dataesr/scientific-paragraphs-categorization huggingface dataset:
For beta diversity analysis, the Jaccard dissimilarity was calculated on the rarefied table using the function decostand from vegan package. Hellinger distance was calculated from the non rarefied table also using the function decostand  (Legendre & Gallagher, 2001). To explore beta diversity patterns, we performed a principal component analysis (PCA) on the Hellinger transformed data using the function rda from vegan. A principal coordinates analysis (PCoA) was performed using the Jaccard dissimilarity using the function pcoa from ape package  (Paradis & Schliep, 2019). To test the influence of the treatments, replicates and time on the microbiome beta diversity, we performed a permutational analysis of variance (PERMANOVA)  (Anderson, 2005). Before running the analysis, the functions betadisper from the package vegan and anova-like permutation test from stats package were used to identify significant deviations on the multivariate beta dispersion of the data for treatments, replicates, time and of the interaction between treatments and time  (Oksanen et al.
, 2015). The effect of treatments and replicates (nested within treatments) was tested using the function nested. npmanova from the package BiodiversityR  (Kindt & Coe, 2005). To test the effect of time and the interaction between treatments and time, we used the function adonis from vegan including treatments, replicates, and time (number of days) as fixed variables in the model, with permutations restricted to the replicates level. HCA was done using the Hellinger distance as previously described. Taxonomy barplots were produced by showing the three most abundant genera (considering the best hits classified by VSEARCH), while the less abundant were merged as "others". IndVal analyses were run with the rarefied table to identify indicative species of the three groups of treatments evidenced in the beta diversity analysis (inside and outside bloom DCM and both surface samples) using the function multipatt from the package indicspecies v1.
Acknowledgments This work was supported by a PhD fellowship from  Sorbonne University  and the  Région Bretagne  to MCdR, the  Centre National de la Recherche Scientifique (CNRS, France) , and the  French Government ''Investissements d'Avenir'' programmes OCEANOMICS  ( ANR-11-BTBR-0008 ). We are grateful to the  Tara Ocean Foundation , led by  Romain Troublé  and  Etienne Bourgois , for the sampling opportunity and facilities onboard Tara, and to all the scientific and logistic team involved in the  Tara Breizh Bloom cruise , notably captain  Martin Herteau  and his crew. We warmly thank  Shai Fainsod ,  Michel Flores ,  Eric Pelletier ,  Daniella Schatz  and  Flora Vincent , for their contribution during the cruise, and  Lydia White  for her help in the statistical analysis. We are thankful to the  Roscoff Bioinformatics platform ABiMS  (http://abims.
sb-roscoff. fr),part of the  Institut Français de Bioinformatique  ( ANR-11-INBS-0013 ) and  BioGenouest network , for sharing computing and storage resources,  Eric Macé  and  INSU (Parc National d'Instrumentation Scientifique)  for supplying the SBE19+ profiler, and to the  RCC  for providing the E. huxleyi cultures. Environmental samples are deposited in the bioproject  PRJEB50692. Reads are deposited under the accession numbers  ERX9644183 -ERX9644242  and biosamples under the accession numbers  ERS10466567-ERS10466596. Samples from the assembly experiment are deposited in the bioproject  PRJEB48747. Reads are deposited under the accession numbers  ERX9767825 -ERX9768004  and biosamples under the accession numbers  ERS10539058-ERS10539153.
The abundance and prevalence filters described above removed about 68% of the total number of ASVs while keeping 99% of the number of reads. The final dataset (filtered ASV table used for further analysis) contained 107 samples (11 from the environment and 96 from cultures) for a total of 6,017,019 reads and 294 prokaryotic ASVs. Community compositioWickham, 2016;R Core Team, 2017)vironmental samples. All the analyses were conducted in R version 4. 0. 2 in Rstudio (1. 1. 442)andthe plots were produced with ggplot2(RStudio Team, 2016;Wickham, 2016;R Core Team, 2017). Taxonomy treemaps of environmental samples were produced at the genus level considering the best hits classified by VSEARCH. To facilitate visualization, low abundant genera (accounting to less than 3% of relative abundance at each sample) present at the raw community table were grouped.
We discretize the simulation domain with a quadrilateral element mesh generated in a previous study  (Xu et al. , 2015)  with the software CUBIT (https://cubit. sandia. gov/). The elements have a size of 200 m on average, which translates into a spatial resolution of about 50 m (there are multiple internal nodes within each element). The treatment of the fault junction follows the convention commonly adopted for fault branching problems  (DeDontney et al. , 2012; Xu et al. , 2015) , where split nodes run continuously across the junction along the main fault but converge to a single non-split node at the junction along the splay fault. Such treatment allows for through-going rupture along the main fault but terminated rupture along the splay fault (zero splay-fault slip at the junction), which is supported by the relative maturity of the main fault (the EAF) and source inversion results  (Melgar et al.
8 mainshock  (Barbot et al. , 2023; Melgar et al. , 2023; Okuwaki et al. , 2023). We carefully choose the values for other model parameters so that the simulated slip is comparable to the one found in source inversions. Unless mentioned otherwise, we assume model parameters are uniformly distributed along each fault. Their specific values may vary from one simulation case to another. Table  1  summarizes the key model parameters and their values. Superscripts 𝑠𝑝 and 𝑚 indicate parameters along the splay fault and the main fault, respectively. We conduct the dynamic rupture simulations with the spectral-element-method software SEM2DPACK  (Ampuero, 2012). We first show the spatiotemporal evolution of Coulomb failure stress changes ∆CFS for a case of successful rupture branching from the splay fault to the main fault (Figure  5 ).
Acknowledgments SX was supported by the  National Key R&D Program of China   2021YFC3000700  and the  NSFC  grant  42074048. YX and JPA were supported by the  EU  project " DT-GEO ,  A Digital Twin for Geophysical Extremes " (No  101058129 ). JPA was also supported by the  French government  through the  UCA-JEDI Investments in the Future project  ( ANR-15-IDEX-01 ) managed by the  National Research Agency (ANR). MvdE was supported by the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 research and innovation program  (grant agreement No. 101041092 -ABYSS ). JP was supported by fellowships from the  Interdisciplinary Institute for Artificial Intelligence 3IA Côte d'Azur.
Data and code availability The software SEM2DPACK is freely available at: https://github. com/jpampuero/sem2dpack. The key input parameters for running numerical simulations are within the paper. The information for the Mw 7. 8 mainshock can be found from the AFAD (Turkey Disaster and Emergency Management Authority) at: https://deprem. afad. gov. tr/event-focal-mechanism, or from the USGS (U. S. Geological Survey) at: https://earthquake. usgs. gov/earthquakes/eventpage/us6000jllz/origin/detail. The aftershock catalog was downloaded from the AFAD at: https://deprem. afad. gov. tr/event-catalog (last accessed on May 29,.
, 2015). We mapped the shoreline angle of coastal terraces using dGPS topographic profiles and 5 and 2 m high-resolution DEM of produced from ALOS (NTTDATA, RESTEC Included © JAXA) and Pleiades CNES stereoscopic satellite images, respectively. With the help of the different data sets (hillshades, slope maps, satellite ortho-images, and field data), we contoured the inner edges of each preserved surfaces corresponding to the fossil cliffs from 0 to 520 m above the current sea level with the ESRI's ArcGIS 10 software. The lateral correlation of coastal terraces across valleys, is based on the continuity of elevations, planform width, and equivalent position in the terrace sequence. We attribute a number to each terrace between T0 and T29 (T0 being the active terrace and T29 the uppermost one).
We define the meaning of "snow scarce conditions" for each ski resort as the worst 20% years in terms of groomed natural snow encountered during the reference time period. The simulation results used in this study are based on the approach implemented to compute the Copernicus Climate Change Service (CS3) Mountain Tourism Meteorological and Snow Indicators (MTMSI) dataset  [34]. This dataset uses the pan-European near-surface meteorological reanalysis UERRA and an ensemble of adjusted EURO-CORDEX regional climate model simulations. Both feed the detailed snow cover model Crocus, which includes a representation of snow grooming and snowmaking  [34, 35]. In the model, snowmaking depends on operating rules and meteorological conditions, in particular wet bulb air temperature (  [34] , see Methods and Extended Data Figure  1 ).
The MTMSI dataset provides state-of-the-art estimates of relevant climate data for the ski tourism industry, yet is insufficient for sectoral risk assessment because it does not consider the location of individual ski resorts. We address this gap by combining the data used to produce the MTMSI dataset with geographical information of 2,234 individual ski resorts across Europe (see Method and Extended Data Figures  1  and 2 ). This enables to compute snow cover conditions (including the snow reliability index -expanding its use when snowmaking is taken into account, see Extended Data Figure  3 ) and water demand for snowmaking, for various values of the snowmaking fractional   2 , reveals the strong heterogeneity in reference snow conditions within and across mountain areas, due to the combined effect of the local climate and the location of ski resorts, and provides the baseline upon which future changes can be analyzed.
Climate data This study uses meteorological and snow cover simulations underpinning the Copernicus Climate Change Service (CS3) Mountain Tourism Meteorological and Snow Indicators (MTMSI) dataset  [1]. Results are provided for all countries of the European Union, candidate countries and members of the European Free Trade Association. Altogether, the dataset covers all EU member states, Albania, Andorra, Montenegro, North Macedonia, Serbia, Turkey, the United Kingdom, Iceland, Liechtenstein, Norway and Switzerland. Within each country, the data is provided for each NUTS-3 (Nomenclature des unités territoriales statistiques) area, by elevation steps of 100 m, on flat terrain. Meteorological data are extracted from the UERRA European reanalysis at 5. 5 km resolution, spanning the time period 1961-2015.
Inventory of European ski resorts We use the OpenSkiMap (https://openskimap. org) global database of ski resorts, which is based on the OpenStreetMap (https://www. openstreetmap. Climate change challenges European ski tourism org) collaborative geospatial database. Specifically, we use the three files provided by OpenSkiMap (Ski Areas, Lifts and Runs). Information from these three files was combined, involving careful checks and occasional removal of lifts from our analysis, which are not associated directly with a ski piste, in order to provide a consistent set of ski resorts and ski lifts, together forming a series of 2,959 ski resorts in Europe. See Extended Data Figure  1.
Further, we apply a geospatial modelling framework to infer the location where snowmaking is applied within a given ski resort, depending on the snowmaking fractional coverage value (0%, 25%, 50% and 75%, see Extended Data Figure  2f )  [2, 8]. Each pixel of the ski resort gravitational domain is associated to a NUTS-3 region (Extended Data Figure  2h ) and a 100 m elevation band (Extended Data Figure  2f ) from the climate and snow cover dataset, for flat terrain conditions. The limitations of the MTMSI dataset regarding the available elevation bands for each NUTS-3 region compared to the actual elevation ranges or the ski resorts made it necessary to reassign some values. For the resorts fully included in mountain NUTS-3 regions, but with missing elevation bands, we assigned the minimum or the maximum available NUTS-3 elevation, depending on whether the missing elevations are at the top or at the bottom of the ski resort, in the limit of a 300 m difference and with minimum elevation not less than 500 m.
The global warming level dataset, providing information for all of the Climate change challenges European ski tourism GCM results used in this study, is obtained from the Santander Meteorology Group (UC-CSIC) ( https://github. com/SantanderMetGroup/ATLAS/blob/ main/warming-levels/CMIP5 Atlas WarmingLevels. csv). For each ski resort, the average value of the frequency of snow reliability index values below the Q ref 20 , across all GCM/RCM pairs corresponding to a given global warming level, is used to relate the risk value to this particular warming level. This resort-level risk value is then averaged for each mountain area, using the surface area of the ski resort as a weighting factor. The corresponding risk value for each mountain area and global warming level is then used to build the burning ember diagram.
Ultimately, burning embers  [12]  represent the snow supply risk to ski tourism under different global warming levels, with and without snowmaking. Due to the lack of a European database on the fraction per ski resort equipped with snowmaking, we apply a homogeneous 25%, 50% and 75% snowmaking fractional coverage to all ski resorts in Europe, notwithstanding actual heterogeneities in snowmaking fractional coverages within and across mountain areas and countries  [9]. By design, the risk level is equal to 20% for the reference period without snowmaking. Hence, a frequency of 20% of snow scarce winters corresponds to an undetectable change in risk level (displayed in white on Figure  3 ). A frequency of 30%, corresponding to a 50% increase in the frequency of snow scarce conditions compared to the reference value (20%), is referred to as "moderate" risk (yellow), while a doubling in the frequency (40%) is referred to as "high" risk (red).
Water and electricity demand for snowmaking Water demand values are generated as a diagnostic of the snow cover simulations. Water demand for snowmaking is illustrated in Extended Data Figures  4, 5 , 6 and 7. See Extended Data Figure  8b  for more information on the calculation of the uncertainty pertaining to the water demand for various global warming levels and snowmaking fractional coverage values. Here we assume an electricity consumption of 3±1 kWh per m 3 of water converted to snow. This value is consistent with recent reports from the ski tourism industry and national and regional reports from local environment and tourism authorities (https://www. wko. at/branchen/ transport-verkehr/seilbahnen/Oekologie und Umwelt. html, http://www. observatoire. savoie. equipement-agriculture. gouv.
Carbon footprint of electrity consumption for snowmaking The carbon footprints are calculated using amounts of electricity generation by type of production (e. g. hard coal, run-of-river hydropower, etc. ) and country as recorded on the ENTSO-E Transparency Platform for 2018 to 2022 (https://transparency. entsoe. eu/dashboard/show). The central estimates were computed using the figures for the year 2019, deemed representative of the situation prior to the outbreak of Covid-19. Data for the years from 2018 to 2022 were used for uncertainty analysis, based on the 6 main countries in terms of water and electricity demand, and for which carbon intensity factors were calculated. Combining electricity demand time series (from past and future climate conditions) with a sampling of the carbon intensity data from these 5 years leads to an estimate of the uncertainty due to the interannual variability of carbon itensity.
The multiannual standard deviation around the mean of the median shows, for the UERRA reanalysis reference time period , varies between 8. 8% (Norway, expected to increase up to the values of 12. 3% and 13. 6% at +2 • C and +4 • C global warming levels, respectively ) and 1. 2% (Sweden, expected to reach 0. 6% and 1. 0% at +2 • C and +4 • C) of the mean value. Values for Italy and Austria, where the mean value is the highest, reach 2. 5% and 3. 7% respectively. This uncertainty is thus much lower than the uncertainty due to the electricity demand for snowmaking, considered in this study. Production for each generation type and country are multiplied by country dependent emission factors (kg CO 2 eq kWh -1 ) from the ecoinvent database  [13] , taking into account life cycle assessment (LCA) components.
Data availability The MTMSI dataset is available on the Copernicus Climate Data Store following this doi: https://doi. org/10. 24381/cds. 2fe6a082 [14]  under the Copernicus licence. We use the OpenSkiMap (https: //openskimap. org) global database of ski resorts, which is based on Climate change challenges European ski tourism the OpenStreetMap (https://www. openstreetmap. org) collaborative geospatial database. The description of the 15 European Environmental Agency (EEA) European mountain areas is available online: https://www. eea. europa. eu/data-and-maps/data/european-mountain-areas. The European Digital Elevation Model (EU-DEM), version 1. 1, with a spatial resolution of 25 m, is available online http://land. copernicus. eu/ pan-european/satellite-derived-products/eu-dem/eu-dem-v1. 1/view.
The global warming level dataset is available from the Santander Meteorology Group (UC-CSIC) (https://github. com/SantanderMetGroup/ATLAS/ blob/main/warming-levels/CMIP5 Atlas WarmingLevels. csv). The carbon footprints are calculated using hourly amounts of electricity generation by type of production (e. g. hard coal, run-of-river hydropower, etc. ) and country as recorded on the ENTSO-E Transparency Platform for 2019 (https://transparency. entsoe. eu/dashboard/show). The Cryosphere 13, 1325-1347 (2019). [7] François, H. ,  Morin   ). Further, we apply a geospatial modelling framework to infer the location where snowmaking is applied within a given ski resort, depending on the snowmaking fractional coverage value (0%, 25%, 50% and 75%) (f). Each pixel of the ski resort gravitational domain is associated to a (NUTS-3, elevation) pair from the climate and snow cover dataset, by steps of 100 m elevation and for flat terrain conditions (g).
, S. , Lafaysse, M. & George-Marcelpoil, E. Crossing numerical simulations of snow conditions with a spatially-resolved socioeconomic database of ski resorts: A proof of concept in the French Alps. Cold Regions Science and Technology 108, 98-112 (2014). [8] François, H. , Morin, S. , Spandre, P. , Lafaysse, M. & George-Marcelpoil, E. Croisement de simulations numériques des conditions d'enneigement avec une base de données socioéconomiques spatialisée des stations de sports d'hiver : description de l'approche et application aux Alpes françaises. La Houille Blanche 4 (2016). [9] Berard-Chenu, L. , François, H. , George, E. & Morin, S. Past changes in natural and managed snow reliability of French Alps ski resorts from 1961 to 2019. The Cryosphere 16, 863-881 (2022). Mountain tourism meteorological and snow indicators for europe from 1950 to 2100 derived from reanalysis and climate projection.
Acknowledgments We are grateful to all organizations and individuals involved in the production and distribution of the datasets used for this work. Work towards this publication has benefited from funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  730203. We thank  N. Ribot  ( Geospatial Solutions ) for support on database management and  E. Maldonado  and  F. Bray  ( INRAE ) for operating the DBMS tools. Figures 2 and3 and Extended Data Figures 1 and8 use images from flaticon. com.
Code availability The Crocus snow cover model used for this work is developed inside the open-source SURFEX project (http://www. umr-cnrm. fr/surfex/, last access : 7 June 2020). For reproducibility of results, the version used in this work is tagged as "C3S-European-Tourism-MTMSI-2019" on the SURFEX git repository. The computer code necessary to reproduce the main results is provided at the Zenodo repository (10. 5281/zenodo. 8047168) [15].
https://doi. org/10. 24381/cds. 2fe6a082 (2020). [15] François, H. & Samacoïts, R. European ski resorts snow reliability. https: //doi. org/10. 5281/zenodo. 8047168 (2023). Climate change challenges European ski tourism Extended Data Spatial modelling of ski resorts Climate modelling of snow conditions Energy production OpenStreetMap GIS Copernicus EUDEM NUT3 regions geospatial dataset Near surface atmospheric reanalysis UERRA 5. 5 km 6-hourly data GCMs: ensemble of CMIP5 model runs levels Global warming European countries Generic OpenSkiMap building spatial dataset spatial dataset for ski resorts (lifts and pistes) Atmospheric data from a subset of mountain grid points from UERRA reanalysis MTMSI geography RCMs: ensemble of model runs EURO-CORDEX daily data ENTSO-E Transparency Platform for 2019 hourly amount of electricity generation by type of production and country ADAMONT statistical Spatial modelling of gravitational envelopes including snowmaking fractional coverage for 2959 ski resorts adjustment method Climate projection 1950-2100 Ecoinvent database: Life cycle assessment based emission factors per country 2625 ski resorts matching the NUTS3 and elevation covered by MTMSI geography Adjusted using the subset of mountain points from the UERRA reanalysis 6-hourly data Carbon intensity of electricity production by country and month EEA European mountain areas 2234 ski resorts within mountain area from EEA Crocus snow cover model including grooming and snowmaking Resort-level frequency of snow scarce winter and future climate season under past Snowmaking fractional coverage Q ref threshold For UERRA 1961-2015 daily For 9 pairs of GCM/RCM for Snow simulations 3 RCPs between 1950-2100 daily By country and month demand Water By country Risk to snow supply at the scale of EEA mountain areas as a function of global warming level UERRA worst snow condition reference defined as 20 th percentile of groomed snow reliability index between 1961 and 1990 (Q ref 20 ) Computed daily for each ski resort Snow reliability index for snowmaking Water demand consumption Electricity Carbon footprint for snowmaking [10]Taranu, I.
S. , Somot, S. , Alias, A. , Boé, J. & Delire, C. Mechanisms behind large-scale inconsistencies between regional and global climate model-based projections over Europe. Climate Dynamics (2022). [11] Ribes, A. et al. An updated assessment of past and future warming over France based on a regional observational constraint. Earth System Dynamics 13, 1397-1415 (2022). [12] Zommers, Z. et al. Burning embers: towards more transparent and robust climate-change risk assessments. Nature Reviews Earth & Environment 1, 516 -529 (2020). [13] Wernet, G. et al. The ecoinvent database version 3 (part i): overview and methodology. Int. J. Life Cycle Assess. (2016). [14] Morin, S. , Samacoïts, R. , François, H. & Abegg, B.
We also provide an open-source Python library of all mentioned algorithms which is available on-line at https://github. com/tchau218/parEMDA. The paper is organized as follows. In Section 2, we first remind sequential smoothing methods in an incremental way which permits to highlight the differences from one algorithm to the other. It starts from the usual particle filter/smoother and ends with the conditional particle smoother referred to as CPFBS. Then, the combination of CPFBS and the SEM algorithms is described. The performances of the different algorithms are compared in Section 3 using numerical experiments on toy models. Section 4 contains conclusions and perspectives.