dataesr/scientific-paragraphs-categorization huggingface dataset:
| Climate Climate estimated as long-term change in precipitation and temperature together with groundwater level is known to affect breeding wader populations  (Thorup, 1998; Bellebaum & Bock, 2009; Schroeder et al. , 2012; Maier, 2013). We used mean of daily maximum air temperature (°C) in April and sum of monthly precipitation for March, April, and May (mm, data from Danish Meteorological Institute). We used data on climate measured at a local coastal weather station at Vestervig. Groundwater at Tipperne and water level at the outlet sluice at Vejlerne were measured, but not included in the environmental analysis due to significant correlations (p < 0. 05) with precipitation in most spring months.
| Nutrients Data on fertilizer from farmland in Denmark were estimated for 1928-2014 as the annual amount of outlet of total-N (tons) to coastal waters  (Conley et al. , 2007)  and updates by  Hansen (2011)  and  Thodsen et al. (2016). Each update was calibrated to the former level. This data set was used for the long-term statistical analyses for Tipperne. From 1989, data on nitrogen (μg/L) concentration were collected in Ringkøbing Fjord (surrounding Tipperne) and for nitrogen in Limfjorden (adjacent to Vejlerne) as a part of the national monitoring program NOVANA (data from Environment Centre Ringkøbing; both sites; Hansen, 2015). These data sets were used for the short-term statistical analyses at Tipperne and Vejlerne.
| Management and predation Habitat management as grazing and mowing is important for maintaining populations of breeding waders while predators reduce breeding success  (Thorup, 1998; Bellebaum & Bock, 2009; Maier, 2013). At Tipperne, we have data on the number of cattle, area grazed (ha), and the relative area mowed since 1931. Due to qualitative information on area mowed, it was classified as 0 (0%-5% of the meadows mowed), 1 (6%-25% mowed), 2 (26%-50% mowed), or 3 (51%-75% mowed). At Vejlerne, data on the number of cattle, the area grazed, and the area mowed were available. We analyzed the effects of foxes on nest predation  (Thorup, 1998; Kjeldsen, 2008). Information about fox abundance was taken from the official Danish bag statistics, which goes back to 1941. From this year up to 1972, we used the national bag size of foxes as a proxy for fox abundance (annual number of foxes shot in Denmark).
2006 ). We measured average tussac height to the nearest 0. 25 m from the ground to the top of the grass, using a graduated pole. We estimated approximate per cent tussac cover by eye. We also measured the lateral length of burrows in a subsample of 18 plots, aligned along two perpendicular transects spanning the full range of breeding habitats on the island. Burrow length was measured to the nearest 10 cm using divisions marked on the flexible probe of a burrowscope (Ridgid micro CA-300 Hand-held Inspection Camera, Elyria, OH, USA). Due to the constraints of another study carried out at the same time, we only monitored single-entrance burrows, which were determined by hand. In addition, we calculated slope in QGIS 2. 8. 3 (QGIS Development Team 2016) using a digital elevation model (10 m resolution) retrieved from the Earthdata search portal, courtesy of NASA Land Processes Distributed Active Archive Center (NASA JPL 2013) and provided by the South Atlantic Environmental Research Institute data centre.
Detailed analysis of the collected material (PI documents, press articles, interview transcripts) was performed using Atlas. ti software. This software allows to analyze themes, cited sources, and arguments in each document. It can perform quantitative analysis concerning, for example, the number of occurrences of the same argument in various public inquiries and in the media. Content analyses were also conducted to characterize how the media handled the information and to cross-reference the arguments proposed by citizens, by stakeholders and in media reports. Thus, it is possible to assess the use of the different types of resources by categories of actors, be they technical or linked to policies, cultural aspects or environmental ones, and to draw causal inferences related to stakeholders' inscriptions in different social worlds.
Acknowledgements This work was conducted within the framework of the European Union's  Horizon 2020 DESTRESS project  (Grant Agreement  691728 ) and of the  EGS Alsace  project, co-funded by  ADEME  and  ES. We thank the  LabEx G-EAU-THERMIE PROFONDE  ( University of Strasbourg, CNRS, ES ), which is co-funded by the  French government  under the " Investissements d' Avenir" program , for their financial support. This article also received support from the  Maison Interuniversitaire des Sciences de l'Homme d' Alsace (MISHA)  and the  Excellence Initiative of the University of Strasbourg. We thank  Nicolas Daigneault  and  Jean-Yves Bart  for their work on the English version of the text. The authors are grateful to the editor and to the reviewers for their helpful comments and advice.
The French Karst National Observatory Service (SNO KARST) was created by the National Institute for Earth Sciences and Astronomy (Institut National des Sciences de l'Univers, INSU) of the French National Research Council (Centre National pour la Recherche Scientifique, CNRS) with the purpose of establishing an appropriate tool for the study of karst aquifers and watersheds. This is achieved through the synergy of several regional observatories all over France. The main objective is to acquire hydrological and physicochemical data by means of high frequency monitoring using common standards and procedures (data corpus, no resampling of raw data, etc. ), and make them available to the international scientific community. The various observatories of SNO KARST are located in different physiographic and climatic contexts (Fig.
Carbonate rocks that host karst systems are eminently prone to erosion and weathering, two processes that are highly dependent on the climatic, hydrologic, and meteorological regimes, but also on anthropogenic activities affecting the inputs to the karst system and/or the management of the land cover. Consequently, karst systems are more sensitive to environmental changes than most other hydrosystems. Here we describe the structure of SNO KARST and its local observatories, the data acquired, and the research questions addressed. These concern mainly (i) the hydrological response of karst to climate variability and anthropogenic changes, (ii) the influence of karst on the geochemical mass balance of watersheds within the critical zone, and (iii) the relationships between the structure and hydrological functioning of karst aquifers and watersheds.
Hydrological Response of Karst Catchments to Large-Scale Atmospheric Circulation Patterns Karst watersheds can display a strongly nonlinear response to meteorological inputs. This is because the interactions between the internal watershed compartments change with the amplitude of climatic variations. To assess the sensitivity of karst systems to climate variability and changes, the relationships between karst hydrological variations and large-scale climate variability was studied using the SNO KARST database. The climatic determinism of long-term interannual (hereafter referred to as low-frequency) karst hydrological variations was investigated. Three sites were used: the Radicatel Chalk karst observatory in Northern France, the Lez/MEDYCYSS observatory in Southern France, and the Moulis/Le Baget observatory in the Pyrenees.
A quasi-Newton inverse method was first applied to test the capability of tomographic pumping test data to identify the structure of hydraulic connectivity in karst aquifers  (Wang et al. , 2016). Although the inverted transmissivity field is highly dependent on a priori information provided on the inversions, the approach allows the connectivity between the major karst conduits to be identified correctly. To address the issue of uniqueness of inversion solutions and quantify the uncertainty in the inversed transmissivity fields, a stochastic Newton inverse method was proposed and applied to the same field dataset  (Wang et al. , 2017). An important finding is that the success of inverse modeling in karst systems strongly depends on whether the connectivity between the boreholes used in the tomographic hydraulic tests is preserved in the a priori model.
Using the Terrieu dataset, the CADI method generates a variety of possible karst networks, the geometrical characteristics of which are in a close agreement with those derived from previous inversions and direct field observations. Further research focuses on better identifying cross-borehole connectivity and representing the spatial arrangement of karst conduit networks. A harmonic pumping technique was applied. Numerical harmonic pumping tests with various pumping locations, amplitudes, and frequencies were first simulated in a synthetic hierarchical network formed by interconnected fractures and karst conduits embedded in a background matrix  (Fischer et al. , 2018). A sensitivity analysis showed that the phase offset of the monitored responses in observation wells allows the degree of connectivity between source and measurement points to be identified.
Research questions involving site and data intercomparisons can be also addressed. Data analysis and modeling approaches can be tested and developed thanks to the data collected at the SNO KARST sites. The added value of using different sites with complementary characteristics is illustrated by the study of the NAO reported above. The recharge areas of the Radicatel, Moulis/Le Baget, and Lez catchments are respectively around 10, 55, and 130 km 2. However, the filtering of the climatic component is similar for the three sites. A number of emerging research issues can be identified: ʶ Coupled modeling of hydrodynamic and geochemical processes. This issue may be addressed in a first step by developing the KarstMod structure, although this is not a compulsory step. ʶ Assessment of mineral-bacteria interactions.
Fish abundance data Abundance data were collected during the Baltic International Trawl Survey (BITS)  (ICES, 2014). Since 2001, the survey has been carried out with a harmonized sampling scheme and a standard gear to sample the demersal fish community in the Baltic Sea. This sampling scheme consists of trawl hauls with a duration of 30 min on average, carried out at a speed of three knots with a demersal otter trawl gear best suited for sampling demersal fish such as gadoids and flatfish. Because of poor survey coverage in shallow areas, we excluded hauls carried out at depths shallower than 20 m. We only included valid hauls carried out during the first quarter of the year (15 February-31 March). In total, our dataset included information from 4086 hauls carried out between 2001 and 2016.
With around 250 hauls per year on average, the sampling has a good spatial coverage of the area defined from the Kattegat to the northern Baltic proper (no hauls were recorded at latitude higher than 59 N). The original dataset was downloaded from the ICES Database for Trawl Surveys (DATRAS; http://datras. ices. dk/Home/Default. aspx; data downloaded on the 22 June 2017). We conducted pre-processing checks to fix mis-identified species and removed non fish species following previous recommendations  (Fung et al. , 2012; Hiddink and Coleby, 2012). We aggregated species to genus or family level when species were not consistently identified (only for Gobiidae and Callionymus spp. ) as suggested by  Fung et al. (2012). Among the pelagic species, only sprat and herring were retained in the analyses since they are the two species with the highest catches in the BITS and are consistently caught in almost all the hauls.
Other pelagic fish species are not properly sampled by the otter trawl gear and were removed from the dataset. Details of data cleaning are given in Supplementary Table  S2. Furthermore, we excluded rare species that occurred in less than 1% of our dataset (i. e. recorded in <40 hauls). The procedure identified 60 rare species (61% of all recorded species), that in total correspond to <0. 01% of the total abundance. The final community dataset consisted of abundance values (expressed in number per trawling hour) of 33 species from 4086 hauls.
Traits dataset Information on the life history of Baltic fish species was retrieved from a previous study  (Pecuchet et al. , 2016)  and the Fishbase database  (Froese and Pauly, 2017). Specifically, trophic level and maximum length (in cm) were extracted from Fishbase  (Froese and Pauly, 2017). Mean fecundity (in number of eggs spawned per adult female in one spawning season), the shape of the caudal fin (in five categories: rounded, truncated, emarginated, forked and continuous) and the body shape (in 4 categories: gadoid-like, flat, elongated, and eel-like) were obtained from  Pecuchet et al. (2016). In total, two qualitative and three quantitative traits were used to characterize the 33 fish species. These five different traits are complementary and describe the diet, habitat and reproduction for all species included in the study  (Pecuchet et al.
Environmental dataset Based on the location and time of each of the 4086 hauls, we extracted nine environmental variables. We selected environmental variables based on their potential or known effect on the demersal fish community, specifically depth, local hydrographic conditions, primary productivity and large-scale climatic conditions. Additionally, we assured that the selected variables were not strongly cross-correlated (Pearson coefficient < 0. 7). Trawl depth was retrieved directly from the information provided in the BITS dataset. Five variables concerning local hydrographic conditions were derived from the Baltic Sea Ice-Ocean Model (BSIOM)  (Lehmann and Hinrichsen, 2000; Lehmann et al. , 2002 Lehmann et al. , , 2014)) , a hydrodynamic model with an oxygen consumption calculation sub-module.
BSIOM provided values of temperature, salinity, and oxygen with a horizontal resolution of 2. 5 km and 60 vertical levels. The temporal evolution of three-dimensional temperature, salinity, and oxygen fields are in good agreement with hydrographic measurements of the ICES database  (Lehmann et al. , 2014). The five variables used in this study were annual bottom temperature, oxygen and salinity, surface temperature at the time of the survey and seasonality of bottom temperature. Annual bottom hydrographic conditions were selected as characteristic of the habitat (temperature: sbt_an, salinity: sbs_an, and oxygen: oxb_an). Average surface temperature during the first quarter (sst_q1) was selected as a snapshot of the hydrographic conditions at the time of the survey. The seasonality of bottom temperature (sbt_ra) was estimated as the range between average monthly temperatures and is an indicator of seasonal variation of the benthic habitat.
Two variables concerning primary productivity were estimated from the Chlorophyll a concentration (mg. m À3 ) of the GlobColour project  (Maritorena et al. , 2010) , merging Ocean Colour products from different sensors. We used a monthly averaged dataset with a spatial resolution of 1 km, downloaded from http://hermes. acri. fr/on 13 June 2017. The two variables selected as indicators of the primary production were Chlorophyll a concentrations averaged over the first quarter (chl_q1) and over the previous year (chl_an). Large-scale climate conditions were represented in our analysis by the North Atlantic Oscillation (NAO) index  (Hurrell, 1995) , which indicates high frequency (7-25 years) atmospheric variations and is known to affect Baltic biotic communities  (Ha ¨nninen et al. , 2000; Mo ¨llmann et al.
Multi-tables multivariate analyses First, each dataset (i. e. species abundance, species traits and environmental data) was analysed according to appropriate ordination methods corresponding to the nature of the variables (Figure  1b , Supplementary Table  S1 ). Correspondence analysis (CA) was computed on the fish abundance dataset (a matrix of species by sample). CA is well suited for abundance data along large environmental gradient because species communities often show a unimodal distribution along a gradient and, using the chisquare distance, CA can highlight differences of species composition profiles  (Legendre and Gallagher, 2001; Greenacre, 2017). Abundance was previously log-transformed (x þ 1) to reduce the influence of the dominant species in the analysis of community structure.
Principal component analysis (PCA) was performed on the environmental dataset with nine quantitative variables (a matrix of environment variables by sample) using the row weights (corresponding to the samples) from the previous CA on the fish abundance dataset in order to permit the comparison between species distribution and environmental conditions. The trait dataset (a matrix of species by trait) contained a mix of quantitative and qualitative variables and was analysed using the  Hill and Smith (1976)  method. This method combined a PCA on quantitative variables and a multiple CA on qualitative variables. Species were weighted according to column weights in the previous CA on the fish abundance dataset, in order to permit the comparison between species distribution and species traits. Second, we used within and between-group analysis to assess and separate spatial and temporal variabilities, with the year of sampling as grouping variable  (Doledec and Chessel, 1987; Franquet et al.
Software and sources All statistical analyses were conducted in the programming environment R 3. 3 (R Core team, 2017). The ade4 package  (Dray and Dufour, 2007)  was used to compute the multivariate analyses. The functional richness was calculated with the FD R package  (Laliberteand Legendre, 2010). Maps were created with the mapdata package  (Becker et al. , 2016). The cleaned datasets and the R-script are available in Supplementary Material S1.
Environmental conditions drive fish community composition Based on a large dataset of more than 4000 samples and using complementary multivariate analyses and statistical tests, we investigate the links between fish communities and environmental conditions in the Baltic Sea. Salinity, decreasing from marine waters in the Kattegat to brackish waters in the Baltic Proper, is the main driver of fish community composition. Along this gradient, our statistical approach is able to identify three sub-assemblages within the overall Baltic fish community. The three subassemblages are nested, with most fish species inhabiting the Kattegat. This finding agrees with the predicted reduction of species richness from marine to brackish water  (Pecuchet et al. , 2016; Smolinski and Radtke, 2016) , also found in the Baltic Sea zoobenthic community  (Bonsdorff, 2006).
Limits of data-driven approach As with any statistical approach, the ability of the methods applied here is limited by the quality and amount of data available. For example, the dataset used covers the 16-year period from 2001 to 2016, i. e. after the regime shift occurring in the Baltic Sea during the late 1980s  (Mo ¨llmann et al. , 2009; Casini, 2013). Including the period prior to the shift would likely increase the importance of the temporal dynamics and the capacity to detect a significant link with environmental variability. Even though the sampling started in 1991, we did not include data prior to 2001 because the sampling was performed using different gears and the sampling scheme of the surveys was different, potentially affecting the robustness of our analysis. The short length of the time series is the main limitation of our study, and it stresses the importance of rigorous and continuous data collection, a very valuable source of information in order to understand, preserve and manage marine ecosystems in a better way.
Management implications and concluding remarks The mismatch between taxonomic and functional diversity, associated with the spatial overlap of sub-assemblages, suggests that the functional redundancy decreases from west to east in our study area. The low functional redundancy in the Baltic Proper implies that its ecosystem is susceptible to changes in external pressures such as hydrography, nutrient inputs, and fisheries overexploitation that can provoke drastic reductions in fish abundances  (Rice et al. , 2013). Therefore, fisheries management in the Baltic Proper should be precautious by taking in consideration the specific local characteristics of the fish community. Our study demonstrates that based on a large dataset of community data, analysed in an innovative and comprehensive way, we can provide a complete view of the effects of environment on the structuring of biotic communities in space, time and functions.
Acknowledgements The authors are grateful to all contributors of the DATRAS database who collected and took part in the  Baltic International Trawl Survey , and to ICES who merged, cleaned and made the data open access. We thank the two anonymous referees and  Marta Coll  for their useful comments that helped improve this manuscript. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No  675997  ( ITN MARmaED ). The results of this publication reflect only the author's view and the Commission is not responsible for any use that may be made of the information it contains. The study is a contribution to the  University of Hamburg Center of Excellence on "Integrated Climate System Analysis and Prediction" (CliSAP)  funded by the  German Science Foundation.
, 2013 (Fröhlich et al. , , 2015)). The ToF-ACSM has mass-to-charge resolution M/ M = 500 and can measure up to m/z 300. Data were obtained with a time resolution of 200 s using Igor-DAQ v2. 0. 20 and the data were analyzed by Tofware version 2. 5. 6 written in Igor Pro (Wave Metrics Inc. , Lake Oswego, Oregon). We calibrated the sampling flow rate, ionization efficiency (IE) of nitrate, and relative IE (RIE) of ammonium and sulfate before and after the campaign. The calibration results before and after the campaign were consistent. A composition-dependent collection efficiency  (Middlebrook et al. , 2012)  factor of approximately 0. 5 (0. 49 ± 0. 17) was applied and confirmed by plotting total NR-PM 1 mass concentration measured by the ToF-ACSM against PM 2. 5 mass concentration published on the website of the National Environment Agency (NEA) of Singapore (Fig.
2 µm) prior to injection to the TOC analyzer. Possible interference of gaseous species on WSOC was also subtracted using the data from back filters, as in the case of the OC analysis. For the inorganic ion analysis, a portion of the filter samples (1 cm ×1 cm) was soaked in a solvent (4. 75 mL Milli-Q and 0. 25 mL ethanol). Ions were extracted by ultrasonication for 15 min, filtered by syringe filters (hydrophilic polyvinylidene fluoride, PVDF; pore size 0. 45 µm), and were subsequently analyzed using ion chromatography (IC, Dionex ICS 1000, Thermo Fisher Scientific K. K. )  (Miyakawa et al. , 2015). The measured OC, EC, WSOC, and inorganic ions are reported in Table  1. The inorganic ions were corrected for blank interference but not corrected for positive artifacts. We use the measured inorganic ions to identify the source of OA factors from the ME-2 analysis.
Public domain data and analysis The NOAA HYSPLIT back-trajectory model  (Rolph et al. , 2017; Stein et al. , 2015)  was used for estimating the origins of air masses. The trajectories were calculated from the observation site at 200 and 500 m above ground level (a. g. l. ) at 06:00 LT of each day during the entire measurement period. In addition, trajectories were also estimated for four time periods (06:00, 12:00, 18:00, and 00:00 LT) of 19 and 20 October from the observation site at 500 m a. g. l. Fire hotspots in Indonesia during October 2015 were retrieved from near-real-time (NRT) Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies/Fire Locations Collection 6 processed by the Land, Atmosphere Near real-time Capability for EOS (Earth Observing System) Fire Information for Resource Management System  (LANCE-FIRMS, 2015).