dataesr/scientific-paragraphs-categorization huggingface dataset:
7 (as in  [94] ). Individuals with intermediate Q-values were considered genetically admixed individuals between hatchery and wild strains. Individual assignment Q-values to the cluster containing all Soueich hatchery individuals (i. e. degree of assignment to the "captive-bred" cluster = individual level of "hatchery ancestry") were compared between SNPs and microsatellites using a Spearman correlation test, as admixed ancestry was not normally distributed. We then compared population differentiation assessment between markers by calculating pairwise Fst between sites using the adegenet R package and mantel R function. Finally, we compared the informativeness of both sets of markers for population structure by calculating the informativeness for assignment (I n;  [98] ). A higher index indicates a higher informativeness of the set of markers.
Acknowledgments We warmly thank the  AAPPMA of Luchon  ( J. Lérimé ,  D. Estrade  and colleagues) and of Hérault ( E. Ravel  and colleagues), ECOGEA ( P. Baran  and colleagues) and members of EDF ( A. Barillier ,  V. Gouraud  and colleagues) for field sampling support. This work was undertaken at SETE, which forms part of the  "Laboratoire d'Excellence" (LABEX)  entitled  TULIP  ( ANR-10-LABX-41 ), and  M. Leitwein  was supported by a grant from  LABEX CeMEB  ( Centre Méditerranéen de l'Environnement et de la Biodiversité, Montpellier ), using genotyping and sequencing facilities of  ISEM (Institut des Sciences de l'Evolution-Montpellier ).
Bacterial Community Composition DNA was extracted from the Millipore filters by a combination of enzymatic cell lysis  (Ghiglione et al. , 2009)  and AllPrep DNA/RNA Mini Kit (QIAGEN) according to the manufacturer's instructions. The protocol for the DNA extraction and the library preparation is fully described in Supplementary Material. We assessed bacterial community composition (BCC) by targeting the V4-V5 region of the 16S rRNA gene  (Parada et al. , 2016)  and using Illumina Miseq 2 × 250 pb paired-end sequencing (Genoscreen, France). Sequences were then demultiplexed and assigned to corresponding samples using CASAVA (Illumina). Forward and reverse reads were merged using PEAR 0. 9. 8 with default options  (Zhang et al. , 2014). Raw sequences were analyzed using MacQiime v. 1. 9. 1 software  (Caporaso et al.
, 2010). Briefly, barcode, primer, shorter sequences (<100 bp in length), and sequences with ambiguous base calls or homopolymer runs exceeding 10 bp were removed. The remaining sequences were assigned to operational taxonomic units (OTUs) and clustered at a 97% threshold using Uclust algorithm  (Edgar, 2010) , both closed and open reference OTU picking, based on the SILVA (release 128) database  (Pruesse et al. , 2007; Quast et al. , 2013). Low abundance OTU (<0. 005%) were filtered as recommended by  Bokulich et al. (2013). Sequences classified as mitochondria or chloroplast were removed from the OTU table, corresponding to 3393 OTUs. A total of 349,009 reads were finally obtained representing 31,132 OTUs. OTU table was normalized by random subsampling to the smallest number of sequences (i.
Data Analysis All plots and statistical analysis were done in R RStudio  (R Core Team, 2015). Alpha diversity calculations and error estimates were averaged, using QIIME script core_diversity_analyses. py  (Caporaso et al. , 2010) , including estimates of rarefaction, Chao1, equitability, Shannon, and Simpson's diversity. In order to compare the sites based on their chemical composition, taxonomic and functional community profiles, seawater samples were sorted in a non-metric multidimensional scaling (NMDS). Pairwise dissimilarities were calculated using Bray-Curtis metrics. Each resulting dissimilarity matrix was used to visualize sample differences via NMDS ordination using the "vegan" package in R  (Oksanen et al. , 2016). To test the null hypothesis that there were no significant difference between the groups discriminated according to sampling stations, sampling dates, and depth, similarities were analyzed by global and pairwise PERMANOVA tests, using "vegan" and "RVAideMemoire" packages, respectively.
To identify taxa that discriminated the sampling sites, the linear discriminant analysis (LDA) effect size method (LEfSe)  (Segata et al. , 2011)  was used. This was performed with the LEfSe online tool in the Galaxy framework, using all default setting for data formatting and LDA effect size  (Goecks et al. , 2010). A similarity percentage (SIMPER) was run with a 90% cutoff and used to rank the percent contribution of individual biomarker to the dissimilarity between site differences  (Clarke, 1993). To investigate the relationships between BCC and measured environmental variables, Spearman rank correlation analysis, and redundancy analysis (RDA) were performed using the "vegan" package in R  (Oksanen et al. , 2016)  (details in Supplementary Material). Briefly, for variable reduction and in order to create an efficient model from the most significant explanatory variables, vegan's ordistep function were applied and among the 31 explanatory variables, 20 significant variables (P < 0.
05) were kept for the following analyses The RDA model was tested by performing partial RDA, and variation partitioning to test the significance of the contribution of both groups of variables and each individual variable. Interactions among the OTUs and between these OTUs and the 31 environmental variables were evaluated using CoNeT (Co-occurrence Network) plugin 1. 1. 1. beta (Faust et al. , 2012) , a plugin in a Cytoscape software 3. 4. 0. A similarity matrix was built with different metrics (Spearman correlation and Kullback-Leibler distance and a mutual information score) from OTUs. This initial network was re-defined by randomization. A permutation matrix representing a null distribution was obtained by resampling OTUs as described in  Faust et al. (2012). In a permutation step, edge specific p-values were computed; however, for the final network, p-values of an edge were merged into one p-value according to Brown's method.
In the final step, the 1 https://www. ncbi. nlm. nih. gov/biosample/10721482 Benjamini-Hochberg multiple testing correction was applied (P < 0. 05). Network characterization was evaluated using betweenness centrality (BC) and closeness centrality (CC) (details in Supplementary Material). Highly connected clusters were identified using the MCODE plugin version 1. 4. 0. beta2  (Smoot et al. , 2011). Network characterization were evaluated using different topological indexes generated by Network Analyzer plugin. Functional profiles were predicted from obtained 16S rRNA gene data using Tax4Fun  (Aßhauer et al. , 2015)  based on KEGG category.
As the sites are not physically separated, there was no discontinuity in both communities and functions but a clear gradient of the relative components throughout the metal gradient. Metal contaminated environments were shown to exert a high selective pressure toward the transfer of several genes, developing thus resistance against high metal concentrations  (Azarbad et al. , 2016). Only two field studies on long-term metal polluted areas have shown shifts in microbial community structure and functions or increased occurrence of resistance genes that have made microbial communities resistant to toxic metals concentrations  (Hemme et al. , 2010; Kang et al. , 2013). However, both taxonomic affiliation of metal-specific metabolic traits and the currently available KEGG database are insufficient to validate the hypothesis that heavy metal stress is a key environmental factor shaping the function of microbial communities.
Experimental redox oscillation scenarios Batch experiments were performed using a bioreactor system described in  Parsons et al. (2013)  and  Markelova et al. (2018). Each bioreactor consisted of two-part glass pressure reaction vessel, contained a suspension volume of 1 L with a headspace of about 300 mL, and incubated in the dark. All glass and plastic parts were washed with 5% HNO 3 then rinsed thoroughly in ultra-pure water. The Eh and pH electrodes (Mettler-Toledo Xerolyt Solid) signals were connected to field-effect transistor (FET) amplifiers with high input impedance. The Eh and pH data were recorded every 1 min using an Agilent 34970A BenchLink Data Logger. Measured Eh was corrected for the reference electrode's standard voltage (þ203 mV) relative to the standard hydrogen electrode (SHE) and referred to Ag/AgCl (3 M KCl) reference electrode.
X-ray diffraction analysis Powder XRD analysis was conducted for initial sediments and on day 35 and 42 at the end of the last oxic and anoxic half-cycles for both matrix A and B. Fewer than 1 mm size sediments were analyzed using Bruker D5000 equipped with a Kevex Si(Li) solid detector and a Cu K a1þ2 radiation source. Intensities were recorded at 25 C over an angular range of 2 e80 2q and using a counting time of 3 s per step. 2. 6. 2. X-ray absorption near edge structure (XANES) 2. 6. 2. 1. Data collection. XANES spectroscopy at the S K-edge (2472 eV) was performed at the XAFS beamline of the Elettra synchrotron (Trieste, Italy). The X-ray energy resolution was achieved by a Si(111) monochromator calibrated relative to the white line of a Na 2 S 2 O 3(s) standard (Sigma-Aldrich) at 2471.
Amorphous mackinawite was synthesized by mixing stoichiometric proportions of Fe(II) and H 2 S from FeCl 2 and Na 2 S solutions. Pyrite was synthesized at ambient temperature by following the protocol described in Le  Pape et al. (2017). 2. 6. 2. 3. Data processing and analysis. XANES spectra were energy calibrated, normalized and background subtracted using standard procedures  (Ravel and Newville, 2005)  using the Athena software. The relative contributions of the different S and As reference compounds to the sample spectra were determined by linear combination fitting (LCF). The LCF procedure was performed using an homemade program based on a Levenberg-Marquardt algorithm. In the case of LCF of XANES data at the As K-edge, model compounds used to fit the experimental spectra were arsenian pyrite, thiol-bound As(III) (As(III)eS), As(III)-sorbed ferrihydrite (As(III)eO), and As(V)-sorbed ferrihydrite (As(V)eO).
For LCF of XANES data at the S K-edge, FeS 2 , cysteine (S cyst ), elemental sulfur (S 0 ), and sulfate were used. For each experimental XANES spectrum, the fit quality was estimated by a classical R-factor (R f ) and a reduced c 2 (Redc 2 ). The uncertainty on each refined parameter was estimated as 3 Â ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi VðpÞRedc 2 p , where V(p) is the variance of the parameter p returned by the Levenberg-Marquardt algorithm routine for the lowest Redc 2 value  (Resongles et al. , 2016).
Geochemical modeling The geochemical code, PhreePlot including the thermodynamic database WATEQ4F. DAT was updated with a schwermannite K sp values of 10 18AE2. 5 as presented by  Bigham et al. (1996)  to calculate the distribution of Fe species  (Appelo and Postma, 2005; Kinniburgh and Cooper, 2011)  3. Results.
Microbial community Four samples were collected in the last anoxic and oxic halfcycle in both A and B matrices, but the 16S rRNA sequences were detected only in three samples (A35 and A42 in Matrix A and B35 in Matrix B). The potential metabolism results and the full taxa data of selected samples are shown in Table  2  and Tables Se1 (Supporting  Information). The array of bacterial phyla identified was of limited diversity and mainly consists of Actinobacteria and Firmicutes, with contributions from Proteobacteria, including Alphaproteobacteria and Betaproteobacteria classes. In matrix A, both the oxic and anoxic cycles probed included a large contribution from an OTU related to Streptococcus sp. (64% and 29%, respectively). This genus includes primarily facultatively anaerobic fermentative organisms.
Iron reduction and sulfate mineral dissolution The general sediment type of the area sampled was previously shown to contain trace minerals like gypsum  (Post and Sloane, 1971). The analytical data of Matrix B suggest the occurrence of some sulfide production through sulfate reduction in the initial anoxic half-cycle, which disappeared after to the first oxic half cycle. In addition, iron-reducing bacteria were identified in the sediment. Isotopic analyses of sulfur and oxygen indicate that sulfate release may not only be related to pyrite oxidation but also to the dissolution of sulfate-containing minerals. The presence of gypsum in the initial sediment and the increase of dissolved Ca 2þ and SO 4 2À concentration in Matrix B samples would likely be related to gypsum dissolution  (Postma, 1983).
5 (SO 4 ) 1. 25 þ 22H þ þ 8e e The IAP values for schwertmannite were calculated with Fe 3þ and SO 4 2À activities using the PHREEQC code and the WATEQ4F database with pH, Eh and total aqueous components as input variables and the updated equilibrium constant of schwertmannite  (Bigham et al. , 1996). The observed saturation index (SI) for schwertmannite was 42. 33 AE 1. 1 for the samples in the pH range between 2. 0 and 3. 5 in the second oxic half-cycle where sulfate concentration values ranged of 90e1000 mg/L (Fig. The observed IAP for schwertmannite is in agreement with the K sp value of 10 18AE2. 5 reported by  Bigham et al. (1996). The transformation of schwertmannite to goethite releases acidity, and may inhibit sulfate reduction in the sediment and support an acidic iron cycle at the sediment-water interface  (Peine et al.
Acknowledgement The authors acknowledge the financial support of the doctoral scholarship from  University Grenoble Alpes and Geochemistry group (ISTerre) , which is part of  Labex OSUG@2020  ( ANR10 LAB56 ). This study has been conducted under the framework of  CARE-RESCIF  initiatives. We would like to thank  Douglas Kent  and  Guillaume Morin  for their advice during the writing and review process. We thank to  Y. Wang  from  EPFL  for help with collecting sediment and fieldwork in Vietnam. Aqueous As species were analyzed on  Plateforme AETE e HydroSciences/OSU OREME, Montpellier, France. We also thank  Francesco D'Acapito  and  Giovanni Lepore  for the assistance during XAS measurements at the LISA beamline at the  ESRF  ( BM08 ),  Giuliana Aquilanti  for the assistance at the XAFS beamline at the Elettra.
All 300 ewes of this study have been treated with doramectine and triclabendazole in September 2016 at the beginning of the survey. Netobimin treatment was applied to control Dicrocoelium dendriticum infections on February 2017 and in January 2018 i. e. to prepare lambing. Another anthelminthic treatment was done in May 2017 (levamisole) and in June 2018 (oral moxidectin). All around the study, ewes from the cell and from the rotational grazing systems have been treated in the same time with the same molecules. Measurements. Rainfall and temperatures data were accessed from a meteorology station located within the farm. Mean graze periods and mean rest periods were calculated plot by plot (rotational grazing system) or cell by cell (cell grazing system) thanks to data recorded by the personnel of Le Mourier farm.
Height of grass was evaluated by the same person during the whole study, plot by plot (both systems), by using a special equipment (JENQUIP). 30 measures were done by hectare and the mean value was reported in the data sheet. These measures were done monthly during the whole period of study on a limited set of plots (three per grazing system). Between 25 and 40 individual fecal samples were collected per rectum in ewes at seven sampling dates (December 2016, May, August and October 2017 and January, May and October 2018). These ewes were always divided into different age classes: ewes born in 2012, 2013 and 2014 from December 2016 to May 2017 dates of sampling; ewes born in 2012, 2013, 2014, 2014 and 2015 from October 2017 to January 2018 sampling sessions and ewes born in 2012, 2013, 2014, 2015 and 2016 from May to October 2018.
Statistical analysis To assess the effect of cooking and nitrite addition on the endogenous nitrosylation, data were analysed by the non-parametric statistical test of Friedman, based on the chi-square test X 2 , with the statistical analysis software from Statistica (V12, Statsoft Inc. Tulsa, USA). The Friedman analysis was preferred to the parametric ANOVA because in the dynamic digestive model, the measurement scale of the different variables was ordinal and not continuous due to the presence of different compartments and to emptying from one compartment to another. As experiments were carried out on a single animal, the muscle was not considered as a factor in our statistical analysis. Only variability inherent to the measurement methods and to the digestive process was taken into account in this study.
| Measurement of explanatory variables We applied a combination of field-based surveys and published spatial data to derive two groups of explanatory variables representing (a) local canopy structure and composition versus (b) landscape structure and topography (Table  1 ). Local-scale canopy structure and composition was assessed between 3 July and 15 August 2017, within a circular plot area with a radius of 9 m around the central tree on which the temperature sensor was attached (Figure  1c ). The plot dimensions were measured with a vertex hypsometer (Vertex IV), and the location of the interpretation point in each cardinal direction was marked with a pole. The coordinates of the plot centre were recorded using a differential global positioning system with an accuracy of c. 1 m. In each cardinal direction, we estimated canopy cover visually, by adding up the species-specific vertical covers of all the plant species in the shrub and tree layer.
We considered CA as an additional variable because its link to microclimate is more mechanistic compared to d. b. h. The height of the tree on which the temperature logger was attached was measured by the mean of two measurements from opposing directions using the vertex hypsometer (Vertex IV). The shade-casting ability (SCA) describes the ability of each tree species to cast a specific level of shade, ranging between one (very low SCA, e. g. , Betula spp. ) and five (very high SCA, e. g. , Fagus sylvatica)  (Verheyen et al. , 2012). We calculated a weighted SCA per plot by using the species-specific canopy cover estimates as weights. This allowed us to test whether canopies composed of tree species with higher SCA scores have a stronger offsetting capacity than those with low SCA scores. Landscape and topographic characteristics were derived from satellite-based global tree cover data with a spatial resolution of c.
To test for nonlinear relationships between the temperature offset and canopy characteristics, in addition to topographic position, we used general additive mixed-effects models (GAMMs) with the gamm function in the "mgcv" package  (Wood, 2017) , and again "region" was added as random term. To complement the nonlinearity check and to identify possible break points or thresholds, we used piecewise regression based on the function segmented in the "segmented" package  (Muggeo, 2017). To investigate the degree to which the relationships between canopy characteristics and temperature offset are transferable to other regions across the temperate deciduous forest biome, we assessed the predictive performance of the model based on a crossvalidation procedure with blocked data splitting, accounting for our hierarchical sampling design ("region" as a random effect)  (Roberts et al.
ACK N OWLED G M ENTS F. Z. was funded by the  Swiss National Science Foundation  (grant no. 172198 ) and the  Isaac Newton Trust. P. D. F. received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  ( ERC Starting Grant   FORMICA 757833 ). K. V. , L. D. and S. L. M. received ERC funding through a  Consolidator Grant  (grant no. 614839: PASTFORWARD). D. C. was funded by  NERC  (grant no. NE/K016377/1 ). M. K. was supported by the  Czech Science Foundation  (grant no. 17-13998S ) and the  Czech Academy of Sciences  (grant no. RVO 67985939 ). F. M. was supported by project  APVV-15-0270. We are grateful to all suppliers of weather-station data, namely the  Deutscher Wetterdienst (DWD) , the  Swedish Meteorological and Hydrological Institute (SMHI) , the  French meteorological institute (Météo France) ,  Denise Pallett  and the  Upper Seeds  automated weather station, and  Zuzana Sitková  and  Katarína Střelcová , who were supported by projects  APVV-16-0325  and  VEGA 1/0367/16.
Moreover, the mechanisms behind the existence of such an intense CO 2 maximum and its relationship with physical and biochemical processes must be elucidated. We evaluated the relevant physical processes that influence the spatial variability of carbonate chemistry and the aragonite saturation state of the Peruvian coastal region. We describe the patterns of relevant water masses with low pH and oxygen concentrations. In particular, we describe shoaling due to mesoscale activity that modulates the position of the upper boundary of the water masses. This shoaling is associated with high DIC and influences the carbocline and aragonite saturation depths. Our oceanographic and biogeochemical data show the effects of physical processes on the spatial and vertical distribution of the CO 2 variables.
All samples were analyzed aboard the ship. For DIC analysis, a LI-7000 gas analyzer (CO 2 /H 2 O, LICOR, Lincoln, NE, USA) was used. The certificate reference material for DIC analysis was provided by the laboratory of Dr. Andrew Dickson of Scripps Institution of Oceanography  (Dickson et al. , 2003). The relative difference averaged ±2 µmol kg -1 (0. 2% error). The pH was measured using a glass electrode at 25 • C on the seawater scale (pHsw) as described by  Chapa-Balcorta et al. (2015). We use the program CO2SYS  (Lewis and Wallace, 1998)  and DIC-pH sw to calculate pCO 2 , in situ pHsw, and the aragonite saturation state ( Arg ). We use the constants proposed by  Mehrbach et al. (1973)  for these calculations. The uncertainty obtained for in situ pH sw , Arg , and pCO 2 was ± 0. 04 ± 0.
Variability of Sea Surface Height Anomalies In order to calculate the relative importance of the different scales of variability for sea surface height anomalies (SSHa) in the study area, the seasonal, interannual, and mesoscale signals were derived following the methods of  Godínez et al. (2010)  and  León-Chávez et al. (2015). A 21-year database (1993-2014) of mean SSHa distributed by AVISO was used (http://www. aviso. altimetry. fr). The seasonal, mesoscale, and interannual signals were separated. The first was obtained using a harmonic fit, while the second and third were obtained by means of orthogonal empirical functions (Equations 3 and 4). A a and A s are the annual and semi-annual amplitudes of the harmonic fit, respectively, while x is the spatial vector, ϕ a and ϕ s are the annual and semi-annual phases, ω is the annual radian frequency, t is the time referred to the beginning of the year, and n is the number of EOF.
SSHaseason is the variance of the seasonal component of SSHa, and σ 2 SSHa is the variance of the original SSHa. LV was calculated per pixel for each time series. The same procedure was used for the interannual and mesoscale components. The complete series was used for this calculation. The signal decomposition during the cruise was obtained by reconstructing SSHa data of the separated variability scales for the dates corresponding to the sampling periods. The seasonal data were reconstructed from the harmonic analysis. The interannual data were reconstructed from the first EOF. The mesoscale data were constructed from the sum of the EOF from 2 to N.