dataesr/scientific-paragraphs-categorization huggingface dataset:
Furthermore, considering the conditional bias of the ensemble forecasts (not shown) revealed that this underestimation occurs mainly for low forecasted gusts. Still, the application of this dataset for storm damage modelling would lead to an underestimation of the estimated storm damage probabilities. The second thing to note in Fig. 2  (left) is the underdispersion, demonstrated by the u-shape. As described in Sec. 3. 5, this indicates an underestimation of the uncertainty on forecasted wind gusts. With increasing forecast lead time, both u-shape as well as the asymmetry in the Talagrand diagram decreases (Fig. 2 , left). This means that both underdispersion and frequency bias decrease with increasing forecast lead time, which might relate to the fact that the ECMWF EPS system as mentioned above is primarily designed for forecasts in the medium range.
Acknowledgement This research was carried out in the  Hans-Ertel-Centre for Weather Research. This research network of Universities, Research Institutes  and the Deutscher Wetterdienst is funded by the  BMVI (Federal Ministry of Transport and Digital Infrastructures). Furthermore, contributions to this work have been funded by the  Federal Ministry of Education and Research in Germany (BMBF)  through the research program MiKlip (FKZ:  01LP1104A ) and by  Munich Re. We wish to thank the  Gesamtverband der Deutschen Versicherungswirtschaft e. V. (GDV)  for providing the loss data. We are also grateful to the  German Weather Service (DWD)  and the  ECMWF  for providing access to the  EPS data. Nat. Hazards Earth Syst. Sci. Discuss. , doi:10. 5194/nhess-2016-182,2016 Manuscript under review for journal Nat.
Morphology, lithology and land use Delineation and morphological characterization of the studied drainage basin was done with the open source software QGIS (QGIS, 2015) on the basis of two different digital elevation models (DEM). We used the 25 m spatial resolution DEM BD Alti of the French National Geographic Institute  (IGN, 2015)  for the coastal rivers and the 30 arc-seconds spatial resolution (equivalent to about 1 km in our latitudes) DEM associated with the WorldClim data layers (see Section 3. 1. 2) for the Rhone River. Lithological information was taken from the SANDRE database (SANDRE, 2015) which distinguishes 13 major rock types with regard to their hydrogeological properties. We simplified them to 8 rock type classes. Finally, land use types were taken from the CORINE database  (CLC, 2006)  which is available from the European Environmental Agency in a 0.
Water discharge and climate Data on water discharge (Q) were obtained from the HYDRO database at the French Ministry of the Environment, centralizing most of the hydrological records in France  (Hydro, 2015). Daily data were recovered for the most downstream gauging station in each basin, for which complete data series are available. Daily water discharges were then associated with daily-averaged SPM data (see Section 3. 2). The series extend over the 1977-2013 period for the coastal rivers and over the 1969-2013 period for the Rhone and its tributaries. In some cases (4. 0% of all data), data gaps exist in the time series. They were filled by applying linear interpolations from correlations with records from neighbouring stations according to  Lespinas et al. (2009). These stations were chosen close to the main stations, avoiding the confluence of additional tributaries in between.
( , 2014)). For the Rhone River and its tributaries, we extracted the average precipitation and temperature data from the WorldClim database  (Hijmans et al. , 2005). Available data consist in monthly data layers in a 30 arc-seconds spatial resolution and represent averages over the 1950-2000 period. All hydroclimatic parameters are summarized in Table  2. We included an additional parameter which is supposed to reflect the torrential character of water discharge (Tr). It has been defined as the ratio of flood discharge (Qf) over mean annual discharge (Qmean). This requires a clear definition of what are flood and non-flood conditions, which is not a trivial task. We followed here the proposition of  Dumas et al. (2015)  and defined as flood discharge the threshold value corresponding to a return period of 1.
Calculation of SPM fluxes Calculation of representative SPM fluxes was done through the fitting of rating curves to existing SPM -Q data pairs. These curves were then applied to the long term daily discharge records of the considered rivers. We compiled a large up-to-date database of Gulf of Lions rivers for this purpose (Table  3 ). Many of the data originate from the French water agency Agence de l'Eau RMC, which measures a series of water quality parameters monthly  (AERMC, 2015). Whenever it was possible, we completed them using data from research studies which focussed on high discharge events. These data have been kindly supplied by the corresponding authors and concern the studies of  Pont (1997)  for the Rhone River,  Serrat (1999)  for the Agly River,  Serrat et al. (2001)  for the Tet River, and Petelet-Giraud and Negrel (2007) for the Herault River.
Variability and controls of sediment yields in the study area SiRCA was consequently retained as the most appropriate rating curve model in our study. As reference period for the flux calculations, we selected 1977-2013 for which the overall discharge records were complete (or poorest in data gaps). The resulting sediment yields (SY) are listed in Table  5a  and give a complete picture of the spatial variability of average sediment yields in the Gulf of Lions drainage basin. They are compared to previous literature estimates whenever they exist (see Table  5b ). SY vary almost within one order of magnitude, from 19 t km À2yr À1 (Aude) to 151 t km À2 yr À1 (Durance). They are generally lower in the coastal river basins than in the Rhone and its tributary basins. Our simulations compare well with other literature estimates, in particular the ones of  Petelet-Giraud and Negrel (2007)  for the Herault River and of  Serrat et al.
We therefore started from the development of a simple and robust approach for the prediction of average SPM fluxes. SiRCA is not fundamentally different compared to most literature approaches, as it uses SPM-Q rating curves to predict the fluxes. It mainly intends to strengthen the SPM measurement for high-discharge conditions, generally under-represented in SPM-Q data sets, in order to avoid possible biases in the resulting fluxes. SiRCA always produced two distinct regression lines which cross at individual discharge thresholds (i. e. a few millimetres per day), separating the rating curve in a transport-dominated and erosion-dominated model part. Such behaviour cannot be fitted   2002 ),  L5-Sempéré et al. (2000). Errors bars are represented for all models. Observed fluxes taken from the study of  Dumas et al.
Acknowledgements This work was supported by the  National Centre of Scientific Research -France  ( Centre National de la Recherche Scientifique -France, CNRS ) through the attribution of a PhD grant to MS and through previous funding of the long-term environmental observation program  ORME (Observatoire Régional Méditerranéen de l'Environnement). Additional financial support was provided by the  Mediterranean Ocean Observing System for the Environment (MOOSE). We are grateful to two anonymous reviewers for their valuable and useful comments during the review process.
Data analysis The mean date of leaf unfolding and the GDH requirement were analyzed using all saplings per treatment, i. e. the mean values were averaged across all saplings of each treatment (in three chambers). The differences between the mean date of leaf unfolding under warming treatments and under controls were defined as the advancement of leaf unfolding under warming treatments. Since chamber is a nested factor, we specified the nested effects of the chambers in the runs of the linear mixed models, and found that the chambers did not significantly affect the results. Tukey's post hoc HSD tests were applied to test pairwise comparisons of the advancement, GDH requirement and temperature sensitivity of leaf unfolding among treatments. The temperature sensitivity of leaf unfolding (S T ) was calculated using linear regression analyses (ordinary least squares regressions) of the dates of leaf unfolding against mean air temperature from 1 January to the day of leaf unfolding for each species and each warming treatment.
All statistical analyses were conducted using SPSS 16. 0 (SPSS Inc. , Chicago, USA). The effectiveness of the phenological models was evaluated with the root mean square error (RMSE) of the difference between the predicted and observed dates of leaf unfolding: 2 1 ( ) 1 n i i i Sim Obs RMSE n = - - ∑ ＝ (6) where Obs i and Sim i are the mean observed and simulated dates of leaf unfolding in chamber i, respectively, and n is the number of dates.
Acknowledgements This work is supported by the  Fonds Wetenschappelijk Onderzoek (FWO) foundation. We acknowledge  Dr. Maarten Op de Beeck ,  Dr. Xiaobo Zheng ,  Marc Wellens  and  Fred Kockelbergh  for their field assistance. Yongshuo H. Fu  is a  Pegasus Marie Curie Fellow. Yongjie P. H. Liu  holds a research grant from the  China Scholarship Council (CSC). Ivan A. Janssens  and  Josep Peñuelas  acknowledge the support of the  European Research Council  with Synergy grant  ERC-2013-SyG-610028  " IMBALANCE-P" and of the University of Antwerp Centre of Excellence "GCE ",  Annette Menzel  acknowledge the support of  ERC  grant  282250  " E3-Extreme Event Ecology " and the  TUM Institute of Advanced Study.
annual progress reports), and personal communications with colleagues working in various countries (especially members of the EIFAAC/ICES/GFCM Eel Working Group). For France, we have analysed all rapports/délibérations published before 1940 by the Conseils Généraux of all départements, and the Bulletins des Sociétés (Impériales) Zoologiques d'Acclimatation between 1854 and 1890 (available at http://gallica. bnf. fr). For Germany, we analysed the Allgemeine Fischerei-Zeitung (earlier known as Bayerischen Fischerei-Zeitung) between 1876 and 1940; the Circulare des Deutschen Fischerei-Vereins between 1880 and 1892; and the Fischerei Zeitung between 1889 and 1940. For other countries, we did not find systematic data sources, so historical trends were compiled from recent reports and personal communications.
More detailed data sources are specified in the text. Though we provide quantitative analyses of the information and have cross-checked our results with contemporary sources, we cannot claim to have covered all periods in all countries. We will consider the relevance of the available material where needed. In calculating geographical distances between sources and destinations, we compare transport by road, train, aeroplane, and even horse cart or foot along unknown routes. We standardise comparisons by calculating great-circle distances, i. e. as the crow flies, which underestimates the true distances, but systematically so. Throughout this article, we apply a standard conversion rate from glass eel to silver eel: 1 kg of glass eel consists of just over 3,000 individuals, approximately 10% of which survive to the silver eel stage and/or a harvestable size of 0.
ACKNOWLEDGEMENTS The eel and its restocking are distributed all over Europe, and restocking has been practised for a long time. We could not have collected information over such a huge area, covering so many years, in so many languages, without the greatly appreciated help of so many colleagues and friends:  Jean-Paul Binnert ,  Janis Birzaks ,  Uwe Braemick ,  Cedric Briand ,  Eleonora Ciccotti ,  Margo Dekker-Woltjer ,  Erik Fladung ,  Guy Fontenelle ,  Martin de Graaf ,  Bas de Groot ,  Reinhold Hanel ,  Cecile Huber ,  Don Jellyman ,  Dominique Juhe-Beaulaton ,  Jan Klein Breteler ,  Linas Ložys ,  Mariana Mesherjakova ,  Tomasz Nermer ,  Jaap Quack ,  Stanisław Robak ,  Tony Robinet ,  Eva Sers ,  Janek Simon ,  Audre Trumpiene ,  Håkan Wickström  and  Klaus Wysujack. We are much indebted to  Stephen Sanborn  (Proper English AB) for furbishing our language, and to two anonymous reviewers and the editor for their critical remarks.
Meanwhile, IPBES has been established, and its regional/subregional assessments are currently beginning  6. Global biodiversity and conservation databases hold much relevant information for these assessment processes, given the importance of consistency between different regions. Such databases are typically delivered as spatially explicit global knowledge products, and so can be analysed to inform regional/ subregional assessment processes. However, both limited GIS capacity and frequently abbreviated timeframes pose severe challenges to the timely preparation of such analyses. Moreover, such regionalisation is sensitive to assumptions in data preparation and analytical settings, which if not standardised among regions can yield non-comparable results. Given the intention that the GEO and IPBES regional/subregional assessments will feed into respective global assessments, this standardisation is critically important.
The generic scoping report for IPBES regional/subregional assessments (IPBES/3/6/Add. 1), the rationale for which is to promote coherence across the assessments, specifies that these will draw on relevant datasets. The variation between these regionalisations is remarkable. It provides great support to the regional assessment approach, given that this variation is masked by global reporting. We leave inference regarding causes of this variation to the assessment processes themselves, but note that contributing factors will surely include variation in underlying biogeography, intensity of anthropogenic drivers, and capacity for conservation response. Disaggregation of other knowledge products would complement our work. Examples could include regionalisation of forest cover  7  , species populations 8 , forest carbon  9  , and protected area management effectiveness, equitability, ecological representativeness, and connectedness  10.
UNEP-WCMC is undertaking such regionalisation for other measures of the state of nature and drivers of change. Other emerging knowledge products, for example to assess risk of ecosystem collapse  11  , and will also be candidates for such disaggregation once they achieve global coverage. Finally, given the dynamic nature of all three knowledge products used here, we emphasise the importance that assessment processes build capacity for customised analysis. For future regional/ subregional analyses, it will be necessary to repeat such analyses, because the data will be outdated by then. If systematic disaggregations of global datasets such as those presented here become unnecessary in the future, because capacity to undertake such analyses has by that point been developed within each region, this would be an excellent measure of the success of the capacity-building efforts associated with processes to assess biodiversity and ecosystem services.
Methods The regionalisations slated for use by GEO  12  (updated to classify the five Central Asian countries into the 'Europe' region) and IPBES  13  (specifically IPBES-3/1 Annexes IV-VII) are documented in Table  1  (available online only) and shown in Fig. 1. Where data are available, we also include 'Areas Beyond National Jurisdiction' (ABNJ) as a region for both GEO and IPBES, because while the high seas are not the subject of regional assessment in these current processes, they may be incorporated into GEO and IPBES in the future. We also include a region for 'Excluded' for IPBES, for completeness (this is the Antarctic, which might be included in a future regional IPBES marine assessment). Importantly, these regions do not have (and do not claim to have) any biogeographic basis. Rather they are established based on assumed policy relevance, given economic, cultural, and political similarities among their constituent countries.
The IUCN Red List of Threatened Species is a dynamic knowledge product. Version 2015-2 includes assessments of 77,340 species against the IUCN Red List Categories and Criteria  19. These data are maintained in an underlying database, the Species Information Service (SIS), and are freely available for non-commercial use according to published terms  28  , and under data licence for commercial use through IBAT  29. All bird species have been assessed six times by BirdLife International 30 , and there have been two assessments of all mammals  31  , amphibians  32  , and reef-building corals  33. Third reassessments are underway for mammals and amphibians, and comprehensive global assessments of reptiles and fishes are far-advanced, with the former building off existing assessments of all chameleons  14  , and all seasnakes  34  , and the latter all sharks and rays  35  , tarpons and ladyfishes  36  , parrotfishes and surgeonfishes  37  , groupers  38  , tunas and billfishes  39  , and hagfishes  40  , as well as angelfishes, blennies, butterflyfishes, picarels, porgies, pufferfishes, seabreams, sturgeon, and wrasses  14.
The Red List Committee comprises equal representation from SSC, the Global Species Programme of the IUCN Secretariat, and the Red List Partnership. The latter comprises a dozen institutions who contribute $200,000 or more annually towards the delivery of the Red List Strategic Plan  57. A Standards & Petitions Sub-Committee, independently appointed by and accountable to the SSC Chair, serves to adjudicate petitions against particular assessments or disputes related to the Red List Categories and Criteria. For larger taxonomic groups that have been assessed comprehensively (i. e. , for which >90% of species have been assessed), listed above, we present total numbers of species occurring in each region/subregion, for GEO (Data citation 1, Fig. 2a, b ) and IPBES (Data citation 2, Fig. 3a, b ).
The numbers of species endemic to each region and subregion are shown separately for GEO (Data citation 3, Fig. 2c, d ) and IPBES (Data citation 4, Fig. 3c, d ). Extinction risk has been assessed for all species in these taxonomic groups. We therefore present in each of the Data citations 1-4 the numbers of species in each Red List Category and the overall percentage threatened (presented as lower, upper and best estimates). For the taxonomic groups that have been assessed multiple times, it is possible to derive Red List Indices, which are indicators of the aggregate rate at which all species in a given taxonomic group are moving towards extinction  58, 59. Critically, the derivation of the Red List Indices requires extracting only those changes in Red List category between assessments that are caused by genuine increases or decreases in extinction risk, while those caused by changing knowledge or revised taxonomy are accounted for so that they do not drive trends in the index  60.
Specifically, this measure is calculated for any given region/subregion as a weighted species richness divided by the number of years in the assessment period, where each species is weighted by: i) the number of genuine category changes in The IUCN Red List of Threatened Species during the assessment period (one category change of increasing extinction risk = -1; one category change of decreasing extinction risk = +1), and ii) by the fraction of the species' range occurring within the region/subregion  76. Rodrigues et al. 76  successfully applied this downscaling technique to grid cells, ecological regions, and countries. We extend this downscaling to the regionalisation slated for GEO (Data citation 5) and IPBES (Data citation 6).
Key Biodiversity Areas (specifically IBAs and AZE sites) Key Biodiversity Areas (KBAs) are sites contributing significantly to the global persistence of biodiversity  77. They are identified by assessment of sites against standard criteria for the presence of threshold levels of significance for threatened biodiversity (based on Red Lists), range-restricted biodiversity, ecological integrity, and biological process. In 2004, IUCN's Membership requested 'a worldwide consultative process to agree a methodology to enable countries to identify Key Biodiversity Areas, drawing on data from the IUCN Red List of Threatened Species and other datasets, building on existing approaches' (WCC-2004-Res-013). An initial formulation of the scientific basis for identification of KBAs was published by Eken et al.
Protected Planet Protected Planet is a knowledge product reporting on the location, status, and management of the world's protected areas, underpinned by the World Database on Protected Areas 96 (http://www. protectedplanet. net). It is based on a United Nations Economic and Social Council (ECOSOC) mandate dating back to 1959 for the compilation of the UN List of Protected Areas (ECOSOC Resolution 713 (XXVIII)), and implemented by IUCN-WCPA and the UNEP-World Conservation Monitoring Centre  97. Protected Planet data are freely available for non-commercial use according to published terms  98  , and under data licence for commercial use through IBAT  29. It follows the IUCN definition of a protected area as 'A clearly defined geographical space, recognised, dedicated and managed, through legal or other effective means, to achieve the long-term conservation of nature with associated ecosystem services and cultural values' across six protected area management categories  99  , where the data are available.
Since 1981 it has been maintained as a database, and for the last decade it has been made available as an online knowledge product  100. In addition to formal, government-reported data, Protected Planet also compiles protected area data from other sources, and is in the process of strengthening its coverage across protected area governance types  101. In the future, the WDPA is likely to be expanded to include data on 'other effective area-based conservation measures', once the definition of such sites has been agreed  97. This definition is still under discussion  102  , and an IUCN-WCPA task force has been created to provide recommendations on such definition. Global summary statistics for the coverage of land and sea by different protected area categories are presented in the Protected Planet reports (Fig.
Data Records Regional and subregional species diversity and endemism For both GEO and IPBES, total numbers of species occurring within each region/subregion, and number of species endemic to each region/subregion, are derived using the drop-down menus for countries of occurrence in the SIS, the database which underlies The IUCN Red List of Threatened Species (which are based on documented occurrence in each country), rather than through GIS analysis of the range maps. The only exception is for ABNJ, within which species occurrences are by definition not coded for national occurrence, and so for which we derive occurrence from GIS analysis of species range maps (none of the species included occur only in ABNJ). We count species as occurring if they occur in at least one of a region's countries; and species as endemic if they are not listed as occurring in any countries outside of a given region (excluding records of vagrants, records of uncertain origin, and introduced populations).
Regional and subregional coverage of protected areas For both GEO and IPBES, we present regional and subregional summary statistics for the coverage of land and sea by different protected area categories. Resulting data can be found in Data citation 9 for GEO and in Data citation 10 for IPBES. Regional and subregional trends in coverage of Key Biodiversity Areas (specifically of IBAs and AZE sites) by protected areas For both GEO and IPBES, we present regional and subregional summary statistics for trends in the proportions of IBAs and of AZE sites fully covered by protected areas, using data on the year of protected area establishment recorded in the January 2013 version of the World Database on Protected Areas. We overlaid digital boundaries of protected areas onto IBAs and AZEs to quantify the degree of overlap.