dataesr/scientific-paragraphs-categorization huggingface dataset:
2016a) , finding both similarities and differences (Supplementary Fig. S5  available on Dryad) that likely reflect differences in the underlying data and analytical steps between our studies. We then performed a biogeographic analysis of palms using our new dated tree. First, we downloaded all geo-referenced species occurrences for palms from GBIF (comprising 724,002 records; downloaded on 1 April 2016; http://doi. org/10. 15468/dl. 2083tb). Then, we performed automated data cleaning steps as implemented in the function GeoClean of the package speciesgeocodeR  (Zizka and Antonelli 2015; Töpel et al. , in press ). The resulting data set was used as input for a bioregionalisation analysis in Infomap Bioregions  (Edler et al. , this issue) , with minimum and maximum cell sizes ranging between 2°and 4°, and minimum and maximum cell capacity ranging between 10 and 100 records per cell.
We then coded the presence or abscence of all species in the palm phylogeny in each of the bioregions identified using SpeciesGeoCoder  (Töpel et al. , in press ) and estimated ancestral ranges using the dispersalextinction-cladogeneis (DEC) model  (Ree and Smith 2008 ) under an unconstrained scenario (i. e. , no time stratification or arbitrarily defined dispersal matrices) in the R-package BioGeoBEARS  (Matzke 2013). We calculated the number of dispersal events through time between all pairs of areas (i. e. , when a lineage disperses, or expands, from area a to area b and vice versa). Number of dispersal events were computed for time bins of 5 Ma across the duration of the whole tree. Under the DEC model, dispersal events take place along branches. As branches can fall into more than one 5 Ma time bin, we weighted the dispersal event relative to the length of that branch that fell in the bin (e.
See text for details on the analysis. of dispersals by the total branch length per time bin  (Antonelli et al. 2015 ) (Supplementary Fig. S6  available on Dryad). The bioregionalisation analysis identified an optimum of 19 bioregions of palms, reflecting major species assemblages among and within continents (Fig. 5a ). Our biogeographical analysis resolved an origin of the palm family in Central and South America (Supplementary Fig. S7  available on Dryad), with dispersal out of the Americas occurring only around 70 Ma. This result in consistent with early hypotheses of palm evolution  (Moore 1973 ), but contrasts with more recent studies that VOL. 66 suggest a Laurasian (North America and Eurasia) origin of the family  (Couvreur et al. 2011; Baker and Couvreur 2013). We show the results from the dispersal through time analyses for two sets of areas: between Central and Northern South America and Eastern Brazil, and across Wallace's Line (Fig.
Simulation of Synthetic Data Sets To obtain a known "generating" tree we simulated a phylogeny that resembles the primate phylogeny obtained with SUPERSMART with regard to its size, depth, number and size of genera, and parameter values of the birth-death process. We estimated the parameter values for the latter by fitting a birth-death model using maximum likelihood, as implemented in the R-package ape  (Paradis et al. 2004 ) to the empirical primate phylogeny. Tips of the simulated tree were then assigned to genera approximating the size and age distribution of genera in the original tree. In this way, genera were kept monophyletic while being shuffled in the replicated phylogeny. Next, we replicated the set of orthologous sequence clusters obtained from the multiple sequence alignments in the original analysis.
To this end we selected substitution models and their parameter values using the R-package phangorn  (Schliep 2011)  on the empirical data sets and applied these when simulating sequences on the simulated tree using the R-package phylosim  (Sipos et al. 2011 ) Simulated alignments that were invariable were removed from the data set. Basic properties of the simulated tree set of alignments are similar to the actual data (Fig. 6 ). Replication of molecular sequence data sets is implemented in SUPERSMART to provide users with the possibility of validating custom analyses. All code and results of the simulation study are available in the Supplementary Material on Dryad.
Tree Inference from the Synthetic Data set The synthetic sequence data set was used as a starting point for phylogenetic inference with SUPERSMART, using ExaBayes and *BEAST for backbone-and subclade inference, respectively. We used the same pipeline settings (e. g. , number of markers in backbone and clade matrices, number of generations for the Bayesian backbone-and clade tree inference, maximal allowed distance within an alignment etc. ). A comparison of the simulated "generating" tree and the tree that was re-estimated from the synthetic data (Fig. 6b ; a fully annotated tree is provided in Supplementary Fig. S8  available on Dryad) shows that both tree topology and node ages agree well. Branch lengths in the reestimated tree appear to be slightly overestimated, possibly because the re-estimated tree was calibrated using TreePL, which systematically overestimate node ages  (Sanderson and Doyle 2001).
To assess the performance of the two-step approach implemented in SUPERSMART compared to other software tools, we compiled matrices from the synthetic data set and used these as input for maximum likelihood inference with the well-established tools ExaML  (Stamatakis and Aberer 2013)  RAxML  (Stamatakis 2014)  and GARLI  (Zwickl 2006). We restricted our comparison to these methods because state-of-the-art Bayesian methods become prohibitive for the dimension of our data set (228 taxa and 289,978 sites). Each tool could, therefore, only be tested with a relatively low number of bootstrap replicates of the input matrix. We quantified the differences in topology between the synthetic generating tree and the inferred trees using the Robinson-Foulds metric, which counts the number of bipartitions that occur in one tree but not in the other, and vice versa.
Our simulation study shows that SUPERSMART is able to estimate topology and divergence dates in a simulated phylogeny with a good degree of accuracy (Fig. 6b ) and that the performance is comparable to other state-ofthe-art phylogenetic inference software. We therefore argue that the three-step approach implemented in SUPERSMART is fruitful and viable for inferring large phylogenies in an entirely Bayesian framework, and capable of accommodating vast amounts of genetic data.
INTERACTIONS WITH OTHER INITIATIVES SUPERSMART is designed as a community-based platform that will complement and interact (rather than compete) with many ongoing initiatives worldwide. Supplementary Figure  S9  available on Dryad outlines some of the anticipated interactions and data exchange possible during different operational levels. For instance, a related application which efficiently assembles sequence data for a prespecified list of target species is PHLAWD, as used in  Zanne et al. (2014). SUPERSMART has similar goals but differs from PHLAWD by dealing more extensively with name resolution, homology/orthology assessment, simultaneously optimizing taxonomic and genetic coverage, time calibration through a curated fossil database, and the extensive support for, and use of, plugin tools.
The large number of ways in which methodological artifacts can be introduced along the way of a complex phylogenetic analysis means that "black boxes" that produce credible results without trial and error are not likely to become available any time soon, and SUPERSMART is no exception to this. Finally, a major limitation of SUPERSMART is computational scalability. With the wealth of genomic data being produced, a next step in development will be to produce backbone trees using full genomes and coalescent models. Another challenge will be to make PhyLoTa updates fully automated, so that GenBank is downloaded and its sequences clustered several times a year. Several additional enhancements are planned and will be added successively. Anyone can join the users' list and request additional features, and those wishing to contribute to the code and project are invited to join the developers' list at https://www.
ACKNOWLEDGMENTS We thank  Johan Nylander ,  Matthias Obst ,  Elisabet Sjökvist ,  Carlos Jaramillo ,  Klas Benjaminsson , and various other colleagues for discussions and support;  Luke Harmon ,  Karen Cranston ,  Daniel Ksepka ,  Walter Jetz , and  James Rosindell  for discussions on the integration of our initiatives; and  James Albert ,  Frank Anderson ,  Karen Cranston ,  Julia Allen , and an anonymous reviewer for constructive suggestions on the manuscript. The code was developed, tested, and benchmarked on the bioinformatics computer cluster Albiorix at the  Department of Biological and Environmental Sciences, University of Gothenburg ; at the  Swedish National Infrastructure for Computing  ( SNIC 2014/3-56  and  SNIC 2015/1-72 ); and at the  HPC infrastructure at Naturalis Biodiversity Center.
e. in the number of free parameters). Classical vicariance as a cladogenetic event is also incorporated by making use of temporally flexible constraints on the connectivity between any two given areas in accordance with the movement of landmasses and dispersal opportunity over time. DECX is built upon a previous implementation in C/C++ and can analyze phylogenies on the order of several thousand tips in a few minutes. We test our model extensively on Pseudo Observed Datasets and on wellcurated and recently published data from various island clades and a worldwide phylogeny of Amphibians (3309 species). We also propose the very first implementation of the DEC model that can specifically account for trees with fossil tips ( i. e. nonultrametric) using the phylogeny of palpimanoid spiders as a case study.
Both these approaches have also inspired a similar use in historical biogeography and an example of the former ( i. e. node constraints) can be found in  Meseguer et al. (2015). Recently, several others have made use of trees with fossil tips  (Mao et al. 2012; Nauheimer et al. 2012; Wood et al. 2013)   ( https://github. com/rhr/lagrangepython ) or in C++ ( https://github. com/blackrim/lagrange ). Both these implementations can lead to an erroneous inference of ancestral ranges when used with nonultrametric trees. In the following, we provide a remedy to this problem by specifically accounting for trees with fossil tips. Improvements in this paper are based on an efficient implementation of the DEC model  (Smith 2009 ) and address the issues of (i) classical vicariance (hereafter VI ), (ii) evolving geological connectivity (hereafter EGC), and (iii) rapid anagenetic change (hereafter RAC ).
Our model will hereafter be referred to as the DEC eXtended model (or DECX in the text); the details of which are presented in the next section. Very importantly, the DECX model does not change the total number of free parameters with respect to the original DEC model (two parameters). We assess the performance of DECX on Pseudo Observed Datasets (hereafter PODs), which are generated for a wide range of lineage diversification scenarios and over an evolving biogeographic context with eight areas. In the following sections, we also apply DECX over a number of published datasets on island clades  (Matzke 2014 ) and on a large amphibian phylogeny with over 3300 species  (Pyron 2014)  for which results from the DEC and DEC+J models are available. Finally, the phylogeny of palpimanoid spiders  (Wood et al.
2  ). For the interested reader, note that this mode of range inheritance is not part of the DEC+J model, available as part of the R package BioGeoBEARS and as presented by  Matzke (2014). It is also important to note that DECX (as in DEC ) does not support sympatric speciation across multiple areas in which daughter ranges copy the entire range of a widely distributed ancestor (as implemented in the BayArea approach). Such large scale sympatric speciation does not seem to be a biologically valid assumption on a macroevolutionary scale but which may be applicable to more recent speciation events  (Coyne and Orr 2004; e. g. wide  sympatric speciation between Colias eurytheme and C. philodice in North America  Wahlberg et al. 2014 , or sympatric speciation within several species of Heliconius in South America,  Kozak et al.
PODs: phylogenies used Timecalibrated trees were generated under various birthdeath scenarios (e. g. expanding diversity, declining diversity, high turnover) which represent the most commonly found patterns of lineage diversification in the literature. We used the R package TESS (v. 2. 0, Höhna 2014) and the function sim. globalBiDe. age for simulating the timecalibrated ultrametric phylogenies under a global time dependent birthdeath process conditioned on the age of the tree. The latter was set to correspond to the sum of the three equally spaced time intervals. Thus, lineage diversification during these three intervals, each lasting over 10 time units, consisted of piecewise constant speciation and extinction rates. Totally, 12 diversification scenarios were considerd and more details on each of these can be found in the Dryad repository (Online Appendix 1 available at https://goo.
Empirical analysis of island clades (Matzke 2014) The DECX model was not specifically developed for studying either island or continental biogeography and our search for recently published and freely available studies using DEC and alternate models on real data yielded Matzke (2014, Table  2 ). The latter contains a compilation of 13 island clades (mostly from the Hawaiian archipelago) for which the biogeographic histories have been inferred under a variety of hypotheses and where model choice was evaluated between the DEC and DEC+J approaches. We made use of these easily accessible datasets  (Matzke 2014, Dryad:  http://dx. doi. org/10. 5061/dryad. 2mc1t ) as it sets a good precedent for the reproducibility of results in the field of historical biogeography. Note that some of the scenarios in Matzke (2014, Table  2 ) specify a ghost source or an "ancient area Z " representing either an island older than the current Hawaiian Islands or the continent.
However, a notable difference from Matzke (2014, Table  2  and Supplementary Table 1 ) is that out of a total of 53 biogeographic analyses we have selected only 44 ( cf. Difficulties with the empirical analysis of island clades ). Note that in this following we shall refer to DECX instead of specifying the DECVI+RAC submodel to be consistent with the fact that we haven't explored the VI and RAC aspects separately on the empirical analyses (as has been done for the PODs analysis). Empirical analysis of Amphibia  (Pyron 2014)  In order to further illustrate the efficiency and performance of the DECX approach when applied to large phylogenies, we chose an Amphibian phylogeny containing 3309 species  (Pyron 2014). We also make use of the species distribution and area delineation as presented by  Pyron (2014)  who applied the BioGeoBEARSDEC and DEC+J models to the Amphibian dataset.
In the following, and only for the Amphibian dataset, we define as model M0 whenever inference was performed without imposing any EGC. Note that we constrain our M0 , akin to  Pyron (2014) , such that the inferred ancestral ranges can only be composed of a maximum of two areas. We first ran the BioGeoBEARSDEC and DEC+J models using the input files provided by  Pyron (2014, Dryad:  http://dx. doi. org/10. 5061/dryad. jm453 ) and adapted the latter for the C++ DEC and DECX analysis. Next, we imposed an EGC covering the lineage diversification history of Amphibians from the present to the past 270 Myrs which we shall refer to as model M1. The EGC for the Amphibian history was thus divided into five time intervals: (i) 0 to 34 Myrs ago, (ii) 34 to 90 Myrs ago, (iii) 90 to 120 Myrs ago, (iv) 120 to 170 Myrs ago, and (v) 170 to 270 Myrs ago.
Empirical analysis of palpimanoids (Wood et al. 2013) Trees with fossil tips ( i. e. nonultrametric) can now be analyzed explicitly by the DECX framework. Previous implementations of DEC (especially the C++ version as available at https://github. com/blackrim/lagrange ) accept trees with fossil tips without complaint and erroneously extend the respective branches up to the present. This is due to an internal procedure for calculating the phylogenetic branch lengths from node age information provided by the user in the NEWICK format. In the DECX implementation, we have corrected this by asking the user to explicitly specify when providing a nonultrametric tree and accordingly account for the correct branch length of the fossil tips. We compare our implementation by reanalyzing the biogeography of palpimanoid spiders (Wood et al.
Implementation The DECX approach for inferring dispersal and extinction parameters along with the ancestral species ranges is essentially coded in C++ and can be freely downloaded from the GitHub repository https://github. com/champost/DECX. This is an overhaul of the original C++ version of the DEC model  (Smith 2009;  https://github. com/blackrim/lagrange ). Among some of the notable improvements, it is now possible to analyze large phylogenies (over 1000 species) which previously resulted in bugs related to the high precision required for the calculation of the likelihood. Another area of interest is the handling of a few highly dispersed (widespread) extant species of a given clade, which is usually an artifact of recent and rapid expansion and which can now be modelled separately. The DECX program is meant to be used from the command line and simply requires a single configuration file and several input files (e.
Inferring geographic range evolution using DECX and DEC+J ). Note that these proportions provide only a qualitative insight into the performance of the DECX model versus the DEC+J model. While  Matzke (2014, pg. 956 ) states that, " Wherever a constrained analysis was run, an unconstrained run was also implemented for comparison. ", we note that this hasn't been followed rigorously. The most obvious example being the 100 Myr old Anolis clade in the Caribbean where only a single unconstrained analysis has been performed. Following Matzke (2014), we provide the input files necessary to redo our analyses, as well as additional information such as the trees displaying the inferred ancestral areas and the raw program output ( i. e. with ln L values, list of ranges within 2 ln L units for every ancestral node, etc.
) in the Dryad repository (Online Appendix 5 available at https://goo. gl/oXDdJz ). Empirical analysis of Amphibia  (Pyron 2014)  For the M0 model ( i. e. no additional constraints) and under BioGeoBEARSDEC we obtained very different values for the ln L and for estimates of dispersal and extinction from those of  Pyron (2014) , though the inferred origin of the Amphibian phylogeny was consistent with our results ( Table  4  ). As expected, C++ DEC agrees more closely with the BioGeoBEARSDEC analysis in our paper except for the extinction parameter. We also found a notable difference in ln L values between the DEC+J analysis performed by  Pyron (2014)  and our results which favour the DEC+J model as the bestfitting model of the historical biogeography of Amphibians. The DECX model fits the Amphibian dataset poorly under the M0 assumption though the inferred origin and dispersal rate remains consistent with the other models.
Other important differences at particular nodes are highlighted within the dashed boxes ( Fig. 6  ). DECX finds an ancestral range composed of the Neotropics, Africa, and India to be split into Neotropics for one clade, and Africa plus India for the second clade. On the contrary, DEC+J finds an ancestral range composed of the Neotropics, Africa, and Australasia to be split into Neotropics for one clade, and Africa plus Australasia for the second clade. All figures displaying the inferred ancestral ranges as well as the files with the raw results ( i. e. ln L, list of ancestral ranges within 2log ln L units, etc. ) are available in the Dryad repository (Online Appendix 6 available at https://goo. gl/oXDdJz ).
Empirical analysis of palpimanoids (Wood et al. 2013) Our results of the biogeography of palpimanoid spiders highlighted the fact that fossils, when used in a total evidence framework, should have a strong influence close of their respective branching points. We thus identified five ancestral nodes ( Fig. 7  ), in the vicinity of the fossil tips, of the palpimanoid phylogeny which differ from the previous analyses by  Wood et al. (2013). In terms of parameter estimates, Wood et al. (  2013 ) found a smaller extinction rate with respect to dispersal ( d =5. 36×10 4 and e =6. 85×10 5 ). Though, we find an extinction rate smaller than dispersal, we also obtain a more than twofold increase in the former ( d =6. 42×10 4 and e =1. 62×10 4 ). The DECX analysis with the same dataset and same parameters resulted in an extinction rate almost twice as high as dispersal rate ( d =2.
Difficulties with the empirical analysis of island clades (Matzke 2014) The central tenet of the DEC+J model lies in its successful application to a notable compilation of island clades. Here, we briefly bring to the notice of the interested reader some of the difficulties we encountered while analysing the entire dataset consisting of 13 island clades from Matzke (2014, SuppData2_all_BioGeoBEARS_inferences available online at http://datadryad. org/resource/doi:10. 5061/dryad. 2mc1t ). 1 ) which were either missing ( Nesophrosyne and Cyrtandra ) or which were conflicting ( Megalagrion5 ) with the results from the aforementioned supplementary data. Also, a detailed comparison of inferred dispersal and extinction rates between the DECX and DEC+J models was hampered by an incorrect reporting of results in Matzke (2014, Supplementary Figure  9 ).
basically amounts to a comparison of the model likelihoods. When DEC is the true underlying model of range evolution Fig. 4 (top row) it always has a lower or equal ln L than the other three models. In this case, the DECVI model agrees almost perfectly with the DEC inferences and note that the other two models ( DECRAC and DECVI+RAC ) disagree in a similar fashion. When DECVI+RAC is the true model Fig. 4 (bottom row) DECRAC inferences are in very good agreement with the former compared to the other two models ( i. e. DEC and DECVI ). Additional results illustrating the effect of the phylogeny, depending on the various lineage diversification scenarios, on model comparison using likelihoods can be found in the Dryad repository (Online Appendix 3 available at https://goo. gl/oXDdJz ). Likelihoods aside, performance was also evaluated by a relative comparison of the inferred dispersal ( d ) and extinction ( e ) parameters with their true values.
Acknowledgements This work is based on a longlasting collaboration initiated during the first postdoc fellowship C. R. B. and the PhD thesis of F. L. C. We warmly thank all people who discussed our ideas and projects in the different labs (e. g. Andrea Meseguer ,  Frédéric Delsuc ,  Gael Kergoat ,  Raphael Leblois ,  Mike Hickerson ). They kindly pushed us to publish this work, and contribute to our ideas. A special thanks goes to  Julien Veyssier  for making DECX accessible to all interested users by helping us create binaries for various platforms. supported by an  ANR  grant  ECOEVOBIOCHEX2011  to  Hélène Morlon , a  Carl Tryggers Stiftelse grant  ( CTS 12 : 24 ), and a grant of the  Marie Curie Actions (BIOMME project ,  IOF627684 ).