dataesr/scientific-paragraphs-categorization huggingface dataset:
Likewise, we doubled all male-only density estimates for amphibians. Our final dataset included 373 estimates for amphibians (two orders, 20 families, 74 species), 1,020 for reptiles (two orders, 31 families, 276 species), 5,726 for birds (33 orders, 128 families, 1,095 species) and 7,849 for mammals (17 orders, 73 families, 604 species) (Figure  1 ; Supporting Information Data S1).
, 2009; AmphibiaWeb, 2015; Feldman, Sabath, Pyron, Mayrose, & Meiri, 2016; Gomez-Mestre, Pyron, & Wiens, 2012; Jones et al. , 2009; Myhrvold et al. , 2015; Novosolov et al. , 2016; Pyron & Burbrink, 2014; Wilman et al. , 2014). Diet in reptiles was classified as herbivore, omnivore or carnivore. Diet in birds was classified in five categories (plant and seed eaters; frugivores/nectarivores; carnivores of invertebrates; carnivores of vertebrates and carrions; omnivores) as decribed by  Wilman et al. (2014). Diet categories for mammals were estimated using the percentage of different food items found in the EltonTraits database  (Wilman et al. , 2014). We classified as herbivores species feeding on 80% on plant material, as carnivores species feeding on 80% on animal material, and all the rest as omnivores.
Richness for amphibians, birds and mammals was obtained by summing the IUCN range maps of species distribution at 18 resolution (IUCN, 2017). Given that range maps are unavailable for most reptiles, we used the richness of lizards and snakes by terrestrial ecoregion [Snake and Lizard Species Richness by ecoregion: World Wildlife Fund (WWF), 2006]. Several of these variables were highly correlated in at least one of the four datasets (Pearson' q > 0. 7; Supporting Information Figure  S1  in Appendix S1); therefore, to avoid collinearity and maintain comparability among the four groups, we retained only NPP, P wq and P cv and excluded T sd , T m and Rich. However, because these excluded variables underlie different hypotheses, we also present the results for three alternative sets of models in the Supporting Information (Appendix S2): one including P cv , P wq and T m ; one including P cv , P wq and T sd ; and one including Rich, P cv and P wq.
| Predictions of models To evaluate the predictive accuracy of the models, we ran a 5-fold cross-validation, repeated 10 times for each of the models (Supporting Information Figure  S2f ). Predictive performances were evaluated using the maximum absolute error (MAE). We also squared the correlation coefficient between the 'Predicted' and the 'Observed' data obtained from all repetitions of the 5-fold cross-validation to calculate a pseudo-R 2. We compared the MAE with that obtained by randomly sampling species in the datasets 1,000 times and calculating MAE using different records of density estimates for the same species. This procedure allowed us to compare our prediction error with the expected natural intraspecific variability. We then extracted the model-averaged parameter estimates to map population density globally.
The 5-fold cross-validation indicated good predictive abilities of the models (Supporting Information Table  S2 ), with pseudo-R 2 between 0. 67 (birds) and 0. 84 (reptiles), MAE between 0. 35 (birds) and 0. 48 (reptiles), and without any consistent bias in the estimation (Figure  4 ). The forecasting errors were within the range of errors calculated using the conspecific density records within the dataset (Supporting Information Figure  S4 ). All models are available as part of the Supporting Information (Data S2). Projected densities differed between the four groups of vertebrates, with amphibians displaying higher densities in tropical and highly productive areas, reptiles in tropical arid and non-seasonal environments, birds and mammals in temperate environments, and with the mammals more associated with wet areas when compared with birds (Figure  5 ).
Traits such as body mass, for example, have been observed to vary along environmental gradients (e. g. , Bergmann's rule) in both ectotherms and endotherms  (Blackburn, Gaston, & Loder, 1999; Olalla-T arraga & Rodríguez, 2007). Overall, our models show good predictive abilities, with errors comparable to the observed interspecific variability. However, as our dataset encompasses a large range of organisms with very different densities, our models cannot and should not be used to predict the abundance of local populations for management purposes. Rather, the projected densities can be used to predict broad differences in abundance between species with different traits and living in different environments. This can find several applications in large-scale systematic conservation planning and biodiversity projections under scenarios of future environmental change.
It is important to note that a substantial part of the variance is analyses where hundreds or thousands of species are jointly considered, such as in conservation planning, but also to derive useful biodiversity metrics that rely on relative densities  (Santini, Belmaker et al. , 2017)  or to forecast population trends under future scenarios of , areas with environmental characteristics beyond those observed in our dataset). Amphibians were filtered further by areas where they are known to be absent (IUCN, 2017), and predictions for both amphibians and reptiles were cropped beyond latitudinal maxima where density estimates were unavailable (Figure  1 ). [Color figure can be viewed at wileyonlinelibrary. com] environmental change  (Ashcroft et al. , 2017). At the same time, the way we built the models makes it possible to map information about the reliability of the predictions.
Of course, a multispecies model cannot be particularly informative at the level of a single species, as it assumes the same scaling of specific environmental variables across all species. Nonetheless, data to develop species-specific models of population density are available for only a bunch of species (e. g. ,  Lewis et al. , 2017) ; therefore, at present, multispecies models can be the best surrogates for most species thus far. As for most macroecological analyses, we were strongly limited by data availability, which led us to a final dataset clearly biased toward certain taxonomic groups and geographical regions (Figure  1 ). This probably influences our current state of knowledge on large-scale patterns in population density. Nonetheless, the lack of phylogenetic autocorrelation suggests that the control for the taxonomic levels in the random effect part of the models corrects for taxonomic bias.
ACKNOWLEDGMENTS We thank  Damien Georges  and  Julien Renaud  for technical assistance, and  Laura J. Pollock  and  Matthew Talluto  for insightful discussions and suggestions. L. S. was supported by the  Brusarosco  fellowship provided by the  Italian Society of Ecology (SItE)  and, together with M. A. J. H. , by the  ERC  project ( 62002139 ERC -CoG SIZE 647224 ). N. J. B. I. was supported by the  U. K. Natural Environment Research Council  ( NE/D009448/2 ).
Acknowledgements The Strathclyde group is grateful for support by the  Leverhulme Trust , the collaboration between the two groups is supported by the  Royal Society (London) , the  CNRS , in particular the  Laboratoire international associé (LIA) 'Solace' , and the  University of Strathclyde. The Nice group acknowledges support from  CNRS ,  UNS , and  Région PACA. The final stage of this work was performed in the framework of the  European Training Network ColOpt , which is funded by the  European Union  ( EU) Horizon 2020 program  under the  Marie Skłodowska-Curie action , grant agreement  721465.
005% abundance and cluster occurrence in a minimum of 2 samples of the total library. Chimeras were removed using vchime of the vsearch package  (Rognes et al. 2016). Affiliation was determined using the Silva SSU database 123  (Quast et al. 2013)  through BLAST  (Altschul et al. 1990) , which allowed multiple affiliation and manual curation. Classification at the class level was used for affiliated Bacteria and Archaea. All analyses were performed on the Galaxy instance of the INRA MIGALE bioinformatics platform (http://migale. jouy. inra. fr). The relative abundance was calculated against the number of total taxonomically assigned reads, and a constrained hierarchical clustering (UPGMA) using Morisita similarity index and 999 bootstraps was calculated. We present here results from CH 4 -related Archaea and Bacteria evaluated at the order/family level.
Fingerprinting of functional gene mcrA Extracted DNA was amplified using primers ME1 (5'-6-FAM-labelled-) and ME2 as described by  Billard et al. (2015)  but with modified hybridation temperature to 54°C. Amplicons were then digested using the Mspl restriction enzyme for 4 h at 37°C. Restriction products were purified using a AxyPrep PCR Clean-up Kit (Axygen, Tewksbury, MA, USA) and separated on an ABI 3730xl DNA Analyzer (BIOfidal DTAMB, Université Lyon 1) using the internal size standard Dye 5 ladder, 50-1000 bp (Gel company, San Francisco, CA, USA). Fragments were then treated using the Peakscanner software, with a fluorescence cutoff of 15 relative fluorescence units, and then processed with the interactive binner method  (Ramette 2009 ) using WS = 2, Sh = 1, and cutoff of 0. 01. The obtained distance values for each operational taxonomic unit (OTU) were exponentially transformed (c = 2) and plotted by principal coordinates analysis (PCoA) using a Bray-Curtis similarity calculation.
2011) , and classified in 95% similar OTUs  (Daebeler et al. 2013)  on Mothur  (Schloss et al. 2009). BLAST was conducted with blastn from the National Center of Biotechnology Institute database  (Johnson et al. 2008) , and the resulting sequences were aligned by amino acids and used to build a phylogenetic tree using the neighbor-joining method  (Saitou and Nei 1987)  with 1000 samples bootstrapped on MEGA  (Kumar et al. 2016). Sequences are available in the NCBI database under the accession numbers MG594064 to MG594136. Cloning results were correlated to T-RFLP fragments by performing in silico T-RFLP on the mcrA sequences using TRiFLe  (Junier et al. 2008). Simulated fragments that varied from experimental fragment size by <3 bp were considered. These results were used to complete sequence occurrence for each site.
Data analysis Data obtained from XRF-scanning were presented as profiles with depth points 2, 4, 6, 8, 10, and 15 cm, selected after smoothing over 2 cm. These points were plotted against Al for easier representation. Percent sand, TOC, C/N, and TS were selected for the same sediment depths. When data were missing for the corresponding depth, the average between 2 intermediate depths was considered. We built 2 community matrixes: the first was an OTU versus sites matrix based on mcrA TRFLP results (relative fluorescence), and the second was an OTU versus sites matrix based on selected 16S rRNA gene abundance of methanotrophic microbial clades. Environmental parameters fitting the matrix samples' depths were compiled in an associated variable table. The functional and microbial dataset were both range-transformed.
We then computed several distance-based redundancy analyses (dbRDA) as an ANOVA to test for the possible influence of environmental variables in driving the communities. First we tested whether depths and sites were significant variables influencing the structure of these communities. We then tested the significance of sedimentary parameters in structuring these communities. The parameters were standardized and their significance was assessed using automatic stepwise (forward) model building for constrained ordination with 5000 permutations. All analyses were performed using the Vegan package  (Oksanen et al. 2007 ) and R 3. 4. 0 (R Core Team 2015).
For the functional dataset, "site" was found to be a significant parameter (p = 0. 002) characterizing the functional communities, confirming the ANOSIM results; however, depth was not found to be significant. Sediment Mn (p = 0. 002), Ca (p = 0. 002), TOC (p = 0. 002), and C/N (p = 0. 026) content were identified as the structuring parameters of the functional community.
Methylomirabilis ranged between 0. 6 and 1. 5% in the first 6 cm and attained 3. 3% at 6 cm, 1. 8% at 7 cm, and decreased to 0. 5% below 9 cm. Archaeal Cand. Methanoperedens reads reached 0. 6-0. 7% at 6-7 cm. No other ANME sequence could be identified within the library. Hierarchical clustering constrained by sediment depth obtained a cophenetic correlation of 0. 77 and allowed us to decipher 3 main clusters (Fig. 8 ) between 1 and 4 cm, 6 and 8 cm (Morisita similarity >0. 9 for both), and 9 and 10 cm (Morisita similarity of ∼0. 9). Sample 5 was identified as an outlier and was removed from the analysis. The 15 cm sample did not show strong similarities with the others. For the 16S microbial dataset, no parameter was found to significantly structure the community.
Acknowledgements This work was supported by the FEEL foundation, " Life Under the Ice  project" (http://wiki. epfl. ch/ladoga). We are grateful to the  INRA MIGALE bioinformatics platform  (http://migale. jouy. inra. fr)for providing help, support, and computational resources. The authors also thank the  Limnology Center from EPFL  for funding this research, and the  Northern Water Problems Institute of Russian Academy of Sciences , as well as  Mischa Haas  and  Alois Zwyssig  ( EAWAG ) for providing technical and logistical support during field expeditions. The authors also thank  Elodie Rumiano ,  Cécile Chardon ,  Nathalie Tissot , and  Jérôme Jeandenand  ( UMR CARRTEL ) for molecular biology work;  Stéphane Pesce  and  Christophe Rosy  ( IRSTEA ) for methane measurements during incubations;  Thomas Junier  ( EPFL ) for his help with TRiFLe; and  Phanélie Berthon (LIBM)  for kindly providing a dry-shipper for field work.
A dataset of 3611 EDF-DTG's discharge measurement using current meters is used in that study. They have been realized over 176 hydrometric stations in France (see Figure  1 ) covering a large sample of rivers, from small mountain streams to large plain rivers, as illustrated in Table  1. Discharges ranges from 0. 01 to 863 m 3 /s, with an averaged value of 8. 45 m 3 /s. Flood discharges were measured using cableways or hydrometric trucks. The studied rivers are mainly small to medium rivers as 90% of discharge, width and hydraulic radius are smaller than 10. 2 m 3 /s, 24. 8 m and 0. 63 m respectively.
Velocity profile analysis method For the 3611 gaugings of the database, verticals of measurement realized close to the banks were removed to avoid side effects producing velocity profiles which are nor representative of the main flow. For each gauging, the depths and velocities of every verticals are normalized with the verticals' total depth and averaged velocity respectively, so as one velocity profile is produced for one gauging using all the verticals of the gauging. Then, three methods (log-law, power-law and surface velocity) are applied to compute α for each gauging.
lsce. ipsl. fr, last access: 22 June 2018), we focused on future changes in surface ozone from 2000 to 2100 above the MB using model outputs from the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP;  Lamarque et al. , 2013). This intercomparison project (ACCMIP) consists of a series of time slice experiments aiming at studying the long-term changes in atmospheric composition between 1850 and 2100. ACCMIP was designed to contribute to the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) and analyses the driving forces of climate change in the simulations being performed in the fifth Coupled Model Intercomparison Project (CMIP5)  (Taylor et al. , 2012). This paper is organized as follows. In Sect. 2, we provide a summary of the datasets used in this study, as well as the analysis approach.
ACCMIP models and observations We used the data from 13 models from the ACCMIP experiment. Note that model outputs are not available for all scenarios and periods (see Tables  1  and 2 ). The chemistrytransport model CICERO-OsloCTM2 was discarded in this study due to the absence of sufficient and required model outputs. Most of the models we used are chemistry-climate models (CCMs), except three models: MOCAGE (Modèle de Chimie Atmosphérique de Grande Echelle) which is a chemical transport model (CTM), using offline meteorological fields from an appropriate simulation of a climate model; STOC-HadAM3 (STOCHEM Lagrangian tropospheric chemistry-transport model coupled to the Hadley Centre atmospheric climate model); and UM-CAM (UK Met Office Unified Model version 4. 5 combined with a detailed -CAM-superfast CMAM - EMAC-DLR - GEOSCCM GFDL AM3 GISS-E2-R HadGEM2 LMDz-OR-INCA - - NCAR CAM3.
The representation of stratospheric chemistry is included in the models, excepted in HadGEM2, LMDz-OR-INCA, STOC-HadAM3 and UM-CAM. LMDz-OR-INCA uses a constant (in time) stratospheric ozone climatology  (Li and Shine, 1995) , whereas the other models without detailed stratospheric chemistry use the time-varying stratospheric ozone dataset of  Cionni et al. (2011). Iglesias-Suarez et al. (2016)  evaluated the stratospheric ozone and associated climate impacts using the ACCMIP simulations in the recent past. They showed that ACCMIP multimodel mean total column ozone trends compare favorably against observations. They also demonstrated how changes in stratospheric ozone are intrinsically linked to climate changes. All anthropogenic and biomass burning emissions are specified for all models. However, the natural emissions are differently specified for the different models.
(2010)  for the Hist simulations, whereas emissions for different RCP simulations are described by  Lamarque et al. (2013). The four RCPs include reductions and redistribution of ozone precursor emissions in future projections except for CH 4. Natural emissions, such as CO and VOCs from vegetation and oceans, and NO x from soil and lightning, were determined by each model group. In this study, we use available surface ozone observations based   (Sofen et al. , 2016). Data are averaged within a global grid of 2 • × 2 •. We use averages from hourly ozone data on a monthly basis from 1990 to 2010.
This study is composed of two parts. The first part consists of a model assessment based on the REF period, in which we compare the outputs of different models to a set of available surface ozone data based on the gridded observations given by  Sofen et al. (2016). We use several statistical diagnostics to assess the performances of different model outputs. The individual model performances and the ACCMIP ensemble mean are compared to the averaged observations over the period 1990-2010. For the evaluation of the different models, we use a set of metrics (see Table  4 ), the correlation coefficient (R), the normalized mean biases (NMB), the mean bias (MnB), the mean absolute gross error (MAGE) and the root mean square error (RMSE). In addition to these metrics, we use two unbiased symmetric metrics introduced by  Yu et al.
These two models are the only ones that do not prescribe CH 4 concentrations in RCP simulations  (Young et al. , 2013). Figure  7b  presents the evolution of total VOC emissions between 2000 and 2100. We note that the inter-model variability is high. This is mainly due to two factors. (1) The VOC module is different from one model to another. In other words, some models have more VOC species than others, and especially isoprene is not included in a few models (CMAM and HadGEM2). (2) The second factor is that the biogenic emissions are not specified in the ACCMIP experiment (but are included in most of the models). VOC emissions are mainly from biogenic origin, which explains this difference  (Lamarque et al. , 2013; Young et al. , 2013). The multimodel average of VOCs decreases from 2000 to 2100 for all RCPs, but these changes are not significant given the very large inter-model variability.
Acknowledgements. This work is funded in France by the  Centre National de Recherches Météorologiques (CNRM) of Météo-France , the region Occitanie and the  Centre National de Recherches Scientifiques (CNRS). The authors thank all stakeholders who contributed to the ACCMIP data production and collection. We thank the two anonymous reviewers for their valuable comments, and we also express our thanks to  Jean-Francois Lamarque  and  Béatrice Josse  for their valuable comments that greatly improved our manuscript. Edited by:  William Lahoz Reviewed  by: three anonymous referees.
Data availability. The model data outputs from the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP) are publicly accessible from the NCAS British Atmospheric Data Centre: http://catalogue. ceda. ac. uk/uuid/ ded523bf23d59910e5d73f1703a2d540 (Atmospheric Chemistry and Climate Model Intercomparison Project, 2011). The gridded global surface ozone metrics data  for atmospheric chemistry model evaluation are publicly available from the Centre for Environmental Data Analysis: https://doi. org/10. 5285/897a3958-5bfc-4311-9bb0-01134bf6aefa (Evans and Sofen, 2016).
5 MIROC-CHEM RCP2. 6 - RCP4. 5 - RCP8. 5 - on the gridded observations given by Sofen et al. (2016) in order to evaluate uncertainty related to model simulations. Sofen et al. (2016) built a consistent gridded dataset for the evaluation of chemical transport and chemistry-climate models from all publicly available surface ozone observa-tions from online databases of the modern era: the World Meteorological Organization (WMO) Global Atmospheric Watch (GAW), Cooperative Programme for Monitoring and Evaluation of the Long-range Transmission of Air Pollutants in Europe (EMEP), European Environment Agency Air-Base (EEA), US Environmental Protection Agency Clean Air Sta-tus and Trends Network (US EPA CASTNET), US EPA Air.
The constant changes in public policy in France are in this respect an opportunity for carrying out impact studies, taking advantage of spatial differentiation or the specific features of local housing markets. The article by Guillaume Bérard and Alain Trannoy uses a new database made available to researchers by the CGEDD, the MEDOC database along with the Fidji database, to measure the impact of the 2014 increase in real estate transfer taxes (RETT) paid by the buyer of a real estate asset. As of the first of January 2014, the départements have been able to increase the departmental share of these rates by more than 0. 7% of the transaction amount (from 3. 8 to 4. 5%). As they have not all done so and especially not all at the same time, this sets the stage for a natural experiment. The authors obtain a significant sales anticipation effect the month prior to the tax hike, more than compensated for by a drop in the number of transactions in the three subsequent months.
In fact, the traditional contrast between society's liberal or social democratic approaches comes back into play. As regards housing, France historically belongs among the second group of countries, and the social housing movement is closely linked with this approach. The study by Christophe André and Thomas Chalaux seeks to establish a new typology across all OECD countries, which are still fairly disparate in terms of living standards, based on a new database created by the OECD for this purpose. The Affordable Housing Database (AHD) will in any case be extremely useful to social science researchers. One lesson learnt from this database is that the social democratic countries of Northern Europe, the Netherlands, Denmark, Sweden, Iceland and Norway, are countries where property owners (including homeowners) are now the majority at between 50% and 70%.