dataesr/scientific-paragraphs-categorization huggingface dataset:
Growth Fertilization with N, P, or N + P significantly stimulated stem growth, 3. 05, 3. 88 and 4. 01-fold, respectively, in comparison with control plots (Figure  2A ;  Vallicrosa, 2023: TS2. xlsx). In all treatments, potential N-fixers had greater growth than species not regarded as N-fixers (Figure  2B ). However, we noted a significant negative interaction between N-fixers and P (0. 42) and N + P (0. 31) fertilization  (Figure 2A, Vallicrosa, 2023: TS2. xlsx)  and a close link between N-fixers and N fertilization (p = 0. 062; Figure  2A ,  Vallicrosa, 2023: TS2. xlsx). F I G U R E 2 (A) Control plots had a yearly relative growth of 0. 0049 cm, while N-fertilized plots, N + P fertilized plots, and P fertilized plots grew 0. 0117, 0. 0126, and 0. 0145 cm, respectively (Figure  2B , https://github.
conditions  (Thuiller et al. , 2008 (Thuiller et al. , , 2013)). Incorporating dispersal ability into such models is a fundamental step in estimating the potential distribution of species  (Jiménez & Soberón, 2022). Recently, several databases of plant traits have become available that contain information on seed morphology and dispersal strategies, for example, TRY  (Kattge et al. , 2020) , LEDA (Kleyer et al. , 2008),   D 3 (Hintze et al. , 2013), BROT 2. 0  (Tavşanoğlu & Pausas, 2018) , Flora of Italy  (Pignatti et al. , 2018) , Pladias  (Chytrý et al. , 2021; Sádlo et al. , 2018) , SID  (Royal Botanic Gardens Kew, 2021)  and Tela Botanica (www. tela-botan ica. org). These databases use different approaches to characterize plant dispersal ability, although most of them provide only information on seed mass and seed shape.
Most databases additionally include categories of dispersal modes (also called dispersal syndromes or dispersal types, e. g. anemochory, hydrochory, zoochory) or dispersal vectors (e. g. wind, water, animals), usually for regionally restricted sets of species. Such characteristics are usually estimated from seed or diaspore morphology and size. However, the morphological adaptations and sizes of seeds and other diaspores do not fully describe the dispersal potential of a given species, which limits the use of such data in ecological studies (e. g. Beckman et al. , 2018; Bullock et al. , 2017; Nogales et al. , 2007; Tamme et al. , 2014; Thompson et al. , 2010 Thompson et al. , , 2011)). Few studies have measured, estimated or mathematically modelled dispersal distances, which seem to be a more reliable indicator of dispersal ability.
Most databases only consider dispersal mode estimated from species morphological characteristics, such as dispersal by wind, water or animals (e. g. Royal Botanic Gardens Kew Seed Information Database, 2021; SID). Indeed, natural dispersal strategy is crucial for colonizing new areas at the landscape scale. However, rare events of human-assisted dispersal should also be considered. Information on the dispersal by humans is directly available in some databases (e. g. LEDA,  Kleyer et al. , 2008) , or it can be inferred for species with native ranges on other continents (e. g. GloNAF; van  Kleunen et al. , 2019). Another group of species that are commonly dispersed by humans are weeds of arable fields or other man-made habitats (compare the characteristic species combinations in the EUNIS Habitat Classification;  Chytrý et al.
, 2020). Dispersal distance is also influenced by the interaction between the environment and plant traits  (Vittoz & Engler, 2007). For example, while some diaspores can be dispersed over long distances in an open landscape, the same diaspores can be dispersed less effectively when the parent plants grow under a closed canopy. Global databases containing information on other relevant traits, such as plant height (TRY;  Kattge et al. , 2020) , and data on habitat preferences of individual plants  (Chytrý et al. , 2020; Mucina et al. , 2016 ) make it possible to combine this information to estimate the actual plant dispersal distances. Vittoz and Engler (2007)  proposed to estimate plant dispersal distances using a clearly defined and methodologically simple approach. They assigned plant species to seven dispersal distance classes based on the morphology of dispersal units (diaspores), other species traits and habitat preferences.
The original dataset of  Vittoz and Engler (2007)  contained 1262 species of Swiss flora. Boulangeat et al. (2012)  extended this dataset to 1786 plant species occurring in Switzerland and the French Alps. However, as both studies focussed on the Alps and nearby areas, they included only a small part of the European vascular flora. Here, we present a new dataset of seed dispersal distance classes and predominant dispersal types for 10,327 widely distributed European vascular plants. We use the approach of  Vittoz and Engler (2007)  with a slight modification of some classes. To evaluate our approach, we compared our seed dispersal distance classes with dispersal distances estimated using the trait-based method proposed by  Tamme et al. (2014).
| Species selection We prepared a list of frequent European vascular plant species based on species occurrence frequency in vegetation plots in the European Vegetation Archive (EVA,  Chytrý et al. , 2016) , a centralized database of European vegetation plots. We standardized species taxonomy and nomenclature according to Euro+Med (http://europ lusmed. org). Subspecies, varieties and forms were merged at the species level. Species in taxonomically difficult groups were merged into aggregates following the EUNIS-ESy expert system for the EUNIS Habitat Classification  (Chytrý et al. , 2020). Hereafter, we use the term 'species' to refer to both species and aggregates. As we aimed to include all frequent species and species that could attain high cover in plant communities, we selected those species that occurred in >1% of plots, had a cover of >25% in >0.
| Trait data We compiled several species traits necessary for assigning species to different dispersal distance classes (dispersal mode, dispersal vector, diaspore type, seed or diaspore mass (further in the text | 1487 LOSOSOVÁ et al. referred to as seed mass), plant height and life form) from the following sources: Flora of GDR  (Frank & Klotz, 1990) , LEDA database (Kleyer et al. , 2008), D 3 database (Hintze et al. , 2013), Ladouceur   et al. (2017), BROT 2. 0 database  (Tavşanoğlu & Pausas, 2018) , Flora of Italy  (Pignatti et al. , 2018) , Pladias database  (Chytrý et al. , 2021) , SID database (Royal Botanic Gardens Kew, 2021) and Tela Botanica database (www. tela-botan ica. org). In addition, we used data from  Vittoz and Engler (2007)  and  Boulangeat et al. (2012) , which also contained assignments to dispersal distance categories.
| Occurrence in habitats We compiled available information on the broadly defined habitats where each species typically occurs  (Chytrý et al. , 2020 (Chytrý et al. , , 2021;; Heinken et al. , 2022; Mucina et al. , 2016; Pignatti et al. , 2018). On this basis, we estimated species affinity for forest or open habitats. Another important aspect was whether or not the species is native to Europe, which indicates human-assisted long-distance dispersal. For the assignment of the alien or native status, we used several additional sources, particularly the list of European alien species in the FloraVeg. EU database (https://flora veg. eu).
| Assignment to dispersal distance classes We summarized the plant characteristics obtained from various sources in a table (Supplement S1) that includes plant height, life form, predominant dispersal mode, seed mass and typical habitat. We also included data on plant geographical origin and the use by humans to distinguish a group of plants intentionally dispersed by humans (Table  1 ). Plants were classified into seven dispersal distance classes based on the above-mentioned characteristics, as illustrated in a decision tree (Figure  1 ). Examples of typical species are shown in Figure  2. In contrast to the original approach of  Vittoz and Engler (2007) , we have slightly modified the definitions of the dispersal distance classes. Their original definition of the dispersal classes combined two different types of information: classes 1-6 were based on species characteristics and natural dispersal vectors, whereas class 7 included human-dispersed species, although the natural dispersal strategy of these species was different and often less efficient than long-distance dispersal by humans.
Vittoz and Engler (2007)   After standardizing nomenclature, the dataset contained 13,333 species. These species had at least partial information necessary for their assignment to the dispersal distance classes. We assigned the priority species with incomplete or no data to dispersal distance classes based on our expert judgement combined with the information available for other species in the same genus. For example, dispersal mode information was available for several species of the genus Abies but not for Abies borisii-regis and A. sibirica. We decided to classify these species in the same dispersal distance class as the other Abies species. However, we did this only for the genera with uniform dispersal traits, similar height and similar habitat preferences. We did not do this for the genera with diverse diaspores (e.
We ran all models with the 'dispeRsal' function  (Tamme et al. , 2014) , which estimates dispersal distances using simple linear regressions and mixed-effect models that account for taxonomic dependencies by using species taxonomy (either as family or order) as a random variable. We determined species families using the 'TPL' function of the R package Taxonstand  (Cayuela et al. , 2017). Finally, to estimate the dispersal distance for each species, we averaged dispersal distances for each model and structure type (i. e. simple regression, species' family or order as a random component). Note: Species are classified into seven dispersal distance classes based on predominant dispersal mode, life form, plant height, dispersal-related diaspore features and habitat preferences. Each class comprises species with similar dispersal distances estimated as upper limits of distances within which 50% and 99% of seeds are dispersed (modified from  Vittoz & Engler, 2007).
| DISCUSS ION We estimated dispersal distances for 10,327 European plant species using dispersal distance classes following the method proposed by  Vittoz and Engler (2007). This semi-quantitative trait provides better information for ecological studies than categorical proxies of dispersal distance, such as dispersal modes and dispersal vectors. Our dataset is based on the definition of the predominant dispersal modes, which are also given for all 10,327 species. We recognize that plants usually combine multiple dispersal vectors and that the efficiency of these vectors varies in space and time  (Beckman et al. , 2020; Bullock et al. , 2017; Sádlo et al. , 2018; Thompson et al. , 2010). We have attempted to overcome this uncertainty by dividing species adapted to human dispersal into two groups reflecting less effective but more frequent natural dispersal and more effective but rare human-assisted dispersal.
Consequently, researchers can decide whether to use only the first six dispersal classes (corresponding to natural dispersal) or all seven dispersal classes (also including rare events of human-assisted dispersal). Six classes are appropriate for landscape-scale studies, while seven classes may be more suitable for studies at the continental scale and over longer time periods. Completeness of data on traits and other characteristics of plant species varies by region, species commonness and habitat preferences. While data coverage is relatively high for Central and Western Europe, less information is generally available for plants from the Balkan Peninsula and Eastern Europe and for rare species. However, the dataset can be extended with new data using our decision tree. We have shown that our classification is consistent with predictions of seed dispersal distances using the 'dispeRsal' function  (Tamme et al.
, 2014). Relatively low dispersal distances estimated using this function for classes 5 and 7 (see Figure  4 ) are due to considering more factors in our approach. Tamme et al. (2014)    Predicted dispersal distance in meters (log an underestimate of dispersal distances for class 5. They also did not account for human dispersal, which is partly based on human preferences for some species rather than on the biological traits of species, resulting in an underestimate of dispersal distances for class 7. The seed dispersal dataset is important for several types of macroecological studies. Dispersal distance has significant implications for the distribution and coexistence of plant species  (Beckman et al. , 2020; Levin et al. , 2003). For example,  Poschlod et al. (1998)  have shown the importance of dispersal ability for the diversity of calcareous grasslands,  Pärtel and Zobel (2007)  have demonstrated its effect on the diversity-productivity relationship, and Ozinga et al.
(  2009 ) have detected its effect on plant diversity losses in northwestern Europe. Our dataset can be used to test similar hypotheses on the continental scale. In addition, the data could be used to describe dispersal patterns in specific vegetation types  (Bullock et al. , 2017; Morgan & Venn, 2017). Knowledge of species dispersal potential is important for island biogeography  (Cody & Overton, 1996; Ottaviani et al. , 2020; Riba et al. , 2009)  and conservation management  (Driscoll et al. , 2014). Classification by dispersal classes or dispersal modes can be considered in vegetation modelling (see e. g. Boulangeat et al. , 2014; Bullock et al. , 2012; Engler et al. , 2009) , predicting species responses under different scenarios of novel environmental conditions  (Beckman et al. , 2020)  or estimating the potential spread of invasive species  (Chapman et al.
, 2017). Our dataset contains all the necessary information to assign species to dispersal distance classes, including relevant traits, predominant dispersal modes, species biogeographical origin (native vs. alien) and habitat preferences. Besides using the dispersal distance classes, it is also possible to work with partial data. For example, in landscapescale studies focused on a single habitat type, one might want to consider only dispersal traits without habitat preferences. The seed dispersal dataset for the European flora is a resource for addressing a variety of ecological and evolutionary questions. We hope this dataset and approach will stimulate research in ecol- prepared the trait data and carried out nomenclature unification. G. M. conducted the statistical analyses. J. R. , S.
FU  N D I N G I N FO R M ATI  O N This research was funded through the  2019-2020 BiodivERsA joint call for research proposals , under the  BiodivClim ERA-Net COFUND programme , and with the funding organizations  Swiss National Science Foundation SNF  (project: FeedBaCks, 193907),  Agence nationale de la recherche  ( ANR-20-EBI5-0001-05 ) and the  Technology Agency of the Czech Republic  ( SS70010002 ).
Role of the Angiosperm Revolution on the insect diversification The apparent temporal gap between insect pollinators and crown angiosperms seems to question the reciprocal diversification model, or coevolution between insects and flowering plants  (Asar et al. , 2022). Relying on qualitative comparisons has limits, and there are little quantitative tests to evaluate whether coevolution has spurred diversification between plants and insects. Here we retrieved the family-level fossil dataset of  Condamine et al. (2016)  to estimate whether origination and/or extinction rates varied through time and whether they could be correlated to the angiosperm radiation. We relied on the Bayesian framework implemented in PyRate  (Silvestro et al. , 2014 (Silvestro et al. , , 2019) )  to simultaneously estimate correlations between diversification dynamics and multiple environmental variables (Lehtonen et al.
We thus compiled the relative diversity trajectories of angiosperms, gymnosperms, and spore plants (mostly composed of ferns) based on previous estimates of plant diversity  (Silvestro et al. , 2015). (iii) Insect diversity. Biotic interactions within insects could also have influenced their diversification. For instance, we could draw hypotheses of diversity dependence such that insects could either impact or be impacted by their own diversity. In other words, the change in their diversity can affect their diversification as proposed in diversity-dependent hypotheses. We thus included the paleodiversity of all insect families to account for diversity dependence within insects as an independent variable. (www. coreldraw. com). Figure  2  was designed based on the results obtained after the data analysis with the software R.
Acknowledgements DP thanks the  Ministry of Economy and Competitiveness of Spain  (project " CRE ",  Spanish AEI/FEDER ,  UE CGL2017-84419 ) for financial support. This is contribution no. 4 of the postdoctoral fellowships programme  Beatriu de Pinós  project  2020 BP 00015 , The  flowering plant success -Influence of beetles , funded to DP by the  Secretary of Universities and Research (Government of Catalonia)  and by the  Horizon 2020 programme of research and innovation  of the  European Union  under the  Marie-Curie  grant agreement no. 801370. FLC is supported by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (project  GAIA , agreement no. 851188 ).
Statistical Analysis All measurements were conducted in least triplicate, and the results were presented as mean ± standard deviation. Analysis of variance (one-way ANOVA, LSD test) was carried out using R (The R Foundation 2014) with a significance level of p < 0. 05. Principal component analysis (PCA) was performed using the FactoMineR and Factoshiny packages  [16]. The dataset was composed of 31 variables and 84 individuals (triplicate of the 28 studied samples). Only the permeate-based powders with a Cas:WP ratio of 91:9 were not considered for analysis. Variables "Hyg_10; Hyg_20. " corresponded to the hygroscopicity values of the powders (in g•100 g -1 DM) extracted from the sorption isotherms at different RHs. Variables "Sph_0. 0-0. 5; Sph_0. 5-0. 6. " represented the percentage of particles according to their sphericity ranging from less than 0.
Principal Component Analysis PCA was performed to obtain a comprehensive understanding of parameters responsible of the distinctive features/characteristics of different powders. Figure  2A  shows the variables associated with PC1 and PC2, which, respectively, accounted for 49. 40 and 29. 03% of the total variance. Other PCs each accounted for less than 8%. Therefore, only PC1 and PC2 were considered. Figure  2B  presents the corresponding biplot of individuals where proximity reflected similarity. The PCA suggested four distinctive groups of variables (Figure  2A ): carried out using R (The R Foundation 2014) with a significance level of p < 0. 05. Principal component analysis (PCA) was performed using the FactoMineR and Factoshiny packages  [16]. The dataset was composed of 31 variables and 84 individuals (triplicate of the 28 studied samples).
Note that negative parameter values and confidence intervals are not displayed. (C) and (D) show predicted outcomes for different initial absolute densities and relative frequencies of the two species, obtained from simulations using parameters estimated with all data (cf. Figure  S1  for each replicate population) when both types of competition had additive or non-additive effects, respectively. In both panels, heat maps show that the identity of the remaining species after 20 generations depends on the initial conditions due to priority effects (see also Figures  S2  and S3  for the full population dynamics). Red area: Tc excludes Tu; Blue area: Tu excludes Tc (only for equal initial frequency of the two species in panel B). (C) The combined effect of food competition and reproductive interference were measured across three consecutives generations (G1 to G3) by using common (food and mating) patches for the entire life cycle of the individuals.
Acknowledgements We are grateful to  Inês Santos ,  Cátia Eira  and  Miguel Cunha  for the maintenance of the spider mite populations and  Lucie de Sousa  for the plants, to  André Mira  and  Alexandra Balola  for assistance with data collection, and to  Emanuel A. Fronhofer  and  Jhelam N. Deshpande  for useful discussions. This work was funded by an  ERC Consolidator Grant  (COMPCON, GA asymmetrical production of sterile hybrid females in our system (due to sex-ratio distortion in crosses between Tu females and Tc males 54 ). Then, we compared the proportion of Tu, Tc, and hybrid females observed in the cage populations every two generations, with those predicted under different scenarios: (1) assuming additive vs. non-additive effects of food competition and reproductive interference (by parametrizing the model with estimates from Experiments 1 and 2, or from Experiment 3, respectively; parameter values in Table S1 vs.
MC was funded through an  FCT PhD fellowship  ( SFRH/BD/136454/2018 ) and IF through a  Junior researcher  contract ( CEECIND/02616/2018 ). VCS acknowledges financial support provided by  FCT  ( CEECINST/00032/2018/CP1523/CT0008 ), including national funds to cE3c ( UIDB/00329/2021 ). This is contribution  ISEM-2023 -XXX of the  Institute of Evolutionary Science of Montpellier (ISEM). OG acknowledges financial support provided by the  Spanish Ministry of Economy and Competitiveness  and by the  European Social Fund  through a  Ramón y Cajal  contract ( RyC 2017-23666 ).
Image analysis and quantification of fluorescence intensity and morphological parameters The images of five sections per condition were analysed with ImageJ software (version 1. 53c). An automated procedure was developed to quantify fluorescence intensity and morphological parameters (Fig. 1 ). In few seconds, this procedure enables to quantify several parameters inherent to cells. Two procedures have been developed to fit tissular specificities of analysed wood samples and their relevancy: tracheid lumens for spruce and vessel lumens for beechwood. The analysis procedure is designed to be applied to stacks of images, created from a collection of the same section at different exposure time (Fig. 2a ). The latter will be duplicated in 2, allowing on the first one to perform different morphological operations in order to detect different parts of the wood sections without altering the fluorescence.
2e ). A different threshold, "Li dark" for spruce or "Minimum" for beechwood, was applied on the lowest intensities of the contrasted image (Fig. 2f  ) and the plugin "Analyze particles" available in ImageJ software was used to select the cells of interest: tracheids or vessels (Fig. 2g ). A lower bound for the size of the particles was set (20 pixels for spruce and 500 pixels for beechwood) so that only cells of interest would be analysed. Several morphological parameters inherent to these cells were calculated (Fig. 2h ), including area, perimeter and circularity. Area corresponds to the area of the lumen, perimeter is the outline of the lumen, and circularity quantifies if a cell is perfectly rectangular (tracheids of spruce) or round (vessels of beechwood).