dataesr/scientific-paragraphs-categorization huggingface dataset:
Quality control Individual records in the RPD were compiled either by the original authors or from published and open-access material by specialists in the collection and interpretation of charcoal records. Records that were obtained from published and open-access material were cross-checked against publications or with the original authors of those publications whenever possible. Null values for metadata fields were identified  during the initial checking procedure, and checks were made with the data contributors to determine whether these genuinely corresponded to missing information. In the database, null values are reserved for fields where the required information is not applicable, for example water depth for terrestrial sites or laboratory sample numbers for correlative dates. We distinguish fields where information could be available but was never recorded or has subsequently been lost (represented by -999999) and fields where we were unable to obtain this information but it could be included in subsequent updates of the database (represented by -777777).
We also distinguish fields where specific metadata are not applicable (represented by -888888), for example basin size for a marine core or water depth for a terrestrial small hollow. Prior to entry in the database, the records were automatically checked using specially designed database scripts (in R) to ensure that the entries to individual fields were in the format expected (e. g. text, decimal numeric, positive integers) or were selected from the pre-defined lists provided for specific fields. Checks were also performed to find duplicated rows (e. g. duplicated sampling depths within the same entity).
Overview of database contents This first version of the RPD contains 1676 individual charcoal records from 1480 sites worldwide. This represents a 128 % increase compared to the number of records in ver-sion 3 of the Global Charcoal Database (GCDv3;  Marlon et al. , 2016; 736 records) , a 79 % increase compared to version 4  (Blarquez, 2018; 935 records) , and a 36 % increase compared to the online version of the GCD (1232 records). The RPD includes 840 records that are not available in any version of the GCD and provides updated or corrected information for a further 485 records that were included in the GCD. Raw data are available for 14 % of the entities and concentration for 67 % of the entities; influx based on the original age models is given for 16 % of the entities. The original age models for 67 (4 %) of the records included in the RPD were derived solely by layer counting, U-Th or Pb dates, or isotopic correlation and therefore are already expressed in calendar ages.
, 2018). The temporal coverage of the records is excellent for the interval starting 22 000 years ago, with 774 records with a minimum resolution of 10 years for the past 2000 years, 1335 records with a minimum resolution of 500 years for the past 12 000 years, and 1382 records with a minimum resolution of 1000 years for the past 22 000 years (Fig. 3 ). There are fewer records for earlier intervals. Nevertheless, there are 70 records that provide evidence for the interval of the last glacial period before the Last Glacial Maximum (22-115 ka) including the response of fire to rapid climate warming (Dansgaard-Oeschger events). Information about site type (Fig. 4a ) is included in the database because this could influence whether the charcoal is of local origin or represents a more regional palaeofire signal.
Conclusions The Reading Palaeofire Database (RPD) is an effort to improve the coverage of charcoal records that can be used to investigate palaeofire regimes. New age models have been developed for 48 % of the records to take account of recent improvements in radiocarbon calibration and age-modelling methods. In addition to expanded coverage and improved age models, considerable effort has been made to include metadata and quality control information to allow the selection of records appropriate to address specific questions and to document potential sources of uncertainty in the interpretation of the records. The first version of the RPD contains 1676 individual charcoal records (entities) from 1480 sites worldwide. Geographic coverage is best for the northern extratropics, but the coverage is good overall except for in semiarid and arid regions.
Temporal coverage is good for the past 2000 years, the Holocene, and back to the LGM, but there are a reasonable number of longer records. The database is publicly available, both as an SQL database and as. csv files. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/essd-14-1109-2022-supplement. Author contributions. SPH and RVD designed the database; RVD, DK, PL, and SPH were responsible for construction of the database; ALD advised on incorporation of data from the GCD and the standardisation of charcoal units; ECS, DG, DK, PL, YS, and LS provided updated age models; the other authors provided original data or metadata and quality control on individual records; SPH wrote the first draft of the paper, and all authors contributed to the final draft.
Competing interests. The contact author has declared that neither they nor their co-authors have any competing interests. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Acknowledgements. Roberto Villegas-Diaz, Sandy P. Harrison, Esmeralda Cruz-Silva, and Yicheng Shen acknowledge support from the ERC-funded project GC2. 0 (Global Change 2. 0: Unlocking the past for a clearer future, grant number 694481). Sandy P. Harrison, Paul Lincoln, Daniel Gallagher, David Kesner, and   (MOBJD313). We would like to thank our many colleagues from the PAGES Global Paleofire Working Group for their contributions to the construction of the Global Charcoal Database, which provided the starting point for the current compilation, and our colleagues from the Leverhulme Centre for Wildfires, Environment and Society for discussions on the use of palaeodata to reconstruct past fire regimes.
We thank Manfred Rösch for providing information on dating for several sites. We also thank Dan Gavin and Jack Williams for helpful reviews of the original manuscript. Financial support. This research has been supported by the Leverhulme Trust (grant no. RC-2018-023), the European Research Council (grant no. 694481), the German Research Foundation (grant no. FE-1096/6-1), the Swiss Government Excellence Postdoctoral Scholarships (grant no. FIRECO 2016. 0310), the National Science Centre of Poland (grant no. 2015/17/B/ST10/01656), the SCIEX Scholarship Fund (grant no. PSPB-013/2010), and the Estonian Research Council (grant no. MOBJD313). Review statement. This paper was edited by David Carlson and reviewed by Daniel Gavin and one anonymous referee.
is available in SQL format at https://doi. org/10. 17864/1947. 000345. The individual tables are also available as. csv files. The R package used to create the new age models is available at https:// github. com/special-uor/ageR (last access: 21 February 2022) and https://doi. org/10. 5281/zenodo. 4636716 (Villegas-Diaz et al. , 2021).
Butterflies are a diverse and charismatic insect group that are thought to have diversified via coevolution with plants and in response to dispersals following key geological events. These hypotheses have been poorly tested at the macroevolutionary scale because a comprehensive phylogenetic framework and datasets on global distributions and larval hosts of butterflies are lacking. We sequenced 391 genes from nearly 2,000 butterfly species to construct a new, phylogenomic tree of butterflies representing 92% of all genera and aggregated global distribution records and larval host datasets. We found that butterflies likely originated in what is now the Americas, ~100 Ma, shortly before the Cretaceous Thermal Maximum, then crossed Beringia and diversified in the Paleotropics. The ancestor of modern butterflies likely fed on Fabaceae, and most extant families were present before the K/Pg extinction.
Background Butterflies have captivated naturalists, scientists, and the general public for centuries, and they have played a central role in studies of speciation, community ecology, plant-insect interactions, mimicry, genetics, and conservation. Despite being the most studied group of insects, butterfly evolutionary history and drivers of their diversification are still poorly understood  1, 2. Due to over a century of efforts by amateur naturalists, butterflies are one of the few insect groups for which substantial trait data (e. g. , geographic and host plant information) exist to test diversification hypotheses. Until now, these data have largely been scattered across the literature, museum collections, and local databases. Butterflies are thought to have diversified in relation to multiple abiotic and biotic factors, including adaptations to novel climates and species interactions, with geographic history and caterpillar-host interactions playing a major role  3.
However, these have not been studied because a synthesis of associated traits and a robust phylogenetic framework at the taxonomic scale needed to examine their evolution has not been available. We sequenced 391 genes from nearly 2,000 butterfly species to construct a new, robust, phylogenomic tree of butterflies representing 92% of all genera, aggregated global distribution records, and assembled a comprehensive host association dataset. Using this tree, we infer the evolutionary timing, biogeographic history, and diversification patterns of butterflies. We address three long-standing questions related to butterfly evolution: 1) did butterflies originate in the northern (Laurasia) or southern (Gondwana) hemisphere?  4  , 2) what plants did the ancestor of butterflies feed on?  5  , and 3) were plants a major driver promoting butterfly diversification?  6.
Results and Discussion To elucidate patterns of global butterfly diversification in space and time, we used targeted exon capture  7  to assemble a dataset of 391 gene regions (161,166 nucleotides and 53,722 amino acids) from 2,244 butterfly species. The majority (1,914 specimens) of butterflies sampled were newly sequenced for this study, and represented all families, tribes, and 92% of recognized genera, with most samples coming from museum collections across the world (see Supplementary Materials). Phylogenomic trees inferred with nucleotides and amino acids were highly congruent, with strong support for the monophyly of all families and nearly all subfamilies with traditional branch support metrics (Table  S1 ), multispecies coalescence (MSC) analyses (Table  S1 ), and 4-cluster likelihood mapping (FcLM) (Table  S2 ).
Soon thereafter, the families Hesperiidae and Nymphalidae experienced dramatic increases in the number of speciation events in the East Palearctic (Fig. S9 ). This marks a period when East Palaearctic habitats changed from dense forest to forest-steppe, temperate grassland, and mixed forest biomes  21. These serve as primary habitats of the diverse modern butterfly groups -fritillaries, satyrs, and grassland skippers, which dominate this region today (Argynnini, Satyrini, and Hesperiinae, respectively). It is often argued that butterfly evolution is closely tied to the diversification of angiosperms  5, 6. Yet, few studies have examined the timing and pattern of butterflies and their hosts on a broad, macroevolutionary scale. To understand the temporal diversification of butterflies and plants, we compiled 31,456 butterfly host records from 186 books and databases (Table  S8 ).
We examined how butterfly lineages increased through time and compared these results with four recent studies that examined plant diversification dynamics. We found that butterfly diversification lagged far behind the origin of angiosperms (Fig. 2 ), corroborating prior studies  7, 22. Ancestral state estimation provided support for Fabaceae as the host plant of the most recent common ancestor of butterflies (Tables S9-S10, Fig. S10 ), a widely accepted hypothesis  5  that has lacked empirical support. The crown age of the most recent common ancestor of Fabaceae is thought to be ~98 Ma  23, 24  , largely coincident with the origin of butterflies. Although the vast majority of butterfly larvae in our dataset are herbivores, a small number also feed on detritus, lichens, or insects (Table  S8 ).
In the best-fitting model, speciation rates vary exponentially through time without extinction (92% of the trees, Akaike ω = 0. 549). This timedependent model is far better supported than the angiosperm-dependent models, the best of which was ranked fourth (Table  S12 ). We also ran the HiSSE model  30  as implemented in the R package hisse  31  to test for a potential impact of host-plant preference on diversification dynamics. In total, we compared 18 different models of HiSSE and BiSSE-like implementations, accounting for hidden states to alleviate recent concerns regarding the reliability of SSE models and the high incidence of false positive results  32. We found little or no indication that association with a specific plant group is linked to butterfly diversification, largely rejecting the hypothesis of hostplant driven diversification (Table  S13 , Data S2).
This finding supports the pattern reported by Ehrlich and Raven 6 that related butterflies feed on related plants. Finally, we used trait-dependent methods to examine whether diversification rates changed following switches between generalist and specialist, and host plant shifts (Table  S13 ). Our results demonstrate that -at least in analyses of Papilionoidea and Hesperiidae, where results were significant -specialists have lower rates of speciation (Fig. S12 ), contradicting the resource-use hypothesis. This study includes the most comprehensive taxonomic sampling of butterflies to date to understand their evolutionary history. We also compiled two comprehensive trait datasets (geography and host plants) that demonstrate the importance of assembling large trait databases to test key hypotheses.
Our study overturns some long-held assumptions of butterfly evolution and provides a framework for future studies of this model insect lineage. The consistency of results obtained using different approaches for each of our analyses suggests that our conclusions are robust. Assembling species-level datasets will be an important future goal to address finescale trends within particular butterfly clades. Our data support the hypothesis that butterflies originated in the Americas in the Late Cretaceous 100 million years after the origin of angiosperms, and that they first fed on legumes. They dispersed from the Americas to the East Palearctic likely across Beringia ~75 Ma before diversifying in the Paleotropics. They appear to have been minimally affected by the K/Pg mass extinction. While some evidence points to a Nearctic origin, the evidence for Nearctic versus Neotropical origin is not strong -we therefore only tentatively conclude that a Laurasian origin is possible.
Diversification rate analyses. We performed a Bayesian analysis of macroevolutionary mixtures using the program BAMM v. 1. 10. 4  58  to detect shifts in diversification rates between clades. The reversible-jump markov chain Monte Carlo was run for 50 million generations and sampled every 50,000 generations. Priors were estimated with the R package BAMMtools v. 2. 1. 6 59 using the command 'setBAMMpriors'. The tree was trimmed in Mesquite v. 3. 6  60  to remove all outgroups. Six analyses were performed using different priors for expected numbers of shifts (5, 10, 20, 30, 40,    and 50 shifts). Because BAMM has been criticized for incorrectly modeling rate shifts on extinct lineages (i. e. , extinct or non-sampled lineages inherit the ancestral diversification process and cannot experience subsequent diversification-rate shifts  61, 62  ), we conducted a lineage-specific birth-death shift analysis  63  in RevBayes  64.
We also utilized the R package castor v. 1. 6. 9  65  to infer deterministic lineage through time (dLTT), pulled speciation rate (PSR), and pulled diversification rate (PDR) plots for Papilionoidea and six of its families, excluding Hedylidae. Finally, we performed diversification analyses in TreePar v. 3. 3 66 and TESS v. 2. 1. 0 67 to determine relative likelihoods of models with different amounts of shifts in diversification and turnover rates. We conducted a series of diversification analyses to evaluate whether there is a correlation between butterflies and plants. For this, we used HiSSE (Hidden State Speciation and Extinction) and a BiSSE-like (Binary State Speciation and Extinction) implementation of HiSSE  30  in the R package hisse  31. We pruned outgroups from the tree (aa154 dated tree, Strategy A) and compared 20 HiSSE models and BiSSE-like implementations of HiSSE.
The BiSSE equivalent of HiSSE tests whether there are different diversification rates associated with the two host-plant use states. Other models were built in the HiSSE framework to test alternative combinations of presence or absence of hidden state and host-plant use associations while also considering different transition rate matrices, net turnover rates τi (speciation plus extinction: λi + μi), and extinction fractions εi (extinction divided by speciation: μi/λi) (Table  S17 ). We tested whether diversification rates were linked to feeding (A) as a larval specialist or generalist (Table  S18 ), (B) on Poales (Table  S19 ) in Papilionoidea, Hesperiidae, and Nymphalidae, (C) on Fabales (Table  S20 ) in Papilionoidea and Nymphalidae, (D) on Brassicales (Table  S21 ) in all butterflies and Pieridae, (E) on Fagales (Table  S22 ), (F) on Poaceae module (Table  S23 ), (G) on Fabaceae module (Table  S24 ), and (H) on Fabaceae in Eudaminae (Tables  S13, S24 ).
Biogeographic analyses. To assess the role of geography on diversification, we first aggregated data from multiple sources to create a global butterfly checklist for each country. Primary data sources included: 1) the Lepidoptera and other life forms database (http://ftp. funet. fi/index/Tree_of_life/insecta/ lepidoptera); 2) WikiSpecies (https://species. wikimedia. org); and 3) the type locality of each species or subspecies in our list of valid butterfly names, which was obtained from 1, above. This initial global checklist was vetted using published country checklists and the ButterflyNet Trait Database (https://butterflytraits. org). Trait data from ca. 100 comprehensive and country-specific field guides have been entered into this database, allowing us to generate species lists to crossvalidate checklists assembled  68.
We estimated the ancestral area of origin and geographic range evolution for butterflies using two approaches: the ML approach of the DECX model  69  as implemented in the C++ version  70, 71  , available at https://github. com/champost/DECX), and with the program BioGeoBEARS v. 1. 1. 2  20. We designated 14 biogeographic regions across the globe (Fig. S13 , Table  S25 ), determined which of these regions were occupied by each species in our tree, and developed a 14-state character matrix. DECX uses a time-calibrated tree, the modern distribution of each species for a set of geographic areas, and a time-stratified geographic model that is represented by connectivity matrices for specified time intervals spanning the evolutionary history of clade of interest  72. Because we could not estimate immigration and emigration rates in DECX, we also ran BioGeoBEARS with 7 and 8 areas (Figs.
S14, S15, Table  S25 ). BioGeoBEARS analyses could not be run with 14 states due to computational limitations due to the complexity of our dataset (2,248 tree tips). The 7 and 8 bioregions largely correspond to the biogeographic realms defined by Udvardy  73. In BioGeoBEARS, we implemented both Dispersal Extinction Cladogenesis (DEC) model  69, 74  and the Likelihood equivalent of the Dispersal-Vicariance approach (DIVALIKE)  75  models and different adjacency matrices (Data S4). Both approaches gave largely consistent results, regardless of the model and parameters used (Tables  S5, S26 ). We performed biogeographic stochastic mapping to examine in-situ speciation, immigration, and emigration between the 7-bioregions in BioGeoBEARS. We followed the protocol of Li et al. 76  with 1,000 simulations with the DEC model.
Larval host plant analyses. Larval host records were compiled from numerous sources (Table  S8 , Data S6). Given the size of our host datasets and the scale of our analyses, we chose to examine relationships between individual butterfly species and host families that are consumed by their larvae. Plant families are commonly adopted as the taxonomic rank used for examining the evolution of host use  77, 78. For each plant-feeding butterfly species in our tree, we quantified host-plant richness and phylogenetic distance using six different metrics implemented in the package picante v. 1. 8. 2  79. To calculate these metrics, we used the calibrated tree of seed plants from Smith and Brown  54. Because the number of host groups was too large for an ancestral state reconstruction (nearly 50 host-plant orders, ~200 plant families plus insect associations), we first reduced the number of host groups by using a network analysis.
The Beckett algorithm  80  , as implemented in the function 'computeModules' from the package bipartite 81 in R v. 3. 6. 2  82 , assigns plants and butterflies to modules and computes the modularity index, Q. By maximizing Q, the algorithm finds groups of butterflies and hosts that interact more with each other than with other taxa in the network. Thus, host-plants that are assigned to the same module tend to be used by the same butterflies. We found 13 modules for butterfly host associations in our module analysis (Table  S28 , Table  S17 ). We then conducted three larval host ancestral state reconstruction analyses using stochastic character mapping with SIMMAP in phytools v. 0. 7-70  83  using the 'make. simmap' command. We reconstructed the ancestral state of (A) generalist versus specialist feeding (2 states, Data S7), (B) plant, lichen, Hemiptera, or Hymenoptera as a food source (4 states, Data S8), and (C) plant module (13 states as described above, Data S9).
We examined the speciation rate of butterflies over time with maximum-likelihood birthdeath models to fit constant-rate (CST), time-dependent (TimeVar), and angiosperm-dependent (AngioVar) models of diversification using the R package RPANDA v. 1. 9  84. These models jointly tested whether the diversification of angiosperms could have fostered the diversification of butterflies in a single statistical framework. We conducted a series of diversification analyses to evaluate whether there is a correlation between butterflies and plants. For this, we used HiSSE (Hidden State Speciation and Extinction) and a BiSSE-like (Binary State Speciation and Extinction) implementation of HiSSE in the R package hisse  30, 31. Fig. 2. Global butterfly diversification over time. Butterfly diversity increased well after the origin of flowering plants.
Colored lines with dots show butterfly diversity compared to vascular plant diversity from three widely accepted, recent plant diversification studies. Mean global temperature and major geological events during the last 350 Ma were calculated from Scotese et al. 85. The angiosperm stem to clade credibility interval is a consensus of seven studies (see section 14 of Extended Online Methods). Fig. 3. Relative mean dispersal rates of butterflies between different bioregions. Numbers associated with each arrow are the average rates from 1000 simulations using biogeographical stochastic mapping in BioGeoBEARS, which were then divided by 100 for ease of comparison (raw values can be found in Data S5).