dataesr/scientific-paragraphs-categorization huggingface dataset:
S3 ). Of these, 517 MAGs belonged to the Bacteria and represented about 65 new families and 202 new genera without cultured representatives based on GTDB-Tk  (27)  and average amino acid identity (AAI) analyses (Fig. 4 , Dataset S3 B and C, and see Materials and Methods and SI Appendix, SI Text and SI Methods), and the 184 archaeal MAGs represented about 27 new families and 83 new potential genera (Fig. 5 , Dataset S3 B and C, and SI Appendix, Fig. S4 ). While MAG diversity (National Center for Biotechnology Information [NCBI] and Genome Taxonomy Database [GTDB] nomenclature in Dataset S3B) might not necessarily be a proxy for whole-community composition  (30) , in our study similar diversity patterns were observed for MAG and 16S rRNA gene amplicon data (SI Appendix, Fig. S5 A  and B ); the functional data also tracked these patterns (Fig.
2 A-C ). When MAG diversity differences were explored statistically, the analysis further confirmed that the MAGs obtained from NWC-A samples were significantly different from those found at NWC-B+UCW (NWC-A versus NWC-B+UCW, ANOSIM R = 0. 931, P = 0. 002; Fig. 2C ). Putative anaerobes related to the genera Archaeoglobus, Thermococcus, Hippea, Nitratifractor, and Mesoaciditoga and the family Caldisericaceae were prevalent MAGs from the NWC-A samples, while putative microaerophilic Aquificae and Thermoprotei (Aeropyrum-related) and putative carboxydotrophic Hydrothermarchaeota MAGs characterized the NWC-B+UCW samples (Dataset S3D). Furthermore, from the LC site samples in particular a diversity of previously undescribed members of the DPANN superphylum were assembled (Datasets S3E and S4 and SI Appendix, Figs.
S5C and S6 and SI Text). One DPANN lineage common to several sites was the Woesearchaeaota. We successfully enriched for a novel woesesarchaeote with only Bacteria (Thermales and Aquificales) in the coculture (Dataset S5A and SI Appendix, SI Text). The reduced metabolic repertoire of the assembled metagenome of this woesearchaeote suggests that it forms a symbiotic association with bacterial hosts (Dataset S5B). Several of the MAGs most likely contributed to the functional differences described above (data available at FigShare  (21). For example, 143 (67%) of NWC-A MAGs, including members of the Epsilonbacteraeota, Thermodesulfobacteria, Archaeoglobi, Thermoprotei, Thermococci, Thermoplasmata, and several DPANN, all encoded for superoxide reductase (dfx), which is often prevalent in anaerobes  (22) , while very few (n = 40, or 19%) MAGs encoded the superoxide dismutase (SOD2) typically found in aerobes.
(42)  and shown in Dataset S1B. Amplicon Amplification and Sequencing of Environmental Samples. Microbial communities were characterized by sequencing the V4 region of the 16S rRNA gene following the Earth Microbiome Protocol  (43)  with primers 515F-Y (5′-GTGYCAGCMGCCGCGGTAA-3′)  (44)  and 806R (5′-GGACTACHVGGGTWTC-TAAT-3′). Twelve-base-pair barcodes were added to primer 806R. Triplicate 25-μL PCR reactions were prepared with 12. 5 μL of GoTaq Master Mix (Promega Corp. ), 0. 5 μL of forward and reverse primers (0. 2 μM final concentration), 10. 5 μL of nuclease-free PCR water (Promega Corp. ), and 1 μL of template DNA. Thermal cycling conditions began with an initial denaturation at 94 °C for 3 min, followed by 35 cycles of 94 °C for 45 s, 50 °C for 60 s, and 72 °C for 90 s. The final extension was at 72 °C for 10 min.
After amplification, PCR products were pooled, visualized using gel electrophoresis, and then quantified using the Quant-iT Picogreen dsDNA kit (Invitrogen). Three hundred nanograms of each product were pooled, cleaned using the Invitrogen PureLink PCR purification kit (Carlsbad), and quantified using the Qubit dsDNA High Sensitivity (HS) kit (Invitrogen). Sequencing was performed on an Illumina MiSeq using the v2 chemistry (2 × 150 base pairs [bp]) at California State University, Northridge, CA. Amplicon Sequence Analysis. Demultiplexed amplicon sequence data were imported into QIIME2 v. 2019. 748 (45). The reads were joined using vsearch, quality-filtered, and denoised using deblur  (46). Diversity analyses (alpha and beta diversity) were performed at a depth of 3,000 sequences per sample.
Taxonomic assignments were performed using the SILVA database classification (v. 132) (47) using the QIIME2 Naive Bayes classifier trained for the V4 region (Silva-132-99-515-806-nb-classifier. qza). All QIIME2 commands are available on FigShare. Metagenome Sequencing of Environmental Samples. Metagenomic libraries were constructed using Nextera DNA Library Prep kits (Illumina) according to the manufacturer's protocols. Paired-end sequencing (2 × 150 bp) of the libraries was done at the Oregon State University Center for Genome Research and Computing on the Illumina HiSeq 3000 platform. Metagenome Assembly. Metagenome reads were trimmed and cleaved of Illumina sequencing adapters and barcodes using Trimmomatic v. 0. 36  (48). Reads were interleaved, and both interleaved and unpaired reads were assembled into contigs using MEGAHIT v.
1. 1. 1-2-g02102e1 (49)  with the parameters -min-contig-len 2000 -k-min 31 -k-max 151 and -k-step 20. In order to evaluate different assembly approaches, the reads of three MEGAHIT assemblies with the lowest ratio of assembly length to clean reads (S009, S017, and S142) were also assembled using metaSPAdes v. 3. 10. 1  (50)  with the following options: -k 33, 55, 77, 99, 127 -only-assembler. Only contigs ≥2,000 bp were retained. The quality of the MEGAHIT assemblies was in general significantly better than those obtained using metaSPAdes (best assembly length in all three using MEGAHIT, and higher maximum and   ), magnesium (Mg), hydrogen sulfide (H 2 S), iron (Fe) and hydrogen (H 2 ) and pH shifts are shown for each site (Dataset S1B). The combination of different geochemical environments intersected by the circulating hydrothermal fluids, and different scales of recharge, may explain some of the shifts in diversity of the microbial communities observed at Brothers volcano.
mean contig lengths in two) and thus MEGAHIT assemblies were chosen for further metagenomic analyses. To generate differential read coverage profiles, reads were mapped to the contigs with Bowtie2 v. 2. 2. 9 (51) and parsed using SAMtools v. 1. 3. 1  (52). Annotation of Metagenome Assemblies. To assess functional differences between samples, proteins on assembled contigs from each metagenome were predicted using Prodigal v. 2. 6. 3  (53)  and annotated with the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (54) using GhostKOALA  (55). KEGG orthology (KO) hits corresponding to catalytic hydrogenase subunits (K00436, K00440, K05922, K06281, K12142, K14090, K14106, K14123, K14126, K18016, K18332, and K23549) were further characterized using HydDB  (24) , and only hydrogenases passing HydDB quality standards were retained.
Gene categories involved in iron transport and metabolism were independently annotated using FeGenie  (56). HydDB-validated hydrogenase hits (n = 21 genes), FeGenie-annotated iron gene categories (n = 7 categories), and a subset of KO hits (n = 95 genes) involved in key energy and carbon acquisition processes and oxygen sensitivity were extracted for further analysis. Each functional gene in the analysis was assigned a coverage value using contig coverage profiles (discussed above). The summed coverage for each functional gene was normalized by the average total coverage for 14 single-copy marker genes (K02950, K02992, K02906, K02926, K02890, K02874, K02931, K02994, K02952, K02948, K02871, K02961, K02881, and K02986), as previously described  (57). Normalized values were also averaged across samples from each area (UC, LC, NWC-A, and NWC-B+UCW), and a heat map was constructed using Morpheus (https://software.
Curation of Representative Genomes. Representative MAGs belonging to taxonomic clades of interest were selected for additional curation using Emergent Self-Organizing Maps  (62)  with the tetramerFreq script package (https://github. com/tetramerFreqs/Binning) (63). After calculating the tetranucleotide frequency of individual MAGs and multiple reference genomes, ESOM maps were trained using the K-batch option with the recommended number of rows and columns. MAGs were manually rebinned, and outlier contigs with <90% confidence were removed. These curated MAGs are denoted on the phylogenetic trees (discussed below) by an asterisk. Phylogenetic Analyses. As a framework for this study, genome bins were used to construct maximum-likelihood phylogenetic trees for the Bacteria (n = 21,780 taxa and 5,035 amino acid positions) and Archaea (n = 1,371 taxa and 5,024 amino acid positions) using the identify, align, and infer steps of the GTDB-Tk v.
0. 2. 2 de novo workflow (data release 86 v. 3, WAG+GAMMA model)  (27). Branch support values were computed using the Shimodaira-Hasegawa (SH) test. Additional phylogenetic trees were constructed of the Chloroflexi (n = 67 taxa and 2,375 amino acid positions) and the Aquificae (n = 48 taxa and 2,373 amino acid positions) using 16 ribosomal proteins  (61) , which were aligned individually using Muscle v. 3. 8. 425 (64) and concatenated, and positions containing ≥90% gaps were also removed. Alignments were then subjected to maximum-likelihood phylogenetic analysis in RAxML v. 8. 2. 8 (65) (PROTGAMMA+LG model) with 1,000 replicate bootstraps. GTDB-Tk trees and ribosomal protein trees were viewed and rooted with Interactive Tree of Life v. 5  (66). Furthermore, we performed phylogenetic analyses of DPANN superphylum MAGs using 48 marker proteins identified in 400 archaeal genomes  (67)  from the Archaeal Clusters of Orthologous Genes (arCOG) database  (68).
All markers were individually aligned with MAFFT v. 7. 407 (69) (mafft-linsi), trimmed with BMGE v. 1. 12 (70) (settings: -m BLOSUM30 -h 0. 55), and concatenated using catfasta2phyml. pl (https://github. com/nylander/catfasta2phyml). IQ-TREE v. 1. 6. 7  (71)  was used to perform maximum-likelihood analysis (C60+LG+F+R mixture model, n = 400 taxa and 9,461 amino acid positions) with an ultrafast bootstrap approximation and SH-like approximate likelihood tests, each run with 1,000 bootstrap replicates. Taxonomic Classification and Novel Taxonomic Rank Designations. MAGs were classified using the GTDB-Tk v. 0. 2. 2 (data release 86 v. 3) classify workflow  (27)  and with the NCBI taxonomy browser. In order to explore novel family and genus level ranks for MAGs, we used both the GTDB-Tk workflow and average amino acid identity (AAI) matrices (using the Enveomics AAI Matrix Tool, enve-omics.
ce. gatech. edu/g-matrix/) to inform our decision. In general, we used the GTDB-Tk assignment first and confirmed the ranking with AAI. In a few cases, compelling evidence based on AAI similarity superseded the GTDB-Tk ranking. For comparison, archaeal taxonomic ranks were also determined using AAI comparisons between MAGs and reference genomes. AAI matrices for the DPANN superphylum were generated separately using CompareM v. 0. 0. 23 (https://github. com/dparks1134/CompareM). Nomenclature. We recognize the archaeal and bacterial nomenclature is in flux. Here we primarily use the NCBI-assigned taxonomies or the GTDBrecommended taxonomy in parentheses. Dataset S3B provides both nomenclatures for reference. We refer to the Epsilonbacteraeota (formerly Epsilonproteobacteria; GTDB Campylobacterota) throughout the text using detailed nomenclature proposed by Waite et al.
(72) , which is largely congruent with GTDB data release 86 v. 3 taxonomy at the class level and lower taxonomic ranks. Detailed taxonomy of the DPANN superphylum MAGs is also included in the phylogenetic tree depicted in SI Appendix, Fig. S6. Annotation and Comparison of MAGs. Open reading frames (ORFs) within MAGs were predicted using Prodigal v. 2. 6. 3  (53)  as implemented in Prokka v. 1. 13 (73). For comparison with assembly annotation data, the MAGs were annotated as described above using GhostKoala  (55) , HydDB  (24) , and FeGenie  (56). Statistical Analyses. To assess site differences at Brothers volcano, Bray-Curtis similarity matrices were generated in PRIMER v. 6. 1. 13 (74) using the relative abundance of key functional genes/gene categories identified in the assemblies (n = 123 genes/gene categories) and the relative abundance of medium-to high-quality MAGs based on normalized read coverage and GTDB-Tk taxonomy (n = 251 taxonomic assignations).
Bray-Curtis dissimilarity matrices comparing communities from Brothers volcano samples and from both Brothers volcano and ELSC-VFR samples were also generated in QIIME2 as part of the "core-metrics-phylogenetic" workflow in the "diversity" plugin, at a sampling depth of 3,000 sequences per sample. Similarity and distance matrices were used to create nonmetric multidimensional scaling (NMDS) plots in PRIMER. One-way ANOSIM was performed on each matrix to identify statistically significant differences between sites, and oneway SIMPER analysis was used to identify specific contributors to the similarities and differences between sites where applicable. Data Availability. Amplicon reads from Brothers volcano and ELSC-VFR are available from the NCBI Sequence Read Archive (see Dataset S1 A and E for accession nos.
). Metagenome reads are also deposited in the Sequence Read Archive (see Dataset S2A for accesson nos. ), and metagenome-assembled genomes are available on GenBank (see Dataset S3B for accession nos. ). Additional data are available on FigShare at https://doi. org/10. 6084/m9. figshare. c. 5099348  (21). ACKNOWLEDGMENTS. We thank the captain and crew of the R/V Thompson and the engineers from Woods Hole Oceanographic Institution for the successful operation of ROV Jason.
The project was funded by NSF grants OCE-1558356 (Principal Investigator S. E. H. ) and OCE-1558795 (Principal Investigator A. -L. R. ). S. L. received a grant from the University of Brest to work in the A. -L. R. laboratory. A travel fund from Interridge enabled A. D. to participate on the R/V Thompson cruise. Funding for this work for C. E. J. d. R. , F. C. T. , V. K. S. , and L. C. S. was provided by the New Zealand government. A. S. was supported by the Swedish Research Council (Vetenskapsrådet starting grant 2016-03559 to A. S. ) and the Nederlandse Organisatie voor Wetenschappelijk Onderzoek (Dutch Research Council) Foundation of the Netherlands Organization for Scientific Research (Women In Science Excel [WISE] fellowship to A. S. ). A. -L. R. and E. S. J. thank Rika Anderson for helpful methodological discussions and Sean Sylva for assistance in shipboard geochemical analysis.
Data We use an ensemble of 18 high-resolution regional climate projections taken from the most up-to-date ensemble produced in the recent years for Africa: CORDEX-AFRICA  (Giorgi et al. , 2009; Jones et al. , 2011). In this ensemble, 5 Regional Climate Models (RCMs) are used to downscale 10 Global Climate Models (GCMs) under the climate scenario 80 RCP8. 5 (Table  1 ). The simulations span the period 2006-2099 at a daily mean time step, and cover Africa (24. 64°W -60. 28°E; 45. 76°S -42. 24°N) at about 50 km (~ 0. 5°) spatial resolution in latitude and longitude. For each simulation and each grid cell, daily time series of surface air temperature and precipitation are retrieved. We evaluate the spatial distribution of daily rainfall simulated in the CORDEX models by comparing it to the satellite 'Climate Hazards Group Infrared Precipitation with Station data (CHIRPS)' rainfall dataset (Supplementary Figure  1 ).
85 The CHIRPS dataset is explicitly designed for monitoring agricultural drought and global environmental change over land. It corresponds to a gridded, quasi-global (50° S-50° N), high-resolution (0. 05°), daily rainfall dataset that covers the time period 1981-2014  (Funk et al. , 2015). In addition, we evaluate the statistical distribution of daily rainfall simulated in the CORDEX models in three cities (Ouagadougou Airport (12. 35° N, 1. 52° W), Dakar-Yoff (14. 72° N, 17. 51° W, and Accra Kotoka International Airport (5. 6° N, 0. 17° W), as seen in Figure  1a ), by comparing it to the 90 CHIRPS dataset (grid point comparison) and to three near-surface daily rain gauge data extracted from the BADOPLUS dataset, as described in  Panthou et al. (2012)  (Supplementary Figure  2 ). We find that for all the CORDEX simulations, the spatial distribution of mean precipitation and to some extent RR1 satisfactorily reproduces the spatial distribution of the CHIRPS dataset (Supplementary Figure  1 ).
The multimodel mean however overestimates the observed RR1, especially along the Guinea Coast. More disagreements are found across models for INT, even though the multimodel 95 mean is in good agreement with the observation. In addition, we find that in Dakar, the CHIRPS and the BADOPLUS datasets show similar statistical distribution of daily mean precipitation, yearly mean precipitation, RR1, and to some extent INT. In Ouagadougou and Accra however, the two observational datasets show similar statistical distributions for daily and yearly mean precipitation, but strongly disagree for RR1 (underestimated in the CHIRPS dataset) and INT (overestimated in the CHIRPS dataset). In other words, the CHIRPS dataset shows more frequent but less intense 100 precipitation than the BADOPLUS dataset in Ouagadougou and Accra.
Finally, we find that the CORDEX simulations generally show a similar statistical distribution than the observations for daily mean and yearly mean precipitation. However, they tend to overestimate (underestimate) the observed RR1 in Accra and Ouagadougou (Dakar), and underestimate the observed (particularly as compared to the BADOPLUS dataset) INT in all three locations. In other words, the CORDEX simulations show less intense precipitation than the observations (especially the BADOPLUS 105 dataset) in all three locations, and less (more) frequent precipitation in Dakar (Ouagadougou and Accra) than the observations. Nevertheless, we find that the observations are always included within the range of the CORDEX simulations. Hence, we conclude that the CORDEX simulations compare satisfactorily well with the observations, and can be used for the purpose of our study.
After DNA extraction, PCR was conducted using the primers to amplify the DNA fragment 12S rRNA  32. Electrophoresis was carried out on a 1. 5% agarose gel in 0. 5 X TBE buffer during 20 min at 135 volts. PCR products were sequenced along both strands using Dynamic ET Terminator Cycle Sequencing kit, and purified using ExoSAP-IT (Amersham). The sequencer used was the Megabase 1,000 apparatus. The DNA sequences were blasted in the Genbank database to determine, if the specimens considered correspond to Phytoseiidae. Molecular analyses were conducted using Mega 6. 0. 6  33. The distance matrices were elaborated using the Kimura 2-parameter model. Molecular identification revealed that the genetic distances between the eight specimens collected in Spain were equal to 0%, showing no differences for the 12S rRNA fragment among the specimens considered.
These specimens differ from those commercialized by Koppert by a low mean distance of 0. 05% (min: 0%; max: 1. 7%), which was within the intraspecific variation usually observed for Phytoseiidae  34. The specimens herein considered belong thus to the species A. limonicus. The accession numbers of these sequences in Genbank are: (i) population from Spain: SUB5946808, seq. 4 to 11 (MN180274-MN180281), (ii) commercial population from Koppert: SUB5946808, seq1 to 3 (MN180271-MN180273). the temperature regime in the field. Climatic data from the sampling location of A. limonicus in Northeastern Spain (La Tallada d'Emporda, Girona, Spain; 42. 0541°N, 3. 0614°E) were generated for the summer periods 2007 to 2017 (June, July, August, September) from the high-resolution gridded data set of daily climate over Europe  35.
The daily maximum summer temperatures (T max ) over eleven years were used to determine the number and duration of heat waves at the location, where A. limonicus is well established in the field. A heat wave was defined as the longest period of consecutive days satisfying the following three conditions: (i) T max must be >30 °C for the first three consecutive days, (ii) the daily T max can be below 30 °C, but not 25 °C, for single days during a heat wave period and (iii) the average T max is >30 °C for the entire period  36. The thermal data evaluation revealed that the field population of A. limonicus in La Tallada d'Emporda was exposed to 44 heat waves with a duration ranging from 4 to 44 days between 2007 and 2017. The statistical values were: mean duration = 11. 9 days, median duration = 8.
Statistical analyses. All statistical analyses were carried out using SPSS 24  42. First, a chi-square test was used to evaluate the temperature effects on the number of males and females from the F1 generation. The sex-ratios of the F1 generations reared under summer conditions (13 males, 10 females) and heat wave conditions (12 males, 15 females) were not affected by the rearing temperatures (P = 0. 395), so that sex could be used as independent factor in the statistical analyses of the F1 generation. Second, generalized linear models (GLMs) were applied to analyze the effects of temperature [F1 generation: summer or heat wave conditions; F2 generation: (1) F1 rearing temperatures (T F1 ): summer or heat wave conditions), (2) F2 rearing temperatures (T F2 ): summer or heat wave   sex separately.
Our dataset did not allow to test for the effect of individual sessions, rat species and age classes on σ due to limited numbers of rat recaptures within each group category. We therefore grouped some sessions and individual categories together based on similarities in the shape of the raw data distribution, as well as environmental seasonality. As a result, sessions 1, 2, 6 (austral summer; n = 44 recaptures) and sessions 3, 4, 5 (austral winter; n = 160 recaptures) were merged, and each rat was assigned to one of the four following groups: R. exulans (n = 38), R. rattus juveniles (n = 68), R. rattus adult males (n = 52), R. rattus adult females (n = 46). Due to the limited number of data points, we did not test for interactions between covariates in SECR models. Our final model combination allowed for testing the effects of a learned response to trapping, species, age, sex, session as well as group of sessions and group of individuals on g 0 ; and group of sessions, group of individuals and rat removal efforts on σ.
Moreover, six weeks after the initial knock-down of the rat population, captures were mainly recorded along the edge of the removal area. Nevertheless, it remains unclear whether these scattered movements and spatio-temporal patterns of captures were driven by immigration, were a consequence of the expansion of existing home ranges in the adjacent CMR area, or simply reflect the higher capture probability of neighboring individuals dwelling in the near vicinity of the removal area. Our analyses of rat home range barrycenters and size (sigma) did not reveal any marked shift in space use nor any enlargement of home ranges within the trappable rat population of the CMR area in response to ongoing control efforts. Our data also suggest that some reestablishement may have been driven by density-dependent enhancement of breeding: a larger proportion of reproductively active males as well as young juveniles (R.
Acknowledgements This study was funded by the  Northern Province of New Caledonia  to REFCOR project (Convention n°  12C240 ,  14C330  and  15C154 ). We are grateful to  Josepho Bahormal ,  Hélène De Méringo ,  Oriana Garcia-Iriarte ,  Raphaël Gouyet ,  Matthieu Mativet ,  Mathilde Méheut ,  Martin Thibault  from  IRD  for their help in the fieldwork, and the  University of Aberdeen  for hosting a visit. We thank the team of  Dayu Biik NGO ,  Alain Couhia ,  Ismaël Farino ,  Djaèk Folger ,  Ronald Tein ,  Aldo Tiempouène ,  Josine Tiavouane ,  Silvano Wanguène  for field logistics and their help in the rat capture experiments. We also thank  Frederic Rigault  and  Jérémy Anso  for their help in preparing maps and figures,  Murray Efford  for support with the secr analyses, and  Pablo Garcia Diaz  for providing comments on a draft manuscript.