dataesr/scientific-paragraphs-categorization huggingface dataset:
The first model uses the phylogenetic tree based on whole genome sequences as provided in  (Li et al. , 2021) , which includes 281 species from which we collected climatic data (referred to as the 'genome tree model'). The second model uses a taxonomy-based cladogram for species based on their taxonomy from kingdom to species level (referred to as the 'taxonomy tree model'), which includes 1137 species from which we collected climatic data. AIC, Akaike's Information Criterion; dAIC, delta AIC (difference between the AIC of each model and the one containing all terms). A dAIC > 10 indicates no support for dropping that term from the model because it results in a large decline in model fit; dAIC between 4 and 7 indicate considerably low support for dropping that term; and dAIC<2 indicates strong support for dropping that term as it improves model fit  (Burnham & Anderson, 2002).
, 2019) ) or yeast phenotypes (as it is commonly seen in most insect gut endosymbionts  (Gibson & Hunter, 2010) ). In addition, as host-symbiont specialization is a long-term evolutionary process  (Chomicki et al. , 2020) , the age of symbiosis might be a predictor of reproductive trait changes (for both host and symbionts). Thus, we propose that the reliance of fungi in other ways of transmission other than spores might explain weaker correlation and symbiotic state we found in some clades. In those cases, variation in spore size might be driven by neutral processes such as drift (which we did not test). In order to test these hypothesis it would be necessary to include more species across different symbiotic lifestyles in phylogenetic studies  (James et al. , 2020)  and the need to populate databases with fungal reproductive traits.
AU T HOR CON T R I BU T ION S CAAT conceived the study together with FSK, WKC, JRP and MCR. WKC downloaded data from Mycobank. FSK developed the text mining algorithm. CAAT, JRP, CD and HZ mined the text data and cleaned spore data entries. CAAT digitized manually the spore size data not present in Mycobank, managed the spore database and assembled the fungal functional database. JRP managed and assembled climatic and geographic data. CAAT and JRP performed statistical analysis with input from WKC and FSK. CAAT wrote the first draft, and all authors contributed to the writing of the paper.
Funding information Alexander von Humboldt- Stiftung, Grant/Award Number: Feodor-Lynen Fellowship ;  Australian Research Council , Grant/Award Number:  DP190103714  and  FT0100590 ;  Bundesministerium für Bildung und Forschung , Grant/ Award Number:  01LC1501A ;  Deutsche Forschungsgemeinschaft , Grant/ Award Number:  HE6183 ;  Deutscher Akademischer Austauschdienst; Division of Environmental Biology , Grant/Award Number:  1623040  and  1655759 ;  Grantová Agentura České Republiky , Grant/Award Number:  21-17749S ;  H2020   European Research Council , Grant/Award Number:  647038  and  694368 ;  Universities Australia.
Statistical analysis To develop the WQI min , the critical environmental parameters influencing WQI were explored using a stepwise multiple linear regression. The Spearman analysis investigated the correlation between the metrics and environmental parameters. A linear regression model investigated the relationships between WQI min and WQI. In addition, the linear regression model also determined the relationship between P-IBI and WQI min with TLI and water quality score. The coefficient of determination (R 2 ) determined the model's goodness of fit. The WQI min and P-IBI spatial distribution maps were generated using ArcGIS 10. 3 software. The Kruskal-Wallis analyzed the spatial and seasonal variation in WQI min and P-IBI values. The locally weighted scatterplot smoothing (LOWESS) method generated fitted curves between P-IBI values and the environmental variables, determining the turning points.
The study also includes simulations with perturbed SST to explore the impacts of enhanced and suppressed sea-surface fluxes. The role of the diabatic processes before and during Ianos's intensification is diagnosed with the use of the Semi-Geotriptic inversion tool (SGT, Cullen 2018), and the baroclinic forcing during Ianos cyclogenesis with the use of the Quasi-Geostrophic omega equation (QG,  Davies 2015). Details of our simulations, the SGT inversion tool, the QG omega equation plus other evaluation tools and the observational datasets employed are provided in 2. A synoptic overview of the Ianos case is given in 3. The results from the simulations are presented in section 4. The main conclusions of this study are described in section 5.
Code availability. MetUM model is available for use under a closed licence agreement, further information at http://www. metoffice. gov. uk/research/modelling-systems/unified-model (last access: 19 October 2023). The SGT inversion tool is available at the MetOffice Science Repository Service (MOSRS) at https://code. metoffice. gov. uk/svn/utils/sgdiagnostic/ (last access: 19 October 2023) Data availability. MetUM simulations are archived in Met Office Operational Storage Environment (MOOSE) at devfc/u-ci649 (control SST), devfc/u-cl004 (+2K SST) and devfc/u-cl005 (-2K SST), available upon request. IFS analysis are available at the ECMWF's Meteorological Archive Retrieval System (MARS) Catalogue.
Satellite Sensor Observations A time series of cloud-free ortho-rectified 22 m resolution DMC and 10 m resolution S2 satellite images were used for deriving the biophysical variables fAPAR (fraction of absorbed photosynthetically active radiation) and fCover (vegetation cover fraction) (Table  1 ). Pre-processing included atmospheric correction  [39] ; cloud and shadow detection  [40]  for DMC;  [41]  for S2; and calculation of the biophysical variables  [42]. The derivation of fAPAR and fCover products from Sentinel-2 and DMC was achieved by using the BV-NET (biophysical variable neural network), initially developed for mediumresolution sensors  [43] , adapted to higher spatial resolution sensors such as SPOT  [44]  and Landsat-8  [45] , and based on neural networks trained on a synthetic dataset of around 50,000 simulations using the PROSAIL model  [46].
Loci showed different amplification successes across subpopulations, with Tunisian DNA less readily amplified. Altogether, 15 loci were considered suitable for genetic analyses of Portuguese subpopulations, 10 for Tunisian samples, and seven, common to both populations, were considered to be informative at the inter-continental level. A preliminary analysis of both datasets revealed two isolated populations, which can correspond to two different species. Furthermore, Tunisian specimens identified by sequencing of 16S rDNA as having I. ricinus or I. inopinatus sequence profiles all clustered together in one single population using the proposed microsatellites. This confirms that taxonomic decisions based only on 16S rRNA gene sequencing can be misleading. The application of the proposed set of microsatellite markers to a larger sample, representative of the south-western Ixodes' distribution range, will be crucial to clarify the distribution of both species.
A fragment of their 16S rRNA gene was amplified and sequenced by using 16S-F and 16-R primers  (Mangold et al. 1997). GenBank accession numbers for the 16S rDNA sequences from Gerês (OP375366 -OP375385) and El Jouza (OP375386 -OP375405) are provided in supplementary table S1 for each tick.
Identification of new microsatellite loci and primer design Genome sequences available under the Whole Genome Shotgun I. ricinus project (GenBank accession number JXMZ02000000)  (Cramaro et al. 2015 (Cramaro et al. , 2017) )  were used to detect potentially amplifiable loci (PALs) with the Palfinder Galaxy Service  (Griffiths et al. 2016 ), available at https://palfinder. ls. manchester. ac. uk/. Pal_finder software, version 0. 02. 04. 8  (Castoe et al. 2012 ) was used to find di-, tetra-, penta-and hexanucleotide repeat motifs, using default settings. Primers were designed with Primer3 and the following settings customised for use with the Type-it® Multiplex PCR kit (Qiagen): primer optimum length = 25 nucleotides (ranging from 18 to 30 nucleotides); minimum GC% content = 45%; maximum GC% content = 65%; optimum melting temperature = 68 °C (ranging from 62 °C to 75 °C).
Genetic distance between all individuals and neighbour-joining-tree Microsatellite-based genetic distances were evaluated as the chord distances of Cavalli-Sforza and Edwards (Cavalli-Sforza and Edwards 1967) (D CSE ). Because null alleles were known to occur (see results), the distance values for their presence were corrected by applying the INA method implemented in FreeNA, after missing data were recoded as homozygous for allele 999 (putative null homozygotes)  (Chapuis and Estoup 2007). The D CSE matrix was used to build a neighbour-joining tree as recommended  (Takezaki and Nei 1996)  with MEGA 7. 0. 26 (Kumar et al. 2016).
To minimize SAD effects, all loci were doublechecked by hunting micro-peaks at long alleles. We also attempted to cure loci with stuttering by pooling alleles close in size. Both methods are described in De  Meeûs et al. (2021). In dioecious species, heterozygote excesses are expected at all loci. Nevertheless, null alleles may display heterozygote deficits at affected loci. Default F IS test is one-sided in Fstat (heterozygote deficit). Two-sided -values were computed as twice the p-value, in case of heterozygote deficit, or twice 1-p-value otherwise. A more accurate estimate of F ST can be made for datasets with null alleles after recoding missing genotypes as homozygous for allele 999 (null allele) with the ENA method as implemented in FreeNA  (Chapuis and Estoup 2007). RecodeData  (Meirmans 2006 ) was used to obtain maximum divergence, and computed the maximum possible F ST (F ST_Max ) with Fstat.
Portuguese (Gerês) subsample raw data Genetic diversity was high for all loci (H S >0. 73). Linkage disequilibrium tests were then optimally powerful for each pair of loci. There were only five loci pairs in significant LD out of the 190 possible pairs (2. 6%), and none stayed significant with the BY-FDR correction (p-values >0. 22). Heterozygote deficits, if any, hardly could come from a Wahlund effect. There was a highly significant and variable F IS = 0. 264 in 95% CI = [0. 174, 0. 36] (two-sided p-value = 0. 0002). Two missing data were observed at locus IRN4, which correlatively displayed the highest F IS = 0. 804. Null alleles probably explain a large part of these heterozygote deficits, and this was confirmed by estimate of expected null homozygotes computed with Brookfield's second method with MicroChecker.
According to this method, none of the observed number of missing data significantly deviated from those expected (all p-values >0. 19). Nevertheless, the importance of F IS at several loci with very few or even no missing genotypes, suggested that other amplification problems (SAD, stuttering) were at stake. SAD was tested on the 17 loci with a heterozygote deficit. Most p-values for SAD tests were below 0. 5, and overall the 17 loci, with the generalized binomial procedure  (Teriokhin et al. 2007; De Meeûs et al. 2009) , the p-value was almost significant (p-value = 0. 0575). This suggested a strong tendency for SAD across all loci, but at various degrees of intensity. Two loci displayed a significant SAD: IRN4 (p-value = 0. 009), and IRic08 (p-value = 0. 0286). As for the results of the exact binomial tests performed for stuttering detection, most p-values were below 0.
6157. On the contrary, seven loci displayed a significant stuttering signature, and most loci displayed quite low p-values, which yielded a very low generalized binomial p-value = 0. 0003. Stuttering correction attempts were then undertaken for all loci with a p-value <0. 5 for stuttering, whenever possible. Stutter corrections concerned 16 loci: IR27, IRN3, IRN7, IRN12, IRN14, IRN17, IRN21, IRN28, IRN32, IRic08, IRic11, IRic13, IRic17, IRL17, IRL40 and IRL69. These corrections are highlighted in allele pooling table  S3 , provided as supplementary material. It worked rather well for most loci, except for IRN14 and IRL17. Further analyses kept initial data for these two loci and kept stuttering corrections for the 14 other loci. With this new dataset, eight locus pairs (4. 2%) were in significant LD (improvement in power), and none stayed significant after BY correction (all p-values = 1).
This was already expected, as discrepancies in amplification success between populations at several loci had been previously detected during sample genotyping. Methods for minimizing the effects of SAD and stuttering described by De  Meeûs et al. (2021)  were applied with good results in this dataset. After double-checking the chromatograms of homozygous individuals for previously undetected micro-peaks, only two loci showed significant SAD in Gerês subpopulation, and none were found to be affected in El Jouza subsample. Stuttering was detected in both populations, but the use of pooling strategies adjusted to allele size differences, allele frequencies and F IS , improved heterozygosity estimates for eight (out of 17) and 14 (out of 16) loci in Gerês and El Jouza subpopulations, respectively.
Conclusion In this work, we have revisited all microsatellite markers previously developed for I. ricinus and specifically tested them with Portuguese and Tunisian tick populations, to ascertain their suitability for this species' population genetics studies in the Mediterranean region. Additionally, new microsatellite loci have been developed to add to the currently available ones. Altogether, 15 suitable loci were identified for genetic analyses of Portuguese subpopulations, 10 loci for Tunisian ones, and seven informative loci to be used in inter-continental studies. A preliminary analysis of both datasets revealed two isolated populations, which are likely to correspond to two different species. The sets of microsatellite markers defined here can now be applied to larger-scale studies in the Mediterranean transition zone, hopefully contributing to define the population structure of I.
22 µm, the relative proportion of Patescibacteria lineages frequently exceeded those of other bacteria in diversity surveys, suggesting that the abundance of ultra-small Patescibacteria is frequently underestimated  (Luef et al. , 2015; Anantharaman et al. , 2016; Danczak et al. , 2017; Vigneron et al. , 2020; Chaudhari et al. , 2021). Most of our knowledge about these microorganisms derives from population genomes recovered from metagenomic datasets  (Brown et al. , 2015; Hug et al. , 2016; Castelle and Banfield, 2018; Tian et al. , 2020). With more than 5000 draft genomes of Patescibacteria in public databases, the metabolic repertoire of the Patescibacteria members is rapidly expanding. Shared features of Patescibacteria include a small genome size with limited known biosynthetic and metabolic pathways  (Castelle and Banfield, 2018; Tian et al.
, 2020). Consistently, a limited catabolic potential has been detected for different Patescibacteria lineages, suggesting a potential niche partitioning based on different carbohydrate utilization  (Danczak et al. , 2017; Vigneron et al. , 2020). However, due to the lack of cultured representatives and ecophysiological studies, these metabolic hypotheses predicted from genomic data have not been tested, leaving the activity and ecological roles of these microorganisms elusive. Taking benefit of an extensive metagenomic (370 Gb of reads) and metatranscriptomic (300 Gb of rRNA-depleted reads) dataset (SRP063590) covering sample heterogeneity as well as seasonal and diel variability of two microbial mats located in brackish lagoons (Salins du Lion and Etang de Berre, France)  (Vigneron et al.
, 2020). Illumina HiSeq sequencing of the libraries led to a total of 670 Gb of sequencing reads (370 GB of metagenomes and 300 GB of rRNA-depleted metatranscriptomes). Datasets were quality filtered using the Trimmomatic v. 0. 39 tool keeping both R1 and R2 reads when reads overlapped  (Bolger et al. , 2014). The 16S rRNA reads were isolated from the metagenomic reads using Infernal v. 1. 1. 4 (Nawrocki and Eddy, 2013) , and their taxonomic assignments were performed with Mothur  (Schloss et al. , 2009)  using BLAST against Silva database release 138 as reference  (Pruesse et al. , 2007). Since metagenomic 16S rRNA reads were only 100-bp long and spanned various regions of the 16S rRNA gene, taxonomic assignments were limited to the genus level and above.
Binning and functional characterization For metagenome-assembled genome (MAG) reconstruction, all quality-filtered sequences were pooled and co-assembled using MEGAHIT v. 1. 2. 9  (Li et al. , 2015) , as previously described  (Vigneron et al. , 2021). Read coverage of the contigs against all metagenomic datasets was carried out using bwa-mem v. 0. 7. 17 (http://bio-bwa. sourceforge. net), and the binning of the contigs longer than 2000 bp was carried out by  MetaBAT-2 (Kang et al. , 2015). Coding density, completeness, and contamination levels of the MAGs were then evaluated using CheckM v. 1. 1. 2 (Parks et al. , 2015)  with cpr_43_markers. hmm files that provide a better estimation of CPR bacteria completeness  (Parks et al. , 2015). Taxonomic affiliation of the MAGs was performed using the GTDB_Tk classify workflow (GTDB-tk release 207) with genomes containing a minimum of 40% of the bacterial marker genes  (Chaumeil et al.
, 2016)  databases on IMG/MER platform using IMG annotation pipeline v. 5. 0. 19  (Markowitz et al. , 2009) , leading to 5. 7 x10 6 genes coding for proteins with the product name (58. 96% of the genes). ORFs were also compared against the CAZY database using dbcan2, and glycoside hydrolase genes were analyzed to infer the catabolic potential  (Zhang et al. , 2018). The type of ribulose-1,5-bisphosphate carboxylase (Rubisco) protein was determined using publicly available HMM profiles of the different Rubisco categories  (Anantharaman et al. , 2016). Metatranscriptomic reads passing quality filtration (4. 12 ± 0. 7 x 10 7 reads passing quality filtration per sample for a total of 1. 24 x 10 9 metatranscriptomic reads) were normalized and then mapped against all ORF identified in the MAGs using bwa-mem with a default value to determine the expression level of each gene under all tested conditions.
Results and discussion The microbial community composition of the two microbial mats was characterized using 16S rRNA genes recovered from the 30 metagenomes. Overall, Patescibacteria constituted a minor but diverse fraction of the microbial mats community, representing in total 2% of the 16S rRNA reads from the metagenomic dataset (Figure  1A ). Nonetheless, spatial and seasonal variations in the Patescibacteria community composition were detected (Supplementary Figure  1 ), suggesting rapid responses to environmental conditions. After co-assembly of the metagenomic datasets and binning of the contigs longer than 2000 bp, 18 MAGs (average completeness: 90 ± 10%) over the 407 MAGs recovered from the mats were affiliated to Patescibacteria, including representatives of the Candidatus Pacebacteria, Dojkabacteria, Gracilibacteria, Moranbacteria, Campbellbacteria, and Kaiserbacteria phyla (Figure  1B ), that were identified as dominant Patescibacteria by 16S rRNA gene analysis.
Genomic composition of Patescibacteria Consistently with publicly available genomes, the Patescibacteria MAGs were reduced in size, and an average of 940 ORFs per MAG were identified, indicating a lower genomic content for a similar completeness level compared with other phyla found in the microbial mats (Figure  2A  and Supplementary Figure  2 ). Comparison against Kegg  (Kanehisa et al. , 2016) , COG  (Galperin et al. , 2021) , TIGRFAM  (Haft et al. , 2003) , and CAZy  (Cantarel et al. , 2009)  databases assigned together a predicted function for 65 ± 4% of the proteins encoded in ORFs (Supplementary Figure  3 ), which are lower than the average number of proteins with a predicted function for the entire microbial mat community (70%) but comparable to Planctomycetes or Cyanobacteria (Figures  2B, C ).
A comparison of the sequence against the NCBI database indicated that the sequence shared 74% similarity with the carbon monoxide dehydrogenase gene from an uncultured marine organism and 67% similarity with the carbon monoxide dehydrogenase gene of Rokubacteria MAGs. Nonetheless, other subunits of the complex were not identified based on sequence similarity. However, it has been suggested that the lack of other subunits may indicate that this enzyme could act on substrates other than CO  (King and Weber, 2007). Therefore, further work is needed to characterize the activity and distribution of putative aerobic carbon monoxide dehydrogenase in Patescibacteria.
In situ activities of Patescibacteria The extended metatranscriptomic dataset (4. 12 ± 0. 7 x 10 7 reads passing quality filtration per sample for a total of 1. 24 x 10 9 metatranscriptomic reads) that covered the sample heterogeneity, as well as the seasonal (April and September) and diel variations of the microbial mats, was mapped against Patescibacteria ORFs (Supplementary Table  2 ). An analysis of the expression level of ribosomal protein genes, DNA polymerases, and the number of expressed genes indicated differential activity levels for the Patescibacteria populations of the microbial mats (Figure  4 ). Only 32% of the expressed genes were transcribed under both day and night conditions, suggesting contrasted diel activities in Patescibacteria (Figure  4  and Supplementary Table  2 ).
This could also illustrate a rapid synchronization of the Patescibacteria to the activity of their potential host. Alternatively, this result might indicate that the genome reduction process is still ongoing. However, the coding density of the Patescibacteria MAGs was similar to the rest of the microbial community (89 ± 3%), providing little support for gene inactivation and erosion  (Liu et al. , 2004). Among the expressed genes, an average of 66. 7% (206 ± 132 ORFs per MAG) encoded proteins with a predicted function (Supplementary Figure  3  and Supplementary dataset). However, only 13% of the genes used for the metabolic characterization of the MAGs were expressed in more than 50% of the samples, and 52% of the samples were not expressed in any of the samples (Figure  3  and Supplementary Figure  4 ).
Furthermore, 45 ± 19% of the most expressed genes in MAGs were not associated with a known function (Supplementary Figure  2 ), confirming that substantial activities remain uncharacterized. These results have major consequences for the extrapolation of the Patescibacteria metabolism and environmental function. For instance, transcripts of alcohol, lactate, or malate dehydrogenases were rarely detected, challenging the fermentative metabolism assumed from the detection of these genes in the genomic data. Although we cannot exclude that some transcripts were not sequenced, these results suggest that essential genes and pathways remain uncharacterized in the microbial dark matter or unidentified due to overly divergent sequences compared with existing databases. This indicates that in situ activities of Patescibacteria could hardly be inferred from metagenomic data alone and that activity-based and cultivation data are mandatory.
FIGUREMetabolic potential and activities identified in the Patescibacteria MAGs of the microbial mats. Completeness and contamination percentages were estimated using CheckM with cpr_ _markers hmm profiles. Dots indicate the detection of the gene in the MAGs and are color-coded based on the identification of corresponding transcripts in the metatranscriptomic dataset. Due to the high number of genes involved, the expression level of complex metabolic pathways (pentose phosphate pathway, glycolysis/neoglucogenesis, and tricarboxylic acid cycle) was not estimated (gray dots).
Funding This work was supported by the  French National Research Agency  [ ANR FUNHYMAT ANR11 BSV7 014 01  and  ANR MICROMER ANR-21-CE34-0014 ] and by the  Excellence Initiative of Université de Pau et des Pays de l'Adour -I- Site E2S UPPA [Hub MeSMic ], a  French Investissements d'Avenir program.