dataesr/scientific-paragraphs-categorization huggingface dataset:
For retrieving the target genes from available spider transcriptomes from Fernández et al. [44] , we downloaded the raw data from NCBI (see Supplementary Table  S2 ). Transcriptome assembly was carried out using Trinity version 2. 0. 3  [59, 60]  under default parameters and employing Trimmomatic (default parameters, as part of the Trinity package) for quality control. Relevant molecular markers were retrieved from the resulting assembled transcriptome using BLASTn  [61]  with an E-value cut-off of less than 10 -20 to account for potential orthologs. A total of 32 transcriptomes were analyzed. Extracted sequences were checked by comparing against the online NCBI BLAST databases and by building single-gene phylogenies to screen for possible contaminants. The topological position of each newly added taxon was first checked, and in the case of the obvious misplacing of the specific gene partition, or the whole taxon if all partitions were involved, was deleted.
The concatenated gene matrix of Wheeler et. al. [43] , which included three mitochondrial (12S rRNA, 16S rRNA and COI) and three nuclear (histone H3, 18S rRNA and 28S rRNA) genes, was used as a backbone matrix for our analyses (see Supplementary Table  S3  for details of taxa included). Taxa from the phylotranscriptomic tree of Fernández et. al. [44]  not present in the Wheeler et. al. [43]  matrix were included to maximize the number of constrained nodes. Sequences from the missing taxa or, if unavailable, from close relatives within the same genus (see Figure  1  for further details) were either (1) downloaded from GenBank, or (2) bioinformatically extracted from the original transcriptome data in Fernández et. al. [44]  (see Supplementary Table  S2  for GenBank transcriptomes accession numbers).
For retrieving the target genes from available spider transcriptomes from Fernández et. al. [44] , we downloaded the raw data from NCBI (see Supplementary Table  S2 ). Transcriptome assembly was carried out using Trinity version 2. 0. 3  [59, 60]  under default parameters and employing Trimmomatic (default parameters, as part of the Trinity package) for quality control. Relevant molecular markers were retrieved from the resulting assembled transcriptome using BLASTn  [61]  with an E-value cut-off of less than 10 -20 to account for potential orthologs. A total of 32 transcriptomes were analyzed. Extracted sequences were checked by comparing against the online NCBI BLAST databases and by building single-gene phylogenies to screen for possible contaminants. The topological position of each newly added taxon was first checked, and in the case of the obvious misplacing of the specific gene partition, or the whole taxon if all partitions were involved, was deleted.
A total of 167 new sequences of the six markers (12S, 16S, COI, H3, 18S and 28S) (126 sequences extracted from transcriptomic data and 41 sequences downloaded from GenBank) were added to the Wheeler et al. [43]  matrix after the removal of non-spider outgroups. The final backbone matrix, hereafter referred to as the B matrix, contained 964 terminals (Table  1 , see Supplementary Table  S2  for the new sequences included). With the inclusion of the species Cepheia longiseta, a representative of the family Synaphridae, the B matrix included 118 of the 120 (98. 3%) spider families currently recognized  [56]. The Lc1 matrix (containing COI sequences of the Ibercoding and NetBiome-MacDiv projects) was added to the B matrix to generate a new matrix hereafter referred to as BLc1 (Table  1 ). Similarly, the Lc128 matrix (containing both COI and 28S sequences of the former projects) was added to the B matrix, hereafter referred to as the BLc128 matrix (Table  1 ).
Maximum Likelihood Analyses Phylogenetic trees, for the different matrices constructed, were inferred using Maximum Likelihood (ML) as implemented in the program RAxML-HPC v. 8. 2. 12  [63] , remotely run on XSEDE at the CIPRES Science Gateway  [64]. The concatenated matrices were partitioned by gene, and an unlinked GTR + CAT substitution model was assigned to each gene. The ML tree was inferred out of 10 iterations of randomized stepwise addition Maximum Parsimony starting trees (-N 10). The effect of incorporating a topological constraint in the phylogenetic reconstruction and on PD estimations was assessed by conducting maximum likelihood analyses on each concatenated data matrix with and without topological constraints (-g), the latter was defined by the F_topological_constraint tree (Table  1 ).
Inferring Ultrametric Trees All resulting ML trees (except Lc1 and Lc128) were made ultrametric using the congruify. phylo function in the R package GEIGER  [65]. The dated phylogeny generated by Fernández et al. [44]  was used as a reference to provide fixed calibration points for the target trees (i. e. , each of the ML trees inferred for the different data matrices with and without topological constraints). The resulting table of calibration points was used to date the target trees using the program treePL  [66]. Recently, Magalhaes et al. (2020)  [67]  have published a time stamped tree of life of spiders based on a reexamination of the available fossil data. Although the location of some of the calibration points differed from those used by Fernández et al. 2018  [44] , the inferred confidence intervals broadly overlapped.
Tree Similarity and Monophyly Levels The level of similarity among trees inferred from the assembled matrices was assessed with the R package phangorn  [68]. Pairwise tree similarities were estimated using Robinson-Foulds (RF) distances  [69] , both absolute and normalized to the maximum possible distance. Low values of RF indicate high similarity between trees. For these analyses, we used all non-ultrametric trees that were pruned to ensure that both trees shared the same labels. The number of monophyletic families and genera recovered in each tree was explored with the R package MonoPhy  [70]. Levels of monophyly between trees were compared by calculating the ratio of monophyletic families/genera taking into account either the total taxa of the corresponding taxonomic level, or excluding the monotypic taxa.
Phylogenetic Community Metrics and Statistical Analyses Phylogenetic diversity for each community was calculated as the Faith's phylogenetic diversity (PD)  [14, 17]. This index measures phylogenetic community richness as the total branch length of the minimum spanning tree linking all species represented in a community. It was calculated with the R package BAT  [71]  for each sampling plot, using each phylogenetic tree. All statistical analyses were conducted under the assumption that the time-stamped tree obtained with the full data set including topological constraints (BLc128_tc_cal) conveys the most accurate representation of the underlying evolutionary relationships of the sampled taxa, as it maximizes its explanatory power by including all available sources of evidence  [72]. This tree is hereafter referred to as the "reference tree" (See Figure  2  and supplementary Figure  S1 ).
We further explored how PD varied across the trees obtained under the different conditions explored, and if the different inferred trees affected the rank of alpha diversity comparisons of the communities. Diversity 2020, 12, x FOR PEER REVIEW 8 of 23 genera recovered in each tree was explored with the R package MonoPhy  [70]. Levels of monophyly between trees were compared by calculating the ratio of monophyletic families/genera taking into account either the total taxa of the corresponding taxonomic level, or excluding the monotypic taxa.
unconstrained, time-stamped vs. non-ultrametric, 28S and COI barcode region vs. COI barcode region-only) (see details in Table  1 ). For example, testing the influence of using topological constraints was accomplished by defining Rank of communities obtained with tree BLc128_tc_cal~Rank of communities obtained with tree BLc128_cal + (1|Region). The fixed factor was one of the trees inferred under specific conditions. The region pool (1: Iberian Peninsula; 0: Macaronesia) was included as a random effect to remove the influence of the geographic origin of the plot (see Table  2  for details of GLMM analyses). The proportion of variance explained by the fixed and the random factor together (conditional R2)  [75, 76]  was calculated using the function r. squaredGLMM in the library MuMIn in R  [75].
The variable importance for each GLMM accomplished was obtained as 1-R2. For each of the factors tested, the mean of the variable importance was calculated to determine which factor had a larger effect on the rank of the PD (see Table  2 ). The true skill statistic (TSS) test  [77]  was applied to check the accuracy of each phylogenetic tree in inferring the same ecological processes (PD overdispersion or PD clustering) as the reference tree. TSS accounts for the accuracy in detecting false negatives and positives, in opposition to true positives and negatives, and it ranges from -1 to + 1, where + 1 indicates perfect agreement, and values of zero or less indicate a performance no better than random. All statistical analyses were performed with the software R  [58]  and the TSS test conducted by applying the formula from  [77]  in Excel.
Both the estimated PD within plots and their ranking varied depending on the tree used (see Supplementary Tables  S4  and S5 ). With the time-stamped trees and averaging over all communities, the percentages of difference in estimated PD between the reference and the alternative trees were higher (range from 6. 5% to 14%) than in the non-ultrametric trees (range from 2% to 10. 2%). Among the time-stamped trees, the unconstrained tree including only the COI barcode data of the local communities (BLc1_cal) performed the worst (i. e. , yielded the most different PD values from the reference tree). Among the non-ultrametric trees, the tree built from COI-only data from local taxa (Lc1) was the worst (see supplementary Table  S4 ). The PD values were on average 10% higher when using phylogenetic trees inferred from species from the local communities alone than when incorporating the backbone matrix.
The monophyly of many families was better recovered, both in constrained and unconstrained phylogenetic trees, when species sampled from local communities contained both COI barcode and 28S sequences, compared with COI barcode data alone. This is in agreement with other studies that suggest that multilocus approaches outperform single locus ones in phylogenetic reconstruction  [20, 81]. The mitochondrial gene COI has a higher mutation rate  [82]  and smaller effective population sizes compared to nuclear genes, which allows for effective sorting at recent lineage splits but becomes rapidly saturated (i. e. , multiple hits on the same positions) proving little resolution at deeper nodes  [83]. Thus, the combination of genes with different substitution rates helps to resolve relationships at different hierarchical levels, from deeper nodes to shallow relationships  [84].
However, the lowest R2 values of the linear regression models suggested that time information and the use of COI barcode data alone have the highest impact on correctly ranking the community plots according to their PD. Sensitivity of the community plot ranking to the tree topology has also been observed in other studies  [19]. It is generally assumed that larger amounts of data result in more resolved and better supported tree inferences. However, little is known about how trees inferred from different amounts of data affect PD estimations. Some studies have found that PD indices estimated from COI barcode sequence-only phylogenies do not significantly differ from those estimated from multigene trees  [27] , while others suggest that multilocus trees outperform single locus ones in estimating phylogenetic diversity  [81].
Use of Time-Stamped vs. Non-Ultrametric Trees in PD Estimation The branch lengths of non-ultrametric trees indicate divergence in characters (genes) used for reconstructing the trees, while chronograms with time-calibrated branch lengths indicate time since species diverged  [26, 87]. The relative branch lengths between two species in a non-ultrametric tree can differ from those in a time-stamped tree, because during the smoothing process of dating the tree, the long branches are shortened while short branches are lengthened. There are some species from our communities (such as Marilynia bicolor, Iberina montana, Pirata piraticus, Titanoeca schineri, Steatoda sp. , Walckenaeria n. sp. and Dictyna pusilla) that have long branches in the non-ultrametric trees (available at the Zenodo Digital Repository at 10.
5281/zenodo. 3941712), thus increasing the PD distances between species compared to the time-stamped trees, on which long and short branches are accordingly shortened or lengthened during the smoothing process of time-calibration. Other studies have also shown a low correlation and significantly different results in phylogenetic diversity indices when using time-stamped versus non-ultrametric trees  [22, 25, 26, 87]. Although all factors tested had a similar effect in failing to correctly rank the community plots according to their PD, most non-ultrametric trees were the least correlated with the rank values of the best resolved tree. Other studies have also shown differences in PD estimations and rank of community plots with different dating methods  [19, 26]. The calibration or not of a phylogenetic tree has an important effect on the ecological patterns inferred, thus affecting the interpretation of PD  [26, 88].
Incorporating all, some or none of the treatments/data listed above will depend on either data availability or tradeoffs between time and resource costs for generating data and the potential downstream benefits. A major improvement both in terms of phylogenetic relationships and estimates of PD was accomplished by simply combining the DNA barcodes with a more conserved marker, in our case 28S. The amount of improvement was similar or even greater than that obtained by combining DNA barcode sequences with a backbone matrix. Although generating an additional marker increases cost, our results indicate this to be a worthy investment. Assembling a backbone matrix, on the other hand, may be a financially more economical alternative, or even complement, if relevant sequences are available within public databases.
In this regard, and contrary to previous suggestions, recent studies have shown that taxonomic inaccuracy in public databases is of limited concern, at least for non-parasitic macroscopic animals  [93] , and several programs are available that automatize the assembly of backbone matrices from public databases (e. g. ,  [94] [95] [96] ). Interestingly, our results revealed that an increase in missing data introduced by combining barcode sequences with a backbone matrix had little effect on the topology of the inferred tree. Surprisingly, the use of topological constraints yielded trees very similar to those from unconstrained searches. Constrained trees slightly increased monophyly indexes for higher taxa but had little effect on patterns of community phylogenetic diversity. Thus, the use of topological constraints may be less important when either additional markers or backbone matrices are available, although they may help to transform trees into chronograms (see below).
Although generating an additional marker increases cost, our results indicate this to be a worthy investment. Assembling a backbone matrix, on the other hand, may be a financially more economical alternative, or even complement, if relevant sequences are available within public databases. In this regard, and contrary to previous suggestions, recent studies have shown that taxonomic inaccuracy in public databases is of limited concern, at least for nonparasitic macroscopic animals  [93] , and several programs are available that automatize the assembly Consistent with findings for other organisms (e. g. , vascular plants  [22, 25, 26, 87]  or reptiles  [97] ), our study demonstrates substantial differences in community phylogenetic structure inferred from non-ultrametric trees (phylograms) compared to their time-stamped counterparts (chronograms).
ACKNOWLEDGMENTS Financial disclosure: Conflict of interest statement: M. K. has received honoraria from  Gilead and Alexion. M. K. D. has served as a consultant for  Pharmacyclics and Daiichi Sankyo. M. H. has received research support/funding from  Takeda Pharmaceutical, Spectrum Pharmaceuticals , and  Astellas Pharma ; served as a consultant for Janssen R&D and Incyte; and served on the speaker's bureau for  Sanofi Genzyme  and  AstraZeneca. D. W. reports research support from  Incyte  and consulting fees from  FATE. S. H. reports honoraria and travel funding from  Pfizer ,  Novartis ,  Gilead ,  Merck ,  Sanofi ,  Mallinckrodt , and  Janssen. M. -A. P. has received honoraria from  Abbvie, Bellicum, Celgene, Bristol-Myers Squibb, Incyte, Kite/Gilead ,  Merck ,  Novartis ,  Nektar Therapeutics ,  Omeros , and Takeda; has served on data safety and monitoring boards for  Cidara Therapeutics, Servier , and  Medigene  and scientific advisory boards for MolMed and  Nex-Immune ; has received research support for clinical trials from  Incyte ,  Kite/Gilead ; and  Miltenyi Biotec ; and serves in a volunteer capacity as a member of the Board of Directors of Be The  Match  ( National Marrow Donor Program ), as well as on the CIBMTR Cellular Immunotherapy Data Resource Executive Committee.
Since the 1960 s, there has been a rapid expansion of drilling activities in the central and northern Adriatic Sea to meet the increasing global energy demand. The discharges of organic and inorganic pollutants, as well as the alteration of the sediment substrate, are among the main impacts associated with these activities. In the present study, we evaluate the response of benthic foraminifera to the activities of three gas platforms in the northwestern Adriatic Sea, with a special focus on the Armida A platform for which extensive geochemical data (organic matter, trace elements, polycyclic aromatic hydrocarbons, other hydrocarbons, and volatile organic compounds) are available. The response to disturbance is assessed by analyzing the foraminiferal diversity using the traditional morphology-based approach and by 18S rDNA-based metabarcoding.
The two methods give congruent results, showing relatively lower foraminiferal diversity and higher dominance values at stations closer to the platforms (< 50 m). The taxonomic compositions of the morphological and metabarcoding datasets are very different, the latter being dominated by monothalamous, mainly soft-walled species. However, compositional changes consistently occur at 50 m from the platform and can be related to variations in sediment grain-size variation and higher concentrations of Ni, Zn, Ba, hydrocarbons and total organic carbon. Additionally, several morphospecies and Molecular Operational Taxonomic Units (MOTUs) show strong correlations with distance from the platform and with environmental parameters extracted from BIOENV analysis. Some of these MOTUs have the potential to become new bioindicators, complementing the assemblage of hardshelled foraminiferal species detected through microscopic analyses.
The aims of the present study are i) to define the potential impact zone around three gas platforms (Armida A, Garibaldi A, and Agostino B) in the northwestern Adriatic Sea (Italy); ii) to compare the performance of metabarcoding and morphological analyses in assessing the impact of Armida A platform on benthic foraminiferal communities; and iii) to identify new potential bioindicators of pollution in the foraminiferal metabarcoding dataset.
The sequencing libraries were then prepared using the Illumina TruSeq® DNA PCR-Free Library Preparation Kit. The libraries were quantified by qPCR using the KAPA Library Quantification Kit. The sequencing was performed on an Illumina MiSeq instrument using paired-end sequencing for 600 cycles with a V3 kit. The raw data is available from the Sequence Read Archive public database under the accession PRJEB29469. To retain only high-quality data, stringent parameters were applied to filter out sequence reads with a mean quality score below 30 and those with any ambiguous bases or mismatches in the tagged primers. Chimeras originating from the artificial recombination of different sequences during the PCR cycles were excluded using the de novo mode in UCHIME  (Edgar et al. , 2011). We then clustered the reads into Molecular Operational Taxonomic Units (MOTUs) using the SWARM v2.
1. 8 algorithm  (Mahé et al. , 2015)  and the default d parameter (i. e. , 1). The representative reads of each MOTU (the most abundant read of each MOTU) were then compared against curated reference sequence database for taxonomic assignment (http://forambarcoding. unige. ch) using the assign_taxonomy. py script from the QIIME v1. 9. 1 toolkit  (Caporaso et al. , 2010)  with default option (uclust method,  Edgar, 2010). Annotations were done using the Last Common Ancestor approach from up to three candidate reference sequences with above 95% of similarity with the queries. In the absence of a universal solution for the HTS filtering and to improve the congruence in terms of alpha diversity, and because most of the rarest MOTUs were unassigned, the MOTUs represented by less than 1000 reads were excluded.
0  (StatSoft, Inc. , 2007). Detrended Correspondence Analysis (DCA) based on the morphospecies (relative abundance > 5%) and MOTUs (relative abundance > 3%) distributions along the transect was performed using the package vegan  (Oksanen et al. , 2019). Graphs were created using the R package Rioja  (Juggins, 2017; R Core Team, 2017). Differences in the beta-diversity of the molecular and morphological datasets were tested by permutational multivariate analysis of variance (PERMANOVA) based on normalized composition tables (log-transformed relative abundances) using Bray-Curtis dissimilarity matrices with 999 permutations. The tests were performed using the adonis function in the vegan R package  (Oksanen et al. , 2016). The PERMA-NOVA analysis was based on nested models with three hierarchical spatial factors: platform (i.
3a-d ). Monothalamea increases in proportion along a distant gradient from the platform (i. e. , the highest values are found between 50 and 1000 m) as does Globothalamea, but the latter represents only a minor component of the molecular dataset (Fig. 3a ). Among Globothalamea, A. aberdoveyensis shows the highest abundances close to the platform, whereas L. scotti records the lowest one. Bolivina sp. , Epistominella sp. , Nonionidae, Nonionoides sp. , N. stella, Nonion sp. , and Stainforthia sp. increase in proportion as the distance from the platform increases (Fig. 3b ). Among Monothalamea, the most striking shift in relative abundance is observed in the case of Micrometula sp. , which is less than 1-2% close to the platform (0-50 m) but greater than 10% at the stations situated more than 250 m from the platform.
Statistical analyses 4. 3. 1. Alpha diversity A comparison of molecular and morphological data shows very congruent results despite of the different scales (Fig. 4 ). In the molecular data, the diversity plots show an overall lower diversification (Fisher's α and H' index) and higher D at stations close to the platforms (0 m, 25 m, and 50 m) (Fig. 4 ). Similarly, lower values of diversity (Fisher's α and H' index) and higher D values are found in the morphological dataset at stations closer to the platforms. Higher diversity values are commonly encountered between the 125 and 2000 m The other platforms, AB and GA, show great variability along a distance gradient, often with higher values of D and lower values of diversity (Fisher's α and H' index) at stations close to the platforms (Figs. S2  and S3 ).