dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction Species distribution models (SDMs) are regularly used to generate predictions of species presence; they rely on correlations between environmental variables and geo-localized species records to understand the environmental variables that drive species presences  (Blach-Overgaard et al. , 2010)  and delineate potential species distributions  (Araújo and Guisan, 2006; Araújo and Peterson, 2012 ). An extensive number of environmental datasets are available nowadays for fitting SDMs; but only a limited number of them should be included when running SDMs. Indeed, although increasing the number of predictors increases the chance of having ecologically relevant ones, it also inflates the risk of overfitting the model  (Merow et al. , 2014)  and of collinearity issues between variables  (Dormann et al.
Distributional data We used the GPS records from the INPN biodiversity database (http://inpn. mnhn. fr), maintained by the French National Natural History Museum, and based on a participative science program (http://frelonasiatique. mnhn. fr), as in  Barbet-Massin et al. (2013). The database totaled to 14,794 records of Asian hornet colonies, from the invaded range (France, Italy, Germany, Spain & Portugal, Belgium), spanning from 2004 to 2016. We did not include presence data from the native range for two reasons. Firstly, this invasion results from a single introduction event  (Arca et al. , 2015) , of one female only, that gave rise to the whole European population of Asian hornets. A single organism cannot encompass the whole population genetic and phenotypic diversities. Furthermore, altered speciese climate relationships during invasion are recurrent for insects, due to their ability to respond quickly to novel environments  (Hill et al.
Environmental data We used a set of 19 climatic variables (averaged from 1950 to 2000) available from the worldclim database (http://www. worldclim. org/) at 2. 5 arc min (~4 km) resolution  (Hijmans et al. , 2005). These variables represent a combination of means, extremes, variability and seasonality of temperature and precipitation data that are known to influence species distribution  (Root et al. , 2003). We used the CORINE (Coordination of Information on the Environment) Land Cover 2006 dataset to study the suitability of each habitat for the hornet (The European Environment Agency (EEA), 2010). This dataset is characterized by its high-spatial (100 m resolution) and thematic resolution. It is composed of 44 different land cover classes (hereafter habitat), each belonging to one of the four following broad categories: artificial surfaces (urban, roads, industrial units, etc.
, 2017) , and that the hornet is an opportunistic forager attracted by food scraps (bins, market places with seafood …) available in urban areas  (Monceau and Thi ery, 2017). Previous studies pointed out to the fact that honeybees represent at least a third of V. velutina diet  (Monceau et al. , 2014). One hypothesis could be that the recent phenomenon of placing high densities of beehives in cities increases the suitability of this habitat for the hornet, by providing it with abundant Table  1  Ranking of the 21 suitable habitats for Vespa velutina nigrithorax. The ranking is based on the ratio between the observed number of nests and the number expected by chance (obtained by random sampling). The higher the ratio, the more suitable the habitat. The Level of anthropization corresponds to first level of classification of the Corine Land Cover database categories.
Indeed, sugar and nectar are essential carbohydrates sources for the founder queens, the workers and the males  (Richter, 2000). Furthermore, these habitats have been proven to host the great diversity and abundance of wild pollinators  (Deguines et al. , 2012) , which also represent significant food sources for the larvae. This result goes along with  Franklin et al. (2017)  observation that >75% of primary nests are found on man-made structures, and 73% of the secondary nests are located in trees. A strength of our result is that it is unlikely to be affected by observation biases. Indeed, we used data from two complementary sampling protocols: a large-scale, citizen-based database and a local exhaustive search for nests in Chiz e. Data analyses from both sampling methods were concordant and showed the same habitats as suitable for the hornet.
Relevance of the multi-scale framework Our method enabled us to filter the climatic prediction according to the suitability and availability of habitats present locally; both drivers are complementary and combining these information is crucial to identify areas where special attention is needed. SDMs that do not consider speciesehabitat relationships may produce unrealistic and overestimated predictions Table  2  Ranking of the 16 land cover types unsuitable by Vespa velutina nigrithorax. The ranking is based on the ratio between the observed number of nests and the number expected by chance (obtained by random sampling). The higher the ratio, the more suitable the habitat. The Level of anthropization corresponds to first level of classification of the Corine Land Cover database categories.
For instance, instead of the percentage of favorable habitats per grid cells, one could use other landscape metrics if these are more relevant regarding the biology of the suited species (connectivity of the patches, Shannon's Diversity Index, heterogeneity of the landscape …). Another application of our method could be to include biotic interactions to refine the predictions; additional filters representing hosts, preys or predators could easily be combined to the existing framework, or Bayesian networks could be coupled to SDMs  (Staniczenko et al. , 2017). Further analyses could also consider species dispersal and demographic parameters  (Keeling et al. , 2017; Robinet et al. , 2016)  to refine the predictions. Finally, combining future climate and land use changes  (Rounsevell et al. , 2006)  datasets would be interesting, in order to evaluate how the interplay between the two drivers will impact the focal species.
Acknowledgments We are most thankful to all persons and organizations that provided records of hornet nests in France and the  SPN (Service du Patrimoine Naturel, MNHN)  who supported the process. The records from Chiz e proceed from a project funded by the  European Community program  ( 797/2004 ) for French beekeeping coordinated by the  French Ministry of Agriculture  (convention FranceAgriMer  14-03R ). We thank  Claire Villemant ,  Fabrice Requier ,  Benoît Geslin ,  Damien Decante  for their useful advice and discussions concerning Vespa Velutina biology. We are also thankful to  C eline Bellard  for her essential help and suggestions to improve this work. This work was supported by grants from the  ANR  ( 14-CE02-0021-01 ), the  Fondation BNP Paribas (INVACOST) ,  BiodivERsA Eranet (FFII)  and the  French Ministry of Ecology.
Data accessibility All environmental layers (climate and habitat) are available publicly. Vespa Velutina presence points are available by filling in the request form available at https://inpn. mnhn. fr/contact/ contacteznous?lg¼en and aggregated data (number of nests by 10 Â 10 km grid cells) are freely available. The output of our model, i. e. the environmental suitability in each grid cell in Europe. It is available at: http://max2. ese. upsud. fr/epc/conservation/pages/Franck/docs/env_suitability. csv.
Despite tremendous progress in understanding phylogenetic relationships across the family (LPWG, 2013a), uncertainty remains regarding relationships amongst the six first branching lineages of legumes and within certain clades (Figs. 1  &  2)  (Wojciechowski & al. , 2004; Bruneau & al. , 2008; LPWG, 2013a). For example, relationships among early-branching papilionoids  (Cardoso & al. , 2012a (Cardoso & al. , , 2013b)) , the large so-called Mimosoideae-Caesalpinieae-Cassieae clade, or MCC clade sensu  Doyle (2011 Doyle ( , 2012) )   (Bruneau & al. , 2008; Manzanilla & Bruneau, 2012; ) , and the Ingeae-Acacia s. str. clade  (Luckow & al. , 2003; Simon & al. , 2009)  all lack resolution and support using conventional DNA sequence datasets (i. e. , a few kilobases of plastid DNA sequence data).
2 , where Duparquetioideae is sister to the clade comprising Dialioideae, Caesalpinioideae and Papilionoideae based on analysis of a much larger plastid gene set). Numbers of genera and species (+ infraspecific taxa) sampled / currently recognised are indicated for each subfamily. The phylogenetic tree can be visualised (e. g. , with FigTree [http://tree. bio. ed. ac. uk/software/figtree/] or Dendroscope [http://dendroscope. org/];  Huson & Scornavacca, 2012) , and downloaded from Supplementary Data: Data file B. B, Schematic phylogeny based on the matK Bayesian analysis showing the six subfamily classification of the Leguminosae, with clade sizes proportional to number of species. A schematic figure illustrating the diversity of the six subfamilies is available for download as a poster (Figs.
g. , rDNA ITS, SucS) (see LPWG, 2013a and references therein). In recent analyses of all 81 plastid genes (Fig. 2 ) and of a large nuclear gene dataset derived from transcriptome sequences (E. J. M. Koenen & al. , in prep. ) , all five of the non-monospecific subfamilies are strongly supported, and the relationships amongst them do not conflict with the matK analyses (see below), although the nuclear gene dataset does not include Duparquetioideae. The analysis presented here includes 3696 legume species (with an additional 48 infraspecific taxa) representing 698 of the currently recognised 765 legume genera (Figs. 1 & S1; Tables  2  & S1 ; Appendix 1). Subfamilies Cercidoideae and Duparquetioideae are fully sampled at the generic level. In the Detarioideae, five genera are not sampled, all of them monospecific, in Dialioideae two monospecific genera are missing, and in Caesalpinioideae, two genera are not sampled (Table  2 , missing genera identified with *).
Papilionoideae are represented by 445 genera, with most of the missing 48 genera belonging to the tribe Loteae and phaseoloid clades. The phylogenetic trees and the underlying alignment and voucher data are available to browse and download from the online Supplementary Data (Table  S1 ; Data Files A-F) and on Data Dryad (DOI: https:// doi. org/10. 5061/dryad. 61pd6). Bayesian analyses (Fig. 1 ) and maximum likelihood (Fig. S1 ) of the matK sequence data resolve the Leguminosae as monophyletic with 1. 0 posterior probability and 100% bootstrap support. Each of the five non-monospecific subfamilies of Leguminosae is also supported with 1. 0 posterior probability and 100% bootstrap support. Relationships amongst subfamilies Cercidoideae, Detarioideae, Duparquetioideae and the clade that groups the remaining legumes (i.
In the mimosoid clade, and in other parts of Caesalpinioideae and Detarioideae, genera are often not supported as monophyletic, and generic-level relationships are often poorly resolved. This can likely be attributed in part to striking substitution rate heterogeneity in plastid genes, and hence variable phylogenetic resolution across legumes, as previously noted by  Lavin & al. (2005)  and  Dugas & al. (2015)  (see also  Figs. 1 & 2). Several recent large-scale angiosperm / rosid phylogenetic analyses  (Zanne & al. , 2014; Li & al. , 2016; Sun & al. , 2016)  included thousands of legume nuclear and plastid and, in some cases, mitochondrial sequences. These analyses contain many taxa that were mis-identified or labelled using outdated taxon names, or are based on apparent sequence contaminants that have been deposited in GenBank without being properly checked and annotated.
Typically, flowers are combined in spicate or capitate inflorescences, often these are in turn combined into compound inflorescences (e. g. , a panicle of globose heads). Pantropical, common in both wet and dry regions, with a handful of species extending to the temperate zone, and less frequently into frost-prone regions. (specimens selected in the single-taxon analyses are indicated by * in Table  S1 ). Full-length gene sequences were preferentially selected. The aligned matrix, accession list with voucher information and GenBank numbers, and tree files (Bayesian and bootstrap majority-rule consensus trees, best-scoring ML tree, 1000 Bayesian posterior trees and 1000 bootstrapped trees, all in newick format) are available as Supplementary Data and in Data Dryad (DOI: https://doi. org/10.
5061/dryad. 61pd6). The final matrix includes 3842 sequences representing 3696 legume species (identified with * in Table  S1 ) and covering 698 of the 765 currently recognised legume genera (Table  2 ). The sampling for subfamily Cercidoideae includes 96 species representing all 12 genera; for Detarioideae, 327 species (plus 3 infraspecific taxa) are included from 79 of the 84 genera; for Dialioideae, 19 species are included, representing 15 of the 17 genera; for Caesalpinioideae, we include 937 species (plus 5 infraspecific taxa), representing 146 of the 148 genera; and for Papilionoideae, 2316 species are included (plus 38 infraspecific taxa), representing 445 of the 503 genera. This represents the most comprehensive generic and species sampling of Leguminosae in a phylogenetic analysis of the family to date.
We also included 100 outgroup sequences, sampled across Eudicots, including relatively dense sampling of the three other families of Fabales (Table  S1 ). Broad sampling of outgroup taxa was included to facilitate downstream analyses requiring branch lengths and wider interfamilial relationships. Sequences for outgroup taxa were obtained from vouchered GenBank sequences (including published complete plastome sequences) and the 1000 Plants Project (OneKP or 1KP), as indicated in Table  S1. Phylogenetic analyses. -We initially built four separate matrices for Papilionoideae, the mimosoid clade, lineages of the former Caesalpinioideae, and the outgroup taxa. For each, an initial alignment for a subset of taxa was made using MACSE v1. 01b  (Ranwez & al. , 2011)  using default settings, in order to obtain an alignment that respects the open reading frame (ORF) and does not allow indels within codons.
g. , grouping together of shorter sequences). We used PartitionFinder v. 2  (Lanfear & al. , 2012)  to determine whether to partition codons separately or not. The program favoured a single partition for all three codon positions together. Complete analyses of the final matrix were implemented using a maximum likelihood approach analysed with RAxML, using the GTRGAMMA model, and support was assessed through 1000 rapid bootstrap replicates. The Bayesian analyses were implemented in PhyloBayes-MPI v. 1. 5a (Lartillot & al. , 2009) , with the GTR model, running two chains until they reached convergence, as determined with Tracer v. 1. 6 (Rambaut & al. , 2014). The two chains were run for a total of 25,891 and 25,512 cycles, and the majority-rule consensus tree produced by the program bpcomp (included in the PhyloBayes package) was based on 1075 posterior trees sampled from both chains.
ACKNOWLEDGEMENTS This project was made possible thanks to ongoing collaboration with researchers worldwide who have collected samples of legumes for DNA studies and who have shared material with members of the  legume systematics community  for over three decades. In particular, we thank the following people for their help with collection, preparation and curation of specimens, and / or with sequencing of the matK locus:  Alexandra Clark ,  Michelle Hart  ( Royal Botanic Gardens, Edinburgh );  Adilson M. Pintor ,  Marcelo T. Nascimento ,  Pablo Prieto  ( Rio de Janeiro );  Aécio A. Santos  ( Goiás, Tocantins );  Antônio S. L. da Silva ,  Camilo Barbosa ,  Catarina S. Carvalho ,  Leandro V. Ferreira ,  Lisandra A. Teixeira ,  Nara Mota ,  Pedro L. Viana ,  Rafael Salomão  (Pará);  Caio V. Vivas ,  José Lima Paixão ,  Tim Baker  (Bahia);  Claudio Nicoletti ,  Geovane S.
However, the expected limit shown in  [65]  is similar to that observed, so we do not ex-We sample this parameter space using MultiNest v2. 18  [79] , dividing the 7dimensional parameter space into 108 boxes, as also described in Table  2. This has two advantages: it enables us to run MasterCode on many nodes in parallel, and it enables us to probe more efficiently for local features in the likelihood function. For each box, we choose a prior such that 80% of the sample has a flat distribution within the nominal range, while 20% of the sample is in normally-distributed tails outside the box. Our resultant total sample overlaps smoothly between boxes, avoiding any spurious features at the box boundaries. The total number of points in our sample is ∼ 125 × 10 6 , of which ∼ 8 × 10 6 have ∆χ 2 < 10.
12 As discussed in more detail later, we used the  Atom  [72]  simulation code for a dedicated verification that points in this region escape all the relevant LHC constraints. These points avoid exclusion by the LHC constraints through a combination of a strong mass degeneracy, m ũR /c Rm χ0 1 50 GeV, leading to strong suppression of the standard / E T signature, and the reduction of the production rate compared to the simplified model that assumes mass degeneracy of all 8 light flavour squarks (see Fig. 1 ). These effects are clearly visible in Fig. 18  of  [2]. Fig. 5  displays the corresponding information in the (m 5 , m 10 ) plane of the SUSY SU(5) GUT model. As already reported in Table  3 , here we see directly that the best-fit point has very small (and slightly negative) m 5 , and that m 10 is somewhat larger, exploiting the possibility that m 5 = m 10 that is offered in this model.
m H u [GeV] other squarks. This leads to the possibility of a ũR /c R -χ0 1 coannihilation strip where m ũR and m cR ∼ 500 GeV, which is visible as a second local minimum of χ 2 with ∆χ 2 > 4 in the centre right panel of Fig. 10. We have checked specifically whether this strip is allowed by the available LHC constraints. To this end, we verified using the Atom simulation code that points along this strip are consistent with the published constraints from the LHC 8-TeV data. We have also checked that this strip is consistent with the preliminary simplified model search for q q+q q at 13 TeV reported by CMS. The left panel of Fig. 13  displays as a solid/dashed blue line the one-dimensional χ 2 function for m ũRm χ0 1 including/omitting the 13-TeV data (the corresponding lines for m cRm χ0 1 are very similar), and the right panel of Fig.
Comparison with Previous Results In previous papers we have studied the CMSSM, NUHM1 and NUHM2 using the LHC 8-TeV results and earlier DM scattering constraints. None of these models are directly comparable to the supersymmetric SU  (5)   Here we compare the supersymmetric SU(5) results found in this paper using LHC 13-TeV data with 'fake' NUHM2 results obtained by selecting a subset of this SU(5) sample with m 5 /m 10 ∈ [0. 9, 1. 1] (which were also displayed as grey lines in Fig. 10 ) and with previous NUHM2 results  [8]. Fig. 20  compares the one-dimensional χ 2 likelihood functions for m g (upper left), m qR (upper right), m t1 (lower left) and m τ1 (lower right) found in the SU(5) model including LHC 13-TeV constraints (solid blue lines) with the restricted fake NUHM2 sample (solid grey lines) and, for comparison, results from our previous NUHM2 analysis that used only the LHC 7-and 8-TeV constraints (dashed grey lines)  [8].
However, we showed in  [8]  that our NUHM2 LHC 7-and 8-TeV results for the exhibited sparticle masses were broadly similar to those for the NUHM1 and the CMSSM  [7] , and we expect the impacts of the LHC 13-TeV data on these models to be comparable to that in the NUHM2. Finally, we ask whether or not there is a significant improvement in the SU(5) fit compared to that in the NUHM2 subsample, thanks to the additional parameter (m 5 and m 10 replacing m 0 ). The NUHM2 subsample has a total χ 2 = 100. 8, which is reduced to 32. 8 when we remove the contributions from HiggsSignals, as discussed earlier. It should be noted that the NUHM2 subsample is statistically significantly smaller than that of the SU(5) sample. The quoted NUHM2 χ 2 represents only an upper bound on the χ 2 of the bestfit point that would be found in a more complete sample of the NUHM2.
Since the NUHM2 model has one less parameter than the SU(5) model, it has 24 degrees of freedom, and its χ 2 probability is 11%. According to the Wilks test  [94] , the probability that the data are represented better by the SU(5) model than by the NUHM2 subsample is 50%, while the F-test  [95]  yields a 40% probability. Therefore we conclude that there is no evidence that the extra parameter of SU(5) provides a significant improvement.
Acknowledgements The work of M. B. , V. C. , M. L. and D. M. -S. is supported by the  European Council via  Grant  BSMFLEET 639068. The work of R. C. is supported in part by the  National Science Foundation  under Grant No. PHY-1151640  at the  University of Illinois Chicago , and in part by  Fermilab , operated by  Fermi Research Alliance, LLC  under Contract No. De-AC02-07CH11359  with the  United States Department of Energy. This work of M. J. D. is supported in part by the  Australian Research Council. The work of J. E. is supported in part by  STFC (UK)  via the research grant  ST/L000326/1 , and the work of H. F. is also supported in part by  STFC (UK). The work of S. H. is supported in part by  CICYT  (grant FPA  2013-40715-P ) and also by the  Spanish MICINN  Consolider- Ingenio 2010 Program  under grant MultiDark  CSD2009-00064.
The work of K. A. O. is supported in part by  DOE  grant  de-sc0011842  at the  University of Minnesota. The work of K. S. is partially supported by the  National Science Centre, Poland , under research grants  DEC-2014/15/B/ST2/02157  and  DEC-2015/18/M/ST2/00054. The work of G. W. is supported in part by the  Collaborative Research Center   SFB676  of the DFG, " Particles, Strings and the early Universe ", and in part by the  European Commission  through the  "HiggsTools" Initial Training Network   PITN-GA-2012-316704.
Specimens were mounted on microscope slides in Marc André II medium  (Massoud 1967 ) and were examined with a compound microscope with differential interference contrast optics at magnifications ranging from 250× to 1000×. Drawings were made with a drawing tube. Specimens for scanning electron microscopy (SEM) observations were preserved in 100% ethanol before critical point drying (Emitech K850, Quorum Technologies Ltd, Laughton, England) and sputter gold coating, and imaged with a Jeol JFC-1200 (JEOL Ltd, Tokyo, Japan) scanning electron microscope. All synonymies, systematics, and literature data up to 2006 are given in  Szeptycki (2007). Head seta designations follow  Rusek et al. (2012) ; terminology for body chaetotaxy and porotaxy follows  Szeptycki (1988 Szeptycki ( , 1993) )  and  Shrubovych (2014) , body indices (PR, CF, BS, TR, EU) follow  Tuxen (1964).
0. 2 (CodonCode Corporation, Centerville, Massachusetts). Sequences were manually checked for misreadings of the chromatograms. Phylogenetic affinities were analyzed by comparing the obtained COI, 18S rRNA, 28S rDNA D1-D2, and 28S rDNA D3-D6 sequences with sequences of other members of Acerentomidae published in previous works  (Shrubovych et al. 2012 (Shrubovych et al. , 2014b (Shrubovych et al. ,c, 2016;; Resch et al. 2014 ) completed with published sequences  (Bu & Wu 2012; Bai & Bu 2013 ) and other unpublished sequences found in BOLD (Table  1 ). Other Acerentomata genera (Neocondeellum [Protentomidae], Proturentomon [Protentomidae], and Hesperentomon [Hesperentomidae]) were used to root the trees (see Table  1 ). Some of the COI sequences downloaded from GenBank or the Barcode of Life Data System (BOLD) were shorter than the barcode 658 bp so the missing ends of the alignment were completed with Ns.