dataesr/scientific-paragraphs-categorization huggingface dataset:
The landmark configurations were scaled, translated, and rotated against the consensus configuration using the GLS Procrustes superimposition method to remove all non-shape differences and to separate the size from shape components of the form  (Rohlf & Slice 1990; Bookstein 1991). The superimposition was performed using R functions of the package geomorph  (Adams & Otárola-Castillo 2013). Each wing was digitized twice by the same experimenter (MG), to account for measurement error. The aligned landmark configurations were projected into the Euclidean space tangent to the curved Kendall's shape space to aid further statistical analyses. The correlation coefficient between the Procrustes distances in the shape space and the Euclidean distances in the linear tangent space equalled 1. 00. This indicates that the curvature of the shape space around our data was negligible  (Rohlf 1999).
The least-squares regression slope through the origin (0. 999) and the correlation coefficient between the two distances were calculated with tps-SMALL v1. 25  (Rohlf 2013c ). After checking of application assumptions, PerMANOVA (Permutational analysis of variance) analyses were performed to assess wing size and wing shape differences between groups. A PCA was performed to assess shape variation among the different groups, using the geomorph function "plotTangentSpace" and to visualize potential differentiation between taxa. Prior to the assignment of the holotype queen B. gelidus, shape variation within the reference dataset and discrimination of the different taxa was assessed by Linear Discriminant Analyses (LDA) of the projected aligned configuration of landmarks. These analyses were performed at species level as a priori grouping by using the software R version 3.
0. 2. The effectiveness of the LDA for discriminating taxon was assessed by the percentages of individuals correctly classified to their original taxon (hit-ratio, HR) in a leave-one-out crossvalidation procedure based on the posterior probabilities of assignment. Given the observed scores of an ''unknown'', the posterior probability (pp) equals the probability of the unit to belong to one group compared to all others. The unit is consequently assigned to the group for which the posterior probability is the highest  (Huberty & Olejnik 2006). Taxonomic affinities of the holotype queen B. gelidus were first assessed based on their score in the predictive discriminant space of shapes. After superimposition of the landmark configurations, aligned coordinates of the specimens from the reference dataset were used to calculate the LDA.
A unique superimposition of both the reference dataset and the assigned specimens is sometimes disregarded while it is of primary importance because GLS Procrustes superimposition is sampling dependent. We included a posteriori the holotype queen B. gelidus in the computed LDA space as ''unknown'' specimen and calculated their score. Assignments of the holotype B. gelidus were estimated by calculating the Mahalanobis Distance between ''unknown'' and group mean of each taxon. We also calculated posterior probabilities of assignment to confirm the assignment to one taxon.
Genetic analyses A total of 938 bp from the COI gene and 925 bp from PEPCK were obtained. All phylogenetic analyses (ML and MB) on each genetic dataset showed a similar topology and identical phylogenetic differentiation (Fig. 2 ). As expected, we found a less structured tree in the PEPCK gene for the lapponicus-sylvicola group. For the two gene fragments, the lapponicus-sylvicola group resulted in two lineages: (a) one comprising all lapponicus and a part of sylvicola specimens (group A, 1. 92% of divergence between COI sequences) and (b) a second lineage comprising the second part of sylvicola (group B) (Fig. 2 ). While genetic analyses based on the mitochondrial gene COI showed a slightly supported divergence between B. lapponicus and B. sylvicola group A (Fig. 2B ), there was no differentiation between these two taxa for the nuclear marker PEPCK (Fig.
Contrary to the COI marker, the phylogenetic affinities inside the group including interacti sp. nov. , monticola, and konradini were not resolved with the nuclear PEPCK fragment (Fig. 2A ). PEPCK and COI sequences of B. interacti sp. nov. have been blasted to NCBI Genbank database. Sequences matched most closely to the studied species complex lapponicus-sylvicola-monticola but with no complete identity (99% of identity and 100% of query cover for PEPCK, 96% of identity and 97% of query cover for COI from B. monticola in Genbank). In our phylogenetic analyses, B. interacti sp. nov. differs significantly from B. melanopygus and B. glacialis (high branch supports and posterior probabilities, Fig. 2A, 2B ). The bGMYC analysis (Fig. 3 ) highlighted nine entities with low probabilities (<0. 05) to be conspecific with the other ones.
interacti sp. nov. (Table  3 ). Morphological, genetic and semio-chemical datasets support the presence of two biologically distinct taxa within sylvicola sampling from Alaska. CLGS results strongly support also the new species B. interacti sp. nov. with different major and indicator compounds from monticola, konradini, lapponicus lapponicus and lapponicus sylvicola. Although we have a restricted sampling and different tree topologies between the two genetic markers, nuclear gene analysis suggests that B. interacti sp. nov. is closer to B. monticola (also consistent with the morphology, see Table  2 ), a strictly European taxon. This could question the distribution of their potential common ancestor around the Arctic Circle. One hypothesis could be that the speciation of B. interacti sp. nov.
Martinet/Rasmont St88, PRAS1045; (2) red, printed with 'HOLOTYPE'; (3) white, printed with "det. Martinet & Rasmont 2016, Bombus interacti Martinet, Brasero & Rasmont". The left anterior leg is missing as it was removed for genetic analysis. The type has been deposited in the Royal Belgian Institute of Natural Sciences in Brussels. Genbank accession numbers: MG280603 (COI), MG280606 (PEPCK).
Acknowledgements We warmly thank  Pierre ROPOLO  for providing data about the lower Aptian Ancyloceras of the  Cassis-La Bédoule area. The constructive comments of the Editor-in-chief and reviewers  Ottilia SZIVES  ( Budapest, Hungary ) and Yves DUTOUR (Aix-en-Provence, France) are highly appreciated and greatly improved the manuscript. We are grateful to  Simon MITCHELL  for the improvement of the English manuscript. J. E. , BAUDOUIN C. , BOSELLI P. , BOSELLI M. , KINDLER P. & FÖLLMI K. B. (2015). -Threesuccessive phases of platform demise during the early Aptian and their association with the oceanic anoxic  Selli episode (Ardèche, France). -Palaeogeography,Palaeoclimatology, Palaeoecology , vol. 418, p. 101-125. RAGAZZI M. (1982, unpublished). -L'Aptiendans les Alpes Maritimes: stratigraphie, paléoécologie, sédimentologie.
Only two probes discriminated between the two soils when assessing bulk soil samples, while 13 probes separated tuberospheres of the resistant cultivar Kariera, and 26 probes those of the susceptible cultivar Agria (Fig. 3A ). Considering the entire dataset including both tuberospheres of the cultivars and bulk soil, the samples from suppressive and conducive soils were significantly distinguished by the signals of 22 probes (Metastats p < 0. 05). Among 13 of them with higher hybridization signals in suppressive soil, the most significantly contributing probes were Aceto3A, Acdp821, Aci1 (targeting the family Acetobacteraceae), PalgiG3 (Paenibacillaceae), Pseu33 (Pseudomonadaceae), Strepto5 (Streptomycetaceae), and Brady4 (Bradyrhizobiaceae). Nine of the probes were significantly higher in conducive soil, and probes Janaga 2 and 3 (Oxalobacteraceae) contributed most significantly to the separation of the two soils (Table  S5A ).
Melting curves were recorded to ensure qPCR specificity. Baseline and threshold calculations were performed with the StepOne v. 2. 2. 2 software. The inhibition was tested by serial DNA dilution from each site, and the dilutions without inhibition of qPCR reactions were used for quantification. All qPCR measurements were done in duplicate. Standards for qPCR were prepared by cloning fragments of target genes from Streptomyces europaeiscabiei DSM 41802 in pGEM-T Easy vector system (Promega). After PCR verification and isolation of cloned constructs by Pure Yield Plasmid Miniprep System (Promega), a linear standard was prepared by cleaving with SalI enzyme (New England Biolabs, UK) in a 200 µl reaction mixture containing 1× reaction buffer, 2 µg circular plasmid, and 20 U restriction endonuclease for 2 h in 37 °C.
16S rRNA gene-based taxonomic microarray. A taxonomic microarray based on DNA probes targeting 16S rRNA genes representing 19 bacterial phyla at different taxonomic levels  23  was used to assess soil samples from potato fields. This microarray was validated previously  23, 25. Twelve probes targeting the genus Streptomyces, as well as S. scabies and relatives (Table  1 ) were added to the previous probe set (1033 probes) in this study. The probe KO 08  51  for genus Streptomyces was obtained via the probeBase server  52  (http://probebase. csb. univie. ac. at). The other 11 probes (20-mers) were designed in this study using ARB sofware  53  (http://www. arb-home. de) and the parameters of the Probe Design function chosen by Sanguin et al. 31, 54. Probe specificity was tested with the Probe Match function in ARB against the reference Silva-104 and with the TestProbe online tool against Silva 126 database 55 (http://www.
Fragments of the eukaryotic18S rRNA gene including the variable region V9 were amplified using primers CS1_Euk1391F/CS2_EukBr 61 (Table  2 ). PCR conditions were according to the standard protocol of the Earth microbiome project (http://www. earthmicrobiome. org): 3 min initial denaturation at 94 °C, followed by 28 cycles of: 45 s denaturation at 94 °C, 60 s annealing at 57 °C, and 90 s extension at 72 °C. Construction of amplicon libraries including the second PCR and sequencing using MiSeq sequencer (Illumina, San Diego, CA) were done at the DNA Services Facility, Research Resources Center, University of Illinois (Chicago, IL). Resulting paired sequence reads were merged, filtered, aligned using reference alignment from the Silva database  55  , and chimera checked using integrated Vsearch tool  62  according to the MiSeq standard operation procedure (Miseq SOP, February 2018)  63  in Mothur v.
1. 39. 5 software  64. A taxonomical assignment of sequence libraries was performed in Mothur using the Silva Small Subunit rRNA Database, release 128  65  adapted for use in Mothur (https://mothur. org/w/images/b/b4/Silva. nr_v128. tgz) as the reference database. Sequences of plastids, mitochondria, and those not classified in the domain Bacteria were discarded. The sequence library was clustered into OTUs using the Uparse pipeline in Usearch v10. 0. 240 software  21  , and the OTU table was further processed using tools implemented in the Mothur software. Distance matrices describing the differences in community composition between individual samples were calculated using the Yue-Clayton theta calculator  66. Analysis of molecular variance (AMOVA)  67  was based on a matrix of Yue-Clayton theta distances.
The consensus sequences were provided by Genewiz. Chromatograms were visualized with Seq-Trace 0. 9. 0  [26]  to check nucleotide variations. The sequences were deposited in GenBank with the following accession numbers: for the predators, MH455610 to MH455618 for COI and MH456371 to MH456401 for 28S; and for Chalcidoidea MH456402 to MH456789 for COI and MH455619 to MH456370 for 28S (S1 Table ).
Molecular identification and phylogenetic analysis We considered sequences differing by one or more nucleotides to correspond to different haplotypes. Analyses were performed on the concatenated 28S and COI haplotypes (multilocus   [22]  (library 'maps'  [23] ). https://doi. org/10. 1371/journal. pone. 0205475. g001 haplotype, abbr. mult-H). Regions of the 28S sequences displaying large numbers of insertions or deletions were removed for the analyses with Gblocks Server  [27]. Haplotype alignment was performed with MEGA version 7  [28]  and the CLUSTALW method  [29]. Blast queries were performed against the NCBI GenBank database and final phylogenetic trees were generated from the haplotypes of each gene with R software  [22] : libraries "ape"  [30] , "ade4"  [31] , and "phangorn"  [32]. Species delineation was performed with (i) the online version of Automatic Barcode Gap Discovery (ABGD)  [33] , with a prior maximal distance P = 0.
All iterations corresponding to the burn-in period (one million iterations) were removed from the output of BayesPhylogenies before subsequent analyses. We used the sump command of MrBayes  [36]  to obtain a summary of BayesPhylogenies outputs and to calculate Bayes factors. Majority-rule consensus trees were then drawn with PAUP � 4 3. 99  [37]  ('contree/Majrule' command) from the output of the BayesPhylogenies analysis (9,000 trees), using the best model selected. Trees were rooted on concatenated Mymaromma anomalum (Blood and Kryger) (Hymenoptera: Mymarommatidae) sequences obtained from GenBank (accession numbers: GQ374772. 1 for 28S and GQ374670. 1 for COI). Mymarommatoidea was recently classified as a sister group of Chalcidoidea  [38].
Molecular and morphological identification Hymenoptera. DNA barcodes were successfully obtained for 763 hymenopteran individuals. For 28S, 731 sequences of 760 bp to 860 bp were obtained. Before Gblocks analysis to identify conserved blocks, the 28S sequences were reduced to 596 bp (without gaps) and assigned to 61 haplotypes. For COI, 386 sequences of 652 bp were obtained, corresponding to 50 haplotypes. COI sequences were shortened for only 10 individuals, with the removal of 5 bp (n = 9) and 31 bp (n = 1) from the start of the sequence. Both genes were successfully sequenced in 348 individuals. Following BLAST queries, we removed the sequences of individuals not belonging to Chalcidoidea. For subsequent analyses, we used data for 506 individuals, corresponding to 50 multilocus haplotypes and seven 28S haplotypes not associated with any COI haplotype.
Within the superfamily Chalcidoidea, ABGD analyses split the multilocus haplotypes into 42 groups, whereas the 28S sequences were split into 31 groups. 1 ), we identified 41 putative species. The intra variation of Signiphora sp. III was not maintained because both had specific 28S haplotype (Table  1 ) and, the bPTP analysis (Table  1 ) and the phylogenetic tree provided support for the monophyly (Fig  2 ). The presence of barcode gap also supported the species division (Table  1 ). For example, within the genus Aphytis, the intraspecific variability was lesser than 0. 005 for COI and 0. 008 for 28S whereas the minimum interspecific distance was 0. 04 and 0. 03, respectively. Morphological identification was possible for 23 of the 41 putative species. For the other 18 putative species, BLAST analyses of the GenBank database matched the DNA barcodes to a taxonomic reference.
Predators. DNA data were obtained for 34 individuals: 25 from Coleoptera Coccinellidae and nine from Neuroptera (Table  2 ). For 28S, 31 sequences of 764 bp to 986 bp were obtained. Before the Gblocks analysis, 28S sequences were reduced to 753 bp and five haplotypes were identified. For COI, eight sequences of 658 bp were obtained and seven haplotypes were.
Only Chrysoperla near comanche was identified morphologically. Molecular identification within Chrysopidae was inconclusive, probably due to the occurrence of cryptic species, as observed in the Northern Hemisphere and Africa  [52]. The larvae of these species are voracious predators known to be generalists but including scale insects among their prey  [51]. Five Coccinellidae species were collected. Coccidophilus transandinus (Sticholotidinae) was identified morphologically, whereas Rhyzobius lophantae (Coccinellidae: Coccidulinae) was clearly identified on the basis of similarities between its DNA sequences and sequences present in the GenBank database. This common biocontrol agent was introduced into Chile in 1931 for the control of Aonidiella aurantii  [19] , and is now available commercially.
Acknowledgments We would like to thank all the growers who allowed us access to their orchards for sample collection. We thank  Paula Molina  and  Angela Romo  for technical assistance in the laboratory. We also thank all those who helped us to collect samples throughout Chile:  Hugo Herrera ,  Francisco Bugueño ,  Sebastian Catalan  and  Isaac Vera  from  Xilema-ANASAC (Quillota) ;  Claudio Salas  from  Instituto de Investigaciones Agropecuarias  ( INIA Intihuasi-La Serena );  Marta Godoy  from  Cabilfrut (Ovalle) ;  Leonardo Fuentes  from  Pontificia Universidad Catolica de Valparaı ´so (Quillota) ;  Alda Romero  from  Pontificia Universidad Catolica de Chile (Santiago) ;  Jean-Claude Malausa  from  Institut National de la Recherche Agronomique (Sophia-Antipolis, France ). This project was supported by the  EU   FP7 Marie Curie IAPP Project  " Colbics " # 324475 , the " Fundacion para la Innovacion Agraria " (FIA) # PYT-2015-0230  and  CONICYT FONDECYT  Postdoctorado No.
This work was supported by: P. A. # 3180344 ,  CONICYT -Fondo Nacional de Desarrollo Cientı ´fico y Tecnologico , Postdoc grant, http://www. conicyt. cl/fondecyt/,No; M. C. # PYT-2015-0230 ,  Fundacion para la Innovacion Agraria , http://www. fia. cl/,No; T. M. # 324475 ,  EU   FP7 Marie Curie IAPP Project  " Colbics ", http://ec. europa. eu/research/fp7/index_en. cfm,No.
The sequences were deposited in GenBank database (https://www. ncbi. nlm. nih. gov/genbank/?) with the following accession numbers: for the predators, MH455610 to MH455618 for COI and MH456371 to MH456401 for 28S; and for Chalcidoidea MH456402 to MH456789 for COI and MH455619 to MH456370.
Acknowledgements This research was partially supported by the  Advanced Functional Materials Center at Linköping University  and the Önnesjö Foundation. The authors thank the  Knut and Alice Wallenberg Foundation  ( Power Paper  project , scholar ) and the  Swedish Foundation for Strategic Research (Synergy project)  for financial support. We would like to thank  ARKEMA  for the blocbuilder-SG1® and the  Aquitaine region  for the funding. The authors thank  Arkema  for the CIFRE PhD fellowship allocated to D. M. This work was performed within the framework of the  Equipex ELORPrintTec   ANR-10EQPX-28-01  and the  LabEx AMADEUS   ANR-10-LABEX0042-AMADEUS  with the help of the  French state Initiative d'Excellence IdEx   ANR-10-IDEX-003-02  and the  LCPO/ Arkema INDUSTRIAL CHAIR "HOMERIC "  ANR-13CHIN-0002-01.
After collecting carboxylate exudates, roots were separated from the aerial part and total root length was determined using a scanner (Calibrated Color Optical scanner STD4800 with special lighting system. S/N URUW009925-6714112, Optical Resolution 4800 dpi, max. scan area: 22 × 30 cm) and WinRHIZO (Regent Instruments Inc. , Quebec, Canada) software. The entire root was then dried at 60 °C for 48 h and weighed. The SRL was calculated as the ratio between total root length and root dry biomass. The second plant of the pot was used to determine the intensity of arbuscular mycorrhizal fungi (AMF) root colonization for wheat and barley only. As most species belonging to Brassicaceae, canola cannot be colonized by AMF  18. A root subsample of the plant was cleared in 10% (w/v) KOH, stained with trypan blue  52  , and 30 random sections of 1 cm were observed under a microscope (100x enlarged) to determine the percentage of root surface colonized by AMF.
, 2001; Li et al. , 2018). The analysis of phenological series falls into the statistical domain of longitudinal data analysis  (Diggle et al. , 2002)  where a relatively large number of individuals are observed repeatedly over time. We here focus on the situation where the time sampling means that count data (i. e. number of newly emerged organs between two successive observation dates) are collected instead of time interval data (i. e. time interval between the emergence of two successive organs)  (Denny et al. 2014). Collecting time interval data requires dating a new organ using a morphological stage which may be a source of uncertainty. This uncertainty is minimized in the case of count data since it only concerns a small proportion of the counted organs (often a single one) because of the sequential development of phytomers that helps to determinate in a reliable way the last emerged organ at a given morphological stage.
We then assessed the degree of For each genotype, the segmentations are presented in two rows as the alternation of phase characteristics (mean and SD of the number of weekly emerged leaves computed from the raw data without grouping in the first row and part of between-individual variance in % in the second row) and limit between phases (limit in the first row and interval with credibility 0. 95 for this limit in the second row). The closeness of the limits between phases defines the alignment of this first set of columns describing the segmentations. The number of phases selected by the slope heuristic, the posterior probabilities of the multiple change-point model selected by the slope heuristic, and the optimal segmentation in the corresponding number of phases are given in the last three columns.
Acknowledgements The authors thank  Karine Guy ,  Marie Noële Démené ,  Laurie Lamothe , and  Fabien Barthoulot  for their help with the experiment,  Christine Granier  for fruitful comments on this article, and a reviewer for insightful comments that helped to improve the presentation of this article. The project was funded by  Nouvelle Aquitaine region  ( FraFlo  project) and  European Union 's  Horizon 2020 research and innovation program  (grant number  679303 ,  GoodBerry  project).
Data and Study Zone For this study, 4 satellite images of different dates were used; a Landsat 8 OLI image (i. e. summer: 15-08-2015), and 4 Sentinel 2A images acquired at different seasons  (i. e. respectively spring: 12-05-2017, summer: 28-08-2016, autumn: 17-10-2016 and winter: 25-01-2017)  were used. Landsat 8 image was pan-sharpened to 15m with 9 bands in VIS-NIR-SWIR domain, Sentinel-2A images were pansharpened at 10m with 13 bands in VIS-NIR-SWIR domain, pan-sharpening step was carried using nearest-neighbour (NN) interpolation method. For Sentinel-2A, the acquisition of four seasonal monoannual images was not possible due to cloud presence, images of 2016 and 2017 were used instead. In parallel, two summer images from airborne hyperspectral imaging system Rikola were used, with respectively 16 bands and 64 bands in VIS-NIR domain at 0.
NDVI Nir_IR Red / Nir_IR Red (1) First, the images were pre-processed if necessary, satellite imagery were pan-sharpened to the highest spatial resolution, no atmospheric correction was needed since the available images were already corrected. Airborne imagery doesn't requires pansharpening since all bands were acquired at the same resolution, they were nevertheless converted to reflectance using MODTRAN radiative transfer model  (Matthew et al. , 2000). First step of the method aims at defining an optimal threshold of NDVI for vegetation pixels extraction, for that purpose, a precise study of NDVI useful bands (i. e. red and infrared) was carried for all the available datasets. The goal was to test NDVI index behaviour over vegetated pixels, for this purpose, ten pixels were randomly chosen from a collection of ground truth vegetation pixels, the bands corresponding to minimum and maximum peaks in red and infrared intervals were extracted for each pixel, optimal NDVI bands will corresponds to the most recurrent bands from the 10 test samples.