dataesr/scientific-paragraphs-categorization huggingface dataset:
The second main genetic cluster included the two colonies from England and the third genetic cluster included the Tunisian colony (Figure  5 ). Increasing K did not change this clustering pattern. The PCA showed similar results, with three main genetic clusters (England, western France, Spanish Basque Country, and Tunisia, Figure  S2 ). While including all colonies, the program MAPI revealed that the Mediterranean Sea corresponded to an area of significantly higher genetic dissimilarity (Figure  6a ). A second analysis excluding the Tunisian colony highlighted a significantly higher genetic dissimilarity between the English colonies on the one hand and the French and Basque colonies on the other. It also showed the existence of genetic homogeneity in the continent (Figure  6b ). We found a significant positive relationship between genetic differentiation and geographic distance within France (slope of the regression = 0.
and the logarithm of the Euclidean geographic distances, implemented in GENEPOP v4. 6. Confidence intervals and significance of regression slope and intercept were assessed by bootstrapping over loci. It was then tested using Mantel tests with 10,000 permutations. IBD analyses were performed on the complete dataset, within France and between pairs of colonies sampled in the different countries. The comparison of the results gathered from these IBD analyses should enable the identification of geographic barriers (Pyrenees mountain range, Channel Sea, and Mediterranean Sea) that might reduce R. ferrumequinum gene flow.
TA B L E 2 Isolation by distance characteristics using the genetic differentiation parameter (F ST /1-F ST ) between colonies against the logarithm of the Euclidian geographical distance Note: 95% confidence intervals (CI) for the slope and the intercept of the IBD were obtained by ABC bootstrapping. Significant p-values of Mantel tests are represented in bold with *** for p <. 001, ** for p <. 01, and * for p <. 05. "MON" is the northern French colony of the study area. Abbreviation: SBC, Spanish Basque Country. Means of the posterior distributions of contemporary migration rate (m) with standard deviation in parentheses Geographic zone Intercept [CI] Slope [CI] p Mantel test Including "MON" All datasets -0. 3395 [-0. 5402, 0. 2411] 0. 0303 [0. 0217, 0. 0475] 0*** Within France -0. 0667 [-0.
0963, -0. 0428] 0. 0063 [0. 0042, 0. 0090] 0*** Between France and England 0. 4177 [0. 0645, 0. 9419] -0. 0229 [-0. 0603, 0. 0025]. 912 Between France and Tunisia -0. 4755 [-1. 7182, 0. 1382] 0. 0457 [0. 0009, 0. 1390]. 001*** Between France and SBC -0. 2547 [-0. 5020, -0. 1373] 0. 0212 [0. 0120, 0. 0412]. 0001*** Excluding "MON" All datasets -0. 3328 [-0. 5279, -0. 2406] 0. 0296 [0. 0217, 0. 0463] 0*** Within France -0. 0242 [-0. 0357, -0. 0142] 0. 0025 [0. 0015, 0. 0036] 0*** Between France and England 0. 1304 [-0. 1185, 0. 4372] -0. 0014 [-0. 0229, 0. 0166]. 563 Between France and Tunisia -0. 2265 [-0. 7600, 0. 2584] 0. 0279 [-0. 0050, 0. 0687]. 013* Between France and SBC -0. 0774 [-0. 1841, 0. 0483] 0. 0073 [-0. 0021, 0. 0158]. 010* TA B L E 3 Migration into Migration from Genetic cluster 1 MON France Basque Genetic cluster 2 England Genetic cluster 3 Tunisia MON 0.
Bioinformatic analyses Paired-end sequences were joined using PEAR v0. 9. 10 (  Zhang et al. , 2014). Only pairs with a minimum overlap of 50 bp and without any uncalled bases were kept. Assembled sequences were filtered using DADA2 v1. 4. 0  (Callahan et al. , 2016). Only sequences with less than one expected error and longer than 100 bp were retained in the dataset. Amplicon sequence variants (ASVs) were inferred using DADA2 and chimeric sequences were removed using the consensus method of the removeBimeras function. Taxonomic assignments were performed using the RDP classifier  (Wang et al. , 2007)   et al. , 2016). The cross-contamination threshold (TCC) was defined as the maximal number of sequences of each ASV found in negative or positive control samples. The false-assignment threshold (TFA) was defined as the highest sequence count of a positive control strain in a noncontrol sample, divided by the total number of sequences of the strain in the whole run and multiplied by the total number of sequences of each ASV.
Images were merged (RGB) using Image J software  (Schneider et al. 2012). Pictures were created using Z Project Stacks  (Campisano et al. 2014) , then cropped, and the light/contrast balance was improved  (Glassner et al. 2015). Images presented in this publication correspond to the average colonization level observed. Comment citer ce document : Fort, T. , Pauvert, C. , Zanne, A. E. , Ovaskainen, O. , Caignard, T. , Barret, M. , Compant, S. , Hampe, A. , Delzon, S. ,  Vacher, C. (2019). Maternal effects and environmental filtering shape seed fungal communities in oak trees. BioRxiv. , DOI : 10. 1101/691121.
The two models had the mother tree as a fixed effect and permutations were constrained by population. The natural logarithm of the total number of sequences per sample (sequencing depth) was introduced as the first effect in all models. In addition, we investigated whether changes in fungal community composition after acorn fall were due to either the substitution of canopy-associated fungal species by ground-associated fungal species or gain of ground-associated fungal species without loss of canopy-associated fungal species, by partitioning Jaccard binary dissimilarities among acorns of the same mother tree using the betapart package v1. 5. 1  (Baselga et al. , 2018). The proportion of fungal species of acorns on the ground also found in acorns in the canopy was calculated for each mother tree.
In addition, we tested the hypothesis that fungal lifestyle modulates fungal ASV responses to environmental variations (H3), by including the trophic mode (saprotroph, plant pathogen or other) and the degree of specialization toward acorns of each ASV in the T matrix. The putative trophic mode of each ASV was determined using the FUNGuild database  (Nguyen et al. , 2016). Comment citer ce document : Fort, T. , Pauvert, C. , Zanne, A. E. , Ovaskainen, O. , Caignard, T. ,  Barret  Their degree of specialization toward acorns was defined as the log-ratio of ASV relative abundance in acorns versus other sample types (i. e. branches, leaves, litter and upper soil), calculated using DESeq2  (Love et al. , 2014). To account for the zero-inflated nature of the data, we applied a hurdle modelling approach.
All seeds Tree population (T) 3 12. 5 <. 01 Seed position (P) 1 12. 6 <. 001 Dissimilarities among seeds were estimated using the binary Jaccard distance. The total number of sequences per sample (sequencing depth, SD) was log-transformed and introduced as the first explanatory variable in all models. The effects of tree population (T), seed position (canopy versus ground, P) and their interaction were tested on the whole seed dataset while the effect of mother tree was tested separately on canopy seeds and ground seeds. PCoAs and HMSC models gave different estimates of the relative contribution of environmental filtering and maternals effects. PCoAs suggested that mother tree identity had a lower influence on fungal community composition than elevation (Fig. 2 ), whereas HMSC models indicated the opposite trend (Table  4 ).
Seedswere either collected in the tree canopy or on the ground. Materials from the seed microenvironment (twigs and leaves, or litter and upper soil) were also collected. The fungal community of all 4 sample types was analyzed using a metabarcoding approach. Only Amplicon sequence variants (ASV) assigned to Ascomycota (A) or Basidiomycota(B)with the UNITE database were kept. Average relative abundances of all ASVs were computed for each sample type, after merging ASVs assigned to the same fungal species. Only ASVs identified at the species level are shown in the table.
Acknowledgements We thank  Albin Lobo  ( University of Copenhagen, Denmark ) and  Jean-Marc Louvet  ( Biogeco, INRA-University of Bordeaux, France ) for their assistance during sampling campaigns. We are also very grateful to  Martine-Martin Clotté ,  Céline Lalanne ,  Patrick Leger ,  Adline Delcamp  ( Biogeco, INRA-University of Bordeaux, France ) and  Jessica Vallance  (SAVE,  INRA-Bordeaux Sciences Agro, France)  for providing materials and reagents for processing the samples and advice on molecular biology protocols. We thank  Samuel Pennarun  and all members of the Genotoul sequencing facility ( Get-PlaGe, Toulouse, France ) for performing MiSeq sequencing, and the  Genotoul bioinformatics facility  ( Bioinfo Genotoul, Toulouse, France ) for providing computing and storage resources.
We also thank  Erwan Guichoux  and all members of the  Genome Transcriptome facility of Bordeaux (PGTB, Cestas, France)  for performing the SNP genotyping. Thanks to  Franck Deniel  and  Gaëtan Burgaud  ( ESIAB, Université de Bretagne Occidentale, France ) for giving us marine fungal strains used as positive controls. We would like to acknowledge  George Washington University  for its contribution to the funding of molecular biology analyses. We also thank the  INRA Meta-Omics of Microbial Ecosystems (MEM)  metaprogramme ( Learnbiocontrol  project), the  INRA Ecosystem Services (EcoServ)  metaprogramme ( IBISC  project), the LABEX  COTE  ( MICROMIC  project;  ANR-10-LABX-45 ), the  LABEX CEBA  ( ANR-10-LABX-25-01 ) and the  Aquitaine Region (Athene project ,  n°2016-1R20301-00007218 ) for additional financial support throughout the project.
To this end, we computed normalized conditional entropy, H(A|B)/H(A), where H(A|B) is given by equation (  1 ), H(A) by equation (  2  (2) The functions 'entropy' and 'condentropy' of the infotheo package  (Meyer, 2014)  for R (R Core Team, 2017) were used for all such calculations. Our code and data are fully available (see Code and data availability).
Dataset We used a corpus of 6859 types of coins ("coins" for short), each being characterized by a unique combination of the images on the obverse and reverse side of a coin ("design"), denomination, issuing authority, and date. The corpus represents the broad reach of coinage practices in the Mediterranean, heavily influenced by the city-states of mainland Greece  (Howgego, 1995, pp. 1-2). In addition to the coinage of Greek city-states and colonies all over the Mediterranean, our corpus also includes the coinage of other ancient Mediterranean civilizations (Parthian, Jewish Hellenistic, Celtic), and the coinage of the Hellenistic states born of Alexander's conquests. We collected the data from two online databases, the Sylloge Nummorum Graecorum (SNG) project of the British Academy  (Carradice and Popescu, n.
d. )  and the "MANTIS" online database of the American Numismatic Society (American Numismatic Society, 2015). Our analysis focused on the period c. 625 BCE -31 BCE, from the invention of coinage to the Roman victory at Actium. 1 The corpus was 1 The study presented here builds upon two preliminary studies that were carried on two distinct datasets, one based on the SNG data (1548 coin types c. 650 -c. 336 BCE), and the other based on MANTIS data (5375 coin types, c. 580 -c. 31 BCE), each testing slightly different predictions. For the main study, reported here, we pooled together the two datasets, and re-tested all the predictions from both studies. Both preliminary studies were preregistered, and their complete results are reported in the Electronic Supplementary Material. constituted by selecting the coins dated to the relevant time period, whose entries included information on issuing authority and denomination, and the descriptions of the images on the obverse and reverse side.
To determine each coin's issuing authority, we combined the information given by the databases in the following way: the information concerning the state authority issuing the coins (e. g. , Corinth, Athens, Alexander III) was prioritized, but if missing, it was replaced by information on the mint. In case both state and mint were missing, we used the coin's geographical provenance (region). We used the coins' denominations as given by our sources (ancient denominations were fairly homogeneous and are well documented). The databases provided us with a detailed description of the images found on the obverse and reverse sides of a coin. However, these descriptions are often subjective and idiosyncratic. We were therefore faced with the task of identifying the individual motifs, normalizing the motif names, and creating standardized coin design descriptions in order to be able to identify unique coin types.
We made a list of all the motifs found on each coin in the database, focusing on individual human figures, animals, plants, objects, and symbols, and excluding inscriptions (monograms, personal or place names). We disregarded the motifs that rarely appear independently and were either more likely to appear as attributes of a particular character (e. g. Herakles' lion skin), or as part of the character's clothing. Variants of the same motif (different kinds of helmets, stars, etc. ) were treated as simple instances of the basic motif. Similarly, we took into account only one occurrence of a motif on a coin, irrespective of how many times that motif appears on that coin. Orthographic and terminological variants were standardized. Finally, we merged all the motifs on a given coin into a single alphabetically arranged string, which we treated as the coin's design.
In both online databases used as data sources, the coins' dates were given as time intervals of varying width. For each coin, this interval was constrained to a single year (the median year in the interval). Since our conditional entropy measures need to be calculated over data bins of roughly homogeneous sizes, we used an unsupervised discretization algorithm (the "discretize" function of the infotheo package in R) to divide the corpus into 18 chronological windows or "date bins", each corresponding to a time interval, and containing an approximately equal number of coins (see Electronic Supplementary Material, section 0. 4). The "date bins" were assigned chronological labels, representing the number of years between the median year of the earliest period until the median year of the period in question.
; Howgego, 1995, p. 51 ff. ). Later, when successors started issuing coinage with their own designs, the diversity of designs increased, and they again became informative as state symbols in the politically fragmented Hellenistic world. As most of the authorities in our dataset produced several distinct denominations, the coins for each of the 18 time-bins have been split according to their issuing authority, to avoid giving undue importance to the minority of authorities that issued many coin types. Without this control, value-relevant information does not appear to rise (Spearman's ρ = 0. 036, p = 0. 888), but it does if we consider the average amount of value-relevant information on the coins of distinct authorities (Fig. 3 ). To show this, we performed two nested regressions using, as data points, the value-relevant information carried by the coins issued by a particular authority in one time-bin, for instance Corinth (413-393), Corinth (315-291), or Athens (315-291).
In the first analysis, valuerelevant information was computed as the conditional entropy of denominations given designs. We first ran a null model nesting each data point by the relevant authority (e. g. , Corinth, Athens). We compared this null model with a model using the median year of each time bin (its DATE) as predictor. That model had a lower AIC than the null model (AIC null = 485 vs. AIC test = 472) and included a negative estimate for the effect of DATE (β = -0. 0002; SE = 0. 0001; t = -3. 942; p < 0. 0001). This result held when we used normalized conditional entropy instead of conditional entropy as our measure of information (AIC null = 496 vs. AIC test = 493, estimate for the effect of DATE: β = -0. 0002; SE = 0. 0001; t = -2. 292; p = 0. 0223). Once again, Alexander's rise was accompanied by a weakening of the amount of information carried by coin designs, most likely due to the indiscriminate use of a few very frequent designs on the coins of distinct denominations, thus confusing them.
(For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article. ) divided our corpus into two subsets, high-and low-value coins, we subdivided each subset into authority-specific bins: e. g. low Corinthian denominations, high Athenian denominations, etc. For each subset, we calculated the designs' value-relevant information, as given by the conditional entropy of denominations given designs. Nested regression was not suitable to test this prediction, because of the structure of the data: the variance of the nesting variable could not be estimated due to the scarcity of different data points per nesting variable. The effect of "high" vs. "low" values was therefore tested using a Wilcoxon rank sum test. Contrary to our predictions, coin designs on high-value coins carried more value-relevant information than designs on low-value coins (W = 18,667; p < 0.
Value-relevant information was computed as the conditional entropy of denominations given designs (in nats), on the y-axis. High entropy indicates low information. Each time bin is indicated on the x-axis as a time interval BCE. Each time bin was further split into sub-bins, according to the coins' issuing authorities (total n = 601 distinct authorities). Conditional entropy was computed over each of these sub-bins (total n = 1476). Each data point indicates the average amount of value-relevant information carried by coins of the distinct authorities represented in the dataset for that period. Error bars = 95% confidence intervals. Fig. 4. The value-relevant information carried by the designs of the low-value and high-value coins issued by 353 authorities. Value-relevant information was measured as the conditional entropy of the coins' denominations given their designs (on the y-axis, in nats).
(a) Data collection We live-captured polar bears from 1992 to 2017 at Svalbard, Norway, from late March to beginning of May -just after females have emerged from maternity dens with their cubs  [11]  -using methods described in  Stirling et al. 1989 [12]. The age of first reproduction for Svalbard females is usually six years of age  [11]. Age was estimated using a premolar tooth extracted from sub-adult and adult bears (cubs were of known age based on size)  [13]. Body straight length (cm), hereafter size, was measured as the dorsal straight-line made from the tip of the nose to the caudal end of the tail bone with bears laying sternally recumbent. Litter size (one to three) was recorded upon capture as the number of cubs-of-the-year a mother had reared to that time.
Thus, the lack of precise knowledge regarding the nation-wide distribution of rock slope permafrost is a major gap for both fundamental research and practical concerns in Norway. Therefore, we have monitored the RST at 25 measurement points, covering various aspects and elevations across a latitudinal transect of Norway. With the collected data we (i) characterize the thermal regime in rock slopes, (ii) calibrate a statistical model of RST and (iii) map permafrost probability for rock slopes at the national scale. In this paper, we present (i) the measurement settings and strategy, (ii) the approaches for statistical modelling and permafrost probability mapping, and (iii) the permafrost distribution in steep slopes at the national scale and for local areas of interest.
No obvious smoothing of the daily RST is found in the presented time series, making them all suitable for statistical modelling. After this visual check, daily RST data are aggregated to mean annual values (MARST). For the eight loggers at Mannen, in Jotunheimen and in Alta, we retrieve the data before achieving a full last year of measurements. Therefore a few days are missing to allow calculating MARST over complete years and we filled the missing data for the last few days by fitting a linear regression with local AT measurements (daily averages). For this, AT time series showing the best correlation with RST time series were chosen (Table  2 , Fig. S1 ). The RST data processing provides 85 MARST multi-year measurement points to fit the regression model. MAAT for the measurement period is calculated for each MARST point, using local meteorological records at different elevations, from which a lapse rate is calculated.
AT data were retrieved from the eKlima portal (http://sharki. oslo. dnmi. no/portal/page?_pageid=73,39035,73_39049&_dad=portal&_schema=PORTAL) from the Norwegian meteorological institute. Similarly to the RST reconstruction, weather stations are chosen based on the best correlation between daily AT and RST records. To derive the most accurate MAAT at location of the RST loggers, we calculate the lapse rate at a daily time step between relatively low-and relatively high-lying weather stations. When AT records are incomplete, an average lapse rate is calculated for the period of the gap (two months overlap on each side of the gap) and for each of the recorded years. This yields a standard lapse rate value for the period of the gap, taking into account that lapse rate values can vary significantly throughout seasons.
Permafrost mapping MAAT and PISR are first mapped over the entire country based on the 10 m resolution DEM. The PISR is calculated using the same parameters as for the model explanatory variable: hourly time step and 100% transmissivity. The MAAT is mapped using the SeNorge2 dataset, which is interpolated from in-situ temperature observations over grid point elevations at 1 km resolution  (Lussana et al. , 2017) , and daily AT grids available from 1957. For the downscaling to a 10 m resolution, we first averaged daily AT data of the normal period 1981-2010 to generate a 1 km resolution MAAT raster. Then, for each 1 km grid cell, an average lapse rate is calculated over a radius of 25 km (Fig  4 ). This regional lapse rate is then used to interpolate 1km MAAT grid to a 10 m resolution. The 25 km radius ensures a precision at the regional scale, representing the relatively high lapse rate of the maritime coasts and the relatively low lapse rates in the more continental areas, while smoothing local patterns and possible artefacts.
Combined with slopes rarely exceeding 55°, this results in a significant debris cover, favouring snow accumulation in winter and permafrost persistence, even with a relatively low probability value. Protruding rock outcrops also exists at Gaustatoppen, but they are generally highly fractured and covered by thin snow in winter, which makes them also favourable to permafrost persistence. In relation to the study by  Steiger et al. (2016) , our approach is an improvement as (i) we calibrate our model with observations and therefore account for the important influence of topographic aspect, (ii) we use an updated and improved spatial interpolation method for air temperature that has been used to build the SeNorge2 dataset  (Lussana et al. 2017) , and (iii) we downscale AT using a regional lapse rate instead of using a standard and uniform one.
Large-scale rock slope deformations in Norway -In the Norwegian mountains, numerous sites with large scale instabilities are identified through systematic mapping and surveillance during the last years (e. g. Hermanns et al. , 2012; Bunkholt et al. , 2013; Oppikofer et al. , 2015). Seven sites comprise a high hazard level, justifying installation of an early warning system, two of which have been equipped with RST loggers in this study. We now used the CryoWall map to assess permafrost occurrence at "critical slopes" inventoried by the Geological Survey of Norway  (Oppikofer et al. , 2015, http ://geo. ngu. no/kart/ustabilefjellparti_mobil/). The database comprises unstable slopes (postglacial deformation that could lead to catastrophic slope failures), deformed rock slopes that can produce rock falls, potential unstable slopes (that match the structural inventory for failure but have no signs of deformation), and morphological lineaments along steep slopes that are the result of erosional processes.