dataesr/scientific-paragraphs-categorization huggingface dataset:
Variant calling analysis Paired-end Illumina libraries from each ecotype (Pt1 to Pt17) were first trimmed using Trimmomatic  [27]  with "ILLUMINACLIP:adapters. fa:2:30:10:2:keepBothReads LEADING:20 TRAILING:20 SLIDINGWINDOW:4:20 MINLEN:40". Reads were then mapped with BWA-mem2  [28]  on P. tricornutum Phatr2 assembly (accession GCA_000150955. 2). Variant calling and filtering were performed with GATK package version 4. 2. 2. 0, following GATK best practices  [29]. In short, HaplotypeCaller module was run with a call confidence of 30, a sample ploidy of 2 and double precision was activated for pair-HMM algorithm. Variants that were called were functionally annotated by snpEff  [30]  with P. tricornutum database v5. 0 and transposable elements annotation from  [7] , an SelectVariant module and some of the longest were validated by PCR.
Repeat detection in novel transcripts Reads from both replicates of Pt1 to Pt17 RNAseq libraries were mapped with HISAT2 2. 0. 5  [39]  using the default parameters and all the mapping information were combined. The reads were then assembled with StringTie 1. 3. 3b  [41]  and novel transcripts were kept. We screened these 656 novel transcripts for repeats with RepeatMasker (RepeatMasker Open-4. 0. 2013-2015 <http://www. repeatmasker. org>) Galaxy Tool wrapper version 4. 0. 9 (slow settings with matrices for 43% GC content), using a manually curated database of 71 reference transposable elements (TE) of P. tricornutum. RepeatMasker was run on the public server at https://usegalaxy. org  [42].
WGCNA network analysis The network was obtained using Weighted Gene Correlation Network Analysis (WGCNA) R package (version 1. 17)  [43]. Before constructing the co-expression network, we filtered out genes having a row median less than 10 reads. The expression matrix was transformed with the vst (Variance Stabilizing Transformation) function from DESeq2 R package (version 1. 32. 0)  [44]. The sequencing steps for the network construction for P. tricornutum accessions have been described previously  [45]. For Network construction the WGCNA R package  [43]  was used to identify network modules from 36 RNA-Seq datasets representing expression data from 18 P. tricornutum accessions (two replicates per accession). First, the quality of the raw counting matrix was checked. A hierarchical clustering analysis based on the "average" method allowed us to identify the Pt1R2 as an outlier, so this sample was filtered out from further analysis.
Acknowledgements We thank  Carine Pruvost  for media preparation. We are grateful to  Achal Rastogi  for his helpful discussions on the bioinformatics methodology related to phylogeny. LT acknowledges support from the region of  Pays de la Loire and Nantes métropole  ( ConnecTalent EPI-ALG project ),  Epicycle   ANR  project ( ANR-19-CE20-0028-02 ) and  Région Pays de la Loire ImpulseAlgae  projects. FY was supported by  CSC  Grant  201906310152. We are grateful to the bioinformatics core facility of  Nantes University (BiRD Biogenouest)  for its technical support.
Acknowledgements. EastGRIP is directed and organized by the  Centre for Ice and Climate at the Niels Bohr Institute, University of Copenhagen. It is supported by funding agencies and institutions in  Denmark  ( A. P. Møller Foundation, University of Copenhagen) ,  USA  ( US National Science Foundation, Office of Polar Programs) ,  Germany  ( Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research) ,  Japan  ( National Institute of Polar Research and Arctic Challenge for Sustainability) ,  Norway (University of Bergen  and  Trond Mohn Foundation) ,  Switzerland  ( Swiss National Science Foundation) ,  France  ( French Polar Institute Paul-Emile Victor ,  Institute for Geosciences and Environmental research) ,  Canada (University of Manitoba)  and  China  ( Chinese Academy of Sciences and Beijing Normal University).
Literature search and data selection We conducted a literature search on the Web of Science and Google Scholar with the title request (("invertebrate" OR [any known order of terrestrial invertebrate]) AND ("space use" OR "home range" OR "foraging" OR "dispersal" OR "movement pattern" OR MRR OR CMR OR telemetry OR "harmonic radar")). Specific words relative to aquatic habitats were specified as unwanted, as well as journal categories like "Toxicology" or "Neurosciences" (see full research strategy in Supporting information). We looked for cited references to similar works within each selected paper and added them to our database when relevant. We found three reviews about movement of specific taxonomic groups, and the original papers were double-checked in order to ensure that the concepts of foraging distance, home range and dispersal were used according to consistent definitions.
For instance,  Mazzi et Dorn (2012)  conducted their review by considering the term 'dispersal' in a very broad sense, including short distance foraging flights which we incorporated in our database as home range measures  (Schlägel et al. 2020). Studies were retained if they provided quantitative information about mean active dispersal distance (m) and/or mean foraging distance (m) or home-range size (m²) for terrestrial invertebrate individuals. We did not compile data of home range size of social insects' colonies (ants or termites colonies for instance) because they do not correspond to individual traits but to emergent collective patterns. We also excluded genetic and simulation studies that only provided indirect measures of movement distances. Translocation experiments were also excluded, because the ability of an individual to return to its initial territory beyond a certain distance does not only depend on its motion capacities, but also on its memory of visual landmarks and other sorts of cues  (Able 1980 ).
Locomotion mode In terrestrial environments, invertebrates actively move in three different ways: they either fly, walk or run (non-alate OR alate species moving on the ground with articulated legs) or crawl (above-ground or below-ground limbless species or larvae). Many invertebrate species undergo a shift in their locomotion mode during their lifetime (e. g. Crawling lepidoptera caterpillars become flying imagos). We therefore associated to each observation in our database the locomotion mode corresponding to the exact life stage of the individuals at the time of the study, and used the corresponding body mass and trophic guild of this life stage.
Trophic guild We classified species as either carnivores, herbivores or decomposers. We performed this classification in broad trophic groups to avoid a multiplication of specific trophic habits in our database that would have small sample sizes and for which we would not have clear a priori predictions. Hence, we classified omnivorous species feeding on both plants and other invertebrates as carnivores. We grouped hematophagous species like ticks or mosquitoes with carnivores. We pooled granivore and palynivore species with herbivores, and xylophagous, saproxylic, fungivore, detritivore and coprophagous animals as decomposer species.
Statistical analysis Taxon-specific allometry of space use. We first performed ordinary least square (OLS) regressions to assess the allometry of space use for each taxonomic order that presented more than seven observations in our database. Since we found that movement distances estimated by ex situ studies are significantly larger than those estimated in situ (dispersal : ANCOVA : F = 78. 161, p = 2e-16; foraging : ANCOVA : F = 36. 945, p = 8. 58e-09), we performed these analyses on 'in situ' studies only, except for the Haplotaxida order for which we only found foraging data from laboratory studies. We log-transformed (Log10) body mass and movement distances for these analyses. For each taxonomic order, we performed either mixed models with tracking method incorporated as a random effect on the intercept value, when several types of tracking methods were used, or simple linear models otherwise.
We made this choice of mixing linear and mixed models to follow the same methodology as the one of  Tamburello et al. (2015)  in their analyses of movement of vertebrate data. Functional and environmental drivers of interspecific variations in space use. We first tested the influence of functional drivers on movement distances, using the full dataset of in situ and ex situ studies. We assessed whether the locomotion mode and trophic guild influenced the coefficients of our initial allometric models "(Dispersal or foraging) Distance ~ Dry Mass". We built mixed models to test each predictor separately for both dispersal and foraging data sets, with tracking method and taxonomic order incorporated as random effects on the intercept value. These random effects allowed us to deal with multiple tracking methods while accounting for phylogenetic interdependence.
We tested interaction effects between body mass and locomotion mode, and between body mass and trophic guild. We finally combined these two hypothesized functional drivers into a "full" model. The relative support of each model was assessed with the Akaike Information Criterion corrected for small samples (AICc). The marginal and conditional R² of the models were calculated to assess the proportion of variance explained respectively by the fixed effects alone, and by both fixed and random effects, with the "r. squaredGLMM" function from MuMIn (Barton 2022). A second analysis aimed at assessing the relative influences of functional and environmental drivers using the dataset of in situ studies only. We built a mixed model with the following potential drivers of space use: body mass, trophic guild, locomotion mode, temperature and GPP.
Phylogenetic signal We also performed a phylogenetic analysis of the interspecific variation in movement distances. We constructed phylogenetic trees with dated branches thanks to the OneTwoTree pipeline  (Drori et al. 2018 ) for a subset of the species in our database (174 out of 216). We calculated the phylogenetic signal in movement distance using Pagel's lambda metric that is adapted to continuous response variables. Pagel's λ ranges from 0 if there is no link between the response variable and phylogeny, to 1 if closely related species respond exactly the same way to the predictor variables  (Pagel 1997 , Pagel 1999). Because phylogenetic relatedness between species is likely to cause an inter-dependency of the observations of movement distances, we computed phylogenetic generalized least squares regressions (PGLS) using the 'nlme' package in R (Pinheiro and Bates 2023) to account for interspecific autocorrelation and we compared the PGLS regression results with the fit of non-phylogenetic, ordinary least square regressions (OLS).
Datasets We assembled a database of 174 scientific articles that met our selection criteria. They provided 401 movement observations of individuals from 216 species, 82 families and 22 orders. These data correspond to observations of dispersal (238, 59%) and foraging (163, 41%) distances, with a heterogeneous distribution between trophic and locomotion groups (see Appendix 3, Table  1  in Supporting information). Biogeographical realms are unequally represented, with 80% of the total observations coming from the Nearctic and Palearctic world regions, while only 14% is from the Southern hemisphere of the globe (Figure  2 ). However, the two environmental variables in our database (temperature and gross primary productivity) still cover a large and continuous spectrum of values (see Appendix 3, Figure  1  in Supporting information).
006, respectively) although models with and without this interaction have close AICc values (Table  2 ). Consistently with our a priori predictions, we found that flying individuals in our database forage further than walkers, themselves moving further than crawlers when controlling for body mass variations (see the blue line that is above the green line that is above the red line in Figure  6c ). We find the same pattern for the dispersal movements, although we observe a small inversion between flyers and walkers movement distances for the largest organisms (Figure  6d ). Flying and crawling individuals show comparable allometric regression slopes, which are negative for foraging and positive for dispersal movements (Figures  6c  and 6d ) when considering full models coefficients (Table  2 ).
DISCUSSION We assembled the first global database of movement patterns of terrestrial invertebrates, focusing on active dispersal and foraging movements. Using this database, we documented allometric relationships between body mass and foraging and dispersal movement distances across major terrestrial invertebrate taxonomic orders. We then assessed whether invertebrate movements were driven by the same functional traits as those evidenced for vertebrate taxa. We found that, as for vertebrate species, phylogeny, locomotion strategy and diet are the main drivers of variation in those relationships, since they explain together more than 80% of the total variability in dispersal and foraging movements. This echoes the finding that 80% of the variability in vertebrate home range is explained by locomotion mode, trophic guild, foraging dimension and taxonomy  (Tamburello et al.
We conclude that, as in vertebrate species, invertebrate movement distances correlate with the cost of transport associated with the movement media  (Shepard et al. 2013). Our prediction that carnivores would move further than herbivores and decomposers to compensate for lower resource densities is partially supported by our foraging data, but not for dispersal. In the present dataset however, we lack information on potential interaction effects between locomotion mode and trophic guild, due to a strong lack of balance between these two variables in our databases. We dealt with an over-representation of flying individuals in the dispersal database, and of walking individuals in the foraging one, with trophic guilds not being equally represented in both databases (see Table  1  in Supporting information).
TABLES Table  1. Regression parameter estimates for body mass across separate taxonomic orders with n ≥ 7 observations. Global regressions ('All orders') include all taxonomic orders in our databases without constraint on minimal number of data. Simple linear models are of the form log10(D) ~ log10(a) + b x log10(DM) where D : movement distance and DM : dry mass. Mixed linear models include the tracking method as a random effect. We used the most frequently used tracking method as the reference for each taxonomic order. log10.
Acknowledgements The authors thank the  National Research Agency (ANR) , the  French government   IDEX-ISITE initiative   16-IDEX-0001  ( CAP 20-25) -CIR 1 -Axis  " Fluxes " and  Clermont Auvergne Metropole  for the funding of this research. Figure 6. Allometry of space use across trophic guilds and species with similar locomotion strategies, taking taxonomic order and tracking method random effects into account. Regression line coefficients are reported in Table 2 (Full models parameters).
Their values are used to classify each unit to a given LCZ type according to a set of rules. GeoClimate can automatically prepare the inputs and calculate the LCZ for two datasets: OpenStreetMap (OSM -available worldwide) and the BD Topo v2. 2 (BDT -a French dataset produced by the national mapping agency). The LCZ are calculated for 22 French communes using these two datasets in order to evaluate the effect of the dataset on the results. About 55% of all areas has obtained the same LCZ type with large differences when differentiating this result by city (from 30% to 82%). The agreement is good for large patches of forest and water as well as for compact mid-rise and open low-rise LCZ types. It is lower for open mid-rise, open high-rise mainly due to height underestimation for OSM buildings located in open areas.
A first stream uses remote sensing images as main input data. A prominent initiative of this remote sensing -approach is the World Urban Database and Access Portal Tool (WUDAPT)  (Ching et al. , 2018). Within this project, a LCZ generator tool has been released  (Demuzere et al. , 2021). It is based on a Random Forest model trained with areas that have been classified by expert to a given LCZ type. The corresponding workflow has been applied to several continents  (Demuzere et al. , 2019 (Demuzere et al. , , 2020)). A second stream involves detailed geographical information (often presented as vector data) processed by Geographic Information System (GIS). It is organized into six main steps  (Quan and Bansal, 2021) : 1. Collection of geographical data 2. Partitioning of the territory using Reference Spatial Unit (RSU -called basic spatial units by  (Quan and Bansal, 2021) ), defined as the smallest spatial unit where calculations are performed 3.
Calculation of several UCPs within each RSU 4. Assignation of a LCZ type to each RSU based on UCP values 5. Post-processing, e. g. merge adjacent RSU for simplification and sizing purpose 6. Evaluation of the LCZ map In the first stream, experts may not use the UCP values to attribute a LCZ type to a given area, thus leading to subjective decision while an objective LCZ referential has been proposed. However, due to the growing accessibility to high quality satellite images, it is applicable to most countries in the world. The second stream is spatially limited to the territory where a specific geographical dataset is available (often a city or a country). Its main advantage is that the UCP used in the LCZ referential are often calculated, inducing a potentially more objective method than the first stream.
This study presents a new automated workflow for LCZ maps construction that address these issues by the following contributions: the workflow has been designed to be generic: it accepts all datasets as soon as the input data is structured following a well-described guideline  (Bocher et al. , 2021)  and can be run with any type of RSU. The GeoClimate workflow is already designed to work with two input data types, namely BD Topo 2. 2 and OpenStreetMap. The first is a French government dataset while the latter is an open source project that provides geographical information worldwide, and therefore tackle the data scarcity issue. the workflow, described in details within this article, is integrated in GeoClimate, a free and open source software and thus the code is fully open and available online 1   (Bocher et al.
Geoclimate library GeoClimate is a free and open source toolbox developed in Groovy language that allows to compute geographical indicators from vector-based data. The current GeoClimate version (0. 0. 1) supports two data source providers : the BDTopo database (version 2. 2, later referred as BDT) produced by the French national mapping agency (IGNF 2 ) and the community database OpenStreetMap (later referred as OSM 3 ). Once the input data source is selected, GeoClimate applies a set of rules to extract and format the required spatial descriptors and then build seven GIS layers: building footprint, road and rail network, water, vegetation and impervious surfaces. Each layer follows a set of specifications to ensure the completeness and logical consistency of the input data and avoid potential geometry inconsistencies.
Third, the rules to edit data in OSM are more restrictive than BDT due to relational data model used by OSM to store and describe a geometry. In BDT the geometries are stored in layers independently of each other (vegetation, water. ), consequently there is a higher probability to find overlaps and gaps between the layers (e. g a surface of impervious that covers a surface of water), than in OSM. Indeed, in OSM all geometries are defined in three tables nodes, ways and relations, therefore the snapping between geometries are more consistent, resulting in a lower number of very small RSU (Fig. 4 ). To compare the LCZ at RSU scale, a first step is then to create as many units as there are RSU intersections between the two datasets, thus leading to an increase of small size units.
General agreement For each LCZ, the areas of geometries who received the same LCZ value using BDT or OSM data as inputs are summed to compute a weighted agreement per LCZ value and a general agreement for the whole commune. A figure comparing the OSM and BDT results is automatically generated for each city using the lczexplore R package 4. It contains the LCZ map created for both datasets, a bi-colored map (red, green) that shows the spatial distribution of the zones in agreement and a confusion table based on percentage of area in agreement for each LCZ type. The Fig. 5  and 6 show a comparison of the LCZ obtained for the city of Dijon and for the rural commune of Saint-Nicolas de Redon, respectively. They illustrate well the main similarities and differences observed for major cities and rural territories.
(2022) , Annecy and Meudon are two cities where the model used to estimate OSM building height fails quite a lot. This is clearly highlighted by their very low agreement for compact mid-rise LCZ (47% for Annecy and 40% for Meudon while the average is 81%). On the contrary, Paris is the city having the highest agreement for almost all The GeoClimate software has a great potential for new collaborations. Along with the lczexplore R package, it can be used to efficiently compare the LCZ produced with GeoClimate to any other method. The influence of each step of the LCZ creation can be investigated separately: impact of the dataset (such as presented in this paper), of the unit of analysis (RSU), impact of the method used for UCPs calculation and impact of the algorithm used to assign a LCZ. GeoClimate also has the potential to interact with the current WUDAPT approach.
all vegetation (low and high) patches in the RSU A V i the area of vegetation patch i A RSU the area of the RSU No https://doi. org/10. 5194/egusphere-2023-371 Preprint. Discussion started: 27 March 2023 c Author(s) 2023. CC BY 4. 0 License. Table 1: Description of the calculation method for each indicator used Indicator abbreviation Indicator name in Geoclimate library (or combination of Geoclimate indicators) of high vegetation patches in the RSU A HV i the area of high vegetation patch i n the number of all vegetation (low and high) patches in the RSU A of industrial buildings in the RSU (buildings having TYPE = 'HEAVY INDUSTRY') of large low-rise buildings in the RSU (buildings having TYPE = 'COMMERCIAL' or TYPE = 'LIGHT INDUSTRY') A BLLR i the area of the large low-rise building i n the number of buildings in the RSU A of residential buildings in the RSU (buildings having TYPE = RESIDENTIAL) A BRES i the area of the residential building i n the number of buildings in the RSU A level of building i No * This calculation is generic for any dataset since Geoclimate is based on a generic model described by Bocher et al.
Acknowledgements. The method presented in this paper has been integrated in the GeoClimate tool and developed within the following research projects:  -Urb-TWin  ( 2020-2023 ):  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No  896069 -PAENDORA2  (Pour la gestion du confort estival :  Données, Outils et Recherche-Action ) ( 2022-2025 ): funded by the  French Agency.
performed. Code and data availability. The LCZ calculation is performed using the GeoClimate 0. 0. 1 software available at https://zenodo. org/record/6372337 while the figures used in the manuscript are created using the lczexplore R package available at https://zenodo. org/record/7646866. All the work performed in this manuscript can be reproduced following the Readme of the following Zenodo repository: https://zenodo. org/record/7687911.