dataesr/scientific-paragraphs-categorization huggingface dataset:
Indeed, previous works  26  provided online access to seagrass spatial data at a broad resolution. As part of the MediSeH project, the collated current and historical data on the P. oceanica distribution were stored within a geodatabase and were made available through the development of an online GIS data viewer (http://www. mareaproject. net/medviewer/), enabling their visualization. Seagrass presence depends on a number of factors such as physical variables (e. g. temperature, salinity, depth, turbidity) which regulate its physiological activity, natural phenomena (climate change) and anthropogenic pressures. Meadows cover the majority of the Western Mediterranean coasts, but they are absent along parts of the Spanish coastline (Tarragona region), near the Ebro's mouth, where freshwater input affects salinity and turbidity, thereby hindering seagrass growth  35.
The comparison of current distribution maps with available historical ones allowed us to assess the changes undergone by meadows over time. As we mentioned in the Results, in areas for which historical data were available, the estimated regression of P. oceanica meadows was 34% in the last 50 years. With reference to the IUCN's draft of Red List criteria for ecosystems  47  , that estimate makes P. oceanica habitat an "endangered" ecosystems. The results of our work showed that the regression of meadows is a generalised phenomenon in the Mediterranean Sea, even though some exceptions exist (e. g. Corsica, parts of the Sardinian coastline and the Valencia region in Spain). However, we should note that historical knowledge is generally fragmented, with different levels of accuracy across each Mediterranean country.
oceanica meadows by 2050  55. The distribution map of P. oceanica, compiled by the MediSeH project and presented here, helps to fill the gap in knowledge on the presence and absence of P. oceanica meadows along the Mediterranean coasts. Furthermore, the work points out the lack of relevant data in different parts of the Mediterranean Sea, with particular reference to the eastern basin. Future mapping and monitoring efforts should target the remaining unmapped coastline (21,500 km) located in the southern and eastern regions of the basin, and in particular along the Algerian, Libyan, Croatian, Montenegrins, Greek and Turkish coasts. New surveys are expected to increase the known extent of P. oceanica meadows. Despite the scarcity of information on the presence and distribution of P. oceanica meadows in some areas, a progressive regression seems to be taking place in those countries where the distribution is well known.
g. France). Despite the remaining data gaps, our effort of collating information on the distribution of P. oceanica and documenting patterns of regression shows that sufficient information exists to identify and prioritize areas where cost-effective schemes for threats reduction could be implemented. Now, the challenge is the identification of reversible threats that can be managed through specific actions capable of reversing present patterns of change and ensuring P. oceanica persistence at Mediterranean scale. The broad distribution of P. oceanica in the Mediterranean Sea indicates that meadows are the result of ecological and evolutionary processes occurring over centuries. Time scales such as these are in contrast to the rapid and acute current impacts, caused directly or indirectly by human activity on seagrasses  57.
Methods We looked for the literature showing current and past distribution maps (i. e. shapefiles, polygons), and point data of presence/absence of P. oceanica in different countries across the Mediterraneand basin. The Web of Science literature database was used, with search terms within the 'Topic' field such as "Posidonia oceanica" and "Distribution" or "Map" or "Regression" or "Decline" or "Progression" or "Recovery" or "Status" or "Cartography" or "Cover" or "Density". Governmental authorities (Ministries, Regional authorities) and other administrative offices of different countries were contacted to access information collated as part of Natura 2000, as well as local unpublished data (grey literature). We enriched the collated dataset using data obtained from national, EU and international projects' websites.
To fill the gaps in knowledge, local experts (e. g. researchers, civil servants in environmental ministries) were contacted for additional information. Ultimately, 263 studies were found to be relevant and these include reviewed papers, unpublished dataset, reports of EU or national projects and websites (see Supplementary Information and Supplementary Table S1 online). Most of the spatial data were not available in geo-referenced digital format (e. g. shapefile, raster file) suitable for graphic software (e. g. ArcGIS, AutoCAD), but as paper maps only such as. jpeg and. pdf. The maps available had a great heterogeneity of spatial resolution, ranging from 1:1,000 to 1:250,000. Collated maps were characterised by various geographical projections, datum and legends, with a suite of non-standardised symbology.
As a consequence, paper maps were manually digitized, the new maps geo-referenced (UTM Projection with WGS 1984 Datum) and incorporated in a GIS database (see Fig. 6  and Supplementary Figure  S1 online). Symbology was simplified and all symbols, which were used by different maps, were unified in two categories: (i) "Posidonia oceanica" and (ii) "dead matte", regardless of the substrate (e. g. rock, sand or matte) or other meadow characteristics (e. g. mosaic of P. oceanica and rocks, P. oceanica and Cymodocea nodosa). However, the original information is still available within the GIS database. The remaining coastlines were classified as (iii) known absence of P. oceanica and (iv) "no data areas", where no information on presence or absence was available. All the collated data and maps were standardized using the Geographical Information System software ArcGIS ® software by Esri (Environmental Systems Resource Institute, ArcMap 9.
3, http://www. esri. com). For the coastline and conspicuous points in the mainland, the OpenStreetMap contributors 59 data coverage (http://www. openstreetmap. org/) was used. The bathymetric lines or points came from different local sources (i. e. Italian "Istituto Idrografico della Marina"), the GEBCO Project 60 , the EUSeaMap Project  34  , or, where present, from the original maps. Supplementary Figure  S1  shows a detailed example of the current and historical distribution of P. oceanica and the bathymetric data available along the coast of the central Tyrrhenian Sea. We considered the most recent map of an area as the present P. oceanica distribution. Older maps available for the same area were considered to be historical information. Despite their low accuracy, these were included in the analysis only if they could be exactly geo-referenced.
After the standardization of maps and the creation of a shapefile representing the whole distribution of P. oceanica meadows, we estimated differences in seagrass extension by comparing current and historical maps we evaluated the extent of regression and calculated meadows' variations caused by regressive phenomena. Where data and maps were available, regression values were calculated by ArcGIS ® software and listed in Table  2 , reporting amounts of lost P. oceanica with relation to the coastline length for which current and historical information was available. In some areas, the literature available on regressive status of P. oceanica was reviewed in order to gauge the reliability of our assessment or to improve the quality of the information of inaccurate maps. As a cross-check, data reported in the literature was compared to information found on maps, with special regard to the positioning of meadows' lower limits.
At a later stage, all the collated and standardized data were incorporated in a GIS database and they were made accessible on the MediSeH online GIS data viewer (http://www. mareaproject. net/medviewer/). The GIS database development was performed in a two-steps procedure. First, an empty geodatabase was created using an ArcGIS ® software and all the information was incorporated. Second, the geoda- tabase was transferred to a geoserver for online visualisation through a Web Map Service (WMS). The MediSeH online GIS data viewer was developed using ALOV Map ® (http://prof. if. ktu. lt:8090/alovmap/), a multi-platform, portable Java ® application for online publication of cartographic datasets and inter- active on last generation web-browser. The final online GIS data viewer was highly customised in order to improve and facilitate the visualization and scalable selection of the extensive number of assembled shapefiles and georeferenced images from the produced datasets.
Acknowledgements This study was supported and financed by the  Commission of the European Union (DG MARE)  within the MAREA Framework contract (Call for tenders  MARE/2009/05_Lot1 ) through the  Specific  Project  MEDISEH  ( SI2. 600741 ): Mediterranean Sensitive Habitats , that received 568. 996 euro. The opinions expressed are those of the authors of the study only and do not represent the Commission's official position. The  European Commission  is thankfully acknowledged. The authors would also like to express their gratitude to those who kindly shared their occurrence data on Posidonia oceanica and without whom this work would not have been possible. In particular, we would like to thank the following persons:  Tatjana Bakran-Petricioli  ( University of Zagreb, Croatia ),  Francesco Bititto (Italy) ,  Enrico Cecchi  ( ARPAT, Italy ),  Stefano Coppo  ( Regione Liguria, Italy ),  Gianna Fabi  ( CNR -IRPEM, Italy ),  Annalisa Fallace  ( University of Trieste, Italy), Sami Ben Haj (Thethis, Tunisia ),  Giuseppe Lembo  ( COISPA, Italy ),  Yassine Ramzi Sghaier  ( University of Tunis, Tunisia ) and  Maria Teresa Spedicato  (COISPA, Italy).
Available data and model implementation We implemented the WASP7 model using site-specific data, which were complemented by literature values obtained from similar environments, as summarized in Supplemental Materials. Although many research studies focusing on the Hg cycle have been carried out in the lagoon, a comprehensive investigation has never been performed. We therefore augmented our seasonal database with additional information obtained from projects carried out by the Regional Environmental Protection Agency (ARPA FVG) at the University of Trieste in the Friuli Venezia Giulia Region, and the National Institute of Oceanography and Experimental Geophysics (OGS). We gathered data on meteo-climatic conditions, suspended particulate matter and Hg species concentrations at the lagoon boundaries (i.
Sediment processes are very important in shallow water environments and play an especially crucial role in MeHg budgets. This work provides a first comprehensive overview of the Hg cycle in the Marano-Grado Lagoon, by integrating Hg data and other environmental parameters with literature values of Hg kinetic processes into a model framework specifically developed for Hg modeling. The model was able to reproduce the average measured Hg T and MeHg values and was used to quantify the Hg budgets between the Marano-Grado Lagoon and its surrounding environments. During its residence in the Marano-Grado Lagoon, Hg underwent several transformations, which led to a net MeHg production of 5 mol/y (1 kg/y) within the sediment compartment. The estimated net export of Hg T from the Lagoon to the Adriatic Sea was 0.
56 kmol/y (i. e. , approximately 115 kg/y) for Hg T , and 4. 1 mol/year for MeHg. The Hg T export corresponded to almost 6% of the Hg T load to the Mediterranean basin (10 kmol/y) estimated by  Rajar et al. (2007) , thus confirming that the Marano-Grado Lagoon is an important source of Hg for the Mediterranean Sea. Results also demonstrated that the Marano-Grado Lagoon was a net emitter of Hg 0 to the atmosphere. Information on Hg inputs to the Adriatic and to the atmosphere therefore appears to be crucial when assessing Hg budgets at a regional scale. Finally, our results also indicated that the most critical shortcoming of the currently available data sets is the lack of complete synoptic measurements and suggest that future experimental efforts should also include information on the photo-transformation rates.
In total eight gridded data sets are used, which cover Switzerland, the Alps, Germany, France, the Carpathians, Sweden, Norway, and Spain (see Fig. 1a ; Table  2 ). Except for Switzerland and France, the data sets are solely based on station data, provided on a daily basis, and cover the entire simulated period 1989 to 2007. The Swiss data set (RdissagH) is derived from a combination of surface stations and four weather radar images and has an hourly frequency starting on May 1, 2003. The French data set (SAFRAN) is a regional reanalysis in which observations where assimilated. It is originally provided on a hourly basis. The Alpine data set includes areas in Germany and France, which overlap with the observational data sets of these countries. For the analysis we compare the simulated precipitation with single observational data sets (region by region) and do not account for differences between observational data sets in the overlapping areas.
(2007)  showed that in their RCM simulations errors in the lateral boundary conditions did not increase nor amplifies. If large-scale errors are present in the lateral boundary conditions the representation of small-scale features in their RCM was rather poor. Exceptions could be found at locations where strong small-scale surface forcing were present. The EURO-CORDEX imitative provides a perfect framework to deepen this analysis and apply it to a large ensemble of GCM driven transient regional climate simulations. Acknowledgments The authors like to thank the coordination and participating institutes of the EURO-CORDEX initiative. This study relied on the availability of high-resolution observation data sets. Therefore, the authors want to thank the German Weather Service (DWD) for making available the REGNIE, the Swedish Meteorological and Hydrological Institute (SMHI) for the PTHBV, the Federal Office of Meteorology and Climatology (MeteoSwiss) for the Rdis-aggH, the Norwegian Meteorological Institute (met.
no) for the KLI-MAGRID, Météo-France for the SAFRAN, the University of Cantabria (UNICAN) for the Spain011 data set, and the CARPATCLIM Database European Commission-JRC, 2013. Part of SMHI contribution was done in the Swedish Mistra-SWECIA programme founded by Mistra (the Foundation for Strategic Environmental Research). Some EURO-CORDEX simulations and analyses were carried out within the framework of the IMPACT2C FP7 project (Grant FP7-ENV. 2011. 1. 1. 6-1). The contribution from CRP-GL (now LIST) was funded by the Luxembourg National Research Fund (FNR) through Grant FNR C09/SR/16 (CLIMPACT). Part of this work was supported by the NHCM-2 project funded by the Austrian Science Fund (FWF) (Project Number P24758-N29). NCAR is funded by the National Science Foundation. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.
Investigators from the DVDP hypothesized the presence of a groundwater system in the Taylor and Wright Valley based on a few surveys of seismic velocities and semiquantitative DC resistivity measurements  22, 27. However, borehole data did not strongly corroborate this idea, and suggested, instead, that frozen ground in the MDV was thicker than predicted. Regardless, the limited spatial coverage of a small number of boreholes restricted extrapolation of subsurface features to the greater MDV system  26. A unique feature, known as Blood Falls appears at the snout of the Taylor Glacier in the upper Taylor Valley (Fig. 1a ). Blood Falls forms as the weight of the glacier pressurizes subglacial materials leading to the expulsion of a cryoconcentrated brine at the glacier front that flows into Lake Bonney.
Discussion Our AEM survey produced regional-scale resistivity data that confirms and expands the overall extent of permafrost and reveals two extensive subsurface brine systems in the MDV. The AEM data correlate well with conductivity profiles from the MDV lakes (Table  1 ). For example, the hypersaline bottom waters of the west lobe of Lake Bonney (30-35 m depth) have a resistivity of 0. 13-0. 12 Om, and the AEM returned a resistivity value of 0. 42 Om for this depth interval. The AEM sensor was flown over the site of the DVDP ground-based resistivity survey and boreholes in the Fryxell Basin (Fig. 1 ) and consistently recorded resistivity around 100 Om or less at depths below 185 m, indicating the presence of brine in sediments. Although not directly comparable, our results are consistent with previous DVDP geophysical measurements  26  (Table  1 ; Supplementary Fig.
Alternatively, these subsurface brines could be a legacy of much older marine deposition. The presence of unfrozen soil extending beyond the current lake margins to elevations approximating the estimates of a high stand Glacial Lake Washburn (Fig. 5 ) supports the large lake hypothesis of Hall and Denton  51. Previous to our study, the MDV lakes were viewed as being isolated from one another. From the surface, Canada Glacier appears to be preventing communication between the surface waters of Lakes Hoare and Fryxell (Fig. 1a ). However, our data suggest that there is flow from the bottom of Lake Hoare into Lake Fryxell (Fig. 3  and Fig. 6 ). The implication of this to the geochemistry of the lakes is profound. It was previously thought that Lake Hoare completely evaporated around 1,200 years ago and its salts blew away.
Methods The sensor system. SkyTEM is a time-domain electromagnetic, helicopter-borne sensor system (AEM) designed for hydrogeophysical and environmental investigations  29, 64. AEM induces eddy currents in the subsurface and measures the decaying (secondary) magnetic fields in a pick-up coil mounted in the tail (Supplementary Fig. 1 ). The decay rates allow for the distinction between electrically conductive brine-bearing sediments and resistive ice-bearing formations. Electromagnetic (EM) data were acquired using the SkyTEM504 AEM system during the Antarctic austral summer (November-December 2011). Flight lines were B200-400 m apart with soundings at 15 m intervals resulting in 1,000 km of acquired flight lines. The system uses a 500-m 2 transmitter loop with four wire turns and a maximum current of 95 Amps to induce eddy currents in the subsurface (Supplementary Fig.
65  AEM data processing. The continuous data set was acquired at an average flight speed of 55 km h À 1 and a nominal flight height above the ground of 35 m. The system transmits two magnetic moments (number of turns multiplied by the area multiplied by the current in the transmitter loop), a low moment for resolution of the near-surface layers and a high moment for resolution of the deeper layers (Supplementary Fig. 3a ). The sensor in this study was equipped with altimeters and inclinometers to record frame altitude, pitch and roll throughout the survey to correct for deviations from horizontal during flight. Before shipment to Antarctica, the system was calibrated at the Danish national reference site  66  to ensure correct data levels. Data (transients and navigation data) were processed in the software package Aarhus Workbench, a software platform for processing and inversion of AEM data  67.
Retrieval of subsurface resistivities is done in a process called inversion where a cost function consisting of the difference between measured and model data and with model regularization constraints minimized in a least square  68. To achieve maximum credibility of the models resulting from the inversion, raw data was corrected for pitch and roll of the transmitter and receiver loops. Data were filtered at late times to achieve a depth of investigation (DOI) of up to B350 m depth without compromising lateral resolution. Flight height is included as an inversion parameter in the inversion scheme with a prior value and a s. d. as determined from the laser altimeters attached to the transmitter frame. The final inversion of the SkyTEM data was done using the quasi three-dimensional (3D) spatially constrained inversion scheme  69  with models discretizing the subsurface into 30 layers logarithmically distributed from the surface to a depth of about 400 m (Supplementary Fig.
Following data processing and inversion, resulting models were gridded in two-dimensional maps, and presented in cross sections (Fig. 3 ) or in 3D (Figs  4  and 5 ). A combination of Aarhus Workbench and the 3D visualization software ParaView was used to generate these figures. indicates the zone between Lake Bonney and Lake Hoare where no connectivity was identified with our survey. Taylor Glacier Lake Bonney Suess Glacier ? Lake Hoare Canada Glacier Lake Fryxell McMurdo Sound.
Acknowledgements The  US National Science Foundation, Office of Polar Programs Antarctic Earth Science and Organism and Ecosystem Programs  (Award #  1043618  to J. A. M. and R. A. V. ) funded this project. Additional support came from  NSF  Award # 1344348  to J. A. M. , Award # 1344349  to S. T. and Award # 1115245  to P. T. D. and  R. A. V. Our  survey would not have been possible without technicians from  Aarhus University Jan Steen Jorgensen  and  Lars Jensen  and helicopter support from PHI pilots  Paul Murphy  and  Ken Kuwahara. We thank  Lindsay Wahl  and  Raytheon Polar Services , in particular  Susie Lyons , for field and logistical support.
Background: In the last decade, thousands of satellite-relayed data loggers (SRDLs) have been deployed, providing large datasets on marine predator movement patterns at sea and their diving behaviour. However, the latter is in a highly summarised, low-resolution form, from which it is difficult to make the sorts of important behavioural inferences that are possible from higher-resolution datasets (such as detection of likely foraging events). The main objective of this study was to develop a simple, but accurate tool to detect and quantify within-dive foraging periods in low-resolution dives. Method: Two southern elephant seals were fitted with a head-mounted time depth recorder (TDR) (recording depth at 1 Hz) and an accelerometer (recording 3 axes of acceleration at 16 Hz) from which prey capture attempts were estimated (PrCA), and a Weddell seal was also fitted with a TDR (1 Hz).
The resulting high-resolution dive profiles were used to: (1) calculate an accurate index of foraging effort based on the detection of vertical sinuosity switches (i. e. hunting highres time); (2) produce an SRDL-equivalent low-resolution dataset using a broken stick algorithm; and (3) from each low-resolution dive calculate a set of candidate foraging effort indices. Results: Hunting lowres time, which is the total time spent in decreased vertical velocity segments of the dive, was the foraging effort index that best correlated with hunting highres time. Hunting highres mode of SES dives (highly sinuous parts of high-resolution dives) was associated with 77 % of total PrCA. In comparison, Hunting lowres segments of SES dives were associated with 68 % of PrCA as well as with four times more PrCA than transit lowres segments.
We studied two deep-diving species of circum-Antarctic seals, SES and Weddell seals, for which numerous large low-resolution datasets (collected and transmitted by SRDLs) are available. Southern elephant seals are capable of travelling long distances from their sub-Antarctic breeding colonies and using diverse habitats such as ice edges, continental shelves and oceanic eddies  [22, 23] , feeding mainly on fish and squid  [24] [25] [26]  and, as recently discovered, on krill  [27]. Their diving behaviour is relatively stereotyped and they mostly forage at the bottom of their dives  [9, 21]. In contrast, Weddell seals mainly reside in coastal or shelf waters in areas of fast ice. They feed mainly on fish, but also on cephalopods and crustaceans in variable proportions according to age, location and season  [28].