dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS The numerous discussions that I have had over the past decade with participants in the BioIceAtmos network and with co-authors of Ellison et al. (2017) contributed greatly to my understanding of many of the concepts presented in this paper. I also thank  V. Phillips ,  Lund University, Sweden , for his input to assure that the information in the text concerning physics was accurate;  D. Sheil ,  A. Makarieva , and  V. Gorshkov  ( authors of the Biotic Pump concept ) for their help in clearly explaining this concept; and  F. Fabre ,  INRA-Bordeaux, France , for providing useful input on the clarity of the content of the manuscript.
Data collection Once the observations were complete, the segments or the attacked parts of the whole trees were wrapped in cellophane sheets, on which the position and number of the pins were marked. The sheets were then removed and photographed in the laboratory, and the successive pictures were stitched together prior to analysis. The Cartesian coordinates of the attack holes were measured using IMAGEJ software (v. 1. 49t)  [33]  with the X-axis corresponding to the positions along the length of the tree, while the Y-axis corresponded to the positions around the circumference of the tree with y = 0 corresponding to the upper rim of the tree. In addition to the entrance holes, patches of bark showing a rough texture (mostly located around branches while the other parts of the bark usually showed a smooth surface; electronic supplementary material, figure  S2 ) were delineated on the cellophane sheet.
Spatial patterns The R  [34]  package spatstat  [35]  was used to analyse the spatial pattern of the attack holes and its departure from complete spatial randomness (CSR, which is synonymous to the homogeneous Poisson point processes  [36] ). Spatial pattern analyses were conducted after computing the region of interest defined by the convex hull of the infested patch, i. e. the most compact convex polygon encompassing all the entrance holes at the end of colonization (figure  3a ) whose area was defined as A hull (dm 2 ). The attack density on the convex hull, hereafter referred to as hull density (λ hull ), was computed at different times during colonization as λ hull = H/A hull (holes dm -2 ).
Simulating random colonization with an inhibition distance The mechanisms impeding additional holes within a certain distance of earlier attacks remain unknown, but they could pertain to inhibition or to a lack of stimulation. In this study, however, we will label these unknown mechanisms as 'inhibition' for the sake of concision. Random colonization with an inhibition distance r (cm) was simulated using a simple sequential inhibition (SSI)  [36, 38]  algorithm provided by the rSSI function of the R package spatstat as follows. At each iteration, a new entrance hole was generated at random within the environment and independently of the preceding holes. If the new hole was closer than the distance r to any pre-existing attack, it was rejected, and another random entrance hole was generated.
Data accessibility. Our data are deposited at Dryad Digital Repository: https://doi. org/10. 5061/dryad. 5q7k4 [61]. Author contributions. E. T. , M. L. , J. -C. G. and E. G. conceived the ideas and designed methodology. E. T. and M. L. collected the data. E. T. , E. G. and J. -L. D. carried out the analyses. E. T. and J. -C. G. led the writing of the manuscript. All authors contributed critically to the drafts and gave final approval for publication.
rsos. royalsocietypublishing. org R. Soc. open sci. 5: 170454. 2. 4. 4. Diggle-Cressie-Loosmore-Ford test The Diggle-Cressie-Loosmore-Ford (dclf) test performs hypothesis tests for goodness of fit of a point pattern dataset to a point process model, based on Monte Carlo simulation from the model. It is a test based on a statistic, U = r max 0.
Sequences analysis Bacterial taxonomic assignments were performed using Ion Reporter Metagenomics 16S software v5. 2 (Life Technologies, Carlsbad, CA, USA) with the following analysis pipeline: (a) a minimum Phred score of Q20; (b) only reads with no mismatches in the barcode, up to three mismatches in the primer sequence, and a minimum length of 150 bp were included, and (c) reads were trimmed by primers at both ends. After primer trimming and length checking, reads were assigned to a hash table to get unique sequences and their abundance. Taxonomic assignment for unique sequences with a minimum of 10 copies was performed using MicroSEQ ID 16S Reference Library v2013. 1 (Thermo Fisher Science) and Greengenes v13. 5  (McDonald et al. , 2012)  databases. Reads were grouped into two bins: 90-97% match (approximating the family level) and >97-99% identity (genus level).
This tool also provided information on relative taxonomic abundance by consensus and by primer within the dataset. Rarefaction curves and alpha diversity indices were calculated using the alpha diversity script in the Quantitative Insights into Microbial Ecology software  (QIIME v. 1. 9. 0; Caporaso et al. , 2010). For the diversity estimates, sequences were rarefied at 88,076 reads, which was the smallest read count across all fish samples (sample Micos female 1). The species richness was estimated with the Chao index, and diversity was determined by Shannon index (that quantifies the uncertainty in predicting the species identity in a random sampling) and Simpson index (that quantifies evenness and dominance of the species in the sample)  (Hill et al. , 2003). The number of unique and shared OTUs (Operational Taxonomic Units) at genus level between sexes and populations was visualized with Venn diagrams using the Euleer package in R software  (R Core Team, 2015).
We evaluated the capability of each sequenced region to describe the stomach microbiota composition through the survey of the relative proportion of bacterial taxonomic groups retrieved by each hypervariable region of the 16S rRNA gene. Posteriorly, a non-metric multidimensional scaling (NMDS) ordination plot using Bray-Curtis dissimilarity matrix was constructed with the vegan package in R to depict community structure patterns in two dimensions. Permutation tests (n = 1,000) were used to fit significant water physico-chemical properties (P < 0. 1) onto the ordination plot as vectors. Analysis of similarity (ANOSIM) was used to test for significant differences in microbiome structure between fish samples (999 permutations, P < 0. 001). To analyze the distribution of OTUs at family level with relative abundances >1% of the total reads across all the samples, a heatmap was constructed using the Manhattan dissimilarity matrix and Ward's hierarchical clustering algorithms with the plots package in R.
ACKNOWLEDGEMENTS We would like to thank  Luis Espinasa ,  Carlos Pedraza Lara ,  Victor Simon ,  Ulises Rivera  and  Angeles Verde , who helped with specimen collection for the current study. We also want to thank to  MR Riddle  for her kind suggestions on an early version of the Manuscript. Laura Margarita Márquez Valdelamar  and  Patricia Rosas Escobar ,  Sequencing Service ,  Instituto de Biología, UNAM (IBUNAM) , for their help with the Library construction of 16S rRNA amplicons for the Metagenomic Ion kit. Pachón cavefish picture was taken by  Carmen Loyola , IBUNAM.
Multibeam bathymetric analysis High-resolution bathymetric data were acquired using Kongsberg EM 302 and 710 multi-beam echo sounders. A comprehensive mapping program initially focused near the six sample collection sites, ROV dive sites A-F (see Sect. 3. 1. 1), and then expanded to fill the remaining unmapped zones across the region  (McCulloch et al. , 2017). Post-cruise, bathymetry maps of the canyon and all dive sites were generated at different spatial resolutions (see  Sect. 3. 1. 1). The multi-beam echosounder data was processed using CARIS HIPS and SIPS 7. 0 software. After loading raw data and applying tide corrections, the data were 'cleaned' using the Swath Editor that consists of interactively selecting and rejecting soundings as well as filtering functions, which automatically detected and rejected outliers.
Dissolved inorganic nutrients, including ammonium (NH4 + ), nitrate+nitrite (commonly referred to as just 'nitrate' or NO3 -), phosphate (HPO4 2-), and silica (Si(OH)4) were analysed at the University of Western Australia on a Lachat autoanalyser using standard spectrophotometric methods. Seawater pH, pCO2, calcite saturation state (Ωcalcite), and aragonite saturation state (Ωaragonite) were calculated post-cruise using MATLAB version 1. 1 of the CO2SYS software  (van Heuven et al. , 2011). Stable C and O isotope measurements of 30 seawater samples (sites A, B, E) were undertaken in the West Australian Biogeochemistry Centre at The University of Western Australia. The stable isotope compositions of oxygen and hydrogen were analysed using an Isotopic Liquid Water Analyser Picarro L1115-i. Each sample was analysed six times and then the first four results were discarded in order to minimize any instrument memory effect.
resolution bathymetric mapping. A Sub-Atlantic Comanche 2000m ROV operated by Neptune Marine Services, was employed for the strategic sampling of key macrobenthos from six dive sites (comprising nine dives). A comprehensive list of all samples collected during the cruise and complementary datasets are reported in the SOI Cruise FK150301 Final Report (McCulloch et Biogeosciences Discuss. , https://doi. org/10. 5194/bg-2018-319 Manuscript under review for journal Biogeosciences Discussion started: 2 August 2018 c Author(s) 2018. CC BY 4. 0 License. al. , 2017, see: https://schmidtocean. org/cruise/perth-canyon-first-deep-exploration/). Note that the dive numbers relate to the nine survey and faunal collection dives (see McCulloch et al. , 2017) rather than the SOI dive files, some of which are offset by one due to SOI D07 being a USBL recovery dive.
Data availability. The imagery and seawater data collected during the R/V Falkor cruise (FK20150301) and cruise reports can be found on the Schmidt Oceans Institute website: https://schmidtocean. org/cruise/perth-canyon-first-deep-exploration/, http://www. marine-geo. org/tools/search/entry. php?id=FK150301, seawater metadata, bottle, and CTD-sensor data also at: http://catalogue. aodn. org. au/geonetwork/srv/eng/metadata. show?uuid=579fc6fe-3f22-4101-94d1-3dc85b7d0b36 (metadata), http://thredds. aodn. org. au/thredds/catalog/UWA/RV_Falkor/2015_03_Perth_Canyon/Bottle_data/cataloghtml (bottle data), and http://www. rvdata. us/catalog/FK150301 (CTD data), and subsequent seawater and carbonate chemical analyses are given in the Supplementary file. Additional seawater data collected from earlier cruises are from the GEOSECSv2 and GLODAPv2 publically available data repositories.
The spatial standard deviation of element mass fraction varies among macro-elements and may be up to 50%. Elements considered in respect with ICOS are carbon, nitrogen, phosphorus, potassium, magnesium and calcium and few microelements iron, copper, zinc and manganese. The values of the foliar mass fraction of selected elements are provided in Annex 2 (extracted from the TRY data basis, November 2015, https://www. try-db. org/TryWeb/Home. php). Botanically, 'leaf' includes both the petiole and limb in broadleaved species. As for 'needle' this is the entire needle outside the basal scales in coniferous species. For sake of simplicity, the petiole of leaves is excluded from the 'leaf' accounted for in LMA and FMF X determinations.
However, the foliage sample composition and timing could be changed in few cases: following a clear-cut or a selective thinning of a forest stand, for crop successions, after regular grasslands harvests etc. -Third, depending on the land use type and canopy composition, only plant species contributing significantly to the biogeochemical cycles of GHG are sampled. XP V03-111 October, 1995. Analyse des produits agricoles et alimentaires -Protocole d'évaluation intralaboratoire d'une méthode alternative d'analyse qualitative par rapport à une méthode de référence (Food and agriculture products analysis -Procedure of intra-laboratory evaluation of an alternative method of quantitative analysis in comparison with a method of reference). Annex 1. From  Poorter et al. , 2009. Annex 2. Values of mass fraction calculated from data extracted from the TRY databasis, November 2015.
Histograms show the distribution of mean foliar element mass fraction per plant genus for 8 elements as calculated from raw data extracted from the plant trait TRY Databasis (https://www. trydb. org/TryWeb/Home. php). Raw data were first converted in g element  100g dry mass -1 and then averaged by plant genus. Box plots give 95% range, median and mean value (diamond) and show outliers as black dots. The inset shows the mean value in g element × 100g dry foliar mass -1 and sample size (number of genus). Caution: Dry mass may have been determined at different temperatures (60 to 105 °C) and analytical methods may vary among data sources as well. Leaf petiole may or may not be included. Annex 3. For non-flat leaves and needles, the area measured with the planimeter or projected on the scanner is not the half area.
, 2012 ). An advantage of such an analysis is to generate a distribution of more than 100 identified target compounds with small amounts of particulate matter (from 5 to 10 mg)  (Jeanneau et al. , 2014) , giving a dataset rich enough to differentiate between sources  (Walling, 2013). Here this analytical approach is combined with a PCA to determine the main sources of POM as a function of the sediment size, the catchment size and the rainfall characteristics. The first objective of this paper is to test the suitability of molecular biomarkers derived from THM-GC-MS as a tool to determine the sources of river POM. The second objective is to investigate how the sources of POM changed as a function of the catchment size, particle size of the sediment and the hydrological characteristics of the rainfall events.
Watershed monitoring and sampling strategy Detailed information on monitoring and sampling is provided in  Rowland et al. (2017). Climatological data were obtained from a local station maintained by the Delaware Environmental Observing System approximately 450 m from the 79 ha catchment outlet. These consist of temperature and GEONOR gage hourly rainfall measurements. Stream discharge estimates were obtained at 20 min intervals using a Parshall flume at 12 ha stream location (nested within the 79 ha watershed, Fig. 1 ) and a discharge rating curve calculated from paired pressure transducer and acoustic Doppler velocity meter measurements at a rectangular concrete culvert at the 79 ha location. Suspended sediments were collected using in situ samplers made of 10 cm diameter capped PVC pipes placed vertically in the middle of the stream.
Compounds were identified on the basis of their full-scan mass spectra by comparison with the NIST library and with published data  (Nierop et al. , 2005; Nierop and Verstraten, 2004). They were quantified assuming similar ionization and detection efficiencies between all compounds. This assumption means that the concentrations must be handled as rough estimations. Target compounds were classified into four categories: low molecular weight organic acids, phenolic compounds including lignin and tannin markers, carbohydrates and fatty acids. The peak area of the selected m/z for each compound was integrated and corrected by a mass spectra factor calculated as the reciprocal of the proportion of the fragment used for the integration and the entire fragmentogram provided by the NIST library (Table  S1 ).
9. When two variables were correlated, the least abundant was removed. Then a second PCA was performed. The variables with a correlation lower than 0. 4 with the two first factors (F1: 29. 8 %; F2: 17. 2 % of variance) were removed, resulting in a new set of 71 variables. A third PCA was calculated and a hierarchical ascendant classification (HAC) was calculated using the coordinates of the individuals (end-members and CSSs) on the nine first factors that explained 90. 5 % of the variance of the dataset. This HAC identified Upland soils and Stream bank sediments as minor contributors. Consequently a fourth PCA was calculated removing upland soils and Stream bank sediments from the potential end-members. Similarly to the three previous PCA, CSSs were considered as additional individuals. The coordinates of CSSs on the two first factors (of 10) of this PCA (F1: 40.
Molecular composition of end-members The number of detected target compounds ranged from 49 (SBed#1) to 112 (FH). A Dixon test for extreme value identified the lowest value (SBed#1) as an outlier (p value = 0. 011). Once this value was removed, the number of detected target compounds ranged from 75 (BaB) to 112 (FH). The low value recorded for one of the SBeds could be due to a combination of low OC content with a low analytical efficiency. This sample was removed from the dataset. The distribution of target compounds into chemical families gives the first overview of the molecular composition of OM in the different end-members (Fig. 2 ). In W, Li and FH, the main compounds are phenolic compounds and high molecular weight fatty acids (> C 20 , HMW) that represent more than 30 % of target compounds.
An additional PCA was then calculated using SBed, Li, FH and W as individuals; CSS as additional individuals; and the previously defined list of 71 variables. The two first factors of this PCA explained 64. 1 % of the variance of this final dataset. The projection of end-members and CSS on the plan obtained with these two factors is illustrated in Fig. 4. This projection allows the following to be differentiated: (i) the three groups of end-members, Li, SBed, and a combination of FH and W, denoted by FH-W, and (ii) POM from the two sampling locations. Moreover, the size classes were also separated. From this 2-D projection, an area was defined for each end-member corresponding to the 95 % confidence interval. The results of the source apportionment calculated using this 2-D projection are listed in Table  2.
A HCA using the nine first PCA factors (90. 4 % of the variance) highlighted BaA, BaB and Up as minor contributors, similarly to this step performed on molecular data alone. Then a second PCA was calculated with FH, W, Li and SBed as potential end-members and the CSS as additional individuals. The two first factors represented 64. 4 % of the variance and resulted in a clear differentiation between Li, SBed and FH-W. The same approach was then applied using the molecular data alone, resulting in the calculation of the proportions of those three end-members in the CSS for 10 different combinations of the position of end-members in the 2-D plan created by the two first factors of the PCA. For each CSS a set of 10 values was created for Li-, SBed-and FH-W-derived POM (Table  S2 ). Student's t test was used to compare these distributions between the modality "molecular data" and the modality "molecular + isotopic, elemental data".
Acknowledgements. This study was funded by  NSF  ESPCoR grant no. IIA 1330238  (NEWRnet) and  USDA NIFA  grant no. 2015-67020-23585. We would like to thank the  Fair Hill Natural Resources Management Area  for allowing us to conduct this study in the  Fair Hill Nature Preserve. Many thanks to students who assisted with sampling, including  Erin Johnson ,  Catherine Winters ,  Chelsea Krieg ,  Shawn Del Percio ,  Margaret Orr  and  Daniel Warner. The authors also thank the three anonymous reviewers who participated in the improvement of the quality of this paper. Edited by:  Tom J. Battin Reviewed  by: three anonymous referees.
Acknowledgements. The senior author thanks  M. Reich ,  A. Gehler  and  T. Stegemann  for making the specimen of Faberaspis available and assisting him during his visit to the university museum in Göttingen. We thank  G. Hundertsmark  ( GZG Museum ) for providing the photograph for Figure 2C. This paper was improved by the helpful review of  P. Janvier  and by that of  T. Märss , which was particularly insightful. The publication costs of this article were covered by the  Estonian Academy of Sciences.
Water sampling Seawater samples were collected during the noon cast of each station at different depths using 12-L Niskin bottles for the determination of various parameters. For the determination of Chla concentration and particulate (phytoplankton + NAP) absorption coefficient [a P (λ)], samples were collected at depths corresponding to different % of PAR  (i. e. , 75, 54, 36, 10, 1, 0. 1%)  and filtered [288 mL for Chla and 2. 25 L for a P (λ)] through 25mm Whatman GF/F filters. After filtration, the latter were immediately stored at -80 °C (liquid N 2 ) in Nunc ® cryogenic vials until analysis. Liposoluble pigments were sampled at all depths (LOV laboratory data, OUTPACE data basis, J. Ras). In addition, samples were taken in duplicate at surface and Deep Chlorophyll Maximum (DCM) as part of a NASA satellite validation program.
surface slicks : the "Simbada" transect, with 7 pump samples between SD3 and SD4 in the Melanesian archipelago (MA) (Appendix B), and the High Frequency HF1 transect (31samples), around LDA, and the HF2 transect in the Fijian archipelago (FI) near LDB (42 samples)  (Dupouy, OUTPACE data basis). Besides radiometric measurements and water sampling, in situ measurements were also conducted for the determination of Trichodesmium spp. colonies and backscattering coefficients (see below).
Statistics Ocean Data View sections Schlitzer, R. , Ocean Data View, http://odv. awi. de, 2016  was employed for the spatial representation of biogeochemical parameters over the vertical (0-150m). The spatial interpolation/gridding of data was performed using Data-Interpolating Variational Analysis (DIVA). Principal component analysis (PCA) was conducted on the basis of Pearson's correlation matrices using XLSTAT 2011. 2. 05 for the surface stations, for AOPs and TChla (HPLC). Biogeosciences Discuss. , https://doi. org/10. 5194/bg-2017-570 Manuscript under review for journal Biogeosciences Discussion started: 15 January 2018 c Author(s) 2018. CC BY 4. 0 License.
Oligotrophic stations are on the right side and mesotrophic stations on the left. PC2 represents 9. 4% of the total variance. The variables that have significant correlation with PC2 are nL w (565), (Chla rich waters) and nL w (490) (Chla poor waters), both on the upper side of the PC2 axis. A series of stations is positively linked to this PC2 axis (LDB4, SD1, SD2, LDA-2, SD7) while LDA-3 and LDA-4 are negatively linked to In comparison, the first 2 PCs for the South East Pacific dataset represent 95% of the total variance, with 89% for PC1 and only 5% for PC2 (Fig. 12c ). The main difference is that nL w (  565 ) is no more linked with PC2 but only to PC1, and that for PC2 nL w (490) has an opposite behavior compared to that in the South West Pacific (correlation is negative instead of positive).
, 20109; Brewin et al. , 2012; Martinez Vincente et al. , 2013; Slade and Boss, 2017). Our H6-backscaterring data at OUTPACE compared to the ones of Diapalis (not shown) show that backscattering is enhanced in the presence of Trichodesmium. The layer of the highest backscattering coefficient is situated above the 10m-FTL Trichodesmium and the relationship between the vertical distribution of b bp , and the vertical structure of colonies, detritus and organisms must be explored further. There is a strong link between particulate backscattering and particulate organic carbon  (Stramski et al. , 2008; Evers-King et al. , 2017). The organic carbon content of Trichodesmium filaments was not estimated in the South West Pacific as trichomes counts are not yet available at all depths and stations  (Dupouy et al.