dataesr/scientific-paragraphs-categorization huggingface dataset:
13  (Batagelj & Mrvar, 1998)  to model the network. We adopted several descriptors to characterize the structure of the Cerrado plant-frugivore network, such as "Nestedness", "Modularity", "Connectance", "Link per Species", "Species Strength", and "Shannon Index for Diversity of Interactions"  (Carreira et al. , 2020; Silva et al. , 2015; Tylianakis et al. , 2010). Details on the interpretation and methods of each metric can be found in the Appendix S1. Lastly, we did proportional analyses considering the total amount of frugivores (lato sensu), their biomass (further details about data collection in subsection 2. 5), and network interactions. We assessed differences in fruit and seed size among disperser groups with a nonparametric Kruskal-Wallis, followed by a Dunn's post hoc test.
The network was modeled by compiling secondary data on plant-frugivore interactions already observed in the biome. All Cerrado vegetation physiognomies were considered (savannah, forest, and grassland formations). Interactions are weighted equally since the network's matrix was binary. The full list of species can be found in Tables  S3  and S4  for dispersers and plants, respectively. Eterovick, 2007; Cáceres, 2002). The large frugivore T. terrestris is also of fundamental importance to the Cerrado network. Besides ingesting different-sized seeds, tapirs have particular relevance for the long-distance dispersal of large-fruited plants since they deposit viable large seeds in places unreachable for other large-bodied dispersers  (O'Farril et al. , 2013). Moreover, tapirs are important fruit consumers in altered areas of the Cerrado  (Hannibal et al.
Some of Cerrado's iconic large-bodied dispersers  (Kuhlmann, 2018a (Kuhlmann, , 2018b)) , like the maned wolf (Chrysocyon brachyurus), greater rhea (Rhea americana), and white-lipped peccary (Tayassu pecari), did not show as much network prominence as expected. We acknowledge, though, they could be underrepresented in our dataset, once recording large dispersers' activity is challenging as many are secretive and nocturnal  (Corlett, 2011; Vidal et al. , 2013). Regarding species traits, the lack of correlation between fruit size and disperser body mass is intriguing. Wider fruits are usually associated with larger frugivores  (Herrel et al. , 2004; Kalko et al. , 1996; Lim et al. , 2020) , and several Cerrado's large-fruited plants exhibit the so-called 'megafaunal syndrome', meaning their main seed dispersers should be large-bodied frugivores  (Guimarães et al.
MBCAs officially registered for the management of GTDs in different countries throughout the world. BPDB: Bio-Pesticides Database, accessible at http://sitem. herts. ac. uk/aeru/bpdb/ index. htm (accessed on 24 May 2023); AU: Australia; BE: Belgium; CA: Canada; CY: Cyprus; CZ: Czech Republic; DE: Germany; EL: Greece; ES: Spain; EU: European Union; FR: France; HR: Croatia; HU: Hungary; IT; Italy; KE; Kenya; LU: Luxembourg; MA: Morocco; NL: The Netherlands; NZ: New Zealand; PL: Poland; PT: Portugal; RO: Romania; SA: South Africa; SI: Slovenia; TR: Turkey; UK; United Kingdom; USA: United States of America; VT: Vietnam; ZM: Zambia.
Funding: This work was supported by the  Excellence Initiative of Université de Pau et des Pays de l'Adour-I-Site   E2S   UPPA [Project Biovine , seed funding], a  French "Investissements d Avenir" program. The  French Research Technology Association (ANRT) , and the  GreenCell Company. It was also funded by the  Industrial Chair "WinEsca " funded by the  ANR (French National Research Agency) , as well as the  JAs Hennessy & Co and GreenCell companies.
ABSTRACT: Understanding the environmental processes shaping connectivity can greatly improve management and conservation actions which are essential in the trailing edge of species' distributions. In this study, we used a dataset built from 32 populations situated in the southern limit of the kelp species Laminaria digitata. By extracting data from 11 microsatellite markers, our aim was to (1) refine the analyses of population structure, (2) compare connectivity patterns and genetic diversity between island and mainland populations and (3) evaluate the influence of sampling year, hydrodynamic processes, habitat discontinuity, spatial distance and sea surface temperature on the genetic structure using a distance-based redundancy analysis (db-RDA). Analyses of population structure enabled to identify well connected populations associated to high genetic diversity, and others which appeared genetically isolated from neighboring populations and showing signs of genetic erosion verifying contrasting ecological (and demographic) status in Brittany and the English Channel.
, 2003; Robuchon et al. , 2014). In this study, we extended the sampling design of previous microsatellite analyses  (Billot et al. 2003 , Valero et al. 2011 , Couceiro et al. 2013 , Robuchon et al. 2014 ) by using a dataset built from 32 populations ranging from the southern range limit of L. digitata to the northernmost population found on the French coast, situated in the Strait of Dover. Using this dataset, we explored the respective and combined effects of sampling year, hydrodynamic processes, habitat discontinuity, spatial distance and SST on the genetic structure at different domain sizes. We specifically aimed to (1) refine the analyses of population structure for this species and identify isolated populations, (2) test whether island populations are disconnected from mainland populations, and thus show signs of genetic erosion and (3) evaluate the relative importance of the environmental factors on the genetic structure at different sampling domain sizes using db-RDA.
1998 ) and five for L. ochroleuca  (Lo4-24, Lo454-17, Lo454-23, Lo454-24, and Lo454-28; Coelho et al. 2014). Alleles were sized using the SM594 size standard  (Mauger et al. 2012 ) and scored manually using GeneMapper 4. 0 (Applied Biosystems). Individuals for which more than one locus did not amplify were removed from the dataset. 2. 3. 3. Preliminary analyses Prior to genetic analyses, a Principal Component Analysis (PCA) was ran using the "adegenet" R package  (Jombart 2008)  to identify potential outliers. Then the presence of null alleles was estimated on the dataset cleaned from potential outliers, using the ENA method in FreeNa  (Chapuis & Estoup 2007). As we expect the frequency of null alleles to increase with mutation rate, we also tested the occurrence of null alleles at each marker by looking at the correlation between the mean number of observed alleles (Na) and the number of individuals for which there was no amplification.
In order to test the independence of each marker, genotypic linkage disequilibrium was calculated for all pairs of markers among each of the populations and across populations using Genepop 4. 7. 5  (Rousset 2008 ) and the Markov chain parameters that were used are: dememorization number 1,000, number of batches 100 and number of iterations per batch 1,000. 2. 4. Population structure Population structure was investigated with pairwise estimates of FST  (Weir & Cockerham 1984)  and their significance was computed with 1,000 permutations on FSTAT  (Goudet 1999). Population structure was additionally assessed with two multivariate methods: a Principal Coordinate Analysis (PCoA) using the R package "ape"  (Paradis & Schliep 2019) and STRUCTURE v2. 3. 4 (Pritchard et al. 2000). The PCoA represents the first step to run the multivariate statistical procedure (db-RDA, see section "Linking environmental variables to genetic structure").
STRUCTURE was run with the admixture model without prior population information and ten independent replicate runs were performed from K = 1 to 20 rather than 32 (corresponding to the number of populations) to reduce computation time. For each replicate, the burn-in was set to 100,000 and the Markov chain Monte Carlo (MCMC) iterations to 500,000 following guidance from  Gilbert et al. (2012). The most likely value of K was determined using both the Evanno ΔK  (Evanno et al. 2005 ) and the log Pr(X|K) methods  (Pritchard & Wen 2003)  obtained using Structure Harvester  (Earl & VonHoldt 2012). Based on the recommendations of  Janes et al. (2017) , we assessed the number of genetic clusters by applying and comparing these two methods. Finally, CLUMPAK software  (Kopelman et al. 2015)  was used to summarize and visualize STRUCTURE outputs.
2. 8. Geographic distance and habitat discontinuity Using ArcGIS 10, we first extracted the bathymetric contour from a 0. 001°-resolution digital terrain model (or DTM, data. shom. fr). Given the shape of the peninsula of Brittany, and in order to standardize the calculation of pair-wise distances between coastal populations, distances were calculated following the 5m isobath. When at least one of the populations was situated on an island, the straight-line distance across the sea to another island or to the closest coastline population was considered. A 0. 009º-resolution raster layer representing the distribution of rocky seabed in the English Channel and around Brittany was prepared from the best available substrate information available at Ifremer in May 2019. The information on the spatial distribution of bedrock comes from the combination of several sources: 1) maps of coastal habitats produced as part of the Rebent project, with the scales varying from 1/2,000 to 1/10,000; 2) maps of existing habitat produced as part of the MESH project, with the scales varying from 1/50,000 to 1/10,0000 and 3) from the local extraction of the information on the presence of rock from topobathymetric Lidar data (DTM of 5 m resolution) (e.
Temperature data Sea-surface temperature (SST) were derived from daily mean satellite imagery (Copernicus product ID: SST_GLO_SST_L4_REP_OBSERVATIONS_010_011) for the years 2016 and 2017 using 0. 05°resolution data obtained in 2019 from the EU Copernicus Marine Service (see  Good et al. , 2020  for product details). The minimum, maximum, average and range of annual SST were calculated for each population. 2. 10. Linking environmental variables to genetic structure Global and partial distance-based redundancy analyses (db-RDA,  Legendre and Andersson 1999)  were conducted to investigate the individual and joint effects of geographic distance, dispersal mediated by ocean currents, sampling years, SST (minimum, maximum, mean and range) and habitat discontinuity on the total explained genetic variation.
The analyses were repeated for domains of different size to assess if and how the contribution of each predictor varied according to it. The "overall scale" corresponded to our whole dataset (i. e. , 32 populations), the "regional scale" accounted for the 29 populations from Brittany (S1 to S29) and a "finer scale" included solely the 24 continuously sampled populations (S1 to S24). Sampling years were taken into account to assess how variation in sampling year explained the contemporary genetic differentiation. The first two axes of the PCoA performed on the FST matrix (see "Population Structure" subsection above) were used as the response variables representing the core of the genetic structure in the db-RDA. The environmental explanatory variables accounting for geographic distance and ocean currents were respectively transformed into distance-based Moran's eigenvector maps (or dbMEM,  Dray et al.
2006 ) and Asymmetric Eigenvector Maps (or AEM,  Blanchet et al. 2008). dbMEMs are derived from spectral graph theory and permit describing the spatial autocorrelation between sampling locations using orthogonal variables (corresponding to eigenfunctions,  Dray et al. 2006). dbMEM eigenvectors result from the matrix of geographic distance and were computed with the "adespatial" package using the default settings for truncations. Only the positive eigenvalues were retained as no negative autocorrelation between sampling positions was expected  (Dray et al. 2006). The construction of AEMs uses the same framework while accounting for asymmetric directional spatial processes  (Blanchet et al. 2008). The mean oceanographic connectivity matrix (see "Lagrangian Modeling" subsection) was transformed into a nodes-to-edges matrix and the edges were weighted by the probability of dispersal before calculating AEM eigenvectors with the "adespatial" package.
The calculations of dbMEM and AEM eigenvectors were iterated at each domain size following the aforementioned method. It is worth noting that the first eigenvectors in both dbMEMs and AEMs (e. g. , AEM1, dbMEM1) are associated to broad-scale patterns while the highest eigenvectors (e. g. , dbMEM5, AEM25) highlight fine-scale patterns. For the global db-RDA, an elastic net regularized regression was performed using the "glmnet" package  (Friedman et al. 2010 ) with an elastic net mixing parameter α fixed at 0. 08 to facilitate the identification of irrelevant variables and the highly correlated ones. This method is considered successful when the number of explanatory variables exceeds the sample size  (Ogutu et al. 2012). The remaining variables were then selected via a stepwise forward procedure using the ordiR2step function from the "vegan" package  (Oksanen et al.
RESULTS 3. 1. Prior analyses The PCA run on the whole dataset allowed to identify 12 L. hyperborea individuals (Figure  S1 , Table  S1 ), a sister species to L. digitata with an overlapping range distribution  (Robuchon et al. 2014). Their identities were additionally confirmed based on allelic size at markers Ld148, Ld158, Ld704, Lo4-24 and Lo454-23 following  Mauger et al. (2021). These individuals were sampled in populations S6 (2 ind. ), S9 (1 ind. ), S21 (3 ind. ), S22 (3 ind. ), S25 (1 ind. ), S28 (1 ind. ) and S30 (1 ind. ) and were removed from our dataset, giving a total of 896 individuals. Null alleles were present in several populations (Table  S2 ). However, differences between FST values in the pairwise comparison were of order 10 -3 (data not shown). Therefore, we concluded that the frequency of null alleles was negligible and our dataset was analyzed without taking into account correction for null alleles.
Acknowledgements We thank  Eric Thiébaut ,  Thierry Comtet ,  Jean-François Arnaud  and  Ophélie Ronce  for helpful discussions and comments. The  OSU Pythéas Institute (Marseille)  is thanked for the use of the highperformance computing (HPC) cluster. We are also grateful to the  Biogenouest genomics core facility (Genomer Plateforme génomique at the Biological Station of Roscoff  in particular  Gwenn Tanguy ) for their technical support. LF was funded the  EU  project  MARFOR Biodiversa /004/2015 ,  Region Bretagne  ( ARED 2017   REEALG ) and the  NOMIS  foundation. JA is funded by  Portuguese national  funds through projects  UIDB/04326/2020 ,  UIDP/04326/2020 ,  LA/P/0101/2020  and  PTDC/BIA-CBI/6515/2020.
While this categorization method is simple and quick, it relies on arbitrary choices for class limits and sub-categories. This becomes even more challenging when dealing with samples of mixed origin  [3]. Consequently, finding the most suitable texture classification scheme that maximizes the contrast between samples becomes necessary. The R package Soil Texture Wizard, which utilizes data generated by Gradistat, can be employed for this purpose. The seminal work of Folk and Ward  [3] , supplemented by Folk's later update  [4] , as well as Passega's contemporary contributions  [5, 6] , laid the foundation for a rational description of grain size data through statistical analysis. These publications primarily employed elementary statistical approaches, such as the first and second statistical moments (mean, mode, skewness, and kurtosis)  [3]  or percentiles (e.
g. , D 50 and D 99 )  [5]. However, the theoretical basis for such usage remains weak as these approaches assume a Gaussian lognormal and unimodal sediment distribution, which is rarely the case  [7, 8]. Semi-quantitative indices like sorting indices (Trask's index  [9] , Inman's index  [10] , Folk and Ward index  [3] , etc. ) are often temporary solutions and tend to correlate with standard deviation  [11]. Nevertheless, despite their limitations, central tendency values (mean, median), shape indices (skewness and kurtosis), and dispersion indices (sorting, etc. ) remain dominant in interpreting and graphically representing grain size distributions. The popularity of software like Gradistat  [12]  and its successive updates (currently version 9. 1) attests to this. These approaches are still valuable for identifying trends, gradients, discontinuities, or depositional events in the sedimentary record.
However, this type of approach remains underutilized  [18] [19] [20] [21]. The optimization of this treatment, particularly in the selection of descriptive variables to be included in multivariate analysis, is seldom discussed. An alternative to statistical approaches is decomposition based on mixing laws. Several authors  [22] [23] [24]  have demonstrated that particle size curves of many samples correspond to mixtures of lognormal sub-samples. It is then possible to propose the decomposition of entire curves into the sums of lognormal functions, wherein the means, modes, and standard deviations serve as relevant data. Several software tools that are not specific to sedimentology can be used for this purpose, such as PeakFit ® or the R package MixTools among others. More recently, Dietze et al.
Core Data Acquisition Three cores, with depths ranging from 6 m to 9 m, were obtained from the alluvial plain as shown in Figure  3  (refer to Table  1  for detailed information). Although the three cores exhibit similar characteristics, this paper focuses on the longest core, CHA01, which reaches a depth of 9 m. CHA01 is situated in the southeastern part of la Baine Island, close to an abandoned navigation channel that served as a detour for navigating obstacles (Figure  3 ). Once extracted, the core was initially split, photographed, and described based on macroscopic texture, sedimentary structures, and the presence of macro-remains. Appl. Sci. 2023, 13, x FOR PEER REVIEW 6 of 27 0. 0001 m. m -1. This segment can be classified as "Laterally stable single channel floodplains type C1" according to the classification system by Nanson and Croke  [42].
Core Data Acquisition Three cores, with depths ranging from 6 m to 9 m, were obtained from the alluvial plain as shown in Figure  3  (refer to Table  1  for detailed information). Although the three cores exhibit similar characteristics, this paper focuses on the longest core, CHA01, which reaches a depth of 9 m. CHA01 is situated in the southeastern part of la Baine Island, close to an abandoned navigation channel that served as a detour for navigating obstacles (Figure  3 ). Once extracted, the core was initially split, photographed, and described based on macroscopic texture, sedimentary structures, and the presence of macro-remains.
Chronology The chronology of core CHA01 was established using AMS radiocarbon dating conducted on plant remains or organic sediments, as outlined in Table  2. Radiocarbon dates obtained were then calibrated using the R package CLAM and the Intcal20. 14C radiocarbon calibration curve  [44, 45]. The radiocarbon ages were calculated with a 95% confidence range (two sigmas) and expressed as calibrated years before present (cal. years B. P. ). A deterministic age-depth model was generated (Figure  4 ), covering the period since the early Atlantic. The base of core CHA01 was estimated to be around 7915 cal. years B. P. However, it should be noted that the top section of the core (the last 4 m) lacks radiocarbon dating and alternative dating methods. Therefore, the age-depth model for the uppermost part of the core should be interpreted cautiously due to this limitation.
Grain Size Analysis A total of 158 sediment samples were collected at 5 cm intervals for grain size analysis. A constant volume of 2 cm 3 was retrieved from each sample for further analysis. The collected sediment samples were subjected to oven drying at 105 °C for 24 h. Subsequently, 5 g of sediment were treated with a sodium hexametaphosphate solution at a concentration of 180 g/L to disperse any aggregates present. Grain size analysis was conducted using a Horiba LA-960V2 laser particle size analyzer. The obtained data represent the volume percentage of each grain class; they were classified into 97 different grain size classes ranging from 0. 01 to 3000 mm.
Grain Size Analysis A total of 158 sediment samples were collected at 5 cm intervals for grain size analysis. A constant volume of 2 cm 3 was retrieved from each sample for further analysis. The collected sediment samples were subjected to oven drying at 105 • C for 24 h. Subsequently, 5 g of sediment were treated with a sodium hexametaphosphate solution at a concentration of 180 g/L to disperse any aggregates present. Grain size analysis was conducted using a Horiba LA-960V2 laser particle size analyzer. The obtained data represent the volume percentage of each grain class; they were classified into 97 different grain size classes ranging from 0. 01 to 3000 mm.
Elementary Statistical Parameters The grain size data were initially processed using the Gradistat software for standard statistical analysis  [12]. This involved extracting basic statistical parameters such as sorting, skewness, and kurtosis  [3]. The method of moments  [46, 47]  is commonly used to describe grain size distributions  [48]. However, it is important to note that these parameters can be highly influenced by outliers located in the tails of the distribution  [49]  and mixing processes. Other commonly used statistical descriptors include the mean, median, and mode(s)  [48]. The mean and median are relatively easy to compute but may not always be reliable. The mean is heavily influenced by extreme values in the distribution's tails, while the median does not adequately account for extreme values  [49].
Textural Analysis Fine grain size classes are often categorized as sand, silt, and clay or a combination of these terms  [50]. However, the splitting of mixed samples into categorical nomenclature remains a topic of discussion because the limit values between these categories are somewhat arbitrary and can be described using either metric or phi scales  [51]. The choice of textural classification scheme used is crucial for interpreting the results. To represent these data, a textural triangular analysis is a simple approach. In this study, the sand-silt-clay content was determined using the Gradistat software  [12]  following Folk's classification system  [52]. The textural data were then processed using the R package Soil Texture Wizard, developed by Moeys  [53]. The Soil Texture Wizard package generates texture triangles and applies various texture classification schemes.
The data set (X) used in EMMA consists of sediment samples described by m grain size distributions and n grain size classes. EMMA generates a modeled data set that represents the linear combination of end-member loadings (individual grain size distributions) and end-member scores (the contribution of each loading to each sample)  [25, 28]. The principles and procedures of the EMMA algorithm have been detailed in previous works  [72, 73]. For this study, the data set comprised 158 samples described by 69 grain size classes after removing classes with zero values. The grain size data were processed using the R package EMMAgeo  [25, 26]  with an extended protocol to parameterize all steps of robust EMMA. In this analysis, a five-end-member mode was selected based on the samples' pre-processing.