dataesr/scientific-paragraphs-categorization huggingface dataset:
The softwares tpsDig v2. 31  [25]  and tpsUtil v1. 76  [26]  were employed to create the landmark datasets (Figure  1 ), while tpsSmall v1. 34  [27]  and tpsRelw v1. 69  [28]  were employed to evaluate the overall shape variation within the chosen datasets. The canonical variate analysis (CVA), as implemented in  [29] , tested the taxa attribution analysing the RW scores, setting the Mahalanobis distance stepwise method and leave-one-out classification. Additionally, the scatterplots of the RW scores were generated using SPSS software, defining the ingroup and outgroup taxa. The matrix of landmark characters was built including the aligned values of each structure. In the phylogenetic analysis, each shape configuration was set as a single character, as recently implemented in TNT  [30] , being that it "allows running a combined analysis of traditional and phylo-morpho characters in an analogous way as two (or more) different gene sequences can be analysed together where each one contributes to the resolution of a phylogeny"  [31].
Phylogenetic Analysis The software TNT v1. 5  [30]  (freely available at: http://www. lillo. org. ar/phylogeny/tnt/) analysed the discrete and continuous datasets first separately and then together, under equal weighting, and while applying different settings for each analysis. An initial parsimony analysis was performed on the discrete dataset (equal weights), with 10,000 random addition sequences to the TBR method, retaining 10 trees for each replication. Subsequently, a new technology search was done selecting sectorial search, ratchet, drift, and tree fusing options, as implemented in TNT. The outgroup method was chosen to root the resulting trees in all of the analyses. The 'multiple taxa' option = ON was chosen to test the hypothesis that the ingroup taxa were well separated from any of the outgroups.
Thus, the Tiaronthophagus species were grouped together, while the outgroup species were not grouped thus the latter ones were not forced together. The landmarks characters were separately examined by traditional search (Wagner parsimony), setting off the option 're-align data landmarks', since the values were already aligned in tpsRelw (see above). Finally, the full dataset merging discrete and landmark equal-weighted characters was analysed with the following initial settings:  (1)  implied weighting: OFF, to give the same weight to any character; (2) lmark confsample: ON, to include the landmark data; and,  (3)  force: ON, to allow for multiple outgroups in the search. The Hendrickx script  [31]  is available at http://phylo. wikidot. com/tntwiki. The landmark data were not re-aligned, since the aligned datasets from tpsRelw were included in the matrix  [21].
Results and discussion were based on the combined analysis. The combined matrix of discrete and landmark characters underwent the heuristic (traditional) search method, as implemented in TNT (Wagner starting trees) with 100,000 random addition sequences, followed by the tree bisection reconnection (TBR) branch-swapping algorithm (100 trees per replication save limit). Additionally, the 'landcombsch. run' [31]  and 'aquickie. run' (TNT package) scripts were applied, with the same initial settings of the Wagner parsimony analysis. The resulting trees of the analyses were thus compared to evaluate whether there were any differences when applying different methods to the combined matrix. Statistics (i. e. , tree length, adjusted homoplasy, consistency index, and retention index) were evaluated for each tree.
The TNT default options for resampling were used to determine branch support by the absolute bremer and relative bremer supports, as calculated using the TNT Bremer function (i. e. , bremer. run). Moreover, the standard bootstrap, symmetric resampling, and jackknife values were calculated setting the traditional search method with nreps = 1000 and when considering the whole landmark configurations for resampling and relative support. The average group support values of each phylogenetic hypothesis were calculated with TNT. Discrete and landmark characters were separately mapped onto the tree, as implemented in the software, and then the synapomorphies were evaluated, retaining the list of the common synapomorphies. The resulting trees were then drawn by TreeGraph2 v2. 14. 00-771beta  [33].
Biogeographical Analysis The distribution of Tiaronthophagus was subdivided into areas using the InfoMap online facility (http://bioregions. mapequation. org/), a powerful interactive web application  [34, 35]  performing bipartite network clustering, whose aim is to identify taxon-specific bioregions from species distribution data. The dataset of the Tiaronthophagus species distribution was run at the InfoMap Bioregions web page (http://bioregions. mapequation. org/), setting max cell size = 4, min cell size = 1, max cell capacity = 100, and min cell capacity = 1. In the analysis, the phylogenetic tree was not time-calibrated. The analysis can also define the most common and indicative species for each area, allowing for evaluating 'endemic species, unique or close to unique to a specific bioregion'  [35].
Discrete and Landmark Characters The 53 discrete characters that were gained by the comparison of the Tiaronthophagus and the outgroups taxa were included in the matrix of characters (Supporting Information Data S2) to be employed in the subsequent phylogenetic analysis. Characters that could be regarded as duplicating the landmark characters (see below) were not included in the dataset. Insects 2019, 10, 64 7 of 50 Before using the twelve landmark characters to build the combined matrix, they were carefully examined to evaluate the differential patterns of shape variation of the ingroup and outgroup taxa for each structure. The evaluation of the configurations, as implemented by tpsSmall, gave a significant result for each of them (correlation ≈ 0. 999), thus allowing the application in the following analyses.
The relative warps analysis (i. e. , PCA) of each configuration highlighted marked differences between the ingroup and outgroup taxa. The scatterplots of the first two relative warps (RWs) showed two well-separated clusters. Here, the plots of the hindwing, epipharynx, female head and pronotum, paramere, and phallobase were given (Figure  2 ). The statistics of each structure were examined, in order to assess the analogous developmental patterns. The 53 discrete characters that were gained by the comparison of the Tiaronthophagus and the outgroups taxa were included in the matrix of characters (Supporting Information Data S2) to be employed in the subsequent phylogenetic analysis. Characters that could be regarded as duplicating the landmark characters (see below) were not included in the dataset.
The RW scores summarize 100% of the overall shape variation  [38]  were used for the canonical variate analysis (CVA). Congruent results were obtained for all of the examined structures, thus it was confirmed that the Tiaronthophagus genus was a well differentiated taxon from the other Onthophagini examined here (Figure  2 ). The evaluation of each dataset providing different patterns of shape variation is noteworthy here. The hindwing plot of RWs 1 and 2 (Figure  2A ) accounted for more than 45% of the overall shape variation, with the first six out of 31 RWs being significantly higher than 5%, and explaining together more than 76% of the overall shape variation. The CVA confirmed the differential pattern, being 96. 9% of cross-validated grouped cases correctly classified. In the plot of the female head (Figure  2B ), two distinct clusters were present.
Phylogenetic Analysis The parsimony analysis (both traditional search and new technology search) of the discrete characters gave 465 trees (tree length = 196, CI = 0. 566, RI = 0. 829); in the strict consensus (results not shown here), the Tiaronthophagus clade was distinct from the outgroup taxa, but the phylogenetic relationships among the species were not fully elucidated. The new technology search gave a similar consensus from six trees in which the Tiaronthophagus clade is defined, but the phylogenetic relationships within the genus also remained unsolved. Additionally, the resampling analyses confirmed that Tiaronthophagus constituted a distinct clade (100% branch support value at basal node of the clade for standard bootstrap, jackknife, and symmetric resampling). The separate parsimony analyses of the landmark datasets (resulting trees not shown here) highlighted a common pattern for the ingroup clade, also evincing the relationships within the genus that constituted a well differentiated group of Onthophagini, as already seen in the geometric morphometric analysis.
Each of the trees that were obtained from a single landmark character gave better resolved relationships among the species than the one from the analysis of the discrete dataset. The landmark characters concur to define the phylogenetic relationships within Tiaronthophagus, thus, according to the former results, the twelve landmark characters were included in the combined matrix. Instead, the combined analysis gave a unique fully resolved tree from parsimony analysis (Figure  3 , being the tree length = 260. 343, adjusted homoplasy = 15. 109, CI = 0. 462, RI = 0. 724, and scores for 247 landmark points = 64. 343). Insects 2019, 10, x 9 of 49 The paramere plot of RWs 1 and 2 (Figure  2F , more than 69% of the overall shape variation explained) also defined two distinct groups, with the overall shape variation accounting for the first four out of 33 RWs about 85%, and 97.
Each of the trees that were obtained from a single landmark character gave better resolved relationships among the species than the one from the analysis of the discrete dataset. Within Tiaronthophagus some clades were defined (T. rufopygus/T. liberianus; T. aequatus/T. schaufussi; T. zambesianus/T. ebenus; and, T. viridiaereus/T. curtipilis). The common synapomorphies were calculated (Supporting Information Data S3), evidencing how the landmark characters greatly contribute to defining the phylogenetic relationships within the Tiaronthophagus species. The unique fully resolved tree that resulted from the analysis using the Hendrickx script was almost identical to the former one (Figure  3 ), with two differences at terminal nodes for T. viridiaereus and T. flexicornis, and T. aequatus and T.
rolandoi (DEFGHI), T. curtipilis (ABGH), T. rufobasalis (DEFH), and T. zambesianus (EFGH), but about 70% of the Tiaronthophagus species are endemic to a single macroarea (Figure  4B ). The landmark characters concur to define the phylogenetic relationships within Tiaronthophagus, thus, according to the former results, the twelve landmark characters were included in the combined matrix. Instead, the combined analysis gave a unique fully resolved tree from parsimony analysis (Figure  3 , being the tree length = 260. 343, adjusted homoplasy = 15. 109, CI = 0. 462, RI = 0. 724, and scores for 247 landmark points = 64. 343). Within Tiaronthophagus some clades were defined (T. rufopygus/T. liberianus; T. aequatus/T. schaufussi; T. zambesianus/T. ebenus; and, T. viridiaereus/T. curtipilis). The common synapomorphies were calculated (Supporting Information Data S3), evidencing how the landmark characters greatly contribute to defining the phylogenetic relationships within the Tiaronthophagus species.
The unique fully resolved tree that resulted from the analysis using the Hendrickx script was almost identical to the former one (Figure  3 ), with two differences at terminal nodes for T. viridiaereus and T. flexicornis, and T. aequatus and T. angolensis, which were here regarded as sister species. The unique tree from the aquickie run (not shown here) was also identical to the parsimony analysis. The branch support values (Figure  3 ) gave congruent results. Although Tiaronthophagus constituted a well differentiated clade from the other Onthophagini taxa, the phylogenetic relationships within the new genus were not fully elucidated, some with branches having low support values.
jossoi from Tanzania, although less marked than in T. saadaniensis. A differential pattern of morphological variation is thus evident in this clade, although the two species present evident differences in both internal and external morphology. Noteworthy, the listed above characters can also help in the recognition within Tiaronthophagus, with clearly highlighted differences among the species (Supporting Information Data S5 and S6), e. g. , in the density, shape, and size of the granulate points of the pronotal disc; position of the large points of the head; density and position of the points on the interstriae; shape of vertex carina (in females) and vertex lamina (in major males), features of the protuberances of pronotum in females (from above); shape of epipharynx, parameres, phallobase sclerotized plate, infundibular wall of vagina, infundibular tube, and posterior expansions of vagina.
Some interesting results were already evinced here within the outgroup dataset, as regarding the relationships between Onthophagus s. str. and Palaeonthophagus, which constitute two distinct clades in the phylogenetic tree (Figure  3 ). The present findings could also suggest that Palaeonthophagus and Onthophagus s. str. must be regarded as distinct genera, as the other Onthophagini taxa included in our analyses. The hypothesis is challenging and it deserves a more careful evaluation in the future, but it is far beyond the purpose of our present analysis, thus we mainly focused on Tiaronthophagus, which is the new genus identified here. Noteworthy, both Onthophagus s. str. and Palaeonthophagus are well-separated from the Tiaronthophagus on the tree. In our analysis, Hamonthophagus/Morettius constitutes the sister clade of Tiaronthophagus.
The forewings can contribute to the taxa discrimination at a lesser rate than the hindwings, although the whole dataset accounted for 95% of the correctly classified cases (meaning that a large number of RWs accounted for the overall shape variation). It should be worth to remember that, as a rule, in taxa that are phylogenetically close, the elytra features commonly used for the identification are mainly related to the variation in colour, puncture, and pubescence than in form. Thus, the elytra shape variation deserves a careful evaluation to gain a better definition of the form variant patterns of this structure. The shape variation patterns of the head are similar in both sexes, also with a common pattern in Tiaronthophagus for this anatomical trait. In the plots, different relationships among taxa were defined for the two sexes, showing that female heads have a more marked shape differentiation than males, both between and within the proposed groups.
Acknowledgments: The iconographic material was produced using the facilities of the  Geometric Morphometrics Laboratory at the Dpt. of Life Sciences and Systems Biology of Torino University , equipped thanks to funds from the  CRT Foundation, Research and Education section (Torino, Italy). We are greatly indebted to the museum curators and private collectors who kindly lent us their material. We also want to thank  Aisha Makeshiyo  (SAMC),  Olivier Montreuil  (MNHN),  Nicolas Margraf  (NHMC),  Hans Huijbregts  (RMNH),  Christoph Germann  (NHMB),  Ditta Balke  (ZSM) and  Max Barclay (BMNH) , for the digital images of the type material of Onthophagus species. We are grateful to  Ruth Muller  ( TMSA, Ditsong National Museum of Natural History, Pretoria, South Africa ),  Terence Bellingan  ( AMGS, Albany Museum, Grahamstown, South Africa ),  Ashley H.
High-throughput sequencing 16S rRNA gene surveys have enabled new insights into the diversity of soil bacteria, and furthered understanding of the ecological drivers of abundances across landscapes. However, current analytical approaches are of limited use in formalizing syntheses of the ecological attributes of taxa discovered, because derived taxonomic units are typically unique to individual studies and sequence identification databases only characterize taxonomy. To address this, we used sequences obtained from a large nationwide soil survey (GB Countryside Survey, henceforth CS) to create a comprehensive soil specific 16S reference database, with coupled ecological information derived from survey metadata. Specifically, we modeled taxon responses to soil pH at the OTU level using hierarchical logistic regression (HOF) models, to provide information on both the shape of landscape scale pHabundance responses, and pH optima (pH at which OTU abundance is maximal).
We identify that most of the soil OTUs examined exhibited a non-flat relationship with soil pH. Further, the pH optima could not be generalized by broad taxonomy, highlighting the need for tools and databases synthesizing ecological traits at finer taxonomic resolution. We further demonstrate the utility of the database by testing against geographically dispersed query 16S datasets; evaluating efficacy by quantifying matches, and accuracy in predicting pH responses of query sequences from a separate large soil survey. We found that the CS database provided good coverage of dominant taxa; and that the taxa indicating soil pH in a query dataset corresponded with the pH classifications of top matches in the CS database. Furthermore we were able to predict query dataset community structure, using predicted abundances of dominant taxa based on query soil pH data and the HOF models of matched CS database taxa.
Whilst we have a wealth of databases and tools for characterizing the taxonomy of matched sequences  (Wang et al. , 2007; McDonald et al. , 2012; Quast et al. , 2013) , databases do not include any associated ecological information on sequences matches. Whilst new software has recently become available that uses text mining to return some ecological data on matched sequences to NCBI, this information is currently limited to descriptions of sequence associated habitat  (Sinclair et al. , 2016). Synthesizing relationships between soil amplicon abundances and environmental parameters is now necessary to progress ecological understanding of soil microbes beyond those few organisms that are readily cultivated. Determining microbial responses across environmental gradients can inform on the realized niche widths of discrete taxa, and may indicate the presence of shared functional traits across taxa  (Martiny et al.
The degree of coupling between response and effect groups can then allow prediction of functional effects under change. For instance if certain phylogenetic groups of taxa decrease due to environmental change, and these taxa also represent an effect group (e. g. , these taxa possess a unique functional gene) then we can expect the function to also decrease. Conversely with uncoupled effect groups (e. g. , responsive taxa all possess a ubiquitous functional gene), the system is likely to be more functionally resistant to change  (Diaz et al. , 2013). Applying such concepts to microbial ecology is a realistic ambition given the extensive availability of amplicon datasets coupled to environmental information, and the increasing feasibility of uncultivated microbial genome assembly from metagenomes or single cell genomics  (Choi et al.
, 2013). Determining the degree of functional redundancy in taxa which respond across soil pH gradients, will permit new insight into the microbial biodiversity mechanisms underpinning soil functionality and resilience to change. Since soil pH is largely predictable from geo-climatic  (Slessarev et al. , 2016)  and land use features  (Wamelink et al. , 2019) ; prediction of the abundances of individual bacterial taxa under environmental change scenarios is likely to be feasible. The immediate challenge is therefore to establish predictive frameworks for many soil bacterial taxa, which can be populated with genomic information as it becomes available; to ultimately facilitate predictions of microbial functional distributions. We believe that attempts to progress understanding of the ecological attributes of environmentally retrieved bacterial taxa can be streamlined immediately by making better use of the extensive amplicon datasets that exist, which already provide an abundance of useful information on taxa-environment responses.
Indeed it has recently been shown that many prokaryotic taxa are distributed globally (particularly dominant OTUs  Delgado-Baquerizo et al. , 2018 ), yet there is currently no way to formally capture their ecological attributes in databases for further microbiological and ecological enquiry other than in Supplementary Material spreadsheets. Here we seek to address this by making available a database of representative sequences from a large 16S rRNA amplicon dataset from over 1,000 soil samples collected across Britain. In addition to providing standard taxonomic annotation, we also seek to add ecological response information to each representative sequence. We focus here on soil pH responses as bacterial communities are known to respond strongly across soil pH gradients  (Griffiths et al. , 2011).
We will firstly model OTU abundances across soil pH using hierarchical logistic regression (HOF)  (Jansen and Oksanen, 2013) , a commonly used approach to examine vegetation responses across ecological gradients which has yet to be widely applied to microbial datasets. We will use model outputs to assign each OTU to a specific pH response group based on abundance optima, and in addition demonstrate the utility of the database in determining the phylogenetic relationships in ecological responses. The utility of the database will be further tested on 16S datasets to compare both the percentage of hits and modeled responses. The OTU database with associated HOF model outputs is released both as an online portal for visualizing individual queries and as flat files for integration into existing bioinformatics pipelines.
Database Coverage The database was constructed from sequences obtained from the 2007 Countryside Survey (CS), a randomly stratified sampling of most soil types and habitats across Great Britain, full details of which are provided elsewhere  (Griffiths et al. , 2011; Reynolds et al. , 2013). Sequencing of 1,113 soils using the universal 341f/806r  (Takahashi et al. , 2014)  primers targeting the V3 and V4 regions of the 16S rRNA gene yielded a total of 39,952 reference sequence OTUs, after clustering at 97% sequence similarity and singleton removal. Coverage was assessed on a filtered dataset of 1,006 samples which had at least 5,000 reads per sample, using sample based species accumulation curves calculated per habitat class and pooled across all habitats (Figure  1 ). The curves for individual habitats, whilst not reaching saturation, reveal some interesting trends with grasslands exhibiting highest biodiversity at the landscape scale, which is likely attributable to the broad range of soil conditions they encompass.
Performance of Database Against Independent Datasets The coverage of this dataset was further assessed through blasting representative sequences from independent 16S datasets for various locations and habitats (detailed in Table  1 ), against all 39,952 CS representative sequences. Here we defined an OTU "hit" as a query OTU that shared 97% identity with a CS OTU and had an e-value equal to or less than 0. 001. We subsequently calculated the percentage of OTUs within each independent dataset meeting this criteria to gain insights into CS dataset coverage. For the two independent soil datasets (query datasets 1 and 2, Table  1 ), we found over 50% of the OTUs in each study had a hit within the CS database. Expectedly, this was in stark contrast to the fresh water query dataset (query dataset 3, Table  1 ) which exhibited much less overlap with the CS database with 33.