dataesr/scientific-paragraphs-categorization huggingface dataset:
MATERIALS AND METHODS From each individual, we extracted total RNA from the frozen leaves using an RNeasy Plant Mini Kit (Qiagen, Valencia, CA, USA), which included treatment with RNase-free DNase I (TaKaRa, Dalian, China) to eliminate DNA contamination. We determined the quality and quantity of the extracted RNA of each sample using the agarose gel electrophoresis and a 2100 Bioanalyzer (Agilent Technologies). We sent the purified RNA to Novogene Corporation (Tianjin, China) for preparation of cDNA libraries and sequencing on an Illumina NovaSeq 6000 platform to generate 150 bp paired-end (PE) reads. We deposited all newly obtained, raw reads in the NCBI Sequence Read Archive under Bioproject accession number PRJNA649046 and BioSample accession numbers from SAMN15655841 to SAMN15655969 (Supplementary Appendix S2 available on Dryad).
Reads Filtering, Orthologs Identification, and Phylogenetic Analysis We performed quality control on the raw reads to ensure the reliability of downstream analyses. Specifically, we filtered raw reads using a custom Perl script (QC_pe. pl, https://github. com/scbgfengchao/; Feng et al. 2017)  to remove the low-quality reads (i. e. , reads containing unknown/ambiguous base higher than 5%, more than 20% bases with quality scores less than 20) to obtain clean reads. We then assembled the cleaned reads de novo for each individual sample using Trinity 2. 6. 6  (Grabherr et al. 2011 ) with K-mer size of 32 and under default settings for other parameters. We used the longest isoform from each Trinity subcomponent to generate unigenes using our script (Trinity2Unigene. pl, https://github. com/scbgfengchao/; Feng et al.
2020 ), and we reduced redundant unigenes using CD-HIT-EST 4. 7 (with the parameter -c 0. 98)  (Fu et al. 2012). We identified coding regions using TransDecoder  (Haas et al. 2013 ) by performing BLAST search against known, annotated proteins in the UniProt and Protein Family (Pfam) databases with an E-value cutoff of 1. 0E-5. We used OrthoFinder 2. 3. 3  (Emms and Kelly 2015, 2019)  to classify the orthogroups of proteins from all the species of Oreocharis and outgroups (with the parameter -S diamond -og). We excluded the orthogroups with the following criteria  (Ran et al. 2018 ): 1) sequences are absent in at least one species; 2) the mean copy number of an orthogroup is more than five; and 3) the median copy number of an orthogroup is more than two. We identified homologs and orthologs from all the samples based on the approach of Yang and Smith (2014) (https://bitbucket.
org/yangya/phylogenomic_ dataset_construction, accessed on October 15, 2019). After excluding orthogroups based on the criteria above, we aligned the remaining orthogroups using MAFFT 6. 864b  (Katoh and Standley 2013) , inferred two rounds maximum likelihood phylogenies using RAxML 8. 2. 11  (Stamatakis 2014) , and pruned terminal branches that were longer than 0. 2 and more than 10 times longer than its sister. We also pruned branches longer than 0. 5. According to definition of  Yang and Smith (2014)  and  Yang et al. (2015) , the remaining trees comprised potential homologs. Using these trees, we obtained 1:1 orthologs (i. e. , the orthologs represented in single copy for each species) by pruning the trees of homologs using the rooted ingroups method (i. e. , prune by extracting ingroup clades and then cut paralogs from root to tip)  (Yang and Smith 2014) , allowing for missing species (i.
To avoid arbitrary topologies detrimentally influencing the species tree, branches arising from nodes with less than 10% UFBS support value were collapsed on each tree  (Zhang et al. 2017)  using the "nw_ed" function of Newick utilities  (Junier and Zdobnov 2010). We input the resulting gene tree files with collapsed nodes into ASTRAL with the "-t 3" option to calculate the species tree with local posterior probability support  (Sayyari and Mirarab 2016). We measured the differences among trees from different gene data sets based on the average Robinson-Foulds (RF) distance  (Robinson and Foulds 1981)  with the "-rf_all" function in IQ-TREE. We performed measurements for trees resulting from the supermatrix approach, SVDquartets method, and ASTRAL approach independently. We used a custom R script (https://figshare.
Quartet Sampling The quartet sampling (QS) method is a quartet-based evaluation system that offers an alternative measure for branch support  (Pease et al. 2018). Under the QS method, one taxon is randomly resampled from each of the four subsets at the focal branch, and then the likelihood values of all three possible phylogenies based on the aligned sequence data are evaluated. This method returns the quartet concordance (QC), quartet differential (QD), quartet informativeness (QI), and quartet fidelity (QF) to assess the confidence, consistency, informativeness of the internal nodes, and the reliability of each terminal branch  (Pease et al. 2018). We conducted the QS analysis using quartet_sampling. py (https://www. github. com/ fephyfofum/quartetsampling, accessed on May 10, 2020) with a maximum of 200 quartet replicates.
Evaluating incomplete lineage sorting Incomplete lineage sorting (ILS) is one of the most fundamental causes of discordance among gene trees. Among new, innovative algorithms for addressing discordance is MSCquartets, which is designed for inference of species trees and species networks under the multispecies coalescent (MSC) model of ILS as well as species tree hypothesis-testing  (Allman et al. 2021; Rhodes et al. 2021). Specifically, MSCquartets determined the number of the three possible quartet topologies for each set of four taxa among all gene trees. These are referred to as quartet count concordance factor (qcCF) and can be compared to generate P-values for judging the fit of gene trees to a species tree. All quartet concordance factors for all choices of four taxa can be viewed using a simplex plot to give a comprehensive view of discordance across the entire gene tree data set  (Allman et al.
2021). To test the degree of ILS in Oreocharis, we carried out MSCquartets analyses. We used the function quartetTreeTest with "T1 model" implemented in the MSCquartets package under different rejection levels with = 0. 01, 0. 001, 1e-04, and 1e-06. For each result, we generated a simplex plot for all four-taxon subsets with color-coded symbols that show either that particular qcCFs are consistent with the MSC of ILS on a species tree at the specified level of statistical significance or that some mechanism other than ILS most likely explains discordance (e. g. , substantial gene tree inference error, hybridization, or gene duplication and loss). When ILS is present, symbols may lie in closer proximity to the centroid indicating substantial ILS or closer to the vertex indicating little ILS.
Estimation of Divergence Times There are presently no known fossils of the Gesneriaceae  (Roalson and Roberts 2016;  no occurrence in Paleobiology Database, accessed on March 10, 2020). Therefore, we used secondary calibrations based on  Roalson and Roberts (2016)  to calibrate a molecular clock and divergence times of lineages within Oreocharis. Roalson and Roberts (2016)  used 12 external fossil calibration points and geologic ages to estimate divergence times among lineages represented by 768 species of Gesneriaceae and found that the crown age of Oreocharis was 12. 33 Ma with a 95% highest posterior density (HPD) between 7. 92 and 16. 60 Ma. We applied the 95% HPD of age estimates from that study as secondary calibration points using uniform prior distributions and used the concatenated data set with 574 genes for the dating analyses.
Temporal Dynamic of Diversification We carried out several different diversification analyses to test the temporal dynamic of diversification. These comprised lineages-through-time (LTT) in the Rpackage APE 5. 3 (Paradis and Schliep 2019), BAMM 2. 5. 0  (Rabosky et al. 2013)  to estimate speciation and extinction rates through time and across clade, RPANDA 1. 2  (Morlon et al. 2016 ) to cross-validate results from BAMM, and the compound Poisson process on Mass-Extinction Times (CoMET,  May et al. 2016 ) model as implemented in the R-package TESS 2. 1  (Höhna et al. 2016)  to test for abrupt changes in diversification rates. To visualize the temporal dynamic of diversification in Oreocharis, we used the LTT function in APE 5. 3 to plot 7,500 trees (after 25% burn-in) from the dating analysis, as well as the MCC tree.
2014)  to process the output of the MCMC, analyze the posterior distribution of rates allowing the estimation of the diversification rates through time and the best configuration of the diversification rate shifts. We discarded the first 25% of the sampled data as burn-in. We used the R-package RPANDA to cross-validate the BAMM inferences because there are debates on the reliability of rate estimates produced by BAMM  (Moore et al. 2016; Meyer and Wiens 2017, but see Rabosky et al. 2017; Rabosky 2018). Within RPANDA, we performed time-dependent diversification models as implemented by  Morlon et al. (2016)  with two constant-rate and four time-dependent models to detect whether rates varied through time and how they varied as follows: 1) speciation is constant and extinction is null (Bcst); 2) both speciation and extinction are constant (BcstDcst); 3) speciation varies through time and extinction is null (BTimeVar); 4) speciation varies through time and extinction is constant (BTimeVarDcst); 5) speciation is constant and extinction varies through time (BcstDTimeVar); and 6) speciation and extinction are both varying through time (BTimeVarDTimeVar).
To accomplish this, we used the diversity-dependent diversification as implemented in the R-package DDD 4. 2  (Etienne and Haegeman 2012)  to infer the effect of diversity on speciation and extinction rates. Within DDD, we used the function "dd_ML" to fit five models 1) speciation declines linearly with diversity and no extinction (DDL), 2) speciation declines linearly with diversity and extinction (DDL+E), 3) speciation declines exponentially with diversity and extinction (DDX+E), 4) extinction increases linearly with diversity (DD+EL), and 5) extinction increases exponentially with diversity (DD+EX). The initial carrying capacity was set to the current species diversity, and the carrying capacity (K) is estimated according to the models and parameters. We tested whether distribution in the HDM influenced diversification rates using Binary State Speciation and Extinction (BiSSE) and Hidden State Speciation and Extinction (HiSSE) models in the R-packages diversitree 0.
Historical Biogeography To reconstruct the ancestral area of origin of Oreocharis, we inferred ancestral ranges and dispersal events across the phylogeny. Based on vegetation types and the species composition, we defined nine geographic regions representing the sampled distribution of Oreocharis according to the floristic regions of China proposed by  Huang et al. (2016)   (Yu et al. 2020). We used the DEC model without founder-event dispersal parameter (Matzke 2014), which appears to have both conceptual and statistical flaws  (Condamine et al. 2018; Ree and Sanmartín 2018). The maximum areas at each node were set to two based on the largest range of any extant species (Supplementary Appendix S2 available on Dryad).
Orthologs Filter and Phylogenetic Analyses Filtering of orthologs based on missing data. -When we allowed 75% missing taxa for each gene, we obtained 2,360 orthologous genes. After filtering orthologous genes that were not represented among the outgroup species or did not result in the outgroup species forming a clade in phylogenetic reconstruction, we obtained 1027 orthologous genes. For the filtered data sets of 5%, 25%, and 50% missing taxa for each gene, we obtained 33, 240, and 574 orthologous genes, respectively, and the final actual amounts of missing data (base pairs) for entire supermatrix ranged from 3. 5% to 40. 8% (Supplementary Appendix S3a available on Dryad). Final alignments for all four data sets are available in Supplementary Appendix S4 available on Dryad. Conflicts among species trees and gene trees.
-The average and pairwise RF distances among species trees are presented in Supplementary Appendix S3a available on Dryad. The ASTRAL tree inferred from the 574 gene trees with up to 50% missing taxa for each gene showed the least conflict among the 12 species trees, with the lowest average distance, 51. 2%, to other trees. Therefore, we used this tree for downstream analyses. The variance of RF distance among inferred species trees was much lower than that among inferred gene trees. No tree based on any one of the 574 genes was completely consistent with the overall species tree topology as inferred from the concatenated and coalescent analyses based on the data sets with varying levels of missing taxa for each gene (Supplementary Appendix S3b available on Dryad). Individual gene-tree topologies estimated with IQ-TREE received average UFBS values ranging from 38.
57% to 94. 63%. The distances for genes in tree space from the consensus species trees were not clearly correlated with the average UFBS, that is, the genes with lower average UFBS were not always further away from the species trees than those with higher average UFBS. Phylogeny of Oreocharis. -Node support was evaluated with either UFBS, local posterior probability (LPP), or bootstrap support (BS). The results showed maximal node support for Oreocharis in all phylogenomic analyses (Fig. 2 ; Supplementary Appendices S5-S7 available on Dryad). Two main clades of Oreocharis, here after called Clade West (W) and Clade East (E), were robustly recovered by all different methods of inference (Fig. 2 ; Supplementary Appendices S5-S7 available on Dryad). These two main clades are roughly separated by the Yungui Plateau (Supplementary Appendix S10 available on Dryad).
Several taxa had variable phylogenetic positions under different methods of inference, but all phylogenetic variance was within the two major clades, not between them (Supplementary Appendix S10 available on Dryad). There was a strong positive relationship between internal branch lengths and nodal support based on LPP (Supplementary Appendices S7 and S9 available on Dryad), and the conflicting nodes were always estimated with relative low node support, suggesting that conflicts occurred most often at short internodes. We chose the tree inferred with 574 genes data set and ASTRAL as the reference tree to discuss phylogenetic relationships within Oreocharis (Supplementary Appendix S7 available on Dryad.
Overall, the LTT plot showed rapid species diversification early in the history of Oreocharis, followed by a slowdown of diversification towards the present (Supplementary Appendix S10 available on Dryad). with high initial speciation rates followed by a steady decline of speciation toward the present and a constant extinction rate since the late Miocene (Fig. 5 ). The RPANDA analyses supported a diversification model with time-variable rates that outperformed constant-rate models (Table  1 ). The BAMM and RPANDA analyses are congruent on inferring a decrease in speciation rate through time and a constant (but low) extinction rate. The CoMET model in TESS also showed a slowdown of speciation rate with a constant extinction rate and no sudden extinction events (Supplementary Appendix S11 available on Dryad).
present (Fig. 5c, d ), and BAMM, RPANDA, and CoMET models corroborated this slowdown in diversification. Smoothing the temperature curve decreased the fit for the temperature-dependent model ( AICc = 13. 61; Table  1  and Supplementary Appendix S12 available on Dryad), showing that the support for the temperaturedependent model was likely not a false positive. The BiSSE analyses suggested that the best-fitting model was a model in which speciation and transition rates were variable and extinction remained equal between the species distributed in HDM and non-HDM (AICc = 709. 27; the second best model with all variable parameters had an AICc = 711. 49; AICc = 2. 22; Table  3 ). Under the best BiSSE model, we found that the diversification rates were significantly different between the HDM and non-HDM regions with 0.
32 versus 0. 06 events/lineage/Ma, respectively (Table  3 , Supplementary Appendix S13 available on Dryad). Moreover, the transition rate from HDM to non-HDM regions was 0. 23 events/lineage/Ma but could not be detected in the opposite direction. Notably, we recovered a significant difference in diversification rates between the HDM and non-HDM regions regardless of the constraint applied on the root state (Table  3 , Supplementary Appendix S14 available on Dryad). The HiSSE analyses revealed that the model including a hidden effect on diversification of state 1 (distributed in the HDM region) was strongly supported against the other SSE models (AICc = 685. 58 vs. AICc = 698. 22) for the second best estimated that the HDM species diversified about ten times faster than non-HDM species (0. 65 vs.
0. 065 events/lineage/Ma; Supplementary Appendix S15 available on Dryad), and also indicated that the diversification of HDM species was maybe influenced by other (unmeasured) traits, that is abiotic factors or intrinsic and/or extrinsic biotic factors. Randomization analyzes of the trait data set showed that the HiSSE-based results were robust to type-I error (Supplementary Appendix S16 available on Dryad), indicating that our inferences are unlikely to be biased.
Ancestral Areas of Origin The DEC-based biogeographic analysis revealed Central China and the Lingnan Mountains Region as the most likely ancestral area of origin of Oreocharis with maximal probability (Fig. 6 ; see Supplementary Appendix S17 available on Dryad for probability at each node). The Clade W most likely originated in Central China region, while the Clade E originated in the region of Lingnan Mountains, indicating a vicariant event early in the history of Oreocharis. The Clade W colonized the Hengduan Mountains ~10 Ma ago from Central China. At least six dispersal events with high probability (p >0. 70) are detected from Hengduan Mountains to other regions, but no other dispersal events into the Hengduan Mountains are detected from other regions, suggesting rare colonizations events, which is similar to the transition rate (q non-HDM→HDM ) estimated with the BiSSE analyses (Table  3 ).
Therefore, there is an incompatibility between the root ancestor as inferred with DEC (non-HDM region) and the inference of zero dispersal event out of non-HDM region as estimated with the SSE models. We also counted six vicariance events and seven dispersal events in the Clade E, and 16 vicariance events and 15 dispersal events in the Clade W (Fig. 6 ). A more detailed biogeographic history is available on Dryad (Supplementary Appendix S17 available on Dryad).
6 , Supplementary Appendix S1 available on Dryad), which is an upland boarding the HDM to the east. The DEC-based analyses estimated an allopatric speciation with the two sister lineages inheriting each half of the ancestral range (Clade W in Central China and Clade E in Lingnan Mountains). This suggests that an early vicariance, likely related to mountain building, climate, or a combination of the two, was a driver of the initial lineage divergence within Oreocharis. Central, southern and south-eastern China have been characterized by relative tectonic stability since the late Cenozoic  (Hsü 1983; López-Pujol et al. 2011 ). If mountain building did directly trigger the initial diversification, we would expect different diversification rates between the western lineage and the eastern lineage due to the different mountain building history.
Finally, clades including HDM species likely originated by vicariance (six times, Fig. 6 ; Supplementary Appendix S17 available on Dryad), and the BiSSE and HiSSE models may be inappropriate in this situation. The onset and intensification of Asian monsoons since the Miocene has been suggested to have impacted species diversification of many plant lineages within East Asia  (Wang et al. 2012; Yao et al. 2016; Kong et al. 2017; Lin et al. 2019; Ding et al. 2020; Li et al. 2021 ), but there have been few attempts to directly test for a relationship between diversification rate and Asian monsoons (but see  Kong et al. 2017). Using a paleoenvironment-dependent diversification model, we found that the diversification rate within Oreocharis is highly correlated with the strength of the East Asian monsoons.
oup. com/sysbio/advance-article/doi/10. 1093/sysbio/syab068/6352738 by guest on 05 October 2021 low statistical support for shallow nodes  (Möller et al. 2011; Chen et al. 2014 ). Furthermore, with such a small number of loci it yields very little statistical power and is inadequate to deal with inconsistencies related to ILS and gene flow. This study provides the most comprehensive phylogeny of Oreocharis to date based on a transcriptomic data set. Our analyses indicated several well-supported and relatively consistent subclades, and both concatenation and coalescent-based approaches generated a species tree with high statistical support for nodes. Nonetheless, our results show widespread conflict between individual gene trees and the species tree, especially for the placements of short branches (Supplementary Appendices S3b and S8 available on Dryad).
Harris et al. 2013)H, Lingnan Mountains Region; and I, South Sea Region. The presence/absence for each species in each region is shown in Supplementary Appendix S2 available on Dryad. We performed ancestral range estimation on the MCC tree (without outgroups,Harris et al. 2013), using the Dispersal-Extinction-Cladogenesis (DEC, Ree and Smith 2008) model of range evolution as implemented in RASP 20200510.
filtered, clean paired-end reads with Q30 values higher than 90% representing 123 populations of Oreocharis. These paired-end reads were assembled into 116,833 (O. obtusidentata) to 327,758 (O. acutiloba) transcripts per transcriptome. The length of N50 for the transcripts ranged from 369 (O. acutiloba) to 1,798 bp (O. pinnatilobata), and the total number of genes with matches in Pfam ranged from 21,616 (O. shweliensis) to 48,143 (O. acutiloba) with an average length of 861 bp. Additional sequence information is summarized in Supplementary Appendix S2 available on Dryad.
). The topology showed a grade of O. leiophylla + O. sinensis and O. uniflora as subtending to all other Oreocharis in Clade E. Most species within Clade E were distributed in Central to East China, except O. parvifolia and O. fargesii, both of which occurred in the adjacent region of Southwestern China (Supplementary Appendix S1 available on Dryad). All the species within Clade W were distributed in the mountains of Southwest China. Oreocharis longituba, O. caobangensis, O. stenosiphon, O. villosa, and O. brachypodus formed a basal grade within Clade W, while other species of Oreocharis in the clade formed two monophyletic groups, which contained all sampled species from the Hengduan Mountains.
ACKNOWLEDGMENTS We would like to thank  Dr. Bryan Carstens ,  Dr. Richard Ree ,  Dr. Yaowu Xing , and two anonymous reviewers for their comments, which helped improve the overall quality of the manuscript. We thank  Dr. Wade R. Roberts  ( University of Arkansas ) for providing the results of divergence time analyses of Gesneriaceae,  Dr. Siavash Mirarab  ( University of California, San Diego ) for comments on species tree reconstruction,  Dr. John Rhodes  ( University of Alaska Fairbanks ) for very detail discussion and comments on MSCquartets analysis.
Harris et al. 2013)H, Lingnan Mountains Region; and I, South Sea Region. The presence/absence for each species in each region is shown in Supplementary Appendix S2 available on Dryad. We performed ancestral range estimation on the MCC tree (without outgroups,Harris et al. 2013), using the Dispersal-Extinction-Cladogenesis (DEC, Ree and Smith 2008) model of range evolution as implemented in RASP 20200510 : A, Hengduan Mountains Region; B, Yunnan Plateau Region; C, Yunnan, Myanmar and Thailand Border Region; D, Central China Region; E, Yunnan, Guizhou and Guangxi Region; F, Beibu Gulf Region; G,.