dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2009). However, in marine environment, m/z 60 is usually dominated by Na 37 Cl  (Ovadnevaite et al. , 2012). To further evaluate the interpretation of primary sources within the selected solution, source-specific ratios can be calculated (e. g. , HOA / CO, HOA/BC and HOA / NO x ) and compared with literature studies. However, in our work this approach could not be systematically applied due to the lack of external data. Bilinear regression  (Allan et al. , 2010)  can be used to estimate these ratios also in the presence of multiple sources; however, this method is more applicable to a single data set rather than an overview of 25 data sets across Europe. The secondary OA components are compared with the secondary inorganic species: the SV-OOA time series are expected to correlate with nitrate (NO 3 ) due to the higher volatility of this component and its partitioning behavior with temperature, while the LV-OOA time series are correlated with sulfate (SO 4 ) since it represents a less volatile fraction  (Lanz et al.
POA and SOA mass spectral variability Figure  7  shows the median mass spectra of HOA (n=25), BBOA (n = 22), SV-OOA (n = 21) and LV-OOA (n = 25) together with the 25th and 75th percentiles. The presented mass spectra (MS) can be considered as reference profiles for European sites in addition to the work presented by  Ng et al. (2011a) , where only for a reduced number of data sets it was possible to retrieve a good separation of primary sources (e. g. , HOA and BBOA). The comparison between the Ng et al. (2011a) MS and our median profiles provides an R 2 equal to 0. 99, 0. 93, 0. 86 and 0. 95 for HOA, BBOA, SV-OOA and LV-OOA, respectively. However, as shown in Fig. 6 , specific m/z express a stronger variability within a source profile compared to others, as later discussed later. The hydrocarbon-like OA (HOA) profile is characterized by peaks corresponding to aliphatic hydrocarbons (including m/z 27,  41, 43, 55, 57, 69, 71, etc.
Acknowledgements. This work was funded through the  European EUCAARI IP , other collaborating initiatives and through national funding through individual members states to the  CLRTAP (Convention on Long-range Transboundary Air Pollution ) as a contribution to the  EMEP monitoring program. The  CEH (Centre for Ecology & Hydrology ) and UK contribution, including much of the synthesizing analysis, was funded by the  UK Department for Environment, Food and Rural Affairs (Defra). Measurements were further funded by the following national sources: the  Spanish Ministry of Science and Innovation , the  Swiss Federal Office for Environment, Helsinki Energy , the  Ministry of Transport and Communications Finland , the  Academy of Finland Center of Excellence Program  (project no. 1118615 ), the  German Umweltbundesamt  (contracts  351 01 031 & 038 ), the  Swedish Research Council and Swedish Environmental Protection Agency , as well as the  Nordic Council of Ministers.
Trees were classified into diameter at breast height (dbh) classes with equal width d, ranging from a minimum dbh for inventory of 10 cm to a maximum dbh of 150 cm. The number K of dbh classes correspondingly varied proportionally to 1=d. The time step of the matrix model was t~1 year. For each species with at least 300 observations, a constant dbh growth rate a was estimated from the M'Baı ¨ki data base as the empirical mean of the dbh increments (including negative increments) over 2 years divided by this period of 2 years. The variations of the growth rate with dbh were not considered here because they are not a condition for l to vary with class width d. A constant recruitment rate was estimated for each species as the ratio of the number of newly recruited at year t over the number of living trees at year t{2, divided by this period of 2 years.
Variations for Celtis zenkeri To illustrate the dependence of the population growth rate l on the class width, we first describe the variations of l for just one species taken from the Mbaki data set. We chose Celtis zenkeri Engl. (Ulmaceae) because it was the most abundant species of the data set (with 7295 observations between 1982 and 2006) and representative of the most commonly observed pattern of variation of l. Its average growth rate was 0. 263 cm yr {1 (standard deviation: 0. 30 cm yr {1 ). Its recruitment rate was 1. 028% yr {1. Its mortality rate was best modeled by model  (10)  with a~{5:348 (std. dev. : 0. 272) and b~0:026 (std. dev. : 0. 009). Therefore, the mortality rate for C. zenkeri was an increasing function of tree dbh, ranging from 0. 6% yr {1 for a dbh of 10 cm to 2.
Acknowledgments We thank the  ARF Project (Appui ala Recherche Forestie  `re) and its seven partners:  AFD (Agence Francaise de Developpement) ,  CIRAD (Centre de Cooperation Internationale en Recherche Agronomique pour le Developpement) ,  ICRA  ( Institut Centrafricain de Recherche Agronomique), MEFCP (Ministere centrafricain des Eaux, Forets, Chasse et Peche ), SCAC/ MAE  ( Service de Cooperation et d'Actions Culturelles), University of Bangui  and  SCAD (SocieteCentrafricaine de Deroulage)  for providing access to the M'Baı ¨ki database. We are grateful to all volunteers appointed by the  SCAC/MAE  and the fieldworkers who participated in the ARF project management, data collection and data capture. We are grateful to  Sylvie Gourlet-Fleury  for her comments on the manuscript.
In addition, they were resistant to all aminoglycosides, to fluoroquinolones, nitrofurantoin, chloramphenicol and trimethoprim-sulphamethoxazole. The MICs of tigecycline and colistin of isolate A measured by E-test were 0. 25 and 0. 094 mg/L, respectively, whereas those of isolate B were 0. 25 and 4 mg/L, respectively. Detection of any carbapenemase activity using the Carba NP test  [3]  gave a positive result. Therefore, PCR assays were performed to identify the type of carbapenemase produced, with a series of primers designed for the detection of bla KPC , bla IMP , bla VIM , bla NDM and bla OXA-48 carbapenemase genes  [4] , and PCR and sequencing identified the bla KPC-2 gene. Pulsed-field gel electrophoresis analysis indicated that both isolates were clonally related, exhibiting identical restriction patterns (data not shown).
01 mol l -1 HCl solution (Titrisol, Merck). Phosphate and silicate in ambient water were measured once at each sampling date and concentrations were determined according to  Aminot & Kérouel (2004)  using an AutoAnalyser. DIC concentrations (mmol l -1 ) were calculated from pH, TA, phosphate and silicate concentrations, temperature, and salinity using the CO2SYS program  (Pierrot et al. 2006). NCP during immersion (mmol m -2 h -1 ) was calculated as the difference between the final and initial concentrations in DIC according to the following equations: (2) (3) where ΔDIC is the change in DIC concentration during the incubation (mmol l -1 ), V is the chamber volume (l), s is the enclosed surface area (m 2 ), Δt is the incubation time (h), CG is the estimated net community calcification (mmol CaCO 3 m -2 h -1 ), and ΔTA is the change in TA (mmol l -1 ).
Environmental parameters Photosynthetically active radiation (PAR, 400-700 nm) irradiance in the air (μmol photons m -2 s -1 ) and shade air temperature (°C) were recorded every minute from sunrise to sunset, using a quantum sensor (LI-192SA, LI-COR) calibrated for in-air operation and an air temperature sensor (LI-1400-101, LI-COR), Underwater PAR (μmol photons m -2 s -1 ) was also measured every minute during underwater experiments using the same quantum sensor, calibrated for in-water operation and connected to a LI-1400 data logger in a watertight case. Underwater PAR was measured level to the top of the leaves in seagrass beds and near the sediment in bare sandy bottoms. Sediment subsurface (first centimeter) temperature was recorded every 15 min on a HOBO U12-015 data logger. NCP DIC CG = × - Δ Δ V s t NCP CO = × Δ Δ 2 V s t CG TA = × Δ Δ V s t 2 1.
Data analysis, daily community metabolism Daily community production and respiration rates were determined for each sampling site and period from P vs. E curves established during emersion and immersion. Aerial irradiance was measured every minute over 12 d in November 2008 and 11 d in January 2010. Average underwater irradiance (E z , μmol photons m -2 s -1 ) was calculated every minute at each subtidal sampling site and during the submersion of intertidal sites according to: (5) (  6 ) where E 0 is the measured aerial irradiance (μmol photons m -2 s -1 ), k PAR is the downwelling irradiance attenuation coefficient for the whole PAR waveband (m -1 ) for each site, z (m) is the depth corrected from tidal effects, E 0* and E z* (μmol photons m -2 s -1 ) are the recorded aerial and underwater irradiance at depth z, respectively, and n is the number of simul taneous records of irradiance in the air and under water.
As no reliable tide table exists for the eastern part of the Banc d'Arguin, a harmonic tidal analysis with nodal corrections was performed for the time series sea-surface height that we established at Iwik from October 5, 2009 to January 8, 2010, using the T_Tide package  (Pawlowicz et al. 2002). To be always immersed, a depth gauge (YSI 600 XL) was placed near the tidal level where the Cymodocea nodosa bed was studied (Gauge 1). From 59 analyzed tidal constituents, 22 constituents were significant at 95% confidence according to linear analysis. The tide is semidiurnal; the amplitudes of tidal heights are 78 cm and 24 cm for the tidal harmonics M2 and S2, respectively, and 8 cm and 4 cm for K1 and O1, respectively. From this analysis, tidal heights relative to the Gauge 1 level were calculated every minute during the 2 sampling periods.
2006 , Holmer 2009) , may change the flow of alkalinity, and may prevent a valid measurement of calcification from TA variation alone. These processes were not assessed during our study, and, consequently, our data cannot be used to estimate any potential calcification rate that may have occurred in the seagrass beds. As such, the DIC correction we used must be considered an approximation. It is commonly assumed that respiration is constant during 24 h periods, an approximation that can have a significant impact on carbon budgets  (Clavier et al. 2008 ). We measured CR both at night and during the day in opaque enclosures, and our estimates should be close to the daily averages. During our study, seagrass biomasses were similar to those previously reported for the Banc d'Arguin  (van Lent et al.
The spatial distribution of a disease is often examined by applying statistical methods to data collected during disease surveillance and then generating a map that describes spatial variation in risk. However, this task is often complicated by heterogeneities in the population of interest  [14]. For example, in oysters, susceptibility to the disease varies with age, origin of oysters, farming practices, and life history traits  [7]. These heterogeneities, if not accounted for, may obscure observation of important disease trends that may be of interest to resource managers. In order to circumvent this issue, disease mortality of oysters was investigated in susceptible animals deployed at 106 locations in the Thau lagoon, on the French Mediterranean coast, before the start of the annual epizootic event.
Temperature and salinity (data not shown) were recorded every 10 min during the entire period of study, using autonomous CTD multiparameter recorders (NKE Instrumentation) deployed at Marseillan, within the bivalve farming area (43u22944. 860 N, 3u34915. 880 W, Figure  1 ). Temperature and salinity were also measured at each location for each sampling date using a WTW ProfiLab LF597-5 salinometer (Sigma-Aldrich, Lyon, France) to obtain information for the entire lagoon. Periodic temperature measurements taken at each location fit with the continuous temperature profile recorded at Marseillan (Figure  2 ). Overall, spatial variation in seawater temperature was 60. 4uC in Thau lagoon.
Statistical Analyses Nonparametric estimates of the survivor function were computed by the Kaplan-Meier method  [38]. Survival time was measured as days from 6 April (time origin), when seawater temperature reached 16uC. The data were read as the number of dead animals within each experimental unit at each time interval for 13 intervals. Sampling areas were used as strata and the resulting survival estimates were compared by using the log-rank test of homogeneity of strata. Two-way mixed model ANOVAs were conducted to determine potential differences in energetic reserves of oysters according to sampling area (Bouzigues, Meze, Marseillan, outside) and time  (6 and 16 April 2011). The term 'Time' was a random factor with two levels of repeated measurements. One-way ANOVAs were conducted to determine potential differences in fatty acid food web markers of oysters according to sampling area.
This means that explanatory variables were added into the model one by one, by selecting at each step the one that minimizes the AIC criteria. This was done until the stopping rule was met, which consists of the smallest residual sum of squares using the leave-one-out cross-validation technique. These statistical analyses were conducted using LIFETEST, FREQ, MIXED, PHREG, CORR, REG and GLMSELECT procedures of the SAS software package (SAS 9. 3, SAS institute, Carry, USA). Spatial structures of the mean survival time, energetic reserves and trophic markers of oysters were described through variograms, allowing quantification of the spatial dependency and its partitioning among distance classes  [42]. Statistical models (linear, exponential and spherical) were fitted to the variograms to produce interpolated maps by kriging for each variable using  [43].
Acknowledgments The authors thank  Marine Miguet  and  Julien Vicario  for their careful lab work, the staff involved in oyster production (Ifremer hatchery and nursery teams) and  Nicolas Keck  and  Helene Boulet  from  Laboratoire departemental veterinaire de l'He  ´rault for OsHV-1 analyses. The authors express their gratitude to  Helen McCombie  for revising the English version of this manuscript,  Coralie Lupo  for providing advice on statistics, and 5 anonymous referees and  David Bushek  for their constructive reviews.
This work was supported by grants from  France Agrimer, Region Languedoc-Roussillon  and  Conseil Ge  ´neral de l'Herault and is part of the  GIGASSAT  project funded by  ANR- AGROBIOSPHERE  No. ANR-12-AGRO-0001-01. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Meteorological data sets Three data sets are used in this paper. The detection methodology is developed with the ERA Interim reanalysis data set  (Dee et al. , 2011)  in Sect. 3. ERAI is a 6-hourly data set at a 0. 75 • × 0. 75 • spatial resolution covering the period from 1979 to 2011, provided by the European Centre for Medium-Range Weather Forecasts. In Sect. 4, two other data sets are used along with ERAI to complete the analysis and test the robustness of the methodology. First, we use the NCEP-DOE (NCEP2) reanalysis from NCEP/NCAR, a 6-hourly data set from 1979 to 2011 with a 2. 5 • × 2. 5 • spatial resolution  (Kanamitsu et al. , 2002). Second, we compute a spatial average of ERA Interim on the NCEP2 2. 5 • resolution (ERAI-2. 5). The geographical window used for the detection of events is restricted to western Europe, where most of the exposure to the windstorm peril is localised; this enables us to check our detected events against databases of insured damages.
Catalogues of extreme windstorms We consider the 10 most damaging events since 1987 ranked by Munich Re (Table  1 ). These events, called "reference storms" hereafter, are used as case studies in order to develop the methodology (Sect. 3). They cover a time period from 1987 to 2010 and are concentrated in the winter season from October to March. As a result, we choose to work with the 6month winters (October-March) from 1987 to 2010 and not the whole period covered by ERA Interim or NCEP2. In order to quantify the quality of the results, we compare the events detected with our method to the ones of the eXtreme Wind Storm (XWS) database 1. The XWS catalogue gathers 50 European winter windstorms from 1981 to 2012. Among those 50 windstorms, 16 are associated with a loss (called insurance storms hereafter) and 34 storms are not associated with a loss (named non-insurance storms).
The selection of XWS events was performed with a wind-based index computed with ERA Interim at 0. 25 • resolution over a wider geographical window than the one we consider. The 44 events with the highest index values were automatically selected, and 6 other events including one insurance storm  (Emma, 2008)  were manually added. The XWS events are considered over the period 1987-2010 shared with our study. The database contains 38 storms over the period with 14 insurance storms, including the 10 reference events from the Munich Re (MR) ranking and 4 others:  Herta (1990) ,  Wiebke (1990 ), Gero (2005)  and Emma  (2008). The remaining 24 non-insurance storms have an index value as high as the insurance storms, although they did not cause as much damages. The ratio of events that actually caused damages (i.
Variables Because damages are ultimately caused by surface winds, studies on damage potential mostly rely on wind-based indices measuring either a proxy of loss or the severity of the event. However, the accurate reproduction of local wind maxima varies according to the considered reanalysis data sets or general circulation model. The surface winds depend on boundary-layer parameterisations, and the low spatial resolution of some data sets may not capture smaller-scale dy-1 http://www. met. reading. ac. uk/~extws/ namical or topographical features that lead to extreme winds. Therefore, we choose here to define the damage potential of an event through the combined information content available from the 10 m wind speed and other variables that are not directly linked to damages, but that may have a predictive value on the physical strength of events.
In order to characterise extra-tropical cyclones with a high potential of wind-related damages over Europe, several variables at different levels of the troposphere have been analysed. We favour variables that are standard outputs from models or that only require simple computation from the initial data. Among all the variables considered, three are retained: the relative vorticity at 850 hPa, the mean sea level pressure and the 10 m wind speed. These variables are commonly used either to detect and track ETCs  (Ulbrich et al. , 2009; Neu et al. , 2013)  or to assess potential impacts of ETCs  (Leckebusch et al. , 2008; Pinto et al. , 2012). We briefly illustrate in Fig. 1  the spatial patterns of these three variables in the case of the major storm Lothar (December 1999). The relative vorticity at 850 hPa (RV850) is either directly provided in the reanalysis data set or model run, or computed as the curl of the velocity field at 850 hPa.
The vorticity field is very sensitive to the spatial resolution; it becomes noisy at finer resolutions, leading to the detection of numerous and intense local-scale features  (Hoskins and Hodges, 2002; Ulbrich et al. , 2009; Hodges et al. , 2011). We also use the anomaly of the mean sea level pressure defined, at each time step, as the difference between the MSLP and its running average over 8 days MSLP 8days , see Eq. (  1 ). The mean sea level pressure is a large-scale field that is much less dependent on the spatial resolution than the relative vorticity at 850 hPa, which means that the pattern of the MSLP field remains roughly the same when upgrading the spatial resolution. Moreover, it is strongly constrained in reanalysis data sets due to the large number and quality of observation data, especially over continents.
The added value of a multi-variable approach It is difficult to further reduce the total number of singlevariable events while keeping the reference storms. For example, raising either the detection or selection threshold is inadequate, since some reference storms would not be detected afterwards. Another way to reduce the number of detected events would be to retain the top-ranked events selected with one variable. However, the reference storms are not top-ranked in any of the three variables, and their ranking does not follow the ranking of the damages they caused (Table 2). This is also the case in the XWS database where the insurance storms are not the 50 most extreme events, with Emma ranked 63rd -below events that did not cause damages. The use of a threshold on the ranking would thus be hazardous with the perspective of using other models.
This result was also highlighted in  Pinto et al. (2012)  who showed that the ranking of storm intensity is model-dependent. Table  2. The 10 reference storms, ranked in the second column according to the insured losses (Munich Re), and from the third to the fifth column, their ranking according to the maximum value they reach in ERA Interim in terms of relative vorticity at 850 hPa (RV850), anomaly of the mean sea level pressure (MSLP anom ) and 10 m wind speed ratio (WND10 ratio ). For example, with the relative vorticity at 850 hPa (RV850), we detect 149 events that we ranked according to the maximum of RV850, reached during the period they are detected over the window. Fig. 4. For each reanalysis data set: total number of events detected with each variable, number of events common to pairs of variables and number of events common to the three variables.
Comparison to the XWS catalogue Table  3  summaries the comparison between the events generated for each variable, the final common events and the insurance storms of the XWS database. The ratio of detected insurance storms over the total number of detected events is also presented. The results show that the 14 insurance storms of the XWS database are detected with the 10 m wind speed ratio. However, the ratio of insurance storms over the total number of events is then 0. 15, which is much lower than the XWS ratio 0. 37. With the relative vorticity and the anomaly of mean sea level pressure, 12 insurance storms are detected, with a ratio of 0. 08 and 0. 10 respectively. The missing insurance storms within the relative vorticity set of events are Gero  (2005)  and Emma (2008), because their trajectory is further north than the spatial window we consider.
The benefit of widening the spatial window, i. e. detecting one more insurance storm, seems small compared to the drawback of detecting many more events in total, with no possible comparison with damage databases. For the current climate and exposure, we consider the initial window adequate enough to capture most of the insurance storms. To conclude on the results of Sect. 3, taking the intersection of event sets generated with three separate variables gives more satisfying results than using a single one. Indeed, the selection of 24 events with potential for wind-related damages over the last 30 years is consistent with records from insurance companies of major damages over the area considered. The final events have been compared to the XWS database. We show that the approach is robust when considering a wider window.
Sensitivity to data set and spatial resolution The ultimate objective of this work is to apply the method to the outputs of general circulation models such as the ones participating in the Coupled Model Intercomparison Project (CMIP5). Most of those models have a spatial resolution coarser than ERAI, especially when run over long periods of time. In order to validate its robustness against spatial resolution, the methodology is applied to the coarser reanalysis data sets NCEP2 and ERAI, downgraded to the 2. 5 • spatial resolution. This will partially separate the resolution effect (ERAI versus ERAI-2. 5) from the model effect (ERAI-2. 5 versus NCEP2), with the caveat that downgrading to 2. 5 • , the output of a model run at 0. 75 • is different from taking the output of a model run at 2. 5 •.