dataesr/scientific-paragraphs-categorization huggingface dataset:
We wish to recognise and acknowledge the very significant cultural role and reverence that the summit of Maunakea has always had within the indigenous Hawaiian community. This paper is based on observations made with the MuSCAT3 instrument, developed by the  Astrobiology Center  and under financial supports by  JSPS KAKENHI  ( JP18H05439 ) and  JST PRESTO  ( JPMJPR1775 ), at Faulkes Telescope North on Maui, HI, operated by the  Las Cumbres Observatory. Some of the observations in the paper made use of the High-Resolution Imaging instrument Zorro obtained under Gemini LLP Proposal Number:  GN/S-2021A-LP-105. Zorro was funded by the  NASA   Exoplanet Exploration Program  and built at the  NASA Ames Research Center by Steve B. Howell, Nic Scott ,  Elliott P. Horch , and  Emmett Quigley. Zorro  was mounted on the Gemini North (and/or South) telescope of the international  Gemini Observatory , a program of  NSF 's  OIR Lab , which is managed by the  Association of Universities for Research in Astronomy (AURA)  under a cooperative agreement with the  National Science Foundation  on behalf of the Gemini partnership: the  National Science Foundation  ( United States ),  National Research Council (Canada) ,  Agencia Nacional de Investigación y Desarrollo (Chile) ,  Ministerio de Ciencia, Tecnología e Innovación (Argentina) ,  Ministério da Ciência, Tecnologia, Inovações e Comunicações (Brazil) , and  Korea Astronomy and Space Science Institute (Republic of Korea ).
We acknowledge funding from the  European Research Council  under the  ERC Grant  Agreement n. 337591-ExTrA. This work has been carried out within the framework of the  NCCR PlanetS  supported by the  Swiss National Science Foundation. This work is based upon observations carried out at the  Observatorio Astronómico Nacional at the Sierra de San Pedro Mártir (OAN-SPM), Baja California, México. We warmly thank the entire technical staff of the  Observatorio Astronómico Nacional at San Pedro Mártir  for their unfailing support to SAINT-EX operations. Research at  Lick Observatory  is partially supported by a generous gift from  Google. L. D. is an F. R. S. -FNRS Postdoctoral Researcher. M. G. and E. J. are F. R. S. - FNRS Senior Research Associates. V. V. G. is an F. R. S. -FNRS Research Associate.
Libraries were paired-end sequenced on one lane of an Illumina HiSeq 4000 (2x150bp, University of Oregon). Read cleaning, mapping, and SNP calling were conducted by SNPsaurus LLC using BBMap tools v. 38. 08 (http://sourceforge. net/projects/bbmap/; see supplementary methods for the used parameters). All reads were mapped to the reference genome of the asexual KG strain of A. japonica  (Ma et al. , 2021 ; BioProject: PRJNA661661, accession number: JADHZF000000000) with an alignment identity threshold of 0. 95 using bbmap (BBMap tools). We used ANGSD v. 0. 93  (Korneliussen, Albrechtsen, & Nielsen, 2014)  for further processing and cleaning the population genomic dataset. Single nucleotide polymorphisms (SNPs) were called in ANGSD  (Korneliussen et al. , 2014)  using the GATK model (GL 2), taking genotype uncertainty into account by calculating genotype likelihoods.
Sites with a phred base quality score lower than 15 were excluded to remove potential erroneous SNP calls (-minMapQ) if not stated otherwise. Then, SNPs with a mean read depth lower than 2X and over 30X were excluded (-setMinDepth 190; i. e. , an average of 2 per individual; setMaxDepth 2850; i. e. , an average of 30 per individual). A minor-allele frequency (MAF) threshold was set to 10% (-minMaf 0. 1) to remove rare variants and putative additional sequencing errors. We removed triallelic SNPs (-skipTriallelic) and only included high-confidence variants by setting theoption -SNP_pval to 1e-6. Additionally, only SNPs occurring in a minimum of 70% of the individuals (i. e. , sites occurring in 67 individuals out of the 95) were kept (-minInd). Finally, the Neighbour-joining tree and the population structure (PCA, PCA admix) analyses required a linkage disequilibrium (LD-) pruned SNP dataset, keeping only unlinked SNPs.
First, we inspected LD-decay among SNPs, considering SNPs that are at most 50kb distant from each other (Fig. S1 ) using ngsLD  (Fox, Wright, Fumagalli, & Vieira, 2019). Then, we generated an LD-pruned SNPs dataset considering an r 2 threshold of 0. 6 between every pair of SNPs up to a distance of 20 kb using ngsLD  (Fox et al. , 2019). The resulting filtered high-quality SNP data set included 88,485 out of 130,571 SNPs in a total of 95 samples and was used for further population genetic analyses.
The flag "--min_epsilon" was set to 1e-10 as recommended for low coverage data (https://github. com/fgvieira/ngsF). Population structure was first investigated by partitioning the genetic variance using a principal component analysis (PCA;  Patterson, Price, & Reich, 2006)  based on genotype likelihoods using PCAngsd  (Meisner & Albrechtsen, 2018). The contribution of each principal component (PC) was calculated in R and plotted using ggplot2  (Wickham, 2016). Based on genotype likelihoods, a pairwise genetic distances matrix was calculated using the ngsDist package  (Vieira, Lassalle, Korneliussen, & Fumagalli, 2016)  based on genotype likelihoods to create a mid-point rooted NJ tree. The NJ tree was inferred using FASTME v. 2. 1. 5. (Lefort, Desper, & Gascuel, 2015)  and midpoint rooted with the R package phytools v.
0. 7. 47. Node support was estimated with 1,000 bootstrap resampling using ngsDist, and support values were placed on the main tree using RaxML v. 8. 2. 11  (Stamatakis, 2014). Finally, the tree was plotted using the R package ggtree v1. 4  (Yu et al. , 2018). Genetic structure and individual wasp ancestry proportions were further investigated based on the approach implemented in PCAngsd  (Meisner & Albrechtsen, 2018) , relying on the estimated individual allelic frequencies  (Meisner & Albrechtsen, 2018). We ran the analysis using five eigenvalues, which capture 61. 8% of the observed genetic variation. We set the maximum number of iterations parameter to 500 and alpha, the sparseness regularization parameter, to 50. We tested different numbers of plausible clusters (K) ranging from 1 to 8.
The weighted FST  (Reynolds, Weir, & Cockerham, 1983)  was then calculated in a sliding window of 10kb for each pair of populations directly from the folded 2d-SFS. We applied the "-whichFST 1" option because it performs better with small sample sizes  (Bhatia, Patterson, Sankararaman, & Price, 2013). Finally, we estimated the 95% confidence interval using the R package Rmisc v1. 5  (Ryan, 2013)  and reported them together with the mean FST values observed across sliding windows.
Estimated error rates and list of unique sequences were then used to dereplicate the data using the dada function. Forward and reverse reads were subsequently merged to obtain the full denoised sequences. Finally, chimeric sequences were removed using the removeBimeraDenovo function. Taxonomy was assigned using the pre-trained classifier Silva 132 taxonomy classifier (clustered at 99% similarity;  Callahan, 2018; Quast et al. , 2013). The DADA2 pipeline created an amplicon sequence variants (ASV) table and a table containing the assigned taxonomy, while an unrooted neighbour-joining (NJ) tree was created using the phangorn v. 2. 7. 1 R package  (Schliep, 2011; Schliep, Potts, Morrison, & Grimm, 2017). These output files, ASV  of reads in all samples (min(sample_sums(x)) using the phyloseq function rarefy_even_depth before analysis.
In addition, out of this rarefied dataset (Wolbachia +), a second phyloseq object was created, in which reads belonging to the genus Wolbachia were removed (Wolbachia -). Taxa abundance of the phyloseq object without Wolbachia (Wolbachia -) was normalised following the edgeR method (microbiomeSeq R package v. 0. 1; Ssekagiri, 2020)  prior to ordination analyses. This second phyloseq object was used to check how the dominance of Wolbachia influences following analyses, as highly dominant species could influence (multivariate) analyses. All statistical analyses were analysed separately for each dataset (rarefied; Wolbachia +) and rarefied without reads from Wolbachia (Wolbachia -). Normality and homogeneous variances of the ASV's and Shannon diversity were tested with the Shapiro Wilks test  (Shapiro & Wilk, 1965)  and fligner test  (Conover, Johnson, & Johnson, 1981).
The observed number of ASV's and Shannon diversity of the bacterial community of A. japonica were tested against reproductive mode (asexual, infected with Wolbachia or sexual, uninfected) in an ANOVA. Variation in community composition (beta diversity) among samples was visualised via a principal coordinates analysis (PCoA) based on Bray-Curtis distance dissimilarity matrices (ordinate function in phyloseq). Differences between the reproductive modes (asexual, sexual), the effect of location, and population genetic structure were tested by permutational multivariate analysis of variance using the function "adonis2" with 999 permutations (PERMANOVA, vegan R package v. 2. 5-7;  Anderson & Willis, 2003; Oksanen et al. , 2016). Moreover, environmental factors were fitted on the ordination with 999 permutations to investigate the influence of environmental conditions on the bacterial community composition using the "envfit" function (vegan R package v.
2. 5-7;  Anderson & Willis, 2003; Oksanen et al. , 2016). Average distances to the group centroid per explaining variable (population cluster, climatic zones, and reproductive mode) were plotted to establish which variable drives clustering patterns (betadisper function in vegan). Shared and unique taxa found between the reproductive modes and genetic cluster were displayed in a Venn diagram depicting taxa count and percentage (ggVennDiagram R package v. 1. 1. 4; Gao, Yu, & Cai, 2021). We used the R package DESeq2 version 1. 36. 0  (Love, Huber, & Anders, 2014)  to identify differentially abundant taxa across the reproductive modes. We created a deseq object using the phyloseq function "phyloseq_to_deseq2" with reproductive mode as factor and the location Sendai removed due to the high number of zeros.
Correlation of host genomic background with host-associated bacterial community We correlated the pairwise genetic distance of the wasps hatched from the field material (G0) to the bacterial community of their respective offspring (G1) to analyse the influence of A. japonica genetic ancestry and population structure on the bacterial community composition. Only samples with the genetic data of the mother (G0) and the bacterial data of their offspring (G1) were included. This resulted in 42 samples, including seven replicates for each of the three locations harbouring sexual wasps (n=21) and three replicates for each of the seven locations harbouring asexual wasps (n=21). This subset was used to correlate pairwise genetic distance and the bacterial community. The pairwise genetic distance was calculated using the ngsDist package  (Vieira et al.
Bacterial community composition The bacterial community composition covering all locations (3 sexual locations, n=7; 7 asexual locations, n=3; total n=42) revealed 566 taxa in a total of 776,870 reads, with 461 taxa assigned to genus level (81. 5%). After rarefaction to 11,993 reads, the minimum reads per sample, 535 taxa remained with 446 taxa assigned to genus level (83. 4%). Removal of reads belonging to the genus Wolbachia from the rarefied dataset led to 523 taxa, 434 of which could be assigned to genus (82. 9%) in 280,433 reads. Overall, 56% of taxa (301 out of 535) were shared between the two reproductive modes (Fig. S4 ). However, the sexual wasps yielded a higher number of unique taxa (35%) than the asexual wasps, with only 9% percent unique taxa (Fig. S4A ). The five genetic clusters distinguished by the population genetic analyses (Fig.
reproduction, and environmental factors, were then used to create a phyloseq object (R package phyloseq v. 1. 34; McMurdie & Holmes, 2013). Sequences identified as chloroplast, mitochondria, or Archaea, and uncharacterised reads at the phylum level were removed from the phyloseq object. The dataset was rarefied to 11,993 reads based on visualisation by a rarefaction curve (ggrare in the phyloseq R package) and determination of the lowest number.
Figure4: Bacterial diversity and community composition is driven by the endosymbiont Wolbachia. Bacterial diversity and community composition of the wasp Asobara japonica. Alpha diversity A, B) was measured as the observed number of species and expressed as Shannon diversity. Variance of bacterial community composition (beta diversity) C, D) was visualized via a Principal Coordinates Analysis (PCoA) based on Bray-Curtis distance dissimilarity matrices. Wasps from seven Wolbachia-infected, asexual populations (Kagoshima, Fukuoka, Kobe, Kyoto, Kanazawa, Tokyo, Sendai) and three uninfected, sexual populations (Iriomote, Okinawa, Amami) are included. Each sample contains a pool of ten female wasps. A) Alpha diversity of the rarefied dataset (Wolbachia +) and B) alpha diversity of the rarefied dataset excluding reads belonging to the genus Wolbachia (Wolbachia -).
Acknowledgements: We would like to thank  Prof. Maeto  for providing us with important contacts in Japan and shelter during our stay in Kobe. We thank  Prof. Kimura  for his advice on planning the collection trip and his support during the collection in Japan. We thank the  Kyoto Stock Center  for providing us with Drosophila culture material. We thank  Rogier Houwerzijl  and  Elzemiek Geuverink  for help in processing the cultures in Groningen. We thank the  Center for Information Technology of the University of Groningen  for their support and providing access to the Peregrine high-performance computing cluster. We would also like to thank the editor,  Quinton Krueger , and the two other anonymous reviewers for their comments on our manuscript, which improved its quality. P. B. was supported by a scholarship from the  Adaptive Life program  from the  University of Groningen , The  Netherlands.
Globalization and economic growth are recognized as key drivers of biological invasions. Alien species have become a feature of almost every biological community worldwide, and rates of new introductions continue to rise as the movement of people and goods accelerates. Insects are among the most numerous and problematic alien organisms, and are mainly introduced unintentionally with imported cargo or arriving passengers. However, the processes occurring prior to insect introductions remain poorly understood. We used a unique dataset of 1,902,392 border interception records from inspections at air, land and maritime ports in Australia, New Zealand, Europe, Japan, the United States of America and Canada to identify key commodities associated with insect movement through trade and travel. A total of 8,939 species were intercepted, and commodity association data were available for 1,242 species recorded between 1960 and 2019.
While progress has been made towards understanding human-mediated dispersal of certain taxa (for example  Suarez et al. , 2005; Brockerhoff et al. , 2006; Ward et al. , 2006; Liebhold et al. , 2012; Meurisse et al. , 2019) , a global analysis of unintentional insect introduction pathways is lacking. Identifying commerce that transports a wide range of insects worldwide would improve our ability to monitor and manage key pathways, particularly in regions with fewer resources available. The exact pathways responsible for historical species introductions are usually unknown, but alien species databases and inventories often assign species to the most likely pathway based on their ecology and the assumptions of the assessor  (Kenis et al. , 2007; Essl et al. , 2015; Pergl et al. , 2017). However, many countries perform inspections of trade goods, mail and personal baggage at ports of entry (i.
Data acquisition and cleaning We analysed records of insects detected during inspections of international air and sea cargo, mail, vessels and passenger baggage at ports of entry. The data consist of interceptions at air, land and maritime ports from 1960 to 2019 in Australia, New Zealand, member countries of the European and Mediterranean Plant Protection Organisation (EPPO), Japan, the United States of America, and Canada. As the number of individuals detected is not recorded in most regions, each interception represents a single arrival event per species. The insects discovered are destroyed, so while interceptions can be considered a proxy for species' unobserved arrival, they do not directly represent introductions. We included only interceptions between 1960 and 2019 for the years where records where available in each region (Appendix S1: Table  S1 ), where the insect was identified to species level, with information available on the associated commodity.
Pooling data across interception regions There are regional differences in inspection methods and targets, the sources, volume and nature of imports, and the years covered (Appendix S1: Table  S1 ,  Turner et al. , 2021). However, the main commodity types associated with insects are similar across all regions, with the majority being plants, wood, and related products (Appendix S1: Figure  S2 ). To test if species share similar commodity associations across regions despite the differences, we analysed the 59 species intercepted more than 20 times in two or more regions. We included a separate commodity profile for each region in which a species was intercepted. We used a partial constrained correspondence analysis (CCA) in the vegan package  (Oksanen et al. , 2019)  to estimate the variance in commodity associations explained by species, once the This article is protected by copyright.
All rights reserved. effect of interception region is removed. A CCA relates a matrix of species' abundance or occurrence to a matrix of explanatory variables. Partial CCA is an extension of this method where you can control for the influence of conditioning variables in an additional matrix  (Legendre and Legendre, 2012). Pooling the interception records across countries allows us to analyse insect arrivals based on a much wider range of taxa and commodities, and to generalize across regions. As there was an overall similarity in the commodities recorded, and species shared similar commodity associations across regions, we pooled the data for further analysis.
Estimating species richness and diversity We used rarefaction and extrapolation methods to estimate total species richness (i. e. the number of species intercepted) and species diversity (i. e. the number and relative abundance of species) associated with different commodities, using the iNEXT package  (Hsieh et al. , 2016). The ChaoRichness() function estimates the asymptote of rarefaction and extrapolation curves and the associated confidence intervals based on the methods proposed in  Chao (1984 Chao ( , 1987)) , giving a conservative lower bound for undetected species richness. Shannon's diversity index considers both the number of species (richness) and their relative abundance (evenness), which helps to distinguish between commodities where species are transported with a similar frequency, and commodities where only a few species are commonly intercepted.
Phylogenetic signal of commodity associations Phylogenetic signal can be defined as the tendency for related species to resemble each other more than they resemble species drawn at random from the tree  (Bloomberg & Garland, 2002). To test whether related species share similar commodity associations, we created a tree based on the taxonomic structure of species using the as. phylo() function in the ape package  (Paradis and Schliep, 2018) , adding branch lengths with the compute. brlen() function. We combined the taxonomic tree with each species' coordinates from the CA, and tested for phylogenetic signal using Abouheif's Cmean in the phylosignal package  (Keck et al. , 2016). The Cmean index was compared to the null hypothesis that the trait values are randomly distributed in the taxonomy  (Keck et al.
Correspondence analysis and hierarchical clustering To explore the relationship between species and the commodities they are transported with, we carried out a correspondence analysis (CA) using the ade4 package  (Dray and Dufour, 2007). We calculated the proportion of interceptions on each HS-2 commodity group for each species, in order to compare their "commodity profiles" using the relative number of interceptions per commodity. Species with less than 20 interceptions were excluded as this provides insufficient replication to characterize commodity associations. There were 1,242 species intercepted a sufficient number of times for analysis. The first eight axes of the CA were retained. We used a hierarchical agglomerative clustering analysis in the cluster package  (Maechler et al. , 2019)  to identify species associated with similar suites of commodities.
Species were clustered based on their coordinates in the CA, using the agnes() function with Ward's clustering method  (Kaufman and Rousseeuw, 1990). We used the permutation test introduced by Greenacre (2011) to determine whether non-random levels of clustering were present, and if so, to indicate at which level the resulting tree can be cut to give the optimal number of clusters. All analyses were conducted in R (R Core Team, 2017) and figures produced using the ggplot2 package  (Wickham, 2009).
Results The dataset comprised 1,902,392 interception events, representing commodity associations for 7,231 species and 1,708 additional genera with no members identified to species level. The species intercepted were mainly Coleoptera (3165 species), Hemiptera (2708 species) and Lepidoptera (1103 species), but also included members of 19 other insect orders. Insects This article is protected by copyright. All rights reserved. were intercepted on 80 different HS-2 commodity groups, belonging to 14 different commodity classes (Appendix S1: Table  S3 ). With the interception region included as a conditioning variable, species explained 46. 7 % of the variance in commodity associations, while the interception region explained just 12. 3 % of the variance in commodity associations. Both variables explained significantly more variance than expected by chance (permutation test for CCA with 999 permutations, interception region: F = 4.
Acknowledgements We are grateful to the  Australian Department of Agriculture Water and Environment ,  Canadian Food Inspection Agency, European Plant Protection Organization, Japan Ministry of Agriculture Forestry and Fisheries, New Zealand Ministry of Primary Industries and USDA APHIS PPQ  for granting access to inspection data. This paper doesn't necessarily reflect the views of APHIS. We thank  Cathy Sue Katsar  and  Robert Griffin  for thoughtful advice and discussions during various phases of this work. SO and CB were supported by an  International Emerging Action entitled "Globalization and insect invasions (GLOBINV) " and funded by the  CNRS. Additional funding was provided by  USDA Forest Service International Programs  and grant  EVA4. 0 ,No. CZ. 02. 1. 01/0. 0/0. 0/16_019/0000803financed  by  OP RDE.
3150-3182. https://doi. org/10. 1257/aer. 20130954Fenn-Moltu, G. ,  S. Ollier ,  B. Caton ,  A. Liebhold ,  H. Nahrung ,  D. Pureswaran ,  R. Turner ,  T. Yamanaka , and  C. Bertelsmeier. 2022a. Alien insect dispersal mediated by the global movement of commodities. Dryad. https://doi. org/10. 5061/dryad. 8931zcrrqFenn-Moltu, G. ,  S. Ollier ,  B. Caton ,  A. Liebhold ,  H. Nahrung ,  D. Pureswaran ,  R. Turner ,  T. Yamanaka , and  C. Bertelsmeier. 2022b. Alien insect dispersal mediated by the global movement of commodities. Zenodo. https://doi. org/10. 5281/zenodo. 6567146Franić, I. , Prospero, S. , Hartmann, M. , Allan, E. ,  Auger-Rozenberg , M. -A, Grünwald, N. J. , Kenis, M. , Roques, A. , Schneider, S. , Sniezko,  R. ,  Williams , W. , Eschen, R. (2019). 'Are traded forest tree seeds a potential source of nonnative pests?',Ecological Applications, 29(7), pp.