dataesr/scientific-paragraphs-categorization huggingface dataset:
In the present study, we first examine species delimitation in the Andrena proxima-complex using genomic nuclear markers, the socalled "ultra-conserved elements" (UCEs), which have recently been used successfully to delimitate closely related bee species  (Gueuning et al. , 2020). Our sampling mostly focuses on Switzerland and specifically targets contact zones among the three taxa of the A. proximacomplex. We also sequenced DNA barcodes to identify specimens that could not be included in the UCE dataset, mostly to refine our knowledge on the distribution of these species in Europe. We then investigated the impacts of Wolbachia infection and phenology as possible factors contributing to reproductive isolation in this complex. Finally, we discuss how these factors could explain the high diversification rate observed in Andrena bees.
COI phylogenetic analysis Prior to analyses, sequences were filtered by eliminating low quality reads and short (<400 bp) sequences. Alignments were made in MAFFT  (Katoh & Standley, 2013). Bayesian phylogenetic trees were built using BEASTv1. 10. 4  (Suchard et al. , 2018)  leaving mostly default settings, except for Tree Prior: Speciation Yule Process, an MCMC chain length of 20,000,000, an Echo state of 10,000 and logging parameters every 1000 steps. Effective Sample Size was examined in Tracer v1. 7. 1; a threshold of 300 was considered acceptable. Finally, a maximum clade credibility tree was built in TreeAnnotator v1. 10. 0 with a burnin of 50,000 and then visualized in FigTree v1. 4. 4. Trees were rooted at the base of the branch leading to the two outgroup taxa Andrena minutula (Kirby) (Bold accession number FBAPD009_11) and Andrena strohmella Stöckhert (ABEE205_17).
UCE library preparation Based on barcoding results, a total of 51 specimens from the COI database were selected for analyses of ultraconserved elements; these specimens covered the geographic range of this study, with a concentration on contact zones for each of the three potential species. DNA was extracted non-invasively from full body specimens (those in ethanol were fully dried beforehand) using Nucleospin Tissue Kits (Macherey-Nagel, Switzerland) following the manufacturer's protocol, with an overnight "Pre-lyse sample" step. DNA was quantified and normalized to 50 ng by Qubit fluorometer (Thermo Fisher Scientific Inc. ) before being sheared using a Bioruptor ultrasonicator (Diagenode) into approximately 400-600 bp fragments. Library preparation was performed using a KAPA HyperPrep Kit following the manufacturer's protocols at 1 /4 volume for all steps excluding PCR.
proxima (blue dots) in England, France, Switzerland and Italy. "seq-cap" program. Loci present across less than 75% of all specimens were filtered out and the remaining loci were concatenated into a fasta file. Additional filtering of wrongly aligned loci was performed using TrimAl  (Capella-Gutiérrez et al. , 2009)  with the "strictplus" method, subsequently eliminating all specimens with over 90% missing data. To root the UCE phylogenetic trees, six UCE datasets published by  Pisanty et al. , 2022  were downloaded from the SRA databank (NCBI). Based on the phylogenetic placement of A. proxima  (Pisanty et al. , 2022) , we selected the following species as outgroups: A. alfkenella (SRR13987887), A. iliaca (SRR13987885), A. minutula (SRR13987697), A. nigrae (SRR13987781), A. yelkouan_1 (SRR13987806), A.
UCE analyses Phylogenetic trees were built with RAxML-NG v1. 1. 0 (Kozlov et al. , 2019)  using the GTR model and the "autoMRE" bootstrap replicate option (0. 03 cut-off). The adegenet R package  (Jombart, 2008)  was primarily used to conduct the multivariate analyses and genetic distance tests. Using the "vcfR" package  (Knaus & Grünwald, 2017) , the VCF file was first imported into R using the function "read. vcfR" and then transformed into genlight format using the "vcfR2genlight" function. The "dudi. pca" function from the ade4 package  (Dray & Dufour, 2007)  was used to perform principal component analyses on unscaled and uncentered data, and the first two resulting PCA components were plotted using ggplot2  (Wilkinson, 2011). For a better understanding of the genetic clusters, discriminant analyses of principal components (DAPC) were performed without species a priori knowledge.
The optimal number of genetic clusters was identified by the solution resulting in the lowest Bayesian Information Criterion (BIC) value using the function "find. clusters". By using both the plotted cumulative variance of the eigenvalues and a cross-validation method implemented in the "optim. a. score" function, the optimal number of PCs to retain was determined. The posterior membership probability results for each specimen were plotted using ggplot2. Using the dartR package  (Gruber et al. , 2018)  with 10,000 permutations, we first computed fixation indexes (Fst) between each species pair. Analyses of molecular variance (AMOVAs) were then used to test the levels of observed genetic difference. Using the "gl. ibd" function from the dartR package, the correlation between genetic and geographical distance between sampling locations, or isolation by distance (IBD), was computed for each species.
Genetic distances were computed based on Nei's genetic distance  (Nei, 1972) , using "stamppNeisD" function from the StAMPP package  (Pembleton et al. , 2013) , and geographical distances were computed according to the "haversine method" using the geosphere package  (Hijmans, Williams, & Vennes, 2019). Population ancestry was inferred by using a maximum likelihood model through the software ADMIXTURE v1. 3  (Alexander et al. , 2009)  with a 5-fold cross validation procedure for k-values from 1 to 15. Finally, species delimitation analyses were performed using the Bayesian Phylogenetics and Phylogeography model (BPP)  (Yang & Rannala, 2010 ). An unguided species delimitation analysis (A11 model;  Yang & Rannala, 2014)  was used to run the BPP analyses on the nexus files obtained following the 75% threshold filtering step, with each file corresponding to one UCE.
ncbi. nlm. nih. gov) to verify that Wolbachia surface protein genes were amplified. These BLAST searches suggested that all of our sequences belonged to the A supergroup of Wolbachia  (Gerth et al. , 2014; Zhou et al. , 1998). We included representative wsp sequences of supergroups A, B, C and D in our phylogenetic analyses to root the tree. Sequences were subsequently aligned in MAFFT  (Katoh & Standley, 2013 ) and a maximum likelihood phylogenetic tree was created using RAxML v7. 2. 8 performing 500 bootstrap replicates with a GTR+G model. This tree was rooted along the branch joining groups C and D with A and B, following the phylogenetic hypothesis of  Gerth et al. (2014). Genbank accession numbers for all wsp sequences included in this study are given in Table  S3.
Phenology We compared the phenology of the three species of the Andrena proxima-complex using two datasets. We only used females since males are more difficult to identify using morphology, and since we only had few males of one species (A. alutacea: three male records) identified using DNA barcodes. Additionally, we only included specimens from Switzerland to avoid geographic variations in phenology. In the first dataset, we used all Swiss female specimens whose identification was verified using DNA barcodes; after removal of duplicate records, as defined as specimens collected in the same site (1 km 2 ) on the same day, a total of 49 specimens were considered (11 females of A. alutacea, 12 of A. ampla and 26 of A. proxima). In the second dataset, we used all female records in the Swiss National Apoidea Databank, which is accessible on GBIF  (Praz, Müller, et al.
, 2022). For the genus Andrena, this dataset was partly assembled before 2010 based on all museum and private collections in Switzerland  (Amiet et al. , 2010) ; all specimens of the A. proxima species complex had originally been databased as one single species, A. proxima sensu lato  (Amiet et al. , 2010). One of us (CP) examined an estimated 80% of these specimens and separated A. ampla and A. alutacea from A. proxima whenever possible; records whose morphological identification was not clear (mostly for worn specimens) were changed to "Andrena proxima aggr. ", which refers to all three species of the complex. For the remaining 20% specimens, which could not be located or examined, the identifications were corrected as follows: specimens collected in the known range of A. ampla (see below) were assigned to A.
ampla; outside this range, the identification of specimens was changed to "Andrena proxima aggr. ". After removal of duplicate records, this dataset included 370 female specimens (55 females of A. alutacea, 106 of A. ampla and 209 of A. proxima;  Praz, Müller, et al. , 2022). Given that the separation between the three species is challenging using morphology, this second dataset may include a small proportion of identification errors; for this reason, we examine phenological differences across these two datasets. To determine whether the three species have distinct phenologies, a Kruskal-Wallis test (with Fisher's least significant difference with Bonferroni corrections as post hoc) was applied for the first dataset since a Shapiro normality test suggested significant deviation from normality, and an Analysis of variance (with Tukey multiple comparisons of means with Bonferroni corrections as post hoc) was used for the second dataset.
Cytochrome oxidase I COI phylogenetic analyses recovered three monophyletic clades for the Andrena proxima-complex: A. alutacea, A. ampla and A. proxima, with maximal support (posterior probability, hereafter pp = 1. 0) for all three taxa (Figure  S1 ). Andrena proxima was sister to a clade composed of the other two species, but the latter clade had low statistical support (pp below 0. 5). Barcode identification of all examined specimens confirms the parapatric distribution of A. ampla and A. proxima in Switzerland and France; Andrena alutacea was mostly found in sympatry with A. proxima, never with A. ampla (Figure  1 ); Andrena alutacea is reported here for the first time in France. All sequences are deposited in the BOLD database (Table  S1 ).
UCE sequencing and variant calling On average, we obtained 802,002 (SD AE 655,603) forward and reverse reads per specimen. The raw reads are deposited in the NCBI database (BioProject Accession number PRJNA851564; Table  S1 ). After retaining only loci matching the UCE reference file and filtering for a 75% matrix completeness, the number of retrieved loci was 860 (1367 with outgroup taxa). Resulting concatenated aligned reads were 638,751 bp long (883,212 bp with outgroup taxa). In total, five specimens did not pass the 90% missing data filter and were therefore excluded from downstream analyses. Details on the datasets analysed here are given in Table  S4. Freebayes identified 30,629 SNPs in total. While looking for outliers, we identified the same five specimens as those determined from the 90% missing data filter applied to the concatenated UCE matrix.
Phenology The three species each have only one generation per year with females of Andrena proxima collected in mid-April through to early July, A. ampla from late May until late July, and A. alutacea from early June to late July. Based on the first dataset examined (barcoded F I G U R E 5 Maximum likelihood phylogenetic tree based on nucleotide data from the wsp gene for Wolbachia detected in specimens of the Andrena proxima-complex. Branch support values are only shown for bootstrap probabilities greater than 50. The outgroup sequences used to root the tree are not shown. Swiss canton of collection for each specimen is noted next to the specimen IDs (GR: Grisons, VS: Valais, SG: St. Gallen, NE: Neuchâtel; VD: Vaud) as well as the French department Ardèche. Three distinct lineages have been identified, referred to as wsp 1, wsp 2 and wsp 3.
Whether Wolbachia infection, proposed here to lead to unidirectional CI among these two species, may further reduce gene flow, remains to be investigated. In theory, unidirectional CI could contribute to the reduced gene flow between these two species if the infection status differs across contact zones; the infected species would have an additional advantage over the uninfected species. However, our limited dataset does now allow to test for this hypothesis. Lastly, we also expect bidirectional CI between A. ampla and A. alutacea, which raises the question of why these two taxa do not occur in sympatry. We suggest that this lack of sympatry may again be associated with the less pronounced differences in phenology between these two species. These less pronounced phenological differences could be less effective in preventing reproductive inference compared to the A.
specimens were collected in ethanol from several locations in Switzerland between May and August 2019. Sampling locations were based on areas of previous records included in the info fauna database (https://lepus. unine. ch/carto/); sites with Apiaceae were surveyed to locate the species. A particular attention was given to possible contact zones, as delimitated in preliminary DNA barcoding studies as well as previous work based on morphology (Schmid-Egger, 2005), which identified three contact zones between Andrena ampla and A. proxima in Switzerland: the regions around Martigny, Valais; Chur, Graubünden; and in Ticino. In addition, specimens collected recently in Switzerland (mostly collected in the context of an ongoing project focused on updating the red list of the Swiss bees).
ACKNOWLEDGMENTS This project was funded by the  Wüthrich et Mathey-Dupraz foundation , the  Evolutionary Entomology Laboratory , the  Swiss Federal Office for Agriculture (FOAG)  and the  Swiss Federal Office for Environment (FOEN). We would like to extend our gratitude to all collaborators who provided specimens for this study, in particular  Paddy Saunders ,  Rosie Earwaker  and  Steven Falk. We also thank  François Claude  for continuous assistance in curating the Swiss National Apoidea Databank, and the curators of all Swiss natural history Museums for access to their collections. Finally, we thank three anonymous reviewers and the editor for their numerous insightful comments and suggestions, which improved our manuscript. Open access funding provided by the  University of Neuchatel.
A. alutacea Raw Illumina sequence reads are available in the Sequence Read Archive (SRA) under BioProject PRJNA851564 (Table  S1 ). COI sequences are available in the BOLD dataset (accession numbers HYMAA362-22-HYMAA492-22), and Wolbachia wsp sequences in the NCBI dataset (accession numbers OP019003-OP019021). Swiss occurrences are available on the GBIF platform at https://doi. org/10. 15468/ksfmzj (see also Table  S1 ).
DISCUSSION In this study, we tackled species delimitation in a controversial group of Andrena using nuclear genetic data in addition to DNA barcodes. We also refined distribution data for the three taxa in this taxonomic complex in Central Europe. In line with the fast diversification rates of the Andrena lineage, our results support the recognition of three closely related species, with a unique distribution and phenology pattern: two pairs of species are parapatric with very narrow contact lines but no known sympatric occurrence (A. ampla/A. proxima and A. ampla/A. alutacea); and one pair of species is often found in sympatry but exhibiting pronounced differences in phenology (A. proxima/ A. alutacea).
Fresh specimens were collected in ethanol from several locations in Switzerland between May and August 2019. Sampling locations were based on areas of previous records included in the info fauna database (https://lepus. unine. ch/carto/); sites with Apiaceae were surveyed to locate the species. A particular attention was given to possible contact zones, as delimitated in preliminary DNA barcoding studies as well as previous work based on morphology (Schmid-Egger, 2005), which identified three contact zones between Andrena ampla and A. proxima in Switzerland: the regions around Martigny, Valais; Chur, Graubünden; and in Ticino. In addition, specimens collected recently in Switzerland (mostly collected in the context of an ongoing project focused on updating the red list of the Swiss bees).
We report the combined results of eight searches for strong gravitational lens systems in the full 5,000 sq. deg. of Dark Energy Survey (DES) observations. The observations accumulated by the end of the third observing season fully covered the DES footprint in 5 filters (grizY), with an i-band limiting magnitude (at10σ) of 23. 44. In four searches, a list of potential candidates was identified using a color and magnitude selection from the object catalogs created from the first three observing seasons. Three other searches were conducted at the locations of previously identified galaxy clusters. Cutout images of potential candidates were then visually scanned using an object viewer. An additional set of candidates came from a data-quality check of a subset of the color-coadd "tiles" created from the full DES six-season data set.
In the CFHTLS Strong Lensing Legacy Survey  (Cabanac et al. 2007 ), 54 systems with promising lenses were identified by the ARCFINDER algorithm  (More et al. 2012 ) in 150 sq. deg. , 49 confirmed strong lens systems identified with RingFinder  (Gavazzi et al. 2014) , and 29 promising and 59 total  (More et al. 2016 ) from a crowdsourcing  (Marshall et al. 2016)  effort. CFHTLS imaging yielded 16 more probable to definite SL systems using neural networkbased searches  (Jacobs et al. 2017). The Survey of Graviationally-lensed Objects in HSC Imaging  (SuGoHI, Sonnenfeld et al. (2018) ) used three different methods, including YattaLens, to search the Hyper Suprime-Cam Subaru Strategic Program (HSC SSP) images. The program yielded 333 galaxygalaxy SL candidates from an area of 442 deg 2. The Kilo Degree Survey images were searched using machine-learning (ML) classifiers that discovered 227 high grade SL candidates, plus an additional 200 candidates discovered serendipitously  (Petrillo et al.
2008) , 68 giant arcs in  Wen et al. (2011 ), ten SL systems in Furlanetto et al. (2013b) , and 37 more in  Sharon et al. (2020). More recently, a search of galaxy clusters in HSC imaging  (Jaelani et al. 2020 ) revealed more than 600 candidate strong lens systems, of which 8 are confirmed spectroscopically. The eBOSS spectroscopic data produced  (Talbot et al. 2021 ) 838 likely galaxy-galaxy SL systems with Einstein radius 1. The DESI Legacy Imaging Surveys team used 14,000 sq. degs. of images from several instruments, including the Dark Energy Camera  (Flaugher et al. 2015) , in combination with a neural network trained on real SL systems, to identify 1,545 candidate systems  (Huang et al. 2020 (Huang et al. , 2021)). Galaxy clusters, among the most massive objects in the universe, are ideal candidates for strong lenses, and optical observations of galaxy cluster catalogs have yielded many SL systems.
Galaxy cluster catalogs selected via optical observations, the Sunyaev-Zel'dovich effect  (Sunyaev & Zeldovich 1972) , or as extended high-flux x-ray sources have been used. Follow-up of South Pole Telescope (SPT)  (Reichardt et al. 2013 ) detections yielded 34 new and 10 previously known SL systems in  Bleem et al. (2015) , and 44 new SL systems in  Bleem et al. (2020). Follow-up of Atacama Cosmology Telescope galaxy cluster identifications  (Hilton et al. 2021 ) yielded 67 new candidates including many in the DES footprint that are characterized here for the first time. The DES data have proven to be a productive source of SL candidate systems. Searches for lensed quasars have yielded four spectroscopically confirmed systems  (Agnello et al. 2015; Ostrovski et al. 2017; Lin et al. 2017) , two new quad SL systems and 96 more quasar lens candidates ripe for spectroscopic follow-up  (Agnello & Spiniello 2019).
Astrometric calibration is performed by matching bright stars on each exposure to reference stellar catalogs (2MASS  (Skrutskie et al. 2006 ) for Y3, GAIA DR2  (GAIA Collaboration et al. 2018 ) for Y6) using scamp  (Bertin 2006). Magitude zeropoints for each CCD on each exposure are determined using the "Forward Global Calibration Module"  (Burke et al. 2018). Finally, exposures in each filter were coadded using SWarp (Bertin 2010) in 10,000 by 10,000 pixel "tiles" 0. 72 degrees on a side. SExtractor was then run on a weighted combination of coadded r + i + z "detection tiles" to form catalogs of objects, and then rerun on the individual filter bands at the location of the detected objects. The separation or "deblending" of closely positioned (or even overlapping) objects is a challenge where the goal is to balance completeness against the spurious separation of features within a single galaxy.
The deblending was performed using the detection images. For Y6, the SourceExtractor source detection threshold was lowered from 10σ (in Y3) to 5σ to detect fainter sources; this was accompanied by a reoptimization of the deblending parameters. As a result, objects are sometimes detected and/or deblended better in one data release than they are in the other. The absolute photometric calibration for both Y3 and Y6 is tied to the spectrophotometric Hubble CALSPEC standard star C26202  (Bohlin et al. 2014 ). Other differences between the Y3 and Y6 processing are described in Dark Energy Survey  Collaboration et al. 2021. All of the previous seasons' observations were reprocessed to produce the new data sets. Each time, object catalogs containing the list of objects, their shapes, and their astrometric and photometric properties were calculated for each coadd tile.
(2017). We completed eight different searches, described below, to identify candidate strong lens systems using the Y3 and Y6 data. One of the searches was a by-product of the Y6 data quality effort. The seven other searches involved identifying and visually scanning a list of potential SL systems. While the origin of the lists was different for each search (those details are described below), the rest of the procedure is in common. The lists were loaded into the "DES Science Portal" (Dark Energy Survey Collaboration et al. 2021), which includes "Target Viewer", a tool for visualizing the DES fields that can also provide catalog information about the objects. The Target Viewer produced 5-color (g, r, i, z, and Y-band) cutout images, roughly 2 across, with one system centered on each page. Cutouts were scanned by either scientists with experience identifying strong lens systems or students trained to do so.
Each person assigned a score of 0 to 2 to each system -0 points if the system was thought to not be a SL candidate, 1 point if it might be, and 2 points if the system was expected to be an instance of strong lensing. Scores from all judges were summed to produce a "rank" between 0 and 10. Systems with a rank of at least 3 were taken as the final list for this paper. The candidate rankings of 3 to 10 span the range from "possible" to "probable" to "definite" strong lens systems with rankings consistent with those used in the Master Lens Database  (Moustakas et al. 2012 ) and other graded samples of strong lens candidates.
All four of these had a grade of 3 or more. As part of the Y6A1 DES data release, we performed a data quality check comprising a visual scan of a subset of the 10,167 1/2-sq. deg. coadd tiles. The scanning took place in two stages. Initially 2,377 tiles were scanned in RA-order starting with RA's close to 0 hrs. Later, we repeated the process on the 1,708 tiles that had not had any targets in the BNA or RNA SL searches. We were looking for obvious quality defects such as the inclusion of problematic single exposures into the coadds. We identified coadds that included exposures that had been taken during an earthquake, some that had a trail from the reflections off the International Space Station, and others that had a subtle astrometric failures that resulted in a "doubling-up" of the objects in one filter band.