dataesr/scientific-paragraphs-categorization huggingface dataset:
To study community assembly rules, we leveraged the unprecedented amount of data available for subterranean spiders in Europe  (36) , namely community composition data for selected caves across the continent  (37) , and standardized traits for all species  (38). A previous analysis of the taxonomic component of this dataset demonstrated a quick turnover in the taxonomic diversity of subterranean spiders across Europe, mediated primarily by geographic distance among caves, and secondarily by the climatic conditions and availability of karst. Conversely, local-scale characteristics of caves exerted a negligible effect on species turnover  (39). Here, we explore the functional dimension of these patterns, testing: i) the relative contribution of environmental filtering and limiting similarity in determining community assembly in caves; ii) how functional diversity decays along environmental gradients.
CONCLUSIONS The use of caves as model systems for investigating (macro-)ecological patterns in space and time is still underexploited  (52). This is partially a problem related to the objective difficulties of working in caves (resulting in a general lack of data at the right resolution) and partly a methodological problem. Nonetheless, thanks to the recent development in databases of species distributions and traits, and the emergence of novel analytical tools, there is a vast potential to leverage these systems as ideal settings in which to model across space. Using an explicit functional diversity approach, we showed that i) even systems with stringent environmental conditions maintain the potential for trait differentiation via biotic interactions, especially in areas of greater habitat connectivity; and ii) the relative influence of environmental filtering and limiting similarity change with scale, along clear ecological gradients.
Community-level data We obtained data for subterranean spider communities across Europe from ref. (37) , which we refer to for a full account of the dataset and the methods used to assemble it. The dataset comprises data from 475 subterranean sites (limestone, volcanic, talus, and salt caves, as well as artificial sites including mines, blockhouses, and cellars; the general term 'cave' is used hereafter) across 27 European countries, covering a latitudinal range from 35° to 70°. The dataset only includes subterranean sites for which spider fauna is exhaustively known. For each site, the spider composition is represented as incidence data-presence/absence of each species. Note that we focused solely on "subterranean spiders"  (36, 38) , excluding "accidental" surface species (55) occasionally found underground.
As broad-scale predictors, we included three climatic variables (mean annual temperature, annual temperature range, cumulative precipitation), one variable reflecting availability of carbonatic rocks (karst), and one biogeographical factor (the distance of each cave to the margin of the glacier in Last Glacial Maximum; ca. 21,000 years ago). We extracted climatic data from WordClim 2 rasters (57) at a resolution of 2. 5 minutes. Although they may fail-short to capture microclimatic variability within caves (  58 ), these broad-scale variables are good surrogates for general subterranean climatic conditions  (59) (60) (61) (62). We extracted the size of the karst patch in which a cave occurs using the World Map of Carbonate Rock Outcrops (version 3. 0). Given that most locations in our database were karst caves, we interpreted this variable as a good proxy of habitat availability in the surrounding of each cave, and an indirect measure of habitat connectivity  (45, 46).
Functional traits For each spider species included in the database, we derived functional traits from ref. (38)  , which we refer to for a full description of the trait data matrix and data collection methods. For the purposes of the analysis, we selected a subset of traits from the whole trait matrix, representing: i) general morphology of species; ii) morphological adaptation to subterranean conditions; and iii) webs and hunting strategies (Figure  1 ). To ensure exact matching between the spider species names in the community and trait matrices, we standardized and updated taxonomy using the function checknames in the R package 'arakno' version 1. 1. 1. (66).
ACKNOWLEDGEMENTS This project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie  grant agreement No  882221  and the  Biodiversa+  project  DarCo. CG-R was funded by  Finnish Cultural Foundation. The authors acknowledge the support of NBFC to CNR, funded by the  Italian Ministry of University  and Research, P. N. R. R. , Missione 4 Componente 2, " Dalla ricerca all'impresa ",  Investimento 1. 4 , Project  CN00000033. The authors wish to acknowledge  CSC -IT Center for Science, Finland , for computational resources. Finally, a special thanks goes to  Federico Riva  for critical feedback on the manuscript. SMa wrote the first draft, with support by CG-R. All authors contributed to the writing with suggestions and critical comments.
These probabilities are typically derived from historical databases, scientific literature reviews, or expert elicitation. However, the available information is often limited and accompanied by uncertainties, making full probabilistic modeling of cascading effects highly complex. To address these challenges and provide decision-makers with reliable inputs for simulation tools, it is essential to develop a comprehensive understanding of cascading effects scenarios at the local level. This involves considering dependent events that can occur simultaneously and quantifying the associated risk scenarios through physics-based modeling.
Specific software, like LOGICHAR, has been developed by Laigle  [34]  to study these phenomena. It is a one-dimensional numerical model that predicts the evolution of the torrent's bed during torrential floods based on hypotheses and existing sediment transport laws. To launch simulations using LOGICHAR, several input data parameters need to be provided. These include the geometric features of the torrent (longitudinal and transverse profiles), the protection system (location and observed height of the dams), the characteristics of the torrent bed (grain size distribution, depth of erodible layer), and the hydrographs of the flood events occurring over the period of simulation (duration, peak discharge, time to peak). In addition, the operator is required to define the scan points (both temporal and spatial) at which they wish to observe the corresponding outputs.
Acknowledgments This work has been partially supported by  MIAI@Grenoble Alpes , ( ANR-19-P3IA-0003 ). We also thank our colleagues from  ONF/RTM (French national  forest office/Mountain restoration department), particularly  Simon Carladous  from Directorate for natural risks and  Benoit Serra , deputy head of  RTM department of Alpes de Haute-Provence , along with their entire team. Their field visits, data provision, and valuable discussions and collaboration have greatly contributed to this project.
, 2017; Catalano et al. , 2020; Orlando et al. , 2009; Schubert et al. , 2016)  as well as 7 other equids  (Bennett et al. , 2017; Jónsson et al. , 2014; Wang et al. , 2020)  were collected. We used a 249 bp-long fragment, the longest common fragment across all available samples, to construct a haplogroup network; we also used a 361 bp-long D-loop fragment for BEAST analysis (Supplementary Table  3 ). Both in a haplogroup network and in Bayesian trees, mtDNA sequences of the three Anatolian equids clustered with published hemione-hydruntine sequences with strong support (Supplementary Figures  2  and 3 ). Moreover, all three new sequences were a sub-branch of the H1 haplotype clade [as defined by  (Bennett et al. , 2017) ]. H1 is the most prevalent mtDNA haplotype among the 19 hydruntine individuals in this dataset, appears exclusive to hydruntine, and was already detected among Anatolian hydruntines of Neolithic and Bronze Ages, c.
We repeated this phylogenetic analysis using whole genome data of 13 equids and using concatenated protein-coding sequences (Methods). A maximum likelihood tree constructed with this dataset indicated reciprocal monophyly of the Asiatic wild asses (i. e. the 9 Asian wild ass genomes representing the kiang, hemione, onager, and hemippe) and the hydruntine clade (represented by the Anatolian wild asses) (Figure  2B ). We further used D-tests to further clarify hemione-hydruntine relationships. We started by testing monophyly of the Anatolian wild ass genomes with D(Horse, AnatoliaX; AnatoliaY, Non-Anatolia), where AnatoliaX and AnatoliaY refer to different Anatolian wild ass genomes, and Non-Anatolia refers to any individual hemione, kiang, onager or hemippe genome (Methods; Supplementary Table  5 ).
, 2017) , may deserve an independent taxonomic status from other hemiones  (Boulbes and van Asperen, 2019; van Asperen, 2012). However, because our genome dataset did not include the full diversity of hemiones (e. g. the Indian khur and Central Asiatic wild ass genomes are not yet sequenced), because we lacked the information about full hydruntine diversity through ages, and also because our Anatolian samples represented a population that appears to have undergone a severe bottleneck, reciprocal monophyly between hemiones and hydruntines may still be considered an open question. It was proposed previously that the European hydruntine may have split from the Central Asiatic wild ass populations and evolved independently in Europe before colonizing Anatolia in the Late Pleistocene/Early Holocene  (Bennett et al, 2017).
Complete mitogenome sequence We generated a complete mitogenome sequence of our highest coverage sample cdh008 (coverage: 140. 04X) by consensus calling of the most common bases using Geneious Prime 2022. 2 with default parameters. Most of the unidentified bases (Ns: 2% of the whole sequence) were observed near the end of the sequence. The sequence was deposited to the NCBI Genbank with accession no: OP448588.
Phylogenetic analyses of mtDNA data Phylogenetic analysis was carried out using mtDNA consensus sequences generated from BAM files using angsd (v. 0. 934)  (Korneliussen et al. , 2014)  with "-doFasta 2", "-minQ 30", "-minMapQ 30" and "-setMinDepth 3" parameters. MEGA X  (Kumar et al. , 2018)  was used to construct phylogenetic trees using maximum likelihood and neighbor-joining. To this end, a 361 bp fragment of the D-loop in the control region of the mitochondrial DNA, as well as a 13,042 bp long consensus sequence of the whole mitochondria were used (Genbank). For the maximum likelihood analysis using 138 D-loop sequences (Supplementary Table  6 ), the substitution model was chosen as Hasegawa-Kishino-Yano (HKY) with a Gamma-distributed rate (no invariant sites) using jModeltest (v. 0. 1.
1)  (Guindon & Gascuel, 2003; Posada, 2008). A maximum composite likelihood model was used for the neighbor-joining tree construction. The nodal support was evaluated by 1000 bootstrap replicates for both methods. For the median joining (MJ) tree, 121 partial D-loop samples were trimmed to 249 bp based on the shortest fragment in the dataset. The MJ network was constructed with NETWORK v. 5 maximum parsimony post processing (http://www. fluxusengineering. com). The mitochondrial DNA divergence times of chosen equid species were estimated with the full mitochondrial DNA sequence using Bayesian inference with BEAST (v. 1. 8. 4)  (Drummond et al. , 2012). For this analysis, the best substitution model was found to be GTR with a Gamma distributed rate and invariant sites using jModeltest (v. 0.
SNP panel construction A genomic variation dataset was constructed using 12 modern-day high coverage (>5x) genomes  (Huang et al. , 2015; Jónsson et al. , 2014; Renaud et al. , 2018; Wang et al. , 2020) , of which six were domesticated donkeys (Equus asinus asinus) and six were Asian wild asses (three Equus hemiones hemionus, one Equus hemiones onager, two Equus hemionus kiang)(Supplementary Table  3 ). The E. h. onager sample was traced back in their lineage to confirm its species. It revealed that this individual's (studbook#818) father (studbook #682) was an onager with known origin while mother was a non-specified onager. Their father's (studbook #682) father was a non-specified onager and mother was a zoo onager (studbook #618). The lineage of #618 individual was captured from Iran in the wild between 1955-1973; 6-7 generations before the birth of #682.
Genotyping and dataset construction We genotyped both modern-day and ancient genomes using either of the SNP panels. All modern-day individuals were genotyped using SAMtools (v. 1. 9) mpileup module in diploid fashion. Ancient individuals were genotyped using sequenceTools (v. 1. 4. 0. 5) (https://github. com/stschiff/sequenceTools) using the option "-randomHaploid". The pileupCaller module of this program was run on ancient samples, genotyping only the positions in the SNP panel. Ancient genomes were pseudohaploidized by randomly choosing a single read and using its allele as the individual's genotype. Genotypes of modern-day and ancient individuals were merged using PLINK (v. 1. 90)  (Purcell et al. , 2007)  and EIGENSOFT (v. 7. 2. 1)  (Patterson et al. , 2006)  to form the final versions of the dataset.
f3-statistics We performed outgroup-f3 statistics on the autosomal chromosomes dataset using popstats  (Skoglund et al. , 2015)  with "--not23" and "--f3 vanilla" parameters, and using the PLINK (v. 1. 90). tped/. tfam file format. Analyses were run in the forms of f3(Horse;Anatolia,Modern), f3(Horse;Modern,Modern), f3(Horse;Anatolia,Anatolia) and f3(Horse;Anatolia,AncientHemione) in a pairwise manner, where Horse represents two horse genomes used as outgroup, Anatolia represents one of three Anatolian ancient wild ass individuals reported in this article, Modern represents a modern-day donkey, Somali wild ass, or Asian wild ass genome, and AncientHemione represents the three published ancient hemione and three ancient hemippe genomes mentioned earlier (Supplementary Table  3 ). A heatmap graph was generated from the f3 results using the heatmap.
D-statistics Gene flow and genetic affinity between ancient and modern groups were inspected with D-statistics using main and secondary dataset in EIGENSTRAT file format (. geno/. ind/. snp), running the "qpDstat" module of AdmixTools (v. 7. 0)  (Patterson et al. , 2012)  with default parameters. The analysis was run on all possible trio combinations, using horse as an outgroup. We limited the analyses to pairs with sufficient numbers of overlapping SNPs (for example excluding historical hemippe individual, Hm_1892). Significance of each comparison was inspected using the Z-score (the ratio of the D-statistic to the standard error, the latter estimated using a jackknife procedure). Results with absolute Z-scores > 3 (corresponding to roughly p<0. 001) were defined as nominally significant.
Resulting fasta files were aligned and trimmed to the same size for each chromosome. A final CDS dataset was constructed by merging all chromosomes and filtering all transitions. CDS selection, aligning-trimming, merging and filtering the transitions were all performed by custom python scripts (filter_gene. py, trim_py merge_py, and remove_transitions. py respectively). A maximum likelihood tree was generated with this CDS dataset using RAxMLv. 8. 2. 12  (Stamatakis, 2014)  with the following strategy: A preliminary tree was constructed using the GTRCAT model, 200 bootstrapping replicates were applied on the preliminary tree using the GTRGMMA model, and a final tree was generated by overlaying the calculated bootstraps on the preliminary tree. The final tree was visualized by R package ape v.
Pairwise McDonald-Kreitman analysis For calculating the Pairwise McDonald-Kreitman (PMK) statistic, we used the Gene Set 2 described above. We again considered the longest transcript per gene as described above and removed CDS that were not a multiple of three in length. For each gene, we first counted the numbers of synonymous substitutions (DS) and nonsynonymous substitutions (DN) between pairs of taxa (here Anatolia vs. Asia, Anatolia vs. Africa, or Africa vs. Asia), and also synonymous polymorphisms (PS) and nonsynonymous polymorphisms (PN) within a taxon (here only for Africa or for Asia). We added 1 to the whole dataset to avoid zero division errors. [Note that we use the ratio between the numbers of substitutions, instead of estimating the rate of changes (i. e. dN and dS), because we are comparing ratios calculated for the same locus across the three lineages, and the denominators would cancel out].
Acknowledgements The authors would like to thank  Dr. Derya Baykara , for helpful comments;  Dr. Mikolaj Lisowski  and  Dr. Jesse Wolfhagen  for their contributions in sample selection; the  Turkish Ministry of Culture and Tourism, Yozgat Museum and Konya Museum  for permissions to work on archaeological samples;  Sevgi Yorulmaz ,  Duygu Deniz Kazancı  and  Dr. Aslıhan Ilgaz  for their assistance, and all  METU CompEvo  research team members for discussion and support. This study was funded by a  Scientific and Technological Research Council of Türkiye (TÜBİTAK)   1001 Grant Programme  (Project No. 117Z991  to F. Ö. ), the  European Commission   Horizon 2020 TWINNING Programme  (Project No. 952317  " NEOMATRIX " to M. S. ) and  European Research Council Consolidator  Grant  H2020 ERC  (no.
Data and code availability Genetic data generated from three Anatolian ancient equid samples were deposited to the European Nucleotide Archive ENA under project code PRJEB52847 as BAM files. The mitochondrial DNA sequence was deposited to the NCBI Genbank with accession no: OP448588. The scripts incorporated into phylogenetic and demographic analyses were uploaded to https://github. com/CompEvoMetu/Hydruntine2023. The scripts related to selection analysis, namely, used for obtaining the polymorphism in the representative gene set, counting DN, DS, PN and PS and calculating PMK can be accessed at https://github. com/rabiafidan/equus.
Latitude and longitude (WGS84 datum) of the sampling sites were determined in the eld with a geographic positioning system (GPS) or later in the laboratory using a geographic information system (GIS). Precipitation (mm) and mean annual temperature (ºC) were estimated based on the WordClim 1. 4. database (http://www. worldclim. org/current), which provides 1-km spatial resolution climate surfaces for global land areas over the period 1970-2000. Mean annual potential evapotranspiration (PET) and mean annual aridity were determined using the Global Aridity and PET database published by the Consortium for Spatial Information (CGIARCSI, www. cgiar-csi. org/) using the worldClim. org database. PET is a measure of the ability of the atmosphere to remove water through evapotranspiration and was calculated as a function of mean annual temperature, daily temperature range, and extra-terrestrial radiation over the years 1950-2000.
Singletons, sequences shorter than the expected amplicon size and sequences occurring in only 1 PCR replicate were removed before using the obliclean command to remove PCR errors. We formed operational taxonomic units (OTUs) by clustering sequences at 97% of similarity using the Sumaclust algorithm 69. OTU abundance was de ned as the sum of reads sharing these similar sequences. In subsequent analyses, each OTU was represented by its most abundant sequence. Each OTU was assigned a taxonomic clade with the ecotag command 68 , using a set of reference databases built with the ecoPCR software  70  from the EMBL database version 136 to re ne taxonomic annotations. Taxonomic annotations with > 80% identities were retained. OTUs peaking in abundance in blank extractions or PCR negative controls were considered contaminants and removed from the analysis.
PCR replicates with a number of reads and OTUs lower or similar to PCR negative controls were considered as dysfunctional PCRs and also removed from the analysis  23. Finally, we removed PCR replicates that had a high Bray-Curtis dissimilarity compared to other PCR replicates from the same sample. At the end of this process, PCR replicates were pooled for each site. The number of reads, OTUs, samples and PCR replicates at the different steps of the bioinformatic curation process are reported in Sup. Table  1. A total of 152 sites were retained for the 18S marker and 167 sites for the 16S marker. Prior to the statistical analysis, the 18S and 16S datasets were rare ed to 15624 and 2311 reads per site, resulting in a total of 76 and 84 sites, respectively. Most rarefaction curves for the 18S (Eukaryota) marker reached a plateau indicating that sequencing depth was adequate to recover the diversity of selected taxa in our samples (Sup.
have a partial correlation coe cient of zero and cannot causally in uence each other). Assuming adequate representation of the major abiotic variables, connections among nodes in partial correlation networks of beta-diversity between pairs of taxa can be used to identify potential biotic interactions among taxa 82. We assessed the strength of the linkages between the beta-diversity of a given taxon with other taxa by analyzing the degree (i. e. number of directly connected nodes) and weighted degree (i. e. total sum of partial correlations between a given node and other directly connected nodes) of the corresponding node in the network. The higher the degree or weighted degree values, the more important the in uence of a taxon on the beta-diversity of other taxa. This analysis was conducted on the subset of samples common to the Eukaryota and Bacteria datasets that were then rare ed to 15883 and 2423 reads respectively, leading to a total of 40 samples for the analysis.
Acknowledgments Molecular analyses were funded by  Université Grenoble Alpes (UGA)  through the  AGIR-POLE-PAGE program  ( DRIME  project,  AGIR-POLE-PAGE 2016 ). We thank the landholders who enabled the collection of samples from private land and the  Commonwealth Environmental Water O ce  for support. We also thank  S. O. Díaz Huezo ,  F. Erzinger ,  J. Gelbrecht ,  M. Graça ,  J. López ,  C. Robinson , V. Rosset  for their help in collecting samples in the eld. DvS and NB are  Serra Húnter  Fellows. PRL was supported by a  Margalida Comas postdoctoral  contract ( PD/031/2018 ) funded by the  Government of the Balearic Islands  and the  European Social Fund , and by a  Juan de la Cierva -Incorporación fellowship  ( IJC2019-041601-I ). MTB was supported by a  David H. Smith Conservation Research Fellowship.
of bacterial and archaeal communities in sediments from dry riverbeds (n = 84). Read proportions (logarithmic scale) correspond to the relative abundances of each taxon per sample and were estimated using the 16S marker dataset rare ed to 2311 reads per sample. The vertical bold line within the box represents the median. The upper and lower limits of the box represent the 75th and 25th percentiles. The horizontal dotted lines on the left and on the right of the box indicate the range of observed values within 1. 5× the interquartile range from the 75th and 25th percentiles. Values outside this range are considered outliers and indicated as points.
To evaluate the uniform distribution of cloud ice at a global scale, large-scale observations of cloud ice are necessary. The combined data set of spaceborne radar from the CloudSat satellite  (Stephens et al. , 2008)  and the lidar from the Cloud-Aerosol Lidar and Infrared Pathfinder Observations satellite (CALIPSO,  Winker et al. , 2009)  allows to retrieve a global data set of cloud ice. Here, we use the liDAR -raDAR (DARDAR  Delanoë and Hogan, 2008, 2010)  dataset. Comparing observed cloud ice with modeled cloud ice is a challenge. Since the cloud ice from the ICON-AES does not include snow (precipitating cloud ice) and convective cloud ice, it is necessary to remove falling and convective cloud ice from the DARDAR data set in order to give a meaningful comparison between model and observations.