dataesr/scientific-paragraphs-categorization huggingface dataset:
S3, S4 and S7 available on Dryad). As :m approached 1, all methods were better at detecting character displacement. Currently, there are no analytical approaches that can disentangle the simultaneous impact of attraction toward a peak and divergence due to competition, though we hope our results will inspire development of such tools. We also note that the ratio of the BM rate parameter 2 and m will also likely impact the ability to detect character displacement, though we have not explored this here. Unlike for divergent character displacement, available statistical methods for detecting convergence in comparative data sets generally do a poor job of detecting convergence, with the simulation method outperforming others (Table  1 ). With whole-data set approaches, Type I error rates are acceptable for phylogenetic analyses (~5%), however, so although detecting convergence is difficult, the risk of mistakenly detecting convergence is low.
In sister-taxa analyses, although Type I error rates are high for PLMMs (Table  2 ), these largely return erroneous divergence results, rather than erroneous convergence  (Figs. 4Bii, v, Supplementary Fig. S12Bii, v available on Dryad). In short, if an empiricist detects convergence in their data set, they can be fairly confident in the result. Yet if empiricists do not detect convergence, this could simply be a result of lower power of the available analytical tools. Currently, there are no tools to fit phylogenetic trait models of convergence between species (e. g. ,  Nuismer and Harmon 2015) ; such tools might more successfully identify convergent character displacement in comparative data sets than the available statistical methods. For both divergent and convergent character displacement scenarios, we found that sister-taxa GLMs and the simulation approach applied to sister-taxa data sets had a mean Type I error rate near 5% (Table  2 ).
However, in some scenarios, the Type I error for sistertaxa GLMs was slightly higher than for the simulation approach (Figs. 2Bi  and 4Bi , Supplementary Tables  available on Dryad ), which suggests that including a model-based estimate of the rate of trait evolution more properly accounts for the effect of divergence than simply including the branch lengths separating sister taxa as a covariate in analyses to control for variation in the amount of time sister taxa have had to diverge from one another (but see Appendix 1 for other extensions of sister-taxa GLMs). The high overall Type I error rate for analyses conducted on sister-taxa data sets may also result from the unrealistic assumption, common to all sister-taxa analyses, that transitions between allopatry and sympatry are uncommon along branches connecting sister taxa  (Weir and Price 2011; Tobias et al.
2014). Supporting this explanation, we found that biogeographic scenarios with high levels of sympatric speciation and low dispersal tended to have overall lower Type I error rates (cf. Figs. 2, 4, Supplementary Figs. S2 and S12 available on Dryad). The statistical properties of analyses used for identifying which traits drive species interactions are less variable than for character displacement scenarios. The statistical methods available to test for causal relationships between phenotypic similarity and interactions between species have very high power. The simulation approach has a low Type I error rate when causal relationships are simulated based on an unmeasured trait, although non-phylogenetic regressions and PLMMs suffer from relatively high Type I error rates (Table  1 ). Thus, we recommend that empiricists interested in predicting pairwise species interactions based on trait data use phylogenetic simulations.
In particular, studies that have used sister-taxa methods to document character displacement using standard regressions or PLMMs may have falsely interpreted a null expectation-larger trait differences between sympatric lineages owing to allopatric speciation-as evidence for divergent character displacement. Conversely, convergent character displacement is often hard to detect with existing methods, suggesting that convergence in signal traits (e. g. ,  Cody 1969 , Cody 1973; Pigot and Tobias 2014; Losin et al. 2016 ) might be more prevalent than previously thought. SUPPLEMENTARY MATERIAL Data available from the Dryad Digital Repository: http://dx. doi. org/10. 5061/dryad. ch0vn.
For a guide to which analytical tools can be applied to each empirical question, see Supplementary Diagram 1 available on Dryad. 1. Non-phylogenetic regressions "Non-phylogenetic regressions" refers to Generalized Linear Models (GLMs) that ignore phylogenetic structure. Though less commonly applied to whole-clade analyses, investigators sometimes use non-phylogenetic regressions for sister-taxa analyses, on the basis that branches connecting sister taxa represent independent evolutionary histories  (Felsenstein 1985). Nonphylogenetic regressions can be used in tests for character displacement or in analyses of predictors of interspecific interactions.
Transcriptome assembly Adapters were clipped from the sequence, low-quality read ends were trimmed (phred score <30), and low-quality reads were discarded (mean phred score <25 or if remaining length <19 bp) using fastq-mcf of the ea-utils package  (Aronesty 2013 ). Transcriptomes were de novo assembled using Trinity (version 2013-02-25)  (Grabherr et al. 2011). Open reading frames (ORF) were identified with TransDecoder (http://transdecoder. github. io/). For each assembled component, only the longest ORF was retained, and gene families were delimited using all against all BLASTP  (Altschul et al. 1990 ) and SiLiX  (Miele et al. 2011). SiLiX parameters were set to i = 0. 6 and r = 0. 6 as they were maximizing the number of 1-to-1 orthologous gene families.
d N /d S calculation Single-copy orthologs were extracted for three different sets of taxa: the 11 asellid species pairs (320 genes), the ibero-aquitanian clade (863 genes), and the alpine-coxalis clade (2257 genes) (Fig. 1 ). Each gene family was then aligned with the following procedure: (1) search and masking of frameshift using MACSE with frameshift cost set to -10 (Ranwez et al. 2011); (2) multiple alignments of the translated sequences with PRANK  (Löytynoja and Goldman 2008)  using the empirical codon model and F option; (3) site masking with Gblocks  (Castresana 2000)  using -t = c, -b5 = h and -b2 set as -b1. After gene concatenation, a transcriptomewide d N /d S ratio was estimated with the free ratio model of CODEML from the package PAML 4. 7a  (Yang 2007). Confidence intervals were obtained using 100 nonparametric bootstrap samples (random sampling of the codon sites with replacement).
This polymorphism data set was also used to measure the direction of selection statistics (DoS)  (Stoletzki and Eyre-Walker 2011)  using: DoS = D n D n + D s - P n P n + P s with D s and D n the number of synonymous and nonsynonymous divergences, and P s and P n the number of synonymous and nonsynonymous polymorphisms. Divergences were measured with PAML 4. 7a  (Yang 2007) , and polymorphisms were measured using the above described pipeline. DoS were measured gene by gene for every species pair and compared for every pair using a Wilcoxon signed rank test or globally using the median DoS per species.
Colonization time For 19 species, we were able to determine the sequence of one opsin gene. Sequences were determined using (1) transcriptome sequences, (2) Sanger sequencing using PCR primers (LWF1a and Scylla) and PCR conditions from  Taylor et al. (2005) , and (3) genomic Illumina reads as detailed below. For the latter, reads were mapped on the closest available opsin sequence following the same approach as for the SNP search, and a consensus was called with the SAMtools program suite. To estimate colonization time, we used the loss of function observed in several subterranean species and postulated that the opsin gene loss of function took place at the time of groundwater colonization. We used a model with two d N /d S ratios, one for the functional opsins (ω surf ) and one for the nonfunctional opsin (ω 1 ), which was set to 1.
2007 ) and Asellus aquaticus (GS of 2. 49 pg)  (Rocchi et al. 1988, and authors' cross validation). The Drosophila were maintained under laboratory conditions at 25°C for two to three generations before GS measurements. Standards were prepared using the protocol described above from five organism heads and were measured in each run (two measurements of D. virilis at the beginning and end of runs and five measurements of A. aquaticus evenly distributed during the runs). The FlowQ bioconductor package  (Gentleman et al. 2004 ) in R software (R Core Team 2013) was used for quality assessment of flow cytometry data. All the flow cytometer analyses were checked for cell number, boundary events, and time anomalies. Cell subsetting known as gating, was first performed manually using the BD FACSDiva software (BD Biosciences).
1. Finally, GS was derived from fluorescence data using D. virilis as a standard for Asellidae and Rissoidae and using A. aquaticus as a standard for Atyidae. Indeed, large GS in Atyidae (previously known GS range from 3. 30 to 7. 20 pg) (http://www. genomesize. com/) prevented the use of D. virilis as a standard.
The difference in ûw , p N /p S , and d N /d S between the two species of a surface-subterranean pair was tested using the proportion of bootstrap replicates supporting a difference (critical level = 5%), and the difference in GS was tested using a Wilcoxon rank-sum test with five measurements of GS (i. e. , five individuals) per species. We also performed ordinary leastsquares models to test for the effect of the ecological status on GS while ignoring phylogenetic relationships among species. To test for the effect of the ecological transition on GS using a wider range of taxa (i. e. , 18 species pairs including Decapoda and Mollusca with five measurements of GS per species), we performed a linear mixed model in R using the nlme package because a chronogram with accurate branch length could not be obtained given the available molecular markers and calibration points.
Acknowledgments This work was supported by the  Agence Nationale de la Recherche  ( ANR-08-JCJC-0120-01   DEEP ,  ANR-15-CE32-0005  Convergenomix); the  Institut Universitaire de France ; the  European Commission  (7th  EU Framework Programme , Contract No. 226874 ,  BioFresh ), the  CNRS  (APEGE No. 70632 , PEPS ExoMod 2014 and Enviromics 2014), the  French Embassy in Denmark , the  Danish National Research Foundation  ( DNRF94 ), and the  Marie-Curie Intra-European Fellowship  ( IEF 302617 ). We thank the following collaborators and organizations for their help in collecting biological material:  Alhama de Granada  municipality,  J. P. Besson ,  M. Bodon ,  M. Bouillon ,  C. Capderrey ,  I. Carreira ,  B. Chatelier ,  C. Colson-Proch ,  M. Creuzé des Chatelliers ,  Cueva de Valporquero ,  D.
Data access Sequence reads and assemblies from this study have been submitted to the European Nucleotide Archive (ENA; http://www. ebi. ac. uk/ena) under study accession number PRJEB14193. Sanger sequences from this study have been submitted to NCBI GenBank (https://www. ncbi. nlm. nih. gov/nucleotide/) under accession numbers KC610091-KC610505 (Supplemental Table  S9 ).
Scan setup and assembly of 3D images was carried out with proprietary Datos/x 2. 0 software. One thousand individual X ray absorption profiles of each mount were taken from marginally different orientations as the sample was rotated on the mounting stage, and combined to build a 3D rendering of the image using Datos/x 2. 0. Image analysis was carried out with Volume Graphics VGStudio MAX version 2. 2 software. Initial 3D renderings contained two pteropods and a calcite standard, which comprised the objects in each balsa wood mount. The 3D image was then split into separate renderings for each object for analysis as illustrated in the S1-S4 Figs. We found that a manual surface determination enabled the hard calcium carbonate shell to be separated from organic material comprising the soft-body of the pteropods and also separated the pteropod shell from the balsa wood mount without clipping the pteropod shell rendering.
The differential X-ray absorption of organic matter, balsa wood, and calcium carbonate translates into different greyscale intensities in the reconstructed 3D image; therefore, it was possible to filter out balsa wood and organic material from the analysed data for volume, wall thickness etc. This process is illustrated with text in S1-S4 Figs. Object volume, average thickness and surface area were determined using standard Volume Graphics analysis. In addition, dimension measurements were taken on 3D renderings of shells. For C. inflexa, three dimension measurements were taken, the width (at widest point), the width at half shell length and length (Fig  2A ). For S. subula, the dimension measurements taken were the total length, width (diameter at widest point) and the "diameter at half length" (Fig  2B ).
pH hindcast The environmental times series run at Point B from Villefranche-sur-Mer Service d'Observation en Milieu Littoral (SOMLIT/CNRS-INSU) was used for pH estimation using the methods detailed in Howes, Stemmann  (18). Weekly temperature and salinity measurements have been undertaken since 1957. Since 2007, pH T , dissolved inorganic carbon (C T ) and total alkalinity (A T ) have also been measured (data provided in the S1 Table ). Water for the determination of dissolved inorganic carbon (C T ) and total alkalinity (A T ) was collected from 10-L Niskin bottles, transferred to combusted glass bottles, overfilled, and poisoned with HgCl 2 as recommended by Dickson et al. (  2007 ). C T and A T are determined potentiometrically  [39, 40]. Further information on environmental sampling methods is provided at the SOMLIT website (http://somlit.
The coeffcients a, b and c were derived from the weekly measurements of total alkalinity taken between 2007 and 2012. Finally, the model was applied to annual averages of temperature and salinity from 1899-2012. The SOMLIT time series only extends back as far as the 1950's, which was insufficient to ascertain an estimate of pH conditions at the time of collection of the historical samples  (1910 and 1921). SOMLIT data was used for hindcasting as far as 1957; for data prior to the start of the SOMLIT series, annual temperature values corresponding to the coordinates of sites 1 and 2 were taken from gridded sea surface temperatures (SST) from the Hadley Data Centre  [43]  (http://rda. ucar. edu/datasets/ds277. 3. ). Salinity data were unavailable for the full period so values were extrapolated from SOMLIT data as the change in surface salinity in the western Mediterranean has been shown to be roughly linear since the year 1900  [7].
Measured and calculated values of alkalinity for the period of 2007-2012 were compared to assess hindcast accuracy. The partial pressure of CO 2 (pCO 2 sw ) was estimated from temperature and concentration of CO 2 in the atmosphere (CO 2 atm , ppm; Eq 4. )  [44]. pCO 2sw ¼ a Â T þ b Â pCO 2atm þ c ð4Þ There is no measurement of CO 2 atm in the Mediterranean region covering the entirety of the study period. The Mauna Loa (19˚53'N-155˚57'W, Hawaii) atmospheric CO 2 time series was used, as this is the only station in the northern hemisphere that has data for the entire study period. To account for geographical differences, the data were compared to the 1979-1997 CO 2 atm time series from the Italian station, Monte Cimone (44˚18'N-10˚7'E). Seasonal differences were minimised by lagging the Manuna Loa data by 2 months and adjusting seasonal maxima and minima of the values from the Mauna Loa time series to obtain the best fit with the data from Monte Cimone.
The coefficients were determined using a linear model for 1 m depth based on pCO 2 sw computed from SOMLIT data from weekly measurements of dissolved inorganic carbon and total alkalinity ranging from January 2007 to March 2012. For values before the start of the Mauna Loa time series (pre 1958) values was taken from the Siple ice core dataset  [45]. Values of pCO 2 sw for the period 2007-2012, derived using hindcast methods were compared with those from the same period that were calculated using the R package seacarb  [46]  from measurements of C T and A T. Using the estimations of A T and pCO 2 sw alongside the measured values of temperature, salinity and pressure, it was possible to calculate seawater pH T using the R package seacarb  [46]. Weekly measurements of pH at Point B began in 2007.
Statistical analysis All statistical analyses were carried out using R  [47]. For direct comparisons between shell thickness, weight and density of the old and new samples, a size filter was applied to the dataset and only individuals of the same length were used. The data were non-parametrically distributed so the Mann Whitney U test was used to analyse differences between the two groups. For the examination of the ontogenetic shell biometrics, a larger set of modern samples was used, which included a range of ontogenetic stages. For this aspect of the analysis, no length filter was applied.
Until now, it has been unclear that such variation existed and has implications for studies of shell thickness that use measurements taken at a single point on the shell. In future biometric work, there is the potential to build on these insights from CT scanning and investigate localized and zonal differences in shell thickness and dimensions. For example CT data could potentially be used to probe changes in the higher thickness and lower thickness areas of the shell indicated in Figs 9 and 10 in isolation from each other. If CT scanning of pteropod shells is to be employed as a method of assessing global change, it is necessary to select standard shell parameters to analyse. The ontogenetic analysis was undertaken to assess which parameters were unaffected by different life stages. The work of Roger et al.
Acknowledgments We thank  Jeannine Rampal  for her generous donation of the "Precious Samples", the  Service d'Observation Rade de Villefranche  and the  Service d'Observation en Milieu Littoral (SOMLIT/ CNRS-INSU)  for their kind permission to use the Point B data. We thank  Aradhna Tripati  ( UCLA ) for her input in the early stage of this study and  Kristina Wilson ,  Vanessa Brillo , and  Tanya Conchas  ( UCLA ) for assistance with CT scanning. We also thank  Rebecca Rudolph  and  Javier Santillan  ( GE technologies ) and  Jeremy Boyce  ( UCLA ) for assistance with CT project design and data analysis. RAE acknowledges support from  National Science Foundation  grant  OCE-1437166. We also thank the  Service National d'Analyse des Paramètres Océaniques du CO 2  for performing the analyses of the carbonate system.
Functional analysis of genes containing nonsynonymous SNPs compared to the sequenced strain (JGI) revealed a strong and significant enrichment of genes involved in metabolism (P value ϭ 0. 0), particularly amino acid metabolism (P value ϭ 1. 57eϪ03), and also of other diverse compounds, including those involved in secondary metabolism (P value ϭ 7. 46 eϪ03). Interestingly, genes involved in DNA repair were also significantly enriched (P value ϭ 3. 37eϪ03). Availability of the genome sequence of T. harzianum B97 will contribute to the investigation into the relevance of different genotypes for phenotypes that are important for efficient alleviation of abiotic stresses and stimulation of plant growth in agriculture. Accession number(s). The genome sequence of T. harzianum B97 has been deposited in GenBank under the accession number MRYK00000000 (BioProject PRJNA357189).
Formic acid (HCOOH) concentrations are often underestimated by models and its chemistry is highly uncertain. HCOOH is, however, among the most abundant atmospheric volatile organic compounds and it is potentially responsible for 15 rain acidity in remote areas. HCOOH data from the Infrared Atmospheric Sounding Interferometer (IASI) are analyzed from 2008 to 2014, to estimate enhancement ratios from biomass burning emissions over seven regions. Fire-affected HCOOH and CO total columns are defined by combining total columns from IASI, geographic location of the fires from MODerate resolution Imaging Spectroradiometer (MODIS) and the surface wind speed field from the European Centre for Medium-Range Weather Forecasts (ECMWF). Robust correlations are found between these fire-affected HCOOH and CO total 20 columns over the selected biomass burning regions, allowing the calculation of enhancement ratios equal to 7.
The CO retrieval characteristics The CO concentrations are retrieved from IASI using the FORLI-CO software  (Hurtmans et al. , 2012) , which uses an 100 optimal estimation method based on  Rodgers (2000). The spectral range used for the retrieval is between 2143 and 2181. 25 cm -1. The CO total columns have been validated for different locations and atmospheric conditions (e. g. De  Wachter et al. , 2012; Kerzenmacher et al. , 2012)  and the comparison with other data have shown good agreement overall, even if some discrepancies were found within CO-enriched plumes (reaching 12% over the Arctic in summer, see  Pommier et al, 2010;  and reaching 17% in comparison with other IR sounders, see  George et al. , 2009). These data were also used previously to 105 study the biomass burning plumes (e.
g. Pochanart et al. , 2003). These external influences interfere with the CO plumes originating from forest fires measured over this region. Despite the overall good match between the number of fires and the variation in HCOOH and CO, we are not certain that the HCOOH and the CO were emitted solely by fires, and the discrimination between a natural and an anthropogenic origin for 165 each compound is challenging. This assessment is particularly obvious for IND and SIB. To isolate the HCOOH and CO signals measured by IASI, potentially emitted by a fire, we decided to only use the data in the vicinity of each MODIS hotspot. To do so, we co-located the IASI data at 50 km around each MODIS pixel and between 0 and 5h for each detected fire, so that each MODIS pixel is associated with a value of HCOOH and CO total columns from IASI.
Importance of the meteorological conditions As shown by earlier studies, the wind speed can have a large influence on the detection of tropospheric plumes of trace gases from space (e. g NO 2  : Beirle et al. , 2011; CO: Pommier et al. , 2013; SO 2 : Fioletov et al. , 2015). We have chosen to attribute a surface wind speed value for each MODIS hotspot. These meteorological fields were taken from the ECMWF (European Centre for Medium-Range Weather Forecasts) reanalysis data (http://data-portal. ecmwf. int/data/d/interim_full_daily) (Dee et 180 al. , 2011). The horizontal resolution of these fields is 0. 125° on longitude and latitude with a 6h time step. As shown in Fig  3 , the three regions where the HCOOH:CO correlations are found to be high (close to 0. 8), correspond to the regions where the surface wind speed was lower, i.
However, due to the methodology used, i. e. averaging the data collected during a few hours (between 0 and 5h from the time registered by MODIS for each detected fire), the calculation of the decay of each compound is not possible. The comparison presented hereafter is therefore mostly illustrative. For both, IND and SEA regions, the emission ratios have been calculated from the emission factors provided in Akagi et al. Thus, based on equation (1), EmR (HCOOH/CO) were calculated and compared with our ER (HCOOH/CO). In this calculation, the vegetation type characterizing each region is important. Some regions are composed by a mix of vegetation types. This is the case for example for AMA and SAF (e. g. White, 1981) , composed by tropical forest and savanna, characterized by an EF CO of 93±27 g/kg and 63±17 g/kg respectively  (Akagi et al.