dataesr/scientific-paragraphs-categorization huggingface dataset:
Revised: 7 September 2020 -Accepted: 26 September 2020 -Published: 12 November 2020 Abstract. Agriculture is the main source of ammonia (NH 3 ) in France, an important gaseous precursor of atmospheric particulate matter (PM). National and global emission inventories are known to have difficulty representing the large spatial and temporal variability inherent to atmospheric NH 3. In this study, we compare NH 3 emissions in France during spring 2011 from one reference inventory, the TNO inventory, and two alternative inventories that account in different manners for both the spatial and temporal variabilities of the emissions: (i) the NH 3 SAT satellite-derived inventory based on IASI NH 3 columns and (ii) the CADASTRE-CIT inventory that combines NH 3 emissions due to nitrogen fertilization calculated with the mechanistic model VOLT'AIR on the database of the CADASTRE_NH 3 framework and other source emissions from the CITEPA.
The IASI observations We use data from the IASI-A instrument, flying on a low Sun-synchronous polar orbit aboard the Metop satellite since October 2006, with Equator crossing times of 09:30 (descending mode) and 21:30 (ascending mode) local sidereal time (LST)  (Clerbaux et al. , 2009 , Clarisse et al. , 2009). The spatial resolution of its observations is about 12 km × 12 km at nadir. The algorithm used to retrieve NH 3 columns from the radiance spectra is described in  Van Damme et al. (2017). Several improvements have been introduced since the description of  Van Damme et al. (2014)  and the version v1 used in our previous study  (Fortems-Cheiney et al. , 2016). In this study, we use the reanalyzed dataset ANNI-NH3-v2. 2R, relying on ERA-Interim meteorological input data from the European Centre for Medium-Range Weather Forecasts (ECMWF) rather than the operationally provided Eumetsat IASI Level 2 (L2) data used for the standard nearreal-time version  (Van Damme et al.
, 2017). We only consider land measurements from the morning overpass, as IASI is more sensitive at this time to the boundary layer, owing to more favorable thermal conditions  (Clarisse et al. , 2010 , Van Damme et al. , 2014). The IASI total columns are averaged into "superobservations" (average of all IASI data within the 0. 5 • × 0. 25 • resolution and for the given CHIMERE physical time step of about 5-10 min). As suggested by  Van Damme et al. (2017) , we no longer use weighted averages for this purpose. We performed a sensitivity test by selecting the IASI pixels for which the retrieval error does not exceed 100 %: the results of the temporal and spatial variability of the NH 3 French emissions presented in Sect. 3 did not significantly change, showing the robustness of the IASI NH 3 product (not shown).
, 2012). It incorporates current knowledge on NH 3 volatilization after application of the main types of organic manure and mineral N fertilizers in the field. It takes into account the major factors known to influence NH 3 volatilization in the field, i. e. , soil properties, weather conditions, cultural practices, and properties of mineral fertilizers and organic products. It runs at an hourly time step at the field scale for a period of several weeks, thus covering the entire volatilization duration of fertilization events. Local features are attributed to each simulation unit, the small agricultural regions (SARs): local weather conditions (SAFRAN, Météo-France); the dominant soil type of the SAR from the European Soil Data Center (ESDC), with soil properties provided by the Harmonized World Soil Database (HWSD) of the Food and Agriculture Organization (FAO); areas cultivated in the crop year 2010-2011 per crop per region, derived from the European Land Parcel Identification System (LPIS, Common Agricultural Policy (CAP) regulations); and nitrogen fertilization management practices, derived from data of the national AGRESTE survey of cultural practices for arable crops and grassland (Department of Statistics and Forecasting of the French Ministry of Agriculture)  (AGRESTE, 2014).
Livestock emissions As for the TNO-GEN inventory, French NH 3 emissions from livestock for the CADASTRE-CIT inventory are generated by using annual national emissions provided by CITEPA for 2011 (Fig. 3a ). Nevertheless, here these emissions have been spatially distributed differently than for the TNO-GEN inventory. This has been done by using the FAO Gridded Livestock of the World database with a resolution of 30 arcsec. The temporalization of the emissions has been performed as a function of temperature and wind speed with the parameterizations of  Skøjth et al. (2011)  for the different subsectors.
However, time series delivered by CADASTRE-CIT and NH 3 SAT are uncorrelated for all regions considered. This result can be partly explained by the lack of data in IASI NH 3 column observations under partially cloudy conditions, and by the fact that available information on agricultural practices is resolved at a 2-week scale. Yet, current results of our study have important implications for air quality modeling over Europe. The important changes in the spatial distribution of NH 3 emissions as a function of soil properties are of general concern not only for France, but for all of Europe. Soils are alkaline or neutral (pH > 6) not only over northeastern France, but also over large parts of Italy, eastern Spain or eastern Germany  (Reuter et al. , 2008). Over these regions, our study suggests potentially larger NH 3 emissions than with a constant emission factor treatment, with impacts on fine-particle formation.
These features should be included in "operational" emission inventories used for air quality modeling. Thus, as a general conclusion, use of observational constrained or process-oriented emission inventories is clearly of added value for estimating better monthly averages over French areas with intensive mineral fertilizer use, but capacity for delivering day-to-day variations is not yet proven. This warrants further studies, including both those that refine hypotheses on days chosen by farmers for fertilizer spread out as a function of meteorological conditions and those that acquire and use continuous surface NH 3 measurements for validating satellite-or process-derived NH 3 emission variability. Financial support. For this study, Audrey Fortems-Cheiney and Karine Dufossé were funded by the Amp'Air and the PolQA Primequal projects, respectively, under agreement numbers ADEME 1660C0013 and 1662C0023.
https://doi. org/10. 5194/acp-20-13481-2020 Atmos. Chem. Phys. , 20, 13481-13495, 2020 2. 3. 1 Fertilization emissions from CADASTRE_NH 3CADASTRE_NH 3 was implemented in order to represent in a realistic way spatiotemporal variability of French NH 3 emissions due to mineral and organic N fertilization and is fully described in Ramanantenasoa et al. (2018)  and in Génermont et al. (2018). It has been constructed through the combined use of two types of resources: the process-based VOLT'AIR model and geo-referenced and temporally explicit databases for soil properties, meteorological conditions and N fertilization.
Belgian F. R. S-FNRS. The authors are indebted to all those who provided input data for the CADASTRE_NH3 2010-2011 crop year: the Service de la Statistique et de la Prospective (SSP), Department of Statistics and Forecasting of the French Ministry of Agriculture, for the French cultural practices survey, supported by a public grant overseen by the French National Research Agency (ANR) as part of the "Investissements d'avenir" program (reference: ANR-10-EQPX-17 -Centre d'Accès Sécurisé aux Données -CASD); Météo-France for the data on weather conditions; the Agence de Service et de Paiement (ASP) and the Observatoire du Développement Rural (ODR) service unit for data on the Land Parcel Identification System (LPIS) for France. The authors are also grateful to Maharavo Marie Julie Ramanantenasoa for her technical assistance, funded by the Cadastre_NH3 project under agreement number ADEME 1081C0031.
Another way to constrain LAI is through the assimilation of direct LAI observations in LDASs. Satellite-derived LAI products benefit from recent advances in remote sensing  (Fang et al. , 2013; Baret et al. , 2013; Xiao et al. , 2013) , and datasets are now available at the global scale and at high resolution. While other studies have assimilated LAI in crop models and at a more local scale (see e. g. Pauwels et al. , 2007; Ines et al. , 2013; Jin et al. , 2018) , such assimilation has been, to our knowledge, seldom performed by LDASs. Jarlan et al. (2008)  and  Sabater et al. (2008)  have succeeded in introducing such an approach in LDASs. The latter study has notably shown that jointly assimilating observations of SSM and LAI can improve the quality of root-zone SM estimates for one location in southwestern France.
, 2017) , (ii) upscaled ground-based observations of gross primary production (GPP) from the FLUXCOM project  (Tramontana et al. , 2016; Jung et al. , 2017)  and (iii) river discharges from the Global Runoff Data Centre (GRDC). The paper is organized as follows: Sect. 2 details the various components involved in LDAS-Monde including the data assimilation schemes. Section 3 describes the experimental setup and the different datasets used in the experiment such as atmospheric forcing or assimilated observations. Section 3 also details the datasets used to assess the performance of the En-SRF and the SEKF. The impact of the EnSRF on LSVs is then assessed in Sect. 4, including the comparison with the SEKF. Finally, the paper discusses the issues encountered during the experiment and provides prospects for future work in Sect.
LDAS-Monde LDAS-Monde is the offline, global-scale and sequentialdata-assimilation system dedicated to land surfaces developed by the Météo-France research centre, CNRM  (Albergel et al. , 2017). Embedded within the open-access SURFEX surface modelling platform  (Masson et al. , 2013 ; https:// www. umr-cnrm. fr/surfex/, last access: 16 January 2020), it consists of the ISBA land surface model coupled with the CTRIP river routing system and data assimilation. Those routines routinely assimilate satellite-based products of SSM and LAI to analyse and update soil moisture and LAI modelled by ISBA. The most recent SURFEX v8. 1 implementation is used in our experiments. We quickly recall the main components of LDAS-Monde and subsequently detail the novel EnSRF approach for the joint assimilation of SSM and LAI.
For arctic regions, hydraulic and thermal soil properties are modified in order to include a dependency on soil organic carbon content  (Decharme et al. , 2016). From a practical point of view, ISBA is run in this paper at a regular 0. 25 • spatial resolution. Each ISBA grid cell is divided into 12 generic patches: nine representing different types of vegetation (deciduous forests, coniferous forests, evergreen forests, C 3 crops, C 4 crops, C 4 irrigated crops, grasslands, tropical herbaceous and wetlands) and three others depicting bare soils, bare rocks and permanent snow or ice surfaces. Each patch covers a varying percentage of grid cells. Denoted α  [p]  for patch p of a given grid cell, this percentage is also known as the patch fraction. Vegetation and soil parameters for each ISBA patch and grid cell are derived from the ECOCLIMAP II land cover database  (Faroux et al.
Data assimilation LDAS-Monde is a sequential data assimilation system with a 24 h assimilation window. Each cycle is divided in two steps: forecast and analysis. Quantities produced during the forecast step (analysis step) are denoted with a superscript f (superscript a ). The state of the studied system is described by x [p] , the control vector that contains every prognostic variable of the ISBA LSM for a patch p and a given grid point. In this paper, we consider LAI and soil moisture from layers 2 (1-4 cm depth; SM2) to 7 (60-80 cm depth; SM7) in the control vector, with soil moisture in layer 1 being driven mostly by atmospheric forcings  (Draper et al. , 2011; Barbu et al. , 2014). As in many LDASs, LDAS-Monde performs DA for each grid point independently (no spatial covariances are considered).
Observations for assimilation In this paper we assimilate observations from the SWI-001 and GEOLAND2 version 1 (GEOV1) LAI datasets, both being distributed by the Copernicus Global Land Service. These satellite-derived products have already been successfully assimilated into LDAS-Monde (e. g. Leroux et al. , 2018; Albergel et al. , 2019). The SWI-001 product consists of soil water indices obtained through a recursive exponential filter  (Albergel et al. , 2008)  using backscatter observations from the ASCAT (Advanced SCATterometer) C-band radar  (Wagner et al. , 1999; Bartalis et al. , 2007). A 1 d timescale is used in the recursive filter in order to estimate the wetness of the first centimetres of the soil. This product is available daily at a 0. 1 • spatial resolution. The raw SWI-001 averaged over the 2008-2017 period can be seen in Fig.
, 2013)  transforming observations of reflectance from SPOT-VGT and PROBA-V satellites into LAI. This dataset is available every 10 d with the finest spatial resolution being 1 km. The GEOV1 LAI averaged over the 2008-2017 period can be seen in Fig. 1b. Following  Barbu et al. (2014) , both observation datasets are interpolated on the model grid (0. 25 • spatial resolution) where and when at least half of the observation grid points are available. As in previous LDAS-Monde studies, we use a 24 h assimilation window, and observations are assimilated at 09:00 UTC.
Validation datasets We consider independent datasets of evapotranspiration (ET), gross primary production (GPP) and river discharges to assess the validity of our approach and measure the influence of the EnSRF on the improvement of LSV reanalyses. Satellite-derived estimates of ET come from the GLEAM v3. 3b product  (Miralles et al. , 2011; Martens et al. , 2017). Daily estimates available for the period 1980-2018 at a 0. 25 • spatial resolution are fully driven by satellite observations and, as such, are independent of LDAS-Monde estimates. Figure  1c  displays GLEAM ET averaged over the period 2008-2017 considered for validation in this paper. Observations of GPP are derived from the FLUXCOM project. This dataset is obtained by merging upscaled measurements from eddy-covariance flux towers and satellite observations using machine learning.
More details can be found in  Tramontana et al. (2016)  and  Jung et al. (2017). The FLUXCOM data are available at a 0. 5 • spatial resolution on a monthly basis for the period 1982-2013. Figure  1d  shows FLUXCOM GPP averaged over the period 2008-2013 considered for validation in this paper. River discharge output data from the CTRIP river routing model are compared to daily streamflow data obtained from the Global Runoff Data Centre (https://www. bafg. de/GRDC, last access: 16 January 2020). Due to the low resolution of CTRIP (0. 5 • spatial resolution), we only consider data for sub-basins with rather large drainage areas (greater than 10 000 km 2 ) with a long enough time series (4 complete years or more over 2008-2017).
This modification has been implemented by  Decharme et al. (2016)  in order to include a dependency on soil organic carbon content. Figure  11  shows analysis increments in SM4 for SEKF (top row) and EnSRF (bottom row) for May, July and September. We see that increments in SM4 tend to be negative in May and September in most parts of the domain and positive in July, particularly in northern Europe for SEKF. The SM4 analyses increments for SEKF and EnSRF tend to be similar, except for arid regions. This makes the SM4 estimates less dependent on the data assimilation method. For analysis increments for SM6, SEKF increments are close to zero for every season (not shown). This implies that the drier estimates are solely due to the joint effect of the ISBA LSM and the updated LAI and soil moisture near the surface.
Evaluation using evapotranspiration and gross primary production We now evaluate the performance of our data assimilation systems using independent satellite-based datasets of ET and GPP. The open loop tends to underestimate ET leading to an averaged negative bias of -0. 328 kg m -2 d -1 reaching -0. 8 kg m -2 d -1 in June and July. Both SEKF and EnSRF reduce this bias to -0. 114 and -0. 059 kg m -2 d -1 , respectively. More statistics on ET can be found in Table  1. EnSRF provides estimates that are more correlated with this independent dataset for almost all grid cells; it improves correlation (between 0. 05 and 0. 1) especially over Spain, northern Africa or around the Caspian Sea, where correlations between the open loop and GLEAM were poorer than for the rest of the domain, showing its positive impact on ET.
Similar conclusions can be drawn from geographical patterns observed for RMSD and nRMSD (not shown; see Table  1  for averaged results). Figure  13  depicts the correlation between GPP from the FLUXCOM dataset and open-loop estimates (a) and the difference between correlations for the open loop and the estimates produced with SEKF (b) and EnSRF (c). As for ET, EnSRF provides GPP estimates that are more correlated to the FLUXCOM dataset than open-loop and SEKF estimates for almost everywhere, on average 0. 817 compared to 0. 784 for the model and 0. 786 for SEKF. The biggest improvements are noticeable around the Caspian Sea (above 0. 05), where correlations between the model and FLUXCOM GPP were poorer than for the rest of the domain. Also contrary to the SEKF, degradations are confined to only few places in Iraq, Iran and close to the Arctic Circle.
For deeper layers (20-80 cm depth), SEKF and EnSRF estimates of soil moisture are similar but are obtained through different mechanisms. While drier soil moisture in SEKF is obtained through the model by transferring information from updated soil moisture at or near the surface, the EnSRF produces soil moisture estimates partly because of the data assimilation routine itself, acting like a bias correction procedure for soil layers either near the surface or in the root zone to compensate for the wet model bias via the correlations between soil moisture in deeper layers and surface soil moisture and LAI. Finally, validation of our approach has been carried out using datasets of ET, GPP and river discharges, showing a moderate positive impact for ET and GPP, but it is a marked positive one for river discharges.
Phylogenetic relatedness is a key diversity measure for the analysis and understanding of how species and communities evolve across time and space. Understanding the nonrandom loss of species with respect to phylogeny is also essential for better-informed conservation decisions. However, several factors are known to influence phylogenetic reconstruction and, ultimately, phylogenetic diversity metrics. In this study, we empirically tested how some of these factors (topological constraint, taxon sampling, genetic markers and calibration) affect phylogenetic resolution and uncertainty. We built a densely sampled, species-level phylogenetic tree for spiders, combining Sanger sequencing of species from local communities of two biogeographical regions (Iberian Peninsula and Macaronesia) with a taxon-rich backbone matrix of Genbank sequences and a topological constraint derived from recent phylogenomic studies.
A variety of methods to build phylogenies from which to calculate PD have been described. The simplest approach uses taxonomic hierarchy as a surrogate of PD, assigning increasing values to distances among species, genera, families, etc. [29, 30]. Pre-existing trees containing target species can also be used  [31] , or super-trees developed by pruning and grafting taxa from different available phylogenetic trees using tools such as those included in the software Phylomatic  [32]. These approaches facilitate community phylogenetic studies, but are not without limitations and drawbacks  [32]. A combination of supertrees together with DNA barcode data for a subset of taxa has also been explored, e. g. ,  [33, 34]. Some phylogenies have been developed by combining multiple, non-dated supertrees and setting all branch lengths to one  [3].
Finally, phylogenetic trees have also been assembled either from genetic markers downloaded from GenBank  [9]  or from next-generation sequencing (NGS) data  [12]. Spiders are among the most diverse and abundant invertebrate predators in terrestrial habitats worldwide, and they play a key role in ecosystem functioning as top-down regulators of above-ground food webs  [35]. However, while many studies have addressed community patterns of taxonomic and functional diversity both at global  [36]  and regional scales  [29, 37, 38] , to date, only two studies have included phylogenetic information in the analysis of spider communities  [39, 40]. Here, we take advantage of the wealth of spider DNA sequence information available in public databases to investigate the effects of (i) taxon sampling, (ii) multi-gene data, (iii) topological constraints and (iv) time information on estimates of PD at the community level.
All reactions were carried out with 5 µL of MyTaq RedMix from Bioline (London, UK), 0. 2 mM of each primer, 2 µL of DNA template and adding ultrapure, distilled water up to a total reaction volume of 20 µL. PCR conditions for COI amplification were as follows: initial denaturation step at 95    C for 1 min) and a final extension step at 72 • C for 10 min. PCR products were cycle-sequenced in both directions at Macrogen Inc. (Amsterdam, Netherlands). If the amplification of one of the genes was unsuccessful, a sequence for the same fragment of the same species was downloaded from GenBank when possible (see supplementary Table  S1  for details in taxon sampling, including GenBank accession numbers). All sequence chromatograms were assembled, visualized, error-checked, edited and aligned in Geneious 11.
Topological Constraint The phylogenomic study of Fernández et al. [44]  was used to construct a topologically constrained tree, hereafter referred to as the F_topological_constraint (Table  1 ). The information of the preferred tree of Fernández et al. [44]  was slightly modified, such that some misspelled taxon names were corrected according to the World Spider Catalog [56] or modified to match our taxon labelling (see details in supplementary Table  S2 ). Additionally, outgroup taxa and terminals that were not present in our final matrix were pruned, and nodes with bootstrap support < 95 were collapsed. Tree manipulation was made with the software TreeGraph2  [57]  and R v3. 6. 1  [58].
Backbone Matrix The concatenated gene matrix of Wheeler et al. [43] , which included three mitochondrial (12S rRNA, 16S rRNA and COI) and three nuclear (histone H3, 18S rRNA and 28S rRNA) genes, was used as a backbone matrix for our analyses (see Supplementary Table  S3  for details of taxa included). Taxa from the phylotranscriptomic tree of Fernández et al. [44]  not present in the Wheeler et al. [43]  matrix were included to maximize the number of constrained nodes. Sequences from the missing taxa or, if unavailable, from close relatives within the same genus (see Figure  1  for further details) were either (1) downloaded from GenBank, or (2) bioinformatically extracted from the original transcriptome data in Fernández et al. [44]  (see Supplementary Table  S2  for GenBank transcriptomes accession numbers).