dataesr/scientific-paragraphs-categorization huggingface dataset:
Although studies implementing field surveys that cover all types of absence data are more likely to discriminate between the potential distribution and the occupied distributional area, this distinction may be arbitrary and the selection of field-confirmed absences in the broad sense already considerably improves model reliability  (Guillera-Arroita et al. , 2015 Leroy et al. , 2018) (Figure 2. 2). Environmental predictor maps (Figure  2. 1) are often freely downloadable and can be merged with occurrence points using a basic GIS application. Global climate data are widely available for researchers to model past, current, and future climate projections (e. g. , WorldClim and CHELSA), rendering climate niche modeling an attractive approach for global change research relative to land-use modeling.
g. ,  Lehsten et al. , 2015; Marshall et al. , 2018; Martin et al. , 2013). Climate change scenarios have been outlined by the IPCC and are freely available at chelsa-climate. org and WorldClim. org. At this point, all data are available for modeling, using dismo or another SDM framework (Figure  2. 5). Models are first trained and tested using the occurrence points and (cropped) environmental maps, finally providing a habitat suitability map that reflects the present distribution. The model results are then extrapolated to predict future distributions under the provided scenarios. These projections do not account for dispersal and evolution, and require fine-tuning and contextualization based on species' life-history traits (through partial dispersal modeling, Figure  2. 6) and genetic markers (through discussion of the adaptive potential, Figure  2.
The cartridge was then installed in its housing  (9)  and it was filled with the wetting phase (the alcohol-rich phase of the demixing) for half an hour at atmospheric pressure. Then, cartridge housing was closed, and tank reservoir was filled by aqueous displacement phase, being system purged at a constant flow of 1 L/min for some minutes, to avoid bubble entrapment. The experiment started with a small initial flux imposed on the pump: this value is settled between 0. 1 to 1 L/min. Data pairs (flow imposed and pressure reached) were recorded at each flux step. In fact, pressure varied slowly before stabilization. This evolution was recorded and the slope of the curve pressure vs. time was calculated. When this slope reached a value of 0. 1 to 1 mbar/s, we considered that equilibrium was reached.
Water Permeability Measurements To check if LLDP analysis affected the performance of the cartridges, a simple measurement of water permeability for these filters was performed before and after the LLDP analysis. For such purpose, the reservoir tank (1) in Figure  2  was filled with ultrapure water, then the filter cartridge was installed into the housing cell (after being carefully dried) and pure water was pumped through the filter initially at a constant flow of 1 L/min to remove air bubbles trapped. Finally, water permeability was obtained from the slope of 50 data points (pressure, flow) obtained by consecutive steps of ~0. 5 L/min until 25 L/m and averaging the pressure data, once equilibrium reached, at each step. Water permeability determination was done again after finishing LLDP analysis (previously used filter was cleaned with pure water and dried again for 24 h) and both permeability values compared.
Water Permeability Measurements To check if LLDP analysis affected the performance of the cartridges, a simple measurement of water permeability for these filters was performed before and after the LLDP analysis. For such purpose, the reservoir tank (1) in Figure  2  was filled with ultrapure water, then the filter cartridge was installed into the housing cell (after being carefully dried) and pure water was pumped through the filter initially at a constant flow of 1 L/min to remove air bubbles trapped. Finally, water permeability was obtained from the slope of 50 data points (pressure, flow) obtained by consecutive steps of ~ 0. 5 L/min until 25 L/m and averaging the pressure data, once equilibrium reached, at each step. Water permeability determination was done again after finishing LLDP analysis (previously used filter was cleaned with pure water and dried again for 24 h) and both permeability values compared.
To mediate competition, similar sympatric species are assumed to use different resources, or the same but geographically separated resources. The two giant petrels (Macronectes spp. ) are intriguing in that they are morphologically similar seabirds with overlapping diets and distributions. To better understand the mechanisms allowing their coexistence, we investigated intra-and interspecific niche segregation at Marion Island (Southern Indian Ocean), one of the few localities where they breed in sympatry. We used GPS tracks from 94 individuals and remote-sensed environmental data to quantify habitat use, combined with blood carbon and nitrogen stable isotope ratios from 90 individuals to characterize their foraging habitat and trophic ecology. Females of both species made distant at-sea foraging trips and fed at a similar trophic level.
We estimated handling time was approximately 5-10 min per bird. We retrieved 96 loggers with data (50 NGPs and 46 SGPs). At retrieval, we measured the birds' culmen length and bill depth (gonys depth). Culmen lengths of males and females are non-overlapping  [32, 34]. We found two clearly distinct groups based on culmen length (see Results and electronic supplementary material, figure  S2 ) and thus putatively assigned individuals with culmen lengths greater than 97 mm as males, and those with culmen lengths less than 93 mm as females. For four birds with no bill measurements, sex was determined genetically using the primers 2550F and 2718R (adapted from  [54] ). We additionally determined the sex of 22 other birds genetically to confirm that sex assignments based on bill measurements were generally valid.
Upon retrieval of GPS data loggers, usually around two weeks after deployment (electronic supplementary material, table  S1 ), approximately 1 ml of blood was collected from the tarsus vein using a slightly heparinized syringe with a 25 gauge needle. Approximately half of the blood sample was stored directly in 70% ethanol. The other half was centrifuged within 3-4 h after collection, separated into red blood cells and plasma, and stored in 70% ethanol. Storage in 70% ethanol was preferred to drying because of the field conditions and it does not significantly affect δ 13 C and δ 15 N values  [55]. Samples were then stored at -20°C as soon as possible until preparation for stable isotope analysis. Blood was sampled from 105 individuals, including 82 of the birds successfully GPS-tracked.
GPS analyses Data analyses were done in the R environment  [56]. GPS loggers recorded locations at approximately 60 min intervals. Tracks were filtered based on a 30 m s -1 speed threshold  [33, 41]  in the argosfilter package  [57]. For visualization and broad description, foraging 'trips' were identified based on displacement from the nest. These trips were delineated by 3 or more hours of successive locations further than 200 m from the nest location. After filtering (which removed approx. 0. 02% of locations) and visual inspection, the tracks of 94 individuals (49 NGPs and 45 SGPs) were retained for further analyses. Data exploration indicated two distinct movement strategies-nearby and distant trips. We quantitatively distinguished these two strategies by calculating quantile breaks on trip distance using the classInt package  [58].
To quantify spatial overlap between species and sexes, we first calculated utilization distributions, using the adehabitatHR package  [59]. Bandwidth (h) selection by least-squares cross validation failed to converge and we thus selected h using the 'ad-hoc' method, whereby an initial, high value for h is chosen and incrementally decreased until the resulting utilization distribution starts to break up (undersmoothing)  [60]. Values were h = 0. 8°for all trips together and h = 0. 005°for nearby trips only. Before calculating utilization distributions, we excluded locations where the preceding displacement required a speed greater than 7. 2 m s -1 , with the rationale that these locations were unlikely to represent foraging and would thus bias the utilization distributions. The speed threshold was calculated using the Fisher-Jenks algorithm in the classInt package  [58].
We calculated 95% utilization distributions to represent most of the foraging range of each group, and 50% utilization distributions to represent core areas. We then calculated an overlap measure-Bhattacharyya's affinity  [61] -among royalsocietypublishing. org/journal/rsos R. Soc. Open Sci. 7: 200649 groups, also using adehabitatHR. We tested the null hypothesis of no spatial segregation by permuting the individual track labels (sex or species, as appropriate) 1000 times and calculating overlap for each permutation (e. g. [15] ). Since interannual variation in resource availability and environmental conditions may influence foraging behaviour, we constrained the permutations by year. However, for other habitat analyses (including the random forest model described below) we pooled the data for different years.
The p-values for the permutation tests were estimated as the proportion of times the observed overlap was greater than the permuted overlap. To characterize the at-sea habitat used by individuals, we collated seven candidate environmental variables at each GPS location using the raster  [62] , raadtools  [63]  and xtractomatic  [64]  packages. These were: sea surface temperature, chlorophyll-a concentration, ocean depth, sea surface height anomaly, meridional wind velocity, zonal wind velocity and eddy kinetic energy (details in electronic supplementary material, table  S2 ). Dynamic variables (except for chlorophyll-a concentration) were obtained at a daily resolution and matched to the date of each location. Chlorophyll-a concentration was matched monthly to lower the amount of missing data due to cloud cover.
We then related these variables to species and sex using a random forest classification model-a fast and accurate method that can classify multiple target classes  [65]. The fitted random forest model predicts, for each at-sea GPS location, to which group (NGP or SGP, male or female) the location belongs based on the environmental covariates. We fitted the model in the randomForest R package  [66] , growing 1000 trees. We assessed variable importance as the mean decrease in Gini index. To avoid collinearity among variables, we tested for pairwise correlation among variables. No variables had Spearman's R > |0. 70|  [67] , so we retained all variables in the models.
2‰, δ 15 N < 0. 1‰; valine: δ 13 C < 0. 2‰, δ 15 N = 0. 1‰; seal bone: δ 13 C < 0. 2‰, δ 15 N < 0. 1‰). All internal standards were calibrated against International Atomic Energy Agency standards. Carbon is expressed in terms of its value relative to Vienna PeeDee Belemnite, while nitrogen is expressed in terms of its value relative to atmospheric nitrogen. Hereafter, we use δ 15 N values from the raw plasma samples and δ 13 C values from the lipid-extracted plasma samples. For further analyses, we used δ 13 C and δ 15 N values for the 90 birds where there was enough plasma for lipid extraction; 75 of these birds were tracked. We assessed multivariate normality with Mardia's skewness and kurtosis coefficients [72], tested in the MVN package  [73]. Differences in stable isotope ratio values among groups (species × sex) were tested using a multivariate analysis of variance (MANOVA), followed by pairwise MANOVAs.
To characterize the isotopic niche of each group, we calculated standard ellipse areas corrected for small sample sizes (SEAc), in the SIBER package  [74]. As a measure of isotopic niche overlap, we calculated SEAc overlap as a proportion of the sum of the non-overlapping SEAc areas. We tested these overlap values against a null distribution using the permutation procedure described above for the tracking data. Again, however, we pooled data from the different years for the general stable isotope analyses. While δ 15 N values are mainly used to indicate the trophic position of animals, they are influenced by differences in baseline δ 15 N values that reflect the isotopic gradients in the Southern Ocean (e. g. [49, 75] ). These gradients are captured mainly in the δ 13 C values, but when comparing δ 15 N values originating royalsocietypublishing.
2009 ). We therefore used the basal leaves to assess leaf resource acquisition (associated with light interception and photosynthate assimilation). The largest basal leaf on each plant was sampled when the first flower bud emerged. Each leaf was placed in a test tube filled with deionised water and was stored in the dark for 24 h at 4°C. We measured the fresh weight (g) and surface area (cm 2 ) of each rehydrated leaf with an Epson Perfection V550 Photo digital image scanner, processing the images obtained with WinFOLIA software. Leaf dry mass (g) was then determined by drying the leaves in an oven at 70°C for 72 h. Specific leaf area (SLA) was calculated as the ratio of leaf surface area to dry mass (m²/kg;  Pérez-Harguindeguy et al. 2013). Leaf dry matter content (LDMC) was calculated as the ratio of dry mass to rehydrated fresh mass (mg/g;  Pérez-Harguindeguy et al.
Climatic data for the original locations of the populations We evaluated the effects of two climate variables on each of the 20 functional traits considered: mean minimum temperature of the coldest month and mean annual precipitation, calculated from 1970 to 2000 (Supplementary material S1). Data were retrieved from WorldClim with the 'raster' package  (Hijmans et al. 2019 ) and R software (R Core Team 2018). We selected these two variables, based on the extensive overlap of values between native and introduced regions and the absence of interdependence between them (R² = 0. 004, F (1/194) = 0. 127, p value = 0. 723). We also tested mean maximum temperature of the warmest month, but we did not retain this variable for the analysis because it was correlated with the other selected variables (maximum temperature and precipitation: R² = 0.
Statistical analyses As a first step, we performed a phylogenetic principal component analysis (PCA) incorporating the 20 traits to explore the multidimensional distribution of individual plants from native and introduced ranges, based on the entire suite of traits. The phylogenetic PCA was used, because it accounts for the non-independence of plants derived from the same source population  (Revell 2009). We used the R package phytools (function phyl. pca) (Revell 2012)  for the analysis. To perform this phylogenetic PCA, we provided, as an additional dataset, a phylogenetic tree depicting the relationships amongst individuals derived from the same source population (i. e. the tree is composed of 14 clades, each containing 14 individuals, simply coded as 14 vectors of the 14 plant identification numbers and was built using read.
We performed two analyses of covariance (ANCOVA) for each of the trait combinations, considering the predictor variable and range as fixed factors and population of origin as a random covariate. We accepted the hypothesis (i. e. that the trait associations differ between native and introduced ranges), based on significant interaction terms in each model along with different slopes of regression lines. Interactions were tested by comparing two different models (with and without the interaction term) in LRTs. Both trade-offs are plotted to illustrate the correlation patterns by range. All analyses were performed with the software R (R Core Team 2018). The "nlme" package (function lme) was used for linear mixed models and the "lme4" package (function glmer) was used for generalised linear mixed models.
Acknowledgements This project was supported by  AgriFutures Australia (Rural Industries Research and Development Corporation) , through funding from the  Australian Government Department of Agriculture , as part of its  Rural R&D for Profit program  ( PRJ--010527 ). We thank  M. van Kleunen  and three anonymous reviewers for their invaluable comments. Warms thanks are addressed to the collaborators who participated in seed collection in Australia and Europe:  Michael Widderick ,  Louise Morin ,  Andy Sheppard ,  Raghu Sathyamurthy  and  Mireille Jourdan. We also thank  Johannes Tavoillot  for technical assistance with data collection. We thank  Florian Fort  and  Odile Fossati  for help with data analysis.
Synthesis and Stereolithographic Shaping of the β-TCP Pellets Synthetic β-Ca 3 (PO 4 ) 2 (β-TCP) powder was prepared by aqueous precipitation using di-ammonium phosphate solution (NH 4 ) 2 HPO 4 (Carlos Erba, Val de Reuil, France) and calcium nitrate solution Ca(NO 3 ) 2 •4H 2 O (Brenntag, Tournan en Brie, France) at 35 • C and pH 6. 4 with an initial Ca/P molar ratio of 1. 5 and an aging time of 20 h  [28]. After filtration, the solids were dried at 80 • C and then calcined at 850 • C. To reduce the powder particle size, the powder was ground for 3 h in a ball mill using a high-density polyethylene (HDPE) milling jar and yttrium-stabilized zirconia grinding media. The macroporous sample models (10. 5 mm-diameter round disk and 1. 8 mm thick, 500 µm macro pores size) were designed by CAD software (Figure  4a ).
The models were then sliced (layer thickness = 50 µm) using Creation Workshop software (Datatree3D, Dallas, TX, USA). The data were transferred to the SLA software and equipment (CryoCeram Printer; CryoBeryl Software, Bry, France) for additive manufacturing. The pellets were printed by stereolithography using a high dry matter content slurry (65% dry matter content w/w) prepared with the synthetic β-TCP and a photosensitive (λ= 350-400 nm) acrylic resin (CryoBeryl Software, France). The curing energy was set to 5 mW/cm 2 with a slice thickness of 50 µm. Printing was followed by a series of washing steps in order to remove unpolymerized matter. The pellets were then thermally rebound and sintered at 1050 • C with a heating rate of 5 • C/min at atmospheric air. The printed pellets are illustrated in Figure  4b.
Synthetic β-Ca3(PO4)2 (β-TCP) powder was prepared by aqueous precipitation using diammonium phosphate solution (NH4)2HPO4 (Carlos Erba, Val de Reuil, France) and calcium nitrate solution Ca(NO3)2•4H2O (Brenntag, Tournan en Brie, France) at 35 °C and pH 6. 4 with an initial Ca/P molar ratio of 1. 5 and an aging time of 20 h  [28]. After filtration, the solids were dried at 80 °C and then calcined at 850 °C. To reduce the powder particle size, the powder was ground for 3 h in a ball mill using a high-density polyethylene (HDPE) milling jar and yttrium-stabilized zirconia grinding media. The macroporous sample models (10. 5 mm-diameter round disk and 1. 8 mm thick, 500 µm macro pores size) were designed by CAD software (Figure  4a ). The models were then sliced (layer thickness = 50 µm) using Creation Workshop software (Datatree3D, Dallas, TX, USA).
The data were transferred to the SLA software and equipment (CryoCeram Printer; CryoBeryl Software, Bry, France) for additive manufacturing. The pellets were printed by stereolithography using a high dry matter content slurry (65% dry matter content w/w) prepared with the synthetic β-TCP and a photosensitive (λ= 350-400 nm) acrylic resin (CryoBeryl Software, France). The curing energy was set to 5 mW/cm 2 with a slice thickness of 50 µm. Printing was followed by a series of washing steps in order to remove unpolymerized matter. The pellets were then thermally rebound and sintered at 1050 °C with a heating rate of 5 °C/min at atmospheric air. The printed pellets are illustrated in Figure  4b.
Results are reported as relative risk (RR) and 95% confidence interval (CI). Outcomes between compliant and non-compliant groups were compared by constructing Cox regression or logistic regression models, as appropriate, adjusted for baseline characteristics and therapies that yielded a P < 0. 10 on univariate analysis. Results are reported as hazard ratios (HRs) or odds ratios (ORs) with associated 95% CI and P values. We performed sensitivity analyses of the adjusted relationship between each compliance component and the primary endpoint, all-cause death, using a multivariable Cox model. Statistical tests were 2-sided and considered significant if they yielded P < 0. 05. Analyses were performed using SAS software, version 9. 4 (SAS Institute Inc. , Cary, NC, USA).
These tests were performed on samples containing the same tested hardener concentration. All the obtained results were normalized with respect to the mass of the corresponding samples, in order to compare them. The rheological tests were performed until the appearance of a viscosity complex modulus stabilization after a continuous increase from the first inflection point, meaning that the curing reaction slowed down a lot. This plateau is assumed to be the end of vitrification, as it is confirmed by the G'' drop meaning that the cured resin has no remaining viscous properties in its final glassy state. As the three tested rheological samples were repeatable, only curves obtained with sample 1 are shown thereafter, and compared to NDT data obtained with Plate 1. After these tests, correlations were searched between the rheological curves and the ones obtained during the infusion of the different plates, especially in-situ PZT capacitance, IRT ΔT, acoustic emission (CAE) and pressure.
We amplified the ITS-1 fragment using the primers from  Vogler and DeSalle (1994)  and digested it using the restriction enzyme Hga-1, which produces different banding patterns for T. pityocampa  (6 bands of 152, 131, 97, 70, 33, and  17 bp based on the GenBank accession EF189684), and T. wilkinsoni (5 bands of 232, 153, 66, 33, and 17 bp, accession EF189687). To check the consistency of results, we cloned and sequenced 13 individuals showing typical banding pattern for the species and 5 individuals with an ambiguous or hybrid-like banding pattern. We obtained high-quality consensus sequences using CodonCode Aligner 1. 63 and aligned using ClustalW 4. 0  (Larkin et al. , 2007). We checked sequences for stop codons and double peaks to avoid pseudogenes. We also used 12 microsatellite markers   Rousselet, Magnoux, & Kerdelhué, 2004).
| Analyses of sequence and RFLP data We built COI haplotype and Pho allelic networks using TCS 1. 21  (Clement, Posada, & Crandall, 2000) , including T. pityocampa and T. wilkinsoni reference sequences downloaded from GenBank (COI: GU385906, EF185140; Pho: JX182493, JX182496). We then calculated the number of haplotypes and alleles using DnaSP-5. 10. 01  (Librado & Rozas, 2009). Genetic p-distances between any COI haplotype (respectively Pho alleles) found in the present study and the reference sequence downloaded from GenBank were further calculated using MEGA 6  (Tamura, Stecher, Peterson, Filipski, & Kumar, 2013). We analyzed between and within-group distances for COI haplotypes assigning lineages and species as groups and using Kimura 2-parameter  (Kimura, 1980)  in MEGA X  (Kumar, Stecher, Li, Knyaz, & Tamura, 2018).
| Population genetic structure We performed a Principal Component Analysis (PCA) using the adegenet 1. 4-2 package  (Jombart, 2008)  implemented in R (R Core Team, 2018). We assigned individuals to clusters according to their membership coefficients (q) using a Bayesian inference method implemented in STRUCTURE 2. 3  (Pritchard, Stephens, & Donnelly, 2000)  with Admixture Model and correlative allelic frequency option. We used 100,000 burn-in cycles followed by 100,000 MCMC simulations and performed 10 iterations for each K (number of clusters) from 1 to 6. We selected the K value that best fit the data by examining the curve of ln P(X|K) and calculating ΔK  (Evanno, Regnaut, & Goudet, 2005). After checking consistency of results over all iterations, we used the greedy algorithm of CLUMPP 1.