dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. The authors are grateful for the financial support given by the  Centre National des Etudes Spatiales (CNES , the  French Space Agency) , the  Université Paris Est Créteil (UPEC)  and the  Centre National des Recherches Scientifiques -Institut National des Sciences de l'Univers (CNRS-INSU)  for achieving this research work and its publication. This study was supported by the project " GEOQAIR-TOSCA " ( Terre, Océan, Surfaces continentals, Atmosphère ) financed by  CNES  and by a  "Chaire d'Excellence" of UPEC  and  CNES. IASI is a joint mission of  EUMETSAT  and  CNES. We acknowledge the support by the data centres ETHER (http://www. pole-ether. fr/)and  NOAA CLASS  (http://www. class. ncdc. noaa. gov)for providing respectively L1 IASI and GOME-2 data sets, which are originally supplied by  EUMETSAT  through the Eumetcast system distribution (http://www.
We compared the values predicted by our approach to values of the SINM's I parameter that were estimated from the taxonomic composition of tropical forest plots, based on various estimation methods (Table  1 ). We also made use of a review of field studies  [18]  (Table  2 ) and of a large database on forest tree species traits from French Guiana  [19]  (Figure  S1 ), in order to determine realistic bounds of seed dispersal distances in the context of tropical rainforests. With respect to predictions of I from a baseline seed dispersal limitation model, additional causes of migration limitation acting at regional scales (e. g. physical and ecological barriers) are expected to yield I estimates substantially below this baseline. On the other hand, excessively high estimates of I may point towards weaknesses of the estimation methods or violations of some important assumptions that underline them  [8, 20].
Moreover, values similar to those estimated from multiple plot datasets were found when single-plot estimates accounted for the phylogenetic relationship between tree species  [23]. Studies inferring I from a network of forest plots are unfortunately rare and limited to the regions mentioned in Table  1 , but the upper range of estimated I values (i. e. 80-150) easily match with I ˜in the Gaussian case for realistic dispersal distances (Figure  2 ). However, smallest values (10,I,20) imply very small dispersal distances for the case of a Gaussian kernel (R 95 d ,10 m, Fig. 2 left ). The same variation of I estimates is possible under a wider range of dispersal distances for the heavily fat-tailed Cauchy, but this would mean that the community is smaller than the reference plot (R c ,1 ha, Fig.
This research was supported by grants from the  French Ministere de l'Ecologie et du Developpement Durable (EcoTrop 05 OSDA)  to C. B. R. , P. C. , R. P. and F. M. , and partly by the program  BRIDGE  ( ANR Biodiv 06 ) to P. C. and  R. P. C. B. R. 's Ph. D. was funded by a  BSTD (DSF)  grant from the  Institut de Recherche pour le Developpement (IRD). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
082 1195. 3 86 m. 10 km Including phylogenetic information [23] I Barro Colorado Island, Panama 50 ha 20788 236 0. 002 41. 66 2. 5 m ,0 J La Planada, Colombia 25 ha 14100 164 0. 003 42. 42 4 m 55 m K Pasoh Forest Reserve, Malaysia 50 ha 29257 674 0. 01 295. 5 18 m. 10 km L Lambir Hills, Malaysia 52 ha 29890 990 0. 008 241. 0 14 m 6205 m Multiple plot datasets M Baro Colarado subplot, Cocoli ,5 ha/plot 1079-2860 99-171 30. 7-54. 2 8 m 419 m and Sherman plots, Panama [9] N Western Ghats, India [5] 1 ha/plot ,400/plt ,45/plt 0. 01-0. 11; 4. 7-50; 10 m 436 m 0. 003-0. 08 1. 3-35 4 O Panama Canal Watershed [24] 1 ha/plot ,400/plt ,78/plt 0. 05-0. 3 21-171 34 m 4334 m.
(2003)  experimental data was used by  Sofiev et al. (2011)  to derive a modification of the  Monahan et al. (1986)  SSSF formulation which resulted in a temperature and salinity dependent SSSF. This modified SSSF was implemented in the dispersion model SILAM  (Sofiev et al. 2006 ) and applied to compute the distribution of sea salt over the North Atlantic and Western Europe, as well as globally. The influence of sea surface temperature and salinity were evaluated using data from several campaigns, long-term in situ and satellite data (MODIS AOD). An approach combining satellite observations, in situ data from six cruises and model results was presented by  Jaegleet al. (2011). These authors compared model results (GEOS-Chem, with the  Gong (2003)  formulation for the SSSF) with MODIS and AERONET AOD observations and in situ aerosol measurements.
Since 2008, an Aerodyne high resolution Time of Flight aerosol mass spectrometer has been continuously deployed at Mace Head and this continuous database of real-time chemical composition has led to further elucidation of sea spray aerosol chemical properties. In particular,  Ovadnevaite et al. (2011a)  report the regular occurrence of significant primary organic aerosol plumes at concentrations often exceeding those reported in heavily polluted air (e. g. 4 μg m À3 ) and extending for periods exceeding 24 h. They also reported a unique primary organic mass spectral fingerprint hitherto unreported. In that study, it was also reported that the organic aerosol was 55 % oxygenated and 45 % hydrocarbon -like. The oxygenated component must have a very low solubility to be consistent with the previous off-line WSOM/WIOM ratios reported.
For other BVOC species, flux measurements rely on techniques using gas chromatography or proton transfer reaction spectrometers as sensors. A few short-term campaign studies from a very limited number of ecosystems have been published to-date (a list of example data are provided in  Rinne et al. 2009 , Pacifico et al. 2011 , Arneth et al. 2007 , Lathiere et al. 2006)  which is insufficient for model evaluation. For future emission estimates large uncertainties exist with respect to how changes in climate, atmospheric CO 2 , N deposition, tropospheric O 3 and human land use/land cover changes interact to alter BVOC emissions in direct and indirect ways  (Arneth et al. 2010; Niinemets et al. 2010a, b). In addition to improved understanding of the processes of BVOC production and emissions in response to these various environmental changes, observation and modelling efforts clearly also have to move towards substances beyond isoprene and monoterpenes to reflect the multiple open questions on BVOC-atmosphere and climate interactions  (Goldstein and Galbally 2007; Holzinger et al.
Anthropogenic Land-Based Emissions Recently  Lamarque et al. (2010)  developed a new emissions dataset covering the 1850-2000 period, based on the combination and harmonisation of published and publicly available datasets, in support of the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5). This so-called ACCMIP emission data set also acts as a starting point for the emission projections up to the year 2100 given by different representative concentrations pathways (RCPs) as used in the AR5  (Van Vuuren et al. 2011). The ACCMIP output is the most recent and widely used global emission data. It is distributed on a 0. 5 Â 0. 5 degree grid and made available through ftp://ftp-ipcc. fz-juelich. de/pub/emissions/. Lamarque et al. (2010)  provide emission estimates for the precursors of secondary anthropogenic aerosol and for primary carbonaceous particulate matter (BC and OC) (Table  4.
Global Biomass Burning Emissions Biomass burning emissions are highly variable from year to year as a result of different environmental and human factors. Schultz et al. (2008)  provided a detailed literature review on continental scale estimates of biomass burning emissions and constructed a global emissions data set with monthly time resolution for the period 1960-2000. The previously discussed ACCMIP historical dataset  (Lamarque et al. 2010 ) provides decadal monthly mean biomass burning emissions, mostly based on  Schulz et al. (2008). As can be seen from Table  4. 2, biomass burning emissions contribute significantly to total global emissions of aerosols and their precursors but its relevance differs by substance, from being quite modest for SO 2 to dominant for OC. Over the past 20 years estimates of biomass burning emissions, including their spatial location, have greatly improved due to the availability of earth observation data from satellites.
The activity data detected from space (burned area or fire radiative power) includes all major grassland, savanna, and forest fires (including deforestation fires) (e. g. Van der Werf et al. 2006 , 2010; Kloster et al. 2010 ). To estimate emissions, satellite-derived burned areas  (Giglio et al. 2010 ) drive the fire module of a biogeochemical model that calculates fuel loads for each month and grid cell, which are then combined with emission factors  (Andreae and Merlet 2001). A good example of this methodology is the Global Fire Emissions Database (GFED) which contains emissions from open fires for the  1997 -2004 period (van der Werf et al. 2006)). A new version of the inventory that covers the 1997-2009 period, called GFED-v3, was made available at the beginning of 2010 (van der  Werf et al.
Comparison and Evaluation of Different Emission Datasets A detailed comparison and evaluation of different emission datasets of anthropogenic and biomass burning emissions for the 1980-2010 period was made by  Granier et al. (2011). They identified large discrepancies between the global and regional emission data sets, showing that there is still no consensus on the best estimates for surface emissions of atmospheric compounds. Hence the data presented in Table  4. 2 have a substantial uncertainty; about 10-30 % for land-based anthropogenic emissions and shipping and 50-80 % for biomass burning  (Granier et al. 2011). For a full description of the various global and regional emission datasets currently available we refer to  Granier et al. (2011). To understand the impact of anthropogenic emissions on marine ecosystems it is important to know not only the absolute emission in a certain base year but also the trend over time.
, 2008; Granier et al. 2011)  to have influenced the cycling of elements and nutrients as well as other processes. Trends in other substances may vary somewhat from the NOx trend depicted here but overall the pattern is consistent. For a full description of the various global and regional emission datasets currently available and emission trends for 1980-2010 we refer to  Granier et al. (2011) ; historic emission trends since 1850 are provided by  Lamarque et al. (2010).
Atmospheric Impacts in HNLC and LNLC Areas 4. 5. 4. 1 Experimental: Large Scale Fertilisation Experiments (Fe, P) Direct assessment of the fate of atmospheric dust deposition and the subsequent biogeochemical impact on HNLC or LNLC waters has been extremely limited due to the episodic nature of such events and the need for researchers to 'be in the right place at the right time'. Such assessments have therefore been indirect, and mainly confined to a comparison of remotelysensed datasets from different satellite sensors  (Boyd et al. 2010). The use of such indirect approaches has in many cases led to mis-attribution of cause (aerosol deposition) and effect (altered ocean biogeochemistry via an aerosol-iron mediated phytoplankton bloom)  (Boyd et al. 2010). Due to the lack of direct information on the 'dustnutrients-biota-biogeochemistry' linkages, we have had to rely on experimental approaches in which a known quantity of nutrients (either iron or phosphate, After T 0 , bigger particles (orange) settle faster than smaller particles (black) which form aggregates with organic matter (green) at an intermediate depth and in a few cases iron and phosphate,  Boyd et al.
The water is highly enriched in calcium (∼600 mg/l) and sulfate (∼2 g/l) but contains also 100 mg/l Na, 24 mg/l Cl, and 120 mg/l Mg as major components (see Table  DR4  in Data Repository Item;  Garcia-Ruiz et al. , 2007). Two water samples of 20 l each were collected from two different hot springs (N1 and N2) in clean plastic containers thoroughly rinsed with the same hot spring water. The containers were immediately transported to the nearest laboratory and the water filtered directly through GTTP TM isopore filters (Millipore, MA, USA) of 0. 22 μm-pore diameter. Filters were fixed in situ in 96% ethanol and stored at -20 • C until DNA was purified. For DNA extraction, the ethanol was removed from the filters by evaporation. The remaining ethanol was filtered through a small 0. 22 μm-pore size filter to retain detached microbial biomass.
g. , contaminants associated to the DNA extraction kit and process, we had previously built a database of potential contaminants  (Gerard et al. , 2009). Naica sequences were not closely related to them. Attempts to amplify dsrAB genes encoding the dissimilatory sulfite reductase diagnostic of sulfate reducers and mcrA genes encoding the methyl coenzyme M reductase characteristic of methanogenic archaea were done using, respectively, the specific primers DSR-1F (ACSCACTG-GAAGCACG) + DSR-4R (GTG TAG CAG TTA CCG CA)  (Perez-Jimenez et al. , 2001)  and ME1F (CGMATGCARATHG-GWATGTC) + ME2R (TCATKGCRTAGTTDGGRTAGT)  (Inagaki et al. , 2004). Archaeal amoA genes encoding the ammonium monooxygenase were amplified with primers Arch-amoAF (STAATGGTCTGGCTTAGACG) and Arch-amoAR (GCGGC-CATCCATCTGTATGT)  (Francis et al.
PHYLOGENETIC ANALYSES Environmental 16S rRNA gene sequences from the Naica Mine were compared with sequences in the database GenBank 3  by BLAST  (Altschul et al. , 1997). We retrieved the closest sequences found in the database and included them in an alignment containing also sequences from the closest cultivated members and some representative sequences of the major taxa found. Sequences were aligned using MUSCLE (MUltiple Sequence Comparison by Log-Expectation;  Edgar, 2004). Ambiguously aligned positions and gaps were eliminated using Gblocks  (Castresana, 2000). A total of 1,079 and 846 unambiguously aligned nucleotide positions were retained for, respectively, archaeal and bacterial sequences in order to carry out subsequent phylogenetic analyses. The resulting sequence alignments were used as input to build phylogenetic trees by approximate maximum likelihood using Fasttree  (Price et al.
, 2010)  with a general time reversible (GTR) model of sequence evolution, and taking among-site rate variation into account by using a four-category discrete approximation of a Γ distribution. ML bootstrap proportions were inferred using 1,000 replicates. For protein (amoA) phylogenetic analyses, amino acid alignments were obtained with MUSCLE and trimmed using Gblocks. The ML phylogenetic tree was reconstructed using Treefinder  (Jobb et al. , 2004)  with the WAG-Γ model of sequence evolution. Phylogenetic trees were visualized using the program FIGTREE 4. The sequences reported in this study have been deposited in GenBank with accession numbers KC481372-KC481399.
, 1998 ; Accession numbers of sequences retrieved from GenBank are given between brackets. Only bootstrap values higher than 50% are given at nodes. The scale bar represents the number of substitutions per a unit branch length. Rastogi et al. , 2009). While many of the marine and soil mesophilic Thaumarchaeota may be nitrifiers, aerobically oxidizing ammonia to nitrite, the metabolism of the deeper Thaumarchaeota branches to which the Naica OTU belongs, remains unknown  (Pester et al. , 2011). The Thermoplasmatales are moderately thermoacidophilic archaea frequent in hot springs. The related lineages to which the Naica sequences resemble have been identified in deep-sea sediments and also in the deep subsurface  (Lin et al. , 2006a; Kato et al. , 2009; Zhang et al. , 2010). This suggests that both archaeal OTUs are autochthonous lineages in the Naica hydrothermal springs.
ACKNOWLEDGMENTS We are grateful to  Eng. Roberto Carlos Reyes  and  Compañía Peñoles  for the facilities provided during the field studies. This work was financed by the  Spanish MINECO  ( Consolider-Ingenio 2010 project  " Factoría de Cristalización " and project  CGL2010-16882 ) and by the  French Agence Nationale de la Recherche  ( ANR-08-GENM-024-001 ).
However, the radar signals are sensitive to wind-induced waves, especially in the C-band, limiting the usefulness of this band for water detection  (Alsdorf et al. , 2007). L-band data are limited by their low orbital repeat cycles, cost and limited archives. Passive microwave data have been used to detect flood extents  (Sippel et al. , 1998; Ticehurst et al. , 2009)  but are also limited by a low spatial resolution. Thermal satellite data have been used to map inundated areas  (Leblanc et al. , 2011) , but to our knowledge, these data have an inadequate monthly timescale for characterizing rapidly changing flood extents. An alternative solution is the use of passive optical/infrared sensors on board the Landsat, Aqua, Terra or SPOT satellites. The MODIS instruments on-board the Terra and Aqua satellites have been providing daily data for 36 spectral bands between 0.
The objective of this study was to quantify the main water fluxes and flooding characteristics in a poorly gauged East African wetland, the Tana River Delta (TRD). To do so, we constructed a lumped hydrological model (the Tana Inundation Model, TIM) that allowed us to determine the role of river fluxes in flooding events and the number of flood events from 2002 to 2011 and to characterize the extent and duration of these floods. Satellite data were used to calibrate and validate the model, and to provide a spatial representation of the flooded zones. The hydrological variables quantified in this study are relevant for the management of the wetlands. After a description of the TRD, focusing in particular on its hydrological characteristics, the MODIS satellite data are analyzed, and the Tana Inundation Model is presented.
The precipitation decreases from the coast at Malindi (1098 ± 306 mm yr 1 , 1962-2008) to the inlands (530 ± 202 mm yr 1 in Garsen, 1972-1986, data  ) )   1963-1986 and 1991-1998). delta are also provided by the Tana Delta Irrigation Project (TDIP, 2 316 0 22. 07 00 S, 40 11 0 00. 55 00 E). Monthly precipitation data (from the KMD) are available from Garissa. Local precipitation most likely does not induce the floods but instead acts as a "wetting" event before the arrival of the flood wave. Precipitation is low in the semi-arid zones surrounding the delta. With an annual precipitation similar to that in Garissa (373 ± 202 mm yr 1 , 1962-2008; KMD) and a low topographical gradient, overland flow and regional groundwater flows to the floodplains are most likely limited, compared to the water brought by the Tana River.
Acquisition and pre-processing of images The MYD09A1 500 m, 8-day composite images (tile H22V09) were downloaded from the NASA website (434 images from 4 July 2002 to 19 December 2011). All pixels with a high aerosol quantity or a MOD35 cloud cover (indices extracted from the surface reflectance state quality layer) in the visual or mid-infrared bands (bands 2 and 5) were masked (LDOPE software tool, MODIS Land quality assessment group:  Roy et al. , 2002). The processed images were then reprojected into geographical coordinates, subset to the studied area (upper left corner: 2 6 0 0 00 S, 39 59 0 56. 4 00 E, lower right corner: 2 6 0 0 00 S, 39 59 0 56. 4 00 E) and transformed into geotiffs (HDF-EOS To GeoTIFF  Conversion Tool, HEG v2. 11, EOS, 2012). Image analysis was carried out on the deltaic floodplains, excluding the river mouth, mangroves and coastal forests.
C. Leauthaud et al. : Flood characterization in the Tana River Delta to model many large rivers with overflow in Africa  (Lamagat et al. , 1993; Bader et al. , 2003; Belaud et al. , 2010). This approach relies on a theoretical analysis of the diffusive wave equation and takes into account differences in propagation trends in low and high flow regimes. According to this method, two empirical relationships were established between the upstream and downstream discharges (Q 1 and Q 2 ), and between the upstream station and a delay time, T. Calibration of the two empirical relations was performed with a subset of Q 1 and Q 2 discharge data  (1 December 1986 to 29 June 1991). Upstream and downstream hydrographs were divided into 20 elementary intervals  (Belaud et al. , 2010) , and then, a linear regression was performed between the discharge rates from the two stations for different delay times, T , and for each interval.
Equations and specifications of the water balance model The TRD was represented as a single reservoir, within which a daily water balance was calculated: dV dt = A • dZ dt = Q 2 + R + L Q 3 E I (6) where V [L 3 ], Z [L] are the volume and the level of water within the reservoir. In the absence of topographical data, the flooded area A was related to water level Z using a logistic equation: A = A max 1 + a • exp r•Z , (7) where A max is the maximum flood extent, while a [ ] and r [L 1 ] determine the shape of the logistic curve. Rainfall input, R, was calculated as R = R D • A, (8) where R D is the daily precipitation at TDIP [L T 1 ]. In the absence of daily precipitation data from 1 March 2007 to 31 December 2007, R D was uniformly distributed daily values calculated from the monthly precipitation data.
Overland flow, L, was calculated as a function of rainfall at Garissa, R SB : L = c R • R SB • (A SB A) , (9) with c R being the runoff coefficient [ ] and A SB [L 2 ] being the sub-basin area after Garsen (13 700 km 2 ). In the absence of daily precipitation data for the sub-basin, R SB was uniformly distributed daily values calculated from the monthly precipitation data at Garissa. The outflow, Q 3 , which is unknown in reality because of its diffuse character, was related to the level of the water stored above the reservoir's outlet at sea level, Z SL  [L] , in a classical manner, in other words: Q 3 = ↵ • (Z Z SL ). (10) Z Z SL represents the head loss between the reservoir and its outlet. ↵ [L 3 T 1 ] and [ ] are the two parameters of the equation. The value of is typically between a minimum of 0.
Acknowledgements. This project was financed by the  French Ministry of Environment  in the framework of the project " Eaux et Territoires ", by the  Research Institute for Development (IRD)  and by the  LISAH laboratory. We thank the  Water Resource Management Authority  for providing the discharge data, the  Kenya Department of Meteorology  and the  Tana Delta Irrigation Project  for the rainfall data. The  National Museums of Kenya (NMK) , the  People, Livestock  and the Environment (PEL) research group at the  International Livestock Research Institute (ILRI) in Nairobi  and the IRD representation in East Africa are acknowledged. We also thank the anonymous reviewers for their useful comments and  J. -S. Bailly  for our discussions on error matrices. Edited by:  B. Schaefli.
Monitoring: use of historical data and statistical analysis Historical data from different sources may be collected and analyzed from a statistical viewpoint to identify possible relationships between biofuel production rates in a given country and land use and land use change. The use of this method is often justified by the fact that if biofuel production in a given country did trigger land conversion elsewhere, evidence for LUC effects should be traceable in past data on land-use worldwide  (Kim and Dale, 2011; Overmars et al. , 2011). Some studies have attempted to find evidence for LUC from historical data. A recent study was conducted by In  Numeri (2012)  on behalf of the French agency for environment and energy management (ADEME) to identify the impacts of biofuel production in France on the French and international markets (imports, exports, prices, etc.