dataesr/scientific-paragraphs-categorization huggingface dataset:
In order to quantify how specialised shags were in spatial use, we used a variance components analysis as described above. This was not done separately for each sex, however, because no difference in spatial use consistency was detected between sexes. In order to identify differences in foraging strategies, an agglomerative hierarchical clustering analysis with Euclidian distance and Ward's linkage criterion  [51]  was performed on mean and CVs of trip spatial metrics for each individual within stages. The function "HCPC" of the FactoMineR package was used to determine the appropriate number of clusters  [52]. Data on diving behaviour were not included in this clustering analysis as only a third of individuals were instrumented with a TDR and only in a single breeding stage. Linear mixed effects models were used to determine if cluster affiliation had a significant impact on mass and isotopic values.
Null models (including no fixed factors and individual nested within breeding stages as random factors) were compared with models additionally including cluster number as a fixed factor. Significant differences between both models fitted by maximum likelihood indicated that clusters varied significantly in terms of the response variable of interest. Post hoc Tukey HSD multiple comparison tests were then conducted to determine more specifically which clusters differed in the multcomp package  [53]. To look at the effect of sex and stage on stable isotopes, linear mixed models were used with blood δ 13 C or blood δ 15 N values as response variables. In the first case and second case, the random effects were individual and individual nested within pair, respectively. In order to assess dietary specialisations, linear mixed effects models were used, with either the isotope values during chick-rearing as response variables and the isotope values during incubation as explanatory variables, or feather isotope values as response variables and blood values (averaged between samples when repeat samples were obtained) as explanatory variables.
Acknowledgments The authors would like to thank fieldwork volunteers, including  Ayala Loisel ,  Elie Gaget ,  Florian Orgeret ,  Joris Laborie  and  Martin Delpuech. The authors also acknowledge the help of  David Pinaud ,  Samantha Patrick ,  Matthew Symonds , and  Lauren Angel  for assistance with statistical analyses. The authors thank the reviewers for their helpful suggestions on how to improve the statistical analyses, and the manuscript. Lastly, we thank  Paco Bustamante ,  Maud Brault-Favrou , and  Gae ¨l Guillou  for their contribution to sample preparation and isotopic analysis, respectively.
Soil sampling For the present study, 4, 16, 16 and 14 large plots with respectively 0, 1, 2 and 16 plant species were sampled at the date of experimental set up (= 50 large plots). In addition, we sampled 37 small plots cultivated with the monocultures missing in large plots (= total of 87 plots). We assessed the realized (actual) plant species richness in 2006 from data of species-specific biomass  [33]. Plant species whose biomass was <5% of the total plant biomass in the plot were excluded for the computation of actual plant richness since such species hardly influenced the characteristics of composite soil samples taken at plot scale. We tested other threshold values (3 to 10%) to compute actual richness and found that the value used here did not affect our conclusions. Soil samples (0-8 cm depth) were collected using corers (8 cm diameter) in October 2006.
These extracts were dried and concentrated at 4 mg/ml in methanol. Compounds analysis was first performed by Gas Chromatography-Mass Spectrometry (GC-MS) using a gas chromatograph HP 6890 coupled with a HP 5973 mass spectrometer (Agilent Technology) controlled by the HP Chemstation software. GC runs were performed on a DB-FFAP column (30 m, 250 μm internal diameter, 0. 25 μm film thickness; Agilent Technology). The injection was operated in splitless mode at 60˚C with a constant helium carrier gas flow adjusted to 1. 0 mL/min. Oven temperature was programmed to increase from 60˚C to 260˚C at 10˚C/min. The injected volume was 1 μl. For mass spectrometry we used Electron ionization mode at 70eV and the scan range was 30-600 amu. Chromatograms obtained were integrated after standardization using the HP Chemstation software.
In addition, UV absorbing compounds were analyzed by High Performance Liquid Chromatography (HPLC) using an Agilent 1200 series HPLC equipped with a diode array detector (DAD) (Agilent technologies). The analysis was carried out at room temperature using a Nucleodur Sphinx C18 column (250 x 4. 6 mm; 5μm; Macherey-Nagel). For polar and apolar extracts of each sample, 20 μl were injected and the column was eluted by two optimized procedures depending on the extract's type (see  Appendix 1). UV data were recorded between 200 to 600 nm. Chromatograms, processed at 280 nm, were integrated after standardization using the Chemstation software. The low molecular weight compounds detected using GC-MS are referred to hereafter as LMW1 organic compounds and those detected using HPLC-DAD are referred to as LMW2 compounds.
Data analysis For polar and apolar extracts, the chromatograms of the different soil samples were compared and the retention time of each peak was aligned to construct an organic compounds matrix. Block effect on soil compound diversity was first tested by analysis of variance, which was not significant (p = 0. 29 and p = 0. 32 respectively for soil LMW1 and LMW2 compound richnesses). Similarly, block effect on soil compound composition was tested by analysis of similarity (ANOSIM) using the Bray-Curtis coefficient (PRIMER-E software, Plymouth, UK), which was not significant for the composition of the LMW1 compounds (p = 0. 12) but was significant for the LMW2 compounds (p = 0. 001). For analyzing plant diversity effect on organic compound diversity, only compounds not detected in bare soil extracts were considered.
The mean richness of both types of soil compounds was compared between plant functional groups or between plant families using analysis of variance followed by Tukey's test. Data normality was tested using the Shapiro test. The organic compound compositions between plant functional groups or between plant families were compared using ANOSIM. We then tested the effect of plant diversity on the richness of soil LMW1 and LMW2 compounds using linear or logarithmic regression. Given the observed relationships, we tested to what extent soil compound richness for a given plant assemblage could be explained by a complementarity effect among plants present in the assemblage as presented in S1 Fig. In this case, it was assumed that each plant species is associated with a given soil compound profile and that a mixture of plant species is associated with the corresponding mixture of compounds brought in by each plant species of the assemblage.
Acknowledgments We are grateful to  Wolfgang Weisser  for allowing easy access to the Jena biodiversity experiment plots, and to the many students from the  Jena University and Microbial Ecology Centre of Lyon  for their help during soil sampling. We are also grateful to  Danis Abrouk  from the  Microbial Ecology Centre and Stephane Dray  from the  Lyon1 University  ( UMR CNRS 5558 ) for their help during statistical analyses.
This work was funded by the  Agence Nationale de la Recherche ANR , part of the  "Discover" project  ( ANR-05-BDIV-010-01 ) and by the  Ecology Department of the National Research Institute for Agronomy Research (INRA EFPA)  and  Ecology Institute of CNRS (CNRS INEE ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. There was no additional external funding received for this study.
Databases, chemical selection, and mixture sampling ExpoCast  (Wambaugh et al. 2013)  provides exposure estimates, with measures of uncertainty, for 1936 chemicals. Those exposure estimates were obtained using far-field, mass-balance human exposure models (USEtox and RAIDAR). In ToxCast (11 December 2013 release), the Tox21-aromatase-inhibition assay is a cell-based assay measuring CYP19A1 (aromatase) gene activity via a fluorescent protein reporter gene. Chemicals acting on aromatase mRNA synthesis, degradation or translation, or on aromatase itself should give positive results in this assay  (Chen et al. 2015). For each chemical x assayed, ToxCast provides the geometric mean and standard error for the parameter values AC 50,x , W x , B x and T x of a Hill function fitted to the concentration-inhibition data (scaled using the positive and negative controls' data): %Inhibition = B x + (T x -B x ) C x W AC 50, x W + C x W (1) In ToxCast, 1102 chemicals are identified as aromatase inhibitors by the Tox21-aromataseinhibition assay (on MCF-7 human breast cells) with "some reasonable confidence in the estimated parameter values".
The robustness of the prediction was evaluated by examining compounds from the training set similar to the target substances, together with literature data and references. F x central estimates were obtained at several oral dose levels and were linearly interpolated between dose levels as needed. Beyond the dose rates of 0. 001 to 10 mg/day, the F x value at the closest bound was used). To take into account the uncertainty affecting the QSAR estimated PK parameters, we randomly sampled F values from Beta distributions (naturally bounded between 0 and 1), with parameters calculated so that the distribution modes corresponded to the interpolated value of F x , with a CV of 20% (for null F x modes, a and b were set to 1 and 50, yielding a median at 0. 01, a 1 st quartile at 0. 006, and a 3 rd quartile at 0.
Software used ACD/Labs Percepta platform modules "ACD/Oral Bioavailability" and "ACD/ PK Explorer" were used for the prediction of oral bioavailability (F) and total body clearance rate constant (K e ), respectively (see Methods S1, Table  S1 , Figures  S2  and S3  in Supplemental Material). GNU MCSim v5. 6. 5 (www. gnu. org/software/mcsim) (Bois 2009 ) was used to build the ovarian cycle model. R v3. 1. 1 (www. R-project. org) (R Development Core Team 2013) with packages parallel, deSolve and EnvStats was used for database processing, numerical integration of the models, and graphics.
Cycle model behavior Our implementation of the ovarian cycle model proposed by  Chen and Ward (2013)   for which the baseline levels are not well matched. There is a large intra-and inter-variability in hormonal levels across women in those datasets (see Supplemental Material Figure  S6 ). We took the model simulated normal cycling of E2 as the "reference cycle" in the following. Constant exposure scenarios result, at steady-state, in a constant level of aromatase inhibition. In that case, perturbation depends only on that parameter (according to the model assumptions) so the distance between the perturbed and reference cycles is a useful measure of effect (see Figure  2 ). As aromatase inhibition increases, cycles become increasingly perturbed and exhibit chaotic features (hence the misalignment of the points in Figure  2 ).
DISCUSSION We linked ToxCast data and ExpoCast estimates of exposures to (mixtures of) aromatase inhibitors, and estimated their effects on the ovarian cycle in women. To our knowledge, this is the first application of computational toxicology and high-throughput testing to assessing the combined effect of exposures to a large number of EDCs. Our approach is predictive and we had to make many assumptions and simplifications. ToxCast and ExpoCast are still incomplete and a full inventory of all the EDCs to which women are exposed is not yet available. So, we were only able to look at a subset of the potential EDCs. We used human exposure estimates from  Wambaugh et al. (2013) , who give summary statistics for the distribution of exposures to individual chemicals. This allowed us to take the correspondingly large uncertainty into account via Monte Carlo simulations.
We respected the distribution of population exposure levels documented by ExpoCast, but lacking co-exposure information, our estimates might be lower or higher than reality. Efforts are ongoing to collect relevant data; for example, in the European Total Diet Study  (Vin et al. 2014 ). An analysis of such data  (Traoré et al. 2016)  shows that among 153 synthetic chemicals studied in seven typical French diets (food associations), three of them are aromatase inhibitors according to ToxCast: zearalenone, triadimenol, and lindane. In this regard, mixtures of 86 or more aromatase inhibitors may seem unrealistic, but only food contaminants were studied in  Traoré et al. (2016). We searched for the 86 selected aromatase inhibitors in a database of consumer products marketed in the United States  (Gabb and Blake 2016).
Briefly, this database was constructed by scraping product information from online retailers. It currently contains 53743 products. Twelve of the 86 aromatase inhibitors were detected in 5701 products (representing 11% of the products in the database). [It is worth noting that none of the 86 aromatase inhibitors are among the volatile fragrance chemicals detected in consumer products  (Steinemann 2015; Steinemann et al. 2011) , so aromatase inhibitors are unlikely to be hidden in generic "fragrance" or "flavor" designations on consumer product labels. ] Two-way combinations of these chemicals were found in 220 products; and the three-way combination of carminic acid, FD&C blue no. 1, and retinol was found in 3 products. This may not seem to point to a large problem but it is an incomplete view of combinatorial exposure.
Consider that 3660 out of 4501 makeup products (81%) in the database contain at least one of the aromatase inhibitors considered (carminic acid, retinol, and artificial colors are common ingredients in makeup), and a typical consumer uses several products each day, possibly even several makeup products. This increases the likelihood of combined exposures. Also, no readily available data address associations for all near-and far-field exposures for the 86 aromatase inhibitors, which are used in industrial or agricultural processes (18), consumer product formulations (12), biocidal applications (38), and pharmaceutical drugs (18). These usage categories are likely to be independent, so focusing on a few known associations would only give partial answers and underestimate global risk. We are striving for a more extensive picture.
To address the potential overestimation of mixture effects when generating purely random associations, we present the results of a very optimistic exposure scenario (with no coexposures at all). This gives a lower bound estimate: 0. 3% of exposures would lead to more than 10% average aromatase inhibition in women. ToxCast aromatase inhibition data were obtained by exposing cells in vitro. We had no easy way to assess the in vitro kinetics of the substances assayed and reconstruction methods  (Armitage et al. 2014)  require input data that we did not have. We assumed that the nominal assay concentrations were those really experienced by the cells and that equivalent extracellular concentrations in vivo would lead to the same levels of aromatase inhibition. That is a typical assumption but it is not necessarily correct  (Coecke et al.
2012). To obtain extracellular concentrations in vivo, we estimated bioavailability and total clearance with QSAR methods and input them in a simple one-compartment PK model with oral exposure only (even though inhalation or dermal exposures might be more relevant). Again, more sophisticated (PBPK) models and additional PK data would give more precise and accurate predictions  (El-Masri et al. 2016; Wambaugh et al. 2015). On the effect side, we only considered chemicals for which the ToxCast dose-response parameters were estimated with reasonable confidence. This is a conservative choice and additional chemicals would have been included if more relaxed criteria had been chosen (at the price of lower confidence in the results). A concern with large databases such as ToxCast is the quality assurance of the data provided.
Aromatase inhibition may not be the most sensitive toxicity endpoint, for example, or it may be due to a burst effect of cytotoxicity  (Judson et al. 2016 ). From the original set of 256 aromatase inhibitors for which we had exposure estimates and ToxCast data, 170 were screened off based on cytotoxicity. Those were mostly weak inhibitors (data not shown). For the screen, we kept only the 86 substances which had an aromatase inhibition AC 90 lower than their cytotoxicity AC 10 -measured by the ToxCast proliferation decrease assay on T47D human breast cells, a cell type similar to the MCF-7 used by the aromatase inhibition assays  (Aka and Lin 2012). We preferred that screen to the omnibus z-score criterion, which aggregates cytotoxicity results from different cell types and species. For the remaining 86 substances, we may still have downplayed other types of toxicity.
Methods S1: PK modules of ACD/Labs. The ACD/Oral Bioavailability module uses a combination of probabilistic and mechanistic models to predict oral bioavailability based on a dataset of oral bioavailability data for 788 substances. The module predicts the fraction of the specified dose that reaches systemic circulation after oral administration (F), with the possibility to explore dose-dependence of F. The default oral dose used in calculations is 50 mg. The mathematical model used for simulations in the Oral Bioavailability (and PK Explorer) modules is based on differential equations that consider solubility in the gastrointestinal tract, passive-absorption in jejunum, elimination (total body clearance), and volume of distribution. Simulations performed with the Oral Bioavailability module ignore firstpass metabolism in liver and gut.
Data collection Data were collected from 1998 to 2001 in forest fragments (FF) and continuous forest (CF) composed of Semi-deciduous Forest (IBGE 2012). The area, perimeter and degree of isolation of fragments were calculated from Landsat satellite TM-5 images, orbits/point 227/62 1999, to identify forests, with fragment profiles converted to vector format and their areas calculated in ArcView 3. 2 software  (ESRI 1996). The continuous-forest area sampled covered hundreds of hectares and the sampling units were established between 1-10 kilometers from each other.
Data analysis Plant density, species richness and DBH distribution were compared between FF and CF using t-tests in the R statistical platform (R Core Team 2014). Phytosociological parameters were estimated following  Mueller-Dombois and Ellemberg (2003). For this, forest fragments (FF) were categorized into small (SF), medium (MF) and large (LF), based on the mean area of the fragments (44. 62 ha); where fragments with an area below 50% of the average value (2. 4-22. 34 ha, N=13) were classified as small, those in the range 22. 34-67. 01 ha (N=10) as medium, and those above 67. 01 h as large (N=2). The structural profiles of forest fragments and continuous forest were analyzed as the frequency distribution of DBH classes. As the forest had many thin individuals, a class amplitude of 5 cm was adopted  (Soares et al.
6a  shows the projected production of cement in GCAM and IMAGE, the production of non-metallic minerals in TIAM-UCL and the production of clinker in DNE21þ, that model material use explicitly. The global cement production in 2010 was 3. 2 Gtonnes  [30]  and the global estimated clinker production was 2. 4 Gtonnes (based on a clinker to cement ratio of 76%) 7  [31]. The IEA shows a   7 Although there is data available on cement production, data on clinker production is not. Therefore, clinker production is usually estimated based on information concerning the clinker to cement ratios. The clinker to cement ratio reported by the WBCSD/CSI (2012) is lower from the clinker/cement ratio of 80% reported in IEA (2012b). For an 80% clinker/cement ratio, the 2010 clinker production would be 2. 56 Gtonnes.
These frogs were sold under three different brands but all come from the same importer. The samples were identified through a barcoding approach based on the 16S gene. The 16S gene was previously recognized as one of the most effective genes for molecular amphibian species identification  (Vences et al. 2005; Grosjean et al. 2015). Moreover, 16S reference sequences were available in the GenBank database for the 15 species potentially collected in Indonesia for commercialization. A diminution in size of adult frogs due to overharvesting has been suggested  (Iskandar, cited in Anonymous 2007). As snout-vent length and tibia length are highly correlated in frogs  (Emerson 1978; Vidal-García et al. 2014) , we used tibia length to obtain an estimate for the body size of commercialized frogs.
Molecular species identification of samples To identify all our samples we first determined the number of haplotypes in our dataset using TCS  (Clement et al. 2000). Each haplotype was then assigned to a species through a nucleotide BLAST approach conducted in GenBank (http://blast. ncbi. nlm. nih. gov) and through a phylogenetic method (Bayesian tree). BLAST species identification was accomplished following three different criteria  (Meier et al. 2006  identifications to the closest match regardless of genetic distance. The BCM criterion is similar to BM, but the query is identified by the closest match with a distance below a defined threshold. Finally, the ASB criterion is similar to the BCM by applying a threshold but it returns all the sequences within it. A query is identified when all the matching sequences below the threshold are conspecific.
For BCM and ASB, a query may provide ambiguous results if sequence divergences of different species are below the threshold (ASB) or sequences from different species are the closest match below the threshold (BCM). A tree-based approach of species delimitation was also used. The tree-based criterion of reciprocal monophyly was used to define species boundaries. In our analysis we included: 1) all the haplotypes recovered in our deep frozen frog legs, 2) all species of the genera Fejervarya and Limnonectes known from Indonesia (except Limnonectes dammermani  (Mertens, 1929) , L. kenepaiensis  (Inger, 1966) , L. rhacodus  (Inger, Boedi & Taufik, 1996) , L. sinuatodorsalis  Matsui, 2015 , L. timorensis (Smith, 1927)  and Fejervarya schlueteri (Werner, 1893) for which no 16S gene sequences are currently available in GenBank), and 3) one sequence of Hoplobatrachus rugulosus (Wiegmann, 1834) and one sequence of Occidozyga laevis (Günther, 1858) included as outgroups.
Depending on 16S gene sequence availability in GenBank, we included one to four individuals per species in our analysis. Sequences were aligned with ClustalW  (Thompson et al. 1994). In order to minimize the number of missing data we kept 553 sites in our final analyses. Evolutionary relationships among sequences were estimated by conducting Bayesian Markov chain Monte Carlo phylogenetic analyses (MCMC) with MrBayes v. 3. 1. 2 (Huelsenbeck & Ronquist 2001 ). MrModeltest v. 3. 04  (Nylander 2004 ) was used to evaluate the fit of 24 nested models of nucleotide substitution to the data. According to the Akaike information criterion, MrModeltest recommended the GTR + I + G model. This model was used in the Bayesian analysis. Three heated chains and one single cold chain were employed, and runs were initiated with random trees.