper. Frédéric Satgé: Contributed to the setup of the methodology and the datasets through discussions with the lead author, Contributed to the discussion of the results and the writing of the paper. Yves Tramblay: Contributed to the setup of the methodology through discussions with the lead author, Contributed to the discussion of the results and the writing of the paper. Dimtri Defrance: Contributed to the discussion of the results and the writing the paper.
Acknowledgments We would like to thank all the agents of the national services who measure the hydroclimatic data. We would also like to thank  HydroSciences Montpellier , which provided the data through their database SIEREM, and  Nathalie Rouché , who carried out critical and investigative analyses on this streamflow data. We would like to acknowledge  Frédéric Satgé  for preprocessing and providing ready-to-use gridded precipitation data, and  GDRI-Sud  and  ActNAO  for funding the research. Finally, we would like to thank all the coauthors for their valuable contributions to this manuscript.
25 • 6 months (Ashouri et al. , 2015) using Artificial Neural Networks -Climate Data Record TAMSAT v. 3 Tropical Applications of Meteorology using SATellite and S,G 1983-present Daily Africa 0. 0375 • ~1 week (Maidment et al. , 2017) ground-based observations v. 3 WFDEI-CRU WATCH Forcing Data ERA Interim (WFDEI) corrected using R,G 1979-2016 3 h Global 0. 5 • Stopped (Weedon et al. , 2014) Climatic Research Unit (CRU) dataset WFDEI-GPCC WATCH Forcing Data ERA Interim (WFDEI) corrected using R,G 1979-2016 3 h Global 0. 5 • Stopped (Weedon et al. , 2014) Global Precipitation Climatology Center (GPCC) dataset.
| Primers and probe validation Several primers and probe sets specific to the white-clawed crayfish have been developed and optimized within the literature (Table  1 ), targeting amplicon length ranging from 96 bp to more than 250 bp, and targeting a region of COI  (Atkinson et al. , 2019; Chucholl et al. , 2021; Troth et al. , 2020)  or 16S genes  (Manfrin et al. , 2022). To choose the most suitable PCR set (primers and probe) for specimens found in France (our study area), we carried out an in-silico alignment using Geneious Pro R10 software (https://www. genei ous. com; Kearse et al. , 2012) , consisting in aligning these experimental primers and probe sets given in the literature (Table  1 ) with the genetic data of A. pallipes from French territory (from GenBank) (Appendix C). The experimental set giving the most satisfactory results (amplicon size < 120 bp and no mismatch) was the one developed by  Atkinson et al.
In-silico tests showed high specificity of primers and probe for A. pallipes, with no amplification of co-occurring or genetically closely related species. For example, when aligned with sequences P. leptodactylus (genetically closed species of A. pallipes; GenBank access number: KU571462 and KU571463), five mismatches were found in reverse primer and probe, and forward primer was not able to bind to the sequence (Appendix D). In-vitro tests, on DNA samples extracted from tissues showed no amplification either on tested species.
ddPCR reactions were also carried out in 25 μL final volume containing: 10 μL of ddPCR Supermix (no dUTP) 2×, 1. 8 μL of each primer (final concentration 900 nM), 1 μL of probe (final concentration of 250 nM), 5. 4 μL of DNA-free water and 5 μL of template. Droplets generation was performed by adding 70 μL of "Droplet Generation Oil for Probes" to each reaction and placed in the BioRad QX200 Droplet Generator. The emulsion was then transferred to a PCR plate sealed with a specific "Pierceable Foil Heat Seal" for 5 s at 180°C in the BioRad PX1 PCR Plate Sealer heating block. Final PCR amplification was carried out using the BioRad C1000 Touch Thermal Cycler thermocycler using the following conditions: 10 min at 95°C, 40 cycles of 30 s at 94°C and 1 min at 60°C then 10 min at 98°C. Results were finally read using the QuantaSoft software on the BioRad QX 200 Droplet Reader.
| Analyses Cartography was performed on QGIS 2. 18 (Las Palmas) software ("occModel" fitting determination and 10,000 iterations MCMC chains). False negative errors were calculated using  Griffin et al. (2019)  modeling (https://seak. shiny apps. io/eDNA/), for qPCR and ddPCR, on samples collected for spatial variation (see above Section 2. 2). The probability of species presence (ψ) was set at 1 for sites where A. pallipes was known to occur and fixed by the random parameters for sites sampled downstream (where the A. pallipes was unknown). Probabilities of eDNA occurrence in a PCR replicate (p) were investigated for qPCR and ddPCR, then both approaches were compared using Student's t-test. The effects of seasonality and PCR method (qPCR/ddPCR) on the detection efficiency/probability were analyzed with a two-way ANOVA, with a significant probability of less than 0.
QGIS Team Development, 2016), with resources provided from the database ©IGN. All statistical analyses were performed using RStudio V1. 1. 463 (R Development Core Team, 2019, RGui v4. 0. 2). The normal distribution of the data and its homogeneity were verified with Shapiro-Wild and Bartlett tests. Effects of covariates (temperature, oxygen, distance, and total volume filtered) on the probability of A. pallipes eDNA occurrence in TA B L E 1 List of primers and probes specific to white-clawed crayfish Austropotamobius pallipes available in the literature. modeled following MacKenzie et al. (2002)  and Royle and Dorazio (2009)  approaches. "eDNAoccupancy" R package was used, following procedures given in Dorazio and Erickson (2018).
( QGIS Team Development, 2016), with resources provided from the database ©IGN. All statistical analyses were performed using RStudio V1. 1. 463 (R Development Core Team, 2019, RGui v4. 0. 2). The normal distribution of the data and its homogeneity were verified with Shapiro-Wild and Bartlett tests. Effects of covariates (temperature, oxygen, distance, and total volume filtered) on the probability of A. pallipes eDNA occurrence in TA B L E 1 List of primers and probes specific to white-clawed crayfish Austropotamobius pallipes available in the literature. modeled following MacKenzie et al. (2002)  and Royle and Dorazio (2009)  approaches. "eDNAoccupancy" R package was used, following procedures given in Dorazio and Erickson (2018).
2022 ) might show divergent patterns in terms of soil seed bank richness and density. We already know that patterns of species diversity can be complicated due to broad differences in beta-diversity  (Keil & Chase 2019; Wilson et al. 2012) , but these relationships have not yet been established for soil seed banks. In this synthesis, we use a global database to examine spatial patterns of soil seed banks in terms of diversity, density and abundance. Specifically, we investigate the relationship of species richness with increasing area sampled, and whether this differs across the world's biomes. We expect that dominant seed-banking strategies in different biomes combine with patterns of diversity in the established vegetation to result in scale-dependent patterns in the soil seed bank across the world.
The global seed bank database To study the effects of spatial scale on soil seed banks, we compiled a global database of seed bank richness, abundance and density  (Auffret et al. , submitted). Briefly, we searched the Web of Science using the search term "seed bank*" OR seedbank*, including papers included in the Web of Science until the end of 2019 (a few papers returned by the search were subsequently published in journals in 2020). All resulting papers were checked for being both community seed bank studies (i. e. , taking soil samples and counting and identifying all seeds, rather than focussing on one or more specific species), and containing information about species richness (number of species), seed abundance (number of seeds), and/or seed density (number of seeds per unit area or volume) in the seed bank.