dataesr/scientific-paragraphs-categorization huggingface dataset:
Overview of the toolbox The toolbox is designed as an easy-to-use, free and open-source solution for the detection, monitoring and impact assessment of IAPs through remote sensing (Fig. 1 ). It consists of a series of tutorials that include exemplary datasets of ground surveys, LiDAR point cloud data, hyperspectral images and several R functions to: • implement an optimal sampling design for ground surveys; • map and model the realized and potential distributions of IAPs within the invaded range, respectively; and • assess the impact of IAPs on some ecosystem functions. The toolbox tutorials are organized into two main sections: • data preparation and • applications (Fig. 1 ). The first section contains three tutorials on hyperspectral data, LiDAR data processing and the implementation of the method and R functions to generate an optimized systemic sampling design.
Data processing There are three main types of data used in the toolbox: hyperspectral data; LiDAR data; and field data. Each type of data requires a specific processing that is explained in a dedicated tutorial. The first tutorial, hyperspectral data processing guidelines, includes a procedure to filter specific bands with values that might add noise to the analysis (e. g. water vapor) and the steps recommended to have the data ready to use in R (R Core Team 2015). The LiDAR guidelines include instructions on how to import the data (in LAS format) into R and extract a high-resolution digital terrain model (DTM), as well as various canopy height metrics and statistics. This part of the tutorial is based on an interface between GRASS GIS 7. 0 (GRASS Development Team 2015) and the R environment, which offers the possibility to ecologists (who are more familiar with R) to use GRASS GIS commands from the R command line via rgrass7 package (Bivand 2016).
Finally, in the third part of this section, highlights an approach that optimizes the sampling of observed presence-absence data of IAS in the field and the handling of absence data for subsequent analyses. The tools are presented as part of the "iSDM" R package  (Hattab et al. 2017) , that provides functions to facilitate the setup of ground surveys that optimally capture the environmental variation available within the studied area through a systematic sampling design within the environmental space and, to assess the probability that observed field absences are contingent and thus should only be used for mapping the realized distribution but not for modeling the potential distribution (Fig. 2 , Hattab et al.
2017 ). These models (cf. SDMs) produce maps depicting the areas of potential invasion risk, the areas that require close surveillance and those that require mitigation actions  (Guisan et al. 2013). In this section, guidelines to run SDMs are provided as well as a set of observations to run SDMs for IAS in a "best practices" framework. This section also combines the outcomes from the LiDAR data processing with those derived from the systematic sampling design proposed in the toolbox. Overlook of the "iSDM" R package functions presented in the toolbox. The last part in the applications' section deals with the assessment of the impact of plant invasions on the ecosystem functioning. The approach focuses on the impact of IAP species establishment on the nutrient status of the native plant communities  (Aerts et al.
Data resources The demonstration dataset was generated to provide an easy-to-download and easy-to-use real-world data set including ground surveys, hyperspectral and LiDAR data of actual alien plant invasion cases. One initial challenge when using LiDAR and hyperspectral data is the large size of the files that often require high computer power. For example, the original hyperspectral data file of one of our study site was 27 Gb and the raw point cloud LiDAR file was 413 Gb, which are typical dataset sizes for airborne imagery. To overcome this high demand of computational power, we developed a checkerboard approach (Fig. 4 ) that reconstructs a hyperspectral/LiDAR image using only information from areas relevant for model calibration and validation, together with information from a set of background areas.
This way we derived snippets from the original images that were in a second step rearranged to an image mosaic (Fig. 5 ). The resulting reconstructed image is similar to a checkerboard where each cell consists of the extracted hyperspectral/LiDAR values for a specific plot. After the reconstruction, plot field data were relocated to match the spatial locations in the reconstructed image and provide a complete but much lighter dataset with real hyperspectral and LiDAR data, and ground data. Example of the workflow used for the mapping of alien plants. The same approach was used for all the tutorials. The dataset consists of two such reconstructed hyperspectral images with 248 spectral bands and spatial resolutions of 1. 8 m x 1. 8 m (Fig. 5A ) and 3 m x 3 m (Fig. 5B ) for the island of Sylt (Germany) and for the forest of Compiègne (France), respectively.
A set of LiDAR-derived rasters with the same spatial resolutions and, a corresponding table of plotbased field. The field data include repositioned geographical coordinates (in the reconstructed image space), grid categories (i. e. calibration, validation, background) and the percentage cover of the invasive alien moss species Campylopus introflexus (Hedw. ) Brid. , 1819 (Sylt island) and the invasive alien tree species Prunus serotina Ehrh. , 1788 (forest of Compiègne). Additional field data are provided for the forest of Compiègne on the community weighted mean of leaf phosphorus and nitrogen concentration for plots with varying native and alien invasive species abundance. The original remote sensing data were acquired in two flights and field campaigns within the DIARS project. For Sylt island, an APEX (Airborne Prism Experiment) sensor covering a spectral range between 412 and 2432 nm was used by The Flemish Institute for Technological Research (VITO) to acquire Image reconstruction process work flow.
Acknowledgements This study is part of the project  DIARS  (Detection of invasive plant species and assessment of their impact on ecosystem properties through remote sensing) funded by the  ERA-Net BiodivERsA , with the national funders: ANR; BelSPO; and  DFG. The authors would like to thank the  Office National des Forêts (ONF)  for granting permission for leaf sampling and for providing the airborne LiDAR data.
Z-boson production in association with jets at 13 TeV The measurement of Z-boson production in association with up to seven jets is reported here using 13 TeV data recorded in 2015 with ATLAS  [9]. The Z boson is reconstructed using both electron and muon decay channels. The cross sections are measured as a function of inclusive and exclusive jet multiplicities and multiplicity ratios, the transverse momentum of the leading jet p jet T and its rapidity y jet , the scalar sum H T of the transverse momenta of all selected leptons and jets, and several other differential distributions involving jets. The jets are reconstructed with anti-k T algorithm  [5]  within the radius of R = 0. 4. For this analysis, they are required to be with p jet T > 30 GeV and |y jet | < 2. 5. Figure  2  shows the comparison of measured cross sections with the various theoretical calculations for inclusive jet multiplicity and as a function of H T for inclusive Z+≥1 jet events.
W -boson production in association with jets at 8 TeV The measurement of W -boson production in association with up to seven jets is performed using 8 TeV ATLAS data, recorded in 2012  [10]. The cross sections are measured as a function of jet multiplicity and eight kinematical observables which are reconstructed from jets and leptonic decay products of the W -boson, allowing to test pQCD under various conditions. The data are measured for W , W + , W -production and the cross section ratio W + /W -. Differential cross section measurements of W + and W -production and their ratio are sensitive to the PDFs for up and down quarks. This measurement is performed in the electron decay channel (W → eν). The W candidates are selected by requiring p e T > 30 GeV, E miss T > 30 GeV and m W T > 60 GeV, in order to reduce the backgrounds.
We applied a multiple correspondence analysis (MCA)  (Husson et al. 2010 ) using the Burt table method  (Burt 1950)  to assess the response patterns of the survey participants. MCA is an extension of correspondence analysis (CA) and elucidates the relationship patterns among several categorical dependent variables  (Burt 1950). We included the following variables in our qualitative data matrix: years of experience, role, region where they have worked (based on regional management units [RMUs] selected by respondents), species they have experience with (based on RMUs selected by respondents), region and species considered as highest priority for future research, research topic with highest priority, and most important barriers hindering research. The questions for 'role' and 'years of experience' were coded as supplementary variables; all other questions were coded as qualitative variables.
Metastudies incorporating data from multiple regions and/or species collected by different organizations can substantially increase the power and significance of research  (National Research Council 2010 , Wallace et al. 2010a , 2011 , Bjorndal et al. 2013 , 2016 , 2017 , Fossette et al. 2014 , Mazaris et al. 2018). This is of importance given that immature turtles are such a large component of the population, and large sample sizes spanning multiple years are needed to draw strong inferences. Thus, establishing collaborations with groups or agencies that have long-term data on immature sea turtles can facilitate data analyses and publication. For instance, there may be valuable databases with fisheries managers on bycaught sea turtles that may provide greater insight into behavior and migration  (Riskas et al.
Further sources of information on immature sea turtles might be found by considering unpublished data housed in various facilities and institutions. In developing countries/low capacity regions (e. g. Indian Ocean, Caribbean), it is important to use local ecological knowledge and integrate the community into monitoring projects  (Stephenson et al. 2016 , Hamel et al. 2018 , Barrios-Garrido et al. 2018). For example, encouraging the access and sharing of data from reporting schemes with small-scale fishers could shed light on occurrence, distribution, hotspots, and threats to immature sea turtles. Also, a way to facilitate knowledge accessibility is to foster data and experience sharing through databases or web portals (e. g. seaturtle. org, Climate Adaptation Knowledge Exchange: cakex. org), so new projects can learn from both success and failure stories, leading to potential savings in time, resources, and efforts.
Acknowledgements. This research was conducted under Florida State University Ethics Approval HSC Number  2015. 16178. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the  U. S. Government. We appreciate the editorial assistance provided by  Daniel Sloane ,  Janine Powell , and  Michael Cherkiss. We are also thankful for the support and comments provided by the editors and reviewers.
, 2016). To this aim, large-scale datasets and novel analytical methods are required  (Manel & Holderegger, 2013). White et al. (2010)  were among the first to show that larval dispersal was a better driver of genetic structure than geography measured as the mere Euclidean distance. Selkoe et al. (2010)  moreover demonstrated the importance of considering other ecological and environmental factors to explain seascape genetic patterns. In their study, they showed that habitat (i. e. , kelp coverage) was the main variable shaping genetic structure for three fish species, thus demonstrating that beyond larval dispersal, the survival capacity within a given habitat also affects gene flow. Seascape genetic patterns can also be shaped by other environmental variables such as temperature or salinity through isolation by adaptation  (Nosil, Funk, & Ortiz-Barrientos, 2009).
| Genetic data and analyses Extraction of genomic DNA was undertaken using the DNeasy Blood & Tissue Kit (Qiagen) according to the manufacturer's protocol. DNA quality was assessed by running 3 μl of each DNA sample on 1% agarose gels. DNA concentration was determined using NanoDrop 8000. A total of 558 DNA samples that fulfilled the quality and quantity criteria (DNA of high molecular weight, with at least 40 μl of DNA available at a concentration >10 ng/μl) were then sequenced. Individuals were genotyped using genotyping-by-sequencing technique (GBS) based on the use of restriction enzyme digestion to sequence a reduced portion of the genome  (Elshire et al. , 2011). Libraries were constructed using restriction enzyme ApeKI (recognition site: GCWGC) following a protocol modified from  Elshire et al.
(2011). Six 96-plex GBS libraries were prepared and sequenced at the Institute of Genomic Diversity at Cornell University using the Illumina HiSeq 2500 (100 bp, single-end reads). Each library was sequenced on a separate HiSeq flowcell lane. Raw read sequences were filtered according to quality base calling, removing sequences with average Phred quality below 25. Trimming was performed to remove low-quality bases at the extremities of the reads (Phred quality below 20). Sequences shorter than 60 bp were discarded from the dataset. For each sequenced library, the number of raw reads and filtered data are provided in Table  S1. SNP calling per pool was performed using the Tassel 3. 0 Universal Network-Enabled Analysis Kit (UNEAK;  Lu et al. , 2013). We refiltered the individual raw sequences with Stacks software  (Catchen, Amores, Hohenlohe, Cresko, & Postlethwait, 2011) , which confirmed the low coverage and high rate of missing data for individual genotype calling.
To overcome this bias, we considered pools of individuals sampled at the same site rather than the individual genotype information. This strategy produced a dataset of 47 "pools" (i. e. , sampling sites) containing between nine and eighteen individuals (Table  S1 ), and whose sequence coverage was >10× at 1153 SNPs. The parameters used in both UNEAK and Stacks are detailed in Appendix S1. We verified that the uneven sample sizes did not influence the estimations of allele frequencies in the 47 pools (see Figure  S2 ). To remove the potential confounding effect of SNPs under selection, we eliminated candidate SNPs (see  Dalongeville, Benestan, Mouillot, Lobreaux, & Manel, 2018). Six genome scan methods were applied to detect selection signal, and the sequences of all the outlier SNPs were blasted on known annotated genes in the NCBI's nr database  (NCBI Resource Coordinators, 2017).
The blast analysis identified 30 sequences corresponding to genes. We removed the 30 SNPs corresponding to these sequences to produce the final allele frequencies at 1123 SNPs in 47 sites for subsequent analyses. Genetic differentiation between the pairs of 47 sites was quantified by the Wright's pairwise F ST , calculated using the R package "polysat"  (Clark & Jasieniuk, 2011). To test for isolation by distance (IBD), we performed a Mantel test between pairwise F ST computed from allele frequencies and marine geographic distances, computed as least-cost path distances with infinite resistance values assigned to landmasses. The Mantel test was performed using the R package "vegan"  (Oksanen et al. , 2016)  and its significance using 10,000 randomizations.
AEMs were computed using the R package "aem"  (Blanchet, Legendre, & Gauthier, 2015). The db-MEMs and AEMs associated with the largest eigenvalues represent the large-scale spatial structure in the data, while those associated with smaller eigenvalues reflect finer-scale variations  (Borcard & Legendre, 2002; Dray et al. , 2006). The spatial scale represented by each db-MEM can be broadly estimated using the method described in Figure  3a. For example, db-MEM 1 describes spatial relationship among the study sites at about 4,500 km (Figure  3b ), and db-MEM 9 describes the spatial relationship at about 600 km (Figure  3c ).
| Variable selection To reduce the number of predictors used in the regressions, we first reduced the number of variable within each type of spatial predictors (db-MEMs and AEMs). As we have a high number of SNP, we used the loading scores of the 47 sites on the first axis of a PCA computed on all SNP allele frequencies as a response variable. The spatial predictors were selected using both forward selection and backward selection based on the Bayesian information criterion (BIC) using the MASS package version 7. 3. 45 (Venables & Ripley, 2002). This step allowed us to build a set of seven db-MEMs and six AEMs. Then, we calculated Pearson's correlation coefficient between each pair of explanatory variables (db-MEMs, AEMs, SST, and SSS) and removed one of the variables when the correlation was higher than 0.
| Regression analyses We performed multiple linear regressions between the allele frequency at each SNP and the twelve explanatory variables accounting for the respective influence of environment, geographic isolation, and larval dispersal (Figure  1c ). The explanatory power of a given model, the Akaike weights (AIC w ;  Burnham & Anderson, 2002) , was calculated from all possible models (i. e. , using all possible combinations of the 12 explanatory variables). The relative importance of each predictor variable at explaining allele frequencies of each SNP was estimated by summing the AIC w values across all models that included that variable and averaged over the 1123 SNPs to obtain their global contribution (ω). Analyses were conducted using the R package MuMIn version 1. 15. 6  (Barton, 2016).
We tested the significance of the differences in mean ω among the twelve predictors using a Kruskal-Wallis test  (Hollander & Wolfe, 1973) , followed by a post hoc Dunn test  (Dunn, 1961)  to check the significance of pairwise differences, using the R package "dunn. test" version 1. 3. 5. To determine whether geographic isolation or larval dispersal had a larger influence on spatial distribution of allele frequencies, we used the likelihood ratio (LR) tests. This test compares the full model (with both db-MEMs and AEMs) to nested models that consider only F I G U R E 3 Coordinates of the sampling sites on db-MEMs and the spatial scale they represent. (a) Theoretical system illustrating how to calculate the scale represented by db-MEMs. The system covers the same longitudinal extent as our study area (4,500 km) and is constructed from 63 sampling points spaced by an equal distance (the smallest distance between our sampling sites-47 km).
The graph presents the coordinates of these sites on the sixth theoretical db-MEM. The scale represented by the vector is the ratio of the longitudinal extent to the number of cycles of the sinusoid. (b,c) Bubble plots illustrating the db-MEMs 1 (b) and 9 (c) corresponding to the potential spatial scales of variability based on the geographic distances among sites. The size of the bubble reflects the coordinate of the site on the db-MEM. Contour lines (in red) show the db-MEM scores. The bubble plots have been created using the "ordisurf" function of the "vegan" R package version 2. 4-6  (Oksanen et al. , 2016)  geographic isolation (five db-MEMs) or only larval dispersal (five AEMs). The LR test was performed only on the best models (AIC min ) for each SNP with at least one significant explanatory variable (  693 SNPs).
Eva Maire, Sebastien Villeger, Johanna Brun-Vitelli, Manuel Muntoni, Claire Seceh, Sofiène Ben Abdelhamid, Achraf Barbena, Itai Granot, Ori Frid, and Shahar Malamud for collecting M. surmuletus samples, as well as all the fishermen that provided the fishes. We are grateful to the SMGE platform, and especially Veronique Arnal and Marie-Pierre Dubois, for technical support during molecular analyses, and we thank Laura Benestan for her expertise in the statistical analyses. This study was funded by the "Total foundation" through the SEACONNECT project, PSL Environment through the project RESMOD (ANR-10-IDEX-0001-02 PSL), and the CNRS project PICS France Canada. Part of the computations presented in this article were performed using the Froggy platform of the CIMENT infrastructure (https://ciment.
Funding information " Total foundation " through the  SEACONNECT  project;  PSL Environment  through the project  RESMOD, Grant/Award Number :  ANR-10-IDEX-0001-02   PSL ;  CNRS  project  PICS France Canada ;  Rhône-Alpes  region, Grant/Award Number:  CPER07_13   CIRA ;  Agence Nationale pour la Recherche  through the  Equip@Meso project ,  Grant/ Award Number :  ANR-10-EQPX-29-01.
A new aerosol module was developed and integrated in the air quality model CHIMERE. Developments include the use of the Model of Emissions and Gases and Aerosols from Nature (MEGAN) 2. 1 for biogenic emissions, the implementation of the inorganic thermodynamic model ISORROPIA 2. 1, revision of wet deposition processes and of the algorithms of condensation/evaporation and coagulation and the implementation of the secondary organic aerosol (SOA) mechanism H 2 O and the thermodynamic model SOAP. Concentrations of particles over Europe were simulated by the model for the year 2013. Model concentrations were compared to the European Monitoring and Evaluation Programme (EMEP) observations and other observations available in the EBAS database to evaluate the performance of the model. Performances were determined for several components of particles (sea salt, sulfate, ammonium, nitrate, organic aerosol) with a seasonal and regional analysis of results.
As dust and sea salt are mainly coarse particles, these two processes can lead to the formation of coarse nitrate, whereas ammonium nitrate will mainly remain in fine particles. To simulate PM concentrations, models have to take into account the microphysics of particles (condensation/evaporation, coagulation, nucleation), chemical mechanisms for the gas-phase chemistry, aerosol thermodynamics, emissions and deposition processes. In the scope of this study, a new aerosol module has been developed in a modified version of the CHIMERE model. This new model version is referred to hereafter as CHIMERE 2017β. The results of the model were evaluated by comparison to measurements of PM 2. 5 and PM 10 concentrations but also of composition (Cl, Na + , SO 2- 4 , NO - 3 , NH + 4 , organic carbon). The aerosol module include the following processes: -Biogenic emissions are computed with the Model of Emissions and Gases and Aerosols from Nature (MEGAN) 2.
Method The CHIMERE 2017β is based on the CHIMERE 2013 version  (Menut et al. , 2013)  which is modified by implementing the new aerosol module and chemical mechanisms. Table  S1  in the Supplement provides a comparison of algorithms between CHIMERE 2017β with CHIMERE 2013 to illustrate the differences between the two versions. CHIMERE 2017β was then evaluated for the simulation of PM concentration and composition over Europe in 2013. The model uses a sectional approach where particles are separated into several diameter bins. In this study, particles were separated into 10 bins from 10 nm to 10 µm.
Biogenic emissions Biogenic emissions are computed with the MEGAN 2. 1 algorithm  (Guenther et al. , 2012) , which is implemented in CHIMERE. It uses meteorological conditions (temperature, solar radiation and soil moisture), the leaf area index and the plant functional type (PFT) to compute biogenic emissions. In this study, the above-canopy model is used. The effects of soil moisture on isoprene emissions are not taken into account because no wilting point (i. e. , the soil moisture level below which plants cannot extract water from soil) database is available over Europe. Therefore, isoprene emissions may be overestimated during dry periods. High spatiotemporal data (30 arcsec every 8 days) generated from MODIS  (Yuan et al. , 2011)  are used for LAI inputs. The 30 arcsec USGS (US Geophysical Survey) land-use database is used to provide information on the plant functional type.