dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding Funding is listed only for the corresponding authors. CT's laboratory is funded by  INSERM, Institut Curie, Ministry of Education , and grants from  INCa  ( INCA-11548 ),  French National Research Agency  ( ANR-10-IDEX-0001-02 PSL*  and  ANR-11-LABX-0043 ), SIDACTION ( 17-1-AAE-1138 ),  Fondation ARC  ( PGA1 RF20180206962 ,  PJA 20171206453 ). KWW and CT receive support from  NIDA  ( DA040385 ). KWW is also supported in part by  NIA   AG057430 ,  NIDA DA047807  and  NIMH MH118164.
Therefore, if conotoxin gene diversity gives rise to varying levels of evolvability, conotoxin gene diversity should be coupled with macroevolutionary speciation rates. We applied exon capture techniques to recover phylogenetic markers and conotoxin loci across 314 species, the largest venom discovery effort in a single study. We paired a reconstructed timetree using 12 fossil calibrations with species-specific estimates of conotoxin gene diversity and used traitdependent diversification methods to test the impact of evolvability on diversification patterns. Surprisingly, did not detect any signal for the relationship between conotoxin gene diversity and speciation rates, suggesting that venom evolution may not be the rate-limiting factor controlling diversification dynamics in Conidae. Comparative analyses showed some signal for the impact of diet and larval dispersal strategy on diversification patterns, though whether or not we detected a signal depended on the dataset and the method.
Bait design We used a targeted sequencing approach to recover markers for phylogenetic inference and obtain an estimate of conotoxin gene diversity from Conidae species. For the phylogenetic markers, we identified loci using a previous Conidae targeted sequencing dataset  (Phuong & Mahardika 2017 ) and the Conidae transcriptome data from  (Phuong et al. 2016 ). In the Conidae targeted sequencing dataset, the authors generated a phylogeny using 5883 loci across 32 species  (Phuong & Mahardika 2017). For our sequencing experiment, we only retained loci that were >180bp and were present in at least 26 out of 32 taxa with at least 10X coverage. We chose to only include longer loci to increase confidence in identifying orthologous fragments in other Conidae species. To identify additional phylogenetic markers from the transcriptome data  (Phuong et al.
2016) , which consisted of venom duct transcriptomes from 12 species, we performed the following: (1) identified reciprocal best blast hits between the assembled transcriptome and the Lottia gigantea protein reference  (Simakov et al. 2013) using BLAST+ v2. 2. 31 (evalue = 1e-10). We also considered fragments that had their best hit to the protein reference, but to a non-overlapping portion (<20% overlapping). (2) mapped reads using bowtie2 v2. 2. 7  (Langmead & Salzberg 2012)  (3) removed duplicates using picard-tools v. 2. 1. 1 (http://broadinstitute. github. io/picard) (4) fixed assembly errors by calling single nucleotide polymorphisms (SNPs) using samtools v1. 3 and bcftools v1. 3  (Li et al. 2009)  (5) aligned sequences per locus using mafft v7. 222  (Katoh et al. 2005)  (6) calculated uncorrected pairwise distances within each locus for all possible pairwise comparisons (7) removed sequences if the uncorrected pairwise distance was greater than the 90 th percentile for those pair of species (8) denoted exon boundaries by comparing the transcriptome sequences to the Lottia gigantea genome reference  (Simakov et al.
2016)  to only include the coding region and 100bp of the untranslated regions (UTRs), (2) merged sequences using cd-hit v4. 6. 4  (Li & Godzik 2006 ) at 97% sequence similarity to reduce redundancy among conotoxin loci, and (3) masked repeats using the RepeatMasker v4. 0. 6 web server  (Smit et al. 2015). This filtered dataset contained 395 conotoxin loci with a total length of 171,317 bp. We submitted the following datasets to MYcroarray (Ann Arbor, Michigan, USA) for bait synthesis: (1) 1749 loci for phylogenetic inference, (2) 12652 conotoxin loci using data from  (Phuong & Mahardika 2017) , (3) a single concatenated sequence using data from  (Phuong & Mahardika 2017) , and (4) 395 additional conotoxin loci using transcriptome data from  (Phuong et al. 2016). We chose to synthesize a MYbaits-3 kit, which included 60,000 bait sequences to accommodate all the targeted loci.
Because our aim was to recover sequences from species throughout Conidae, each bait sequence was 120bp in length, which increases the efficiency of recovering divergent fragments. We used a 2X tiling density strategy (a new probe every 60bp) across the sequences from datasets (1) and (  2 ) and used a 4X tiling density strategy (a new probe every 30bp) across datasets (3) and (4). We chose to increase the tiling density for datasets (3) and (4) because the boundaries between exons were not denoted and we wanted to ensure effective capture of the conotoxin loci. The set of probe sequences will be made available on DRYAD following publication.
Conotoxin assembly, processing, and filtering Commonly used assembly programs are known to poorly reconstruct all copies of multilocus gene families  (Lavergne et al. 2015; Phuong et al. 2016). To address this issue, we followed the conotoxin assembly workflow outlined in  (Phuong & Mahardika 2017). Briefly, we first mapped reads back to our assembled contigs using the 'very sensitive local' and no discordant' options. Then, we identified conotoxins within our dataset by using blastn v2. 2. 31 (word size = 11, evalue = 1e-10) to associate our assembled contigs (from SPAdes) with conotoxins we targeted in the bait design. We generated a set of unique conotoxin 'seed sequences' (a short stretch [~100bp] of conotoxin-blasted sequence) using a combination of of the pysam module (https://github.
This approach is described in further detail in  (Phuong & Mahardika 2017). We note that the final conotoxin sequences per sample consisted of exon fragments, where each sequence represents a single conotoxin exon flanked by any adjacent noncoding region. We updated our conotoxin reference database because we targeted additional conotoxin transcripts from  (Phuong et al. 2016). We used blastn v2. 2. 31 (word size = 11, evalue =1e-10) and EXONERATE v2. 2. 0 to define exon/intron boundaries for these additional conotoxin transcripts and added them to our conotoxin reference database. The final conotoxin reference database consisted of conotoxin sequences with the coding regions denoted and gene superfamily annotated. We also annotated the conotoxin sequences for functional region (e. g. , signal, pre, mature, post) using blastn v2.
2. 31 (word size = 11, evalue = 1e-10) with a conotoxin reference database that was previously categorized by functional region  (Phuong & Mahardika 2017). With the final conotoxin reference database, we performed blastn v2. 2. 31 (word size = 11, evalue = 1e-10) searches between the conotoxin reference and every sample's re-assembled conotoxin sequences. We retained sequences if they could align across the entire coding region of the reference sequence. We guessed the coding region for each retained sequence by aligning the query sequence with the reference conotoxin using mafft v7. 222 and denoting the coding region as the region of overlap with the exon in the reference conotoxin. We fixed misassemblies by mapping reads with bowtie2 (very sensitive local and no discordant options enabled, score min = L, 70, 1) back to each conotoxin assembly and marked duplicates using picard-tools v2.
1. 1  (Grabherr et al. 2011 ) reduced redundancy in these transcriptomes with cap3 and cd-hit (percent identity = 99%). We used blastn (word size = 11, evalue=1e-10) to associate contigs with the phylogenetic markers present in our dataset. We used bowtie2 v2. 2. 7 (very sensitive local and no discordant enabled), samtools v1. 3, and bcftools 1. 3 to map reads and call SNPs. We removed sequences if they were below 4X coverage for > 30% of the sequence and masked bases if they were below 4X coverage. We also removed sequences if they had a heterozygosity value two standard deviations away from the mean heterozygosity within a sample. We used to mafft v7. 222 to align loci across a total of 373 samples. We inferred phylogenies under both maximum likelihood  (Stamatakis 2006 ) and coalescent-based methods  (Mirarab & Warnow 2015).
We assigned 12 additional fossils (Table  S2 , Fig. S1  (Duda Jr. et al. 2001; Hendricks 2009 Hendricks , 2015 Hendricks , 2018)) ) to nodes throughout the phylogeny as minimum age constraints, which MCMCtree treats as soft bounds on the minimum age  (Yang 2007). Further information on fossil placement on nodes can be found in the Supplementary. A recent paper showed that the number of species in Lautoconus may be overestimated  (Abalde et al. 2017 ). To account for potential artificial inflation in the species richness of this clade, we artificially removed half the unique species in Lautoconus from our dataset and ran all dating analyses and downstream diversification analyses on this secondary dataset.
Trait dependent diversification We tested for the impact of evolvability (measured as conotoxin gene diversity) on diversification patterns using two trait dependent diversification methods, focusing on the genus Conus. We focused our hypothesis testing on Conus because conotoxin diversity is wellcharacterized in this group  (Phuong et al. 2016 ) and the sequence capture approach used in this study likely represents uniform sampling in conotoxin gene diversity across the genus. This is in contrast to other genera in Conidae, such as Conasprella or Profundiconus, where low conotoxin diversity values are likely the result of poor knowledge of the venom repertoire of these genera (Fig. S2 ) First, we used BiSSE (binary state speciation and extinction,  (Maddison et al. 2007))  implemeneted in the R package diversitree (FitzJohn 2012), which employs a maximum likelihood approach to estimate the impact of a binary trait on speciation, extinction, and transition rates between character states.
Finally, we used STRAPP (Structured Rate Permutations on Phylogenies,  (Rabosky & Huang 2016 ) implemented in the R package BAMMtools  (Rabosky et al. 2014). STRAPP is a semi-parametric approach that tests for trait dependent diversification by comparing a test statistic with a null distribution generated by permutations of speciation rates across the tips of the phylogeny  (Rabosky & Huang 2016). We generated the empirical correlation (method = Spearman's rank correlation) between speciation rates and conotoxin gene diversity and compared this test statistic with the null distribution of correlations generated by permutations of evolutionary rates across the tree. We performed a two-tailed test with the alternative hypothesis that there is a correlation between speciation rates and total conotoxin gene diversity.
S3 ). Our final conotoxin sequence dataset consists of exon fragments and we do not have information on exon coherence (which exons pair together on the same gene). We were unable to assemble full conotoxin genes because conotoxin introns are long (>1 kilobases,  (Wu et al. 2013) ) and exceed the average insert size of our sequencing experiment (~350bp). We recovered fragments from all 58 gene superfamilies we targeted and obtained 159,670 sequences containing some or all of the mature toxin region (Table  S3 ). Total conotoxin gene diversity per species (estimated by summing across all signal region exon fragments and sequences containing the entire coding region) ranged from 5 to 1280 copies in Conus, 31 to 88 copies in Profundiconus, and 7 to 164 in Conasprella (Table  S1 ). Total conotoxin diversity was 311 copies for Californiconus californicus, 12 copies for Pygmaeconus tralli, and 30 copies for the outgroup taxon, Bathyoma sp (Table  S1 ).
Diversification patterns We found that most branching events within each genus began to occur in the Miocene and continued until the present (Fig. 1 ). When analyzing the entire dataset, we found support for diversification rate heterogeneity, where BAMM identified at least one rate shift across Conidae (Fig. 1, S10 ). Across the 95% credible set of distinct shift configurations, BAMM detected an increase in diversification rates on the branch leading to Lautoconus, a clade consisting mainly of species endemic to the Cape Verde islands (Fig. 1, S10 ). However, when examining an artificially reduced dataset consisting of half the species within Lautoconus, we detect no rate shift or a decrease in diversification rates leading to the Conus clade (Fig. S11 ).
33), followed by piscivory (0. 24), and vermivory (0. 16). For the larval dispersal trait, we found support for trait-dependent speciation rates in the full dataset (delta AIC > 2, Table  S9 ), where species with short-lived larvae had higher speciation rates  (0. 27 vs. 0. 16 ). However, this result was not significant when examining the reduced dataset (Table  S9 ).
1 , S10, S11). This is congruent with diversification rates estimated from the fossil record  (Kohn 1990) , suggesting that the accumulation of species during the Miocene may have been a function of an increased number of lineages present rather than an increase in diversification rates. The number of species we included in the subgenus Lautoconus had an impact on the BAMM diversification analyses. On the full dataset, BAMM detected an increase in diversification rates leading to Lautoconus (Fig. 1 , S10), a known and documented radiation of cone snails  (Duda & Rolán 2005; Cunha et al. 2005). However, when we remove half the species in response to recent work suggesting taxonomic inflation in this subgenus  (Abalde et al. 2017) , we do not detect the same shift. Rather, there is partial support for no shift across Conidae, or a slight decrease in diversification rates leading to Conus (Fig.
However, we did find some signal for the impact of diet and larval dispersal strategy on diversification rates when using the BiSSe and MuSSE methods (Table  S8 , S9). Further work is needed to be fully confident in this signal given high false positive rates in these methods  (Abosky 2017; Rabosky & Goldberg 2017)  and given that our results depended on which dataset was used. What is remarkable about these results is the lack of any signal on the impact of venom gene diversity on diversification rates in cone snails, even as we found some signal for trait-dependent diversification in other Conidae characters. If this lack of signal is real, several biological factors may explain this decoupling between conotoxin gene diversity and speciation rates. A critical assumption in Conus biology is that ecological diversification driven by diet specialization is a major factor governing diversification dynamics in cone snails  (Duda & Palumbi 1999; Duda Jr.
Acknowledgements A majority of the sampling material in this paper originates from numerous shore-based expeditions and deep sea cruises, conducted respectively by MNHN and Pro- Natura International (PNI)  as part of the  Our Planet Reviewed programme  ( SANTO 2006 ,  ATIMO VATAE ,  MAINBAZA ,  INHACA 2011 ,  GUYANE 2014 ,  PAPUA NIUGINI ,  KAVIENG 2014 ), by  MNHN  and  AAMP (Pakaihi i Te Moana) , and/or by  MNHN  and  Institut de Recherche pour le Développement (IRD)  as part of the  Tropical Deep-Sea Benthos programme  ( AURORA 2007 ,  BIOPAPUA ,  EBISCO ,  EXBODI ,  MADEEP ,  MIRIKY ,  TAIWAN 2013 ,  NANHAI 2014 ,  BIOPAPUA ,  SALOMONBOA 3 ,  CONCALIS ,  EXBODI ,  SALOMON BOA3 ,  KARUBENTHOS 2015 ,  NORFOLK 2, TERRASSES). Scientific partners included the  University of Papua New Guinea (UPNG) ;  National Fisheries College, Kavieng ;  Institut d'Halieutique et Sciences Marines (IH.
This work used the  Extreme Science and Engineering Discovery Environment (XSEDE) , which is supported by  National  Science Foundation grant number ACI-1053575. This work was supported by two Grants-in-Aid of research from Sigma Xi, a Grants-in-Aid of Research from the Society for Integrative and Comparative Biology, a research grant from the Society of Systematic Biologists, a Student Research Award from the American Society of Naturalists, a National Science Foundation Graduate Research Opportunities Worldwide to Australia, the Lerner Gray Fund for Marine Research from the American Museum of Natural History, research.
Plant material and DNA extraction C. mauritiana is a sub-canopy tree widely distributed in the forest of low and intermediary altitude from Reunion Island. In its natural range, the species is present in diverse conditions of temperature (13-24 °C in mean annual temperature) and rainfall (from 1100 to up to 5000 mm in median annual precipitation). Apart from lowlands, the original habitat of C. mauritiana in Reunion Island was almost well preserved from human disturbances. Collecting missions were organized mainly based on previous records available in the "Mascarine" database (Mascarine, système d'information sur la flore et les habitats de La Réunion) developed by the Conservatoire Botanique National de Mascarin (CBNM, Saint-Leu, Reunion Island). Sampling was designed to cover the environmental range of habitats and the geographic distribution of the species in Reunion Island as well as possible.
DArTseq analysis Genomic DNA from each accession was standardized to a concentration of 50-100 ng μl -1 and sent to Diversity Array Technology Pty. Ltd. Australia (http://www. diversityarrays. com/). One DNA sample was duplicated to be used as technical replicate. Samples were genotyped by Diversity Arrays Technology Pty Ltd using the DArTseq platform  (Sansaloni et al. 2011). The enzyme combination PstI/HapII was selected for library construction and fragments were sequenced using Illumina technology (Illumina, San Diego, CA, USA). Genotype data were generated using proprietary analytical pipelines (DArT Pty Ltd). Identical sequences were collapsed into "fastqcall files". These files were then processed to produce "SNP" tables (codominant markers) and "silicoDArT" tables (dominant markers).
Data filtering for clustering analysis The DArTseq-derived SNP dataset was subset for phylogenetic analysis purposes. Regarding C. mauritiana from Reunion Island, seven accessions were selected to cover the natural distribution of the species. Concerning the other DArTseq genotyped species, all accessions were included. The resulting dataset included 24 accessions. SNP showing missing values (not available, NAs) higher than 8% in all Coffea accessions were discarded. SNP with minor allele frequency (MAF) lower than 0. 02 (just one alternative allele) among the seven C. mauritiana from Reunion Island were filtered out. Regarding the other Coffea accessions, no MAF filtering was performed. The genetic distance (simple matching) between accessions was calculated from the filtered SNPs using DARwin 6.
Data filtering for population genetic studies and assessment of genetic diversity The DArTseq-derived SNP data set corresponding to the C. mauritiana samples from Reunion Island was filtered to only retain individuals with NAs < 25%. SNPs with NAs > 5% and MAF < 0. 002 were discarded. The expected heterozygosity (He), and the observed heterozygosity (Ho) were calculated for the 34 source populations, using ARLEQUIN version 3. 5. 2 (Excoffier et al. 2005). Next, average He and Ho were calculated per geographic regions of origin (Table  1 ). Otherwise, the SilicoDArT data set was filtered to avoid markers with NAs >5%; markers for which a restriction site was detected only once; markers whose location on C. canephora genome could not be determined. Individuals were also filtered to only retain those with NAs <5%.
Assessment of genetic diversity structure in C. mauritiana A tree-based method was first performed using DARwin. Genetic distance was computed between each pair of individuals (at least 80% of pairwise completion; 1000 bootstraps) using a simple matching distance and the corresponding neighbor-joining tree was computed. Next, the sNMF program (sparse non-negative matrix factorization algorithms) implemented in the LEA R package v. 2. 0. 0. (Frichot and François 2015)  was used to infer individual ancestry coefficients. The program relies on sparse non-negative matrix factorization (sNMF) algorithms to estimate population clustering, and to compute least squares estimates of the ancestry coefficients  (Frichot et al. 2014). sNMF algorithms were run 100 times for each putative number of ancestral populations (K), ranging from K = 1-30 (α = 1000).
SNP filtering and Treemix analysis The topology of relationships and migrations between the K populations of C. mauritiana were assessed by Treemix v. 1. 12 (Pickrell and Pritchard 2012) using population allelic frequencies estimated from the above mentioned filtered SNP dataset. As C. myrtifolia and C. bernardiniana were genotyped for three accessions respectively, these species were set as outgroups. The DArTseq-derived SNP dataset was subset for including C. mauritiana from Reunion Island, C. myrtifolia and C. bernardiniana samples. The resulting dataset was filtered to discard SNPs with missing data and markers with MAF <0. 002. Two different filtering methods were used to account for linkage disequilibrium, depending on whether information was available on the position on the coffee reference genome or not.
When information on the position of the SNP was available, SNPs were filtered for r² < 0. 2 using the bigsnpr and bigstatsr R package (function snp_clumping;  Privé et al. 2018). When no information on position was available, only one randomly chosen SNP per sequence was retained. The dataset was divided into windows of 20 SNPs to account for potential remaining linkage disequilibrium by jackknifing. Up to three migration edges were tested (m ranging from 1-3). Each Treemix analysis was computed three times to ensure consistency of the results. As all three runs were consistent, only one run is presented in the results. The three-population test f3(X;Y,W) included in the TreeMix package was performed to test for mixture events for all combinations of three points out of the K clusters of C.
As positive eigenvectors better describe the geographical distance  (Dray et al. 2006) , we kept the eigenvalues of first four positive vectors (geo1, geo2, geo3, geo4) to account for patterns of IBD. (ii) Environmental variables. Climatic variables that minimized collinearity were selected previous to dbRDA. Taking into consideration both the results of Pearson's correlations and PCA analyses, five climatic variables were retained: Tmean, Trange, CDD, PET, RCI (Fig. S1 , Table  S2 ). dbRDA was performed using the function capscale in the vegan R package v. 2. 4-2  (Oksanen et al. 2017). To identify the best model, the dbRDA was run on nested models, which optimizes the model by taking the Akaike information criterion (AIC) into consideration. Both forward and backward processes were used to select direction.
SNP filtering and assessment of genetic diversity The complete DArTseq-derived SNP dataset was filtered for quality criteria leading to 3953 SNPs from 323 C. mauritiana samples from Reunion Island. This dataset was considered for analysis of genetic diversity and population structure. Total expected heterozygosity (He) and observed heterozygosity (Ho) among the 34 C. mauritiana source populations collected in Reunion Island were 0. 14 and 0. 18, respectively (Table  1 ). He values were low among source populations from the north-western and western parts of the island (He values <0. 15), except for source population 8 (He = 0. 16). Source populations from the east (mean He = 0. 17), north-east (mean He = 0. 16) and south (mean He = 0. 16) showed the highest He values. The same pattern was observed regarding Ho values.