dataesr/scientific-paragraphs-categorization huggingface dataset:
| Clustering of individuals without hybrids All four different methods exhibited very good performances in terms of TPR (most results above 90%) and near perfect TNR, showing that clusters present in the simulated dataset were overall well recovered by all approaches (Figure  1 ). Runtime analysis showed that snapclust was on average 27 times faster than BAPS and about 120,000 times faster than STRUCTURE, with an average analysis time below a second (Table  S1 ). Multivariate linear regression captured a large fraction of the variation in logit(TPR) values (Adjusted R 2 : 61%, Table  S2 ). Predicted TPR and TNR we pooled by methods across all simulations to compare overall performances of the different approaches. The results showed marginal variations in performances of the methods, with mean predicted TPR varying from 96.
This latter aspect in particular is worth investigating, as our method does not, unlike Bayesian approaches (e. g. BAPS, STRUCTURE) include a natural measure of uncertainty in the form of distributions of group membership probabilities for each individual. For snapclust, an alternative approach to assess statistical uncertainty may be to use bootstrap, in which case the method would be to run a large number of times (e. g. 100) on datasets obtained by random resampling (with replacement) of the loci. Such approach would provide a distribution of group membership probabilities for each individual (one per run), and thereby a measure of uncertainty. Bootstrap on loci can readily be implemented, using existing tools for genetic data handling  (Jombart, 2008; Jombart & Ahmed, 2011; Kamvar et al.
With this in mind, we implemented four different statistics measuring the goodness-of-fit of clustering solutions, which should hopefully provide the needed flexibility for future investigations of the "true K. " In our simulations, the number of loci and levels of genetic differentiation varied independently, so that the resolution of the datasets may not have been sufficient for detecting some of the hybrid classes, especially the second-generation backcrosses  (Vähä & Primmer, 2006). While this was not a problem for comparing the relative performances of snapclust and NEWHYBRIDS, ensuring sufficient resolution should be a primary concern in empirical studies. Ideally, further work will formulate guidelines for defining the minimum resolution required for recovering specific hybrid classes.
of the adegenet package to obtain F1 hybrids (P1 × P2), first-generation backcrosses (BC1: F1 × P1 and F1 × P2), and secondgeneration backcrosses (BC2: (F1 × P1) × P1 and (F1 × P2) × P2). Each simulated dataset was formed by 100 individuals from P1 and P2 each, and 10 individuals from each hybrid class (i. e. 50 hybrids in total).
I G U R E 2 Comparison of snapclust (red) and NEWHYBRIDS (blue) for the identification of hybrids using simulated data. Notes: This figure shows the distributions of (a) the mean proportion of correct group assignment and (b) the support (i. e. group membership probability) for the true class across all simulated datasets. Three hybrid classes are considered in the simulations in addition to the parental class: first-generation hybrids (F1), first-generation backcrosses (BC1) and second-generation backcrosses (BC2). This width of the enveloppes reflects the density of points.
I G U R E 3 Illustration of snapclust using simulated hybrids from cattle breed microsatellite data. Notes: (a) Representation of the Akaike Criterion value according to the number of populations (K) considered. (b) Representation of the individual probability of assignment obtained with the function snapclust. em for the different types of individuals present in the dataset. (c) Representation of the first axis of the discriminant analysis of principal components carried out on the hybrid groups found using the snapclust analysis.
ACKNOWLEDGEMENTS T. J. is funded by the  Medical Research Council Centre for Outbreak Analysis and Modelling  and the  National Institute for Health Research-Health Protection Research Unit  for Modelling Methodology. M. -P. B. and D. P. are supported by the  LabEx ECOFECT  ( ANR-11-LABX-0048 ). We thank  Jérome Goudet  for some insightful discussions on genetic data simulations. Simulation analyses with STRUCTURE and NEWHYBRIDS were performed, using the computing facilities of the  CC LBBE/PRABI.
hybridize of the adegenet package to obtain F1 hybrids (P1 × P2), first-generation backcrosses (BC1: F1 × P1 and F1 × P2), and secondgeneration backcrosses (BC2: (F1 × P1) × P1 and (F1 × P2) × P2). Each simulated dataset was formed by 100 individuals from P1 and P2 each, and 10 individuals from each hybrid class (i. e. 50 hybrids in total).
F I G U R E 2 Comparison of snapclust (red) and NEWHYBRIDS (blue) for the identification of hybrids using simulated data. Notes: This figure shows the distributions of (a) the mean proportion of correct group assignment and (b) the support (i. e. group membership probability) for the true class across all simulated datasets. Three hybrid classes are considered in the simulations in addition to the parental class: first-generation hybrids (F1), first-generation backcrosses (BC1) and second-generation backcrosses (BC2). This width of the enveloppes reflects the density of points.
F I G U R E 3 Illustration of snapclust using simulated hybrids from cattle breed microsatellite data. Notes: (a) Representation of the Akaike Criterion value according to the number of populations (K) considered. (b) Representation of the individual probability of assignment obtained with the function snapclust. em for the different types of individuals present in the dataset. (c) Representation of the first axis of the discriminant analysis of principal components carried out on the hybrid groups found using the snapclust analysis.
1a ). The vertical discretization scheme of the emulator does not change the total input and respiration as simulated by ORCHIDEE in the case where erosion and land use change processes are switched off. We apply the same scheme for all three SOC pools, assuming that each SOC pool is equally distributed across all layers of the soil profile, while the ratios between the pools per soil layer are equal to those from ORCHIDEE. We base this assumption on the fact that there is very little information or data to constrain the pool ratios globally, mainly because the three SOC pools cannot be directly related to measurements  (Elliott et al. , 1996). Furthermore, neither the emulator nor the ORCHIDEE LSM model we used include soil processes that may affect these pool ratios with depth, such as vertical mixing by soil organisms, diffusion, leaching, changes in soil texture (SOC protection and stabilization by clay particles), and limitations by oxygen and by access to deep organic matter by microbes.
The belowground litter input for the active SOC pool is the sum of a fraction of the belowground structural and metabolic litter pools of ORCHIDEE being recalculated by the emulator to change with depth, while the belowground litter input for the slow SOC pool is equal to a fraction of the belowground structural litter pool only. This setting is consistent with the structure of the SOC module of the ORCHIDEE LSM to ensure that the emulator reproduces the same C pool dynamics as the LSM. The litter fractions are based on the Century model as introduced by  Parton et al. (1987)  and later implemented inside ORCHIDEE  (Krinner et al. , 2005). We assume that the subsoil receives carbon only from belowground litter and that this input decreases exponentially with depth following the root profile of ORCHIDEE.
(13c) In case there was no solution for the k 0i at a certain time step we took the values from the previous time step. The annual average soil erosion rate (E, t ha -1 year -1 ) is calculated by the Adj. RUSLE  (Naipal et al. , 2015 (Naipal et al. , , 2016) )  according to E = S × R × K × C, ( 14 ) where R is the rainfall erosivity factor (MJ mm ha -1 h -1 year -1 ), K is the soil erodibility factor (t ha h ha -1 MJ -1 mm -1 ), C is the land cover factor (dimensionless), and S is the slope steepness factor (dimensionless). The S factor is calculated using the slope from a 1 km resolution digital elevation model (DEM) that has been scaled using the fractal method to a resolution of 150 m  (Naipal et al. , 2015). In this way the spatial variability of a high-resolution slope dataset can be captured.
For ORCHIDEE We used 6-hourly climate data supplied by the CRU-NCEP (version 5. 3. 2) global database (https://crudata. uea. ac. uk/ cru/data/ncep/; last access: 16 July 2018) available at 0. 5 • resolution to perform simulations with the full ORCHIDEE model for constructing the emulator. CRU-NCEP climate data were only available for the period 1901-2012. To be able to run ORCHIDEE for the period 1850-1900, we randomly projected the climate forcing after 1900 to the years before 1900. The random projection of the climate data is necessary to avoid the risk of including the effects of extreme climate conditions multiple times when only a certain decade is used repeatedly. The historical changes in PFT fractions were derived from the historical annual PFT maps of  Peng et al. (2017). These PFT maps were available at a resolution of 0.
Daily precipitation data for the period 1850-2005 to calculate soil erosion rates is derived from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), product ISIMIP2b  (Frieler et al. , 2017). These data are based on model output of the Coupled Model Intercomparison Project Phase 5 (CMIP5 output of IPSL-CM5A-LR  (Taylor et al. , 2012) , which are bias corrected using observational datasets and the method of  Hempel et al. (2013)  and made available at a resolution of 0. 5 • (Fig. 2 ). We chose these data as input to the Adj. RUSLE model, because the dataset extended to 1850, in contrast to the CRU-NCEP data. Also, this dataset being bias corrected provides a better distribution of extreme events and frequencies of dry and wet days  (Frieler et al. , 2017) , which is important for the calculation of rainfall erosivity (R factor).
The ISIMIP precipitation data were regridded using the bilinear interpolation method to the resolution of the Adj. RUSLE model, before being used to calculate the R factor. This was necessary because the erosivity equations from the Adj. RUSLE model are calibrated at this specific resolution  (Naipal et al. , 2015). Data on soil bulk density and other soil parameters to calculate the soil erodibility factor (K), available at the resolution of 1 km, have been taken from the Global Soil Dataset for use in Earth System Models (GSDE)  (Shangguan et al. , 2014). The K factor has been calculated at the resolution of 1 km before being regridded to 5 arcmin using the bilinear interpolation method. We also used the SOC concentration in the soil from GSDE, which was derived using the "aggregating first" approach, to compare to our SOC stocks from simulations with the emulator.
Model simulations To be able to understand and estimate the different direct and indirect effects of soil erosion on the SOC dynamics, we propose a factorial simulation framework (Fig. 3  and Table  1 ). This framework allows isolating or combining the main processes that link soil erosion to the SOC pool, namely the influence from climate variability, LUC, and atmospheric CO 2 increase. The different model simulations described in this section will be based on this framework. We performed two different simulations with the full OR-CHIDEE model to produce the required data input for the emulator for the period 1850-2005. For this we first performed a spinup with ORCHIDEE to get steady-state carbon pools for the year 1850. We chose the period 1850-2005 based on the ISIMIP2b precipitation data availability and the fact that this period underwent a significant intensification in agriculture globally and a substantial rise in atmospheric CO 2 concentrations.
We did not perform a more in-depth comparison with SOC global observations as our emulator and the original ORCHIDEE LSM do not include various soil processes that have been proven to affect SOC substantially such as vertical mixing, diffusion, priming, changes in soil texture, and carbon-rich organic soils formation. The ORCHIDEE LSM model we use to build the emulator also lacks processes such as nitrogen and phosphorus limitations, which affect the productivity and SOC decomposition  (Goll et al. , 2017; Guenet et al. , 2016). The emulator also simulates only the removal of SOC but not the subsequent SOC transport and deposition after erosion, and there is a general uncertainty in the simulation of underlying processes that govern the SOC dynamics  (Todd-Brown et al. , 2014). Finally, large uncertainties in the global soil databases  (Hengl et al.
Model limitations, uncertainties, and next steps One of the uncertainties in our modeling approach is related to the application of the Adj. RUSLE model at the global scale and the estimation of the model parameters for various different environmental conditions and biomes. Although the Adj. RUSLE model was extensively validated using large high-resolution datasets, we calculated an uncertainty range for the R and C factors of the model to investigate the sensitivity of the emulator to the uncertainty in soil erosion rates. In Sect. 4. 1 we show that including soil erosion in the emulator decreases the land carbon sink due to the large SOC losses on agricultural land triggered by erosion that reduce the SOC stocks significantly. Without soil erosion (S3) the global agricultural SOC stock increases by 60 Pg C due to agricultural expansion, while soil erosion reduces this increase by 11 Pg C in the minimum soil erosion scenario (S1 minimum) and by 18 Pg C in the maximum soil erosion scenario (S1 maximum).
Acknowledgements. Funding was provided by the  Laboratory for Sciences of Climate and Environment (LSCE) ,  CEA ,  CNRS , and  UVSQ. Ronny Lauerwald  acknowledges funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement no. 703813  for the  Marie Sklodowska-Curie European Individual Fellowship  " C-Leak". Philippe Ciais  acknowledges support from the  European Research Council Synergy  grant  ERC-2013-SyG-610028   IMBALANCE-P. Bertrand Guenet  acknowledges support from the project  ERANETMED2-72-209  ASSESS. We also thank  Adrian Chappell  and  Daniel S. Goll  for their useful comments during the early stages of this research and  Dan Zhu  for helping out with the model simulations. Edited by: Akihiko Ito Reviewed by: two anonymous referees.
Forest biomass is a renewable energy source, more climate-friendly than fossil fuels and widely available in Europe. The wood energy chain has been suggested as a means to re-activate forest management and improve the value of forest stands in marginalized rural areas. However, wall-to-wall estimates of forest biomass, needed to design the location and size of power and heat biomass plants in any given territory, are notoriously difficult to obtain. This paper tests an algorithm to predict forest biomass using publicly available Landsat satellite imagery in the Liguria region, northern Italy. We used regional forest inventory data to train and validate an artificial neural network (ANN) classifier that uses remotely-sensed information such as three principal components of Landsat-5 TM spectral bands, the Enhanced Vegetation Index (EVI), and topography, to retrieve aboveground live tree volume.
Percent root mean square error was -9% and -23% for conifers and broadleaves respectively in the calibration dataset, and -27% and -24% in the validation dataset. The reconstructed volume map was updated to present day using current volume increment rates reported by the Italian National Forest Inventory. A wall-to-wall map of forest biomass from harvest residues was finally produced based on species-specific wood density, biomass expansion factors, volume logged for timber assortments, forest accessibility, and topography. Predicted aboveground forest volume ranged from 81 to 391 m 3 ha -1. In forests available for wood supply (70% of the total), planned volume removals averaged 25. 4 m 3 ha -1 , or 18. 7% of the average standing stock across. Biomass available for bioenergy supply was 1.
Image and data analysis We chose one high-quality image (cloud cover <5%) acquired from Landsat 5 Thematic Mapper TM (path 194, row 029) on 7 June 1993, i. e. , at the peak of the growing season, and contemporary to the last forest survey in which field plots were measured for the calibration of biomass models (see below). The image had a ground resolution of 30 m and was orthorectified based on the Advanced Spaceborne Thermal Emission and Reflection Radiometer Digital Elevation Model (ASTER DEM) with the same spatial resolution. The six reflective spectral bands (1-5, 7) of Landsat TM were converted to top-of-atmosphere reflectance and corrected for atmospheric thickness, with the thermal infrared band (6) serving as additional input for cloud masking  (Bodart et al. 2011). Ground-based volume was predicted from data measured by the most recent regional forest inventory, which was carried out in 1993.
2002). Smallvolume plots (<50 m 3 ha -1 ) were excluded from the training dataset. We used a multilayer Perceptron ANN  (Hagan et al. 2014 ) trained with a Levenberg-Marquardt Error Backpropagation (EBP) algorithm  (Hagan & Menhaj 1994) , and fitted the ANN separately for plots belonging to broadleavesor conifers-dominated forest cover types (no mixed forest cover types existed in the regional classification -Bocci & Vissani 2010). For each of the two groups, we divided field plots into a training (80%) and a validation (20%) set. The ANN had a single hidden layer of neurons (Fig. 2 ). The hyperbolic tangent and a linear function were selected as transfer functions in the hidden and the output layer, respectively. The Levemberg-Marquardt parameter was set to 0. 001. A routine was in charge of repeating the training of the ANN and recording (in a.
Available biomass supply To demonstrate the practical relevance of wall-to-wall biomass mapping, we used the predicted volume to estimate the current supply of biomass residues available for bioenergy (i. e. , tops and branches) across the study region. This required the following steps: In this demonstration, we assume that the bioenergy assessment must be carried out in the year 2005. This allowed to further improve the accuracy of our estimates by rescaling the updated pixel-level volume predictions obtained in (a) so as to be consistent with region-wide statistics of forest volume by cover type provided by the Italian National Forest Inventory for the same year. It was not possible to obtain volume estimates contemporary to the year of bioenergy assessment by using field-measured and remotely sensed data from 2005.
In fact, although plot data from the Italian National Forest Inventory have recently been made available, the geographic precision of plot coordinates has been downgraded to 1 km, i. e. , more than three orders of magnitude coarser than a Landsat pixel footprint, which we deem inadequate to accurately calibrate spatially-explicit volume prediction models. For each pixel, forest volume was updated to the year 2005 by adding the current increment recorded for each forest cover type by the Italian National Forest Inventory for Liguria region  (Gasparini & Tabacchi 2011 -Tab. 1) , times the number of years needed by the update (12). To account for the loss of standing volume due to harvest during the update period, we reduced current increment by 6. 36% each year, following data on the average harvest intensity for the last decade in the region  (Penco 2014).
Losses due to wildfire were estimated using regional data on mean yearly percent burned area by forest cover type  (Penco 2014) ; total burned area over the update period was converted into the loss of growing stock by assuming 100% mortality. Finally, we predicted loss due to pest and diseases by estimating the proportion of forest area described by the Italian National Forest Inventory as being damaged by "parasites" in the region and converted this into volume loss by assuming a mortality of 10%. Finally, to reconcile the updated pixel-level predictions with national forest inventory statistics, for each cover type we calculated the ratio of the average stock per hectare (sum of pixel volume divided by cover type area) to the average stock per hectare reported by the 2005 inventory (Tab.
iForest -Biogeosciences and Forestry Forest biomass assessment by satellite imagery vest was reduced using two coefficients equal to slope and distance from roads of each pixel relative to their maximum values  (Barbati et al. 2012). Vector layers for protected areas, rivers, and public roads were downloaded from an online regional cartographic database (http://www. cartografia. regione. liguria. it). To schedule silvicultural interventions for the next future (i. e. , a 20-year period), local silviculturists were consulted to design the following sustainable management scheduled based on current forest structure, legal constraints (i. e. , requirement to retain 60-80 standards ha -1 in coppices, 60-120 m 3 ha -1 in even-aged forests, and 75-150 in uneven-aged forests -Regione Liguria 1999), market demand, and criteria of ecological and economic sustainability: (i) conifer forests: group selection, 30% volume removed; (ii) beech coppices: conversion to high forest, 40% volume removed; (iii) beech high forests: selective thinning, 30% volume removed; (iv) other broadleaves: coppice with standards, 70% volume removed; (v) any pixel with volume < 100 m 3 ha -1 : no management in the next 20 years; (vi) any pixel belonging to Natura 2000 protected areas or in forests designated for direct protection from hydrogeological hazards: volume removed was further reduced by 25%.
97 m 3 ha -1 ; 95% confidence interval for the estimated mean volumes per hectare were 209. 5 -218. 5 m 3 ha -1 for broadleaves, and 154. 8 -267. 2 m 3 ha -1 for conifers. After predicting volume in year 1993 for all pixels of the study region, updating it based on forest growth and losses, and av-eraging it over all the area covered by each forest type, we obtained estimates of the growing stock that were overestimating NFI data by 56% (average weighted over the area covered by each forest cover type. Errors ranged from 16% underestimation in Abies alba to 223% overestimation in mixed broadleaves forests (Tab. 1). However, this forest cover types occupy a very small proportion of the landscape. Large prediction errors might be related to unforeseen forest disturbances that have occurred during the update period.
2014 ) and management type (high forests and coppices). Data fusion between model-assisted estimates and national forest inventory statistics at the provincial level helped increase the accuracy of volume estimates and made them consistent with official forest statistics. Principal component analysis of Landsat bands helped avoided collinearity in the model structure. Elevation, slope, EVI, and PC1 were the most important predictors in the volume modeling algorithm. The importance of elevation (especially in broadleaves) and of slope conforms to an expected topographical gradient of site productivity (smallest on steeper slopes). EVI is directly linked to photosynthetic activity and leaf area and is immune from saturation problems that affect the more commonly used NDVI  (Huete et al. 2002)   iForest -Biogeosciences and Forestry Fig.