dataesr/scientific-paragraphs-categorization huggingface dataset:
Écotourisme et projets de développement économique communautaire dans les villages riverains pour une protection intégrale du PNB To achieve these objectives, the analysis of the land cover and vegetation change help to understand the environmental dynamics of the Banco forest. The inventory and mapping of the types of risk and their impact on PNB enable to show the most vulnerable areas due to anthropogenic pressures and urbanization. The study of the types of human pressures in PNB is conducted through observations to detect spatial changes in recent years. The study also relies on an aerial photograph of 1955 and Spot satellite image taken in 1998. Aerial photography was acquired at the Centre of Cartography and Remote Sensing and the National Geographic Institute (IGN-Paris). The satellite image was obtained with the ISIS program of Spot Image.
More details on the MAT and on the WAPLS are given in  Peyron et al. (2013). Both methods are calibrated using the high-quality and taxonomically consistent dataset compiled by  Dormoy et al. (2009). This dataset contains more than 2000 samples from the Mediterranean region to which were added 26 modern surface samples from around Lake Trifoglietti (Calabria, southern Italy) collected by  Joannin et al. (2012) , and from around Mount Altesina and Lake Preola (Sicily). Pinus pollen was removed from the modern and fossil samples because of its over-representation in marine pollen assemblages. In this study, the quantitative climate reconstructions for the core MD 90-917 were performed using annual (TANN), coldest month (MTCO) and warmest month (MTWA) temperature, and annual (PANN), spring (Pspr), summer (Psum), autumn (Paut) and winter (Pwin) precipitation to emphasize changes in seasonality.
Illite presents a basal peak at 10 Å on the three tests (natural, glycolated, and heated). Kaolinite is characterized by peaks at 7 and 3. 57 Å on the untreated sample and after saturation in ethylene glycol. Both peaks disappear or are strongly reduced after heating. Semiquantitative estimation of clay mineral abundances, based on the pseudo-voigt deconvolution for the doublet kaolinitechlorite (3. 57 Å-3. 53 Å), was performed using the software MacDiff developed by  Petschick (2001). Here we use the illite to kaolinite and smectite to kaolinite ratios in order to evaluate the balance between inputs from rivers, respectively Po River (illite) and eastern Apennine rivers (smectite), versus eolian inputs from the south (kaolinite). These ratios are calculated using the semi quantitative estimation of each clay mineral.
The 7700 cal yr BP event shows a major increase in the illite / kaolinite ratio, indicating major discharges from the Po River. This pattern of increasing inflow from the Po River Fig. 9. Comparison of the Psum (yellow) and Pwin (red) precipitation changes reconstructed from MD 90-917 pollen data (MAT) and from bottom to top: lake level highstands in western Europe  (Magny et al. , 2012 column) ; sea surface salinity reconstructed from MD 90-917 δ 18 O record  (Siani et al. , 2013; blue) , I/K (pink) and S/K (green) ratios from MD 90-917 and δ 18 O speleothem records from the Renella cave (Italy)  (Zhornyak et al. , 2011; purple)  and Soreq cave (Israel)  (Bar-Matthews et al. , 1998; Bar-Matthews and Ayalon, 2011; grey). For MD 90-917 records, thin, light curves represent the original data and accentuated curves are the 3 pts-smoothed curves.
Acknowledgements. This study is a part of the  LAMA ANR  projects ( MSHE Ledoux ,  USR 3124 , CNRS) financially supported by the  French CNRS (National Centre for Scientific Research). The authors received funding from  French CNRS  and  CEA  for their research. S. Goring is funded through the  NSF-Macrosystems program , grant # EF1065656. This research has been supported by the  Institut Paul Emile Victor  for the Prometée cruise of the  R/V Marion Dufresne. Palynological samples were processed by  J. P. Cazet  and  I. Dormoy. This is LSCE contribution no. 4587. Edited by: M. -F. Loutre The publication of this article is financed by  CNRS-INSU.
En effet, les données notées spontanément par des naturalistes dans des bases de données Prise en compte de la biodiversité dans les projets d'aménagement : comment a. La biodiversité ferait courir un risque aux projets ? Pour un naturaliste c'est bien évidemment le projet qui fait courir un risque à la biodiversité. Mais du point de vue du maître d'ouvrage, c'est parfois l'inverse comme l'exprime RFF dans sa présentation en commission particulière du débat public sur la ligne POCL (Paris-Orléans-Clermont-Ferrand-Lyon) « quels sont les risques que les enjeux de biodiversité font peser sur le projet ? » (RFF, 2011). RFF décline ces risques en « risque de faisabilité » (obtenir les autorisations pour le projet), « risque juridique » (sécuriser les autorisations), « risque technico-financier » (réaliser les mesures compensatoires) et « risque d'acceptabilité » (partager les enjeux et les risques).
La diffusion de la connaissance sur la biodiversité est identifiée comme un enjeu particulier pour permettre d'intégrer cette thématique dans les décisions publiques (ARPE PACA, 2012). Les DREAL pourraient contribuer à nourrir ces observatoires par les études d'impact dont elles ont connaissance mais en ont-elles les moyens humains et techniques ? Le partage de bases de données est en effet une expertise à part entière qui exige à la fois réflexion sur les formats des données, sur la validation de la qualité des données et sur leur mise à disposition. La prise en compte des continuités écologiques dans les projets d'aménagement et en particulier dans les projets très fragmentant comme les infrastructures de transport terrestre va amener un regard nouveau sur la biodiversité. D'une prise en compte exclusivement ciblée sur des espèces et espaces protégés parce que soumis à réglementation et nécessitant des démarches spécifiques en cas de perturbation et/ou de destruction 10 , la prise en compte de la biodiversité ordinaire et de son fonctionnement imposera une vision plus systémique des impacts d'une infrastructure.
De la même manière avoir une liste synthétique ou les fichiers des relevés bruts, des résultats déjà traités et analysés ne permettra pas la même utilisation de l'étude. Il est important pour commencer à mutualiser les données notamment financées par des fonds publics, de prévoir des formats et attributs de fichiers de données qui permettent le transfert vers des bases de données mutualisées. Au delà des résultats, le financeur souhaite-t-il que le prestataire lui apporte des conseils concernant la préservation ou la gestion du patrimoine naturel présent, des propositions d'actions pour augmenter les potentialités du site pour accueillir une biodiversité plus importante ?Si des actions sont attendues, selon quels critères les actions proposées doivent-elles être hiérarchisées (coût de mise en oeuvre, délai de réalisation, contrainte d'entretien, pérennité, gain en biodiversité…) ?L'étude doit-elle proposer une méthode d'évaluation de la mise en oeuvre et de l'efficacité de l'aménagement ou du projet ?Si oui, il est nécessaire de préciser les données à relever, les protocoles de suivi, la périodicité, les indicateurs à calculer…ConclusionNous avons montré que malgré les belles et anciennes intentions de la loi de 1976, la qualité des études d'impact laisse encore à désirer et que les avancées réglementaires vont nécessiter de proposer des méthodes non encore opérationnelles pour prendre en compte les continuités écologiques, les services rendus par la nature ordinaire et les impacts cumulés.
Pistes d'amélioration 3. 1. Pour les études d'impact 21 Le chantier est conséquent puisqu'en 2011, 4200 études d'impact sont réalisées par an en France (Lavoux, 2011) et ce nombre risque d'augmenter avec l'extension de son champ sur le d'application apportée par la réforme de 2012. La première recommandation serait que Type de projet territoire concerné effectivement par le remembrement. La présentation du milieu les différents guides 9 rédigés pour la prise en compte de l'environnement dans les études Contexte réglementaire Maître d'ouvrage naturel et des espèces locales semble reposer essentiellement sur les bases de données d'impact soient connus des maîtres d'ouvrages et utilisés, car ils reprennent les bases de Ligne voie ferrée publiques (ZNIEFF et Natura 2000), communiquées par la DREAL (direction régionale de Etude d'impact RFF ce qu'il faut respecter à minima.
Remerciements 69 Nous tenons à remercier  Céline Granjou , chercheuse à l' Institut national de Recherche en Sciences et Technologies pour l'Environnement et l'Agriculture (IRSTEA ), pour son aide et pour la communication de ses travaux en la matière, qui nous ont permis de disposer d'une excellente base théorique. Nos remerciements vont aussi à Pierre Migot, directeur des études et de la recherche et  Marie-Solange Landry, documentaliste de l'ONCFS. Enfin ,  Samuel Danflous  pour sa maîtrise de la langue anglaise.
During post-processing, canopy opening information is extracted by means of specific algorithms. A spatial index, calibrated to a low-resolution radiometer, indicates present and past degrees of opening. This process produces estimations of forest degradation that can describe lowresolution remotely-sensed data (such as MODIS) used in continental mapping. These estimations are then cross-referenced with continental land cover maps to gain insights into present-day forest categories. This tool was developed under the CoForChange project, whose overall objective is to predict changes in forests and in tree species distribution in the Congo Basin as a result of global change, and to develop decision-making tools. The article focuses on a full-scale example in real time, in the rainforest located at the borders of the Central African Republic, the Republic of the Congo and Cameroon, which was analysed on a year-by-year basis with thirty years of LANDSAT image archives.
, 2010; LAURANCE et al. , 2009; BROADBENT et al. , 2008). Then several software developments are proposed to estimate the logging opening in large areas  (SOUZA et al. , 2003)  and the vegetation capacity to recover  (BROADBENT et al. , 2006; DE WASSEIGE & DEFOURNY, 2004). LAPORTE et al. (2007)  showed that it is possible to digitize manually the road networks of a large region using remotely-sensed data, then ASNER (  2009 ) developed a software able to process data in a semi-automated way -but its access is restricted -, finally  PITHON et al. (2013)  also developed a semi-automated software -but it can only be applied to specific data. Today, the forestry community still needs an operational tool capable informing on the logging impact and capacity of the forest to recover. This tool must be able to extract small objects and aggregate them in time and space.
Methodological development The processing workflow was prototyped with the box image processing software (ArcGIS, ENVI and SAGA). The process was then recoded into a semi-automated application developed in C++ by ORFEO (wiki. orfeo-toolbox. org), an image processing library created by CNES and a few shell scripts. It was applied standard data pre-processing with calibration to standardize the reflectance of each image (from digital number to top of atmosphere reflectance). Specific indices to enhance the spectral contrast between forest and bare soil were calculated. Two indices were necessary, the normalized difference vegetation index (NDVI = NIR-R/NIR+R) and the green-red index (GR = G-R/G+R), to increase the contrast between photosynthetic active surface and bare soil surface. A local contrast process using a spatial filter improves this contrast  (GOND et al.
, 2004). Bare soils were identified using spectral channels and indices (a pixel is considered as bare soil when R>0. 006 and GR>0. 019 and NDVI+GR>0. 052). A cloud and water mask was applied to all data to avoid confusion with bare soils (a pixel is considered as cloud or water when B>0. 12 and SWIR<0. 04). A morphological filter from the ORFEO toolbox (SHAPE functions) was applied to eliminate isolated pixels and extract the road network (an object is considered as bare soil/road when SHAPE_Size>50 pixels or SHAPE_Elongation>3 pixels; figure  2 ). Then a Boolean model (0 or 1) was created considering forest (0) and bare soil (1). This process avoids missing values along road networks and masks isolated pixels. Finally, a yearly synthesis was used to develop a year-toyear database for comparisons and analyses over time.
Modelling the weights of criteria Different criteria have varying levels of influence on the risk of contaminant transfer. Because of this, the values need to be weighted within the model. The numerical values assigned to these weightings were obtained using SRF (Simos-Roy-Figueira) software  (Figueira and Roy, 2002) , with the help of expert agronomists in ranking criteria based on their importance in physical processes, and then using software with a "card game" approach (Table  2 ). Table  2  shows the final results of the weighting. Surface water vulnerability criteria receive a weighting of 47%, nitrogen pressure 30%, and the two BEPs "vulnerability regulators": 23%. In order to account for the imprecise nature of some data, we also introduced the notion of thresholds to the model. We established three different thresholds: Indifference (q), Preference (p), and Veto (v).
Indicator of Hydrography Step 1: Combination of indicators The second stage involved aggregating contamination risk values at different spatial levels (watersheds in this case). This was achieved by calculating average values based on the surface area of the watershed. The level of aggregation depends on the area chosen by the public managers of agriculture and the environment, and the area in which water needs to be protected. This information provides adequate scope for scale changing. Data were gathered over the full area at the smallest scale, allowing for a more precise description. The general flowchart of the method is showed in the Figure  4. The application of the method in the Coteaux de Gascogne area (map on Figure  1 ) shows how the risk assessment can be carried out for a watershed of some km 2 , right up to a hydrographic basin covering several thousand square kilometres.
Acknowledgements We would like to thank the  EU-Interreg SUDOE IV B  for funding this study, as part of the "Aguaflash" project. Our thanks also go to the association of Auradé farmers and especially their technician Vincent Gobert, for his active cooperation in this project. We thank also the laboratory teams of the  Irstea-REBX unit and INPT-Ecolab  for field samples and chemical analysis;  Daniel Uny , geomatician in the Irstea-ADBX unit, for supporting the development of geomatic applications, and  James Emery , English teacher and translator, for his help in proofreading this paper.
, 2011; Pineault et al. , 2013). Nevertheless, it is impossible to dismiss the large organic matter flux from the North America Mackenzie River. Previous studies comparing heterotrophic carbon demand versus local PP in the region typically proposed that riverine organic carbon input is the major factor explaining the imbalance between the two (e. g. ,  Garneau et al. , 2008; Ortega-Retuerta et al. , 2012). While this might be appropriate for microbial communities, metazooplankton activity (dominated by copepods) is rather linked to pulses in fresh food supply during spring-summer (e. g. ,  Forest et al. , 2011 Forest et al. , , 2012)). Similarly, carbon turnover by benthic organisms is largely related to the distribution of phyto-detritus in surface sediments  (Renaud et al. , 2007). Here we used data from the ArcticNet and Malina oceanographic expeditions that successively investigated the southeastern Beaufort Sea in July-August 2009 from the Canadian research icebreaker CCGS Amundsen.
Mixed-layer depth Air--sea CO 2 exchange Diagram showing which variables have been directly measured on the field during ArcticNet-Malina, versus which ones have been calculated using a combination of in situ data and statistical models, or rather estimated using knowledge from the literature or deduced from previous regional studies. This schematic aims at facilitating the reading of the methodological Sects. 2. 2-2. 6. The biophysical context at a regional scale prior and during the field campaign was documented using sea ice, wind and ocean color PP estimates gathered from different sources. For a detailed description of equations linking metabolic pathways of the planktonic and benthic compartments, please see Appendix B in  Forest et al. (2011).
Sea ice conditions, wind reanalysis and ocean color remote sensing Daily averaged sea ice concentrations (% of cover) were obtained from the Ifremer-CERSAT team (http://cersat. ifremer. fr/) as initially acquired from the Special Sensor Microwave Imager (SSM/I) located onboard the orbiting sensor of the Defense Meteorological Satellite Program (DMSP). The CERSAT team processes ice maps at 12. 5 km resolution using the daily brightness temperature maps from the National Snow and Ice Data Center (NSIDC;  Maslanik and Stroeve, 1999)   Daily mean wind velocity and direction (meridional and zonal wind at 10 m) were obtained from the National Centers for Environmental Prediction (NCEP) North American Regional Reanalysis (NARR) (http://www. esrl. noaa. gov/psd/ data/gridded/data. narr. html). The NARR data are obtained from the high-resolution NCEP Eta Model (32 km, 45 layers) together with the Regional Data Assimilation System (RDAS) and converted to a Northern Lambert Conformal Conic grid.
Phenol red and m-cresol purple were used as indicators. The pH of brackish water samples (salinity < 20) was measured with a potentiometric electrode setup. In situ seawater CO 2 fugacity (f CO 2 SW) and total inorganic carbon (TIC) concentration were calculated from A T and pH measurements with the freeware CO2SYS  (Lewis and Wallace, 1998)  and the carbonate dissociation constants of  Mehrbach et al. (1973)  as refit by  Dickson and Millero (1987). In addition to bottle sampling, a continuous sampling ofpCO 2 SW was conducted using an underway system (General Oceanics model 8050;  Pierrot et al. , 2009)  delivering water from a nominal depth of ∼ 5 m and equipped with a flow-through CTD (Idronaut Ocean Seven 315) as described in  Else et al. (2013a). CO 2 data from the underway system were corrected for thermodynamic effects and were discarded when the flow to the equilibrator was below 2 L per min.
Validated pCO 2 SW data from the underway system were merged with the bottle data set in a unique database to provide a comprehensive overview of sea surface CO 2 concentrations across the study area. Conversion of f CO 2 to pCO 2 and vice-versa was made using the virial equation of  Weiss (1974). Sea-to-air CO 2 fluxes were calculated using the bulk formulation, linearly scaled to sea ice concentration (e. g. ,  Bates et al. , 2006; Mucci et al. , 2010; Else et al. , 2012) : F CO 2 = kα(pCO 2 SW -pCO 2 ATM)(100 -SIC/100), (7) where F CO 2 is the flux of CO 2 (mmol m -2 d -1 , negative values indicate a sink into the ocean while positive values indicate a source for the atmosphere), k is the gas transfer velocity, α is the solubility of CO 2 in seawater, pCO 2 is the partial pressure of CO 2 in the atmosphere (ATM) and surface seawater (SW), and SIC is the sea ice concentration from 0 to 100 %.
The two data sets were subsequently compared for coherency and combined into one single database. Samples for the determination of total organic carbon were collected into 50 mL glass Schott bottles, immediately acidified with H 2 SO 4 and stored for further analysis. Prior to oxidation, samples were bubbled with a high-purity oxygen/nitrogen gas stream for 15 min. Persulfate wet-oxidation was used to digest the organic matter in these unfiltered samples  (Raimbault et al. (1999b). The concentration of DOC was calculated from the total organic fraction by subtracting POC values obtained from the corresponding GF/F fraction. Reference water from the Sargasso Sea was used to verify the accuracy of DOC measurements (Hansell Laboratory, Bermuda Biological Station for Research). All reagents and blanks were prepared using fresh Millipore Milli-Q plus water.
Data analyses, mapping and net community production estimates The various data sets collected during ArcticNet-Malina were analyzed to explore spatial variability and large-scale differences in physical, biogeochemical and ecological functioning. Sub-regions and sub-layers were defined as follows. The mixed-layer depth was used to delimit the boundary between the surface and subsurface layer, while the subsurface layer extended down to 100 m depth maximum. The threshold between the on-shelf and off-shelf region was set at the 100 m isobath. The northward bound of the off-shelf zones was set to the 2000 m isobath. The western, central and eastern regions used for averaging diverse values corresponded to the areas delimited by 136-141 All observational data maps presented in this study were computed using the Gridfit function developed for Matlab (MathWorks, USA) by  D'Errico (2006).
Acknowledgements. This work was supported in the field by the officers and crew members of the  CCGS Amundsen. We express gratitude to  L. Prieur ,  M. Picheral  and  C. Marec  for the deployment of the CTD/UVP-rosette and for the processing of UVP data. We thank  P. Guillot  for the validation of CTD data. We gratefully thank  S. Lessard ,  M. Poulin  and  M. Gosselin  for phytoplankton taxonomy data and for comments on the initial manuscript. Special thanks to  L. Stemmann ,  A. Mucci ,  E. Ortega-Retuerta ,  F. Joux ,  W. Jeffrey ,  H. Link ,  P. Archambault , and  G. Chaillou  from the Malina team for sharing part of their data sets. Thanks to  N. Garcia  and  J. Gagnon  for the measurement of nutrients and primary production on the field. Special thanks  M. -B. Gagné  for the processing and extraction of satellite-derived PP rates.
Le traitement de l'information n'est plus considéré dans sa relation avec un individu unique mais dans le cadre de réseaux interorganisationnels  (Noucher, 2009). Les architectures orientées services (ou Service Oriented Architecture, SOA) qui sont ainsi déployées permettent des couplages externes « lâches » entre applications d'organisations différentes grâce à l'utilisation de couches d'interfaces interopérables. Elles visent à décomposer une fonctionnalité en un ensemble de fonctions basiques, appelées « services ». Il s'agit le plus souvent de services web de type WMS 3  pour les données raster (cartes scannées, orthophotoplans, etc. ), de type WFS  4  pour les données vectorielles (bases de données topographiques, zonages environnementaux, etc. ), de type CSW 5  pour les métadonnées (description des données) voire de type TJS 6  pour les données statistiques (recensement par exemple).
L'originalité des IDG est ici d'opérer un renouvèlement dans les méthodes de transfert des connaissances environnementales. En effet, les administrations centrales ne sont plus des organisations qui accumulent les données publiques mais les noeuds de réseaux de bases de données. Ceci révèle un double mouvement. Les administrations locales sont confortées dans leur rôle de création des données, conformément au principe de subsidiarité : ce sont les acteurs les plus proches des phénomènes sociaux ou naturels qui sont les plus légitimes pour produire les données. Les données doivent être produites, stockées et entretenues au niveau local. Mais le principe de subsidiarité dans la création et la gestion des données publiques ne se fait pas au détriment des administrations centrales dans la mesure où celles-ci ont à présent les moyens techniques pour que le maintien des données au niveau local n'empêche pas leur agrégation nationale ou transnationale.