dataesr/scientific-paragraphs-categorization huggingface dataset:
RAD-seq Data Processing and de novo Assembly After filtering the raw short-read sequences, it is necessary to assemble the reads pairs into sets of reads (stacks) and merge overlapping stacks into putative loci, which will then be used for population genomic analyses. Since there is no reference-genome available for the Eliurus genus, we conducted a de-novo assembly of RAD-seq nuclear loci in STACKS v2. 2 using ustacks, cstacks, sstacks, tsv2bam, gstacks and populations programs, which are described in details in Supplementary Appendix S1. The assembly of nuclear loci from RADseq data relies on several parameters (m, M, n). The choice of the best combination of these parameters can be challenging, since several factors can influence the amount and quality of genomic information recoverable from the dataset, such as the biology and natural history of the studied population(s) or the deployed RAD-seq technique  (Paris et al.
2017 ). Therefore, we followed the protocol proposed by  Paris et al. (2017)  to optimize the three main parameters of the de novo assembly (m, M, n). The analysis for the optimization of de novo assembly parameters was performed on a subset of the whole dataset, following  Rochette and Catchen (2017). In particular,  Rochette and Catchen (2017)  found that adding all samples does not increase significantly the number of widely shared catalog loci in the dataset and can add noise in the estimation of m, M and n. The individuals used for building the catalog were selected by considering i) those with highest coverage and ii) maximizing the geographic distribution of the selected individuals, as a proxy for maximizing the genetic diversity in the dataset. We thus performed the optimization analysis on twenty individuals: five E.
carletoni, four E. minor, five E. ellermani, five E. myoxinus and one Eliurus sp. nova (see Supplementary Appendix S3: Fig. S2  and Supplementary Appendix S2: Table  S1 ). These were also the individuals used for building the catalog. The optimization procedure consisted in iterating a range of values (2 to 8) for the m, M and n parameters, separately, while keeping all other parameters fixed at default values (m = 3, M = 2 and n = 1). For each parameter combination we extracted the genomic information shared by either 40%, 60% or 80% of the individuals in the dataset. In order to identify the best parameter combination, we extracted and assessed several assembly metrics: i) individual coverage; ii) the number of assembled loci; iii) the number of polymorphic loci; iv) the number of SNPs; v) the variable; and vi) fixed sites identified across the whole population.
Mitochondrial DNA Analyses We Table  S2, S3 ). Genetic data were not available for E. petteri, E. danieli and E. penicillatus. As outgroup we used mtDNA cytb of Rattus norvegicus (EU349782. 1). The complete dataset consisted of 263 mtDNA cytb sequences of 1064 bp. We aligned and visually checked the sequences using the Clustal Omega method  (Sievers et al. 2011). We performed format conversion of DNA alignments using ALTER  (Glez-Peña et al. 2010). We used two statistical methods for phylogenetic analysis: a Bayesian MCMC approach implemented in MRBAYES v3. 2. 1  (Ronquist et al. 2012 ) and a maximum-likelihood (ML) approach implemented in RAXML v8. 2. X  (Stamatakis 2014). Information on the parameters used in the mtDNA phylogenetic analyses are described in Supplementary Appendix S1. We measured pairwise mtDNA distance within and between species using the dist.
Phylogenomic Analysis We employed a maximum-likelihood approach (RAXML v8. 2. 11; Stamatakis 2014) to infer evolutionary relationships among the five Eliurus taxa. We performed concatenated analyses on datasets composed of fixed sites, among which invariant sites were removed. The concatenated analysis assumes that all the sites included in the multialignment share the same species tree topology  (Felsenstein 1981). We set the unpartitioned General Time-Reversible Categorization (GTRCAT) molecular model with joint branch length optimization, the '-f a' option to conduct 1000 rapid Bootstrap analysis, and the 'autoMRE_IGN' option to carry out bootstrap convergence test, which determines whether sufficient bootstrap replicates have been run to get stable support values in the inferred phylogenetic tree.
We also carried out a partitioned analysis on the N15. pop5. p4. min15 dataset, including all sites (variant + fixed), setting each catalog locus as a separate partition. We applied an ascertainment bias correction for binary SNP data in the RAXML analysis based on Paul O. Lewis formula  (Lewis 2001). The partitioned analysis estimates the model parameters of the most likely evolutionary model for each partition while fixing the global set of branch lengths (genealogy). As for the concatenated analysis, we set GTRCAT molecular model for each partition. In this analysis we also used the argument '-M', to estimate a separate set of branch lengths for every partition. For each maximum-likelihood analysis, the best likelihood tree with bootstrap supported value was visualized using ggtree and treeio R packages  (Yu et al.
Genomic Clustering analyses Clustering methods were used to identify genetic groups within the Eliurus dataset. We used the widely-used maximum likelihood model-based approach implemented in ADMIXTURE v1. 3  (Alexander et al. 2009 ). This program uses the same likelihood model as STRUCTURE software  (Pritchard et al. 2000) , but instead of sampling the posterior probability of a model given the data, it maximises the likelihood of the model. In the model, it is assumed that individuals are sampled from an admixed population with contributions from K a priori clusters. Then, individuals are assigned to K-clusters by maximizing the likelihood of the model. For each dataset, we ran the program for K values ranging from 1 to 8, and repeated the analysis for 30 independent runs. ADMIXTURE analysis was carried out using the default optimization method and the default termination criterion, which stops the analysis when the log-likelihood increases by less than 10 -4 between iterations.
K values were compared using the cross-validation method by setting --cv = 20 and by comparing likelihoods across runs. We used CLUMPAK  (Kopelman et al. 2015)  to permute the 30 independent runs so that clusters align across runs. We selected the best K number of clusters considering the lower average cross-validation, but we also explored admixture models for K -1 and K + 1. The final assignment of individuals to the best K-value was obtained by taking the mode containing most runs (Major. Cluster). In addition, we used a model-free approach, the Principal Components Analysis (PCA), through the function glPca implemented in the R package adegenet 2. 0. 1  (Jombart and Ahmed 2011). PCA constructs low-dimensional projections of the data that explain the gross variation in marker genotypes, and thus it can be useful to assess whether most of the variation in the genomic dataset can be explained by species divergence.
ADMIXTURE and PCA analyses were performed on the N124. pop5. p4. r80. min100 dataset. Admixture and PCA analyses were also performed on the GLs-based ANGSD dataset using the NGSadmix and PCAngsd programs  (Skotte et al. 2013). NgsAdmix was run for 20 iterations per K value (1 to 12) and a stringent tolerance of 10 -6 for convergence. We compared K values using the ΔK procedure  (Evanno et al. 2005).
Species Validation We used three approaches to test the hypothesis that the newly identified taxon (Eliurus sp. nova) was a new species: i) guided-tree species delimitation analyses; ii) estimation of the gdi-index; iii) comparison of isolation-by-distance patterns within versus between taxa; and iv) comparison of GC-content across taxa. Species delimitation analysis with guided tree was performed in BPP  (Yang and Rannala 2010; Flouri et al. 2018 Flouri et al. , 2020) )  using loci full-length FASTA file of the three individuals per species dataset (N15. pop5. p4. min15 dataset). We used six combinations of priors for divergence time () and population size (θ). The θ and  priors consisted of an inverse gamma distribution with α = 2 (θ) or α = 3 () and β equal to either 0. 002 or 0. 02 or 0.
myoxinus -Eliurus sp. nova). We performed this analysis on the STACKS generated datasets (including only variant sites) by estimating pairwise genetic distances using the stamppNeisD function in R (R Core Team 2022) which computes the Nei's genetic distance  (Nei 1972). It has been shown that across animal genomes, GC-content correlates with various aspects of genome architecture such as genome size, gene density, gene expression, replication timing, and recombination  (Vinogradov 1998; Bernardi and Bernardi 1986; Bernardi 2007; Figuet et al. 2015). Few case studies have shown that GC-content may also be informative on the evolutionary relationships of species (e. g. ,  Du et al. 2010; Huttener et al. 2019 ). Accordingly, we asked whether differences in GC content could be observed among the five Eliurus species in our dataset, as a proxy for differences in genomic architecture among Eliurus species.
Morphological Analyses Four morphological variables (head length, tail length, body length and weight) were consistently recorded for the Eliurus individuals. They were used here to assess whether morphological differences could be detected between the five Eliurus taxa. For increasing the power of the morphological analyses, we used a dataset composed of 325 Eliurus individuals, which included the 124 individuals used in phylogenetic and species validation analyses, as well as 200 individuals that were excluded from the above analysis due to computational cost. We carried out Discriminant Analysis of Principal Component (DAPC) to identify which morphological variable contributes the most to the phenotypic structure observed across the five Eliurus taxa. DAPC analysis was performed using the dapc function in adegenet R package  (Jombart 2008; Jombart and Ahmed 2011) , fixing species identity as prior grouping.
Furthermore, we compared the distribution of each morphological variable across Eliurus species and assess significant differences through multiple comparison Tukey's test, as implemented in the TukeyHSD R function (R Core Team 2022). In addition, we assessed whether morphology would correlate with altitude and 19 bioclimatic variables. For each individual, altitude and bioclimatic data were extracted from the WorldClim database (www. worldclim. org; Fick and Hijmans 2017) at a spatial resolution of 30 seconds of a degree using the getData function in the raster R package  (Hijmans 2022). Correlations between each morphological and bioclimatic variable were measured using Phylogenetic Generalized Least Squares (PGLS) analysis  (Harvey and Pagel 1991). Briefly, PGLS analysis correlates two variables while accounting for the phylogenetic relatedness among individuals, given that closely related taxa tend to have more similar traits than random.
The PGLS analysis was carried out in R using the gls function of nlme R package  (Pinheiro and Bates 2000; Pinheiro et al. 2022). Covariance structure was based on the model of Brownian evolution through the corBrownian function. The phylogenetic tree used to account for phylogenetic relatedness was generated from the full Eliurus dataset (N = 347), including also few individuals for which morphological data were not available or incomplete (N = 23) (Supplementary Appendix S3: Fig. S3 ). All individuals from the dataset were assumed to belong to the same 'population' (p = 1) and loci were included in the final dataset only if 80% of the individuals were genotyped at a given locus. Moreover, in order to reduce linkage disequilibrium among genotyped SNPs, we considered only one random SNP per RAD locus.
minor and E. myoxinus. NGSadmix results showed clear genetic differences between Eliurus sp. nova and the other Eliurus species at K = 5, while revealing population structure within E. carletoni at K= 6 (Supplementary Appendix S3: Fig. S10 ). The PCA analyses performed on genotype calls (STACKS dataset) and likelihood (ANGSD dataset) showed five distinct clusters of individuals (Fig. 3  and Supplementary Appendix S3: PCA and ADMIXTURE analyses separate individuals according to the hypothesized species identification (E. carletoni; E. myoxinus; E. ellermani; E. minor) while identifying the hypothetically new Eliurus species (Eliurus sp. nova) as being genetically distinct from the others, and, in particular, from the sister-species E. myoxinus. Both analyses have been performed using the N124.
Eliurus Species Delimitation The phylogenomic and genomic structure analyses above appear to support the hypothesis of a new distinct Eliurus taxon: Eliurus sp. nova. The species delimitation analyses carried out with BPP, using all sites (variant and invariant) of the N15. pop5. p4. min15 dataset, for E. myoxinus, E. minor and Eliurus sp. nova, strongly supported the delimitation of the three taxa as distinct species. This topology was always the best species tree with a posterior probability of 1. 0, across all runs, algorithms and priors (Fig. 4a ). The N15. pop5. p4. min15 dataset was also used to estimate divergence time and population size parameters using BPP. The estimated values were then used to calculate the gdi-index, which was essentially equal to 1 across all runs and combination of priors (Fig.
nova-E. myoxinus (Spearman rho= -0. 187; p-value= 0. 0017). The results showed also that, for similar geographical distances, Eliurus sp. nova -E. myoxinus genetic differences were significantly higher than within taxa genetic differences (Fig. 4d ). Similar patterns could be observed for two well-recognized Eliurus species, E. ellermani and E. minor, for which the average proportion of genetic differences is slightly higher than Eliurus sp. nova-E. myoxinus species pairs ( -  = 12. 3%; Supplementary Appendix S3: Fig. S13 ). The percentage of GC content across the Eliurus species included in our dataset ranged between 46. 06% (E. carletoni) and 46. 18% (E. myoxinus) (Fig. 4e ). We did not observe significant differences among E. ellermani, E. minor and Eliurus sp. nova, however we found significantly different (but partially overlapping) GC content distributions between Eliurus sp.
Morphological Differences among Eliurus Species The DAPC analysis showed that among the four morphological variables available in our dataset, "head length" had the most weight for discriminating the five Eliurus species (Fig. 5a ; Supplementary Appendix S3: Fig. S14b ). In particular, the Tukey multiple comparison of means revealed significant differences in head length between two groups of species, namely E. carletoni -E. ellermani versus E. minor -E. myoxinus -Eliurus sp. nova (p-value<0. 01). The test was also significant for the comparison between E. myoxinus and E. minor -Eliurus sp. nova (p-value<0. 01) (Fig. 5b ). Similar differences were detected also for "weight" and "body length" (Supplementary Appendix S3: Fig. S14c, e ), as expected since they are likely positively correlated.
Morphological analyses thus detected significant differences between the two sister taxa E. myoxinus and Eliurus sp. nova, the latter being consistently smaller than E. myoxinus across the three significant morphological variables. Comparisons with previously published morphological data confirmed the interspecies morphological differences observed in our dataset (Supplementary Appendix S3: Fig. S15 ). Moreover, the PGLS analysis on each morphological variable showed that "body length", "tail length" and "weight" were negatively correlated with altitude (slope = -0. 01, -0. 03, -0. 03; p-value = 0. 05, <0. 01, <0. 01, respectively), whereas no correlation could be detected for "head length" (Fig. 5 ; Supplementary Appendix S2: Table  S5 ). The PGLS analysis carried out on 19 bioclimatic variables exhibited results consistent with those obtained from altitude, overall showing a decline in body size with decreasing temperature.
Nuclear Genomic Diversity of Eliurus Species in Northern Madagascar The comparison of individuals genotyped at both mtDNA and nuclear DNA have confirmed the presence of five Eliurus taxa in our dataset (Supplementary Appendix S3: Fig. S3 ). The results based on nuclear genome-wide data confirmed the tree topology recovered using MrBayes from mtDNA cytb, and were in agreement with the topology obtained in previous studies (e. g. ,  Jansa et al. 2019). Genome-wide PCA and clustering analysis have further confirmed the genomic differentiation across the five Eliurus taxa (Fig. 3  and Supplementary Appendix S3: Fig. S10 ). We did not find clear signature of admixed coancestry among Eliurus species, which may lead to exclude events of recent admixture or hybridization among them, at this stage (Fig.
ACKNOWLEDGMENTS We thank all the Malagasy MSc students, field assistants, volunteers, local guides and cooks that contributed significantly to this work, by sharing their knowledge and participating at the collection of the data. We thank in particular  Jacquis Randriamaroson  for his valuable help and support over several fieldwork seasons. We also thank  Filipa Borges  for her help with PCR amplification of mtDNA data. We thank the  Direction Générale du Ministère de l'Environnement et des Forêts de Madagascar, Direction Régionale de l'Environnement, Ecologie et des Forêts (DREEF DIANA and SAVA) ,  Madagascar's Ad Hoc Committee for Fauna and Flora and Organizational Committee for Environmental Research (CAFF/CORE) ,  Conservation International ,  WWF ,  Missouri Botanical Garden  and the Fanamby NGO.
FUNDING This work was supported by the 2015-2016 BiodivERsA COFUND call for research proposals, with the national funders  Agence Nationale de la Recherche  (grant number  ANR-16-EBI3-0014 ),  Fundação para a Ciência e Tecnologia  (grant number  Biodiversa/0003/2015 ) and  German Bundesministerium für Bildung und Forschung  (grant number  01LC1617A ). This work was also supported by the  Fundação para a Ciência e Tecnologia  (grant numbers  PTDC/BIA-BEC/100176/2008 ,  PTDC/BIA-BIC/4476/2012 ,  PTDC-BIA-EVL/30815/2017  to L. C. , SFRH/BD/64875/2009 to J. S. ,  PD/BD/114343/2016  to G. M. S), by the  Laboratoire d'Excellence (LABEX)  entitled  TULIP  (grant numbers  ANR-10-LABX-41  and  ANR-11-IDEX-0002-02 ) as well as the the  LIA BEEG-B (Laboratoire International Associé-Bioinformatics, Ecology, Evolution, Genomics and Behaviour)  and the  Investissement d'Avenir grant  of the  Agence Nationale de la Recherche  (grant number  CEBA :  ANR-10-LABX-25-01 ).
Conclusion This review emphasizes how geochronology applied to absolute dating of folds, faults and fractures in fold-and-thrust belts complement classical approaches mainly based on the sedimentary record and provides new key insights into the timing, sequence, duration and rate of deformation. Increasing the regional absolute age datasets helps unravel the main faulting and folding episodes, thus bridging the gap that the study of only a single structure could be leading to. Clearly the more we date, the clearer the pattern of regional thrusting and folding. The extensive combination of absolute dates of folding and thrusting with independent studies using classical time markers makes it increasingly easier to depict a full picture of the main events forming and deforming a FTB. Absolute dates for folding and thrusting not only support and refine a regional tectonic history, but also enable us to quantify the duration and rate of shortening accommodated at the mesoscale and the macroscale, as well as the rate of propagation of deformation.
Most inhabitants of the Arctic live near the coastline, including fjord systems where socio-ecological coupling with coastal communities is dominant. It is therefore critically important that the key aspects of Arctic fjords be measured as well as possible. Much work has been done to monitor temperature and salinity, but an indepth knowledge of the light environment throughout Arctic fjords is lacking. This is particularly problematic knowing the importance of light for benthic ecosystem engineers such as macroalgae, which also play a major role in ecosystem function. Here we document the creation and implementation of a high resolution (~50-150 m) gridded dataset for surface photosynthetically available radiation (PAR), diffuse attenuation of PAR through the water column (KPAR), and PAR available at the seafloor (bottom PAR) for seven Arctic fjords distributed throughout Svalbard, Greenland, and Norway, during the period 2003-2022.
In addition to bottom PAR being available at a monthly resolution over this time period, all variables are available as a global average, annual averages, and monthly climatologies. Throughout most Arctic fjords, the interannual variability of monthly bottom PAR is too large to determine any long term trends. However, in some fjords, bottom PAR has increased in spring and autumn, and decreased in summer. While a full investigation into these causes is beyond the scope of the description of the dataset presented here, it is hypothesised that this shift is due to a decrease in seasonal ice cover (i. e. enhanced surface PAR) in the shoulder seasons, and an increase in coastal runoff (i. e. increased turbidity/decreased surface PAR) in summer. A demonstration of the usability of the dataset is given by showing how it can be combined with known PAR requirements of macroalgae to track the change in time of the potential distribution area for macroalgal habitats within fjords.
Bathymetry data In order to accurately calculate PARB, it was necessary to utilise the highest resolution bathymetric data available. The base layer used for all sites was v4. 2 of the International Bathymetric Chart of the Arctic Ocean (IBCAO;  Jakobsson et al. , 2020) , an Arctic specific data product produced by the General Bathymetric Chart of the Oceans (GEBCO). IBCAO contains all bathymetric data from 64°N to the pole at a gridded resolution of 200 m on the IBCAO Polar Stereographic projection (WGS 84; EPSG:3996). However, higher resolutions were often available within the focal study sites for this dataset, and these were used wherever possible. For Northern Norway (Porsangerfjorden) and Svalbard (Kongsfjorden, Isfjorden, and Storfjorden), data with a 50 m resolution were available from the Norwegian mapping authority To match with the gridded satellite data (see 2.
, 2002 Lee et al. , , 2005) )  can be used to derive KPAR in coastal and turbid coastal waters with reasonable accuracy. Therefore, the KPAR values provided in this dataset were calculated using the remote sensing reflectance at 555 nm (Rrs(  555 )) from MODIS-Aqua using  Saulquin et al. (2013)  with the updated formulation of the QAA  (Lee et al. , 2013).
P-functions We define a "P-function" as the percentage of the surface area in a shallow (depth ≤ 50 m) or coastal (depth ≤ 200 m) zone that receives PARB greater than a threshold. The P-function was introduced by  (Gattuso et al. , 2006 (Gattuso et al. , , 2020) )  and can be calculated for a given region (i. While a more detailed explanation may be found in section 2. 5. 2 of  Gattuso et al. (2020) , it is relevant to the dataset being presented here to see how the data for the P-functions were calculated. Let E be a value of irradiance (expressed in mol photons m -2 d -1 ) and d a given day. For this day, let Sa,d be the available surface (i. e. the total surface of pixels for which an irradiance value is available), and sd(E) the total surface of pixels  EQ2: P I ( E )=100 ∑ i=1 n s d i ( E)/ ∑ i=1 n S a , d i We may apply this by letting P be a P-function and Sgeo the surface of the shallow coastal area of the fjord (0-50 m), the area receiving PARB above a given threshold (expressed as s(E) and measured in mol photons m -2 d -1 ) is: EQ3: s ( E)=S geo P ( E) 100 The threshold value assigned to E in eq.