dataesr/scientific-paragraphs-categorization huggingface dataset:
Question 3: Temporal dynamics of invasion costs The cost over time of all UK invasive species was calculated via the summarizeCosts function of the 'invacost' R package  (Leroy et al. 2020). This function illustrates the dynamics of costs over time, projecting the mean cost per decade, as well as the mean cost over the entire reported period (i. e. from 1976 to 2019; the last year with robust, reported costs). Using first record information from the sTwist database, we used linear regression to examine the relationship between the length of time a species has been reported as invasive in the UK and its total invasion cost. First record information was available for 35 species reported in InvaCost (of the 42 species with individual cost entries). Both time since introduction and total economic costs were modelled on a log 10 scale to normalise residuals and homogenise variances.
Question 2: Taxonomic biases in invasion costs Overall, cost data in the UK were reported for 42 invasive species in InvaCost (with individual cost entries; n = 56 including species within 'mixed' entries). However, there were 520 unique invasive species in the UK reported in InvaCost, sTwist, GISD or GB-NNSIP, thus meaning that approximately 8% of known invasive species in the UK have documented economic costs (Fig. 2a ). Invasive species with reported cost data mainly belonged to the Mammalia (21%), Magnoliopsida (16%), Insecta (11%) and Aves (11%) classes (Fig. 2b ). Cost contributions were highly uneven across species overall (Fig. 3 ). Considering total costs, the European rabbit O. cuniculus contributed 62%, followed by Japanese The top three highest-contributing species are labelled on each subplot, for example, the European rabbit ranks as the costliest species a overall, for c damage costs and amongst the terrestrial organisms (f) and animal kingdom (h), representing 62%, 82%, 66% and 77% of costs in the respective categories.
2020)  nor the ash dieback fungus (Hymenoscyphus fraxineus)  (Broome et al. 2018) , despite their impact and concurrent management responses. The 2019 Environmental Audit Committee recognised a lack of consolidated information across UK organisations for these and other invasive species. This can lead to lost opportunities in managing new invasions in the UK, such as the delayed response in tackling the arrival of oak processionary moth (Thaumetopoea processionea) in 2006  (EAC 2019). This now established invasive species is a serious concern for forestry and public health and its unpredictable outbreaks make it difficult and costly to manage  (Godefroid et al. 2020). Overall, relative to three of the most robust databases of invasive species in the UK and beyond (sTwist, GISD and GB-NNSIP), numbers of species represented in Inva-Cost comprised less than one tenth and the few which are present reflect a bias towards intensively studied invasive species.
Acknowledgements The authors acknowledge the  French National Research Agency  ( ANR-14-CE02-0021 ) and the  BNP-Paribas Foundation Climate Initiative  for funding the  InvaCost  project that allowed the construction of the InvaCost database. The present work was conducted following a workshop funded by the  AXA Research Fund Chair of Invasion Biology  and is part of the  AlienScenario  project funded by  BiodivERsA and Belmont-Forum  call 2018 on biodiversity scenarios. RNC acknowledges funding from the  Alexander von Humboldt Foundation. AB acknowledges funding from the  Natural Environmental Research Council  (grant no. NE/L002485/1 ). CD was funded by the  Biodi-vERsA-Belmont Forum  Project " Alien Scenarios " ( BMBF/PT DLR 01LC1807C ). We acknowledge  Dr. Gethin Thomas  for assistance with Welsh translations,  ROS Educational Consultancy Ltd & Garnock Media Ltd  for assistance with Irish translations,  Dr.
We analyzed the carbon and nitrogen stable isotopes (δ 13 C and δ 15 N) in seal whiskers to define isotopic niches at a colony level and quantify their overlap. Finally, we identified the foraging areas of individuals based on telemetry data in order to specify where the overlap could occur, and to determine if the overlap may be due to individual cases by looking at individuals' stable isotope values. Identifying the potential for competition between sympatric harbour and grey seals at their range limit, where resources are potentially limited, would allow us to describe the nature of competitive interactions (i. e. , implicated time periods, prey types, spatial location), and potentially anticipate, and hopefully mitigate, the impacts they could have on population dynamics in this area.
Benthic flatfish were divided into two functional groups of small and large benthic flatfish, based on the substantial ecological differences between juvenile flatfish and mature/adult flatfish despite being taxonomically associated: juvenile flatfish are dependent on nursery grounds in estuarine and coastal waters during their early life stage (e. g. , about 2 years for S. solea in the EEC) and are then recruited to the adult stage during which they move to deeper offshore waters to forage (e. g. ,  Riou et al. , 2001; Rochette et al. , 2010). Benthic flatfish with relative body lengths greater than or equal to 200 mm were considered as large, and anything below that was considered small. Diet data were set in a two-dimensional matrix of proportion of total reconstructed prey mass summed by functional group (six columns) for each seal scat sample (270 lines).
It was constructed on all non-empty scats without prior distinction between the two seal species. Diet matrix was scaled with the function "scale" from the base in R (version 4. 0. 2, R Core Team, 2020) to perform an agglomerative hierarchical cluster analysis. This cluster analysis grouped seal scats that had a similar prey composition. The hierarchical cluster analysis was performed using a Euclidian distance procedure to estimate similarity between scats and employed the Ward. D2 algorithm to gather scats in groups  (Murtagh & Legendre, 2014). The optimal number of clusters was determined using the "NbClust" function   Charrad et al. , 2014). Cluster analysis was run using the "clustCoDa" function in the rob-Compositions package (version 2. 3. 0) in R. The functional composition of the diet associated with each cluster was presented by calculating the percentage of total prey mass of all scats within that cluster by functional group for each seal species separately.
0, 20. 0). Parameter estimation was done using Hamilton Monte Carlo methods as implemented in software Stan  (Carpenter et al. , 2017). Four chains were initialized using the default options in package rstan (version 2. 21. 0, Stan Development Team, 2019) and run for a total of 2,000 iterations. The first 1,000 iterations served as warm-up, and the remaining 1,000 were thinned to yield a sample of four draws per chain. Parameter convergence was assessed using the Gelman-Rubin-Brooks r statistics (� r < 1. 05).
The tags provided data on their locations at the sea surface using a GPS sensor, as well as on their diving behavior. The following dive data were registered when a seal reached depths exceeding 1. 5 m: start date and time, maximum depth, dive duration, the "percent area" (to calculate Time Allocation at Depth index, TAD;  Fedak et al. , 2001) , and nine intermediate-depth points (each 10% of the dive duration). Based on the analysis of this type of data, a "vertical approach" was developed to identify the likely foraging areas using some of the same individuals  (Planque et al. , 2020). Using two dive parameters-the TAD index describing dive shape and vertical descent speed-we selected the faster U-shaped dives for each individual, as they likely represent foraging behavior. We defined likely foraging areas as the areas where the highest spatial densities of faster U-shaped dives occurred  (Planque et al.
, 2017). Another possible limitation is that isotopic values of prey consumed could vary interannually (e. g. ,  Kurle et al. , 2011) , which could have impacted harbour and grey seal isotopic niches in 2008 and 2011-2012, respectively; however, to our knowledge, there are no data of any such shifts in the EEC and we think that significant variations are unlikely given the high concordance observed between results from all approaches used. The foraging areas identified for the individuals we sampled showed an overlap in feeding grounds in the coastal waters around the Baie de Somme (BDS) haulout site, where all eight harbour seals focused their foraging effort, and four of the nine grey seals spent considerable time (G03, G05, G10, and G12) as part of their more extensive feeding areas. This is consistent with the general spatial foraging patterns observed elsewhere for these species (e.
DATA AVA I L A B I L I T Y S TAT E M E N T The data used in this study are permanently available on SEANOE repositories under the license "Creative Commons BY-NC" and are freely downloadable: diet data from https://doi. org/10. 17882/ 76780, stable isotopes data from https://doi. org/10. 17882/ 76528, and dive data from https://doi. org/10. 17882/ 80016. The three scripts developed in this study to perform all presented analyses are freely available on GitHub repositories: diet analysis from https:// github. com/YannP lanqu e/Diet_Clust er_and_Overlap, isotopic niche analysis from https://github. com/YannP lanqu e/Isoto pic_Niche_ Overlap, and dive data analysis to identify individual foraging areas (based on the methodology by  Planque et al. , 2020)  from https:// github. com/YannP lanqu e/Forag ing_Areas_with_Dive_Data (written in R language).
Grain legumes are highly valuable plant species, as they produce seeds with high protein content. Increasing seed protein production and improving seed nutritional quality represent an agronomical challenge in order to promote plant protein consumption of a growing population. In this study, we used the genetic diversity, naturally present in Medicago truncatula, a model plant for legumes, to identify genes/loci regulating seed traits. Indeed, using sequencing data of 162 accessions from the Medicago HAPMAP collection, we performed genome-wide association study for 32 seed traits related to seed size and seed composition such as seed protein content/concentration, sulfur content/ concentration. Using different GWAS and postGWAS methods, we identified 79 quantitative trait nucleotides (QTNs) as regulating seed size, 41 QTNs for seed composition related to nitrogen (i.
e. longitude, latitude, altimeter) and 19 bioclimatic values obtained from the WorldClim database (http://world clim. org). These bioclimatic values (called BIO1 to BIO19) mainly represent temperature and rainfall values measured monthly, quarterly or annually (see details in Fig. 1 legend ). A global correlation analysis was performed to identify correlations between seed phenotypic traits themselves and with their geographical and bioclimatic values (Fig. 1 ). Results showed that all seed traits related to seed size (i. e. weight, area, perimeter, minellipse and majellipse) were highly correlated (Pearson coefficient correlation, PCC > 0. 9), which validated the accuracy of our measurements. Similar results were obtained for seed color values (i. e. PCC > 0. 85 for CH1, CH2, CH3). Regarding seed content, we observed that nitrogen and sulfur contents were also highly correlated with seed size traits (PCC > 0.
Seed composition analysis with elemental CHNS analyzer (162 accessions). Seed composition was characterized using a CHNS elemental analyser, which measured the percentage (w/w) of carbon (C), hydrogen (H), nitrogen (N) and sulfur (S). Mature seeds were ground in liquid nitrogen and dried in an oven at 90 °C for 48 h. Then, triplicates of approximately 5 mg of powder were analyzed using an Elementar Vario Micro cube analyzer (Germany) using flash combustion of the sample based on the "Dumas" method. Concentrations of C, H, N, S were determined by the Elementar Vario software based on exact seed weights. From which, carbon-nitrogen ratios (C/N ratio) were calculated to provide an accurate overview of the global seed composition. Nitrogen and sulfur contents per seed for each accession (i. e.
After transformation, Shapiro-Wilk tests  57  were performed to validate the normality and traits that did not reach normality were discarded of following GWAS analyses. Supplementary Table  S1  provides seed trait values before and after Box Cox transformation, respective lambda values for each trait and corresponding p-values of the Shapiro-Wilk test after transformation. Genome-wide association studies and post-GWAS analyses. Single nucleotide polymorphisms (SNP) data were obtained by whole genome sequencing of the 262 Medicago accessions from the M. truncatula Hapmap project  9. From the 6 million SNPs originally identified in Medicago genome version 4, 4,852,061 SNPs were successfully mapped to the fifth version of the Medicago genome (Mtv5 5 ) and were used for subsequent analyses. The population structure and the kinship matrix used in this study were the same as previously described in Bonhomme et al.
58  and le Signor et al. 7  , respectively. Two models were used to perform GWAS: (1) a classical single locus method using a mixed linear model called EMMA (Efficient Mixed-Model Association  15  with the kindship matrix and the population structure as inputs; (2) a multi-locus model called FarmCPU (Fixed and random model Circulating Probability Unification  16  ) with correction of the population structure, both with a statistical test p-value threshold of 1%. The Manhattan and quantile-quantile (QQ) plots were plotted using the R package rMVP (https ://githu b. com/xiaol ei-lab/rMVP). PostGWAS analysis was performed to correct for the linkage disequilibrium (LD) using PLINK algorithm  22  with the "clump" function and the following options: clumb-kb-radius of 15, which represents the genomic range (in kilobases) to identify SNP in LD and clump-r2 of 0.
7, which represents the r-squared threshold to identify correlation between SNPs. All GWAS result files were transformed into gwas files (Supplementary Tables  S2 to S5 ) readable in web-application JBrowse 17 containing the M. truncatula genome version 5 such as https ://Medic ago. toulo use. inra. fr/Mtrun A17r5. 0-ANR/ or in personal desktop genome viewer such as the freely available Integrative Genome Viewer (IGV  18  , http://softw are. broad insti tute. org/softw are/igv/). Over-representation analyses (ORA) of candidate genes were performed using ClusterProfiler package available in R using hypergeometrical test (p-values) with a Bonferroni correction (q-values)  59. RNA-seq analysis in major plant organs. Expression of Medicago transcripts in major plant organs was determined from existing experiments.
Acknowledgements Seeds of HAPMAP accessions used in this study were obtained from the  Medicago HAPMAP germplasm resource center  (http://www. Medic agoha pmap. org/hapmap/germp lasm), and from the  Genetic Improvement and Adaptation of Mediterranean  and tropical plants unit (AGAP,  INRA   Montpellier, JM Prosperi ). This research was conducted in the framework of the regional programme "Objectif Végétal, Research,  Education and Innovation in Pays de la Loire ", supported by the  French Region Pays de la Loire ,  Angers Loire Métropole  and the  European Regional Development Fund. This study was also supported by the  China Scholarship Council  ( CSC No. 201704910863 ) from the  Ministry of Education of P. R. China. Finally , authors would like to thank  Jaiana Malabarba  for manuscript proofreading.
Identification of three canonical RAB5 homologs in petunia Examination of RNA sequencing (RNA-seq) data indicated that petunia petals express three canonical RAB5 genes, namely, RAB5a, RAB5a1, and RAB5a2 (Figure  1B ; Figure  S1A ). RAB5a is expressed primarily in petals and in the vasculature of leaves and stems and is strongly reduced in an1, ph3, and ph4 mutant petals (Figures 1B-1D; Figure  S1B ), suggesting a role in the vacuolino pathway. RAB5a1 and RAB5a2, by contrast, are not controlled by AN1, PH3, or PH4 (Figures  1B  and 1C ) and are expressed in a broad range of tissues (Figure  1D ). To examine the relationship of these petunia proteins with the canonical Arabidopsis RAB5s (ARA7 and RHA1) and AtARA6, which belongs to a distinct plant-specific clade of RAB5-like proteins, we retrieved highly similar proteins from genome databases and performed phylogenetic analyses (Figure  1E ).
ACKNOWLEDGMENTS Authors are in debt with the staff of the LCAM (Van Leeuwenhoek Centre for Advanced Microscopy) of the University of Amsterdam for their assistance with technical issues during the microscopic observation. We want to thank Jihed Chouaref for help with the preparation of figures. We are grateful to (C) Schematic representation of the RAB5a-mediated pathway for vacuolino formation. Dark-green arrows denote two ''direct'' canonical protein sorting pathways to CV observed in a variety of cells  (Cui et al. , 2020; Ebine et al. , 2014). A very similar direct pathway operates in petal mesophyll cells and petal epidermis cells from mutants with a defective MBWW complex. Light-green arrows denote a pathway involved in vacuolar biogenesis in developing root cells  (Cui et al. , 2019).
, 2001)  and vacuolino  (Faraco et al. , 2017). ERD2-RFP were constructed by amplifying (primers 7474 + 7475) the CDS of ERD2 cDNA of A. thaliana and subcloning it into the Gateway destination vector pK7RWG2,0 by BP and LR reactions. RFP-KDEL  (Faraco et al. , 2017) , RFP-LeRAB11  (Rehman et al. , 2008)  and CHERRY:BP80 (BP80 = the transmembrane domain/cytoplasmic tail sequence of the BP80 cDNA)  (De Benedictis et al. , 2013; Tse et al. , 2004)  were described in previous works. All the primers used in this study are reported in Table  S1. Vectors applied in this study are presented in Table  S2. Phylogenetic analysis and sequence alignment Amino acid sequences of RAB5 homologs from different plant species were selected from the Petunia Genome SGN dedicated database (https://solgenomics. net/), Phytozome databases (https://phytozome-next.
jgi. doe. gov/), PLAZA (https://bioinformatics. psb. ugent. be/plaza/), and Ensembl (http://www. ensembl. org//useast. ensembl. org/?redirectsrc=//www. ensembl. org%2F). Multiple sequences alignments were conducted by MUSCLE. The phylogenetic tree was produced by maximum-likelihood using the online tool PhyML 3. 0 (http://www. atgc-montpellier. fr/phyml/). G-blocks were applied to curate the alignment and 300 bootstrap replicates were used to assess the branch support  (Dereeper et al. , 2008). Amino acid sequence alignment was performed by ClustalX version 2. 1, and further edited by program GeneDoc version 2. 7.
01 Macerozyme Duchefa Biochemie Lot # 015810. 01 Gamborg's B-5 basal salt SIGMA Lot # SLCB4231 Wortmannin LC Laboratoriesâ Cat # W-2990 Deposited data RNaseq data This study Genbank: Bioproject PRJNA753066 RAB5a mRNA This study Genbank: MH986793 RAB5a1 mRNA This study Genbank: MH986794 RAB5a2 mRNA This study Genbank: MH986795 PhARA6 mRNA This study Genbank: MK896357 PhVPS9 mRNA This study Genbank: MK896358 Experimental models: Organisms/strains P. hybrida W138 Spelt et al. , 2000 N/A P. hybrida R182 (rab5a X2042 ) This study N/A P. hybrida W225 (an1 W225 ) Spelt et al. , 2000 N/A P. hybrida R162 (ph4) Quattrocchio et al. , 2006 N/A P. hybrida R167 (ph3) Verweij et al. , 2016 N/A P. hybrida V23 (ph1) Faraco et al. , 2014 N/A P. hybrida M1xV30 Amsterdam Petunia collection N/A 35S:RAB5a in P.
Exploratory Activity and Social Interaction tests The following behavioral variables were registered: 1) Automatic registration with EthoVision XT software (version 11. 0; Noldus Information Technology, The Netherlands) of the tracking score (distance) during testing time with differentiation of place near the wire holder (5 cm around) of the cage and total time spent in the opposite to wire holder corner; 2) Manual registration with Observer XT (version 7. 0; Noldus Information Technology, The Netherlands) was used for following behavior indicators of communicativeness/sociability: 1) the number and/or duration of 1) rearing (exploratory activity); 2) grooming (self-oriented behavior licking of the fur on the flanks or abdomen, washing over the head from ear to snout); 3) approaches to the wire holder and the total time (s) spent near it (moving near the wire holder, smelling and touching it with nose, one or more paws, getting on a wire holder and a contact with it by four paws).
Effects of chronic LiCl treatment on the expression of serotonergic genes in the midbrain raphe nuclei of defeated mice (Fig. 6 ) The measurement data is provided by the BioRad Amplifier software (USA). Chronic LiCl treatment induced overexpression of the Tph2 gene, encoded the limiting enzyme of serotonin synthesis, in comparison with the controls and Sal-treated losers (both P < 0. 01); the Slc6a4 gene, encoded serotonin transporter in comparison with the control and Sal-treated losers (P < 0. 05 and P < 0. 01, respectively); the Htr1a gene, encoded 5HT1a receptors, in comparison with the controls and Sal-treated losers (for both P < 0. 01); and the Htr5b genes, encoded 5HT5b receptors, in comparison with the controls and Sal-treated losers (P < 0. 01 and P < 0. 05, respectively) in the midbrain raphe nuclei of defeated mice.
Plankton form the base of the marine food web and are sensitive indicators of environmental change. Plankton time series are therefore an essential part of monitoring progress towards global biodiversity goals, such as the Convention on Biological Diversity Aichi Targets, and for informing ecosystem-based policy, such as the EU Marine Strategy Framework Directive. Multiple plankton monitoring programmes exist in Europe, but differences in sampling and analysis methods prevent the integration of their data, constraining their utility over large spatio-temporal scales. The Plankton Lifeform Extraction Tool brings together disparate European plankton datasets into a central database from which it extracts abundance time series of plankton functional Published by Copernicus Publications.
5618 C. Ostle et al. : The Plankton Lifeform Extraction Tool groups, called "lifeforms", according to shared biological traits. This tool has been designed to make complex plankton datasets accessible and meaningful for policy, public interest, and scientific discovery. It allows examination of large-scale shifts in lifeform abundance or distribution (for example, holoplankton being partially replaced by meroplankton), providing clues to how the marine environment is changing. The lifeform method enables datasets with different plankton sampling and taxonomic analysis methodologies to be used together to provide insights into the response to multiple stressors and robust policy evidence for decision making. Lifeform time series generated with the Plankton Lifeform Extraction Tool currently inform plankton and food web indicators for the UK's Marine Strategy, the EU's Marine Strategy Framework Directive, and for the Convention for the Protection of the Marine Environment of the North-East Atlantic (OSPAR) biodiversity assessments.
The Plankton Lifeform Extraction Tool currently integrates 155 000 samples, containing over 44 million plankton records, from nine different plankton datasets within UK and European seas, collected between 1924 and 2017. Additional datasets can be added, and time series can be updated. The Plankton Lifeform Extraction Tool is hosted by The Archive for Marine Species and Habitats Data (DASSH) at https://www. dassh. ac. uk/lifeforms/ (last access: 22 November 2021,  Ostle et al. , 2021). The lifeform outputs are linked to specific, DOI-ed, versions of the Plankton Lifeform Traits Master List and each underlying dataset.