dataesr/scientific-paragraphs-categorization huggingface dataset:
e. molluscs) can be predicted with 89-100% accuracy by temperature plus salinity and productivity (the last two having lesser but significant effects). Toma sov ych et al. (2015)  showed that bivalve species inhabiting seasonally variable mid-latitude environments have broader thermal ranges than species inhabiting less seasonal tropical environments, supporting Stevens' (1989) climatic variability hypothesis. By contrast, in a study based on shallow marine invertebrates of the northern hemisphere,  Pappalardo et al. (2015)  found that range limits predicted from water temperature and species temperature tolerance did not match well with real observations but rather corresponded to patterns of ocean circulation. In addition, using the marine mollusc database of  Roy et al. (1998 ), Parmesan et al.
Acknowledgements. We are grateful to  Kenneth De Baets ,  Sally Thomas  and to an anonymous reviewer for their constructive comments, which helped us to improve the manuscript. We thank  Bruno Vrielynck  ( UPMC ) for the reconstruction of the early Pliensbachian palaeogeographic map of the western Tethys and for the calculation of fossil locality palaeocoordinates that boosted several studies, including this work. Romain Vaucher  is thanked for insightful comments regarding the relationship between western Tethys geomorphology and climate. This is a contribution by the team ' SAMBA' of the laboratory Biog eosciences, Dijon (France).
and Table2). Results of analyses conducted on datasets only species with more than one occurrence are consistent with those conducted on complete datasets(Zaca€ ı et al. 2018, table Western Tethys MED province NWE province A B C Latitudinal range 0 25 30 35 40 45 50 55 25 30 35 40 45 44 46 48 50 52 54 Latitudinal midpoint Latitudinal midpoint Latitudinal midpoint Early Pliensbachian D E F Latitudinal range 5 0 35 40 45 50 34 36 38 40 42 46 48 50 Davoei G H I Latitudinal range 0 25 30 35 40 45 50 25 30 35 40 44 46 48 50 52 54 Ibex J K L Latitudinal range 5 0 25 30 35 40 45 50 55 25 30 35 40 45 46 48 50 52 54 Latitudinal midpoint Latitudinal midpoint Latitudinal midpoint Jamesoni containing.
Strain identification After 48 hours of incubation in microaerophilic conditions, pure colonies were isolated on Columbia agar and identified by proteomic analysis using matrix-assisted desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) following the same protocol as previously described by Seng et al. [7]  with a Microflex spectrometer (Bruker Daltonics, Leipzig, Germany). All obtained spectra of strain SN6 were imported into the MALDI BioTyper software (version 2. 0; Bruker) and analysed by standard pattern matching (with default parameter settings) and compared to those of the BioTyper database and our own collection. Thus, a score of >2 allowed identification at the species level, and a score of <1. 7 did not allow any identification. If the identification of the spectrum from colonies selected and purified several times by subculturing on Columbia agar failed, then the 16S rRNA gene was amplified and complete genome sequencing was carried out as previously described  [8].
The nucleotide sequence obtained was corrected using Chromas Pro 1. 34 software (Technelysium, Tewantin, Australia) and compared to the nucleotide database using the BLAST similarities web services in the online PubMed National Center for Biotechnology Information (NCBI) database (http://blast. ncbi. nlm. nih. gov. gate1. inist. fr/Blast. cgi). As suggested previously, similarity level thresholds of 98. 65% and 95% allowed the definition of a new species or a new genus, respectively  [9, 10].
Genome sequencing and assembly A EZ1 DNA tissue kit was used to extract the DNA of strain SN6 on the EZ1 biorobot (Qiagen, Courtaboeuf, France) after pretreatment by lysozyme incubation at 37°C as previously described  [13]. Genomic DNA (gDNA) was quantified by a Qubit assay with the high sensitivity kit (Life Technologies) and sequenced on the MiSeq Technology (Illumina, San Diego, CA, USA) with the mate-pair strategy as previously described  [14]. The Nextera Mate sample collection kit (Illumina) was used to mix DNA previously barcoded with 11 other projects. An assembly of six different software packages (Velvet  [15] , Spades  [16]  and Soap Denovo  [17] ), on trimmed (MiSeq and Trimmomatic  [18]  or untrimmed data (only MiSeq software) was created from a pipeline and allowed to perform genome assembly.
Genome annotation and comparison Prodigal allowed to predict open reading frames (ORFs) using default parameters  [19]  and those that were spanning a sequencing gap region (contained N) were excluded. BLASTP with an E value of 1e-03, coverage of 0. 7 and 30% identity was used to search the predicted bacterial protein sequences against the Clusters of Orthologous Groups (COGs) database. If no hit was found, it searched against the NR database using BLASTP (E value of 1e-03, coverage of 0. 7 and 30% identity) and an E value of 1e-05 was used if the sequence's length was shorter than 80 aa. The tRNAScanSE  [20]  and RNAmmer  [21]  tools were used to find transfer RNA genes and ribosomal RNA genes, respectively. The number of transmembrane helices and the lipoprotein signal peptides were predicted using Phobius  [22].
ORFans were identified if all the BLASTP performed did not yield positive results (E value smaller than 1e-05 for ORFs with sequence length inferior to 80 aa or E value smaller than 1e-03 for ORFs with sequence size larger than 80 aa). These different parameter thresholds had already been used in previous works to define ORFans. The genomes of each species from the 16S RNA tree used in the comparison were automatically retrieved using Xegen software (PhyloPattern), and the NCBI FTP was used to recover the complete genome sequence, proteome sequence and Orfeome  [23]. When the complete genome of one specific strain was not available, we used the complete genome of the same species. All proteomes were analysed with proteinOrtho  [24]. For each couple of genomes, a similarity score defined by the mean value of nucleotide similarity between all couples of orthologous genes was computed by average genomic identity of orthologous gene sequences (AGIOS) software.
Strain identification and phylogenetic analyses The first colonies of strain SN6 were isolated after direct inoculation of the stool sample on Columbia agar plates under microaerobic condition at 37°C for 48 hours. The bacterial spectrum obtained by MALDI-TOF MS did not match against the Bruker or our own database. Thus, it was incremented in our database (Fig. 1 ). The 16S rRNA sequenced showed that strain SN6 was phylogenetically clustered in the genus of Xanthomonas and presented a sequence identity of 98. 08% with Xanthomonas campestris strain ATCC33913 (NR_074936), the phylogenetically closest species with standing in nomenclature  [3]  (Fig. 2 ), which putatively classifies strain SN6 as a member of a new species within the genus Xanthomonas in the phylum Proteobacteria. Thus, we propose the creation of the new species Xanthomonas massiliensis (Table  1 ).
The 16S rRNA gene of Xanthomonas massiliensis strain SN6 is 1508 bp long and was deposited with the accession number AA00102 in the 16S IHU bank and LN881611 in GenBank. A comparison between the spectrum of the strain's protein level and that of the closely related species on the 16S rRNA tree and present in our database was performed in a gel view (Fig. 3 ).
In all genomes compared, the distribution of genes into COGs categories is identical (Fig. 6 ). Among Xanthomonas species with standing in nomenclature, AGIOS values ranged from 64. 76% between Xanthomonas campestris pv. campestris and Stenotrophomonas acidaminiphila to 79. 65% between Xanthomonas sacchari and Pseudoxanthomonas suwonensis. When comparing Xanthomonas massiliensis sp. nov. to other species, AGIOS values were in the same range, from 66. 21% with Xanthomonas vesicatoria to 80. 88% with Xanthomonas sacchari (Table  7 ). Among the species with standing in nomenclature, we found that by using the digital DNA-DNA hybridization (dDDH) with the GGDC software, values ranged from 20. 8% between Xanthomonas vesicatoria and Pseudoxanthomonas suwonensis to 32. 1% between Xanthomonas gardneri and Xanthomonas axonopodis.
When comparing Xanthomonas massiliensis to other species, the dDDH value ranged FIG. 5. Graphical circular map of genome of Xanthomonas massiliensis strain SN6 T. From outside to center: on forward strand coloured by COGs categories (only genes assigned to COGs), genes on reverse strand coloured by COGs categories (only gene assigned to COGs), RNA genes (tRNAs green, rRNAs red), GC content and GC skew. COGs, Clusters of Orthologous Groups database. FIG. 6. Distribution of functional classes of predicted genes according to clusters of orthologous groups of proteins.
Predominant fatty acids were 13-methyl-tetradecanoic acid followed by 9-methyl-decanoic acid. The DNA G + C content is about 70. 52%. The 16S rRNA gene and genome sequences were deposited in GenBank under accession number LN881611 and FCOY00000000, respectively. The type strain is Xanthomonas massiliensis strain SN6 T (= CSUR P2129 = DSM 100900) and was isolated from human faeces.
Acknowledgements The authors thank the  Xegen Company  (http://www. xegen. fr/)for automating the genomic annotation process, and  M. Lardiere  ( MEPHI ) for English-language editorial review. This study was supported by the  Fondation Méditerranée Infection  and the  French government  under the 'Investissements d'avenir' with the reference Mediterranée Infection  10-IAHU-3.
227925 Energy production and conversion G 130 4. 144087 Carbohydrate transport and metabolism E 193 6. 152375 Amino acid transport and metabolism F 65 2. 0720434 Nucleotide transport and metabolism H 103 3. 283392 Coenzyme transport and metabolism I 146 4. 654128 Lipid transport and metabolism P 157 5. 0047817 Inorganic ion transport and metabolism Q 80 2. 5502074 Secondary metabolites biosynthesis, transport and catabolism R 227 7. 236213 General function prediction only S 150 4. 7816386 Function unknown - 935 29. 805548 Not in COGs COGs, Clusters of Orthologous Groups database. NMNI.
ACKNOWLEDGMENTS M. P. Veldhuis ,  M. P. Berg , and  H. Olff  together developed the concept. M. P. Veldhuis wrote the first draft of the manuscript. M. Loreau contributed the modeling sections and all authors contributed substantially to the revisions. M. Loreau was supported by the  TULIP Laboratory of Excellence  ( ANR-10-LABX-41 ). We thank  Eric S. Higgs  and two anonymous reviewers for their valuable comments on earlier versions of the manuscript. This project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No  641918. [Correction added 12 June, 2018 after online publication. The last funding statement was mistakenly omitted from the original manuscript. ].
, 2014; Wu et al. , 2013). Thus, in our study, drugs with different octanol-water partition coefficients and pKa values were selected. FLM and CBZ were quantified in all samples nearly at all concentration levels, finding, as expected, higher concentrations in the plants at high irrigation concentration. As it can be seen, data show a clear trend of accumulation in root samples for both drugs, reaching very high concentrations in the case of high level exposure concentration for CBZ. However in leaf samples, it can be seen how initially the plant accumulates the highest concentration of drug and later these values gradually decrease until almost disappears, this results do not agree with those previously reported by  Tanoue et al. (2012)  and  Wu et al. (2014)  who found higher concentrations for CBZ in leaves than in roots.
Acknowledgements This work is supported by the Project  CTM2015-67902-C-1-P  from the  "Dirección General de Investigación y Gestión del Plan Nacional de I+D+i (Ministerio de Educación, Cultura y Deporte, Spain )". Authors also want to thank  FEDER  Project  UNSE10-1E-429  and  Servicio de Microanálisis de la Universidad de Sevilla  ( CITIUS, Celestino Mutis ) for the support offered with UPLC-QTOF/MS instrument.
Ionomics Sampled tissues were dried and then mineralized in HNO 3 by using a Microwave Digestion System (Multiwave ECO). Chloroplast and mitochondrial fractions were mineralized in HNO 3 at 100-120 • C according to  Vigani et al. (2017). Metal content was determined by inductively coupled plasma-mass spectrometry (ICP/MS, aurora M90 BRUKER) according to  Vigani et al. (2013a Vigani et al. ( , 2017)). Data collected are from three independent experiments.
A six elements silicon drift diode detector was used to collect the fluorescence signal. XRF data were analyzed by using PyMca software (version 5. 1. 1). Zinc distribution maps are displayed by using the same grayscale for all the samples so that they can be directly visually compared. The relative amount of Zn in chloroplasts and mitochondria was obtained from the XRF sum spectrum of each organelle by dividing the peak area of the Kα line of the element multiplied per 10 3 for the area of the scatter signal, used to normalize the data for synchrotron current variations. The sum spectra were extrapolated from XRF maps by using PyMca and selecting the pixels forming the image of each organelle. All the spectra from each pixel were then summed to have the final sum spectrum. Eight chloroplasts and four mitochondria were considered for each sample.
1 ). Briefly, in the 2013 DS experiment, we divided 296 genotypes into six groups with a 10 d interval based on days to flowering data collected from a previous experiment conducted in the 2012 wet season (WS), our only source of flowering dates for this population grown at IRRI. While the expected range of flowering was 29 March to 8 April 2013 (Fig. 1A ), we observed deviation in days to flowering in the 2013 DS experiment, where the staggered sowing was based on the 2012 WS data. Therefore, in the 2014 DS experiment, we regrouped the 296 genotypes into eight groups with a 7 d interval using 2013 DS flowering data to improve synchrony within the whole population. The expected date of flowering was 28 March to 5 April 2014 for these genotypes (Fig. 1B ). In each year, the sowing date chosen for the stress treatment was the same as for the non-stress treatment of the same genotype.
Genetic analysis of marker-trait associations Two hundred and ninety-one genotypes in 2013 and 288 genotypes in 2014 had complete phenotypic data. However, 20 genotypes were missing from the 45 699 (46K) SNPs dataset resulting in 271 genotypes in 2013 and 268 in 2014, used for GWAS analysis. The detailed genotypeby-sequencing protocol of SNP genotyping, population structure, and linkage disequilibrium (LD) for this population is explained in  Kadam et al. (2017). The GWAS was performed on a set of 271 (  2013 ) and 268 (2014) genotypes separately, with 267 genotypes being common across both years. Two GWAS methods were used to test the marker-trait associations: single-locus and multi-locus analysis. Single-locus analysis is a one-dimensional scan, typically identifying associations between single markers and traits.
Therefore, to make a more logical comparison for the same traits, we reanalysed the number of spikelets per panicle from  Rebolledo et al. (2016)  and yield and yield components from  Kikuchi et al. (2017) , using the same SNP datasets and analysis methods that are used in this study. This comparative analysis identified one locus on chromosome 2 (30 518 548) for panicle weight (equivalent to grain yield) from  Kikuchi et al. (2017)  that overlapped with grain yield locus (Q2: 30 523 925; different SNP but falls within the same LD block; Table  3 ; Supplementary Table  S10 ) from 2013 non-stress conditions. In addition, there was also no overlap of a significant marker for grain yield and its components when comparing with other studies using different mapping panels under reproductive-stage water deficit  (Ma et al.
Acknowledgements This work was supported by an anonymous private donor who provided the financial support, via  Wageningen University Fund , to the first author's PhD fellowship. We also thank  The Federal Ministry for Economic Cooperation and Development, Germany , and the  USAID-Bill & Melinda Gates Foundation  for their financial support. We also thank the  GRiSP (Global Rice Science Partnerships ; now renamed to  RICE CRP consortium)  for establishing the  PRAY Global Phenotyping Network. Dr C. G. van der Linden  and  Dr P. S. Bindraban  are acknowledged for their valuable advice.
Results Approach. We evaluate the PD-FD relationship for different species pools (taxonomic families and geographical assemblages, i. e. , sets of species co-occurring at a given scale) using a large global dataset including trait, phylogenetic, and geographic range data for 4616 species of mammals, 9993 species of birds, and 1536 species of tropical fish. Specifically, we measure FD as functional richness (see Methods) and compute, for any given species pool, an estimate of surrogacy  27, 28  (S PD-FD , Fig. 1 ). S PD-FD represents the amount of FD sampled by the set of species chosen to maximize PD, relative to the FD sampled by optimal set of species selected to maximize FD directly, with both components controlled for the expected FD from a random species set of the same size. S PD-FD will be positive if the averaged PD-maximized set contains more FD than the averaged random set, and negative if not.
This is particularly the case in spatial assemblages containing multiple taxonomic orders, which are both phylogenetically and ecologically divergent from one another. Interestingly, the PD-FD relationship was not consistent across taxonomic scale: we found that, in contrast to patterns at the family level, for certain mammalian and avian orders (which are older than the families described above), using PD to select species is much worse for capturing FD than choosing species at random (see, for example, the Afrosoricidae, Chiroptera, and Charadriiformes in Supplementary Figure  6 ). We then explored whether we can explain this variability within-and between-datasets, and in particular, why for some assemblages/clades, a PD-prioritization strategy fails to capture more FD than random choice.
Evolutionary biologists commonly focus on "unusual" clades with rapid divergences (e. g. , cichlids); we show here that divergence does not have to be that spectacular (e. g. , African carnivores) to alter the PD-FD relationship. Third, we found that while this strategy, on average, captures FD well, it is also somewhat unreliable, and 36% of the time will not capture more FD than random choice. This means that while the PD gambit can be a bet worth taking, it is still a bet with associated risk, not a sure thing. Our objective in this paper is to test the phylogenetic gambit using empirical datasets. This means that we do not aim to provide a coherent prioritization strategy  34  , or ready-to-use conservation guidelines. Indeed, we simplistically and implicitly assume that species will either be saved or go extinct, and we have not linked our various scenarios to any particular policy position or conservation objective other than maximizing FD within a phylogenetic clade or region  28, 30.
Indeed, if it were not for this relative paucity of data, we could simply prioritize species based on their unique contribution to FD directly. Although there have been massive and well-funded efforts to collect and curate trait data from across the Tree of Life  [40] [41] [42]  , we are still far from having comprehensive coverage. Furthermore, despite recent progress  43  , it is still not fully understood which traits are most relevant for responses to environmental change, or that contribute most to certain ecosystem functions and services, and how these vary among systems. Our analysis suffers from a similar data limitation. We chose these traits because they are frequently collected in ecological studies, not because we know they are ecologically important. Our assumption is that their phylogenetic distribution is typical of those traits that are most desirable for the purpose of conservation and that our primary results are therefore widely applicable.
For birds, we repeated our analysis using the full ranges. Finally, we evaluated the robustness of our result to the spatial resolution considered by repeating our analysis at a resolution of 100 × 100 km (number of cells was 13,330) for birds and mammals; we present these results in the supplementary materials, as they are qualitatively identical to those conducted at 200 × 200 km (Supplementary Figure  1 ). For fishes, we used a database of 1536 species, for which we had distribution data, phylogenetic and functional data. Distribution data were extracted from a global-scale distribution database  46. Species composition was then extracted from grid cells of 5°x5°, corresponding to approximately 555 × 555 km at the equator  47. This grain size of the grid was chosen because it represents a good compromise between the desired resolution and the geographical density of information.
For the taxonomic analysis of mammal families, we removed two families (Dipodidae and Echimyidae) because of their very poor phylogenetic resolution (i. e. , polytomies for an important number of species). Trait data. For birds and mammals, four traits (diet, (log-transformed) body mass, activity cycle, and foraging height) were extracted from Elton Traits1. 0  42. These traits are generally assumed to appropriately represent Eltonian niche dimensions within an assemblage or clade of mammals or birds  53, 54. For fishes, we used a previously published database  12. We used 6 categorical traits: size, mobility, period of activity, schooling, vertical position in the water column, and diet (for a full description of the dataset, see Mouillot et al. 12  ). These traits have already been used to investigate community assembly rules  55  and to seek vulnerable fish functions  11.
Surrogacy estimates. We use a common approach  27, 28  to quantify the extent to which a given surrogate (here, the maxPD choice) reaches a certain objective (here, maximize FD). Species from a given pool (i. e. , for each dataset (clade and assemblages) independently,) were prioritized and selected according to (1) the objective, i. e. , maximize FD, producing the "optimal curve" (maxFD curve in Fig. 1 ) the surrogate, i. e. , maximize PD, producing the "surrogate curve" (maxPD curve in Figs. 1  and 3 ) at random (random curve in Fig. 1 ), i. e. , producing the "random curve" (Fig. 1 ). To compute a "surrogacy" estimate of PD (S PD-FD ), we compare the position of the surrogate curve (1) to the random curve (2) relative to the optimal curve (2) (Fig. 1  and Eq. 2) across the deciles of species richness of the pool (given as an interval 0-1): S PDÀFD ¼ Z 1 0 FD maxPD À FD random FD maxFD À FD random ð2Þ This surrogacy metric is at 100% when the surrogate perfectly meets the objective (i.
Acknowledgements This paper is a joint effort of the sCAP working group funded by sDIV, the  Synthesis Centre of the German Centre for Integrative Biodiversity Research Halle-Jena-Leipzig  and the  Canadian Institute for Ecology and Evolution. F. M. was supported by an  NSERC   Accelerator Grant  to A. O. M. and a Banting Post-doctoral fellowship. G. V. D. R. thanks  Mike Steel. We thank  Risa Sergeant  at the  University of Ottawa  for hospitality, the  Crawford Lab for Evolutionary Studies at SFU  for discussion. M. W. C. , D. M. , and C. M. T. are members of the FREE working group at the  CESAB-FRB.
Code availability. R functions developed in this paper are available at https:// github. com/FloMazel/FD_PD_Max Data availability. Mammal and bird datasets are publicly available (see methods). The Fish phylogeny is available on figshare (https://doi. org/10. 6084/m9. figshare. 6430982. v1). The distribution and trait fish datasets are available upon request to D. M. and F. L.