dataesr/scientific-paragraphs-categorization huggingface dataset:
1. 3  40. Five replicates, each testing for one to eight clusters (K) and 10-fold cross-validation was performed. The software fineRADstructure v. 0. 3. 1  41  was used to infer genomic variation among individuals by clustering them according to similarity of their RAD haplotypes in a coancestry matrix. Haplotypes were exported using "populations" in Stacks (see above), additionally filtering for a minor allele frequency above 0. 02 and a mean log-likelihood greater than -10. 0. The script "Stacks2fineRAD. py" 41 was used to convert haplotypes of loci with maximally 20 variable sites to the fineRADstructure input format, resulting in a set of haplotypes for 65,912 RAD loci (Supplementary Fig. 1 ). The coancestry matrix was inferred using RADpainter, and the MCMC clustering algorithm in fineSTRUCTURE v.
4  110  was used to infer clusters of shared ancestry, setting the number of burnin iterations to 100,000, the sample iterations to 100,000, and the thinning interval to 1000. Finally, to reflect the relationships within the coancestry matrix, the inferred clusters were arranged according to a tree inferred with fineSTRUCTURE, using 100,000 hill-climbing iterations and allowing all possible tree comparisons. Detecting contemporary hybridization. Based on the results of morphological and genomic PCA (Fig. 1 , Supplementary Figs. 3, 5 ), analyses with ADMIXTURE (Supplementary Fig. 7 ) and fineRADstructure (Supplementary Fig. 8 ), and previous reports  18, 111  , we suspected that our dataset included recent hybrids between four species pairs: A. marmorata and A. megastoma, A. marmorata and A.
Detecting past introgression. As our analyses of contemporary hybridization identified several backcrossed individuals, we assumed that, despite their old divergence times, tropical eel species may have remained connected by continuous or episodic gene flow. We thus tested for signals of past introgression among the seven species using multiple complementary approaches. Our first approach was motivated by the observation that A. interioris clustered with A. marmorata and A. luzonensis in the Bayesian species-tree analyses with SNAPP, but appeared as the sister to A. bicolor and A. obscura in the maximum-likelihood phylogeny generated with IQ-TREE, with strong support in both cases. Assuming that this discordance might have resulted from past introgression  112  , we thus applied genealogy interrogation  48  to the dataset used for IQ-TREE analyses, composed of 1360 RAD loci with a total length of 393,708 bp.
We expected that the true speciestree topology would be supported by the largest number of loci, and that introgression would, if present, increase the support for one of the alternative hypotheses relative to the other  52, 113. As a second approach for the detection of past introgression, we calculated Patterson's D statistic  49, 50  from biallelic SNPs included in the RAD-sequencing derived dataset of 619,353 SNPs (Supplementary Table  1 ). As this statistic is applicable to quartets of species in which one is the outgroup to all others and two species (labeled P1 and P2) are sister taxa, we calculated the D statistic separately for all species quartets compatible with the species tree inferred through genealogy interrogation. In this species tree, A. mossambica is the sister to all other species and A.
marmorata, A. megastoma, and A. obscura, in combination with the available reference genome assembly for A. anguilla 57 , allowed us to calculate D statistics for this species quartet from a fully genomic dataset. To this end, WGS reads of the three species were mapped against the A. anguilla reference assembly using BWA MEM, and sorted and indexed using SAMtools. Duplicates were marked using Picard tools v. 2. 6. 0 (http://broadinstitute. github. io/picard/), and indels were realigned using GATK v. 3. 4. 64  115. Per-species mean read coverage (71. 31×, 64. 80×, and 48. 97× for A. marmorata, A. megastoma, and A. obscura, respectively) was calculated with bedtools v. 2. 26. 0  116. SNP calling was performed using SAMtools' "mpileup" command, requiring a minimum mapping quality (MQ) of 30 and a base quality (BQ) greater than 30, before extracting the consensus sequence using BCFtools v.
1. 6. The consensus sequences were converted to FASTQ format via SAMtools' "vcfutils" script for bases with a read depth (DP) between 15 and 140, and subsequently used to calculate the genome-wide D statistic with A. obscura as P1, A. marmorata as P2, A. megastoma as P3, and A. anguilla as the outgroup. The dataset of 619,353 RAD-sequencing-derived SNPs (Supplementary Table  1 ) was further used to calculated the f 4 statistic 51 as a separate measure of introgression signals, for the same species quartets as the D statistic. The f 4 statistic is based on allele-frequency differences between the species pair formed by P1 and P2 and the species pair formed by P3 and the outgroup (as the f 4 statistic does not assume a rooted topology, P3 and the outgroup form a pair when P1 and P2 are monophyletic), and like the D statistic, the f 4 statistic is expected to be zero in the absence of introgression.
Acknowledgements Funding for this study was provided by the  Austrian Science Fund (FWF , project  P28381-B29  to  R. Schabetsberger ) and the  Norwegian Research Council  ( FRIPRO  project  275869  to M. M. ). We thank  Anthony Acou ,  David Boseto ,  Donna Kalfatak ,  Rilloy Leaana ,  Finn Økland ,  Christine Pöllabauer ,  Alexander Scheck ,  Ursula Sichrowsky , and  Meelis Tambets  for assistance with field work,  Franz Gassner  for help with data analysis,  Ian Goodhead  for his support in the laboratory, and  Milan Malinsky  for advice on wholegenome alignment. We further thank  Olaf L. F. Weyl  and acknowledge the  DST/NRF South African Research Chair in Inland Fisheries and Freshwater Ecology  (Grant No. 110507 ) and the  NRF-SAIAB Collections Platform  for the provision of genetic tissue samples.
Data availability The raw RADseq data are deposited on the NCBI SRA database with project number PRJNA590038. Genome assemblies and WGS reads for A. marmorata, A. megastoma, and A. obscura are deposited on ENA with project number PRJEB32187. Haplotype files, alignment files, SNP datasets in VCF format, and input and output of phylogenetic analyses are available from the associated Dryad repository (https://doi. org/10. 5061/dryad. ncjsxksr1). Previously available datasets used in this study include the NCBI accessions CM002536, NC_006531, GCA_000002035. 4, GCA_000695075, GCA_000470695, NM_001020485, and DQ649453. The source data underlying Figs. 1b, c, 3c, d , and Supplementary Figs. 3-5, 7, and 15-18 are provided as a Source Data file.
Acknowledgments Republication of this study in the present volume was supported by the  Index of Medieval Art  ( Princeton University ) and The Medieval Iberian Treasury in Context: Collections, Connections, and Representations on the  Peninsula and Beyond (National Research Challenge Grant ,  Spanish Ministry of Science, Innovation, and Universities , aei/ feder,  RTI2018-098615-B-I00 ,  2019-2022 ,  PI Therese Martin ).
Tropical forests are being deforested worldwide, and the remaining fragments are suffering from biomass and biodiversity erosion. Quantifying this erosion is challenging because ground data on tropical biodiversity and biomass are often sparse. Here, we use an unprecedented dataset of 1,819 field surveys covering the entire Atlantic Forest biodiversity hotspot. We show that 83-85% of the surveys presented losses in forest biomass and tree species richness, functional traits and conservation value. On average, forest fragments had 25-32% less biomass, 23-31% fewer species, and 33%, 36% and 42% fewer individuals of late-successional, large-seeded and endemic species, respectively. Biodiversity and biomass erosion were both lower inside strictly protected conservation units, particularly in large ones.
The data set covers the entire range of environmental conditions, landscape contexts, and disturbance histories of the Atlantic Forest (Supplementary Fig. 2 , Supplementary Table  2 ). It also contains information on multiple species properties, including plant functional traits (i. e. , wood density, maximum height, seed mass), ecological groups (or successional status, e. g. pioneer) and their conservation value (i. e. , threat status and endemism level), which enable to assess human-induced impacts on community composition sensu lato. Using this unprecedented data set, we quantified forest degradation impacts on the aboveground biomass stocks, tree species richness, and multiple species properties. More specifically, we assess the extent and magnitude of those impacts by asking: (i) how pervasive negative impacts are across this biodiversity hotspot? (ii) how much these biodiversity and biomass losses represent compared to low-disturbance Atlantic Forests? And (iii) can protected areas mitigate those losses? Next, we explore the implications of our results to the conservation of what's left of this biodiversity hotspot by (iv) projecting forest degradation impacts to the remaining Atlantic Forest area to estimate the total amount of carbon lost.
Our approach based on CWMs of species properties, does not allow us to distinguish which groups of species are causing this homogenization, but we can draw some propositions. Widespread tree species in the Atlantic Forest are often small-seeded pioneers that proliferate at forest edges and small fragments  19, 31, 32. However, we found only a weak correlation between the loses of endemism level and of seed mass and ecological groups (r= 0. 14 and 0. 17, respectively; Supplementary Fig. 12 ). This suggests that not all species proliferating in disturbed Atlantic Forests are small-seeded pioneers. In addition, because exotics represented only 0. 3% of the trees in our data set, the proliferation of widespread native species is the most probable cause of the Atlantic Forest homogenization. Wood density and maximum tree height, both related to carbon storage potential, presented the smallest changes.
In this context, selecting the appropriate species to maximize restoration outcomes becomes a more efficient strategy  39. To help the species selection for restoration we provide a list of 242 common trees species (Supplementary Data 2) that have a high potential to increase the carbon storage and the conservation value of disturbed Atlantic Forest fragments (see Supplementary Methods for details). The re-introduction of these species could mitigate effects of local extirpation in the short term, while the connectivity of the landscape can be restored and allow the frugivore fauna to return in the long run. Seedlings from hardwood, tall or endemic species may be less available from nurseries and thus increase restoration costs  39, 40. So, local seed production cooperatives and nurseries able to propagate these species will be essential for the support of the Atlantic Forest restoration  39  , particularly regarding the re-introduction of large-seeded Atlantic Forest endemics.
Forest survey and species data We obtained 1,819 tree community surveys of natural Atlantic Forests available from the Neotropical Tree Community database (TreeCo)  23. We considered all types of forest formations in Brazil, Paraguay and Argentina (Supplementary Fig. 1 , Supplementary Data 1), with the exception of dry deciduous and early secondary forests. Moreover, we considered only surveys including trees with diameter at breast height (dbh) ≥3, ≥5 and ≥10 cm and using plots or the point-centred quarter method. Surveys represented a total effort of 1. 45 million trees and 1,238 hectares (Supplementary Table  1 ) and they covered a wide spectrum of environmental conditions and patch/landscape metrics (Supplementary Table  2 ). For each survey we extracted the tree density (trees ha -1 ), basal area (m 2 ha -1 ), species richness, sampling method (plots and point-centred quarter method), arrangement of sample units (contiguous and systematic/random), sampled area (ha), dbh inclusion criteria (cm) and geographical coordinates.
We verified the precision of the geographical coordinates provided to ensure that they corresponded to the forest fragment studied, otherwise the survey was discarded. Whenever needed, plot coordinates were corrected, based on maps or the site description provided in the study, including internet searches of any valuable information on the fragment, farm or park location. We compiled data on species abundances and basal area for surveys with a sampling effort ≥0. 1 ha and with species data presented in an extractable format (see Supplementary Methods). For all surveys providing information on voucher specimens associated with morpho-species, we checked for identification updates performed by taxonomists using the speciesLink network (http://splink. cria. org. br). Although only about one-third of the surveys provided vouchers, this effort improved the taxonomic resolution of around 10% of our records.
We also completed missing information on ecological groups for typical pioneer Neotropical genera (e. g. , Cecropia, Trema, Vernonanthura). We computed community weighted means (CWM) for the species properties in order to summarize the community composition sensu lato of each survey. For continuous species properties, the CWM was simply the average of each property weighted by the total number of individuals in the community. For wood density and maximum height, we used total basal area instead of the number of individuals of each species to calculate the CWM. For wood density, CWM was obtained after removing palms, palmoids, cacti, and tree ferns. For maximum height, we removed shrubs prior to the calculation of CWM. For seed mass, we removed tree ferns prior to the calculation of CWM. To compute the CWMs for the categorical properties, we treated them as ordinal categorical data: Pioneer < Early secondary < Late secondary < Climax for ecological groups; Not threatened/Not evaluated/Least concern < Data deficient/Near threatened < Vulnerable < Endangered < Critically endangered for extinction level; and Exotic/Naturalized < Not endemic < Northern/Eastern/Southern South-America < Regional endemic < Local endemic for the endemism level.
Site descriptors For each survey we obtained climatic (e. g. , mean air temperature and rainfall) and topographic information (i. e. , altitude, slope declivity and aspect) from different sources (see Supplementary Methods). We also obtained soil classes from the original surveys, which were checked for their consistency using soil maps at state and national scales. Missing data and inconsistencies between sources were double-checked to assure soil data quality and homogeneity. We used soil classes to infer average soil properties for plant growth by cross-referencing them with a database of physical and chemical soil properties (see Supplementary Methods). We used forest cover maps (30 m resolution)  1  to extract 4×4 km landscapes centred on the survey coordinates and we used a 70% canopy closure threshold to classify maps into forest or non-forest pixels.
Thus, these coarse classes are the best information available to allow analysis across the Atlantic Forest. We assigned each survey a biogeographical region  54. To avoid an excessive subdivision of regions, we reassigned Atlantic Forest surveys mapped as Cerrado, Caatinga, Campo Rupestre enclaves, Atlantic Coast Restingas and Southern Atlantic Mangroves to the closest region (Supplementary Fig. 1 ). We included the Uruguayan biogeographical region, representing the forests in the transition to the pampas region of South Brazil as part of the Atlantic Forest  21, 53. The proportion of each region in our sample was similar to their contribution to the remaining Atlantic Forest area with few exceptions (Serra do Mar: 29% in the data set and 21% of remaining area, Araucaria: 24% and 13%, Alto Paraná: 21% and 31%, Bahia Inland: 8% and 11%, Bahia Coastal: 6% and 7%, Uruguay: 5% and 3%, Northeast: 5% and 4%, Atlantic Dry: 2% and 10%).
Simulating strategies of forest restoration We used the regression models fitted to biodiversity and biomass data to simulated two simple scenarios of forest restoration. The 'fragment restoration' scenario assumes no increase in landscape forest cover and aims at restoring forest disturbance levels to 50% of the lower level observed for the Atlantic Forest. This is realistic target, because restoring and maintaining disturbance levels below this threshold, although possible, would be too expensive. The 'landscape restoration' scenario mimics restoration activities around existing fragments, increasing fragment size and thus landscape integrity (i. e. , greater forest cover) and connectivity (i. e. , smaller fragment distance). This scenario implicitly assumes that restoration activities should focus on the restoration around existing forest fragments, which would increase the 'area' of restoration and thus maximize the success of the restoration project while minimizing its costs  64.
Acknowledgments We thank  Ary T. Oliveira-Filho ,  Danilo Mori ,  Markus Gastauer ,  Melina Melito ,  Natália Targhetta ,  Geison Castro ,  Luiz F. S. Magnago  and  Carolina Bello  for their great help in supported by grants  99/08515-0, 99/09635-0  and  04/04820-3  ( FAPESP)  and grant  DE-FC26-01NT411151  ( US Department of Energy ).
Results and discussion 2. 1. SOLiD sequencing, de novo assembly, transcript annotation and SSR mining RNA-Seq was performed for cDNA libraries from the mRNA of mango fruit cultivar Chaunsa White. Raw reads were ltered and assembled into transcripts for annotation. Aer ltering raw reads, a total of 2 000 000 clean reads were generated. These reads were assembled into 12 876 redundant transcripts and 2674 non-redundant transcripts. The non-redundant transcripts showed a GC content of 50%. BLASTx database annotation of the non-redundant transcripts characterized 4169 predicted, 3807 characterized, 3217 hypothetical proteins and uncharacterized proteins (Fig. 1a ). BLASTx database annotation also revealed a maximum number of hits was from Citrus sinensis and Citrus clementine followed by Glycine max, Vitis vinifera and Jatropha curcas with more than 85% sequence similarity (Fig.
1b ). The transcripts were assigned with Enzyme Commission numbers to characterize their role in biological processes. Pathway analysis using the KEGG database (https:// www. genome. jp/kegg/pathway. html) represented a total of 80 metabolic pathways in Chaunsa White mango fruit. Several transcripts were annotated into pathways including glycolysis, Krebs cycle, pentose phosphate pathway, fructose, and mannose metabolism and oxidative phosphorylation. A total of 71% of non-redundant transcripts were annotated by BlastKOALA to various biological processes such as genetic information, cellular processes, environmental information processing, energy metabolism, carbohydrate metabolism, amino acid metabolism and enzyme families (Fig. 1c ). Enzyme class distribution indicated that the highest number of transcripts in Chaunsa mango fruit encodes for aldolases followed by transferases, synthases, oxidases and dehydrogenases (Fig.
RNA-Seq data processing, de novo assembly and annotation RNA-Seq was carried out to characterize the mango fruit transcriptome and to identify the transcripts conferring stress resistance in mango fruit. The 5500 SOLiD™ sequencer generated raw data in XSQ format. 67 The XSQ format le was splitted and converted to csFASTA and Qual format les using SOLiD™ LifeScope soware XSQ tool. Low-quality raw reads were removed and ltered by SOLiD_preprocess_lter v2. pl Perl script. 68 The pre-processing of raw reads generated high-quality clean reads in color-space FASTq format. More than 2 million ltered reads were then assembled by SATRAP (SOLiD Assembler TRAnslation Program) de novo assembler,  69  by using k-mers set of 31, 29, 27 and 25. SATRAP is a RNA-Seq de novo assembly pipeline that integrates the short read assemblers, Oases and Velvet, for de novo assembly and translation of colorspace sequences into a base-space format and nally, into accurate transcripts.
Non-redundant transcripts were extracted by CD-HIT-EST program 70 at a 90% similarity threshold. These transcripts were submitted for alignment and comparison using BLASTx with a signicant E-value threshold #10 À5 through DIAMOND program,  71  to protein databases including the non-redundant protein database (https://blast. ncbi. nlm. nih. gov/), SWISS-PROT (https://web. expasy. org/), Kyoto Encyclopedia of Genes and Genomes (KEGG) database (https://www. genome. jp/ kegg/) and BlastKOALA tool (https://www. kegg. jp/blastkoala/) for classication and annotation of transcripts into biological terms such as molecular function, biological processes, and cellular components. 72 GMATA tool  73  was used for the identication of the most abundant monomer and dimer motifs in transcripts. RSEM soware  74  was used for abundance estimation of gene transcripts based on their expected counts in the high throughput short single-end read based RNA-Seq transcripts.
The pathways were identied using the SOLiD sequencing data based on the candidate gene name. Each transcript sequence was conrmed using the Basic Local Alignment Search Tool (BLAST) program in the National Center for Biotechnology Information (NCBI) GenBank database (https://blast. ncbi. nlm. nih. gov/Blast). Transcript IDs and their functions were further annotated by querying the mango RNA-Seq Database of mango exocarp genes including 107 744 annotated unigenes (http:// bioinfo. bti. cornell. edu/cgi-bin/mango/index. cgi). 8 Primers for quantitative real-time PCR analysis were designed using transcripts from an available public database mango gene transcript. 8 ene-specic primers for quantitative real-time PCR were designed by using the Primer-BLAST tool (https:// www. ncbi. nlm. nih.
Transcriptional level analysis by reverse transcriptionquantitative real-time PCR (RT-qPCR) RT-qPCR experiment was performed in three biological replicates per stage to calculate the expression level of each transcript. High-quality RNA was extracted from the different tissues of mango and cDNA synthesis for quantitative PCR was carried out using the Revert Aid First Strand cDNA synthesis kit   The IC 50 values for DPPH radical scavenging assay were analyzed by using the EZ-t enzyme kinetics soware program (Perrella Scientic Inc. Amherst, MA, USA) and the data obtained from in vitro experiments were expressed as mean AE standard error.
Statistical analysis The qRT_PCR was performed using three biological replicates per stage, each consisting of 10 mesocarp discs. For real-time quantitative PCR results, statistical analysis of the whole data was performed by a two-way ANOVA test with one discriminating factor being mango fruit developmental stage. For each gene at a particular developmental stage, the highest expression at a particular mango fruit development stage was considered as 1 for normalization. The normalized gene expression data were used for graphical presentation (Fig. 2a  and b ). The values were expressed as means AE standard deviation. The data acquired from GC-MS was subjected to functional analysis and metabolic pathways annotation by an online available MetaboAnalyst tool was used. 77 C-MS chromatograms supporting the results are all provided in ESI Table  S1.
Results and discussion Observations of growth-lifespan trade-offs globally. We here compile and analyse tree-ring datasets including 110 different species from the tropics to high latitudes to assess the existence of growth-lifespan trade-offs (see "Methods"). We find that taxa with fast early growth rates have short maximum lifespans and vice versa (Fig. 1a ), confirming a widely known trade-off between these traits across species  13, 15. Relationships at the tree level, within species, show remarkably similar relationships between early growth and lifespan (Fig. 1b-d ). For example, in Picea mariana from Quebec (Fig. 1b ), fast early growth strongly selects against trees reaching old age, while the oldest ages within this dataset are overwhelmingly from trees that grew slowly when they were young (Supplementary Figure  3 ).