dataesr/scientific-paragraphs-categorization huggingface dataset:
g. approaching coastal waters or entering a lagoon). When [fdom] data was not available, the filtered absorption and attenuation were linearly interpolated between filter events before being removed from the total absorption and attenuation. From November 13, 2016 to May 6, 2017, the [BB3] was located upstream of the switch system, thus measured total (non-filtered) water all the time. During this period, the volume scattering coefficient of seawater was removed from the raw data counts to obtain the particulate backscattering coefficient [bbp]. The biofouling and instrument drift were estimated comparing values before and after each cleaning events. The biofouling was estimated between two cleaning events by fitting an exponential or linear model to the raw data before removing it from the signal.
Sample measurements technical validation. For nutrients [NUT] samples a quality check was done in several steps. First a visual inspection was done to determine if samples were overfilled or not frozen vertically which may induce sample leakage during the frosting procedure. Secondly any readings too close to detection limits or when duplicate measurements differed by more than 10% were flagged. In this last case, when the difference between two values of the same sample is greater than 10%, it is considered that the high value is not acceptable and is not reported. Finally, the overall quality of the dataset was established by comparing measurements values with Copernicus Marine Services modelling outputs. For trace metals ([MTE-USC]), any samples in which concentrations were close to detection limits were flagged.
A standard produced by the GEOTRACES program (coastal surface seawater standard) was included in each sample run. If the metal concentrations of the standard were outside the GEOTRACES community consensus values, the sample run was rejected. Trace metal concentrations had an average error of 5%. [HPLC] samples were analysed as described in Ras et al. 2008. All pigments peaks were inspected and quality controlled as good, acceptable or qualitative. Any measurements below detection limits were disregarded. [FCM] samples were analysed with a FACS Canto II Flow Cytometer equipped with a 488 nm laser  67  and every measurement where cell populations were either complicated, needed manual curation or were impossible were flagged.
Overall, we can conclude that [HSN-NET-300] and [MANTA-NET-300] are collecting plankton with a relatively similar efficiency even if the larger sampling volume of [MANTA-NET-300] allows a better collection of larger, rare, organisms, as seen from spectra extending to larger sizes (Fig. 4A ). Nevertheless, these results show that the use of [HSN-NET-300] may be really useful for underway zooplankton sampling in the situations when it is not possible to stop the ship for regular sampling or on ships of opportunity. Overall biogeochemical data validity. To assess the overall quality and homogeneity of the collected environmental parameters, we conducted a quick multivariate exploration of the dataset to compare it with known biogeography of biogeochemical provinces  74, 75  and their associated biogeochemical signatures.
For this, we first used data simplified at the site version (see section 4 of Data records), selected only datasets providing a full overview over the geographical range of the expedition, used a box-cox transformation and centred-reduced each variable to equally consider those. This dataset was then analysed through a PCA analysis (Fig. 5 ). The 3 first components of the PCA analysis were recovered to code for a RGB (red, green, blue) color-coding of each station and better visualize the biogeochemical signature of the station on a map. Finally, those were compared with known biogeochemical provinces extracted from  75. Despite the different temporal resolution between instantaneous sampling and biogeochemical provinces representing a consensus over several years and seasons, we can see that the main biogeochemical provinces (and associated macroscale oceanic features) as well as their progressive boundaries are well captured by our sampling scheme.
Usage Notes We recommend paying close attention to the various quality flags provided with the raw datasets to avoid using lower quality data if needed. Similarly, to provide the more complete set of observations for each sample, we provided the lag in time (dt), as well as distance in horizontal (dxy) and vertical (dz) space, between the collection timing, latitude/longitude and depth/altitude of the sample and that of the environmental context provided. Depending on the scientific question, future users are encouraged to carefully define reasonable time lag and distances to consider in their study, to avoid including unrealistic associations between samples. Moreover, we extracted contextual data at the event level to simplify the data extraction task. We also provide simplified version at the site level by combining and cross-calibrating all similar variables (e.
g. using different sources of SST data to fill gaps of missing data and obtain one merged SST variable). We prioritised observations originating from in-situ samples over satellite data, and over modelled data (MERCATOR), and evaluated their correspondence by linear regressions. Potential biases of satellite and modelled data in comparison to in-situ data were corrected applying the slope and intercept of their linear regression to force satellite and modelled data to best match in-situ data. Similarly, we also chose to interpolate some environmental variables that were sampled only few hours before or after the site itself to maximize data recovery for each sampling station. We acknowledge merging different sources of data can introduce differences in variance depending on the source of data used, therefore, we encourage the user to cautiously evaluate the relevance of this merged dataset for their study.
Considering the intrinsic heterogeneity of variance between the different datasets, and their potential non-normal distribution, we recommend using appropriate normalisation methods before any multivariate statistical analysis. Here we chose to use box-cox transformation and centred-reduced each variable. In this version of the dataset the satellite data used is 8-days averages while the in-situ measurements are instantaneous measurements of optical properties averaged over the station sampling period. The 8-days averaging tends to attenuate extreme values and reduces the potential differences between stations. While suited for macro-ecological processes which depend on large temporal and spatial variations of their environment, the use of 8-day average satellite products could be inaccurate to study shorter life cycles of the pico-, nano and micro-plankton.
Moreover, phytoplankton can adjust their light harvesting pigment concentrations according to light exposure, nutrient availability and temperature. These variations are negligible over periods shorter than a day but can become significant over 3-5 days  80  ,referencestherein. Therefore, we advise the users to cautiously use the merged bio-optical variables of this dataset and to verify its compatibility with the research question and potentially replace this 8-day average with shorter time observations if available. As presented in section "3. 3. Continuous measurements", the [poc] was estimated from the underway system, both using the measured [cp]  28  ,.
code availability The different codes used to process the different datasets are indicated within the text and are repeated here and includes: -Inline optical processing (https://github. com/OceanOptics/InLineAnalysis) -Satellite products used  38, [40] [41] [42] [43] [44] [45]  -Mercator products  [46] [47] [48] 57  used. -Astronomical almanac to calculate sun/moon position and day-nights parameters from sites positions and time  49, 50. -Additional parameters of the carbonate system were calculated with CO2SYS. m v3. 1. 1  33  using in situ temperature, total alkalinity, total dissolved inorganic carbon, salinity, phosphate and silicate concentrations as inputs together with recommended parameters  [34] [35] [36] [37]  (K1K2 = 10; KSO4 = 3; KF = 2; BOR = 2). -Ecotaxa 64 server github (https://github.
24e correspondence between total particulate scattering [bp] estimated from the [ACS] and [bbp] was investigated for the whole expedition. [bbp]valueswere discarded when [bbp]/[bp] was unusually low ( < 0. 002; see range of [bbp]/[bp] in natural waters 66 ). A similar modelling and correction for biofouling than the one performed for the [BB3] was applied to the [WSCD] data. The [PAR], [TSG], [BB3], [ACS], and [WSCD] data were processed following the last recommendations for processing inline24, using custom software available at https://github. com/OceanOptics/ InLineAnalysis. The entire time series of measurement were automatically QC to remove artifacts and manually checked and QC for obviously inaccurate measurements due to saturated sensor, low flow rate, bubbles, or poor filtered seawater measurements.
acknowledgements We are keen to thank the commitment of the people and the following institutions for their financial and scientific support that made this singular expedition possible: CNRS, PSL,  CSM ,  EPHE, Genoscope/CEA ,  Inserm ,  Université Côte d' Azur ,  ANR , agnès b. ,  UNESCO-IOC , the  Veolia Environment Foundation ,  Région Bretagne, Billerudkorsnas ,  Amerisource Bergen Company, Lorient Agglomeration, Smilewave, Oceans by Disney , the  Prince Albert II de Monaco Foundation ,  L'Oréal, Biotherm ,  France Collectivités ,  Fonds Français pour l'Environnement Mondial (FFEM) , the  Ministère des Affaires Européennes et Etrangères , the  Museum National d'Histoire Naturelle, Etienne BOURGOIS , the  Tara Ocean Foundation 's teams and crew. Tara Pacific  would not exist without the continuous support of the participating institutes.
The authors also particularly thank  Serge Planes ,  Denis Allemand  and the  Tara Pacific consortium. This study has been conducted using E. U. Copernicus Marine Service Information and Mercator Ocean products. We acknowledge funding from the  Investissement  d'avenir project  France Génomique  ( ANR-10-INBS-09 ). FL is supported by  Sorbonne Université ,  Institut Universitaire de France  and the  Fondation CA-PCA. The in-line and atmospheric optics dataset was collected and analysed with support from  NASA   Ocean Biology and Biogeochemistry program  under grants  NNX13AE58G  and  NNX15AC08G  and the  HPLC processing under NSF  award  2025402  to  University of Maine. JMF is supported by a research grant from  Scott Eric Jordan  and the  Bernard & Norton Wolf Family Foundation. NCo was supported by a grant from the  Simons Foundation/SFARI  ( 544236 ).
Work from MC, RMM, ER, DF, PF, EG is supported by the  French Government (National Research Agency, ANR)  through the grant " Coralgene "  ANR-17-CE02-0020  as well as the  "Investments for the Future" programs LABEX SIGNALIFE   ANR-11-LABX-0028  and  IDEX  UCAJedi  ANR-15-IDEX-01. NC and YL were supported by the  "Laboratoire d'Excellence" LabexMER  ( ANR-10-LABX-19 ) and cofunded by a grant from the  French government  under the program " Investissements d' Avenir ". FL, SP, CdV and ZM are funded by the  European Union 's  Horizon 2020 research and innovation programme "Atlantic Ecosystems Assessment ,  Forecasting and Sustainability" (AtlantECO)  under grant agreement No  862923. ED and FL have been supported by the  French National Research Agency  under the grant number  ANR-22-CE02-0025  (project  COR-Resilience ).
FL and ZM are supported by the  Belmont Forum  project " World Wide Web of Plankton Image Curation" (WWWPIC, grant  No  ANR-18-BELM-003 ). The support of  Pr. Alan Fuchs, President of CNRS, was crucial for the success of the surface sampling undertaken during the Tara Pacific expedition. We thank  A. Gavilli  from  TECA Inc. France , and  E. Tanguy  and  D. Delhommeau  from the  Institut de la Mer ,  Villefranche-sur-Mer  for the helpful collaboration in the conception of the  High Speed Net  and the Dolphin systems. The AT and CT data were analysed at the SNAPO-CO2 service facility at LOCEAN laboratory and supported by  CNRS-INSU  and  OSU Ecce-Terra. We thank the  EMBRC platform PIQv  for image analysis and CCPv for samples storage and supported by  EMBRC-France , whose French state funds through the  ANR  within the  Investments of the Future program  under reference  ANR-10-INBS-02.
52betw], adimensional): this diagnostic draws inspiration from Lagrangian Flow Network Theory52. It can identify regions which act as bottlenecks for the circulation, Number of Name measurements Variables Link/doi Reference Environmental context at the granularity of sampling events Inline sensors + Almanach + Copernicus + Modis 4155 all Inline data with n, sd, quartiles, local sun/moon set/rize, local zenith, nutrients, hydrology, plancton quantities, Chla, https://zenodo. org/record/6445609 96 Aqua (2 and 12 pixels around) PAR, PIC, POC, T, GSM, KD490 (with n, sd, and quartiles).
| Morphological data We carried out state-of-the-art morphological species identification with NUMOBAT (Seifert, 2021) using three to six individuals from each nest sampled for genomic data. Since both the genomic analysis and morphological identification required the use of whole individuals, we used different individuals from each nest for these separate purposes, which is justified by the fact that wood ant nests are genetically homogeneous  (Kulmuni et al. , 2020; Nouhaud, Martin, et al. , 2022; Pamilo, 1982). We measured altogether 17 morphological characteristics (See Table  S2  for a description of the characteristics, and Table  S3  for primary morphological data), producing species assignment probabilities at the nest level. The morphological characteristics measured and their recording is described in detail in  Seifert (2021).
| Variant identification Raw Illumina reads and adapter sequences were trimmed using  TRIMMOMATIC (v0. 39; Bolger et al. , 2014)  before mapping against a Finnish F. aquilonia × F. polyctena hybrid reference genome  (Nouhaud, Beresford, et al. , 2022)  using BWA  MEM (v0. 7. 17; Li, 2013). Duplicates were removed using PICARD TOOLS (v2. 21. 4; http:// broad insti tute. github. io/picard). Average read depth was 16. 0×, as computed across individuals with  MOSDEPTH (v0. 3. 3; Pedersen & Quinlan, 2018)  after duplicate removal (Table  S4 ).
Nuclear genomic data Single nucleotide variants (SNPs) for the nuclear genome were called with the FREEBAYES software (v1. 3. 1, -k option was used for disabling population priors;  Garrison & Marth, 2012)  and the VCF was normalised using  VT (v2015. 11. 10; Tan et al. , 2015). Sites located at less than two base pairs from indels were excluded, as were sites supported by only Forward or Reverse reads, using  BCFTOOLS (v1. 10; Danecek et al. , 2021). Multinucleotide variants were decomposed with the vcfallelicprimitives command from VCFLIB (v1. 0. 1; Garrison et al. , 2022). The following steps were computed with BCFTOOLS. Only biallelic SNPs with quality equal or higher than 30 were kept. Sites were then filtered based on individual sequencing depth: individual genotypes that had more than twice the mean depth of the individual in question were set as missing.
| Phylogenetic network analysis Using the nuclear genomic SNP data from all sequenced samples, we For the phylogenetic network, we computed pairwise genetic distance between all pairs of samples using the script distMat. py from github. com/simon hmart in/genom ics_general. A phylogenetic network was computed from the distance matrix with SplitsTree (version 4. 17. 1; Huson & Bryant, 2006) , using the NeighbourNet approach  (Bryant & Moulton, 2004 ) and default parameters.
| Ancestry heterozygosity and hybrid index Finally, we computed both ancestry heterozygosity and the hybrid index (HI) for our Finnish samples that were shown to be admixed between F. aquilonia and the F. polyctena/F. rufa clades, to provide more information on the admixed populations: in combination, these measures are informative about the stage of the hybrids (F1 We thus recovered 305 Ancestry-Informative Markers (AIMs) that were used to compute HI and ancestry heterozygosity. For these analyses, we excluded two admixed samples with more than 50% missing data for the AIMs (see Table  S1 ).
| Ambient temperature and precipitation To address our second aim of assessing whether temperature or precipitation correlate with the occurrences of hybrids between F. aquilonia and F. polyctena, and specifically, if the hybrids occur in warmer and drier habitats than cold-adapted F. aquilonia, we analysed climate data at Finnish nest locations. We compared nonadmixed F. aquilonia populations with populations that were admixed between F. aquilonia and the F. polyctena/F. rufa clade. To restrict the comparison to the sympatric range, all nests north of the northernmost F. rufa locality (in the absence of nonadmixed F. polyctena populations, and since F. rufa was revealed to be involved in the F. aquilonia × F. polyctena hybridisation) at 63. 36°N latitude, were excluded from this analysis, leaving a total of 33 nest sites, one per population (see Table  S1 ).
We generated climatic time series for each of these nests extending back 1, 5, and 20 years from the nest-specific field sampling date. The time series data were extracted from digital rasters of daily mean air temperature and daily precipitation sum (sourced from the  To test whether F. aquilonia and hybrid nest sites diverged climatically within their sympatric range, we fitted Bayesian logistic regression models using the R package brms (Bürkner, 2017) with nest status (hybrid vs. F. aquilonia) as the response variable. We fitted 10 separate models, each with either one of the climatic covariates, or site latitude or longitude, as the explanatory variable. We did not include multiple explanatory variables in a single model due to high multicollinearity between the climatic variables and the coordinates, and the relatively low sample size.
aquilonia locations. Latitude and longitude had slight negative effects, reflecting fewer hybrid nest occurrences in the north and the east of the sympatric area than in the south and the west. Winter precipitation did not have any consistent effects on the probability of hybrids occurrence with our dataset. The estimated effects were weaker but showed a similar trend when the three Åland island nests were removed. Crucially, among others the 20-year period results on winter upper quartile temperatures and spring precipitation still retained significance. F I G U R E 3 The absolute values of these effect estimates are highly correlated with the difference in the expected log pointwise predictive density (elpd-diff) calculated with loo, which was used to rank the models according to their predictive performance (Figure  S4 ).
| Genomic distinctness in sympatry, and mosaic hybrid zone with broad implications between differentially adapted F. rufa group species Despite our results on extensive hybridisation and the finding that all the studied species hybridise with one or more other species in sympatry, we detected both whole-genome and mitochondrial divergence among most of them in Finland. Specifically, four out of the five studied species-F. aquilonia, F. lugubris, F. rufa and F. pratensis-were found in nonadmixed populations in Finland. Our current dataset, in which all the F. polyctena populations are admixed in sympatry, does not allow testing whether F. polyctena and F. rufa represent different species or ecotypes of the same species. The mosaic hybrid zone that we detected (ca 60°N to 63°N) greatly expands the known area and extent of wood ant hybridisation in Finland  (Beresford et al.
, 2017; Pamilo & Kulmuni, 2022; Seifert, 2021). We report 10 new populations that are admixed between F. aquilonia and the F. polyctena/F. rufa clade, bringing the total within this dataset, comprising both published and unpublished genomes, to 14. The hybrid zone is likely not restricted to F I G U R E 5 Bayesian logistic regression using climatic data from a total of 33 sympatric F. aquilonia populations and populations admixed between F. aquilonia and the F. polyctena/F. rufa clade ('Admixed 1') shows that they occur in different microclimates. Hybrid nest locations are warmer both in the winter and in the spring, and receive less precipitation in the spring than F. aquilonia nest locations. The climate data spans 20 years backward from the nest sampling year. Covariates that significantly correlate with hybrid occurrence have nonoverlapping credibility intervals (95% credibility intervals are given) with the midline (zero).
After these steps, 1,829,565 SNPs from 89 individuals with sequencing depths of at least 8× remained. All the genetic analyses (detailed below) were performed by subsampling 9770 genome-wide SNPs. These were obtained by thinning the final dataset with 20 kb distances to minimise linkage disequilibrium, as well as by excluding chromosome number three (social chromosome with reduced recombination due to supergene inversions; Brelsford et al. , 2020) and shorter contigs with no known genomic location.
NFI and data collection was funded by the Academy of Finland(#200870 and #114380). Timo Domisch, Pirjo Appelgren, Gergely Várkonyi, Riitta Savolainen and Kari Vepsäläinen provided additional ant samples. We would also like to thank Raphal Martin-Roy for helping to collect the within-nest temperature data, Jaakko Kuurne for involvement in the preliminary mitochondrial data analyses, and the SpecIAnt research group and Perttu Seppä for comments and support. Warm thanks for Martyn 1365294x, 0, Downloaded from https://onlinelibrary. wiley. com/doi/10. 1111/mec. 16992 by Cochrane France, Wiley Online Library on [28/06/2023]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License.
DATA AVA I L A B I L I T Y S TAT E M E N T Genetic data (filtered nuclear and mitochondrial SNP datasets used in this study), temperature and precipitation data (broadscale climatic data on temperature and precipitation, and smaller-scale within-nest and ambient temperature data), and related scripts have been archived in Dryad at https://doi. org/10. 5061/dryad. 4f4qr fjhf.
, 2010) , geomorphological features encompassing till and glacio-fluvial deposits. They are partly cultivated  (Hannesdóttir et al. , 2010). The periglacial landforms are numerous and could induce variation in hydraulic parameters. South of Breiðamerkurjökull in particular there is a large sandur with many drumlins and eskers. The geological map of Iceland at a scale of 1/500 000 was published by the Icelandic Institute of Natural History  (Jóhannesson and Saemundsson, 1998) , and is to date the only published geological map covering the study area. The drilling logs of 66 boreholes, drilled for different purposes in the past, were collected through the ISOR database (maps. is/os) or directly at the archives of the drilling company Raektunarsamand Flóa og Skeiða (RFS). Twenty-four of these logs allowed to check the geological map and provided data on the formations thicknesses.
To correct for the atmospheric pressure we used 3 Baro Diver type DI800 and 1 Baro Micro-Diver DI500. A participatory approach involving Glacier Adventure, a tourism and educational company based in Hali, allows the monthly monitoring of 3 of the boreholes in the basalt aquifer all year round with an Hydrotechnik Water level meter. All boreholes and springs were localised using a differential GPS in September 2021 (elevations obtained in table  1 ). Finally we carried slug tests out in July and September 2022 in the 5 boreholes in the till and glacio-fluvial deposits and in 6 boreholes in the basalt formation, using slugs (diameters 10 cm and 5 cm) and an automatic pressure probe. One to three repetitions of the test were conducted in each borehole, with data acquired every half second.