dataesr/scientific-paragraphs-categorization huggingface dataset:
Yet, the mean values approximate the general pattern of growth in these features for a typical individual  (Creighton and Strauss, 1986). Data were also recorded on the mean length of the gestation period. Upon termination of the breeding experiments (27 August 2000), animals were sacrificed and preserved as vouchers deposited in the collections of the Museum National d'Histoire Naturelle (MNHN, Paris). For 23 specimens (see vouchers numbers in Appendix 1) with known age, the eye-lens (eye crystalline) could be preserved, cleaned, dried, and weighed with a dedicated analytical balance (model AE-240 of Mettler-Toledo company, Bekasi -Indonesia) to the nearest tenth of mg. Among various "saturation" models available for linking eye-lens weight and age, we selected the Gompertz equation as implemented in the software PAST  (Hammer et al.
, 2011). Other saturation models do exist, such as Bertalanffy, or Michaelis-Menten models, or else polynomial, but we used Gompertz' for the sake of comparison with published studies (see discussions in  Creighton and Strauss, 1986; Zullinger et al. , 1984). The parameters obtained for each metric feature were the asymptotic length (mm) or mass (g) A; the constant growth rate (b) and the age at the first inflexion point of the growth curve (c) in the Gompertz equation. Estimates of adult size were based either on measurements of wild-caught specimens or on the asymptotic size calculated from the growth curves using the Gompertz model. Choosing 90% of the asymptotic size provides a landmark from within the range of observed data that indicates the age at which a constant proportion of total exponential growth has occurred (further details in  Creighton and Strauss, 1986).
For assessing the role of sexual size dimorphism, parametric (paired t-tests) and non-parametric tests (Mann-Whitney) were done with the software PAST  (Hammer et al. , 2011). For comparing the estimated asymptotic growth values of the captive-bred animals, we used biometric data for 40 wild-caught Oecomys rutilus (20 females, 20 males) originating from different localities in French Guiana (see Appendix 2 for details). Our data set includes 3 juveniles (with only two erupted molars), 4 sub-adults (with three molars but no dental wear), and 33 adults. Adult wild-caught females include 7 pregnant and 4 lactating individuals. All procedures with animals (captures in the wild, rearing in captivity, handling, euthanasy) followed the ethical guidelines of the American Society of Mammalogists for the use of wild mammals in research  (Sikes and Gannon 2011).
Acknowledgements The friendly hospitality of Pierre Charles-Dominique, director of the Nouragues field station, has been much appreciated, as was the technical help of  Jean-François Mauffrey  in the field. At Montpellier University,  Delphine Bosch  generously offered the use of a dedicated analytical balance for weighing eye-lenses at the  Earth Sciences Department , and  Elisabeth Cabirou  executed the cleaning of the eye crystalline lenses with much care. With his usual kindness,  Nilton Caceres  translated the summary into  Brazilian Portuguese , and  Philip Dunn  helped improve my awful English language -many thanks!This work has benefited from an " Investissements d'Avenir " grant managed by  Agence Nationale de la Recherche  (CEBA,.
, 2015) , for assessing soil drought risk  (Schwärzel et al. , 2009; Poggio et al. , 2010; Leenaars et al. , 2018) , and estimating transport and leaching of pollutants  (Marchetti et al. 1997). Many agricultural and ecological models have AWC, , or as input variables [e. g. , STICS  (Brisson et al. , 1998) , CENTURY  (Parton et al. , 1987) , APSIM  (O'Leary et al. , 2016)  SWAT  (Arnold et al. , 1987; Arnold and Fohrer, 2005) ]. Thus, spatially explicit predictions of AWC at high resolution are relevant for upscaling simulation models at regional or national scale, and assessing the provision of some ecosystem services (eg. ,  Dominati et al. , 2010)  like water quantity and quality regulation, carbon 1 Abbreviations: available water capacity, AWC; digital soil mapping, DSM; GlobalSoilMap, GSM; French soil mapping and inventory program dataset, IGCS; French soil monitoring network, RMQS; pedotransfer function, PTF; soil moisture at field capacity, ; soil moisture at permanent wilting point,.
sequestration and provision of food, feed, fuel and fiber. Furthermore, uncertainty and scenario analysis should also include the uncertainty of AWC estimates when forecasting carbon sequestration, crop yield and biomass production, and planning efficient water use  (Leenaars et al. , 2018). Therefore, information of AWC and its spatial variability is important for planning  (Poggio et al. , 2010) , and can help researchers and policy-makers towards the achievement of several United Nations Sustainable Development Goals (e. g. , ensuring food security and promoting sustainable agriculture, mitigating climate change, and sustainable water management). Measuring soil hydraulic properties is time-consuming and requires many human and economic resources. National soil databases rarely contain sufficient georeferenced AWC measurements for applying geostatistical or regression models  (Padarian et al.
Calibration data from the French Soil Mapping and Inventory program For the DSM model, the calibration data of particle size distribution and coarse elements came from the French soil mapping and inventory program dataset (Inventaire Gestion et Conservation des Sols: IGCS)  (Laroche et al. , 2014). Data from 81,671 soil profiles and soil cores was extracted from the IGCS dataset. The IGCS observations were originally collected for different studies with the objective of delineating soil-mapping units  (Arrouays et al. , 2004). Hence, the distribution of the observations was irregular through mainland France  (Mulder et al. , 2016). Whereas some areas were densely sampled, several areas had very few data or were even practically empty of observations (Figure  2 ). The horizon data of the profiles was standardized for the six depth intervals specified by the GlobalSoilMap project (i.
e. , 0-5 cm; 5-15 cm; 15-30 cm; 30-60 cm; 60-100 cm; 100-200 cm) (Table  1 ). For that purpose, we applied equal-area quadratic splines  (Bishop et al. , 1999)   sites  (Jolivet et al. , 2006). Hence, we used data from the first RMQS campaign  (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012)  as an independent evaluation sample for particle size distribution and coarse elements predictions  (Brus et al. , 2011). At each RMQS site, a soil pit of approximately 120 cm by 90 cm was dug to the appearance of parent material, and fully described. Samples were collected from each horizon of the soil profile and analyzed at the laboratory for determining the content of sand, silt, and clay using the pipette method  (ISO 13317-2:2001). The soil surveyors estimated visually the content of coarse elements (% volume) on the three faces of the soil pit.
In May 2018, the database had data of particle size distribution from 1622 RMQS sites. The particle size distribution of some RMQS sites mostly located in forested areas have not been analyzed in the laboratory yet (Figure  3 ). Similarly, soil profile data of coarse elements was available for 1662 RMQS sites. The soil horizons were not sampled following any systematic sampling scheme, and were located mainly in the southwest or northern half of France (Figure  3 ). The land use was mostly agricultural, with cereals, (wheat, corn, sorghum, oats), sugar beet, and oleaginous crops (rapeseed, sunflower), and some pastures. The parent material varied between loamy materials, calcareous rocks, alluvial deposits, sandy aeolian deposits, and crystalline rocks. This independent evaluation dataset had measurements of particle size distribution, coarse elements, bulk density, and volumetric soil moisture content measured on soil aggregates after equilibrium at -10 kPa (θ2.
Environmental covariates We selected 44 covariates describing the scorpan factors soil, climate, vegetation, relief and parent material (Table  2 ). Climatic variables came from the French SAFRAN atmospheric analysis system  (Durand et al. 1993). We used a Digital Elevation Model from SRTM (Shuttle Radar Topography Mission) at 90 m (USGS, 2004) to derive primary and secondary relief covariates in ArcGIS (ESRI, Redlands, WA). Soil and parent material were characterized by predominant soil type and parent material by soil mapping unit of the French Soil Geographical Database  (Gis Sol, 2011) , erosion rates  (Cerdan et al. , 2010) , geophysical gravimetric data, and the Index of River Network Development and Persistence (IRNDP). Vegetation and land use were classified according to Corine Land Cover 2006 data  (European Environmental Agency, 2007) , ECOCIMAP-II  (Faroux et al.
, 2003) , and BD Foret (Institut National de l'Information Géographique et Forestière, 2012). Two vegetation indices derived from remote sensing data were used to describe the photosynthetic capacity of the vegetation cover, the enhanced vegetation index (EVI) and the normalized difference vegetation index (NDVI)  (Huete et al. , 2002). The MOD13A1 MODIS/Terra Vegetation Indices 16-day composite products at 500 m resolution were retrieved from the online NASA Earthdata Search, courtesy of the NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC) (https://earthdata. nasa. gov/) (Didan, 2015). The vegetation indices were collected for the months of January (i. e. , minimum vegetation activity) and June (i. e. , maximum vegetation activity) for the period 2002-2014. The median of these vegetation indices over the 13 years for each month were used as covariates.
All the covariates were projected to the Lambert 93 (EPSG: 2154) associated to the Réseau Géodésique Français 1993 (RGF93), aligned with the SRTM, and resampled to 90 m resolution using nearest neighbor interpolation. Data pre-processing was done in GRASS (GRASS Development Team, 2016), the R software v. 3. 2. 2 (R Core Team, 2015), and the Geospatial Data Abstraction Library v. 2. 0. 1 (GDAL/OGR contributors, 2015).
Pedotransfer functions The volumetric soil moisture content (cm 3 cm -3 ) at field capacity or pF = 2. 0 (θ2. 0) and at permanent wilting point or pF = 4. 2 (θ4. 2) for the fine fraction were estimated using PTFs developed by Román Dobarco et al. (  2019 ) with the French SOLHYDRO database  (Bruand et al. , 2003; Al Majou et al. , 2008b). These PTFs use the content of clay (%) and sand (%) as predictor variables: θ2. 0= 0. 278 + 2. 45 10 -3 clay -1. 35 10 -3 sand  [9]  θ4. 2= 0. 08 + 4. 01 10 -3 clay -2. 93 10 -4 sand  [10]  The uncertainty of the PTFs' coefficients was calculated by non-parametric bootstrapping  (Efron and Tibshirani, 1993). The variance-covariance matrices of the PTFs' coefficients are presented in Table  3. These PTFs were chosen because: 1) the calibration dataset is representative of a large proportion of sand and clay contents found across France, 2) estimates of uncertainty for the PTFs' coefficients are available, and 3) the propagation of error due to both soil input variables and PTFs' coefficients can be easily calculated with first order Taylor series.
Evaluation of the functional DSM predictions Observed horizon data from the RMQS were used for independent evaluation of the predictions of clay, sand, silt, and coarse elements. The measurements of GEVARNOVIA horizons were compared with the predictions of volumetric soil moisture contents of the fine fraction (θFC, θPWP). This dataset is not representative of all the pedoclimatic conditions in France and therefore the evaluation statistics may be biased and not suitable for evaluating the whole France DSM approach. However, it is the best data available on θFC and θPWP in France at the moment. For each independent observation (i. e. an observation of a soil property on a given soil horizon), we calculated the weighted average of the GSM predictions overlapping the horizon attached to the observation.
This was done because the sample support differed between the evaluation dataset and the predictions. Hence, a prediction was calculated as R w = ∑ \ x \ yz{ A Y A)* , where R w is the estimated value, n is the number of GSM layers overlapping the horizon, yz{ A is the GSM prediction for the layer i, t is the total thickness of the horizon (cm), and ' A is the thickness (cm) of the GSM layer i that overlaps the predicted horizon. The evaluation statistics consisted on the root mean square error (RMSE), coefficient of determination (R 2 ), Lin's concordance correlation coefficient  (Lin, 1989) , and the bias, or mean error of prediction. The concordance evaluates both the accuracy and the precision of the prediction, and it is defined as: | = !}~• € ~• ~• € 8 •~• 8 •(W w ‚ W ‚) 8 [13] where R w ‚ and R ‚ are the means of the predicted and observed values, j W w ! and j W ! their respective variances, and | the correlation between predicted and observed values.
| can range between -1 and 1, and a value closer to 1 indicates a better fit with the 45° line, or agreement between predictions and observations. In addition, we assessed the estimation of the prediction uncertainty with the prediction interval coverage probability (PICP)  (Shrestha and Solomatine, 2006). ƒ" ƒ = Vu…Y\ ( † † x ‡ W x ‡ ˆ † x ) Y Q 100 [14] where n is the number of observations in the evaluation dataset, and the numerator the counts that an observation R A fits within its prediction limits. For a 90 % confidence level, the uncertainty is optimally estimated when the PICP value is close to 90 %. The prediction interval limits for the estimates of the observed horizon data were calculated as R w ± 1. 64 j OE • assuming a normal distribution of the estimated variance (j OE • ! ) around the mean (R w).
, (2008b)  applied their PTFs with information on horizon type, horizon thickness, texture, and bulk density provided by the descriptions of soil typological unit (STU) from the 1:1 000 000 Soil Geographical Database of France  (King et al. , 1995). In these previous studies, the AWC by soil mapping unit was calculated based on the proportion of STU present in each soil mapping unit  (Al Majou et al. , 2008b)   2011 ) and this study. Piedallu et al. (2001)  used texture class estimated by the surveyors in the field, which may enlarge the measurement error compared to laboratory analysis of particle size distribution. However, the high density of observations across metropolitan France may compensate partly the error in the input texture data resulting in a good description of the spatial pattern of AWC.
General approach for mapping AWC There are multiple modelling trajectories for mapping AWC depending on at which step are applied the PTFs and the spatial extrapolation  (Styc and Lagacherie, 2018). We first modelled spatially the AWC input variables, and when possible, jointly, as for particle size distribution, with the aim of capturing their spatial patterns and the relationships between the soil forming factors and the soil properties, thus improving the accuracy of AWC predictions. This approach is similar to that followed by  Ugbaje and Reuter (2013)  for GlobalSoilMap AWC for Nigeria and  Tóth at al. (2017)  for the European Soil Hydraulic Database (EU-SoilHydroGrids). The methodology chosen for predicting AWC has the advantage that 1) the results can be easily updated when more accurate predictions for the soil input properties or more reliable PTFs are available and 2) some input soil properties (here particle size distribution) come as a side product of AWC estimation.
The predictions of particle size distribution had similar R 2 (Table  5 ) than previous GlobalSoilMap products for France  (Mulder et al. , 2016) , which had R 2 between 0. 25 -0. 44 for clay, 0. 21 -0. 42 for silt, and 0. 19 -0. 33 for sand. The concordance coefficients by  Mulder et al. (2016)  ranged between 0. 34 -0. 53 for clay, 0. 37 -0. 61 for silt, and 0. 46 -0. 63 for sand, which are comparable to the 0. 49, 0. 43, and 0. 66 of this study (Table  5 ). This is not surprising because both studies shared the data from the IGCS in the calibration dataset (although  Mulder et al. (2016)  merged this dataset with the RMQS one), some of the environmental covariates, and applied the cubist algorithm. However, predicting particle size distribution with regression-cokriging allowed us to account for the spatial correlation between particle size fractions of the same GSM layer, hence quantifying the uncertainty of AWC more accurately.
On the other hand, the results for coarse elements were less accurate and precise in this study, with R 2 = 0. 14 and a concordance coefficient of 0. 26 compared to a R 2 between 0. 17 -0. 28 and a concordance coefficient between 0. 30 -0. 46 by  Mulder et al. (2016)  for log transformed coarse elements. One possible explanation for this slight performance discrepancy could be, at least for topsoil layers, the better precision of RMQS observations (compared to the IGCS), which was not used for training the models in the present study. Mapping coarse elements is specially challenging. The calibration and evaluation data (volume of coarse elements) was estimated visually by the soil surveyors. More precise methods for measuring the volume of coarse elements are very time consuming and can only be applied in a limited number of sites in national soil monitoring surveys  (Jolivet et al.
, 2018). More generally, we were not able to capture the spatial patterns of distribution of coarse elements, especially in deep layers. Indeed, the input data itself may be partly biased, as coarse elements in surface layers are much more easily estimated than in deeper ones, especially when doing observations by coring. It is also possible that the chosen covariates did not represent well the processes driving the distribution of coarse elements, or these were not accurate enough (e. g. , the scale of parent material was 1:1 000 000). Nevertheless, next versions of GlobalSoilMap with more accurate predictions on coarse elements can be incorporated in the proposed AWC modelling framework for reducing the prediction error and uncertainty of AWC predictions. Styc and Lagacherie (2018) compared six possible trajectories for mapping AWC in the Languedoc-Roussillon (France).
Limitations of the produced maps and future directions The predictive ability of the spatial predictions for and decreased considerably in comparison to applying the PTFs to measured horizon data (Table  6 ). The change in R 2 for and was of ΔR 2 = -0. 33, which in relative terms consisted in a 61 % and 53% reduction compared to the R 2 of the PTFs. The RMSE increased in ΔRMSE = 0. 013 cm 3 cm -3 for and ΔRMSE = 0. 015 cm 3 cm -3 for that suppose a relative increase of 25 % and 36 % respectively compared to the RMSE of the PTFs. We acknowledge that we could evaluate the spatial predictions of and , which is not always possible for soil hydraulic properties due to the lack of georeferenced observations. However, the evaluation statistics are of limited validity at national extent due to the sample size, distribution and representativeness of pedoclimatic conditions of the evaluation dataset (Table  6  and Figure  3 ).
Spatial soil inference systems  (Lagacherie and McBratney, 2006)  predicting AWC across large areas could apply the most appropriate PTF for each pixel. Another possible limitation is that both the DSM learning dataset (IGCS) and the PTFs deal mostly with cultivated soils. The proposed predictions might not be accurate for other soils such as forest soils, where soil properties other than particle size distribution such as soil organic carbon content and the frequent high amount of coarse elements might shift soils outside of the validity domain of PTFs used here  (Román Dobarco et al. , 2019). Another future development is related to the definition of the available water content itself. We set at pF = 2. 0 for metropolitan France based on samples collected mainly in in the Paris basin and southwest of France  (Al Majou et al.
, 2008a). For this set of samples,  Bruand et al. (2004)  indicated that at the field corresponded, in the laboratory, to soil moisture content for soil matric potentials between pF = 1. 5 and pF = 2. 0. Hence, our predictions may underestimate AWC. Conversely, the is often considered at pF = 2. 5 by European PTFs  (Toth et al. , 2015). The definition of regarding the soil water potential is another factor that influences the uncertainty of AWC. The ongoing expansion of the database of soil hydraulic properties for France will support the choice of the optimum upper limit of AWC, which may not be the same for different horizon types.
The end-users of the AWC maps will be essential for evaluating the usefulness of the maps for assessing the provision of ecosystem services and modelling ecological processes, and to indicate limitations in their exploitation due to the AWC prediction uncertainty. The reproducible modelling framework allows replacing each component of the AWC calculation (PTFs, soil input properties) when more accurate maps are developed thanks to the selection of covariates that characterize better the processes driving the spatial distribution of soil input properties, the implementation of new regression algorithms, and the acquisition of new soil profile data. Indeed, key aspects for improving AWC estimates are expanding the calibration data on coarse elements (especially for deeper layers), incorporating the nature and soil hydraulic properties of coarse elements into the calculation, improving the estimates of clay and sand and their mineralogy, and improving the prediction of soil depth.
Acknowledgements This study was carried in the framework of the  Groupement d'intérêt scientifique Sol  ( Gis Sol ) and financed by the  French Environment and Energy Management Agency (ADEME) , the contract number is 32000753. The GEVARNOVIA dataset was created with contributions from  INRA ,  ARVALIS-Institut du végétal, GEVES, and Terres-Inovia. We would like to thank  Alain Bouthier ,  Marie-Hélène Bernicot ,  Luc Champolivier , and  Aya Labidi  for sharing the  GEVARNOVIA  dataset for the analyses included in this article. We would also like to thank  Anne Richer-de-Forges  for her help with the calibration dataset for coarse elements, and  Line Boulonne  and  Jean-Philippe Chenu  for their help with the RMQS dataset.
, elevation above channel network, slope height, mid-slope position, multiresolution valley bottom flatness index(Gallant and Dowling, 2003), multiresolution ridgetop flatness index(Gallant and  Dowling, 2003), topographic wetness index(Böhner et  al. , 2002), compound topographic index, curvature, longitudinal curvature, transversal curvature, exposition, heat load index, linear aspect, roughness, surface area ratio, slope position, surface relief ratio Source Variables Soil forming factor Scale/ resolution Reference SAFRAN Mean annual potential evapotranspiration, precipitation, and temperature statistics (minimum, median, mean, maximum) C 8 km Quintana-Seguí et al. (2008) SRTM Elevation, slopeR 90 m USGS (2004) Erosion rate S, R 1:1000000 Cerdan et al. (2010) French Soil Geographical Soil type, parent material S, P 1:1000000 Gis Sol (2011) Database Index of Development and Persistence of Hydrological Network P 1:50000 Mardhel and Gravier (2005) Gravimetric data Gravimetric data: Bouger anomaly, free-air bouguer anomaly, Bouguer gravity anomaly.
Microscopy and image analysis Samples were observed using a phase contrast microscope (Olympus BX 50 instrument, Rungis, France) with a BX-FLA reflected light fluorescence attachment. Pictures were taken with a black and white cool SNAP-EZ camera (Tucson, USA). Images were processed with Micromanager software, ImageJ and PMC Capture Pro software. Samples, stained with Live/Dead (Invitrogen) or Nile red (Sigma) as described below, were observed in epifluorescence microscopy, and pictures were taken separately with the corresponding filters and processed with PMC Capture Pro software  (De Sarrau et al. , 2013b).